U.S. patent application number 13/596579 was filed with the patent office on 2013-02-07 for methods for diagnosis, prognosis and methods of treatment.
This patent application is currently assigned to Nodality, Inc.. The applicant listed for this patent is Alessandra Cesano, Wendy J. Fantl, James R. Hackett, Santosh K. Putta, David B. Rosen, Jing Shi, Michael Walker. Invention is credited to Alessandra Cesano, Wendy J. Fantl, James R. Hackett, Santosh K. Putta, David B. Rosen, Jing Shi, Michael Walker.
Application Number | 20130034862 13/596579 |
Document ID | / |
Family ID | 43925842 |
Filed Date | 2013-02-07 |
United States Patent
Application |
20130034862 |
Kind Code |
A1 |
Fantl; Wendy J. ; et
al. |
February 7, 2013 |
Methods for Diagnosis, Prognosis and Methods of Treatment
Abstract
The present invention provides an approach for the determination
of the activation states of a plurality of proteins in single
cells. This approach permits the rapid detection of heterogeneity
in a complex cell population based on activation states, expression
markers and other criteria, and the identification of cellular
subsets that exhibit correlated changes in activation within the
cell population. Moreover, this approach allows the correlation of
cellular activities or properties. In addition, the use of
modulators of cellular activation allows for characterization of
pathways and cell populations. Several exemplary diseases that can
be analyzed using the invention include AML, MDS, and MPN.
Inventors: |
Fantl; Wendy J.; (San
Francisco, CA) ; Rosen; David B.; (Mountain View,
CA) ; Cesano; Alessandra; (Redwood City, CA) ;
Putta; Santosh K.; (Foster City, CA) ; Hackett; James
R.; (San Jose, CA) ; Walker; Michael;
(Mountain View, CA) ; Shi; Jing; (South San
Francisco, CA) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
Fantl; Wendy J.
Rosen; David B.
Cesano; Alessandra
Putta; Santosh K.
Hackett; James R.
Walker; Michael
Shi; Jing |
San Francisco
Mountain View
Redwood City
Foster City
San Jose
Mountain View
South San Francisco |
CA
CA
CA
CA
CA
CA
CA |
US
US
US
US
US
US
US |
|
|
Assignee: |
Nodality, Inc.
South San Francisco
CA
|
Family ID: |
43925842 |
Appl. No.: |
13/596579 |
Filed: |
August 28, 2012 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
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13083156 |
Apr 8, 2011 |
8273544 |
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13596579 |
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12910769 |
Oct 22, 2010 |
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13083156 |
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12460029 |
Jul 10, 2009 |
8227202 |
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12910769 |
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61382793 |
Sep 14, 2010 |
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61373199 |
Aug 12, 2010 |
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61350864 |
Jun 2, 2010 |
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61265743 |
Dec 1, 2009 |
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61265585 |
Dec 1, 2009 |
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61254131 |
Oct 22, 2009 |
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61079766 |
Jul 10, 2008 |
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61085789 |
Aug 1, 2008 |
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61120320 |
Dec 5, 2008 |
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Current U.S.
Class: |
435/7.1 |
Current CPC
Class: |
A61K 31/7068 20130101;
A61K 31/4709 20130101; G01N 2800/52 20130101; G01N 33/57426
20130101; G01N 33/5094 20130101; G01N 33/50 20130101; A61K 31/7076
20130101; G01N 33/6893 20130101; A61K 31/513 20130101; G01N 33/5044
20130101; G01N 33/5041 20130101; G01N 33/574 20130101; G01N 33/5047
20130101; G01N 33/5073 20130101; G01N 33/53 20130101; G01N 2800/22
20130101; A61K 31/704 20130101; G01N 33/5055 20130101; G01N
33/57407 20130101; G01N 2333/705 20130101 |
Class at
Publication: |
435/7.1 |
International
Class: |
G01N 21/64 20060101
G01N021/64; G01N 21/76 20060101 G01N021/76 |
Claims
1. A method for determining the activation state of a signal
transduction pathway signaling protein in a leukocyte-containing
sample comprising: (a) activating the activatable proteins of at
least one signal transduction pathway in the leukocytes of the
sample by exposing the leukocyte-containing sample to a pan-kinase
activator; (b) preserving the activated sample with a preservative;
(c) unmasking intracellular epitopes of the preserved leukocytes in
the sample; (d) contacting the unmasked intracellular epitopes of
the preserved leukocytes with a plurality of fluorescently labeled
capture molecules, said plurality of capture molecules comprising
at least two different capture molecules capable of binding to the
activated state of at least two different unmasked intracellular
epitopes of preserved, activated leukocytes in the sample and at
least one control capture molecule, wherein the control capture
molecule binds to an epitope on the preserved leukocytes that is
unactivated by the pan-kinase activator; (e) detecting fluorescence
of the preserved, activated leukocytes captured by the binding of
the capture molecules to the activated state of the unmasked
intracellular epitopes; (f) detecting fluorescence of the preserved
leukocytes captured by the binding of the control capture molecule;
and (g) comparing the fluorescence of the detected preserved,
activated leukocytes captured by the capture molecules to the
fluorescence of the detected preserved leukocytes captured by the
control capture molecule.
2. The method of claim 1, further comprising: h) evaluating the
compared fluorescence measured in step g) against compared
fluorescence measured in an unactivated reference sample.
3. The method of claim 2, wherein the unactivated reference sample
is a second aliquot of the sample.
4. The method of claim 2, wherein the unactivated reference sample
is a standardized reference sample.
5. The method of claim 1, wherein the activation is performed for
about 1 minute to about 10 minutes.
6. The method of claim 1, wherein the activation is performed for
at least about 30 minutes.
7. The method of claim 2, wherein the sample is from a patient and
the evaluation of the fluorescence indicates that the patient has a
signal transduction associated disease or condition when the
fluorescence of the activated and unactivated samples are
approximately comparable.
8. The method of claim 7, wherein the signal transduction
associated disease or condition is inflammation, autoimmune,
allergic, fever, sepsis, cancer, diabetes, or heart failure.
9. The method of claim 8, wherein the signal transduction
associated disease or condition is sepsis.
10. The method of claim 8, further comprising repeating steps a) to
g) with a sample from the patient after the patient has received a
therapeutic agent to treat the inflammation, fever, sepsis, cancer,
diabetes, or heart failure and monitoring the effectiveness of that
therapeutic agent by monitoring for a change in the detected
fluorescence between the activated and unactivated samples.
11. The method of claim 8, wherein the sample is from a patient
receiving a kinase inhibitor.
12. The method of claim 11, wherein the evaluating of the compared
fluorescence indicates that the kinase inhibitor is effective in
treating the signal transduction associated disease or condition
patient when a change is determined in the detected fluorescence
between the activated and unactivated samples.
13. The method of claim 2, wherein the sample has been exposed to a
putative kinase inhibitor and the method further comprises
ascertaining the effectiveness of the kinase inhibitor when the
activated sample does not demonstrate a change in fluorescence of
the activatable proteins of the at least one signal transduction
pathway.
14. The method of claim 13, wherein the putative kinase inhibitor
is a putative inhibitor of ERK or PI3K, further comprising
monitoring for the inhibition of ribosomal S6, wherein inhibition
of both ERK and PI3K are required for ribosomal S6 inhibition by:
i) exposing the sample to a known ERK inhibitor and a putative PI3K
inhibitor and monitoring for ribosomal S6 inhibition; or ii)
exposing the sample to a known PI3K inhibitor and a putative ERK
inhibitor and monitoring for ribosomal S6 inhibition.
15. The method of claim 1, comprising measuring the activity of at
least a second signal transduction pathway.
16. The method of claim 1, wherein said intracellular epitopes
comprise phosphorylated epitopes.
17. The method of claim 1, wherein said unmasking comprises
contacting the fixed cells with an alcohol and a detergent.
18. The method of claim 16, wherein said alcohol is added at a
concentration between approximately 25 percent and approximately 90
percent.
19. The method of claim 17, wherein said alcohol is selected from
the group consisting of ethanol and methanol.
20. The method of claim 1, wherein said preservative is aldehyde,
paraformaldehyde, or formaldehyde.
21. The method of claim 16, wherein said detergent is at a
concentration between approximately 0.1 percent and approximately
10 percent.
22. The method of claim 21, wherein said detergent is selected from
the group consisting of Triton X-100, Nonidet P-40 (NP-40), and
Brij-58.
23. The method of claim 1, wherein said detection is accomplished
by cytometry.
24. The method of claim 1, wherein said signal transduction pathway
protein is selected from the group consisting of PI3K, ribosomal S6
protein, p44/42 MAP kinase, TYK2, p38 MAP kinase, PKC, PKA, SAPK,
ELK, JNK, cJun, RAS, Raf, MEK 1/2, MEK 3/6, MEK 4/7, ZAP-70, LAT,
SRC, LCK, ERK 1/2, Rsk 1, PYK2, SYK, PDK1, GSK3, FKHR, AFX, PLCg,
PLC.gamma., FAK, CREB, aIII.beta.3, FcsRI, BAD, p70S6K, STAT1,
STAT2, STAT3, STATS, STATE, and combinations thereof.
25. The method of claim 24, wherein said signal transduction
pathway proteins are p38 and ERK and PI3K or ribosomal S6.
26. The method of claim 24, wherein said signal transduction
pathway proteins are p38, ERK, and ribosomal S6.
27. The method of claim 24, wherein the first protein is JNK and
the second protein is ribosomal S6.
28. The method of claim 1, wherein said pan-kinase activator is a
toll-like receptor 4 (TLR4) activator or lipopolysaccharide
(LPS).
29. The method of claim 1, wherein the capture molecule is an
antibody or antigen binding fragment thereof.
30. The method of claim 29, wherein said antibody is specific for a
phosphorylation state of said signal transduction pathway
protein.
31. The method of claim 30, wherein said
phosphorylation-state-specific antibody is selected from the group
consisting of anti-phospho-p44/42 MAP kinase (Thr202/Tyr204),
anti-phospho-TYK2 (Tyr1054/1055), anti-phospho-p38 MAP kinase
(Thr180/Tyr182), phospho-PKC-PAN substrate antibody,
phospho-PKA-substrate antibody, anti-phospho-SAPK/JNK
(Thr183/Tyr185), anti-phospho-tyrosine (P-tyr-100), anti-p44/42
MAPK, anti-phospho-MEK1/2 (Ser217/221), anti-phospho-p90RSK
(Ser381), anti-p38 MAPK, anti-JNK/SAPK, anti-phospho-Raf1 (Ser259),
anti-phosphoElk-1 (Ser383), anti-phospho-CREB (Ser133),
anti-phosphoSEK1/MKK4 (Thr261), anti-phospho-Jun (Ser 63),
anti-phosphoMKK3/MKK6 (Ser189/207), anti-AKT, anti-phospho FKHR,
anti-FKHR, anti-phospho-Gsk3 alp21, anti-pAFX, anti-PARP, anti-BAD,
anti-BADser 112, anti-BADser 136, anti-phospho-BADser 155,
anti-p27, anti-p21, anti-cFLIP, antiMYC, anti-p53, anti-NFKB,
anti-Ikk.alpha., anti-Ikk.beta., anti-phospho-tyrosine, and
anti-phospho-threonine.
32. The method of claim 1, wherein said fluorescent label is
selected from the group consisting of a chemiluminescent label and
FRET label.
33. The method of claim 1, wherein the sample is whole blood.
34. The method of claim 1, wherein the sample comprises the
leukocytes isolated from a whole blood sample.
35. A kit for monitoring the activation state of a signal
transduction pathway comprising: a) a pan-kinase activator; and b)
at least two different capture molecules that bind at least one
signal transduction pathway protein selected from the group
consisting of p38, ERK, PI3K, JNK, and ribosomal S6, wherein at
least one of the capture molecules binds to either PI3K, JNK, or
ribosomal S6.
36. The kit of claim 36, wherein said pan kinase activator is a
toll-like receptor 4 activator or LPS.
37. The kit of claim 36, wherein at least one of the capture
molecules binds to either PI3K, JNK, or p38 and another of the
capture molecules binds to ribosomal S6.
38. A method for determining the activation state of a signal
transduction pathway signaling protein in a leukocyte-containing
sample, said method comprising: (a) activating the activatable
proteins of at least one signal transduction pathway in the
leukocytes of the sample by exposing the leukocyte-containing
sample to a pan-kinase activator; (b) permeabilizing the activated
sample; (c) contacting the permeabilized sample with a plurality of
fluorescently labeled binding elements, said plurality of binding
comprising at least two different state-specific binding element
capable of binding to the activated state of at least two different
intracellular epitopes of permeabilized, activated leukocytes in
the sample and at least one control binding element, wherein the
control binding element binds to an epitope on the permeabilized
leukocytes that is unactivated by the pan-kinase activator; (d)
detecting fluorescence of the permeabilized, activated leukocytes
captured by the binding of the binding elements to the activated
state of the intracellular epitopes; (e) detecting fluorescence of
the permeabilized leukocytes captured by the binding of the control
binding element; and (f) comparing the fluorescence of the detected
permeabilized, activated leukocytes captured by the binding
elements to the fluorescence of the detected permeabilized
leukocytes captured by the control binding element.
Description
CROSS-REFERENCE
[0001] This application is a continuation application of U.S.
application Ser. No. 13/083,156 filed Apr. 8, 2011, which claims
priority to U.S. application Ser. No. 12/910,769, filed Oct. 22,
2010, which claims priority to U.S. application No. 61/382,793,
filed Sep. 14, 2010, U.S. application No. 61/374,613 filed Aug. 18,
2010, U.S. application No. 61/373,199, filed Aug. 12, 2010, U.S.
application No. 61/350,864, filed Jun. 2, 2010, U.S. application
No. 61/265,743, filed Dec. 1, 2009, U.S. application No.
61/265,585, filed Dec. 1, 2009, U.S. application No. 61/254,131,
filed Oct. 22, 2009. This application is a continuation in part of
U.S. application Ser. No. 12/460,029 filed Jul. 10, 2009 which
claims priority to U.S. Ser. No. 61/079,766 filed Jul. 10, 2008,
U.S. Ser. No. 61/085,789 filed Aug. 1, 2008, U.S. Ser. No.
61/104,666 filed Oct. 10, 2008 and U.S. Ser. No. 61/120,320 filed
Dec. 5, 2008. Each of these applications is hereby expressly
incorporated by reference in their entirety.
BACKGROUND OF THE INVENTION
[0002] Many conditions are characterized by disruptions in cellular
pathways that lead, for example, to aberrant control of cellular
processes, with uncontrolled growth and increased cell survival.
These disruptions are often caused by changes in the activity of
molecules participating in cellular pathways. For example,
alterations in specific signaling pathways have been described for
many cancers. Despite the increasing evidence that disruption in
cellular pathways mediate the detrimental transformation, the
precise molecular events underlying these transformations in
diseases remain unclear. As a result, therapeutics may not be
effective in treating conditions involving cellular pathways that
are not well understood. Thus, the successful diagnosis of a
condition and use of therapies will require knowledge of the
cellular events that are responsible for the condition
pathology.
[0003] Acute myeloid leukemia (AML), myelodysplastic syndrome
(MDS), and myeloproliferative neoplasms (MPN) are examples of
disorders that arise from defects of hematopoietic cells of myeloid
origin. These hematopoietic disorders are recognized as clonal
diseases, which are initiated by somatic and/or inherited mutations
that cause dysregulated signaling in a progenitor cell. The wide
range of possible mutations and accompanying signaling defects
accounts for the diversity of disease phenotypes and response to
therapy observed within this group of disorders. For example, some
leukemia patients respond well to treatment and survive for
prolonged periods, while others die rapidly despite aggressive
treatment. Some patients with myelodysplastic syndrome suffer only
from anemia while others transform to an acute myeloid leukemia
that is difficult to treat. Despite the emergence of new therapies
to treat these disorders the percentage of patients who do not
benefit from current treatment is still high. Patients that are
resistant to therapy experience significant toxicity and have very
short survival times. While various staging systems have been
developed to address this clinical heterogeneity, they cannot
accurately predict at diagnosis the prognosis or predict response
to a given therapy or the clinical course that a given patient will
follow.
[0004] Accordingly, there is a need for a biologically based
clinically relevant re-classification of these disorders that can
inform on disease management at the individual level. This
classification, based upon the biologic commonalities of the
disorders above, will aid clinicians in both prognosis and
therapeutic selection at the individual patient level thus
improving patient outcomes e.g. survival and quality of life.
[0005] There are also needs for a biologically based clinically
relevant re-classification of these disorders to aid in new drug
target identification and drug screening for agents that may be
active against myeloid malignancies.
SUMMARY OF THE INVENTION
[0006] In some embodiments, the invention provides methods of
diagnosing, prognosing, or determining progression of acute
leukemia, myelodysplastic syndrome or myeloproliferative neoplasms
in an individual, the method comprising: A] classifying one or more
hematopoietic cells associated with acute leukemia, myelodysplastic
syndrome or myeloproliferative neoplasms in the individual by a
method comprising: a) subjecting a cell population comprising the
one or more hematopoietic cells from the individual to a plurality
of modulators in a plurality of cultures, b) characterizing a
plurality of pathways in one or more cells from the plurality of
cultures by determining an activation level of at least one
activatable element within a plurality of pathways, and c)
classifying one or more hematopoietic cells based on the pathways
characterization; and B] making a decision regarding diagnosis,
prognosis or progression of acute leukemia, myelodysplastic
syndrome or myeloproliferative neoplasms in the individual, where
the decision is based on the classification of the cells. In some
embodiments, the acute leukemia is acute myeloid leukemia. In some
embodiments, the pathways are selected from the group consisting of
apoptosis, cell cycle, signaling, or DNA damage pathways.
[0007] In some embodiments, the method provides of diagnosing,
prognosing, determining progression, predicting a response to a
treatment or choosing a treatment for acute leukemia,
myelodysplastic syndrome or myeloproliferative neoplasms in an
individual, the method comprising: (1) classifying one or more
hematopoietic cells associated with acute leukemia, myelodysplastic
syndrome or myeloproliferative neoplasms in the individual by a
method comprising: a) subjecting a cell population comprising the
one or more hematopoietic cells from the individual to no
modulator, CD40L, FLT3L, IGF-1, IL-27, IL-3, IL-6, M-CSF, SCF,
Thapsigargin, SDF-1.alpha. or PMA, b) determining an activation
level of p-CREB in one or more cells from the individual, and c)
classifying the one or more hematopoietic cells based on the
activation levels of p-CREB; and (2) making a decision regarding a
diagnosis, prognosis, progression, response to a treatment or a
selection of treatment for acute leukemia, myelodysplastic syndrome
or myeloproliferative neoplasms in the individual based on the
classification of the one or more hematopoietic cells. In some
embodiments, the individual is selected from the group consisting
of De Novo patient, intermediate risk cytogenetics and high risk
cytogenetics, and the cell population is subjected to SDF-1.alpha..
In some embodiments, the individual is an individual with Secondary
acute leukemia or less than 60 years old, and the cell population
is subjected to PMA. In some embodiments, the individual is less
than 60 years old, and the population is subjected to Thapsigargin.
In some embodiments, the individual has a FLT3 mutation, and the
cell population is subjected to FLT3L or PMA. In some embodiments,
classifying further comprises identifying a difference in kinetics
of the activation level.
[0008] In some embodiments, the invention provides methods of
diagnosing, prognosing, determining progression, predicting a
response to a treatment or choosing a treatment for acute leukemia,
myelodysplastic syndrome or myeloproliferative neoplasms in an
individual, the method comprising: (1) classifying one or more
hematopoietic cells associated with acute leukemia, myelodysplastic
syndrome or myeloproliferative neoplasms in the individual by a
method comprising: a) subjecting a cell population comprising the
one or more hematopoietic cells from the individual to no
modulator, CD40L, H2O2, SCF, SDF-1.alpha. TNF.alpha., LPS, PMA,
FLT3L and Thapsigargin, b) determining an activation level of p-Erk
in one or more cells from the individual, and c) classifying the
one or more hematopoietic cells based on the activation levels of
p-Erk; and (2) making a decision regarding a diagnosis, prognosis,
progression, response to a treatment or a selection of treatment
for acute leukemia, myelodysplastic syndrome or myeloproliferative
neoplasms in the individual based on the classification of the one
or more hematopoietic cells. In some embodiments, the individual is
60 years old or older, and the cell population is subjected to LPS.
In some embodiments, the individual is less than 60 years old; and
the cell population is subjected to no modulator, PMA or
Thapsigargin. In some embodiments the individual has a FLT3
mutation, and the cell population is subjected to FLT3L. In some
embodiments, the classifying further comprises identifying a
difference in kinetics of the activation level.
[0009] In some embodiments, the invention provides methods of
diagnosing, prognosing, determining progression, predicting a
response to a treatment or choosing a treatment for acute leukemia,
myelodysplastic syndrome or myeloproliferative neoplasms in an
individual, the method comprising: (1) classifying one or more
hematopoietic cells associated with acute leukemia, myelodysplastic
syndrome or myeloproliferative neoplasms in the individual by a
method comprising: a) subjecting a cell population comprising the
one or more hematopoietic cells from the individual to no
modulator; FLT3L, H2O2, SCF, IGF-1, M-CSF, b) determining an
activation level of p-plc.gamma.2 in one or more cells from the
individual, and c) classifying the one or more hematopoietic cells
based on the activation levels of p-plc.gamma.2; and (2) making a
decision regarding a diagnosis, prognosis, progression, response to
a treatment or a selection of treatment for acute leukemia,
myelodysplastic syndrome or myeloproliferative neoplasms in the
individual based on the classification of the one or more
hematopoietic cells. In some embodiments, the individual is a de
Novo patient, and the cell population is subjected to SCF or
FLT3L.
[0010] In some embodiments, the invention provides methods of
diagnosing, prognosing, determining progression, predicting a
response to a treatment or choosing a treatment for acute leukemia,
myelodysplastic syndrome or myeloproliferative neoplasms in an
individual, the method comprising: (1) classifying one or more
hematopoietic cells associated with acute leukemia, myelodysplastic
syndrome or myeloproliferative neoplasms in the individual by a
method comprising: a) subjecting a cell population comprising the
one or more hematopoietic cells from the individual to no
modulator, FLT3L or SCF, b) determining an activation level of p-S6
in one or more cells from the individual, and c) classifying the
one or more hematopoietic cells based on the activation levels of
p-S6; and (2) making a decision regarding a diagnosis, prognosis,
progression, response to a treatment or a selection of treatment
for acute leukemia, myelodysplastic syndrome or myeloproliferative
neoplasms in the individual based on the classification of the one
or more hematopoietic cells. In some embodiments, the individual is
a de Novo patient; and the cell population is subjected to no
modulator or SCF. In some embodiments, the individual has a FLT3
mutation. In some embodiments, classifying further comprises
identifying a difference in kinetics of the activation level.
[0011] In some embodiments, the invention provides methods of
diagnosing, prognosing, determining progression, predicting a
response to a treatment or choosing a treatment for acute leukemia,
myelodysplastic syndrome or myeloproliferative neoplasms in an
individual, the method comprising: (1) classifying one or more
hematopoietic cells associated with acute leukemia, myelodysplastic
syndrome or myeloproliferative neoplasms in the individual by a
method comprising: a) subjecting a cell population comprising the
one or more hematopoietic cells from the individual to no
modulator, G-CSF, IL-27, IL-3, IL-6, IFN.alpha., IFNg, IL-10, or
GM-CSF, b) determining an activation level of p-Stat 3 in one or
more cells from the individual, and c) classifying the one or more
hematopoietic cells based on the activation levels of p-Stat 3; and
(2) making a decision regarding a diagnosis, prognosis,
progression, response to a treatment or a selection of treatment
for acute leukemia, myelodysplastic syndrome or myeloproliferative
neoplasms in the individual based on the classification of the one
or more hematopoietic cells. In some embodiments, the individual is
a de Novo patient, and the cell population is subjected to IL-3. In
some embodiments, the individual is an individual with secondary
acute leukemia, and the cell population is subjected to IFN.alpha..
In some embodiments, the individual is 60 years old or older; and
the cell population is subjected IL-27. In some embodiments, the
individual is less than 60 years old; and the cell population is
subjected to GM-CSF, IFN.alpha., IFNg, IL-10 or IL-6. In some
embodiments, the individual is an individual with intermediate or
high risk cytogenetics; and the cell population is subjected to
IFN.alpha., IFNg, G-CSF, IL-10, IL-27 or IL-6. In some embodiments,
the individual has a FLT3 mutation; and the cell population is
subjected to IL-27, G-CSF, or IFN.alpha..
[0012] In some embodiments, the invention provides methods of
diagnosing, prognosing, determining progression, predicting a
response to a treatment or choosing a treatment for acute leukemia,
myelodysplastic syndrome or myeloproliferative neoplasms in an
individual, the method comprising: (1) classifying one or more
hematopoietic cells associated with acute leukemia, myelodysplastic
syndrome or myeloproliferative neoplasms in the individual by a
method comprising: a) subjecting a cell population comprising the
one or more hematopoietic cells from the individual to G-CSF, IL-6,
IFN.alpha., GM-CSF, IFNg, IL-10, or IL-27, b) determining an
activation level of p-Stat 5 in one or more cells from the
individual, and c) classifying the one or more hematopoietic cells
based on the activation levels of p-Stat 5; and (2) making a
decision regarding a diagnosis, prognosis, progression, response to
a treatment or a selection of treatment for acute leukemia,
myelodysplastic syndrome or myeloproliferative neoplasms in the
individual based on the classification of the one or more
hematopoietic cells. In some embodiments, the individual is a de
Novo patient; and the cell population is subjected to IL-6. In some
embodiments, the individual is an individual with secondary acute
leukemia; and the cell population is subjected to IFN.alpha.. In
some embodiments, the individual is less than 60 years old, and the
cell population is subjected to GM-CSF, IFN.alpha., IFNg, IL-10 or
IL-6. In some embodiments the individual is an individual with
intermediate or high risk cytogenetics; and the cell population is
subjected to IFN.alpha., FNg, G-CSF, IL-10, IL-27 or IL-6. In some
embodiments, the individual has a FLT3 mutation, and the cell
population is subjected to IL-27, IFN.alpha., or G-CSF.
[0013] In some embodiments, the invention methods of diagnosing,
prognosing, determining progression, predicting a response to a
treatment or choosing a treatment for acute leukemia,
myelodysplastic syndrome or myeloproliferative neoplasms in an
individual, the method comprising: (1) classifying one or more
hematopoietic cells associated with acute leukemia, myelodysplastic
syndrome or myeloproliferative neoplasms in the individual by a
method comprising: a) subjecting a cell population comprising the
one or more hematopoietic cells from the individual to H2O2 or SCF,
b) determining an activation level of p-SLP 76 in one or more cells
from the individual, and c) classifying the one or more
hematopoietic cells based on the activation levels of p-SLP 76; and
(2) making a decision regarding a diagnosis, prognosis,
progression, response to a treatment or a selection of treatment
for acute leukemia, myelodysplastic syndrome or myeloproliferative
neoplasms in the individual based on the classification of the one
or more hematopoietic cells. In some embodiments, the individual is
an individual with intermediate or high risk cytogenetics; and the
cell population is subjected to H2O2.
[0014] In some embodiments, the invention provides methods of
diagnosing, prognosing, determining progression, predicting a
response to a treatment or choosing a treatment for acute leukemia,
myelodysplastic syndrome or myeloproliferative neoplasms in an
individual, the method comprising: (1) classifying one or more
hematopoietic cells associated with acute leukemia, myelodysplastic
syndrome or myeloproliferative neoplasms in the individual by a
method comprising: a) subjecting a cell population comprising the
one or more hematopoietic cells from the individual to H2O2, b)
determining an activation level of p-Lck in one or more cells from
the individual, and c) classifying the one or more hematopoietic
cells based on the activation levels of p-Lck; and (2) making a
decision regarding a diagnosis, prognosis, progression, response to
a treatment or a selection of treatment for acute leukemia,
myelodysplastic syndrome or myeloproliferative neoplasms in the
individual based on the classification of the one or more
hematopoietic cells.
[0015] In some embodiments, the invention provides methods of
diagnosing, prognosing, determining progression, predicting a
response to a treatment or choosing a treatment for acute leukemia,
myelodysplastic syndrome or myeloproliferative neoplasms in an
individual, the method comprising: (1) classifying one or more
hematopoietic cells associated with acute leukemia, myelodysplastic
syndrome or myeloproliferative neoplasms in the individual by a
method comprising: a) subjecting a cell population comprising the
one or more hematopoietic cells from the individual to SCF, FLT3L,
M-CSF or H2O2, b) determining an activation level of p-Akt in one
or more cells from the individual, and c) classifying the one or
more hematopoietic cells based on the activation levels of p-Akt;
and (2) making a decision regarding a diagnosis, prognosis,
progression, response to a treatment or a selection of treatment
for acute leukemia, myelodysplastic syndrome or myeloproliferative
neoplasms in the individual based on the classification of the one
or more hematopoietic cells. In some embodiments, the individual is
60 years old, older than 60 years old, an individual with
intermediate risk cytogenetics or an individual with high risk
cytogenetics. In some embodiments, the individual has a FLT3
mutation; and the cell population is subjected to FLT3L or SCF. In
some embodiments, classifying further comprises identifying a
difference in kinetics of the activation level.
[0016] In some embodiments, the invention provides methods of
diagnosing, prognosing, determining progression, predicting a
response to a treatment or choosing a treatment for acute leukemia,
myelodysplastic syndrome or myeloproliferative neoplasms in an
individual, the method comprising: (1) classifying one or more
hematopoietic cells associated with acute leukemia, myelodysplastic
syndrome or myeloproliferative neoplasms in the individual by a
method comprising: a) subjecting a cell population comprising the
one or more hematopoietic cells from the individual to no
modulator, b) determining an activation level of p-Stat 6 in one or
more cells from the individual, and c) classifying the one or more
hematopoietic cells based on the activation levels of p-Stat 6; and
(2) making a decision regarding a diagnosis, prognosis,
progression, response to a treatment or a selection of treatment
for acute leukemia, myelodysplastic syndrome or myeloproliferative
neoplasms in the individual based on the classification of the one
or more hematopoietic cells. In some embodiments, the individual is
a de Novo patient.
[0017] In some embodiments, the invention methods of diagnosing,
prognosing, determining progression, predicting a response to a
treatment or choosing a treatment for acute leukemia,
myelodysplastic syndrome or myeloproliferative neoplasms in an
individual, the method comprising: (1) classifying one or more
hematopoietic cells associated with acute leukemia, myelodysplastic
syndrome or myeloproliferative neoplasms in the individual by a
method comprising: a) subjecting a cell population comprising the
one or more hematopoietic cells from the individual to no
modulator, Etoposide, Daunorubicin, AraC, or a combination thereof
b) determining an activation level of p-Chk2 in one or more cells
from the individual, and c) classifying the one or more
hematopoietic cells based on the activation levels of p-Chk2; and
(2) making a decision regarding a diagnosis, prognosis,
progression, response to a treatment or a selection of treatment
for acute leukemia, myelodysplastic syndrome or myeloproliferative
neoplasms in the individual based on the classification of the one
or more hematopoietic cells. In some embodiments, the individual is
a de Novo patient, or an individual with secondary acute leukemia;
and the cell population is subjected to etoposide. In some
embodiments, the individual is an individual with secondary acute
leukemia; and the cell population is subjected to no modulator. In
some embodiments, the individual is less than 60 years old; and the
cell population is subjected to Daunorubicin, AraC, Etoposide or a
combination thereof.
[0018] In some embodiments, the invention provides methods of
diagnosing, prognosing, determining progression, predicting a
response to a treatment or choosing a treatment for acute leukemia,
myelodysplastic syndrome or myeloproliferative neoplasms in an
individual, the method comprising: (1) classifying one or more
hematopoietic cells associated with acute leukemia, myelodysplastic
syndrome or myeloproliferative neoplasms in the individual by a
method comprising: a) subjecting a cell population comprising the
one or more hematopoietic cells from the individual to no
modulator, Daunorubicin, AraC, Etoposide Staurosporine, ZVAD or a
combination thereof; b) determining an activation level of c-PARP
in one or more cells from the individual, and c) classifying the
one or more hematopoietic cells based on the activation levels of
c-PARP; and (2) making a decision regarding a diagnosis, prognosis,
progression, response to a treatment or a selection of treatment
for acute leukemia, myelodysplastic syndrome or myeloproliferative
neoplasms in the individual based on the classification of the one
or more hematopoietic cells. In some embodiments, the individual is
an individual with secondary acute leukemia, and the cell
population is subjected to no modulator or etoposide. In some
embodiments, the individual is less than 60 years old; and the cell
population is subjected to no modulator, Daunorubicin, AraC,
Etoposide, Staurosporine, ZVAD or a combination thereof. In some
embodiments, the individual has a FLT3 mutation, and the cell
population is subjected to Etoposide.
[0019] In some embodiments, the invention methods of diagnosing,
prognosing, determining progression, predicting a response to a
treatment or choosing a treatment for acute leukemia,
myelodysplastic syndrome or myeloproliferative neoplasms in an
individual, the method comprising: (1) classifying one or more
hematopoietic cells associated with acute leukemia, myelodysplastic
syndrome or myeloproliferative neoplasms in the individual by a
method comprising: a) subjecting a cell population comprising the
one or more hematopoietic cells from the individual to G-CSF;
GM-CSF, IFN.alpha., IFNg, IL-10, IL-27 and IL-6, b) determining an
activation level of p-Stat 1 in one or more cells from the
individual, and c) classifying the one or more hematopoietic cells
based on the activation levels of p-Stat 1; and (2) making a
decision regarding a diagnosis, prognosis, progression, response to
a treatment or a selection of treatment for acute leukemia,
myelodysplastic syndrome or myeloproliferative neoplasms in the
individual based on the classification of the one or more
hematopoietic cells. In some embodiments, the individual is an
individual with secondary acute leukemia; and the cell population
is subjected to G-CSF or IFN.alpha.. In some embodiments, the
individual is less than 60 years old, and the cell population is
subjected to GM-CSF, IFN.alpha., IFNg, IL-10 or IL-6. In some
embodiments, the individual is an individual with intermediate or
high risk cytogenetics, and the cell population is subjected to
IFN.alpha., IFNg, IL-10, IL-27 or IL-6.
[0020] In some embodiments, the invention provides methods of
diagnosing, prognosing, determining progression, predicting a
response to a treatment or choosing a treatment for acute leukemia,
myelodysplastic syndrome or myeloproliferative neoplasms in an
individual, the method comprising: (1) classifying one or more
hematopoietic cells associated with acute leukemia, myelodysplastic
syndrome or myeloproliferative neoplasms in the individual by a
method comprising: a) subjecting a cell population comprising the
one or more hematopoietic cells from the individual to
Staurosporine, ZVAD or a combination thereof, b) determining an
activation level of cytochrome C in one or more cells from the
individual, and c) classifying the one or more hematopoietic cells
based on the activation levels of cytochrome C; and (2) making a
decision regarding a diagnosis, prognosis, progression, response to
a treatment or a selection of treatment for acute leukemia,
myelodysplastic syndrome or myeloproliferative neoplasms in the
individual based on the classification of the one or more
hematopoietic cells. In some embodiments, the individual is less
than 60 years old.
[0021] In some embodiments, the invention methods of diagnosing,
prognosing, determining progression, predicting a response to a
treatment or choosing a treatment for acute leukemia,
myelodysplastic syndrome or myeloproliferative neoplasms in an
individual, the method comprising: (1) classifying one or more
hematopoietic cells associated with acute leukemia, myelodysplastic
syndrome or myeloproliferative neoplasms in the individual by a
method comprising: a) determining an activation level of at least
three activatable elements in the presence of a modulator as listed
in Tables 23, or 24 or FIG. 36, and b) classifying the one or more
hematopoietic cells based on the activation levels of the
activatable elements; and (2) making a decision regarding a
diagnosis, prognosis, progression, response to a treatment or a
selection of treatment for acute leukemia, myelodysplastic syndrome
or myeloproliferative neoplasms in the individual based on the
classification of the one or more hematopoietic cells. In some
embodiments, at least one activatable element is from an apoptosis
pathway. In some embodiments, at least two activatable elements are
from an apoptosis pathway. In some embodiments, the activation
level of the at least three activatable elements is selected from
the group consisting of (i) p-Akt in the presence of SCF, (ii)
p-Akt in the presence of FLT3L, (iii) p-Chk2 in the presence of
Etoposide; (iv) c-PARP+ in the presence of no modulator and (v)
p-Erk 1/2 in the presence of PMA. In some embodiments, the
activation level of the at least three activatable elements is
selected from the group consisting of (i) p-Akt in the presence of
SCF, (ii) p-Akt in the presence of FLT3L, (iii) p-Chk2 in the
presence of Etoposide; (iv) c-PARP+ in the presence of no modulator
and (v) p-Erk 1/2 in the presence of PMA; and at least two
activatable elements are from an apoptosis pathway.
[0022] In some embodiments of the methods, the methods further
comprise determining the levels of a drug transporter, growth
factor receptor and/or a cytokine receptor. In some embodiments,
the cytokine receptor, growth factor receptor or drug transporter
are selected from the group consisting of MDR1, ABCG2, MRP,
P-Glycoprotein, CXCR4, FLT3, and c-kit.
[0023] In some embodiments, the activation level is determined by a
process comprising the binding of a binding element which is
specific to a particular activation state of the particular
activatable element. In some embodiments, the binding element
comprises an antibody.
[0024] In some embodiments, the step of determining the activation
level comprises the use of flow cytometry, immunofluorescence,
confocal microscopy, immunohistochemistry,
immunoelectronmicroscopy, nucleic acid amplification, gene array,
protein array, mass spectrometry, patch clamp, 2-dimensional gel
electrophoresis, differential display gel electrophoresis,
microsphere-based multiplex protein assays, ELISA, and label-free
cellular assays to determine the activation level of one or more
intracellular activatable element in single cells.
INCORPORATION BY REFERENCE
[0025] All publications, patents, and patent applications mentioned
in this specification are herein incorporated by reference to the
same extent as if each individual publication, patent, or patent
application was specifically and individually indicated to be
incorporated by reference.
BRIEF DESCRIPTION OF THE DRAWINGS
[0026] The novel features of the invention are set forth with
particularity in the appended claims. A better understanding of the
features and advantages of the present invention will be obtained
by reference to the following detailed description that sets forth
illustrative embodiments, in which the principles of the invention
are utilized, and the accompanying drawings of which:
[0027] FIG. 1 shows some examples of cellular pathways. For
example, cytokines such as G-CSF or growth factors such as FLT-3
Ligand (FLT3L) will activate their receptors resulting in
activation of intracellular signaling pathways. Also,
chemotherapeutics, such as AraC can be transported inside the cell
to cause effects, such as DNA damage, caspase activation, PARP
cleavage, etc.
[0028] FIG. 2A shows the use of four metrics used to analyze data
from cells that may be subject to a disease, such as AML. For these
metrics the median (mean can be used as well) fluorescence
intensity (MFI) was computed for the cells in one of the gated
populations measured under various conditions of staining and
stimulation. For example, the "basal" metric is calculated by
subtracting the MFI of cells in the absence of a stimulant and
stain (autofluorescence) from the MFI for cell measured in the
absence of a stimulant (autofluorescence)
(log.sub.2(MFI.sub.Unstimulated Stained)-log.sub.2(MFI.sub.Gated
Unstained). The "total phospho" metric is calculated by measuring
the fluorescence of a cell that has been stimulated with a
modulator and stained with a labeled antibody and then subtracting
the value for autofluorescence (log.sub.2(MFI.sub.Stimulated
Stained)-log.sub.2(MFI.sub.Gated Unstained). The "fold change"
metric is the measurement of the fluorescence of a cell that has
been stimulated with a modulator and stained with a labeled
antibody and then subtracting the value for unstimulated stained
cells (log.sub.2(MFI.sub.Stimulated
Stained)-log.sub.2(MFI.sub.Unstimulated Stained). The "quadrant
frequency" metric is the percentage of cells in each quadrant of
the contour plot. FIG. 2B shows that additional metrics can also be
derived directly from the distribution of cell for a protein in a
gated population for various conditions. NewlyPos=% of newly
positive cells by modulator, based on a positive gate for a stain.
AUC unstim=Area under the curve of frequency of un-modulated cells
and modulated cells for a stain. NewlyPos: % Positive Cells
modulated-% Positive Cellsunmodulated. FIG. 2B measures the
frequency of cells with a described property such as cells positive
for cleaved PARP (% PARP+), or cells positive for p-S6 and p-Akt.
Similarly, measurements examining the changes in the frequencies of
cells may be applied such as the Change in % PARP+which would
measure the % PARP+.sub.stimulated Stained-% PARP+.sub.Unstimulated
Stained. The AUC.sub.unstim metric also measures changes in
population frequencies measuring the frequency of cells to become
positive compared to an unstimulated condition.
[0029] FIG. 3 shows a diagram of apoptosis pathways.
[0030] FIG. 4 shows the use of signaling nodes to select patients
for specific targeted therapies.
[0031] FIG. 5a depicts a gating analysis to define leukemic blast
population. FIG. 5b shows that cell surface markers did not
identify resistance-associated myeloblasts subpopulations.
[0032] FIG. 6 shows that an examination of signaling profiles
revealed differences in relapse and diagnosis samples for SCF and
FLT3L.
[0033] FIG. 7 shows that c-kit expression is not predictive of SCF
responsiveness.
[0034] FIG. 8 shows univariate analysis for first study. Univariate
analysis of modulated signaling and functional apoptosis nodes
stratify NR and CR patient groups. (A) Stratification of NR and CR
patient groups with SCF modulated, but not Basal, p-Akt signaling.
(B) Stratification of NR and CR patients using functional apoptosis
assays. The frequency of p-CHK2-, Cleaved PARP+(c-PARP+) Apoptotic
cells (upper left quadrant of the flow cytometry plots) after
overnight exposure to Etoposide is used to quantify apoptosis. The
circle in the lower right quadrant highlights cells that mount a
DNA Damage Response but fail to undergo apoptosis
[0035] FIG. 9 shows combinations of independent nodes from distinct
pathways improve stratification for first study. Examples
demonstrate how corners and thresholds for the classifiers are set.
(O: CR, X: NR) (A) Doublet combination of nodes i.e. SCF induced
p-Erk and IL-27 induced p-Stat3. (B) Triplet combinations of nodes
i.e. SDF-.alpha. induced p-Akt, IL-27 induced p-Stat3, and
Etoposide induced p-CHK2-, c PARP+ cells. C) Comparison of AUCs of
ROCs of raw data vs. AUCs of ROCs on bootstrapped data to
illustrate robustness of individual combinations. Combinations with
AUCs of ROCs above 0.95 on the raw data are shown.
[0036] FIG. 10 contains "box and whisker" plots and scatter plots
that illustrate the different ranges of signaling observed in
FLT3-WT and BMMC cells.
[0037] FIG. 11 contains distribution plots that illustrate the
different ranges of signaling observed in FLT3-WT and BMMC cells
and distinct FLT3L responsive subpopulations in both sets of
cells.
[0038] FIG. 12 illustrates FLT3L signaling kinetics in FLT3-WT AML
and healthy bone marrow myeloblast (BMMC).
[0039] FIG. 13 depicts a table comparing FLT3 Receptor and FLT3L
induced signaling between normal BM Myeloblast and FLT3-WT AML.
[0040] FIG. 14 depicts the variance in signaling among different
FLT3 subgroups.
[0041] FIG. 15 contains "box and whisker" plots that demonstrate
the range of values of both FLT3 receptor levels and FLT3L-induced
S6 signaling.
[0042] FIG. 16 contains "bar and whisker" plots that demonstrate
the observed differences between FLT3-WT and FLT3-ITD samples. FIG.
16(a) illustrates differences in FLT3L-induced Stat signaling. FIG.
16(b) illustrates differences in IL-27-induced Stat signaling. FIG.
16(c) illustrates differences in Etoposide-induced apoptosis.
[0043] FIG. 17 graphically depicts stratifying nodes that
distinguished FLT3-ITD from FLT3-WT samples.
[0044] FIG. 18 tabulates the correlations between nodes that
stratify FLT3-ITD from FLT3-WT samples.
[0045] FIG. 19 provides a schematic overview of bivariate
modeling.
[0046] FIG. 20 contains scatter plots that illustrate the signaling
profiles of clinical outliers relative to other study samples. FIG.
20(a) illustrates FLT3L-induced S6 signaling in the clinical
outliers relative to FLT3-ITD and FLT3-WT samples. FIG. 20 (b)
illustrates IL-27-induced Stat signaling in the clinical outliers
relative to FLT3-ITD and FLT3-WT samples. FIG. 20(c) illustrates
IL-27-induced Stat signaling in the clinical outliers relative to
FLT3-ITD and FLT3-WT samples.
[0047] FIG. 21 tabulates the correlations between nodes that
stratify FLT3-ITD from FLT3-WT samples.
[0048] FIG. 22 tabulates results from a univariate analysis of
differences between FLT3-ITD and FLT3-WT signaling.
[0049] FIG. 23 depicts a summary table of common stratifying
pathways between FLT3-WT and FLT3-ID signaling in AML samples.
[0050] FIG. 24 depicts FLT3L-induced p-S6, p-Erk and p-Akt
signaling in different FLT3 subgroups.
[0051] FIG. 25 depicts IL-27-induced p-Stat1, p-Stat3 and p-Stat5
signaling in different FLT3 subgroups.
[0052] FIG. 26 tabulates results from a univariate analysis of
differences between FLT3-ITD and FLT3-WT signaling.
[0053] FIG. 27 list all combinations of nodes for which the
bivariate model of the combination had an AUC greater than the best
single node/metric within the combination
[0054] FIG. 28 demonstrates the stratification that PCA achieves
when applied to induced nodes in pathways and basal nodes in the
same pathways.
[0055] FIG. 29 illustrates three distinct responses to apoptosis
and DNA damage repair (DNA) that were observed in AML blasts.
[0056] FIG. 30(a) is a scatter plot comparing etoposide versus
staurosporine-mediated apoptosis. FIG. 30(b) contains distribution
plots that illustrate sample-specific differences in sensitivity to
etoposide and staurosporine-mediated apoptosis.
[0057] FIG. 31(a) illustrates the selection of staurosporine
refractory and responsive cells. FIG. 31(b) contains scatter plots
which illustrate IL-27-induced and G-CSF-induced Stat signaling
responses in the staurosporine outliers. FIG. 31(c) contains
scatter plots that compare a principle component representing Stat
pathway activity (derived from PCA of the nodes associated Stat
pathway). FIG. 31(d) tabulates the Pearson and Spearman
correlations between staurosporine response and individual
nodes.
[0058] FIG. 32(a) illustrates the selection of etoposide and
staurosporine refractory and responsive cells. FIG. 32(b) contains
scatter-plots which illustrate FLT3-induced and SCF-induced PI3K
signaling response samples with high or low apoptosis responses to
etoposide and staurosporine. FIG. 31(c) contains scatter-plots that
compare a principle component representing PI3K pathway activity
(derived from PCA of the nodes associated PI3K pathway). FIG. 32(d)
tabulates the Pearson and Spearman correlations between
staurosporine/etoposide response and individual nodes in the PI3K
pathway.
[0059] FIG. 32(a) and FIG. 33(b) contain distribution plots that
illustrate distinct subpopulations of AML samples and the
differences in Etoposide, Staurosporine, FLT3L and G-CSF-induced
signaling between the distinct subpopulations of AML.
[0060] FIG. 34 depicts a model score vs. the predicted probability
for the BBLRS model on the training data (unadjusted). Both the
true outcome and the predicted probability (along with 95%
confidence limits) of Complete Response (CR) are shown on the
y-axis.
[0061] FIG. 35 depicts FLT3 Ligand induced signaling of p-S6 at 5,
10, and 15 min time points in healthy bone marrow myeloblast (BM
Mb, and leukemic blast from AML donors with or without FLT3-ITD
mutation.
[0062] FIG. 36 tabulates a list of stratifying nodes.
DETAILED DESCRIPTION OF THE INVENTION
[0063] The present invention incorporates information disclosed in
other applications and texts. The following patent and other
publications are hereby incorporated by reference in their
entireties: Haskell et al, Cancer Treatment, 5.sup.th Ed., W.B.
Saunders and Co., 2001; Alberts et al., The Cell, 4.sup.th Ed.,
Garland Science, 2002; Vogelstein and Kinzler, The Genetic Basis of
Human Cancer, 2d Ed., McGraw Hill, 2002; Michael, Biochemical
Pathways, John Wiley and Sons, 1999; Weinberg, The Biology of
Cancer, 2007; Immunobiology, Janeway et al. 7.sup.th Ed., Garland,
and Leroith and Bondy, Growth Factors and Cytokines in Health and
Disease, A Multi Volume Treatise, Volumes 1A and 1B, Growth
Factors, 1996. Other conventional techniques and descriptions can
be found in standard laboratory manuals such as Genome Analysis: A
Laboratory Manual Series (Vols. I-IV), Using Antibodies: A
Laboratory Manual, Cells: A Laboratory Manual, PCR Primer: A
Laboratory Manual, and Molecular Cloning: A Laboratory Manual (all
from Cold Spring Harbor Laboratory Press), Stryer, L. (1995)
Biochemistry (4th Ed.) Freeman, New York, Gait, "Oligonucleotide
Synthesis: A Practical Approach" 1984, IRL Press, London, Nelson
and Cox (2000), Lehninger, Principles of Biochemistry 3rd Ed., W.
H. Freeman Pub., New York, N.Y. and Berg et al. (2002)
Biochemistry, 5th Ed., W. H. Freeman Pub., New York, N.Y.; and
Sambrook, Fritsche and Maniatis. "Molecular Cloning A laboratory
Manual" 3rd Ed. Cold Spring Harbor Press (2001), all of which are
herein incorporated in their entirety by reference for all
purposes.
[0064] Patents and applications that are also incorporated by
reference include U.S. Pat. Nos. 7,381,535 and 7,393,656 and U.S.
Ser. Nos. 10/193,462; 11/655,785; 11/655,789; 11/655,821;
11/338,957, 61/048,886; 61/048,920; 61/048,657; and 61/079,766.
Some commercial reagents, protocols, software and instruments that
are useful in some embodiments of the present invention are
available at the Becton Dickinson Website
http://www.bdbiosciences.com/features/products/, and the Beckman
Coulter website, http://www.beckmancoulter.com/Default.asp?bhfv=7.
Relevant articles include High-content single-cell drug screening
with phosphospecific flow cytometry, Krutzik et al., Nature
Chemical Biology, 23 Dec. 2007; Irish et al., FLt3 ligand Y591
duplication and Bcl-2 over expression are detected in acute myeloid
leukemia cells with high levels of phosphorylated wild-type p53,
Neoplasia, 2007, Irish et al. Mapping normal and cancer cell
signaling networks: towards single-cell proteomics, Nature, Vol. 6
146-155, 2006; and Irish et al., Single cell profiling of
potentiated phospho-protein networks in cancer cells, Cell, Vol.
118, 1-20 Jul. 23, 2004; Schulz, K. R., et al., Single-cell
phospho-protein analysis by flow cytometry, Curr Protoc Immunol,
2007, 78:8.17.1-20; Krutzik, P. O., et al., Coordinate analysis of
murine immune cell surface markers and intracellular
phosphoproteins by flow cytometry, J Immunol 2005 Aug. 15;
175(4):2357-65; Krutzik, P. O., et al., Characterization of the
murine immunological signaling network with phosphospecific flow
cytometry, J Immunol 2005 Aug. 15; 175(4):2366-73; Shulz et al.,
Current Protocols in Immunology 2007, 78:8.17.1-20; Stelzer et al.
Use of Multiparameter Flow Cytometry and Immunophenotyping for the
Diagnosis and Classification of Acute Myeloid Leukemia,
Immunophenotyping, Wiley, 2000; and Krutzik, P. O. and Nolan, G.
P., Intracellular phospho-protein staining techniques for flow
cytometry: monitoring single cell signaling events, Cytometry A.
2003 October; 55(2):61-70; Hanahan D., Weinberg, The Hallmarks of
Cancer, CELL, 2000 Jan. 7; 100(1) 57-70; Krutzik et al, High
content single cell drug screening with phosphospecific flow
cytometry, Nat Chem. Biol. 2008 February; 4(2):132-42. Experimental
and process protocols and other helpful information can be found at
http:/proteomices.stanford.edu. The articles and other references
cited below are also incorporated by reference in their entireties
for all purposes.
[0065] One embodiment of the present invention involves the
classification, diagnosis, prognosis of disease or outcome after
administering a therapeutic to treat the disease; exemplary
diseases include AML, MDS and MPN. Another embodiment of the
invention involves monitoring and predicting outcome of disease.
Another embodiment is drug screening using some of the methods of
the invention, to determine which drugs may be useful in particular
diseases. In other embodiments, the invention involves the
identification of new druggable targets, that can be used alone or
in combination with other treatments. The invention allows the
selection of patients for specific target therapies. The invention
allows for delineation of subpopulations of cells associated with a
disease that are differentially susceptible to drugs or drug
combinations. In another embodiment, the invention allows to
demarcate subpopulations of cells associated with a disease that
have different genetic subclone origins. In another embodiment, the
invention provides for the identification of a cell type, that in
combination other cell type(s), provide ratiometric or metrics that
singly or coordinately allow for surrogate identification of
subpopulations of cells associated with a disease, diagnosis,
prognosis, disease stage of the individual from which the cells
were derived, response to treatment, monitoring and predicting
outcome of disease. Another embodiment involves the analysis of
apoptosis, drug transport and/or drug metabolism. In performing
these processes, one preferred analysis method involves looking at
cell signals and/or expression markers. One embodiment of cell
signal analysis involves the analysis of phosphorylated proteins
and the use of flow cytometers in that analysis. In one embodiment,
a signal transduction-based classification of AML, MDS, or MPN can
be performed using clustering of phospho-protein patterns or
biosignatures. See generally FIG. 1.
[0066] In some embodiments, the present invention provides methods
for classification, diagnosis, prognosis of disease and outcome
after administering a therapeutic to treat the disease by
characterizing a plurality of pathways in a population of cells. In
some embodiments, a treatment is chosen based on the
characterization of plurality of pathways in single cells. In some
embodiments, characterizing a plurality of pathways in single cells
comprises determining whether apoptosis pathways, cell cycle
pathways, signaling pathways, or DNA damage pathways are functional
in an individual based on the activation levels of activatable
elements within the pathways, where a pathway is functional if it
is permissive for a response to a treatment. For example, when the
apoptosis, cell cycle, signaling, and DNA damage pathways are
functional the individual can respond to treatment, and when at
least one of the pathways is not functional the individual can not
respond to treatment. In some embodiments, if the apoptosis and DNA
damage pathways are functional the individual can respond to
treatment.
[0067] In some embodiments, the characterization of pathways in
conditions such as AML, MDS and MPN shows disruptions in cellular
pathways that are reflective of increased proliferation, increased
survival, evasion of apoptosis, insensitivity to anti-growth
signals and other mechanisms. In some embodiments, the disruption
in these pathways can be revealed by exposing a cell to one or more
modulators that mimic one or more environmental cue. FIG. 1 shows
an example of how biology determines response to therapy. For
example, without intending to be limited to any theory, a
responsive cells treated with Ara-C will undergo cell death through
activation of DNA damage and apoptosis pathways. However, a
non-responsive cell might escape apoptosis through disruption in
one or more pathways that allows the cell to survive. For instance,
a non-responsive cell might have increased concentration of a drug
transporter (e.g., MPR-1), which causes Ara-C to be removed from
the cells. A non-responsive cell might also have disruptions in one
or more pathways involve in proliferation, cell cycle progression
and cell survival that allows the cell to survive. A non-responsive
cell may have a DNA damage response pathway that fails to
communicate with apoptosis pathways. A non-responsive cell might
also have disruptions in one or more pathways involve in
proliferation, cell cycle progression and cell survival that allows
the cell to survive. The disruptions in these pathways can be
revealed, for example, by exposing the cell to a growth factor such
as FLT3L or G-CSF. In addition, the revealed disruptions in these
pathways can allow for identification of target therapies that will
be more effective in a particular patient and can allow the
identification of new druggable targets, which therapies can be
used alone or in combination with other treatments. Expression
levels of proteins, such as drug transporters and receptors, may
not be as informative by themselves for disease management as
analysis of activatable elements, such as phosphorylated proteins.
However, expression information may be useful in combination with
the analysis of activatable elements, such as phosphorylated
proteins.
[0068] The discussion below describes some of the preferred
embodiments with respect to particular diseases. However, it should
be appreciated that the principles may be useful for the analysis
of many other diseases as well.
Introduction
[0069] Hematopoietic cells are blood-forming cells in the body.
Hematopoiesis (development of blood cells) begins in the bone
marrow and depending on the cell type, further maturation occurs
either in the periphery or in secondary lymphoid organs such as the
spleen or lymph nodes. Hematopoietic disorders are recognized as
clonal diseases, which are initiated by somatic and/or inherited
mutations that cause dysregulated signaling in a progenitor cell.
The wide range of possible mutations and accompanying signaling
defects accounts for the diversity of disease phenotypes observed
within this group of disorders. Hematopoietic disorders fall into
three major categories: Myelodysplastic syndromes,
myeloproliferative disorders, and acute leukemias. Examples of
hematopoietic disorders include non-B lineage derived, such as
acute myeloid leukemia (AML), Chronic Myeloid Leukemia (CML), non-B
cell acute lymphocytic leukemia (ALL), myelodysplastic disorders,
myeloproliferative disorders, polycythemias, thrombocythemias, or
non-B atypical immune lymphoproliferations. Examples of B-Cell or B
cell lineage derived disorder include Chronic Lymphocytic Leukemia
(CLL), B lymphocyte lineage leukemia, Multiple Myeloma, acute
lymphoblastic leukemia (ALL), B-cell pro-lymphocytic leukemia,
precursor B lymphoblastic leukemia, hairy cell leukemia or plasma
cell disorders, e.g., amyloidosis or Waldenstrom's
macroglobulinemia.
[0070] Acute myeloid leukemia (AML), myelodysplastic syndrome
(MDS), and myeloproliferative neoplasms (MPN) are examples of
distinct myeloid hematopoietic disorders. However, it is recognized
that these disorders share clinical overlap in that 30% of patients
with MDS and 5-10% of patients with MPN will go on to develop
AML.
Cell-Signaling Pathways and Differentiating Factors Involved
[0071] a. AML
[0072] Alterations of kinases and phosphatases lead to
inappropriate signal transduction, whereas alterations of
transcription factors give rise to inappropriate gene expression.
Both of these mechanisms contribute to the pathogenesis of AML by
the induction of increased proliferation, reduced apoptosis and
block of differentiation. The dysregulation of one or more of the
key signaling pathways (e.g., RAS/MAPK, PI3K/AKT, and JAK/STAT) is
believed to result in growth factor-independent proliferation and
clonal expansion of hematopoietic progenitors (HOX deregulation in
acute myeloid leukemia. Journal of Clinical Investigation. 2007,
vol. 117, no. 4, p. 865-868.) See generally Table 1 below which
depicts pathways relevant for AML Biology. In some embodiments, the
pathways depicted in Table 1 are characterized using the methods
described herein by exposing cells to the modulators listed in the
table and measuring the readout listed in the table, for each
corresponding pathways. Disruption in one or more pathways can be
revealed by exposing the cells to the modulators. This can then be
used for classification, diagnosis, prognosis of AML, selection of
treatment and/or predict outcome after administering a
therapeutic.
TABLE-US-00001 TABLE 1 Pathway Readout Modulator Pathway Readout
Modulator DNA Damage p-Chk1, p-Chk2, p-ATM, p-ATR, p- Etoposide,
Ara-C/Daunorubicin, Drug H2AX Pump Inhibitors, Mylotarg Drug
transporters MDR-1, ABCG2, MPR Drug Pump Inhibitors Apoptosis
Bcl-2, Mcl-1, cytochrome c, survivin, Staurosporine, Etoposide,
Ara- XIAP PARP, Caspses 3, 7 and 8 C/Daunorubicin, Drug Pump
Inhibitors, Mylotarg, Zvad, Caspase Inhibitors, Phosphatases Shp-1,
Shp-2,, CD45 H.sub.20.sub.2 JAK/STAT p-Stat 1, 3, 4, 5, 6 Cytokine
and Growth Factors Cell Cycle Myc, Ki-67, Cyclins, DNA stains, p-
Cytokine and Growth Factors, RB, p16, p21, p27, p15, cyclin D1,
Mitogens, Apoptosis inducing agents, cyclin B1, p-Cdk1,
p-histoneH3, p- CDC25 MAPK Ras, p-Mek, p-Erk, p-S6, p-38 Cytokine
and Growth Factors, Mitogens, PI3K-AKT p-Akt, p-S6, p-PRAS40,
p-GSK3, p- Cytokines, Growth Factors, Mitogens, TSC2, p-p70S6K,
4-EBP1, p-FOXO chemokines, Receptor Tyrosine Kinase proteins (RTK)
ligands FLT3 and other RTKs p-PLCg 1/2, p-CREB, total CREB, Flt3L,
Receptor Tyrosine Kinase p-Akt, p-Erk, p-S6 (RTK) ligands
Angiogenesis PLC.gamma.1, p-Akt, p-Erk VEGF stim Wnt/b-catenin
Active B-Catenin, Myc, Cyclin D RTK ligands, growth factors
Survival PI3K, PLCg, Stats RKT Growth Factors
[0073] There are two main classes of receptors which play an
important role in hematopoiesis: Receptors with intrinsic tyrosine
kinase activity (RTKs) and those that do not contain their own
enzymatic activity and often consist of heterodimers of a
ligand-binding alpha subunit and a signal transducing beta subunit,
which is frequently shared between a subset of cytokine receptors.
Cytoplasmic tyrosine kinases phosphorylate cytokine receptors
thereby creating docking sites for signaling molecules resulting in
activation of a specific intracellular signaling pathway. Of the
first class, Kit and FLt3 receptor have been shown to play an
important role in the pathogenesis of AML. Extracellular ligand
binding regulates the intracellular substrate specificity, affinity
and kinase activity of these proteins. Therefore, the receptor
transmits its signal through binding and/or phosphorylation of
intracellular signaling intermediates. Despite these differences,
the signals transmitted by both classes of receptors ultimately
converge on one or more of the key signaling pathways, such as the
Ras/Raf/MAPK, PI3K/AKT, and JAK/STAT pathways.
[0074] The STAT (signal transducer and activator of transcription)
family of proteins, especially STAT3 and STAT5, are emerging as
important players in several cancers. (Yu 2004--STATs in cancer.
(2008) pp. 9). Of particular relevance to AML, the STATs have been
shown to be critical for myeloid differentiation and survival, as
well as for long-term maintenance of normal and leukemic stem
cells. (Schepers et al. STAT5 is required for long-term maintenance
of normal and leukemic human stem/progenitor cells. Blood (2007)
vol. 110 (8) pp. 2880-2888) STAT signaling is activated by several
cytokine receptors, which are differentially expressed depending on
the cell type and the stage of differentiation. Intrinsic or
receptor-associated tyrosine kinases phosphorylate STAT proteins,
causing them to form a homodimer. The activated STAT dimer is able
to enter the cell nucleus and activate the transcription of target
genes, many of which are involved in the regulation of apoptosis
and cell cycle progression. Apart from promoting proliferation and
survival, some growth factor receptors and signaling intermediates
have been shown to play specific and important roles in myeloid
differentiation. For example, G-CSF- or TPO-induced activation of
the Ras-Raf-MAP Kinase pathway promotes myeloid or megakaryocytic
differentiation in the respective progenitor cells by the
activation of c/EBP.alpha. (frequently inactivated in myeloid
leukemias) and GATA-1, respectively. (B. STEFFEN et al. Critical
Reviews in Oncology/Hematology. 2005, vol. 56, p. 195-221.)
[0075] Phosphatases:
[0076] One of the earliest events that occurs after engagement of
myeloid receptors is the phosphorylation of cellular proteins on
serine, threonine, and tyrosine residues 8, 9, 10. The overall
level of phosphorylated tyrosine residues is regulated by the
competing activities of protein tyrosine kinases (PTKs) and protein
tyrosine phosphatases (PTPs). Decreases in the activity of tyrosine
phosphatases may also contribute to an increase in cellular
tyrosine phosphorylation following stimulation.
[0077] SHP-1 (PTPN6) is a non-receptor protein tyrosine phosphatase
that is expressed primarily in hematopoietic cells. The enzyme is
composed of two SH2 domains, a tyrosine phosphatase catalytic
domain and a carboxy-terminal regulatory domain (Yi, T. L. et al.
(1992) Mol Cell Biol 12, 836-46). SHP-1 removes phosphates from
target proteins to down regulate several tyrosine kinase regulated
pathways. In hematopoietic cells, the N-terminal SH2 domain of
SHP-1 binds to tyrosine phosphorylated erythropoietin receptors
(EpoR) to negatively regulate hematopoietic growth (Yi, T. et al.
(1995) Blood 85, 87-95). Following ligand binding in myeloid cells,
SHP-1 associates with IL-3R .beta. chain and down regulates
IL-3-induced tyrosine phosphorylation and cell proliferation (Yi,
T. et al. (1993) Mol Cell Biol 13, 7577-86). Because SHP-1
downregulates signaling pathways emanating from receptor tyrosine
kinases, cytokine receptors, multi-chain recognition receptors and
integrins, it is considered a potential tumor suppressor (Wu, C. et
al. (2003) Gene 306, 1-12, Bhattacharya, R. et al. (2008) J Mol
Signal 3, 8).
[0078] SHP-2 (PTPN11) is a ubiquitously expressed, nonreceptor
protein tyrosine phosphatase (PTP). It participates in signaling
events downstream of receptors for growth factors, cytokines,
hormones, antigens and extracellular matrices in the control of
cell growth, differentiation, migration and death (Qu, C. K. (2000)
Cell Res 10, 279-88). Activation of SHP-2 and its association with
Gab1 is critical for sustained Erk activation downstream of several
growth factor receptors and cytokines (Maroun, C. R. et al. (2000)
Mol Cell Biol 20, 8513-25.).
[0079] In AML, when active SHP-1 and SHP-2 dephosphorylates protein
kinase (See Koretzky G A et al. Nat Rev Immunol 2006 January;
6(1):67-78. Review). Treatment of cells with a general tyrosine
phosphatase inhibitor such as H.sub.2O.sub.2 results in an increase
in phosphorylation of intracellular signalling molecules. In this
experiment, AML patients that were complete responders (CR) to one
cycle of standard 7+3 induction therapy showed higher levels of
phosphorylated PLC.gamma.2 and SLP-76 upon H.sub.2O.sub.2 treatment
when compared with non-responders (NR).
[0080] FLt3 Ligand Mutations:
[0081] During normal hematopoietic development, the FLT3 receptor
functions in the differentiation and proliferation of multipotent
stem cells and their progeny in the myeloid, B cell, and T cell
lineages. (Gilliland, G. D., and Griffin, J. D. The roles of FLT3
in hematopoesis and leukemia. Blood (2002) 100: 1532-42). FLT3
receptor expression is normally restricted to hematopoietic
progenitors, and genetic ablation experiments have shown that FLT3
is required for the maturation of these early cells, but is not
required in mature cells (Rosnet O., et al, Human FLT3/FLK2
receptor tyrosine kinase is expressed at the surface of normal and
malignant hematopoietic cells. Leukemia (1996) 10; 238-48;
Mackarehtschian K., et al. Targeted disruption of the flk2/flt3
gene leads to deficiencies in primitive hematopoietic progenitors.
Immunity (1995) 3: 147-61).
[0082] Mutations in FLT3 are found in 25-45% of all AML patients
(Renneville A., et al, Cooperating gene mutations in acute myeloid
leukemia: a review of the literature. Leukemia (2008) 22: 915-31).
Of the AML-associated FLT3 mutations, the most common is the
internal tandem duplication (ITD), which is found in 25-35% of
adult AML patients (Id). The ITD is an in-frame duplication of
3-400 nucleotides that encodes a lengthened FLT3 juxtamembrane
domain (JMD) (Schnittger S., et al. FLT3 internal tandem
duplication in 234 children with acute myeloid leukemia (AML):
prognostic significance and relation to cellular drug resistance.
Blood (2003) 102: 2387-94.). In vitro studies have shown that
FLT3/ITDs promote ligand-independent receptor dimerization, leading
to autonomous phosphorylation and constitutive activation of the
receptor (Gilliand, G. D, and Griffin, J. D. Blood (2002) 100:
1532-42). Structural studies of FLT3 suggest that in the wild-type
receptor, the JMD produces steric hindrance that prevents
autodimerization (Griffith, J., et al. The Structural Basis for
Autoinhibition of FLT3 by the Juxtamembrane Domain. Molecular Cell
(2004) 13: 169-78). The ITD-associated lengthening of the JMD
appears to remove this hindrance, resulting in autodimerization and
constitutive FLT3 kinase activity. The second class of FLT3
mutation, found in 5-10% of AML patients, comprises missense point
mutations in exon 20--commonly in codons D835, I836, N841, or
Y842--which produce amino acid substitutions in the activation loop
of the FLT3 tyrosine kinase domain (TKD) (Yamamoto Y., et al,
Activating mutation of D835 within the activation loop of FLT3 in
human hematologic malignancies. Blood (2001) 97: 2434-39).
Investigators have also identified several AML-associated point
mutations in the FLT3 JMD (Stirewalt D. L., et al. Novel FLT3 point
mutations within exon 14 found in patients with acute myeloid
leukemia. Br. J. Haematol (2004) 124: 481-84), and one in the
N-terminal portion of the Tyrosine Kinase Domain (Schittenheim M.
M., et al. FLT3 K663Q is a novel AML-associated oncogenic kinase:
determination of biochemical properties and sensitivity to
sunitnib. Leukemia (2006) 20: 2008-14.).
[0083] The AML-associated FLT3 mutations generally cause
ligand-independent autophosphorylation of the FLT3 receptor and
subsequent activation of downstream signaling pathways, such as
PI3K, Ras, and JAK/STAT (Renneville, et al. (2008) 22: 915-31).
However, the FLT3-ITD and TKD mutations are associated with
significant biological differences (Renneville, et al. (2008) 22:
915-31). FLT3-ITD mutations constitutively induce STAT5
phosphorylation, while FLT3-TKD mutations only weakly induce STAT5
phosphorylation (Choudry, C. et al. AML-associated Flt3 kinase
domain mutations show signal transduction differences compared with
Flt3-ITD mutations. Blood (2005) 106: 265-73). Furthermore,
FLT3-ITD, but not TKD mutations suppress expression of the
transcription factors, c/EBP.alpha. and Pu.1, which function in
myeloid differentiation. Additionally, neither class of FLT3
mutation is sufficient to induce AML, suggesting that additional
mechanisms may be involved (Renneville, et al. (2008) 22: 915-31).
Many investigational new drugs are targeted to FLT3 receptor kinase
activity (Gilliland, G. D., and Griffin, J. D. Blood (2002) 100:
1532-42). However, the different cell signaling profiles of
AML-associated mutations suggest that different AML patients will
exhibit distinct responses to inhibition of FLT3 kinase activity.
Pre-screening patient cell samples for a response to a FLT3 kinase
inhibitor drug, for example by examining the effects of drug
treatment on pSTAT5 levels, may predict whether a patient will
respond to that drug.
[0084] Clinically, FLT3-TKD mutations correlate with shorter
clinical response duration and worse overall survival than for
patients carrying the FLT3-TKD or wild-type alleles (Meshinchi, S
and Applebaum, F Clin. Can. Res. (2009) 13: 4263-4269; Frohling et
al. Prognostic significance of activating FLT3 mutations in younger
adults (16 to 60 years) with acute myeloid leukemia and normal
cytogenetics: a study of the AML Study Group Ulm. Blood (2002) 100:
4372-80.). The presence of the FLT3-ITD mutation and the ratio of
the FLT3-ITD mutation to other FLT3 alleles are predictive of
clinical response duration, cumulative incidence of relapse, and
patient overall survival (Renneville, et al. (2008) 22:
915-31).
[0085] In healthy myeloid lineages, G-CSF--promotes cell
proliferation through activation of JAK/STAT signaling (Touw, I.
P., and Marijke, B., Granulocyte colony-stimulating factor: key
factor or innocent bystander in the development of secondary
myeloid malignancy? (2007). J. Natl. Cancer. Inst. 99: 183-186). A
class of AML-associated mutations produces truncated G-CSF
receptor, and causes hyperreponsiveness to G-CSF stimulation
(Gert-Jan, M. et al. G-CSF receptor truncations found in SCN/AML
relieve SOCS3-controlled inhibition of STAT5 but leave suppression
of STAT3 intact. Blood (2004) 104: 667-74.). Stimulation of AML
patient blast cells with G-CSF in vitro revealed potentiated Stat3
and Stat5 phosphorylations that correlated with poor response to
chemotherapy (Irish, J. M., et al. Single Cell Profiling of
Potentiated Phospho-Protein Networks in Cancer Cells. Cell (2004)
118: 217-28.).
[0086] The process of angiogenesis may contribute to leukemic cell
survival and a resultant resistance to chemotherapy-triggered cell
death. Vascular endothelial growth factor (VEGF) is a major
determinant of angiogenesis. A significant proportion of de novo
and secondary AML blast populations produce and secrete VEGF
protein. Moreover, blasts from some patients with newly diagnosed
AML exhibit relative overexpresssion of VEGF Receptor R2 (Padro T,
Bieker R, Ruiz S, et al. Overexpression of vascular endothelial
growth factor (VEGF) and its cellular receptor KDR (VEGFR-2) in the
bone marrow of patients with acute myeloid leukemia. Leukemia 2002;
16:1302). Furthermore, the incorporation of the anti-VEGF
monoclonal antibody bevacizumab (Avastin) into an AML combination
therapy reportedly improved tumor clearance rates. (Karp, J. E., et
al. Targeting Vascular Endothelial Growth Factor for Relapsed and
Refractory Adult Acute Myelogenous Leukemias. Clinical Cancer Res.
(2004) 10: 3577-85).
[0087] In addition to Flt3, a variety of other genes are mutated in
AML and can be divided into two classes based on whether they
confer a favorable or non-favorable prognosis. Mutations in the
chaperone protein-encoding gene NPM1 have been found in 30% of
adults with de novo AML, but not in adults with secondary AML
(Renneville, et al. (2008) 22: 915-31). Among patients with
cytogenetically normal AML, NPM1 mutations are predictive of higher
rates of response to induction therapy and longer overall survival,
but only in the absence of FLT3-ITD mutations. Mutations in the
basic region leucine zipper-encoding gene CEBPA are found in 15-19%
of AML patients, and are predictive of longer overall survival and
longer complete response duration (Baldus, C. D., et al. Clinical
outcome of de novo acute myeloid leukemia patients with normal
cytogenetics is affected by molecular genetic alterations: a
concise review. British J. Haematology (2007) 137: 387-400).
[0088] Mutated genes that confer a non-favorable prognosis include
ERG which encodes a transcription factor activated by signal
transduction pathways that regulates cell differentiation,
proliferation, and tissue invasion (Baldus, C. D., et al. British
J. Haematology (2007) 137: 387-400.). Overexpression of ERG in AML
patients is predictive of a higher rate of relapse and shorter
overall survival (Marcucci et al, Overexpression of the ETS-related
gene, ERG, predicts a worse outcome in acute myeloid leukemia with
normal karyotype: a Cancer and Leukemia Group B study. J. Clinical
Oncology (2005) 23: 9234-42). High expression of BAALC in younger
AML patients (under 60 years old) is associated with lower rates of
disease-free survival and overall survival (Baldus et al, BAALC
expression predicts clinical outcome of de novo acute myeloid
leukemia patients with normal cytogenetics: a Cancer and Leukemia
Group B study. Blood (2003) 102: 1613-18). Overexpression of MN1 in
AML patients is associated with a lower rate of response to
induction therapy (Baldus, C. D., et al. British J. Haematology
(2007) 137: 387-400.). Gain-of-function mutations in the receptor
tyrosine kinase-encoding gene c-KIT are predictive of shorter
overall complete response duration and overall survival in AML
patients, and may also be predictive of response to treatment with
tyrosine kinase inhibitors (Renneville, et al. (2008) 22: 915-31).
Mutations in the Wilm's Tumor 1 (WT1) gene are found in 10-15% of
AML cases, and in cytogenetically normal AML patients, are
predictive of failure to achieve complete response to chemotherapy
(Renneville, et al. (2008) 22: 915-31). Point mutations in the RAS
oncogenes are found in 10-20% of AML patients, but prognostic uses
of these mutations have not yet been identified (Renneville, et al.
(2008) 22: 915-31).
[0089] RAS Mutations:
[0090] Ras proteins normally act as signaling switches, which
alternate between the active (GTP-bound) and inactive (GDP-bound)
states. Somatic point mutations in codons 12, 13 and 61 of the NRAS
and KRAS genes occur in many myeloid malignancies, resulting in
persistently active forms of the protein. Analyses of patients with
MDS revealed a very high risk of transformation to AML in patients
with N-RAS mutations, providing evidence that these mutations might
represent an important progression factor in MDS. Under the two-hit
model put forth by Gilliland et al., RAS mutations are likely to
provide a growth advantage, which when combined with a secondary
mutation that blocks differentiation, results in AML. Supporting
this model, N-RAS or K-RAS mutations were found in 22% of cases of
core binding factor AML (CBF-AML), which is defined by AML1-ETO or
CBF.beta.-MYH11 gene fusions known to disrupt differentiation.
(Boissel et al. Incidence and prognostic impact of c-Kit, FLT3
LIGAND, and Ras gene mutations in core binding factor acute myeloid
leukemia (CBF-AML). Leukemia (2006) vol. 20 (6) pp. 965-970)
[0091] One embodiment of the invention will look at any of the cell
signaling pathways described above in classifying diseases, such as
AML. Modulators can be designed to investigate these pathways and
any relevant parallel pathways.
[0092] In some embodiments, the invention provides a method for
diagnosis, prognosis, determining progression, predicting response
to treatment or choosing a treatment for AML, the method comprising
the steps of (a) subjecting a cell population from the individual
to a plurality of distinct modulators in separate cultures, (b)
characterizing a plurality of pathways in one or more cells from
the separate cultures comprising determining an activation level of
at least one activatable element in at least three pathways, where
the pathways are selected from the group consisting of apoptosis,
cell cycle, signaling, or DNA damage pathways, and (c) correlating
the characterization with diagnosis, prognosis, determining
progression, predicting response to treatment or choosing a
treatment for AML, in an individual, where the pathways
characterization is indicative of the diagnosis, prognosis,
determining progression, response to treatment or the appropriate
treatment for AML. In some embodiments the activatable elements and
modulators are selected from the activatable elements and
modulators listed in Tables 1, 2, 3 or 5. In some embodiments, the
activatable elements and modulators are selected from the
activatable elements and modulators listed in Table 12 and are used
to predict response duration in an individual after treatment. In
some embodiments the modulator is selected from the group
consisting of FLT3L, GM-CSF, SCF, G-CSF, SDF1.alpha., LPS, PMA,
Thapsigargin, IFNg, IFNa, IL-27, IL-3, IL-6, IL-10, ZVAD,
H.sub.2O.sub.2, Staurosporine, Etoposide, Mylotarg, Daunorubicin,
and AraC. In some embodiments, the individual has a predefined
clinical parameter and the characterization of multiple pathways in
combination with the clinical parameter is indicative of the
diagnosis, prognosis, determining progression, predicting response
to treatment or choosing a treatment for AML, in an individual.
Examples of predetermined clinical parameters include, but are not
limited to, age, de novo acute myeloid leukemia patient, secondary
acute myeloid leukemia patient, or a biochemical/molecular marker.
In some embodiments, the individual is over 60 years old. In some
embodiments, the individual is under 60 years old. In some
embodiments, when the individual is under 60 years old the
activatable elements and modulators are selected from the
activatable elements and modulators listed in Table 6. In some
embodiments, where the individual is over 60 years the activatable
elements and modulators are selected from the activatable elements
and modulators listed in Table 7. In some embodiments, where the
individual is a secondary acute myeloid leukemia patient the
activatable elements and modulators are selected from the
activatable elements and modulators listed in Table 8 and Table 9.
In some embodiments, where the individual is a de novo acute
myeloid leukemia patient the activatable elements and modulators
are selected from the activatable elements and modulators listed in
Table 10 and Table 11. In some embodiments, where the individual
has a wild type FLT3 the activatable elements and modulators are
selected from the activatable elements and modulators listed in
Table 13.
[0093] In some embodiments, the activatable elements can demarcate
AML cell subpopulations that have different genetic subclone
origins. In some embodiments, the activatable elements can
demarcate AML subpopulations that, in combination with additional
surface molecules, can allow for surrogate identification of AML
cell subpopulations. In some embodiments, the activatable elements
can demarcate AML subpopulations that can be used to determine
other protein, epitope-based, RNA, mRNA, siRNA, or metabolic
markers that singly or coordinately allow for surrogate
identification of AML cell subpopulations, disease stage of the
individual from which the cells were derived, diagnosis, prognosis,
response to treatment, or new druggable targets. In some
embodiments, the pathways characterization allows for the
delineation of AML cell subpopulations that are differentially
susceptible to drugs or drug combinations. In other embodiments,
the cell types or activatable elements from a given cell type will,
in combination with activatable elements in other cell types,
provide ratiometric or metrics that singly or coordinately allow
for surrogate identification of AML cell subpopulations, disease
stage of the individual from which the cells were derived,
diagnosis, prognosis, response to treatment, or new druggable
targets.
[0094] b. MDS
[0095] Regulation of hematopoiesis in MDS is complex and multiple
factors are involved. Genetic alterations in signaling molecules
have been extensively studied in MDS. These molecules include
transcription factors, receptors for growth factors, RAS.sup.-
signaling molecules, and cell cycle regulators.
[0096] In the early stages of MDS, there is an increased frequency
of apoptosis resulting in intramedullary apoptotic bodies. Advanced
MDS, which may transform to AML, is characterized by increased
proliferation and antiapoptotic factors, such as mutations in p53,
RAS, C-MPL or FMS. (Aul et al. Evaluating the prognosis of patients
with myelodysplastic syndromes. Ann Hematol (2002) vol. 81 (9) pp.
485-97)
[0097] Genetic alterations in the RAS signaling pathway are
frequently seen in MDS. The RAS signaling pathway normally promotes
cellular proliferation and differentiation. By contrast, pathogenic
RAS pathway mutations generally cause continuous kinase activity
and signal transduction. The cell surface receptor for macrophage
colony stimulating factor (M-CSF), encoded by the FMS gene,
normally promotes cellular proliferation and differentiation of
monocyte and macrophages, and is upstream of RAS signaling.
Activating mutations in this gene are found in 10% of MDS cases,
and are associated with poor survival and increased risk of
transformation to AML. (PADUA R A, et al. RAS, FMS and p53
mutations and poor clinical outcome in myelodysplasias: a 10-year
follow-up. Leukemia, 1998, vol. 12, p. 887-892; TOBAL K, et al.
Mutation of the human FMS gene (M-CSF receptor) in myelodysplastic
syndromes and acute myeloid leukemia. Leukemia, 1990, vol. 4, p.
486-489.)
[0098] Activating mutations in FLT3, a receptor-type tyrosine
kinase also upstream of RAS signaling, have been reported in 3-5%
of MDS cases. (Georgiou et al. Serial determination of FLT3
mutations in myelodysplastic syndrome patients at diagnosis, follow
up or acute myeloid leukemia transformation: incidence and their
prognostic significance. Br J Haematol (2006) vol. 134 (3) pp.
302-6) Inactivation of the neurofibromatosis type 1 (NF1) gene,
normally a negative regulator of RAS signaling, has also been
implicated in the progression of MDS. (Stephenson J, et al.
Possible co-existence of RAS activation and monosomy 7 in the
leukemic transformation of myelodysplastic syndromes. Leukemia Res
1995; 19:741-8). Gain-of-function mutations have also been reported
in PTPN11 in patients with MDS. (NEUBAUER A, et al. Mutations in
the ras proto-oncogenes in patients with myelodysplastic syndromes.
Leukemia. 1994, vol. 8, p. 638-641). Among the RAS genes
themselves, mutations of the N-ras gene are the most frequent and
are detected in 20 to 30 percent of human leukemias and
approximately 16 percent of MDS cases. K-RAS mutations are found at
approximately half that frequency. The majority of studies suggest
that RAS mutations in MDS are associated with poor survival and
increased probability of developing AML. (YUNIS J J, et al.
Mechanisms of ras mutation in myelodysplastic syndrome. Oncogene.
1989, vol. 4, p. 609-614; Aul et al. Evaluating the prognosis of
patients with myelodysplastic syndromes. Ann Hematol (2002) vol. 81
(9) pp. 485-97).
[0099] Although less frequently, AML1, C/EBP.alpha., TEL (ETV6) and
p53 genes are also a target of mutations in MDS. AML1-binding sites
exist upstream of several genes encoding factors and receptors that
determine the lineage specificity of hematopoietic cells. (OKUDA T,
et al. AML1, the target of multiple chromosomal translocations in
human leukemia, is essential for normal fetal liver hematopoiesis.
Cell. 1996, vol. 84, p. 321-30.) C/EBP.alpha. is an important
mediator of granulocyte differentiation and regulates the
expression of multiple granulocyte-specific genes including the
granulocyte colony-stimulating factor (G-CSF) receptor, neutrophil
elastase and myeloperoxidase. C/EBP.alpha. knockout mice display a
profound block in granulocyte differentiation (COLLINS S J, et al.
Multipotent hematopoietic cell lines derived from C/EBP.alpha.
(-/-) knockout mice display granulocyte
macrophage-colony-stimulating factor,
granulocyte-colony-stimulating factor and retinoic acid-induced
granulocytic differentiation. Blood. 2001, vol. 98, p. 2382-8).
This suggests that any mutation in C/EBP.alpha. will result in
defective hematopoiesis. TEL function is essential for the
establishment of hematopoiesis of all lineages in the bone marrow,
suggesting a critical role for TEL in the normal transition of the
hematopoietic activity from fetal liver to bone marrow. Experiments
conducted on the role of TEL genes indicate an ineffective
hematopoiesis in the case of an alteration in these genes. (WANG L
C, et al. The TEL/ETV6 gene is required specifically for
hematopoiesis in the bone marrow. Genes and Development. 1998, vol.
12, p. 2392-402). Mutations or deletions causing inactivation of
the p53 gene in both the alleles have been shown to predispose the
cells to neoplastic transformation. Inactivation is detected in 5
to 10 percent of cases of clinically advanced MDS, indicating that
p53 mutations may play a role in leukemic progression of MDS.
(SUGIMOTO K, et al. Mutations of the p53 gene in MDS and
MDS-derived leukemia. Blood. 1993, vol. 81, p. 3022-6.)
[0100] Apoptotic genes (increased bcl-2 expression) (KUROTAKI H, et
al. Apoptosis, bcl-2 expression and p53 accumulation in MDS, MDS
derived acute myeloid leukemia and de novo acute myeloid leukemia.
Acta Haematologica, 2000, vol. 102, p. 115-123.) And mutations in
genes including CHK2, p53, MLL have been implicated in the
pathogenesis of MDS (HOFMANN W K, et al. Mutation analysis of the
DNA-damage checkpoint gene CHK2 in myelodysplastic syndromes and
acute myeloid leukemias. Leukemia Research, 2001, vol. 25, p.
333-338; KIKUKAWA M, et al. Study of p53 in elderly patients with
myelodysplastic syndromes by immunohistochemistry and DNA analysis.
American Journal of Pathology. 1999, vol. 155, p. 717-721; POPPE B,
et al. Expression analyses identify MEL as a prominent target of
11q23 amplification and support an etiologic role for MLL gain of
function in myeloid malignancies. Blood. 2004, vol. 103, p.
229-235.)
[0101] Dysregulation of genes that encode angiogenic factors
involved in the growth of hematopoietic cells may play important
role in pathogenesis of MDS. (PRUNERI G, et al. Angiogenesis in
myelodysplastic syndromes. British Journal of Cancer, 1999, vol.
81, p. 1398-1401.) The immunomodulatory cytokine, TNF-.alpha. has
been shown to express strong inhibitory activity in hematopoiesis.
(BROXMEYER H E, et al. The suppressive influences of human tumor
necrosis factors on bone marrow hematopoietic progenitor cells from
normal donors and patients with leukemia: synergism of tumor
necrosis factor and interferon-gamma. Journal of immunology. 1986,
vol. 36, p. 4487-4495.) Other cytokines reportedly involved in the
processes leading to ineffective hematopoiesis in MDSs include
TGF-.beta., IL-1.beta., and TNF-related signaling molecules
TRADD/FADD, RIP, and TNF-related apoptosis inducing ligand (TRAIL)
(SAWANOBORI M, et al. Expression of TNF receptors and related
signaling molecules in the bone marrow from patients with
myelodysplastic syndromes. Leukemia Research, 2003, vol. 27, p.
583-591; PLASILOVA M, et al. TRAIL (Apo2L) suppresses growth of
primary human leukemia and myelodysplasia progenitors. Leukemia,
2002, vol. 16, p. 67-73.)
[0102] One embodiment of the invention will look at any of the cell
signaling pathways described above in classifying diseases, such as
MDS. Modulators can be designed to investigate these pathways and
any relevant parallel pathways.
[0103] In some embodiments, the invention provides a method for
diagnosing, prognosing, determining progression, predicting
response to treatment or choosing a treatment for MDS or rationale
combinations of drugs, or identification of new potentially
druggable targets the method, the method comprising the steps of
(a) subjecting a cell population from the individual to a plurality
of distinct modulators in separate cultures, (b) characterizing a
plurality of pathways in one or more cells from the separate
cultures comprising determining an activation level of at least one
activatable element in at least three pathways, where the pathways
are selected from the group consisting of apoptosis, cell cycle,
signaling, or DNA damage pathways, and (c) correlating the
characterization with diagnosing, prognosing, determining
progression, predicting response to treatment or choosing a
treatment for MDS, in an individual, where the pathways
characterization is indicative of the diagnosing, prognosing,
determining progression, response to treatment or the appropriate
treatment for MDS. In some embodiments, the individual has a
predefined clinical parameter and the characterization of multiple
pathways in combination with the clinical parameter is indicative
of the diagnosis, prognosis, determining progression, predicting
response to treatment or choosing a treatment for MDS, in an
individual. Examples of predetermined clinical parameters include,
but are not limited to, biochemical/molecular markers. In some
embodiments, the activatable elements can demarcate MDS cell
subpopulations that have different genetic subclone origins. In
some embodiments, the activatable elements can demarcate MDS
subpopulations that, in combination with additional surface
molecules, can allow for surrogate identification of MDS cell
subpopulations. In some embodiments, the activatable elements can
demarcate MDS subpopulations that can be used to determine other
protein, epitope-based, RNA, mRNA, siRNA, or metabolic markers that
singly or coordinately allow for surrogate identification of MDS
cell subpopulations, disease stage of the individual from which the
cells were derived, diagnosis, prognosis, response to treatment, or
new druggable targets. In some embodiments, the pathways
characterization allows for the delineation of MDS cell
subpopulations that are differentially susceptible to drugs or drug
combinations. In other embodiments, the cell types or activatable
elements from a given cell type will, in combination with
activatable elements in other cell types, provide ratiometric or
metrics that singly or coordinately allow for surrogate
identification of MDS cell subpopulations, disease stage of the
individual from which the cells were derived, diagnosis, prognosis,
response to treatment, or new druggable targets.
[0104] c. MPN
[0105] Dysregulation of the JAK-STAT signaling pathway has been
implicated in the development and progression of MPN. Alterations
in gene expression occur due to the activation of the JAK/STAT
pathway by exogenous stimuli (sepsis or G-CSF treatment), or
endogenously through activating mutations (e.g. JAK2-V617F. (ROBERT
KRALOVICS, et. al. Altered gene expression in myeloproliferative
neoplasms correlates with the activation of signaling by the V617F
mutation of JAK2. Blood. November 2005, vol. 106, no. 10, p.
3374-3376.) Several distinct MPN, polycythemia vera, essential
thrombocythemia, and myelofibrosis are found to have JAK2-V617F
mutation, supporting the concept that hyperactivation of JAK-STAT
signaling is involved in the development of MPN. JAK2 mutations are
present in virtually all cases of polycythemia vera, 41 to 72
percent in essential thrombocythemia, and 39 to 57 percent in
primary myelofibrosis. (BAXTER E J, et al. Acquired mutation of the
tyrosine kinase JAK2 in human myeloproliferative neoplasms. Lancet.
2005, vol. 365, no. 9464, p. 1054-1061.) Studies have found 15
gene-expression markers which were elevated in patients with PV,
including polycythemiarubra vera 1 (PRV1) and nuclear factor
erythroid-derived 2 (NF-E2), as well as one marker, ANKRD15, which
was down-regulated. (ROBERT KRALOVICS, et. al. Altered gene
expression in myeloproliferative neoplasms correlates with the
activation of signaling by the V617F mutation of Jak2. Blood.
November 2005, vol. 106, no. 10, p. 3374-3376.)
[0106] JAK3 important lymphoid development/myeloid differentiation.
Loss of function of JAK3 leads to an autosomal recessive form of
severe combined immunodeficiency. Gain of function mutations in
JAK3 have been shown to lead to acute megakaryocytic leukemia.
Leukemia and Lymphoma March 2008 49 (3):388-397
[0107] Phosphatases have been implicated in MPN biology. These
include SHP-1 (Src homology 2 domain containing tyrosine
Phosphatase 1), SHP-2 (Src homology 2 domain containing tyrosine
phosphatase 2), TC-PTP (T-cell PTP), RPTPa (Receptor protein
tyrosine phosphatase a), DEP (Density enhanced phosphatase),
PTP-MEG1 (Protein tyrosine phosphatase MEG1), PTP-MEG2 (Protein
tyrosine phosphatase MEG2). PTP-MEG2 is thought to be deregulated
in Normally PTP-MEG2 decreases as cells differentiate, however
PTP-MEG2 displays increased activity in PV.
[0108] One embodiment of the invention will look cell signaling
pathways described above in classifying and diagnosing MPN and
identification of new potentially druggable targets. Modulators can
be designed to investigate these pathways and any relevant parallel
pathways.
[0109] In some embodiments, the invention provides a method for
diagnosing, prognosing, determining progression, predicting
response to treatment or choosing a treatment for MPN or rationale
combination of different drugs, the method comprising the steps of
(a) subjecting a cell population from the individual to a plurality
of distinct modulators in separate cultures, (b) characterizing a
plurality of pathways in one or more cells from the separate
cultures comprising determining an activation level of at least one
activatable element in at least three pathways, where the pathways
are selected from the group consisting of apoptosis, cell cycle,
signaling, or DNA damage pathways, and (c) correlating the
characterization with diagnosing, prognosing, determining
progression, predicting response to treatment or choosing a
treatment for MPN, in an individual, where the pathways
characterization is indicative of the diagnosing, prognosing,
determining progression, response to treatment or the appropriate
treatment for MPN. In some embodiments, the individual has a
predefined clinical parameter and the characterization of multiple
pathways in combination with the clinical parameter is indicative
of the diagnoses, prognoses, determining progression, predicting
response to treatment or choosing a treatment for MPN, in an
individual. Examples of predetermined clinical parameters include,
but are not limited to, biochemical/molecular marker.
General Methods
[0110] Embodiments of the invention may be used to diagnose,
predict or to provide therapeutic decisions for disease treatment,
such as MDS, AML, or MPN. In some embodiments, the invention may be
used to identify new druggable targets and to design drug
combinations. The following will discuss instruments, reagents,
kits, and the biology involved with these and other diseases. One
aspect of the invention involves contacting a hematopoietic cell
with a modulator; determining the activation states of a plurality
of activatable elements in the cell; and classifying the cell based
on said activation state.
[0111] In some embodiments, this invention is directed to methods
and compositions, and kits for analysis, drug screening, diagnosis,
prognosis, for methods of disease treatment and prediction. In some
embodiments, the present invention involves methods of analyzing
experimental data. In some embodiments, the physiological status of
cells present in a sample (e.g. clinical sample) is used, e.g., in
diagnosis or prognosis of a condition, patient selection for
therapy using some of the agents identified above, to monitor
treatment, modify therapeutic regimens, and to further optimize the
selection of therapeutic agents which may be administered as one or
a combination of agents. Hence, therapeutic regimens can be
individualized and tailored according to the data obtained prior
to, and at different times over the course of treatment, thereby
providing a regimen that is individually appropriate. In some
embodiments, a compound is contacted with cells to analyze the
response to the compound.
[0112] In some embodiments, the present invention is directed to
methods for classifying a sample derived from an individual having
or suspected of having a condition, e.g., a neoplastic or a
hematopoietic condition. The invention allows for identification of
prognostically and therapeutically relevant subgroups of conditions
and prediction of the clinical course of an individual. The methods
of the invention provide tools useful in the treatment of an
individual afflicted with a condition, including but not limited to
methods for assigning a risk group, methods of predicting an
increased risk of relapse, methods of predicting an increased risk
of developing secondary complications, methods of choosing a
therapy for an individual, methods of predicting duration of
response, response to a therapy for an individual, methods of
determining the efficacy of a therapy in an individual, and methods
of determining the prognosis for an individual. The present
invention provides methods that can serve as a prognostic indicator
to predict the course of a condition, e.g. whether the course of a
neoplastic or a hematopoietic condition in an individual will be
aggressive or indolent, thereby aiding the clinician in managing
the patient and evaluating the modality of treatment to be used. In
another embodiment, the present invention provides information to a
physician to aid in the clinical management of a patient so that
the information may be translated into action, including treatment,
prognosis or prediction.
[0113] In some embodiments, the invention is directed to methods of
characterizing a plurality of pathways in single cells. Exemplary
pathways include apoptosis, cell cycle, signaling, or DNA damage
pathways. In some embodiments, the characterization of the pathways
is correlated with diagnosing, prognosing or determining condition
progression in an individual. In some embodiments, the
characterization of the pathways is correlated with predicting
response to treatment or choosing a treatment in an individual. In
some embodiments, the characterization of the pathways is
correlated with finding a new druggable target. In some
embodiments, the pathways' characterization in combination with a
predetermined clinical parameter is indicative of the diagnosis,
prognosis or progression of the condition. In some embodiments, the
pathways' characterization in combination with a predetermined
clinical parameter is indicative of a response to treatment or of
the appropriate treatment for an individual. In some embodiments,
the characterization of the pathways in combination with a
predetermined clinical parameter is indicative a new druggable
target.
[0114] In some embodiments, the invention is directed to methods
for determining the activation level of one or more activatable
elements in a cell upon treatment with one or more modulators. The
activation of an activatable element in the cell upon treatment
with one or more modulators can reveal operative pathways in a
condition that can then be used, e.g., as an indicator to predict
course of the condition, to identify risk group, to predict an
increased risk of developing secondary complications, to choose a
therapy for an individual, to predict response to a therapy for an
individual, to determine the efficacy of a therapy in an
individual, and to determine the prognosis for an individual. In
some embodiments, the operative pathways can reveal whether
apoptosis, cell cycle, signaling, or DNA damage pathways are
functional in an individual, where a pathway is functional if it is
permissive for a response to a treatment. In some embodiments, when
apoptosis, cell cycle, signaling, and DNA damage pathways are
functional the individual can respond to treatment, and if at least
one of the pathways is not functional the individual can not
respond to treatment. In some embodiments, when the apoptosis and
DNA damage pathways are functional the individual can respond to
treatment. In some embodiments, the operative pathways can reveal
new druggable targets.
[0115] In some embodiments, the invention is directed to methods of
determining a phenotypic profile of a population of cells by
exposing the population of cells to a plurality of modulators in
separate cultures, determining the presence or absence of an
increase in activation level of an activatable element in the cell
population from each of the separate culture and classifying the
cell population based on the presence or absence of the increase in
the activation of the activatable element from each of the separate
culture. In some embodiments at least one of the modulators is an
inhibitor. In some embodiments, the presence or absence of an
increase in activation level of a plurality of activatable elements
is determined. In some embodiments, each of the activatable
elements belongs to a particular pathway and the activation level
of the activatable elements is used to characterize each of the
particular pathways. In some embodiments, a plurality of pathways
are characterized by exposing a population of cells to a plurality
of modulators in separate cultures, determining the presence or
absence of an increase in activation levels of a plurality of
activatable elements in the cell population from each of the
separate culture, wherein the activatable elements are within the
pathways being characterized and classifying the cell population
based on the characterizations of said multiple pathways. In some
embodiments, the activatable elements and modulators are selected
from the activatable elements and modulators listed in Tables 1, 2,
3 or 5. In some embodiments, the activatable elements and
modulators are selected from the activatable elements and
modulators listed in Table 12 and are used to predict response
duration in an individual after treatment.
[0116] In some embodiments, the invention is directed to methods
for classifying a cell by determining the presence or absence of an
increase in activation level of an activatable element in the, in
combination with additional expression markers. In some
embodiments, expression markers or drug transporters, such as CD34,
CD33, CD45, HLADR, CD11B FLT3 Ligand, c-KIT, ABCG2, MDR1, BCRP,
MRP1, LRP, and others noted below, can also be used for stratifying
responders and non-responders. The expression markers may be
detected using many different techniques, for example using nodes
from flow cytometry data (see the articles and patent applications
referred to above). Other common techniques employ expression
arrays (commercially available from Affymetrix, Santa Clara
Calif.), taqman (commercially available from ABI, Foster City
Calif.), SAGE (commercially available from Genzyme, Cambridge
Mass.), sequencing techniques (see the commercial products from
Helicos, 454, US Genomics, and ABI) and other commonly know assays.
See Golub et al., Science 286: 531-537 (1999). Expression markers
are measured in unstimulated cells to know whether they have an
impact on functional apoptosis. This provides implications for
treatment and prognosis for the disease. Under this hypothesis, the
amount of drug transporters correlates with the response of the
patient and non-responders may have more levels of drug
transporters (to move a drug out of a cell) as compared to
responders. In some embodiments, the invention is directed to
methods of classifying a cell population by contacting the cell
population with at least one modulator that affects signaling
mediated by receptors selected from the group comprising of growth
factors, mitogens and cytokines. In some embodiments, the invention
is directed to methods of classifying a cell population by
contacting the cell population with at least one modulator that
affects signaling mediated by receptors selected from the group
comprising SDF-1a, IFN-.alpha., IFN-.gamma., IL-10, IL-6, IL-27,
G-CSF, FLT-3L, IGF-1, M-CSF, SCF, PMA, and Thapsigargin;
determining the activation states of a plurality of activatable
elements in the cell comprising; and classifying the cell based on
said activation states and expression levels. In some embodiments,
the cell population is also exposed in a separate culture to at
least one modulator that slows or stops the growth of cells and/or
induces apoptosis of cells. In some embodiments, the modulator that
slows or stops the growth of cells and/or induces apoptosis of
cells is selected from the group consisting of, Etoposide,
Mylotarg, AraC, daunorubicin, staurosporine,
benzyloxycarbonyl-Val-Ala-Asp (OMe) fluoromethylketone (ZVAD),
lenalidomide, EPO, and azacitadine, decitabine. In some
embodiments, the cell population is also exposed in a separate
culture to at least one modulator that is an inhibitor. In some
embodiments the inhibitor is H.sub.2O.sub.2. In some embodiments,
the expression of a growth factor receptor, cytokine receptor
and/or a drug transporter is also measured. In some embodiments,
the methods comprise determining the expression level at least one
protein selected from the group comprising ABCG2, C-KIT receptor,
and FLT3 LIGAND receptor. Another embodiment of the invention
further includes using the modulators IL-3, IL-4, GM-CSF, EPO, LPS,
TNF-.alpha., and CD40L. In some embodiments, the cell population in
a hematopoietic cell population. In some embodiments, the invention
is directed to methods of correlating and/or classifying an
activation state of an AML, MDS or MPN cell with a clinical outcome
in an individual by subjecting the AML, MDS or MPN cell from the
individual to a modulator, determining the activation levels of a
plurality of activatable elements, and identifying a pattern of the
activation levels of the plurality of activatable elements to
determine the presence or absence of an alteration in signaling,
where the presence of the alteration is indicative of a clinical
outcome. In some embodiments, the activatable elements can
demarcate AML, MDS or MPN cell subpopulations that have different
genetic subclone origins. In some embodiments, the activatable
elements can demarcate AML, MDS or MPN subpopulations that can be
used to determine other protein, epitope-based, RNA, mRNA, siRNA,
or metabolomic markers that singly or coordinately allow for
surrogate identification of AML, MDS or MPN cell subpopulations,
disease stage of the individual from which the cells were derived,
diagnosis, prognosis, response to treatment, or new druggable
targets. In some embodiments, the pathways characterization allows
for the delineation of AML, MDS or MPN cell subpopulations that are
differentially susceptible to drugs or drug combinations. In other
embodiments, the cell types or activatable elements from a given
cell type will, in combination with activatable elements in other
cell types, provide ratiometric or metrics that singly or
coordinately allow for surrogate identification of AML, MDS or MPN
cell subpopulations, disease stage of the individual from which the
cells were derived, diagnosis, prognosis, response to treatment, or
new druggable targets.
[0117] The subject invention also provides kits for use in
determining the physiological status of cells in a sample, the kit
comprising one or more modulators, inhibitors, specific binding
elements for signaling molecules, and may additionally comprise one
or more therapeutic agents. The above reagents for the kit are all
recited and listed in the present application below. The kit may
further comprise a software package for data analysis of the
cellular state and its physiological status, which may include
reference profiles for comparison with the test profile and
comparisons to other analyses as referred to above. The kit may
also include instructions for use for any of the above
applications.
[0118] In some embodiments, the invention provides methods,
including methods to determine the physiological status of a cell,
e.g., by determining the activation level of an activatable element
upon contact with one or more modulators. In some embodiments, the
invention provides methods, including methods to classify a cell
according to the status of an activatable element in a cellular
pathway. In some embodiments, the cells are classified by analyzing
the response to particular modulators and by comparison of
different cell states, with or without modulators. The information
can be used in prognosis and diagnosis, including susceptibility to
disease(s), status of a diseased state and response to changes, in
the environment, such as the passage of time, treatment with drugs
or other modalities. The physiological status of the cells provided
in a sample (e.g. clinical sample) may be classified according to
the activation of cellular pathways of interest. The cells can also
be classified as to their ability to respond to therapeutic agents
and treatments. The physiological status of the cells can provide
new druggable targets for the development of treatments. These
treatments can be used alone or in combination with other
treatments. The physiological status of the cells can be used to
design combination treatments.
[0119] One or more cells or cell types, or samples containing one
or more cells or cell types, can be isolated from body samples. The
cells can be separated from body samples by centrifugation,
elutriation, density gradient separation, apheresis, affinity
selection, panning, FACS, centrifugation with Hypaque, solid
supports (magnetic beads, beads in columns, or other surfaces) with
attached antibodies, etc. By using antibodies specific for markers
identified with particular cell types, a relatively homogeneous
population of cells may be obtained. Alternatively, a heterogeneous
cell population can be used. Cells can also be separated by using
filters. For example, whole blood can also be applied to filters
that are engineered to contain pore sizes that select for the
desired cell type or class. Rare pathogenic cells can be filtered
out of diluted, whole blood following the lysis of red blood cells
by using filters with pore sizes between 5 to 10 .mu.m, as
disclosed in U.S. patent application Ser. No. 09/790,673. Once a
sample is obtained, it can be used directly, frozen, or maintained
in appropriate culture medium for short periods of time. Methods to
isolate one or more cells for use according to the methods of this
invention are performed according to standard techniques and
protocols well-established in the art. See also U.S. Ser. Nos.
61/048,886; 61/048,920; and 61/048,657. See also, the commercial
products from companies such as BD and BCI as identified above.
[0120] See also U.S. Pat. Nos. 7,381,535 and 7,393,656. All of the
above patents and applications are incorporated by reference as
stated above.
[0121] In some embodiments, the cells are cultured post collection
in a media suitable for revealing the activation level of an
activatable element (e.g. RPMI, DMEM) in the presence, or absence,
of serum such as fetal bovine serum, bovine serum, human serum,
porcine serum, horse serum, or goat serum. When serum is present in
the media it could be present at a level ranging from 0.0001% to
30%.
[0122] In some embodiments, the cells are hematopoietic cells.
Examples of hematopoietic cells include but are not limited to
pluripotent hematopoietic stem cells, B-lymphocyte lineage
progenitor or derived cells, T-lymphocyte lineage progenitor or
derived cells, NK cell lineage progenitor or derived cells,
granulocyte lineage progenitor or derived cells, monocyte lineage
progenitor or derived cells, megakaryocyte lineage progenitor or
derived cells and erythroid lineage progenitor or derived
cells.
[0123] The term "patient" or "individual" as used herein includes
humans as well as other mammals. The methods generally involve
determining the status of an activatable element. The methods also
involve determining the status of a plurality of activatable
elements.
[0124] In some embodiments, the invention provides a method of
classifying a cell by determining the presence or absence of an
increase in activation level of an activatable element in the cell
upon treatment with one or more modulators, and classifying the
cell based on the presence or absence of the increase in the
activation of the activatable element. In some embodiments of the
invention, the activation level of the activatable element is
determined by contacting the cell with a binding element that is
specific for an activation state of the activatable element. In
some embodiments, a cell is classified according to the activation
level of a plurality of activatable elements after the cell have
been subjected to a modulator. In some embodiments of the
invention, the activation levels of a plurality of activatable
elements are determined by contacting a cell with a plurality of
binding elements, where each binding element is specific for an
activation state of an activatable element.
[0125] The classification of a cell according to the status of an
activatable element can comprise classifying the cell as a cell
that is correlated with a clinical outcome. In some embodiments,
the clinical outcome is the prognosis and/or diagnosis of a
condition. In some embodiments, the clinical outcome is the
presence or absence of a neoplastic or a hematopoietic condition
such as acute myeloid leukemia (AML), myelodysplastic syndrome
(MDS) or myeloproliferative neoplasms (MPN). In some embodiments,
the clinical outcome is the staging or grading of a neoplastic or
hematopoietic condition. Examples of staging include, but are not
limited to, aggressive, indolent, benign, refractory, Roman Numeral
staging, TNM Staging, Rai staging, Binet staging, WHO
classification, FAB classification, IPSS score, WPSS score, limited
stage, extensive stage, staging according to cellular markers,
occult, including information that may inform on time to
progression, progression free survival, overall survival, or
event-free survival.
[0126] The classification of a cell according to the status of an
activatable element can comprise classifying a cell as a cell that
is correlated to a patient response to a treatment. In some
embodiments, the patient response is selected from the group
consisting of complete response, partial response, nodular partial
response, no response, progressive disease, stable disease and
adverse reaction.
[0127] The classification of a rare cell according to the status of
an activatable element can comprise classifying the cell as a cell
that can be correlated with minimal residual disease or emerging
resistance. See U.S. No. 61/048,886 which is incorporated by
reference.
[0128] The classification of a cell according to the status of an
activatable element can comprise selecting a method of treatment.
Example of methods of treatments include, but are not limited to
chemotherapy, biological therapy, radiation therapy, bone marrow
transplantation, Peripheral stem cell transplantation, umbilical
cord blood transplantation, autologous stem cell transplantation,
allogeneic stem cell transplantation, syngeneic stem cell
transplantation, surgery, induction therapy, maintenance therapy,
watchful waiting, and other therapy.
[0129] A modulator can be an activator, an inhibitor or a compound
capable of impacting cellular signaling networks. Modulators can
take the form of a wide variety of environmental cues and inputs.
Examples of modulators include but are not limited to growth
factors, mitogens, cytokines, adhesion molecules, drugs, hormones,
small molecules, polynucleotides, antibodies, natural compounds,
lactones, chemotherapeutic agents, immune modulators,
carbohydrates, proteases, ions, reactive oxygen species, radiation,
physical parameters such as heat, cold, UV radiation, peptides, and
protein fragments, either alone or in the context of cells, cells
themselves, viruses, and biological and non-biological complexes
(e.g. beads, plates, viral envelopes, antigen presentation
molecules such as major histocompatibility complex). One exemplary
set of modulators, include but are not limited to SDF-1.alpha.,
IFN-.alpha., IFN-.gamma., IL-10, IL-6, IL-27, G-CSF, FLT-3L, IGF-1,
M-CSF, SCF, PMA, Thapsigargin, H.sub.2O.sub.2, Etoposide, Mylotarg,
AraC, daunorubicin, staurosporine, benzyloxycarbonyl-Val-Ala-Asp
(OMe) fluoromethylketone (ZVAD), lenalidomide, EPO, azacitadine,
decitabine, IL-3, IL-4, GM-CSF, EPO, LPS, TNF-.alpha., and
CD40L.
[0130] In some embodiments, the modulator is an activator. In some
embodiments the modulator is an inhibitor. In some embodiments, the
invention provides methods for classifying a cell by contacting the
cell with an inhibitor, determining the presence or absence of an
increase in activation level of an activatable element in the cell,
and classifying the cell based on the presence or absence of the
increase in the activation of the activatable element. In some
embodiments, a cell is classified according to the activation level
of a plurality of activatable elements after the cells have been
subjected to an inhibitor. In some embodiments, the inhibitor is an
inhibitor of a cellular factor or a plurality of factors that
participates in a signaling cascade in the cell. In some
embodiments, the inhibitor is a phosphatase inhibitor. Examples of
phosphatase inhibitors include, but are not limited to
H.sub.2O.sub.2, siRNA, miRNA, Cantharidin, (-)-p-Bromotetramisole,
Microcystin LR, Sodium Orthovanadate, Sodium Pervanadate, Vanadyl
sulfate, Sodium oxodiperoxo(1,10-phenanthroline)vanadate,
bis(maltolato)oxovanadium(IV), Sodium Molybdate, Sodium Perm
olybdate, Sodium Tartrate, Imidazole, Sodium Fluoride,
.beta.-Glycerophosphate, Sodium Pyrophosphate Decahydrate,
Calyculin A, Discodermia calyx, bpV(phen), mpV(pic), DMHV,
Cypermethrin, Dephostatin, Okadaic Acid, NIPP-1,
N-(9,10-Dioxo-9,10-dihydro-phenanthren-2-yl)-2,2-dimethyl-propion-
amide, .alpha.-Bromo-4-hydroxyacetophenone, 4-Hydroxyphenacyl Br,
.alpha.-Bromo-4-methoxyacetophenone, 4-Methoxyphenacyl Br,
.alpha.-Bromo-4-(carboxymethoxy)acetophenone,
4-(Carboxymethoxy)phenacyl Br, and
bis(4-Trifluoromethylsulfonamidophenyl)-1,4-diisopropylbenzene,
phenylarsine oxide, Pyrrolidine Dithiocarbamate, and Aluminium
fluoride. In some embodiments, the phosphatase inhibitor is
H.sub.2O.sub.2.
[0131] In some embodiments, the methods of the invention provide
methods for classifying a cell population or determining the
presence or absence of a condition in an individual by subjecting a
cell from the individual to a modulator and an inhibitor,
determining the activation level of an activatable element in the
cell, and determining the presence or absence of a condition based
on the activation level. In some embodiments, the activation level
of a plurality of activatable elements in the cell is determined.
The inhibitor can be an inhibitor as described herein. In some
embodiments, the inhibitor is a phosphatase inhibitor. In some
embodiments, the inhibitor is H.sub.2O.sub.2. The modulator can be
any modulator described herein. In some embodiments, the methods of
the invention provides for methods for classifying a cell
population by exposing the cell population to a plurality of
modulators in separate cultures and determining the status of an
activatable element in the cell population. In some embodiments,
the status of a plurality of activatable elements in the cell
population is determined. In some embodiments, at least one of the
modulators of the plurality of modulators is an inhibitor. The
modulator can be at least one of the modulators described herein.
In some embodiments, at least one modulator is selected from the
group consisting of SDF-1.alpha., IFN-.alpha., IFN-.gamma., IL-10,
IL-6, G-CSF, FLT-3L, IGF-1, M-CSF, SCF, PMA, Thapsigargin,
H.sub.2O.sub.2, Etoposide, Mylotarg, AraC, daunorubicin,
staurosporine, benzyloxycarbonyl-Val-Ala-Asp (OMe)
fluoromethylketone (ZVAD), lenalidomide, EPO, azacitadine,
decitabine, IL-3, IL-4, GM-CSF, EPO, LPS, TNF-.alpha., and CD40L or
a combination thereof. In some embodiments of the invention, the
status of an activatable element is determined by contacting the
cell population with a binding element that is specific for an
activation state of the activatable element. In some embodiments,
the status of a plurality of activatable elements is determined by
contacting the cell population with a plurality of binding
elements, where each binding element is specific for an activation
state of an activatable element.
[0132] In some embodiments, the methods of the invention provide
methods for determining a phenotypic profile of a population of
cells by exposing the population of cells to a plurality of
modulators (recited herein) in separate cultures, determining the
presence or absence of an increase in activation level of an
activatable element in the cell population from each of the
separate cultures and classifying the cell population based on the
presence or absence of the increase in the activation of the
activatable element from each of the separate culture. In some
embodiments, the phenotypic profile is used to characterize
multiple pathways in the population of cells.
[0133] Patterns and profiles of one or more activatable elements
are detected using the methods known in the art including those
described herein. In some embodiments, patterns and profiles of
activatable elements that are cellular components of a cellular
pathway or a signaling pathway are detected using the methods
described herein. For example, patterns and profiles of one or more
phosphorylated polypeptides are detected using methods known in art
including those described herein.
[0134] In some embodiments, cells (e.g. normal cells) other than
the cells associated with a condition (e.g. cancer cells) or a
combination of cells are used, e.g., in assigning a risk group,
predicting an increased risk of relapse, predicting an increased
risk of developing secondary complications, choosing a therapy for
an individual, predicting response to a therapy for an individual,
determining the efficacy of a therapy in an individual, and/or
determining the prognosis for an individual. That is that cells
other than cells associated with a condition (e.g. cancer cells)
are in fact reflective of the condition process. For instance, in
the case of cancer, infiltrating immune cells might determine the
outcome of the disease. Alternatively, a combination of information
from the cancer cell plus the immune cells in the blood that are
responding to the disease, or reacting to the disease can be used
for diagnosis or prognosis of the cancer.
[0135] In some embodiments, the invention provides methods to carry
out multiparameter flow cytometry for monitoring phospho-protein
responses to various factors in acute myeloid leukemia, MDS, or MPN
at the single cell level. Phospho-protein members of signaling
cascades and the kinases and phosphatases that interact with them
are required to initiate and regulate proliferative signals in
cells. Apart from the basal level of protein phosphorylation alone,
the effect of potential drug molecules on these network pathways
was studied to discern unique cancer network profiles, which
correlate with the genetics and disease outcome. Single cell
measurements of phospho-protein responses reveal shifts in the
signaling potential of a phospho-protein network, enabling
categorization of cell network phenotypes by multidimensional
molecular profiles of signaling. See U.S. Pat. No. 7,393,656. See
also Irish et. al., Single cell profiling of potentiated
phospho-protein networks in cancer cells. Cell. 2004, vol. 118, p.
1-20.
[0136] Flow cytometry is useful in a clinical setting, since
relatively small sample sizes, as few as 10,000 cells, can produce
a considerable amount of statistically tractable multidimensional
signaling data and reveal key cell subsets that are responsible for
a phenotype. See U.S. Pat. Nos. 7,381,535 and 7,393,656. See also
Krutzik et al, 2004).
[0137] Cytokine response panels have been studied to survey altered
signal transduction of cancer cells by using a multidimensional
flow cytometry file which contained at least 30,000 cell events. In
one embodiment, this panel is expanded and the effect of growth
factors and cytokines on primary AML samples studied. See U.S. Pat.
Nos. 7,381,535 and 7,393,656. See also Irish et. al., CELL July 23;
118(2):217-28. In some embodiments, the analysis involves working
at multiple characteristics of the cell in parallel after contact
with the compound. For example, the analysis can examine drug
transporter function; drug transporter expression; drug metabolism;
drug activation; cellular redox potential; signaling pathways; DNA
damage repair; and apoptosis.
[0138] In some embodiments, the modulators include growth factors,
cytokines, chemokines, phosphatase inhibitors, and pharmacological
reagents. The response panel is composed of at least one of:
SDF-1.alpha., IFN-.alpha., IFN-.gamma., IL-10, IL-6, IL-27, G-CSF,
FLT-3L, IGF-1, M-CSF, SCF, PMA, Thapsigargin, H.sub.2O.sub.2,
Etoposide, Mylotarg, AraC, daunorubicin, staurosporine,
benzyloxycarbonyl-Val-Ala-Asp (OMe) fluoromethylketone (ZVAD),
lenalidomide, EPO, azacitadine, decitabine, IL-3, IL-4, GM-CSF,
EPO, LPS, TNF-.alpha., and CD40L.
[0139] The response of each phospho-protein node is compared to the
basal state and can be represented by calculating the log.sub.2
fold difference in the Median Fluorescence Intensity (MFI) of the
stimulated sample divided by the unstimulated sample. The data can
be analyzed using any of the metrics described herein including the
metric described in FIG. 2. However, other statistical methods may
be used. The growth factor and the cytokine response panel included
detection of phosphorylated Stat1, Stat3, Stat5, Stat6,
PLC.gamma.2, S6, Akt, Erk1/2, CREB, p38, and NF-KBp-65. In some
embodiments, a diagnosis, prognosis, a prediction of outcome such
as response to treatment or relapse is performed by analyzing the
two or more phosphorylation levels of two or more proteins each in
response to one or more modulators. The phosphorylation levels of
the independent proteins can be measured in response to the same or
different modulators. Grouping of data points increases predictive
value.
[0140] In some embodiments, the AML or other panel of modulators is
further expanded to examine the process of DNA damage, apoptosis,
drug transport, drug metabolism, and the use of peroxide to
evaluate phosphatase activity. Analysis can assess the ability of
the cell to undergo the process of apoptosis after exposure to the
experimental drug in an in vitro assay as well as how quickly the
drug is exported out of the cell or metabolized. The drug response
panel can include but is not limited to detection of phosphorylated
Chk2, Cleaved Caspase 3, Caspase 8, PARP and mitochondria-released
Cytoplasmic Cytochrome C. Modulators may include Stauro, Etoposide,
Mylotarg, AraC, and daunorubicin. Analysis can assess phosphatase
activity after exposure of cells to phosphatase inhibitors
including but not limited to hydrogen peroxide (H.sub.2O.sub.2),
H.sub.2O.sub.2+SCF and H.sub.2O.sub.2+IFN.alpha.. The response
panel to evaluate phosphatase activity can include but is not
limited to the detection of phosphorylated Slp76, PLCg2, Lck, S6,
Akt, Erk, Stat1, Sta3, and Stat5. Later, the samples may be
analyzed for the expression of drug transporters such as MDR1/PGP,
MRP1 and BCRP/ABCG2. Samples may also be examined for XIAP,
Survivin, Bcl-2, MCL-1, Bim, Ki-67, Cyclin D1, ID1 and Myc.
[0141] Another method of the present invention is a method for
determining the prognosis and therapeutic selection for an
individual with acute myelogenous leukemia (AML). Using the
signaling nodes and methodology described herein, multiparametric
flow could separate a patient into "cytarabine responsive", meaning
that a cytarabine based induction regimen would yield a complete
response or "cytarabine non-responsive", meaning that the patient
is unlikely to yield a complete response to a cytarabine based
induction regimen. Furthermore, for those patients unlikely to
benefit from cytarabine based therapy, the individual's blood or
marrow sample could reveal signaling biology that corresponds to
either in-vivo or in-vitro sensitivity to a class of drugs
including but not limited to direct drug resistance modulators,
anti-Bcl-2 or pro-apoptotic drugs, proteosome inhibitors, DNA
methyl transferase inhibitors, histone deacetylase inhibitors,
anti-angiogenic drugs, farnesyl transferase inhibitors, FLt3 ligand
inhibitors, or ribonucleotide reductase inhibitors. An individual
with AML with a complete response to induction therapy could
further benefit from the present invention. The individual's blood
or marrow sample could reveal signaling biology that corresponds to
likelihood of benefit from further cytarabine based chemotherapy
versus myeloablative therapy followed by and stem cell transplant
versus reduced intensity therapy followed by stem cell
transplantation.
[0142] In some embodiments, the invention provides a method for
diagnosing, prognosing, determining progression, predicting
response to treatment or choosing a treatment for AML, MDS or MPN
in an individual where the individual has a predefined clinical
parameter, the method comprising the steps of (a) subjecting a cell
population from the individual to a plurality of distinct
modulators in separate cultures, (b) characterizing a plurality of
pathways in one or more cells from the separate cultures comprising
determining an activation level of at least one activatable element
in at least three pathways, where (i) the pathways are selected
from the group consisting of apoptosis, cell cycle, signaling, or
DNA damage pathways (ii) at least one of the pathways being
characterized in at least one of the separate cultures is an
apoptosis or DNA damage pathway, (iii) the distinct modulators
independently activate or inhibit said one or more pathways being
characterized, and (c) correlating the characterization with
diagnosing, prognosing, determining progression, predicting
response to treatment or choosing a treatment for AML, MDS or MPN
in an individual, where the pathways characterization in
combination with the clinical parameter is indicative of the
diagnosing, prognosing, determining progression, response to
treatment or the appropriate treatment for AML, MDS or MPN.
Examples of predetermined clinical parameters include, but are not
limited to, age, de novo acute myeloid leukemia patient, secondary
acute myeloid leukemia patient, or a biochemical/molecular marker.
In some embodiments, the individual is over 60 years old. In some
embodiments, the individual is under 60 years old. In some
embodiments the activatable elements and modulators are selected
from the activatable elements and modulators listed in Tables 1, 2,
3 or 5. In some embodiments, the activatable elements and
modulators are selected from the activatable elements and
modulators listed in Table 12 and are used to predict response
duration in an individual after treatment. In some embodiments the
modulator is selected from the group consisting of FLT3L, GM-CSF,
SCF, G-CSF, SDF1a, LPS, PMA, Thapsigargin, IFNg, IFNa, IL-27, IL-3,
IL-6, IL-10, ZVAD, H.sub.2O.sub.2, Staurosporine, Etoposide,
Mylotarg, Daunorubicin, and AraC. In some embodiments, when the
individual is under 60 years old the activatable elements and
modulators are selected from the activatable elements and
modulators listed in Table 6. In some embodiments, where the
individual is over 60 years the activatable elements and modulators
are selected from the activatable elements and modulators listed in
Table 7. In some embodiments, where the individual is a secondary
acute myeloid leukemia patient the activatable elements and
modulators are selected from the activatable elements and
modulators listed in Table 8 and Table 9. In some embodiments,
where the individual is a de novo acute myeloid leukemia patient
the activatable elements and modulators are selected from the
activatable elements and modulators listed in Table 10 and Table
11. In some embodiments, where the individual has a wild type FLT3
the activatable elements and modulators are selected from the
activatable elements and modulators listed in Table 13.
[0143] In some embodiments, the invention provides a method for
predicting a response to a treatment or choosing a treatment for
AML, MDS or MPN in an individual, the method comprising the steps:
(a) subjecting a cell population from the individual to at least
two distinct modulators in separate cultures; (b) determining an
activation level of at least one activatable element from each of
at least three pathways selected from the group consisting of
apoptosis, cell cycle, signaling, and DNA damage pathways in one or
more cells from each said separate cultures, where at least one of
the activatable elements is from an apoptosis or DNA damage
pathway, and where the activatable elements measured in each
separate culture are the same or the activatable elements measured
in each separate culture are different; and (c) predicting a
response to a treatment or choosing a therapeutic for AML, MDS or
MPN in the individual based on the activation level of said
activatable elements. In some embodiments, the method further
comprises determining whether the apoptosis, cell cycle, signaling,
or DNA damage pathways are functional in the individual based on
the activation levels of the activatable elements, wherein a
pathway is functional if it is permissive for a response to a
treatment, where if the apoptosis, cell cycle, signaling, and DNA
damage pathways are functional the individual can respond to
treatment, and where if at least one of the pathways is not
functional the individual can not respond to treatment. In some
embodiments, the method further comprises determining whether the
apoptosis, cell cycle, signaling, or DNA damage pathways are
functional in the individual based on the activation levels of the
activatable elements, wherein a pathway is functional if it is
permissive for a response to a treatment, where if the apoptosis
and DNA damage pathways are functional the individual can respond
to treatment. In some embodiments, the method further comprises
determining whether the apoptosis, cell cycle, signaling, or DNA
damage pathways are functional in the individual based on the
activation levels of the activatable elements, wherein a pathway is
functional if it is permissive for a response to a treatment, where
a therapeutic is chosen depending of the functional pathways in the
individual. In some embodiments the activatable elements and
modulators are selected from the activatable elements and
modulators listed in Tables 1, 2, 3 or 5. In some embodiments, the
activatable elements and modulators are selected from the
activatable elements and modulators listed in Table 12 and are used
to predict response duration in an individual after treatment. In
some embodiments the modulator is selected from the group
consisting of FLT3L, GM-CSF, SCF, G-CSF, SDF1a, LPS, PMA,
Thapsigargin, IFNg, IFNa, IL-27, IL-3, IL-6, IL-10, ZVAD,
H.sub.2O.sub.2, Staurosporine, Etoposide, Mylotarg, Daunorubicin,
and AraC.
[0144] In some embodiments, the invention provides a method of
predicting a response to a treatment or choosing a treatment for
AML, MDS or MPN in an individual, the method comprising the steps
of: (a) subjecting a cell population from said individual to at
least three distinct modulators in separate cultures, wherein: (i)
a first modulator is a growth factor or mitogen, (ii) a second
modulator is a cytokine, (iii) a third modulator is a modulator
that slows or stops the growth of cells and/or induces apoptosis of
cells or, the third modulator is an inhibitor; (b) determining the
activation level of at least one activatable element in one or more
cells from each of the separate cultures, where: (i) a first
activatable element is an activatable element within the PI3K/AKT,
or MAPK pathways and the activation level is measured in response
to the growth factor or mitogen, (ii) a second activatable element
is an activatable element within the STAT pathway and the
activation level is measured in response to the cytokine, (iii) a
third activatable element is an activatable element within an
apoptosis pathway and the activation level is measured in response
to the modulator that slows or stops the growth of cells and/or
induces apoptosis of cells, or the third activatable element is
activatable element within the phospholipase C pathway and the
activation level is measured in response to the inhibitor, or the
third activatable element is a phosphatase and the activation level
is measured in response to the inhibitor; and (c) correlating the
activation levels of said activatable elements with a response to a
treatment or with choosing a treatment for AML, MDS or MPN in the
individual. Examples of predefined clinical parameters include age,
de novo acute myeloid leukemia patient, secondary acute myeloid
leukemia patient, or a biochemical/molecular marker. In some
embodiments, the cytokine is selected from the group consisting of
G-CSF, IFNg, IFNa, IL-27, IL-3, IL-6, and IL-10. In some
embodiments, the growth factor is selected from the group
consisting of FLT3L, SCF, G-CSF, and SDF1a. In some embodiments,
the mitogen is selected from the group consisting of LPS, PMA, and
Thapsigargin. In some embodiments, the modulator that slows or
stops the growth of cells and/or induces apoptosis of cells is
selected from the group consisting of Staurosporine, Etoposide,
Mylotarg, Daunorubicin, and AraC.
[0145] In some embodiments, activation levels of an activatable
element within the STAT pathway higher than a threshold level in
response to a cytokine are indicative that an individual can not
respond to treatment. In some embodiment, a treatment is chosen
based on the ability of the cells to respond to treatment. In some
embodiments, the activatable element within the STAT pathway is
selected from the group consisting of p-Stat3, p-Stat5, p-Stat1,
and p-Stat6 and the cytokine is selected from the group consisting
of IFNg, IFNa, IL-27, IL-3, IL-6, IL-10, and G-CSF. In some
embodiments, the activatable element within the STAT pathway is
Stat 1 and the cytokine is IL-27 or G-CSF.
[0146] In some embodiments, activation levels of an activatable
element within the PI3K/AKT, or MAPK pathway higher than a
threshold level in response to a growth factor or mitogen is
indicative that an individual can not respond to treatment. In some
embodiment, a treatment is chosen based on the ability of the cells
to respond to treatment with a modulator. In some embodiments, the
activatable element within the PI3K/AKT, or MAPK pathway is
selected from the group consisting of p-ERK, p38 and pS6 and the
growth factor or mitogen is selected from the group consisting of
FLT3L, SCF, G-CSF, SDF1a, LPS, PMA, and Thapsigargin.
[0147] In some embodiments, activation levels of an activatable
element within the phospholipase C pathway higher than a threshold
level in response to an inhibitor is indicative that an individual
can respond to treatment. In some embodiment, a treatment is chosen
based on the ability of the cells to respond to treatment. In some
embodiments, the activatable element within the phospholipase C
pathway is selected from the group consisting of p-Slp-76, and
Plcg2 and the inhibitor is H.sub.2O.sub.2.
[0148] In some embodiments, activation levels of an activatable
element within the apoptosis pathway higher than a threshold in
response to a modulator that slows or stops the growth of cells
and/or induces apoptosis of cells is indicative that an individual
can respond to treatment. In some embodiment, a treatment is chosen
based on the ability of the cells to respond to treatment. In some
embodiments, the activatable element within the apoptosis pathway
is selected from the group consisting of Parp+, Cleaved Caspase 8,
and Cytoplasmic Cytochrome C, and the modulator that slows or stops
the growth of cells and/or induces apoptosis of cells is selected
from the group consisting of Staurosporine, Etoposide, Mylotarg,
Daunorubicin, and AraC.
[0149] In some embodiments, activation levels of an activatable
element within the apoptosis pathway higher than a threshold in
response to a modulator that slows or stops the growth of cells
and/or induces apoptosis of cells and activation levels of an
activatable element within the STAT pathway higher than a threshold
level in response to a cytokine is indicative that an individual
can not respond to treatment. In some embodiments, the activatable
element within the apoptosis pathway is selected from the group
consisting of Parp+, Cleaved Caspase 8, and Cytoplasmic Cytochrome
C, and the modulator that slows or stops the growth of cells and/or
induces apoptosis of cells is selected from the group consisting of
Staurosporine, Etoposide, Mylotarg, Daunorubicin, and AraC. In some
embodiments, the activatable element within the STAT pathway is
selected from the group consisting of p-Stat3, p-Stat5, p-Stat1,
and p-Stat6 and the cytokine is selected from the group consisting
of IFNg, IFNa, IL-27, IL-3, IL-6, IL-10, and G-CSF. In some
embodiments, the activatable element within the STAT pathway is
Stat 1 and the cytokine is IL-27 or G-CSF.
[0150] In some embodiments, the methods of the invention further
comprise determining an activation level of an activatable element
within a DNA damage pathway in response to a modulator that slows
or stops the growth of cells and/or induces apoptosis of cells. In
some embodiments, the activatable element within a DNA damage
pathway is selected from the group consisting of Chk2, ATM, ATR and
14-3-3 and the modulator that slows or stops the growth of cells
and/or induces apoptosis of cells is selected from the group
consisting of Staurosporine, Etoposide, Mylotarg, Daunorubicin, and
AraC.
[0151] In some embodiments, activation levels higher than a
threshold of an activatable element within a DNA damage pathway and
activation levels lower than a threshold of an activatable element
within the apoptosis pathway in response to a modulator that slows
or stops the growth of cells and/or induces apoptosis of cells are
indicative of a communication breakdown between the DNA damage
response pathway and the apoptotic machinery and that an individual
can not respond to treatment. In some embodiment, a treatment is
chosen based on the ability of the cells to respond to
treatment.
[0152] In some embodiments, the methods of the invention further
comprise determining an activation level of an activatable element
within a cell cycle pathway in response to a modulator that slows
or stops the growth of cells and/or induces apoptosis of cells. In
some embodiments, the activatable element within a DNA damage
pathway is selected from the group consisting of Cdc25, p53,
CyclinA-Cdk2, CyclinE-Cdk2, CyclinB-Cdk1, p21, and Gadd45 and the
modulator that slows or stops the growth of cells and/or induces
apoptosis of cells is selected from the group consisting of
Staurosporine, Etoposide, Mylotarg, Daunorubicin, and AraC.
[0153] In some embodiments, the methods of the invention further
comprise determining the levels of a drug transporter and/or a
cytokine receptor. In some embodiments, the cytokine receptors or
drug transporters are selected from the group consisting of MDR1,
ABCG2, MRP, P-Glycoprotein, CXCR4, FLT3, and c-kit. In some
embodiments, levels higher than a threshold of the drug transporter
and/or said cytokine receptor are indicative that an individual can
not respond to treatment. In some embodiment, a treatment is chosen
based on the ability of the cells to respond to treatment.
[0154] In some embodiments, the methods of the invention further
comprise determining the activation levels of an activatable
element within the Akt pathway in response to an inhibitor, where
activation levels higher that a threshold of the activatable
element within the Akt pathway in response to the inhibitor are
indicative that the individual can not respond to treatment. In
some embodiment, a treatment is chosen based on the ability of the
cells to respond to treatment.
[0155] In some embodiments, activation levels higher than a
threshold of an activatable element in the PI3K/AKT pathway in
response to a growth factor is indicative that the individual can
not respond to treatment. In some embodiments, the activatable
element in the PI3K/Akt pathway is Akt and the growth factor is
FLT3L.
[0156] In some embodiments, activation levels higher than a
threshold of an activatable element in the apoptosis pathway in
response to a modulator that slows or stops the growth of cells
and/or induces apoptosis of cells is indicative that the individual
can respond to treatment. In some embodiments, the activatable
element within the apoptosis pathway is Parp+ and the modulator
that slows or stops the growth of cells and/or induces apoptosis of
cells is selected from the group consisting of Staurosporine,
Etoposide, Mylotarg, Daunorubicin, and AraC.
[0157] In some embodiments, the invention provides a method of
predicting a response to a treatment or choosing a treatment for
AML in an individual where the individual is a secondary acute
myeloid leukemia patient, the method comprising the steps of (a)
subjecting a cell population from the individual to IL-27 and G-CSF
in separate cultures, (b) determining an activation level of pStat1
in one or more cells from each separate culture, (c) predicting a
response to a treatment or choosing a treatment for AML, in the
individual, where if the activation levels of pStat1 are higher
than a threshold level in response to both IL-27 and G-CSF the
individual can not respond to treatment and if the levels of pStat1
are lower than a threshold in response to both IL-27 and G-CSF the
individual can respond to treatment. In some embodiments, the
treatment is chemotherapy agent. Examples of chemotherapy agents
include, but are not limited to, cytarabine (ara-C), daunorubicin
(Daunomycin), idarubicin (Idamycin), mitoxantrone and
6-thioguanine. In some embodiments, the treatment is allogeneic
stem cell transplant or autologous stem cell transplant.
[0158] In some embodiments, the invention provides a method of
predicting a response to a treatment or choosing a treatment for
AML, MDS or MPN in an individual, the method comprising the steps
of: (a) subjecting a cell population from the individual to FLT3L,
(b) determining an activation level of pAkt in one or more cells
from the population (c) predicting a response to a treatment or
choosing a treatment for AML, MDS or MPN in the individual, where
if the activation levels of pAkt are higher than a predetermined
threshold in response to FLT3L the individual can not respond to
treatment. In some embodiments, the method further comprises the
steps of: (d) subjecting a cell population from said individual to
IL-27 in a separate culture, (e) determining an activation level of
Stat1 in one or more cells from the separate culture, (f)
predicting a response to a treatment or choosing a treatment for
AML, MDS or MPN in the individual, where if the activation levels
of pStat1 are higher than a predetermined threshold in response to
IL-27 the individual can not respond to treatment. In some
embodiments where the individual is over 60 years old the method
further comprises the step of: (g) subjecting a cell population
from the individual to H2O2 in a separate culture, (h) determining
an activation level of Plcg2 in one or more cells from the separate
culture (i) predicting a response to a treatment or choosing a
treatment for AML, MDS or MPN in the individual, wherein if the
activation levels of Plcg2 are higher than a predetermined
threshold in response to H2O2 the individual can not respond to
treatment. In some embodiments where the individual is under 60
years old the method further comprises the steps of (g) subjecting
a cell population from said individual to Etoposide in a separate
culture, (h) determining an activation level of Parp in one or more
cells from the separate culture, and (i) predicting a response to a
treatment for AML, MDS or MPN in said individual, where if the
activation levels of Parp are higher than a predetermined threshold
in response to Etoposide the individual can respond to treatment.
In some embodiments, the treatment is chemotherapy agent. Examples
of chemotherapy agents include, but are not limited to, cytarabine
(ara-C), daunorubicin (Daunomycin), idarubicin (Idamycin),
mitoxantrone and 6-thioguanine. In some embodiments, the treatment
is allogeneic stem cell transplant or autologous stem cell
transplant.
[0159] In some embodiments, the invention provides methods of
prediction response to a treatment and/or risk of relapse for AML,
MDS or MPN in an individual, the method comprising the steps of:
(a) subjecting a cell population from the individual to SCF, (b)
determining an activation level of pAkt and S6 in one or more cells
from the population (c) predicting a response to a treatment,
choosing a treatment or predicting risk of relapse for AML, MDS or
MPN in the individual, where if the activation levels of pAkt and
S6 are higher than a predetermined threshold in response to SCF the
individual can not respond to treatment or will have a higher
probability of relapse.
[0160] In some embodiments, a diagnosis, prognosis, a prediction of
outcome such as response to treatment or relapse is performed by
analyzing the two or more phosphorylation levels of two or more
proteins each in response to one or more modulators. The
phosphorylation levels of the independent proteins can be measured
in response to the same or different modulators. Grouping of data
points increases predictive value.
[0161] In some embodiments, the invention provides a method of
diagnosing, prognosing or predicting a response to a treatment or
choosing a treatment for AML, MDS or MPN in an individual, the
method comprising the steps of: (a) subjecting a cell population
from the individual in separate cultures to at least two modulators
selected from the group consisting of Staurosporine, Etoposide,
Mylotarg, Daunorubicin, AraC, CD40L, G-CSF, IGF-1, IFNg, IFNa,
IL-27, IL-3, IL-6, IL-10, FLT3L, SCF, G-CSF, SDF1a, LPS, PMA,
Thapsigargin and H2O2; b) determining the activation level of at
least three activatable elements selected from the group consisting
of p-Slp-76, p-Plcg2, p-Stat3, p-Stat5, p-Stat1, p-Stat6, p-Creb,
Parp+, Chk2, p-65/RelA, p-Akt, p-S6, p-ERK, Cleaved Caspase 8,
Cytoplasmic Cytochrome C, and p38; and (c) diagnosing, prognosing,
or predicting a response to a treatment or choosing a treatment for
AML, MDS or MPN based on the activation levels of the activatable
elements. In some embodiments, the method further comprises
determining the expression of a cytokine receptor or drug
transporter selected from the group consisting of MDR1, ABCG2, MRP,
P-Glycoprotein, CXCR4, FLT3, and c-Kit.
[0162] In some embodiments, the invention provides methods of
diagnosing, prognosing, determining progression, predicting a
response to a treatment or choosing a treatment for acute leukemia,
myelodysplastic syndrome or myeloproliferative neoplasms in an
individual, the methods comprising the steps of: (1) classifying
one or more hematopoietic cells associated with acute leukemia,
myelodysplastic syndrome or myeloproliferative neoplasms in the
individual by a method comprising: a) subjecting a cell population
comprising the one or more hematopoietic cells from the individual
to CD40L, b) determining an activation level of at least one
activatable element selected from the group consisting of p-CREB
and p-Erk in one or more cells from the individual, and c)
classifying the one or more hematopoietic cells based on the
activation levels of the activatable element; and (2) making a
decision regarding a diagnosis, prognosis, progression, response to
a treatment or a selection of treatment for acute leukemia,
myelodysplastic syndrome or myeloproliferative neoplasms in the
individual based on the classification of said one or more
hematopoietic cells.
[0163] In some embodiments, the inventions provides methods of
diagnosing, prognosing, determining progression, predicting a
response to a treatment or choosing a treatment for acute leukemia,
myelodysplastic syndrome or myeloproliferative neoplasms in an
individual, the methods comprising the steps of: (1) classifying
one or more hematopoietic cells associated with acute leukemia,
myelodysplastic syndrome or myeloproliferative neoplasms in the
individual by a method comprising: a) subjecting a cell population
comprising the one or more hematopoietic cells from the individual
to FLT3L, b) determining an activation level of at least one
activatable element selected from the group consisting of p-CREB,
p-plc.gamma.2, p-Stat5, p-Erk, p-Akt and p-S6 in one or more cells
from the individual, and c) classifying said one or more
hematopoietic cells based on the activation levels of the
activatable element; and (2) making a decision regarding a
diagnosis, prognosis, progression, response to a treatment or a
selection of treatment for acute leukemia, myelodysplastic syndrome
or myeloproliferative neoplasms in the individual based on said
classification of said one or more hematopoietic cells. In some
embodiment, the individual has a FLT3 mutation. In some
embodiments, classifying further comprises identifying a difference
in kinetics of said activation level.
[0164] In some embodiments, the invention provides methods of
diagnosing, prognosing, determining progression, predicting a
response to a treatment or choosing a treatment for acute leukemia,
myelodysplastic syndrome or myeloproliferative neoplasms in an
individual, said methods comprising the steps of: (1) classifying
one or more hematopoietic cells associated with acute leukemia,
myelodysplastic syndrome or myeloproliferative neoplasms in said
individual by a method comprising: a) subjecting a cell population
comprising said one or more hematopoietic cells from said
individual to G-CSF, b) determining an activation level of at least
one activatable element selected from the group consisting of
p-Stat 3, and p-Stat 5 in one or more cells from said individual,
and c) classifying said one or more hematopoietic cells based on
said activation levels of said activatable element; and (2) making
a decision regarding a diagnosis, prognosis, progression, response
to a treatment or a selection of treatment for acute leukemia,
myelodysplastic syndrome or myeloproliferative neoplasms in said
individual based on said classification of said one or more
hematopoietic cells.
[0165] In some embodiments, the invention provides methods of
diagnosing, prognosing, determining progression, predicting a
response to a treatment or choosing a treatment for acute leukemia,
myelodysplastic syndrome or myeloproliferative neoplasms in an
individual, said methods comprising the steps of: (1) classifying
one or more hematopoietic cells associated with acute leukemia,
myelodysplastic syndrome or myeloproliferative neoplasms in said
individual by a method comprising: a) subjecting a cell population
comprising said one or more hematopoietic cells from said
individual to H2O2 and SCF, b) determining an activation level of
at least one activatable element selected from the group consisting
of p-Erk, p-plc.gamma.2, and p-SLP 76 in one or more cells from
said individual, and c) classifying said one or more hematopoietic
cells based on said activation levels of said activatable element;
and (2) making a decision regarding a diagnosis, prognosis,
progression, response to a treatment or a selection of treatment
for acute leukemia, myelodysplastic syndrome or myeloproliferative
neoplasms in said individual based on said classification of said
one or more hematopoietic cells.
[0166] In some embodiments, the invention provides methods of
diagnosing, prognosing, determining progression, predicting a
response to a treatment or choosing a treatment for acute leukemia,
myelodysplastic syndrome or myeloproliferative neoplasms in an
individual, said methods comprising the steps of: (1) classifying
one or more hematopoietic cells associated with acute leukemia,
myelodysplastic syndrome or myeloproliferative neoplasms in said
individual by a method comprising: a) subjecting a cell population
comprising said one or more hematopoietic cells from said
individual to H2O2, b) determining an activation level of p-Lck in
one or more cells from said individual, and c) classifying said one
or more hematopoietic cells based on said activation levels of said
activatable element; and (2) making a decision regarding a
diagnosis, prognosis, progression, response to a treatment or a
selection of treatment for acute leukemia, myelodysplastic syndrome
or myeloproliferative neoplasms in said individual based on said
classification of said one or more hematopoietic cells.
[0167] In some embodiments, the invention provides method of
diagnosing, prognosing, determining progression, predicting a
response to a treatment or choosing a treatment for acute leukemia,
myelodysplastic syndrome or myeloproliferative neoplasms in an
individual, said methods comprising the steps of: (1) classifying
one or more hematopoietic cells associated with acute leukemia,
myelodysplastic syndrome or myeloproliferative neoplasms in said
individual by a method comprising: a) subjecting a cell population
comprising said one or more hematopoietic cells from said
individual to IGF-1, b) determining an activation level of at least
one activatable element selected from the group consisting of
p-CREB, and p-plc.gamma.2 in one or more cells from said
individual, and c) classifying said one or more hematopoietic cells
based on said activation levels of said activatable element; and
(2) making a decision regarding a diagnosis, prognosis,
progression, response to a treatment or a selection of treatment
for acute leukemia, myelodysplastic syndrome or myeloproliferative
neoplasms in said individual based on said classification of said
one or more hematopoietic cells.
[0168] In some embodiments, the invention provides methods of
diagnosing, prognosing, determining progression, predicting a
response to a treatment or choosing a treatment for acute leukemia,
myelodysplastic syndrome or myeloproliferative neoplasms in an
individual, said methods comprising the steps: (1) classifying one
or more hematopoietic cells associated with acute leukemia,
myelodysplastic syndrome or myeloproliferative neoplasms in said
individual by a method comprising: a) subjecting a cell population
comprising said one or more hematopoietic cells from said
individual to a modulator selected from the group consisting of
IL-27, IL-3 or IL-6, b) determining an activation level of at least
one activatable element selected from the group consisting of
p-CREB and p-Stat 3 in one or more cells from said individual, and
c) classifying said one or more hematopoietic cells based on said
activation levels of said activatable element; and (2) making a
decision regarding a diagnosis, prognosis, progression, response to
a treatment or a selection of treatment for acute leukemia,
myelodysplastic syndrome or myeloproliferative neoplasms in said
individual based on said classification of said one or more
hematopoietic cells.
[0169] In some embodiments, the invention provides methods of
diagnosing, prognosing, determining progression, predicting a
response to a treatment or choosing a treatment for acute leukemia,
myelodysplastic syndrome or myeloproliferative neoplasms in an
individual, said methods comprising the steps: (1) classifying one
or more hematopoietic cells associated with acute leukemia,
myelodysplastic syndrome or myeloproliferative neoplasms in said
individual by a method comprising: a) subjecting a cell population
comprising said one or more hematopoietic cells from said
individual to M-CSF, b) determining an activation level of at least
one activatable elements selected from the group consisting of
p-plc.gamma.2, p-Akt and p-CREB in one or more cells from said
individual, and c) classifying said one or more hematopoietic cells
based on said activation levels of said activatable element; and
(2) making a decision regarding a diagnosis, prognosis,
progression, response to a treatment or a selection of treatment
for acute leukemia, myelodysplastic syndrome or myeloproliferative
neoplasms in said individual based on said classification of said
one or more hematopoietic cells.
[0170] In some embodiments, the invention provides methods of
diagnosing, prognosing, determining progression, predicting a
response to a treatment or choosing a treatment for acute leukemia,
myelodysplastic syndrome or myeloproliferative neoplasms in an
individual, said methods comprising the steps of: (1) classifying
one or more hematopoietic cells associated with acute leukemia,
myelodysplastic syndrome or myeloproliferative neoplasms in said
individual by a method comprising: a) determining the basal levels
of at least one activatable element selected from the group
consisting of p-CREB, p-Erk, p-plc.gamma.2, p-Stat 3, and p-Stat 6
in one or more cells from said individual, and b) classifying said
one or more hematopoietic cells based on said activation levels of
said activatable element; and (2) making a decision regarding a
diagnosis, prognosis, progression, response to a treatment or a
selection of treatment for acute leukemia, myelodysplastic syndrome
or myeloproliferative neoplasms in said individual based on said
classification of said one or more hematopoietic cells.
[0171] In some embodiments, the invention provides method of
diagnosing, prognosing, determining progression, predicting a
response to a treatment or choosing a treatment for acute leukemia,
myelodysplastic syndrome or myeloproliferative neoplasms in an
individual, said methods comprising the steps of: (1) classifying
one or more hematopoietic cells associated with acute leukemia,
myelodysplastic syndrome or myeloproliferative neoplasms in said
individual by a method comprising: a) subjecting a cell population
comprising said one or more hematopoietic cells from said
individual to SCF, b) determining an activation level of at least
one activatable element selected from the group consisting of
p-CREB, and p-plc.gamma.2 in one or more cells from said
individual, and c) classifying said one or more hematopoietic cells
based on said activation levels of said activatable element; and
(2) making a decision regarding a diagnosis, prognosis,
progression, response to a treatment or a selection of treatment
for acute leukemia, myelodysplastic syndrome or myeloproliferative
neoplasms in said individual based on said classification of said
one or more hematopoietic cells.
[0172] In some embodiments, the invention provides methods of
diagnosing, prognosing, determining progression, predicting a
response to a treatment or choosing a treatment for acute leukemia,
myelodysplastic syndrome or myeloproliferative neoplasms in an
individual, said methods comprising the steps of: (1) classifying
one or more hematopoietic cells associated with acute leukemia,
myelodysplastic syndrome or myeloproliferative neoplasms in said
individual by a method comprising: a) subjecting a cell population
comprising said one or more hematopoietic cells from said
individual to a modulator selected from the group consisting of
SDF-1.alpha. and TNF.alpha., b) determining an activation level of
p-Erk in one or more cells from said individual, and c) classifying
said one or more hematopoietic cells based on said activation
levels of said activatable element; and (2) making a decision
regarding a diagnosis, prognosis, progression, response to a
treatment or a selection of treatment for acute leukemia,
myelodysplastic syndrome or myeloproliferative neoplasms in said
individual based on said classification of said one or more
hematopoietic cells.
[0173] In some embodiments, the invention provides methods of
diagnosing, prognosing, determining progression, predicting a
response to a treatment or choosing a treatment for acute leukemia,
myelodysplastic syndrome or myeloproliferative neoplasms in an
individual, said methods comprising the steps of: (1) classifying
one or more hematopoietic cells associated with acute leukemia,
myelodysplastic syndrome or myeloproliferative neoplasms in said
individual by a method comprising: a) subjecting a cell population
comprising said one or more hematopoietic cells from said
individual to Thapsigargin, b) determining an activation level of
p-CREB in one or more cells from said individual, and c)
classifying said one or more hematopoietic cells based on said
activation levels of said activatable element; and (2) making a
decision regarding a diagnosis, prognosis, progression, response to
a treatment or a selection of treatment for acute leukemia,
myelodysplastic syndrome or myeloproliferative neoplasms in said
individual based on said classification of said one or more
hematopoietic cells.
[0174] In some embodiments, the invention provides methods of
diagnosing, prognosing, determining progression, predicting a
response to a treatment or choosing a treatment for acute leukemia,
myelodysplastic syndrome or myeloproliferative neoplasms in an
individual, said methods comprising the steps of: (1) classifying
one or more hematopoietic cells associated with acute leukemia,
myelodysplastic syndrome or myeloproliferative neoplasms in said
individual by a method comprising: a) determining an activation
level of at least three activatable elements in the presence of a
modulator as listed in Tables 23 or 24, and b) classifying said one
or more hematopoietic cells based on said activation levels of said
activatable elements; and (2) making a decision regarding a
diagnosis, prognosis, progression, response to a treatment or a
selection of treatment for acute leukemia, myelodysplastic syndrome
or myeloproliferative neoplasms in said individual based on said
classification of said one or more hematopoietic cells. In some
embodiments, the activation level of said at least three
activatable elements being selected from the group consisting of
(i) p-Akt in the presence of SCF, (ii) p-Akt in the presence of
FLT3L, (iii) p-Chk2 in the presence of Etoposide; (iv) c-PARP+ in
the presence of no modulator and (v) p-Erk 1/2 in the presence of
PMA.
[0175] In some embodiments, the invention provides methods of
diagnosing, prognosing, determining progression, predicting a
response to a treatment or choosing a treatment for acute leukemia,
myelodysplastic syndrome or myeloproliferative neoplasms in an
individual, wherein said individual is a De Novo patient or a
patient with a FLT3 mutation, said methods comprising the steps of:
(1) classifying one or more hematopoietic cells associated with
acute leukemia, myelodysplastic syndrome or myeloproliferative
neoplasms in said individual by a method comprising: a) subjecting
a cell population comprising said one or more hematopoietic cells
from said individual to SCF or FLT3L, b) determining an activation
level of at least one activatable element selected from the group
consisting of p-S6, and p-plc.gamma.2 in one or more cells from
said individual, and c) classifying said one or more hematopoietic
cells based on said activation levels of said activatable element;
and (2) making a decision regarding a diagnosis, prognosis,
progression, response to a treatment or a selection of treatment
for acute leukemia, myelodysplastic syndrome or myeloproliferative
neoplasms in said individual based on said classification of said
one or more hematopoietic cells. In some embodiments, classifying
further comprises identifying a difference in kinetics of said
activation level.
[0176] In some embodiments, the invention provides methods of
diagnosing, prognosing, determining progression, predicting a
response to a treatment or choosing a treatment for acute leukemia,
myelodysplastic syndrome or myeloproliferative neoplasms in an
individual, wherein said individual is a De Novo patient, said
methods comprising the steps of: (1) classifying one or more
hematopoietic cells associated with acute leukemia, myelodysplastic
syndrome or myeloproliferative neoplasms in said individual by a
method comprising: a) subjecting a cell population comprising said
one or more hematopoietic cells from said individual to Etoposide,
b) determining an activation level of p-Chk2 in one or more cells
from said individual, and c) classifying said one or more
hematopoietic cells based on said activation levels of said
activatable element; and (2) making a decision regarding a
diagnosis, prognosis, progression, response to a treatment or a
selection of treatment for acute leukemia, myelodysplastic syndrome
or myeloproliferative neoplasms in said individual based on said
classification of said one or more hematopoietic cells.
[0177] In some embodiments, the invention provides methods of
diagnosing, prognosing, determining progression, predicting a
response to a treatment or choosing a treatment for acute leukemia,
myelodysplastic syndrome or myeloproliferative neoplasms in an
individual, wherein said individual is a De Novo patient, said
methods comprising the steps of: (1) classifying one or more
hematopoietic cells associated with acute leukemia, myelodysplastic
syndrome or myeloproliferative neoplasms in said individual by a
method comprising: a) subjecting a cell population comprising said
one or more hematopoietic cells from said individual to FLT3L, b)
determining an activation level of p-plc.gamma.2 in one or more
cells from said individual, and c) classifying said one or more
hematopoietic cells based on said activation levels of said
activatable element; and (2) making a decision regarding a
diagnosis, prognosis, progression, response to a treatment or a
selection of treatment for acute leukemia, myelodysplastic syndrome
or myeloproliferative neoplasms in said individual based on said
classification of said one or more hematopoietic cells.
[0178] In some embodiments, the invention provides methods of
diagnosing, prognosing, determining progression, predicting a
response to a treatment or choosing a treatment for acute leukemia,
myelodysplastic syndrome or myeloproliferative neoplasms in an
individual, wherein said individual is a De Novo patient, said
methods comprising the steps of: (1) classifying one or more
hematopoietic cells associated with acute leukemia, myelodysplastic
syndrome or myeloproliferative neoplasms in said individual by a
method comprising: a) subjecting a cell population comprising said
one or more hematopoietic cells from said individual to IL-3, b)
determining an activation level of p-Stat 3 in one or more cells
from said individual, and c) classifying said one or more
hematopoietic cells based on said activation levels of said
activatable element; and (2) making a decision regarding a
diagnosis, prognosis, progression, response to a treatment or a
selection of treatment for acute leukemia, myelodysplastic syndrome
or myeloproliferative neoplasms in said individual based on said
classification of said one or more hematopoietic cells.
[0179] In some embodiments, the invention provides methods of
diagnosing, prognosing, determining progression, predicting a
response to a treatment or choosing a treatment for acute leukemia,
myelodysplastic syndrome or myeloproliferative neoplasms in an
individual, wherein said individual is a De Novo patient, said
methods comprising the steps of: (1) classifying one or more
hematopoietic cells associated with acute leukemia, myelodysplastic
syndrome or myeloproliferative neoplasms in said individual by a
method comprising: a) subjecting a cell population comprising said
one or more hematopoietic cells from said individual to IL-6, b)
determining an activation level p-Stat 5 in one or more cells from
said individual, and c) classifying said one or more hematopoietic
cells based on said activation levels of said activatable element;
and (2) making a decision regarding a diagnosis, prognosis,
progression, response to a treatment or a selection of treatment
for acute leukemia, myelodysplastic syndrome or myeloproliferative
neoplasms in said individual based on said classification of said
one or more hematopoietic cells
[0180] In some embodiments, the invention provides methods of
diagnosing, prognosing, determining progression, predicting a
response to a treatment or choosing a treatment for acute leukemia,
myelodysplastic syndrome or myeloproliferative neoplasms in an
individual, wherein said individual is a De Novo patient, said
methods comprising the steps of: (1) classifying one or more
hematopoietic cells associated with acute leukemia, myelodysplastic
syndrome or myeloproliferative neoplasms in said individual by a
method comprising: a) determining an activation level of at least
one activatable element selected from the group consisting of
p-Erk, and p-Stat 6 in one or more cells from said individual, and
b) classifying said one or more hematopoietic cells based on said
activation levels of said activatable element; and (2) making a
decision regarding a diagnosis, prognosis, progression, response to
a treatment or a selection of treatment for acute leukemia,
myelodysplastic syndrome or myeloproliferative neoplasms in said
individual based on said classification of said one or more
hematopoietic cells.
[0181] In some embodiments, the invention provides methods of
diagnosing, prognosing, determining progression, predicting a
response to a treatment or choosing a treatment for acute leukemia,
myelodysplastic syndrome or myeloproliferative neoplasms in an
individual, wherein said individual is a De Novo patient, said
methods comprising the steps of: (1) classifying one or more
hematopoietic cells associated with acute leukemia, myelodysplastic
syndrome or myeloproliferative neoplasms in said individual by a
method comprising: a) subjecting a cell population comprising said
one or more hematopoietic cells from said individual to
SDF-1.alpha., b) determining an activation level of p-CREB in one
or more cells from said individual, and c) classifying said one or
more hematopoietic cells based on said activation levels of said
activatable element; and (2) making a decision regarding a
diagnosis, prognosis, progression, response to a treatment or a
selection of treatment for acute leukemia, myelodysplastic syndrome
or myeloproliferative neoplasms in said individual based on said
classification of said one or more hematopoietic cells.
[0182] In some embodiments, the invention provides methods of
diagnosing, prognosing, determining progression, predicting a
response to a treatment or choosing a treatment for acute leukemia,
myelodysplastic syndrome or myeloproliferative neoplasms in an
individual, wherein said individual is an individual with Secondary
acute leukemia, myelodysplastic syndrome or myeloproliferative
neoplasms, said methods comprising the steps of: (1) classifying
one or more hematopoietic cells associated with acute leukemia,
myelodysplastic syndrome or myeloproliferative neoplasms in said
individual by a method comprising: a) subjecting a cell population
comprising said one or more hematopoietic cells from said
individual to Etoposide, b) determining an activation level of at
least one activatable element selected from the group consisting of
p-Chk2, and c-PARP in one or more cells from said individual, and
c) classifying said one or more hematopoietic cells based on said
activation levels of said activatable element; and (2) making a
decision regarding a diagnosis, prognosis, progression, response to
a treatment or a selection of treatment for acute leukemia,
myelodysplastic syndrome or myeloproliferative neoplasms in said
individual based on said classification of said one or more
hematopoietic cells.
[0183] In some embodiments, the invention provides methods of
diagnosing, prognosing, determining progression, predicting a
response to a treatment or choosing a treatment for acute leukemia,
myelodysplastic syndrome or myeloproliferative neoplasms in an
individual, wherein said individual is an individual with Secondary
acute leukemia, myelodysplastic syndrome or myeloproliferative
neoplasms, said methods comprising the steps of: (1) classifying
one or more hematopoietic cells associated with acute leukemia,
myelodysplastic syndrome or myeloproliferative neoplasms in said
individual by a method comprising: a) subjecting a cell population
comprising said one or more hematopoietic cells from said
individual to G-CSF; b) determining an activation level of p-Stat 1
in one or more cells from said individual, and c) classifying said
one or more hematopoietic cells based on said activation levels of
said activatable element; and (2) making a decision regarding a
diagnosis, prognosis, progression, response to a treatment or a
selection of treatment for acute leukemia, myelodysplastic syndrome
or myeloproliferative neoplasms in said individual based on said
classification of said one or more hematopoietic cells.
[0184] In some embodiments, the invention provides method of
diagnosing, prognosing, determining progression, predicting a
response to a treatment or choosing a treatment for acute leukemia,
myelodysplastic syndrome or myeloproliferative neoplasms in an
individual, wherein said individual is an individual with Secondary
acute leukemia, myelodysplastic syndrome or myeloproliferative
neoplasms, said methods comprising the steps of: (1) classifying
one or more hematopoietic cells associated with acute leukemia,
myelodysplastic syndrome or myeloproliferative neoplasms in said
individual by a method comprising: a) subjecting a cell population
comprising said one or more hematopoietic cells from said
individual to IFN.alpha., b) determining an activation level of at
least one activatable element selected from the group consisting of
p-Stat 1, p-Stat 3 and p-Stat 5 in one or more cells from said
individual, and c) classifying said one or more hematopoietic cells
based on said activation levels of said activatable element; and
(2) making a decision regarding a diagnosis, prognosis,
progression, response to a treatment or a selection of treatment
for acute leukemia, myelodysplastic syndrome or myeloproliferative
neoplasms in said individual based on said classification of said
one or more hematopoietic cells.
[0185] In some embodiments, the invention provides methods of
diagnosing, prognosing, determining progression, predicting a
response to a treatment or choosing a treatment for acute leukemia,
myelodysplastic syndrome or myeloproliferative neoplasms in an
individual, wherein said individual is an individual with Secondary
acute leukemia, myelodysplastic syndrome or myeloproliferative
neoplasms, said methods comprising the steps of: (1) classifying
one or more hematopoietic cells associated with acute leukemia,
myelodysplastic syndrome or myeloproliferative neoplasms in said
individual by a method comprising: a) determining an activation
level of at least one activatable element selected from the group
consisting of p-Chk2, and c-PARP in one or more cells from said
individual, and b) classifying said one or more hematopoietic cells
based on said activation levels of said activatable element; and
(2) making a decision regarding a diagnosis, prognosis,
progression, response to a treatment or a selection of treatment
for acute leukemia, myelodysplastic syndrome or myeloproliferative
neoplasms in said individual based on said classification of said
one or more hematopoietic cells.
[0186] In some embodiments, the invention provides methods of
diagnosing, prognosing, determining progression, predicting a
response to a treatment or choosing a treatment for acute leukemia,
myelodysplastic syndrome or myeloproliferative neoplasms in an
individual, wherein said individual is an individual with Secondary
acute leukemia, myelodysplastic syndrome or myeloproliferative
neoplasms, said methods comprising the steps: (1) classifying one
or more hematopoietic cells associated with acute leukemia,
myelodysplastic syndrome or myeloproliferative neoplasms in said
individual by a method comprising: a) subjecting a cell population
comprising said one or more hematopoietic cells from said
individual to PMA, b) determining an activation level of p-CREB in
one or more cells from said individual, and c) classifying said one
or more hematopoietic cells based on said activation levels of said
activatable element; and (2) making a decision regarding a
diagnosis, prognosis, progression, response to a treatment or a
selection of treatment for acute leukemia, myelodysplastic syndrome
or myeloproliferative neoplasms in said individual based on said
classification of said one or more hematopoietic cells.
[0187] In some embodiments, the invention provides methods of
diagnosing, prognosing, determining progression, predicting a
response to a treatment or choosing a treatment for acute leukemia,
myelodysplastic syndrome or myeloproliferative neoplasms in an
individual, wherein said individual 60 years old or older, said
methods comprising the steps of: (1) classifying one or more
hematopoietic cells associated with acute leukemia, myelodysplastic
syndrome or myeloproliferative neoplasms in said individual by a
method comprising: a) subjecting a cell population comprising said
one or more hematopoietic cells from said individual to H2O2, b)
determining an activation level of p-Akt in one or more cells from
said individual, and c) classifying said one or more hematopoietic
cells based on said activation levels of said activatable element;
and (2) making a decision regarding a diagnosis, prognosis,
progression, response to a treatment or a selection of treatment
for acute leukemia, myelodysplastic syndrome or myeloproliferative
neoplasms in said individual based on said classification of said
one or more hematopoietic cells.
[0188] In some embodiments, the invention provides methods of
diagnosing, prognosing, determining progression, predicting a
response to a treatment or choosing a treatment for acute leukemia,
myelodysplastic syndrome or myeloproliferative neoplasms in an
individual, wherein said individual is 60 years old or older, said
methods comprising the steps of: (1) classifying one or more
hematopoietic cells associated with acute leukemia, myelodysplastic
syndrome or myeloproliferative neoplasms in said individual by a
method comprising: a) subjecting a cell population comprising said
one or more hematopoietic cells from said individual to IL-27, b)
determining an activation level of p-Stat 3 in one or more cells
from said individual, and c) classifying said one or more
hematopoietic cells based on said activation levels of said
activatable element; and (2) making a decision regarding a
diagnosis, prognosis, progression, response to a treatment or a
selection of treatment for acute leukemia, myelodysplastic syndrome
or myeloproliferative neoplasms in said individual based on said
classification of said one or more hematopoietic cells.
[0189] In some embodiments, the invention provides methods of
diagnosing, prognosing, determining progression, predicting a
response to a treatment or choosing a treatment for acute leukemia,
myelodysplastic syndrome or myeloproliferative neoplasms in an
individual, wherein said individual is 60 years old or older, said
methods comprising the steps of: (1) classifying one or more
hematopoietic cells associated with acute leukemia, myelodysplastic
syndrome or myeloproliferative neoplasms in said individual by a
method comprising: a) subjecting a cell population comprising said
one or more hematopoietic cells from said individual to LPS, b)
determining an activation level of p-Erk in one or more cells from
said individual, and c) classifying said one or more hematopoietic
cells based on said activation levels of said activatable element;
and (2) making a decision regarding a diagnosis, prognosis,
progression, response to a treatment or a selection of treatment
for acute leukemia, myelodysplastic syndrome or myeloproliferative
neoplasms in said individual based on said classification of said
one or more hematopoietic cells.
[0190] In some embodiments, the invention provides methods of
diagnosing, prognosing, determining progression, predicting a
response to a treatment or choosing a treatment for acute leukemia,
myelodysplastic syndrome or myeloproliferative neoplasms in an
individual, wherein said individual is less than 60 years old, said
methods comprising the steps of: (1) classifying one or more
hematopoietic cells associated with acute leukemia, myelodysplastic
syndrome or myeloproliferative neoplasms in said individual by a
method comprising: a) subjecting a cell population comprising said
one or more hematopoietic cells from said individual to a modulator
selected from the group consisting of Daunorubicin, AraC, Etoposide
and a combination thereof, b) determining an activation level of at
least one activatable element selected from the group consisting of
p-Chk2, and c-PARP in one or more cells from said individual, and
c) classifying said one or more hematopoietic cells based on said
activation levels of said activatable element; and (2) making a
decision regarding a diagnosis, prognosis, progression, response to
a treatment or a selection of treatment for acute leukemia,
myelodysplastic syndrome or myeloproliferative neoplasms in said
individual based on said classification of said one or more
hematopoietic cells.
[0191] In some embodiments, the invention provides methods of
diagnosing, prognosing, determining progression, predicting a
response to a treatment or choosing a treatment for acute leukemia,
myelodysplastic syndrome or myeloproliferative neoplasms in an
individual, wherein said individual is less than 60 years old, said
methods comprising the steps: (1) classifying one or more
hematopoietic cells associated with acute leukemia, myelodysplastic
syndrome or myeloproliferative neoplasms in said individual by a
method comprising: a) subjecting a cell population comprising said
one or more hematopoietic cells from said individual to a modulator
selected from the group consisting of GM-CSF, IFNa, IFNg, IL-10 and
IL-6, b) determining an activation level of at least one
activatable element selected from the group consisting of p-Stat 1,
p-Stat 3, and p-Stat 5 in one or more cells from said individual,
and c) classifying said one or more hematopoietic cells based on
said activation levels of said activatable element; and (2) making
a decision regarding a diagnosis, prognosis, progression, response
to a treatment or a selection of treatment for acute leukemia,
myelodysplastic syndrome or myeloproliferative neoplasms in said
individual based on said classification of said one or more
hematopoietic cells.
[0192] In some embodiments, the invention provides methods of
diagnosing, prognosing, determining progression, predicting a
response to a treatment or choosing a treatment for acute leukemia,
myelodysplastic syndrome or myeloproliferative neoplasms in an
individual, wherein said individual is less than 60 years old, said
methods comprising the steps of: (1) classifying one or more
hematopoietic cells associated with acute leukemia, myelodysplastic
syndrome or myeloproliferative neoplasms in said individual by a
method comprising: a) determining an activation level of at least
one activatable element selected from the group consisting of
c-PARP, and p-Erk in one or more cells from said individual, and b)
classifying said one or more hematopoietic cells based on said
activation levels of said activatable element; and (2) making a
decision regarding a diagnosis, prognosis, progression, response to
a treatment or a selection of treatment for acute leukemia,
myelodysplastic syndrome or myeloproliferative neoplasms in said
individual based on said classification of said one or more
hematopoietic cells.
[0193] In some embodiments, the invention provides methods of
diagnosing, prognosing, determining progression, predicting a
response to a treatment or choosing a treatment for acute leukemia,
myelodysplastic syndrome or myeloproliferative neoplasms in an
individual, wherein said individual is less than 60 years old, said
methods comprising the steps of: (1) classifying one or more
hematopoietic cells associated with acute leukemia, myelodysplastic
syndrome or myeloproliferative neoplasms in said individual by a
method comprising: a) subjecting a cell population comprising said
one or more hematopoietic cells from said individual to a modulator
selected from the group consisting of PMA and Thapsigargin, b)
determining an activation level of at least one activatable element
selected from the group consisting of p-CREB, and p-Erk in one or
more cells from said individual, and c) classifying said one or
more hematopoietic cells based on said activation levels of said
activatable element; and (2) making a decision regarding a
diagnosis, prognosis, progression, response to a treatment or a
selection of treatment for acute leukemia, myelodysplastic syndrome
or myeloproliferative neoplasms in said individual based on said
classification of said one or more hematopoietic cells.
[0194] In some embodiments, the invention provides methods of
diagnosing, prognosing, determining progression, predicting a
response to a treatment or choosing a treatment for acute leukemia,
myelodysplastic syndrome or myeloproliferative neoplasms in an
individual, wherein said individual is less than 60 years old, said
methods comprising the steps of: (1) classifying one or more
hematopoietic cells associated with acute leukemia, myelodysplastic
syndrome or myeloproliferative neoplasms in said individual by a
method comprising: a) subjecting a cell population comprising said
one or more hematopoietic cells from said individual to a modulator
selected from the group consisting of Staurosporine, ZVAD and a
combination thereof, b) determining an activation level of at least
one activatable element selected from the group consisting of
cytochrome C, and c-PARP in one or more cells from said individual,
and c) classifying said one or more hematopoietic cells based on
said activation levels of said activatable element; and (2) making
a decision regarding a diagnosis, prognosis, progression, response
to a treatment or a selection of treatment for acute leukemia,
myelodysplastic syndrome or myeloproliferative neoplasms in said
individual based on said classification of said one or more
hematopoietic cells.
[0195] In some embodiments, the invention provides methods of
diagnosing, prognosing, determining progression, predicting a
response to a treatment or choosing a treatment for acute leukemia,
myelodysplastic syndrome or myeloproliferative neoplasms in an
individual, wherein said individual has an intermediate or high
risk cytogenetics, said methods comprising the steps of: (1)
classifying one or more hematopoietic cells associated with acute
leukemia, myelodysplastic syndrome or myeloproliferative neoplasms
in said individual by a method comprising: a) subjecting a cell
population comprising said one or more hematopoietic cells from
said individual to a modulator selected from the group consisting
of G-CSF, IFN.alpha., IFNg, IL-10, IL-27 and IL-6, b) determining
an activation level of at least one activatable element selected
from the group consisting of p-Stat 1, p-Stat 3, and p-Stat 5 in
one or more cells from said individual, and c) classifying said one
or more hematopoietic cells based on said activation levels of said
activatable element; and (2) making a decision regarding a
diagnosis, prognosis, progression, response to a treatment or a
selection of treatment for acute leukemia, myelodysplastic syndrome
or myeloproliferative neoplasms in said individual based on said
classification of said one or more hematopoietic cells.
[0196] In some embodiments, the invention provides methods of
diagnosing, prognosing, determining progression, predicting a
response to a treatment or choosing a treatment for acute leukemia,
myelodysplastic syndrome or myeloproliferative neoplasms in an
individual, wherein said individual has an intermediate or high
risk cytogenetics, said methods comprising the steps of: (1)
classifying one or more hematopoietic cells associated with acute
leukemia, myelodysplastic syndrome or myeloproliferative neoplasms
in said individual by a method comprising: a) subjecting a cell
population comprising said one or more hematopoietic cells from
said individual to H2O2, b) determining an activation level of at
least one activatable element selected from the group consisting of
p-Akt, and p-Slp 76 in one or more cells from said individual, and
c) classifying said one or more hematopoietic cells based on said
activation levels of said activatable element; and (2) making a
decision regarding a diagnosis, prognosis, progression, response to
a treatment or a selection of treatment for acute leukemia,
myelodysplastic syndrome or myeloproliferative neoplasms in said
individual based on said classification of said one or more
hematopoietic cells.
[0197] In some embodiments, the invention provides methods of
diagnosing, prognosing, determining progression, predicting a
response to a treatment or choosing a treatment for acute leukemia,
myelodysplastic syndrome or myeloproliferative neoplasms in an
individual, wherein said individual has an intermediate or high
risk cytogenetics, said methods comprising the steps of: (1)
classifying one or more hematopoietic cells associated with acute
leukemia, myelodysplastic syndrome or myeloproliferative neoplasms
in said individual by a method comprising: a) subjecting a cell
population comprising said one or more hematopoietic cells from
said individual to FLT3L or SCF, b) determining an activation level
of at least one activatable element of p-Akt in one or more cells
from said individual, and c) classifying said one or more
hematopoietic cells based on said activation levels of said
activatable element; and (2) making a decision regarding a
diagnosis, prognosis, progression, response to a treatment or a
selection of treatment for acute leukemia, myelodysplastic syndrome
or myeloproliferative neoplasms in said individual based on said
classification of said one or more hematopoietic cells. In some
embodiments, classifying further comprises identifying a difference
in kinetics of said activation level.
[0198] In some embodiments, the invention provides methods of
diagnosing, prognosing, determining progression, predicting a
response to a treatment or choosing a treatment for acute leukemia,
myelodysplastic syndrome or myeloproliferative neoplasms in an
individual, wherein said individual has an intermediate or high
risk cytogenetics, said methods comprising the steps of: (1)
classifying one or more hematopoietic cells associated with acute
leukemia, myelodysplastic syndrome or myeloproliferative neoplasms
in said individual by a method comprising: a) subjecting a cell
population comprising said one or more hematopoietic cells from
said individual to SDF-1.alpha., b) determining an activation level
of p-CREB in one or more cells from said individual, and c)
classifying said one or more hematopoietic cells based on said
activation levels of said activatable element; and (2) making a
decision regarding a diagnosis, prognosis, progression, response to
a treatment or a selection of treatment for acute leukemia,
myelodysplastic syndrome or myeloproliferative neoplasms in said
individual based on said classification of said one or more
hematopoietic cells.
[0199] In some embodiments, the invention provides methods of
diagnosing, prognosing, determining progression, predicting a
response to a treatment or choosing a treatment for acute leukemia,
myelodysplastic syndrome or myeloproliferative neoplasms in an
individual, wherein said individual has an intermediate or high
risk cytogenetics, said methods comprising the steps of: (1)
classifying one or more hematopoietic cells associated with acute
leukemia, myelodysplastic syndrome or myeloproliferative neoplasms
in said individual by a method comprising: a) subjecting a cell
population comprising said one or more hematopoietic cells from
said individual to FLT3L or PMA b) determining an activation level
of p-CREB in one or more cells from said individual, and c)
classifying said one or more hematopoietic cells based on said
activation levels of said activatable element; and (2) making a
decision regarding a diagnosis, prognosis, progression, response to
a treatment or a selection of treatment for acute leukemia,
myelodysplastic syndrome or myeloproliferative neoplasms in said
individual based on said classification of said one or more
hematopoietic cells. In some embodiments, the individual has a FLT3
mutation. In some embodiments, classifying further comprises
identifying a difference in kinetics of said activation level.
[0200] In some embodiments, the invention provides methods of
diagnosing, prognosing, determining progression, predicting a
response to a treatment or choosing a treatment for acute leukemia,
myelodysplastic syndrome or myeloproliferative neoplasms in an
individual, wherein said individual has an intermediate or high
risk cytogenetics, said methods comprising the steps of: (1)
classifying one or more hematopoietic cells associated with acute
leukemia, myelodysplastic syndrome or myeloproliferative neoplasms
in said individual by a method comprising: a) subjecting a cell
population comprising said one or more hematopoietic cells from
said individual to Ara-C, Etoposide and Daunorubicin, b)
determining an activation level of at least one activatable element
selected from the group consisting of p-Chk2, and p-PARP in one or
more cells from said individual, and c) classifying said one or
more hematopoietic cells based on said activation levels of said
activatable element; and (2) making a decision regarding a
diagnosis, prognosis, progression, response to a treatment or a
selection of treatment for acute leukemia, myelodysplastic syndrome
or myeloproliferative neoplasms in said individual based on said
classification of said one or more hematopoietic cells.
[0201] In some embodiments, the invention provides methods of
diagnosing, prognosing, determining progression, predicting a
response to a treatment or choosing a treatment for acute leukemia,
myelodysplastic syndrome or myeloproliferative neoplasms in an
individual, wherein said individual has an intermediate or high
risk cytogenetics, said methods comprising the steps of: (1)
classifying one or more hematopoietic cells associated with acute
leukemia, myelodysplastic syndrome or myeloproliferative neoplasms
in said individual by a method comprising: a) subjecting a cell
population comprising said one or more hematopoietic cells from
said individual to FLT3L, b) determining an activation level of
p-Erk in one or more cells from said individual, and c) classifying
said one or more hematopoietic cells based on said activation
levels of said activatable element; and (2) making a decision
regarding a diagnosis, prognosis, progression, response to a
treatment or a selection of treatment for acute leukemia,
myelodysplastic syndrome or myeloproliferative neoplasms in said
individual based on said classification of said one or more
hematopoietic cells. In some embodiments, classifying further
comprises identifying a difference in kinetics of said activation
level.
[0202] In some embodiments, the invention provides methods of
diagnosing, prognosing, determining progression, predicting a
response to a treatment or choosing a treatment for acute leukemia,
myelodysplastic syndrome or myeloproliferative neoplasms in an
individual, said methods comprising the steps of: (1) classifying
one or more hematopoietic cells associated with acute leukemia,
myelodysplastic syndrome or myeloproliferative neoplasms in said
individual by a method comprising: a) determining an activation
level of at least two activatable elements in the presence of a
modulator as listed in FIG. 36, and b) classifying said one or more
hematopoietic cells based on said activation levels of said
activatable elements; and (2) making a decision regarding a
diagnosis, prognosis, progression, response to a treatment or a
selection of treatment for acute leukemia, myelodysplastic syndrome
or myeloproliferative neoplasms in said individual based on said
classification of said one or more hematopoietic cells. In some
embodiments, at least one of the activatable elements is an
activatable element from an apoptosis pathway.
[0203] In some embodiments, the invention provides methods of
diagnosing, prognosing, determining progression, predicting a
response to a treatment or choosing a treatment for acute leukemia,
myelodysplastic syndrome or myeloproliferative neoplasms in an
individual, said methods comprising the steps of: (1) classifying
one or more hematopoietic cells associated with acute leukemia,
myelodysplastic syndrome or myeloproliferative neoplasms in said
individual by a method comprising: a) determining an activation
level of at least three activatable elements in the presence of a
modulator as listed in FIG. 36, and b) classifying said one or more
hematopoietic cells based on said activation levels of said
activatable elements; and (2) making a decision regarding a
diagnosis, prognosis, progression, response to a treatment or a
selection of treatment for acute leukemia, myelodysplastic syndrome
or myeloproliferative neoplasms in said individual based on said
classification of said one or more hematopoietic cells. In some
embodiments, at least one of the activatable elements is an
activatable element from an apoptosis pathway. In some embodiments,
at least two of the activatable elements are activatable elements
from an apoptosis pathway. In some embodiments, at least two of the
activatable elements are activatable elements from an apoptosis
pathway and the third activatable element is p-Erk 1/2 in the
presence of PMA.
[0204] In some embodiments, the invention provides methods of
diagnosing, prognosing, determining progression, predicting a
response to a treatment or choosing a treatment for acute leukemia,
myelodysplastic syndrome or myeloproliferative neoplasms in an
individual, said methods comprising the steps of: (1) classifying
one or more hematopoietic cells associated with acute leukemia,
myelodysplastic syndrome or myeloproliferative neoplasms in said
individual by a method comprising: a) determining an activation
level of at least four activatable elements in the presence of a
modulator as listed in FIG. 36, and b) classifying said one or more
hematopoietic cells based on said activation levels of said
activatable elements; and (2) making a decision regarding a
diagnosis, prognosis, progression, response to a treatment or a
selection of treatment for acute leukemia, myelodysplastic syndrome
or myeloproliferative neoplasms in said individual based on said
classification of said one or more hematopoietic cells. In some
embodiments, at least one of the activatable elements is an
activatable element from an apoptosis pathway. In some embodiments,
at least two of the activatable elements are activatable elements
from an apoptosis pathway.
[0205] In some embodiments, the invention provides methods of
diagnosing, prognosing, determining progression, predicting a
response to a treatment or choosing a treatment for acute leukemia,
myelodysplastic syndrome or myeloproliferative neoplasms in an
individual, wherein said individual has a FLT3 mutation, the method
comprising: (1) classifying one or more hematopoietic cells
associated with acute leukemia, myelodysplastic syndrome or
myeloproliferative neoplasms in said individual by a method
comprising: a) subjecting a cell population comprising said one or
more hematopoietic cells from said individual to G-CSF, IL-6,
IFN.alpha., GM-CSF, IFNg, IL-10, or IL-27, b) determining an
activation level of p-Stat 1, p-Stat 3 or p-Stat 5 in one or more
cells from said individual, and c) classifying said one or more
hematopoietic cells based on said activation levels; and (2) making
a decision regarding a diagnosis, prognosis, progression, response
to a treatment or a selection of treatment for acute leukemia,
myelodysplastic syndrome or myeloproliferative neoplasms in said
individual based on said classification of said one or more
hematopoietic cells. In some embodiments, classifying further
comprises identifying a difference in kinetics of said activation
level.
[0206] In some embodiments, the invention provides methods for
predicting response to a treatment for AML, MDS or MPN, wherein the
positive predictive value (PPV) is higher than 60, 70, 80, 90, 95,
or 99.9%. In some embodiments, the invention provides methods for
predicting response to a treatment for AML, MDS or MPN, wherein the
PPV is equal or higher than 95%. In some embodiments, the invention
provides methods for predicting response to a treatment for AML,
MDS or MPN, wherein the negative predictive value (NPV) is higher
than 60, 70, 80, 90, 95, or 99.9%. In some embodiments, the
invention provides methods for predicting response to a treatment
for AML, MDS or MPN, wherein the NPV is higher than 85%.
[0207] In some embodiments, the invention provides methods for
predicting risk of relapse at 2 years, wherein the PPV is higher
than 60, 70, 80, 90, 95, or 99.9%. In some embodiments, the
invention provides methods for predicting risk of relapse at 2
years, wherein the PPV is equal or higher than 95%. In some
embodiments, the invention provides methods for predicting risk of
relapse at 2 years, wherein the NPV is higher than 60, 70, 80, 90,
95, or 99.9%. In some embodiments, the invention provides methods
for predicting risk of relapse at 2 years, wherein the NPV is
higher than 80%. In some embodiments, the invention provides
methods for predicting risk of relapse at 5 years, wherein the PPV
is higher than 60, 70, 80, 90, 95, or 99.9%. In some embodiments,
the invention provides methods for predicting risk of relapse at 5
years, wherein the PPV is equal or higher than 95%. In some
embodiments, the invention provides methods for predicting risk of
relapse at 5 years, wherein the NPV is higher than 60, 70, 80, 90,
95, or 99.9%. In some embodiments, the invention provides methods
for predicting risk of relapse at 5 years, wherein the NPV is
higher than 80%. In some embodiments, the invention provides
methods for predicting risk of relapse at 10 years, wherein the PPV
is higher than 60, 70, 80, 90, 95, or 99.9%. In some embodiments,
the invention provides methods for predicting risk of relapse at 10
years, wherein the PPV is equal or higher than 95%. In some
embodiments, the invention provides methods for predicting risk of
relapse at 10 years, wherein the NPV is higher than 60, 70, 80, 90,
95, or 99.9%. In some embodiments, the invention provides methods
for predicting risk of relapse at 10 years, wherein the NPV is
higher than 80%.
[0208] In some embodiments, the p value in the analysis of the
methods described herein is below 0.05, 04, 0.03, 0.02, 0.01,
0.009, 0.005, or 0.001. In some embodiments, the p value is below
0.001. Thus in some embodiments, the invention provides methods for
diagnosing, prognosing, determining progression or predicting
response for treatment of AML, MDS or MPN wherein the p value is
below 0.05, 04, 0.03, 0.02, 0.01, 0.009, 0.005, or 0.001. In some
embodiments, the p value is below 0.001. In some embodiments, the
invention provides methods for diagnosing, prognosing, determining
progression or predicting response for treatment of AML, MDS or MPN
wherein the AUC value is higher than 0.5, 0.6, 07, 0.8 or 0.9. In
some embodiments, the invention provides methods for diagnosing,
prognosing, determining progression or predicting response for
treatment of AML, MDS or MPN wherein the AUC value is higher than
0.7. In some embodiments, the invention provides methods for
diagnosing, prognosing, determining progression or predicting
response for treatment of AML, MDS or MPN wherein the AUC value is
higher than 0.8. In some embodiments, the invention provides
methods for diagnosing, prognosing, determining progression or
predicting response for treatment of AML, MDS or MPN wherein the
AUC value is higher than 0.9.
[0209] Another method of the present invention is a method for
determining the prognosis and therapeutic selection for an
individual with myelodysplasia or MDS. Using the signaling nodes
and methodology described herein, multiparametric flow cytometry
could separate a patient into one of five groups consisting of:
"AML-like", where a patient displays signaling biology that is
similar to that seen in acute myelogenous leukemia (AML) requiring
intensive therapy, "Epo-Responsive", where a patient's bone marrow
or potentially peripheral blood, shows signaling biology that
corresponds to either in-vivo or in-vitro sensitivity to
erythropoietin, "Lenalidomide responsive", where a patient's bone
marrow or potentially peripheral blood, shows signaling biology
that corresponds to either in-vivo or in-vitro sensitivity to
Lenalidomide, "Auto-immune", where a patient's bone marrow or
potentially peripheral blood, shows signaling biology that
corresponds to sensitivity to cyclosporine A (CSA) and
anti-thymocyte globulin (ATG).
[0210] In those cases where an individual is classified as
"AML-like", the individual's blood or marrow sample could reveal
signaling biology that corresponds to either in-vivo or in-vitro
sensitivity to cytarabine or to a class of drugs including but not
limited to direct drug resistance modulators, anti-Bcl-2 or
pro-apoptotic drugs, proteosome inhibitors, DNA methyl transferase
inhibitors, histone deacetylase inhibitors, anti-angiogenic drugs,
farnesyl transferase inhibitors, FLt3 ligand inhibitors, or
ribonucleotide reductase inhibitors.
[0211] In some embodiments of the invention, different gating
strategies can be used in order to analyze only blasts in the
sample of mixed population after treatment with the modulator.
These gating strategies can be based on the presence of one or more
specific surface marker expressed on each cell type. In some
embodiments, the first gate eliminates cell doublets so that the
user can focus on singlets. The following gate can differentiate
between dead cells and live cells and subsequent gating of live
cells classifies them into blasts, monocytes and lymphocytes. A
clear comparison can be carried out to study the effect of
potential modulators, such as G-SCF on activatable elements in:
ungated samples, blasts, monocytes, granulocytes and lymphocytes by
using two-dimensional contour plot representations of Stat5 and
Stat3 phosphorylation (x and Y axis) of patient samples. The level
of basal phosphorylation and the change in phosphorylation in both
Stat3 and Stat5 phosphorylation in response to G-CSF can be
compared. G-CSF increases both STAT3 and STAT5 phosphorylation and
this dual signaling can occur concurrently (subpopulations with
increases in both pSTAT 3 and pSTAT5) or individually
(subpopulations with either an increase in phospho pSTAT 3 or
pSTAT5 alone). The advantage of gating is to get a clearer picture
and more precise results of the effect of various activatable
elements on blasts.
[0212] In some embodiments, a gate is established after learning
from a responsive subpopulation. That is, a gate is developed from
one data set. This gate can then be applied retrospectively or
prospectively to other data sets (See FIGS. 5, 6, and 7). The cells
in this gate can be used for the diagnosis or prognosis of a
condition. The cells in this gate can also be used to predict
response to a treatment or for treatment selection. The mere
presence of cells in this gate may be indicative of a diagnosis,
prognosis, or a response to treatment. In some embodiments, the
presence of cells in this gate at a number higher than a threshold
number may be indicative of a diagnosis, prognosis, or a response
to treatment.
[0213] Some methods of analysis, also called metrics are: 1)
measuring the difference in the log of the median fluorescence
value between an unstimulated fluorochrome-antibody stained sample
and a sample that has not been treated with a stimulant or stained
(log(MFI.sub.Unstimulated Stained)-log(MFI.sub.Gated Unstained)),
2) measuring the difference in the log of the median fluorescence
value between a stimulated fluorochrome-antibody stained sample and
a sample that has not been treated with a stimulant or stained
(log(MFI.sub.Stimulated Stained) log(MFI.sub.Gated Unstained)), 3)
Measuring the change between the stimulated fluorochrome-antibody
stained sample and the unstimulated fluorochrome-antibody stained
sample log(MFI.sub.Stimulated Stained)-log(MFI.sub.Unstimulated
Stained), also called "fold change in median fluorescence
intensity", 4) Measuring the percentage of cells in a Quadrant Gate
of a contour plot which measures multiple populations in one or
more dimension 5) measuring MFI of phosphor positive population to
obtain percentage positivity above the background; and 6) use of
multimodality and spread metrics for large sample population and
for subpopulation analysis. Other metrics used to analyze data are
population frequency metrics measuring the frequency of cells with
a described property such as cells positive for cleaved PARP (%
PARP+), or cells positive for p-S6 and p-Akt (See FIG. 2B).
Similarly, measurements examining the changes in the frequencies of
cells may be applied such as the Change in % PARP+which would
measure the % PARP+.sub.stimulated Stained-% PARP+.sub.Unstimulated
Stained. The AUC.sub.unstim metric also measures changes in
population frequencies measuring the frequency of cells to become
positive compared to an unstimulated condition (FIG. 2B). The
metrics described in FIG. 2B can be use to measure apoptosis. For
example, these metrics can be applied to cleaved Caspase-3 and
Caspase-8, e.g., Change in % Cleaved Caspase-3 or Cleaved
Caspase-8.
[0214] Other possible metrics include third-color analysis (3D
plots); percentage positive and relative expression of various
markers; clinical analysis on an individual patient basis for
various parameters, including, but not limited to age, race,
cytogenetics, mutational status, blast percentage, CD34+
percentage, time of relapse, survival, etc. See FIG. 2. In
alternative embodiments, there are other ways of analyzing data,
such as third color analysis (3D plots), which can be similar to
Cytobank 2D, plus third D in color.
Disease Conditions
[0215] The methods of the invention are applicable to any condition
in an individual involving, indicated by, and/or arising from, in
whole or in part, altered physiological status in a cell. The term
"physiological status" includes mechanical, physical, and
biochemical functions in a cell. In some embodiments, the
physiological status of a cell is determined by measuring
characteristics of cellular components of a cellular pathway.
Cellular pathways are well known in the art. In some embodiments
the cellular pathway is a signaling pathway. Signaling pathways are
also well known in the art (see, e.g., Hunter T., Cell 100(1):
113-27 (2000); Cell Signaling Technology, Inc., 2002 Catalogue,
Pathway Diagrams pgs. 232-253). A condition involving or
characterized by altered physiological status may be readily
identified, for example, by determining the state in a cell of one
or more activatable elements, as taught herein.
[0216] In some embodiments, the present invention is directed to
methods for classifying one or more cells in a sample derived from
an individual having or suspected of having a condition. Example
conditions include AML, MDS, or MPN. In some embodiments, the
invention allows for identification of prognostically and
therapeutically relevant subgroups of the conditions and prediction
of the clinical course of an individual. In some embodiments, the
invention provides methods of classifying a cell according to the
activation levels of one or more activatable elements in a cell
from an individual having or suspected of having a condition. In
some embodiments, the classification includes classifying the cell
as a cell that is correlated with a clinical outcome. The clinical
outcome can be the prognosis and/or diagnosis of a condition,
and/or staging or grading of a condition. In some embodiments, the
classifying of the cell includes classifying the cell as a cell
that is correlated with a patient response to a treatment. In some
embodiments, the classifying of the cell includes classifying the
cell as a cell that is correlated with minimal residual disease or
emerging resistance.
Activatable Elements
[0217] The methods and compositions of the invention may be
employed to examine and profile the status of any activatable
element in a cellular pathway, or collections of such activatable
elements. Single or multiple distinct pathways may be profiled
(sequentially or simultaneously), or subsets of activatable
elements within a single pathway or across multiple pathways may be
examined (again, sequentially or simultaneously). In some
embodiments, apoptosis, signaling, cell cycle and/or DNA damage
pathways are characterized in order to classify one or more cells
in an individual. The characterization of multiple pathways can
reveal operative pathways in a condition that can then be used to
classify one or more cells in an individual. In some embodiments,
the classification includes classifying the cell as a cell that is
correlated with a clinical outcome. The clinical outcome can be the
prognosis and/or diagnosis of a condition, and/or staging or
grading of a condition. In some embodiments, the classifying of the
cell includes classifying the cell as a cell that is correlated
with a patient response to a treatment. In some embodiments, the
classifying of the cell includes classifying the cell as a cell
that is correlated with minimal residual disease or emerging
resistance.
[0218] As will be appreciated by those in the art, a wide variety
of activation events can find use in the present invention. In
general, the basic requirement is that the activation results in a
change in the activatable protein that is detectable by some
indication (termed an "activation state indicator"), preferably by
altered binding of a labeled binding element or by changes in
detectable biological activities (e.g., the activated state has an
enzymatic activity which can be measured and compared to a lack of
activity in the non-activated state). What is important is to
differentiate, using detectable events or moieties, between two or
more activation states (e.g. "off" and "on").
[0219] The activation state of an individual activatable element is
either in the on or off state. As an illustrative example, and
without intending to be limited to any theory, an individual
phosphorylatable site on a protein can activate or deactivate the
protein. Additionally, phosphorylation of an adapter protein may
promote its interaction with other components/proteins of distinct
cellular signaling pathways. The terms "on" and "off," when applied
to an activatable element that is a part of a cellular constituent,
are used here to describe the state of the activatable element, and
not the overall state of the cellular constituent of which it is a
part. Typically, a cell possesses a plurality of a particular
protein or other constituent with a particular activatable element
and this plurality of proteins or constituents usually has some
proteins or constituents whose individual activatable element is in
the on state and other proteins or constituents whose individual
activatable element is in the off state. Since the activation state
of each activatable element is measured through the use of a
binding element that recognizes a specific activation state, only
those activatable elements in the specific activation state
recognized by the binding element, representing some fraction of
the total number of activatable elements, will be bound by the
binding element to generate a measurable signal. The measurable
signal corresponding to the summation of individual activatable
elements of a particular type that are activated in a single cell
is the "activation level" for that activatable element in that
cell. The measurable signal can be produced by the binding element
and/or the activatable element. The measurable signal can be
produced by the activatable element after the activatable element
has been dissociated from the binding element.
[0220] Activation levels for a particular activatable element may
vary among individual cells so that when a plurality of cells is
analyzed, the activation levels follow a distribution. The
distribution may be a normal distribution, also known as a Gaussian
distribution, or it may be of another type. Different populations
of cells may have different distributions of activation levels that
can then serve to distinguish between the populations.
[0221] In some embodiments, the basis for classifying cells is that
the distribution of activation levels for one or more specific
activatable elements will differ among different phenotypes. A
certain activation level, or more typically a range of activation
levels for one or more activatable elements seen in a cell or a
population of cells, is indicative that that cell or population of
cells belongs to a distinctive phenotype. Other measurements, such
as cellular levels (e.g., expression levels) of biomolecules that
may not contain activatable elements, may also be used to classify
cells in addition to activation levels of activatable elements; it
will be appreciated that these levels also will follow a
distribution, similar to activatable elements. Thus, the activation
level or levels of one or more activatable elements, optionally in
conjunction with levels of one or more levels of biomolecules that
may or may not contain activatable elements, of cell or a
population of cells may be used to classify a cell or a population
of cells into a class. Once the activation level of intracellular
activatable elements of individual single cells is known they can
be placed into one or more classes, e.g., a class that corresponds
to a phenotype. A class encompasses a class of cells wherein every
cell has the same or substantially the same known activation level,
or range of activation levels, of one or more intracellular
activatable elements. For example, if the activation levels of five
intracellular activatable elements are analyzed, predefined classes
of cells that encompass one or more of the intracellular
activatable elements can be constructed based on the activation
level, or ranges of the activation levels, of each of these five
elements. It is understood that activation levels can exist as a
distribution and that an activation level of a particular element
used to classify a cell may be a particular point on the
distribution but more typically may be a portion of the
distribution.
[0222] In some embodiments, the basis for classifying cells may use
the position of a cell in a contour or density plot. The contour or
density plot represents the number of cells that share a
characteristic such as the activation level of activatable proteins
in response to a modulator. For example, when referring to
activation levels of activatable elements in response to one or
more modulators, normal individuals and patients with a condition
might show populations with increased activation levels in response
to the one or more modulators. However, the number of cells that
have a specific activation level (e.g. specific amount of an
activatable element) might be different between normal individuals
and patients with a condition. Thus, a cell can be classified
according to its location within a given region in the contour or
density plot. In other embodiments, the basis for classifying cells
may use a series of population clusters whose centers, centroids,
boundaries, relative positions describe the state of a cell, the
diagnosis or prognosis of a patient, selection of treatment, or
predicting response to treatment or to a combination of treatments,
or long term outcome.
[0223] In some embodiments, the basis for classifying cells may use
an N-dimensional Eigen map that describe the state of a cell, the
diagnosis or prognosis of a patient, selection of treatment, or
predicting response to treatment or to a combination of treatments,
or long term outcome.
[0224] In other embodiments, the basis for classifying cells may
use a Bayesian inference network of activatable elements
interaction capabilities that together, or in part, describe the
state of a cell, the diagnosis or prognosis of a patient, selection
of treatment, or predicting response to treatment or to a
combination of treatments, or long term outcome. See U.S.
publication no. 2007/0009923 entitled Use of Bayesian Networks for
Modeling Signaling Systems, incorporated herein by reference on its
entirety.
[0225] In addition to activation levels of intracellular
activatable elements, levels of intracellular or extracellular
biomolecules, e.g., proteins, may be used alone or in combination
with activation states of activatable elements to classify cells.
Further, additional cellular elements, e.g., biomolecules or
molecular complexes such as RNA, DNA, carbohydrates, metabolites,
and the like, may be used in conjunction with activatable states or
expression levels in the classification of cells encompassed
here.
[0226] In some embodiments, cellular redox signaling nodes are
analyzed for a change in activation level. Reactive oxygen species
(ROS) are involved in a variety of different cellular processes
ranging from apoptosis and necrosis to cell proliferation and
carcinogenesis. ROS can modify many intracellular signaling
pathways including protein phosphatases, protein kinases, and
transcription factors. This activity may indicate that the majority
of the effects of ROS are through their actions on signaling
pathways rather than via non-specific damage of macromolecules. The
exact mechanisms by which redox status induces cells to proliferate
or to die, and how oxidative stress can lead to processes evoking
tumor formation are still under investigation. See Mates, J M et
al., Arch Toxicol. 2008 May: 82(5):271-2; Galaris D., et al.,
Cancer Lett. 2008 Jul. 18; 266(1)21-9.
[0227] Reactive oxygen species can be measured. One example
technique is by flow cytometry. See Chang et al., Lymphocyte
proliferation modulated by glutamine: involved in the endogenous
redox reaction; Clin Exp Immunol 1999 September; 117(3): 482-488.
Redox potential can be evaluated by means of an ROS indicator, one
example being 2',7'-dichlorofluorescein-diacetate (DCFH-DA) which
is added to the cells at an exemplary time and temperature, such as
37.degree. C. for 15 minutes. DCF peroxidation can be measured
using flow cytometry. See Yang K D, Shaio M F. Hydroxyl radicals as
an early signal involved in phorbol ester-induced monocyte
differentiation of HL60 cells. Biochem Biophys Res Commun. 1994;
200:1650-7 and Wang J F, Jerrells T R, Spitzer J J. Decreased
production of reactive oxygen intermediates is an early event
during in vitro apoptosis of rat thymocytes. Free Radic Biol Med.
1996; 20:533-42.
[0228] In some embodiments, other characteristics that affect the
status of a cellular constituent may also be used to classify a
cell. Examples include the translocation of biomolecules or changes
in their turnover rates and the formation and disassociation of
complexes of biomolecule. Such complexes can include multi-protein
complexes, multi-lipid complexes, homo- or hetero-dimers or
oligomers, and combinations thereof. Other characteristics include
proteolytic cleavage, e.g. from exposure of a cell to an
extracellular protease or from the intracellular proteolytic
cleavage of a biomolecule.
[0229] In some embodiments, cellular pH is analyzed. See June, C H
and Moore, and J S, Curr Protoc Immulon, 2004 December; Chapter
5:Unit 5.5; Leyval, D et al., Flow cytometry for the intracellular
pH measurement of glutamate producing Corynebacterium glutamicum,
Journal of Microbiological Methods, Volume 29, Issue 2, 1 May 1997,
Pages 121-127; Weider, E D, et al., Measurement of intracellular pH
using flow cytometry with carboxy-SNARF-1. Cytometry, 1993
November; 14(8):916-21; and Valli, M, et al., Intracellular pH
Distribution in Saccharomyces cerevisiae Cell Populations, Analyzed
by Flow Cytometry, Applied and Environmental Microbiology, March
2005, p. 1515-1521, Vol. 71, No. 3.
[0230] In some embodiments, the activatable element is the
phosphorylation of immunoreceptor tyrosine-based inhibitory motif
(ITIM). An immunoreceptor tyrosine-based inhibition motif (ITIM),
is a conserved sequence of amino acids (S/I/V/LxYxxI/V/L) that is
found in the cytoplasmic tails of many inhibitory receptors of the
immune system. After ITIM-possessing inhibitory receptors interact
with their ligand, their ITIM motif becomes phosphorylated by
enzymes of the Src family of kinases, allowing them to recruit
other enzymes such as the phosphotyrosine phosphatases SHP-1 and
SHP-2, or the inositol-phosphatase called SHIP. These phosphatases
decrease the activation of molecules involved in cell signaling.
See Barrow A, Trowsdale J (2006). "You say ITAM and I say ITIM,
let's call the whole thing off: the ambiguity of immunoreceptor
signalling". Eur J Immunol 36 (7): 1646-53. When phosphorylated,
these phospho-tyrosine residues provide docking sites for the Shps
which may result in transmission of inhibitory signals and effect
the signaling of neighboring membrane receptor complexes (Paul et
al., Blood (2000 96:483).
[0231] ITIMs can be analyzed by flow cytometry.
[0232] Additional elements may also be used to classify a cell,
such as the expression level of extracellular or intracellular
markers, nuclear antigens, enzymatic activity, protein expression
and localization, cell cycle analysis, chromosomal analysis, cell
volume, and morphological characteristics like granularity and size
of nucleus or other distinguishing characteristics. For example, B
cells can be further subdivided based on the expression of cell
surface markers such as CD19, CD20, CD22 or CD23.
[0233] Alternatively, predefined classes of cells can be aggregated
or grouped based upon shared characteristics that may include
inclusion in one or more additional predefined class or the
presence of extracellular or intracellular markers, similar gene
expression profile, nuclear antigens, enzymatic activity, protein
expression and localization, cell cycle analysis, chromosomal
analysis, cell volume, and morphological characteristics like
granularity and size of nucleus or other distinguishing cellular
characteristics.
[0234] In some embodiments, the physiological status of one or more
cells is determined by examining and profiling the activation level
of one or more activatable elements in a cellular pathway. In some
embodiments, a cell is classified according to the activation level
of a plurality of activatable elements. In some embodiments, a
hematopoietic cell is classified according to the activation levels
of a plurality of activatable elements. In some embodiments, 1, 2,
3, 4, 5, 6, 7, 8, 9, 10 or more activatable elements may be
analyzed in a cell signaling pathway. In some embodiments, the
activation levels of one or more activatable elements of a
hematopoietic cell are correlated with a condition. In some
embodiments, the activation levels of one or more activatable
elements of a hematopoietic cell are correlated with a neoplastic
or hematopoietic condition as described herein. Examples of
hematopoietic cells include, but are not limited to, AML, MDS or
MPN cells.
[0235] In some embodiments, the activation level of one or more
activatable elements in single cells in the sample is determined.
Cellular constituents that may include activatable elements include
without limitation proteins, carbohydrates, lipids, nucleic acids
and metabolites. The activatable element may be a portion of the
cellular constituent, for example, an amino acid residue in a
protein that may undergo phosphorylation, or it may be the cellular
constituent itself, for example, a protein that is activated by
translocation, change in conformation (due to, e.g., change in pH
or ion concentration), by proteolytic cleavage, degradation through
ubiquitination and the like. Upon activation, a change occurs to
the activatable element, such as covalent modification of the
activatable element (e.g., binding of a molecule or group to the
activatable element, such as phosphorylation) or a conformational
change. Such changes generally contribute to changes in particular
biological, biochemical, or physical properties of the cellular
constituent that contains the activatable element. The state of the
cellular constituent that contains the activatable element is
determined to some degree, though not necessarily completely, by
the state of a particular activatable element of the cellular
constituent. For example, a protein may have multiple activatable
elements, and the particular activation states of these elements
may overall determine the activation state of the protein; the
state of a single activatable element is not necessarily
determinative. Additional factors, such as the binding of other
proteins, pH, ion concentration, interaction with other cellular
constituents, and the like, can also affect the state of the
cellular constituent.
[0236] In some embodiments, the activation levels of a plurality of
intracellular activatable elements in single cells are determined.
In some embodiments, at least about 2, 3, 4, 5, 6, 7, 8, 9, 10 or
more than 10 intracellular activatable elements are determined
[0237] Activation states of activatable elements may result from
chemical additions or modifications of biomolecules and include
biochemical processes such as glycosylation, phosphorylation,
acetylation, methylation, biotinylation, glutamylation,
glycylation, hydroxylation, isomerization, prenylation,
myristoylation, lipoylation, phosphopantetheinylation, sulfation,
ISGylation, nitrosylation, palmitoylation, SUMOylation,
ubiquitination, neddylation, citrullination, amidation, and
disulfide bond formation, disulfide bond reduction. Other possible
chemical additions or modifications of biomolecules include the
formation of protein carbonyls, direct modifications of protein
side chains, such as o-tyrosine, chloro-, nitrotyrosine, and
dityrosine, and protein adducts derived from reactions with
carbohydrate and lipid derivatives. Other modifications may be
non-covalent, such as binding of a ligand or binding of an
allosteric modulator.
[0238] One example of a covalent modification is the substitution
of a phosphate group for a hydroxyl group in the side chain of an
amino acid (phosphorylation). A wide variety of proteins are known
that recognize specific protein substrates and catalyze the
phosphorylation of serine, threonine, or tyrosine residues on their
protein substrates. Such proteins are generally termed "kinases."
Substrate proteins that are capable of being phosphorylated are
often referred to as phosphoproteins (after phosphorylation). Once
phosphorylated, a substrate phosphoprotein may have its
phosphorylated residue converted back to a hydroxyl one by the
action of a protein phosphatase that specifically recognizes the
substrate protein. Protein phosphatases catalyze the replacement of
phosphate groups by hydroxyl groups on serine, threonine, or
tyrosine residues. Through the action of kinases and phosphatases a
protein may be reversibly phosphorylated on a multiplicity of
residues and its activity may be regulated thereby. Thus, the
presence or absence of one or more phosphate groups in an
activatable protein is a preferred readout in the present
invention.
[0239] Another example of a covalent modification of an activatable
protein is the acetylation of histones. Through the activity of
various acetylases and deacetylylases the DNA binding function of
histone proteins is tightly regulated. Furthermore, histone
acetylation and histone deactelyation have been linked with
malignant progression. See Nature, 2004 May 27; 429(6990):
457-63.
[0240] Another form of activation involves cleavage of the
activatable element. For example, one form of protein regulation
involves proteolytic cleavage of a peptide bond. While random or
misdirected proteolytic cleavage may be detrimental to the activity
of a protein, many proteins are activated by the action of
proteases that recognize and cleave specific peptide bonds. Many
proteins derive from precursor proteins, or pro-proteins, which
give rise to a mature isoform of the protein following proteolytic
cleavage of specific peptide bonds. Many growth factors are
synthesized and processed in this manner, with a mature isoform of
the protein typically possessing a biological activity not
exhibited by the precursor form. Many enzymes are also synthesized
and processed in this manner, with a mature isoform of the protein
typically being enzymatically active, and the precursor form of the
protein being enzymatically inactive. This type of regulation is
generally not reversible. Accordingly, to inhibit the activity of a
proteolytically activated protein, mechanisms other than
"reattachment" must be used. For example, many proteolytically
activated proteins are relatively short-lived proteins, and their
turnover effectively results in deactivation of the signal.
Inhibitors may also be used. Among the enzymes that are
proteolytically activated are serine and cysteine proteases,
including cathepsins and caspases respectively.
[0241] In one embodiment, the activatable enzyme is a caspase. The
caspases are an important class of proteases that mediate
programmed cell death (referred to in the art as "apoptosis").
Caspases are constitutively present in most cells, residing in the
cytosol as a single chain proenzyme. These are activated to fully
functional proteases by a first proteolytic cleavage to divide the
chain into large and small caspase subunits and a second cleavage
to remove the N-terminal domain. The subunits assemble into a
tetramer with two active sites (Green, Cell 94:695-698, 1998). Many
other proteolytically activated enzymes, known in the art as
"zymogens," also find use in the instant invention as activatable
elements.
[0242] In an alternative embodiment the activation of the
activatable element involves prenylation of the element. By
"prenylation", and grammatical equivalents used herein, is meant
the addition of any lipid group to the element. Common examples of
prenylation include the addition of farnesyl groups, geranylgeranyl
groups, myristoylation and palmitoylation. In general these groups
are attached via thioether linkages to the activatable element,
although other attachments may be used.
[0243] In alternative embodiment, activation of the activatable
element is detected as intermolecular clustering of the activatable
element. By "clustering" or "multimerization", and grammatical
equivalents used herein, is meant any reversible or irreversible
association of one or more signal transduction elements. Clusters
can be made up of 2, 3, 4, etc., elements. Clusters of two elements
are termed dimers. Clusters of 3 or more elements are generally
termed oligomers, with individual numbers of clusters having their
own designation; for example, a cluster of 3 elements is a trimer,
a cluster of 4 elements is a tetramer, etc.
[0244] Clusters can be made up of identical elements or different
elements. Clusters of identical elements are termed "homo" dimers,
while clusters of different elements are termed "hetero" clusters.
Accordingly, a cluster can be a homodimer, as is the case for the
.beta..sub.2-adrenergic receptor.
[0245] Alternatively, a cluster can be a heterodimer, as is the
case for GABA.sub.B-R. In other embodiments, the cluster is a
homotrimer, as in the case of TNF.alpha., or a heterotrimer such
the one formed by membrane-bound and soluble CD95 to modulate
apoptosis. In further embodiments the cluster is a homo-oligomer,
as in the case of Thyrotropin releasing hormone receptor, or a
hetero-oligomer, as in the case of TGF.beta.1.
[0246] In a preferred embodiment, the activation or signaling
potential of elements is mediated by clustering, irrespective of
the actual mechanism by which the element's clustering is induced.
For example, elements can be activated to cluster a) as membrane
bound receptors by binding to ligands (ligands including both
naturally occurring and synthetic ligands), b) as membrane bound
receptors by binding to other surface molecules, or c) as
intracellular (non-membrane bound) receptors binding to
ligands.
[0247] In some embodiments, the activatable element is a protein.
Examples of proteins that may include activatable elements include,
but are not limited to kinases, phosphatases, lipid signaling
molecules, adaptor/scaffold proteins, cytokines, cytokine
regulators, ubiquitination enzymes, adhesion molecules,
cytoskeletal/contractile proteins, heterotrimeric G proteins, small
molecular weight GTPases, guanine nucleotide exchange factors,
GTPase activating proteins, caspases, proteins involved in
apoptosis, cell cycle regulators, molecular chaperones, metabolic
enzymes, vesicular transport proteins, hydroxylases, isomerases,
deacetylases, methylases, demethylases, tumor suppressor genes,
proteases, ion channels, molecular transporters, transcription
factors/DNA binding factors, regulators of transcription, and
regulators of translation. Examples of activatable elements,
activation states and methods of determining the activation level
of activatable elements are described in US Publication Number
20060073474 entitled "Methods and compositions for detecting the
activation state of multiple proteins in single cells" and US
Publication Number 20050112700 entitled "Methods and compositions
for risk stratification" the content of which are incorporate here
by reference. See also U.S. Ser. Nos. 61/048,886; 61/048,920; and
Shulz et al., Current Protocols in Immunology 2007,
78:8.17.1-20.
[0248] In some embodiments, the protein is selected from the group
consisting of HER receptors, PDGF receptors, Kit receptor, FGF
receptors, Eph receptors, Trk receptors, IGF receptors, Insulin
receptor, Met receptor, Ret, VEGF receptors, TIE1, TIE2, FAK, Jak1,
Jak2, Jak3, Tyk2, Src, Lyn, Fyn, Lck, Fgr, Yes, Csk, Abl, Btk,
ZAp70, Syk, IRAKs, cRaf, ARaf, BRAF, Mos, Lim kinase, ILK, Tpl,
ALK, TGF.beta. receptors, BMP receptors, MEKKs, ASK, MLKs, DLK,
PAKs, Mek 1, Mek 2, MKK3/6, MKK4/7, ASK1, Cot, NIK, Bub, Myt 1,
Wee1, Casein kinases, PDK1, SGK1, SGK2, SGK3, Akt1, Akt2, Akt3,
p90Rsks, p70S6 Kinase, Prks, PKCs, PKAs, ROCK 1, ROCK 2, Auroras,
CaMKs, MNKs, AMPKs, MELK, MARKs, Chk1, Chk2, LKB-1, MAPKAPKs, Pim1,
Pim2, Pim3, IKKs, Cdks, Jnks, Erks, IKKs, GSK3.alpha., GSK3.beta.,
Cdks, CLKs, PKR, PI3-Kinase class 1, class 2, class 3, mTor,
SAPK/JNK1,2,3, p38s, PKR, DNA-PK, ATM, ATR, Receptor protein
tyrosine phosphatases (RPTPs), LAR phosphatase, CD45, Non receptor
tyrosine phosphatases (NPRTPs), SHPs, MAP kinase phosphatases
(MKPs), Dual Specificity phosphatases (DUSPs), CDC25 phosphatases,
Low molecular weight tyrosine phosphatase, Eyes absent (EYA)
tyrosine phosphatases, Slingshot phosphatases (SSH), serine
phosphatases, PP2A, PP2B, PP2C, PP1, PPS, inositol phosphatases,
PTEN, SHIPs, myotubularins, phosphoinositide kinases,
phopsholipases, prostaglandin synthases, 5-lipoxygenase,
sphingosine kinases, sphingomyelinases, adaptor/scaffold proteins,
Shc, Grb2, BLNK, LAT, B cell adaptor for PI3-kinase (BCAP), SLAP,
Dok, KSR, MyD88, Crk, CrkL, GAD, Nck, Grb2 associated binder (GAB),
Fas associated death domain (FADD), TRADD, TRAF2, RIP, T-Cell
leukemia family, IL-2, IL-4, IL-8, IL-6, interferon .gamma.,
interferon .alpha., suppressors of cytokine signaling (SOCs), Cbl,
SCF ubiquitination ligase complex, APC/C, adhesion molecules,
integrins, Immunoglobulin-like adhesion molecules, selectins,
cadherins, catenins, focal adhesion kinase, p130CAS, fodrin, actin,
paxillin, myosin, myosin binding proteins, tubulin, eg5/KSP, CENPs,
.beta.-adrenergic receptors, muscarinic receptors, adenylyl cyclase
receptors, small molecular weight GTPases, H-Ras, K-Ras, N-Ras,
Ran, Rac, Rho, Cdc42, Arfs, RABs, RHEB, Vav, Tiam, Sos, Dbl, PRK,
TSC1,2, Ras-GAP, Arf-GAPs, Rho-GAPs, caspases, Caspase 2, Caspase
3, Caspase 6, Caspase 7, Caspase 8, Caspase 9, Bcl-2, Mcl-1,
Bcl-XL, Bcl-w, Bcl-B, A1, Bax, Bak, Bok, Bik, Bad, Bid, Bim, Bmf,
Hrk, Noxa, Puma, IAPs, XIAP, Smac, Cdk4, Cdk 6, Cdk 2, Cdk1, Cdk 7,
Cyclin D, Cyclin E, Cyclin A, Cyclin B, Rb, p16, p14Arf, p27KIP,
p21CIP, molecular chaperones, Hsp90s, Hsp70, Hsp27, metabolic
enzymes, Acetyl-CoAa Carboxylase, ATP citrate lyase, nitric oxide
synthase, caveolins, endosomal sorting complex required for
transport (ESCRT) proteins, vesicular protein sorting (Vsps),
hydroxylases, prolyl-hydroxylases PHD-1, 2 and 3, asparagine
hydroxylase FIH transferases, Pin1 prolyl isomerase,
topoisomerases, deacetylases, Histone deacetylases, sirtuins,
histone acetylases, CBP/p300 family, MYST family, ATF2, DNA methyl
transferases, Histone H3K4 demethylases, H3K27, JHDM2A, UTX, VHL,
WT-1, p53, Hdm, PTEN, ubiquitin proteases, urokinase-type
plasminogen activator (uPA) and uPA receptor (uPAR) system,
cathepsins, metalloproteinases, esterases, hydrolases, separase,
potassium channels, sodium channels, multi-drug resistance
proteins, P-Gycoprotein, nucleoside transporters, Ets, Elk, SMADs,
Rel-A (p65-NFK), CREB, NFAT, ATF-2, AFT, Myc, Fos, Spl, Egr-1,
T-bet, .beta.-catenin, HIFs, FOXOs, E2Fs, SRFs, TCFs, Egr-1,
.beta.-catenin, FOXO STAT1, STAT 3, STAT 4, STAT 5, STAT 6, p53,
WT-1, HMGA, pS6, 4EPB-1, eIF4E-binding protein, RNA polymerase,
initiation factors, elongation factors.
[0249] In another embodiment the activatable element is a nucleic
acid. Activation and deactivation of nucleic acids can occur in
numerous ways including, but not limited to, cleavage of an
inactivating leader sequence as well as covalent or non-covalent
modifications that induce structural or functional changes. For
example, many catalytic RNAs, e.g. hammerhead ribozymes, can be
designed to have an inactivating leader sequence that deactivates
the catalytic activity of the ribozyme until cleavage occurs. An
example of a covalent modification is methylation of DNA.
Deactivation by methylation has been shown to be a factor in the
silencing of certain genes, e.g. STAT regulating SOCS genes in
lymphomas. See Leukemia. See February 2004; 18(2): 356-8. SOCS1 and
SHP1 hypermethylation in mantle cell lymphoma and follicular
lymphoma: implications for epigenetic activation of the Jak/STAT
pathway. Chim C S, Wong K Y, Loong F, Srivastava G.
[0250] In another embodiment the activatable element is a small
molecule, carbohydrate, lipid or other naturally occurring or
synthetic compound capable of having an activated isoform. In
addition, as pointed out above, activation of these elements need
not include switching from one form to another, but can be detected
as the presence or absence of the compound. For example, activation
of cAMP (cyclic adenosine mono-phosphate) can be detected as the
presence of cAMP rather than the conversion from non-cyclic AMP to
cyclic AMP.
[0251] In some embodiments of the invention, the methods described
herein are employed to determine the activation level of an
activatable element, e.g., in a cellular pathway. Methods and
compositions are provided for the classification of a cell
according to the activation level of an activatable element in a
cellular pathway. The cell can be a hematopoietic cell. Examples of
hematopoietic cells include but are not limited to pluripotent
hematopoietic stem cells, granulocyte lineage progenitor or derived
cells, monocyte lineage progenitor or derived cells, macrophage
lineage progenitor or derived cells, megakaryocyte lineage
progenitor or derived cells and erythroid lineage progenitor or
derived cells.
[0252] In some embodiments, the cell is classified according to the
activation level of an activatable element, e.g., in a cellular
pathway comprises classifying the cell as a cell that is correlated
with a clinical outcome. In some embodiments, the clinical outcome
is the prognosis and/or diagnosis of a condition. In some
embodiments, the clinical outcome is the presence or absence of a
neoplastic or a hematopoietic condition. In some embodiments, the
clinical outcome is the staging or grading of a neoplastic or
hematopoietic condition. Examples of staging include, but are not
limited to, aggressive, indolent, benign, refractory, Roman Numeral
staging, TNM Staging, Rai staging, Binet staging, WHO
classification, FAB classification, IPSS score, WPSS score, limited
stage, extensive stage, staging according to cellular markers such
as ZAp70 and CD38, occult, including information that may inform on
time to progression, progression free survival, overall survival,
or event-free survival.
[0253] In some embodiments, methods and compositions are provided
for the classification of a cell according to the activation level
of an activatable element, e.g., in a cellular pathway wherein the
classification comprises classifying a cell as a cell that is
correlated to a patient response to a treatment. In some
embodiments, the patient response is selected from the group
consisting of complete response, partial response, nodular partial
response, no response, progressive disease, stable disease and
adverse reaction.
[0254] In some embodiments, methods and compositions are provided
for the classification of a cell according to the activation level
of an activatable element, e.g., in a cellular pathway wherein the
classification comprises classifying the cell as a cell that is
correlated with minimal residual disease or emerging
resistance.
[0255] In some embodiments, methods and compositions are provided
for the classification of a cell according to the activation level
of an activatable element, e.g., in a cellular pathway wherein the
classification comprises selecting a method of treatment. Example
of methods of treatments include, but are not limited to,
chemotherapy, biological therapy, radiation therapy, bone marrow
transplantation, Peripheral stem cell transplantation, umbilical
cord blood transplantation, autologous stem cell transplantation,
allogeneic stem cell transplantation, syngeneic stem cell
transplantation, surgery, induction therapy, maintenance therapy,
and watchful waiting.
[0256] Generally, the methods of the invention involve determining
the activation levels of an activatable element in a plurality of
single cells in a sample.
Signaling Pathways
[0257] In some embodiments, the methods of the invention are
employed to determine the status of an activatable element in a
signaling pathway. In some embodiments, a cell is classified, as
described herein, according to the activation level of one or more
activatable elements in one or more signaling pathways. Signaling
pathways and their members have been described. See (Hunter T. Cell
Jan. 7, 2000;100(1): 13-27). Exemplary signaling pathways include
the following pathways and their members: The MAP kinase pathway
including Ras, Raf, MEK, ERK and elk; the PI3K/Akt pathway
including PI-3-kinase, PDK1, Akt and Bad; the NF-.kappa.B pathway
including IKKs, IkB and the Wnt pathway including frizzled
receptors, beta-catenin, APC and other co-factors and TCF (see Cell
Signaling Technology, Inc. 2002 Catalog pages 231-279 and Hunter
T., supra.). In some embodiments of the invention, the correlated
activatable elements being assayed (or the signaling proteins being
examined) are members of the MAP kinase, Akt, NFkB, WNT,
RAS/RAF/MEK/ERK, JNK/SAPK, p38 MAPK, Src Family Kinases, JAK/STAT
and/or PKC signaling pathways. See FIG. 1 generally.
[0258] In some embodiments, the status of an activatable element
within the PI3K/AKT, or MAPK pathways in response to a growth
factor or mitogen is determined. In some embodiments, the
activatable element within the PI3K/AKT or MAPK pathway is selected
from the group consisting of Akt, p-Erk, p38 and pS6 and the growth
factor or mitogen is selected from the group consisting of FLT3L,
SCF, G-CSF, SCF, G-CSF, SDF1a, LPS, PMA and Thapsigargin.
[0259] In some embodiments, the status of an activatable element
within JAk/STAT pathways in response to a cytokine is determined.
In some embodiments, the activatable element within the JAK/STAT
pathway is selected from the group consisting of p-Stat3, p-Stat5,
p-Stat1, and p-Stat6 and the cytokine is selected from the group
consisting of IFNg, IFNa, IL-27, IL-3, IL-6, IL-10, and G-CSF. In
some embodiments, the activatable element within the STAT pathway
is Stat 1 and the cytokine is IL-27 or G-CSF.
[0260] In some embodiments, the status of an activatable element
within the phospholipase C pathway in response to an inhibitor is
determined. In some embodiments, the activatable element within the
phospholipase C pathway is selected from the group consisting of
p-Slp-76, and Plcg2 and the inhibitor is H2O2.
[0261] In some embodiments, the status of a phosphatase in response
to an inhibitor is determined. In some embodiments, the inhibitor
is H2O2.
[0262] In some embodiments, the methods of the invention are
employed to determine the status of a signaling protein in a
signaling pathway known in the art including those described
herein. Exemplary types of signaling proteins within the scope of
the present invention include, but are not limited to kinases,
kinase substrates (i.e. phosphorylated substrates), phosphatases,
phosphatase substrates, binding proteins (such as 14-3-3), receptor
ligands and receptors (cell surface receptor tyrosine kinases and
nuclear receptors)). Kinases and protein binding domains, for
example, have been well described (see, e.g., Cell Signaling
Technology, Inc., 2002 Catalogue "The Human Protein Kinases" and
"Protein Interaction Domains" pgs. 254-279).
[0263] Nuclear Factor-kappaB (NF-.kappa.B) Pathway:
[0264] Nuclear factor-kappaB (NF-kappaB) transcription factors and
the signaling pathways that activate them are central coordinators
of innate and adaptive immune responses. More recently, it has
become clear that NF-kappaB signaling also has a critical role in
cancer development and progression. NF-kappaB provides a
mechanistic link between inflammation and cancer, and is a major
factor controlling the ability of both pre-neoplastic and malignant
cells to resist apoptosis-based tumor-surveillance mechanisms. In
mammalian cells, there are five NF-.kappa.B family members, RelA
(p65), RelB, c-Rel, p50/p105 (NF-.kappa.B1) and p52/p100
(NF-.kappa.B2) and different NF-.kappa.B complexes are formed from
their homo and heterodimers. In most cell types, NF-.kappa.B
complexes are retained in the cytoplasm by a family of inhibitory
proteins known as inhibitors of NF-.kappa.B (I.kappa.Bs).
Activation of NF-.kappa.B typically involves the phosphorylation of
I.kappa.B by the I.kappa.B kinase (IKK) complex, which results in
I.kappa.B ubiquitination with subsequent degradation. This releases
NF-.kappa.B and allows it to translocate freely to the nucleus. The
genes regulated by NF-.kappa.B include those controlling programmed
cell death, cell adhesion, proliferation, the innate- and
adaptive-immune responses, inflammation, the cellular-stress
response and tissue remodeling. However, the expression of these
genes is tightly coordinated with the activity of many other
signaling and transcription-factor pathways. Therefore, the outcome
of NF-.kappa.B activation depends on the nature and the cellular
context of its induction. For example, it has become apparent that
NF-.kappa.B activity can be regulated by both oncogenes and tumor
suppressors, resulting in either stimulation or inhibition of
apoptosis and proliferation. See Perkins, N. Integrating
cell-signaling pathways with NF-.kappa.B and IKK function. Reviews:
Molecular Cell Biology. January, 2007; 8(1): 49-62, hereby fully
incorporated by reference in its entirety for all purposes. Hayden,
M. Signaling to NF-.kappa.B. Genes & Development. 2004; 18:
2195-2224, hereby fully incorporated by reference in its entirety
for all purposes. Perkins, N. Good Cop, Bad Cop: The Different
Faces of NF-.kappa.B. Cell Death and Differentiation. 2006; 13:
759-772, hereby fully incorporated by reference in its entirety for
all purposes.
[0265] Phosphatidylinositol 3-kinase (PI3-K)/AKT Pathway:
[0266] PI3-Ks are activated by a wide range of cell surface
receptors to generate the lipid second messengers
phosphatidylinositol 3,4-biphosphate (PIP.sub.2) and
phosphatidylinositol 3,4,5-trisphosphate (PIP.sub.3). Examples of
receptor tyrosine kinases include but are not limited to FLT3
LIGAND, EGFR, IGF-1R, HER2/neu, VEGFR, and PDGFR. The lipid second
messengers generated by PI3Ks regulate a diverse array of cellular
functions. The specific binding of PI3,4P.sub.2 and PI3,4,5P.sub.3
to target proteins is mediated through the pleckstrin homology (PH)
domain present in these target proteins. One key downstream
effector of PI3-K is Akt, a serine/threonine kinase, which is
activated when its PH domain interacts with PI3, 4P.sub.2 and
PI3,4,5P.sub.3 resulting in recruitment of Akt to the plasma
membrane. Once there, in order to be fully activated, Akt is
phosphorylated at threonine 308 by 3-phosphoinositide-dependent
protein kinase-1 (PDK-1) and at serine 473 by several PDK2 kinases.
Akt then acts downstream of PI3K to regulate the phosphorylation of
a number of substrates, including but not limited to forkhead box 0
transcription factors, Bad, GSK-3.beta., I-.kappa.B, mTOR, MDM-2,
and S6 ribosomal subunit. These phosphorylation events in turn
mediate cell survival, cell proliferation, membrane trafficking,
glucose homeostasis, metabolism and cell motility. Deregulation of
the PI3K pathway occurs by activating mutations in growth factor
receptors, activating mutations in a PI3-K gene (e.g. PIK3CA), loss
of function mutations in a lipid phosphatase (e.g. PTEN),
up-regulation of Akt, or the impairment of the tuberous sclerosis
complex (TSC1/2). All these events are linked to increased survival
and proliferation. See Vivanco, I. The Phosphatidylinositol
3-Kinase-AKT Pathway in Human Cancer. Nature Reviews: Cancer. July,
2002; 2: 489-501 and Shaw, R. Ras, PI(3)K and mTOR signaling
controls tumor cell growth. Nature. May, 2006; 441: 424-430, Marone
et al., Biochimica et Biophysica Acta, 2008; 1784, p 159-185 hereby
fully incorporated by reference in their entirety for all
purposes.
[0267] Wnt Pathway:
[0268] The Wnt signaling pathway describes a complex network of
proteins well known for their roles in embryogenesis, normal
physiological processes in adult animals, such as tissue
homeostasis, and cancer. Further, a role for the Wnt pathway has
been shown in self-renewal of hematopoietic stem cells (Reya T et
al., Nature. 2003 May 22; 423(6938):409-14). Cytoplasmic levels of
.beta.-catenin are normally kept low through the continuous
proteosomal degradation of .beta.-catenin controlled by a complex
of glycogen synthase kinase 3.beta. (GSK-3.beta.), axin, and
adenomatous polyposis coli (APC). When Wnt proteins bind to a
receptor complex composed of the Frizzled receptors (Fz) and low
density lipoprotein receptor-related protein (LRP) at the cell
surface, the GSK-3/axin/APC complex is inhibited. Key intermediates
in this process include disheveled (Dsh) and axin binding the
cytoplasmic tail of LRP. Upon Wnt signaling and inhibition of the
.beta.-catenin degradation pathway, .beta.-catenin accumulates in
the cytoplasm and nucleus. Nuclear .beta.-catenin interacts with
transcription factors such as lymphoid enhanced-binding factor 1
(LEF) and T cell-specific transcription factor (TCF) to affect
transcription of target genes. See Gordon, M. Wnt Signaling:
Multiple Pathways, Multiple Receptors, and Multiple Transcription
Factors. J of Biological Chemistry. June, 2006; 281(32):
22429-22433, Logan C Y, Nusse R: The Wnt signaling pathway in
development and disease. Annu Rev Cell Dev Biol 2004, 20:781-810,
Clevers H: Wnt/beta-catenin signaling in development and disease.
Cell 2006, 127:469-480. Hereby fully incorporated by reference in
its entirety for all purposes.
[0269] Protein Kinase C (PKC) Signaling:
[0270] The PKC family of serine/threonine kinases mediates
signaling pathways following activation of receptor tyrosine
kinases, G-protein coupled receptors and cytoplasmic tyrosine
kinases. Activation of PKC family members is associated with cell
proliferation, differentiation, survival, immune function,
invasion, migration and angiogenesis. Disruption of PKC signaling
has been implicated in tumorigenesis and drug resistance. PKC
isoforms have distinct and overlapping roles in cellular functions.
PKC was originally identified as a phospholipid and
calcium-dependent protein kinase. The mammalian PKC superfamily
consists of 13 different isoforms that are divided into four
subgroups on the basis of their structural differences and related
cofactor requirements cPKC (classical PKC) isoforms (.alpha.,
.beta.I, .beta.II and .gamma.), which respond both to Ca2+ and DAG
(diacylglycerol), nPKC (novel PKC) isoforms (.delta., .epsilon.,
.theta. and .eta.), which are insensitive to Ca2+, but dependent on
DAG, atypical PKCs (aPKCs, /.lamda., .zeta.), which are responsive
to neither co-factor, but may be activated by other lipids and
through protein-protein interactions, and the related PKN (protein
kinase N) family (e.g. PKN1, PKN2 and PKN3), members of which are
subject to regulation by small GTPases. Consistent with their
different biological functions, PKC isoforms differ in their
structure, tissue distribution, subcellular localization, mode of
activation and substrate specificity. Before maximal activation of
its kinase, PKC requires a priming phosphorylation which is
provided constitutively by phosphoinositide-dependent kinase 1
(PDK-1). The phospholipid DAG has a central role in the activation
of PKC by causing an increase in the affinity of classical PKCs for
cell membranes accompanied by PKC activation and the release of an
inhibitory substrate (a pseudo-substrate) to which the inactive
enzyme binds. Activated PKC then phosphorylates and activates a
range of kinases. The downstream events following PKC activation
are poorly understood, although the MEK-ERK (mitogen activated
protein kinase kinase-extracellular signal-regulated kinase)
pathway is thought to have an important role. There is also
evidence to support the involvement of PKC in the PI3K-Akt pathway.
PKC isoforms probably form part of the multi-protein complexes that
facilitate cellular signal transduction. Many reports describe
dysregulation of several family members. For example alterations in
PKC.epsilon. have been detected in thyroid cancer, and have been
correlated with aggressive, metastatic breast cancer and PKC.sub.
was shown to be associated with poor outcome in ovarian cancer.
(Knauf J A, et al. Isozyme-Specific Abnormalities of PKC in Thyroid
Cancer Evidence for Post-Transcriptional Changes in PKC Epsilon.
The Journal of Clinical Endocrinology & Metabolism. Vol. 87,
No. 5, pp 2150-2159; Zhang L et al. Integrative Genomic Analysis of
Protein Kinase C (PKC) Family Identifies PKC{iota} as a Biomarker
and Potential Oncogene in Ovarian Carcinoma. Cancer Res. 2006, Vol
66, No. 9, pp 4627-4635)
[0271] Mitogen Activated Protein (MAP) Kinase Pathways:
[0272] MAP kinases transduce signals that are involved in a
multitude of cellular pathways and functions in response to a
variety of ligands and cell stimuli. (Lawrence et al., Cell
Research (2008) 18: 436-442). Signaling by MAPKs affects specific
events such as the activity or localization of individual proteins,
transcription of genes, and increased cell cycle entry, and
promotes changes that orchestrate complex processes such as
embryogenesis and differentiation. Aberrant or inappropriate
functions of MAPKs have now been identified in diseases ranging
from cancer to inflammatory disease to obesity and diabetes. MAPKs
are activated by protein kinase cascades consisting of three or
more protein kinases in series: MAPK kinase kinases (MAP3Ks)
activate MAPK kinases (MAP2Ks) by dual phosphorylation on S/T
residues; MAP2Ks then activate MAPKs by dual phosphorylation on Y
and T residues MAPKs then phosphorylate target substrates on select
S/T residues typically followed by a proline residue. In the ERK1/2
cascade the MAP3K is usually a member of the Raf family. Many
diverse MAP3Ks reside upstream of the p38 and the c-Jun N-terminal
kinase/stress-activated protein kinase (JNK/SAPK) MAPK groups,
which have generally been associated with responses to cellular
stress. Downstream of the activating stimuli, the kinase cascades
may themselves be stimulated by combinations of small G proteins,
MAP4Ks, scaffolds, or oligomerization of the MAP3K in a pathway. In
the ERK1/2 pathway, Ras family members usually bind to Raf proteins
leading to their activation as well as to the subsequent activation
of other downstream members of the pathway.
[0273] a. Ras/RAF/MEK/ERK Pathway:
[0274] Classic activation of the RAS/Raf/MAPK cascade occurs
following ligand binding to a receptor tyrosine kinase at the cell
surface, but a vast array of other receptors have the ability to
activate the cascade as well, such as integrins, serpentine
receptors, heterotrimeric G-proteins, and cytokine receptors.
Although conceptually linear, considerable cross talk occurs
between the Ras/Raf/MAPK/Erk kinase (MEK)/Erk MAPK pathway and
other MAPK pathways as well as many other signaling cascades. The
pivotal role of the Ras/Raf/MEK/Erk MAPK pathway in multiple
cellular functions underlies the importance of the cascade in
oncogenesis and growth of transformed cells. As such, the MAPK
pathway has been a focus of intense investigation for therapeutic
targeting. Many receptor tyrosine kinases are capable of initiating
MAPK signaling. They do so after activating phosphorylation events
within their cytoplasmic domains provide docking sites for
src-homology 2 (SH2) domain-containing signaling molecules. Of
these, adaptor proteins such as Grb2 recruit guanine nucleotide
exchange factors such as SOS-1 or CDC25 to the cell membrane. The
guanine nucleotide exchange factor is now capable of interacting
with Ras proteins at the cell membrane to promote a conformational
change and the exchange of GDP for GTP bound to Ras. Multiple Ras
isoforms have been described, including K-Ras, N-Ras, and H-Ras.
Termination of Ras activation occurs upon hydrolysis of RasGTP to
RasGDP. Ras proteins have intrinsically low GTPase activity. Thus,
the GTPase activity is stimulated by GTPase-activating proteins
such as NF-1 GTPase-activating protein/neurofibromin and p120
GTPase activating protein thereby preventing prolonged Ras
stimulated signaling. Ras activation is the first step in
activation of the MAPK cascade. Following Ras activation, Raf
(A-Raf, B-Raf, or Raf-1) is recruited to the cell membrane through
binding to Ras and activated in a complex process involving
phosphorylation and multiple cofactors that is not completely
understood. Raf proteins directly activate MEK1 and MEK2 via
phosphorylation of multiple serine residues. MEK1 and MEK2 are
themselves tyrosine and threonine/serine dual-specificity kinases
that subsequently phosphorylate threonine and tyrosine residues in
Erk1 and Erk2 resulting in activation. Although MEK1/2 have no
known targets besides Erk proteins, Erk has multiple targets
including Elk-1, c-Ets1, c-Ets2, p90RSK1, MNK1, MNK2, and TOB. The
cellular functions of Erk are diverse and include regulation of
cell proliferation, survival, mitosis, and migration. McCubrey, J.
Roles of the Raf/MEK/ERK pathway in cell growth, malignant
transformation and drug resistance. Biochimica et Biophysica Acta.
2007; 1773: 1263-1284, hereby fully incorporated by reference in
its entirety for all purposes, Friday and Adjei, Clinical Cancer
Research (2008) 14, p 342-346.
[0275] b c-Jun N-Terminal Kinase (JNK)/Stress-Activated Protein
Kinase (SAPK) Pathway:
[0276] The c-Jun N-terminal kinases (JNKs) were initially described
as a family of serine/threonine protein kinases, activated by a
range of stress stimuli and able to phosphorylate the N-terminal
transactivation domain of the c-Jun transcription factor. This
phosphorylation enhances c-Jun dependent transcriptional events in
mammalian cells. Further research has revealed three JNK genes
(JNK1, JNK2 and JNK3) and their splice-forms as well as the range
of external stimuli that lead to JNK activation. JNK1 and JNK2 are
ubiquitous, whereas JNK3 is relatively restricted to brain. The
predominant MAP2Ks upstream of JNK are MEK4 (MKK4) and MEK7 (MKK7).
MAP3Ks with the capacity to activate JNK/SAPKs include MEKKs
(MEKK1, -2, -3 and -4), mixed lineage kinases (MLKs, including
MLK1-3 and DLK), Tpl2, ASKs, TAOs and TAK1. Knockout studies in
several organisms indicate that different MAP3Ks predominate in
JNK/SAPK activation in response to different upstream stimuli. The
wiring may be comparable to, but perhaps even more complex than,
MAP3K selection and control of the ERK1/2 pathway. JNK/SAPKs are
activated in response to inflammatory cytokines; environmental
stresses, such as heat shock, ionizing radiation, oxidant stress
and DNA damage; DNA and protein synthesis inhibition; and growth
factors. JNKs phosphorylate transcription factors c-Jun, ATF-2,
p53, Elk-1, and nuclear factor of activated T cells (NFAT), which
in turn regulate the expression of specific sets of genes to
mediate cell proliferation, differentiation or apoptosis. JNK
proteins are involved in cytokine production, the inflammatory
response, stress-induced and developmentally programmed apoptosis,
actin reorganization, cell transformation and metabolism. Raman, M.
Differential regulation and properties of MAPKs. Oncogene. 2007;
26: 3100-3112, hereby fully incorporated by reference in its
entirety for all purposes.
[0277] c. p38 MAPK Pathway:
[0278] Several independent groups identified the p38 Map kinases,
and four p38 family members have been described (.alpha., .beta.,
.gamma., .delta.). Although the p38 isoforms share about 40%
sequence identity with other MAPKs, they share only about 60%
identity among themselves, suggesting highly diverse functions. p38
MAPKs respond to a wide range of extracellular cues particularly
cellular stressors such as UV radiation, osmotic shock, hypoxia,
pro-inflammatory cytokines and less often growth factors.
Responding to osmotic shock might be viewed as one of the oldest
functions of this pathway, because yeast p38 activates both short
and long-term homeostatic mechanisms to osmotic stress. p38 is
activated via dual phosphorylation on the TGY motif within its
activation loop by its upstream protein kinases MEK3 and MEK6.
MEK3/6 are activated by numerous MAP3Ks including MEKK1-4, TAOs,
TAK and ASK. p38 MAPK is generally considered to be the most
promising MAPK therapeutic target for rheumatoid arthritis as p38
MAPK isoforms have been implicated in the regulation of many of the
processes, such as migration and accumulation of leucocytes,
production of cytokines and pro-inflammatory mediators and
angiogenesis, that promote disease pathogenesis. Further, the p38
MAPK pathway plays a role in cancer, heart and neurodegenerative
diseases and may serve as promising therapeutic target. Cuenda, A.
p38 MAP-Kinases pathway regulation, function, and role in human
diseases. Biochimica et Biophysica Acta. 2007; 1773: 1358-1375;
Thalhamer et al., Rheumatology 2008; 47:409-414; Roux, P. ERK and
p38 MAPK-Activated Protein Kinases: a Family of Protein Kinases
with Diverse Biological Functions. Microbiology and Molecular
Biology Reviews. June, 2004; 320-344 hereby fully incorporated by
reference in its entirety for all purposes.
[0279] Src Family Kinases:
[0280] Src is the most widely studied member of the largest family
of nonreceptor protein tyrosine kinases, known as the Src family
kinases (SFKs). Other SFK members include Lyn, Fyn, Lck, Hck, Fgr,
Blk, Yrk, and Yes. The Src kinases can be grouped into two
sub-categories, those that are ubiquitously expressed (Src, Fyn,
and Yes), and those which are found primarily in hematopoietic
cells (Lyn, Lck, Hck, Blk, Fgr). (Benati, D. Src Family Kinases as
Potential Therapeutic Targets for Malignancies and Immunological
Disorders. Current Medicinal Chemistry. 2008; 15: 1154-1165) SFKs
are key messengers in many cellular pathways, including those
involved in regulating proliferation, differentiation, survival,
motility, and angiogenesis. The activity of SFKs is highly
regulated intramolecularly by interactions between the SH2 and SH3
domains and intermolecularly by association with cytoplasmic
molecules. This latter activation may be mediated by focal adhesion
kinase (FAK) or its molecular partner Crk-associated substrate
(CAS), which plays a prominent role in integrin signaling, and by
ligand activation of cell surface receptors, e.g. epidermal growth
factor receptor (EGFR). These interactions disrupt intramolecular
interactions within Src, leading to an open conformation that
enables the protein to interact with potential substrates and
downstream signaling molecules. Src can also be activated by
dephosphorylation of tyrosine residue Y530. Maximal Src activation
requires the autophosphorylation of tyrosine residue Y419 (in the
human protein) present within the catalytic domain. Elevated Src
activity may be caused by increased transcription or by
deregulation due to overexpression of upstream growth factor
receptors such as EGFR, HER2, platelet-derived growth factor
receptor (PDGFR), fibroblast growth factor receptor (FGFR),
vascular endothelial growth factor receptor, ephrins, integrin, or
FAK. Alternatively, some human tumors show reduced expression of
the negative Src regulator, Csk. Increased levels, increased
activity, and genetic abnormalities of Src kinases have been
implicated in both solid tumor development and leukemias. Ingley,
E. Src family kinases: Regulation of their activities, levels and
identification of new pathways. Biochimica et Biophysica Acta.
2008; 1784 56-65, hereby fully incorporated by reference in its
entirety for all purposes. Benati and Baldari., Curr Med. Chem.
2008; 15(12):1154-65, Finn (2008) Ann Oncol. May 16, hereby fully
incorporated by reference in its entirety for all purposes.
[0281] Janus kinase (JAK)/Signal Transducers and Activators of
Transcription (STAT) Pathway:
[0282] The JAK/STAT pathway plays a crucial role in mediating the
signals from a diverse spectrum of cytokine receptors, growth
factor receptors, and G-protein-coupled receptors. Signal
transducers and activators of transcription (STAT) proteins play a
crucial role in mediating the signals from a diverse spectrum of
cytokine receptors growth factor receptors, and G-protein-coupled
receptors. STAT directly links cytokine receptor stimulation to
gene transcription by acting as both a cytosolic messenger and
nuclear transcription factor. In the Janus Kinase (JAK)-STAT
pathway, receptor dimerization by ligand binding results in JAK
family kinase (JFK) activation and subsequent tyrosine
phosphorylation of the receptor, which leads to the recruitment of
STAT through the SH2 domain, and the phosphorylation of conserved
tyrosine residue. Tyrosine phosphorylated STAT forms a dimer,
translocates to the nucleus, and binds to specific DNA elements to
activate target gene transcription, which leads to the regulation
of cellular proliferation, differentiation, and apoptosis. The
entire process is tightly regulated at multiple levels by protein
tyrosine phosphatases, suppressors of cytokine signaling and
protein inhibitors of activated STAT. In mammals seven members of
the STAT family (STAT1, STAT2, STAT3, STAT4, STAT5a, STAT5b and
STATE) have been identified. JAKs contain two symmetrical
kinase-like domains; the C-terminal JAK homology 1 (JH1) domain
possesses tyrosine kinase function while the immediately adjacent
JH2 domain is enzymatically inert but is believed to regulate the
activity of JH1. There are four JAK family members: JAK1, JAK2,
JAK3 and tyrosine kinase 2 (Tyk2). Expression is ubiquitous for
JAK1, JAK2 and TYK2 but restricted to hematopoietic cells for JAK3.
Mutations in JAK proteins have been described for several myeloid
malignancies. Specific examples include but are not limited to:
Somatic JAK3 (e.g. JAK3A572V, JAK3V722I, JAK3p132T) and fusion JAK2
(e.g. ETV6-JAK2, PCM1-JAK2, BCR-JAK2) mutations have respectively
been described in acute megakaryocytic leukemia and acute
leukemia/chronic myeloid malignancies, JAK2 (V617F, JAK2 exon 12
mutations) and MPL MPLW515L/K/S, MPLS505N) mutations associated
with myeloproliferative disorders and myeloproliferative neoplasms.
JAK2 mutations, primarily JAK2V617F, are invariably associated with
polycythemia vera (PV). This mutation also occurs in the majority
of patients with essential thrombocythemia (ET) or primary
myelofibrosis (PMF) (Tefferi n., Leukemia & Lymphoma, March
2008; 49(3): 388-397). STATs can be activated in a JAK-independent
manner by src family kinase members and by oncogenic FLt3
ligand-ITD (Hayakawa and Naoe, Ann N Y Acad. Sci. 2006 November;
1086:213-22; Choudhary et al. Activation mechanisms of STAT5 by
oncogenic FLt3 ligand-ITD. Blood (2007) vol. 110 (1) pp. 370-4).
Although mutations of STATs have not been described in human
tumors, the activity of several members of the family, such as
STAT1, STAT3 and STAT5, is dysregulated in a variety of human
tumors and leukemias. STAT3 and STAT5 acquire oncogenic potential
through constitutive phosphorylation on tyrosine, and their
activity has been shown to be required to sustain a transformed
phenotype. This was shown in lung cancer where tyrosine
phosphorylation of STAT3 was JAK-independent and mediated by EGF
receptor activated through mutation and Src. (Alvarez et al.,
Cancer Research, Cancer Res 2006; 66) STAT5 phosphorylation was
also shown to be required for the long-term maintenance of leukemic
stem cells. (Schepers et al. STAT5 is required for long-term
maintenance of normal and leukemic human stem/progenitor cells.
Blood (2007) vol. 110 (8) pp. 2880-2888) In contrast to STAT3 and
STAT5, STAT1 negatively regulates cell proliferation and
angiogenesis and thereby inhibits tumor formation. Consistent with
its tumor suppressive properties, STAT1 and its downstream targets
have been shown to be reduced in a variety of human tumors
(Rawlings, J. The JAK/STAT signaling pathway. J of Cell Science.
2004; 117 (8):1281-1283, hereby fully incorporated by reference in
its entirety for all purposes).
Drug Transporters
[0283] A key issue in the treatment of many cancers is the
development of resistance to chemotherapeutic drugs. Of the many
resistance mechanisms, two classes of transporters play a major
role. The human ATP-binding cassette (ABC) superfamily of proteins
consists of 49 membrane proteins that transport a diverse array of
substrates, including sugars, amino acids, bile salts lipids,
sterols, nucleotides, endogenous metabolites, ions, antibiotics
drugs and toxins out of cells using the energy of hydrolysis of
ATP. ATP-binding-cassette (ABC) transporters are evolutionary
extremely well-conserved transmembrane proteins that are highly
expressed in hematopoietic stem cells (HSCs). The physiological
function in human stem cells is believed to be protection against
genetic damage caused by both environmental and naturally occurring
xenobiotics. Additionally, ABC transporters have been implicated in
the maintenance of quiescence and cell fate decisions of stem
cells. These physiological roles suggest a potential role in the
pathogenesis and biology of stem cell-derived hematological
malignancies such as acute and chronic myeloid leukemia
(Raaijmakers, Leukemia (2007) 21, 2094-2102, Zhou et al., Nature
Medicine, 2001, 7, p 1028-1034
[0284] Several ABC proteins are multidrug efflux pumps that not
only protect the body from exogenous toxins, but also play a role
in uptake and distribution of therapeutic drugs. Expression of
these proteins in target tissues causes resistance to treatment
with multiple drugs. (Gillet et al., Biochimica et Biophysica Acta
(2007) 1775, p 237, Sharom (2008) Pharmacogenomics 9 p105). A more
detailed discussion of the ABC family members with critical roles
in resistance and poor outcome to treatment is discussed below
[0285] The second class of plasma membrane transporter proteins
that play a role in the uptake of nucleoside-derived drugs are the
Concentrative and Equilibrative Nucleoside Transporters (CNT and
ENT, respectively), encoded by gene families SLC28 and SLC29
(Pastor-Anglada (2007) J. Physiol. Biochem 63, p 97). They mediate
the uptake of natural nucleosides and a variety of
nucleoside-derived drugs, mostly used in anti-cancer therapy. In
vitro studies, have shown that one mechanism of nucleoside
resistance can be mediated through mutations in the gene for
ENT1/SLC29A1 resulting in lack of detectable protein (Cai et al.,
Cancer Research (2008) 68, p 2349). Studies have also described in
vivo mechanisms of resistance to nucleoside analogues involving low
or non-detectable levels of ENT1 in Acute Myeloid Leukemia (AML),
Mantle Cell lymphoma and other leukemias (Marce et al., Malignant
Lymphomas (2006), 91, p 895).
[0286] Of the ABC transporter family, three family members account
for most of the multiple drug resistance (MDR) in humans;
P-glycoprotein (Pgp/MDR1/ABCB1), MDR-associated protein (MRP1,
ABCC1) and breast cancer resistance protein (BCRP, ABCG2 or MXR).
Pgp/MDR1 and ABCG2 can export both unmodified drugs and drug
conjugates, whereas MRP1 exports glutathione and other drug
conjugates as well as unconjugated drugs together with free
glutathione. All three ABC transporters demonstrate export activity
for a broad range of structurally unrelated drugs and display both
distinct and overlapping specificities. For example, MRP1 promotes
efflux of drug-glutathione conjugates, vinca alkaloids,
camptothecin, but not taxol. Examples of drugs exported by ABCG2
include mitoxantrone, etoposide, daunorubicin as well as the
tyrosine kinase inhibitors Gleevec and Iressa. In treatment
regimens for leukemias, one of the main obstacles to achieving
remission is intrinsic and acquired resistance to chemotherapy
mediated by the ABC drug transporters. Several reports have
described correlations between transporter expression levels as
well as their function, evaluated through the use of fluorescent
dyes, with resistance of patients to chemotherapy regimens.
Notably, in AML, studies have shown that expression of Pgp/MDR1 is
associated with a lower rate of complete response to induction
chemotherapy and a higher rate of resistant disease in both elderly
and younger AML patients (Leith et al., Blood (1997) 89 p 3323,
Leith et al., Blood (1999) 94, p 1086). Legrand et al., (Blood
(1998) 91, p 4480) showed that Pgp/MDR1 and MRP1 function in CD34+
blast cells are negative prognostic factors in AML and further, the
same group showed that a high level of simultaneous activity of
Pgp/MDR1 and MRP1 was predictive of poor treatment outcome (Legrand
et al., (Blood (1999) 94, p 1046). In two more recent studies,
elevated expression of Pgp/MDR1 and BCRP in CD34+/CD38- AML
subpopulations were found in 8 out of 10 non-responders as compared
to 0 out of 10 in responders to induction chemotherapy (Ho et al.,
Experimental Hematology (2008) 36, p 433). In a second study,
evaluation of Pgp/MDR1, MRP1, BCRP/ABCG2 and lung resistance
protein showed that the more immature subsets of leukemic stem
cells expressed higher levels of these proteins compared more
mature leukemic subsets (Figueiredo-Pontes et al., Clinical
Cytometry (2008) 74B p 163).
[0287] Experimentally, it is possible to correlate expression of
transporter proteins with their function by the use of inhibitors
including but not limited to cyclosporine (measures Pgp function),
probenecid (measures MRP1 function), fumitremorgin C, and a
derivative Ko143, reserpine (measures ABCG2 function). Aalthough
these molecules inhibit a variety of transporters, they do permit
some correlations to be made between protein expression and
function (Legrand et al., (Blood (1998) 91, p 4480), Legrand et
al., (Blood (1999) 94, p 1046, Zhou et al., Nature Medicine, 2001,
7, p 1028-1034, Sarkardi et al., Physiol Rev 2006 86:
1179-1236).
[0288] Extending the use of these inhibitors, they can be used to
make correlations within subpopulations of cells gated both for
phenotypic markers denoting stages of development along
hematopoietic and lymphoid lineages, as well as reagents that
recognize the transporter proteins themselves. Thus it will be
possible to simultaneously measure protein expression and
function.
[0289] Expression levels of drug transporters and receptors may not
be as informative by themselves for disease management as analysis
of activatable elements, such as phosphorylated proteins. However,
expression information may be useful in combination with the
analysis of activatable elements, such as phosphorylated proteins.
In some embodiments, the methods described herein analyze the
expression of drug transporters and receptors in combination with
the analysis of one or more activatable elements for the diagnosis,
prognosis, selection of treatment, or predicting response to
treatment for a condition.
DNA Damage and Apoptosis
[0290] The response to DNA damage is a protective measure taken by
cells to prevent or delay genetic instability and tumorigenesis. It
allows cells to undergo cell cycle arrest and gives them an
opportunity to either: repair the broken DNA and resume passage
through the cell cycle or, if the breakage is irreparable, trigger
senescence or an apoptotic program leading to cell death (Wade
Harper et al., Molecular Cell, (2007) 28 p 739-745, Bartek J et
al., Oncogene (2007) 26 p 7773-9).
[0291] Several protein complexes are positioned at strategic points
within the DNA damage response pathway and act as sensors,
transducers or effectors of DNA damage. Depending on the nature of
DNA damage for example; double stranded breaks, single strand
breaks, single base alterations due to alkylation, oxidation etc,
there is an assembly of specific DNA damage sensor protein
complexes in which activated ataxia telangiectasia mutated (ATM)
and ATM- and Rad3 related (ATR) kinases phosphorylate and
subsequently activate the checkpoint kinases Chk1 and Chk2. Both of
these DNA-signal transducer kinases amplify the damage response by
phosphorylating a multitude of substrates. Both checkpoint kinases
have overlapping and distinct roles in orchestrating the cell's
response to DNA damage.
[0292] Maximal kinase activation of Chk2 involves phosphorylation
and homo-dimerization with ATM-mediated phosphorylation of T68 on
Chk2 as a preliminary event. This in turn activates the DNA repair.
As mentioned above, in order for DNA repair to proceed, there must
be a delay in the cell cycle. Chk2 seems to have a role at the G1/S
and G2/M junctures and may have overlapping functions with Chk1.
There are multiple ways in which Chk1 and Chk2 mediate cell cycle
suspension. In one mechanism Chk2 phosphorylates the CDC25A and
CDC25C phosphatases resulting in their removal from the nucleus
either by proteosomal degradation or by sequestration in the
cytoplasm by 14-3-3. These phosphatases are no longer able to act
on their nuclear CDK substrates. If DNA repair is successful cell
cycle progression is resumed (Antoni et al., Nature reviews cancer
(2007) 7, p 925-936).
[0293] When DNA repair is no longer possible the cell undergoes
apoptosis with participation from Chk2 in p53 independent and
dependent pathways. Chk2 substrates that operate in a
p53-independent manner include the E2F1 transcription factor, the
tumor suppressor promyelocytic leukemia (PML) and the polo-like
kinases 1 and 3 (PLK1 and PLK3). E2F1 drives the expression of a
number of apoptotic genes including caspases 3, 7, 8 and 9 as well
as the pro-apoptotic Bcl-2 related proteins (Bim, Noxa, PUMA).
[0294] In its response to DNA damage, the p53 activates the
transcription of a program of genes that regulate DNA repair, cell
cycle arrest, senescence and apoptosis. The overall functions of
p53 are to preserve fidelity in DNA replication such that when cell
division occurs tumorigenic potential can be avoided. In such a
role, p53 is described as "The Guardian of the Genome (Riley et
al., Nature Reviews Molecular Cell Biology (2008) 9 p 402-412). The
diverse alarm signals that impinge on p53 result in a rapid
increase in its levels through a variety of post translational
modifications. Worthy of mention is the phosphorylation of amino
acid residues within the amino terminal portion of p53 such that
p53 is no longer under the regulation of Mdm2 The responsible
kinases are ATM, Chk1 and Chk2. The subsequent stabilization of p53
permits it to transcriptionally regulate multiple pro-apoptotic
members of the Bcl-2 family, including Bax, Bid, Puma, and Noxa
(Discussion below).
[0295] The series of events that are mediated by p53 to promote
apoptosis including DNA damage, anoxia and imbalances in
growth-promoting signals are sometimes termed the `intrinsic
apoptotic" program since the signals triggering it originate within
the cell. An alternate route of activating the apoptotic pathway
can occur from the outside of the cell mediated by the binding of
ligands to transmembrane death receptors. This extrinsic or
receptor mediated apoptotic program acting through their receptor
death domains eventually converges on the intrinsic, mitochondrial
apoptotic pathway as discussed below (Sprick et al., Biochim
Biophys Acta. (2004) 1644 p 125-32).
[0296] Key regulators of apoptosis are proteins of the Bcl-2
family. The founding member, the Bcl-2 proto-oncogene was first
identified at the chromosomal breakpoint of t(14:18) bearing human
follicular B cell lymphoma. Unexpectedly, expression of Bcl-2 was
proved to block rather than promote cell death following multiple
pathological and physiological stimuli (Danial and Korsemeyer, Cell
(2204) 116, p 205-219). The Bcl-2 family has at least 20 members
which are key regulators of apoptosis, functioning to control
mitochondrial permeability as well as the release of proteins
important in the apoptotic program. The ratio of anti- to
pro-apoptotic molecules such as Bcl-2/Bax constitutes a rheostat
that sets the threshold of susceptibility to apoptosis for the
intrinsic pathway, which utilizes organelles such as the
mitochondrion to amplify death signals. The family can be divided
into 3 subclasses based on structure and impact on apoptosis.
Family members of subclass 1 including Bcl-2, Bcl-X.sub.L and Mcl-1
are characterized by the presence of 4 Bcl-2 homology domains (BH1,
BH2, BH3 and BH4) and are anti-apoptotic. The structure of the
second subclass members is marked for containing 3 BH domains and
family members such as Bax and Bak possess pro-apoptotic
activities. The third subclass, termed the BH3-only proteins
include Noxa, Puma, Bid, Bad and Bim. They function to promote
apoptosis either by activating the pro-apoptotic members of group 2
or by inhibiting the anti-apoptotic members of subclass 1 (Er et
al., Biochimica et Biophysica Act (2006) 1757, p 1301-1311,
Fernandez-Luna Cellular Signaling (2008) Advance Publication
Online).
[0297] The role of mitochondria in the apoptotic process was
clarified as involving an apoptotic stimulus resulting in
depolarization of the outer mitochondrial membrane leading to a
leak of cytochrome C into the cytoplasm. Association of Cytoplasmic
cytochrome C molecules with adaptor apoptotic protease activating
factor (APAF) forms a structure called the apoptosome which can
activate enzymatically latent procaspase 9 into a cleaved activated
form. Caspase 9 is one member of a family of cysteine
aspartyl-specific proteases; genes encoding 11 of these proteases
have been mapped in the human genome. Activated caspase 9,
classified as an intiator caspase, then cleaves procaspase 3 which
cleaves more downstream procaspases, classified as executioner
caspases, resulting in an amplification cascade that promotes
cleavage of death substrates including poly(ADP-ribose) polymerase
1 (PARP). The cleavage of PARP produces 2 fragments both of which
have a role in apoptosis (Soldani and Scovassi Apoptosis (2002) 7,
p 321). A further level of apoptotic regulation is provided by
smac/Diablo, a mitochondrial protein that inactivates a group of
anti-apoptotic proteins termed inhibitors of apoptosis (IAPs)
(Huang et al., Cancer Cell (2004) 5 p 1-2). IAPs operate to block
caspase activity in 2 ways; they bind directly to and inhibit
caspase activity and in certain cases they can mark caspases for
ubiquitination and degradation.
[0298] Members of the caspase gene family (cysteine proteases with
aspartate specificity) play significant roles in both inflammation
and apoptosis. Caspases exhibit catalytic and substrate recognition
motifs that have been highly conserved. These characteristic amino
acid sequences allow caspases to interact with both positive and
negative regulators of their activity. The substrate preferences or
specificities of individual caspases have been exploited for the
development of peptides that successfully compete for caspase
binding. In addition to their distinctive aspartate cleavage sites
at the p1 position, the catalytic domains of the caspases require
at least four amino acids to the left of the cleavage site with p4
as the prominent specificity-determining residue. WEHD, VDVAD, and
DEVD are examples of peptides that preferentially bind caspase-1,
caspase-2 and caspase-3, respectively. It is possible to generate
reversible or irreversible inhibitors of caspase activation by
coupling caspase-specific peptides to certain aldehyde, nitrile or
ketone compounds. These caspase inhibitors can successfully inhibit
the induction of apoptosis in various tumor cell lines as well as
normal cells. Fluoromethyl ketone (FMK)-derivatized peptides act as
effective irreversible inhibitors with no added cytotoxic effects.
Inhibitors synthesized with a benzyloxycarbonyl group (also known
as BOC or Z) at the N-terminus and O-methyl side chains exhibit
enhanced cellular permeability thus facilitating their use in both
in vitro cell culture as well as in vivo animal studies.
Benzyloxycarbonyl-Val-Ala-Asp (OMe) fluoromethylketone (ZVAD) is a
caspase inhibitor. See Misaghi, et al., z-VAD-fmk inhibits
peptide:N-glycanase and may result in ER stress Cell Death and
Differentiation (2006) 13, 163-165.
[0299] The balance of pro- and anti-apoptotic proteins is tightly
regulated under normal physiological conditions. Tipping of this
balance either way results in disease. An oncogenic outcome results
from the inability of tumor cells to undergo apoptosis and this can
be caused by over-expression of anti-apoptotic proteins or reduced
expression or activity of pro-apoptotic protein
[0300] FIG. 3 shows the role of apoptosis in AML.
[0301] In some embodiments, the status of an activatable element
within an apoptosis pathway in response to a modulator that slows
or stops the growth of cells and/or induces apoptosis of cells is
determined. In some embodiments, the activatable element within the
apoptosis pathway is selected from the group consisting of PARP+,
Cleaved Caspase 8, and Cytoplasmic Cytochrome C, and the modulator
that slows or stops the growth of cells and/or induces apoptosis of
cells is selected from the group consisting of Staurosporine,
Etoposide, Mylotarg, Daunorubicin, and AraC.
[0302] In some embodiments, the status of an activatable element
within a DNA damage pathway in response to a modulator that slows
or stops the growth of cells and/or induces apoptosis of cells is
determined. In some embodiments, the activatable element within a
DNA damage pathway is selected from the group consisting of Chk1,
Chk2, ATM, and ATR and the modulator that slows or stops the growth
of cells and/or induces apoptosis of cells is selected from the
group consisting of Staurosporine, Etoposide, Mylotarg,
Daunorubicin, and AraC.
[0303] In some embodiments, interrogation of the apoptotic
machinery will also be performed by etoposide with or without ZVAD,
an inhibitor of caspases, or a combination of Cytarabine and
Daunorubicin at clinically relevant concentrations based on peak
plasma drug levels. The standard dose of Cytarabine, 100 mg/m2,
yields a peak plasma concentration of approximately 40 nM, whereas
high dose Cytarabine, 3 g/m2, yields a peak plasma concentration of
2 uM. Daunorubicin at 25 mg/m2 yields a peak plasma concentration
of 50 ng/ml and at 50 mg/m2 yields a peak plasma concentration of
200 ng/ml. Our in vitro apoptosis assay will use concentrations of
Cytarabine up to 2 uM, and concentrations of Daunorubicin up to 200
ng/ml.
[0304] Etoposide phosphate (brand names: Eposin, Etopophos,
Vepesid, VP-16) is an inhibitor of the enzyme topoisomerase II and
a semisynthetic derivative of podophyllotoxin, a substance
extracted from the mandrake root Podophyllum peltatum. Possessing
potent antineoplastic properties, etoposide binds to and inhibits
topoisomerase II and its function in ligating cleaved DNA
molecules, resulting in the accumulation of single- or
double-strand DNA breaks, the inhibition of DNA replication and
transcription, and apoptotic cell death. Etoposide acts primarily
in the G2 and S phases of the cell cycle. See the NCI Drug
Dictionary at
http://www.cancer.gov/Templates/drugdictionary.aspx?CdrID=39207.
Cell Cycle
[0305] The cell cycle, or cell-division cycle, is the series of
events that take place in a cell leading to its division and
duplication (replication). The cell cycle consists of five distinct
phases: G1 phase, S phase (synthesis), G2 phase (collectively known
as interphase) and M phase (mitosis). M phase is itself composed of
two tightly coupled processes: mitosis, in which the cell's
chromosomes are divided between the two daughter cells, and
cytokinesis, in which the cell's cytoplasm divides forming distinct
cells. Activation of each phase is dependent on the proper
progression and completion of the previous one. Cells that have
temporarily or reversibly stopped dividing are said to have entered
a state of quiescence called G0 phase.
[0306] Regulation of the cell cycle involves processes crucial to
the survival of a cell, including the detection and repair of
genetic damage as well as the prevention of uncontrolled cell
division. The molecular events that control the cell cycle are
ordered and directional; that is, each process occurs in a
sequential fashion and it is impossible to "reverse" the cycle.
[0307] Two key classes of regulatory molecules, cyclins and
cyclin-dependent kinases (CDKs), determine a cell's progress
through the cell cycle. Many of the genes encoding cyclins and CDKs
are conserved among all eukaryotes, but in general more complex
organisms have more elaborate cell cycle control systems that
incorporate more individual components. Many of the relevant genes
were first identified by studying yeast, especially Saccharomyces
cerevisiae genetic nomenclature in yeast dubs many these genes cdc
(for "cell division cycle") followed by an identifying number,
e.g., cdc25.
[0308] Cyclins form the regulatory subunits and CDKs the catalytic
subunits of an activated heterodimer; cyclins have no catalytic
activity and CDKs are inactive in the absence of a partner cyclin.
When activated by a bound cyclin, CDKs perform a common biochemical
reaction called phosphorylation that activates or inactivates
target proteins to orchestrate coordinated entry into the next
phase of the cell cycle. Different cyclin-CDK combinations
determine the downstream proteins targeted. CDKs are constitutively
expressed in cells whereas cyclins are synthesised at specific
stages of the cell cycle, in response to various molecular
signals.
[0309] Upon receiving a pro-mitotic extracellular signal, G1
cyclin-CDK complexes become active to prepare the cell for S phase,
promoting the expression of transcription factors that in turn
promote the expression of S cyclins and of enzymes required for DNA
replication. The G1 cyclin-CDK complexes also promote the
degradation of molecules that function as S phase inhibitors by
targeting them for ubiquitination. Once a protein has been
ubiquitinated, it is targeted for proteolytic degradation by the
proteasome. Active S cyclin-CDK complexes phosphorylate proteins
that make up the pre-replication complexes assembled during G1
phase on DNA replication origins. The phosphorylation serves two
purposes: to activate each already-assembled pre-replication
complex, and to prevent new complexes from forming. This ensures
that every portion of the cell's genome will be replicated once and
only once. The reason for prevention of gaps in replication is
fairly clear, because daughter cells that are missing all or part
of crucial genes will die. However, for reasons related to gene
copy number effects, possession of extra copies of certain genes
would also prove deleterious to the daughter cells.
[0310] Mitotic cyclin-CDK complexes, which are synthesized but
inactivated during S and G2 phases, promote the initiation of
mitosis by stimulating downstream proteins involved in chromosome
condensation and mitotic spindle assembly. A critical complex
activated during this process is an ubiquitin ligase known as the
anaphase-promoting complex (APC), which promotes degradation of
structural proteins associated with the chromosomal kinetochore.
APC also targets the mitotic cyclins for degradation, ensuring that
telophase and cytokinesis can proceed. Interphase: Interphase
generally lasts at least 12 to 24 hours in mammalian tissue. During
this period, the cell is constantly synthesizing RNA, producing
protein and growing in size. By studying molecular events in cells,
scientists have determined that interphase can be divided into 4
steps: Gap 0 (G0), Gap 1 (G1), S (synthesis) phase, Gap 2 (G2).
[0311] Cyclin D is the first cyclin produced in the cell cycle, in
response to extracellular signals (e.g. growth factors). Cyclin D
binds to existing CDK4, forming the active cyclin D-CDK4 complex.
Cyclin D-CDK4 complex in turn phosphorylates the retinoblastoma
susceptibility protein (Rb). The hyperphosphorylated Rb dissociates
from the E2F/DP1/Rb complex (which was bound to the E2F responsive
genes, effectively "blocking" them from transcription), activating
E2F. Activation of E2F results in transcription of various genes
like cyclin E, cyclin A, DNA polymerase, thymidine kinase, etc.
Cyclin E thus produced binds to CDK2, forming the cyclin E-CDK2
complex, which pushes the cell from G1 to S phase (G1/S
transition). Cyclin B along with cdc2 (cdc2-fission yeasts
(CDK1-mammalia)) forms the cyclin B-cdc2 complex, which initiates
the G2/M transition. Cyclin B-cdc2 complex activation causes
breakdown of nuclear envelope and initiation of prophase, and
subsequently, its deactivation causes the cell to exit mitosis.
[0312] Two families of genes, the Cip/Kip family and the INK4a/ARF
(Inhibitor of Kinase 4/Alternative Reading Frame) prevent the
progression of the cell cycle. Because these genes are instrumental
in prevention of tumor formation, they are known as tumor
suppressors.
[0313] The Cip/Kip family includes the genes p21, p27 and p57. They
halt cell cycle in G1 phase, by binding to, and inactivating,
cyclin-CDK complexes. p21 is a p53 response gene (which, in turn,
is triggered by DNA damage eg. due to radiation). p27 is activated
by Transforming Growth Factor .beta. (TGF .beta.), a growth
inhibitor.
[0314] The INK4a/ARF family includes p16INK4a, which binds to CDK4
and arrests the cell cycle in G1 phase, and p14arf which prevents
p53 degradation.
[0315] Cell cycle checkpoints are used by the cell to monitor and
regulate the progress of the cell cycle. Checkpoints prevent cell
cycle progression at specific points, allowing verification of
necessary phase processes and repair of DNA damage. The cell cannot
proceed to the next phase until checkpoint requirements have been
met.
[0316] Several checkpoints are designed to ensure that damaged or
incomplete DNA is not passed on to daughter cells. Two main
checkpoints exist: the G1/S checkpoint and the G2/M checkpoint.
G1/S transition is a rate-limiting step in the cell cycle and is
also known as restriction point. An alternative model of the cell
cycle response to DNA damage has also been proposed, known as the
postreplication checkpoint. p53 plays an important role in
triggering the control mechanisms at both G1/S and G2/M
checkpoints.
[0317] A disregulation of the cell cycle components may lead to
tumor formation. As mentioned above, some genes like the cell cycle
inhibitors, RB, p53 etc., when they mutate, may cause the cell to
multiply uncontrollably, forming a tumor. Although the duration of
cell cycle in tumor cells is equal to or longer than that of normal
cell cycle, the proportion of cells that are in active cell
division (versus quiescent cells in G0 phase) in tumors is much
higher than that in normal tissue. Thus there is a net increase in
cell number as the number of cells that die by apoptosis or
senescence remains the same.
[0318] In some embodiments, the status of an activatable element
within a cell cycle pathway in response to a modulator that slows
or stops the growth of cells and/or induces apoptosis of cells is
determined. In some embodiments, the activatable element within a
DNA damage pathway is selected from the group consisting of, Cdc25,
p53, CyclinA-Cdk2, CyclinE-Cdk2, CyclinB-Cdk1, p21, and Gadd45. In
some embodiments, the modulator that slows or stops the growth of
cells and/or induces apoptosis of cells is selected from the group
consisting of Staurosporine, Etoposide, Mylotarg, Daunorubicin, and
AraC.
Modulators
[0319] In some embodiments, the methods and composition utilize a
modulator. A modulator can be an activator, a therapeutic compound,
an inhibitor or a compound capable of impacting a cellular pathway.
Modulators can also take the form of environmental cues and
inputs.
[0320] Modulation can be performed in a variety of environments. In
some embodiments, cells are exposed to a modulator immediately
after collection. In some embodiments where there is a mixed
population of cells, purification of cells is performed after
modulation. In some embodiments, whole blood is collected to which
a modulator is added. In some embodiments, cells are modulated
after processing for single cells or purified fractions of single
cells. As an illustrative example, whole blood can be collected and
processed for an enriched fraction of lymphocytes that is then
exposed to a modulator. Modulation can include exposing cells to
more than one modulator. For instance, in some embodiments, cells
are exposed to at least 2, 3, 4, 5, 6, 7, 8, 9, or 10 modulators.
See U.S. Patent Application 61/048,657 which is incorporated by
reference.
[0321] In some embodiments, cells are cultured post collection in a
suitable media before exposure to a modulator. In some embodiments,
the media is a growth media. In some embodiments, the growth media
is a complex media that may include serum. In some embodiments, the
growth media comprises serum. In some embodiments, the serum is
selected from the group consisting of fetal bovine serum, bovine
serum, human serum, porcine serum, horse serum, and goat serum. In
some embodiments, the serum level ranges from 0.0001% to 30%. In
some embodiments, the growth media is a chemically defined minimal
media and is without serum. In some embodiments, cells are cultured
in a differentiating media.
[0322] Modulators include chemical and biological entities, and
physical or environmental stimuli. Modulators can act
extracellularly or intracellularly. Chemical and biological
modulators include growth factors, mitogens, cytokines, drugs,
immune modulators, ions, neurotransmitters, adhesion molecules,
hormones, small molecules, inorganic compounds, polynucleotides,
antibodies, natural compounds, lectins, lactones, chemotherapeutic
agents, biological response modifiers, carbohydrate, proteases and
free radicals. Modulators include complex and undefined biologic
compositions that may comprise cellular or botanical extracts,
cellular or glandular secretions, physiologic fluids such as serum,
amniotic fluid, or venom. Physical and environmental stimuli
include electromagnetic, ultraviolet, infrared or particulate
radiation, redox potential and pH, the presence or absences of
nutrients, changes in temperature, changes in oxygen partial
pressure, changes in ion concentrations and the application of
oxidative stress. Modulators can be endogenous or exogenous and may
produce different effects depending on the concentration and
duration of exposure to the single cells or whether they are used
in combination or sequentially with other modulators. Modulators
can act directly on the activatable elements or indirectly through
the interaction with one or more intermediary biomolecule. Indirect
modulation includes alterations of gene expression wherein the
expressed gene product is the activatable element or is a modulator
of the activatable element.
[0323] In some embodiments the modulator is selected from the group
consisting of growth factors, mitogens, cytokines, adhesion
molecules, drugs, hormones, small molecules, polynucleotides,
antibodies, natural compounds, lactones, chemotherapeutic agents,
immune modulators, carbohydrates, proteases, ions, reactive oxygen
species, peptides, and protein fragments, either alone or in the
context of cells, cells themselves, viruses, and biological and
non-biological complexes (e.g. beads, plates, viral envelopes,
antigen presentation molecules such as major histocompatibility
complex). In some embodiments, the modulator is a physical stimuli
such as heat, cold, UV radiation, and radiation. Examples of
modulators, include but are not limited to SDF-1.alpha.,
IFN-.alpha., IFN-.gamma., IL-10, IL-6, IL-27, G-CSF, FLT-3L, IGF-1,
M-CSF, SCF, PMA, Thapsigargin, H.sub.2O.sub.2, Etoposide, Mylotarg,
AraC, daunorubicin, staurosporine, benzyloxycarbonyl-Val-Ala-Asp
(OMe) fluoromethylketone (ZVAD), lenalidomide, EPO, azacitadine,
decitabine, IL-3, IL-4, GM-CSF, EPO, LPS, TNF-.alpha., and
CD40L.
[0324] In some embodiments, the modulator is an activator. In some
embodiments the modulator is an inhibitor. In some embodiments,
cells are exposed to one or more modulator. In some embodiments,
cells are exposed to at least 2, 3, 4, 5, 6, 7, 8, 9, or 10
modulators. In some embodiments, cells are exposed to at least two
modulators, wherein one modulator is an activator and one modulator
is an inhibitor. In some embodiments, cells are exposed to at least
2, 3, 4, 5, 6, 7, 8, 9, or 10 modulators, where at least one of the
modulators is an inhibitor.
[0325] In some embodiments, the cross-linker is a molecular binding
entity. In some embodiments, the molecular binding entity is a
monovalent, bivalent, or multivalent is made more multivalent by
attachment to a solid surface or tethered on a nanoparticle surface
to increase the local valency of the epitope binding domain.
[0326] In some embodiments, the inhibitor is an inhibitor of a
cellular factor or a plurality of factors that participates in a
cellular pathway (e.g. signaling cascade) in the cell. In some
embodiments, the inhibitor is a phosphatase inhibitor. Examples of
phosphatase inhibitors include, but are not limited to
H.sub.2O.sub.2, siRNA, miRNA, Cantharidin, (-)-p-Bromotetramisole,
Microcystin LR, Sodium Orthovanadate, Sodium Pervanadate, Vanadyl
sulfate, Sodium oxodiperoxo(1,10-phenanthroline)vanadate,
bis(maltolato)oxovanadium(IV), Sodium Molybdate, Sodium Perm
olybdate, Sodium Tartrate, Imidazole, Sodium Fluoride,
.beta.-Glycerophosphate, Sodium Pyrophosphate Decahydrate,
Calyculin A, Discodermia calyx, bpV(phen), mpV(pic), DMHV,
Cypermethrin, Dephostatin, Okadaic Acid, NIPP-1,
N-(9,10-Dioxo-9,10-dihydro-phenanthren-2-yl)-2,2-dimethyl-propionamide,
.alpha.-Bromo-4-hydroxyacetophenone, 4-Hydroxyphenacyl Br,
.alpha.-Bromo-4-methoxyacetophenone, 4-Methoxyphenacyl Br,
.alpha.-Bromo-4-(carboxymethoxy)acetophenone,
4-(Carboxymethoxy)phenacyl Br, and
bis(4-Trifluoromethylsulfonamidophenyl)-1,4-diisopropylbenzene,
phenylarsine oxide, Pyrrolidine Dithiocarbamate, and Aluminium
fluoride. In some embodiments, the phosphatase inhibitor is
H.sub.2O.sub.2.
[0327] In some embodiments, the activation level of an activatable
element in a cell is determined by contacting the cell with at
least 2, 3, 4, 5, 6, 7, 8, 9, or 10 modulators. In some
embodiments, the activation level of an activatable element in a
cell is determined by contacting the cell with at least 2, 3, 4, 5,
6, 7, 8, 9, or 10 modulators where at least one of the modulators
is an inhibitor. In some embodiments, the activation level of an
activatable element in a cell is determined by contacting the cell
with an inhibitor and a modulator, where the modulator can be an
inhibitor or an activator. In some embodiments, the activation
level of an activatable element in a cell is determined by
contacting the cell with an inhibitor and an activator. In some
embodiments, the activation level of an activatable element in a
cell is determined by contacting the cell with two or more
modulators.
[0328] In some embodiments, a phenotypic profile of a population of
cells is determined by measuring the activation level of an
activatable element when the population of cells is exposed to a
plurality of modulators in separate cultures. In some embodiments,
the modulators include H.sub.2O.sub.2, PMA, SDF1 .alpha., CD40L,
IGF-1, IL-7, IL-6, IL-10, IL-27, IL-4, IL-2, IL-3, thapsigardin
and/or a combination thereof. For instance a population of cells
can be exposed to one or more, all or a combination of the
following combination of modulators: H.sub.2O.sub.2, PMA;
SDF1.alpha.; CD40L; IGF-1; IL-7; IL-6; IL-10; IL-27; IL-4; IL-2;
IL-3; thapsigardin;. In some embodiments, the phenotypic profile of
the population of cells is used to classify the population as
described herein.
Gating
[0329] In another embodiment, a user may analyze the signaling in
subpopulations based on surface markers. For example, the user
could look at: "stem cell populations" by CD34+ CD38- or CD34+
CD33- expressing cells; drug transporter positive cells; i.e. FLT3
LIGAND+ cells; or multiple leukemic subclones based on CD33, CD45,
HLA-DR, CD11b and analyzing signaling in each subpopulation. In
another alternative embodiment, a user may analyze the data based
on intracellular markers, such as transcription factors or other
intracellular proteins; based on a functional assay (i.e. dye
negative "side population" aka drug transporter+ cells, or
fluorescent glucose uptake, or based on other fluorescent markers.
In some embodiments, a gate is established after learning from a
responsive subpopulation. That is, a gate is developed from one
data set after finding a population that correlates with a clinical
outcome. This gate can then be applied retrospectively or
prospectively to other data sets.
[0330] In some embodiments where flow cytometry is used, prior to
analyzing of data the populations of interest and the method for
characterizing these populations are determined For instance, there
are at least two general ways of identifying populations for data
analysis: (i) "Outside-in" comparison of Parameter sets for
individual samples or subset (e.g., patients in a trial). In this
more common case, cell populations are homogenous or lineage gated
in such a way as to create distinct sets considered to be
homogenous for targets of interest. An example of sample-level
comparison would be the identification of signaling profiles in
tumor cells of a patient and correlation of these profiles with
non-random distribution of clinical responses. This is considered
an outside-in approach because the population of interest is
pre-defined prior to the mapping and comparison of its profile to
other populations. (ii) "Inside-out" comparison of Parameters at
the level of individual cells in a heterogeneous population. An
example of this would be the signal transduction state mapping of
mixed hematopoietic cells under certain conditions and subsequent
comparison of computationally identified cell clusters with lineage
specific markers. This could be considered an inside-out approach
to single cell studies as it does not presume the existence of
specific populations prior to classification. A major drawback of
this approach is that it creates populations which, at least
initially, require multiple transient markers to enumerate and may
never be accessible with a single cell surface epitope. As a
result, the biological significance of such populations can be
difficult to determine. The main advantage of this unconventional
approach is the unbiased tracking of cell populations without
drawing potentially arbitrary distinctions between lineages or cell
types.
[0331] Each of these techniques capitalizes on the ability of flow
cytometry to deliver large amounts of multiparameter data at the
single cell level. For cells associated with a condition (e.g.
neoplastic or hematopoietic condition), a third "meta-level" of
data exists because cells associated with a condition (e.g. cancer
cells) are generally treated as a single entity and classified
according to historical techniques. These techniques have included
organ or tissue of origin, degree of differentiation, proliferation
index, metastatic spread, and genetic or metabolic data regarding
the patient.
[0332] In some embodiments, the present invention uses variance
mapping techniques for mapping condition signaling space. These
methods represent a significant advance in the study of condition
biology because it enables comparison of conditions independent of
a putative normal control. Traditional differential state analysis
methods (e.g., DNA microarrays, subtractive Northern blotting)
generally rely on the comparison of cells associated with a
condition from each patient sample with a normal control, generally
adjacent and theoretically untransformed tissue. Alternatively,
they rely on multiple clusterings and reclusterings to group and
then further stratify patient samples according to phenotype. In
contrast, variance mapping of condition states compares condition
samples first with themselves and then against the parent condition
population. As a result, activation states with the most diversity
among conditions provide the core parameters in the differential
state analysis. Given a pool of diverse conditions, this technique
allows a researcher to identify the molecular events that underlie
differential condition pathology (e.g., cancer responses to
chemotherapy), as opposed to differences between conditions and a
proposed normal control.
[0333] In some embodiments, when variance mapping is used to
profile the signaling space of patient samples, conditions whose
signaling response to modulators is similar are grouped together,
regardless of tissue or cell type of origin. Similarly, two
conditions (e.g. two tumors) that are thought to be relatively
alike based on lineage markers or tissue of origin could have
vastly different abilities to interpret environmental stimuli and
would be profiled in two different groups.
[0334] When groups of signaling profiles have been identified it is
frequently useful to determine whether other factors, such as
clinical responses, presence of gene mutations, and protein
expression levels, are non-randomly distributed within the groups.
If experiments or literature suggest such a hypothesis in an
arrayed flow cytometry experiment, it can be judged with simple
statistical tests, such as the Student's t-test and the X.sup.2
test. Similarly, if two variable factors within the experiment are
thought to be related, the Pearson, and/or Spearman are used to
measure the degree of this relationship.
[0335] Examples of analysis for activatable elements are described
in US publication number 20060073474 entitled "Methods and
compositions for detecting the activation state of multiple
proteins in single cells" and US publication number 20050112700
entitled "Methods and compositions for risk stratification" the
content of which are incorporate here by reference.
Detection
[0336] In practicing the methods of this invention, the detection
of the status of the one or more activatable elements can be
carried out by a person, such as a technician in the laboratory.
Alternatively, the detection of the status of the one or more
activatable elements can be carried out using automated systems. In
either case, the detection of the status of the one or more
activatable elements for use according to the methods of this
invention is performed according to standard techniques and
protocols well-established in the art.
[0337] One or more activatable elements can be detected and/or
quantified by any method that detect and/or quantitates the
presence of the activatable element of interest. Such methods may
include radioimmunoassay (RIA) or enzyme linked immunoabsorbance
assay (ELISA), immunohistochemistry, immunofluorescent
histochemistry with or without confocal microscopy, reversed phase
assays, homogeneous enzyme immunoassays, and related non-enzymatic
techniques, Western blots, whole cell staining,
immunoelectronmicroscopy, nucleic acid amplification, gene array,
protein array, mass spectrometry, patch clamp, 2-dimensional gel
electrophoresis, differential display gel electrophoresis,
microsphere-based multiplex protein assays, label-free cellular
assays and flow cytometry, etc. U.S. Pat. No. 4,568,649 describes
ligand detection systems, which employ scintillation counting.
These techniques are particularly useful for modified protein
parameters. Cell readouts for proteins and other cell determinants
can be obtained using fluorescent or otherwise tagged reporter
molecules. Flow cytometry methods are useful for measuring
intracellular parameters.
[0338] In some embodiments, the present invention provides methods
for determining an activatable element's activation profile for a
single cell. The methods may comprise analyzing cells by flow
cytometry on the basis of the activation level of at least two
activatable elements. Binding elements (e.g. activation
state-specific antibodies) are used to analyze cells on the basis
of activatable element activation level, and can be detected as
described below. Alternatively, non-binding elements systems as
described above can be used in any system described herein.
[0339] Detection of cell signaling states may be accomplished using
binding elements and labels. Cell signaling states may be detected
by a variety of methods known in the art. They generally involve a
binding element, such as an antibody, and a label, such as a
fluorochrome to form a detection element. Detection elements do not
need to have both of the above agents, but can be one unit that
possesses both qualities. These and other methods are well
described in U.S. Pat. Nos. 7,381,535 and 7,393,656 and U.S. Ser.
Nos. 10/193,462; 11/655,785; 11/655,789; 11/655,821; 11/338,957,
61/048,886; 61/048,920; and 61/048,657 which are all incorporated
by reference in their entireties.
[0340] In one embodiment of the invention, it is advantageous to
increase the signal to noise ratio by contacting the cells with the
antibody and label for a time greater than 5, 6, 7, 8, 9, 10, 11,
12, 13, 14, 15, 16, 17, 18, 19, 20, 24 or up to 48 or more
hours.
[0341] When using fluorescent labeled components in the methods and
compositions of the present invention, it will recognized that
different types of fluorescent monitoring systems, e.g., Cytometric
measurement device systems, can be used to practice the invention.
In some embodiments, flow cytometric systems are used or systems
dedicated to high throughput screening, e.g. 96 well or greater
microtiter plates. Methods of performing assays on fluorescent
materials are well known in the art and are described in, e.g.,
Lakowicz, J. R., Principles of Fluorescence Spectroscopy, New York:
Plenum Press (1983); Herman, B., Resonance energy transfer
microscopy, in: Fluorescence Microscopy of Living Cells in Culture,
Part B, Methods in Cell Biology, vol. 30, ed. Taylor, D. L. &
Wang, Y.-L., San Diego: Academic Press (1989), pp. 219-243; Turro,
N. J., Modern Molecular Photochemistry, Menlo Park:
Benjamin/Cummings Publishing Col, Inc. (1978), pp. 296-361.
[0342] Fluorescence in a sample can be measured using a
fluorimeter. In general, excitation radiation, from an excitation
source having a first wavelength, passes through excitation optics.
The excitation optics cause the excitation radiation to excite the
sample. In response, fluorescent proteins in the sample emit
radiation that has a wavelength that is different from the
excitation wavelength. Collection optics then collect the emission
from the sample. The device can include a temperature controller to
maintain the sample at a specific temperature while it is being
scanned. According to one embodiment, a multi-axis translation
stage moves a microtiter plate holding a plurality of samples in
order to position different wells to be exposed. The multi-axis
translation stage, temperature controller, auto-focusing feature,
and electronics associated with imaging and data collection can be
managed by an appropriately programmed digital computer. The
computer also can transform the data collected during the assay
into another format for presentation. In general, known robotic
systems and components can be used.
[0343] Other methods of detecting fluorescence may also be used,
e.g., Quantum dot methods (see, e.g., Goldman et al., J. Am. Chem.
Soc. (2002) 124:6378-82; Pathak et al. J. Am. Chem. Soc. (2001)
123:4103-4; and Remade et al., Proc. Natl. Sci. USA (2000)
18:553-8, each expressly incorporated herein by reference) as well
as confocal microscopy. In general, flow cytometry involves the
passage of individual cells through the path of a laser beam. The
scattering the beam and excitation of any fluorescent molecules
attached to, or found within, the cell is detected by
photomultiplier tubes to create a readable output, e.g. size,
granularity, or fluorescent intensity.
[0344] In some embodiments, the activation level of an activatable
element is measured using Inductively Coupled Plasma Mass
Spectrometer (ICP-MS). A binding element that has been labeled with
a specific element binds to the activatable. When the cell is
introduced into the ICP, it is atomized and ionized. The elemental
composition of the cell, including the labeled binding element that
is bound to the activatable element, is measured. The presence and
intensity of the signals corresponding to the labels on the binding
element indicates the level of the activatable element on that cell
(Tanner et al. Spectrochimica Acta Part B: Atomic Spectroscopy,
2007 March; 62(3):188-195.).
[0345] The detecting, sorting, or isolating step of the methods of
the present invention can entail fluorescence-activated cell
sorting (FACS) techniques, where FACS is used to select cells from
the population containing a particular surface marker, or the
selection step can entail the use of magnetically responsive
particles as retrievable supports for target cell capture and/or
background removal. A variety of FACS systems are known in the art
and can be used in the methods of the invention (see e.g.,
WO99/54494, filed Apr. 16, 1999; U.S. Ser. No. 20010006787, filed
Jul. 5, 2001, each expressly incorporated herein by reference).
[0346] In some embodiments, a FACS cell sorter (e.g. a
FACSVantage.TM. Cell Sorter, Becton Dickinson Immunocytometry
Systems, San Jose, Calif.) is used to sort and collect cells based
on their activation profile (positive cells) in the presence or
absence of an increase in activation level in an activatable
element in response to a modulator. Other flow cytometers that are
commercially available include the LSR II and the Canto II both
available from Becton Dickinson. See Shapiro, Howard M., Practical
Flow Cytometry, 4th Ed., John Wiley & Sons, Inc., 2003 for
additional information on flow cytometers.
[0347] In some embodiments, the cells are first contacted with
fluorescent-labeled activation state-specific binding elements
(e.g. antibodies) directed against specific activation state of
specific activatable elements. In such an embodiment, the amount of
bound binding element on each cell can be measured by passing
droplets containing the cells through the cell sorter. By imparting
an electromagnetic charge to droplets containing the positive
cells, the cells can be separated from other cells. The positively
selected cells can then be harvested in sterile collection vessels.
These cell-sorting procedures are described in detail, for example,
in the FACSVantage.TM.. Training Manual, with particular reference
to sections 3-11 to 3-28 and 10-1 to 10-17, which is hereby
incorporated by reference in its entirety. See the patents,
applications and articles referred to, and incorporated above for
detection systems.
[0348] Fluorescent compounds such as Daunorubicin and Enzastaurin
are problematic for flow cytometry based biological assays due to
their broad fluorescence emission spectra. These compounds get
trapped inside cells after fixation with agents like
paraformaldehyde, and are excited by one or more of the lasers
found on flow cytometers. The fluorescence emission of these
compounds is often detected in multiple PMT detectors which
complicates their use in multiparametric flow cytometry. A way to
get around this problem is to compensate out the fluorescence
emission of the compound from the PMT detectors used to measure the
relevant biological markers. This is achieved using a PMT detector
with a bandpass filter near the emission maximum of the fluorescent
compound, and cells incubated with the compound as the compensation
control when calculating a compensation matrix. The cells incubated
with the fluorescent compound are fixed with paraformaldehyde, then
washed and permeabilized with 100% methanol. The methanol is washed
out and the cells are mixed with unlabeled fixed/permed cells to
yield a compensation control consisting of a mixture of fluorescent
and negative cell populations.
[0349] In another embodiment, positive cells can be sorted using
magnetic separation of cells based on the presence of an isoform of
an activatable element. In such separation techniques, cells to be
positively selected are first contacted with specific binding
element (e.g., an antibody or reagent that binds an isoform of an
activatable element). The cells are then contacted with retrievable
particles (e.g., magnetically responsive particles) that are
coupled with a reagent that binds the specific element. The
cell-binding element-particle complex can then be physically
separated from non-positive or non-labeled cells, for example,
using a magnetic field. When using magnetically responsive
particles, the positive or labeled cells can be retained in a
container using a magnetic filed while the negative cells are
removed. These and similar separation procedures are described, for
example, in the Baxter Immunotherapy Isolex training manual which
is hereby incorporated in its entirety.
[0350] In some embodiments, methods for the determination of a
receptor element activation state profile for a single cell are
provided. The methods comprise providing a population of cells and
analyze the population of cells by flow cytometry. Preferably,
cells are analyzed on the basis of the activation level of at least
two activatable elements. In some embodiments, a multiplicity of
activatable element activation-state antibodies is used to
simultaneously determine the activation level of a multiplicity of
elements.
[0351] In some embodiment, cell analysis by flow cytometry on the
basis of the activation level of at least two elements is combined
with a determination of other flow cytometry readable outputs, such
as the presence of surface markers, granularity and cell size to
provide a correlation between the activation level of a
multiplicity of elements and other cell qualities measurable by
flow cytometry for single cells.
[0352] As will be appreciated, the present invention also provides
for the ordering of element clustering events in signal
transduction. Particularly, the present invention allows the
artisan to construct an element clustering and activation hierarchy
based on the correlation of levels of clustering and activation of
a multiplicity of elements within single cells. Ordering can be
accomplished by comparing the activation level of a cell or cell
population with a control at a single time point, or by comparing
cells at multiple time points to observe subpopulations arising out
of the others.
[0353] The present invention provides a valuable method of
determining the presence of cellular subsets within cellular
populations. Ideally, signal transduction pathways are evaluated in
homogeneous cell populations to ensure that variances in signaling
between cells do not qualitatively nor quantitatively mask signal
transduction events and alterations therein. As the ultimate
homogeneous system is the single cell, the present invention allows
the individual evaluation of cells to allow true differences to be
identified in a significant way.
[0354] Thus, the invention provides methods of distinguishing
cellular subsets within a larger cellular population. As outlined
herein, these cellular subsets often exhibit altered biological
characteristics (e.g. activation levels, altered response to
modulators) as compared to other subsets within the population. For
example, as outlined herein, the methods of the invention allow the
identification of subsets of cells from a population such as
primary cell populations, e.g. peripheral blood mononuclear cells
that exhibit altered responses (e.g. response associated with
presence of a condition) as compared to other subsets. In addition,
this type of evaluation distinguishes between different activation
states, altered responses to modulators, cell lineages, cell
differentiation states, etc.
[0355] As will be appreciated, these methods provide for the
identification of distinct signaling cascades for both artificial
and stimulatory conditions in complex cell populations, such a
peripheral blood mononuclear cells, or naive and memory
lymphocytes.
[0356] When necessary cells are dispersed into a single cell
suspension, e.g. by enzymatic digestion with a suitable protease,
e.g. collagenase, dispase, etc; and the like. An appropriate
solution is used for dispersion or suspension. Such solution will
generally be a balanced salt solution, e.g. normal saline, PBS,
Hanks balanced salt solution, etc., conveniently supplemented with
fetal calf serum or other naturally occurring factors, in
conjunction with an acceptable buffer at low concentration,
generally from 5-25 mM. Convenient buffers include HEPES1 phosphate
buffers, lactate buffers, etc. The cells may be fixed, e.g. with 3%
paraformaldehyde, and are usually permeabilized, e.g. with ice cold
methanol; HEPES-buffered PBS containing 0.1% saponin, 3% BSA;
covering for 2 min in acetone at -200 C; and the like as known in
the art and according to the methods described herein.
[0357] In some embodiments, one or more cells are contained in a
well of a 96 well plate or other commercially available multiwell
plate. In an alternate embodiment, the reaction mixture or cells
are in a cytometric measurement device. Other multiwell plates
useful in the present invention include, but are not limited to 384
well plates and 1536 well plates. Still other vessels for
containing the reaction mixture or cells and useful in the present
invention will be apparent to the skilled artisan.
[0358] The addition of the components of the assay for detecting
the activation level or activity of an activatable element, or
modulation of such activation level or activity, may be sequential
or in a predetermined order or grouping under conditions
appropriate for the activity that is assayed for. Such conditions
are described here and known in the art. Moreover, further guidance
is provided below (see, e.g., in the Examples).
[0359] In some embodiments, the activation level of an activatable
element is measured using Inductively Coupled Plasma Mass
Spectrometer (ICP-MS). A binding element that has been labeled with
a specific element binds to the activatable. When the cell is
introduced into the ICP, it is atomized and ionized. The elemental
composition of the cell, including the labeled binding element that
is bound to the activatable element, is measured. The presence and
intensity of the signals corresponding to the labels on the binding
element indicates the level of the activatable element on that cell
(Tanner et al. Spectrochimica Acta Part B: Atomic Spectroscopy,
2007 March; 62(3):188-195.).
[0360] As will be appreciated by one of skill in the art, the
instant methods and compositions find use in a variety of other
assay formats in addition to flow cytometry analysis. For example,
DNA microarrays are commercially available through a variety of
sources (Affymetrix, Santa Clara Calif.) or they can be custom made
in the lab using arrayers which are also know (Perkin Elmer). In
addition, protein chips and methods for synthesis are known. These
methods and materials may be adapted for the purpose of affixing
activation state binding elements to a chip in a prefigured array.
In some embodiments, such a chip comprises a multiplicity of
element activation state binding elements, and is used to determine
an element activation state profile for elements present on the
surface of a cell.
[0361] In some embodiments, a chip comprises a multiplicity of the
"second set binding elements," in this case generally unlabeled.
Such a chip is contacted with sample, preferably cell extract, and
a second multiplicity of binding elements comprising element
activation state specific binding elements is used in the sandwich
assay to simultaneously determine the presence of a multiplicity of
activated elements in sample. Preferably, each of the multiplicity
of activation state-specific binding elements is uniquely labeled
to facilitate detection.
[0362] In some embodiments confocal microscopy can be used to
detect activation profiles for individual cells. Confocal
microscopy relies on the serial collection of light from spatially
filtered individual specimen points, which is then electronically
processed to render a magnified image of the specimen. The signal
processing involved confocal microscopy has the additional
capability of detecting labeled binding elements within single
cells, accordingly in this embodiment the cells can be labeled with
one or more binding elements. In some embodiments the binding
elements used in connection with confocal microscopy are antibodies
conjugated to fluorescent labels, however other binding elements,
such as other proteins or nucleic acids are also possible.
[0363] In some embodiments, the methods and compositions of the
instant invention can be used in conjunction with an "In-Cell
Western Assay." In such an assay, cells are initially grown in
standard tissue culture flasks using standard tissue culture
techniques. Once grown to optimum confluency, the growth media is
removed and cells are washed and trypsinized The cells can then be
counted and volumes sufficient to transfer the appropriate number
of cells are aliquoted into microwell plates (e.g., Nunc.TM. 96
Microwell.TM. plates). The individual wells are then grown to
optimum confluency in complete media whereupon the media is
replaced with serum-free media. At this point controls are
untouched, but experimental wells are incubated with a modulator,
e.g. EGF. After incubation with the modulator cells are fixed and
stained with labeled antibodies to the activation elements being
investigated. Once the cells are labeled, the plates can be scanned
using an imager such as the Odyssey Imager (LiCor, Lincoln Nebr.)
using techniques described in the Odyssey Operator's Manual v1.2.,
which is hereby incorporated in its entirety. Data obtained by
scanning of the multiwell plate can be analyzed and activation
profiles determined as described below.
[0364] In some embodiments, the detecting is by high pressure
liquid chromatography (HPLC), for example, reverse phase HPLC, and
in a further aspect, the detecting is by mass spectrometry.
[0365] These instruments can fit in a sterile laminar flow or fume
hood, or are enclosed, self-contained systems, for cell culture
growth and transformation in multi-well plates or tubes and for
hazardous operations. The living cells may be grown under
controlled growth conditions, with controls for temperature,
humidity, and gas for time series of the live cell assays.
Automated transformation of cells and automated colony pickers may
facilitate rapid screening of desired cells.
[0366] Flow cytometry or capillary electrophoresis formats can be
used for individual capture of magnetic and other beads, particles,
cells, and organisms.
[0367] Flexible hardware and software allow instrument adaptability
for multiple applications. The software program modules allow
creation, modification, and running of methods. The system
diagnostic modules allow instrument alignment, correct connections,
and motor operations. Customized tools, labware, and liquid,
particle, cell and organism transfer patterns allow different
applications to be performed. Databases allow method and parameter
storage. Robotic and computer interfaces allow communication
between instruments.
[0368] In some embodiment, the methods of the invention include the
use of liquid handling components. The liquid handling systems can
include robotic systems comprising any number of components. In
addition, any or all of the steps outlined herein may be automated;
thus, for example, the systems may be completely or partially
automated. See U.S. Ser. No. 61/048,657.
[0369] As will be appreciated by those in the art, there are a wide
variety of components which can be used, including, but not limited
to, one or more robotic arms; plate handlers for the positioning of
microplates; automated lid or cap handlers to remove and replace
lids for wells on non-cross contamination plates; tip assemblies
for sample distribution with disposable tips; washable tip
assemblies for sample distribution; 96 well loading blocks; cooled
reagent racks; microtiter plate pipette positions (optionally
cooled); stacking towers for plates and tips; and computer
systems.
[0370] Fully robotic or microfluidic systems include automated
liquid-, particle-, cell- and organism-handling including high
throughput pipetting to perform all steps of screening
applications. This includes liquid, particle, cell, and organism
manipulations such as aspiration, dispensing, mixing, diluting,
washing, accurate volumetric transfers; retrieving, and discarding
of pipet tips; and repetitive pipetting of identical volumes for
multiple deliveries from a single sample aspiration. These
manipulations are cross-contamination-free liquid, particle, cell,
and organism transfers. This instrument performs automated
replication of microplate samples to filters, membranes, and/or
daughter plates, high-density transfers, full-plate serial
dilutions, and high capacity operation.
[0371] In some embodiments, chemically derivatized particles,
plates, cartridges, tubes, magnetic particles, or other solid phase
matrix with specificity to the assay components are used. The
binding surfaces of microplates, tubes or any solid phase matrices
include non-polar surfaces, highly polar surfaces, modified dextran
coating to promote covalent binding, antibody coating, affinity
media to bind fusion proteins or peptides, surface-fixed proteins
such as recombinant protein A or G, nucleotide resins or coatings,
and other affinity matrix are useful in this invention.
[0372] In some embodiments, platforms for multi-well plates,
multi-tubes, holders, cartridges, minitubes, deep-well plates,
microfuge tubes, cryovials, square well plates, filters, chips,
optic fibers, beads, and other solid-phase matrices or platform
with various volumes are accommodated on an upgradable modular
platform for additional capacity. This modular platform includes a
variable speed orbital shaker, and multi-position work decks for
source samples, sample and reagent dilution, assay plates, sample
and reagent reservoirs, pipette tips, and an active wash station.
In some embodiments, the methods of the invention include the use
of a plate reader.
[0373] In some embodiments, thermocycler and thermoregulating
systems are used for stabilizing the temperature of heat exchangers
such as controlled blocks or platforms to provide accurate
temperature control of incubating samples from 0.degree. C. to
100.degree. C.
[0374] In some embodiments, interchangeable pipet heads (single or
multi-channel) with single or multiple magnetic probes, affinity
probes, or pipetters robotically manipulate the liquid, particles,
cells, and organisms. Multi-well or multi-tube magnetic separators
or platforms manipulate liquid, particles, cells, and organisms in
single or multiple sample formats.
[0375] In some embodiments, the instrumentation will include a
detector, which can be a wide variety of different detectors,
depending on the labels and assay. In some embodiments, useful
detectors include a microscope(s) with multiple channels of
fluorescence; plate readers to provide fluorescent, ultraviolet and
visible spectrophotometric detection with single and dual
wavelength endpoint and kinetics capability, fluorescence resonance
energy transfer (FRET), luminescence, quenching, two-photon
excitation, and intensity redistribution; CCD cameras to capture
and transform data and images into quantifiable formats; and a
computer workstation.
[0376] In some embodiments, the robotic apparatus includes a
central processing unit which communicates with a memory and a set
of input/output devices (e.g., keyboard, mouse, monitor, printer,
etc.) through a bus. Again, as outlined below, this may be in
addition to or in place of the CPU for the multiplexing devices of
the invention. The general interaction between a central processing
unit, a memory, input/output devices, and a bus is known in the
art. Thus, a variety of different procedures, depending on the
experiments to be run, are stored in the CPU memory.
[0377] These robotic fluid handling systems can utilize any number
of different reagents, including buffers, reagents, samples,
washes, assay components such as label probes, etc.
[0378] Any of the steps above can be performed by a computer
program product that comprises a computer executable logic that is
recorded on a computer readable medium. For example, the computer
program can execute some or all of the following functions: (i)
exposing reference population of cells to one or more modulators,
(ii) exposing reference population of cells to one or more binding
elements, (iii) detecting the activation levels of one or more
activatable elements, (iv) characterizing one or more cellular
pathways and/or (v) classifying one or more cells into one or more
classes based on the activation level.
[0379] The computer executable logic can work in any computer that
may be any of a variety of types of general-purpose computers such
as a personal computer, network server, workstation, or other
computer platform now or later developed. In some embodiments, a
computer program product is described comprising a computer usable
medium having the computer executable logic (computer software
program, including program code) stored therein. The computer
executable logic can be executed by a processor, causing the
processor to perform functions described herein. In other
embodiments, some functions are implemented primarily in hardware
using, for example, a hardware state machine. Implementation of the
hardware state machine so as to perform the functions described
herein will be apparent to those skilled in the relevant arts.
[0380] The program can provide a method of determining the status
of an individual by accessing data that reflects the activation
level of one or more activatable elements in the reference
population of cells.
Analysis
[0381] Advances in flow cytometry have enabled the individual cell
enumeration of up to thirteen simultaneous parameters (De Rosa et
al., 2001) and are moving towards the study of genomic and
proteomic data subsets (Krutzik and Nolan, 2003; Perez and Nolan,
2002). Likewise, advances in other techniques (e.g. microarrays)
allow for the identification of multiple activatable elements. As
the number of parameters, epitopes, and samples have increased, the
complexity of experiments and the challenges of data analysis have
grown rapidly. An additional layer of data complexity has been
added by the development of stimulation panels which enable the
study of activatable elements under a growing set of experimental
conditions. See Krutzik et al, Nature Chemical Biology February
2008. Methods for the analysis of multiple parameters are well
known in the art. See U.S. Ser. No. 61/079,579 for gating
analysis.
[0382] In some embodiments where flow cytometry is used, flow
cytometry experiments are performed and the results are expressed
as fold changes using graphical tools and analyses, including, but
not limited to a heat map or a histogram to facilitate evaluation.
One common way of comparing changes in a set of flow cytometry
samples is to overlay histograms of one parameter on the same plot.
Flow cytometry experiments ideally include a reference sample
against which experimental samples are compared. Reference samples
can include normal and/or cells associated with a condition (e.g.
tumor cells). See also U.S. Ser. No. 61/079,537 for visualization
tools
[0383] The patients are stratified based on nodes that inform the
clinical question using a variety of metrics. To stratify the
patients between those patients with No Response (NR) versus a
Complete Response (CR), a prioritization of the nodes can be made
according to statistical significance (such as p-value from a
t-test or Wilcoxon test or area under the receiver operator
characteristic (ROC) curve) or their biological relevance. See FIG.
2, and the methods described herein for methods for analyzing the
cell signaling pathway data. For example, FIG. 2 shows four methods
to analyze data, such as from AML patients. Other characteristics
such as expression markers may also be used. For example the fold
over isotype can be used (e.g., log 2(MFIstain)-Log 2(MFIisotype))
or % positive above Isotype.
[0384] FIG. 2 shows the use of four metrics used to analyze data
from cells that may be subject to a disease, such as AML. For
example, the "basal" metric is calculated by measuring the
autofluorescence of a cell that has not been stimulated with a
modulator or stained with a labeled antibody. The "total phospho"
metric is calculated by measuring the autofluorescence of a cell
that has been stimulated with a modulator and stained with a
labeled antibody. The "fold change" metric is the measurement of
the total phospho metric divided by the basal metric. The quadrant
frequency metric is the frequency of cells in each quadrant of the
contour plot
[0385] A user may also analyze multimodal distributions to separate
cell populations. In some embodiments, metrics can be used for
analyzing bimodal and spread distribution. In some embodiments, a
Mann-Whitney U Metric is used.
[0386] In some embodiments, metrics that calculate the percent of
positive above unstained and metrics that calculate MFI of positive
over untreated stained can be used.
[0387] A user can create other metrics for measuring the negative
signal. For example, a user may analyze a "gated unstained" or
ungated unstained autofluorescence population as the negative
signal for calculations such as "basal" and "total". This is a
population that has been stained with surface markers such as CD33
and CD45 to gate the desired population, but is unstained for the
fluorescent parameters to be quantitatively evaluated for node
determination. However, every antibody has some degree of
nonspecific association or "stickyness" which is not taken into
account by just comparing fluorescent antibody binding to the
autofluorescence. To obtain a more accurate "negative signal", the
user may stain cells with isotype-matched control antibodies. In
addition to the normal fluorescent antibodies, in one embodiment,
(phospho) or non phosphopeptides which the antibodies should
recognize will take away the antibody's epitope specific signal by
blocking its antigen binding site allowing this "bound" antibody to
be used for evaluation of non-specific binding. In another
embodiment, a user may block with unlabeled antibodies. This method
uses the same antibody clones of interest, but uses a version that
lacks the conjugated fluorophore. The goal is to use an excess of
unlabeled antibody with the labeled version.
[0388] In another embodiment, a user may block other high protein
concentration solutions including, but not limited to fetal bovine
serum, and normal serum of the species in which the antibodies were
made, i.e. using normal mouse serum in a stain with mouse
antibodies. (It is preferred to work with primary conjugated
antibodies and not with stains requiring secondary antibodies
because the secondary antibody will recognize the blocking serum).
In another embodiment, a user may treat fixed cells with
phosphatases to enzymatically remove phosphates, then stain.
[0389] In alternative embodiments, there are other ways of
analyzing data, such as third color analysis (3D plots), which can
be similar to Cytobank 2D, plus third D in color.
[0390] One embodiment of the present invention is software to
examine the correlations among phosphorylation or expression levels
of pairs of proteins in response to stimulus or modulation. The
software examines all pairs of proteins for which phosphorylation
and/or expression was measured in an experiment. The Total phosho
metric (sometimes called "FoldAF") is used to represent the
phosphorylation or expression data for each protein; this data is
used either on linear scale or log 2 scale. See FIG. 2, metric 3
for Total Phospho.
[0391] For each protein pair under each experimental condition
(unstimulated, stimulated, or treated with drug/modulator), the
Pearson correlation coefficient and linear regression line fit are
computed. The Pearson correlation coefficients for samples
representing responding and non-responding patients are calculated
separately for each group and compared to the unperturbed
(unstimulated) data. The following additional metrics are derived:
[0392] 1. Delta CRNR unstim: the difference between Pearson
correlation coefficients for each protein pair for the responding
patients and for the non-responding patients in the basal or
unstimulated state. [0393] 2. Delta CRNR stim: the difference
between Pearson correlation coefficients for each protein pair for
the responding patients and for the non-responding patients in the
stimulated or treated state. [0394] 3. DeltaDelta CRNR: the
difference between Delta CRNRstim and Delta CRNRunstim.
[0395] The correlation coefficients, line fit parameters (R,
p-value, and slope), and the three derived parameters described
above are computed for each protein-protein pair. Protein-protein
pairs are identified for closer analysis by the following criteria:
[0396] 1. Large shifts in correlations within patient classes as
denoted by large positive or negative values (top and bottom
quartile or 10.sup.th and 90.sup.th percentile) of the DeltaDelta
CRNR parameter. [0397] 2. Large positive or negative (top and
bottom quartile or 10.sup.th and 90.sup.th percentile) Pearson
correlation for at least one patient group in either unstimulated
or stimulated/treated condition. [0398] 3. Significant line fit
(p-value<=0.05 for linear regression) for at least one patient
group in either unstimulated or stimulated/treated condition.
[0399] All pair data is plotted as a scatter plot with axes
representing phosphorylation or expression level of a protein. Data
for each sample (or patient) is plotted with color indicating
whether the sample represents a responder (generally blue) or
non-responder (generally red). Further line fits for responders,
non-responders and all data are also represented on this graph,
with significant line fits (p-value<=0.05 in linear regression)
represented by solid lines and other fits represented by dashed
line, enabling rapid visual identification of significant fits.
Each graph is annotated with the Pearson correlation coefficient
and linear regression parameters for the individual classes and for
the data as a whole. The resulting plots are saved in PNG format to
a single directory for browsing using Picassa. Other visualization
software can also be used.
[0400] Each protein pair can be further annotated by whether the
proteins comprising the pair are connected in a "canonical"
pathway. In the current implementation canonical pathways are
defined as the pathways curated by the NCI and Nature Publishing
Group. This distinction is important; however, it is likely not an
exclusive way to delineate which protein pairs to examine. High
correlation among proteins in a canonical pathway in a sample may
indicate the pathway in that sample is "intact" or consistent with
the known literature. One embodiment of the present invention
identifies protein pairs that are not part of a canonical pathway
with high correlation in a sample as these may indicate the
non-normal or pathological signaling. This method will be used to
identify stimulator/modulator-stain-stain combinations that
distinguish classes of patients.
[0401] In some embodiments, nodes and/or nodes/metric combinations
can be analyzed and compared across sample for their ability to
distinguish among different groups (e.g., CR vs. NR patients) using
classification algorithms. Any suitable classification algorithm
known in the art can be used. Examples of classification algorithms
that can be used include, but are not limited to, multivariate
classification algorithms such as decision tree techniques:
bagging, boosting, random forest, additive techniques: regression,
lasso, bblrs, stepwise regression, nearest neighbors or other
methods such as support vector machines.
[0402] In some embodiments, nodes and/or nodes/metric combinations
can be analyzed and compared across sample for their ability to
distinguish among different groups (e.g., CR vs. NR patients) using
random forest algorithm. Random forest (or random forests) is an
ensemble classifier that consists of many decision trees and
outputs the class that is the mode of the class's output by
individual trees. The algorithm for inducing a random forest was
developed by Leo Breiman (Breiman, Leo (2001). "Random Forests".
Machine Learning 45 (1): 5-32. doi:10.1023/A:1010933404324) and
Adele Cutler. The term came from random decision forests that was
first proposed by Tin Kam Ho of Bell Labs in 1995. The method
combines Breiman's "bagging" idea and the random selection of
features, introduced independently by Ho (Ho, Tin (1995). "Random
Decision Forest". 3rd Int'l Conf. on Document Analysis and
Recognition. pp. 278-282; Ho, Tina (1998). "The Random Subspace
Method for Constructing Decision Forests". IEEE Transactions on
Pattern Analysis and Machine Intelligence 20 (8): 832-844.
doi:10.1109/34.709601) and Amit and Geman (Amit, Y.; Geman, D.
(1997). "Shape quantization and recognition with randomized trees".
Neural Computation 9 (7): 1545-1588.
doi:10.1162/neco.1997.9.7.1545) in order to construct a collection
of decision trees with controlled variation.
[0403] In some embodiments, nodes and/or nodes/metric combinations
can be analyzed and compared across sample for their ability to
distinguish among different groups (e.g., CR vs. NR patients) using
lasso algorithm. The method of least squares is a standard approach
to the approximate solution of overdetermined systems, i.e. sets of
equations in which there are more equations than unknowns. "Least
squares" means that the overall solution minimizes the sum of the
squares of the errors made in solving every single equation. The
best fit in the least-squares sense minimizes the sum of squared
residuals, a residual being the difference between an observed
value and the fitted value provided by a model.
[0404] In some embodiments, nodes and/or nodes/metric combinations
can be analyzed and compared across sample for their ability to
distinguish among different groups (e.g., CR vs. NR patients) using
BBLRS model building methodology.
[0405] a. Description of the BBLRS Model Building Methodology
[0406] Production of Bootstrap Samples:
[0407] A large number of bootstrap samples are first generated with
stratification by outcome status to insure that all bootstrap
samples have a representative proportion of outcomes of each type.
This is particularly important when the number of observations is
small and the proportion of outcomes of each type is unbalanced.
Stratification under such a scenario is especially critical to the
composition of the out of bag (OOB) samples, since only about
one-third of observations from the original sample will be included
in each OOB sample.
[0408] Best Subsets Selection of Main Effects:
[0409] Best subsets selection is used to identify the combination
of predictors that yields the largest score statistic among models
of a given size in each bootstrap sample. Models having from 1 to
233 N/10 are typically entertained at this stage, where N is the
number of observations. This is much larger than the number of
predictors generally recommended when building a generalized linear
prediction model (Harrell, 2001) but subsequent model building
rules are applied to reduce the likelihood of over-fitting. At the
conclusion of this step, there will be a "best" main effects model
of each size for each bootstrap sample, though the number of unique
models of each size may be considerably fewer.
[0410] Determination of the Optimal Model Size (for Main
Effects):
[0411] Each of the unique "best" models of each size, identified in
the previous step, are fit to each of a subset of the bootstrap
samples, where the number of bootstrap samples in the subset is
under the control of the user (i.e. a tuning parameter) so that the
processing time required at this step can be controlled. For each
of the bootstrap samples in the subset, the median SBC of the
"best" models of the same size is calculated and the model size
yielding the lowest median SBC in that bootstrap sample is
identified. The optimal model size is then determined as the size
for which the median SBC is smallest most often over the subset of
bootstrap samples.
[0412] Identification of the Top Models of the Best Size:
[0413] At this stage, all previously identified "best" models of
the optimal size are fit to every bootstrap sample. A number of top
models are then selected as those with the highest values of the
margin statistic (a measure from the logistic model of the
difference in the predicted probabilities of CR, between NR
patients with the highest predicted probabilities and CR patients
with the lowest predicted probabilities). In order to limit the
processing time required in subsequent steps, the number of top
models selected is under the control of the user.
[0414] Identification of Important Two-Way Interactions:
[0415] For each of the top main effects models identified in the
previous step, models are constructed on every bootstrap sample,
with main effects forced into the model and with stepwise selection
used to identify important two-way interactions among the set of
all possible pair-wise combinations of the main effects. The
nominal significance level for entry and removal of interaction
terms is under the control of the user. Significance levels greater
than 0.05 are often used for entry because of the low power many
studies have to detect interactions and because safeguards against
over-fitting are applied subsequently.
[0416] At this stage, collections of full models (main effects and
possibly some two-way interactions among them) have been
constructed (on the set of all bootstrap samples) for each unique
set of main effects identified in the previous step. The top full
models in each collection are then chosen as those constructed most
frequently over all bootstrap samples, where winners are decided
among tied models by the lowest mean SBC and then the highest mean
AUROC. The number of full models in each collection that are
advanced to the next step is under the control of the user.
[0417] Selection of the Effects in the Final Model:
[0418] Each full model advanced to this step is fit to every
bootstrap sample and the median margin statistic for each model
over the bootstrap samples is calculated. The model with the
highest median margin statistic is selected as the final model. If
there are ties, the model with the lowest mean SBC is selected.
[0419] Technically, the procedure described here results in the
selection of the effects (main effects and possibly two-way
interactions) to be included in the final model, but not
specification of the model itself. The latter includes the effects
and the specific regression coefficients associated with the
intercept and each of the model effects.
[0420] Specification of the Final Model:
[0421] The effects in the final model are then fit to the complete
dataset using Firth's method to apply shrinkage to the regression
coefficient estimates. The model effects and their estimated
regression coefficients (plus the estimate of the intercept)
comprise the final model.
[0422] Another method of the present invention relates to display
of information using scatter plots. Scatter plots are known in the
art and are used to visually convey data for visual analysis of
correlations. See U.S. Pat. No. 6,520,108. The scatter plots
illustrating protein pair correlations can be annotated to convey
additional information, such as one, two, or more additional
parameters of data visually on a scatter plot.
[0423] Previously, scatter plots used equal size plots to denote
all events. However, using the methods described herein two
additional parameters can be visualized as follows. First, the
diameter of the circles representing the phosphorylation or
expression levels of the pair of proteins may be scaled according
to another parameter. For example they may be scaled according to
expression level of one or more other proteins such as transporters
(if more than one protein, scaling is additive, concentric rings
may be used to show individual contributions to diameter).
[0424] Second, additional shapes may be used to indicate subclasses
of patients. For example they could be used to denote patients who
responded to a second drug regimen or where CRp status. Another
example is to show how samples or patients are stratified by
another parameter (such as a different stim-stain-stain
combination). Many other shapes, sizes, colors, outlines, or other
distinguishing glyphs may be used to convey visual information in
the scatter plot.
[0425] In this example the size of the dots is relative to the
measured expression and the box around a dot indicates a NRCR
patient that is a patient that became CR (Responsive) after more
aggressive treatment but was initially NR (Non-Responsive).
Patients without the box indicate a NR patient that stayed NR.
[0426] Applying the methods of the present invention, the Total
Phospho metric for p-Akt and p-Stat1 are correlated in response to
peroxide ("HOOH") treatment. (Total phoshpho is calculated as shown
in FIG. 2, metric #3). On log 2 scale the Pearson correlation
coefficient for p-Akt and p-Stat1 in response to HOOH for samples
from patients who responded to first treatment is 0.89 and the
p-value for linear regression line fit is 0.0075. In contrast there
appeared to be no correlation observed for p-Akt and p-Stat1 in
HOOH treated samples from patients annotated as "NR"
(non-responder) or "NRCR" (initial non-responder, who responded to
later more intensive treatment). Further there are no significant
correlations observed for these proteins in any patient class for
untreated samples.
[0427] The Total phospho metric for p-Erk and p-CREB also appeared
to be correlated in response to IL-3, IL-6, and IL-27 treatment in
samples from non-responding patients (NR and NR-CR). When
considering all data in log 2 scale the Pearson correlation
coefficients for p-Erk and p-CREB in response to IL-3, IL-6, and
IL-27 for samples from patients who did not respond to first
treatment are 0.74, 0.76, 0.81, respectively, and the respective
p-values for linear regression line fits are <0.0001,
<0.0001, and <0.0001. In contrast there appeared to be no
correlation observed for p-Erk and p-Creb in IL-3, IL-6, and IL-27
experiments for patients annotated as "CR". (Not shown). Table 2
below shows nodes identified by a fold change metric. Table 3 below
shows node identified by a variety of methods. In some embodiments,
the nodes depicted in Table 2 and 3 are used according to the
methods described herein for classification, diagnosis, prognosis
of AML, MDS or MPN or for the selection of treatment and/or predict
outcome after administering a therapeutic.
TABLE-US-00002 TABLE 2 Nodes identified by Fold Change Metric
Relevant Biology/ Node Metric Known Role in AML p-Val AUC SDF-1
.fwdarw. p-Akt Fold Change BM Chemokine .025 .71 SCF.fwdarw. p-Akt
Fold Change Stem Cell Growth Factor .018 .809 Upreg, Mutated In AML
SCF.fwdarw. p-S6 Fold Change Stem Cell Growth Factor .055 .66
Upreg, Mutated In AML FLT3L.fwdarw. p-Akt Fold Change Growth Factor
.003 .82 Mutated In AML FLT3L.fwdarw. p-S6 Fold Change Growth
Factor .026 .66 Mutated In AML G-CSF.fwdarw. p-Stat3 Fold Change
Myeloid Growth Factor .090 .68 G-CSF.fwdarw. p-Stat5 Fold Change
Myeloid Growth Factor .038 .70 Peroxide .fwdarw. p- Fold Change
Phosphatase Inhibition .02 .78 Slp-76 Novel AML Biology
Peroxide.fwdarw. p- Fold Change Phosphatase Inhibition .09 .75
Plc.gamma.2 Novel AML Biology IFNa.fwdarw. p-Stat1 Fold Change .017
.747 IFN.gamma..fwdarw. p-Stat1 Fold Change .038 .707
Thapsi.fwdarw. p-S6 Fold Change Pharmacological stim .020 .707 PMA
.fwdarw. p-Erk Fold Change Pharmacological stim .062 .702
TABLE-US-00003 TABLE 3 Nodes Identified by Variety of Metrics
Relevant Biology/ Node Metric Known Role in AML p-Val AUC Etoposide
.fwdarw. Quadrant DNA damage & 001 82 cleaved PARP+ Gate
Apoptosis p-Chk2- Frequency p-Creb Basal Over-expressed in .0005
.87 AML p-Erk Basal Activated in AML .02 .77 p-Stat6 Basal Novel
AML Biology .008 .76 p-Plc.gamma.2 Basal Novel AML Biology .007 .79
p-Stat3 Basal Activated in AML .005 .81 IL-27.fwdarw. p-Stat3 Total
p-Stat3 Active in AML .00004 .80 IL-10.fwdarw. p-Stat3 Total
p-Stat3 Active in AML .0009 .84 IL-6 .fwdarw. p-Stat3 Total
p-pStat3 Active in .001 .77 AML Etopo + Zvad .fwdarw. Total
Apoptosis Cleaved Caspse 3 ABCG2 % Positive Drug Transporter .00093
.75 Above Isotype C-KITR Fold over Growth Factor .012 .78 Isotype
Receptor FLT3R Fold over Growth Factor .0004 .82 Isotype
Receptor
[0428] In some embodiments, analyses are performed on healthy
cells. In some embodiments, the health of the cells is determined
by using cell markers that indicate cell health. In some
embodiments, cells that are dead or undergoing apoptosis will be
removed from the analysis. In some embodiments, cells are stained
with apoptosis and/or cell death markers such as PARP or Aqua dyes.
Cells undergoing apoptosis and/or cells that are dead can be gated
out of the analysis. In other embodiments, apoptosis is monitored
over time before and after treatment. For example, in some
embodiments, the percentage of healthy cells can be measured at
time zero and then at later time points and conditions such as: 24
h with no modulator, and 24 h with Ara-C/Daunorubicin. In some
embodiments, the measurements of activatable elements are adjusted
by measurements of sample quality for the individual sample, such
as the percent of healthy cells present.
[0429] In some embodiments, a regression equation will be used to
adjust raw node readout scores for the percentage of healthy cells
at 24 hours post-thaw. In some embodiments, means and standard
deviations will be used to standardize the adjusted node readout
scores.
[0430] Before applying the SCNP classifier, raw node-metric signal
readouts (measurements) for samples will be adjusted for the
percentage of healthy cells and then standardized. The adjustment
for the percentage of healthy cells and the subsequent
standardization of adjusted measurements is applied separately for
each of the node-metrics in the SCNP classifier.
[0431] The following formula can be used to calculate the adjusted,
normalized node-metric measurement (z) for each of the node-metrics
of each sample.
z=((x-(b.sub.0+b.sub.1.times.pcthealthy))-residual_mean)/residual.sub.---
sd,
where x is the raw node-metric signal readout, b.sub.0 and b.sub.1
are the coefficients from the regression equation used to adjust
for the percentage of healthy cells (pcthealthy), and residual_mean
and residual_sd are the mean and standard deviation, respectively,
for the adjusted signal readouts in the training set data. The
values of b.sub.0, b.sub.1, residual_mean, and residual_sd for each
node-metric are included in the embedded object below, with values
of the latter two parameters stored in variables by the same name.
The values of the b.sub.0 and b.sub.1 parameters are contained on
separate records in the variable named "estimate". The value for
b.sub.0 is contained on the record where the variable "parameter"
is equal to "Intercept" and the value for b.sub.1 is contained on
the record where the variable "parameter" is equal to
"percenthealthy24 Hrs". The value of pcthealthy will be obtained
for each sample as part of the standard assay output. The SCNP
classifier will be applied to the z values for the node-metrics to
calculate the continuous SCNP classifier score and the binary
induction response assignment (pNR or pCR) for each sample.
[0432] In some embodiments, the measurements of activatable
elements are adjusted by measurements of sample quality for the
individual cell populations or individual cells, based on markers
of cell health in the cell populations or individual cells.
Examples of analysis of healthy cells can be found in U.S.
application Ser. No. 61/374,613 filed Aug. 18, 2010, the content of
which is incorporated herein by reference in its entirety for all
purposes.
[0433] In some embodiments, the invention provides methods of
diagnosing, prognosing, determining progression, predicting a
response to a treatment or choosing a treatment for acute leukemia,
myelodysplastic syndrome or myeloproliferative neoplasms in an
individual, the method comprising: (1) classifying one or more
hematopoietic cells associated with acute leukemia, myelodysplastic
syndrome or myeloproliferative neoplasms in said individual by a
method comprising: a) subjecting a cell population comprising said
one or more hematopoietic cells from said individual to modulator
conditions, b) determining an activation level of activatable
elements in one or more cells from said individual, and c)
classifying said one or more hematopoietic cells based on said
activation levels in response to modulator conditions using
multivariate classification algorithms such as decision tree
techniques: bagging, boosting, random forest, additive techniques:
regression, lasso, bblrs, stepwise regression, nearest neighbors or
other methods such as support vector machines (2) making a decision
regarding a diagnosis, prognosis, progression, response to a
treatment or a selection of treatment for acute leukemia,
myelodysplastic syndrome or myeloproliferative neoplasms in said
individual based on said classification of said one or more
hematopoietic cells. In some embodiments, classifying further
comprises identifying a difference in kinetics of said activation
level. In some embodiments, the measurements of activatable
elements are made only in healthy cells as determined using markers
of cell health. In some embodiments, the measurements of
activatable elements are adjusted by measurements of sample quality
for the individual sample, such as the percent of healthy cells
present.
Drug Screening
[0434] Another embodiment of the present invention is a method for
screening drugs that are in development and indicated for patients
that have been diagnosed with acute myelogenous leukemia (AML),
myelodysplasia (MDS) or myelodyspastic syndrome (MPN).
[0435] Using the signaling nodes and methodology described herein,
multiparametric flow cytometry could be used in-vitro to predict
both on and off-target cell signaling effects. Using an embodiment
of the present invention, the bone marrow or peripheral blood
obtained from a patient diagnosed with AML, MDS or MPN could be
divided and part of the sample subjected to a therapeutic.
Modulators (e.g. GM-CSF or PMA) could then be added to the
untreated and treated specimens. Activatable elements (e.g.
JAKs/STATs/AKT), including the proposed target of the therapeutic,
or those that may be affected by the therapeutic (off-target) can
then be assessed for an activation state. This activation state can
be used to predict the therapeutics' potential for on and off
target effects prior to first in human studies.
[0436] Using the signaling nodes and methodology described herein,
one embodiment of the present invention, such as multiparametric
flow cytometry, could be used after in-vivo exposure to a
therapeutic in development for patients that have been diagnosed
with AML, MDS or MPN to determine both on and off-target effects.
Using an embodiment of the present invention, the bone marrow or
peripheral blood (fresh, frozen, ficoll purified, etc.) obtained
from a patient diagnosed with AML or MDS at time points before and
after exposure to a given therapeutic may be subjected to a
modulator as above. Activatable elements (e.g. JAKs/STATs/AKT),
including the proposed target of the therapeutic, or those that may
be affected by the therapeutic (off-target) can then be assessed
for an activation state. This activation state can then be used to
determine the on and off target signaling effects on the bone
marrow or blast cells.
[0437] The apoptosis and peroxide panel study may reveal new
biological classes of stratifying nodes for drug screening. Some of
the important nodes could include changes on levels of p-Lck,
pSlp-76, p PLC.gamma.2, in response to peroxide alone or in
combination with growth factors or cytokines. These important nodes
are induced Cleaved Caspase 3 and Cleaved Caspase 8, and etoposide
induced p-Chk2, peroxide (H.sub.2O.sub.2) induced p-SLP-76,
peroxide (H.sub.2O.sub.2) induced p-PLC.gamma.2 and peroxide
(H.sub.2O.sub.2) induced P-Lck. The apoptosis panel may include but
is not limited to, detection of changes in phosphorylation of Chk2,
changes in amounts of cleaved caspase 3, cleaved caspase 8, cleaved
poly (ACP ribose) polymerase PARP, cytochrome C released from the
mitochondria these apoptotic nodes are measured in response to
agents that included but are not limited to DNA damaging agents
such as Etoposide, Mylotarg, AraC and daunorubicin either alone or
in combination as well as to the global kinase inhibitor
staurosporine.
[0438] Using the signaling nodes and methodology described herein,
multiparametric flow cytometry could be used to find new target for
treatment (e.g. new druggable targets). Using an embodiment of the
present invention, the bone marrow or peripheral blood obtained
from a patient diagnosed with AML, MDS or MPN could be divided and
part of the sample subjected to one or more modulators (e.g. GM-CSF
or PMA). Activatable elements (e.g. JAKs/STATs/AKT) can then be
assessed for an activation state. This activation state can be used
to predict find new target molecule for new existing therapeutics.
These therapeutics can be used alone or in combination with other
treatments for the treatment of AML, MDS or MPN.
Kits
[0439] In some embodiments the invention provides kits. Kits
provided by the invention may comprise one or more of the
state-specific binding elements described herein, such as
phospho-specific antibodies. A kit may also include other reagents
that are useful in the invention, such as modulators, fixatives,
containers, plates, buffers, therapeutic agents, instructions, and
the like.
[0440] In some embodiments, the kit comprises one or more of the
phospho-specific antibodies specific for the proteins selected from
the group consisting of PI3-Kinase (p85, p110a, p110b, p110d),
Jak1, Jak2, SOCs, Rac, Rho, Cdc42, Ras-GAP, Vav, Tiam, Sos, Dbl,
Nck, Gab, PRK, SHP1, and SHP2, SHIP1, SHIP2, sSHIP, PTEN, Shc,
Grb2, PDK1, SGK, Akt1, Akt2, Akt3, TSC1,2, Rheb, mTor, 4EBP-1,
p70S6Kinase, S6, LKB-1, AMPK, PFK, Acetyl-CoAa Carboxylase, DokS,
Rafs, Mos, Tpl2, MEK1/2, MLK3, TAK, DLK, MKK3/6, MEKK1,4, MLK3,
ASK1, MKK4/7, SAPK/JNK1,2,3, p38s, Erk1/2, Syk, Btk, BLNK, LAT,
ZAp70, Lck, Cbl, SLP-76, PLC.gamma., PLC.gamma.2, STAT1, STAT 3,
STAT 4, STAT 5, STAT 6, FAK, p130CAS, PAKs, LIM K1/2, Hsp90, Hsp70,
Hsp27, SMADs, Rel-A (p65-NFKB), CREB, Histone H2B, HATs, HDACs,
PKR, Rb, Cyclin D, Cyclin E, Cyclin A, Cyclin B, p16, p14Arf,
p27KIP, p21CIP, Cdk4, Cdk6, Cdk7, Cdk1, Cdk2, Cdk9, Cdc25,A/B/C,
Abl, E2F, FADD, TRADD, TRAF2, RIP, Myd88, BAD, Bcl-2, Mcl-1,
Bcl-XL, Caspase 2, Caspase 3, Caspase 6, Caspase 7, Caspase 8,
Caspase 9, IAPs, Smac, Fodrin, Actin, Src, Lyn, Fyn, Lck, NIK,
I.kappa.B, p65(RelA), IKK.alpha., PKA, PKC.alpha., PKC.beta.,
PKC.theta., PKC.delta., CAMK, Elk, AFT, Myc, Egr-1, NFAT, ATF-2,
Mdm2, p53, DNA-PK, Chk1, Chk2, ATM, ATR, .beta. catenin, CrkL,
GSK3.alpha., GSK3.beta., and FOXO. In some embodiments, the kit
comprises one or more of the phospho-specific antibodies specific
for the proteins selected from the group consisting of Erk, Syk,
Zap70, Lck, Btk, BLNK, Cbl, PLC.gamma.2, Akt, RelA, p38, S6. In
some embodiments, the kit comprises one or more of the
phospho-specific antibodies specific for the proteins selected from
the group consisting of Akt1, Akt2, Akt3, SAPK/JNK1,2,3, p38s,
Erk1/2, Syk, ZAp70, Btk, BLNK, Lck, PLC.gamma., PLC.gamma.2, STAT1,
STAT 3, STAT 4, STAT 5, STAT 6, CREB, Lyn, p-S6, Cbl, NF-.kappa.B,
GSK3.beta., CARMA/Bcl10 and Tcl-1.
[0441] Kits provided by the invention may comprise one or more of
the modulators described herein. In some embodiments, the kit
comprises one or more modulators selected from the group consisting
of H.sub.2O.sub.2, PMA, BAFF, April, SDF1 .alpha., CD40L, IGF-1,
Imiquimod, polyCpG, IL-7, IL-6, IL-10, IL-27, IL-4, IL-2, IL-3,
thapsigardin and a combination thereof.
[0442] The state-specific binding element of the invention can be
conjugated to a solid support and to detectable groups directly or
indirectly. The reagents may also include ancillary agents such as
buffering agents and stabilizing agents, e.g., polysaccharides and
the like. The kit may further include, where necessary, other
members of the signal-producing system of which system the
detectable group is a member (e.g., enzyme substrates), agents for
reducing background interference in a test, control reagents,
apparatus for conducting a test, and the like. The kit may be
packaged in any suitable manner, typically with all elements in a
single container along with a sheet of printed instructions for
carrying out the test.
[0443] Such kits enable the detection of activatable elements by
sensitive cellular assay methods, such as IHC and flow cytometry,
which are suitable for the clinical detection, prognosis, and
screening of cells and tissue from patients, such as leukemia
patients, having a disease involving altered pathway signaling.
[0444] Such kits may additionally comprise one or more therapeutic
agents. The kit may further comprise a software package for data
analysis of the physiological status, which may include reference
profiles for comparison with the test profile.
[0445] Such kits may also include information, such as scientific
literature references, package insert materials, clinical trial
results, and/or summaries of these and the like, which indicate or
establish the activities and/or advantages of the composition,
and/or which describe dosing, administration, side effects, drug
interactions, or other information useful to the health care
provider. Such information may be based on the results of various
studies, for example, studies using experimental animals involving
in vivo models and studies based on human clinical trials. Kits
described herein can be provided, marketed and/or promoted to
health providers, including physicians, nurses, pharmacists,
formulary officials, and the like. Kits may also, in some
embodiments, be marketed directly to the consumer.
[0446] In some embodiments, the invention provides a kit
comprising: (a) at least two modulators selected from the group
consisting of Staurosporine, Etoposide, Mylotarg, Daunorubicin,
AraC, G-CSF, IFNg, IFNa, IL-27, IL-3, IL-6, IL-10, FLT3L, SCF,
G-CSF, SCF, G-CSF, SDF1a, LPS, PMA, Thapsigargin and H2O2; b) at
least three binding elements specific to a particular activation
state of the activatable element selected from the group consisting
of p-Slp-76, p-Plcg2, p-Stat3, p-Stat5, p-Stat1, p-Stat6, P-Creb,
Parp+, Chk2, Rel-A (p65-NFKB), p-AKT, p-S6, p-ERK, Cleaved Caspase
8, Cytoplasmic Cytochrome C, and p38; and (c) instructions for
diagnosis, prognosis, determining acute myeloid leukemia
progression and/or predicting response to a treatment for acute
myeloid leukemia in an individual. In some embodiments, the kit
further comprises a binding element specific for a cytokine
receptor or drug transporter are selected from the group consisting
of MDR1, ABCG2, MRP, P-Glycoprotein, CXCR4, FLT3, and c-kit. In
some embodiments, the binding element is an antibody.
[0447] The following examples serve to more fully describe the
manner of using the above-described invention, as well as to set
forth the best modes contemplated for carrying out various aspects
of the invention. It is understood that these examples in no way
serve to limit the true scope of this invention, but rather are
presented for illustrative purposes. All references cited herein
are expressly incorporated by reference in their entireties.
EXAMPLES
Example 1
Materials and Methods
[0448] The present illustrative example represents how to analyze
cells in one embodiment of the present invention. There are several
steps in the process, such as the stimulation step, the staining
step and the flow cytometry step. The stimulation step of the
phospho-flow procedure can start with vials of frozen cells and end
with cells fixed and permeabilized in methanol. Then the cells can
be stained with an antibody directed to a particular protein of
interest and then analyzed using a flow cytometer.
[0449] The materials used in this invention include thawing medium
which comprises PBS-CMF+10% FBS+2 mM EDTA; 70 um Cell Strainer
(BD); anti-CD45 antibody conjugated to Alexa 700 (Invitrogen) used
at 1 ul per sample; propidium iodide (PI) solution (Sigma 10 ml, 1
mg/ml) used at 1 ug/ml; RPMI+1% FBS medium; media A comprising
RPMI+1% FBS+1.times.Penn/Strep; Live/Dead Reagent, Amine Aqua
(Invitrogen); 2 ml, 96-Deep Well, U-bottom polypropylene plates
(Nunc); 300 ul 96-Channel Extended-Length D.A.R.T. tips for Hydra
(Matrix); Phosphate Buffered Saline (PBS) (MediaTech); 16%
Paraformaldehyde (Electron Microscopy Sciences); 100% Methanol
(EMD) stored at -20 C; Transtar 96 dispensing apparatus (Costar);
Transtar 96 Disposable Cartridges (Costar, Polystyrene, Sterile);
Transtar reservoir (Costar); and foil plate sealers.
[0450] a. Thawing Cell and Live/Dead Staining:
[0451] Frozen cells are thawed in a 37.degree. C. water bath and
gently resuspended in the vial and transferred to the 15 mL conical
tube. The 15 mL tube is centrifuged at 930 RPM (200.times.g) for 8
minutes at room temperature. The supernatant is aspirated and the
pellet is gently resuspended in 1 mL media A. The cell suspension
is filtered through a 70 um cell strainer into a new 15 mL, tube.
The cell strainer is rinsed with 1 mL media A and another 12 ml of
media A into the 15 mL, tube. The cells are mixed into an even
suspension. A 20 .mu.L aliquot is immediately removed into a
96-well plate containing 180 .mu.L PBS+4% FBS+CD45 Alexa 700+PI to
determine cell count and viability post spin. After the
determination, the 15 mL tubes are centrifuged at 930 RPM
(200.times.g) for 8 minutes at room temperature. The supernatant is
aspirated and the cell pellet is gently resuspended in 4 mL, PBS+4
.mu.l, Amine Aqua and incubated for 15 min in a 37.degree. C.
incubator. 10 mL RPMI+1% FBS is added to the cell suspension and
the tube is inverted to mix the cells. The 15 mL tubes are
centrifuged at 930 RPM (200.times.g) for 8 minutes at room
temperature. The cells are resuspended in Media A at the desired
cell concentration (1.25.times.10.sup.6/mL). For samples with low
numbers of cells (<18.5.times.10.sup.6), the cells are
resuspended in up to 15 mL media. For samples with high numbers of
cells (>18.5.times.10.sup.6), the volume is raised to 10 mL with
media A and the desired volume is transferred to a new 15 mL tube,
and the cell concentration is adjusted to 1.25.times.10.sup.6
cells/ml. 1.6 mL of the above cell suspension (concentration at
1.25.times.10.sup.6 cells/ml) is transferred into wells of a
multi-well plate. From this plate, 80 ul is dispensed into each
well of a subsequent plate. The plates are covered with a lid
(Nunc) and placed in a 37.degree. C. incubator for 2 hours to
rest.
[0452] b. Cell Stimulation:
[0453] A concentration for each stimulant that is five folds more
(5.times.) than the final concentration is prepared using Media A
as diluent. 5.times. stimuli are arrayed into wells of a standard
96 well v-bottom plate that correspond to the wells on the plate
with cells to be stimulated.
Preparation of fixative: Stock vial contains 16% paraformaldehyde
which is diluted with PBS to a concentration that is 1.5.times..
The stock vial is placed in a 37.degree. C. water bath.
[0454] Adding the stimulant: The cell plate(s) are taken out of the
incubator and placed in a 37.degree. C. water bath next to the
pipette apparatus. The cell plate is taken from the water bath and
gently swirled to resuspend any settled cells. With pipettor, the
stimulant is dispensed into the cell plate and vortexed at "7" for
5 seconds. The deep well plate is put back into the water bath.
[0455] Adding Fixative: 200 .mu.l of the fixative solution (final
concentration at 1.6%) is dispensed into wells and then mixed on
the titer plate shaker on high for 5 seconds. The plate is covered
with foil sealer and incubated in a 37.degree. C. water bath for 10
minutes. The plate is spun for 6 minutes at 2000 rpm at room
temperature. The cells are aspirated using a 96 well plate
aspirator (VP Scientific). The plate is vortexed to resuspend cell
pellets in the residual volume. The pellet is ensured to be
dispersed before the Methanol step (see cell permeabilization) or
clumping will occur.
[0456] Cell Permeabilization: Permeability agent, for example
methanol, is added slowly and while the plate is vortexing. To do
this, the cell plate is placed on titer plate shaker and made sure
it is secure. The plate is set to shake using the highest setting.
A pipetter is used to add 0.6 mls of 100% methanol to the plate
wells. The plate(s) are put on ice until this step has been
completed for all plates. Plates are covered with a foil seal using
the plate roller to achieve a tight fit. At this stage the plates
may be stored at -80.degree. C.
[0457] c. Staining Protocol
[0458] Reagents for staining include FACS/Stain Buffer-PBS+0.1%
Bovine serum albumen (BSA)+0.05% Sodium Azide; Diluted Bead Mix-1
mL FACS buffer+1 drop anti-mouse Ig Beads+1 drop negative control
beads. The general protocol for staining cells is as follows,
although numerous variations on the protocol may be used for
staining cells:
[0459] Cells are thawed if frozen. Cells are pelleted at 2000 rpm 5
minutes. Supernatant is aspirated with vacuum aspirator. Plate is
vortexed on a "plate vortex" for 5-10 seconds. Cells are washed
with 1 mL FACS buffer. Repeat the spin, aspirate and vortex steps
as above. 50 .mu.L of FACS/stain buffer with the desired,
previously optimized, antibody cocktail is added to two rows of
cells at a time and agitate the plate. The plate is covered and
incubated in a shaker for 30 minutes at room temperature (RT).
During this incubation, the compensation plate is prepared. For the
compensation plate, in a standard 96 well V-bottom plate, 20 .mu.L
of "diluted bead mix" is added per well. Each well gets 5 .mu.L of
1 fluorophor conjugated control IgG (examples: Alexa488, PE, Pac
Blue, Aqua, Alexa647, Alexa700). For the Aqua well, add 200 uL of
Aqua-/+ cells. Incubate the plate for 10 minutes at RT. Wash by
adding 200 .mu.L FACS/stain buffer, centrifuge at 2000 rpm for 5
minutes, and remove supernatant. Repeat the washing step and
resuspend the cells/beads in 200 .mu.L FACS/stain buffer and
transfer to a U-bottom 96 well plate. After 30 min, 1 mL FACS/stain
buffer is added and the plate is incubated on a plate shaker for 5
minutes at room temperature. Centrifuge, aspirate and vortex cells
as described above. 1 mL FACS/stain buffer is added to the plate
and the plate is covered and incubated on a plate shaker for 5
minutes at room temperature. Repeat the above two steps and
resuspend the cells in 75 .mu.l FACS/stain buffer. The cells are
analyzed using a flow cytometer, such as a LSRII (Becton
Disckinson). All wells are selected and Loader Settings are
described below: Flow Rate: 2 uL/sec; Sample Volume: 40 uL; Mix
volume: 40 uL; Mixing Speed: 250 uL/sec; # Mixes: 5; Wash Volume:
800 uL; STANDARD MODE. When a plate has completed, a Batch analysis
is performed to ensure no clogging.
[0460] d. Gating Protocol
[0461] Data acquired from the flow cytometer are analyzed with
Flowjo software (Treestar, Inc). The Flow cytometry data is first
gated on single cells (to exclude doublets) using Forward Scatter
Characteristics Area and Height (FSC-A, FSC-H). Single cells are
gated on live cells by excluding dead cells that stain positive
with an amine reactive viability dye (Aqua-Invitrogen). Live,
single cells are then gated for subpopulations using antibodies
that recognize surface markers as follows: CD45++, CD33- for
lymphocytes, CD45++, CD33++ for monocytes+granulocytes and CD45+,
CD33+ for leukemic blasts. Signaling, determined by the antibodies
that interact with intracellular signaling molecules, in these
subpopulation gates that select for "lymphs", "monos+grans, and
"blasts" is analyzed.
[0462] e. Gating of Flow Cytometry Data to Identify Live Cells and
the Lymphoid and Myeloid Subpopulations:
[0463] Flow cytometry data can be analyzed using several
commercially available software programs including FACSDiva.TM.,
FlowJo, and Winlist.TM.. The initial gate is set on a two-parameter
plot of forward light scatter (FSC) versus side light scatter (SSC)
to gate on "all cells" and eliminate debris and some dead cells
from the analysis. A second gate is set on the "live cells" using a
two-parameter plot of Amine Aqua (a dye that brightly stains dead
cells, commercially available from Invitrogen) versus SSC to
exclude dead cells from the analysis. Subsequent gates are be set
using antibodies that recognize cell surface markers and in so
doing define cell subsets within the entire population. A third
gate is set to separate lymphocytes from all myeloid cells (acute
myeloid leukemia cells reside in the myeloid gate). This is done
using a two-parameter plot of CD45 (a cell surface antigen found on
all white blood cells) versus SSC. The lymphocytes are identified
by their characteristic high CD45 expression and low SSC. The
myeloid population typically has lower CD45 expression and a higher
SSC signal allowing these different populations to be
discriminated. The gated region containing the entire myeloid
population is also referred to as the P1 gate.
[0464] f. Phenotypic Gating to Identify Subpopulations of Acute
Myeloid Leukemia Cells:
[0465] The antibodies used to identify subpopulations of AML blast
cells are CD34, CD33, and CD11b. The CD34.sup.+ CD11b.sup.- blast
population represents the most immature phenotype of AML blast
cells. This population is gated on CD34 high and CD11b negative
cells using a two-parameter plot of CD34 versus CD11b. The CD33 and
CD11b antigens are used to identify AML blast cells at different
stages of monocytic differentiation. All cells that fall outside of
the CD34.sup.+ CD11b.sup.- gate described above (called "Not
CD34+") are used to generate a two-parameter plot of CD33 versus
CD11b. The CD33.sup.+ CD11b.sup.hi myeloid population represents
the most differentiated monocytic phenotype. The CD33.sup.+
CD11b.sup.hi intermediate and CD33.sup.+ CD11b.sup.lo populations
represent less differentiated monocytic phenotypes.
[0466] The data can then be analyzed using various metrics, such as
basal level of a protein or the basal level of phosphorylation in
the absence of a stimulant, total phosphorylated protein, or fold
change (by comparing the change in phosphorylation in the absence
of a stimulant to the level of phosphorylation seen after treatment
with a stimulant), on each of the cell populations that are defined
by the gates in one or more dimensions. These metrics are then
organized in a database tagged by: the Donor ID, plate
identification (ID), well ID, gated population, stain, and
modulator. These metrics tabulated from the database are then
combined with the clinical data to identify nodes that are
correlated with a pre-specified clinical variable (for example;
response or non response to therapy) of interest.
Example 2
[0467] Multi-parameter flow cytometric analysis was performed on
peripheral blasts taken at diagnosis from 9 AML patients who
achieved a complete response (CR) and 24 patients who were
non-responders (NR) to one cycle of standard 7+3 induction therapy
(100-200 mg/m2 cytarabine and 60 mg/m2 daunorubicin). The signaling
nodes were organized into 4 biological categories: 1) Protein
expression of receptors and drug transporters 2) Response to
cytokines and growth factors, 3) Phosphatase activity, and 4)
Apoptotic signaling pathways.
[0468] The data showed that expression of the receptors for c-Kit
and FLT3 Ligand and the drug transporter ABCG2, were increased in
patients who achieved an NR versus CR (data not shown). Readouts
from the cytokine-Stat response panels and the growth factor-Map
kinase and PI3-Kinase response panels (see Table 4) revealed
increased signaling in blasts taken from NR patients versus blasts
taken from patients who clinically responded to therapy. To
determine the role of phosphatases, peroxide, (H.sub.2O.sub.2) a
physiologic phosphatase inhibitor revealed increased phosphatase
activity in CRs versus NRs for some signaling molecules and
increased phosphatase activity in NRs versus CRs for others. In the
absence of treatment with H.sub.2O.sub.2, CRs had lower levels of
phosphorylated PLC.gamma.2 and SLP-76 versus NRs, and attained
higher levels of phosphorylated PLC.gamma.2 and SLP-76 upon
H.sub.2O.sub.2 treatment. In contrast, H.sub.2O.sub.2 revealed
higher levels of p-Akt in NR patients versus CR patients. Lastly,
interrogation of the apoptotic machinery using agents such as
staurosporine and etoposide showed that NR patient blasts failed to
undergo cell death, as determined by cleaved PARP and cleaved
Caspase 8. Of note, in NR patient blasts, these agents did promote
an increase in phosphorylated Chk2 suggesting a communication
breakdown between the DNA damage response pathway and the apoptotic
machinery. In contrast, blasts from CR patients showed significant
populations of cells with cleaved PARP and caspase 8 consistent
with their clinical outcomes.
[0469] In this study, 152 signaling nodes per patient sample were
measured by multi-parameter flow cytometry and revealed distinct
signaling profiles that correlate with patient response to ara-C
based induction therapy. This study identified 29 individuals nodes
strongly associated (i.e. AUC>0.7, p value 0.05) with clinical
response to 1 cycle of ara-C based induction therapy. Most of these
nodes were highly correlated. Table 4 below shows 26 of the 29
nodes strongly associated with clinical responses. Expression
levels of c-Kit, Flt-3L receptors and ABCG2 drug transporter also
associated with clinical responses.
[0470] Alterations were seen in expression for the c-Kit and Flt-3L
receptors, the ABCG2 drug transporter, cytokine and growth factor
pathway response, phosphatase activity and apoptotic response, all
of which could stratify the NR from the CR patient subsets.
[0471] It was also determined that evoked signaling to biologically
relevant modulators reveals nodes that stratify non-responding
patients from complete responders in this AML sample set. For
example, FIG. 4 shows different activation profiles for NR
patients. The operative pathways in these patients can be used to
predict response to a treatment or to choose a specific treatment
for the patients. FIG. 4 shows that NR patients in subset 1 have
high levels of p-Stat3 and p-Stat5 in response to G-CSF. This
suggests that JAK, Src and other new therapeutics could be good
candidates for the treatment of these patients. In addition, FIG. 4
shows that NR patients in subset 2 have high levels of p-Akt and
p-S6 in response to FLT3L. This suggests that inhibitors to FLT3R,
PI-3K/mTor and other new therapeutics could be good candidates for
the treatment of these patients. FIG. 4 also shows that NR patients
in subset 2 have high levels of p-Stat3 and p-Stat5 in response to
G-CSF, high levels of p-Akt and p-S6 in response to FLT3L, and high
levels of p-Akt and p-S6 in response to SCF. This suggests that
inhibitors to JAK, Src, FLT3R, PI-3K/mTor, RKT inhibitors and other
new therapeutics could be good candidates for the treatment of
these patients.
[0472] However, some patients with a functional apoptosis response
to Etoposide as measured by p-Chk2 and cleaved PARP have a CR
phenotype despite having high levels of p-Stat3 and p-Stat5 in
response to G-CSF (data not shown). Even though high levels of
p-Stat3 and p-Stat5 in response to G-CSF is associated with NR, if
the apoptotic machinery is still active the patient might be able
to respond to treatment. This suggests that there may be a
requirement for more than one signaling pathway to prevent or veto
apoptosis. In this case G-CSF signaling is not able alone to
prevent apoptosis. These results indicate that multivariate
analysis of signaling nodes can improve the specificity of the
patient stratification.
[0473] Although univariate analysis of signaling nodes can stratify
patients based on response to induction therapy as several
predictive nodes were independent of each other, multivariate
analysis of signaling nodes can improve specificity while providing
insight into the pathophysiology of the disease/potential response
to therapy. Combination of two independent nodes, p-Stat5-CSF and
p-Akt-FLT3L, can classify correctly all CR (but one CRp) and
misclassify only 5 NR (not shown).
[0474] Additionally, Phospho-Flow technology allows detection of
multiple signaling subpopulations within the AML blast population
which could be instrumental in disease monitoring and following
rare populations after therapy. See FIG. 4 and not shown. Overall,
phospho-flow identifies patient subgroups of AML with different
clinical outcomes to induction therapy, reveals mechanisms of
potential pathophysiology, and provides a tool for personalized
treatment options based on unique patient-specific signaling
networks and for disease monitoring under therapeutic pressure.
TABLE-US-00004 TABLE 4 Thapsi- Stauro- Etopo- Unstim IFNa IFNg
IL-27 IL-6 IL-10 G-CSF FLT-3L SCF SDF-1a gargin PMA sporine side
H.sub.2O.sub.2 p- NR NR Stat1(Y701) p- NR NR NR NR NR Stat3(Y703)
p- NR Stat5(Y694) p- NR Stat6(Y641) p-S6 NR NR NR (S235/236)
p-Akt(S473) NR NR NR p-Erk NR NR (T202/Y204) p-PLCg2 NR CR (Y759)
p-SLP76 CR (Y128) p- NR CREB(S133) Cleaved CR CR PARP Cleaved CR
Caspase 8 Cleaved CR CR Caspase 3 NR = Nodes in which activation is
greater in a NR patient than in a CR patient CR = Nodes in which
activation is greater in a CR patient than an NR patient
Example 3
[0475] An analysis of a heterogeneous population of AML patients
may be conducted as outlined above. The results may show the
following. In some embodiments, univariate analysis is performed on
relatively homogeneous clinical groups, such as patents over 60
years old, patients under 60 years old, de novo AML patients, and
secondary AML patients. In other embodiments the groups may be
molecularly homogeneous groups, such as Flt-3-ITD WT. For example,
in patients over 60 years old, NRs may have a higher H.sub.2O.sub.2
response than CRs and/or a higher FLT3L response than CRs. In
patients under 60, NRs may have a higher IL-27 response than CRs
and/or CRs may induce apoptosis to Etoposide or Ara-C/Daunorubicin
more than NRs. In de novo AML, CRs may induce apoptosis (cleaved
PARP) in response to Etoposide or Ara-C/Daunorubicin, they may have
higher total p-S6 levels than NRs, or NRs may have higher
H.sub.2O.sub.2 response than CRs. In secondary AML, NRs may have
higher H.sub.2O.sub.2 responses than CRs, NRs may have higher
FLT3L, SCF response than CRs, NRs may have higher G-CSF, IL-27
response than CRs, and there may be overlapping nodes with the over
60 patient set. The following tables may illustrate the above. The
tables show the node, metric, and patient subpopulations. For
example, the node can be shown as the node (readout) followed by
the stimulant/modulator, and in some instances the receptor through
which they act (Table 11 also lists some labels that can be
employed in the readout). The metric is the way the result may be
calculated (see definitions above and in the figures; ppos is
percent positive). The leukemic blast cell subpopulations are: P1
all leukemic cells, S1 most immature blast population, S3 most
mature blast population and S2 median mature blast population. All
nodes: AUC.gtoreq.0.7, p values.ltoreq.0.05, lowest N=4
TABLE-US-00005 TABLE 5 Univariate analysis of All patients can
reveal predictive signaling nodes for Response Failed Pts removed,
NR = Resistant only Node Metric P1 S1 S2 S3
Cleaved.PARP.Ara.C.Daunorubicin.- Fold X X HCl TotalPhospho X X
Cleaved.PARP.Etoposide Fold X Flt3.CD135.Mouse.IgG1 ppos X
p.Akt.Hydrogen.Peroxide Fold X p.Chk2.Ara.C.Daunorubicin.HCl Fold X
p.CREB.SDF.1a.CXCL12 Fold X TotalPhospho X
p.PLCg2.Hydrogen.Peroxide Fold X p.S6.SCF TotalPhospho X
p.SLP.76.Hydrogen.Peroxide Fold X p.Stat1.IL.27 Fold X TotalPhospho
X X p.Stat3.IL.27 Fold X X TotalPhospho X p.Stat5.IL.27 Fold X
SCF.R.c.kit.CD117.IgG1. Fold X SCF.R.c.kit.CD117.IgG2b Fold X ppos
X X MDR.Family.ABCG2.BRCP1.IgG1. ppos X P.glycoprotein.MDR1.IgG1
Fold X
TABLE-US-00006 TABLE 6 Univariate analysis of Young Pts (Age<60)
can reveal predictive signaling nodes for Response Failed Pts
removed, NR = Resistant only Node Metric P1 S1 S2
Cleaved.PARP.Etoposide Fold X X X TotalPhospho X X
Cleaved.PARP.No.Modulator TotalPhospho X p.Akt.SCF Fold X
p.CREB.SDF.1a.CXCL12 Fold X p.ERK.FLT.3.Ligand Fold X p.Stat1.IL.27
Fold X X TotalPhospho X X p.Stat3.IL.27 Fold X X TotalPhospho X
X
TABLE-US-00007 TABLE 7 Univariate analysis of Age>60 patients
can reveal predictive signaling nodes CR vs. NR: Failed Pts
removed, NR = Resistant only Node Metric P1 S2 S3
p.Akt.Hydrogen.Peroxide Fold X p.Akt.FLT.3.Ligand Fold X X X
p.ERK.FLT.3.Ligand Fold X p.PLCg2.Hydrogen.Peroxide TotalPhospho X
p.S6.FLT.3.Ligand Fold X X X p.S6.SCF Fold X X
p.SLP.76.Hydrogen.Peroxide Fold X
TABLE-US-00008 TABLE 8 Univariate analysis of 2ndary AML pts can
reveal predictive signaling nodes for Response: Including Failed
Pts Node Metric P1 S1 S2 S3 p.Akt.Hydrogen.Peroxide Fold X
p.Akt.FLT.3.Ligand Fold X p.Akt.SDF.1a.CXCL12 Fold X
p.ERK.FLT.3.Ligand Fold X X p.PLCg2.Hydrogen.Peroxide Fold X
TotalPhospho X p.S6.FLT.3.Ligand Fold X p.S6.A.SCF Fold X
p.SLP.76.Hydrogen.Peroxide Fold X p.Stat1.G.CSF Fold X
p.Stat1.A.IL.27 Fold X X TotalPhospho X p.Stat3.A.G.CSF Fold X
p.Stat3.IL.27 Fold X TotalPhospho X p.Stat5.G.CSF Fold X
TotalPhospho X SCF.R.c.kit.CD117.Mouse.IgG1. Fold X ppos X X
TABLE-US-00009 TABLE 9 Univariate analysis of 2ndary AML pts can
reveal predictive signaling nodes for Response: Failed Pts removed,
NR = Resistant only Node Metric P1 S1 S2 S3 p.Akt.Hydrogen.Peroxide
Fold X p.Akt.FLT.3.Ligand Fold X p.Akt.SCF TotalPhospho X
p.ERK.FLT.3.Ligand Fold X X p.ERK.SCF Fold X
p.PLCg2.Hydrogen.Peroxide Fold X p.S6.FLT.3.Ligand Fold X p.S6.SCF
Fold X X p.Stat1.IL.27 Fold X X TotalPhospho X p.Stat3.G.CSF Fold X
p.Stat3.IL.27 Fold X p.Stat5.G.CSF Fold X
SCF.R.c.kit.CD117.Mouse.IgG1. Fold X ppos X X
TABLE-US-00010 TABLE 10 Univariate analysis of DeNovo AML can
reveal predictive signaling nodes for Response: Including Failed
Pts Node Metric P1 S1 S2 S3 Cleaved.PARP.Etoposide Fold X
Cytochrome.C.Staurosporine.Z.- Fold X VAD.Caspase.Inhibitor
TotalPhospho X X Cytochrome.C. No.Modulator TotalPhospho X X
p.Akt.Hydrogen.Peroxide Fold X p.Akt.FLT.3.Ligand TotalPhospho X
p.Akt.SCF Fold X X TotalPhospho X p.Akt.SDF.1a.CXCL12 Fold X
p.CREB.SDF.1a.CXCL12 Fold X p.ERK.Thapsigargin Fold X X
p.ERK.No.Modulator TotalPhospho X p.Stat1.GM.CSF TotalPhospho X
p.Stat1.IL.10 Fold X TotalPhospho X p.Stat1.IL.3 TotalPhospho X
p.Stat1.A.IL.6 Fold X TotalPhospho X X X p.Stat3.GM.CSF
TotalPhospho X X X p.Stat3.IFN.g Fold X X X TotalPhospho X X X
p.Stat3.Y705.PE.A.IL.10 Fold X X X TotalPhospho X X X
p.Stat3.Y705.PE.A.IL.3 TotalPhospho X p.Stat3.Y705.PE.A.IL.6 Fold X
TotalPhospho X X p.Stat5.G.CSF Fold X TotalPhospho X p.Stat5.IL.10
Fold X X X p.Stat5.IL.3 Fold X p.Stat5.IL.6 Fold X X X
p.Stat6.No.Modulator TotalPhospho X X pERK.LPS Fold X
SCF.R.c.kit.CD117.IgG1. Fold X ppos X X SCF.R.c.kit.CD117.IgG2b
Fold X X ppos X X X.MDR.Family.MRP.1.IgG2a Fold X ppos X
P.glycoprotein.MDR1.IgG2a Fold X
TABLE-US-00011 TABLE 11 Univariate analysis of De Novo AML patients
can reveals predictive signaling nodes CR vs. NR: Removed Failed
Pts. NR = Resistant Node Metric P1 S1 S2 S3
Cleaved.PARP.Cytosine.b.arabino.furanoside.Daunorubicin.HCl Fold X
TotalPhospho X Cleaved.PARP.D214.FITC.A.Etoposide Fold X X X
p.Akt.S473.Alexa.Fluor.488.A.Hydrogen.Peroxide Fold X
p.Akt.S473.Alexa.Fluor.647.A.FLT.3.Ligand TotalPhospho X
p.Akt.S473.Alexa.Fluor.647.A.SCF Fold X X TotalPhospho X
p.Akt.S473.Alexa.Fluor.647.A.SDF.1a.CXCL12 Fold X
p.CREB.S133.PE.A.SDF.1a.CXCL12 Fold X
p.S6.S235.236.Alexa.Fluor.488.A.FLT.3.Ligand TotalPhospho X X
p.S6.S235.236.Alexa.Fluor.488.A.PMA TotalPhospho X X
p.S6.S235.236.Alexa.Fluor.488.A.SCF TotalPhospho X X
p.S6.S235.236.Alexa.Fluor.488.A.Thapsigargin TotalPhospho X X
p.SLP.76.Y128.Alexa.Fluor.647.A.Hydrogen.Peroxide Fold X
p.Stat5.Y694.Alexa.Fluor.647.A.G.CSF TotalPhospho X
p.Stat5.Y694.Alexa.Fluor.647.A.IFN.a.2b Fold X
SCF.R.c.kit.CD117.APC.A.Mouse.IgG2b Fold X
TABLE-US-00012 TABLE 12 Univariate analysis of All patients can
reveal predictive signaling nodes for Response Duration Node Metric
P1 S1 S2 S3 Cleaved.PARP.araC.Daunorubicin.- Fold X HCl
Cleaved.PARP.Etoposide Fold X CXCR4.IgG1 Fold X X X CXCR4.IgG1 ppos
X p.Akt.Hydrogen.Peroxide Fold X X TotalPhospho X
p.Akt.SDF.1a.CXCL12 TotalPhospho X p.ERK.FLT.3.Ligand Fold X
p.PLCg2.Hydrogen.Peroxide TotalPhospho X X p.S6.Thapsigargin
TotalPhospho X p.SLP.76.Hydrogen.Peroxide TotalPhospho X X
p.Stat3.IL.10 Fold X p.Stat5.IL.6 TotalPhospho X
MDR.Family.ABCG2.BRCP1.IgG1. Fold X MDR.Family.ABCG2.IgG2b ppos X
X
TABLE-US-00013 TABLE 13 Univariate analysis of Flt3 WT Pts can
reveal predictive signaling nodes for Response Duration Node Metric
P1 S1 S2 S3 Cleaved.PARP.araC.Daunorubicin.- Fold X X HCl
Cleaved.PARP.Etoposide Fold X TotalPhospho X CXCR4.IgG1 Fold X X
ppos X X CXCR4.IgG1 Fold X CXCR4.No.Modulator TotalPhospho X X
p.Akt.Hydrogen.Peroxide Fold X TotalPhospho X p.ERK.FLT.3.Ligand
Fold X X p.PLCg2.Hydrogen.Peroxide Fold X TotalPhospho X
p.S6.Thapsigargin TotalPhospho X p.SLP.76.Hydrogen.Peroxide
TotalPhospho X MDR.Family.ABCG2.BRCP1.IgG2b ppos X X
MDR.Family.MRP.IgG2a Fold X
Example 4
[0476] Multi-parameter flow cytometric analysis was performed on
BMMC samples taken at diagnosis from 61 AML patients. The samples
were balanced for complete response (CR) and non-responders (NR)
after 1 to 3 cycles of induction therapy and de novo versus
secondary AML. Nodes in Tables 2 to 10 were examined.
[0477] 10 nodes are common in stratifying NR and CR between the
studies in Example 2 and these studies. Table 14 shows the common
stratifying nodes.
TABLE-US-00014 TABLE 14 Cytokine Pathways: 5 Nodes IL-27 p-Stat 3
and p-Stat 1 IL-27 p-Stat 1 IL-6 p-Stat 3 IL-10 p-Stat 3 IFNa
p-Stat 1 Growth Factors: 4 Nodes Flt3L p-Akt and p-S6 SCF p-Akt and
p-S6 Apoptosis Pathways Etoposide or AraC/Dauno Cleaved
PARP.sup.+
[0478] In secondary analysis patient subpopulations were stratified
by clinical variables. Patients are stratified by age, de novo
acute myeloid leukemia patient, secondary acute myeloid leukemia
patient, or a biochemical/molecular marker.
[0479] Patients were stratified by age (as split variable<60
years old vs. >60 years old and as co-variate). In patients
younger than 60 years old, NRs have higher H2O2 and FLT3L responses
than CRs. In patients younger than 60 years old, NRs have higher
IL-27 response than CRs. In addition, in patients younger than 60
years old, CRs induce apoptosis to Etoposide or Ara-C/Daunorubicin
more than NRs.
[0480] Patients were stratified by de novo versus secondary AML.
Stratifying nodes for de novo group show overlapping nodes with
patients younger than 60 year old. Stratifying nodes for secondary
group show overlapping nodes with patients older than 60 year old
group.
[0481] Patients were stratified by FLT3 ITD mutation vs. FLT3 wild
type phenotypes. The signaling was significantly different between
the patients with FLT3 ITD mutation vs. FLT3 wild type.
Parp-cytosine.b.arabino.furanoside is an example of an identified
node informative on relapse risk in patients who achieved CR and
have FLT3 WT and normal karyotype disease (not shown).
[0482] Individual nodes can be combined for analysis. Several
methods can be used for the analysis.
[0483] The nodes can be analyzed using additive linear models to
discover combinations that provide better accuracy of prediction
for response to induction therapy than the individual nodes. These
models can also include clinical covariates like age, gender,
secondary AML that may already be predictive of the outcome. Only
nodes that add to the accuracy of the model after accounting for
these clinical covariates are considered to be useful. The formula
below is an example of how additive linear models can be used
Response(CR or
NR)=a+b*C.sub.1+c*C.sub.2+d*Node.sub.1+e*Node.sub.2
C1 and C2 are the clinical covariates that are considered to be
predictive of response, Node1 and Node2 are the two nodes from the
biological data. The coefficient a, b, c, d, e are determined by
the regression process. The significance of the coefficients if
tested against them being equal to zero; i.e. if the p-value for
d=0 if very small (say <0.05), then the contribution from the
Node1 is considered to the important. Several such models can be
explored to find combinations of nodes that are complimentary.
Examples of methods for exploring multiple such models include
bootstrapping, and stepwise regression.
[0484] Analysis methods can include additive lineal models, such as
the model represented in the following equations
CR or NR=a+b*Age(categorical)+c*Node for "all blast" population
[0485] Incorporating age as a clinical variable increases the
significance of the resulting combination model (not shown).
[0486] The nodes can be analyzed using independent combinations of
nodes. This method seeks threshold along different node axes
independently. This model among clinical sub-groups improves
predictive value (not shown).
[0487] The nodes can be analyzed using decision trees model. This
model involves the hierarchical splitting of data. This model might
mimic a more natural decision process. Each node is evaluated on
sub-set of data at each level of the tree.
[0488] Both independent node combinations and decision tree provide
node combinations of interest.
[0489] Results from the BMMC samples were compared with PBMC
samples from the same patients in 10 of the patients. The samples
were compared for sub-populations and signaling. The same
phenotypic sub-populations are present in PBMC and BMMC, but in
different percentage. It was observed that 2/3 of nodes correlate
(i.e. Pearson>0.8 or Spearman>0.8) in "all blast" population
of PBMC vs. BMMC. The correlations are node and subpopulation
specific.
Example 5
[0490] This example evaluated whether single cell network profiling
(SCNP), in which cells are modulated and their signaling response
ascertained by multiparametric flow cytometry, could be used to
functionally characterize signaling pathways associated with in
vivo AML chemotherapy resistance. Morphologic and functional
heterogeneity of myeloblasts was observed in paired samples
obtained from two patients at diagnosis and at first relapse.
Notably, a subpopulation of leukemic cells characterized by
simultaneous SCF-mediated increases in the levels of phosphorylated
(p-) Akt and p-S6 (SCF:p-Akt/p-S6), was identified in the relapsed
samples from both patients. This SCF responsive subpopulation,
although dominant in the relapse samples, was present and
detectable at a much lower frequency in the diagnostic samples.
Application of this finding to an independent set of 47 AML
diagnostic samples identified seven patients, six of whom
experienced disease relapse. The presence of an SCF:pAkt/p-S6
subpopulation was independent from c-Kit (SCF receptor) expression
levels on the AML blasts and from patient age, cytogenetics and
FLT-3 mutational status. This example shows that longitudinal SCNP
analysis can provide unique insights into the nature of AML
chemoresistance allowing for the identification of subpopulations
of cells present at diagnosis with unique signaling characteristics
predictive of higher rates of relapse.
[0491] Materials and Methods
[0492] Patient Samples
[0493] All AML bone marrow mononuclear cells (BMMC) were derived
from the bone marrow (BM) of AML patients treated at MD Anderson
Cancer Center (MDACC) between September 1999 and September 2006.
Clinical data were de-identified in compliance with Health
Insurance Portability and Accountability Act regulations.
Patient/sample inclusion criteria required a diagnosis of
French-American-British (FAB) classification of M0 through M7 AML
(excluding M3) AML, collection prior to therapy initiation and at
least 50% viability upon sample thaw. For the identification of
chemoresistant signaling profiles, two longitudinally paired BMMC
samples at diagnosis (collection prior to the initiation of
induction chemotherapy) and first relapse, were examined. An
independent test set comprised of 47 BMMC samples collected at
diagnosis from AML patients with a disease response of CR after
high dose cytarabine based chemotherapy was used to assess the
ability of the identified signaling profiles to predict disease
relapse. Healthy, unstimulated BMMC (n=2) were purchased from a
commercial source (All Cells) to serve as a control. All samples
underwent fractionation over Ficoll-Hypaque prior to
cryopreservation with 90% fetal bovine serum and 10% dimethyl
sulfoxide and storage in liquid nitrogen.
[0494] SCNP Assay
[0495] The SCNP assay measured response to growth factors and
cytokines involved in hematopoietic progenitor or myeloid biology
(SCF, FLT3L, G-CSF, IL-27), drug transporter (ABCG2, MRP-1) and
chemokine receptors (CXCR4) associated with adverse disease
prognosis in AML, and the c-Kit growth factor receptor for SCF. The
SCF and FLT3L-mediated PI3K/Akt and MAPK pathway is important for
maintaining the hematopoietic stem cell pool; G-CSF-mediated
Jak/STAT pathway activation is important for neutrophilic
differentiation of hematopoietic progenitor cells; interleukin
(IL)-27 mediated Jak/STAT pathway activation is important in
regulating proliferation and differentiation of hematopoietic stem
cells; CXCR4 expression is associated with disease relapse and
decreased survival; and drug transporter expression levels (i.e.
ABCG2 and MRP-1) are known to be associated with adverse prognosis
in AML. All together, approximately 20 signaling nodes were
evaluated in each sample.
[0496] SCNP assays were performed as described previously.
Cryopreserved samples were thawed at 37.degree. C. and washed once
in warm PBS containing 10% FBS (HyClone, Waltham, Mass., USA) and 2
mM EDTA. The cells were re-suspended, filtered to remove debris and
washed in RPMI 1640 (MediaTech, Manassas, Va., USA) cell culture
media containing 1% FBS before staining with Aqua LIVE/DEAD
viability dye (Invitrogen, Carlsbad, Calif., USA) to distinguish
non-viable cells. The cells were re-suspended in RPMI containing 1%
FBS, aliquoted to 100,000 cells/condition and rested for 1-2 hours
at 37.degree. C. Cells were incubated for 15 minutes at 37.degree.
C. with each of the following signaling modulators: fms-like
tyrosine kinase receptor-3 ligand (FLT3L, 50 ng/ml; eBiosciences,
San Diego, Calif., USA); granulocyte colony-stimulating factor
(G-CSF, 50 ng/ml; R&D Systems, Minneapolis, Minn., USA);
interleukin-27 (IL-27, 50 ng/ml, R&D Systems); stem cell factor
(SCF, 20 ng/ml, R&D Systems). After exposure to modulators,
cells were fixed with a final concentration of 1.6%
paraformaldehyde (Electron Microscopy Sciences, Hatfield, Pa., USA)
for 10 minutes at 37.degree. C. Cells were pelleted and then
permeabilized with 100% ice-cold methanol (Sigma-Aldrich, St.
Louis, Mo., USA) and stored at -80.degree. C. overnight.
Subsequently, cells were washed with FACS buffer containing
phosphate buffered saline (PBS, Fisher Scientific, Waltham, Mass.,
USA), 0.5% bovine serum albumin (BSA, Ankeny, Iowa, USA), 0.05%
NaN.sub.3 (Mallinckrodt, Hazelwood, Mo., USA), pelleted and stained
with cocktails of fluorochrome-conjugated antibodies. As an
exploratory effort, when sufficient number of cells were available,
simultaneous measurement of c-Kit expression and SCF induced
signaling was also performed. Antibodies were available from
commercial vendors such as BD, Bechman Coulter, Invitrogen and
R&D Systems.
[0497] Flow Cytometry Data Acquisition and Analysis
[0498] Flow cytometry data was acquired on an LSR II and/or CANTO
II flow cytometer using the FACS DIVA software (BD Biosciences, San
Jose, Calif.). All flow cytometry data were gated using either
FlowJo (TreeStar Software, Ashland, Oreg.), or WinList (Verity
House Software, Topsham, Me.). 3D Visual analysis was performed
using Spotfire (Tibco, Somerville, Mass., USA). Dead cells and
debris were excluded by forward scatter, side scatter, and Aqua
viability dye staining Surface markers consisting of CD45, CD34,
CD11b and CD33 and right-angle light-scatter characteristics
identified phenotypes consistent with myeloid leukemia cells. The
percentage of cells expressing c-Kit was calculated by the
frequency of cells with an intensity level greater than the 95th
percentile for isotype control antibody staining CXCR4, MRP-1, and
ABCG2 expression levels were calculated as a fold difference
compared to the mean fluorescent intensity value obtained by the
corresponding isotype control antibody.
[0499] Gating applied to the second data set to assay SCF, FLT3L,
G-CSF, and IL-27 responsiveness was defined by the basal state
(unstimulated) fluorescence of downstream readouts (e.g. p-Akt,
p-S6, STAT3). This gating was performed on healthy BM samples which
were run in each study as controls since absolute values were not
comparable between the studies due to differences in experimental
configurations (e.g. reagent and cytometer calibrations). The
choice of normal BM to define the cut off for the activated
subpopulation in AML marrow was based on the potential for
constitutively activated pathways in AML samples.
[0500] Statistical Analysis
[0501] Given the relatively small number of samples, comparisons
between the readouts from diagnostic and relapse samples were
performed visually. After resistance-associated nodes were
identified, Fisher's exact test was applied to compute the
probability of association of the nodes with disease relapse
occurring by chance in an independent data set. R statistics
package was used for this purpose.
Results
[0502] Patient and Sample Characteristics
[0503] Modulated SCNP was evaluated on longitudinally paired
diagnosis and relapse AML samples from two patients with AML.
Clinical characteristics of the patients are shown in Table 15.
Both patients received high dose cytarabine based induction
chemotherapy with disease response of CR followed by relapse within
one year. Cytogenetic analysis revealed prognostically unfavorable
translocations of AML1-EVIL and DEK-NUP214 [t(6;9)] in patients one
and two respectively. In addition, patient two had FLT3-ITD
positive leukemia, a known poor prognostic marker for relapse risk
and overall survival and associated with the DEK-NUP214
translocation in the majority of cases.
[0504] Healthy control BMMC (N=2) were derived from young healthy
male volunteers (age=18 and 20 years respectively).
TABLE-US-00015 TABLE 15 Clinical Characteristics of Patient Donors
for Longitudinally Paired Diagnosis and Relapse Samples CR Age
Sample Secondary Cytogenetic FLT3 Induction Induction Duration
Donor (Years) Gender Source AML FAB Cytogenetics Group ITD
Chemotherapy Response Relapse (Weeks) 1 77.8 M BM No M0 46, XY,
unfavorable NEG IDA + HDAC* CR Yes 46.143 t(3; 21) (q26; q22) 2
34.8 F BM No M2 t(6; 9) unfavorable POS IA + ZARNESTRA** CR Yes
11.143 *Idarubicin + high dose Ara-C **Idarubicin + Ara-C +
Zarnestra
[0505] Comparison of Diagnosis and Relapse AML Samples
[0506] Longitudinally paired diagnostic and relapse samples from
two AML patients were processed as described in Materials and
Methods to assess whether specific cell subpopulations could be
identified (using cell surface phenotypes and/or signaling
profiles) in the relapsed sample in a greater percentage than
observed in the corresponding diagnostic sample. Next, in an
independent and larger group of diagnostic patient samples, the
presence of blasts with the previously identified cell profiles
were examined for their association with disease relapse.
[0507] Myeloblast Subpopulations Defined by Surface Markers
[0508] The two diagnostic and first relapse samples were first
compared for expression of conventional surface markers used to
define myeloblast maturity as shown in FIG. 5a. Samples from both
patients displayed different proportions of CD34+ CD11b-
(immature), CD33+CD11b+ (mature) and all other blasts
(intermediate-neither mature nor immature) phenotypes from each
other and between diagnosis and relapse. Subpopulations based on
these characteristics of myeloblast maturity were not informative
of relapse risk for either patient sample (FIG. 5b). The levels of
the chemokine receptor CXCR4 and drug transporters ABCG2 and MRP-1
were similar between diagnosis and relapse samples and were also
not informative for disease relapse (not shown).
[0509] Myeloblast Subpopulations Defined by Intracellular Signaling
Profiles
[0510] Examination of intracellular signaling profiles revealed
functionally distinct cell subsets in the otherwise phenotypically
similar relapse and diagnosis samples (FIG. 6). Specifically, when
the relapse samples from Patient 1 and Patient 2 were modulated
using SCF, both p-Akt and p-S6 were induced in 3.2% and 31.7% of
cells respectively (FIG. 6). A similar finding of an increased
percentage of myeloblasts subpopulations defined by intracellular
signaling profiles in relapse versus diagnosis samples was observed
when FLT3L (inducing p-S6 and p-Akt, FIG. 6), and IL-27 or G-SCF
(inducing p-STAT3 and p-STAT5) were used as modulators (not
shown).
[0511] To investigate whether similar cells were present at the
time of diagnosis, which would support the concept of selection, or
absent, supporting the idea of an induced change, we looked for the
presence of cells with similar functional responses to SCF, FLT3L,
IL-27 and G-CSF in the corresponding diagnosis samples. While no
IL-27 responsive subpopulation was identified, SCF, FLT3L and G-CSF
responsive cells were observed in the diagnostic AML bone marrow
samples (FIG. 6), although in much lower percentage (.about.1%).
Back-gating of the SCF responsive cells in the relapse samples
revealed that the SCF:p-Akt/p-S6 signaling profile was found in
phenotypically diverse cell subpopulations despite similar
categorization by conventional surface markers (not shown,
CD34+CD33+CD11b- for both Patient 1 and Patient 2 yet each patient
displays distinct SCF-responsive cell subpopulation). In the two
normal BM samples, an SCF-responsive subpopulation was present and
was comparable between the samples; These SCF responsive cells were
phenotypically distinct from the SCF-responsive cellsin the
leukemia samples and characterized by CD34+ CD33- CD11b- (not
shown).
[0512] Testing the Predictive Value at Diagnosis for Disease
Relapse of Resistance-Associated Signaling Nodes
[0513] After identifying the resistance-associated signaling nodes
in the relapsed samples, we analyzed the nodes in the valuer for
being predictive for poor outcome [early relapse].
[0514] Predictive Value of SCF:p-Akt/p-S6 Subpopulation in an
Independent Sample Set
[0515] We first applied the SCF:p-Akt/p-S6 gating scheme (as
defined in Materials and Methods) to an independent set of
diagnostic AML samples. All patients received high dose cytarabine
based induction chemotherapy with disease response of complete
remission. Of these, 27 experienced disease relapse (CR Rel) while
20 remained in complete continuous remission (CCR) for two or more
years. Patients from whom this independent sample set was obtained
were young (41/47<60) and a high proportion (20/47) had FAB M2
AML.
[0516] In seven diagnostic AML samples a subset of leukemic blast
cells, which responded to SCF modulation by phosphorylation of
p-Akt and p-S6, were observed (not shown). Of those seven, six
patients experienced disease relapse within two years (p=0.21,
Fisher's exact test) from remission while the seventh patient had a
complete remission lasting more than two years; interestingly this
AML sample had favorable cytogenetics t(8;21) (not shown). Of note,
all of the patients with this SCF responsive profile were less than
60 years old and with the exception mentioned above, they all had
intermediate or high risk cytogenetics; six out of seven also had
an early myeloid FAB classification of M1 or M2. Also of note, the
occurrence of the SCF:pAkt/pS6 subpopulation was independent of the
presence of FLT3-ITD: only one of the six samples was positive for
FLT3-ITD mutation. Importantly, the predictive value of the
combination of FLT3-ITD and SCF:p-Akt/p-S6 for disease relapse was
greater than either biomarker individually (p=0.03, Fisher's Exact
Test).
[0517] c-Kit (SCF Receptor) Expression is Not Predictive of SCF
Responsiveness
[0518] We next examined whether expression of c-Kit, the receptor
for SCF, could function as a surrogate marker for the
SCF:p-Akt/p-S6 phenotype. Although only samples that expressed
c-Kit were able to respond to SCF, no association between c-Kit
expression levels and likelihood of leukemia relapse (FIG. 7a) was
observed suggesting that c-Kit expression is a necessary but not
sufficient condition for intra-cellular signaling. In line with
this observation, the removal of non-c-Kit expressing samples
improved relapse prediction (FIG. 7b). Furthermore, when blast
cells from an AML sample were simultaneously examined for c-Kit and
the downstream signaling marker p-Akt, intra-patient heterogeneity
in c-Kit expression and response to SCF within c-kit expressing
cells was observed (FIG. 7c).
[0519] Predictive Value of Other Resistance-Associated Signaling
Node Subpopulations in an Independent Sample Set
[0520] We also examined whether FLT3L:p-Akt/p-S6, G-CSF:p-STAT3/5
or IL-27:p-STAT3/5 signaling nodes predicted poor outcome in the
same independent set of diagnostic AML samples. Unlike SCF:
p-Akt/p-S6 gate, no association was found with disease relapse (not
shown).
[0521] Discussion
[0522] Relapse due to chemoresistant residual disease is a major
cause of death in both adult and pediatric patients with AML and
aberrant signal transduction within pathways that control cell
proliferation and survival is thought to play an important role in
secondary chemoresistance. In this study we used SCNP as a strategy
to identify specific signaling pathway profiles associated with in
vivo chemoresistance using paired diagnosis and relapse samples.
While performed on a limited number of paired AML samples, our
study provides unique insights into the nature of AML secondary
chemoresistance in rare cell populations, identifying a
functionally characterized cell subset associated with likelihood
of early relapse when the assay was applied in a separate patient
cohort.
[0523] A subset of leukemia cells with enhanced activity within the
PI3 kinase/Akt cascade (SCF:p-Akt/p-S6) was found to be commonly
expanded in the two leukemia samples collected at relapse.
Importantly, the presence of cell subpopulations expressing this
same signaling profile at diagnosis was associated with disease
relapse after complete response to induction chemotherapy in an
independent sample set of AML diagnostic samples. Although the
SCF:p-Akt/p-S6 profile was not present in all patients with
relapsed disease, all but one sample that contained a subpopulation
of >3% SCF:p-Akt/p-S6 cells relapsed within two years of
remission. These data support the marked biologic heterogeneity at
the basis of AML secondary chemoresistance and lend merit to the
approach of studying signaling profiles in functionally distinct
subpopulations in longitudinally collected AML samples before and
after therapy to identify poor-prognostic cell populations. While
the SCF:p-Akt/p-S6 profile was predictive for relapse, other
profiles (i.e. G-CSF:p-STAT 3/5, FLT3L:p-Akt/p-S6 and IL-27:p-STAT
3/5) were not associated with poor outcome in this sample set.
Whether these nodes have clinical significance remains to be
determined Analysis of additional paired samples is likely to
reveal other pathway nodes predictive of chemoresistance or
relapse. The data also supports the concept that the cells that
give rise to resistance are selected from amongst the diversity of
leukemic blasts present at diagnosis, as opposed to induction of
cells with new characteristics. This implies that recognition of
resistance prone characteristics at diagnosis could be used to
select and apply therapies that target these cells mechanistically
on an individualized basis at the time of diagnosis. Thus, the
results described herein could be used to prevent chemoresistance
from emerging and improve clinical outcome.
[0524] PI3K/Akt signaling is known to play a fundamental role in
opposing apoptosis and has been shown to be associated with
resistance to a variety of chemotherapeutic agents, including those
used to induce remission in AML and with inferior survival in AML.
Importantly, the prognostic value of the presence of the
SCF:p-Akt/p-S6 profile was independent from other known prognostic
factors for relapse in AML including age and the presence of FLT3
ITD mutation. In the tested sample set the combination of the
SCF:p-Akt/p-S6 phenotype with FLT-3 ITD mutational status resulted
in higher predictive value for disease relapse than that either
marker alone. Further studies are warranted to determine whether
these findings, including the significance of this phenotype
occurring predominantly in early myeloid (FAB M1-M2) leukemia, hold
true in larger independent sample sets.
[0525] The receptor tyrosine kinase Kit and its ligand SCF are
expressed on early hematopoietic cells and are essential for the
proliferation and survival of these cells.(34) Kit is expressed on
over 70% of pediatric and adult AML and activating mutations of
c-Kit are associated with poor outcome in the core binding factor
subset of adult AML. While this study did not examine molecular
aberrations aside from FLT-3 mutational status, we show that c-Kit
expression could not substitute for the poor prognostic
SCF:p-Akt/p-S6 phenotype. In addition, heterogeneity of c-Kit was
observed within individual leukemia samples with some blast
subpopulations expressing high levels and other populations showing
no cell surface c-Kit expression. Furthermore, the simultaneous
examination of c-Kit and p-Akt revealed distinct c-Kit positive
cell populations within an individual AML sample that had different
signaling capabilities. This strategy will provide the ability to
examine signaling in future studies only in the cells that express
c-Kit. Taken together, these data reveal the diversity of c-Kit
expression and function in the context of AML, underscore the
complexity and heterogeneity of each individual's AML, and suggest
further studies incorporating dual cell surface and intracellular
profiling.
[0526] Currently there are no measures to indicate why patients
with similar clinically appearing disease have different responses
to therapy with some remaining disease free while others undergo
disease relapse and ultimately succumb. SCNP permits an accurate
characterization of each individual's leukemia signaling pathway
phenotype and biologic heterogeneity allowing for a more efficient
delineation of the normality or pathology of leukemic
subpopulations. This study shows that leukemic cell populations
differ quantitatively and qualitatively before and after in-vivo
therapeutic pressure in AML and that SCNP offers a novel approach
to identify chemotherapy-resistant subpopulations that may
predispose patients to disease relapse.
Example 6
[0527] a. Exposure of AML Blasts In Vitro to Staurosporine and
Etoposide Reveals Three Distinct Apoptosis Profiles
[0528] Jak/Stat and PI3 kinase pathways are tied to cancer cell
survival. For this reason, apoptotic proficiency in AML samples was
determined in response to etoposide and staurosporine exposure in
vitro. In addition, the ability of etoposide and staurosporine to
induce a DNA damage response was also evaluated for these
samples.
[0529] Single cell network profiling using flow cytometry was used
to determine DNA damage response and apoptosis in AML blasts after
in vitro exposure to staurosporine and etoposide. After treatment
of samples with staurosporine for 6 h or etoposide for 24 hours,
cells were stained with Amine Aqua viability dye then fixed,
permeabilized and incubated with a cocktail of
fluorochrome-conjugated antibodies that delineated AML blasts by
their surface markers and measured levels of intracellular
signaling molecules within the canonical intrinsic apoptosis
pathway: cleavage products of Caspase 3, Caspase-8, and PARP.
[0530] The data showed three distinct apoptosis responses of AML
blasts after in vitro exposure to staurosporine and etoposide (not
shown). The metric used to analyze this data was "Apoptosis" and is
a measure of apoptosis and cell death induced by a drug. A viable
cell will be Aqua negative and PARP negative and a measure of cell
death is PARP and/or Aqua positivity.
"Apoptosis"=% of PARP.sup.- Aqua.sup.-.sub.unstim% of PARP.sup.-
Aqua.sup.-.sub.Drug.
[0531] If initially before exposure to a drug a sample has 80% of
cells that are PARP.sup.- Aqua.sup.- (live/healthy) and after
treatment the sample has 30% of cells that are PARP.sup.-
Aqua.sup.- then the drug induced an apoptotic response in 50% of
the cells.
[0532] In the first profile, staurosporine, a multi-kinase
inhibitor and inducer of apoptosis, failed to induce apoptosis
(Staurosporine Resistant profile). Samples responsive to
staurosporine were then classified by their responses to Etoposide,
a topoisomerase II inhibitor which identified a second signature in
which AML blasts were competent to undergo an apoptotic response to
staurosporine but not to etoposide (Etoposide Resistant Profile).
The third profile described AML blasts that were competent to
undergo apoptosis in response to both agents (Apoptosis Competent
Profile).
[0533] Co-incubation of samples with a pan-Caspase inhibitor,
Z-VAD, revealed different apoptotic mechanisms among leukemic
samples. Various changes in the levels of Cleaved Caspase-3 and
PARP were observed upon co-incubation with Z-VAD revealing
contributions of both caspase-dependent (Z-VAD sensitive) and
caspase-independent (Z-VAD insensitive) pathways of apoptosis, (not
shown). For example, Z-VAD inhibited cleavage of caspase 3 and PARP
to near completion (0341,0521) suggesting that in these samples
apoptosis was predominantly caspase-dependent. In other samples
(8303, 8402) PARP cleavage was only partially inhibited by Z-VAD
treatment suggesting the presence of caspase-independent mechanisms
of apoptosis. Samples that were classified by the "Apoptosis
Competent profile" were enriched for Z-VAD in sensitive samples,
suggesting the presence of both caspase dependent and independent
cell death pathways in these samples suggesting that in these
samples cells have a choice of cell death pathways (not shown).
[0534] Mechanistically, treatment of cells with etoposide (but not
staurosporine) will result in DNA damage which will halt the cell
cycle through activation of cell cycle checkpoint kinases and give
the cell time to repair the damage. If attempts to repair DNA are
unsuccessful, cells undergo apoptosis (Huang et al., Molecular
Cancer therapeutics 2008 and see references therein). In this study
DNA damage was determined by measuring the ATM phosphorylation
site, T68, on Chk2. In this AML sample set different DNA Damage and
Apoptosis in responses were seen between samples exposed in vitro
to Etoposide. The spectrum of responses included samples which
failed to elicit a DNA damage and apoptosis response (8314),
samples in which there was a DNA damage response but no apoptosis
(0521, 8390) and samples in which both responses were intact (5688,
8303, 8402). Analysis of the in vitro apoptotic responses in the
context of FLT3 mutations revealed a range of apoptosis responses
in both molecular classes. Notably, samples in which staurosporine
and etoposide induced the greatest apoptotic responses were those
that expressed FLT3 ITD. As discussed above, given the range of
signaling responses within a molecularly classified group, in this
case FLT3 ITD mutations, further analysis of networks should be
performed to characterize samples and classify patients and their
potential response to therapeutic agents.
[0535] The apoptosis profile revealed for each AML sample after in
vitro exposure to staurosporine and etoposide was compared to the
clinical response documented post induction therapy. Strikingly,
the "Staurosporine Resistant" and "Etoposide Resistant" apoptosis
profiles were completely comprised of AML samples from clinical NR
patient samples. In contrast, the "Apoptosis Competent" profile
comprised all samples from clinical CR patients. Of note, several
samples from NR patients fell into the Apoptosis CompetentProfile".
Thus, in vitro apoptosis assays in leukemic samples could
potentially model in vivo clinical responsiveness to
chemotherapy.
[0536] b. Jak/Stat and PI3K Signaling Confer Resistance to
Apoptosis in AML Blasts
[0537] To understand how proliferation and survival signaling
relate to apoptotic potential, JAK/STAT and PI3K/S6 pathway
activity in leukemic samples was analyzed in the context of the
apoptotic profiles described above. While some differences in the
basal unstimulated levels of phosphorylated STAT proteins were
observed between apoptotic signature groups, stimulation with
cytokines revealed variable JAK/STAT activity among the apoptosis
categories described above. Robust Jak/Stat responses were seen
upon treatment with G-CSF (p-Stat3, p-Stat5) or GM-CSF (p-Stat5) in
all samples from the "Staurosporine Resistant` apoptosis category,
consistent with Stat proteins providing a survival function. In the
two other apoptotic categories, the G-CSF-mediated increases in
p-Stat3 and p-Stat5 were variable suggesting that in these
patients, G-CSF signaling provides an apoptosis-independent pathway
for analysis and potential patient stratification.
[0538] Consistent with the role of augmented Stat signaling in
"staurosporine resistant" samples, IL-27-induced levels of total
p-Stat1 and p-Stat3 were all greater in this apoptotic
sub-category. "Etoposide Resistant" samples had varying levels of
IL-27-mediated Stat signaling and the lowest levels of induced Stat
phosphorylation were observed in the "Apoptosis Competent" category
(not shown).
[0539] The NR patients within the "apoptosis Competent" Profile
displayed higher IL-27 induced p-Stat than CR patients again
emphasizing the need to evaluate multiple pathways in patient
samples in order to reach meaningful clinical decisions.
[0540] Consistent with their roles in survival, there was an
inverse correlation between levels of growth factor-mediated-p-Akt
and p-S6 signaling and apoptotic response. Greater induced p-Akt
and p-S6 levels were observed in samples where there was a low
level of induced apoptosis (Staurosporine and/or Etoposide
Resistant categories). In contrast in the "Apoptosis Competent
Profile" there were low levels of growth factor-mediated increases
in p-Akt and p-S6 (not shown).
[0541] Other myeloid cytokines and chemokines known to stimulate
the PI3K/S6 and pathway are G-CSF, GM-CSF, and SDF-1.alpha..
Overall, these modulators mediated the greatest increase in p-Akt
and p-S6 levels in the "Staurosporine Resistant` category
consistent with the survival role conferred by the PI3K pathway.
Notably, two different cytokines, G-CSF and GM-CSF provided a
similar signaling output (p-Stat5, p-S6) in this apoptotic
category. Pathway characterization of AML blasts highlights the
different signaling mechanisms utilized to evade apoptosis (for
example: sample 8093, NR, "Etoposide resistant", induced Jak/Stat
signaling elevated, sample 0521, NR, "Etoposide Resistant", induced
PI3K/S6 signaling elevated, sample 4353, NR, "Staurosporine
Resistant", induced Jak/Stat and PI3K/S6 pathways elevated
[0542] c. Analysis of Signaling and Apoptosis in the context of
FLT3 Mutations
[0543] Analysis of the in vitro apoptotic responses in the context
of FLT3 mutations revealed that AML samples expressing FLT3 ITD
have relatively intact apoptotic machinery compared with AML
samples expressing wild type FLT3 (not shown). However, apoptosis
responses to both staurosporine and etoposide varied between
samples within FLT3 ITD+ or WT subgroups, demonstrating that
molecular characterization alone is not sufficient to classify
patients and their potential response to therapeutics. In other
analyses FLT3-ITD patients had higher basal p-Stat5 and cytokine
induced p-Stat5 levels than FLT3-WT patients although a large
spread of responses was seen in either FLT3-ITD or FLT3-WT
patients. Also, FLT3-ITD patients had lower basal and FLT3L induced
p-S6 than FLT3-WT patients. Again a spread of responses was seen
within FLT3 WT or FLT3-ITD subgroups demonstrating how single cell
network profiling can further characterize samples within a
molecularly-defined patient subgroup
Example 7
[0544] Scenarios of how this invention might be used to advance the
diagnosis or prognosis of disease, or the ability to predict or
assess response to therapy are outlined in the following two
paragraphs.
[0545] A 49 year-old individual presents to their primary medical
doctor with the chief complaint of fatigue and bruising. A complete
blood count reveals increased white blood cells, decreased
hemoglobin and hematocrit, low platelets and circulating blasts. A
bone marrow aspirate is obtained and flow cytometry reveals an
immature myeloid blast population. The patient is diagnosed with
acute myeloid leukemia and the physician and patient must determine
the best course of therapy. Using an embodiment of the present
invention, the bone marrow or peripheral blood of the patient might
be removed and modulators such as GMCSF or PMA added. Activatable
elements such asp-Stat3, p-Stat5 and p-Akt might classify this
patient as one of the 25% of patients diagnosed with AML less than
60 years old who will not benefit from cytarabine based induction
therapy. This invention may also reveal signaling biology within
this patient's blasts population that suggests to the physician
that the patient should be treated with a DNA methyl transferase
inhibitor. With this invention, the patient would then be spared
the toxicities associated with cytarabine therapy and could be
placed on a clinical trial where he would receive a therapy from
which he would likely benefit.
[0546] A 52 year-old female presents to her primary medical doctor
with the chief complaint of fatigue and bruising. A complete blood
count reveals normal numbers of white blood cells, decreased
hemoglobin and hematocrit, and low platelets. A bone marrow
aspirate and biopsy is obtained and flow cytometry and histology
reveals tri-lineage myelodysplasia. The patient is diagnosed with
MDS. Using an embodiment of the present invention, the bone marrow
or peripheral blood of the patient might be removed and modulators
such as GMCSF or PMA added. Activatable elements such as STAT3,
STAT5 and AKT might reveal that the biology associated with this
patient's MDS is likely of auto-immune origin. The physician
promptly places this patient on CSA and ATG. Within 6 weeks she
shows complete normalization of her complete blood count.
Example 8
[0547] This example relates to the publication "Dynamic Single-Cell
Network Profiles in Acute Myelogenous Leukemia Are Associated with
Patient Response to Standard Induction Therapy". Kornblau S M,
Minden M D, Rosen D B, Putta S, Cohen A, Covey T, Spellmeyer D C,
Fantl W J, Gayko U, Cesano A. Clinical Cancer Research. 2010 Jul.
15; 16(14): 3721-33 January 31. This publication is incorporate
herein by reference in its entirety for all purposes.
[0548] Traditional prognostic markers in acute myeloid leukemia
(AML) use static features present at diagnosis. This study reports
measurements of single cell network profiling (SCNP) in response to
external modulators as a new tool to recognize and interpret
disease heterogeneity in the context of therapeutic applications.
Intracellular signaling profiles from two sequential training
cohorts of diagnostic non-M3 AML patient samples (n=34 and 88)
showed high reproducibility (Pearson correlation
coefficients.gtoreq.0.8). In the first training study univariate
analysis identified multiple "nodes" (modulated readouts of
proteins in signaling pathways) relevant to myeloid biology and
correlated with disease response to conventional induction therapy
(i.e. AUC of ROC>0.66; p<0.05). Importantly combining
independently predictive nodes improved disease response
stratification (AUC of ROC up to 1.0). Extrapolation of the assay
to a second independent set of samples revealed similar findings
after accounting for clinical covariates. In particular, for
patients<60 years old, the presence of intact apoptotic pathways
was associated with complete response (CR), while FLT3 ligand
mediated increase in phospho (p)-Akt and p-Erk correlated to NRs in
patients.gtoreq.60 years. Findings were independent of cytogenetic
and FLT3 mutational status. These data support the value of SCNP in
AML disease characterization and management.
[0549] Introduction
[0550] Acute Myeloid Leukemia (AML) displays biologic and clinical
heterogeneity due to a complex range of cytogenetic and molecular
aberrations resulting in downstream effects on gene expression,
protein function and cell signal transduction pathways, ultimately
affecting proliferation and cellular differentiation. While
morphology and cytochemical stains historically have formed the
basis for AML classification, and emerging technologies such as
gene expression profiling, microRNA profiling, epigenetic profiling
and more recently proteomic profiling have been used to elucidate
the biologic heterogeneity of AML, and have provided useful
insights into the disease biology and its correlation with clinical
outcomes. While individual molecular changes have shown to be
associated with disease-free and overall survival, only karyotype,
high expression levels of the brain and acute leukemia cytoplasmic
(BAALC), and meningioma 1 (MN 1) genes at presentation have
demonstrated an association with response to induction
chemotherapy. (Marcucci et al. Curr Opin Hematol. 2005; 12:68-75;
Langer C, Marcucci et al. J Clin Oncol. 2009; 27:3198-3204.)
However, although these findings offer directionally predictive
information at a population level, no validated means currently
exist to predict the disease response to standard AML induction
chemotherapy at the individual patient level.
[0551] Recently, reverse-phase protein arrays (RPPA) generated
proteomic profiles that characterized aberrantly regulated
signaling networks in AML samples and were found to correlate with
known morphologic features, cytogenetics and outcome. (Kornblau et
al. Blood. 2009; 113:154-164.) Single cell network profiling (SCNP)
using multiparametric flow cytometry is a newer approach for
analyzing and interpreting protein expression and
post-translational protein modifications under modulated conditions
at the single cell level. This approach interrogates the physiology
of signaling pathways by measuring network properties beyond those
detected in resting cells (e.g. failure of a pathway to become
activated, hyper/hyposensitivity of the pathway to physiologic
stimulators, altered response kinetics and rewiring of canonical
pathways), thus revealing otherwise unseen functional heterogeneity
in apparently morphologically and molecularly homogeneous disease
groups. When applied to pathways shown to be important in disease
pathology, this method of mapping signaling networks has potential
applications in the development of predictive/diagnostic tests for
therapeutic response and for improved efficiency of drug
development. (Irish et al. Cell. 2004; 118:217-228; Irish et al.
Nat Rev Cancer. 2006; 6:146-155; Krutzik et al. Nat. Methods. 2006;
3:361-368; and Sachs et al. Science. 2005; 308:523-529.)
[0552] To utilize modulated SCNP to reveal AML network biology as a
guide for disease management, two independent sample sets from
newly diagnosed adult patients with AML (non-M3) were tested
sequentially. Since multiple signaling pathways may be dysregulated
in AML and impact responsiveness to therapy, a wide range of
pathways that regulate proliferation, survival, DNA damage,
apoptosis and drug transport were evaluated in response to
modulators important in myeloid biology. Analyses evaluated assay
performance, identified a signaling profile associated with
response to standard induction chemotherapy (first training study)
and extrapolated the identified profile to a fully independent set
of AML samples (second training study). The results of the two
studies illustrate the value of quantitatively measuring single
cell signaling networks under modulated conditions to stratify AML
patients for outcome to standard induction chemotherapy.
[0553] Materials and Methods
[0554] Patient Samples
[0555] Two independent sets of cryopreserved samples were analyzed
sequentially. The first set consisted of 35 peripheral blood
mononuclear cell (PBMC) samples derived from AML patients. The
second set consisted of 134 cryopreserved bone marrow mononuclear
cell (BMMC) samples derived from AML patients. These samples were
the same samples used in the previous examples. Sample inclusion
criteria required collection prior to initiation of induction
chemotherapy, AML classification by the French-American-British
(FAB) criteria as M0 through M7 (excluding M3) and availability of
clinical annotations.
[0556] In the first study, induction chemotherapy consisted of at
least one cycle of standard cytarabine-based induction therapy
(i.e. daunorubicin 60 mg/m.sup.2.times.3 days, cytarabine 100-200
mg/m.sup.2 continuous infusion.times.7 days); responses were
measured after one cycle of induction therapy. In the second study,
cytarabine (200 mg/m.sup.2 to 3 g/m.sup.2) was used in combination
with an anthracycline (daunorubicin or idarubicin) or an additional
anti-metabolite (e.g. fludarabine or troxacitabine), and sometimes,
an experimental agent (Table 16). Responses in this set were
measured after completion of induction therapy (>90% after one
cycle). Standard clinical and laboratory criteria were used for
defining complete response (CR) in both studies. Leukemia samples
obtained from patients who did not meet the criteria for CR or
samples obtained from those who died during induction therapy were
considered non-complete response (NR) for the primary analyses.
Both studies had one patient that met all the criteria for a
clinical CR, with the exception of platelet recovery. Classified as
"CRp," these samples were included in the CR group for all primary
analysis. The univariate analyses were also repeated with the CRp
patients classified into the NR sample group for sensitivity
analysis.
TABLE-US-00016 TABLE 16 Demographic and Baseline Characteristics
for Evaluable Patients/Samples in Both Studies Characteristic CR
No. 1 NR No. 1 All Pts No. 1 P No. 1 CR No. 2 NR No. 2 All Pts No.
2 P No. 2 N 9 25 34 57 31 88 Age (yr) Median 57 47.4 49.1 0.084
51.2 61.6 55.2 0.004 Range 38.2-74.8 20.7-70.2 20.7-74.8 27.0-79.0
25.0-76.3 25.0-79.0 Age Group <60 yr 5 (56%) 20 (80%) 25 (74%)
0.201 51 (89%) 15 (48%) 66 (75%) <.001 >=60 yr 4 (44%) 5
(20%) 9 (26%) 6 (11%) 16 (52%) 22 (25%) Sex F 7 (78%) 14 (56%) 21
(62%) 0.427 32 (56%) 16 (52%) 48 (55%) 0.823 M 2 (22%) 11 (44%) 13
(38%) 25 (44%) 15 (48%) 40 (45%) Cytogentic Favorable 0 (0%) 1 (4%)
1 (3%) 0.639 7 (12%) 0 (0%) 7 (8%) 0.004 Group Intermediate 8 (89%)
18 (72%) 26 (76%) 29 (51%) 9 (29%) 38 (43%) Unfavorable 0 (0%) 3
(12%) 3 (9%) 21 (37%) 22 (71%) 43 (49%) Not Done 1 (11%) 3 (12%) 4
(12%) 0 (0%) 0 (0%) 0 (0%) FAB M0 0 (0%) 2 (8%) 2 (6%) 0.474 1 (2%)
1 (3%) 2 (2%) 0.794 M1 2 (22%) 2 (8%) 4 (12%) 8 (14%) 1 (3%) 9
(10%) M2 1 (11%) 5 (20%) 6 (18%) 22 (39%) 14 (45%) 36 (41%) M4 1
(11%) 7 (28%) 8 (24%) 14 (25%) 8 (26%) 22 (25%) M5 3 (33%) 2 (8%) 5
(15%) 8 (14%) 4 (13%) 12 (14%) M6 0 (0%) 0 (0%) 0 (0%) 2 (4%) 2
(6%) 4 (5%) Other/Unknown 2 (22%) 7 (28%) 9 (27%) 2 (4%) 1 (3%) 3
(3%) Race White 3 (33%) 17 (68%) 20 (59%) 0.201 15 (26%) 15 (48%)
30 (34%) 0.127 Asian 5 (56%) 5 (20%) 10 (29%) 1 (2%) 1 (3%) 2 (2%)
Other* 1 (11%) 2 (8%) 3 (9%) 10 (18%) 1 (3%) 11 (13%) Unknown 0
(0%) 1 (4%) 1 (3%) 31 (54%) 14 (45%) 45 (51%) FLT3-ITD Negative 4
(44%) 14 (56%) 18 (53%) 0.641 44 (77%) 23 (74%) 67 (76%) 0.477
Positive 5 (56%) 10 (40%) 15 (44%) 11 (19%) 5 (16%) 16 (18%)
Unknown 0 (0%) 1 (4%) 1 (3%) 2 (4%) 3 (10%) 5 (3%) Secondary No 8
(89%) 25 (100%) 33 (97%) 0.265 47 (82%) 14 (45%) 61 (69%) <.001
AML Yes 1 (11%) 0 (0%) 1 (3%) 10 (18%) 17 (55%) 27 (31%) Poor No 5
(56%) 18 (72%) 23 (68%) 0.425 22 (39%) 3 (10%) 25 (28%) 0.004
Prognosis.dagger. Yes 4 (44%) 7 (28%) 11 (32%) 35 (61%) 28 (90%) 63
(72%) Induction Standard 3 + 7 9 (100%) 25 (100%) 34 (100%) n/a 0
(0%) 0 (0%) 0 (0%) Therapy Fludarabine + HDAC 0 (0%) 0 (0%) 0 (0%)
11 (19%) 2 (6%) 13 (15%) 0.222 IA + Zarnestra 0 (0%) 0 (0%) 0 (0%)
18 (32%) 9 (29%) 27 (31%) IDA + HDAC 0 (0%) 0 (0%) 0 (0%) 17 (30%)
9 (29%) 26 (30%) Other 0 (0%) 0 (0%) 0 (0%) 11 (19%) 11 (35%) 22
(25%) There are 25 primary refractory patients and 6 failed
patients in Study No. 2. The two-sample t-test was used to compare
mean ages of CR and NR patients. Fisher's Exact test was used to
compare CR and NR patients with respect to categorical variables
with two levels. The standard Chi-Square test was used to compare
CR and NR patients with respect to categorical variables with three
or more levels. *The "Other" values for race are based on Black and
Hispanic sub groups .dagger.Poor prognosis is defined as having one
or more of the following high risk features: age .gtoreq.60 years,
unfavorable cytogenetics, FLT3 ITD positive or secondary AML
[0557] SCNP Assays
[0558] Cocktails of fluorochrome-conjugated antibodies were used to
measure phosphorylated intracellular signaling molecules, cell
lineage markers, and drug transporters in AML cells. Measurements
were taken at basal state and after extracellular modulation with
growth factors or cytokines.
[0559] A pathway "node" (FIG. 1) was defined as a combination of
specific proteomic readout in the presence or absence of a specific
modulator. Up to 147 nodes (including eight surface receptors and
transporters) using 27 modulators were assessed in the two studies
(Table 17).
[0560] Samples with 6.8 and 4.7 million cells were required to test
all planned experimental nodes in the first and second studies,
respectively. In both studies, evaluable samples were defined as
those that yielded a minimum of 100,000 viable cells. In addition,
500 cells were required in the myeloid blast population for any
condition to be included in analysis for a given sample. In the
first set, 34 of 35 patients had evaluable samples, although some
samples did not have enough cells for the testing of all planned
nodes (Table 17). There were also two cryopreserved vials of each
sample, allowing for assessment of assay reproducibility. In the
second set, the number of viable cells recovered after thawing
(median 1.1 million cells) was significantly less than expected and
only 88 of the 134 samples were evaluable.
TABLE-US-00017 TABLE 17 All Nodes, with Biological Categories,
Flouorochrome Read-Outs, and Number of Patients Assessed in Both
Studies Read-Out (antibody) Read-Out (antibody) Biological Num. Pts
Num. Pts Dye: Alexa 488 or Read-Out (antibody) Dye: Alexa 647 or
Modulator Category No. 1 No. 2 FITC Dye: PE APC Ara-C &
Daunorubicin Apoptosis n/a 42 c-PARP Dauno p-Chk2 (T68) CD40L CCG
34 n/a p-S6 (S235) p-CREB (S133) p-Erk 1/2 (T202/204)* CD40L CCG 34
n/a p-p38 (T180/Y182) p-Erk 1/2 (T202/204)* p-NFkB p 65 (S529) EPO
CCG 34 n/a p-Stat1 (Y701) p-Stat3 (Y705) p-Stat5 (Y694) Etoposide
Apoptosis n/a 62 c-PARP n/a p-Chk2 (T68) Etoposide Apoptosis 28 n/a
BCL-2 c-PARP* p-Chk2 (T68) Etoposide Apoptosis 27 n/a c-Caspase 3
c-PARP* None Etoposide + ZVAD Apoptosis 28 n/a BCL-2 c-PARP* p-Chk2
(T68) Etoposide + ZVAD Apoptosis 29 n/a c-Caspase 3 c-PARP* n/a
Flt3L CCG 34 76 p-S6 (S235) p-Erk 1/2 (T202/204) p-Akt (S473) Flt3L
CCG 34 n/a p-CREB (S133) p-Plc.gamma.2 (Y759) p-Stat5 (Y694) Flt3L
CCG n/a 9 p-Plc.gamma.2 (Y759) p-CREB (S133) p-Stat5 (Y694) G-CSF
CCG 34 63 p-Stat1 (Y701) p-Stat3 (Y705) p-Stat5 (Y694) G-CSF CCG 34
n/a p-S6 (S235) p-Erk 1/2 (T202/204) p-Akt (S473) GM-CSF CCG 34 14
p-Stat1 (Y701) p-Stat3 (Y705) p-Stat5 (Y694) GM-CSF CCG 34 n/a p-S6
(S235) p-Erk 1/2 (T202/204) p-Akt (S473) H.sub.2O.sub.2 Phosphatase
n/a 65 p-Akt (S473) p-Plc.gamma.2 (Y759) p-SLP76 (Y128)
H.sub.2O.sub.2 Phosphatase 29 n/a p-Stat1 (Y701) p-Stat3 (Y705)
p-Stat5 (Y694) H.sub.2O.sub.2 Phosphatase 29 n/a p-Lck (Y505)
p-Plc.gamma.2 (Y759) p-SLP76 (Y128) H.sub.2O.sub.2 Phosphatase 29
n/a p-S6 (S235) p-Erk 1/2 (T202/204) p-Akt (S473) H.sub.2O.sub.2 +
IFN.alpha. Phosphatase 29 n/a p-Stat1 (Y701) p-Stat3 (Y705) p-Stat5
(Y694) H.sub.2O.sub.2 + SCF Phosphatase 29 n/a p-Lck (Y505)
p-Plc.gamma.2 (Y759) p-SLP76 (Y128) H.sub.2O.sub.2 + SCF
Phosphatase 29 n/a p-S6 (S235) p-Erk 1/2 (T202/204) p-Akt (S473)
IFN.alpha. CCG 34 46 p-Stat1 (Y701) p-Stat3 (Y705) p-Stat5 (Y694)
IFN.gamma. CCG 34 21 p-Stat1 (Y701) p-Stat3 (Y705) p-Stat5 (Y694)
IGF-1 CCG 34 n/a p-S6 (S235) p-CREB (S133)* p-Erk 1/2 (T202/204)
IGF-1 CCG 34 n/a p-CREB (S133)* p-Plc.gamma.2 (Y759) p-Stat5 (Y694)
IL-10 CCG 34 24 p-Stat1 (Y701) p-Stat3 (Y705) p-Stat5 (Y694) IL-27
CCG 34 56 p-Stat1 (Y701) p-Stat3 (Y705) p-Stat5 (Y694) IL-27 CCG 34
n/a p-S6 (S235) p-CREB (S133) p-Erk 1/2 (T202/204) IL-3 CCG 34 13
p-Stat1 (Y701) p-Stat3 (Y705) p-Stat5 (Y694) IL-3 CCG 34 n/a p-S6
(S235) p-CREB (S133) p-Erk 1/2 (T202/204) IL-4 CCG 34 9 None
p-Stat6 (Y641) p-Stat5 (Y694) IL-6 CCG 34 15 p-Stat1 (Y701) p-Stat3
(Y705) p-Stat5 (Y694) IL-6 CCG 34 n/a p-S6 (S235) p-CREB (S133)
p-Erk 1/2 (T202/204) LPS CCG 34 27 p-p38 (T180/Y182) p-Erk 1/2
(T202/204) p-NFkB p 65 (S529) M-CSF CCG 34 9 p-S6 (S235) p-Erk 1/2
(T202/204) p-Akt (S473) M-CSF CCG 34 n/a p-CREB (S133)
p-Plc.gamma.2 (Y759) p-Stat5 (Y694) None/Phenotypic Surface Markers
n/a 48 CXCR4 MRP1 ABCG2 None/Phenotypic Surface Markers n/a 51
Flt3R n/a C-Kit Stain None/Phenotypic Surface Markers 31 n/a EPO-R
Flt3R C-Kit Stain None/Phenotypic Surface Markers 31 n/a n/a CXCR4
ABCG2 Stain None/Phenotypic Surface Markers 31 n/a MCSF-R TNF-R
CD40 Stain PMA CCG 34 46 p-S6 (S235) p-CREB (S133 p-Erk 1/2
(T202/204) SCF CCG 34 74 p-S6 (S235) p-Erk 1/2 (T202/204) p-Akt
(S473) SCF CCG 34 n/a p-CREB (S133) p-Plc.gamma.2 (Y759) p-Stat5
(Y694) SCF CCG n/a 9 p-Plc.gamma.2 (Y759) p-CREB (S133) p-Stat5
(Y694) SDF-1.alpha. CCG n/a 93 n/a p-CREB (S133) p-Akt (S473)
SDF-1.alpha. CCG 34 n/a p-S6 (S235) p-Erk 1/2 (T202/204) p-Akt
(S473) Stauro Apoptosis n/a 9 c-Caspase 8 c-PARP Cytochrome C
Stauro Apoptosis 26 n/a BCL-2 c-PARP* c-Caspase 8 Stauro Apoptosis
30 n/a c-Caspase 3 c-PARP* None Stauro + ZVAD Apoptosis n/a 16
c-Caspase 8 c-PARP Cytochrome C Stauro + ZVAD Apoptosis 26 n/a
BCL-2 c-PARP* c-Caspase 8 Stauro + ZVAD Apoptosis 30 n/a c-Caspase
3 c-PARP* n/a Thapsigargin CCG 34 43 p-S6 (S235) p-CREB (S133 p-Erk
1/2 (T202/204) TNF CCG 34 9 p-p38 (T180/Y182) p-Erk 1/2 (T202/204)
p-NFkB p 65 (S529) *Read-Out was assessed twice and all data was
included for analysis. Metrics are defined in Materials and Methods
Each modulator and read-out combination is a node. Unmodulated,
basal levels were also measured. In #1, there were 18 basal, 121
modulated, and 8 surface markers for a total node count of 147. In
#2, there were 16 basal, 69 modulated, and 5 surface markers for a
total node count of 90. Akt indicates protein kinase B; APC,
allophyco-cyanin; Ara-C, cytarabine; ATP-binding cassette,
subfamily G, member 2; BCL, CD, cluster of differentiation; c-,
cleaved-; CCG, cytokine, chemokine, growth factor; C-kit, CD117;
CREB, cAMP response element binding; CXCR, CXC chemokine receptor;
EPO, erythropoietin; Erk, Extracellular signal-regulated kinase;
FITC, fluorescein isothiocyanate; FLT3, fms-like tyrosine kinase;
G-CSF, granulocyte colony stimulating factor; GM-CSF, granulocyte
macrophage stimulating factor; H2O2, hydrogen peroxide; IFN,
interferon; IGF, insulin-like growth factor; IL, interleukin;
M-CSF, macrophage colony stimulating factor; MDR, p-glycoprotein;
NFkB, Nuclear Factor-Kappa B; p-, phospho-; p38, map kinase family
protein 38; PARP, Extracellular signal-regulated kinase; PE,
phycoerythrin; Plc.gamma., phospholipase c-gamma; S6, ribosomal
protein S6; SCF, stem cell factor; SDF, stromal cell derived
factor; Stat, signal transducer and activator of transcription;
Stauro, staurosporine; TNF, tumor necrosis factor; ZVAD, ZVAD-FMK
caspase inhibitor
[0561] Cyropreserved samples were thawed at 37.degree. C., washed
and centrifuged in PBS, 10% FBS and 2 mM EDTA. The cells were
re-suspended, filtered to remove debris and washed in RPMI cell
culture media, 1% FBS, then stained with Live/Dead Fixable Aqua
Viability Dye to distinguish non-viable cells. The cells were then
re-suspended in RPMI, 1% FBS, aliquoted to 100,000 cells/condition
and rested for 1-2 hours at 37.degree. C. prior to SCNP assays.
Each condition included two to five phenotypic markers for cell
population gating (eg, CD45, CD33), up to three intracellular
stains or up to three additional surface markers or control
antibodies for an eight-color flow cytometry assay.
[0562] Functional assays were performed as previously described.
Cells were incubated with modulators (Table 18), at 37.degree. C.
for 3-15 minutes, fixed with 1.6% paraformaldehyde (final
concentration) for 10 minutes at 37.degree. C., pelleted and
permeabilized with 100% ice-cold methanol and stored at -80.degree.
C. For functional apoptosis assays, cells were incubated for 24
hours with cytotoxic drugs (i.e. etoposide or Ara-C and
daunorubicin), re-stained with Live/Dead Fixable Aqua Viability Dye
before fixation and permeabilization, washed with FACS Buffer (PBS,
0.5% BSA, 0.05% NaN.sub.3), pelleted and stained with fluorescent
dye-conjugated antibodies to both surface antigens (CD33, CD45) and
the signaling protein targets (Table 18B).
TABLE-US-00018 TABLE 18A List of Modulators and Technical
Conditions of Use in Both Studies Modulator Final Treatment
Modulator Concentration Duration Manufacturer (Location) Ara-C 0.5
ug/mL 24 h Sigma Aldrich (St Louis, MO) CD40L 0.5 ug/mL 7.5' and
15' R&D (Minneapolis, MN) Daunorubicin 100 ng/mL 24 h Sigma
Aldrich (St Louis, MO) Erythropoetin 1 IU/mL 15' R&D
(Minneapolis, MN) Etoposide 30 mg/mL 24 h Sigma Aldrich (St Louis,
MO) FCS 1.0% various HyClone (Waltham, MA) Flt3L 50 ng/mL 15'
eBiosciences (San Diego, CA) G-CSF 50 ng/mL 15' R&D
(Minneapolis, MN) G-CSF 50 ng/mL 15' Pepro (Rocky Hill, NJ) GM-CSF
2 ng/mL 15' BD (San Jose, CA) H2O2 3 mM 15' JT Baker (Phillipsburg,
NJ) IFN.alpha. 10000 IU/ML 15' Schering (Kenilworth, NJ) IFN.gamma.
5 ng/mL 15' BD (San Jose, CA) IGF-1 6.66 ng/mL 15' R&D
(Minneapolis, MN) IL-10 25 ng/mL 15' BD (San Jose, CA) IL-27 50
ng/mL 15' R&D (Minneapolis, MN) IL-3 50 ng/mL 15' BD (San Jose,
CA) IL-4 5 ng/mL 15' BD (San Jose, CA) IL-6 25 ng/mL 15' R&D
(Minneapolis, MN) LPS 1 ug/mL 7.5' Sigma Aldrich (St Louis, MO)
M-CSF 2 ng/mL 15' R&D (Minneapolis, MN) PMA 400 nM 15' Sigma
Aldrich (St Louis, MO) SCF 20 ng/mL 15' R&D (Minneapolis, MN)
SDF-1.alpha. 2 ng/mL 3' R&D (Minneapolis, MN) Stauro 2.33 ug/mL
6 h Sigma Aldrich (St Louis, MO) Thapsigargin 1 uM 15' EMD
Biosciences (Darmstadt, Germany) TNF.alpha. 20 ng/mL 7.5' BD (San
Jose, CA) Z-VAD-FMK 100 uM 24 h R&D (Minneapolis, MN) Caspase
Inhibitor
TABLE-US-00019 TABLE 18B Antibodies Used in Both Studies Species
& Manufacturer Antibody Isotype (Location) Label ABCG2 Mouse
IgG2b R&D (Minneapolis, MN) APC BCL-2 Mouse IgG1, k BD (San
Jose, CA) FITC CD11b Mouse IgG1 Beckman (Miami, FL) Pac Blue CD33
Mouse IgG1 Beckman (Miami, FL) Biotin CD33 Mouse IgG1 BD (San Jose,
CA) Pac Blue CD34 Mouse IgG1 BD (San Jose, CA) PerCP CD40 Mouse
IgG1, k BD (San Jose, CA) APC CD45 Mouse IgG1 Invitrogen (Carlsbad,
CA) Ax700 C-Kit Mouse IgG1 R&D (Minneapolis, MN) APC c-Caspase
3 Rabbit IgG BD (San Jose, CA) FITC c-Caspase 8 (Asp391) Rabbit IgG
CST (Danvers, MA) Unlabeled c-PARP(Asp214) Mouse IgG1, k BD (San
Jose, CA) PE c-PARP(Asp214) Mouse IgG1, k BD (San Jose, CA) FITC
Control Ig Ms IgG1 eBio (San Diego, CA) FITC Control Ig Mouse
IgG2a, k BD (San Jose, CA) PE Control Ig Rat IgG1 MBL (Woburn, MA)
FITC Control Ig Mouse IgG2b R&D (Minneapolis, MN) APC Control
Ig Mouse IgG1 BD (San Jose, CA) PE Control Ig Mouse IgG1, k BD (San
Jose, CA) FITC Control Ig Mouse IgG1, k BD (San Jose, CA) APC
Control Ig Mouse IgG1, k BD (San Jose, CA) PE CXCR4 Mouse IgG2a, k
BD (San Jose, CA) PE CXCR4 Rat IgG1 MBL (Woburn, MA) FITC
Cytochrome C Mouse IgG2b, k BD (San Jose, CA) Ax647 EpoR Mouse
IgG2b R&D (Minneapolis, MN) FITC Flt3R Mouse igG1 R&D
(Minneapolis, MN) PE Flt3R Mouse IgG1 Ebio (San Diego, CA) FITC
goat anti-rabbit Goat IgG Invitrogen (Carlsbad, CA) Ax488 goat
anti-rabbit Goat IgG Invitrogen (Carlsbad, CA) Ax647 M-CSFR Mouse
IgG1 R&D (Minneapolis, MN) FITC MRP-1 Mouse IgG1 R&D
(Minneapolis, MN) PE p-Akt (S473) Rabbit IgG CST (Danvers, MA)
Ax647 p-Akt (S473) Rabbit IgG CST (Danvers, MA) Ax488 p-Chk2 (T68)
Rabbit IgG CST (Danvers, MA) Unlabeled p-CREB (pS133) Rabbit IgG
CST (Danvers, MA) Ax488 p-CREB (pS133) Mouse IgG1, k BD (San Jose,
CA) PE p-Erk 1/2 (T202/204) Mouse IgG1 BD (San Jose, CA) Ax647
p-Erk 1/2 (T202/204) Mouse IgG1 BD (San Jose, CA) PE p-Lck (Y505)
Mouse IgG1 BD (San Jose, CA) Ax488 p-NF-kB p65 (pS529) Mouse IgG2b,
k BD (San Jose, CA) Ax647 p-p38 MAPK (pT180/pY182) Mouse IgG1 BD
(San Jose, CA) Ax488 p-Plc.gamma.2 (Y759) Mouse IgG1, k BD (San
Jose, CA) PE p-Plc.gamma.2 (Y759) Mouse IgG1, k BD (San Jose, CA)
Ax488 p-S6 (S235/236) Rabbit IgG CST (Danvers, MA) Ax488 p-SLP76
(pY128) Mouse IgG1, k BD (San Jose, CA) Ax647 p-Stat1 (pY701) Mouse
IgG2a BD (San Jose, CA) Ax488 p-Stat3 (pY705) Mouse IgG2a, k BD
(San Jose, CA) PE p-Stat5 (pY694) Mouse IgG1 BD (San Jose, CA)
Ax647 p-Stat6 (pY641) Mouse IgG2a BD (San Jose, CA) PE TNF-R1 Mouse
IgG2a Beckman (Miami, FL) PE Non-Antibody Stains n/a Manufacturer
(Location) Dye Amine Aqua Viability Dye n/a Invitrogen (Carlsbad,
CA) Aqua Streptavidin-Qdot 605 n/a Invitrogen (Carlsbad, CA) Qdot
605 Abbreviations are defined in Table 17
[0563] Data Acquisition and Cytometry Analysis
[0564] Data was acquired using FACS DIVA software on both LSR II
and CANTO II Flow Cytometers (BD). For all analyses, dead cells and
debris were excluded by forward scatter (FSC), side scatter (SSC),
and Amine Aqua Viability Dye measurement. Leukemic cells were
identified as cells that lacked the characteristics of mature
lymphocytes (CD45.sup.++, CD33.sup.-) and that fit the CD45 and
CD33 versus right-angle light-scatter characteristics consistent
with myeloid leukemia cells.
[0565] Statistical Analysis and Stratifying Node Selection
[0566] a) Metrics
[0567] The median fluorescence intensity (MFI) was computed for
each node from the fluorescence intensity levels for the cells in
the myeloid population. The MFI values were then used to compute a
variety of metrics by comparing them to baseline or background
values, including the unmodulated condition, cellular
autofluorescence and antibody isotype controls. The following
metrics were computed: [0568] 1. Basal MFI
("Basal")=log.sub.2(MFI.sub.Unmodulated
Stained)-log.sub.2(MFI.sub.Gated Unstained (Autofluoresence)),
designed to measure the basal levels of a certain protein under
unmodulated conditions. [0569] 2. Fold Change MFI
("Fold")=log.sub.2(MFI.sub.Modulated
Stained)-log.sub.2(MFI.sub.Unmodulated Stained), a measure of the
change in the activation state of a protein under modulated
conditions. [0570] 3. Total Phospho MFI
("TotalPhospho")=log.sub.2(MFI.sub.Modulated
Stained)-log.sub.2(MFI.sub.Gated Unstained (Autofluorescence)), a
measure of the total levels of a protein under modulated
conditions. [0571] 4. Relative Protein Expression ("Rel.
Expression")=log.sub.2(MFI.sub.stain)-log.sub.2(MFI.sub.control), a
measure of the levels of surface marker staining relative to
control antibody staining [0572] 5. Percent Cell Positivity
("PercentPos")=a measure of the frequency of cells that have
surface markers staining at an intensity level greater than the
95.sup.th percentile for isotype control antibody staining [0573]
6. An additional metric was designed to measure the levels of
cellular apoptosis in response to cytotoxic drugs: Quadrant
("Quad")=a measure of the percentage of cells in a flow cytometry
quadrant region defined by p-Chk2 and c-PARP i.e. the % of cells
that are both p-Chk2- and c-PARP+.
[0574] b) Reproducibility Analysis
[0575] In the first study, two cryopreserved vials for all
evaluable patient samples (n=34) were processed separately to
assess overall assay reproducibility. Pearson and Spearman rank
correlations were computed for each node/metric combination between
the two data sets.
[0576] c) Univariate Analysis
[0577] All node/metric combinations were analyzed and compared
across samples for their ability to distinguish between the CR and
NR sample groups. Student t-test and Wilcoxon p values were
computed for each node/metric combination. In addition, the area
under the receiver operator characteristic (ROC) curve was computed
to assess the diagnostic accuracy of each node/metric combination
(FIG. 8).
[0578] In the first study a total of 304 node/metric combinations
were independently tested for differences between patient samples
whose response to standard induction therapy was CR vs. NR. No
corrections for multiple testing were applied to the p-values.
Instead, simulations were performed by randomly permuting the
clinical variable to estimate the number of node/metric
combinations that might appear to be significant by chance. For
each node/metric combination N.sup.cr donors were randomly chosen
(without replacement) and assigned to the CR category (where
N.sup.cr is the number of actual CRs in the original data set for
that node/metric) and the remaining donors were assigned to the NR
category. By comparing each node/metric to the permuted clinical
variable, the student t-test p-values were computed. This process
was repeated 10,000 times. The results were used to estimate the
number of node/metrics expected to be significant by chance at the
various p-values and compared with the empirical p-values for the
number of node/metric combination found to be significant from the
original data.
[0579] The statistical software package R, version 2.7.0 was
used.
[0580] Correlations Between Node/Metric Combinations:
[0581] Correlations between all pairs of node/metric combination
were assessed by computing Pearson and Spearman rank
correlations.
[0582] e) Combinations of Node/Metrics
[0583] Nodes that can potentially complement each other to improve
the accuracy of prediction of response to therapy were also
explored. Given the small size of the data set, a straightforward
"corner classifier" approach for picking combinations was adopted.
Combinations that had an AUC greater than any included individual
node/metric were tested for their robustness via a bootstrapping
approach.
[0584] The corners classifier is a rule-based algorithm for
dividing subjects into two classes (in this case the dichotomized
response to induction therapy) using one or more numeric variables
(defined in our study as a node/metric combination). This method
works by setting a threshold on each variable, then combining the
resulting intervals with "and" operator (e.g. X<10, and
Y>50). This creates a rectangular region expected to hold most
members of the class previously identified as the target (in this
study clinical CR or NR sample groups). Threshold values can be
chosen by minimizing an error criterion, however here in order to
capture all CRs these values were set to either the maximum or the
minimum value for each node/metric for all CRs. The accuracy of the
corner classifier was measured by ranking the donors by their
distance to the boundary. Donors that were inside the boundary were
assigned a negative distance. This ranked list was used to compute
an AUC under the ROC for the classifier. This AUC will be referred
to as the `minimum distance AUC`.
[0585] A "bagging", aka "bootstrapped aggregation", was used to
internally cross-validate the results of the above statistical
model. Bootstrap resamples were drawn 1,000 times. For each
resample a new corner classifier was computed, which was used to
predict the class membership of those patients excluded from the
resample. After repeating the resampling operation, each patient
acquires a list of predicted class memberships based on classifiers
computed using other patients. These predicted values were used to
create an ROC curve and to calculate its AUC, which will be
referred to as the `Bootstrap AUC`. The minimum distance AUC and
bootstrap AUC together provide an estimate of the accuracy as well
as the robustness of a combination of node/metrics.
[0586] Results
[0587] First Study:
[0588] a) Patient and Sample Characteristics.
[0589] Thirty-four evaluable AML PBMC samples were tested in the
first study (Table 16 and 19). The sample set in this study was
biased toward younger (<60 years), female patients whose
leukemia did not respond to induction chemotherapy. Compared to the
typical distribution of AML patients, Asian ethnicity (29%) and
intermediate-risk cytogenetic (76%) samples were overrepresented,
though ethnicity was in alignment with the Toronto population.
Furthermore, 10 of 18 (56%) cytogenetically normal (CN) samples
tested expressed the FLT3 ITD phenotype, overall indicating a poor
prognostic group of patients.sup.17-19.
TABLE-US-00020 SUPPLEMENTAL TABLE 19 Demographic and Baseline
Characteristics for All Patients (Intend To Diagnose) in Both
Studies Characteristic CR 1 NR 1 All Pts 1 P1 CR 2 NR 2 All Pts 2
P2 N 10 25 35 88 46 134 Age (yr) Median 59.9 47.4 49.8 0.050 51.8
61.7 55.5 Range 38.2-74.8 20.7-70.2 20.7-74.8 27.0-79.0 25.0-85.2
25.0-85.2 <.001 Age Group <60 yr 5 (50%) 20 (80%) 25 (71%)
0.107 71 (81%) 22 (48%) 93 (69%) <.001 >=60 yr 5 (50%) 5
(20%) 10 (29%) 17 (19%) 24 (52%) 41 (31%) Sex F 7 (70%) 14 (56%) 21
(60%) 0.704 46 (52%) 24 (52%) 70 (52%) 1.000 M 3 (30%) 11 (44%) 14
(40%) 42 (48%) 22 (48%) 64 (48%) Cytogentic Group Favorable 0 (0%)
1 (4%) 1 (3%) 0.588 10 (11%) 0 (0%) 10 (7%) <.001 Intermediate 9
(90%) 18 (72%) 27 (77%) 48 (55%) 12 (26%) 60 (45%) Unfavorable 0
(0%) 3 (12%) 3 (9%) 30 (34%) 34 (74%) 64 (48%) Not Done 1 (10%) 3
(12%) 4 (11%) 0 (0%) 0 (0%) 0 (0%) FAB M0 0 (0%) 2 (8%) 2 (6%)
0.316 2 (2%) 1 (2%) 3 (2%) 0.697 M1 2 (20%) 2 (8%) 4 (11%) 13 (15%)
3 (7%) 16 (12%) M2 1 (10%) 5 (20%) 6 (17%) 31 (35%) 22 (48%) 53
(40%) M4 1 (10%) 7 (28%) 8 (23%) 21 (24%) 11 (24%) 32 (24%) M5 4
(40%) 2 (8%) 6 (17%) 15 (17%) 5 (11%) 20 (15%) M6 0 (0%) 0 (0%) 0
(0%) 3 (3%) 2 (4%) 5 (4%) Other & Unk. 2 (20%) 7 (28%) 9 (26%)
3 (3%) 2 (4%) 5 (4%) Race White 4 (40%) 17 (68%) 21 (60%) 0.306 30
(34%) 20 (43%) 50 (37%) 0.473 Other & Unk.* 6 (60%) 8 (32%) 14
(40%) 58 (66%) 26 (57%) 84 (63%) FLT3-ITD Negative 4 (40%) 14 (56%)
18 (51%) 0.615 67 (76%) 35 (76%) 102 (76%) 0.867 Positive 5 (50%)
10 (40%) 15 (43%) 17 (19%) 8 (17%) 25 (19%) Unknown 1 (10%) 1 (4%)
2 (6%) 4 (5%) 3 (7%) 7 (5%) Secondary AML No 9 (90%) 25 (100%) 34
(97%) 0.286 73 (83%) 20 (43) 93 (69%) <.001 Yes 1 (10%) 0 (0%) 1
(3%) 15 (17%) 26 (57%) 41 (31%) Poor Prognosis.dagger. No 2 (20%)
11 (44%) 13 (37%) 0.184 28 (32%) 3 (7%) 31 (23%) <.001 Yes 8
(80%) 14 (56%) 22 (63%) 60 (68%) 43 (93%) 103 (77%) Induction
Therapy 7 + 3 Ara- 10 (100%) 25 (100%) 35 (100%) n/a 0 (0%) 0 (0%)
0 (0%) C/Dauno Fludarabine + 0 (0%) 0 (0%) 0 (0%) 18 (20%) 2 (4%)
20 (15%) 0.075 HDAC IA + Zarnestra 0 (0%) 0 (0%) 0 (0%) 20 (23%) 11
(24%) 31 (23%) IDA + HDAC 0 (0%) 0 (0%) 0 (0%) 24 (27%) 15 (33%) 39
(29%) Other 0 (0%) 0 (0%) 0 (0%) 26 (30%) 18 (39%) 44 (33%) There
were 38 primary refractory patients and 8 failed patients in Study
No 2. The two sample t-test was used to compare mean ages of CR and
NR patients. Fishers Exact test was used to compare CR and NR
patient samples with respect to categorical variables with two
levels. The standard Chi-Square test was used to compare CR and NR
patients with respect to categorical variables with three or more
levels. *The "Other" values for race are based on Black, Asian, and
Hispanic sub groups .dagger.Poor prognosis is defined as having one
ore more of the following high risk features: age >60 years,
unfavorable cytogenetics, FLT3 ITD positive or secondary AML
[0590] b) Assay Reproducibility.
[0591] Good correlation (Pearson coefficient.gtoreq.0.8) was found
between the data from the repeated assays (covering the thawing,
stimulating, staining, gating and data analysis steps of the
assays) performed using duplicate vials. As expected, assay
reproducibility was better for nodes with a large range of
signaling (not shown) as measured by standard deviation (SD), e.g.
read outs for: SCF/p-Akt, FLT3L/p-Akt and G-CSF/p-Stat5.
Node/metric combinations with less reproducible results included
those with a very low range of signaling and SD, including
G-CSF/p-Stat1, II27/p-CREB, SDF1-.alpha./p-Erk (Table 20).
TABLE-US-00021 TABLE 20 Reproducibility: Study No. 1 Biological
Num. Pearson Spearman SD Node: Modulator/Read-Out Metric Category
Pts Coefficient Coefficient R2 Value FLT3L/p-Akt Fold CCG 34 0.92
0.82 0.84 0.59 FLT3L/p-Akt TotalPhospho CCG 34 0.92 0.94 0.85 0.95
FLT3L/p-Erk Fold CCG 34 0.69 0.56 0.48 0.23 FLT3L/p-Erk
TotalPhospho CCG 34 0.63 0.61 0.39 0.58 FLT3L/p-S6 Fold CCG 34 0.92
0.72 0.84 0.70 FLT3L/p-S6 TotalPhospho CCG 34 0.84 0.82 0.70 0.86
G-CSF/p-Stat1 Fold CCG 33 0.14 0.19 0.02 0.18 G-CSF/p-Stat1
TotalPhospho CCG 33 0.30 0.47 0.09 0.26 G-CSF/p-Stat3 Fold CCG 33
0.85 0.83 0.73 1.01 G-CSF/p-Stat3 TotalPhospho CCG 33 0.80 0.76
0.64 1.14 G-CSF/p-Stat5 Fold CCG 33 0.86 0.76 0.74 0.97
G-CSF/p-Stat5 TotalPhospho CCG 33 0.87 0.85 0.76 1.25
IFN.alpha./p-Stat1 Fold CCG 34 0.59 0.55 0.34 0.47
IFN.alpha./p-Stat1 TotalPhospho CCG 34 0.73 0.72 0.54 0.52
IFN.alpha./p-Stat3 Fold CCG 34 0.77 0.79 0.59 0.56
IFN.alpha./p-Stat3 TotalPhospho CCG 34 0.73 0.71 0.53 0.74
IFN.alpha./p-Stat5 Fold CCG 34 0.75 0.78 0.57 0.85
IFN.alpha./p-Stat5 TotalPhospho CCG 34 0.92 0.92 0.85 1.30
IFN.gamma./p-Stat1 Fold CCG 34 0.52 0.49 0.27 0.67
IFN.gamma./p-Stat1 TotalPhospho CCG 34 0.69 0.66 0.47 0.71
IFN.gamma./p-Stat3 Fold CCG 34 0.52 0.39 0.27 0.28
IFN.gamma./p-Stat3 TotalPhospho CCG 34 0.56 0.62 0.32 0.42
IFN.gamma./p-Stat5 Fold CCG 34 0.46 0.52 0.21 0.52
IFN.gamma./p-Stat5 TotalPhospho CCG 34 0.82 0.82 0.68 0.83
IL-27/p-CREB Fold CCG 34 0.34 0.37 0.11 0.21 IL-27/p-CREB
TotalPhospho CCG 34 0.78 0.78 0.61 0.74 IL-27/p-Erk Fold CCG 34
0.01 -0.05 0.00 0.18 IL-27/p-Erk TotalPhospho CCG 34 0.78 0.66 0.61
0.72 IL-27/p-S6 Fold CCG 34 0.21 0.10 0.04 0.06 IL-27/p-S6
TotalPhospho CCG 34 0.70 0.82 0.48 0.40 none/p-Akt Basal CCG 34
0.94 0.96 0.89 0.57 none/p-CREB Basal CCG 34 0.81 0.73 0.66 0.72
none/p-Erk (AF647) Basal CCG 34 0.93 0.90 0.86 0.67 none/p-Erk (PE)
Basal CCG 34 0.72 0.69 0.52 0.52 none/p-S6 Basal CCG 34 0.83 0.79
0.68 0.36 none/p-Stat1 Basal CCG 34 0.42 0.53 0.17 0.21
none/p-Stat3 Basal CCG 34 0.49 0.53 0.24 0.37 none/p-Stat5 Basal
CCG 34 0.88 0.88 0.77 0.82 PMA/p-CREB Fold CCG 34 0.85 0.85 0.73
0.92 PMA/p-CREB TotalPhospho CCG 34 0.86 0.90 0.75 1.23 PMA/p-Erk
Fold CCG 34 0.74 0.75 0.55 0.85 PMA/p-Erk TotalPhospho CCG 34 0.83
0.81 0.70 1.21 PMA/p-S6 Fold CCG 34 0.95 0.95 0.90 0.86 PMA/p-S6
TotalPhospho CCG 34 0.92 0.94 0.85 0.82 SCF/p-Akt Fold CCG 34 0.86
0.83 0.74 0.53 SCF/p-Akt TotalPhospho CCG 34 0.93 0.91 0.87 0.71
SCF/p-Erk Fold CCG 34 0.39 0.39 0.15 0.18 SCF/p-Erk TotalPhospho
CCG 34 0.68 0.61 0.46 0.50 SCF/p-S6 Fold CCG 34 0.91 0.91 0.83 0.56
SCF/p-S6 TotalPhospho CCG 34 0.86 0.84 0.75 0.62 SDF-1.alpha./p-Akt
Fold CCG 34 0.87 0.85 0.76 0.42 SDF-1.alpha./p-Akt TotalPhospho CCG
34 0.91 0.90 0.83 0.70 SDF-1.alpha./p-Erk Fold CCG 34 0.38 0.49
0.15 0.22 SDF-1.alpha./p-Erk TotalPhospho CCG 34 0.58 0.53 0.34
0.64 SDF-1.alpha./p-S6 Fold CCG 34 0.12 0.17 0.01 0.09
SDF-1.alpha./p-S6 TotalPhospho CCG 34 0.66 0.59 0.44 0.35
Thapsigargin/p-CREB Fold CCG 34 0.89 0.91 0.80 0.70
Thapsigargin/p-CREB TotalPhospho CCG 34 0.90 0.89 0.81 0.95
Thapsigargin/p-Erk Fold CCG 34 0.94 0.56 0.88 0.43
Thapsigargin/p-Erk TotalPhospho CCG 34 0.94 0.89 0.87 0.89
Thapsigargin/p-S6 Fold CCG 34 0.91 0.79 0.82 0.40 Thapsigargin/p-S6
TotalPhospho CCG 34 0.86 0.81 0.74 0.50 Table is sorted
alphabetically by node Node/metrics with a t-test p value or
Wilcoxon p value of .ltoreq..05 and an AUC of .gtoreq..66 are shown
Metris are defined in Materials and Methods Abbreviations are
defined in Table 17
[0592] c) Univariate Analysis.
[0593] In the first study, 147 nodes were assessed for their
association with clinical response to standard AML induction
therapy. The chosen nodes represented four biologic categories
thought to be relevant to AML disease pathophysiology (FIG. 1): a)
nodes modulated by myeloid cytokines, chemokines and growth
factors; b) nodes modulated by intracellular phosphatases; c)
protein expression levels of drug transporters and surface myeloid
growth factor receptors; and d) nodes related to apoptosis. Each
node was assessed using 2-3 metrics, creating 304 node/metrics.
Univariate analysis, unadjusted for multiple testing, was performed
on all node/metrics, which were then ranked by AUC of the ROC
plots. Fifty-eight node/metrics (Table 21) from all four biological
categories had an AUC above 0.66 and a p value.ltoreq.0.05 (Student
t-test or Wilcoxon), a cut off chosen to be higher than the AUC of
the ROC plot for age (an accepted prognostic factor for this
disease). Sixty-six nodes were not considered candidates for future
development and remove prior to the second cohort due to low
induced signaling or high correlation with other nodes. As
expected, significant heterogeneity was found across most of the
nodes measured, highlighting both the diverse biology underlying
the disease and the ability of modulated SCNP to quantitatively
resolve this heterogeneity at the single cell level. Furthermore,
different populations of cells with differing degrees of
responsiveness were observed within a patient for a given
node/metric combination.
TABLE-US-00022 TABLE 21 Univariate Analysis of Node/Metrics for
Study No. 1 Num. AUC of Mean Value of Node: Modulator/Read-Out
Metric Biologic Category CRs/NRs t-test P Wilcoxon P ROC CRs/NRs
ABCG2 PercentPos Surface Markers 8/23 0.009 0.034 0.76 6.51/8.14
CD40L/p-CREB TotalPhospho CCG 9/25 0.004 0.003 0.83 1.55/2.66
CD40L/p-Erk TotalPhospho CCG 9/25 0.013 0.015 0.77 1.18/1.64 cKit
Rel. Expression Surface Markers 8/23 0.012 0.018 0.78 1.63/2.41
cKit PercentPos Surface Markers 8/23 0.047 0.082 0.71 41.6/59.6
EPO/p-Stat1 TotalPhospho CCG 9/25 0.053 0.037 0.74 0.20/0.42
EPO/p-Stat3 TotalPhospho CCG 9/25 0.003 0.002 0.84 0.72/1.23
Etoposide & ZVAD/c-Caspase 3 TotalPhospho Apoptosis 7/20 0.084
0.048 0.76 1.48/0.67 Etoposide & ZVAD/p-Chk2-, c-PARP+ Quad
Apoptosis 7/22 0.019 0.010 0.83 0.22/0.10 Etoposide/p-Chk2-,
c-PARP+ Quad Apoptosis 7/22 0.010 0.015 0.81 0.49/0.27 FLT3R
TotalPhospho Surface Markers 8/23 0.014 0.026 0.77 1.81/2.58 FLT3R
Rel. Expression Surface Markers 8/23 0.004 0.006 0.82 1.32/2.23
FLT3L/p-Akt Fold CCG 9/25 0.003 0.004 0.82 0.18/0.64 FLT3L/p-CREB
TotalPhospho CCG 9/25 0.014 0.012 0.78 1.50/2.12
FLT3L/p-plc.gamma.2 TotalPhospho CCG 9/25 0.007 0.006 0.80
1.88/2.80 FLT3L/p-S6 Fold CCG 9/25 0.026 0.154 0.66 0.28/0.81
G-CSF/p-Stat3 TotalPhospho CCG 9/25 0.056 0.050 0.72 1.66/2.70
G-CSF/p-Stat5 Fold CCG 9/25 0.038 0.072 0.71 0.47/1.13
GM-CSF/p-Stat3 TotalPhospho CCG 9/25 0.002 0.005 0.81 0.84/1.24
H.sub.2O.sub.2 & SCF/p-Erk TotalPhospho Phosphatase 7/22 0.047
0.122 0.70 2.16/2.57 H.sub.2O.sub.2 & SCF/p-plc.gamma.2 Fold
Phosphatase 7/22 0.102 0.032 0.77 0.47/-0.14 H.sub.2O.sub.2 &
SCF/p-SLP 76 Fold Phosphatase 7/22 0.026 0.042 0.76 1.37/0.06
H.sub.2O.sub.2/p-Lck Fold Phosphatase 7/22 0.163 0.050 0.75
0.42/0.12 H.sub.2O.sub.2/p-SLP 76 Fold Phosphatase 7/22 0.024 0.028
0.78 1.35/0.08 IFN.alpha./p-Stat1 Fold CCG 9/25 0.017 0.030 0.75
0.55/0.78 IFN.gamma./p-Stat1 Fold CCG 9/25 0.039 0.072 0.71
0.53/0.90 IFN.gamma./p-Stat3 TotalPhospho CCG 9/25 0.002 0.003 0.83
0.74/1.30 IGF-1/p-CREB TotalPhospho CCG 9/25 0.006 0.004 0.82
1.52/2.29 IGF-1/p-Plc.gamma.2 TotalPhospho CCG 9/25 0.006 0.005
0.81 1.91/2.76 IL-10/p-Stat1 TotalPhospho CCG 9/25 0.035 0.037 0.74
0.20/0.47 IL-10/p-Stat3 TotalPhospho CCG 9/25 0.001 0.002 0.84
0.82/1.69 IL-27/p-CREB TotalPhospho CCG 9/25 0.003 0.002 0.84
1.40/2.35 IL-27/p-Stat1 TotalPhospho CCG 9/25 0.001 0.003 0.83
0.41/0.82 IL-27/p-Stat3 TotalPhospho CCG 9/25 <0.001 <0.001
0.90 1.07/1.86 IL-3/p-CREB TotalPhospho CCG 9/25 0.004 0.002 0.84
1.64/2.57 IL-3/p-Stat1 Fold CCG 9/25 0.018 0.024 0.76 0.05/-0.01
IL-3/p-Stat3 Fold CCG 9/25 0.052 0.026 0.76 0.13/-0.05 IL-3/p-Stat3
TotalPhospho CCG 9/25 0.039 0.102 0.69 1.05/1.29 IL-6/p-CREB
TotalPhospho CCG 9/25 0.020 0.019 0.76 1.70/2.43 IL-6/p-Stat3
TotalPhospho CCG 9/25 0.001 0.015 0.77 1.08/1.84
M-CSF/p-Plc.gamma.2 TotalPhospho CCG 9/25 0.006 0.005 0.81
1.86/2.81 none/p-CREB Basal CCG 9/25 0.001 0.001 0.87 1.58/2.53
none/p-Erk Basal CCG 9/25 0.028 0.015 0.77 1.69/2.09
none/p-Plc.gamma.2 Basal CCG 9/25 0.008 0.009 0.79 1.73/2.48
none/p-Stat3 Basal CCG 9/25 0.005 0.005 0.81 0.89/1.33 none/p-Stat6
Basal CCG 9/25 0.008 0.019 0.76 0.62/0.96 SCF/p-Akt Fold CCG 9/25
0.018 0.007 0.81 0.12/0.57 SCF/p-CREB TotalPhospho CCG 9/25 0.016
0.030 0.75 1.38/1.92 SCF/p-Erk Fold CCG 9/25 0.043 0.041 0.73
-0.05/0.11 SCF/p-Erk TotalPhospho CCG 9/25 0.049 0.030 0.75
1.87/2.28 SCF/p-Plc.gamma.2 TotalPhospho CCG 9/25 0.006 0.006 0.80
1.87/2.81 SDF-1.alpha./p-Akt Fold CCG 9/25 0.025 0.067 0.71
0.20/0.53 SDF-1.alpha./p-Akt TotalPhospho CCG 9/25 0.045 0.120 0.68
0.57/1.04 SDF-1.alpha./p-Erk TotalPhospho CCG 9/25 0.056 0.041 0.73
1.80/2.28 Thapsigargin/p-CREB TotalPhospho CCG 9/25 0.034 0.027
0.75 1.90/2.76 Thapsigargin/p-S6 Fold CCG 9/25 0.021 0.076 0.70
0.04/0.32 Thapsigargin/p-S6 TotalPhospho CCG 9/25 0.018 0.045 0.73
0.31/0.68 TNF.alpha./p-Erk TotalPhospho CCG 9/25 0.033 0.050 0.72
1.25/1.65 Node/metrics with a t-test p value or Wilcoxon p value of
.ltoreq..05 and an AUC of .gtoreq..66 are shown Negative mean CR/NR
values represent down regulation as compared to
reference/control/normalization Table is sorted alphabetically by
node Metrics are defined in Materials and Methods Abbreviations are
defined in Supplemental Table 1
[0594] Importantly, measurements of basal levels of phosphorylated
signaling proteins, such as p-Stat5, p-Akt and p-S6, were not
informative in classifying patient samples by clinical response
(with AUC of the ROCs values of 0.62, 0.52, and 0.51, respectively
(Table 22). However, G-CSF, SCF or Flt3L mediated phosphorylation
resulted in significant increases in the Fold metric between
patient samples categorized by response and AUC of the ROC values,
which increased to 0.71, 0.82, and 0.66 respectively (Table 22),
allowing patient stratification into CR or NR categories. The
SCF/p-Akt read out is an example shown in FIG. 8A. These data
suggest that increased growth factor-mediated signaling occurred in
samples derived from NR patients, consistent with the previous
findings of Irish et al..sup.4 Interestingly, the basal expression
of cell surface receptors Flt3R and c-Kit also stratified patient
samples as CR versus NR with AUC of the ROC plots of 0.82 and 0.78
respectively, confirming a role for these receptors in treatment
prediction (Table 21).
TABLE-US-00023 TABLE 22 Modulated Readouts are More Predictive than
Basal in Study No. 1 Biologic Num. Mean Value of Node:
Modulator/Read-Out Metric Category CRs/NRs t-test P Wilcoxon P AUC
of ROC CRs/NRs none/p-Akt Basal CCG 9/25 0.644 0.908 0.52 0.48/0.58
FLT3L/p-Akt Fold CCG 9/25 0.003 0.004 0.82 0.18/0.64 SCF/p-Akt Fold
CCG 9/25 0.018 0.007 0.81 0.12/0.57 none/p-S6 Basal CCG 9/25 0.673
0.969 0.51 0.28/0.34 FLT3L/p-S6 Fold CCG 9/25 0.026 0.154 0.66
0.28/0.81 none/p-Stat5 Basal CCG 9/25 0.304 0.298 0.62 1.77/2.11
G-CSF/p-Stat5 Fold CCG 9/25 0.038 0.072 0.71 0.47/1.13 Metrics are
defined in Materials and Methods Abbreviations are defined in Table
17
[0595] Responses to DNA damage and apoptosis were determined by
measuring levels of p-Chk2.sup.43 and cleaved c-PARP respectively,
after exposure of samples to etoposide, a topoisomerase II
inhibitor. Notably, decreased levels of p-Chk2 and increased levels
of c-PARP were seen in CR samples, indicating that the DNA damage
response pathway was able to activate apoptosis in these patient
samples. In contrast, most NR samples showed accumulated levels of
p-Chk2 and low levels of c-PARP suggesting a block in the signals
that relay DNA damage to the apoptotic machinery. These data
suggest that an efficient relay of signals from the DNA damage
response pathway to the apoptotic machinery may be necessary for
response to induction therapy.
[0596] Because of the high number of variables tested on a
relatively small sample set, an assessment of false discovery rate
was performed (see Material and Methods). The number of observed
node/metrics with a Student Hest p.ltoreq.0.05 in our data set was
56, which is higher than expected after random assignment (not
shown). Therefore, the estimated probability that the number of
nodes found to be significant from the experimental data occurred
by chance is less than 0.02.
[0597] Sensitivity univariate analysis was performed to test the
effect of inclusion of the CRp sample within the NR sample cohort.
These analyses resulted in an increase in AUC of the ROC plots for
the majority of nodes examined, suggesting that the biology of the
blasts contained within the CRp sample was more similar to NR than
CR samples (Table 23).
TABLE-US-00024 TABLE 23 Sensitivity Analysis for Study No. 1:
Univariate Analysis of Node/Metrics with CRp Patient Included in NR
Group AUC of Mean Value of Num. Node Metric Biologic Category
t-test P Wilcoxon P ROC CRs/NRs CRs/NRs ABCG2 Rel. Expression
Surface Markers 0.002 0.022 0.79 0.14/0.33 7/24 ABCG2 PercentPos
Surface Markers 0.003 0.017 0.80 6.32/8.13 7/24 CD40L/p-CREB Total
Phospho CCG 0.001 <.001 0.89 1.37/2.67 8/26 CD40L/p-Erk Total
Phospho CCG 0.027 0.039 0.75 1.18/1.62 8/26 cKit Rel. Expression
Surface Markers 0.007 0.012 0.81 1.53/2.41 7/24 cKit Ppos CCG 0.024
0.033 0.77 38.42/59.75 7/24 EPO/p-Stat1 Total Phospho CCG 0.050
0.025 0.76 0.17/0.42 8/26 EPO/p-Stat3 Total Phospho CCG <.001
<.001 0.90 0.64/1.23 8/26 Etoposide + ZVAD/Chk2-PARP+ Quad
Apoptosis 0.044 0.025 0.80 0.23/0.11 6/23 Etoposide 24 h/Chk2-PARP+
Quad Apoptosis 0.026 0.025 0.80 0.49/0.28 6/23 FLT3L/p-Akt Fold CCG
<.001 <.001 0.90 0.10/0.65 8/26 FLT3L/p-CREB Fold CCG 0.013
0.096 0.70 0.07/0.36 8/26 FLT3L/p-CREB Total Phospho CCG 0.004
0.003 0.84 1.39/2.13 8/26 FLT3L/p-Erk Fold CCG 0.013 0.013 0.79
0.08/0.33 8/26 FLT3L/p-Plc.gamma.2 Total Phospho CCG 0.008 0.004
0.83 1.81/2.78 8/26 FLT3L/p-Plc.gamma.2 Fold CCG 0.144 0.049 0.74
-0.14/-0.08 8/26 FLT3L/p-S6 Fold CCG <.001 0.056 0.73 0.14/0.83
8/26 FLT3R Rel. Expression Surface Markers <.001 0.001 0.89
1.16/2.24 7/24 FLT3R PercentPos Surface Markers 0.009 0.008 0.83
49.72/76.39 7/24 FLT3R Total Phospho Surface Markers 0.037 0.061
0.74 1.84/2.55 7/24 G-CSF/p-Stat3 Fold CCG 0.010 0.031 0.75
0.60/1.52 8/26 G-CSF/p-Stat3 Total Phospho CCG 0.013 0.009 0.80
1.40/2.74 8/26 G-CSF/p-Stat5 Fold CCG 0.006 0.022 0.77 0.33/1.15
8/26 GM-CSF/p-Stat3 Total Phospho CCG 0.004 0.007 0.81 0.83/1.23
8/26 IFN.gamma./p-Stat1 Fold CCG 0.006 0.015 0.78 0.45/0.91 8/26
IFN.alpha./p-Stat1 Fold CCG 0.004 0.009 0.80 0.50/0.79 8/26
IFN.gamma./p-Stat1 Total Phospho CCG 0.027 0.012 0.79 0.67/1.27
8/26 IFN.gamma./p-Stat3 Total Phospho CCG 0.001 0.001 0.88 0.68/1.3
8/26 IFN.gamma./p-Stat5 Total Phospho CCG 0.058 0.043 0.74
1.62/2.35 8/26 IGF-1/p-CREB PE Total Phospho CCG 0.003 0.001 0.87
1.42/2.29 8/26 IGF-1/p-CREB Alexa488 Total Phospho CCG 0.097 0.053
0.73 1.11/1.62 8/26 IGF-1/p-Plc.gamma.2 Total Phospho CCG 0.004
0.003 0.84 1.82/2.76 8/26 Il-3/P-Stat1 Fold CCG 0.042 0.062 0.73
0.05/-0.01 8/26 IL-10/p-Stat1 Total Phospho CCG 0.033 0.025 0.76
0.17/0.47 8/26 IL-10/p-Stat3 Total Phospho CCG <.001 <.001
0.89 0.72/1.69 8/26 IL-27/p-CREB Total Phospho CCG <.001
<.001 0.90 1.25/2.36 8/26 IL-27/p-Stat1 Total Phospho CCG 0.002
0.003 0.84 0.39/0.81 8/26 IL-27/p-Stat3 Total Phospho CCG <.001
<.001 0.93 1.01/1.85 8/26 IL-3/p-CREB Total Phospho CCG 0.001
0.001 0.88 1.51/2.58 8/26 IL-3/p-Stat3 Fold CCG 0.062 0.042 0.75
0.15/-0.04 8/26 IL-6/p-CREB Total Phospho CCG 0.008 0.006 0.82
1.58/2.44 8/26 IL-6/p-Stat3 Total Phospho CCG 0.002 0.025 0.76
1.08/1.81 8/26 M-CSF/p-Akt Fold CCG 0.035 0.059 0.73 -0.16/0.05
8/26 M-CSF/p-CREB Total Phospho CCG 0.067 0.039 0.75 1.26/1.76 8/26
M-CSF/p-Plc.gamma.2 Total Phospho CCG 0.007 0.006 0.82 1.79/2.8
8/26 none/p-CREB Basal CCG <.001 <.001 0.92 1.47/2.53 8/26
none/p-Erk Basal CCG 0.051 0.035 0.75 1.69/2.07 8/26
none/p-Plc.gamma.2 Basal CCG 0.011 0.017 0.78 1.70/2.46 8/26
none/p-Stat3 Basal CCG 0.004 0.003 0.84 0.85/1.32 8/26 none/p-Stat6
Basal CCG 0.017 0.031 0.75 0.61/0.95 8/26 PMA/p-Erk Fold CCG 0.039
0.035 0.75 1.46/2.03 8/26 SCF/p-Akt Fold CCG 0.023 0.005 0.83
0.09/0.56 8/26 SCF/p-CREB Total Phospho CCG 0.013 0.020 0.77
1.32/1.92 8/26 SCF/p-Erk Fold CCG 0.040 0.031 0.75 -0.06/0.11 8/26
SCF/p-Plc.gamma.2 Total Phospho CCG 0.007 0.006 0.82 1.80/2.79 8/26
SDF-1.alpha./p-Akt Fold CCG 0.008 0.024 0.77 0.15/0.54 8/26
SDF-1.alpha./p-Akt Total Phospho CCG 0.034 0.077 0.71 0.52/1.04
8/26 SDF-1.alpha./p-Erk Total Phospho CCG 0.053 0.043 0.74
1.75/2.27 8/26 Thapsigargin/p-CREB Total Phospho CCG 0.025 0.015
0.78 1.79/2.77 8/26 Thapsigargin/p-S6 Fold CCG 0.018 0.051 0.73
0.03/0.31 8/26 Thapsigargin/p-S6 Total Phospho CCG 0.028 0.070 0.72
0.31/0.67 8/26 Table is sorted alphabetically by node Node/metrics
with a t-test p value or Wilcoxon p value of .ltoreq..05 and an AUC
of .gtoreq..66 are shown Negative mean CR/NR values represent down
regulation as compared to reference/control/normalization Metrics
are defined in Materials and Methods Abbreviations are defined in
Table 17
[0598] d) Correlations Between Nodes/Metric Combinations.
[0599] Although nodes were analyzed independently in the primary
analysis, several of the top-ranking node/metric combinations
appeared to be correlated with each other. The correlations between
nodes were studied for modulated signaling and surface marker
levels. The Pearson correlation coefficients using the fold metrics
were computed for all nodes with an AUC of the ROCs>0.66 and
p<0.05 to evaluate correlations of induced signaling. The heat
map of the pair wise correlation matrix (not shown) demonstrates
that some nodes, often mapping in the same pathway, such as
IL3/p-Stat1 and IL3/p-Stat3, and Flt3L/p-Akt and Flt3L/p-S6 were
highly correlated. Other nodes such as SCF/p-Akt and IL-3/Stat3
were independent of each other, suggesting that they may be
combined to compute a multivariate model with higher predictive
value. Notably, comparison of Flt3R and c-KitR expression levels to
their ligand-activated pathway readouts demonstrated a poor
correlation (i.e. <0.5 correlation coefficient, not shown).
These data underscore the additive value of measuring the modulated
signaling activity compared to measuring expression level of the
surface receptors associated with that specific pathway.
[0600] e) Combination of Nodes.
[0601] To evaluate nodes that might provide a superior
stratification when combined with each other, all node/metrics with
an AUC greater than equal to 0.66 were chosen to be part of
combination analysis. There were 4465 possible two-node/metric
combinations and 138415 possible three-node combinations.
Combinations that had a minimum distance AUC greater than the best
single node/metric (AUC=0.90) were analyzed further. Table 24
provides as list of nodes that appear most frequent (>3%) among
the two or three node/metric combinations. All triplets of nodes
with a minimum distance AUC great than 0.95 were also analyzed
using the bootstrap procedure described in material and methods.
Bootstrapping analysis (FIG. 9C) suggested that some of these
combinations might be more robust in distinguishing CRs from the
NRs (e.g. SDF1.alpha./p-Akt/Fold with IL-27/p-Stat3/TotalPhospho
and etoposide/p-Chk2-, c-PARP+/Quad). While no restrictions were
placed on the nodes chosen for each combination, several of the
highest ranking combinations contained nodes from multiple
biological pathways.
TABLE-US-00025 TABLE 24 List of Unique Nodes in Combinations for
Study No. 1. Frequency of Frequency of Node in Best AUC in Node in
Best AUC in AUC of Node included in Biological Two-Node Two-Node
Three-Node Three Node Single any Combination Model Metric Category
Combinations Combination Combinations Combinations Node cKit Rel.
Expression Surface Marker 17.07 0.98 17.25 1.00 0.78 IL-27/p-Stat3
TotalPhospho CCG 25.00 0.97 15.24 1.00 0.90 IL-3/p-Creb
TotalPhospho CCG 9.15 0.96 10.05 1.00 0.84 IGF-1/p-Plc.gamma.2
TotalPhospho CCG 8.54 0.95 9.28 1.00 0.81 ABCG2 Percent Pos.
Surface Marker 7.93 0.97 9.26 1.00 0.76 cKit Percent Pos. Surface
Marker 5.49 0.94 7.97 1.00 0.71 GM-CSF/p-Stat3 TotalPhospho CCG
5.49 0.92 7.42 1.00 0.81 FLT3R Rel. Expression Surface Marker 6.10
0.94 6.45 1.00 0.82 IL-6/p-Stat3 TotalPhospho CCG 2.44 0.93 6.37
1.00 0.77 IFN.gamma./p-Stat3 TotalPhospho CCG 7.32 0.95 5.85 1.00
0.83 FLT3R TotalPhospho Surface Marker 3.66 0.95 5.76 0.98 0.77
Etoposide/p-Chk2-, c-PARP+ Quad Apoptosis 4.88 0.95 5.71 1.00 0.81
Etoposide & ZVAD/p-Chk2-, c-PARP+ Quad Apoptosis 4.88 0.97 5.61
1.00 0.83 SCF/p-Akt Fold CCG 4.88 0.95 5.40 1.00 0.81 SCF/p-Erk
Fold CCG 3.05 0.92 5.06 1.00 0.73 Etoposide/c-PARP TotalPhospho
Apoptosis 2.44 0.95 4.96 1.00 0.71 Etoposide/BCL2 Fold Apoptosis
4.27 0.93 4.87 1.00 0.70 IL-27/p-Stat5 TotalPhospho CCG 1.83 0.93
4.82 1.00 0.66 FLT3L/p-Creb TotalPhospho CCG 4.27 0.98 4.62 1.00
0.78 none/p-Stat3 Basal CCG 3.05 0.93 4.60 1.00 0.81
IFN.alpha./p-Stat1 Fold CCG 2.44 0.96 4.38 1.00 0.75 Etoposide
& ZVAD/c-Caspase3 TotalPhospho Apoptosis 3.05 0.94 4.18 1.00
0.76 Etoposide/p-Chk2 Fold Apoptosis 1.83 0.94 4.05 1.00 0.73
none/p-Creb Basal CCG 5.49 0.93 3.94 0.98 0.87 EPO/p-Stat3
TotalPhospho CCG 4.88 0.95 3.91 0.98 0.84 IL-3/p-Stat3 TotalPhospho
CCG 2.44 0.92 3.83 0.99 0.69 FLT3L/p-Akt Fold CCG 5.49 0.96 3.59
0.99 0.82 Etoposide/p-Chk2+, c-PARP- Quad Apoptosis 1.83 0.93 3.57
1.00 0.74 H.sub.2O.sub.2/p-Lck Fold CCG 1.83 0.93 3.56 1.00 0.75
IGF-1/p-Creb TotalPhospho CCG 2.44 0.95 3.52 1.00 0.82 FLT3L/p-Erk
Fold CCG 1.22 0.92 3.44 1.00 0.72 Thapsigargin/p-Creb TotalPhospho
CCG 1.22 0.90 3.42 1.00 0.75 IL-10/p-Stat3 TotalPhospho CCG 4.88
0.94 3.41 0.98 0.84 CD40L/p-Creb TotalPhospho CCG 1.83 0.92 3.24
0.98 0.83 ABCG2 Rel. Expression Surface Marker 1.22 0.93 3.20 1.00
0.70 none/p-Chk2-, c-PARP+ Quad Apoptosis 1.22 0.93 3.19 1.00 0.69
All unique nodes with a minimum frequency of 3% are shown and table
is sorted by frequency. Metrics are defined in Materials and
Methods Abbreviations are defined in Table 17
[0602] Second Study:
[0603] The second study was performed to assess whether the
stratifying signaling profiles developed from the first study could
be extrapolated to a fully independent set of AML samples obtained
from a different center. In this sample set, 90 nodes were assessed
for association with clinical response to standard and high-dose
AML induction therapy using the same metrics as the first study.
Eighty-seven of the nodes overlapped with the first study (Table
17). Of these, 21 node/metrics were selected for the primary
endpoint analysis based on a multistep selection process that
considered univariate stratification power, reproducibility (when
available), node combination analysis and minimum representation in
the four biological categories relevant to AML disease
pathophysiology.
[0604] a) Patient and Sample Characteristics.
[0605] Of the 134 cryopreserved AML BMMC samples in the study, 46
samples were not evaluable due to insufficient viable cells after
thawing. In addition, due to the low recovery of viable cells after
thawing, the number of cells per sample varied and many samples did
not yield enough cells to analyze all planned nodes (Table 17).
Both the original 134 and the analyzed sample set in this study
(n=88) were representative of the United States AML patient
population and response rates, except for an over-representation of
female gender and younger age at diagnosis (Table 16 and Table
19)]. As expected, age, cytogenetic groups and secondary
malignancies were statistically associated with response to
induction therapy (Table 16).
[0606] b) Univariate Analysis of Pre-Specified 21 Node/Metric
Selected from the First Study (Primary Endpoint).
[0607] Univariate analysis, unadjusted for multiple testing, was
performed on the 21 pre-specified node/metrics selected for their
performance in the first study, and ranked by p-value (Table 25).
Based on this analysis, only two node/metric combinations,
PMA/p-Erk Fold, and IL-27/p-Stat3 TotalPhoshpo had AUCs of the ROC
above 0.66 (0.67 and 0.68, respectively) and a p value.ltoreq.0.05
(0.047 and 0.048, respectively) in stratifying patients for
response to induction therapy. Therefore, no further analysis using
these 21 pre-specified node/metrics combinations was performed.
TABLE-US-00026 TABLE 25 Extrapolation of Univariate Analysis for 21
Node/Metrics from Study No. 1 to Study No. 2 (Primary Endpoint
Analysis No. 2) Node: Biological Num. AUC of Wilcoxon Num. AUC of
Wilcoxon Modulator/Read-Out Metric Category CRs/NRs 1 ROC 1 t-test
P1 Test P1 CRs/NRs 2 ROC 2 t-test P2 Test P2 PMA/p-ERK Fold CCG
9/25 0.70 0.063 0.079 33/9 0.67 0.047 0.135 IL-27/p-Stat3
TotalPhospho CCG 9/25 0.90 <0.001 <0.001 44/13 0.68 0.073
0.048 H.sub.2O.sub.2/p-PLC.gamma.2 Fold Phosphatase 7/22 0.75 0.097
0.055 48/19 0.56 0.454 0.427 ABCG2 PercentPos Surface Marker 8/23
0.76 0.009 0.034 37/11 0.55 0.516 0.646 FLT3R Rel. Expression
Surface Marker 8/23 0.82 0.004 0.006 40/11 0.62 0.609 0.233
H.sub.2O.sub.2/p-SLP 76 Fold Phosphatase 7/22 0.78 0.024 0.028
48/18 0.59 0.287 0.238 SCF/p-Akt Fold CCG 9/25 0.81 0.018 0.007
51/24 0.60 0.081 0.178 CKit Rel. Expression Surface Marker 8/23
0.78 0.012 0.018 40/11 0.55 0.498 0.660 FLT3L/p-Akt Fold CCG 9/25
0.82 0.003 0.004 52/26 0.50 0.555 0.962 IFN.alpha./p-Stat1 Fold CCG
9/25 0.75 0.017 0.030 35/11 0.56 0.590 0.542 none/p-PLC.gamma.2
Basal CCG 9/25 0.79 0.008 0.009 47/16 0.55 0.666 0.526
Etoposide/p-Chk2-, Quadrant Apoptosis 7/22 0.81 0.010 0.015 43/19
0.57 0.425 0.396 c-PARP+ none/p-ERK Basal CCG 9/25 0.77 0.028 0.015
46/16 0.54 0.491 0.658 none/p-Stat3 Basal CCG 9/25 0.81 0.005 0.005
47/16 0.53 0.738 0.722 none/p-CREB Basal CCG 9/25 0.87 0.001 0.001
47/16 0.51 0.929 0.882 G CSF/p-Stat3 Fold CCG 9/25 0.68 0.091 0.111
47/17 0.51 0.974 0.951 SDF-1.alpha./p-Akt Fold CCG 9/25 0.71 0.025
0.067 39/22 0.59 0.293 0.273 G CSF/p-Stat5 Fold CCG 9/25 0.71 0.038
0.072 47/17 0.53 0.868 0.721 SCF/p-S6 Fold CCG 9/25 0.66 0.055
0.163 50/24 0.51 0.852 0.922 Thapsigargin/p-S6 Fold CCG 9/25 0.70
0.021 0.076 32/11 0.51 0.684 0.902 FLT3L/p-S6 Fold CCG 9/25 0.66
0.026 0.154 51/26 0.51 0.889 0.842 Metrics are defined in Materials
and Methods Abbreviations are defined in Table 17
[0608] c) Univariate Analysis of All Nodes/Metric Combinations
(Secondary Endpoint).
[0609] Univariate analysis, unadjusted for multiple testing, was
performed testing all 182 node-metric combinations and ranking them
by the resulting AUC of the ROCs. Seventeen node-metrics met the
cut-off criteria (i.e. AUC values above 0.66 with a p
value<0.05; Table 26). This number was lower than expected based
on the results of the first study but higher than expected by
chance.
TABLE-US-00027 TABLE 26 Univariate Analysis of Node/Metrics for All
Patients in Study No. 2 Biological Num. AUC of Mean Value Node:
Modulator/Read-Out Metric Category CRs/NRs ROC t-test P Wilcoxon P
of CRs/NRs Ara-C & Dauno/c-PARP Fold Apoptosis 35/11 0.67 0.042
0.089 1.99/0.82 Etoposide/c-PARP Fold Apoptosis 58/29 0.66 0.023
0.016 0.79/0.25 H.sub.2O.sub.2/p-Akt Fold Phosphatase 48/19 0.66
0.065 0.044 0.68/0.91 IFN.gamma./p-Stat3 Fold CCG 16/5 0.83 0.021
0.032 -0.02/0.2 IL-10/p-Stat3 Fold CCG 19/5 0.84 0.012 0.023
0.08/0.39 IL-10/p-Stat5 Fold CCG 19/5 0.80 0.011 0.044 0.09/0.43
IL-27/p-Stat1 TotalPhospho CCG 44/13 0.74 0.012 0.009 1.66/2.63
IL-27/p-Stat3 Fold CCG 44/14 0.71 0.032 0.019 0.22/0.58
IL-27/p-Stat3 TotalPhospho CCG 44/13 0.68 0.073 0.048 1.88/2.43
IL-3/p-Stat5 Fold CCG 9/5 0.78 0.022 0.112 1.99/0.44 IL-6/p-Stat1
Fold CCG 10/5 0.94 0.034 0.005 -0.01/0.26 IL-6/p-Stat3 Fold CCG
10/5 0.86 0.069 0.032 0.12/1.09 IL-6/p-Stat3 TotalPhospho CCG 10/5
0.88 0.083 0.019 1.76/2.98 IL-6/p-Stat5 Fold CCG 10/5 0.90 0.008
0.013 0.13/0.55 none/p-Erk Basal CCG 33/9 0.66 0.026 0.152
1.05/2.14 PMA/p-Erk Fold CCG 33/9 0.67 0.047 0.135 2.82/1.74
Thapsigargin/p-Erk Fold CCG 31/9 0.68 0.014 0.112 1.22/0.36 Table
is sorted alphabetically by node Node/metrics with a t-test p value
or Wilcoxon p value of .ltoreq..05 and an AUC of .gtoreq..66 are
shown Negative mean CR/NR values represent down regulation as
compared to reference/control/normalization Metrics are defined in
Materials and Methods Abbreviations are defined in Table 17
TABLE-US-00028 TABLE 27 Demographic and Baseline Characteristics
for All Patients (Intend To Diagnose) and Non-Evaluable Patients in
Study No. 2. Non- Non- All Non- P Value P Value Evaluable Evaluable
Evaluable Non- Characteristic All CRs All NRs All Pts All CRs NRs
Pts Eval N 88 46 134 31 15 46 Age (yr) Median 51.8 61.7 55.5 53.7
65 56.2 0.048 Range 27.0-97.0 25.0-85.2 25.0-85.2 <.001 28.2,
77.8 43.4, 85.2 28.2, 85.2 Age Group <60 yr 71 (81%) 22 (48%) 93
(69%) <.001 20 (65%) 7 (47%) 27 (59%) 0.341 >=60 yr 17 (19%)
24 (52%) 41 (31%) 11 (35%) 8 (53%) 19 (41%) Sex F 46 (52%) 24 (52%)
70 (52%) 1 14 (45%) 8 (53%) 22 (48%) 0.755 M 42 (48%) 22 (48%) 64
(48%) 17 (55%) 7 (47%) 24 (52%) Cytogentic Group Favorable 10 (11%)
0 (0%) 10 (7%) <.001 3 (10%) 0 (0%) 3 (7%) 0.005 Intermediate 48
(55%) 12 (26%) 60 (45%) 19 (61%) 3 (20%) 22 (48%) Unfavorable 30
(34%) 34 (74%) 64 (48%) 9 (29%) 12 (80%) 21 (46%) FAB M0 2 (2%) 1
(2%) 3 (2%) 0.697 1 (3%) 0 (0%) 1 (2%) 0.621 M1 13 (15%) 3 (7%) 16
(12%) 5 (16%) 2 (13%) 7 (15%) M2 31 (35%) 22 (48%) 53 (40%) 9 (29%)
8 (53%) 17 (37%) M4 21 (24%) 11 (24%) 32 (24%) 7 (23%) 3 (20%) 10
(22%) M5 15 (17%) 5 (11%) 20 (15%) 7 (23%) 1 (7%) 8 (17%) M6 3 (3%)
2 (4%) 5 (4%) 1 (3%) 0 (0%) 1 (2%) Other/Unknown 3 (3%) 2 (4%) 5
(4%) 1 (3%) 1 (7%) 2 (4%) Race White 30 (34%) 20 (43%) 50 (37%)
0.473 15 (48%) 5 (33%) 20 (43%) 0.346 Other & Unk.* 58 (66%) 26
(57%) 84 (63%) 16 (52%) 10 (66%) 26 (57%) FLT3-ITD Negative 67
(76%) 35 (76%) 102 (76%) 0.867 23 (74%) 12 (80%) 35 (76%) 0.602
Positive 17 (19%) 8 (17%) 25 (19%) 6 (19%) 3 (20%) 9 (20%) Unknown
0 (0%) 0 (0%) 0 (0%) 2 (6%) 0 (0%) 2 (4%) Secondary AML No 73 (83%)
20 (43) 93 (69%) <.001 26 (84%) 6 (40%) 32 (70%) 0.005 Yes 15
(17%) 26 (57%) 41 (31%) 5 (16%) 9 (60%) 14 (30%) Poor
Prognosis.dagger. No 28 (32%) 3 (7%) 31 (23%) <.001 6 (19%) 0
(0%) 6 (13%) 0.068 Yes 60 (68%) 43 (93%) 103 (77%) 25 (81%) 15
(100%) 40 (87%) Induction Therapy Fludarabine + 18 (20%) 2 (4%) 20
(15%) 0.075 7 (23%) 0 (0%) 7 (15%) 0.17 HDAC IA + Zarnestra 20
(23%) 11 (24%) 31 (23%) 2 (6%) 2 (13%) 4 (9%) IDA + HDAC 24 (27%)
15 (33%) 39 (29%) 7 (23%) 6 (40%) 13 (28%) Other 26 (30%) 18 (39%)
44 (33%) 15 (48%) 7 (47%) 22 (48%) *The "Other" values for race are
based on Black, Asian, and Hispanic sub groups .dagger.Poor
prognosis is defined as having one or of the following high risk
features: age >60 years, unfavorable cytogenetics, FLT3 ITD
positive or secondary AML
[0610] We hypothesized that this was a consequence of the higher
heterogeneity in demographic and base line characteristic present
in this sample set, compared to the first study (Table 16),
suggesting the need to examine the data using clinical
covariates.
[0611] d) Nodes Associated With Disease Response to Induction
Chemotherapy in Patient Subsets as Defined by Clinical
Covariates.
[0612] 1. Age:
[0613] Age, a covariate known to be associated with clinical
outcomes in AML, was independently used to test the node/metric
combinations for their association with clinical response to
induction therapy. Using age as a dichotomous criteria (<60
versus .gtoreq.60 years), 28 node/metrics stratified patients for
response to induction therapy in the <60 years patient group
(Table 28B). Despite the small sample set (n.about.20), analysis of
the older patient cohort samples also revealed unique nodes that
distinguished CR from NR samples in this study (Table 28A). These
included FLT3L induced increase in p-Erk and p-Akt and
H.sub.2O.sub.2 induced increase in p-AKT and p-PLC.gamma.2. Since
H.sub.2O.sub.2 is a tyrosine phosphatase inhibitor.sup.44 increases
in p-AKT and p-PLC.gamma.2 following H.sub.2O.sub.2 treatment
(phosphatase inhibition) in NR samples, suggests altered
phosphatase activity may be associated with refractory disease in
older patients. Furthermore, incorporation of age as a clinical
variable in combination with specific nodes (e.g. IL-27/p-Stat3)
increased the predictive value of either age or the node itself,
demonstrating the ability of multiparameter flow cytometry to
improve on age, an important clinical prognostic indicator for
response to induction chemotherapy (not shown).
TABLE-US-00029 TABLE 28 Univariate Analysis of Node/Metrics for
Study No. 2 within Age Sub-Groups Biological Num. AUC of Mean Value
Node: Modulator/Read-Out Metric Category CRs/NRs ROC t-test P
Wilcoxon P of CRs/NRs A: Patients age 60 and older FLT3L/p-Akt Fold
CCG 7/14 0.85 0.011 0.010 0.00/0.36 FLT3L/p-Erk Fold CCG 6/14 0.77
0.034 0.062 0.01/0.21 FLT3L/p-S6 Fold CCG 6/14 0.80 0.004 0.041
-0.06/0.67 H.sub.2O.sub.2/p-Akt Fold Phosphatase 7/9 0.78 0.029
0.071 0.45/0.88 H.sub.2O.sub.2/p-Akt TotalPhospho Phosphatase 7/9
0.79 0.026 0.055 0.84/1.33 H.sub.2O.sub.2/p-Plc.gamma.2
TotalPhospho Phosphatase 7/9 0.84 0.013 0.023 1.19/1.86
IL-27/p-Stat3 Fold CCG 6/8 0.83 0.091 0.043 -0.19/0.48 LPS/p-Erk
Fold CCG 2/5 1.00 0.026 0.095 -0.33/-0.16 SCF/p-S6 Fold CCG 6/13
0.74 0.030 0.106 0.14/0.70 B: Patients Less than 60 Years old Ara-C
& Dauno/p-Chk2-, c-PARP+ Quad Apoptosis 29/4 0.85 0.001 0.021
23.35/7.48 Etoposide/c-PARP Fold Apoptosis 49/14 0.74 0.115 0.007
0.89/0.28 Etoposide/p-Chk2-, c-PARP+ Quad Apoptosis 39/7 0.72 0.010
0.071 21.17/9.58 GM-CSF/p-Stat3 TotalPhospho CCG 8/2 1.00 0.069
0.044 1.51/2.35 IFN.alpha./p-Stat1 Fold CCG 33/4 0.75 0.050 0.114
1.72/2.60 IFN.alpha./p-Stat1 TotalPhospho CCG 33/4 0.82 0.059 0.039
2.67/3.84 IFN.alpha./p-Stat3 TotalPhospho CCG 33/4 0.79 0.014 0.065
2.62/3.44 IFN.gamma./p-Stat3 TotalPhospho CCG 14/2 1.00 <0.001
0.017 1.60/2.71 IFN.gamma./p-Stat1 Fold CCG 14/2 0.96 0.036 0.033
1.35/2.96 IFN.gamma./p-Stat1 TotalPhospho CCG 14/2 0.96 0.163 0.033
2.40/4.13 IFN.gamma./p-Stat5 Fold CCG 14/2 1.00 0.009 0.017
0.68/1.67 IL-10/p-Stat3 TotalPhospho CCG 17/2 1.00 0.007 0.012
1.67/2.90 IL-27/p-Stat1 TotalPhospho CCG 38/5 0.84 0.048 0.016
1.73/3.12 IL-27/p-Stat3 Fold CCG 38/6 0.80 0.080 0.019 0.29/0.72
IL-27/p-Stat3 TotalPhospho CCG 38/5 0.83 0.047 0.014 1.97/3.06
IL-6/p-Stat1 Fold CCG 9/2 1.00 0.202 0.036 -0.02/0.3 IL-6/p-Stat3
Fold CCG 9/2 1.00 0.271 0.036 0.13/1.67 IL-6/p-Stat3 TotalPhospho
CCG 9/2 1.00 0.172 0.036 1.77/4.10 IL-6/p-Stat5 Fold CCG 9/2 0.89
0.003 0.145 0.11/0.58 MRP-1 PercentPos Surface Markers 33/4 0.70
0.018 0.222 33.19/14.20 none/c-PARP TotalPhospho Apoptosis 14/2
0.96 0.305 0.033 1.80/-0.35 none/p-Erk Basal CCG 31/3 0.68 0.021
0.348 0.98/1.96 PMA/p-CREB Fold CCG 33/4 0.82 0.003 0.039 0.78/1.55
PMA/p-CREB TotalPhospho CCG 33/4 0.84 0.002 0.025 3.72/5.00
Staurosporine & ZVAD/Cytochrome-C TotalPhospho Apoptosis 10/2
1.00 0.107 0.030 6.40/8.27 Staurosporine/c-PARP Fold Apoptosis 6/2
1.00 0.036 0.071 3.47/7.06 Thapsigargin/p-CREB TotalPhospho CCG
30/4 0.83 0.024 0.031 2.83/3.71 Thapsigargin/p-Erk Fold CCG 29/3
0.67 0.019 0.365 1.28/0.40 Node/metrics with a t-test p value or
Wilcoxon p value of .ltoreq..05 and an AUC of .gtoreq..66 are
shown. Metrics are defined in Materials and Methods Abbreviations
are defined in Table 17
[0614] 2. Presence or Absence of Secondary AML:
[0615] Due to overlapping baseline disease characteristics of the
groups when stratified by age versus presence/absence of secondary
AML, the univariate analysis of samples group resulted in similar
stratifying nodes (Tables 28 and 29). This suggests that at least
in this sample set, age at diagnosis can be considered a surrogate
marker for different disease biology. When age was examined as a
variable across the secondary AML sample subset no correlation
between age and response to therapy was found (FIG. 9), suggesting
that the underlying biology of secondary AML is different from that
of de novo AML, and age is not prognostic for response in secondary
AML.
TABLE-US-00030 TABLE 29 Univariate Analysis of Node/Metrics for
Study No. 2 within De Novo and Secondary AML Sub-Groups Biologic
Num. AUC of Mean Value Node: Modulator/Read-out Metric Category
CRs/NRs ROC t-test P Wilcoxon P of CRs/NRs A: Patients with De Novo
AML Etoposide/p-Chk2 Fold Apoptosis 46/14 0.67 0.033 0.058
0.59/0.27 FLT3L/p-PLCy2 TotalPhospho CCG 4/3 1.00 0.023 0.057
1.26/1.95 GM-CSF/pStat3 TotalPhospho CCG 8/4 0.97 0.007 0.008
1.51/2.22 IFN.gamma./p-Stat3 Fold CCG 14/4 0.89 0.014 0.018
-0.04/0.24 IFN.gamma./p-Stat3 TotalPhospho CCG 14/4 0.89 0.026
0.018 1.59/2.48 IL-10/p-Stat3 Fold CCG 17/4 0.93 0.005 0.011
0.05/0.45 IL-10/p-Stat3 TotalPhospho CCG 17/4 0.93 0.014 0.006
1.63/2.68 IL-10/p-Stat5 Fold CCG 17/4 0.84 0.027 0.04 0.06/0.43
IL-3/p-Stat1 TotalPhospho CCG 8/4 0.88 0.040 0.048 1.03/1.71
IL-3/p-Stat3 TotalPhospho CCG 8/4 0.88 0.134 0.048 1.46/2.40
IL-3/p-Stat5 Fold CCG 8/4 0.78 0.048 0.154 1.87/0.39 IL-6/p-Stat1
Fold CCG 8/4 0.91 0.088 0.028 0.00/0.19 IL-6/p-Stat1 TotalPhospho
CCG 8/4 0.88 0.026 0.048 1.06/1.67 IL-6/p-Stat3 TotalPhospho CCG
8/4 0.91 0.092 0.028 1.77/3.22 IL-6/p-Stat5 Fold CCG 8/4 0.88 0.023
0.048 0.12/0.47 none/p-Erk Basal CCG 30/6 0.78 0.015 0.029
0.97/2.48 none/p-Stat6 Basal CCG 16/4 0.88 0.077 0.026 1.02/1.34
SCF/p-Akt Fold CCG 44/11 0.76 0.001 0.008 0.52/-0.20 SCF/p-PLCy2
TotalPhospho CCG 4/3 1.00 0.037 0.057 1.29/1.96 SCF/p-S6 Fold CCG
43/11 0.67 0.013 0.098 1.05/0.43 SDF-1.alpha./p-CREB TotalPhospho
CCG 26/3 0.87 0.115 0.037 3.13/1.92 Stauro & ZVAD/Cytochrome C
TotalPhospho Apoptosis 10/4 0.90 0.092 0.024 6.40/8.04
Thapsigargin/p-Erk Fold CCG 28/6 0.74 0.010 0.067 1.28/0.27 B:
Patients with Secondary AML Etoposide/p-Chk2-, c-PARP+ Quad
Apoptosis 8/9 0.83 0.026 0.021 32.71/13.24 Etoposide/p-Chk2+,
c-PARP- Quad Apoptosis 8/9 0.85 0.012 0.015 20.98/55.02 FLT3L/p-Akt
Fold CCG 8/13 0.77 0.025 0.045 0.19/0.60 FLT3L/p-Erk Fold CCG 8/13
0.82 0.004 0.019 0.00/0.32 FLT3L/p-S6 Fold CCG 8/13 0.78 0.006
0.037 0.12/1.02 FLT3R Rel. Expression Surface Marker 5/5 0.88 0.042
0.056 1.23/1.10 G-CSF/p-Stat1 Fold CCG 6/10 0.75 0.049 0.118
0.00/0.36 G-CSF/p-Stat3 Fold CCG 6/10 0.78 0.024 0.073 0.06/0.96
G-CSF/p-Stat5 Fold CCG 6/10 0.70 0.044 0.193 0.08/1.07
G-CSF/p-Stat5 TotalPhospho CCG 6/9 0.78 0.047 0.088 2.58/3.91
IFN.alpha./p-Stat1 Fold CCG 3/5 1.00 0.020 0.036 0.91/2.63
IFN.alpha./p-Stat1 TotalPhospho CCG 3/5 1.00 0.013 0.036 2.01/3.59
IFN.alpha./p-Stat3 Fold CCG 3/5 1.00 0.002 0.036 0.23/1.01
IFN.alpha./p-Stat5 TotalPhospho CCG 3/5 1.00 0.022 0.036 3.03/4.60
IL-27/p-Stat1 Fold CCG 6/8 0.83 0.014 0.043 0.32/1.90 IL-27/p-Stat1
TotalPhospho CCG 6/7 0.88 0.013 0.022 1.50/3.19 IL-27/p-Stat3 Fold
CCG 6/8 0.98 0.001 0.001 -0.01/0.76 IL-27/p-Stat3 TotalPhospho CCG
6/7 0.79 0.048 0.101 1.61/2.60 none/p-Chk2-, c-PARP+ Quad Apoptosis
7/11 0.81 0.062 0.035 31.05/13.79 PMA/p-CREB Fold CCG 3/5 1.00
0.010 0.036 0.04/1.27 SCF/p-S6 Fold CCG 7/13 0.84 0.001 0.014
0.21/1.28 Node/metrics with a t-test p value or Wilcoxon p value of
.ltoreq..05 and an AUC of .gtoreq..66 are shown Negative mean CR/NR
values represent down regulation as compared to
reference/control/normalization Metrics are defined in Materials
and Methods Abbreviations are defined in Table 17
[0616] 3. Cytogenetics:
[0617] Since cytogenetic group was a predictive clinical covariate
with all patients in the favorable cytogenetic group demonstrating
a CR, we evaluated whether nodes could predict response after
incorporation of cytogenetic group as a covariate for the patients
with intermediate and high-risk cytogenetics. Within the
limitations of the small sample set, several nodes, including the
IL-27/p-Stat1, p-Stat3 and p-Stat5 nodes, could significantly add
to the predictive value of cytogenetic group (Table 30). As
expected, FLT3 mutational status was not predictive of response to
induction therapy in this data set (Table 16 and Table 31).
TABLE-US-00031 TABLE 30 Univariate Analysis of Node/Metrics for
Study No. 2 for Patients with Intermediate or High Risk
Cytogenetics with Cytogenetic Group as a Covariate. P value P Value
Biologic Num. AUC for AUC AUC for AUC of P Value Node:
Modulator/Read-Out Metric Category CRs/NRs model model Cyto Cyto
Node Node Ara-C & Dauno/p-Chk2-, c-PARP- Quad Apoptosis 29/11
0.74 0.009 0.60 0.042 0.57 0.036 H.sub.2O.sub.2/p-Akt Fold
Phosphatase 42/19 0.8 <0.001 0.69 0.022 0.66 0.026
H.sub.2O.sub.2/p-Slp 76 Fold Phosphatase 42/18 0.78 <0.001 0.72
0.007 0.59 0.071 IFN.gamma./p-Stat3 Fold CCG 16/5 0.84 0.01 0.54
0.532 0.83 0.056 IL-10/p-Stat3 Fold CCG 19/5 0.84 0.01 0.55 0.548
0.84 0.058 IL-27/p-Stat1 TotalPhospho CCG 39/13 0.81 <0.001 0.66
0.040 0.74 0.019 IL-27/p-Stat1 Fold CCG 39/14 0.76 0.002 0.66 0.015
0.66 0.038 IL-27/p-Stat3 Fold CCG 39/14 0.81 <0.001 0.66 0.009
0.71 0.010 IL-27/p-Stat3 TotalPhospho CCG 39/13 0.76 0.002 0.66
0.024 0.68 0.072 IL-27/p-Stat5 Fold CCG 39/14 0.78 0.001 0.66 0.009
0.62 0.041 IL-27/p-Stat5 TotalPhospho CCG 38/13 0.76 0.003 0.65
0.032 0.62 0.052 IL-6/p-Stat5 Fold CCG 10/5 0.98 0.001 0.60 0.243
0.94 0.089 SDF-1.alpha./p-CREB Fold CCG 33/22 0.74 0.001 0.67 0.090
0.69 0.033 SDF-1.alpha./p-CREB TotalPhospho CCG 26/9 0.84 0.001
0.75 0.023 0.66 0.090 Table is sorted alphabetically by node
Node/metrics with a t-test p value or Wilcoxon p value of
.ltoreq..05 and an AUC of .gtoreq..66 are shown Metrics are defined
in Materials and Methods Abbreviations are defined in Supplemental
Table 1
TABLE-US-00032 TABLE 31 Demographic and Baseline Characteristics of
Intermediate and High Risk Cytogenetic Groups in Study No. 2 Int.
Risk Int. Risk All Int. Int. Risk High Risk High Risk All High High
Risk Characteristic CRs NRs Risk Pts P-Value CRs NRs Risk Pts.
P-Value N 29 9 38 21 22 43 Age (yr) Median 53.6 59.3 56.1 0.071
51.2 61.7 55.8 0.143 Range 27.0-79.0 45.6-68.6 27.0-79.0 34.8-77.8
25.0-76.3 25.0-77.8 Age Group <60 yr 26 (90%) 5 (56%) 31 (82%)
0.041 18 (86%) 10 (45%) 28 (65%) 0.01 >=60 yr 3 (10%) 4 (44%) 7
(18%) 3 (14%) 12 (55%) 15 (35%) Sex F 17 (59%) 6 (67%) 23 (61%) 1
11 (52%) 10 (45%) 21 (49%) 0.763 M 12 (41%) 3 (33%) 15 (39%) 10
(48%) 12 (55%) 22 (51%) FAB M0 0 (0%) 0 (0%) 0 (0%) 1 (5% ) 1 (5%)
2 (5%) 0.831 M1 6 (21%) 1 (11%) 7 (18%) 0.943 2 (10%) 0 (0%) 2 (5%)
M2 11 (38%) 4 (44%) 15 (39%) 8 (38%) 10 (45%) 18 (42%) M4 5 (17%) 2
(22%) 7 (18%) 5 (24%) 6 (27%) 11 (26%) M5 5 (17%) 2 (22%) 7 (18%) 3
(14%) 2 (9%) 5 (12%) M6 1 (3%) 0 (0%) 1 (3%) 1 (5%) 2 (9%) 3 (7%)
Other/Unknown 1 (3%) 0 (0%) 1 (3%) 1 (5%) 1 (5%) 2 (5%) Race White
7 (24%) 5 (56%) 12 (32%) 0.362 4 (19%) 10 (45%) 14 (33%) 0.141
Other & 22 (76%) 4 (44%) 26 (68%) 17 (81%) 12 (55%) 29 (66%)
Unknown* FLT3-ITD Negative 20 (69%) 6 (67%) 26 (68%) 0.821 17 (81%)
17 (77%) 34 (79%) 0.555 Positive 8 (28%) 3 (33%) 11 (29%) 3 (14%) 2
(9%) 5 (12%) Unknown 1 (3%) 0 (0%) 1 (3%) 1 (5%) 3 (14%) 4 (9%)
Secondary AML No 25 (86%) 5 (56%) 30 (79%) 0.071 16 (76%) 9 (41%)
25 (58%) 0.031 Yes 4 (14%) 4 (44%) 8 (21%) 5 (24%) 13 (59%) 18
(42%) Poor Prognosis.dagger. No 16 (55%) 3 (33%) 19 (50%) 0.252 0
(0%) 0 (0%) 0 (0%) Yes 13 (45%) 6 (67%) 19 (50%) 21 (100%) 22
(100%) 43 (100%) Induction Fludarabine + 0 (0%) 0 (0%) 0 (0%) 4
(19%) 2 (9%) 6 (14%) 0.691 Therapy HDAC IA + Zarnestra 12 (41%) 3
(33%) 15 (39%) 0.492 6 (29%) 6 (27%) 12 (28%) IDA + HDAC 10 (34%) 2
(22%) 12 (32%) 7 (33%) 7 (32%) 14 (33%) Other 7 (24%) 4 (44%) 11
(29%) 4 (19%) 7 (32%) 11 (26%) The two-sample t test was used to
compare mean ages of CR and NR patients. Fisher's Exact test was
used to compare CR and NR patients with respect to categorical
variables with two levels. The standard Chi-Square test was used to
compare CR and NR patients with respect to categorical variables
with three or more levels. *The "Other" values for race are based
on Black, Asian, and Hispanic sub groups .dagger.Poor prognosis is
defined as having one or more of the following high risk features:
age >60 years, unfavorable cytogenetics, FLT3 ITD positive or
secondary AML
[0618] Discussion
[0619] The two studies reported here show that AML characterization
using modulated SCNP can be performed with high technical accuracy
and reproducibility to quantitatively characterize the biology of
AML in individual patients. Furthermore, this characterization is
predictive of disease outcome in response to specific therapeutic
interventions and distinct from other known prognostic factors
(such as age, secondary AML and cytogenetics). Basal protein
expression profiling patterns as measured by RPPA in AML was
recently shown to correlate with known morphologic features,
cytogenetics and clinical outcomes (Kornblau et al. Blood. 2009;
113:154-164). While these studies show high sensitivity,
throughput, and reproducibility for baseline measurements they
cannot provide any evaluation of the dynamic response to stimuli of
a specific cell population or of single cells in a heterogeneous
cell population. Resistance or relapse is thought to arise from
rare populations of blasts with different characteristics that
enable them to survive induction therapy. We therefore hypothesize
that the ability to measure the adaptability of individual cells
(or subpopulations) to different modulation and assessing
intra-patient clonal heterogeneity, will provide knowledge with
greater informative content and relevance with respect to
responsiveness and the crucial characteristics that give rise to
disease persistence.
[0620] The data presented are from two independent, sequentially
tested patient sample sets (total n=122) obtained from the leukemic
cell banks of two centers, PMH/UHN and MDACC. The sets differ
substantially in sample number, source of leukemic cells and
patient clinical characteristics. The first, smaller study tested
PBMCs, collected from predominately female patients<60 years,
whose disease did not respond to standard induction chemotherapy.
The second training study included 88 evaluable BMMC AML samples
obtained mostly from patients<60 years old, with a more typical
rate of responsiveness to cytarabine (plus additional drugs in
most) based induction therapy.
[0621] The differences in source of leukemic blasts and induction
therapy were hypothesized to be unimportant for the interpretation
of the study results. It has previously been shown that protein
levels in AML cells do not appear to exhibit biologically relevant
differences between specimen sources (Kornblau et al. Blood. 2009;
113:154-164) and clinical outcome appears to be independent of
cytarabine dose (100 mg/m.sup.2-3 g/m.sup.2) (Sekeres et al. Blood.
2009; 113:28-36). Both patient cohorts lacked sufficient leukemia
samples from older patients responsive to induction chemotherapy
limiting the strength of the observations for this subset of
patients.
[0622] Despite the above limitations, many important observations
could be made: First the SCNP assay demonstrates the level of
robustness and reproducibility needed for clinical application. The
first study began with a large panel of nodes selected for their
role in myeloid biology. In particular, pathways known to be
altered in multiple malignancies and involved in cell survival,
proliferation and DNA damage were probed. Throughout normal myeloid
differentiation these pathways are tightly regulated by a variety
of cytokines and growth factors used in SCNP assays. For example,
SCF and Flt3L are important for maintaining the hematopoietic stem
cell pool (Lyman et al. Blood. 1998; 91:1101-1134; Kikushige et al.
J Immunol 2008; 180:7358-7367); G-CSF is important for neutrophilic
differentiation of hematopoietic progenitor cells (Touw et al.
Front Biosci. 2007; 12:800-815); IL-6 family members including IL-6
and IL-27 regulate proliferation, differentiation and functional
maturation of cells belonging to multiple hematopoietic lineages
(Seita et al. Blood. 2008; 111:1903-1912) and IL-10 modulates the
immune response of monocytes and macrophages and was previously
shown to play a role in AML blast proliferation (Bruserud et al.
Cytokines Cell Mol. Ther. 1998; 4:187-198). Consistent with this
knowledge, the first training study univariate analysis identified
58/304 statistically significant node/metrics (i.e. AUC of the
ROC>0.66 with a p value<0.05), predictive for clinical
response to induction therapy. These included G-CSF induced
Jak/Stat signaling, previously shown to be potentiated in AML
(Irish et al. Cell. 2004; 118:217-228) and new observations of
IL-27, IL-10 and IL-6 mediated signaling. Furthermore, transformed
cells evade apoptosis by activating survival pathways or by
disabling apoptotic DNA damage machinery or signaling. Therefore,
Caspase-dependent apoptosis was also used to characterize patient
responses after in vitro exposure of AML samples to etoposide and
Ara-C/daunorubicin. Importantly both etoposide and
Ara-C/daunorubicin activated apoptosis were shown to stratify
patients by clinical outcome in both studies.
[0623] The external validity of these original observations was
then tested in the second training study, which included a larger
sample set that was more representative of the general US AML
population but more heterogeneous in terms of baseline disease
characteristics. The analysis of the data from the two studies
suggests that the difference in baseline characteristics of donors
in the two studies played a significant role in the differences
observed in the stratifying nodes between the two studies. However,
similar trends existed for some of the stratifying nodes (such as
p-Stat1 and p-Stat3 response to IL-27 and cleaved PARP to
etoposide) were observed across the two studies when similar
subsets of patients (although small) where compared. Another
important observation that emerged from this second study was the
ability of SCNP assays to reveal different pathways that correlated
with patient outcome within patient subgroups defined by clinical
prognostic characteristics such as age, cytogenetics and presence
or absence of secondary leukemia. Specifically, in patients younger
than 60 years of age, intact communication between DNA damage
response and apoptosis after in vitro exposure to chemotherapeutic
agents emerged as an important biologic characteristic that
identified CR samples. By contrast, for patients over age 60 or
with secondary AML lack of response to induction chemotherapy was
associated with increased Flt3L induced p-Akt and p-Erk.
Importantly, combining age with some predictive nodes (such as
IL-27 mediated p-Stat1 or p-Stat3), increased the AUC of the ROCs
from 0.65 for age alone to 0.87 and 0.89, respectively, with highly
significant p values (not shown). This shows that SCNP assays can
distinguish AML disease biology beyond age.
[0624] Finally, although univariate analysis of signaling nodes
stratified patient samples based on leukemic response to induction
therapy, the combination of independently predictive nodes improved
predictive value significantly.
[0625] In summary, this study demonstrated, in two very diverse
patient cohorts, the potential value of using leukemia signaling
biology to stratify patient samples into those that likely will or
will not respond to ara-c based induction chemotherapy. These
results emphasize the value of comprehensive functional assessment
of biologically relevant signaling pathways in AML blasts as a
basis for the development of highly predictive tests for response
to therapy.
Example 9
[0626] This example relates to publication "Functional
Characterization of FLT3 Receptor Signaling Deregulation in AML by
Single Cell Network Profiling (SCNP)". Rosen D B, Minden M D,
Kornblau S M, Cohen A, Gayko U, Putta S, Woronicz J, Evensen E,
Fantl W J, Cesano A. PLoS ONE. 2010. October; In Press. This
publication is incorporated herein by reference it its entirety for
all purposes.
[0627] This example identifies intracellular signaling pathways
associated with FLT3 ITD in two independent cohorts of diagnostic
AML samples that serve as an improvement over current clinical
tools in the identification of clinically meaningful altered FLT 3
and has implications for cohort selection in the development of
FLT3 inhibitors. The two cohorts of data were further analyzed to
investigate the differences in signaling between FLT-WT and FLT-ITD
samples. The first cohort of data ("study 1") comprised the 34
samples from University Health Network outlined in Table 16 and
Table 19. The second cohort of data ("study 2") comprised an 83
sample subset of MD Anderson Cancer Center data outlined in Table
16 (and Table 19). The 83 sample subset was selected based on known
FLT3 mutation status. Both cohorts of data were used to investigate
differences in FLT3 signaling between leukemic blasts and control
data.
[0628] FLT3 WT Signaling in Healthy Control and AML Samples
[0629] In order to further characterize wild-type FLT receptor
signaling in AML, we compared FLT3L-induced signaling in the
myeloblast population of control BMMC samples with FLT3L-induced
signaling in the leukemic blast population of FLT3-WT AML samples.
FLT3L activated the MAPK and PI3K pathways, inducing increased
levels of p-Akt and p-S6 in both BMMC and FLT3-WT AML samples at
early time points (4 minutes, 10 minutes). However, kinetic
differences between the two sets of samples were observed at later
time points (FIG. 12). In the BMMC samples, activation of p-Akt and
p-S6 was largely diminished by 15 minutes, likely due to regulatory
feedback mechanisms. In the FLT3-WT AML samples, sustained p-Erk,
p-CREB and p-Akt activation was observed in a number of samples at
15 minutes (FIG. 13). These results demonstrate that kinetic
differences in signaling at different time points can be used to
distinguish FLT3-WT AML samples from healthy BMMCs.
[0630] Variance in intensity of cell signaling may be used to
distinguish FLT3-WT and healthy cells. FIGS. 10, 11, 12 and 13
illustrate the ranges of signaling observed in FLT3-WT and BMMC
samples. FIG. 1 contains "box and whisker" plots of FLT3 levels and
FLT3L-induced S6 signaling for both the FLT3-WT AML and BMMC
samples. In BMMC samples, FLT3L induced a narrow range of S6
signaling. In FLT3-WT AML samples, FLT3L induced a wide range of S6
signaling. In agreement, standard deviations from measures of FLT3
signaling were higher in FLT3-WT AML than in healthy BMMb. In
addition, the variance in FLT3 receptor signaling was statistically
different (p-value=0.003, Levene's test) between the FLT3-WT AML
and healthy BMMb samples (FIG. 14) In the BMMC samples, the S6
signaling did not co-occur with increased Stat5 signaling (not
shown) however in FLT3-WT AML p-Stat5 was induced by FLT3L in some
samples.
[0631] FIG. 10 also contains scatter-plots that compare
FLT3L-induced S6 signaling with FLT3 receptor levels. From the
scatter-plots, it is shown that the FLT3L-induced S6 signaling is
independent of FLT3 receptor levels in both cohorts (i.e. there is
no linear correlation between FLT3 expression and S6 signaling),
although there may be a threshold level of FLT3 receptor required
for S6 signaling.
[0632] Although individual samples displayed uniform FLT3 receptor
staining, induction of p-S6 was only observed in a fraction of
cells, suggesting the presence of distinct FLT3L responsive and
non-responsive subpopulations in healthy and AML samples. FIG. 11
illustrates FLT3L responsive and FLT3L non-responsive
subpopulations in BMMC samples. Accordingly, FLT3L-induced p-S6
signaling may be used in gating or other types of analyses in order
to select a cell subpopulation with a distinct disease/response
phenotype.
[0633] Signaling Differences and Classification of FLT3-WT and
FLT3-ITD
[0634] Univariate analysis, unadjusted for multiple testing, was
performed sequentially and independently on the two study cohorts
in order to identify signaling nodes that distinguished with
FLT3-ITD from FLT3-WT AML patient samples. In study 1, 75 of the
304 node/metrics tested distinguished FLT3-ITD from FLT3-WT AML
patient samples with an AUC of ROC>0.7 and p<0.05. Results
from study 1 are tabulated in FIG. 22. In study 2, 35 of the 201
node/metrics distinguished FLT3-ITD from FLT3-WT AML patient
samples with an AUC of ROC>0.7 and p<0.05. Results from study
2 are tabulated in FIG. 26. Results from both studies include the
AUC, Wilcoxon and t-test p-value for each node, and the number/mean
value of the samples in the FLT3-ITD and FLT3-WT AML groups with
common stratifying nodes summarized in FIG. 23. Although the
majority of the discussion herein is directed to nodes that had
similar responses within the two cohorts of data, some differences
were observed between the two cohorts of data. These differences
may have been due to the different clinical characteristics of the
two cohorts of data, specifically biases in the data from UHN.
[0635] Analysis of the false discovery rate for both studies showed
this frequency to be significantly greater than the number of
signaling nodes that would be expected to be significantly
different between the two groups by chance (t-test p-value=0.0009).
Stratifying nodes that distinguished FLT3-ITD from FLT3-WT samples
in both studies represented distinct biological networks including
Jak/Stat, PI3K and apoptosis pathway readouts (FIG. 22, FIG.
26).
[0636] FLT3 Signaling and Receptor Levels in FLT3-WT and FLT3-ITD
Samples
[0637] Both FLT3-ITD and FLT3-WT samples expressed similar ranges
of FLT3 receptor levels. Basal levels of p-Erk, p-Akt, and p-S6 did
not differ significantly between FLT3-ITD and FLT3-WT samples.
However, we observed distinct FLT3L-induced signaling responses in
the two sets of samples. With FLT3L induction, FLT3-ITD samples
showed lower levels of induced and total PI3K and MAPK pathway
activation compared to FLT3-WT samples.
[0638] Differences in the PI3K pathway activation were evidenced by
FLT3L induction of p-S6 which, in univariate analysis, provided
discrimination between FLT3-WT and FLT3-ITD samples in study 1 and
study 2 with p-values of 0.038 and 0.036, respectively (Wilcoxon
p-values). FIG. 15 contains "bar and whisker" plots that
demonstrate the range of values of both FLT3 receptor levels and
FLT3L-induced S6 signaling. These plots illustrate that FLT3-ITD
exhibits a much narrower range and lower values of S6 signaling as
compared to FLT3-WT.
[0639] Distinct Jak/Stat Signaling in FLT3-WT and FLT3-ITD
Samples
[0640] Variance in response to a stimulator may also be used to
distinguish samples based on their mutational status. IL-27 induced
a wide range of p-Stat responses in the FLT3-WT samples. FLT3-ITD
samples displayed minimal responsiveness to IL-27 stimulation.
[0641] FIG. 16(b) illustrates the differences in IL-27-induced
Jak/Stat pathway response between FLT3-WT and FLT3-ITD.
IL-27-induced Stat signaling activity was reduced in FLT3-ITD
samples with significantly lower induction of p-Stat3 (t-test
p-value<0.029) and p-Stat5 (t-test p-value<0.038) in both
studies. The fold induction of p-Stat responsive to IL-27
(IL-27.fwdarw.p-Stat 3 Fold) signaling node in univariate analysis
distinguished FLT3-WT and FLT3-ITD in both samples (AUC 0.69 in
study 1 and AUC 0.73 in study 2, respectively). Notably, FLT3-ITD
samples displayed higher basal levels of p-Stat5 and p-Stat1
compared with FLT3-WT samples in Study 1.
[0642] Distinct Apoptotic Responses in FLT3-WT and FLT3-ITD
Samples
[0643] Etoposide-induced DNA damage and apoptosis was measured to
identify FLT3-mutation-based differences in DNA Damage response
(DDR) and apoptotic machinery. Increased p-Chk2 and cleaved PARP
were used to measure the ability of etoposide to induce DNA damage
and apoptosis, respectively. FIG. 16(c) illustrates the differences
in etoposide-induced DNA damage between FLT3-WT and FLT3-ITD
samples. As measured using total cleaved PARP induced by etoposide
(etoposide.fwdarw.c-PARP|Total), FLT3-ITD samples were more
sensitive to in vitro apoptosis than FLT3-WT samples (AUC 0.82 in
study 1 and AUC 0.73 in study 2). Similar results were observed in
both study 1 and in study 2 using other mechanistically-distinct
apoptosis-inducing agents such as staurosporine, a pan kinase
inhibitor, and in study 2, Ara-C/Daunorubicin. Accordingly, a wide
range of apoptosis-inducing agents may be used to induce signaling
that stratifies FLT3-ITD from FLT3-WT samples.
[0644] Stratifying nodes that distinguished FLT3-ITD from FLT3-WT
samples in both studies represented distinct biological networks
including Jak/Stat, PI3K and apoptosis pathway readouts and are
summarized graphically in FIG. 17.
[0645] FLT3L and IL-27 Induced Signaling in FLT3-ITD, NPM1
Molecular Subgroups
[0646] IL-27 induced Jak/Stat signaling and FLT3L induced PI3K and
Raf/Ras/MAPK signaling responses was assessed in FLT3 receptor and
NPM1 molecular defined subgroups. For all nodes analyzed, the
FLT3-WT/NPM-WT subgroup demonstrated the most variable signaling
responses and often contained samples with the most elevated
signaling (FIG. 24, 25). In contrast, within FLT3-ITD/NPM1 mutated
patients, IL-27-induced and FLT3L-induced signaling appeared more
uniform and generally lower compared to FLT3-WT/NPM-WT samples.
FLT3-WT/NPM1-WT samples demonstrated the highest variance among
FLT3 NPM1 subgroups for IL-27 and FLT3L signaling and demonstrated
significantly higher variance compared to both FLT3-ITD subgroups
(FIG. 14). Of note, the largest differences in variance were
observed between FLT3-WT/NPM-WT and FLT3-ITD/NPM-Mutated samples
(FIG. 14).
Correlations Between Nodes
[0647] Several of the top-ranking nodes stratifying FLT3-ITD from
FLT3-WT samples were analyzed to identify co-variance in
FLT3-mutation-dependent signaling. FIG. 18 and FIG. 21 illustrate
the correlations between the top ranking nodes. Pearson correlation
coefficients were computed for all signaling nodes from study 1
with a t-test p-value.ltoreq.0.05 demonstrated correlation between
nodes belonging to the same pathway. For example, nodes within the
Stat pathway (IL-27.fwdarw.p-Stat3 Fold and
IL-27.fwdarw.p-Stat5|Fold) exhibited a correlation of R=0.81. The
same signaling protein was observed to have similar reactions to
different modulators with a correlation of R=0.87
(Thapsigargin.fwdarw.p-CREB Fold and PMA.fwdarw.p-CREB). Nodes
measuring signaling events in different pathways were less
correlated (e.g. Thapsigargin.fwdarw.p-CREB|Fold and
IL-27.fwdarw.p-Stat5|Fold (R=0.04).
[0648] The identification of high correlation values between
similar nodes affirms the quality of results and allows us to
identify FLT3-mutation-stratifying nodes that can be used
interchangeably in a classifier such as a bivariate model or a
multivariate model. Conversely, identification of
FLT3-mutation-stratifying nodes with a poor correlation value
allows us to identify pairs of nodes that may complement each other
for increased classification accuracy.
[0649] Association Between Multiple Signaling Nodes and Flt3 ITD
Status--Multivariate Analysis Using Linear Regression
[0650] FIG. 19 provides a schematic overview of bivariate modeling.
Bivariate modeling combines different signaling nodes to generate a
model that provides better stratification of FLT3-ITD and FLT3-WT
AML samples than the individual nodes. We evaluated all possible
pairs of the 75 signaling nodes with AUC of the ROC>0.7 and
p-value<0.05 (tabulated in FIG. 22) for their ability to improve
stratification of the FLT3 mutational status. This modeling
exercise was performed to identify potential combinations within or
across pathways that might form the basis of future studies. All
combinations of nodes that had an AUC greater than the best single
node/metric within the combination were tabulated in FIG. 27. The
AUC for the tabulated models ranged from 0.89 to 0.98. As discussed
above, the probability of two nodes to complement one another was
higher if the nodes participated in different signal transduction
pathways: e.g. combining the nodes IL-6.fwdarw.p-Stat5|Total
(AUC=0.84) and FLT3L.fwdarw.p-S6|Total (AUC=0.80) yields an
improved AUC of 0.98.
[0651] Clinical Implications
[0652] To better understand the clinical implications of the
FLT3-mutation-stratifying nodes, we independently examined the
FLT3-mutation-stratifying signaling profiles in samples from two
groups of Cytogenetically Normal (CN) AML patients. Each group of
patients represented clinically extreme "outliers" based on their
mutation status: 1) FLT3-WT AML who experienced disease relapse
within 3 months after initial remission (i.e. rapid relapse) and 2)
FLT3-ITD AML in complete continuous disease remission for two or
more years. In study 2 there were 2 FLT3-WT and 2 FLT3-ITD samples
associated with these clinical characteristics.
[0653] The wide range of signaling responses observed in FLT3-WT
AML samples made identification of signaling outliers challenging.
FIG. 20(a) provides a scatter-plot of the signaling profiles in the
two rapid relapse FLT3-WT samples (MD3-19 and MD3-37) showing
attenuated p-S6 and p-Erk in response to FLT3L, similar to the
FLT3L-induced signaling observed in FLT3-ITD samples (see FIG. 15,
FIG. 16(a) for FLT3-ITD FLT3L-induced signaling). FIGS. 20(b) and
20(c) provide scatter-plots showing minimal IL-27-induced Stat
phosphorylation in MD3-19, similar to FLT3-ITD samples (see FIG.
16(b) for FLT3-ITD IL-27-induced Stat signaling), suggesting that
these rapid relapse FLT3-WT samples might share similar biology
with FLT3-ITD samples in certain pathways.
[0654] Identification of FLT3-ITD signaling outliers was aided by
the narrow range of signaling responses of this sample set. In the
CN FLT3-ITD sample group, two patients remained in complete
continuous remission for two or more years. One patient (MD2-22)
had been treated with chemotherapy alone and the other (MD3-22) was
treated with an allogeneic stem cell transplant (as per NCCN
guidelines). Since MD3-22 received high intensity post-remission
therapy we focused on signaling associated with sample MD2-22.
[0655] MD2-22 obtained from a patient who received high dose Ara-C
similar to what is recommended for "low risk" cytogenetic leukemia.
We found that the FLT3-ITD MD2-22 sample signaling profile was
closer to FLT3-WT as illustrated by the first two principal
components of PCA Analysis (not shown). This observation was
further reinforced by the number of nodes (16) for which MD2-22 was
an outlier among the FLT3-ITD group (i.e. outside of 1.5 times the
inter-quartile from the median for FLT3-ITD). These nodes included
those from the Jak/Stat pathway (e.g., IFN.alpha..fwdarw.p-Stat1,
p-Stat3, p-Stat5; G-CSF.fwdarw.p-Stat3, p-Stat5), the CREB pathway
(e.g. PMA.fwdarw.p-CREB); and the PI3K and MAPK pathways (e.g.,
FLT3L.fwdarw.p-S6, p-Akt; SCF.fwdarw.p-S6, p-Akt). A following
molecular analysis of this sample indicated the presence of an NPM1
gene mutation although this information was not available at the
time of post-remission treatment.
[0656] An analysis within FLT3-WT AML samples, demonstrated that
higher measures of induced apoptosis (i.e.
Ara-C/Dauno.fwdarw.C-PARP|Fold) were associated with CR duration
greater than two years (AUCROC: 0.92) These data show the ability
of SCNP to provide information, independent from molecular
determinations relevant to the clinical decision making of AML.
[0657] Discussion
[0658] These data suggest that assessing patient samples for the
presence of FLT3 receptor deregulation may inform clinical decision
making regarding standard treatment as well as serving as a tool
for patient stratification in studies attempting to evaluate
specific inhibitors of the FLT3 receptor. This functional
assessment of biologically relevant signaling pathways in AML
blasts shows the spectrum of deregulated signal transduction not
previously described in primary AML samples.
[0659] The current investigation represents the first analysis
comparing pathway activity and inducibility in the absence or
presence of modulators known to activate Jak/Stat,
PI3-kinase/Akt/S6 and the Ras/Raf/Erk/S6, phosphatase/reactive
oxygen species, and DDR/apoptosis pathways in FLT3-WT and FLT3-ITD
AML samples. We found that FLT3L induced differential signaling in
FLT3-WT AML independently of the presence of FLT3 mutations as
compared to the healthy BMMC. These data show that SCNP uncovers
important heterogeneity in AML and has potential as a platform for
understanding leukemia pathway dependence in the individual
patient, information that will be valuable for the selection of
therapeutic strategies in the era of personalized medicine.
[0660] Although FLT3 receptor levels were similar between the
FTL3-WT and FLT3-ITD AML groups in this study, FLT3-ITD samples
displayed attenuated responses to FLT3L, as measured by induced
levels of p-Erk, p-Akt and p-CREB versus their FLT3-WT counterparts
While increased levels of basal p-Erk and p-Akt have been reported
in FLT3-ITD expressing cell lines, our data demonstrated comparable
levels of basal p-Erk and p-Akt among FLT3-ITD and FLT3-WT primary
AML samples. These data suggest the greater dependence of FLT3L
inducibility of these signaling networks in FLT3-WT AML and
demonstrate FLT3L-independence in FLT3-ITD samples.
[0661] Consistent with these studies FLT3-ITD samples expressed
increased basal levels of p-Stat1, p-Stat3 and p-Stat5 compared to
FLT3-WT samples in Study 1 and in both studies FLT3-ITD AML samples
displayed a uniformly limited range in basal p-Stat5 levels
compared to FLT3-WT samples. Additionally, in contrast to signaling
in healthy myeloid blasts, FLT3L induced p-Stat5 in some FLT3-WT
samples, demonstrating deregulated FLT3 receptor signaling even in
the absence of FLT3 mutational alterations.
[0662] Different signaling responses were also observed between
FLT3-WT and FLT3-ITD samples for IL-27 induced Jak/Stat pathway
activity. Most studies characterizing the biology of IL-27 have
been performed on lymphocytes where this cytokine plays a major
role in immune regulation. However, the IL-27 receptor is present
on other cell types, including those of the myeloid lineage, where
its activation has been shown to enhance proliferation and
differentiation of mouse and human hematopoietic stem/progenitor
cells. In Study 1, increased levels of basal p-Stat1 and p-Stat5
were observed for FLT3-ITD compared to FLT3-WT samples. Our data
suggest these FLT3-ITD samples are less responsive to IL-27
mediated Stat signaling, likely because they already display
elevated Stat pathway activity. This growth factor independence
could contribute to the poor clinical outcome observed within
FLT3-ITD patients.
[0663] Analysis of the apoptosis pathways showed that FLT3-ITD
samples were more sensitive to in vitro etoposide and other
apoptosis inducing agents than FLT3-WT samples. While these results
using cryopreserved diagnostic samples may seem somewhat
counterintuitive to the clinical findings that FLT3-ITD patients
have a worse overall survival and shorter duration of remission, to
date the presence of FLT3-ITD has not been associated with response
to induction therapy.
[0664] The clinical implications of our observations suggest that
SCNP analysis could be applied to clinical decision-making as well
as to evaluating responsiveness to inhibitors of FLT3 receptor
signaling and/or other activated pathways. Despite the limited
sample size and the exploratory nature of the analyses some
interesting observations emerged. Specifically, we identified
FLT3-WT AML samples whose SCNP responses resembled those of
FLT3-ITD AML and furthermore behaved clinically like high risk AML.
Conversely, we found a case of FLT3-ITD AML that functionally
resembled FLT3-WT, and behaved clinically like low-risk AML. These
data suggest SCNP has the potential to provide improved prognostic
information beyond FLT3 molecular characterization alone. Lastly,
multiple therapeutics that target FLT3 receptor (e.g., CEP701,
PKC412, AB220) are in development for the treatment of AML. To
date, the characterization of AML based on the mutational status of
the FLT3 gene has shown not to be very informative in predicting
the activity of any of these FLT3 receptor inhibitors and their
effects on signaling transduction remains unknown. In this regard,
SCNP could be used as a tool to identify AML patients who could
benefit from administration of such inhibitors alone or in
combinations with other standard agents and/or targeted inhibitors.
Further studies in the context of clinical trials are
warranted.
Example 10
[0665] This example relates to publication "Distinct Patterns of
DNA Damage Response and Apoptosis Correlate with Jak/Stat and
PI3Kinase Response Profiles in Human Acute Myelogenous Leukemia".
Rosen D B, Putta S, Covey T, Huang Y W, Nolan, G P, Cesano, A,
Minden M D, Fantl W J. PLoS ONE. 2010 August; 5(8): e12405. This
publication is incorporated herein by reference in its entirety for
all purposes.
[0666] This example further characterizes the data outlined above
with regards to Example 6 based on the activities of their
intracellular signaling pathways. Analysis of Jak/Stat, PI3K, DNA
damage response (DDR) and apoptosis pathway activities demonstrated
biologically distinct patient-specific profiles, even within
cytogenetically and cell surface uniform patient sub-groups. Thus,
while AML is known to be clinically heterogeneous, the biology
described in this study shows that the heterogeneity in the disease
may be represented by a limited number of intracellular signaling
pathways highlighting survival pathways, DDR and their link to
apoptosis.
[0667] Principle Component Analysis (PCA) was used in addition to
our standard metrics for measuring activation levels. PCA is a
dimension reduction technique commonly used to represent
multi-dimensional data according to the strongest "trends" or
associations in the data. Here, we used PCA to represent several
nodes in the same pathway according to a trend or direction in the
data. PCA was performed for Jak/Stat and PI3K nodes using both
"Fold and "Total" metrics of induced pathway activity along with
the corresponding basal nodes.
[0668] The application of PCA to multi-dimensional data
representing the same pathway is beneficial for several reasons. As
discussed above with respect to Example 10, nodes that are part of
the same pathway can have a similar response and exhibit covariance
over different samples or even cells Accordingly, combining the
data into one metric may adequately represent the entire pathway.
Also, since PCA identifies the strongest trend in the data, the use
of PCA allows for the representation of small variations in a
signaling pathway in a single metric. Accordingly, PCA-based
metrics may provide the ability to distinguish small variations in
signaling pathways associated with disease.
[0669] Univariate analysis was also used to identify nodes/metrics
that stratified patients based on their disease response to
standard induction therapy. Each node/metric combination was
evaluated using univariate analyses. Jak/Stat and PI3K nodes that
stratified clinical CR and NR patients (Area Under the Curve of the
Receiver Operator Characteristic (AUCROC)>0.6 and
p-value<0.05) were used for principle component analyses and for
selecting examples of the node/metrics that were used to construct
the heat-maps.
[0670] Results
[0671] As described above with regards to Example 6, SCNP analysis
of the Jak/Stat and PI3K signaling pathways was carried out in AML
blasts after their exposure to a panel of modulators.
[0672] Jak/Stat Pathway Activity
[0673] To assess the activity and inducibility of the Jak/Stat
pathway, samples were treated with G-CSF, IL-6, IL-27, IL-10,
IFN.alpha. and IFN.gamma., known to activate the Jak/Stat pathway.
AML samples were characterized by the magnitude of their basal
Jak/Stat pathway activity as well as by the induced responses (Fold
metric) and total level of Jak/Stat pathway activation (Total
metric). The latter two metrics used paralleled each other. Low or
absent levels of induced phosphorylation of Stat 1, Stat 3 and Stat
5 proteins were associated with gated AML blasts from CR patients
exemplified by the 2D flow plots observed for responses of sample
UHN.sub.--0713 to G-CSF and IL-27 (not shown). In contrast,
potentiated Jak/Stat signaling was observed as well as increased
pathway activity in cells taken from patients whose leukemia was
non-responsive to induction chemotherapy, as observed in a 2D flow
plot for myeloid-gated cells for sample UHN.sub.--9172 (not shown).
In most NR patient samples Jak/Stat signaling was elevated in a
cell subpopulation in response to multiple cytokines, whereas cells
of most CR patients were largely non-responsive. IL-27 and
IL-6-mediated-phosphorylation of Stat3 were closely correlated, as
would be expected for two cytokines sharing the gp130 common signal
transduction receptor subunit.
[0674] PI3K Pathway Activity
[0675] A second major survival pathway interrogated in this study
was PI3K, known to play a role in most cancers. Converging signals
from the PI3K/mTor and Ras/Erk pathways result in phosphorylation
of ribosomal protein S6 which correlates with increased protein
translation of mRNA transcripts that encode proliferation and
survival promoting proteins.
[0676] Analogously to activation of the Jak/Stat pathway,
application of known activators of the PI3K pathway including
FLT3L, SCF and SDF-1a broadly grouped AML, samples by the magnitude
of their signal transduction responses (Fold metric) and overall
pathway activity (Total metric) represented by measurements of
p-Akt and p-S6. In the same manner that low levels of modulated
Jak/Stat responses and Jak/Stat pathway activity were seen in
leukemic cells from CR patients, samples in which p-Akt/p-S6
signaling was low or absent were also associated with clinical
responsiveness to chemotherapy. Additionally, in the same manner
that high levels of induced Jak/Stat responses and high levels of
Jak/Stat pathway activity were seen in leukemic cells from NR
patients, elevated PI3K pathway responses were also associated with
clinical non-response to chemotherapy as observed by a 2D flow plot
for sample UHN.sub.--4353 (not shown). Importantly, no associations
could be made between cytogenetic risk category and the French
American British category (FAB) within these signaling
responses.
[0677] Correlated Measures of Induced JAK/STAT and PI3K Signaling
Reveals AML Blasts with Distinct Pathway Responses
[0678] In order to evaluate the effect of modulation on both the
Jak/Stat and PI3K pathway activities, PCA was performed for each
pathway in its basal state as well as its functionally activated
state. The PCA analysis for the activated states of the pathways
combined readouts from multiple modulators known to activate the
Jak/Stat and PI3K pathways. Induced pathway activity, rather than
basal pathway activity, could more readily reveal distinct
patient-specific functional response patterns. FIGS. 28(a) and (b)
demonstrate the stratification that PCA achieves when applied to
induced nodes in pathways is significantly better than for basal
nodes. This is to be expected because since PCA identifies the
strongest trend in the data. If the pathways don't have a
multiplicity of different states due to induction, PCA will not be
helpful in segregating the different states.
[0679] FIG. 28(b) illustrates the multiple response profiles
observed in the modulated AML samples. In the modulated samples,
activity was high or low for both pathways or high for one and low
for the other pathway. Interestingly, although the number of
samples from CR patients (shown in FIG. 28(b) as filled blue
circles) is low (n=9), a low signaling capacity in both Jak/Stat
and PI3K/S6 pathways was associated with clinical response to
chemotherapy. In contrast, augmented signaling responses from one
or both the Jak/Stat and PI3K pathways were observed in most
samples from chemotherapy refractory patients (i.e. NR patients,
shown in FIG. 28(b) as unfilled red squares). A sub-group of the NR
AML blast samples low level signaling responses in both Jak/Stat
and PI3K pathways (lower-left-hand quadrant) were observed,
suggesting that other pathways could be contributing to clinical
refractoriness to chemotherapy. These data suggest that activation
of the PI3K and Jak/Stat pathways might oppose response to
chemotherapy. Further, the stratification between different AML
samples achieved using PCA demonstrates that principle component of
pathway activity is a useful metric for characterizing
heterogeneity in AML samples and stratifying different subtypes of
AML cells.
[0680] Measurements of DDR and Apoptosis with In Vitro Exposure to
Etoposide and Staurosporine
[0681] As described above with regards to Example 6(a), DDR and
apoptosis was measured using Chk2 and cleaved PARP after exposure
of AML blasts to etoposide, a topoisomerase II inhibitor that
induces double stranded breaks. FIG. 29 illustrates the three
distinct responses that were observed: (1) AML blasts with a
defective DDR and failure to undergo apoptosis (2) AML blasts with
proficient DDR and failure to undergo apoptosis (3) AML blasts with
proficient DDR and apoptosis. All CR samples were exemplified by
the third profile whereas NR samples were exemplified by all three
response profiles
[0682] Staurosporine induced apoptosis responses were evaluable in
26/33 of the AML samples. FIG. 30(a) is a scatter plot comparing
etoposide versus staurosporine-mediated apoptosis. FIG. 30(a) shows
percentage of cells within an AML sample undergoing apoptosis and
for no sample was this value 100% at the time points chosen in this
study. All samples with blast subsets refractory to in vitro
etoposide exposure, regardless of their staurosporine response,
were derived from the NR patient sample subgroup. Apoptosis
responses identified all CR patients as apoptosis competent to both
agents. However, a negative apoptotic response could not predict
all NR patients, underscoring the fact that in vitro responses
alone to apoptosis stimulating agents are only part of the equation
that describes a clinical outcome.
[0683] FIG. 30(b) shows examples of different response profiles for
different AML samples (both NR and CR) in response to Etoposide or
Staurosporine. Notably some samples were sensitive to staurosporine
yet refractory to etoposide (UHN.sub.--0401). This implies that the
apoptotic machinery per se was intact in these cells and that the
resultant refractory response to etoposide could be the result of
ineffective communication between the machinery of the DDR with
that of apoptosis (exemplified by sample UHN.sub.--0401). Other
categories of response shown are relative refractoriness to both
agents (exemplified by sample UHN.sub.--8190) or responsiveness to
both agents (exemplified by sample UHN.sub.--8303). Treatment with
distinct apoptosis inducing agents revealed distinct percentages of
apoptotic (c-PARP+) and non-apoptotic (c-PARP-) subpopulations of
cells within an individual AML sample. This indicates that within
an AML sample there are blast cell subsets with different
sensitivities to each agent.
[0684] Associations Between In Vitro Apoptosis Profiles and
Jak/Stat and PI3K Pathway Activity
[0685] The Jak/Stat and PI3K pathway activities observed in
leukemic samples were further analyzed in the context of the in
vitro apoptotic responses illustrated in FIG. 30(a). FIG. 31(a)
illustrates the selection of staurosporine refractory and
responsive cells. FIG. 31(b) contains scatter plots which
illustrate IL-27-induced and G-CSF-induced Stat signaling responses
in the staurosporine outliers. FIG. 31(c) contains scatter plots
that compare a principle component representing Stat pathway
activity (derived from PCA of the nodes associated Stat pathway).
FIG. 31(d) tabulates the Pearson and Spearman correlations between
staurosporine response and individual nodes.
[0686] As shown in FIG. 31(b), Jak/Stat signaling responses were of
variable magnitude for samples with relatively low or high
responsiveness to etoposide as well as samples that were sensitive
to staurosporine (UHN.sub.--5643, UHN.sub.--0521, UHN.sub.--5684
and (C)). In the four samples with the lowest relative response
(relative refractoriness) (UHN.sub.--4353, UHN.sub.--9172,
UHN.sub.--8314) to staurosporine, Jak/Stat pathway responses were
augmented.
[0687] The Pearson and Spearman coefficients tabulated in FIG.
31(d) demonstrated a statistically significant negative correlation
between staurosporine induced apoptosis and Jak/Stat signaling in
this AML sample set, with outliers clearly apparent. Statistical
significance was found for the Jak/Stat PCA value with even greater
statistical significance observed for individual nodes such as IL-6
or IL-27 induced Stat signaling. Pearson and Spearman coefficients
revealed a lack of correlation for Jak/Stat signaling with
etoposide response.
[0688] The PI3K pathway activities observed in leukemic samples
were further analyzed in the context of the in vitro apoptotic
responses illustrated in FIG. 30(a). FIG. 32(a) illustrates the
selection of etoposide and staurosporine refractory and responsive
cells. FIG. 32(b) contains scatter-plots which illustrate
FLT3-induced and SCF-induced PI3K signaling response samples with
high or low apoptosis responses to etoposide and staurosporine.
FIG. 32(c) contains scatter-plots that compare a principle
component representing PI3K pathway activity (derived from PCA of
the nodes associated PI3K pathway). FIG. 32(d) tabulates the
Pearson and Spearman correlations between staurosporine/etoposide
response and individual nodes in the PI3K pathway.
[0689] As shown in FIG. 32(b), we observed an inverse correlation
between levels of growth factor (SCF and FLT3L) and chemokine
(SDF-1.alpha.)-mediated-p-Akt and p-S6 signaling and in vitro
apoptotic response as characterized through etoposide and
staurosporine. The Pearson and Spearman correlation coefficients
tabulated in FIG. 32(d) demonstrate that this relationship is
statistically significant. FIG. 32(d) demonstrates that the PCA
metric for induced PI3K pathway activity has better negative
correlation with staurosporine and etoposide response than
individual node/metrics. These results confirm that PCA is a
valuable tool for capturing signaling heterogeneity that may
correlate to, or predict, clinical response.
[0690] The scatter-plots in FIG. 32(b) demonstrate that induced
PI3K pathway signaling tended to be lower for samples that were
apoptosis proficient to both etoposide and staurosporine
(UHN.sub.--5684, UHN.sub.--8825 and UHN.sub.--8451). As shown in
FIG. 32(b), greater induced p-Akt and p-S6 levels were observed in
samples refractory to staurosporine and/or etoposide
(UHN.sub.--0341, UHN.sub.--5643 and UHN.sub.--4353).
[0691] When taken together, trends for apoptosis, Jak/Stat and PI3K
pathway activities (FIGS. 30, 31, and 32) and clinical outcomes
suggest that there are limited number of signaling pathway profiles
associated with CR patients (i.e. CR patients are homogeneous in
signaling), whereas in NR patients many different pathway
mechanisms may have evolved for the leukemia to be refractory to
chemotherapy (i.e. NR patients are heterogeneous in signaling). All
samples from CR patients had blast cell subsets that were sensitive
to in vitro staurosporine and etoposide-mediated apoptosis and in
general had low Jak/Stat and PI3K pathway responses. Most clinical
NR samples that were competent to undergo in vitro apoptosis had an
absent or low PI3K response, suggesting that other pathways could
be contributing to refraction to therapies that induce apoptosis.
All other NR samples were refractory to in vitro etoposide and/or
staurosporine exposure with different degrees of elevated Jak/Stat
and/or PI3K pathway activation. Since PCA metrics of pathway
activation had a clear correlation with apoptotic response, which
in turn was predictive of therapeutic response (CR/NR), it can be
inferred that PCA metrics of pathway activation provide another
valuable metric that can be used to stratify patients as to their
clinical response type, but also to further stratify and
biologically characterize NR patients according to heterogeneity
underlying the disease.
[0692] Associations Between In Vitro Apoptosis Profiles and Cell
Subpopulations
[0693] Analysis of CD33 and CD45 surface expression of all samples
within this AML cohort defined three patient samples with two
distinguishable leukemic cell subpopulations, referred to as Blast
1 and Blast 2. In all cases, Blast 1 was defined as a cell subset
with higher CD33 and CD45 levels, whereas Blast 2 cells had lower
levels of these surface proteins. Given the distinct signaling
profiles identified for cell subsets within samples harboring only
one myeloid blast population as defined by CD33 and CD45
expression, in the preceding data of this study, it seemed likely
that samples harboring two myeloid blast populations could harbor
distinct signaling profiles.
[0694] SCNP revealed distinguishable signaling responses within
individual cells in each blast population measured simultaneously.
FIGS. 33 (a) and 33 (b) include the data from two of the three
samples with available data for signaling and apoptosis nodes, both
from NR patients. FIG. 33 (a) demonstrates that blast populations 1
and 2 from sample UHN.sub.--0577 were refractory to
etoposide-mediated apoptosis although both populations exhibited
DDR, albeit to different magnitudes as seen by the frequencies of
blasts with increased phosphorylation of p-Chk2. Exposure of the
samples to staurosporine revealed that the apoptotic machinery was
intact in both blast populations suggesting that etoposide
refractoriness was the result of disabled communication between DDR
and the apoptotic machinery. Comparison of each blast subset for
its response to G-CSF revealed minimal increases in p-Stat3 and
p-Stat5. However, inspection of the PI3K path-way revealed that
Blast 1, but not Blast 2 had two discernible blast cell subsets
with different levels of p-Akt and p-S6 in the basal state. Blast 2
had only one "low" level p-Akt and p-S6 blast cell subset.
Furthermore, in Blast 1, FLT3L was able to induce both p-Akt and
p-S6 signaling in the "low level" basal population. In contrast,
for Blast 2 the predominant response to FLT3L was an increase in
p-S6 alone. Using the metric of "total" as a measure of overall
pathway activity, there was greater overall pathway activity for
Blast 1 than for Blast 2 in both the basal and FLT3L-potentiated
states reflecting significant contributions of both basal and
evoked signaling responses.
[0695] As shown in FIG. 33(b), the two blast populations in sample
UHN.sub.--8093 were both refractory to etoposide possibly through
different mechanisms since there was a greater p-Chk2 response in
Blast 1 and a reduced DDR in Blast 2. Blast 1 was very responsive
to staurosporine which indicated that the apoptotic machinery is
intact and that the etoposide refractoriness in Blast 1 could be
accounted for by failure of DDR to communicate with the apoptotic
machinery. In contrast, Blast 2 was refractory to
staurosporine-mediated apoptosis. Notably, in Blast 2 G-CSF
mediated greater increases in phosphorylated Stat3 and Stat5
compared to the increases seen in Blast 1. This was reflected by
both the "fold" and "total" metrics. Inspection of PI3K pathway
activity revealed that only a small blast cell subset responded to
FLT3L treatment with the majority of cells remaining unresponsive.
These data suggest that the higher activity seen for the Jak/Stat
pathway for Blast 2 may account for its refractoriness to in vitro
apoptosis and non-response in the clinic consistent with the data
in FIG. 31.
[0696] Discussion
[0697] The current study was designed to determine whether
heterogeneity in individual AML samples can be characterized based
on in vitro functional performance tests using SCNP to measure
survival pathways, DDR and in vitro apoptosis. The major findings
were that: (i) an individual sample can be comprised of leukemic
blast subsets with distinct Jak/Stat, PI3K, DDR and apoptosis
pathway responses, (ii) exposure of samples to modulators allowed
these pathway responses to be revealed, (iii) PI3K pathway activity
was high in most samples that were refractory to apoptosis-inducing
agents in vitro, (iv) Jak/Stat pathway activity was high in samples
refractory to staurosporine but only in some samples refractory to
etoposide, (v) in vitro DDR and apoptosis profiles were variable in
leukemic blasts between different samples and also within the same
sample and (vi) SCNP of the pathways chosen reveal a restricted
number of profiles for AML blasts from CR patients and multiple
profiles for AML blasts from NR patients.
[0698] Thus, responders to chemotherapy demonstrated little
variation in the signaling potential of the pathways evaluated
(that is, cells remained relatively unperturbed by environmental
stimuli applied). As such, in the CR samples both the potentiated
responses to myeloid activators of the Jak/Stat and PI3K pathways,
as well as "basal" pathway activity tended to be low whereas DDR
with subsequent apoptosis was robust after in vitro etoposide
exposure. By contrast, robust Jak/Stat and PI3K responses were
revealed in most NR samples. These data are consistent with, and
expand upon previous findings linking functional alterations in
Jak/Stat signal transduction with poor response to chemotherapy in
AML patients. In addition, all samples with impaired DDR or
proficient DDR without subsequent apoptosis were NRs. A subset of
NR samples were competent to undergo in vitro apoptosis and had low
PI3K and Jak/Stat pathway responses suggesting that in these
samples alternative pathways could be contributing to clinical
refractoriness to chemotherapy.
[0699] This study used 34 diagnostic PBMC samples taken from
patients for which clinical out-comes were blinded. However, the
sample set was unintentionally biased with samples predominantly
from NR, female patients of younger age with intermediate
cytogenetics. In spite of these limitations, univariate analysis of
this sample set and an independent sample set from a separate
institution revealed common nodes for CR and NR stratification
suggesting that survival, DDR and apoptosis pathways may be
relevant ways to characterize AML disease subtypes.
[0700] The data suggest that while DDR, Jak/Stat, and PI3K pathways
might serve as useful indicators of the biological underpinnings of
therapeutic responses, additional inquiry or pathways might be
required to more fully complete the characterization of response.
The proliferative and survival properties of the Jak/Stat and PI3K
pathways most likely play a central role in AML leukemogenesis as
well as in refractoriness and resistance to clinically used DNA
damaging agents. For instance, Stat transcription factors are known
to play a critical role in normal and leukemic hematopoiesis
targeting transcription of genes involved in prolife-ration,
survival and differentiation. Receptors that signal through Stat3
and Stat5 are present on AML blasts where they can be activated by
a wide variety of growth factors, interleukins and cytokines.
Furthermore, in a recent study, the level of Stat5 transcriptional
activity was shown to regulate the balance between proliferation
and differentiation in hematopoietic stem cells/progenitor cells by
activating specific genes associated with these processes. The same
group showed that high levels of Stat5 activity disrupted
myelopoiesis. In the current study, CR samples showed low or absent
Jak/Stat responses and a subset of NR samples showed high
magnitudes of Jak/Stat responses while the remaining NRs displayed
a continuum of responses. These data suggest that certain levels of
Stat activity may be necessary to generate the appropriate
transcriptional program necessary for maintaining a particular
clonal state of an AML blast cell subset.
[0701] In addition, deregulation of the PI3K/mTor signaling pathway
has been described in 50-80% of AML cases contributing to the
survival and proliferation of AML blast cells. Many causes for
pathway deregulation have been cited such as activating mutations
in FLT3 and Kit receptors, overexpression of the PI3K class 1A
p110.delta. isoform as well as gain of function mutations in N- and
K-Ras. In this study, PI3K pathway activity was determined by
measuring levels of p-Akt and p-S6 ribosomal protein as pathway
readouts in response to myeloid modulators, FLT3L, SCF and SDF-1a.
Consistent with its role in cancer cell survival, potentiated
levels of p-Akt and p-S6 were lower in CRs and elevated in clinical
NRs, although the two clinical categories were not mutually
exclusive since several NR samples had low potentiated PI3K pathway
activity.
[0702] Moreover, alternative mechanisms of refractoriness could
arise from increased DDR, failure to undergo DDR and/or inoperative
communication between DDR and apoptosis. For a response to a DNA
damaging agent, DNA lesions recruit multi-protein DNA damage sensor
complexes that associate with DNA damage transducer proteins such
as ataxia telangectasia mutated (ATM), a kinase which upon
activation phosphorylates Thr68 (T68) of the checkpoint kinase
Chk2. The resultant delay in cell cycle progression provides cells
with a chance to repair the DNA damage. If repair fails, cells
undergo apoptosis. In this study three DDR/apoptosis profiles
distinguished AML samples. In the first, minimal p-Chk2 response
was observed and consequently no apoptotic response. In the second
profile there seemed to be a failure for DDR to translate into
apoptosis and in the third, DDR, apoptosis and their communication
was intact. Notably, all clinical responsive patients fell into
this latter category. Further sample cohorts are needed to see
whether this association between in vitro apoptotic sensitivity and
clinical response holds, potentially providing a valuable means for
predicting clinical outcomes.
[0703] The robust activation of two major survival pathways shown
in a subset of AML samples provided a rationale for evaluating
apoptotic proficiency in this sample cohort. In vitro exposure of
samples to etoposide and staurosporine, two agents that induce
apoptosis by different mechanisms, identified distinct blast
subsets with different responses to each agent between individual
samples and also within the same sample. Samples sensitive to both
agents were taken from CR patients. However, this apoptotic
proficiency was also observed in some NR patient samples. There are
several explanations to account for the unexpected in vitro
apoptotic response of NR samples, principally that the in vitro
apoptotic responses were not measured with the drugs used
clinically (Ara-C/Daunorubicin) by which the NRs were categorized.
Further, although Etoposide, Ara-C and Daunorubicin all induce DNA
damage they have different mechanisms of action and are substrates
for different transporters and thus might not mimic the in vivo
responses. It is also possible that the AML biology characterized
for these samples is not represented by clinical definitions of NR
and CR. Furthermore, in all cases, only a fraction of cells undergo
apoptosis and the phenotype of the non-responding cells may account
for the apparent disconnect between apoptosis seen in vitro versus
the clinical NR.
[0704] In order to understand whether there was a link between
signaling by survival pathways and in vitro apoptotic responses,
correlations were computed. When evaluated for Jak/Stat and PI3K
pathway activity, most samples refractory in vitro to either or
both etoposide and staurosporine had a cell subset that displayed
potentiated PI3K signaling. In contrast, samples refractory to
staurosporine displayed elevated Jak/Stat pathway activity whereas
there were variable levels of Jak/Stat pathway activity across a
range of etoposide induced responses. Given the fine balance
between levels of p-Stat 5 that, via a transcriptional program in
vivo, regulate blast cell proliferation versus disruption of
differentiation, the in vitro experimental conditions utilized here
may not have allowed these more subtle changes to be observed
between Stat activity and DDR induced apoptosis. It is very likely
that these two common survival pathways are playing a major role in
conferring refractoriness to chemotherapy, but that alternative, as
yet unrevealed, pathways also make a contribution.
[0705] Several AML samples within this cohort had two blast cell
populations discernible by their surface phenotype suggestive of
cell populations representing different stages of differentiation.
Of the two samples described in this manuscript, SCNP revealed that
each blast cell population had its own distinct signaling and
apoptosis profiles. Given the opportunity to apply SCNP assays to
samples taken over time from the same patient it may be possible to
determine which blast population confers refractoriness to
chemotherapy.
[0706] Further correlations to defined genetic abnormalities
driving these signaling observations could underscore their
potential roles in driving AML disease; such as analysis of
intracellular signaling pathways in the context of FLT3 mutational
status. The output from such studies could be to guide the choice
of available investigational and approved agents to provide benefit
for AML patients refractory to current chemotherapy regimens.
[0707] These data also demonstrate the applicability and utility of
using principle component analysis as a metric that can be used to
stratify patient data according to signaling pathway response.
However, these data also suggest accuracy of stratification can be
improved by first identifying distinct sub-populations of AML
blasts. For example, the diversity of different signaling pathway
responses in NR AML was observed not only within a heterogeneous of
samples but also within the same blast from a sample. Likewise,
different sub-populations of cells in a single sample demonstrated
different sensitivities to apoptosis, as demonstrated in FIG.
30(b). Therefore, these results demonstrate the applicability of
sequential analyses such as decision trees or gating analyses, to
AML sample data in order to identify and characterize variation in
signaling pathway response in distinct sub-populations of
heterogeneous AML samples. The identified signaling pathway
responses may then be statistically associated with apoptosis
profiles that can be used to inform patient treatment.
[0708] Samples associated with a multiplicity of sub-populations
with different signaling pathway responses can be further
characterized according to the relative amounts of each
sub-populations (e.g. by a percentage values or ratios). Reports
may be generated for physicians that characterize the
sub-populations of an AML sample, their relative amounts and the
unique biology (e.g. mutational status, signaling mechanisms, etc.)
allowing physicians to make informed treatment decisions based on
the heterogeneity of the patient's leukemia.
Example 11
[0709] SCNP assays were performed on 77 bone marrow samples from
pediatric AML patients enrolled in POG trial 9421 of which 67 were
evaluable/had sufficient data for analysis and were enriched for
non-responders (NR). 80 combinations of modulators and
intra-cellular proteins (signaling nodes) were investigated
including nodes involved in the phosphoinositide 3-kinase (PI3K),
Janus Kinases (JAK), signal transducers and activators of
transcription (STAT) and the DNA damage response and apoptosis
pathways. Basal and modulated protein levels in leukemic blasts
were measured using several metrics (e.g., fold change, total level
of phosphorylation, and a rank based method Uu measuring the
proportion of cells that shift from baseline), and nodes were
examined in univariate and multivariate analyses for their ability
to discriminate between AML responsive (CR, n=46) and
non-responsive (NR, n=21) to anthracycline/cytarabine-based
induction therapy. Furthermore, nodes were examined for their
ability to identify patients likely to be in complete continuous
remission (CCR, n=23) or relapse (CR-Rel, n=23) within 4 years.
Univariate analysis revealed 19 nodes associated with disease
response to conventional induction therapy and 9 associated with
CR-Rel (i.e., area under the operator/receiver curve (AUC of
ROC)>0.65; p<0.05). As in adult studies, nodes involved in
the apoptotic response to agents inducing DNA damage (e.g.,
etoposide.fwdarw.c-PARP AUC 0.83, AraC+Daunorubicin.fwdarw.c-PARP
AUC 0.76, AraC+Daunorubicin.fwdarw.p-Chk2 AUC 0.71) showed higher
levels of apoptosis in CR samples than in NR samples. Similarly,
FLT3 and SCF phosphorylation levels of PI3K pathway members S6 (AUC
0.70) and ERK (AUC 0.65) were also higher in CR samples, while
hydrogen peroxide as a modulator (acting either as a reactive
oxygen species or as a phosphatase inhibitor) revealed lower p-Akt
and p-PLC gamma levels in NR samples (AUC 0.70 for both). In
multivariate analysis combination of 2-8 nodes (representing
apoptosis, Jak/Stat and PI3K pathways) resulted in classifiers with
good performance characteristics (bootstrap adjusted AUC 0.84-0.88)
in predicting response to induction therapy. Increased sensitivity
to etoposide and anthracycline/cytarabine was also associated with
CCR in univariate analysis (AUC 0.77 and 0.68 respectively).
Thapsigargin, a modulator known to raise intracellular calcium,
induced p-Erk, p-CREB and p-S6 less in CR-Rel than in CCR samples.
To predict the response to therapy, multivariate classifiers were
better than individual nodes at discriminating between CR-Rel and
CCR groups (adjusted AUC>0.8). Additional analyses that evaluate
independence and ability to combine clinical or molecular
predictors (e.g., cytogenetics, FLT3-ITD) with SCNP data will be
presented. Tables 32 and 33 show important nodes for stratifying
pediatrics patients into CR vs. NR (Table 32) and relapse (Table
33).
TABLE-US-00033 TABLE 32 Important Nodes for stratifyng CR vs. NR
Node Importance Etoposide*1440_0_*1*0.1_DMSO*Cleaved
PARP_D214_*Blue_E-A*Ua 1.351 Thapsigargin*15_0_*5*0.05_DMSO*p-ERK
1/2_T202/Y204_*Red_C-A*AdjFoldP1 0.633
IL-27*15_0_*3*None*p-Stat3_S727_*Blue_D-A*AdjFoldP1 0.539
G-CSF*15_0_*3*None*p-Stat3_S727_*Blue_D-A*AdjFoldP1 0.532 Unstim/No
Modulator*1440_0_*1*None*Cleaved PARP_D214_*Blue_E-A*Ua 0.511
Ara-C+Daunorubicin-HCl*1440_0_+1440_0_*1*None*Cleaved
PARP_D214_*Blue_E-A*Ua 0.489
Staurosporine*360_0_*2*0.05_DMSO*Cleaved PARP_D214_*Blue_E-A*Ua
0.456 Etoposide*1440_0_*1*0.1_DMSO*Cleaved PARP_D214_*Blue_E-A*Uu
0.449 GM-CSF*15_0_*3*None*p-Stat3_S727_*Blue_D-A*AdjFoldP1 0.404
IL-27*15_0_*3*None*p-Stat1_Y701_*Blue_E-A*AdjFoldP1 0.373
SCF*15_0_*7*None*p-ERK 1/2_T202/Y204_*Blue_D-A*AdjFoldP1 0.369
FLT-3 Ligand*15_0_*7*None*p-S6_S235/236_*Blue_E-A*AdjFoldP1 0.364
Hydrogen Peroxide*15_0_*4*None*p-Akt_S473_*Blue_E-A*Ua 0.353 FLT-3
Ligand*5_0_*7*None*p-S6_S235/236_*Blue_E-A*AdjFoldP1 0.349
G-CSF*15_0_*3*None*p-Stat5_Y694_*Red_C-A*AdjFoldP1 0.341 Hydrogen
Peroxide*15_0_*4*None*p-Akt_S473_*Blue_E-A*AdjFoldP1 0.332 Hydrogen
Peroxide*15_0_*4*None*p-SLP-76_Y128_*Red_C-A*AdjFoldP1 0.305
Ara-C+Daunorubicin-HCl*360_0_+360_0_*1*None*p-Chk2_T68_*Red_C-A*Ua
0.303 IL-27*15_0_*3*None*p-Stat5_Y694_*Red_C-A*AdjFoldP1 0.288
IL-10*15_0_*3*None*p-Stat3_S727_*Blue_D-A*AdjFoldP1 0.285
IFN-a-2b*15_0_*3*None*p-Stat3_S727_*Blue_D-A*Ua 0.261 FLT-3
Ligand*15_0_*6*None*p-Stat3_Y705_*Blue_D-A*AdjFoldP1 0.26
G-CSF*15_0_*3*None*p-Stat3_S727_*Blue_D-A*Ua 0.255 Unstim/No
Modulator*360_0_*1*None*p-Chk2_T68_*Red_C-A*Ua 0.246 Unstim/No
Modulator*0*1*0.1_DMSO*Cleaved PARP_D214_*Blue_E-A*Ua 0.243
TABLE-US-00034 TABLE 33 Important nodes for stratifying CR-Rel vs.
CCR Node Importance
G-CSF*15_0_*3*None*p-Stat5_Y694_*Red_C-A*AdjFoldP1 0.458 Unstim/No
Modulator*360_0_*1*None*Cleaved PARP_D214_*Blue_E-A*Ua 0.422
Unstim/No Modulator*360_0_*1*0.1_DMSO*Cleaved
PARP_D214_*Blue_E-A*Ua 0.379
Thapsigargin*15_0_*5*0.05_DMSO*p-CREB_S133_*Blue_D-A*AdjFoldP1
0.366 Etoposide*360_0_*1*0.1_DMSO*Cleaved PARP_D214_*Blue_E-A*Ua
0.365 Etoposide*360_0_*1*0.1_DMSO*Cleaved PARP_D214_*Blue_E-A*Uu
0.356 Thapsigargin*15_0_*5*0.05_DMSO*p-ERK
1/2_T202/Y204_*Red_C-A*AdjFoldP1 0.319
IL-3*15_0_*3*None*p-Stat5_Y694_*Red_C-A*Ua 0.316
Thapsigargin*15_0_*5*0.05_DMSO*p-S6_S235/236_*Blue_E-A*Ua 0.306
G-CSF*15_0_*3*None*p-Stat1_Y701_*Blue_E-A*AdjFoldP1 0.305
IL-3*15_0_*3*None*p-Stat3_S727_*Blue_D-A*AdjFoldP1 0.299 Unstim/No
Modulator*0+0*9*None*CXCR4*Blue_E-A*RelExpr 0.298
IL-27*15_0_*3*None*p-Stat5_Y694_*Red_C-A*AdjFoldP1 0.292
G-CSF*15_0_*3*None*p-Stat5_Y694_*Red_C-A*Ua 0.249
Thapsigargin*15_0_*5*0.05_DMSO*p-S6_S235/236_*Blue_E-A*AdjFoldP1
0.248 IL-27*15_0_*3*None*p-Stat3_S727_*Blue_D-A*AdjFoldP1 0.232
GM-CSF*15_0_*3*None*p-Stat5_Y694_*Red_C-A*Ua 0.232
Ara-C+Daunorubicin-HCl*360_0_+360_0_*1*None*Cleaved
PARP_D214_*Blue_E-A*Ua 0.224
Staurosporine*360_0_*2*0.05_DMSO*Cleaved PARP_D214_*Blue_E-A*Uu
0.218 GM-CSF*15_0_*3*None*p-Stat3_S727_*Blue_D-A*AdjFoldP1 0.217
SCF*5_0_*7*None*p-S6_S235/236_*Blue_E-A*AdjFoldP1 0.216
IL-10*15_0_*3*None*p-Stat5_Y694_*Red_C-A*AdjFoldP1 0.213
IL-27*15_0_*3*None*p-Stat1_Y701_*Blue_E-A*AdjFoldP1 0.212
IL-27*15_0_*3*None*p-Stat5_Y694_*Red_C-A*Ua 0.202
GM-CSF*15_0_*3*None*p-Stat1_Y701_*Blue_E-A*AdjFoldP1 0.197
[0710] Conclusion: The training study data show the value of
performing quantitative SCNP under modulated conditions as the
basis for developing highly predictive tests for response to
induction chemotherapy in pediatric patients with newly diagnosed
AML. Independent validation studies will follow.
Example 12
[0711] Modulated single cell network profiling (SCNP) was used to
evaluate the activation state of intracellular signaling molecules
(i.e. nodes), including phosphorylated (p)-Akt, p-Erk, p-S6,
p-Stat5 and cleaved-PARP, at baseline and after treatment with
specific modulators [including cytokines (such as IL-27) growth
factors (such as FLT3 ligand) and drugs (such a cytosine
arabinoside)] in 7 healthy bone marrow mononuclear blasts (BMMb)
and leukemic myeloblasts, characterized for FLT3 receptor mutation
status, from 44 AML patients (38 FLT-WT and 6 FLT3-ITD), aged>60
years (ECOG trial E3999). A total of 64 node-metrics were
analyzed.
[0712] Signaling profiles differed significantly in FLT3-ITD vs.
FLT3-WT AML, and in FLT3-WT vs. BMMb (shown in FIG. 35 for a
representative node, FLT3 ligand induced p-S6). Specifically,
compared to BMMb, FLT3-ITD blasts uniformly showed increased basal
p-Stat5 levels, decreased FLT3 ligand-induced activation of PI3K
and Raf/Ras/Erk pathways, minimal IL-27 induced activation of the
Jak/Stat pathway, and higher apoptotic responses to DNA-damaging
agents. Two AMLs harboring a low FLT3-ITD mutant burden, however,
exhibited a signaling pattern similar to FLT3-WT AMLs. By contrast,
FLT3-WT samples displayed heterogeneous signaling profiles,
overlapping both with those of FLT3-ITD and BMMb samples,
suggesting that a fraction of FLT3-WT AML exhibit FLT3 receptor
pathway deregulation even without FLT3-ITD. Conclusions This study
showed that SCNP, which provides a detailed view of intracellular
signaling networks at the single-cell level, subclassified patients
with AML beyond their molecularly determined FLT3 mutation status.
In particular, a fraction of FLT3-WT AML signaled as if containing
a FLT3 receptor length mutation while FLT3-ITD with low mutational
load signaled like FLT3-WT AMLs. The clinical relevance of this
observation, both for disease prognosis and response to kinase
inhibitors, will be revealed only if AML patients are accrued to
kinase inhibition trials irrespective of FLT3 receptor mutation
status. The wide range of signaling responses observed in FLT3-WT
AML suggests that disease across FLT3-WT patients is heterogeneous,
likely promoted through distinct mutations and alterations, giving
rise to distinct signaling profiles in individual patients Our data
also provide evidence for the co-existence of differentially
signaling blast populations in individual patients. The potential
impact of signaling heterogeneity on clinical response needs to be
assessed and may require an individualized combination of treatment
modalities.
Example 13
[0713] We combined signaling pathway analysis and drug response
profiling in Acute Myeloid Leukemia (AML) samples using Single Cell
Network Profiling (SCNP) assays. This technology allow for the
simultaneous measurement of the activation state of multiple
signaling proteins at the single cell level.
[0714] Cryopreserved peripheral blood mononuclear cell (PBMC) blood
samples from patients with AML (N=6) were analyzed in two
experimental arms. #1 Signaling Arm: The effect of various kinase
inhibitors--tandutinib (Flt3); GDC-0941 (PI3Kinase); CI-1040 (MEK);
CP-690550 (JAK3 and JAK2); and rapamycin (mTor)--on multiple
signaling proteins in the JAK/STAT, MAPK, PI3K, and mTor pathways
was measured in the basal and evoked condition (via 15 minutes
growth factors stimulation) with various fluorochrome labeled
phospho-specific antibodies in cell subsets defined by the
expression of CD34, cKit, CD3, and light scatter properties. #2
Apotosis/Cytostasis Arm: The leukemic cells were driven into cell
cycle using IL-3, stem cell factor, and Flt3 ligand, followed by a
48-hr incubation with a combination of one to five aforementioned
kinase inhibitors for a total of 30 treatments per sample. The TKIs
impact was measured on distal functional readouts, including
apoptosis (cleaved PARP) and cell cycle (CyclinB1-S/G2 phase;
p-Histone H3-M phase). All results were compared with results from
bone marrow samples from healthy donors (N=6).
[0715] Each patient's sample generated a unique signaling profile
after short modulation with growth factors (SCF, Flt3L, IL-27,
G-CSF) with a broad range of responses (e.g. the percentage of SCF,
G-CSF and FLT-3L responsive cells ranged between 6%-49%, 3%-56%,
and 3%-22% respectively). The magnitude of signaling (fluorescence
change from basal state) was also quantified in multiple cell
subsets defined by surface receptor expression. Overall, patient
samples could be grouped based on their signaling profile,
proliferative potential, and sensitivity to kinase inhibitor
treatment. Specifically, two samples with the greatest SCF and
G-CSF signaling response also showed the most robust in vitro
proliferation and were most sensitive to the JAK inhibitor
CP-690,550 (1 .mu.M) (as measured by cytostasis readouts). Whereas,
two other samples that displayed only modest SCF and G-CSF
signaling, but robust Flt3L signaling expanded slowly in culture
and were particularly sensitive to the cytostatic effects of
GDC-0941 (1 uM) or tandutinib (1 uM), both as single agent and in
combination. Finally, the last two AML samples had weak growth
factor signaling and did not proliferate in culture and therefore
could not be tested for drug induced cytostasis. Of note, each
individual patient sample showed distinct sensitivity (as measured
by cytostasis and apoptosis) to different drug combinations. This
was in contrast to the bone marrow samples from healthy donors
which showed considerable similarity in response across all
inhibitor combinations.
[0716] This study provides data for the utility of SCNP to dissect
the pathophysiologic heterogeneity of hematologic tumors and assess
their differential response to single and combination therapies.
Ultimately, this functional pathway profiling and drug sensitivity
assay could be used in a clinical trial setting to stratify
patients.
Example 14
[0717] To compare the results of SCNP assays between paired fresh
and cryopreserved samples in a multicenter prospective study. 13
fresh BM and PB samples were prospectively collected from pediatric
or adult non-M3 AML patients at 3 academic centers and shipped over
night. Samples were required to have 2 million viable cells per
aliquot for SCNP assays, and underwent ficoll separation and
mononuclear cells were divided into 2 aliquots--one processed
fresh, and the second cryopreserved for 1 month, and then thawed
and processed for the SCNP assay. 70 SCNP node-metrics (i.e.
proteomic readouts in the presence or absence of modulator),
identified previously as candidate proteomic signatures for several
assays in development (including PIK3, Jak/STAT and DNA
damage/apoptosis pathways) were investigated. The assay readouts
for blast cells from a fresh aliquot were compared to the results
from a cryopreserved aliquot by linear regression, Bland-Altman,
and Lin's concordance analysis.
[0718] The analysis of paired aliquots from 13 patients, with
median WBC of 27.9 (3-60) 10e3/ul, showed that cryopreservation did
not affect sample quality as measured by percent of cells that were
negative for cleaved PARP expression (R.sup.2=0.92 cryopreserved
vs. fresh). The majority of unmodulated node-metrics (59%) and
modulated node-metrics (68%, see Table) had a good correlation
between the two preparations as measured by linear regression i.e.,
R.sup.2>0.64. The node-metrics with a lower R.sup.2 were using
either a dim fluorophore (i.e. Alexa-647) and/or were within the
low signal range (e.g., Erk basal); and therefore were not good
candidates for future test development. Results using both Bland
Altman and Lin's Concordance methods showed good concordance.
[0719] These studies highlight the importance of cryopreservation
of AML samples at clinical sites and by cooperative groups. These
results demonstrate that cryopreservation maintains the activation
signaling potential of AML blasts. SCNP assays developed and
validated using cryopreserved samples can be applied to fresh
samples and integrated prospectively into frontline clinical trials
and clinical practice.
TABLE-US-00035 TABLE 34 Goodness of fit (R.sup.2) values from
regressing Cryo against Fresh for modulated node-metrics. Fold and
U.sub.u (rank based) metrics measure changes in signaling protein
levels due to modulation. A = Alexa Assay Read- R.sup.2 for R.sup.2
for Modulator out Color Fold U.sub.u Cytarabine + cPARP FITC 0.71
0.63 Daunorubicin pChk2 A. 647 0.38 0.37 Etoposide cPARP FITC 0.78
0.49 pChk2 A 647 0.52 0.37 FLT3L pAkt A 647 0.13 0.09 pErk 1/2 PE
0.46 0.55 pS6 A 488 0.89 0.94 G-CSF pStat1 A 488 0.73 0.72 pStat3
PE 0.88 0.94 pStat5 A 647 0.89 0.85 H.sub.2O.sub.2 pAkt A 488 0.79
0.85 pPLCy2 PE 0.83 0.89 pSlp76 A 647 0.80 0.82 IL-27 pStat1 A 488
0.92 0.93 pStat3 PE 0.94 0.90 pStat5 A 647 0.93 0.92 PMA pCreb PE
0.92 0.93 pErk 1/2 A 647 0.94 0.90 pS6 A 488 0.93 0.92 SCF pAkt A
647 0.49 0.09 pErk 1/2 PE 0.15 0.18 pS6 A 488 0.86 0.75
Example 15
[0720] Objectives:
[0721] The objective of this study was to compare by SCNP the
functional effects of a panel of compounds simultaneously on
different signaling pathways (such as the phosphoinositide 3-kinase
(PI3K) and the Janus Kinases (Jak) signal transducers and
activators of transcription (Stat) pathway) relevant both to the
biology of the disease and the development of new therapeutics, in
paired, diagnostic, cryopreserved PB mononuclear cells (PBMC) and
BMMC samples from 44 AML patients. A paired sample was defined as a
BMMC and PBMC specimen collected from the same patient on the same
day.
[0722] Methods:
[0723] Modulated SCNP using a multiparametric flow cytometry
platform was used to evaluate the activation state of intracellular
signaling molecules in leukemic blasts under basal conditions and
after treatment with specific modulators (Table 35). The SCNP
phosphoflow assay was performed on 88 BMMC/PBMC pairs from ECOG
trial, E3999. The relationship between readouts of modulated
intracellular proteins ("nodes") between BMMC and PBMC was assessed
using linear regression, Bland-Altman method or Lin's concordance
correlation coefficient.
[0724] Table 35 shows the goodness of fit (R.sup.2) values from the
linear regression analysis for both the basal levels and the
modulated levels of intracellular signaling proteins. Most of the
signaling nodes show strong correlations (R.sup.2>0.64) with
several of the exceptions belonging to nodes with weak response to
modulation (e.g. SCF->p-Akt) or antibodies with dim fluorophores
(i.e. Alexa 647). The lack of response is however, consistent
between the tissue types for the weak response nodes. Using a rank
based metric that is less sensitive to the absolute intensity
levels seem to perform better for the antibodies with dim
fluorophores. Results from other methods; Bland Altman and Lin's
Concordance also show good concordance between the tissue
types.
[0725] The data presented here demonstrate: 1) Specimen source (BM
or PB) does not affect proteomic signaling in patients with AML and
circulating blasts. 2) PB myeloblasts can be used as a sample
source for Nodality SCNP assays to identify functionally distinct
leukemic blats cell populations with distinct sensitivities to
therapy. 3) The ability to use PB as a sample source will greatly
improve the utility of these assays. In particular, our results
will facilitate the monitoring of cellular signaling effects
following the administration of targeted therapies, e.g., kinase
inhibitors, at time-points when BM aspirates are not clinically
justifiable.
TABLE-US-00036 TABLE 35 Goodness of fit (R.sup.2) values from
regressing PB against BM. SCNP Nodes with R.sup.2 > 0.64 are
highlighted Fold and U.sub.u metrics measures increase (or
decrease) in signaling protein levels due to modulation. Fold
metrics measure the shift intensity, while U.sub.u (rank based)
metrics measure the proportion of cells that shift from baseline.
##STR00001##
[0726] One method of further improving the concordance between PB
and BM specimens could be to adjust the biological measurements by
a measure of the presence of subpopulations within the leukemic
sample, or by differences in the cell maturity of subpopulations.
This could be done for example by measuring the relative presence
of CD34+ cells in PB and BM leukemic samples and adjusting the
signaling of each tissue based on the % of CD34+ cells in the
tissue type. Similarly the signaling or biological measurements of
each cell within the sample could be scaled or adjusted according
to the relative expression of a specific surface marker on that
cell such as CD34 or CD11b or another marker of cell lineage or
cell maturity.
Example 16
[0727] SCNP assays were performed on paired, bone marrow (BM) and
peripheral blood (PB), samples from 44 AML patients (de novo,
evolved from an antecedent MDS or MPN or treatment related), >60
years old, enrolled on ECOG trial E3999. Based on two previous
training studies, 38 combinations of modulators and intra-cellular
proteins (signaling nodes along the phosphoinositide 3-kinase
(PI3K), the Janus Kinases (Jak) signal transducers and activators
of transcription (Stat) and the DNA damage response and apoptosis
pathways) were investigated. Basal and modulated protein levels and
the effect of modulation on proteins levels in the leukemic blast
cells were expressed using a variety of metrics. A total of 64
node/metric combinations (dimensions) were used to build
multi-parametric classifiers (ranging from 2 to 10 nodes/metrics)
using different modeling methodologies (including random forest,
boosting, lasso and a bootstrapped best subsets logistic modeling
approach that shrinks regression coefficients (BBLRS)) able to
predict the likelihood of response to induction therapy. The
performance characteristics of the classifiers built on the BM
samples were then evaluated independently on the paired PB
samples,
[0728] Several promising models with high area under the
operator/receiver curve (AUROC) values (indicating strong agreement
between actual clinical responses and responses as predicted by the
model) were developed based on SCNP proteomic read outs for BM
samples. The observed and predicted values from the current best
BBLRS model are shown in FIG. 34. The unadjusted AUROC of this
model is 0.98 and the expected AUROC for the model when applied to
an independent (validation) sample is 0.84. Five signaling nodes
are represented in this model; they include nodes belonging to
growth factor-induced survival pathways (PI3K, RAS/MAPK) as well as
DNA damage response and apoptosis pathways. When the predictive
accuracy of the lead SCNP classifier was compared to that of a
model based on traditional clinical/molecular predictors (i.e. the
combination of age, therapy-related AML, and karyotype) the
adjusted AUROC of the SCNP classifier far surpassed that of the
clinical predictors (adjusted AUROC=0.61 for clinical/molecular
predictors vs. adjusted AUROC=0.84 for the SCNP classifier).
Finally, when the nodes in the best BBLRS model developed on data
from BM samples were used to model read outs from the paired PB
samples, the adjusted AUROC of the resulting BBLRS model was
comparable to that of the model fit to BM samples.
[0729] This training set data show the value of performing
quantitative SCNP under modulated conditions as the basis for
developing highly predictive tests for response to induction
chemotherapy. Most importantly, the predictions made by the SCNP
classifier are independent of established prognostic factors, such
as age and cytogenetics The ability of one set of nodes to
accurately predict response in paired BM or PB samples from
individual patients suggests that the predictive power of the SCNP
assay is independent of sample source, further improving the
practicality of the test. Independent validation studies are
ongoing.
[0730] While preferred embodiments of the present invention have
been shown and described herein, it will be obvious to those
skilled in the art that such embodiments are provided by way of
example only. Numerous variations, changes, and substitutions will
now occur to those skilled in the art without departing from the
invention. It should be understood that various alternatives to the
embodiments of the invention described herein may be employed in
practicing the invention. It is intended that the following claims
define the scope of the invention and that methods and structures
within the scope of these claims and their equivalents be covered
thereby.
* * * * *
References