U.S. patent application number 12/996489 was filed with the patent office on 2011-08-11 for survival predictor for diffuse large b cell lymphoma.
This patent application is currently assigned to The United States of America, as represented by the Secretary, Department of Health and Human Serv. Invention is credited to Rita M. Braziel, Joseph M. Connors, Sandeep S. Dave, Jan Delabie, Richard I. Fisher, Randy D. Gascoyne, Elias Campo Guerri, Harald Holte, Elaine S. Jaffe, Pedro Jares, Stein Kvaloy, Georg Lenz, Andrew T. Lister, Armando Lopez-Guillermo, Hans-Konrad Muller-Hermelink, German Ott, Lisa Rimsza, Andreas Rosenwald, Erlend B. Smeland, Louis M. Staudt, Dennis Weisenburger, Wyndham H. Wilson, George Wright.
Application Number | 20110195064 12/996489 |
Document ID | / |
Family ID | 41165407 |
Filed Date | 2011-08-11 |
United States Patent
Application |
20110195064 |
Kind Code |
A1 |
Rimsza; Lisa ; et
al. |
August 11, 2011 |
SURVIVAL PREDICTOR FOR DIFFUSE LARGE B CELL LYMPHOMA
Abstract
The invention provides methods and materials related to a gene
expression-based survival predictor for diffuse large B cell
lymphoma (DLBCL) patients.
Inventors: |
Rimsza; Lisa; (Tucson,
AZ) ; Lister; Andrew T.; (London, GB) ;
Weisenburger; Dennis; (Elkhom, NE) ; Delabie;
Jan; (Omaha, NE) ; Smeland; Erlend B.; (Oslo,
NO) ; Holte; Harald; (Oslo, NO) ; Kvaloy;
Stein; (Oslo, NO) ; Braziel; Rita M.; (West
Linn, OR) ; Fisher; Richard I.; (Pittsford, NY)
; Jares; Pedro; (Barcelona, ES) ; Lopez-Guillermo;
Armando; (Barcelona, ES) ; Guerri; Elias Campo;
(Barcelona, ES) ; Jaffe; Elaine S.; (Great Falls,
VA) ; Lenz; Georg; (Berlin, DE) ; Wilson;
Wyndham H.; (Washington, DC) ; Wright; George;
(Rockville, MD) ; Dave; Sandeep S.; (Chapel Hill,
NC) ; Staudt; Louis M.; (Silver Spring, MD) ;
Gascoyne; Randy D.; (North Vancouver, CA) ; Connors;
Joseph M.; (Vancouver, CA) ; Muller-Hermelink;
Hans-Konrad; (Wrzburg, DE) ; Rosenwald; Andreas;
(Wrzburg, DE) ; Ott; German; (Bietigeim-Bissingen,
DE) |
Assignee: |
The United States of America, as
represented by the Secretary, Department of Health and Human
Serv
Bethesda
MD
Arizona Bloard of Regents on behalf of the University of
Arizona
Tuscon
AZ
Queen Mary and Westfiled College, University of London
London
NE
Board of Regents of the University of Nebraska
Lincoln
OR
Oslo University Hospital HF
Oslo
NY
Oregon Health & Science University
Portland
BC
University of Rochester
Rochester
Hospital Clinic
Barrcelona
Universitat De Barcelona
Barcelona
British Columbia Cancer Agency Branch
Vancouver
Julius-Mazimilians-University of Wuerzburg
Wrzburg
|
Family ID: |
41165407 |
Appl. No.: |
12/996489 |
Filed: |
June 5, 2009 |
PCT Filed: |
June 5, 2009 |
PCT NO: |
PCT/US2009/046421 |
371 Date: |
February 24, 2011 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
|
|
61059678 |
Jun 6, 2008 |
|
|
|
Current U.S.
Class: |
424/133.1 ;
506/7; 702/19 |
Current CPC
Class: |
C12Q 2600/112 20130101;
G01N 33/57407 20130101; G01N 2800/56 20130101; C12Q 2600/106
20130101; G01N 2800/52 20130101; A61P 35/00 20180101; C12Q 1/6886
20130101 |
Class at
Publication: |
424/133.1 ;
506/7; 702/19 |
International
Class: |
A61K 39/395 20060101
A61K039/395; C40B 30/00 20060101 C40B030/00; A61P 35/00 20060101
A61P035/00; G06F 19/00 20110101 G06F019/00 |
Claims
1-32. (canceled)
33. A method of predicting the survival outcome of a subject
suffering from diffuse large B cell lymphoma (DLBCL), which method
comprises: a) isolating gene expression product from one or more
DLBCL biopsy samples from a subject; b) obtaining a gene expression
profile from the gene expression product, wherein the expression
profile comprises an expression level for each gene in a germinal
center B cell (GCB) gene expression signature and a stromal-1 gene
expression signature; c) determining a GCB signature value and a
stromal-1 signature value from the gene expression profile; and d)
calculating a survival predictor score using an equation that
includes subtracting [(x)*(the GCB signature value)] and
subtracting [(y)*(the stromal-1 signature value)], wherein (x) and
(y) are scale factors and wherein a lower survival predictor score
indicates a more favorable survival outcome and a higher survival
predictor score indicates a less favorable survival outcome for the
subject.
34. The method of claim 33, wherein the GCB signature value
corresponds to the average of the expression levels of the genes in
the GCB gene expression signature and the stromal-1 signature value
corresponds to the average of the expression levels of the genes in
the stromal-1 gene expression signature.
35. The method of claim 33, wherein the method comprises: b)
obtaining a gene expression profile from the gene expression
product, wherein the expression profile comprises an expression
level for each gene in a GCB expression signature, a stromal-1 gene
expression signature, and a stromal-2 gene expression signature; c)
determining a GCB signature value, a stromal-1 signature value, and
a stromal-2 signature value from the gene expression profile; and
d) calculating a survival predictor score using an equation that
includes subtracting [(x)*(the GCB signature value)] and
subtracting [(y)*(the stromal-1 signature value)], and adding
[(z)*(the stromal-2 signature value)], wherein (x), (y), and (z)
are scale factors and wherein a lower survival predictor score
indicates a more favorable survival outcome and a higher survival
predictor score indicates a less favorable survival outcome for the
subject.
36. The method of claim 35, wherein the the GCB signature value
corresponds to the average of the expression levels of the genes in
the GCB gene expression signature, the stromal-1 signature value
corresponds to the average of the expression levels of the genes in
the stromal-1 gene expression signature, and the stromal-2
signature value corresponds to the average of the expression levels
of the genes in the stromal-2 gene expression signature.
37. The method of claim 35, wherein the method comprises: d)
calculating a survival predictor score using the equation: survival
predictor score=8.11-[0.419*(the GCB signature value)]-[1.015*(the
stromal-1 signature value)]+[0.675*(the stromal-2 signature
value)], wherein a lower survival predictor score indicates a more
favorable survival outcome and a higher survival predictor score
indicates a less favorable outcome for the subject.
38. A method of evaluating a therapeutic regimen for a DLBCL
patient based on survival outcome, wherein the method comprises: a)
isolating gene expression product from one or more DLBCL biopsy
samples from a subject; b) obtaining a gene expression profile from
the gene expression product, wherein the expression profile
comprises an expression level for each gene in a germinal center B
cell (GCB) gene expression signature and a stromal-1 gene
expression signature; c) determining a GCB signature value and a
stromal-1 signature value from the gene expression profile; and d)
calculating a survival predictor score using an equation that
includes subtracting [(x)*(the GCB signature value)] and
subtracting [(y)*(the stromal-1 signature value)], wherein (x) and
(y) are scale factors and wherein (i) a lower survival predictor
score indicates a more favorable survival outcome and a therapeutic
regimen that includes chemotherapy and Rituximab (R-CHOP) and (ii)
a higher survival predictor score indicates a less favorable
survival outcome and a therapeutic regimen other than R-CHOP.
39. The method of claim 38, wherein the DCLBCL patient suffered
relapse from R-CHOP treatment.
40. The method of claim 38, wherein the GCB signature value
corresponds to the average of the expression levels of the genes in
the GCB gene expression signature and the stromal-1 signature value
corresponds to the average of the expression levels of the genes in
the stromal-1 gene expression signature.
41. The method of claim 38, wherein the method comprises: b)
obtaining a gene expression profile from the gene expression
product, wherein the expression profile comprises an expression
level for each gene in a GCB expression signature, a stromal-1 gene
expression signature, and a stromal-2 gene expression signature; c)
determining a GCB signature value, a stromal-1 signature value, and
a stromal-2 gene signature value from the gene expression profile;
and d) calculating a survival predictor score using an equation
that includes subtracting [(x)*(the GCB signature value)] and
subtracting [(y)*(the stromal-1 signature value)], and adding
[(z)*(the stromal-2 signature value)], wherein (x), (y), and (z)
are scale factors and wherein (i) a lower survival predictor score
indicates a more favorable survival outcome and a therapeutic
regimen that includes chemotherapy and Rituximab (R-CHOP) and (ii)
a higher survival predictor score indicates a less favorable
survival outcome and a therapeutic regimen other than R-CHOP.
42. The method of claim 41, wherein the the GCB signature value
corresponds to the average of the expression levels of the genes in
the GCB gene expression signature, the stromal-1 signature value
corresponds to the average of the expression levels of the genes in
the stromal-1 gene expression signature, and the stromal-2
signature value corresponds to the average of the expression levels
of the genes in the stromal-2 gene expression signature.
43. The method of claim 41, wherein the method comprises: d)
calculating a survival predictor score using the equation: survival
predictor score=8.11-[0.419*(the GCB signature value)]-[1.015*(the
stromal-1 signature value)]+[0.675*(the stromal-2 signature value)]
and wherein (i) a lower survival predictor score indicates a more
favorable survival outcome and a therapeutic regimen that includes
chemotherapy and Rituximab (R-CHOP) and (ii) a higher survival
predictor score indicates a less favorable survival outcome and a
therapeutic regimen other than R-CHOP.
44. A method of treating a subject with DLBCL, the method
comprising evaluating a therapeutic regimen for a DLBCL patient
according to the method of claim 38 and providing a therapeutic
regimen based on the indication of the survival predictor score,
wherein a lower survival predictor indicates treatment with a
therapeutic regimen that includes chemotherapy and Rituximab
(R-CHOP) and a higher survival predictor score indicates a less
favorable survival outcome and a therapeutic regimen other than
R-CHOP.
45. The method of claim 44, wherein evaluating a therapeutic
regimen includes the method of claim 41.
46. A method of predicting the survival outcome of a subject
suffering from diffuse large B cell lymphoma (DLBCL), which method
comprises: a) obtaining one or more DLBCL biopsy samples from a
subject; b) isolating gene expression product from the one or more
DLBCL biopsy samples; c) obtaining a gene expression profile from
the gene expression product, wherein the expression profile
comprises an expression level for each gene in a germinal center
B-cell gene (GCB) expression signature, a stromal-1 gene expression
signature, and a stromal-2 gene expression signature; c)
determining a GCB signature value, a stromal-1 signature value, and
a stromal-2 signature value from the gene expression profile; and
d) calculating a survival predictor score using the equation:
survival predictor score=A-[(x)*(the GCB signature
value)]-[(y)*(the stromal-1 signature value)]+[(z)*(the stromal-2
signature value)], wherein A is an offset term and (x), (y), and
(z) are scale factors; and e) calculating the probability of a
survival outcome for the subject beyond an amount of time t
following treatment for DLBCL, wherein the subject's probability of
the survival outcome P(SO) is calculated using the equation:
P(SO)=SO.sub.0(t).sup.(exp((s)*survival predictor score)), wherein
SO.sub.0(t) is the probability of the survival outcome, which
corresponds to the largest time value smaller than t in a survival
outcome curve, and wherein (s) is a scale factor.
47. The method of claim 46, wherein the survival outcome is overall
survival and the method comprises: d) calculating a survival
predictor score using the equation: survival predictor
score=[0.419*(the GCB signature value)]-[1.015*(the stromal-1
signature value)]+[0.675*(the stromal-2 signature value)]; and e)
calculating the probability of overall survival after time t for
the subject, wherein the subject's probability of overall survival
P(OS) is calculated using the equation:
P(OS)=OS.sub.0(t).sup.(exp(survival predictor score)), wherein
OS.sub.0(t) is the probability of overall survival, which
corresponds to the largest time value smaller than t in an overall
survival curve, and wherein scale factor (s)=1.
48. The method of claim 46, wherein the survival outcome is
progression free survival and the method comprises: d) calculating
a survival predictor score using the equation: survival predictor
score=8.11-[0.419*(the GCB signature value)]-[1.015*(the stromal-1
signature value)]+[0.675*(the stromal-2 signature value)]; and e)
calculating the probability of progression free survival after time
t for the subject, wherein the subject's probability of progression
free survival P(PFS) is calculated using the equation
P(PFS)=F.sub.0(t).sup.(exp(0.976*survival predictor score)),
wherein F.sub.0(t) is the probability of progression free survival,
which corresponds to the largest time smaller than t in a survival
curve, and wherein wherein scale factor (s)=0.976.
49. The method of claim 46, wherein the the GCB signature value
corresponds to the average of the expression levels of the genes in
the GCB gene expression signature, the stromal-1 signature value
corresponds to the average of the expression levels of the genes in
the stromal-1 gene expression signature, and the stromal-2
signature value corresponds to the average of the expression levels
of the genes in the stromal-2 gene expression signature.
50. The method of claim 46, wherein the treatment for DLCBL
includes the administration Rituximab.
51. The method of claim 46, wherein the method further includes
providing the subject with the calculated probability of the
survival outcome after time t.
52. A method of evaluating a subject for antiangiogenic therapy of
DLBCL, which method comprises: a) isolating gene expression product
from one or more DLBCL biopsy samples from a subject; b) obtaining
a gene expression profile from the gene expression product, wherein
the profile includes an expression level for each gene in a
stromal-2 signature; c) determining the subject's stromal-2
signature value from the gene expression profile; and d)
determining whether the subject's stromal-2 signature value is
higher or lower than a standard stromal-2 value, wherein
antiangiogenic therapy is indicated by a stromal-2 signature value
that is higher than the standard stromal-2 value and antiangiogenic
therapy is not indicated by a stromal-2 signature value that is not
higher than the standard stromal-2 value.
Description
CROSS-REFERENCE TO RELATED APPLICATION
[0001] This application claims the benefit of U.S. Provisional
Patent Application No. 61/059,678, filed on Jun. 6, 2008, the
disclosure of which is incorporated by reference.
INCORPORATION-BY-REFERENCE OF MATERIAL SUBMITTED ELECTRONICALLY
[0002] Incorporated by reference in its entirety herein is a
computer-readable nucleotide sequence listing submitted
concurrently herewith and identified as follows: One 1,231,630 Byte
ASCII (Text) file named "704777_ST25.TXT," created on Jun. 5,
2009.
BACKGROUND OF THE INVENTION
[0003] The current standard of care for the treatment of diffuse
large B cell lymphoma (DLBCL) includes anthracycline-based
chemotherapy regimens such as CHOP in combination with the
administration of the anti-CD20 monoclonal antibody Rituximab. This
combination regimen (R-CHOP) can cure about 60% of patients and has
improved the overall survival of DLBCL patients by 10-15% (Coiffier
et al., N. Engl. J. Med., 346: 235-42 (2002)). Nonetheless, the
molecular basis of response or resistance to this therapy is
unknown.
[0004] DLBCL is a molecularly heterogeneous disease (Staudt et al.,
Adv. Immunol., 87: 163-208 (2005)), and different molecular
subtypes of DLBCL can have very different prognoses following
treatment. For example, gene expression profiling has identified
two molecular subtypes of DLBCL that are biologically and
clinically distinct (Rosenwald et al., N. Engl. J. Med., 346:
1937-47 (2002); Alizadeh et al., Nature, 403: 503-11 (2000)). The
germinal center B cell-like (GCB) DLBCL subtype likely arises from
normal germinal center B cells, whereas the activated B cell-like
(ABC) DLBCL subtype may arise from a post-germinal center B cell
that is blocked during plasmacytic differentiation. Many oncogenic
mechanisms distinguish these subtypes: GCB DLBCLs have recurrent
t(14,18) translocations, whereas ABC DLBCLs have recurrent trisomy
3 and deletion of the INK4a/ARF locus as well as constitutive
activation of the anti-apoptotic NF-kB signalling pathway
(Rosenwald et al., N. Engl. J. Med., 346: 1937-47 (2002); Bea et
al., Blood, 106: 3183-90 (2005); Tagawa et al., Blood, 106: 1770-77
(2005); Davis et al., J. Exp. Med., 194:1861-74 (2001); Ngo et al.,
Nature, 441: 106-10 (2006); Lenz et al., Science, 319: 1676-79
(2008)). When treated with CHOP-like chemotherapy, the overall
survival rates of patients with GCB DLBCL and ABC DLBCL were 60%
and 30%, respectively (Wright et al., Proc. Nat'l. Acad. Sci. USA,
100: 9991-96 (2003)). Thus, the prognosis for different DLBCL can
vary widely.
[0005] A separate analytical approach identified four gene
expression signatures that reflect distinct DLBCL tumor attributes
and that were associated with distinct survival profiles in
CHOP-treated DLBCL patients (Rosenwald et al., N. Engl. J. Med.,
346: 1937-47 (2002)). A "germinal center B cell" (GCB) signature
was associated with a favorable prognosis and paralleled the
distinction between ABC and GCB DLBCL. The "proliferation"
signature was associated with an adverse prognosis and included MYC
and its target genes. The "MHC class II" signature was silenced in
the malignant cells in a subset of DLBCL cases, an event that was
associated with inferior survival (Rosenwald et al., N. Engl. J.
Med., 346: 1937-47 (2002); Rimsza et al., Blood, 103: 4251-58
(2004)). A fourth prognostic signature, termed "lymph node"
signature was associated with favorable prognosis and included
components of the extracellular matrix, suggesting that it reflects
the nature of the tumor-infiltrating non-malignant cells. These
signatures predicted survival in a statistically independent
fashion, indicating that multiple biological variables dictate the
response to CHOP chemotherapy in DLBCL.
[0006] Reports have suggested that the benefit of Rituximab
immunotherapy might be restricted to certain molecular subtypes of
DLBCL. High expression of BCL-2 or low expression of BCL-6 was
associated with inferior survival with CHOP therapy. However, this
distinction disappeared with R-CHOP therapy (Mounier et al., Blood,
101: 4279-84 (2003); Winter et al., Blood, 107: 4207-13 (2006)).
Immunohistochemistry has also been used to distinguish DLBCLs with
a germinal center versus post-germinal center phenotype. Although
such immunohistochemical phenotypes were prognostically significant
in CHOP-treated cases, they were not prognostic for R-CHOP-treated
cases (Nyman et al., Blood, 109: 4930-35 (2007)).
[0007] Accordingly, there is a need for new methods of
distinguishing among DLBCL subtypes that is prognostically
significant for R-CHOP-treated patients.
BRIEF SUMMARY OF THE INVENTION
[0008] The invention provides methods and arrays related to a gene
expression-based survival predictor for DLBCL patients, including
patients treated with the current standard of care, which includes
chemotherapy and the administration of Rituximab.
[0009] The invention provides a method of predicting the survival
outcome of a subject suffering from diffuse large B cell lymphoma
(DLBCL) that includes obtaining a gene expression profile from one
or more DLBCL biopsy samples from the subject. The gene expression
profile, which can be derived from gene expression product isolated
from the one or more biopsy samples, includes an expression level
for each gene in a germinal center B cell (GCB) gene expression
signature and each gene in a stromal-1 gene expression signature.
From the gene expression profile, a GCB signature value and a
stromal-1 signature value are derived. From these values, a
survival predictor score can be calculated using an equation that
includes subtracting [(x)*(the GCB signature value)] and
subtracting [(y)*(the stromal-1 signature value)]. In the equation,
(x) and (y) are scale factors. A lower survival predictor score
indicates a more favorable survival outcome, and a higher survival
predictor score indicates a less favorable survival outcome for the
subject.
[0010] The invention also provides a method of generating a
survival estimate curve for subjects suffering from DLBCL.
Generally the method includes obtaining a gene expression profile
from one or more DLBCL biopsy samples taken from each member of a
plurality of subjects. Each gene expression profile, which can be
derived from gene expression product isolated from the one or more
biopsy samples taken from each subject, includes an expression
level for each gene in a GCB expression signature, a stromal-1 gene
expression signature, and a stromal-2 gene expression signature.
For each subject, the GCB signature value, the stromal-1 signature
value, and the stromal-2 signature value are determined from the
subject's gene expression profile, and, for each subject, a
survival predictor score is generated. Each subject's survival
outcome following treatment for DLBCL is tracked. A survival
estimate curve is generated which correlates the probability of the
tracked survival outcome with time following treatment for DLBCL
and which also correlates the tracked outcome over time with the
survival predictor score for the subjects.
[0011] The invention additionally provides a method of predicting
the survival outcome of a subject suffering from DLBCL. Generally,
the method includes obtaining a gene expression profile from one or
more DLBCL biopsy samples from the subject. The gene expression
profile, which can be derived from gene expression product isolated
from the one or more biopsy samples, includes an expression level
for each gene in a GCB gene expression signature, each gene in a
stromal-1 gene expression signature, and each gene in a stromal-2
gene expression signature. The GCB signature value, the stromal-1
signature value, and the stromal-2 signature value are determined
from the gene expression profile. The method then includes
calculating a survival predictor score using the equation:
survival predictor score=A-[(x)*(the GCB signature
value)]-[(y)*(the stromal-1 signature value)]+[(z)*(the stromal-2
signature value)].
In this equation, A is an offset term, and (x), (y), and (z) are
scale factors. The method further includes calculating the
probability of a survival outcome for the subject beyond an amount
of time t following treatment for DLBCL, wherein the subject's
probability of the survival outcome P(SO) is calculated using the
equation:
P(SO)=SO.sub.0(t).sup.(exp((s)*survival predictor score))
In this equation, SO.sub.0(t) is the probability of the survival
outcome, which corresponds to the largest time value smaller than t
in a survival outcome curve, and wherein (s) is a scale factor.
[0012] Furthermore, the invention provides a method of evaluating a
subject for antiangiogenic therapy of DLBCL. The method includes
obtaining a gene expression profile from one or more DLBCL biopsy
samples from the subject. The gene expression profile, which can be
derived from gene expression product isolated from the one or more
biopsy samples, includes an expression level for each gene in a
stromal-2 signature. The subject's stromal-2 signature value is
then derived from the gene expression profile and evaluated to
determine whether the subject's stromal-2 signature value is higher
or lower than a standard stromal-2 value. If the subject's
stromal-2 signature value is higher than the standard stromal-2
value, then antiangiogenic therapy is indicated, and the subject
can be treated with antiangiogenic therapy. If the subject's
stromal-2 signature value is not higher than the standard stromal-2
value, then antiangiogenic therapy is not indicated.
[0013] The invention also provides a second method of evaluating a
subject for antiangiogenic therapy of DLBCL. The method includes
obtaining a gene expression profile from one or more DLBCL biopsy
samples from the subject. The gene expression profile, which can be
derived from gene expression product isolated from the one or more
biopsy samples, includes an expression level for each gene in a
stromal-1 signature and in a stromal-2 signature. The subject's
stromal-1 signature value and stromal-2 signature value are then
derived from the gene expression profile. The stromal-1 signature
value is subtracted from the stomal-2 signature value to thereby
obtain the subject's stromal score. The subject's stromal score is
evaluated to determine whether it is higher or lower than a
standard stromal score. If the subject's stromal score is higher
than the standard stromal score, then antiangiogenic therapy is
indicated, and the subject can be treated with antiangiogenic
therapy. If the subject's stromal score is not higher than the
standard stromal-score, then antiangiogenic therapy is not
indicated.
[0014] Additionally, the invention provides a machine-readable
medium containing a digitally encoded GCB signature value, a
digitally encoded stromal-1 signature value, a digitally encoded
stromal-2 signature, or any combination of the foregoing signature
values obtained from a subject suffering from DLBCL.
[0015] In another embodiment the invention provides a
machine-readable medium containing the digitally encoded survival
predictor score obtained using a method disclosed herein for
predicting the survival outcome of a subject suffering from diffuse
large B cell lymphoma (DLBCL). In yet another embodiment, the
invention provides a machine-readable medium containing the
survival estimate curve obtained using a method disclosed herein
for generating a survival estimate curve for subjects suffering
from DLBCL. In still another embodiment, the invention provides a
machine-readable medium containing the digitally encoded
probability of survival calculated according to a method disclosed
herein for predicting the survival outcome (e.g., progression-free
survival or overall survival) of a subject suffering from DLBCL.
Furthermore, the invention provides a machine-readable medium
containing the digitally encoded stromal score generated by a
method disclosed herein for evaluating a subject for antiangiogenic
therapy of DLBCL.
[0016] The invention also provides a targeted array comprising at
least one probe or at least one set of probes for each gene in a
germinal center B cell gene (GCB) expression signature, a stromal-1
gene expression signature, and a stromal-2 gene expression
signature. The array can include probes for fewer than 20,000 genes
or fewer than 10,000 genes.
BRIEF DESCRIPTION OF THE DRAWINGS
[0017] FIG. 1A is a Kaplan-Meier estimates plot depicting the
probability of progression-free-survival versus time (in years) of
patients with GCB DLBCL and ABC DLBCL. The plot indicates that GCB
patients have a more favorable, i.e., higher probability of
progression-free survival rate than ABC patients for at least five
years following R-CHOP therapy.
[0018] FIG. 1B a Kaplan-Meier estimates plot depicting the
probability of overall survival versus time (in years) of patients
with GCB DLBCL and ABC DLBCL. The plot indicates that GCB patients
have a more favorable, i.e., higher probability, of overall
survival than ABC patients for at least five years following R-CHOP
therapy.
[0019] FIG. 1C is a series of four Kaplan-Meier estimates plots
depicting the probabilities of overall survival versus time (in
years) in DLBCL patients. Each of the four plots correlates the
probability of overall survival with the lymph node/stromal-1,
germinal center B cell, proliferation, or MHC class II gene
expression signature, respectively. Moreover, in each plot, the
average expression of the signature genes in each biopsy sample was
used to rank cases and divide the cohort into quartile groups as
indicated.
[0020] FIG. 2A is a pair of Kaplan-Meier estimates plots depicting
the probability of progression-free-survival and the probability of
overall survival, as indicated, versus time (in years) among DLBCL
patients treated with R-CHOP. Patient samples were ranked according
to a bivariate model created using the germinal center B cell (GCB)
and stromal-1 signatures and divided into quartile groups.
[0021] FIG. 2B is a pair of Kaplan-Meier plots depicting the
probability of progression-free-survival and the probability of
overall survival, as indicated, versus time (in years) among DLBCL
patients treated with R-CHOP. Patient samples were ranked according
to a survival predictor score derived from a model incorporating
the germinal center B cell, stromal-1, and stromal-2 signatures and
divided into quartile groups.
[0022] FIG. 2C is a series of three Kaplan-Meier estimates plots
depicting the probability of overall survival versus time (in
years) among R-CHOP treated DLBCL patients in the indicated low,
intermediate, or high IPI risk groups. Patient samples were
stratified according to the same survival predictor score used in
FIG. 2B, except that the first and second quartiles were merged,
and the third and fourth quartiles were merged.
[0023] FIG. 3 depicts the expression levels of the indicated GCB
cell, stromal-1, and stromal-2 signature genes in ABC, GCB, and
unclassified DLBCL biopsy samples. Relative levels of gene
expression are depicted according to the scale shown. Shown at the
bottom are the signature averages for each patient. Also shown is
the stromal score, which is the component of the survival model
contributed by the difference between the stromal-2 and stromal-1
signature averages. The survival predictor score is shown for each
patient and was used to order the cases, after grouping into ABC
DLBCL, GCB DLBCL, and unclassified categories.
[0024] FIG. 4A depicts the relative gene expression of stromal-1,
stromal-2, and germinal center B cell signatures in CD19+ malignant
and CD19- non-malignant subpopulations of cells isolated from three
biopsy specimens from patients with DLBCL. Stromal-1 and stromal-2
signature genes were more highly expressed in the non-malignant
cells, whereas the germinal center B cell signature genes were more
highly expressed in the malignant cells. The log 2 ratios of gene
expression levels in the CD19- subpopulation to those in the CD19+
subpopulations are depicted according to the scale shown.
[0025] FIG. 4B depicts the results of gene enrichment analysis
comparing the stromal-1 gene signature with mesenchyme-1 and
mesenchyme-2 signatures (from normal mesenchymal origin cells),
with a monocyte signature expressed more highly in normal blood
monocytes than in blood B, T, and NK cells, and in a pan-T cell
signature expressed more highly in blood T cells than in blood B
cells, NK cells, and monocytes. While a relationship was seen
between stromal-1 signature and mesenchyme-1, mesenchyme-2, and
monocyte signatures, no relationship was observed between the
stromal-1 signature and a pan-T cell signature expressed more
highly in blood T cells than in blood B cells, NK cells, and
monocytes. The relative levels of gene expression are depicted
according to the scale shown.
[0026] FIG. 5A is a Kaplan-Meier estimates plot depicting the
probability of overall survival versus time (in years) in DLBCL
cases segregated according to SPARC protein expression levels, as
indicated.
[0027] FIG. 5B is a pair of images showing the identification of
tumor blood vessels by immunohistochemical analysis of CD34+
endothelial cells in representative DLBCL biopsies having low or
high blood vessel density (CD34+ objects/.mu.M.sup.2), as
indicated.
[0028] FIG. 5C is a plot depicting the correlation between the
tumor blood vessel density and the stromal score in analyzed DLBCL
biopsies.
[0029] FIG. 6A is a Kaplan-Meier estimates plot depicting the
probability of overall survival versus time (in years) for "LLMPP
CHOP" patients with DLBCL following therapy. The plot indicates
that in this cohort, patients with GCB DLBCL show significantly
superior overall survival compared to patients with ABC DLBCL
following CHOP therapy.
[0030] FIG. 6B is a is a Kaplan-Meier estimates plot depicting
depicting the probability of overall survival versus time (in
years) for "MMMLNP CHOP" patients with DLBCL following therapy. In
this cohort, patients with GCB DLBCL show significantly superior
overall survival compared to patients with ABC DLBCL following CHOP
therapy.
[0031] FIG. 7 is a set of four Kaplan-Meier estimates plots
depicting the probability of overall survival versus time (in
years) in a "MMMLNP CHOP" cohort. Each of the four plots correlates
the probability of overall survival with the lymph node/stromal-1,
germinal center B cell, proliferation, or MHC class II gene
expression signature, respectively. Moreover, in each plot, the
average expression of the signature genes in each biopsy sample was
used to rank cases and divide the cohort into quartile groups as
indicated.
[0032] FIG. 8A is a Kaplan-Meier estimates plot depicting the
probability of overall survival versus time (in years) in a "LLMPP
CHOP" cohort, which was divided according to MHC class II signature
expression levels. Patients with low MHC class II signature
expression have significantly inferior overall survival compared to
patients with normal MHC class II expression.
[0033] FIG. 8B is a Kaplan-Meier estimates plot depicting the
probability of overall survival versus time (in years) in a "MMMLNP
CHOP" cohort, which was divided according to MHC class II signature
expression levels. Patients with low MHC class II signature
expression have significantly inferior overall survival compared to
patients with normal MHC class II expression.
[0034] FIG. 8C is a Kaplan-Meier estimates plot depicting the
probability of overall survival versus time (in years) in a "LLMPP
R-CHOP" cohort, which was divided according to MHC class II
signature expression levels. There was no significant difference in
the overall survival of patients with low MHC class II signature
expression as compared to patients with normal MHC class II
expression.
[0035] FIG. 9A is a pair of Kaplan-Meier estimates plots depicting
the probabilities of progression-free survival or overall survival,
as indicated, versus time (in years) among patients grouped into
quartiles according to a gene expression model consisting of
stromal-1 signature, GCB signature, and signature 122 following
R-CHOP therapy.
[0036] FIG. 9B is a pair Kaplan-Meier estimates plots depicting the
probabilities of overall survival versus time (in years) among
"MMMLNP CHOP" cohort patients grouped into quartiles according to a
gene expression model consisting of either stromal-1 signature and
GCB signature or stromal-1, GCB signature, and signature 122, as
indicated, following CHOP therapy.
[0037] FIG. 9C is a Kaplan-Meier estimates plot depicting the
probabilities of overall survival versus time (in years) among
"MMMLNP CHOP" cohort patients grouped into quartiles according to a
gene expression model consisting of stromal-1 signature, GCB
signature, and stromal-2 signature following CHOP therapy.
[0038] FIG. 10A is a Kaplan-Meier estimates plot depicting the
overall survival among low revised International Prognostic Index
(IPI) risk group patients stratified according to the gene
expression-based outcome predictor score. After grouping patients
into quartiles according to gene expression-based outcome predictor
score, quartiles 1 and 2 were merged (Low Model Score), and
quartiles 3 and 4 were merged (High Model Score).
[0039] FIG. 10B is a Kaplan-Meier estimates plot depicting the
overall survival among intermediate revised International
Prognostic Index (IPI) risk group patients stratified according to
the gene expression-based outcome predictor. After grouping
patients into quartiles according to gene expression-based outcome
predictor score, quartiles 1 and 2 were merged (Low Model Score),
and quartiles 3 and 4 were merged (High Model Score).
[0040] FIG. 10C is a Kaplan-Meier estimates plot depicting the
overall survival among high revised International Prognostic Index
(IPI) risk group patients stratified according to the gene
expression-based outcome predictor. After grouping patients into
quartiles according to gene expression-based outcome predictor
score, quartiles 1 and 2 were merged (Low Model Score), and
quartiles 3 and 4 were merged (High Model Score).
[0041] FIG. 11 depicts normal mesenchymal-1 and normal
mesenchymal-2 signature gene expression in various normal
tissues.
DETAILED DESCRIPTION OF THE INVENTION
[0042] The invention provides a gene expression-based survival
predictor for DLBCL patients, including those patients receiving
the current standard of care, R-CHOP. The survival predictor can be
used to determine the relative probability of a survival outcome in
a specific subject. The survival predictor can also be used to
predict; i.e., determine the expected probability that a survival
outcome will occur by a defined period following treatment for
DLBCL. Such prognostic information can be very useful to both the
patient and the physician. Patients with survival predictor scores
that indicate inferior outcome with R-CHOP therapy could be
candidates for a different therapeutic regimen, if, for example,
they relapse from R-CHOP treatment. The survival predictor can also
be used in the design of clinical studies and analysis of clinical
data to provide a quantitative survey of the types of DLBCL
patients from which clinical data was gathered. The predictor can
be used to improve one or more comparisons between data from
different sources (e.g., from different clinical trials), by
enabling comparisons with respect to patient characteristics, which
are manifested in the gene expression levels that determine and,
thus, are embodied in the predictor. Furthermore, the invention
provides information that can be very valuable to a DLBCL patient,
since the patient may be inclined to order his or her life quite
differently, depending on whether the patient has a high or low
probability of surviving and/or remaining progression-free for a
period of time following treatment.
[0043] The following abbreviations are used herein: ABC, activated
B cell-like diffuse large B cell lymphoma; CHOP, cyclophosphamide,
doxorubicine, vincristine, and prednisone; CI, confidence interval;
COP, cyclophosphamide, vincristine, and prednisone; DLBCL, diffuse
large B cell lymphoma; DOD, dead of disease; ECOG, Eastern
Cooperative Oncology Group; FACS, fluorescence-activated cell
sorting; FH, follicular hyperplasia; FISH, fluorescence in situ
hybridization; FL, follicular lymphoma; GC, germinal center; GCB,
germinal center B cell-like diffuse large B cell lymphoma; IPI,
International Prognostic Index; LPC, lymphoplasmacytic lymphoma;
MHC, major histocompatibility complex; NA, not available or not
applicable; NK, natural killer; PCR, polymerase chain reaction;
RQ-PCR, real-time quantitative PCR; RT-PCR, reverse transcriptase
polymerase chain reaction; SAGE, serial analysis of gene
expression; WHO, World Health Organization.
[0044] The term "R-CHOP" as used herein refers generally to any
therapeutic regimen that includes chemotherapy and the
administration of Rituximab. Accordingly, while the term can refer
to a Rituximab combination therapy that includes a CHOP regimen of
cyclophosphamide, doxorubicine, vincristine, and prednisone, the
term R-CHOP can also refer to therapy that includes Rituximab in
combination with a chemotherapeutic regimen other than CHOP.
[0045] The phrase "gene expression data" as well as "gene
expression level" as used herein refers to information regarding
the relative or absolute level of expression of a gene or set of
genes in a cell or group of cells. The level of expression of a
gene may be determined based on the level of RNA, such as mRNA,
encoded by the gene. Alternatively, the level of expression may be
determined based on the level of a polypeptide or fragment thereof
encoded by the gene. Gene expression data may be acquired for an
individual cell, or for a group of cells such as a tumor or biopsy
sample. Gene expression data and gene expression levels can be
stored on computer readable media, e.g., the computer readable
medium used in conjunction with a microarray or chip reading
device. Such gene expression data can be manipulated to generate
gene expression signatures.
[0046] The term "microarray," "array," or "chip" refers to a
plurality of nucleic acid probes coupled to the surface of a
substrate in different known locations. The substrate is preferably
solid. Microarrays have been generally described in the art in, for
example, U.S. Pat. No. 5,143,854 (Pirrung), U.S. Pat. No. 5,424,186
(Fodor), U.S. Pat. No. 5,445,934 (Fodor), U.S. Pat. No. 5,677,195
(Winkler), U.S. Pat. No. 5,744,305 (Fodor), U.S. Pat. No. 5,800,992
(Fodor), and U.S. Pat. No. 6,040,193 (Winkler), and Fodor et al.,
Science, 251: 767-777 (1991).
[0047] The term "gene expression signature" or "signature" as used
herein refers to a group of coordinately expressed genes. The genes
making up this signature may be expressed in a specific cell
lineage, stage of differentiation, or during a particular
biological response. The genes can reflect biological aspects of
the tumors in which they are expressed, such as the cell of origin
of the cancer, the nature of the non-malignant cells in the biopsy,
and the oncogenic mechanisms responsible for the cancer (Shaffer et
al., Immunity, 15: 375-385 (2001)). Examples of gene expression
signatures include lymph node, proliferation (Rosenwald et al., New
Engl. J. Med., 346: 1937-1947 (2002)), MHC class II, ABC DLBCL
high, B cell differentiation, T-cell, macrophage, immune
response-1, and immune response-2 signatures (U.S. Patent
Application Publication No. 2007/0105136 (Staudt)).
[0048] The term "signature value" as used herein corresponds to a
mathematical combination of measurements from expression levels of
the genes in a gene expression signature. An exemplary signature
value is a signature average which corresponds to the average or
mean of the individual expression levels in a gene expression
signature.
[0049] The phrase "survival predictor score" as used herein refers
to a score generated by a multivariate model used to predict
survival based on gene expression. A subject with a higher survival
predictor score is predicted to have poorer survival than a subject
with a lower survival predictor score.
[0050] The term "survival" or "overall survival" as used herein may
refer to the probability or likelihood of a subject surviving for a
particular period of time. Alternatively, it may refer to the
likely term of survival for a subject, such as expected mean or
median survival time for a subject with a particular gene
expression pattern.
[0051] The term "progression free survival" as used herein can
refer to the probability or likelihood of a subject surviving
without significant progression or worsening of disease for a
particular period of time. Alternatively, it may refer to the
likely term for a subject of survival without significant
progression or worsening of disease, such as expected mean or
median survival time for a subject with a particular gene
expression pattern without significant progression or worsening of
disease.
[0052] The term "survival outcome" as used herein may refer to
survival, overall survival, or progression free survival.
[0053] The phrase "scale factor" as used herein refers to a factor
that relates change in gene expression to prognosis. An example of
a scale factor is a factor obtained by maximizing the partial
likelihoods of the Cox proportional hazards model.
[0054] The gene expression signatures, signature values, survival
predictor scores, stromal scores, survival estimate curves, and
probabilities of survival disclosed herein may be stored in
digitally encoded format on computer readable media, e.g., computer
readable media used in conjunction with microarray or chip reading
devices or computer readable media used to store patient data
during treatment for DLBCL. Such media and the specialized devices
that use them, e.g., for diagnostic and clinical applications, are
known in the art.
[0055] The invention provides a method for predicting a survival
outcome in a subject diagnosed with DLBCL using gene expression
data. Such data may be gathered using any effective method of
quantifying gene expression. For example, gene expression data may
be measured or estimated using one or more microarrays. The
microarrays may be of any effective type, including, but not
limited to, nucleic acid based or antibody based. Gene expression
may also be measured by a variety of other techniques, including,
but not limited to, PCR, quantitative RT-PCR, real-time PCR, RNA
amplification, in situ hybridization, immunohistochemistry,
immunocytochemistry, FACS, serial analysis of gene expression
(SAGE) (Velculescu et al., Science, 270: 484-87 (1995)), Northern
blot hybridization, or western blot hybridization.
[0056] Nucleic acid microarrays generally comprise nucleic acid
probes derived from individual genes and placed in an ordered array
on a support. This support may be, for example, a glass slide, a
nylon membrane, or a silicon wafer. Gene expression patterns in a
sample are obtained by hybridizing the microarray with the gene
expression product from the sample. This gene expression product
may be, for example, total cellular mRNA, rRNA, or cDNA obtained by
reverse transcription of total cellular mRNA. The gene expression
product from a sample is labeled with a radioactive, fluorescent,
or other label to allow for detection. Following hybridization, the
microarray is washed, and hybridization of the gene expression
product to each nucleic acid probe on the microarray is detected
and quantified using a detection device such as a phosphorimager or
scanning confocal microscope.
[0057] There are two broad classes of microarrays: cDNA and
oligonucleotide arrays. cDNA arrays consist of hundreds or
thousands of cDNA probes immobilized on a solid support. These cDNA
probes are usually 100 nucleotides or greater in size. There are
two commonly used designs for cDNA arrays. The first is the
nitrocellulose filter array, which is generally prepared by robotic
spotting of purified DNA fragments or lysates of bacteria
containing cDNA clones onto a nitrocellulose filter (Southern et
al., Genomics, 13: 1008-17 (1992); Southern et al., Nucl Acids Res
22: 1368-73 (1994); Gress et al., Oncogene, 13: 1819-30 (1996);
Pietu et al., Genome Res., 6: 492-503 (1996)). The other commonly
used cDNA arrays is fabricated by robotic spotting of PCR fragments
from cDNA clones onto glass microscope slides (Schena et al.,
Science, 270: 467-70 (1995); DeRisi et al., Nature Genet., 14:
457-60 (1996); Schena et al., Proc. Nat'l. Acad. Sci. USA, 93:
10614-19 (1996); Shalon et al., Genome Res., 6: 639-45 (1996);
DeRisi et al., Science, 278: 680-86 (1997); Heller et al., Proc.
Nat'l. Acad. Sci. USA, 94: 2150-55 (1997); Lashkari et al., Proc.
Nat'l. Acad. Sci. USA, 94: 13057-62 (1997)). These cDNA microarrays
are simultaneously hybridized with two fluorescent cDNA probes,
each labeled with a different fluorescent dye (typically Cy3 or
Cy5). In this format, the relative mRNA expression in two samples
is directly compared for each gene on the microarray.
Oligonucleotide arrays differ from cDNA arrays in that the probes
are 20- to 25-mer oligonucleotides. Oligonucleotide arrays are
generally produced by in situ oligonucleotide synthesis in
conjunction with photolithographic masking techniques (Pease et
al., Proc. Nat'l. Acad. Sci. USA, 91: 5022-26 (1994); Lipshutz et
al., Biotechniques 19: 442-47 (1995); Chee et al., Science, 274:
610-14 (1996); Lockhart et al., Nature Biotechnol., 14: 1675-80
(1996); Wodicka et al., Nature Biotechnol., 15: 1359-6714 (1997)).
The solid support for oligonucleotide arrays is typically a glass
or silicon surface.
[0058] Methods and techniques applicable to array synthesis and use
have been described in, for example, U.S. Pat. No. 5,143,854
(Pirrung), U.S. Pat. No. 5,242,974 (Holmes), U.S. Pat. No.
5,252,743 (Barrett), U.S. Pat. No. 5,324,633 (Fodor), U.S. Pat. No.
5,384,261 (Winkler), U.S. Pat. No. 5,424,186 (Fodor), U.S. Pat. No.
5,445,934 (Fodor), U.S. Pat. No. 5,451,683 (Barrett), U.S. Pat. No.
5,482,867 (Barrett), U.S. Pat. No. 5,491,074 (Aldwin), U.S. Pat.
No. 5,527,681 (Holmes), U.S. Pat. No. 5,550,215 (Holmes), U.S. Pat.
No. 5,571,639 (Hubbell), U.S. Pat. No. 5,578,832 (Trulson), U.S.
Pat. No. 5,593,839 (Hubbell), U.S. Pat. No. 5,599,695 (Pease), U.S.
Pat. No. 5,624,711 (Sundberg), U.S. Pat. No. 5,631,734 (Stern),
U.S. Pat. No. 5,795,716 (Chee), U.S. Pat. No. 5,831,070 (Pease),
U.S. Pat. No. 5,837,832 (Chee), U.S. Pat. No. 5,856,101 (Hubbell),
U.S. Pat. No. 5,858,659 (Sapolsky), U.S. Pat. No. 5,936,324
(Montagu), U.S. Pat. No. 5,968,740 (Fodor), U.S. Pat. No. 5,974,164
(Chee), U.S. Pat. No. 5,981,185 (Matson), U.S. Pat. No. 5,981,956
(Stern), U.S. Pat. No. 6,025,601 (Trulson), U.S. Pat. No. 6,033,860
(Lockhart), U.S. Pat. No. 6,040,193 (Winkler), U.S. Pat. No.
6,090,555 (Fiekowsky), and U.S. Pat. No. 6,410,229 (Lockhart), and
U.S. Patent Application Publication No. 2003/0104411 (Fodor).
[0059] Microarrays may generally be produced using a variety of
techniques, such as mechanical or light directed synthesis methods
that incorporate a combination of photolithographic methods and
solid phase synthesis methods. Techniques for the synthesis of
microarrays using mechanical synthesis methods are described in,
for example, U.S. Pat. No. 5,384,261 (Winkler) and U.S. Pat. No.
6,040,193 (Winkler). Although a planar array surface is preferred,
the microarray may be fabricated on a surface of virtually any
shape, or even on a multiplicity of surfaces. Microarrays may be
nucleic acids on beads, gels, polymeric surfaces, fibers such as
fiber optics, glass, or any other appropriate substrate. See, for
example, U.S. Pat. No. 5,708,153 (Dower), U.S. Pat. No. 5,770,358
(Dower), U.S. Pat. No. 5,789,162 (Dower), U.S. Pat. No. 5,800,992
(Fodor), and U.S. Pat. No. 6,040,193 (Winkler).
[0060] Microarrays can be packaged in such a manner as to allow for
diagnostic use, or they can be all-inclusive devices. See, for
example, U.S. Pat. No. 5,856,174 (Lipshutz) and U.S. Pat. No.
5,922,591 (Anderson).
[0061] Microarrays directed to a variety of purposes are
commercially available from Affymetrix (Santa Clara, Calif.). For
instance, these microarrays may be used for genotyping and gene
expression monitoring.
[0062] Gene expression data can be used to identify genes that are
coordinately regulated. Genes that encode components of the same
multi-subunit protein complex are often coordinately regulated.
Coordinate regulation is also observed among genes whose products
function in a common differentiation program or in the same
physiological response pathway. Recent application of gene
expression profiling to the immune system has shown that lymphocyte
differentiation and activation are accompanied by parallel changes
in expression among hundreds of genes. Gene expression databases
may be used to interpret the pathological changes in gene
expression that accompany autoimmunity, immune deficiencies,
cancers of immune cells and of normal immune responses.
[0063] Scanning and interpreting large bodies of relative gene
expression data is a formidable task. This task is greatly
facilitated by algorithms designed to organize the data in a way
that highlights systematic features, and by visualization tools
that represent the differential expression of each gene as varying
intensities and hues of color (Eisen et al., Proc. Nat'l. Acad.
Sci. USA, 95: 14863-68 (1998)). The development of microarrays,
which are capable of generating massive amounts of expression data
in a single experiment, has greatly increased the need for faster
and more efficient methods of analyzing large-scale expression data
sets. In order to effectively utilize microarray gene expression
data for the prediction of survival in DLBCL patients, there is a
need for new algorithms to be developed, which can identify
important information and convert it to a more manageable format.
In addition, the microarrays used to generate this data can be
streamlined to incorporate probe sets that are useful for survival
outcome prediction.
[0064] Mathematical analysis of gene expression data is a rapidly
evolving science based on a rich mathematics of pattern recognition
developed in other contexts (Kohonen, Self-Organizing Maps,
Springer Press (Berlin 1997)). Mathematical analysis of gene
expression data can be used, for example, to identify groups of
genes that are coordinately regulated within a biological system,
to recognize and interpret similarities between biological samples
on the basis of similarities in gene expression patterns, and/or to
recognize and identify those features of a gene expression pattern
that are related to distinct biological processes or
phenotypes.
[0065] Mathematical analysis of gene expression data often begins
by establishing the expression pattern for each gene on an array
across a number (n) of experimental samples. The expression pattern
of each gene can be represented by a point in n-dimensional space,
with each coordinate specified by an expression measurement in one
of the n samples (Eisen et al., Proc. Nat'l. Acad. Sci. USA, 95:
14863-68 (1998)). A clustering algorithm that uses distance metrics
can then be applied to locate clusters of genes in this
n-dimensional space. These clusters indicate genes with similar
patterns of variation in expression over a series of experiments.
Clustering methods that have been applied to microarray data in the
past include hierarchical clustering (Eisen et al., supra),
self-organizing maps (SOMs) (Tamayo et al., Proc. Nat'l. Acad. Sci.
USA, 96: 2907-12 (1999)), k-means (Tavazoie et al., Nature Genet.,
22: 281-85 (1999)), and deterministic annealing (Alon et al., Proc.
Nat'l. Acad. Sci. USA, 96: 6745-50 (1999)).
[0066] A variety of different algorithms, each emphasizing distinct
orderly features of the data, may be required to glean the maximal
biological insight from a set of samples (Alizadeh et al., J. Clin.
Immunol., 18: 373-79 (1998)). One such algorithm, hierarchical
clustering, begins by determining the gene expression correlation
coefficients for each pair of the n genes studied. Genes with
similar gene expression correlation coefficients are grouped next
to one another in a hierarchical fashion. Generally, genes with
similar expression patterns under a particular set of conditions
can encode protein products with related roles in the physiological
adaptation to those conditions. Novel genes of unknown function
that are clustered with a large group of functionally related genes
likely participate in similar or related biological process.
Likewise, other clustering methods mentioned herein can also group
genes together that encode proteins with related biological
function.
[0067] In such clustering methods, genes that are clustered
together reflect a particular biological function, and are termed
gene expression signatures (Shaffer et al., Immunity 15: 375-85
(2001)). One general type of gene expression signature includes
genes that are characteristically expressed in a particular cell
type or at a particular stage of cellular differentiation or
activation. Another general type of gene expression signature
includes genes that are regulated in their expression by a
particular biological process such as proliferation, or by the
activity of a particular transcription factor or signaling
pathway.
[0068] The pattern of gene expression in a biological sample can
provide a distinctive and accessible molecular picture of its
functional state and identity (DeRisi et al., Science, 278: 680-86
(1997); Cho et al., Mol. Cell., 2: 65-73 (1998); Chu et al.,
Science, 282: 699-705 (1998); Holstege et al., Cell., 95: 717-728
(1998); Spellman et al., Mol. Biol. Cell, 9: 3273-97 (1998)). Each
cell transduces variations in its environment, internal state, and
developmental state into readily measured and recognizable
variations in its gene expression patterns. Two different samples
with related gene expression patterns are therefore likely to be
biologically and functionally similar to one another. Thus, a
specific gene expression signature in a sample can provide
important biological insights into its cellular composition and the
function of various intracellular pathways within those cells.
[0069] Databases of gene expression signatures have proven useful
in elucidating the complex gene expression patterns of various
cancers. For example, the expression pattern of genes in the
germinal center B cell signature in a lymphoma biopsy indicates
that the lymphoma includes cells derived from the germinal center
stage of differentiation. In the same lymphoma biopsy, the
expression of genes from the T cell signature can be used to
estimate the degree of infiltration of the tumor by host T cells,
while the expression of genes from the proliferation signature can
be used to quantitate the tumor cell proliferation rate. In this
manner, gene expression signatures provide an "executive summary"
of the biological properties of a tumor specimen. Gene expression
signatures can also be helpful in interpreting the results of a
supervised analysis of gene expression data. A supervised analysis
generates a list of genes with expression patterns that correlate
with survival. Gene expression signatures can be useful in
assigning these "predictive" genes to functional categories. In
building a multivariate model of survival based on gene expression
data, this functional categorization helps to limit the inclusion
of multiple genes in the model that measure the same aspect of
tumor biology.
[0070] This following approach was utilized to create the survival
prediction models for DLBCL of the invention. Gene expression
profiles were used to create multivariate models for predicting
survival. The methods for creating these models were "supervised"
in that they used clinical data to guide the selection of genes to
be used in the prognostic classification. The method identified
genes with expression patterns that correlated with the length of
overall survival following chemotherapy. Generally the process for
identifying the multivariate model for predicting survival included
the following steps: [0071] 1. Genes were identified having
expression patterns univariately associated with a particular
clinical outcome using a Cox proportional hazards model. Generally,
a univariate p-value of <0.01 is considered the cut-off for
significance (however, another criterion can be used). These genes
were termed "predictor" genes. [0072] 2. Within a set of predictor
genes, gene expression signatures were identified. [0073] 3. For
each gene expression signature significantly associated with
survival, the average expression of each component genes within
this signature was used to generate a gene expression signature
value. [0074] 4. A multivariate Cox model of clinical outcome using
the gene expression signature values was built. [0075] 5.
Additional genes were added to the model, which added to the
statistical power of the model.
[0076] The model of the invention generates a survival predictor
score, with a higher score being associated with worse clinical
outcome. The resulting model can be used separately to predict a
survival outcome. Alternatively, the model can be used in
conjunction with one or more other models, disclosed herein or in
other references, to predict a survival outcome.
[0077] The present invention discloses several gene expression
signatures related to the clinical outcome of DLBCL patients. The
signatures were identified using the clinical data and methods
described below in Examples 1 and 2. Three of these gene expression
signatures are the germinal center B cell (GCB) signature, the
stromal-1 signature, and the stromal-2 signature. Each component
gene of these signatures is identified in Table 1 according to its
GenBank accession number, its GeneID assigned by Entrez Gene, a
common gene symbol, and a descriptive gene title. Table 1 also
provides the Affymetrix Probe Set ID, which can be used (e.g., on
the Affymetrix U133+ (Affymetrix, Santa Clara, Calif.) microarray)
to determine the gene expression level for the indicated gene. The
computer-readable sequence listing filed herewith includes a
representative fragment sequence (of about 100 by or greater) for
each genomic target sequence listed in Table 1, followed by the
sequence for each probe in the corresponding Affymetrix probe set
listed in Table 1.
TABLE-US-00001 TABLE 1 GenBank Entrez Affymetrix Signature
Accession No. GeneID Gene Symbol Gene Title Probe Set ID GCB
NM_052932 114908 TMEM123 transmembrane protein 211967_at 123 GCB
NM_001014380 84056 KATNAL1 katanin p60 subunit A-like 1 227713_at
GCB NM_004665 8875 VNN2 vanin 2 205922_at GCB NM_004760 9263 STK17A
serine/threonine kinase 202693_s_at 17a (apoptosis-inducing) GCB
CR590554 Full-length cDNA clone 228464_at CS0DF007YJ21 of Fetal
brain of Homo sapiens (human) GCB NM_017599 55591 VEZT vezatin,
adherens 223089_at junctions transmembrane protein GCB NM_018351
55785 FGD6 FYVE, RhoGEF and PH 1555136_at domain containing 6 GCB
NM_001007075 51088 KLHL5 kelch-like 5 (Drosophila) 226001_at GCB
NM_004845 9468 PCYT1B phosphate 228959_at cytidylyltransferase 1,
choline, beta GCB AK026881 CDNA: FLJ23228 fis, 226799_at clone
CAE06654 GCB NM_018440 55824 PAG1 phosphoprotein 225626_at
associated with glycosphingolipid microdomains 1 GCB NM_004965 3150
HMGN1 high-mobility group 200944_s_at nucleosome binding domain 1
GCB NM_001706 604 BCL6 B cell CLL/lymphoma 6 228758_at (zinc finger
protein 51) GCB NM_020747 57507 ZNF608 zinc finger protein 608
229817_at GCB NM_001001695 400941 FLJ42418 FLJ42418 protein
231455_at GCB NM_015055 23075 SWAP70 SWAP-70 protein 209306_s_at
GCB NM_005607 5747 PTK2 PTK2 protein tyrosine 208820_at kinase 2
GCB XM_027236 23508 TTC9 tetratricopeptide repeat 213172_at domain
9 GCB BQ213652 440864 LOC440864 hypothetical gene 1569034_a_at
supported by BC040724 GCB NM_005574 4005 LMO2 LIM domain only 2
204249_s_at (rhombotin-like 1) GCB NM_014667 9686 VGLL4 vestigial
like 4 212399_s_at (Drosophila) GCB NM_002221 3707 ITPKB inositol
1,4,5- 203723_at trisphosphate 3-kinase B GCB NM_000902 4311 MME
membrane metallo- 203434_s_at endopeptidase (neutral endopeptidase,
enkephalinase) GCB NM_012446 23635 SSBP2 single-stranded DNA
203787_at binding protein 2 GCB NM_024613 79666 PLEKHF2 pleckstrin
homology 222699_s_at domain containing, family F (with FYVE domain)
member 2 GCB AV705976 Transcribed locus 204681_s_at GCB NM_012108
26228 BRDG1 BCR downstream 220059_at signaling 1 GCB NM_014397
10783 NEK6 NIMA (never in mitosis 223158_s_at gene a)-related
kinase 6 GCB NM_018981 54431 DNAJC10 DnaJ (Hsp40) homolog,
225174_at subfamily C, member 10 GCB NM_001379 1786 DNMT1 DNA
(cytosine-5-)- 227684_at methyltransferase 1 GCB NM_006152 4033
LRMP lymphoid-restricted 35974_at membrane protein GCB NM_024701
79754 ASB13 ankyrin repeat and SOCS 218862_at box-containing 13 GCB
NM_006085 10380 BPNT1 3'(2'), 5'-bisphosphate 232103_at
nucleotidase 1 GCB NM_023009 65108 MARCKSL1 MARCKS-like 1 200644_at
GCB NM_033121 88455 ANKRD13A ankyrin repeat domain 224810_s_at 13A
GCB NM_015187 23231 KIAA0746 KIAA0746 protein 235353_at GCB
NM_175739 327657 SERPINA9 serpin peptidase inhibitor, 1553499_s_at
clade A (alpha-1 antiproteinase, antitrypsin), member 9 GCB
NM_001012391 400509 RUNDC2B RUN domain containing 1554413_s_at 2B
GCB XM_034274 4603 MYBL1 v-myb myeloblastosis 213906_at viral
oncogene homolog (avian)-like 1 Stromal-1 NM_024579 79630 C1orf54
chromosome 1 open 219506_at reading frame 54 Stromal-1 NM_001645
341 APOC1 apolipoprotein C-I 213553_x_at Stromal-1 NM_001562 3606
IL18 interleukin 18 (interferon- 206295_at gamma-inducing factor)
Stromal-1 NM_014479 27299 ADAMDEC1 ADAM-like, decysin 1 206134_at
Stromal-1 NM_003465 1118 CHIT1 chitinase 1 208168_s_at
(chitotriosidase) Stromal-1 NM_000954 5730 PTGDS prostaglandin D2
211748_x_at synthase 21 kDa (brain) Stromal-1 NM_001056 6819
SULT1C1 sulfotransferase family, 211470_s_at cytosolic, 1C, member
1 Stromal-1 NM_018000 55686 MREG melanoregulin 219648_at Stromal-1
NM_001018058 22797 TFEC transcription factor EC 206715_at Stromal-1
NM_000239 4069 LYZ lysozyme (renal 213975_s_at amyloidosis)
Stromal-1 NM_006834 10981 RAB32 RAB32, member RAS 204214_s_at
oncogene family Stromal-1 NM_000416 3459 IFNGR1 interferon gamma
202727_s_at receptor 1 Stromal-1 NM_004666 8876 VNN1 vanin 1
205844_at Stromal-1 NM_031491 83758 RBP5 retinol binding protein 5,
223820_at cellular Stromal-1 NM_001276 1116 CHI3L1 chitinase 3-like
1 209396_s_at (cartilage glycoprotein-39) Stromal-1 NM_138434
113763 C7orf29 chromosome 7 open 227598_at reading frame 29
Stromal-1 NM_001005340 10457 GPNMB glycoprotein 201141_at
(transmembrane) nmb Stromal-1 NM_002294 3920 LAMP2
lysosomal-associated 203041_s_at membrane protein 2 Stromal-1
NM_002888 5918 RARRES1 retinoic acid receptor 221872_at responder
(tazarotene induced) 1 Stromal-1 NM_172248 1438 CSF2RA colony
stimulating factor 2 210340_s_at receptor, alpha, low- affinity
(granulocyte- macrophage) Stromal-1 NM_018344 55315 SLC29A3 solute
carrier family 29 219344_at (nucleoside transporters), member 3
Stromal-1 NM_032413 84419 C15orf48 chromosome 15 open 223484_at
reading frame 48 Stromal-1 NM_001001851 80760 ITIH5 inter-alpha
(globulin) 1553243_at inhibitor H5 Stromal-1 NM_000211 3689 ITGB2
integrin, beta 2 1555349_a_at (complement component 3 receptor 3
and 4 subunit) Stromal-1 NM_005213 1475 CSTA cystatin A (stefin A)
204971_at Stromal-1 NM_003874 8832 CD84 CD84 molecule 205988_at
Stromal-1 NM_000228 3914 LAMB3 laminin, beta 3 209270_at Stromal-1
NM_005651 6999 TDO2 tryptophan 2,3- 205943_at dioxygenase Stromal-1
NM_001005266 283651 C15orf21 chromosome 15 open 242649_x_at reading
frame 21 Stromal-1 AV659177 Transcribed locus 230391_at Stromal-1
NM_001747 822 CAPG capping protein (actin 201850_at filament),
gelsolin-like Stromal-1 NM_000784 1593 CYP27A1 cytochrome P450,
family 203979_at 27, subfamily A, polypeptide 1 Stromal-1 NM_052998
113451 ADC arginine decarboxylase 228000_at Stromal-1 NM_016240
51435 SCARA3 scavenger receptor class 219416_at A, member 3
Stromal-1 Z74615 COL1A1 Collagen, type I, alpha 1 217430_x_at
Stromal-1 NM_052947 115701 ALPK2 alpha-kinase 2 228367_at Stromal-1
NM_021136 6252 RTN1 reticulon 1 210222_s_at Stromal-1 AL049370
Full-length cDNA clone 213100_at CL0BB018ZE07 of Neuroblastoma of
Homo sapiens (human) Stromal-1 NM_006042 9955 HS3ST3A1 heparan
sulfate 219985_at (glucosamine) 3-O- sulfotransferase 3A1 Stromal-1
NM_000041 348 APOE apolipoprotein E 203382_s_at Stromal-1 NM_004994
4318 MMP9 matrix metallopeptidase 9 203936_s_at (gelatinase B, 92
kDa gelatinase, 92 kDa type IV collagenase) Stromal-1 NM_001831
1191 CLU clusterin 222043_at Stromal-1 NM_002305 3956 LGALS1
lectin, galactoside- 201105_at binding, soluble, 1 (galectin 1)
Stromal-1 NM_032024 83938 C10orf11 chromosome 10 open 223703_at
reading frame 11 Stromal-1 NM_001025201 1123 CHN1 chimerin
(chimaerin) 1 212624_s_at Stromal-1 NM_003489 8204 NRIP1 nuclear
receptor 202599_s_at interacting protein 1 Stromal-1 NM_032646
94015 TTYH2 tweety homolog 2 223741_s_at (Drosophila) Stromal-1
NM_001312 1397 CRIP2 cysteine-rich protein 2 208978_at Stromal-1
NM_023075 65258 MPPE1 metallophosphoesterase 1 213924_at Stromal-1
NM_004364 1050 CEBPA CCAAT/enhancer binding 204039_at protein
(C/EBP), alpha Stromal-1 NM_000248 4286 MITF microphthalmia-
207233_s_at associated transcription factor Stromal-1 NM_002185
3575 IL7R interleukin 7 receptor 226218_at Stromal-1 NM_021638
60312 AFAP actin filament associated 203563_at protein Stromal-1
NM_003786 8714 ABCC3 ATP-binding cassette, 208161_s_at sub-family C
(CFTR/MRP), member 3 Stromal-1 730351 LOC730351 hypothetical
protein 229407_at LOC730351 Stromal-1 NM_012153 26298 EHF ets
homologous factor 225645_at Stromal-1 NM_004887 9547 CXCL14
chemokine (C--X--C motif) 222484_s_at ligand 14 Stromal-1 NM_002030
2359 FPRL2 formyl peptide receptor- 230422_at like 2 Stromal-1
NM_001321 1466 CSRP2 cysteine and glycine-rich 207030_s_at protein
2 Stromal-1 NM_001945 1839 HBEGF heparin-binding EGF-like 203821_at
growth factor Stromal-1 NM_031412 23710 GABARAPL1 GABA(A) receptor-
208869_s_at associated protein like 1 Stromal-1 NM_006022 8848
TSC22D1 TSC22 domain family, 215111_s_at member 1 Stromal-1
NM_016174 51148 CEECAM1 cerebral endothelial cell 224794_s_at
adhesion molecule 1 Stromal-1 NM_015103 23129 PLXND1 plexin D1
212235_at Stromal-1 NM_003270 7105 TSPAN6 tetraspanin 6 209109_s_at
Stromal-1 NM_000887 3687 ITGAX integrin, alpha X 210184_at
(complement component 3 receptor 4 subunit) Stromal-1 NM_001864
1346 COX7A1 cytochrome c oxidase 204570_at subunit VIIa polypeptide
1 (muscle) Stromal-1 CR599008 GPR157 Full-length cDNA clone
227970_at CS0DJ007YL22 of T cells (Jurkat cell line) Cot 10-
normalized of Homo sapiens (human) Stromal-1 NM_198580 376497
SLC27A1 solute carrier family 27 226728_at (fatty acid
transporter), member 1 Stromal-1 NM_025106 80176 SPSB1
splA/ryanodine receptor 226075_at domain and SOCS box containing 1
Stromal-1 NM_020130 56892 C8orf4 chromosome 8 open 218541_s_at
reading frame 4 Stromal-1 NM_173833 286133 SCARA5 scavenger
receptor class 229839_at A, member 5 (putative) Stromal-1 NM_007223
11245 GPR176 G protein-coupled 227846_at receptor 176 Stromal-1
NM_013437 29967 LRP12 low density lipoprotein- 219631_at related
protein 12 Stromal-1 NM_007332 8989 TRPA1 transient receptor
228438_at potential cation channel, subfamily A, member 1 Stromal-1
NM_152744 221935 SDK1 sidekick homolog 1 229912_at (chicken)
Stromal-1 NM_001409 1953 MEGF6 multiple EGF-like- 226869_at domains
6 Stromal-1 NM_012082 23414 ZFPM2 zinc finger protein, 219778_at
multitype 2 Stromal-1 NM_080430 140606 SELM selenoprotein M
226051_at Stromal-1 NM_030971 81855 SFXN3 sideroflexin 3
217226_s_at Stromal-1 NM_003246 7057 THBS1 thrombospondin 1
201109_s_at Stromal-1 NM_003882 8840 WISP1 WNT1 inducible signaling
235821_at
pathway protein 1 Stromal-1 NM_005202 1296 COL8A2 collagen, type
VIII, alpha 2 221900_at Stromal-1 NM_003711 8611 PPAP2A
phosphatidic acid 210946_at phosphatase type 2A Stromal-1 NM_004995
4323 MMP14 matrix metallopeptidase 202828_s_at 14
(membrane-inserted) Stromal-1 NM_001005336 1759 DNM1 dynamin 1
215116_s_at Stromal-1 NM_153717 2121 EVC Ellis van Creveld
219432_at syndrome Stromal-1 NM_173462 89932 PAPLN papilin,
proteoglycan-like 226435_at sulfated glycoprotein Stromal-1
XM_496707 441027 FLJ12993 hypothetical LOC441027 229623_at
Stromal-1 NM_001839 1266 CNN3 calponin 3, acidic 228297_at
Stromal-1 NM_015429 25890 ABI3BP ABI gene family, member 223395_at
3 (NESH) binding protein Stromal-1 NM_002840 5792 PTPRF protein
tyrosine 200636_s_at phosphatase, receptor type, F Stromal-1
NM_001001522 6876 TAGLN transgelin 1555724_s_at Stromal-1 NM_017637
54796 BNC2 basonuclin 2 229942_at Stromal-1 NM_003391 7472 WNT2
wingless-type MMTV 205648_at integration site family member 2
Stromal-1 NM_015461 25925 ZNF521 zinc finger protein 521 226677_at
Stromal-1 NM_006475 10631 POSTN periostin, osteoblast 210809_s_at
specific factor Stromal-1 NM_005418 6764 ST5 suppression of
202440_s_at tumorigenicity 5 Stromal-1 NM_005203 1305 COL13A1
collagen, type XIII, alpha 1 211343_s_at Stromal-1 NM_000681 150
ADRA2A adrenergic, alpha-2A-, 209869_at receptor Stromal-1
NM_006622 10769 PLK2 polo-like kinase 2 201939_at (Drosophila)
Stromal-1 AL528626 Full-length cDNA clone 228573_at CS0DD001YA12 of
Neuroblastoma Cot 50- normalized of Homo sapiens (human) Stromal-1
AF180519 23766 GABARAPL3 GABA(A) receptors 211458_s_at associated
protein like 3 Stromal-1 NM_024723 79778 MICALL2 MICAL-like 2
219332_at Stromal-1 NM_057177 117583 PARD3B par-3 partitioning
228411_at defective 3 homolog B (C. elegans) Stromal-1 NM_004949
1824 DSC2 desmocollin 2 226817_at Stromal-1 NM_032784 84870 RSPO3
R-spondin 3 homolog 228186_s_at (Xenopus laevis) Stromal-1
NM_007039 11099 PTPN21 protein tyrosine 226380_at phosphatase, non-
receptor type 21 Stromal-1 NM_031935 83872 HMCN1 hemicentin 1
235944_at Stromal-1 AK022877 Clone TUA8 Cri-du-chat 213169_at
region mRNA Stromal-1 AK127644 CDNA FLJ45742 fis, 236297_at clone
KIDNE2016327 Stromal-1 AK056963 Full length insert cDNA 226282_at
clone ZE03F06 Stromal-1 NM_000899 4254 KITLG KIT ligand 226534_at
Stromal-1 NM_002387 4163 MCC mutated in colorectal 226225_at
cancers Stromal-1 NM_198270 4810 NHS Nance-Horan syndrome 228933_at
(congenital cataracts and dental anomalies) Stromal-1 NM_183376
91947 ARRDC4 arrestin domain 225283_at containing 4 Stromal-1
NM_000216 3730 KAL1 Kallmann syndrome 1 205206_at sequence
Stromal-1 NM_001008224 55075 UACA uveal autoantigen with
223279_s_at coiled-coil domains and ankyrin repeats Stromal-1
NM_133493 135228 CD109 CD109 molecule 226545_at Stromal-1 NM_005545
3671 ISLR immunoglobulin 207191_s_at superfamily containing
leucine-rich repeat Stromal-1 NM_014365 26353 HSPB8 heat shock 22
kDa protein 8 221667_s_at Stromal-1 NM_014476 27295 PDLIM3 PDZ and
LIM domain 3 209621_s_at Stromal-1 NM_020962 57722 NOPE likely
ortholog of mouse 227870_at neighbor of Punc E11 Stromal-1
NM_018357 55323 LARP6 La ribonucleoprotein 218651_s_at domain
family, member 6 Stromal-1 NM_012323 23764 MAFF v-maf 36711_at
musculoaponeurotic fibrosarcoma oncogene homolog F (avian)
Stromal-1 NM_003713 8613 PPAP2B phosphatidic acid 212230_at
phosphatase type 2B Stromal-1 NM_023016 65124 ANKRD57 ankyrin
repeat domain 57 227034_at Stromal-1 NM_032777 25960 GPR124 G
protein-coupled 65718_at receptor 124 Stromal-1 NM_001554 3491
CYR61 cysteine-rich, angiogenic 201289_at inducer, 61 Stromal-1
NM_145117 89797 NAV2 neuron navigator 2 218330_s_at Stromal-1
NM_001002292 79971 GPR177 G protein-coupled 228950_s_at receptor
177 Stromal-1 NM_001401 1902 EDG2 endothelial differentiation,
204036_at lysophosphatidic acid G- protein-coupled receptor, 2
Stromal-1 NM_198282 340061 TMEM173 transmembrane protein 224929_at
173 Stromal-1 NM_014934 22873 DZIP1 DAZ interacting protein 1
204556_s_at Stromal-1 NM_001901 1490 CTGF connective tissue growth
209101_at factor Stromal-1 NM_024600 79652 C16orf30 chromosome 16
open 219315_s_at reading frame 30 Stromal-1 NM_138370 91461
LOC91461 hypothetical protein 225380_at BC007901 Stromal-1
NM_014632 9645 MICAL2 microtubule associated 212472_at
monoxygenase, calponin and LIM domain containing 2 Stromal-1
NM_032866 84952 CGNL1 cingulin-like 1 225817_at Stromal-1 NM_003687
8572 PDLIM4 PDZ and LIM domain 4 211564_s_at Stromal-1 BM544548
Transcribed locus 236179_at Stromal-1 NM_001856 1307 COL16A1
collagen, type XVI, alpha 1 204345_at Stromal-1 XM_087386 57493
HEG1 HEG homolog 1 213069_at (zebrafish) Stromal-1 NM_003887 8853
DDEF2 development and 206414_s_at differentiation enhancing factor
2 Stromal-1 NM_002844 5796 PTPRK protein tyrosine 203038_at
phosphatase, receptor type, K Stromal-1 NM_022138 64094 SMOC2 SPARC
related modular 223235_s_at calcium binding 2 Stromal-1
NM_001006624 10630 PDPN podoplanin 204879_at Stromal-1 NM_003174
6840 SVIL supervillin 202565_s_at Stromal-1 NM_002845 5797 PTPRM
protein tyrosine 1555579_s_at phosphatase, receptor type, M
Stromal-1 NM_002889 5919 RARRES2 retinoic acid receptor 209496_at
responder (tazarotene induced) 2 Stromal-1 NM_006094 10395 DLC1
deleted in liver cancer 1 210762_s_at Stromal-1 NM_022463 64359 NXN
nucleoredoxin 219489_s_at Stromal-1 AK027294 CDNA FLJ14388 fis,
229802_at clone HEMBA1002716 Stromal-1 NM_005711 10085 EDIL3
EGF-like repeats and 225275_at discoidin I-like domains 3 Stromal-1
NM_000177 2934 GSN gelsolin (amyloidosis, 200696_s_at Finnish type)
Stromal-1 NM_016639 51330 TNFRSF12A tumor necrosis factor
218368_s_at receptor superfamily, member 12A Stromal-1 NM_004460
2191 FAP fibroblast activation 209955_s_at protein, alpha Stromal-1
NM_000064 718 C3 complement component 3 217767_at Stromal-1
NM_016206 389136 VGLL3 vestigial like 3 227399_at (Drosophila)
Stromal-1 NM_004339 754 PTTG1IP pituitary tumor- 200677_at
transforming 1 interacting protein Stromal-1 NM_003255 7077 TIMP2
TIMP metallopeptidase 224560_at inhibitor 2 Stromal-1 NM_002998
6383 SDC2 syndecan 2 (heparan 212158_at sulfate proteoglycan 1,
cell surface-associated, fibroglycan) Stromal-1 NM_012223 4430
MYO1B myosin IB 212364_at Stromal-1 NM_020650 57333 RCN3
reticulocalbin 3, EF-hand 61734_at calcium binding domain Stromal-1
AL573464 Transcribed locus 229554_at Stromal-1 AK001903 CDNA
FLJ11041 fis, 227140_at clone PLACE1004405 Stromal-1 NM_005928 4240
MFGE8 milk fat globule-EGF 210605_s_at factor 8 protein Stromal-1
NM_000943 5480 PPIC peptidylprolyl isomerase 204518_s_at C
(cyclophilin C) Stromal-1 NM_001008397 493869 LOC493869 similar to
RIKEN cDNA 227628_at 2310016C16 Stromal-1 AK025431 768211 RELL1
receptor expressed in 226430_at lymphoid tissues like 1 Stromal-1
NM_000297 5311 PKD2 polycystic kidney disease 203688_at 2
(autosomal dominant) Stromal-1 NM_002975 6320 CLEC11A C-type lectin
domain 211709_s_at family 11, member A Stromal-1 NM_001920 1634 DCN
decorin 211813_x_at Stromal-1 NM_001723 667 DST dystonin
215016_x_at Stromal-1 CR749529 MRNA; cDNA 227554_at DKFZp686I18116
(from clone DKFZp686I18116) Stromal-1 NM_000165 2697 GJA1 gap
junction protein, 201667_at alpha 1, 43 kDa (connexin 43) Stromal-1
NM_012104 23621 BACE1 beta-site APP-cleaving 217904_s_at enzyme 1
Stromal-1 NM_001957 1909 EDNRA endothelin receptor type A
204464_s_at Stromal-1 NM_138455 115908 CTHRC1 collagen triple helix
repeat 225681_at containing 1 Stromal-1 NM_001331 1500 CTNND1
catenin (cadherin- 208407_s_at associated protein), delta 1
Stromal-1 NM_001613 59 ACTA2 actin, alpha 2, smooth 200974_at
muscle, aorta Stromal-1 NM_002192 3624 INHBA inhibin, beta A
(activin A, 210511_s_at activin AB alpha polypeptide) Stromal-1
NM_000935 5352 PLOD2 procollagen-lysine, 2- 202620_s_at
oxoglutarate 5- dioxygenase 2 Stromal-1 NM_015170 23213 SULF1
sulfatase 1 212354_at Stromal-1 NM_006039 9902 MRC2 mannose
receptor, C type 2 37408_at Stromal-1 NM_005261 2669 GEM GTP
binding protein 204472_at overexpressed in skeletal muscle
Stromal-1 NM_001008707 2009 EML1 echinoderm microtubule 204797_s_at
associated protein like 1 Stromal-1 NM_001031679 253827 MSRB3
methionine sulfoxide 225782_at reductase B3 Stromal-1 NM_001004125
286319 TUSC1 tumor suppressor 227388_at candidate 1 Stromal-1
NM_005965 4638 MYLK myosin, light chain kinase 202555_s_at
Stromal-1 NM_016205 56034 PDGFC platelet derived growth 218718_at
factor C Stromal-1 NM_015976 51375 SNX7 sorting nexin 7 205573_s_at
Stromal-1 NM_130830 131578 LRRC15 leucine rich repeat 213909_at
containing 15 Stromal-1 NM_002026 2335 FN1 fibronectin 1
212464_s_at Stromal-1 NM_006855 11015 KDELR3 KDEL (Lys-Asp-Glu-Leu)
204017_at endoplasmic reticulum protein retention receptor 3
Stromal-1 NM_002292 3913 LAMB2 laminin, beta 2 (laminin S)
216264_s_at Stromal-1 NM_002658 5328 PLAU plasminogen activator,
205479_s_at urokinase Stromal-1 NM_005529 3339 HSPG2 heparan
sulfate 201655_s_at proteoglycan 2 (perlecan) Stromal-1 NM_001235
871 SERPINH1 serpin peptidase inhibitor, 207714_s_at clade H (heat
shock protein 47), member 1, (collagen binding protein 1) Stromal-1
AJ318805 CDNA FLJ44429 fis, 227061_at clone UTERU2015653 Stromal-1
NM_000396 1513 CTSK cathepsin K 202450_s_at Stromal-1 NM_031302
83468 GLT8D2 glycosyltransferase 8 227070_at domain containing 2
Stromal-1 NM_080821 116151 C20orf108 chromosome 20 open 224690_at
reading frame 108 Stromal-1 NM_002345 4060 LUM lumican 201744_s_at
Stromal-1 NM_005110 9945 GFPT2 glutamine-fructose-6- 205100_at
phosphate transaminase 2 Stromal-1 NM_002941 6091 ROBO1 roundabout,
axon 213194_at guidance receptor, homolog 1 (Drosophila) Stromal-1
NM_005429 7424 VEGFC vascular endothelial 209946_at growth factor C
Stromal-1 NM_002213 3693 ITGB5 integrin, beta 5 201125_s_at
Stromal-1 XM_051017 23363 OBSL1 obscurin-like 1 212775_at Stromal-1
NM_181724 338773 TMEM119 transmembrane protein 227300_at 119
Stromal-1 NM_003474 8038 ADAM12 ADAM metallopeptidase 213790_at
domain 12 (meltrin alpha) Stromal-1 NM_018222 55742 PARVA parvin,
alpha 217890_s_at Stromal-1 NM_006478 10634 GAS2L1 growth
arrest-specific 2 31874_at
like 1 Stromal-1 NM_000093 1289 COL5A1 collagen, type V, alpha 1
212489_at Stromal-1 NM_006288 7070 THY1 Thy-1 cell surface antigen
208851_s_at Stromal-1 CD357685 TIMP2 Transcribed locus, 231579_s_at
strongly similar to XP_511714.1 similar to Metalloproteinase
inhibitor 2 precursor (TIMP-2) (Tissue inhibitor of
metalloproteinases-2) (CSC-21K) [Pan troglodytes] Stromal-1
NM_003247 7058 THBS2 thrombospondin 2 203083_at Stromal-1 NM_000088
1277 COL1A1 collagen, type I, alpha 1 1556499_s_at Stromal-1
NM_006832 10979 PLEKHC1 pleckstrin homology 209210_s_at domain
containing, family C (with FERM domain) member 1 Stromal-1
NM_021961 7003 TEAD1 TEA domain family 224955_at member 1 (SV40
transcriptional enhancer factor) Stromal-1 AK128814 CDNA FLJ25106
fis, 213675_at clone CBR01467 Stromal-1 NM_153367 219654 C10orf56
chromosome 10 open 212423_at reading frame 56 Stromal-1 AK092048
MRNA; cDNA 227623_at DKFZp313C0240 (from clone DKFZp313C0240)
Stromal-1 NM_005245 2195 FAT FAT tumor suppressor 201579_at homolog
1 (Drosophila) Stromal-1 NM_001129 165 AEBP1 AE binding protein 1
201792_at Stromal-1 NM_002403 4237 MFAP2 microfibrillar-associated
203417_at protein 2 Stromal-1 NM_004342 800 CALD1 caldesmon 1
201616_s_at Stromal-1 NM_005576 4016 LOXL1 lysyl oxidase-like 1
203570_at Stromal-1 NM_199511 151887 CCDC80 coiled-coil domain
225242_s_at containing 80 Stromal-1 NM_012098 23452 ANGPTL2
angiopoietin-like 2 213001_at Stromal-1 NM_002210 3685 ITGAV
integrin, alpha V 202351_at (vitronectin receptor, alpha
polypeptide, antigen CD51) Stromal-1 NM_000366 7168 TPM1
tropomyosin 1 (alpha) 210986_s_at Stromal-1 NM_198474 283298 OLFML1
olfactomedin-like 1 217525_at Stromal-1 NM_001424 2013 EMP2
epithelial membrane 225078_at protein 2 Stromal-1 NM_032575 84662
GLIS2 GLIS family zinc finger 2 223378_at Stromal-1 NM_007173 11098
PRSS23 protease, serine, 23 226279_at Stromal-1 NM_001015880 9060
PAPSS2 3'-phosphoadenosine 5'- 203060_s_at phosphosulfate synthase
2 Stromal-1 NM_015645 114902 C1QTNF5 C1q and tumor necrosis
223499_at factor related protein 5 Stromal-1 AK130049 CDNA FLJ26539
fis, 213429_at clone KDN09310 Stromal-1 NM_001849 1292 COL6A2
collagen, type VI, alpha 2 209156_s_at Stromal-1 NM_001014796 4921
DDR2 discoidin domain receptor 225442_at family, member 2 Stromal-1
NM_015463 25927 C2orf32 chromosome 2 open 226751_at reading frame
32 Stromal-1 AK055628 ADAM12 CDNA FLJ31066 fis, 226777_at clone
HSYRA2001153 Stromal-1 NM_014799 9843 HEPH hephaestin 203903_s_at
Stromal-1 NM_004385 1462 CSPG2 chondroitin sulfate 221731_x_at
proteoglycan 2 (versican) Stromal-1 NM_152330 122786 FRMD6 FERM
domain containing 6 225481_at Stromal-1 BQ917964 PPP4R2 Transcribed
locus 235733_at Stromal-1 NM_002615 5176 SERPINF1 serpin peptidase
inhibitor, 202283_at clade F (alpha-2 antiplasmin, pigment
epithelium derived factor), member 1 Stromal-1 NM_032348 54587
MXRA8 matrix-remodelling 213422_s_at associated 8 Stromal-1
NM_006106 10413 YAP1 Yes-associated protein 1, 224894_at 65 kDa
Stromal-1 NM_020182 56937 TMEPAI transmembrane, prostate 222449_at
androgen induced RNA Stromal-1 CB999028 Transcribed locus 226834_at
Stromal-1 NM_001711 633 BGN biglycan 201261_x_at Stromal-1
NM_006902 5396 PRRX1 paired related homeobox 1 226695_at Stromal-1
NM_000428 4053 LTBP2 latent transforming growth 204682_at factor
beta binding protein 2 Stromal-1 NM_004369 1293 COL6A3 collagen,
type VI, alpha 3 201438_at Stromal-1 NM_000393 1290 COL5A2
collagen, type V, alpha 2 221730_at Stromal-1 NM_015419 25878 MXRA5
matrix-remodelling 209596_at associated 5 Stromal-1 NM_001102 87
ACTN1 actinin, alpha 1 208637_x_at Stromal-1 NM_000877 3554 IL1R1
interleukin 1 receptor, 202948_at type I Stromal-1 NM_015927 7041
TGFB1I1 transforming growth factor 209651_at beta 1 induced
transcript 1 Stromal-1 NM_032772 84858 ZNF503 zinc finger protein
503 227195_at Stromal-1 NM_020440 5738 PTGFRN prostaglandin F2
receptor 224937_at negative regulator Stromal-1 NM_000138 2200 FBN1
fibrillin 1 202765_s_at Stromal-1 NM_031442 83604 TMEM47
transmembrane protein 209656_s_at 47 Stromal-1 NM_001734 716 C1S
complement component 208747_s_at 1, s subcomponent Stromal-1
NM_002290 3910 LAMA4 laminin, alpha 4 202202_s_at Stromal-1
CN312045 PPP4R2 Transcribed locus, weakly 222288_at similar to
NP_001013658.1 protein LOC387873 [Homo sapiens] Stromal-1 NM_000089
1278 COL1A2 collagen, type I, alpha 2 202403_s_at Stromal-1
NM_004530 4313 MMP2 matrix metallopeptidase 2 201069_at (gelatinase
A, 72 kDa gelatinase, 72 kDa type IV collagenase) Stromal-1
NM_001387 1809 DPYSL3 dihydropyrimidinase-like 3 201431_s_at
Stromal-1 NM_138389 92689 FAM114A1 family with sequence 213455_at
similarity 114, member A1 Stromal-1 NM_006670 7162 TPBG trophoblast
glycoprotein 203476_at Stromal-1 NM_000304 5376 PMP22 peripheral
myelin protein 210139_s_at 22 Stromal-1 NM_002775 5654 HTRA1 HtrA
serine peptidase 1 201185_at Stromal-1 NM_002593 5118 PCOLCE
procollagen C- 202465_at endopeptidase enhancer Stromal-1 NM_003118
6678 SPARC secreted protein, acidic, 212667_at cysteine-rich
(osteonectin) Stromal-1 NM_007085 11167 FSTL1 follistatin-like 1
208782_at Stromal-1 NM_001080393 727936 predicted glycosyl-
235371_at transferase 8 domain containing 4 Stromal-1 NM_018153
84168 ANTXR1 anthrax toxin receptor 1 224694_at Stromal-1 NM_001733
715 C1R complement component 212067_s_at 1, r subcomponent
Stromal-1 NM_001797 1009 CDH11 cadherin 11, type 2, OB- 207173_x_at
cadherin (osteoblast) Stromal-1 NM_016938 30008 EFEMP2
EGF-containing fibulin- 209356_x_at like extracellular matrix
protein 2 Stromal-2 NM_014601 30846 EHD2 EH-domain containing 2
45297_at Stromal-2 NM_017789 54910 SEMA4C sema domain, 46665_at
immunoglobulin domain (Ig), transmembrane domain (TM) and short
cytoplasmic domain, (semaphorin) 4C Stromal-2 NM_000484 351 APP
amyloid beta (A4) 200602_at precursor protein (peptidase nexin-II,
Alzheimer disease) Stromal-2 NM_004684 8404 SPARCL1 SPARC-like 1
(mast9, 200795_at hevin) Stromal-2 NM_002291 3912 LAMB1 laminin,
beta 1 201505_at Stromal-2 NM_000210 3655 ITGA6 integrin, alpha 6
201656_at Stromal-2 NM_000552 7450 VWF von Willebrand factor
202112_at Stromal-2 NM_001233 858 CAV2 caveolin 2 203323_at
Stromal-2 NM_006404 10544 PROCR protein C receptor, 203650_at
endothelial (EPCR) Stromal-2 NM_000609 6387 CXCL12 chemokine
(C--X--C motif) 203666_at ligand 12 (stromal cell- derived factor
1) Stromal-2 NM_002253 3791 KDR kinase insert domain 203934_at
receptor (a type III receptor tyrosine kinase) Stromal-2 NM_001442
2167 FABP4 fatty acid binding protein 203980_at 4, adipocyte
Stromal-2 NM_016315 51454 GULP1 GULP, engulfment 204237_at adaptor
PTB domain containing 1 Stromal-2 NM_006307 8406 SRPX
sushi-repeat-containing 204955_at protein, X-linked Stromal-2
NM_000163 2690 GHR growth hormone receptor 205498_at Stromal-2
NM_000950 5638 PRRG1 proline rich Gla (G- 205618_at carboxyglutamic
acid) 1 Stromal-2 NM_002666 5346 PLIN perilipin 205913_at Stromal-2
NM_000459 7010 TEK TEK tyrosine kinase, 206702_at endothelial
(venous malformations, multiple cutaneous and mucosal) Stromal-2
NM_004797 9370 ADIPOQ adiponectin, C1Q and 207175_at collagen
domain containing Stromal-2 NM_000442 5175 PECAM1
platelet/endothelial cell 208981_at adhesion molecule (CD31
antigen) Stromal-2 NM_198098 358 AQP1 aquaporin 1 (Colton blood
209047_at group) Stromal-2 NM_021005 7026 NR2F2 nuclear receptor
209120_at subfamily 2, group F, member 2 Stromal-2 NM_014220 4071
TM4SF1 transmembrane 4 L six 209386_at family member 1 Stromal-2
NM_001001549 2887 GRB10 growth factor receptor- 209409_at bound
protein 10 Stromal-2 NM_006108 10418 SPON1 spondin 1, extracellular
209436_at matrix protein Stromal-2 NM_001003679 3953 LEPR leptin
receptor 209894_at Stromal-2 NM_000599 3488 IGFBP5 insulin-like
growth factor 211959_at binding protein 5 Stromal-2 NM_001753 857
CAV1 caveolin 1, caveolae 212097_at protein, 22 kDa Stromal-2
NM_005841 10252 SPRY1 sprouty homolog 1, 212558_at antagonist of
FGF signaling (Drosophila) Stromal-2 NM_015345 23500 DAAM2
dishevelled associated 212793_at activator of morphogenesis 2
Stromal-2 NM_015234 221395 GPR116 G protein-coupled 212950_at
receptor 116 Stromal-2 NM_006108 10418 SPON1 spondin 1,
extracellular 213993_at matrix protein Stromal-2 NM_016215 51162
EGFL7 EGF-like-domain, multiple 7 218825_at Stromal-2 NM_022481
64411 CENTD3 centaurin, delta 3 218950_at Stromal-2 XM_371262 64123
ELTD1 EGF, latrophilin and 219134_at seven transmembrane domain
containing 1 Stromal-2 NM_016563 51285 RASL12 RAS-like, family 12
219167_at Stromal-2 NM_006094 10395 DLC1 deleted in liver cancer 1
224822_at Stromal-2 NM_019035 54510 PCDH18 protocadherin 18
225975_at Stromal-2 NM_019055 54538 ROBO4 roundabout homolog 4,
226028_at magic roundabout (Drosophila) Stromal-2 NM_002207 3680
ITGA9 integrin, alpha 9 227297_at Stromal-2 XM_930608 641700 ECSM2
endothelial cell-specific 227779_at molecule 2 Stromal-2 XM_037493
85358 SHANK3 SH3 and multiple ankyrin 227923_at repeat domains 3
Stromal-2 NM_052954 116159 CYYR1 cysteine/tyrosine-rich 1 228665_at
Stromal-2 NM_002837 5787 PTPRB protein tyrosine 230250_at
phosphatase, receptor type, B Stromal-2 NM_019558 3234 HOXD8
homeobox D8 231906_at Stromal-2 NM_001442 2167 FABP4 fatty acid
binding protein 235978_at 4, adipocyte Stromal-2 NM_024756 79812
MMRN2 multimerin 2 236262_at Stromal-2 BQ897248 Transcribed locus
242680_at Stromal-2 NM_020663 57381 RHOJ ras homolog gene family,
243481_at member J Stromal-2 AK091419 CDNA FLJ34100 fis, 1558397_at
clone FCBBF3007597 Stromal-2 NM_015719 50509 COL5A3 collagen, type
V, alpha 3 52255_s_at Stromal-2 NM_012072 22918 CD93 CD93 molecule
202878_s_at Stromal-2 NM_000300 5320 PLA2G2A phospholipase A2,
group 203649_s_at IIA (platelets, synovial fluid) Stromal-2
NM_019105 7148 TNXB tenascin XB 206093_x_at Stromal-2 NM_030754
6289 SAA2 serum amyloid A2 208607_s_at Stromal-2 NM_019105 7148
TNXB tenascin XB 208609_s_at Stromal-2 NM_014220 4071 TM4SF1
transmembrane 4 L six 209387_s_at family member 1 Stromal-2
NM_000668 125 ADH1B alcohol dehydrogenase IB 209612_s_at (class I),
beta polypeptide Stromal-2 NM_000668 125 ADH1B alcohol
dehydrogenase IB 209613_s_at (class I), beta polypeptide Stromal-2
NM_001354 1646 AKR1C2 aldo-keto reductase 209699_x_at
family 1, member C2 (dihydrodiol dehydrogenase 2; bile acid binding
protein; 3- alpha hydroxysteroid dehydrogenase, type III) Stromal-2
NM_001032281 7035 TFPI tissue factor pathway 210664_s_at inhibitor
(lipoprotein- associated coagulation inhibitor) Stromal-2
NM_001001924 57509 MTUS1 mitochondrial tumor 212096_s_at suppressor
1 Stromal-2 NM_019105 7148 TNXB tenascin XB 213451_x_at Stromal-2
NM_004449 2078 ERG v-ets erythroblastosis 213541_s_at virus E26
oncogene homolog (avian) Stromal-2 NM_018407 55353 LAPTM4B
lysosomal associated 214039_s_at protein transmembrane 4 beta
Stromal-2 NM_000331 6288 SAA1 serum amyloid A1 214456_x_at
Stromal-2 NM_019105 7148 TNXB tenascin XB 216333_x_at Stromal-2
NM_001034954 10580 SORBS1 sorbin and SH3 domain 218087_s_at
containing 1 Stromal-2 NM_017734 54873 PALMD palmdelphin
218736_s_at Stromal-2 NM_024756 79812 MMRN2 multimerin 2
219091_s_at Stromal-2 NM_006744 5950 RBP4 retinol binding protein
4, 219140_s_at plasma Stromal-2 NM_001034954 10580 SORBS1 sorbin
and SH3 domain 222513_s_at containing 1
[0078] The DLBCL survival predictors of the invention were
generated using expression data and methods described in Examples 1
and 2, below. The first bivariate survival predictor incorporates
the GCB and stromal-1 gene expression signatures. Fitting the Cox
proportional hazards model to the gene expression data obtained
from these two signatures resulted in a bivariate model survival
predictor score calculated using the following generalized
equation:
Bivariate DLBCL survival predictor score=A-[(x)*(GCB signature
value)]-[(y)*(stromal-1 signature value)].
In this equation, A is an offset term, while (x) and (y) are scale
factors. The GCB signature value and the stromal-1 signature value
can correspond to the average of the expression levels of all genes
in the GCB signature and the stromal-1 signature, respectively. A
lower survival predictor score indicates a more favorable survival
outcome, and a higher survival predictor score indicates a less
favorable survival outcome for the subject.
[0079] The bivariate survival predictor was refined into a
multivariate survival predictor that incorporates GCB, stromal-1,
and stomal-2 gene expression signatures. Fitting the Cox
proportional hazards model to the gene expression data obtained
from these three signatures resulted in a multivariate model
survival predictor score calculated using the following generalized
equation:
General multivariate DLBCL survival predictor score=A-[(x)*(GCB
signature value)]-[(y)*(stromal-1 signature value)]+[(z)*(stromal-2
signature value)].
In this equation, A is an offset term, while (x), (y), and (z) are
scale factors. The GCB signature value, the stromal-1 signature
value, and the stromal-2 signature value can correspond to the
average of the expression levels of all genes in the GCB signature,
the stromal-1 signature, and the stromal-2 signature, respectively.
A lower survival predictor score indicates a more favorable
survival outcome and a higher survival predictor score indicates a
less favorable survival outcome for the subject.
[0080] In one embodiment, the invention provides the following
multivariate survival predictor equation:
Multivariate DLBCL survival predictor score=8.11-[0.419*(GCB
signature value)]-[1.015*(stromal-1 signature
value)]+[0.675*(stromal-2 signature value)]
In this equation, a lower survival predictor score indicates a more
favorable survival outcome, and a higher survival predictor score
indicates a poorer survival outcome for the subject.
[0081] In other embodiments of the multivariate DLBCL survival
predictor score equation, the offset term (A) or (8.11) can be
varied without affecting the equation's usefulness in predicting
clinical outcome. Scale factors (x), (y), and (z) can also be
varied, individually or in combination. For example, scale factor
(x) can be from about 0.200 or more, from about 0.225 or more, from
about 0.250 or more, from about 0.275 or more, from about 0.300,
from about 0.325 or more, from about 0.350 or more, from about
0.375 or more, or from about 0.400 or more. Alternatively, or in
addition, scale factor (x) can be about 0.625 or less, about 0.600
or less, about 0.575 or less, about 0.550 or less, about 0.525 or
less, about 0.500 or less, about 0.475 or less, about 0.450 or
less, or about 0.425 or less. Thus, scale factor (z) can be one
that is bounded by any two of the previous endpoints. For example
scale factor (x) can be a value from 0.200-0.625, from 0.350-0.550,
from 0.350-0.475, or from 0.400-0.425. Similarly, scale factor (y)
can be from about 0.800 or more, from about 0.825 or more, from
about 0.850 or more, from about 0.875 or more, from about 0.900 or
more, from about 0.925 or more, from about 0.950 or more, from
about 0.975 or more, or from about 1.000 or more. Alternatively, or
in addition, scale factor (y) can be, e.g., about 1.250 or less,
e.g., about 1.225 or less, about 1.200, about 1.175 or less, about
1.150 or less, about 1.125 or less, about 1.100 or less, about
1.075 or less, about 1.050 or less, or about 1.025 or less. Thus,
scale factor (y) can be one that is bounded by any two of the
previous endpoints. For example, scale factor (y) can be a value
from 0.800-1.250, a value from 0.950-1.1025, a value from
0.950-1.200 or a value from 1.000-1.025. Also similarly, scale
factor (z) can be from about 0.450 or more, about 0.475 or more,
about 0.500 or more, about 0.525 or more, about 0.550 or more,
about 0.575 or more, about 0.600 or more, about 0.625 or more, or
about 0.650 or more. Alternatively, or in addition, scale factor
(z) can be, e.g., about 0.900 or less, e.g., about 0.875 or less,
about 0.850, about 0.825 or less, about 0.800 or less, about 0.775
or less, about 0.750 or less, or about 0.725 or less. Thus, scale
factor (z) can be one that is bounded by any two of the previous
endpoints. For example, scale factor (z) can be a value from
0.450-0.900, any value from 0.650-0.725, any value from 0.625-0.775
or any value from 0.650-0.700.
[0082] Furthermore, the invention includes any set of scale factors
(x), (y), and (z) in conjunction in the general multivariate DLBCL
survival predictor score that creates a function that is
monotonically related to a multivariate DLBCL survival predictor
score equation using any combination of the foregoing specified
scale factor (x), (y), and (z) values.
[0083] In some embodiments of the invention, a survival predictor
score can be calculated using fewer than all of the gene components
of the GCB signature, the stromal-1 signature, and/or the stromal-2
signature listed in Table 1. For example, the survival prediction
equations disclosed herein can be calculated using mathematical
combinations of the expressions of 98% (38), 95% (37), 93% (36), or
90% (35) of the genes listed in Table 1 for the GCB signature,
about 99% (about 280), about 98% (about 277), 97% (about 275),
about 96% (about 272), about 95% (about 270), about 94% (about
266), about 93% (about 263), about 92% (about 260), about 91%
(about 257), or about 90% (about 255) of the genes listed in Table
1 for the stromal-1 signature, and/or 99% (71), 97% (70), 96% (69),
95% (68) 93% (67), 92% (66), or 90% (65) of the genes listed in
Table 1 for the stromal-2 signature (instead of using all of the
genes corresponding to a gene signature in Table 1 to calculate the
GCB signature value, the stromal-1 signature value, and/or
stromal-2 signature value, respectively). In other embodiments, the
survival prediction equations disclosed herein can be calculated
using mathematical combinations of the expressions of 88% (34
genes), 85% (33 genes), 82% (32 genes), 80% (31 genes) of the genes
listed in Table 1 for the GCB signature, about 89% (about 252),
about 88% (about 249), about 87% (about 246), about 86% (about
243), about 85% (about 241), about 84% (about 238), about 83%
(about 235), about 82% (about 232), about 81% (about 229), or about
80% (about 226) of the genes listed in Table 1 for the stromal-1
signature, and/or 89% (64), 88% (63), 86% (62), 85% (61), 83% (60),
82% (59) or 80% (58) of the genes listed in Table 1 for the
stromal-2 signature (instead of using all of the genes
corresponding to a gene signature in Table 1 to calculate the GCB
signature value, the stromal-1 signature value, and/or stromal-2
signature value, respectively).
[0084] The invention also provides a method of using a DLBCL
survival predictor score to predict the probability of a survival
outcome beyond an amount of time t following treatment for DLBCL.
The method includes calculating the probability of a survival
outcome for a subject using the following general equation:
P(SO)=SO.sub.0(t).sup.(exp((s)*(survival predictor score)))
In this equation, P(SO) is the subject's probability of the
survival outcome beyond time t following treatment for DLBCL,
SO.sub.0(t) is the probability of survival outcome, which
corresponds to the largest time value smaller than t in a survival
outcome curve, and (s) is a scale factor. Treatment for DLBCL can
include chemotherapy and the administration of Rituximab. A
survival curve can be calculated using statistical methods, such as
the Cox Proportional Hazard Model. Additional information regarding
survival outcome curves is set forth in Lawless, Statistical Models
and Methods for Lifetime Data, John Wiley and Sons (New York 1982)
and Kalbfleisch et al., Biometrika, 60: 267-79 (1973).
[0085] In one embodiment, the method of the invention includes
calculating the probability of overall survival for a subject
beyond an amount of time t following treatment for DLBCL. The
method includes calculating the probability of a survival outcome
for a subject using the following general equation:
P(OS)=SO.sub.0(t).sup.(exp(survival predictor score))
In the equation, P(OS) is the subject's probability of overall
survival beyond time t following treatment for DLBCL, SO.sub.0(t)
is the curve probability of survival outcome, which corresponds to
the largest time value in a survival curve which is smaller than t,
and the general equation scale factor (s)=1. Treatment for DLBCL
can include chemotherapy alone or in combination with the
administration of Rituximab (R-CHOP).
[0086] In another embodiment, the method of the invention includes
calculating the probability of progression-free survival for a
subject beyond an amount of time t following treatment for DLBCL.
The method includes calculating the probability of a survival
outcome for a subject using the following general equation:
P(PFS)=SO.sub.0(t).sup.(exp(0.976*(survival predictor score)))
In this equation, P(PFS) is the subject's probability of
progression-free survival beyond time t following treatment for
DLBCL, SO.sub.0(t) is the curve probability of progression-free
survival, which corresponds to the largest time value in a survival
curve which is smaller than t, and the general equation scale
factor (s)=0.976. The treatment for DLBCL can include chemotherapy
alone or in combination with the administration of Rituximab
(R-CHOP).
[0087] The foregoing equations for P(OS) and P(PFS) were generated
by maximizing the partial likelihoods of the Cox proportional
hazards model within the LLMPP CHOP data described below in
Examples 1 and 2. Separate single variable Cox proportional hazards
models were considered for overall survival P(OS) and for
progression free survival P(PFS) based on this model score
formulation. The single variable scale factor (1.0 for overall
survival and 0.997 for progression free survival) were generated
for each model by maximization of the partial likelihoods within
the R-CHOP patients described below in Examples 1 and 2.
[0088] In other embodiments, the scale factor in the foregoing
P(PFS) can be varied such that (instead of 0.976) scale factor (s)
is a value between 0.970 and 0.980, e.g. 0.971, 0.972, 0.973,
0.973, 0.974, 0.975, 0.977, 0.978, and 0.979.
[0089] The invention also provides a method of selecting a subject
for antiangiogenic therapy of DLBCL based on the subject's high
relative expression of stromal-2 signature genes. As discussed more
fully below in Example 4, the stromal-2 signature includes a number
of genes whose expression or gene products are related to
angiogenesis. Thus, high relative expression of stromal-2 signature
genes in DLBCL can be indicative of high angiogenic activity.
Moreover, high relative expression of stromal-2 signature genes can
be related to the heavy infiltration of some DLBCL tumors with
myeloid lineage cells. Accordingly, subjects with high relative
expression of stromal-2 signature genes are good candidates for
treatment with antiangiogenic therapy, either alone or in
combination with other anti-oncogenic therapies. Furthermore, as
also discussed more fully in Example 4, a stromal score, which was
obtained by subtracting the stromal-1 signature value from the
stromal-2 signature value, was observed to correlate with high
tumor blood vessel density.
[0090] In this regard, the antiangiogenic monoclonal antibody to
vascular endothelial growth factor bevacizumab has been clinically
tested in patients with DLBCL (Ganjoo et al., Leuk. Lymphoma, 47:
998-1005 (2006)). Other antiangiogenic therapies can include small
molecule inhibitors of SDF-1 receptor, such as CXCR4 (Petit et al.,
Trends Immunol., 28: 299-307 (2007). Still another example of an
antiangiogenic therapy can include blocking antibodies to the
myeloid lineage cell marker CTGF, which has been implicated in
angiogenesis. Moreover, anti-CTGF antibodies have been shown to
have anti-cancer activity in pre-clinical models of cancer (Aikawa
et al., Mol. Cancer Ther., 5: 1108-16 (2006)).
[0091] In one embodiment, the method of the invention for selecting
a subject for antiangiogenic therapy includes obtaining a gene
expression profile from a DLBCL biopsy from the subject. The
subject's stromal-2 signature value is determined. The subject's
stromal-2 signature value is then compared to a standard stromal-2
value. A standard stromal-2 value corresponds to the average of
multiple stromal-2 signature values in DLBCL biopsy samples from a
plurality of randomly selected subjects with DLBCL, e.g., more than
10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 150, 200, or 250 randomly
selected subjects with DLBCL. If the subject's stromal-2 signature
value is significantly higher than the standard stromal-2 value,
then the subject can be treated with anti-angiogenic therapy.
[0092] In another embodiment, the method of the invention for
selecting a subject for anti-angiogenic therapy includes obtaining
a gene expression profile from a DLBCL biopsy from the subject. The
subject's stromal 1 signature value and stromal-2 signature value
are determined. The stromal-1 signature value is then subtracted
from the stromal-2 signature value to obtain a stomal score. The
subject's stromal score is then compared to a standard stromal
score. A standard stromal score corresponds to the average of
multiple stromal scores (each stromal score=[stromal-2 signature
value])-[stromal-1 signature value]) derived from DLBCL biopsy
samples from a plurality of randomly selected subjects with DLBCL,
e.g., more than 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 150, 200,
or 250 randomly selected subjects with DLBCL. If the subject's
stromal score is significantly higher than the standard stromal
score, then the subject can be treated with anti-angiogenic
therapy.
[0093] The invention further provides a targeted array that can be
used to detect the expression levels of all or most of the genes in
a germinal center B cell gene (GCB) expression signature, a
stromal-1 gene expression signature, and/or a stromal-2 gene
expression signature. A targeted array, as used herein, is an array
directed to a limited set of genes and thus differs from a whole
genome array. The targeted array of the invention can include
probes for fewer than 20,000 genes, fewer than 15,000 genes, fewer
than 10,000 genes, fewer than 8,000 genes, fewer than 7,000 genes,
fewer than 6,000 genes, fewer than 5,000 genes, or fewer than 4,000
genes. Generally, the targeted array includes probes for at least
80% of the genes in a germinal center B cell gene (GCB) expression
signature, a stromal-1 gene expression signature, and/or a
stromal-2 gene expression signature. The targeted arrays of the
invention can be used, for example, to detect expression levels for
use in the methods described herein.
[0094] The invention provides a targeted array that includes probes
for all of the genes in the stromal-1 gene expression signature.
The invention also provides a targeted array that includes probes
for all of the genes in the stromal-2 gene expression signature.
Additionally, the invention provides a targeted array that includes
probes for all of the genes in the stromal-1 gene expression
signature and all of the genes in the stromal-2 gene expression
signature. Moreover, the invention provides a targeted array that
includes probes for all of the genes in the stromal-1 gene
expression signature, all of the genes in the stromal-2 gene
expression signature, and all of the genes in the GCB
signature.
[0095] In certain embodiments, the arrays of the invention can
include 98% (38), 95% (37), 93% (36), or 90% (35) of the genes
listed in Table 1 for the GCB signature, about 99% (about 280),
about 98% (about 277), 97% (about 275), about 96% (about 272),
about 95% (about 270), about 94% (about 266), about 93% (about
263), about 92% (about 260), about 91% (about 257), or about 90%
(about 255) of the genes listed in Table 1 for the stromal-1
signature, and/or 99% (71), 97% (70), 96% (69), 95% (68) 93% (67),
92% (66), or 90% (65) of the genes listed in Table 1 for the
stromal-2 signature (instead of all of the genes listed in Table 1
for the GCB signature average, the stromal-1 signature average,
and/or stromal-2 signature average, respectively). In certain
embodiments, the arrays of the invention can include 88% (34
genes), 85% (33 genes), 82% (32 genes), 80% (31 genes) of the genes
listed in Table 1 for the GCB signature, about 89% (about 252),
about 88% (about 249), about 87% (about 246), about 86% (about
243), about 85% (about 241), about 84% (about 238), about 83%
(about 235), about 82% (about 232), about 81% (about 229), or about
80% (about 226) of the genes listed in Table 1 for the stromal-1
signature, and/or 89% (64), 88% (63), 86% (62), 85% (61), 83% (60),
82% (59) or 80% (58) of the genes listed in Table 1 for the
stromal-2 signature (instead of all of the genes listed in Table 1
for the GCB signature average, the stromal-1 signature average,
and/or stromal-2 signature average, respectively).
[0096] The following examples further illustrate the invention but,
of course, should not be construed as in any way limiting its
scope.
EXAMPLE 1
[0097] This example demonstrates that significant differences were
found between the survival outcomes for R-CHOP treated ABC DLBCL
and GCB DLBCL patients and that survival outcome correlated with
three prognostic gene expression signatures.
[0098] Pre-treatment tumor biopsy specimens and clinical data were
obtained from 414 patients with de novo DLBCL treated at 10
institutions in North America and Europe and studied according to a
protocol approved by the National Cancer Institute's Institutional
Review Board. Patients included in a "LLMP CHOP cohort" of 181
patients were treated with anthracycline-based combinations, most
often cyclophosphamide, doxorubicin, vincristine, and prednisone
(CHOP) or similar regimens, as previously described (Rosenwald et
al., N. Engl. J. Med., 346: 1937-47 (2002)). The remaining 233
patients constituted an R-CHOP cohort that received similar
chemotherapy plus Rituximab. The median follow-up in the R-CHOP
cohort was 2.1 years (2.8 years for survivors). A panel of expert
hematopathologists confirmed the diagnosis of DLBCL using current
WHO criteria. Additional clinical patient characteristics for the
R-CHOP cohort are described in Table 2. Additional analysis used a
second "MMMNLP CHOP" cohort of 177 patients studied by the
Molecular Mechanisms of Non-Hodgkin's Lymphoma Network Project
(Hummel et al., N. Engl. J. Med., 354: 2419-30 (2006)).
TABLE-US-00002 TABLE 2 Clinical characteristics of DLBCL patients
treated with R-CHOP % Germinal % Activated % % center B cell- B
cell-like Unclassified Total like DLBCL DLBCL DLBCL Characteristic
(N = 233) (N = 107) (N = 93) (N = 33) P-value Age >60 yr 52 47
63 39 0.02 Ann Arbor stage > II 54 48 62 50 0.06 Lactate 48 43
58 41 0.06 Dehydrogenase >1x Normal No. of extranodal 15 14 15
14 0.8 sites >1 Eastern Cooperative 25 17 33 27 0.02 Oncology
Group (ECOG) performance status International <0.001 Prognostic
Index (IPI) Score 0 or 1 41 55 21 50 2 or 3 46 33 63 38 4 or 5 13
12 15 12 Revised IPI Score <0.001 0 19 27 5 28 1 or 2 56 52 64
48 3-5 25 21 31 24
[0099] Gene expression profiling was performed using Affymetrix
U133+2.0 microarrays. Gene expression profiling data are available
through the National Center for Biotechnology Information web site
as described in Lenz et al., New Engl. J. Med, 359: 2313-23 (2008),
at page 2314. All gene expression array data were normalized using
MAS 5.0 software, and were log 2 transformed. To account for
technical differences in the microarray processing between the
R-CHOP cohort data and the LLMPP CHOP cohort data, the expression
values of each gene in the R-CHOP cohort data were adjusted so that
its median matched the median of the LLMPP CHOP data.
[0100] Gene expression signature identification and survival
predictor model development were based solely on the data from the
LLMPP CHOP training set. No prior survival analysis or subgroup
analysis was performed with the test sets (MMMLNP CHOP and R-CHOP
cohorts). The Cox model was used to identify genes associated with
survival in the CHOP training set and to build multivariate
survival models. The models and their associated scaling
coefficients were fixed based on the CHOP training set and then
evaluated on the test sets. The P-values of survival effects of
continuous variables such as gene expression or signature
expression were calculated with the Cox likelihood ratio test. The
significance of survival effects based on discrete variables such
as lymphoma subtype or International Prognostic Index (IPI) was
calculated using the log rank test. Validation P-values presented
are one-sided in the direction observed in the training set. All
other P-values were two sided. Survival curves were estimated using
the Kaplan-Meier method.
[0101] All aspects of gene expression signature identification and
survival predictor model development were based solely on the data
from the CHOP training set. No prior survival analysis or subgroup
analysis was performed with the test sets (MMMLNP CHOP and R-CHOP
cohorts). The Cox model was used to identify genes associated with
survival in the CHOP training set and to build multivariate
survival models. The models and their associated scale factors were
fixed based on the CHOP training set, and then evaluated on the
test sets.
[0102] Since ABC and GCB DLBCL subtypes have distinct overall
survival rates with CHOP chemotherapy (Rosenwald et al., N. Engl.
J. Med., 346: 1937-47 (2002); Alizadeh et al., Nature,
403:503-11(2000); Hummel et al., N. Engl. J. Med., 354:2419-30
(2006); Monti, Blood, 105:1851-61(2005)), whether this distinction
remains prognostically significant among patients treated with
R-CHOP was tested (Coiffier et al, N. Engl. J. Med., 346: 235-42
(2002)). Gene expression profiles were determined for pre-treatment
biopsy samples from a "training set" of 181 patients treated with
CHOP or CHOP-like chemotherapy alone and from a "test set" of 233
patients treated with R-CHOP. The patients in these two cohorts
were comparable with respect to age range and distribution of the
clinical prognostic variables that constitute the International
Prognostic Index (IPI) (Table 2). In the R-CHOP cohort, patients
with GCB DLBCL had better survival rates than those with ABC DLBCL.
Specifically, R-CHOP treated GCB DLBCL and ABC DLBCL patients had
3-year overall survival rates of 84% and 56%, respectively, and
3-year progression-free survival rates of 74% and 40%, respectively
(FIGS. 1A and 1B). In the CHOP training set, and in a second
"MMMLMP" CHOP cohort (Hummel et al., supra), the overall survival
rates for ABC DLBCL and GCB DLBCL were lower than in the R-CHOP
cohort (FIG. 6). Multivariate analysis indicated that the relative
benefit (i.e., change in survival outcome) due to R-CHOP therapy
(as compared to CHOP) was not significantly different between ABC
and GCB DLBCL.
[0103] Four gene expression signatures have been previously shown
to have prognostic significance in DLBCL patients treated with CHOP
(Rosenwald et al., supra). Of these, the GCB signature and lymph
node signature were associated with favorable survival, and the
proliferation signature was associated with inferior survival
within the CHOP training set, in the MMMLNP CHOP cohort (see the
corresponding signature panels in FIG. 7), and in the R-CHOP cohort
(see corresponding signature panels in FIG. 1C). Thus, the
biological differences among DLBCL tumors reflected by these three
signatures remain prognostically important in Rituximab treated
patients, even though Rituximab treatment generally improved
survival in DLBCL.
[0104] The remaining fourth gene expression signature, the MHC
class II signature, which was associated with survival in the CHOP
training set when treated as a continuous variable, was not
associated with survival in the R-CHOP cohort (see MHC class II
signature panel in FIG. 1C). Moreover, tumors with extremely low
"outlier" expression of this signature were associated with
inferior survival in both CHOP cohorts (see FIGS. 8A and 8B), but
not in the R-CHOP cohort (see FIG. 8C).
[0105] The foregoing results indicate that Rituximab immunotherapy
combined with chemotherapy (R-CHOP) benefits both the ABC and GCB
subtypes of DLBCL and that gene expression signatures that
predicted survival in the context of CHOP chemotherapy retained
their prognostic power among R-CHOP-treated patients.
[0106] The foregoing results also indicate that the biological
variation among DLBCL tumors, as measured by gene expression
signatures, has a consistent relationship to therapeutic response
regardless of the treatment regimen used. There is a striking
difference in 3-year progression-free survival between ABC DLBCL
patients and GCB DLBCL patients treated with R-CHOP (40% vs. 74%).
This difference is likely due to genetic and biological differences
between these DLBCL subtypes (Staudt et al., Adv. Immunol., 87:
163-208 (2005)).
[0107] Hence, future clinical trials in DLBCL should incorporate
quantitative methods to discern these biological differences so
that patient cohorts in different trials can be compared and
treatment responses can be related to defined tumor phenotypes.
EXAMPLE 2
[0108] This example demonstrates the development of GCB, stromal-1,
and stromal-2 survival signatures and a related multivariate model
of survival for R-CHOP-treated DLBCL.
[0109] Unless otherwise indicated, patient cohorts and methods of
gene expression analysis are as described in Example 1.
[0110] In the LLMPP CHOP cohort data, 936 genes were identified as
associated with poor prognosis p<0.01 (1-sided). For genes
having multiple array probe sets associated with survival, only the
probe set with the strongest association with survival was used.
The expression values of the probe sets in the LLMPP CHOP cohort
data were then clustered. The largest cluster with an average
correlation of >0.6 and containing myc was identified as the
proliferation survival signature. 1396 genes were identified as
associated with favorable outcome. The largest cluster with average
correlation of >0.6 and containing BCL6 was identified as the
germinal center B cell (GCB) survival signature. A cluster with
average correlation of >0.6 and containing FM was identified as
the stromal-1 survival signature, whereas another cluster with
average correlation of >0.6 containing HLADRA was identified as
the MHC class II survival signature. The expression levels of genes
within each signature were then averaged to create a "signature
average" for each biopsy specimen. For the MMMLNP CHOP data set,
the average was calculated for those array elements represented on
the Affymetrix U133A microarray.
[0111] From the four prognostic clusters or signatures, two
signatures, the stromal-1 and the GCB signatures were used to
create the best two variable survival model. Neither the
proliferation nor the MHC class II signatures added to the
prognostic value of this two variable model. This bivariate model
performed well in the MMMLNP CHOP cohort (FIG. 9A) and in the
R-CHOP cohort (FIG. 2A).
[0112] The CHOP training set was used to discover and refine
signatures that added to the prognostic significance of this
bivariate model, and the resulting multivariate models were tested
in the R-CHOP cohort. 563 genes were identified as adding to the
model in the direction of adverse prognosis. These genes were
clustered by hierarchical clustering, and three clusters of more
than 10 genes with an average correlation of >0.6 were
identified. In addition, 542 genes were identified which added to
the stromal-1 and GCB signature model in the direction of favorable
prognosis. These genes were clustered, and two clusters of more
than 10 genes with an average correlation of >0.6 were
identified. Signature averages were determined for these clusters,
and three variable models containing the stromal-1 and GCB
signature and each of the cluster averages were formed on the
MMMLNP CHOP and R-CHOP data sets. Of the five cluster averages, two
were found to add statistical significance (p<0.02) in the
MMMLNP CHOP data as compared to a model containing the stromal-1
and GCB signatures alone. By contrast, in the R-CHOP data, three of
the five cluster averages were found to add significance
(p<0.02) to the bivariate model. One of these cluster averages
added significantly to the bivariate model in both the MMMLNP CHOP
and R-CHOP data. This signature, designated Signature 122, was also
found to add to the stromal-1 and GCB signature far more
significantly than any of the four other signatures on the LLMPP
CHOP data and, thus, was retained for further analysis.
[0113] Signature 122 added significantly to the bivariate model in
both the MMMLNP CHOP cohort (p=0.011) and in the R-CHOP cohort
(p=0.001) (FIGS. 9B and 9C). This Signature 122 positively
correlated with the stromal-1 signature, although it was associated
with adverse survival when added to the bivariate model. To further
refine our model, we identified genes that were more correlated
with Signature 122 than with the stromal-1 signature (p<0.02).
These genes were organized by hierarchical clustering, and three
sets of correlated genes (r>0.6) were observed. One of these
clusters, the stromal-2 signature, added to the significance of the
bivariate model in both the MMMLNP CHOP cohort (p=0.002) and the
R-CHOP cohort (p<0.001) (FIGS. 2B and 9D).
[0114] A multivariate survival model was formed by fitting a Cox
model with the GCB, stromal-1, and stomal-2 signatures to the LLMPP
CHOP cohort data shown in Table 3. This final multivariate model
with its associated scaling coefficients was then evaluated on the
MMLLMPP CHOP and R-CHOP cohort data sets. Survival predictor scores
from the final model were used to divide the R-CHOP cohort into
quartile groups with 3-year overall survival rates of 89%, 82%,
74%, and 48%, and 3-year progression-free survival rates of 84%,
69%, 61% and 33% (FIG. 2B). The survival predictor scores from the
final model are illustrated in FIG. 3 along with the three
component signatures and representative genes of each
signature.
TABLE-US-00003 TABLE 3 Time to Status at Time to death, Status at
last Germinal death or last last follow up progression, or follow
up Center Stromal-1 Stromal-2 follow up (1 = dead, last follow up
(1 = progressed or died, Signature Signature Signature Model
Patient (years) 0 = alive) (years) 0 = no progression) Average
Average Average Score 2 2.75 0 2.75 0 9.238 8.778 7.475 0.376 3
2.67 0 2.67 0 9.942 8.227 7.102 0.387 5 1.27 1 0.72 1 8.859 9.033
8.716 1.113 21 2.39 0 2.40 0 10.573 8.519 6.959 -0.270 22 2.38 0
2.38 0 8.737 8.686 7.598 0.761 23 2.52 0 2.52 0 10.694 10.322 8.817
-0.897 24 5.11 0 5.11 0 11.376 7.854 7.598 0.500 26 4.01 0 4.01 0
9.829 9.956 8.507 -0.372 28 3.96 0 3.96 0 10.957 9.277 8.248 -0.330
41 0.52 1 0.52 1 9.273 9.437 8.202 0.183 47 1.53 1 0.77 1 9.548
8.802 8.061 0.617 48 0.37 1 0.12 1 8.660 8.279 6.891 0.729 49 2.37
0 2.35 1 10.915 8.988 6.847 -0.965 53 3.89 0 2.23 1 9.530 9.792
9.693 0.721 61 0.90 1 0.46 1 8.649 8.038 8.104 1.798 65 4.04 0 4.04
0 10.744 9.330 7.930 -0.508 66 4.04 0 4.04 0 10.714 10.016 7.536
-1.459 95 0.62 1 0.44 1 9.244 9.197 8.105 0.373 96 5.37 0 5.37 0
10.107 8.723 7.608 0.157 97 5.07 0 5.07 0 9.777 9.192 7.359 -0.349
98 0.94 1 0.59 1 8.794 7.711 7.367 1.571 99 0.40 1 0.40 1 9.024
9.272 9.160 1.101 103 0.03 1 0.02 1 8.883 8.190 7.742 1.301 104
3.76 0 3.76 0 9.785 9.866 7.929 -0.652 106 2.95 0 2.95 0 10.585
7.797 6.824 0.367 107 2.94 0 2.94 0 11.535 8.358 6.660 -0.711 108
2.73 0 2.73 0 9.653 8.495 7.550 0.539 109 0.16 1 0.11 1 9.301 9.376
7.994 0.092 110 2.46 0 2.46 0 10.254 8.980 7.324 -0.357 111 2.44 0
2.44 0 10.137 10.691 8.948 -0.949 113 2.12 0 2.12 0 10.746 8.555
6.942 -0.390 114 1.98 0 0.88 1 8.562 8.159 7.120 1.047 115 1.92 0
1.92 0 10.313 9.385 8.157 -0.231 118 1.64 0 1.64 0 10.209 10.194
8.231 -0.959 119 1.60 0 1.60 0 11.059 8.852 7.479 -0.461 1087 0.05
1 0.05 1 8.756 8.491 7.949 1.188 1089 5.12 0 1.27 1 9.863 9.135
8.034 0.129 1091 5.15 0 5.15 0 10.454 9.918 8.742 -0.437 1092 5.06
0 5.07 0 9.452 9.467 8.912 0.556 1093 3.83 1 1.62 1 9.915 9.138
7.747 -0.090 1096 4.02 0 4.02 0 8.887 9.236 7.795 0.274 1097 1.26 1
1.08 1 11.219 9.234 8.321 -0.347 1098 3.53 0 3.53 0 9.117 9.236
7.655 0.082 1099 3.07 0 0.91 1 9.284 8.798 7.741 0.515 1101 5.64 0
5.64 0 9.803 9.466 8.156 -0.101 1108 3.30 0 3.30 0 9.195 10.456
9.065 -0.237 1109 3.78 0 3.78 0 11.008 10.051 8.273 -1.120 1164
0.19 1 0.16 1 9.242 10.307 10.548 0.896 1167 1.49 1 0.45 1 9.809
9.105 8.784 0.687 1168 0.42 1 0.30 1 8.718 8.368 7.149 0.790 1169
1.71 1 1.22 1 11.512 8.108 7.507 0.125 1172 2.82 0 2.82 0 11.137
8.871 8.153 -0.057 1173 0.87 1 0.79 1 11.324 9.914 8.514 -0.950
1175 1.06 1 0.56 1 9.107 10.310 9.063 -0.053 1179 2.53 0 2.53 0
9.506 9.437 8.461 0.260 1181 1.72 0 1.72 0 10.688 9.018 7.647
-0.360 1184 4.74 0 2.97 1 10.812 8.979 7.922 -0.187 1185 3.71 0
3.71 0 10.431 8.397 7.317 0.156 1186 3.43 0 3.43 0 8.688 8.944
8.552 1.164 1187 5.23 0 5.23 0 10.072 10.192 8.667 -0.604 1189 5.13
0 5.13 0 10.109 9.212 7.967 -0.097 1190 3.66 0 3.66 0 10.713 10.409
8.910 -0.930 1192 0.16 1 0.16 1 8.825 9.903 8.061 -0.199 1195 4.36
0 4.36 0 11.539 7.567 6.873 0.234 1197 3.13 0 3.13 0 10.287 10.365
9.549 -0.275 1200 0.31 1 0.31 1 9.432 8.950 9.805 1.692 1206 6.51 0
6.51 0 10.410 9.946 8.925 -0.323 1211 6.25 0 6.25 0 11.596 7.908
6.524 -0.372 1215 5.35 0 5.35 0 10.504 9.061 7.550 -0.392 1216 0.46
1 0.29 1 10.017 9.010 7.794 0.028 1219 0.51 1 0.51 1 10.614 10.014
8.619 -0.683 1220 2.24 1 2.25 1 8.850 9.400 8.036 0.286 1221 3.94 0
3.95 0 8.777 7.489 6.672 1.334 1222 3.53 0 3.53 0 10.463 9.310
7.019 -0.986 1224 3.22 0 2.11 1 9.751 9.505 8.453 0.082 1225 2.95 0
2.95 0 8.613 8.313 7.668 1.240 1226 0.08 1 0.08 1 9.229 8.851 7.950
0.625 1228 2.78 0 0.99 1 11.532 8.261 6.932 -0.428 1230 0.59 1 0.54
1 9.369 6.951 6.956 1.825 1231 1.41 0 1.41 0 10.248 8.788 8.011
0.303 1232 2.49 0 0.68 1 10.362 8.528 7.975 0.495 1233 2.50 0 2.50
0 9.239 10.581 8.470 -0.784 1236 2.56 0 2.56 0 9.156 10.000 7.805
-0.608 1238 0.16 1 0.16 1 9.488 9.055 8.256 0.517 1239 2.24 0 2.24
0 8.886 8.978 7.838 0.564 1240 1.48 0 1.48 0 10.474 9.073 7.702
-0.288 1241 1.41 1 1.17 1 9.044 9.054 7.451 0.160 1251 2.72 0 2.72
0 8.410 8.687 7.082 0.549 1252 0.01 1 0.01 1 11.167 8.070 7.358
0.206 1255 5.17 0 5.17 0 9.501 9.411 7.887 -0.099 1271 4.72 0 4.73
0 10.718 8.452 7.060 -0.194 1272 5.68 0 5.68 0 9.161 9.080 7.668
0.231 1275 1.89 1 1.48 1 9.257 8.559 8.607 1.354 1277 5.06 0 5.07 0
11.091 9.938 8.274 -1.038 1279 4.87 0 4.87 0 9.309 10.085 9.676
0.504 1281 3.36 0 not available (n/a) n/a 9.535 9.969 9.090 0.132
1284 3.51 0 3.51 0 10.922 9.680 8.481 -0.567 1288 1.54 0 n/a n/a
9.430 8.896 8.037 0.554 1289 0.03 1 0.03 1 8.915 9.052 8.002 0.589
1290 5.23 0 5.23 0 10.432 10.426 8.154 -1.340 1291 0.04 1 0.04 1
11.319 8.246 7.323 -0.059 1292 0.10 1 0.10 1 8.667 8.764 8.110
1.058 1293 4.81 0 4.81 0 11.116 9.842 8.083 -1.081 1294 0.53 1 0.53
1 10.138 10.181 8.501 -0.733 1295 5.16 0 5.17 0 9.445 9.694 7.739
-0.463 1296 4.79 0 4.79 0 10.228 9.064 8.852 0.600 1297 4.24 0 4.24
0 9.524 7.990 7.008 0.740 1298 4.56 0 4.56 0 9.022 9.000 7.695
0.389 1331 3.29 0 3.29 0 11.004 9.488 8.289 -0.536 1334 2.87 0 2.87
0 11.434 9.509 8.109 -0.859 1335 1.38 1 0.90 1 9.586 8.545 7.423
0.431 1336 2.44 0 2.44 0 10.844 9.704 7.706 -1.082 1337 0.02 1 0.02
1 8.521 7.788 7.860 1.941 1449 1.62 0 1.62 0 9.604 8.463 8.030
0.917 1450 1.30 0 0.53 1 8.571 8.112 7.241 1.173 1451 1.84 0 1.85 0
10.637 9.205 7.759 -0.452 1453 1.71 0 1.71 0 10.964 9.089 8.226
-0.157 1454 0.62 0 0.62 0 11.106 8.514 7.604 -0.052 1553 2.93 0
1.92 1 8.975 9.284 7.475 -0.029 1612 5.37 0 5.37 0 10.526 9.471
7.809 -0.643 1613 5.81 0 n/a n/a 10.868 9.695 7.730 -1.067 1614
4.36 1 4.36 1 10.358 9.226 8.765 0.322 1617 0.52 0 0.52 0 10.332
8.723 7.180 -0.227 1618 1.70 0 0.98 1 11.233 8.956 7.852 -0.387
1619 0.25 1 0.25 1 8.646 8.028 7.123 1.146 1620 2.17 0 2.17 0
11.647 8.385 7.343 -0.325 1623 2.80 0 2.80 0 9.611 9.484 8.249
0.024 1626 1.76 0 1.76 0 11.236 9.495 8.108 -0.763 1628 3.13 0 1.23
1 8.714 7.972 7.149 1.192 1645 2.85 0 2.85 0 10.146 9.476 8.914
0.258 1647 2.79 0 2.80 0 10.485 10.495 8.707 -1.058 1650 0.75 1
0.75 1 8.830 7.346 6.486 1.333 1651 1.66 0 1.66 0 9.190 7.949 6.829
0.801 1652 1.64 0 n/a n/a 8.798 8.943 8.331 0.969 1702 1.05 0 1.05
1 9.008 8.217 8.078 1.447 1703 0.70 1 0.70 1 9.499 8.637 7.790
0.621 1704 3.14 0 3.14 0 9.908 9.231 7.503 -0.347 1705 3.94 0 3.94
0 8.933 8.445 8.187 1.321 1707 2.80 0 2.80 0 10.610 9.348 7.872
-0.510 1742 3.27 0 n/a n/a 10.033 8.715 7.412 0.063 1746 1.91 0
1.55 1 9.249 8.705 8.205 0.937 1747 1.48 0 1.48 0 10.162 8.866
7.602 -0.016 1756 3.47 0 3.47 0 10.815 9.638 7.248 -1.312 1761 0.23
1 0.23 1 9.842 10.192 8.664 -0.511 1762 5.20 0 5.20 0 10.583 9.333
7.445 -0.772 1763 5.51 0 5.51 0 8.917 8.925 8.084 0.771 1766 1.59 0
1.59 0 10.919 10.037 8.389 -0.990 1782 1.09 0 1.09 0 10.753 9.600
8.332 -0.516 1788 0.39 1 0.24 1 10.364 8.738 8.914 0.915 1861 0.56
1 0.19 1 9.728 8.604 7.594 0.427 1867 1.17 1 0.38 1 8.903 11.501
10.559 -0.166 1916 1.41 0 n/a n/a 9.295 11.197 11.508 0.619 1920
1.32 0 1.32 0 10.165 9.630 8.789 0.009 1927 1.53 0 1.53 0 9.195
10.261 9.791 0.451 1928 0.72 0 0.72 0 9.769 8.510 7.330 0.328 1939
0.47 1 0.47 1 9.097 9.363 7.647 -0.043 2002 1.29 0 1.30 0 9.469
9.542 8.600 0.262 2006 1.23 0 1.23 0 10.434 8.223 7.162 0.227 2067
2.18 0 2.18 0 10.244 11.186 9.391 -1.197 2070 0.31 0 0.12 1 10.486
10.680 10.353 -0.135 2162 0.38 1 0.38 1 10.934 10.020 7.960 -1.268
2270 1.59 0 1.59 0 10.117 9.904 8.506 -0.440 2271 1.60 0 1.60 0
8.995 9.349 8.261 0.428 2274 0.41 0 0.41 0 8.863 7.623 7.222 1.533
2283 1.19 0 1.19 0 10.501 8.361 6.741 -0.226 2291 0.87 1 0.85 1
10.732 10.184 9.436 -0.353 2299 0.93 0 0.93 0 10.661 9.905 8.189
-0.883 2301 0.61 0 0.61 0 9.852 9.903 8.352 -0.432 2306 0.68 0 0.68
0 8.586 8.759 8.191 1.151 2309 0.43 0 0.43 0 10.839 7.671 6.860
0.413 2311 0.80 0 0.80 0 10.901 7.797 6.912 0.294 2318 0.99 0 0.99
0 10.283 9.403 8.655 0.100 2321 0.82 0 0.82 0 9.691 8.956 7.404
-0.044 2411 0.67 0 0.67 0 8.986 8.383 7.854 1.137 2415 0.62 0 0.62
0 9.296 10.509 9.551 -0.005 2444 3.99 0 3.99 0 10.154 9.871 9.026
-0.071 2445 3.36 0 3.36 0 8.788 8.184 7.964 1.497 2479 0.51 0 0.51
0 11.151 9.023 8.199 -0.186 2482 4.54 0 4.54 0 10.373 9.847 8.208
-0.691 2483 3.89 1 3.89 1 9.241 8.902 7.742 0.428 2484 2.69 1 1.90
1 10.279 9.619 8.312 -0.349 2485 4.43 0 4.43 0 9.957 9.865 8.439
-0.378 2486 4.37 0 n/a n/a 10.698 10.203 8.041 -1.301 2487 4.34 0
4.34 0 11.227 9.909 8.260 -1.076 2488 4.20 0 4.21 0 9.510 8.709
7.615 0.426 2490 4.02 0 4.02 0 10.510 10.961 8.956 -1.374 2491 0.50
1 0.25 1 9.047 8.554 7.624 0.784 2492 3.96 0 3.96 0 9.904 10.901
9.140 -0.935 2497 3.44 0 3.44 0 9.221 9.438 8.065 0.111 2498 3.37 0
3.37 0 9.318 9.427 8.003 0.040 2500 3.31 0 3.31 0 11.014 9.406
7.375 -1.074 2501 3.28 0 n/a n/a 8.822 8.551 7.750 0.966 2503 2.99
0 2.99 0 8.301 7.967 6.929 1.222 2504 2.78 0 2.78 0 10.145 8.004
7.017 0.472 2505 2.76 0 2.76 0 11.036 8.442 7.136 -0.266 2507 0.86
1 0.54 1 9.737 9.475 8.988 0.480 2508 2.58 0 2.58 0 8.678 9.389
8.230 0.498 2509 0.96 1 0.76 1 8.895 10.441 9.088 -0.081 2511 1.55
1 1.06 1 9.225 9.267 9.191 1.042 2512 2.45 0 2.45 0 11.047 10.465
9.337 -0.838 2513 0.61 1 0.61 1 10.855 10.378 8.395 -1.305 2514
2.18 0 2.18 0 10.477 9.832 7.498 -1.198 2515 2.13 0 2.13 0 9.295
10.519 9.788 0.145 2516 2.07 0 2.07 0 10.575 10.592 8.642 -1.238
2517 2.04 0 0.76 1 9.385 9.163 8.328 0.498 2584 0.68 0 0.68 0
10.759 9.356 8.135 -0.404 2599 4.05 0 4.05 0 10.629 9.158 7.724
-0.425 2600 1.01 1 0.54 1 9.785 8.619 7.291 0.184 2601 1.22 1 0.88
1 9.385 8.044 7.178 0.859 2603 4.43 0 4.43 0 9.582 10.707 9.803
-0.156 2604 0.84 0 0.36 1 9.844 10.511 8.382 -1.026 2609 8.89 0
2.55 1 8.981 8.775 7.506 0.507 2610 0.74 0 0.74 0 10.793 8.964
7.421 -0.502 2611 0.66 0 0.66 0 10.353 10.233 9.032 -0.518 2612
1.17 1 1.13 1 10.290 9.028 8.287 0.230 2613 1.66 0 1.66 0 10.997
9.089 7.749 -0.493 2614 0.21 1 0.21 1 8.768 7.850 7.100 1.261 2615
0.48 0 0.48 0 11.359 9.470 7.647 -1.100 2639 10.29 0 10.30 0 11.085
10.385 8.003 -1.674 2641 1.38 0 1.38 0 9.199 8.818 7.340 0.259 2642
3.67 0 3.67 0 10.731 8.777 7.167 -0.458 2643 5.49 0 5.49 0 10.236
10.578 8.473 -1.197 2645 0.19 0 n/a n/a 11.130 9.997 8.254 -1.129
2646 0.18 1 0.18 1 8.893 7.648 6.871 1.260 2648 0.25 0 0.25 0 8.855
7.745 7.060 1.303 2649 2.13 0 2.13 0 9.688 10.354 9.885 0.214 2650
2.43 0 n/a n/a 10.007 10.052 8.861 -0.305 2651 1.61 0 n/a n/a
10.660 9.452 7.831 -0.665 2652 1.84 0 1.84 0 11.378 9.247 7.684
-0.856 2653 1.88 0 1.88 0 11.182 9.638 7.781 -1.106 2654 1.43 0
1.43 0 8.791 9.395 8.905 0.902 2813 3.97 0 3.97 0 10.701 9.366
8.258 -0.306 2814 0.81 1 0.70 1 10.561 9.176 9.275 0.632
[0115] The International Prognostic Index (IPI), which is based on
5 clinical variables, predicts survival in both CHOP-treated and
R-CHOP-treated patients (Shipp et al., N. Engl. J. Med., 329:987-94
(1993); Sehn et al., Blood, 109: 1857-61 (2007)). The inventive
gene expression-based survival model retained its prognostic
significance among R-CHOP-treated patients segregated according to
IPI into high, intermediate and low IPI risk groups, both as
originally defined (Shipp et al., supra) (p<0.001) (FIG. 2C) and
as recently modified for R-CHOP-treated DLBCL (Sehn et al., supra)
(p<0.001) (FIG. 10).
[0116] The foregoing results indicate that the gene
expression-based multivariate model can be used to identify large
disparities in survival among patients with different DLBCL gene
signature profiles. Thus, survival predictor scores were used to
divide patients into least and most favorable quartile groups
having 3-year progression-free survival rates of 33% and 84%,
respectively. Given its statistical independence from the IPI, the
gene expression-based survival predictor provides a complementary
view of DLBCL variation that can be considered when analyzing data
from DLBCL clinical trials. Additionally, the foregoing results
indicate that whole-genome gene expression profiles in conjunction
with the survival model described herein can be used to provide
optimal predictions of expected survival outcomes for subjects
suffering from DLBCL.
EXAMPLE 3
[0117] This example demonstrates the use of a survival predictor
score to predict the probability of progression free and overall
survival outcomes at a period of time t following R-CHOP treatment
in accordance with the invention.
[0118] RNA is isolated from a patient's DLBCL biopsy and hybridized
to a U133+ array from Affymetrix (Santa Clara, Calif.). The array
is scanned, and MAS 5.0 algorithm is applied to obtain signal
values normalized to a target intensity of 500. Signal values are
log 2 transformed to intensity values. For genes of interest with
multiple probe sets, the intensity value of the multiple probe sets
are averaged to obtain a single intensity value for each gene. The
single intensity values of genes in the GCB signature are averaged
to obtain a GCB signature average of 9.2. The single intensity
values of genes in the stromal-1 signature are averaged to obtain a
stromal-1 signature average of 8.5. The single intensity values of
genes in the stromal-2 signature are averaged to obtain a stromal-2
signature average of 7.2.
[0119] The patient's survival predictor score is calculated using
the following equation 8.11-[0.419*(GCB signature
average)]-[1.015*(stromal-1 signature average)]+[0.675*(stromal-2
signature average)], such that the survival predictor
score=8.11-[0.419*(9.2)]-[1.015*(8.5)]+[0.675*(7.2)]=0.389
[0120] Table 4 includes values from a progression free survival
curve generated using baseline hazard functions calculated from the
R-CHOP patient data described in Table 3. The curve was generated
in accordance with the methods of Kalbfleisch and Prentice,
Biometrika, 60: 267-279 (1973), which involves maximizing the full
likelihood, under the assumption that the true scaling coefficients
were equal to prior estimates. In Table 4, F.sub.0(t) is the
probability of progression free survival for each indicated time
period following R-CHOP treatment (t-RCHOP).
TABLE-US-00004 TABLE 4 t-RCHOP (years) F.sub.0 (t) 0.000 1.000
0.008 0.997 0.016 0.993 0.025 0.990 0.030 0.987 0.036 0.983 0.049
0.980 0.082 0.977 0.096 0.973 0.107 0.970 0.118 0.967 0.120 0.963
0.156 0.960 0.156 0.956 0.159 0.953 0.178 0.950 0.192 0.946 0.211
0.943 0.233 0.939 0.241 0.936 0.246 0.932 0.252 0.928 0.290 0.925
0.298 0.921 0.307 0.918 0.364 0.914 0.381 0.910 0.381 0.907 0.400
0.903 0.441 0.899 0.446 0.895 0.463 0.891 0.468 0.887 0.515 0.884
0.517 0.880 0.531 0.876 0.534 0.872 0.537 0.868 0.537 0.864 0.539
0.860 0.561 0.856 0.586 0.852 0.611 0.848 0.679 0.843 0.698 0.839
0.698 0.834 0.720 0.830 0.747 0.826 0.756 0.821 0.761 0.816 0.767
0.812 0.786 0.807 0.849 0.803 0.879 0.798 0.884 0.793 0.898 0.789
0.912 0.784 0.977 0.779 0.986 0.774 1.046 0.770 1.057 0.765 1.076
0.760 1.128 0.755 1.166 0.750 1.216 0.745 1.227 0.740 1.270 0.735
1.481 0.729 1.547 0.724 1.624 0.718 1.900 0.711 1.919 0.705 2.105
0.699 2.231 0.692 2.245 0.685 2.352 0.678 2.546 0.671 2.968 0.662
3.890 0.648 4.364 0.623
[0121] The patient's probability of 2 year progression free
survival is calculated using the equation:
P(PFS)=F.sub.0(t).sup.(exp(0.976*survival predictor score)), where
F.sub.0(t) is the F.sub.0(t) value that corresponds to the largest
time value smaller than 2 years in the progression free survival
curve. In Table 4, the largest time value smaller than 2 is 1.919,
and the corresponding PF.sub.0(t) value is 0.705. Accordingly, the
patient's probability of 2 year progression free survival
P(PFS)=0.705.sup.(exp(0.976*survival predictor
score))=0.705.sup.1.462=0.600 or about 60%.
[0122] Table 5 includes values from an overall survival curve
generated using baseline hazard functions calculated from the
R-CHOP patient data described in Table 3. The curve was made
according to the method of Kalbfleisch and Prentice, Biometrika,
60: 267-279 (1973), which involves maximizing the full likelihood,
under the assumption that the true scaling coefficients were equal
to our estimates. In Table 5, OS.sub.0(t) is the probability of
overall survival for each indicated time period following R-CHOP
treatment (t-RCHOP).
TABLE-US-00005 TABLE 5 t-RCHOP (years) OS.sub.0 (t) 0.000 1.000
0.008 0.997 0.016 0.994 0.030 0.991 0.033 0.988 0.036 0.984 0.049
0.981 0.082 0.978 0.096 0.975 0.156 0.972 0.156 0.969 0.159 0.965
0.178 0.962 0.192 0.959 0.211 0.956 0.233 0.952 0.246 0.949 0.307
0.946 0.367 0.942 0.380 0.939 0.386 0.935 0.402 0.932 0.416 0.928
0.463 0.925 0.468 0.921 0.504 0.918 0.515 0.914 0.517 0.910 0.531
0.907 0.556 0.903 0.586 0.900 0.610 0.896 0.619 0.892 0.698 0.888
0.747 0.885 0.807 0.881 0.862 0.877 0.868 0.873 0.873 0.869 0.895
0.864 0.944 0.860 0.963 0.856 1.010 0.852 1.057 0.848 1.169 0.843
1.169 0.839 1.215 0.835 1.262 0.830 1.273 0.826 1.382 0.821 1.412
0.817 1.492 0.812 1.527 0.807 1.552 0.802 1.708 0.796 1.889 0.791
2.244 0.784 2.693 0.777 3.826 0.763 3.889 0.749 4.363 0.724
[0123] The patient's probability of 2 year overall survival is
calculated using the equation: P(OS)=OS.sub.0(t).sup.(exp(survival
predictor score)), where OS.sub.0(t) is the value that corresponds
to the largest time value in the overall survival curve which is
smaller than 2 years. In Table 5, the largest time value smaller
than 2 is 1.889, and the corresponding OS.sub.0(t) value is 0.791.
Accordingly, the patient's probability of 2 year overall survival
is P(PFS)=0.791.sup.(exp(0.389))=0.791.sup.1.4476=0.707 or
70.7%.
EXAMPLE 4
[0124] This example demonstrates the biological basis for DLBCL
prognostic signatures.
[0125] Unless otherwise indicated, cohorts and methods of gene
expression analysis are described in Examples 1 and 2. Furthermore,
cell suspensions from three biopsies were separated by flow
cytometry into a CD19+ malignant subpopulation and a CD19-
non-malignant subpopulation. Gene expression profiling was
performed following two rounds of linear amplification from total
RNA (Dave et al., N. Engl. J. Med., 351: 2159-69 (2004)). After
MAS5.0 normalization, genes were selected that had a log 2 signal
value greater than 7 in either the CD19+ or CD19- fractions in at
least two of the sorted samples.
[0126] To assess whether the gene expression signatures in the
final survival model of Example 2 were derived from the malignant
lymphoma cells or from the host microenvironment, three DLBCL
biopsy samples were fractionated into CD19+ malignant cells and
CD19- non-malignant cells by flow sorting. Most germinal center B
cell signature genes were more highly expressed in the malignant
fraction, whereas genes from the stromal-1 and stromal-2 signatures
were more highly expressed in the non-malignant stromal fraction
(FIG. 4A), hence their name. Since these two signatures were
synergistic in predicting survival, they were combined into a
"stromal score" (FIG. 3), high values of which were associated with
adverse outcome.
[0127] The germinal center B cell signature relates to the
distinction between the ABC and GCB DLBCL subtypes (FIG. 3). By
contrast, the genes defining the stromal-1 signature encodes
components of the extracellular matrix, including fibronectin,
osteonectin, various collagen and laminin isoforms, and the
anti-angiogenic factor thrombospondin (FIG. 3 and Table 1). This
signature also encodes modifiers of collagen synthesis (LOXL1,
SERPINHI), proteins that remodel the extracellular matrix (MMP2,
MMP9, MMP14, PLAU, TIMP2), and CTGF, a secreted protein that can
initiate fibrotic responses (Frazier et al., J. Invest. Dermatol.,
107(3): 404-11 (1996)). In addition, the stromal-1 signature
includes genes characteristically expressed in cells of the
monocytic lineage, such as CEBPA and CSF2RA.
[0128] The stromal-1 signature is significantly related to several
previously curated gene expression signatures (Shaffer et al.,
Immunol. Rev., 210: 67-85 (2006)) based on gene set enrichment
analysis (Subramanian et al., Proc. Nat'l. Acad. Sci. USA, 102(43):
15545-50 (2005)). Two of these signatures include genes that are
coordinately expressed in normal mesenchymal tissues but not in
hematopoietic subsets, many of which encode extracellular matrix
proteins (false discovery rate (FDR)<0.001) (FIGS. 4B and 11)
(Su et al., Proc. Nat'l. Acad. Sci. USA, 101: 6062-7 (2004)). Also
enriched was a "monocyte" signature, comprised of genes that are
more highly expressed in CD14+ blood monocytes than in B cells, T
cells, or NK cells (FDR=0.014) (FIG. 4B). By contrast, a pan-T cell
signature was not related to the stromal-1 signature (FIG. 4B).
These findings suggest that high expression of the stromal-1
signature identifies tumors with vigorous extracellular matrix
deposition and infiltration by cells in the monocytic lineage.
[0129] In this regard, the stromal-1 signature gene product
fibronectin was prominently localized by immunohistochemistry to
fibrous strands running between the malignant cells in DLBCL biopsy
samples, in keeping with its role in extracellular matrix
formation. By contrast, the protein products of three other
stromal-1 gene--MMP9, SPARC, and CTGF--were localized primarily in
histiocytic cells that infiltrated the DLBCL biopsies. By
immunofluorescence, SPARC and CTGF colocalized with CD68, which is
a marker for cells in the monocytic lineage. As expected for a
stromal-1 gene product, SPARC protein levels were associated with
favorable overall survival (FIG. 5A).
[0130] The stromal-1 signature includes genes that are coordinately
expressed in many normal mesenchymal tissues, most of which encode
proteins that form or modify the extracellular matrix. The
localization of fibronectin to fibrous strands insinuated between
the malignant lymphoma cells suggests that the stromal-1 signature
reflects the fibrotic nature of many DLBCL tumors. This fibrotic
reaction may be related to another stromal-1 signature component,
CTGF, which participates in many fibrotic responses and diseases,
and promotes tumor growth and metastasis of epithelial cancers
(Shi-Wen et al., Cytokine Growth Factor Rev., 19: 133-44
(2008)).
[0131] The foregoing results also indicate that the stromal-1
signature reflects a monocyte-rich host reaction to the lymphoma
that is associated with the abundant deposition of extracellular
matrix. Tumors with high expression of the stromal-1 signature were
infiltrated by cells of the myeloid lineage, which include cells
that have been implicated in the pathogenesis of epithelial
cancers, including tumor-associated macrophages, myeloid-derived
suppressor cells, and Tie2-expressing monocytes (reviewed in Wels
et al., Genes Dev., 22: 559-74 (2008)). In animal models, these
myeloid lineage cells promote tumor cell invasion by secreting
matrix metalloproteinases such as MMP9, suppress T cell immune
responses, and initiate angiogenesis.
[0132] Several stromal-2 signature genes encode well-known markers
of endothelial cells. These include von-Willebrand factor (VWF) and
CD31 (PECAM1), as well as other genes specifically expressed in
endothelium such as EGFL7, MMRN2, GPR116, and SPARCL (Table 1).
This signature also includes genes encoding key regulators of
angiogenesis, such as, for example, KDR (VEGF receptor-2); Grb10,
which mediates KDR signaling; integrin alpha 9, which enhances VEGF
signaling; TEK, the receptor tyrosine kinase for the cytokine
angiopoietin; ROBO4, an endothelial-specific molecular guidance
molecule that regulates angiogenesis; and ERG, a transcription
factor required for endothelial tube formation. The stromal-2
signature genes CAV1, CAV2, and EHD2 encode components of caveolae,
which are specialized plasma membrane structures that are abundant
in endothelial cells and required for angiogenesis (Frank et al.,
Arterioscler. Thromb. Vasc. Biol., 23: 1161-8 (2003); Woodman et
al., Am. J. Pathol., 162: 2059-68 (2003)). Although the stromal-2
signature includes a large number of genes expressed in endothelial
cells, other genes are expressed exclusively in adipocytes,
including ADIPOQ, FABP4, RBP4, and PLIN.
[0133] Quantitative tests were done to determine whether expression
of the stromal-2 signature relative to the stromal-1 signature
(i.e., high stromal score) is related to high tumor blood vessel
density, given the connection between many stromal-2 signature
genes and angiogenesis. More specifically, the stromal-1 signature
averages were subtracted from the stromal-2 signature average to
thereby obtain a stromal score for each biopsy. Tests showed a
quantitative measure of blood vessel density correlated
significantly with the stromal score (r=0.483, p=0.019) (see FIGS.
5B and 5C), such that higher blood vessel densities correlated with
higher stromal scores.
[0134] Thus, the stromal-1 and stromal-2 gene expression signatures
reflect the character of the non-malignant cells in DLBCL tumors,
and the stromal-2 signature may represent an "angiogenic switch" in
which the progression of a hyperplastic lesion to a fully malignant
tumor is accompanied by new blood vessel formation (Hanahan et al.,
Cell, 86: 353-64 (1996)). DLBCL tumors with high relative
expression of the stomal-2 signature were associated with increased
tumor blood vessel density and adverse survival. Significant
macrophage infiltration in some DLBCL tumors may predispose to
angiogenesis since, in experimental models, tumor-associated
macrophages accumulate prior to the angiogenic switch and are
required for the switch to occur (Lin et al., Cancer Res., 66:
11238-46 (2006)). Additionally, CXCL12 (SDF-1), a stromal-2
signature component, is a chemokine secreted either by fibroblasts
or endothelial cells that can promote angiogenesis by recruiting
CXCR4+ endothelial precursor cells from the bone marrow (Orimo et
al., Cell, 121: 335-48 (2005)). Moreover, an antagonist of
angiogenesis, thrombospondin-2 (Kazerounian et al., Cell Mol. Life
Sci., 65: 700-12 (2008)), is a stromal-1 signature component, which
may explain why tumors with low relative expression of this
signature had an elevated blood vessel density. Furthermore, the
expression of adipocyte-associated genes in DLBCL tumors with high
stromal-2 signature expression may play a role in angiogenesis
since some cells in adipose tissue may have the potential to
differentiate into endothelial cells (Planat-Benard et al.,
Circulation, 109: 656-63 (2004)). Alternatively, the expression of
adipose-associated genes may reflect the recruitment of bone
marrow-derived mesenchymal stem cells, which home efficiently to
tumors (Karnoub et al., Nature, 449: 557-63 (2007)) and can
stabilize newly formed blood vessels (Au et al., Blood, 111:
4551-4558 (2008)).
[0135] The foregoing results indicate that the stromal-1 and
stromal-2 gene signatures can be used to generate a stromal score
that correlates with increased blood vessel density. Thus, the
stromal score can be used to determine if a DLBCL patient is likely
to benefit from administration of antiangiogenic therapy (alone, or
in conjunction with another DLBCL therapeutic regimen).
[0136] All references, including publications, patent applications,
and patents, cited herein are hereby incorporated by reference to
the same extent as if each reference were individually and
specifically indicated to be incorporated by reference and were set
forth in its entirety herein.
[0137] The use of the terms "a" and "an" and "the" and similar
referents in the context of describing the invention (especially in
the context of the following claims) are to be construed to cover
both the singular and the plural, unless otherwise indicated herein
or clearly contradicted by context. The terms "comprising,"
"having," "including," and "containing" are to be construed as
open-ended terms (i.e., meaning "including, but not limited to,")
unless otherwise noted. Recitation of ranges of values herein are
merely intended to serve as a shorthand method of referring
individually to each separate value falling within the range,
unless otherwise indicated herein, and each separate value is
incorporated into the specification as if it were individually
recited herein. All methods described herein can be performed in
any suitable order unless otherwise indicated herein or otherwise
clearly contradicted by context. The use of any and all examples,
or exemplary language (e.g., "such as") provided herein, is
intended merely to better illuminate the invention and does not
pose a limitation on the scope of the invention unless otherwise
claimed. No language in the specification should be construed as
indicating any non-claimed element as essential to the practice of
the invention.
[0138] Preferred embodiments of this invention are described
herein, including the best mode known to the inventors for carrying
out the invention. Variations of those preferred embodiments may
become apparent to those of ordinary skill in the art upon reading
the foregoing description. The inventors expect skilled artisans to
employ such variations as appropriate, and the inventors intend for
the invention to be practiced otherwise than as specifically
described herein. Accordingly, this invention includes all
modifications and equivalents of the subject matter recited in the
claims appended hereto as permitted by applicable law. Moreover,
any combination of the above-described elements in all possible
variations thereof is encompassed by the invention unless otherwise
indicated herein or otherwise clearly contradicted by context.
Sequence CWU 0 SQTB SEQUENCE LISTING The patent application
contains a lengthy "Sequence Listing" section. A copy of the
"Sequence Listing" is available in electronic form from the USPTO
web site
(http://seqdata.uspto.gov/?pageRequest=docDetail&DocID=US20110195064A1).
An electronic copy of the "Sequence Listing" will also be available
from the USPTO upon request and payment of the fee set forth in 37
CFR 1.19(b)(3).
0 SQTB SEQUENCE LISTING The patent application contains a lengthy
"Sequence Listing" section. A copy of the "Sequence Listing" is
available in electronic form from the USPTO web site
(http://seqdata.uspto.gov/?pageRequest=docDetail&DocID=US20110195064A1).
An electronic copy of the "Sequence Listing" will also be available
from the USPTO upon request and payment of the fee set forth in 37
CFR 1.19(b)(3).
* * * * *
References