U.S. patent application number 14/413510 was filed with the patent office on 2015-06-11 for markers associated with human double minute 2 inhibitors.
This patent application is currently assigned to NOVARTIS AG. The applicant listed for this patent is Swann Gaulis, Sebastien Jeay. Invention is credited to Swann Gaulis, Sebastien Jeay.
Application Number | 20150159222 14/413510 |
Document ID | / |
Family ID | 49304035 |
Filed Date | 2015-06-11 |
United States Patent
Application |
20150159222 |
Kind Code |
A1 |
Gaulis; Swann ; et
al. |
June 11, 2015 |
MARKERS ASSOCIATED WITH HUMAN DOUBLE MINUTE 2 INHIBITORS
Abstract
The invention provides methods of monitoring differential gene
expression of biomarkers to determine patient sensitivity to Human
Double Minute inhibitors (MDM2i), methods of determining the
sensitivity of a cell to an MDM2i by measuring biomarkers and
methods of screening for candidate MDM2i.
Inventors: |
Gaulis; Swann; (Basel,
CH) ; Jeay; Sebastien; (Niffer, FR) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
Gaulis; Swann
Jeay; Sebastien |
Basel
Niffer |
|
CH
FR |
|
|
Assignee: |
NOVARTIS AG
Basel
CH
|
Family ID: |
49304035 |
Appl. No.: |
14/413510 |
Filed: |
July 25, 2013 |
PCT Filed: |
July 25, 2013 |
PCT NO: |
PCT/IB2013/056122 |
371 Date: |
January 8, 2015 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
|
|
61677859 |
Jul 31, 2012 |
|
|
|
Current U.S.
Class: |
514/253.05 ;
435/6.11; 435/6.12; 435/6.13; 435/7.1; 435/7.92; 506/16; 506/9;
544/363 |
Current CPC
Class: |
G01N 33/5748 20130101;
G01N 2333/4704 20130101; G01N 33/574 20130101; C12Q 1/6883
20130101; C12Q 2600/156 20130101; G01N 2800/52 20130101; C12Q
1/6886 20130101; G01N 33/57496 20130101; C12Q 2600/158 20130101;
A61K 31/496 20130101; C12Q 2600/106 20130101; G01N 33/6875
20130101; G01N 2333/9015 20130101; C12Q 2600/136 20130101; C12Y
603/02019 20130101; G01N 33/5011 20130101; C07D 401/12 20130101;
G01N 2333/4748 20130101; A61P 35/00 20180101 |
International
Class: |
C12Q 1/68 20060101
C12Q001/68; G01N 33/50 20060101 G01N033/50; G01N 33/574 20060101
G01N033/574; A61K 31/496 20060101 A61K031/496 |
Claims
1. A method of predicting the sensitivity of a cancer patient for
treatment with a Human Double Minute 2 inhibitor (MDM2i), the
method comprising: assaying for gene expression of at least one
biomarker selected from Table 2 in a cancer sample obtained from
the patient; and the gene expression of the at least one biomarker
indicates that the patient is sensitive to treatment with an
MDM2i.
2. The method of claim 1, wherein more than one biomarker is
selected from Table 2.
3. The method of claim 1, comprising assaying for gene expression
of the biomarkers MDM2, CDKN1A, ZMAT3, DDB2, FDXR, RPS27L, BAX,
RRM2B, SESN1, CCNG1, XPC, TNFRSF10B and AEN.
4. The method of claim 1, wherein p53 is assayed for a mutation
prior to assay of the at least one biomarker, and the presence of a
p53 mutation indicates decreased sensitivity to an MDM2i.
5. The method of claim 1, wherein the cancer sample is selected
from the group consisting of breast, lung, pancreas, ovary, central
nervous system (CNS), endometrium, stomach, large intestine, colon,
esophagus, bone, urinary tract, hematopoietic, lymphoid, liver,
skin, melanoma, kidney, soft tissue sarcoma and pleura.
6. The method of claim 1, wherein an mRNA or protein of at least
one biomarker is measured.
7. The method of claim 1, wherein the MDM2i is an
isoquinolinone.
8. The method of claim 1, wherein the MDM2i is selected from Table
1.
9. A method of treating a cancer patient comprising: assaying for
gene expression of at least one biomarker selected from Table 2 in
a cancer sample obtained from the patient; and the presence of at
least one biomarker indicates sensitivity of the patient to an
MDM2i; and administering to the patient an MDM2i.
10. The method of claim 9, wherein more than one biomarker is
selected from Table 2.
11. The method of claim 9, comprising assaying for the biomarkers
MDM2, CDKN1A, ZMAT3, DDB2, FDXR, RPS27L, BAX, RRM2B, SESN1, CCNG1,
XPC, TNFRSF10B and AEN.
12. The method of claim 9, wherein p53 is assayed for a mutation
prior to assay of the at least one biomarker and the presence of a
p53 mutation indicates decreased sensitivity to an MDM2i.
13. The method of claim 9 further comprising obtaining a biological
sample from the patient prior to the administration of the MDM2i
and comparing the differential gene expression of at least one
biomarker from Table 2 in the MDM2i untreated cancer sample with an
MDM2i treated cancer sample, wherein increased gene expression
indicates continued patient sensitivity.
14. The method of claim 9, wherein the cancer sample is selected
from the group consisting of breast, lung, pancreas, ovary, central
nervous system (CNS), endometrium, stomach, large intestine, colon,
esophagus, bone, urinary tract, hematopoietic, lymphoid, liver,
skin, melanoma, kidney, soft tissue sarcoma and pleura.
15. The method of claim 9, wherein the MDM2i is an
isoquinolinone.
16. The method of claim 9, wherein the MDM2i is selected from Table
1.
17. The method of claim 9, wherein the MDM2i is administered in a
therapeutically effective amount.
18. A method of predicting the sensitivity of a cancer cell to a
Human Double Minute 2 inhibitor (MDM2i), the method comprising:
assaying for gene expression of at least one biomarker selected
from Table 2 in the cancer cell obtained from a patient sample and
the gene expression of the at least one biomarker selected from
Table 2 indicates that the cancer cell is sensitive to treatment
with an MDM2i.
19. The method of claim 18, wherein more than one biomarker is
selected from Table 2.
20. The method of claim 18, comprising assaying for the biomarkers
MDM2, CDKN1A, ZMAT3, DDB2, FDXR, RPS27L, BAX, RRM2B, SESN1, CCNG1,
XPC, TNFRSF10B and AEN.
21. The method of claim 18, wherein p53 is assayed for a mutation
prior to assay of the at least one biomarker and the presence of a
p53 mutation indicates decreased sensitivity to an MDM2i.
22. The method of claim 18, wherein the cancer sample is selected
from the group consisting of breast, lung, pancreas, ovary, central
nervous system (CNS), endometrium, stomach, large intestine, colon,
esophagus, bone, urinary tract, hematopoietic, lymphoid, liver,
skin, melanoma, kidney, soft tissue sarcoma and pleura.
23. The method of claim 18, wherein an mRNA or protein of at least
one biomarker is measured.
24. The method of claim 18, wherein the MDM2i is an
isoquinolinone.
25. The method of claim 18, wherein the MDM2i is selected from
Table 1.
26. The method of claim 18, wherein the MDM2i is administered in a
therapeutically effective amount.
27. A method of determining the sensitivity of a cancer cell to a
Human Double Minute 2 inhibitor (MDM2i), the method comprising: a)
contacting a cancer cell with at least one MDM2i; b) measuring gene
expression of at least one biomarker selected from Table 2 in the
cell contacted with the MDM2i and expression of at least one
biomarker indicates sensitivity to an MDM2i; and c) comparing the
cell proliferation of cancer cells treated with at least one MDM2i
with an untreated or placebo treated cancer cell, wherein there is
reduced cell proliferation of the treated cancer cell when compared
with the untreated or placebo treated cancer cell.
28. The method of claim 27, wherein the reduced cell proliferation
of the cancer cell contacted with at least one MDM2i has an IC50 of
less than 1 .mu.M.
29. The method of claim 27, wherein the MDM2i is an
isoquinolinone.
30. The method of claim 27, wherein the MDM2i is selected from
Table 1.
31. The method of claim 27, wherein the cell is contacted by the
MDM2i at least two different time points.
32. The method of claim 27, wherein the cell is contacted by two
different MDM2i at step a).
33. The method of claim 27, wherein the cell is contacted by the
two different MDM2i at the same time.
34. The method of claim 27, wherein the cell is contacted by two
different MDM2i at different time points.
35. The method of claim 27, wherein the cancer cell is selected
from the group consisting of breast, lung, pancreas, ovary, central
nervous system (CNS), endometrium, stomach, large intestine, colon,
esophagus, bone, urinary tract, hematopoietic, lymphoid, liver,
skin, melanoma, kidney, soft tissue sarcoma and pleura.
36. The method of claim 27, wherein an mRNA or protein of at least
one biomarker is measured.
37. The method of claim 27, comprising assaying for the biomarkers
MDM2, CDKN1A, ZMAT3, DDB2, FDXR, RPS27L, BAX, RRM2B, SESN1, CCNG1,
XPC, TNFRSF10B and AEN.
38. The method of claim 27, wherein the steps b) and c) are
repeated at a time points selected from the group consisting of: 4
hours, 8 hours, 16 hours, 24 hours, 48 hours, 3 days, 1 week, 1
month and several months after administration of each dose of
MDM2i.
39. A method of screening for MDM2i candidates the method
comprising: a) contacting a cell with a MDM2i candidate; b)
measuring gene expression of at least one biomarker selected from
Table 2 in the cell contacted with the MDM2i candidate and
expression of the at least one biomarker indicates sensitivity to
an MDM2i; and c) comparing the reduction in cell proliferation from
the cell contacted with the MDM2i candidate with the reduction in
cell proliferation of a cell contacted with an MDM2i taken from
Table 1 and the cell proliferation of an untreated or placebo
treated cell.
40. The method of claim 39, wherein assaying for the biomarkers
MDM2, CDKN1A, ZMAT3, DDB2, FDXR, RPS27L, BAX, RRM2B, SESN1, CCNG1,
XPC, TNFRSF10B and AEN.
41. The method of claim 39, wherein the MDM2i candidate increases
gene expression of at least one biomarker of Table 2.
42. The method of claim 39, wherein the cell is a cancer cell
selected from the group consisting of breast, lung, pancreas,
ovary, central nervous system (CNS), endometrium, stomach, large
intestine, colon, esophagus, bone, urinary tract, hematopoietic,
lymphoid, liver, skin, melanoma, kidney, soft tissue sarcoma and
pleura.
43. The method of claim 39, wherein the expression of an mRNA or
protein of at least one biomarker of Table 2 is measured.
44. The method of claim 39, comprising assaying for the biomarkers
MDM2, CDKN1A, ZMAT3, DDB2, FDXR, RPS27L, BAX, RRM2B, SESN1, CCNG1,
XPC, TNFRSF10B and AEN.
45. Composition comprising an MDM2i for use in treatment of cancer
in a selected cancer patient population, wherein the cancer patient
population is selected on the basis of showing gene expression of
at least one biomarker selected from Table 2 in a cancer cell
sample obtained from said patients.
46. The composition of claim 45, wherein the cancer cells sample is
selected from the group consisting of breast, lung, pancreas,
ovary, central nervous system (CNS), endometrium, stomach, large
intestine, colon, esophagus, bone, urinary tract, hematopoietic,
lymphoid, liver, skin, melanoma, kidney, soft tissue sarcoma and
pleura
47. The composition of claim 45 or 46, wherein the patients are
selected on the basis of an gene expression of the biomarkers MDM2,
CDKN1A, ZMAT3, DDB2, FDXR, RPS27L, BAX, RRM2B, SESN1, CCNG1, XPC,
TNFRSF10B and AEN.
48. A kit for predicting the sensitivity of a cancer patient for
treatment with a Human Double Minute 2 inhibitor (MDM2i) comprising
i) means for detecting the expression of the biomarkers MDM2,
CDKN1A, ZMAT3, DDB2, FDXR, RPS27L, BAX, RRM2B, SESN1, CCNG1, XPC,
TNFRSF10B and AEN; and ii) instructions how to use said kit.
49. Use of the kit according to claim 48 for any of the methods of
claims 1 to 37.
Description
FIELD OF THE INVENTION
[0001] The present invention relates to the field of
pharmacogenomics, and the use of biomarkers useful in determining
patient sensitivity prior to treatment, following patient response
after treatment, cancer sensitivity and screening of compounds.
BACKGROUND
[0002] p53, also known as tumor protein 53, is a tumor suppressor
gene involved in the prevention of cancer, often referred to as the
gatekeeper or guardian of the genome (Levine, Cell 1997,
88:323-331). The p53 gene encodes for a transcription factor that
is normally quiescent, and becoming activated when the cell is
stressed or damaged, such as when DNA damage incurred from a
mutagen. If the cell is stressed or damaged, p53 acts to limit the
damage, or barring that, trigger the apoptotic pathway so the
damaged cell is eliminated and no longer a threat to the organism
(Vogelstein et al., Nature 2000, 408:307-310). An analysis of
different cancers showed that p53 is mutated in about 50% of human
cancers (Hollstein et al., Nucleic Acids Res. 1994, 22:3551-3555:
Hollstein et al., Science 1991, 253(5015): 49-53). Humans who are
heterozygous for p53, with only a single functional copy, will
develop tumors early in adulthood, a disorder known as Li-Fraumeni
syndrome (Varley et al., Hum. Mutat. 2003, 21(3):313-320). However,
as much as p53 regulates the cell's fate, p53 is regulated by
another protein known as MDM2.
[0003] Double minute 2 protein (MDM2) was discovered as a negative
regulator of p53 (Fakharzadeh et al., EMBO J. 1991,
10(6):1565-1565). MDM2 encodes an E3 ligase containing a p53
binding domain and a nuclear export signal sequence, and upon
complexing with p53, removes it from the nucleus and
ubiquitinylates it, which promotes the degradation of the p53
protein via the ubiquitin-proteosome pathway (Haupt et al., Nature
1997, 387(6630):296-299; Piette et al., Oncogene 1997
15(9):1001-1010). In addition, MDM2 directly inhibits the activity
of p53 by binding to the p53 transactivation domain, also
preventing p53 mediated gene expression (Wu et al., Genes Dev.
1993, 7:1126-1132). Thus, MDM2 regulates p53 in multiple ways.
[0004] MDM2 is overexpressed in a number of cancers, for example,
liposarcoma, glioblastoma, and leukemia (Momand et al., Nucleic
Acids Res. 1998, 26(15):3453-3459). Overexpression of MDM2 can
interfere with the activities of p53, preventing apoptosis and
growth arrest of the tumor (de Rozieres et al., Oncogene 2000,
19(13):1691-1697). Overexpression of MDM2 correlates with poor
prognosis in glioma, and acute lympocytic leukemia (Onel et al.,
Mol. Cancer Res. 2004, 2(1):1-8).
[0005] As MDM2 is an inhibitor of p53, therapeutics which prevent
the binding of MDM2 to p53 would prevent the degradation of p53,
allowing free p53 to bind and mediate gene expression in cancer
cells, resulting in cell cycle arrest and apoptosis. There are
previous reports of small molecule inhibitors of the p53-MDM2
interaction (Vassilev et al., Science, 2004, 303(5659):844-888;
Zhang et al., Anticancer drugs, 2009 20(6):416-424; Vu et al.,
Curr. Topics Microbiol. Immuno., 2011, 348:151-172). The mode of
binding of these compounds and a crystal structure of the human
MDM2--Nutlin complex as well as a scaffold and pockets of the p53
binding site on MDM2 are also known (Vassilev, supra). The first of
these MDM2 inhibitors, known as the Nutlins, bind MDM2 and occupy
the p53 binding pocket, preventing the formation of the MDM2-p53
complex. This leads to less degradation of the p53 protein, and
expression of p53 target genes. Cancer cell lines treated with
Nutlins showed growth arrest and increased apoptosis. For example,
the SJSA-1 osteosarcoma line contains amplified copies of the MDM2
gene. Treatment of this line with Nutlin-3 reduced proliferation
and increased apoptosis (Vassilev et al., Science, 2004,
303(5659):844-888). The SJSA-1 cell line was used in creating
xenographs in mouse. Administration of Nutlin-3 reduced xenograft
growth by 90%. To investigate the effect the Nutlin compounds had
on non-cancerous cells, human and mouse normal fibroblasts were
treated with Nutlin-3 and while the proliferation of the cells was
slowed, they retained their viability (Vassilev, supra).
[0006] Finding biomarkers which indicate which patient should
receive a therapeutic is useful, especially with regard to cancer.
This allows for more timely and aggressive treatment as opposed to
a trial and error approach. In addition, the discovery of
biomarkers which indicate that cells continue to be sensitive to
the therapy after administration is also useful. These biomarkers
can be used to monitor the response of those patients receiving the
therapeutic. If biomarkers indicate that the patient has become
insensitive to the treatment, then the dosage administered can be
increased, decreased, completely discontinued or an additional
therapeutic administered. As such, there is a need to develop
biomarkers associated with MDM2 inhibitors. This approach ensures
that the correct patients receive the appropriate treatment and
during the course of the treatment the patient can be monitored for
continued MDM2 inhibitor sensitivity.
[0007] In the development of MDM2 inhibitors, specific biomarkers
will aid in understanding the mechanism of action upon
administration. The mechanism of action may involve a complex
cascade of regulatory mechanisms in the cell cycle and differential
gene expression. This analysis is done at the pre-clinical stage of
drug development in order to determine the particular sensitivity
of cancer cells to the MDM2 inhibitor candidate and the activity of
the candidate. Of particular interest in the pharmacodynamic
investigation is the identification of specific markers of
sensitivity and activity, such as the ones disclosed herein.
SUMMARY OF THE INVENTION
[0008] The invention relates to the analysis that a number of genes
identified in Table 2 act as specific biomarkers in determining the
sensitivity of cells to MDM2 inhibitors (henceforth "MDM2i"). The
invention relates to the analysis that at least one of the
biomarkers in Table 2 provides a "gene signature" for MDM2i that
has increased accuracy and specificity in predicting which cancer
cells are sensitive to MDM2i. The method analyzes the gene
expression or protein level of at least one of the biomarkers in
Table 2 in a cancer sample taken from a patient which predicts the
sensitivity of the cancer sample to an MDM2i. The pattern of
expression level changes may be indicative of a favorable response
or an unfavorable one. In addition, the gene signature provided in
Table 2 has increased predictive value because it also indicates
that the p53 pathway is functional. This is an unexpected result as
many tumors contain a mutated p53 and a non-functional pathway
which provides the tumor with a growth advantage. The invention is
an example of "personalized medicine" wherein patients are treated
based on a functional genomic signature that is specific to that
individual.
[0009] The predictive value of at least one biomarker in Table 2
can also be used after treatment with an MDM2i to determine if the
patient remains sensitive to the treatment. Once the MDM2i
therapeutic has been administered, the biomarkers are used to
monitor the continued sensitivity of the patient to MDM2i
treatment. The disclosure also relates to the up or down regulation
of the expression of the identified genes after MDM2i treatment.
This is useful in determining that patients receive the correct
course of treatment. The invention comprises a method of predicting
and monitoring the sensitivity of a patient to MDM2i treatment. The
method includes the step of administration of an MDM2i to the
patient and measurement of biomarker gene expression on a
biological sample obtained from the patient. The response of the
patient is evaluated based on the detection of gene expression of
at least one biomarker from Table 2. Detection and/or alteration in
the level of expression of at least one biomarker is indicative of
the sensitivity of the patient to the treatment. The pattern of
expression level can be indicative of a favorable patient response
or an unfavorable one.
DESCRIPTION OF THE DRAWINGS
[0010] FIG. 1A shows the in vitro potency of the MDM2i in
disrupting p53-MDM2 interaction. FIG. 1B shows the in vitro potency
of the MDM2i on the proliferation of cancer cells.
[0011] FIG. 2 shows the p53 status (mutant or wild type) and the
sensitivity of the cancer cells to MDM2i(2). The X axis is the
crossing point for sensitivity and the Y axis is the Amax value.
FIG. 2 demonstrates the sensitivity of cells to MDM2i(2) and p53
mutational status, mt (mutant) or wild type (wt). The mt panel
displays the MDM2i(2) sensitivity profiles for p53 mutated CCLE
cell lines, the wt panel displays the MDM2i(2) sensitivity profiles
for p53 wild-type CCLE cell lines. Amax is defined as the maximal
effect level (the inhibition at the highest tested MDM2i(2)
concentration, calibrated to a reference inhibitor), and IC50 is
defined as of the .mu.M concentration at which MDM2i(2) response
reached an absolute inhibition of -50 with respect to the reference
inhibitor. Cell line count is broken down by MDM2i(2) chemical
sensitivity and p53 mutation status, and associated statistics.
Data is also displayed as a contingency table with associated
statistics.
[0012] FIG. 3 are biomarkers showing the fold change in expression
and the statistical significance of the overexpression value. FIG.
3 shows selected MDM2i chemical sensitivity predictive biomarkers
and their associated statistics. The gene identities are official
HUGO symbols. The `Expr` prefix indicates these biomarkers are of
the transcript expression type.
[0013] FIG. 4 is a Western blot of sensitive and insensitive
representative cells treated with an MDM2i(1) for 4 hours at
various concentrations and then probed for selected p53 target
genes as pharmacodynamic biomarker representatives.
[0014] FIG. 5 is a graph demonstrating the increase in predictive
value of the gene signature as opposed to a larger set of
biomarkers. FIG. 5 shows cross-validation performances of MDM2i(2)
chemical sensitivity predictive models. Performances of models
derived from three predictive feature sets are evaluated using
sensitivity (fraction of correctly predicted sensitive cell lines),
specificity (fraction of correctly predicted insensitive cell
lines), PPV (positive predicted value, fraction of sensitive cell
lines predicted as such) and NPV (negative predictive value,
fraction of insensitive cell lines predicted as such). Error bars
are one standard deviation of the means over the 5 iterations of
5-fold cross-validations. The model using the 13 selected biomarker
depicted in Table 2 achieves better predictivity of MDM2i(2)
chemical sensitivity, and this independently of the performance
measure, but most strikingly when PPV is considered as the
performance indicator.
[0015] FIG. 6 is a list of cell lines, each analyzed for the gene
signature and the prediction of whether the cell line is sensitive
or insensitive and the IC50 when treated with an MDM2i.
[0016] FIG. 7A-C shows the dose-dependent inhibition of tumor
growth in the SJSA-1 xenograft model (predicted to be sensitive by
the gene signature) following treatment with MDM2i(1), and the
concomitant induction of p21(CDKN1A) expression as a representative
pharmacodynamic biomarker.
[0017] FIG. 8 shows the expression of p21(CDKN1A) at the protein
level after treatment of MDM2i(1) sensitive cells at efficacious
doses.
[0018] FIG. 9 is a model of tumor samples predicted to be sensitive
using the gene signature in the OncExpress database and the Primary
Tumor Bank. FIG. 9 shows lineage specific correlations of MDM2i
chemical sensitivity predictions in human primary tumor collections
and CCLE cell line data. The left panel shows the correlation
between the fractions of predicted sensitive samples from the human
primary tumor sample collection and the observed sensitivity ratio
in the cell line data. The right panel shows the correlation
between the fractions of predicted sensitive samples from the human
primary tumor sample collection and the predicted sensitive ratios
in the primary tumor xenograft collection. Samples/Xenografts/Cell
lines are organized by lineage. The dashed line is the identity
line.
[0019] FIG. 10 represents the Positive Predictive Values (PPV)
achieved by the MDM2i(2) sensitivity predictive models, built from
multiple combinations of biomarkers described in Table 2. FIG. 10
shows Positive Predicted Values (PPV) achieved by MDM2i(2)
sensitivity predictive models built from multiple combinations of
biomarkers. The combinatorial and single-gene model PPVs are shown
as box-and-whisker plots and compared to the PPVs given by the two
p53 mutation status and thirteen biomarker models (the whiskers
extend 1.5 times the interquartile range, the black line is the
median, the notches are an estimation of the median confidence
interval, the box width is proportional to the number of data
points, the outliers are not shown; `bm(s`): biomarker(s);
`allExMt`: p53 all exon mutation model; `ex5to8mt`: p53 exon 5 to 8
mutation model).
[0020] FIG. 11 represents the specificities achieved by the MDM2i
sensitivity predictive models, built from multiple combinations of
biomarkers described in Table 2. FIG. 11 shows specificities
achieved by MDM2i(2) sensitivity predictive models built from
multiple combinations of biomarkers. The combinatorial and
single-gene model specificities are shown as box-and-whisker plots
and compared to the PPVs given by the two p53 mutation status and
thirteen biomarker models (the whiskers extend 1.5 times the
interquartile range, the black line is the median, the notches are
an estimation of the median confidence interval, the box width is
proportional to the number of data points, the outliers are not
shown; `bm(s`): biomarker(s); `allExMt`: p53 all exon mutation
model; `ex5to8mt`: p53 exon 5 to 8 mutation model).
[0021] FIG. 12 represents the sensitivities achieved by the MDM2i
sensitivity predictive models, built from multiple combinations of
biomarkers described in Table 2. FIG. 12 shows the sensitivities
achieved by MDM2i(2) sensitivity predictive models built from
multiple combinations of biomarkers. The combinatorial and
single-gene model sensitivities are shown as box-and-whisker plots
and compared to the PPVs given by the two p53 mutation status and
thirteen biomarker models (the whiskers extend 1.5 times the
interquartile range, the black line is the median, the notches are
an estimation of the median confidence interval, the box width is
proportional to the number of data points, the outliers are not
shown; `bm(s`): biomarker(s); `allExMt`: p53 all exon mutation
model; `ex5to8mt`: p53 exon 5 to 8 mutation model).
[0022] FIG. 13 represents the PPV achieved by the MDM2i sensitivity
predictive models, built from multiple combinations of biomarkers
described in Table 2, together with p53 mutation status. FIG. 13
shows the Positive Predicted Values (PPV) achieved by MDM2i(2)
sensitivity predictive models built from combining biomarkers with
p53 mutation status. For a description of the plot refer to FIG.
10, 11 or 12; `bm(s`): biomarker(s); `mt`: p53 exon 5 to 8
mutations; `allExMt`: p53 all exon mutation model; `ex5to8mt`: p53
exon 5 to 8 mutation model).
[0023] FIG. 14 represents the specificities achieved by the MDM2i
sensitivity predictive models, built from multiple combinations of
biomarkers described in Table 2, together with p53 mutation status.
FIG. 14 shows the specificities achieved by MDM2i(2) sensitivity
predictive models built from combining biomarkers with p53 mutation
status. For a description of the plot refer to FIG. 10, 11 or 12;
`bm(s`): biomarker(s); `mt`: p53 exon 5 to 8 mutations; `allExMt`:
p53 all exon mutation model; `ex5to8mt`: p53 exon 5 to 8 mutation
model).
[0024] FIG. 15 represents the sensitivities achieved by the MDM2i
sensitivity predictive models, built from multiple combinations of
biomarkers described in Table 2, together with p53 mutation status.
FIG. 15 shows the sensitivities achieved by MDM2i(2) sensitivity
predictive models built from combining biomarkers with p53 mutation
status. For a description of the plot refer to FIG. 10, 11 or 12;
`bm(s`): biomarker(s); `mt`: p53 exon 5 to 8 mutations; `allExMt`:
p53 all exon mutation model; `ex5to8mt`: p53 exon 5 to 8 mutation
model).
[0025] FIG. 16 is a graph showing the response of human primary
xenograft models to MDM2i(1) and their predicted
sensitivity/insensitivity. FIG. 16 represents human primary
xenograft models that are predicted to be sensitive to MDM2i using
the biomarkers disclosed in Table 2 are confirmed sensitive to a
daily dosing of 100 mg/kg MDM2i(1), supporting the use of the
described predictive model in cancer patient populations
(HMEX=melanoma; HCOX=colorectal cancer; HSAX=liposarcoma;
HKIX=renal cell carcinoma; CHLI or HLIX=hepatic cancer; HBRX=breast
cancer; HPAX=pancreatic cancer; HLUX=lung cancer).
[0026] FIG. 17 is a graph showing the response to MDM2i(1) of human
primary xenograft models that are pre-selected for wild type p53
and then predicted to be sensitive/insensitive. FIG. 17 shows
wild-type p53 human primary xenograft models that are predicted to
be sensitive to MDM2i using the biomarkers disclosed in Table 2 are
confirmed sensitive to a daily dosing of 100 mg/kg MDM2i(1),
supporting the use of the described predictive model in wild-type
p53 pre-selected cancer patient populations (HMEX=melanoma;
HCOX=colorectal cancer; HSAX=liposarcoma; HKIX=renal cell
carcinoma; CHLI=hepatic cancer; HPAX=pancreatic cancer; HLUX=lung
cancer).
DESCRIPTION OF THE INVENTION
[0027] The aspects, features and embodiments of the present
invention are summarized in the following items and can be used
respectively alone or in combination:
[0028] 1. A method of predicting the sensitivity of a cancer
patient for treatment with a Human Double Minute 2 inhibitor
(MDM2i), the method comprising: measuring gene expression of at
least one biomarker selected from Table 2 in a cancer sample
obtained from the patient, and gene expression of the at least one
biomarker indicates that the patient is sensitive to treatment with
an MDM2i.
[0029] 2. A method of treating a cancer patient comprising:
measuring gene expression of at least one biomarker selected from
Table 2 in a cancer sample obtained from the patient; determining
sensitivity of the patient to an MDM2i; and administering to the
patient an MDM2i.
[0030] 3. A method of treating a cancer patient comprising:
assaying for gene expression of at least one biomarker selected
from Table 2 in a cancer sample obtained from the patient; the
presence of at least one biomarker indicates sensitivity of the
patient to an MDM2i; and administering to the patient an MDM2i.
[0031] 4. A method of predicting the sensitivity of a cancer cell
to a Human Double Minute 2 inhibitor (MDM2i), the method
comprising: assaying for gene expression of at least one biomarker
selected from Table 2 in the cancer cell obtained from a patient
cancer sample and the gene expression of the at least one biomarker
selected from Table 2 indicates that the cancer cell is sensitive
to treatment with an MDM2i.
[0032] 5. A method of predicting the sensitivity of a cancer cell
to a Human Double Minute 2 inhibitor (MDM2i), the method
comprising: measuring gene expression of at least one biomarker
selected from Table 2 in the cell; and the gene expression of the
at least one biomarker selected from Table 2 indicates that the
cancer cell is sensitive to treatment with an MDM2i.
[0033] 6. A method of screening for MDM2i candidates the method
comprising:
a) contacting a cell with a MDM2i candidate; b) measuring gene
expression of at least one biomarker selected from Table 2 in the
cell contacted with the MDM2i candidate and expression of the at
least one biomarker indicates sensitivity to an MDM2i; and c)
comparing the reduction in cell proliferation from the cell
contacted with the MDM2i candidate with the reduction in cell
proliferation of a cell contacted with an MDM2i taken from Table 1
and the cell proliferation of an untreated or placebo treated
cell.
[0034] 7. A method of determining the sensitivity of a cancer cell
to a Human Double Minute 2 inhibitor (MDM2i), the method
comprising: a) contacting a cancer cell with at least one MDM2i; b)
measuring gene expression of at least one biomarker selected from
Table 2 in the cell contacted with the MDM2i; c) comparing the cell
proliferation of cancer cells treated with at least one MDM2i with
an untreated or placebo treated cancer cell, wherein there is
reduced cell proliferation of the treated cancer cell when compared
with the untreated to placebo treated cancer cell.
[0035] 8. The method of any one of items 1 to 7, wherein more than
one biomarker is selected from Table 2.
[0036] 9. The method of item 1 or 8, wherein at least two, at least
three, at least four, at least five, at least six, at least seven,
at least eight, at least nine, at least ten, at least eleven, at
least twelve or all thirteen biomarkers are selected from Table
2.
[0037] 10. The method of any one of items 1 to 9, wherein p53 is
selected as a biomarker in addition to any biomarker selected from
Table 2.
[0038] 11. The method of any one of items 1 to 10, comprising the
biomarkers MDM2, CDKN1A, ZMAT3, DDB2, FDXR, RPS27L, BAX, RRM2B,
SESN1, CCNG1, XPC, TNFRSF10B and/or AEN.
[0039] 12. The method of any one of items 1 to 11, wherein
comparing the gene expression of the at least one biomarker with
gene expression of a control sample indicates a functional p53 gene
pathway.
[0040] 13. The method of any one of items 1 to 4 or 8 to 12,
wherein the cancer sample is selected from the group consisting of
breast, lung, pancreas, ovary, central nervous system (CNS),
endometrium, stomach, large intestine, colon, esophagus, bone,
urinary tract, hematopoietic, lymphoid, liver, skin, melanoma,
kidney, soft tissue sarcoma and pleura.
[0041] 14. The method of any one of items 1 to 13, wherein an mRNA
or protein of at least one biomarker is measured.
[0042] 15. The method of any one of items 1, 2 or 5 to 11, wherein
the expression of the at least one biomarker is increased in the
cancer sample when compared to a control.
[0043] 16. The method of any one of items 1 to 15, wherein the
MDM2i is selected from Table 1.
[0044] 17. The method of any one of items 1 to 16, wherein the
MDM2i is a compound that binds to a p53 binding pocket of MDM2.
[0045] 18. The method of any one of items 1 to 16, wherein the
MDM2i is a compound that binds to substantially the same p53
binding pocket of MDM2 as Nutlin-3a or the MDM2i from the Table
1.
[0046] 19. The method of any one of items 1 to 18, wherein the
MDM2i is a compound that prevents the protein-protein interaction
between p53 and MDM2.
[0047] 20. The method of any one of items 1 to 19, wherein the
MDM2i is a compound that inhibits cell proliferation by inducing
the p53 pathway activity.
[0048] 21. The method of any one of items 2 to 20 further
comprising obtaining a biological sample from the patient prior to
the administration of the MDM2i.
[0049] 22. The method of any one of items 2 to 21, wherein the
MDM2i is administered in a therapeutically effective amount.
[0050] 23. The method of any one of items 4 to 14, or 16 to 22,
wherein the gene expression of the at least one biomarker is
increased in the cancer cell.
[0051] 24. The method of any one of items 3 to 5 or 7 to 23,
wherein the IC50 of the cancer cell contacted with at least one
MDM2i is less than 1 .mu.M
[0052] 25. The method of any one of items 3 to 5, or 7 to 25,
wherein the cell is contacted by the MDM2i at least at two
different time points.
[0053] 26. The method of any one of items 3 to 5, or 7 to 26,
wherein the cell is contacted by two different MDM2i.
[0054] 27. The method of item 26, wherein the cell is contacted by
the two different MDM2i at the same time.
[0055] 28. The method of item 26, wherein the cell is contacted by
two different MDM2i at different time points.
[0056] 29. The method of any one of items 1 to 28, wherein the
MDM2i is an isoquinolinone.
[0057] 30. A method of screening for MDM2i candidates, the method
comprising: a) contacting a cell with a MDM2i candidate; b)
measuring gene expression of at least one biomarker selected from
Table 2 in the cell contacted with the MDM2i candidate; c) the gene
expression of the at least one biomarker indicates sensitivity to
an MDM2i; and d) comparing the reduction in cell proliferation from
the cell contacted with the MDM2i candidate with the reduction in
cell proliferation of a cell contacted with an MDM2i taken from
Table 1 and the cell proliferation of an untreated or placebo
treated cell.
[0058] 31. The method of item 30, wherein the gene expression of
the MDM2i candidate is compared with the gene expression of an
MDM2i selected from Table 1.
[0059] 32. The method of item 30 or 31, wherein the MDM2i candidate
increases gene expression of at least one, at least two, at least
three, at least four, at least five, at least six, at least seven,
at least eight, at least nine, at least ten, at least eleven, at
least twelve or all thirteen biomarkers from Table 2.
[0060] 33. The method of any one of items 30 to 32, wherein the
cell is a cancer cell selected from the group consisting of breast,
lung, pancreas, ovary, central nervous system (CNS), endometrium,
stomach, large intestine, colon, esophagus, bone, urinary tract,
hematopoietic, lymphoid, liver, skin, melanoma, kidney, soft tissue
sarcoma and pleura.
[0061] 34. The method of any one of items 30 to 33, wherein the
expression of an mRNA or protein of at least one biomarker of Table
2 is measured.
[0062] 35. The method of any one of items 30 to 34, comprising the
biomarkers MDM2, CDKN1A, ZMAT3, DDB2, FDXR, RPS27L, BAX, RRM2B,
SESN1, CCNG1, XPC, TNFRSF10B and AEN.
[0063] 36. Composition comprising an MDM2i for use in treatment of
cancer in a selected cancer patient population, wherein the cancer
patient population is selected on the basis of gene expression of
at least one biomarker selected from Table 2 in a cancer cell
sample obtained from said patients.
[0064] 37. The composition of item 36, wherein at least two, at
least three, at least four, at least five, at least six, at least
seven, at least eight, at least nine, at least ten, at least
eleven, at least twelve or all thirteen biomarkers are selected
from Table 2.
[0065] 38. The composition of items 36 or 37, wherein p53 is
selected as a biomarker in addition to any biomarker selected from
Table 2.
[0066] 39. The composition of any one of items 36 to 38, wherein
the biomarker is MDM2, CDKN1A, ZMAT3, DDB2, FDXR, RPS27L, BAX,
RRM2B, SESN1, CCNG1, XPC, TNFRSF10B and/or AEN.
[0067] 40. The composition of any one of items 36 to 39, wherein
the MDM2i is selected from Table 1.
[0068] 41. The composition of any one of items 36 to 40, wherein
the MDM2i is a compound that binds to a p53 binding pocket of
MDM2.
[0069] 42. The composition of any one of items 36 to 41, wherein
the MDM2i is a compound that binds to substantially the same p53
binding pocket of MDM2 as Nutlin-3a or the MDM2i from the Table
1.
[0070] 43. The composition of any one of items 36 to 42, wherein
the MDM2i is a compound that prevents the protein-protein
interaction between p53 and MDM2.
[0071] 44. The composition of any one of items 36 to 43, wherein
the MDM2i is a compound that inhibits cell proliferation by
inducing the p53 pathway activity.
[0072] 45. The composition of any one of items 36 to 44, wherein
the MDM2i is an isoquinolinone
[0073] 46. The composition of any one of items 36 to 45, wherein
the cancer cell sample is selected from the group consisting of
breast, lung, pancreas, ovary, central nervous system (CNS),
endometrium, stomach, large intestine, colon, esophagus, bone,
urinary tract, hematopoietic, lymphoid, liver, skin, melanoma,
kidney, soft tissue sarcoma and pleura.
[0074] 47. The composition of any one of items 36 to 46, wherein
the patients are selected on the basis of gene expression of the
biomarkers MDM2, CDKN1A, ZMAT3, DDB2, FDXR, RPS27L, BAX, RRM2B,
SESN1, CCNG1, XPC, TNFRSF10B and AEN.
[0075] 48. The composition of any one of items 36, 37, or 39 to 47
for use in treatment of a cancer in a selected cancer patient
population, wherein the cancer patient population is selected from
a wild-type p53 pre-selected cancer patient population.
[0076] 49. A kit for predicting the sensitivity of a cancer patient
for treatment with a Human Double Minute 2 inhibitor (MDM2i)
comprising: i) means for detecting the expression of any one of the
biomarkers from the table 2, preferably more than one, particularly
at least two, at least three, at least four, at least five, at
least six, at least seven, at least eight, at least nine, at least
ten, at least eleven, at least twelve or all thirteen biomarkers
selected from Table 2; and ii) instructions how to use said
kit.
[0077] 50. The kit of item 49, wherein the biomarkers are MDM2,
CDKN1A, ZMAT3, DDB2, FDXR, RPS27L, BAX, RRM2B, SESN1, CCNG1, XPC,
TNFRSF10B or/and AEN.
[0078] 51. The kit of item 49 or 50 further comprising means for
detecting the expression of p53.
[0079] 52. Use of the kit according to item 49 to 51 for any of the
methods of items 1 to 32.
[0080] In one aspect, the disclosure provides for methods of
analyzing at least one of the biomarkers identified in Table 2 in a
sample containing cancer cells wherein expression of at least one
biomarker indicates if the cancer cell will be sensitive to MDM2i
treatment. The pattern of expression changes can be indicative of a
favorable patient response or of an unfavorable one and patients
can be selected or rejected based on the expression of at least one
biomarker from Table 2. Alternatively, all of the biomarkers in
Table 2 can be assayed for as a single set.
[0081] After treatment with an MDM2i, the disclosure provides for
methods of analyzing at least one of the biomarkers identified in
Table 2 in a sample containing cancer cells wherein expression of
the biomarker after MDM2i treatment indicates that the patient is
still sensitive to MDM2i treatment. Detection and/or alteration in
the level of expression of at least one biomarker is indicative of
the MDM2i sensitivity, and this correlates with a response of the
patient to the treatment. Alternatively, all of the biomarkers in
Table 2 can be assayed for as a single set. The pattern of
expression can be indicative of a favorable patient response or of
an unfavorable one.
[0082] Accordingly, the disclosure provides for a method of
predicting the sensitivity of a cancer patient for treatment with a
Human Double Minute 2 inhibitor (MDM2i), the method comprising:
assaying for gene expression of at least one biomarker selected
from Table 2 in a cancer sample obtained from the patient; and the
gene expression of the at least one biomarker indicates that the
patient is sensitive to treatment with an MDM2i.
[0083] The method wherein more than one biomarker is selected from
Table 2.
[0084] The method comprising assaying for gene expression of the
biomarkers MDM2, CDKN1A, ZMAT3, DDB2, FDXR, RPS27L, BAX, RRM2B,
SESN1, CCNG1, XPC, TNFRSF10B and AEN.
[0085] The method wherein p53 is assayed for a mutation prior to
assay of the at least one biomarker, and the presence of a p53
mutation indicates decreased sensitivity to an MDM2i.
[0086] The method wherein the cancer sample is selected from the
group consisting of breast, lung, pancreas, ovary, central nervous
system (CNS), endometrium, stomach, large intestine, colon,
esophagus, bone, urinary tract, hematopoietic, lymphoid, liver,
skin, melanoma, kidney, soft tissue sarcoma and pleura.
[0087] The method wherein an mRNA or protein of at least one
biomarker is measured.
[0088] The method wherein the MDM2i is an isoquinolinone.
[0089] The method wherein the MDM2i is selected from Table 1.
[0090] A method of treating a cancer patient comprising: assaying
for gene expression of at least one biomarker selected from Table 2
in a cancer sample obtained from the patient; the presence the at
least one biomarker indicates sensitivity of the patient to an
MDM2i; and administering to the patient an MDM2i.
[0091] The method wherein more than one biomarker is selected from
Table 2.
[0092] The method comprising assaying for the biomarkers MDM2,
CDKN1A, ZMAT3, DDB2, FDXR, RPS27L, BAX, RRM2B, SESN1, CCNG1, XPC,
TNFRSF10B and AEN.
[0093] The method wherein p53 is assayed for a mutation prior to
assay of the at least one biomarker and the presence of a p53
mutation indicates decreased sensitivity to an MDM2i.
[0094] The method further comprising obtaining a biological sample
from the patient prior to the administration of the MDM2i, and
comparing the differential gene expression of at least one
biomarker from Table 2 in the MDM2i untreated cancer sample with an
MDM2i treated cancer sample, wherein increased gene expression
indicates continued patient sensitivity.
[0095] The method wherein the cancer sample is selected from the
group consisting of breast, lung, pancreas, ovary, central nervous
system (CNS), endometrium, stomach, large intestine, colon,
esophagus, bone, urinary tract, hematopoietic, lymphoid, liver,
skin, melanoma, kidney, soft tissue sarcoma and pleura.
[0096] The method wherein the MDM2i is an isoquinolinone.
[0097] The method wherein the MDM2i is selected from Table 1.
[0098] The method wherein the MDM2i is administered in a
therapeutically effective amount.
[0099] A method of predicting the sensitivity of a cancer cell to a
Human Double Minute 2 inhibitor (MDM2i), the method comprising:
assaying for gene expression of at least one biomarker selected
from Table 2 in the cancer cell obtained from a patient sample and
the gene expression of the at least one biomarker selected from
Table 2 indicates that the cancer cell is sensitive to treatment
with an MDM2i.
[0100] The method wherein more than one biomarker is selected from
Table 2.
[0101] The method comprising assaying for the biomarkers MDM2,
CDKN1A, ZMAT3, DDB2, FDXR, RPS27L, BAX, RRM2B, SESN1, CCNG1, XPC,
TNFRSF10B and AEN.
[0102] The method wherein p53 is assayed for a mutation prior to
assay of the at least one biomarker and the presence of a p53
mutation indicates decreased sensitivity to an MDM2i.
[0103] The method wherein the cancer sample is selected from the
group consisting of breast, lung, pancreas, ovary, central nervous
system (CNS), endometrium, stomach, large intestine, colon,
esophagus, bone, urinary tract, hematopoietic, lymphoid, liver,
skin, melanoma, kidney, soft tissue sarcoma and pleura.
[0104] The method wherein a mRNA or protein of at least one
biomarker is measured.
[0105] The method wherein the MDM2i is an isoquinolinone.
[0106] The method wherein the MDM2i is selected from Table 1.
[0107] The method wherein the MDM2i is administered in a
therapeutically effective amount.
[0108] A method of determining the sensitivity of a cancer cell to
a Human Double Minute 2 inhibitor (MDM2i), the method comprising:
a) contacting a cancer cell with at least one MDM2i; b) measuring
gene expression of at least one biomarker selected from Table 2 in
the cell contacted with the MDM2i and expression of at least one
biomarker indicates sensitivity to an MDM2i; and c) comparing the
cell proliferation of cancer cells treated with at least one MDM2i
with an untreated or placebo treated cancer cell; wherein there is
reduced cell proliferation of at the treated cancer cell when
compared with the untreated or placebo treated cancer cell.
[0109] The method wherein the reduced cell proliferation of the
cancer cell contacted with at least one MDM2i has an IC50 of less
than 1 .mu.M.
[0110] The method wherein the MDM2i is an isoquinolinone.
[0111] The method wherein the MDM2i is selected from Table 1.
[0112] The method wherein the cell is contacted by the MDM2i at
least two different time points.
[0113] The method wherein the cell is contacted by two different
MDM2i at step a).
[0114] The method wherein the cell is contacted by the two
different MDM2i at the same time.
[0115] The method wherein the cell is contacted by two different
MDM2i at different time points.
[0116] The method wherein the cancer cell is selected from the
group consisting of breast, lung, pancreas, ovary, central nervous
system (CNS), endometrium, stomach, large intestine, colon,
esophagus, bone, urinary tract, hematopoietic, lymphoid, liver,
skin, melanoma, kidney, soft tissue sarcoma and pleura.
[0117] The method wherein an mRNA or protein of at least one
biomarker is measured.
[0118] The method comprising assaying for the biomarkers MDM2,
CDKN1A, ZMAT3, DDB2, FDXR, RPS27L, BAX, RRM2B, SESN1, CCNG1, XPC,
TNFRSF10B and AEN.
[0119] The method wherein the steps b) and c) are repeated at time
points selected from the group consisting of: 4 hours, 8 hours, 16
hours, 24 hours, 48 hours, 3 days, 1 week, 1 month and several
months after administration of each dose of MDM2i.
[0120] A method of screening for MDM2i candidates the method
comprising:
a) contacting a cell with a MDM2i candidate; b) measuring gene
expression of at least one biomarker selected from Table 2 in the
cell contacted with the MDM2i candidate and expression of the at
least one biomarker indicates sensitivity to an MDM2i; and c)
comparing the reduction in cell proliferation from the cell
contacted with the MDM2i candidate with the reduction in cell
proliferation of a cell contacted with an MDM2i taken from Table 1
and the cell proliferation of an untreated or placebo treated
cell.
[0121] The method wherein assaying for the biomarkers MDM2, CDKN1A,
ZMAT3, DDB2, FDXR, RPS27L, BAX, RRM2B, SESN1, CCNG1, XPC, TNFRSF10B
and AEN.
[0122] The method wherein the MDM2i candidate increases gene
expression of at least one biomarker of Table 2.
[0123] The method wherein the cell is a cancer cell selected from
the group consisting of breast, lung, pancreas, ovary, central
nervous system (CNS), endometrium, stomach, large intestine, colon,
esophagus, bone, urinary tract, hematopoietic, lymphoid, liver,
skin, melanoma, kidney, soft tissue sarcoma and pleura.
[0124] The method wherein the expression of an mRNA or protein of
at least one biomarker of Table 2 is measured.
[0125] The method comprising assaying for the biomarkers MDM2,
CDKN1A, ZMAT3, DDB2, FDXR, RPS27L, BAX, RRM2B, SESN1, CCNG1, XPC,
TNFRSF10B and AEN.
[0126] Composition comprising an MDM2i for use in treatment of
cancer in a selected cancer patient population, wherein the cancer
patient population is selected on the basis of showing gene
expression of at least one biomarker selected from Table 2 in a
cancer cell sample obtained from said patients.
[0127] The composition wherein the cancer cells sample is selected
from the group consisting of breast, lung, pancreas, ovary, central
nervous system (CNS), endometrium, stomach, large intestine, colon,
esophagus, bone, urinary tract, hematopoietic, lymphoid, liver,
skin, melanoma, kidney, soft tissue sarcoma and pleura
[0128] The composition wherein the patients are selected on the
basis of gene expression of the biomarkers MDM2, CDKN1A, ZMAT3,
DDB2, FDXR, RPS27L, BAX, RRM2B, SESN1, CCNG1, XPC, TNFRSF10B and
AEN.
[0129] A kit for predicting the sensitivity of a cancer patient for
treatment with a Human Double Minute 2 inhibitor (MDM2i)
comprising
i) means for detecting the expression of the biomarkers MDM2,
CDKN1A, ZMAT3, DDB2, FDXR, RPS27L, BAX, RRM2B, SESN1, CCNG1, XPC,
TNFRSF10B and AEN; and ii) instructions how to use said kit.
[0130] Use of the kit for any of the methods of listed above.
DEFINITIONS
[0131] As used in the specification and claims, the singular form
"a", "an" and "the" include plural references unless the context
clearly dictates otherwise. For example, the term "a cell" includes
a plurality of cells, including mixtures thereof.
[0132] All numerical designations, e.g., pH, temperature, time,
concentration, and molecular weight, including ranges, are
approximations which are varied (+) or (-) by increments of 0.1. It
is to be understood, although not always explicitly stated that all
numerical designations are preceded by the term "about." It also is
to be understood, although not always explicitly stated, that the
reagents described herein are merely exemplary and that equivalents
of such are known in the art.
[0133] The terms "marker" or "biomarker" are used interchangeably
herein. A biomarker is a nucleic acid or polypeptide and the
presence, absence or differential expression of the nucleic acid or
polypeptide is used to determine sensitivity to any MDM2i. For
example, CDKN1A is a biomarker.
[0134] "MDM2" refers to an E3 ubiquitin-protein ligase that
mediates the ubiquitination of p53, permits the nuclear export of
p53 and triggers p53 degradation. Unless specifically stated
otherwise, MDM2 as used herein refers to human MDM2-accession
numbers NM.sub.--002392/NP.sub.--002383 (SEQ ID NO. 1/SEQ ID NO.
2).
[0135] "p53" refers to the tumor suppressor protein, and refers to
human p53 (p53 accession numbers: DNA--NM.sub.--000546,
protein--NP.sub.--000537).
[0136] A cell is "sensitive" or displays "sensitivity" for
inhibition with an MDM2i when at least one of the biomarkers
disclosed in Table 2 is differentially expressed. Alternatively, a
cell is "sensitive" for inhibition with an MDM2i when all of the
biomarkers disclosed in Table 2 as a set are differentially
expressed. In one embodiment a cell is "sensitive" or displays
"sensitivity", or is "sensitive to treatment" when IC50 of an MDM2i
for reducing cell proliferation is less than 10 .mu.M, preferably
less than 1 .mu.M.
[0137] A "control cell", a "control tissue" and a "control sample"
refer to baseline control, or a cancer cell, tissue or a sample,
respectively, that is insensitive to MDM2i (i.e. IC50 of a MDM2i
for reducing cell proliferation is more than 10 .mu.M; or when
"sensitive" is defined with IC50 of a MDM2i for reducing cell
proliferation being less than 1 .mu.M, then "insensitive" denotes
IC50 of a MDM2i for reducing cell proliferation is more than 1
.mu.M).
[0138] A "normal sample" refers to non-cancerous tissue or
cell.
[0139] The terms "nucleic acid" and "polynucleotide" are used
interchangeably and refer to a polymeric form of nucleotides of any
length, either deoxyribonucleotides or ribonucleotides or analogs
thereof. Polynucleotides can have any three-dimensional structure
and may perform any function. The following are non-limiting
examples of polynucleotides: a gene or gene fragment (for example,
a probe, primer, EST or SAGE tag), exons, introns, messenger RNA
(mRNA), transfer RNA, ribosomal RNA, ribozymes, cDNA, recombinant
polynucleotides, branched polynucleotides, plasmids, vectors,
isolated DNA of any sequence, isolated RNA of any sequence, nucleic
acid probes, and primers. A polynucleotide can comprise modified
nucleotides, such as methylated nucleotides and nucleotide analogs.
If present, modifications to the nucleotide structure can be
imparted before or after assembly of the polymer. The sequence of
nucleotides can be interrupted by non-nucleotide components. A
polynucleotide can be further modified after polymerization, such
as by conjugation with a labeling component. The term also refers
to both double- and single-stranded molecules. Unless otherwise
specified or required, any embodiment of this invention that is a
polynucleotide encompasses both the double-stranded form and each
of two complementary single-stranded forms known or predicted to
make up the double-stranded form.
[0140] A "gene" refers to a polynucleotide containing at least one
open reading frame (ORF) that is capable of encoding a particular
polypeptide or protein after being transcribed and translated. A
polynucleotide sequence can be used to identify larger fragments or
full-length coding sequences of the gene with which they are
associated. Methods of isolating larger fragment sequences are
known to those of skill in the art.
[0141] "Gene expression" or alternatively a "gene product" refers
to the nucleic acids or amino acids (e.g., peptide or polypeptide)
generated when a gene is transcribed and translated.
[0142] The term "polypeptide" is used interchangeably with the term
"protein" and in its broadest sense refers to a compound of two or
more subunit amino acids, amino acid analogs, or peptidomimetics.
The subunits can be linked by peptide bonds. In another embodiment,
the subunit may be linked by other bonds, e.g., ester, ether,
etc.
[0143] As used herein the term "amino acid" refers to either
natural and/or unnatural or synthetic amino acids, and both the D
and L optical isomers, amino acid analogs, and peptidomimetics. A
peptide of three or more amino acids is commonly called an
oligopeptide if the peptide chain is short. If the peptide chain is
long, the peptide is commonly called a polypeptide or a
protein.
[0144] The term "isolated" means separated from constituents,
cellular and otherwise, in which the polynucleotide, peptide,
polypeptide, protein, antibody or fragment(s) thereof, are normally
associated with in nature. For example, an isolated polynucleotide
is separated from the 3' and 5' contiguous nucleotides with which
it is normally associated within its native or natural environment,
e.g., on the chromosome. As is apparent to those of skill in the
art, a non-naturally occurring polynucleotide, peptide,
polypeptide, protein, antibody, or fragment(s) thereof, does not
require "isolation" to distinguish it from its naturally occurring
counterpart. In addition, a "concentrated," "separated" or
"diluted" polynucleotide, peptide, polypeptide, protein, antibody
or fragment(s) thereof, is distinguishable from its naturally
occurring counterpart in that the concentration or number of
molecules per volume is greater in a "concentrated" version or less
than in a "separated" version than that of its naturally occurring
counterpart. A polynucleotide, peptide, polypeptide, protein,
antibody, or fragment(s) thereof, which differs from the naturally
occurring counterpart in its primary sequence or, for example, by
its glycosylation pattern, need not be present in its isolated form
since it is distinguishable from its naturally occurring
counterpart by its primary sequence or, alternatively, by another
characteristic such as glycosylation pattern. Thus, a non-naturally
occurring polynucleotide is provided as a separate embodiment from
the isolated naturally occurring polynucleotide. A protein produced
in a bacterial cell is provided as a separate embodiment from the
naturally occurring protein isolated from a eukaryotic cell in
which it is produced in nature.
[0145] A "probe" when used in the context of polynucleotide
manipulation refers to an oligonucleotide that is provided as a
reagent to detect a target potentially present in a sample of
interest by hybridizing with the target. Usually, a probe will
comprise a label or a means by which a label can be attached,
either before or subsequent to the hybridization reaction. Suitable
labels include, but are not limited to radioisotopes,
fluorochromes, chemiluminescent compounds, dyes, and proteins,
including enzymes.
[0146] A "primer" is a short polynucleotide, generally with a free
3'--OH group that binds to a target or "template" potentially
present in a sample of interest by hybridizing with the target, and
thereafter promoting polymerization of a polynucleotide
complementary to the target. A "polymerase chain reaction" ("PCR")
is a reaction in which replicate copies are made of a target
polynucleotide using a "pair of primers" or a "set of primers"
consisting of an "upstream" and a "downstream" primer, and a
catalyst of polymerization, such as a DNA polymerase, and typically
a thermally-stable polymerase enzyme. Methods for PCR are well
known in the art, and taught, for example in PCR: A Practical
Approach, M. MacPherson et al., IRL Press at Oxford University
Press (1991). All processes of producing replicate copies of a
polynucleotide, such as PCR or gene cloning, are collectively
referred to herein as "replication." A primer can also be used as a
probe in hybridization reactions, such as Southern or Northern blot
analyses (Sambrook et al., Molecular Cloning: A Laboratory Manual,
2nd edition (1989)).
[0147] As used herein, "expression" refers to the process by which
DNA is transcribed into mRNA and/or the process by which the
transcribed mRNA is subsequently translated into peptides,
polypeptides or proteins. If the polynucleotide is derived from
genomic DNA, expression may include splicing of the mRNA in a
eukaryotic cell.
[0148] "Differentially expressed" as applied to a gene, refers to
the differential production of the mRNA transcribed and/or
translated from the gene or the protein product encoded by the
gene. A differentially expressed gene may be overexpressed or
underexpressed as compared to the expression level of a control
cell. However, as used herein, overexpression is an increase in
gene expression and generally is at least 1.25 fold or,
alternatively, at least 1.5 fold or, alternatively, at least 2
fold, or alternatively, at least 3 fold or alternatively, at least
4 fold expression over that detected in a control counterpart cell
or tissue. As used herein, underexpression, is a reduction of gene
expression and generally is at least 1.25 fold, or alternatively,
at least 1.5 fold, or alternatively, at least 2 fold or
alternatively, at least 3 fold or alternatively, at least 4 fold
expression under that detected in a control counterpart cell or
tissue. The term "differentially expressed" also refers to where
expression in a cancer cell or cancerous tissue is detected and
present but expression in a control cell is undetectable.
[0149] A high expression level of the gene may occur because of
over expression of the gene or an increase in gene copy number. The
gene may also be translated into increased protein levels because
of deregulation or absence of a negative regulator.
[0150] A "gene expression profile" refers to a pattern of
expression of at least one biomarker that recurs in multiple
samples and reflects a property shared by those samples, such as
tissue type, response to a particular treatment, or activation of a
particular biological process or pathway in the cells. Furthermore,
a gene expression profile differentiates between samples that share
that common property and those that do not with better accuracy
than would likely be achieved by assigning the samples to the two
groups at random. A gene expression profile may be used to predict
whether samples of unknown status share that common property or
not. Some variation between the levels of at least one biomarker
and the typical profile is to be expected, but the overall
similarity of the expression levels to the typical profile is such
that it is statistically unlikely that the similarity would be
observed by chance in samples not sharing the common property that
the expression profile reflects.
[0151] The term "cDNA" refers to complementary DNA, i.e. mRNA
molecules present in a cell or organism made into cDNA with an
enzyme such as reverse transcriptase. A "cDNA library" is a
collection of all of the mRNA molecules present in a cell or
organism, all turned into cDNA molecules with the enzyme reverse
transcriptase, then inserted into "vectors" (other DNA molecules
that can continue to replicate after addition of foreign DNA).
Exemplary vectors for libraries include bacteriophage (also known
as "phage"), viruses that infect bacteria, for example, lambda
phage. The library can then be probed for the specific cDNA (and
thus mRNA) of interest.
[0152] As used herein, "solid phase support" or "solid support",
used interchangeably, is not limited to a specific type of support.
Rather a large number of supports are available and are known to
one of ordinary skill in the art. Solid phase supports include
silica gels, resins, derivatized plastic films, glass beads,
plastic beads, alumina gels, microarrays, and chips. As used
herein, "solid support" also includes synthetic antigen-presenting
matrices, cells, and liposomes. A suitable solid phase support may
be selected on the basis of desired end use and suitability for
various protocols. For example, for peptide synthesis, solid phase
support may refer to resins such as polystyrene (e.g., PAM-resin
obtained from Bachem Inc., Peninsula Laboratories), polyHIPE(R).TM.
resin (obtained from Aminotech, Canada), polyamide resin (obtained
from Peninsula Laboratories), polystyrene resin grafted with
polyethylene glycol (TentaGelR.TM., Rapp Polymere, Tubingen,
Germany), or polydimethylacrylamide resin (obtained from
Milligen/Biosearch, California).
[0153] A polynucleotide also can be attached to a solid support for
use in high throughput screening assays. PCT WO 97/10365, for
example, discloses the construction of high density oligonucleotide
chips. See also, U.S. Pat. Nos. 5,405,783; 5,412,087 and 5,445,934.
Using this method, the probes are synthesized on a derivatized
glass surface to form chip arrays. Photoprotected nucleoside
phosphoramidites are coupled to the glass surface, selectively
deprotected by photolysis through a photolithographic mask and
reacted with a second protected nucleoside phosphoramidite. The
coupling/deprotection process is repeated until the desired probe
is complete.
[0154] As an example, transcriptional activity can be assessed by
measuring levels of messenger RNA using a gene chip such as the
Affymetrix.RTM. HG-U133-Plus-2 GeneChips. High-throughput,
real-time quantitation of RNA of a large number of genes of
interest thus becomes possible in a reproducible system.
[0155] The terms "stringent hybridization conditions" refers to
conditions under which a nucleic acid probe will specifically
hybridize to its target subsequence, and to no other sequences. The
conditions determining the stringency of hybridization include:
temperature, ionic strength, and the concentration of denaturing
agents such as formamide. Varying one of these factors may
influence another factor and one of skill in the art will
appreciate changes in the conditions to maintain the desired level
of stringency. An example of a highly stringent hybridization is:
0.015M sodium chloride, 0.0015M sodium citrate at 65-68.degree. C.
or 0.015M sodium chloride, 0.0015M sodium citrate, and 50%
formamide at 42.degree. C. (see Sambrook, supra). An example of a
"moderately stringent" hybridization is the conditions of: 0.015M
sodium chloride, 0.0015M sodium citrate at 50-65.degree. C. or
0.015M sodium chloride, 0.0015M sodium citrate, and 20% formamide
at 37-50.degree. C. The moderately stringent conditions are used
when a moderate amount of nucleic acid mismatch is desired. One of
skill in the art will appreciate that washing is part of the
hybridization conditions. For example, washing conditions can
include 02.X-0.1.times.SSC/0.1% SDS and temperatures from
42-68.degree. C., wherein increasing temperature increases the
stringency of the wash conditions.
[0156] When hybridization occurs in an antiparallel configuration
between two single-stranded polynucleotides, the reaction is called
"annealing" and those polynucleotides are described as
"complementary." A double-stranded polynucleotide can be
"complementary" or "homologous" to another polynucleotide, if
hybridization can occur between one of the strands of the first
polynucleotide and the second. "Complementarity" or "homology" (the
degree that one polynucleotide is complementary with another) is
quantifiable in terms of the proportion of bases in opposing
strands that are expected to form hydrogen bonding with each other,
according to generally accepted base-pairing rules.
[0157] A polynucleotide or polynucleotide region (or a polypeptide
or polypeptide region) has a certain percentage (for example, 80%,
85%, 90%, 95%, 98% or 99%) of "sequence identity" to another
sequence means that, when aligned, that percentage of bases (or
amino acids) are the same in comparing the two sequences. This
alignment and the percent homology or sequence identity can be
determined using software programs known in the art, for example
those described in Current Protocols in Molecular Biology, Ausubel
et al., eds., (1987) Supplement 30, section 7.7.18, Table 7.7.1.
Preferably, default parameters are used for alignment. A preferred
alignment program is BLAST, using default parameters. In
particular, preferred programs are BLASTN and BLASTP, using the
following default parameters: Genetic code=standard; filter=none;
strand=both; cutoff=60; expect=10; Matrix=BLOSUM62; Descriptions=50
sequences; sort by=HIGH SCORE; Databases=non-redundant.
[0158] The term "cell proliferative disorders" shall include
dysregulation of normal physiological function characterized by
abnormal cell growth and/or division or loss of function. Examples
of "cell proliferative disorders" include but are not limited to
hyperplasia, neoplasia, metaplasia, and various autoimmune
disorders, e.g., those characterized by the dysregulation of T cell
apoptosis.
[0159] As used herein, the terms "neoplastic cells," "neoplastic
disease," "neoplasia," "tumor," "tumor cells," "cancer," and
"cancer cells," (used interchangeably) refer to cells which exhibit
relatively autonomous growth, so that they exhibit an aberrant
growth phenotype characterized by a significant loss of control of
cell proliferation (i.e., de-regulated cell division). Neoplastic
cells can be malignant or benign. A metastatic cell or tissue means
that the cell can invade and destroy neighboring body
structures.
[0160] The term "cancer" refers to cancer diseases including, for
example, breast, lung, pancreas, ovary, central nervous system
(CNS), endometrium, stomach, large intestine, colon, esophagus,
bone, urinary tract, hematopoietic, lymphoid, liver, skin,
melanoma, kidney, soft tissue sarcoma and pleura.
[0161] The term "PBMC" refers to peripheral blood mononuclear cells
and includes "PBL"--peripheral blood lymphocytes.
[0162] "Suppressing" tumor growth indicates a reduction in tumor
cell growth when contacted with an MDM2i compared to tumor growth
without contact with an MDM2i compound. Tumor cell growth can be
assessed by any means known in the art, including, but not limited
to, measuring tumor size, determining whether tumor cells are
proliferating using a 3H-thymidine incorporation assay, measuring
glucose uptake by FDG-PET (fluorodeoxyglucose positron emission
tomography) imaging, or counting tumor cells. "Suppressing" tumor
cell growth means any or all of the following states: slowing,
delaying and stopping tumor growth, as well as tumor shrinkage.
[0163] A "composition" is a combination of active agent and another
carrier, e.g., compound or composition, inert (for example, a
detectable agent or label) or active, such as an adjuvant, diluent,
binder, stabilizer, buffers, salts, lipophilic solvents,
preservative, adjuvant or the like. Carriers also include
pharmaceutical excipients and additives, for example; proteins,
peptides, amino acids, lipids, and carbohydrates (e.g., sugars,
including monosaccharides and oligosaccharides; derivatized sugars
such as alditols, aldonic acids, esterified sugars and the like;
and polysaccharides or sugar polymers), which can be present singly
or in combination, comprising alone or in combination 1-99.99% by
weight or volume. Carbohydrate excipients include, for example;
monosaccharides such as fructose, maltose, galactose, glucose,
D-mannose, sorbose, and the like; disaccharides, such as lactose,
sucrose, trehalose, cellobiose, and the like; polysaccharides, such
as raffinose, melezitose, maltodextrins, dextrans, starches, and
the like; and alditols, such as mannitol, xylitol, maltitol,
lactitol, xylitol sorbitol (glucitol) and myoinositol.
[0164] Exemplary protein excipients include serum albumin such as
human serum albumin (HSA), recombinant human albumin (rHA),
gelatin, casein, and the like. Representative amino acid/antibody
components, which can also function in a buffering capacity,
include alanine, glycine, arginine, betaine, histidine, glutamic
acid, aspartic acid, cysteine, lysine, leucine, isoleucine, valine,
methionine, phenylalanine, aspartame, and the like.
[0165] The term "carrier" further includes a buffer or a pH
adjusting agent; typically, the buffer is a salt prepared from an
organic acid or base. Representative buffers include organic acid
salts such as salts of citric acid, ascorbic acid, gluconic acid,
carbonic acid, tartaric acid, succinic acid, acetic acid, or
phthalic acid; Tris, tromethamine hydrochloride, or phosphate
buffers. Additional carriers include polymeric excipients/additives
such as polyvinylpyrrolidones, ficolls (a polymeric sugar),
dextrates (e.g., cyclodextrins, such as
2-hydroxypropyl-quadrature-cyclodextrin), polyethylene glycols,
flavoring agents, antimicrobial agents, sweeteners, antioxidants,
antistatic agents, surfactants (e.g., polysorbates such as TWEEN
20.TM. and TWEEN 80.TM.), lipids (e.g., phospholipids, fatty
acids), steroids (e.g., cholesterol), and chelating agents (e.g.,
EDTA).
[0166] As used herein, the term "pharmaceutically acceptable
carrier" encompasses any of the standard pharmaceutical carriers,
such as a phosphate buffered saline solution, water, and emulsions,
such as an oil/water or water/oil emulsion, and various types of
wetting agents. The compositions also can include stabilizers and
preservatives and any of the above noted carriers with the
additional provisio that they be acceptable for use in vivo. For
examples of carriers, stabilizers and adjuvants, see Remington's
Pharmaceutical Science., 15th Ed. (Mack Publ. Co., Easton (1975)
and in the Physician's Desk Reference, 52nd ed., Medical Economics,
Montvale, N.J. (1998).
[0167] An "effective amount" is an amount sufficient to effect
beneficial or desired results. An effective amount can be
administered in one or more administrations, applications or
dosages.
[0168] A "subject," "individual" or "patient" is used
interchangeably herein, which refers to a vertebrate, preferably a
mammal, more preferably a human. Mammals include, but are not
limited to, mice, simians, humans, farm animals, sport animals, and
pets.
[0169] An "inhibitor" of MDM2 as used herein reduces the
association of p53 and MDM2. This inhibition may include, for
example, reducing the association of p53 and MDM2 before they are
bound together, or reducing the association of p53 and MDM2 after
they are bound together, thus freeing both molecules.
[0170] A number of genes have now been identified as biomarkers for
MDM2i. The presence or decrease or increase of gene expression of
one or more of the biomarkers identified herein and in Table 2 can
be used to determine patient sensitivity to any MDM2i, for example,
the expression of a biomarker indicates that a cancer patient is
sensitive to and would favorably respond to administration of an
MDM2i. As another example, after treatment with an MDM2i, a patient
sample can be obtained and the sample assayed for sensitivity to
discover if the patient is still sensitive to the MDM2i treatment.
Alternatively, all of the biomarkers in Table 2 can be assayed for
as a single set.
[0171] MDM2 inhibitors (MDM2i) are compounds which are inhibitors
of the p53-MDM2 association, and are useful in conjunction with the
methods or uses of the invention. MDM2i are useful in
pharmaceutical compositions for human or veterinary use where
inhibition of the p53-MDM2 association is indicated, e.g., in the
treatment of tumors and/or cancerous cell growth. In particular,
such compounds are useful in the treatment of human cancer, since
the progression of these cancers may be at least partially
dependent upon overriding the "gatekeeper" function of p53, for
example the overexpression of MDM2. MDM2i compounds are useful in
treating, for example, carcinomas (e.g., breast, lung, pancreas,
ovary, central nervous system (CNS), endometrium, stomach, large
intestine, colon, esophagus, bone, urinary tract, hematopoietic,
lymphoid, liver, skin, melanoma, kidney, soft tissue sarcoma and
pleura A listing of exemplary MDM2i compounds is found in Table 1
(see WO 2011076786). The MDM2i compounds found in Table 1 are of
the isoquinolinone class of molecules. Other MDM2i that bind to a
p53 binding pocket of MDM2, particularly to substantially the same
p53 binding pocket of MDM2 as Nutlin-3a or substantially where the
exemplary MDM2i from the Table 1 binds, can also be applied in the
methods or uses of the invention. MDM2i used according to present
embodiments can be structurally related to the one described in
Table 1 (i.e. MDM2i(1) and MDM2i(2)) or to Nutlin 3a, such as, for
example, substituted isoquinolinones, or quinazolinones. The
methods included herein can also be used with other compounds such
as the spiro-oxindoles, imidazolyl indole and cis-imidazoline (see
Shangary et al., Mol. Cancer Ther. 2008 7(6): 1533-1542: Furet et
al., BioOrg.Med.Chem. Let. 2012 22:3498-3502 and Carol et al.,
Pediatr. Blood Cancer 2012 pages 1-9, published online Jul. 2,
2012, prior to inclusion into journal). MDM2i as used herein
prevents the protein-protein interaction between p53 and MDM2
and/or inhibits cell proliferation by inducing the p53 pathway
activity.
TABLE-US-00001 TABLE 1 MDM2i compounds ##STR00001## MDM2i(1)
##STR00002## MDM2i(2)
[0172] Measurement of Gene Expression
[0173] Detection of gene expression can be by any appropriate
method, including for example, detecting the quantity of mRNA
transcribed from the gene or the quantity of cDNA produced from the
reverse transcription of the mRNA transcribed from the gene or the
quantity of the polypeptide or protein encoded by the gene. These
methods can be performed on a sample by sample basis or modified
for high throughput analysis. For example, using Affymetrix.TM.
U133 microarray chips.
[0174] In one aspect, gene expression is detected and quantitated
by hybridization to a probe that specifically hybridizes to the
appropriate probe for that biomarker. The probes also can be
attached to a solid support for use in high throughput screening
assays using methods known in the art. WO 97/10365 and U.S. Pat.
Nos. 5,405,783, 5,412,087 and 5,445,934, for example, disclose the
construction of high density oligonucleotide chips which can
contain one or more of the sequences disclosed herein. Using the
methods disclosed in U.S. Pat. Nos. 5,405,783, 5,412,087 and
5,445,934, the probes of this invention are synthesized on a
derivatized glass surface. Photoprotected nucleoside
phosphoramidites are coupled to the glass surface, selectively
deprotected by photolysis through a photolithographic mask, and
reacted with a second protected nucleoside phosphoramidite. The
coupling/deprotection process is repeated until the desired probe
is complete.
[0175] In one aspect, the expression level of a gene is determined
through exposure of a nucleic acid sample to the probe-modified
chip. Extracted nucleic acid is labeled, for example, with a
fluorescent tag, preferably during an amplification step.
Hybridization of the labeled sample is performed at an appropriate
stringency level. The degree of probe-nucleic acid hybridization is
quantitatively measured using a detection device. See U.S. Pat.
Nos. 5,578,832 and 5,631,734.
[0176] Alternatively any one of gene copy number, transcription, or
translation can be determined using known techniques. For example,
an amplification method such as PCR may be useful. General
procedures for PCR are taught in MacPherson et al., PCR: A
Practical Approach, (IRL Press at Oxford University Press (1991)).
However, PCR conditions used for each application reaction are
empirically determined. A number of parameters influence the
success of a reaction. Among them are annealing temperature and
time, extension time, Mg 2+ and/or ATP concentration, pH, and the
relative concentration of primers, templates, and
deoxyribonucleotides. After amplification, the resulting DNA
fragments can be detected by agarose gel electrophoresis followed
by visualization with ethidium bromide staining and ultraviolet
illumination.
[0177] In one embodiment, the hybridized nucleic acids are detected
by detecting one or more labels attached to the sample nucleic
acids. The labels can be incorporated by any of a number of means
well known to those of skill in the art. However, in one aspect,
the label is simultaneously incorporated during the amplification
step in the preparation of the sample nucleic acid. Thus, for
example, polymerase chain reaction (PCR) with labeled primers or
labeled nucleotides will provide a labeled amplification product.
In a separate embodiment, transcription amplification, as described
above, using a labeled nucleotide (e.g. fluorescein-labeled UTP
and/or CTP) incorporates a label in to the transcribed nucleic
acids.
[0178] Alternatively, a label may be added directly to the original
nucleic acid sample (e.g., mRNA, polyA, mRNA, cDNA, etc.) or to the
amplification product after the amplification is completed. Means
of attaching labels to nucleic acids are well known to those of
skill in the art and include, for example nick translation or
end-labeling (e.g. with a labeled RNA) by kinasing of the nucleic
acid and subsequent attachment (ligation) of a nucleic acid linker
joining the sample nucleic acid to a label (e.g., a
fluorophore).
[0179] Detectable labels suitable for use in the present invention
include any composition detectable by spectroscopic, photochemical,
biochemical, immunochemical, electrical, optical or chemical means.
Useful labels in the present invention include biotin for staining
with labeled streptavidin conjugate, magnetic beads (e.g.,
Dynabeads.TM.) fluorescent dyes (e.g., fluorescein, texas red,
rhodamine, green fluorescent protein, and the like), radiolabels
(e.g., 3H, 125I, 35S, 14C, or 32P) enzymes (e.g., horse radish
peroxidase, alkaline phosphatase and others commonly used in an
ELISA), and calorimetric labels such as colloidal gold or colored
glass or plastic (e.g., polystyrene, polypropylene, latex, etc.)
beads. Patents teaching the use of such labels include U.S. Pat.
Nos. 3,817,837; 3,850,752; 3,939,350; 3,996,345; 4,277,437;
4,275,149; and 4,366,241.
[0180] Detection of labels is well known to those of skill in the
art. Thus, for example, radiolabels may be detected using
photographic film or scintillation counters, fluorescent markers
may be detected using a photodetector to detect emitted light.
Enzymatic labels are typically detected by providing the enzyme
with a substrate and detecting the reaction product produced by the
action of the enzyme on the substrate, and calorimetric labels are
detected by simply visualizing the colored label.
[0181] The detectable label may be added to the target (sample)
nucleic acid(s) prior to, or after the hybridization, such as
described in WO 97/10365. These detectable labels are directly
attached to or incorporated into the target (sample) nucleic acid
prior to hybridization. In contrast, "indirect labels" are joined
to the hybrid duplex after hybridization. Generally, the indirect
label is attached to a binding moiety that has been attached to the
target nucleic acid prior to the hybridization. For example, the
target nucleic acid may be biotinylated before the hybridization.
After hybridization, an avidin-conjugated fluorophore will bind the
biotin bearing hybrid duplexes providing a label that is easily
detected. For a detailed review of methods of labeling nucleic
acids and detecting labeled hybridized nucleic acids see Laboratory
Techniques in Biochemistry and Molecular Biology, Vol. 24:
Hybridization with Nucleic Acid Probes, P. Tijssen, ed. Elsevier,
N.Y. (1993).
[0182] Detection of Polypeptides
[0183] Expression level of the biomarker can also be determined by
examining protein expression or the protein product at least one of
the biomarkers listed in Table 2. Determining the protein level
involves measuring the amount of any immunospecific binding that
occurs between an antibody that selectively recognizes and binds to
the polypeptide of the biomarker in a sample obtained from a
patient and comparing this to the amount of immunospecific binding
of at least one biomarker in a control sample. The amount of
protein expression of the biomarker can be increased or reduced
when compared with control expression. Alternatively, all of the
biomarkers in Table 2 can be assayed for as a single set.
[0184] A variety of techniques are available in the art for protein
analysis. They include but are not limited to radioimmunoassays,
ELISA (enzyme linked immunosorbent assays), "sandwich"
immunoassays, immunoradiometric assays, in situ immunoassays (using
e.g., colloidal gold, enzyme or radioisotope labels), western blot
analysis, immunoprecipitation assays, immunofluorescent assays,
flow cytometry, immunohistochemistry, confocal microscopy,
enzymatic assays, surface plasmon resonance and PAGE-SDS.
[0185] Assaying for Biomarkers and MDM2i Treatment
[0186] Once a patient has been predicted to be sensitive to an
MDM2i, administration of any MDM2i to a patient can be effected in
one dose, continuously or intermittently throughout the course of
treatment. Methods of determining the most effective means and
dosage of administration are well known to those of skill in the
art and will vary with the composition used for therapy, the
purpose of the therapy, the target cell being treated, and the
subject being treated. Single or multiple administrations can be
carried out with the dose level and pattern being selected by the
treating physician. Suitable dosage formulations and methods of
administering the agents may be empirically adjusted.
[0187] At least one of the biomarkers provided in Table 2 can be
assayed for after MDM2i administration in order to determine if the
patient remains sensitive to the MDM2i treatment. In addition, at
least one biomarker can be assayed for in multiple time points
after a single MDM2i administration. For example, an initial bolus
of an MDM2i is administered, at least one biomarker from Table 2 is
assayed for at 1 hour, 2 hours, 3 hours, 4 hours, 8 hours, 16
hours, 24 hours, 48 hours, 3 days, 1 week or 1 month or several
months after the first treatment. Alternatively, all of the
biomarkers in Table 2 can be assayed for as a single set.
[0188] The at least one biomarker in Table 2 can be assayed for
after each MDM2i administration, so if there are multiple MDM2i
administrations, then at least one biomarker can be assayed for
after each administration to determine continued patient
sensitivity. The patient could undergo multiple MDM2i
administrations and the biomarkers then assayed at different time
points. For example, a course of treatment can require
administration of an initial dose of MDM2i, a second dose a
specified time period later, and still a third dose hours after the
second dose. At least one biomarker of Table 2 could be assayed for
at 1 hour, 2 hours, 3 hours, 4 hours, 8 hours, 16 hours, 24 hours,
48 hours, 3 days, 1 week or 1 month or several months after
administration of each dose of MDM2i. Alternatively, all of the
biomarkers in Table 2 can be assayed for as a single set.
[0189] It is also within the scope of the invention that different
biomarkers are assayed for at different time points. Without being
bound to any one theory, due to mechanism of action of the MDM2i or
of the biomarker, the response to the MDM2i is delayed and at least
one biomarker from Table 2 is assayed for at any time after
administration to determine if the patient remains sensitive to
MDM2i administration. An assay for at least one biomarker in Table
2 after each administration of MDM2i will provide guidance as to
the means, dosage and course of treatment. Alternatively, all of
the biomarkers in Table 2 can be assayed for as a single set.
[0190] Finally, there is administration of different MDM2 is and
followed by assaying for at least one biomarker in Table 2. In this
embodiment, more than one MDM2i is chosen and administered to the
patient. At least one biomarker from Table 2 can then be assayed
for after administration of each different MDM2i. This assay can
also be done at multiple time points after administration of the
different MDM2i. For example, a first MDM2i could be administered
to the patient and at least one biomarker assayed at 1 hour, 2
hours, 3 hours, 4 hours, 8 hours, 16 hours, 24 hours, 48 hours, 3
days, 1 week or 1 month or several months after administration. A
second MDM2i could then be administered and at least one biomarker
could be assayed for again at 1 hour, 2 hours, 3 hours, 4 hours, 8
hours, 16 hours, 24 hours, 48 hours, 3 days, 1 week or 1 month or
several months after administration of the second MDM2i. In each
case, all of the biomarkers in Table 2 can be assayed for as a
single set.
[0191] Another aspect of the invention provides for a method of
assessing for suitable dose levels of an MDM2i, comprising
monitoring the differential expression of at least one of the genes
identified in Table 2 after administration of the MDM2i. For
example, after administration of a first bolus of MDM2i, at least
one biomarker of Table 2 is analyzed and based on this result, an
increase or decrease in MDM2i dosage is recommended. After
administration of the adjusted dosage of MDM2i the analysis of at
least one biomarker will determine whether the patient is still
sensitive to the adjusted dose and that the adjusted dose is
providing the expected benefit, e.g., suppressing tumor growth.
Alternatively, all of the biomarkers in Table 2 can be assayed for
as a single set for assessing sensitivity to the dose of the
MDM2i.
[0192] Kits for assessing the activity of any MDM2i can be made.
For example, a kit comprising nucleic acid primers for PCR or for
microarray hybridization for the biomarkers listed in Table 2 can
be used for assessing MDM2i sensitivity. Alternatively, a kit
supplied with antibodies for at least one of the biomarkers listed
in Table 2 would be useful in assaying for MDM2i sensitivity.
[0193] It is well known in the art that cancers can become
resistant to chemotherapeutic treatment, especially when that
treatment is prolonged. Assaying for differential expression of at
least one of the biomarkers in Table 2 can be done after prolonged
treatment with any chemotherapeutic to determine if the cancer is
sensitive to the MDM2i. For example, kinase inhibitors such as
Gleevec.RTM. will strongly inhibit a specific kinase, but may also
weakly inhibit other kinases. There are also other MDM2i, for
example, the Nutlin family of compounds. If the patient has been
previously treated with another chemotherapeutic or another MDM2i,
it is useful information for the patient to assay for at least one
of the biomarkers in Table 2 to determine if the tumor is sensitive
to an MDM2i. This assay can be especially beneficial to the patient
if the cancer goes into remission and then re-grows or has
metastasized to a different site.
[0194] Screening for MDM2 Inhibitors
[0195] It is possible to assay for at least one biomarker listed in
Table 2 to screen for other MDM2i. This method comprises assaying a
cell with at least one biomarker from Table 2, which predicts if
the cell is sensitive to an MDM2i candidate inhibitor, the cell is
then contacted with the candidate MDM2i and the IC50 of the treated
cell is compared with a known MDM2i contacting a sensitive cell.
For example, for cells predicted to be sensitive to any MDM2i as
determined by the differential expression of at least one biomarker
in Table 2, the candidate MDM2i will have an IC50 3 .mu.M. The
measurement of at least one biomarker from Table 2 expression can
be done by methods described previously, for example, PCR or
microarray analysis. Alternatively, all of the biomarkers in Table
2 can be assayed for as a single set.
TABLE-US-00002 TABLE 2 Accession SEQ ID NO. Gene Name number
(nucleotide/protein) MDM2 NM_002392/ SEQ ID NO. 1/ NP_002383 SEQ ID
NO. 2 CDKN1A NM_000389/ SEQ ID NO. 3/ NP_000380 SEQ ID NO. 4 ZMAT3
NM_022470/ SEQ ID NO. 5/ NP_071915 SEQ ID NO. 6 DDB2 NM_000107/ SEQ
ID NO. 7/ NP_000098 SEQ ID NO. 8 FDXR NM_004110/ SEQ ID NO. 9/
NP_004101 SEQ ID NO. 10 RPS27L NM_015920/ SEQ ID NO. 11/ NP_057004
SEQ ID NO. 12 BAX NM_004324/ SEQ ID NO. 13/ NP_004315 SEQ ID NO. 14
RRM2B NM_015713/ SEQ ID NO. 15/ NP_056528 SEQ ID NO. 16 SESN1
NM_014454/ SEQ ID NO. 17/ NP_055269 SEQ ID NO. 18 CCNG1 NM_004060/
SEQ ID NO. 19/ NP_004051 SEQ ID NO. 20 XPC NM_004628/ SEQ ID NO.
21/ NP_004619 SEQ ID NO. 22 TNFRSF10B NM_003842/ SEQ ID NO. 23/
NP_003833 SEQ ID NO. 24 AEN NM_022767/ SEQ ID NO. 25/ NP_073604 SEQ
ID NO. 26
EXAMPLES
Example 1
Both MDM2i(1) and MDM2i(2) are Equally Potent p53-MDM2 Inhibitors
in Biochemical and Cellular Assays
[0196] TR-FRET Assay for IC.sub.50 determination: standard assay
conditions consisted of 60 .mu.L total volume in white 384-well
plates (Greiner Bio-One: Frickenhausen, Germany), in PBS buffer
containing 125 mM NaCl, 0.001% Novexin, 0.01% Gelatin, 0.2%
Pluronic F-127, 1 mM DTT and 1.7% final DMSO). Both MDM2i(1) and
MDM2i(2) were added at different concentrations to 0.1 nM
biotinylated MDM2 (human MDM2 amino acids 2-188, internal
preparations), 0.1 nM Europium-labeled streptavidin (Perkin Elmer:
Waltham, Mass., USA) and 10 nM Cy5-p53 peptide (Cy5-p53 aa18-26,
internal preparation). After incubation at room temperature for 15
minutes, samples were measured on a GeniosPro reader (Tecan:
Mannedorf, Germany). FRET assay readout was calculated from the raw
data of the two distinct fluorescence signals measured in time
resolved mode (fluorescence 665 nm/fluorescence 620 nm x 1000).
IC.sub.50 values are calculated by curve fitting using XLfit.RTM.
(Fit Model #205). This data is shown in FIG. 1A.
[0197] Determination of binding rate constants (K.sub.on,
K.sub.off): the rapid mixing tool of GeniosPro reader (Tecan:
Mannedorf, Germany) was used to study fast binding kinetics (single
well mode). Microplates containing the inhibitor and 20 nM
Cy5-labeled p53 peptide in 50 .mu.l assay buffer were placed in the
reader. After 10 min equilibration at 25.degree. C., binding
reactions were initiated by injecting 50 .mu.l of buffer containing
0.2 nM biotinylated MDM2 and 0.2 nM europium-streptavidin at 475
.mu.l/s. Fluorescence was measured at 665 nM and at various time
intervals, the first one 0.6 sec after injection. In the absence of
inhibitor, Cy5 fluorescence was maximal already at 0.6 sec and
remained stable for at least 15 min. In the presence of MDM2i(1)
and MDM2i(2) fluorescence decreased slowly and measurements were
made until steady-state was achieved. Control fluorescence was
taken as the difference between wells containing 1% DMSO and wells
containing 10 .mu.M Nutlin-3 as a control. The inhibitory effect at
each time point was calculated as percent of the corresponding
control. Progress curves obtained in the presence of different
concentrations of inhibitor were combined and fitted as a whole.
Nonlinear regression was performed with XLfit.RTM. using a novel
fit methodology that was designed to obtain precise K.sub.on and
K.sub.off values, based on the following respective equations:
Fit=[Imin+((Imax-(((Ki nH)*Imax)/((y nH)+(Ki
nH))))*(1-exp(((-1)*(koff+((y*koff)/Ki)))*x)))] and
Fit=[Imin+((Imax-(((Ki nH)*Imax)/((y nH)+(Ki
nH))))*(1-exp(((-1)*(kon*(Ki+y)))*x)))], where Ki represents the
constant of inhibition, Imin represents the minimum inhibition (in
%), Imax represents the maximum inhibition (in %), nH represents
the Hill coefficient, x represents the time and y represents the
inhibitor concentration). This data is shown in FIG. 1A.
[0198] Cell proliferation inhibition and GRIP p53 translocation
assay: Effects of MDM2i(1) and MDM2i(2) on cellular growth and loss
of viability is measured in both p53 wild-type (SJSA-1 and HCT116
p53.sup.wt/wt cells) and p53 mutant cell lines (SAOS2 and HCT116
p53.sup.-/- cells) using a standard proliferation assay based on
the DNA-interacting fluorescent dye YOPRO (Invitrogen: Lucern,
Switzerland). Briefly, cells are plated in 96-well plates overnight
at 37.degree. C. and are treated with increasing concentrations of
MDM2i(1) or MDM2i(2) for 72 hours. Cell concentration in each well
is then determined using the DNA-interacting fluorescent dye YOPRO
according to the manufacturer's instructions and the fluorescent
signal is measured using a Gemini-EM standard plate reader
(Molecular Devices: Sunnyvale, Calif., USA). IC.sub.50 values are
calculated by curve fitting using XLfit.RTM. (Fit Model #201) and
this data is shown in FIG. 1A.
[0199] The mechanistic p53-MDM2 Redistribution assay (GRIP assay)
is used to directly monitor in cells the ability of compounds to
modulate the p53-MDM2 protein-protein interaction. In this fully
engineered assay, the p53 protein is tagged with a fluorescent
GFP-label and is bound to MDM2 protein which is anchored in the
cytoplasm of the cells. The treatment of the cells with specific
compounds causes the dissociation of the interaction between the
two proteins and the translocation of the released p53-GFP protein
from the cytoplasm to the nuclei. This effect is detected and
quantified using a high content imaging platform using the
ArrayScan-VTi (Cellomics), following the fluorescent signal over
time (see FIG. 1B, GRIP p53 translocation assay).
[0200] Altogether, using both in vitro and cellular assays, the
results presented in FIGS. 1A and 1B show that both MDM2i(1) and
MDM2i(2) are comparable potent p53-MDM2 protein-protein interaction
inhibitors in vitro, inhibiting the p53-MDM2 protein-protein
interaction, hampering tumor cell proliferation in a p53-dependent
manner, and inducing p53 accumulation and translocation to the
nucleus. This data is shown in FIG. 1B; note that there are large
discrepancies in the IC50 between the cell lines. This is also an
indication that there are differences in the sensitivity between
the two cell lines to an MDM2i, and thus a determination of
sensitivity can be useful in determining which patients receive the
therapeutic.
Example 2
The p53 Mutational Status is Associated with MDM2i Chemical
Sensitivity in Cell Lines
[0201] The association of p53 mutation to MDM2i(2) chemical
sensitivity in a panel of cancer-relevant cell lines was tested by
Fisher's Exact test. The cell line panel is the one covered by the
Cancer Cell Line Encyclopedia (CCLE) initiative (Barretina J.,
Caponigro G., Stransky N., Venkatesan K., et al. The Cancer Cell
Line Encyclopedia enables predictive modeling of anticancer drug
sensitivity. Nature 483:603-7, 2012). A detailed genomic, genetic
and pharmacologic characterization was conducted on the CCLE cell
lines.
[0202] p53 mutation status in CCLE cell lines is taken from a data
source of gene-level genetic alterations, for example,
point-mutations, insertions, deletions and complex genetic
alterations, compiled from the Sanger center COSMIC data and
internal sources including Exome Capture Sequencing. This comprises
data from approximately 1,600 cancer-related genes over the CCLE
cell lines. Analysis of the CCLE panel revealed 244 cell lines
containing a mutant p53, and 112 cell lines that expressed wild
type p53 (p53 accession numbers: DNA--NM.sub.--000546,
protein--NP.sub.--000537).
[0203] The MDM2i(2) chemical sensitivity was determined from the
pharmacologic characterization of the CCLE cell lines. The cell
lines were separated in two groups according to MDM2i(2)
sensitivity. One group contains the cell lines sensitive to
MDM2i(2) compound, while the other group encompasses those being
chemically insensitive to the MDM2i(2) compound. Such
stratification resulted in two groups of 47 sensitive and 309
insensitive cell lines, respectively. From such in vitro chemical
sensitivity data, the prediction that a cell would be sensitive to
an MDM2i treatment was estimated to be 13%. The statistical testing
of the p53 mutation to sensitivity groups association shows an
association between p53 mutation (mt) and the chemical sensitivity
to MDM2i(2) (FIG. 2). The mt panel in FIG. 2 displays the MDM2i(2)
sensitivity profiles for p53 mutated CCLE cell lines, the wild type
(wt) panel displays the MDM2i(2) sensitivity profiles for p53
wild-type CCLE cell lines. Amax is defined as the maximal effect
level (the inhibition at the highest tested MDM2i(2) concentration,
calibrated to MG132, a proteaseome inhibitor used as a reference,
as described in the CCLE publication referenced above, and IC50 is
defined as of the .mu.M concentration at which MDM2i(2) response
reached an absolute inhibition of -50 with respect to the reference
inhibitor.
Cell line count broken down by MDM2i(2) chemical sensitivity and
p53 mutation status, and associated statistics. Data is also
displayed as a contingency table with associated statistics.
[0204] This data indicates it is more likely for a cell line to
show sensitivity to MDM2i(2) if its p53 status is wild type.
Indeed, the majority of p53 mutated cell lines are found
insensitive to the compound, whereas more than two-third of p53
wild type cell lines are sensitive.
[0205] From this data we can conclude a p53 wild type genotype is
the first indication of MDM2i sensitivity, and therefore it is the
first stratification biomarker to be considered for selecting
cancer patients responsive to an MDM2i.
Example 3
Prediction of Cell Line Chemical Sensitivity to MDM2i from Genomic
Data and Clinical Implication
[0206] The two cell line sensitivity groups, given by MDM2i(2)
treatment, are compared with the aim of identifying the biomarkers
differentiating the sensitive cell lines from the insensitive cell
lines, prior to any MDM2i treatment. Such biomarkers are used to
predict the sensitivity of any MDM2i treatment. The biomarkers
analyzed are the following types: 1) gene-level expression values
generated by the Affymetrix GeneChip.TM. technology with the
HG-U133 plus 2 array, summarized according to the RMA normalization
method; 2) gene-level chromosome copy number values, obtained with
the Affymetrix SNP6.0 technology (Affymetrix Santa Clara, Calif.,
USA) and processed using the Affymetrix apt software, and expressed
as log 2 transformed ratios to a collection of HapMap reference
normal samples; 3) gene-level genetic alterations or mutations, as
described above in Example 2; 4) pathway-level expression values,
summarizing pathway expression levels by a standardized average
approach over the genes contributing to the pathways, as referenced
in the GeneGo Metacore.RTM. knowledge base; 5) cell line lineage
(cell line tissue of origin); 6) gene-level Tumor suppressor
status, summarizing the activation status of a selection of tumor
suppressor genes, by integrating the genetic alteration, copy
number and expression information. Such genomic data was generated
in the context of the CCLE cell line genomic and genetic
characterization, and covers a total of about 45,000 genomic
features.
[0207] Wilcoxon signed-rank tests or Fisher's exact tests are used
to compare the two cell line group genomic features, depending on
the feature type. The features having continuous values (gene
expression and copy number, pathway expression features) are
subjected to Wilcoxon signed-ranked test, those having discrete
values (genetic alteration, tumor suppressor status and lineage
features), to Fisher's exact test, for differential profile
evaluation between sensitive and insensitive cell line groups. The
significant features, discriminating the sensitive cell line group
from the insensitive one, are the ones passing a false discovery
rate-controlled p-value cutoff. Irrespective of the p-value limit,
a minimum or maximum number of features per feature type are also
required. To minimize the impact of the high degree of correlation
among the features on the feature selection step, the feature data
is clustered before the statistical tests as a pre-processing step.
This step is performed at the feature type-level using the Frey's
and Dueck's Affinity Propagation method (Clustering by Passing
Messages Between Data Points. Frey B. and Dueck D. Science
315:972-6, 2007), and retrieves a set of features representing the
most variability.
[0208] The cell line sensitivity groups or classes are defined as
follows for this two-class comparison aiming at biomarker
identification: a sensitive group of 47 sensitive cell lines, and
an insensitive group of 204 insensitive cell lines. The 204 cell
lines making this insensitive group are the most insensitive ones
from the 309 insensitive cell line set mentioned in Example 2. This
feature selection step yielded a total of about 200 significant
features, having a significant differential profile to
differentiate the sensitive cell line group from the insensitive
one, and thus having the required properties to be considered as
markers to predict the chemical sensitivity of samples to an MDM2i.
As described above in Example 2, a relevant biomarker is the p53
mutation status itself. The statistics of the feature selection
step (p-value 1.17E-21) confirmed its role in predicting
sensitivity to an MDM2i. Furthermore, the odds-ratio associated
with p53 status (0.024) indicates p53 mutation is more represented
in MDM2i insensitive cell lines. Still noteworthy and still
according to the statistics of the feature selection step, most of
the predictive biomarkers are found to be a subset of p53
transcriptional target genes. These are shown in Table 2/FIG. 3.
Their fold-changes indicate the transcripts of these biomarkers are
more expressed in the MDM2i sensitive cell line population, as
shown in FIG. 3. This is likely indicative of a level of p53
functional activity pre-existing before any treatment in cell lines
that are sensitive to an MDM2i.
[0209] That the biomarkers in Table 2/FIG. 3 are reflective of a
functional p53 pathway in MDM2i sensitive cells is verified in FIG.
4. In FIG. 4, cells have been treated with increasing
concentrations of MDM2i(1) for 4 hours, prior to cell lysis. Whole
cell lysates were prepared using a cell lysis buffer containing 50
mM Tris-HCl pH 7.5, 120 mM NaCl, 1 mM EDTA, 6 mM EGTA pH 8.5, 1%
NP-40, 20 mM NaF, 1 mM PMSF and 0.5 mM Na-Vanadat, proteins were
separated on NuPAGE 4-12% Bis-Tris Gel (Invitrogen # NP0322BOX,
Lucerne; Switzerland), transferred onto Nitrocellulose Protran.RTM.
BA 85 membranes (Whatman #10 401 261: Piscataway, N.J., USA) at 1.5
mA/cm.sup.2 membrane for 2 h using a semi-dry blotting system, and
immunoblotted with either an anti-phospho-p53 (Ser.sup.15) (1/1000;
Cell Signaling Technology #9284: Beverly, Mass., USA) rabbit
polyclonal antibody, or an anti-p53 (Ab-6) (Pantropic, clone DO-1,)
(1/1000; Calbiochem # OP43 San Diego, Calif., USA), an anti-MDM2
(Ab-1, clone IF2) (1/1000; Calbiochem # OP46), an
anti-p21.sup.WAF1(Ab-1, clone EA10) (1/500; Calbiochem # OP64: San
Diego, Calif., USA) or an anti-a-Tubulin (Sigma # T5168: St. Louis,
Mo., USA) mouse monoclonal antibodies, as indicated. As shown in
FIG. 4, increasing concentrations of MDM2i(1) induces stabilization
of p53 protein levels, with no significant increase of phospho-p53
in both C3A and COLO 792 cells. Interestingly, treatment with
MDM2i(1) also induces a strong de novo expression of both p53
target genes p21(CDKN1A) and MDM2 in C3A sensitive cells, but not
in COLO-792 insensitive cell line. Altogether, this data indicates
that sensitivity to an MDM2i inhibitor is directly related to the
presence of an intrinsic functional p53 pathway, that the
biomarkers in Table2/FIG. 3, taken together as a set or alone,
point directly to p53 pathway functionality before treatment, and
indicates these biomarkers have a strong ability to predict patient
sensitivity that correlates with the mechanism of action of
p53.
[0210] The significant genomics features are used as the basis
features for naive Bayes probabilistic modeling of the two MDM2i
chemical sensitivity groups, or classes. The goal of the modeling
step is to derive a classification scheme or classifier that
predicts the patient's response (either sensitive or insensitive)
of an unknown sample with a certain confidence. The predictive
model is defined by training a naive Bayes algorithm over the
entire chemically characterized CCLE cell line population
stratified into the two above-mentioned sensitivity classes. The
performance of the classifier is evaluated through 5 repeats of
5-fold cross-validations of the data used to train the model. The
model performance is summarized with the following class-level
measures: sensitivity, specificity, positive predictive value, and
negative predictive value. The sensitive class is used as the
reference for the sensitivity and positive predicted value
calculations. The default output of the naive Bayes algorithm is a
score or probability, for each predicted sample to be assigned to
one class or the other. A probability threshold is defined to
transform the probability scores into a sensitive or insensitive
class-level prediction. The probability threshold is defined as the
probability maximizing the sensitivity and specificity calculated
over all predicted samples. The entire and nearly-identical
procedure is described in more details in the CCLE publication
referenced in Example 2.
[0211] To demonstrate a better predictive power can be achieved
from at least one biomarker found in Table 2, and alternatively,
the biomarkers in Table 2 as a set, than from p53 mutation status
alone, or from the entire predictive feature set of about 200
genes, naive Bayes models from each of these feature sets were
trained, and their performance assessed by cross-validation, as
above mentioned, and compared (FIG. 5). FIG. 5 demonstrates the
selected biomarkers found in Table 2 outperform both the p53
mutation status and the larger group of about 200 significant
features, and provide a substantial improvement in predicting
patient responsiveness to an MDM2i. This is particularly striking
when performances are evaluated by positive predictive value
(PPV).
[0212] A positive predicted value (PPV) of 76% suggests that 76% of
the predicted sensitive cell lines will be sensitive to MDM2i
treatment. As an extrapolation to a clinical setting, a PPV of 76%
also suggests that 76% of cancer patients, predicted as sensitive
to MDM2i(2) from tumor biopsies, would show clinical response upon
MDM2i(2) treatment. This enrichment of clinical response by patient
stratification, using the biomarkers in Table 2 and associated with
the naive Bayes predictive model, was compared to the baseline
clinical response rate without prior patient stratification. The
baseline clinical response rate estimated from the chemical
sensitivity data is 19% Thus, in a clinical perspective; the
biomarkers of Table 2 have a clear increase of the clinical
response in the predicted sensitive patient population. This
increase in prediction is greater and more specific than assaying
for p53 status alone or all of the approximately 200 genomic
features initially selected. This can be seen in FIG. 5, where the
"All 200 biomarkers" feature bar is the PPV of the approximately
200 genomic features initially selected. The PPV reported for "All
200 biomarkers" feature is 59%. The PPV for using p53 alone is 56%
(FIG. 5). The PPV of the set of biomarkers disclosed in Table 2 is
76% ("Table 2 selected biomarkers"). This is a surprising result,
as in general, the larger data set of 200 genomic features would
provide more data points and more insight into prediction of MDM2i
sensitivity. However, this is not the case, as the biomarkers in
Table 2, when taken as a set, provide a 17% increase in predictive
value. This is important in the clinic, as now 17 additional
patients out of 100 would be predicted to receive the correct
treatment.
[0213] In order to test the predictive value of the biomarkers of
Table 2, 52 cell lines that were not previously examined in the
pharmacologic characterization as part of the CCLE project, were
assayed for their sensitivity to MDM2i in proliferation assays.
Briefly, cells are plated in 96-well plates overnight at 37.degree.
C. and are treated with increasing concentrations of an MDM2i for
72 hours. Cell concentration in each well is then determined using
the CellTiter-Glo Luminescent Cell Viability Assay.RTM. (Promega
Cat. # G7571/2/3: Madison Wis., USA), according to the
manufacturer's instructions and the luminescent signal is measured
using a SYNERGY HT plate reader (BioTek: Winooski, Vt., USA).
IC.sub.50 values are calculated by curve fitting using XLfit.RTM.
(FIG. 6). Sensitivity of cell lines to an MDM2i is determined by
comparing the observed IC.sub.50 of all cells that were tested in
cell proliferation inhibition assay as described in Example 1. The
cut-off for sensitivity was determined at IC.sub.50 3 .mu.M for
both MDM2i(1) and MDM2i(2). Predictions of sensitivity for every
cell lines are performed using the predictive model as described
above, to confirm the values disclosed in FIG. 5. The cell lines
are from a variety of tumors. For example; melanoma (COLO-829,
COLO-849, IGR-1, MEL-JUSO, SK-MEL-1, SK-MEL-31, UACC-62, UACC-257),
leukemia (BV173, EOL-1, GDM-1, HuNS1, L-540, MV-4-11, OCI-LY3,
RS4:11, SUP-B15, HDLM2, JM1), breast cancer (CAL51, EFM-192,
HCC202), pancreatic cancer (DAN-G), hepatic cancer (JHH-5) and lung
cancer (RERF-LCK-KJ). This assay was done with the all of the
biomarkers disclosed in Table 2 as a single set. Overall, 36/52
cell lines were predicted to be sensitive to an MDM2i, and of these
36 cell lines 24 were sensitive, resulting in a positive predictive
value of 66%, again a significant increase in predictive value
(PPV) over assaying for p53 alone. With regard to screening out the
non-responding cells, 16/52 cell lines were predicted to be
insensitive to a p53-MDM2 inhibitor, and 13/16 were found to indeed
be insensitive, leading to a significant negative predictive value
(NPV) of 81%. Overall, these data are similar to the predictive
model performances described in FIG. 5. The actual in vitro testing
of unrelated cell lines allowed testing of the MDM2i chemical
sensitivity predictive model, hence validating the biomarkers
disclosed in Table 2.
Example 4
MDM2i Treatment Inhibits Tumor Growth Inhibition In Vivo which is
Correlated to a Dose-Dependent Increase of p21(CDKNIA) mRNA and
Protein Levels in Tumors
[0214] To further validate the predictive biomarkers disclosed in
FIG. 3, in vivo human xenograft models either from human primary
samples or from cell lines were directly injected and grown in
tumors subcutaneously in mice and then assessed for MDM2i
sensitivity. All the animals were allowed to adapt for 4 days and
housed in a pathogen-controlled environment (5 mice/Type III cage)
with access to food and water ad libitum. Animals were identified
with transponders. Studies were performed according to procedures
covered by permit number 1975 issued by the Kantonales Veterinaramt
Basel-Stadt and strictly adhered to the Eidgenossisches
Tierschutzgesetz and the Eidgenossische Tierschutzverordnung.
Subcutaneous tumors were induced by concentrating
3.0.times.10.sup.6 SJSA-1 osteosarcoma cells in 100 .mu.l of PBS
(without Ca.sup.2+ and Mg.sup.2+) and injecting in the right flank
of Harlan nude mice. The administration of MDM2i began 12-14 days
post cell injection. MDM2i was prepared immediately before each
administration. MDM2i compounds were dissolved in 0.5% HPMC
(hydroxypropylmethylcellulose) and were injected daily (q24h) at
25, 50 or 100 mg/kg. Tumor volumes (TVol), determined from caliper
measurements (using the formula l.times.w.times.h.times..pi./6)
were measured three times per week. Tumor response was quantified
by the change in tumor volume (endpoint minus starting value in
mm.sup.3) as the T/C, i.e.
( .DELTA. TVol drug .DELTA. TVol vehicle .times. 100 ) .
##EQU00001##
In the case of a tumor regression, the tumor response was
quantified by the percentage of regression of the starting TVoI,
i.e.
( .DELTA. TVol drug TVol Day 0 .times. 100 ) . ##EQU00002##
The body-weight (BW) of the mice was measured three times per week
allowing calculation at any particular time-point relative to the
day of initiation of treatment (day 0) of both the percentage
change in BW (.DELTA. % BW). As shown in FIG. 7A, a 10-day
treatment of SJSA-1 xenografted tumors with MDM2i(1) led to a
dose-dependent tumor growth inhibition with a significant T/C of
50% at 25 mg/kg q24h and of 3% (stasis) at 50 mg/kg q24h. At 100
mg/kg q24h for 10 days, MDM2i(1) treatment induced a significant
tumor regression of 65% (FIG. 7B). All doses were well tolerated at
q24h schedule, as indicated by the mean body weight curves over
time.
[0215] Anti-tumor activity of MDM2i(1) was correlated with a
significant dose-dependent induction of p21(CDKN1A) mRNA levels in
tumors (FIG. 7C). Briefly, total RNA was purified from cell pellets
using the QIAshredder.RTM. (79654, Qiagen:Valencia Calif., USA) and
RNeasy Mini Kit.RTM. (74106, Qiagen: Valencia Calif., USA)
according to the manufacturer's instructions, with the exception
that no DNA digestion was performed. Total RNA was eluted with 50
.mu.L of RNase-free water. Total RNA was quantitated using the
spectrophotometer ND-1000 Nanodrop.RTM. (Wilmington Del., USA). The
qRT-PCR (Quantitative Reverse Transcriptase Polymerase Chain
Reaction) was set up in triplicate per sample using the One-Step RT
qPCR Master Mix Plus (RT-QPRT-032X, Eurogentec: Seraing, Belgium),
with either control primers and primers for human p21(CDKN1A)
(Hs00355782_m1, Applied Biosystems: Carlsbad Calif., USA) or mouse
p21(CDKN1A) (Mm00432448_m1, Applied Biosystems: Carlsbad Calif.,
USA), namely TaqMan Gene Expression kit assays (20.times. probe dye
FAM.TM. (or VIC)-TAMRA (or MGB); Applied Biosystems: Carlsbad
Calif., USA). More specifically, a master mix was prepared on ice
for a final concentration of: 1x Master Mix buffer, 1x primer
solution, and 1x Euroscript reverse transcriptase, combined with
H.sub.2O, total volume: 8 .mu.L/well. A MicroAmp Optical 384-well
Reaction Plate (4309849, Applied Biosystems) was fixed on the
bench, and 2 .mu.L of mRNA (concentration: 10 or 20 ng/.mu.l) (or
water for negative control) were pipetted in triplicate, followed
by addition of 8 .mu.L/well of master mix. The plate was then
covered with a MicroAmp Optical Adhesive film kit (4313663, Applied
Biosystems: Carlsbad Calif., USA), centrifuged for 5 min at 1000
rpm at 4.degree. C. and placed in a 7900 HT Fast Real-Time PCR
System (Applied Biosystems: Carlsbad Calif., USA). The program was
run with one cycle of 48.degree. C. for 30 min, one cycle of
95.degree. C. for 10 min, and finally 40 cycles of alternating
95.degree. C. for 15 sec and 60.degree. C. for 1 min. The number of
cycles (CT) was determined, 2.sup.-CT values were calculated, and
the value normalized by dividing with the 2.sup.-CT value obtained
from the GAPDH control. Fold increase over control (i.e. DMSO- or
vehicle-treated animals) was calculated and plotted in the bar
graph.
[0216] In addition, the anti-tumor activity of MDM2i(1) was
correlated with a significant dose-dependent induction of
p21(CDKN1A) protein levels in tumors, as judged by
immunohistochemistry (FIG. 8). SJSA-1 xenograft tumors were
collected and a 3-4 mm slice out of the middle of the tumor was
removed, transferred into pre-labelled histo-cassettes and
immersion-fixed in neutral buffered formalin (NBF) 10% (v/v) (pH
6.8-7.2) (J. T. Baker, Winter Garden, Fla., USA), pre-cooled at
4.degree. C. Tumors were then fixed at room temperature for 24
hours, followed by processing in the TPC 15Duo (Tissue Processing
Center, Medite) for paraffinization. Subsequently, the tumor slices
were embedded in paraffin and from each paraffin block several 3
.mu.m thick sections were cut on a rotary microtome (Mikrom
International AG, Switzerland), spread in a 48.degree. C.
water-bath, mounted on glass slides (SuperFrost Plus, Thermo
Scientific:Waltham Mass., USA), and dried in an oven either at
37.degree. C. overnight or at 60.degree. C. for 30 min. Dry tissue
section were processed for immunohistochemistry (IHC) staining.
p21(CDKN1A) immunohistochemistry has been performed using the mouse
monoclonal antibody clone SX118 from Dako (Cat. No. M7202 Dako:
Carpenteria Calif., USA) at a dilution of 1:50.
Immunohistochemistry has been performed on a Ventana Discovery XT
automated immunostainer using the N-Histofine Mousestain Kit
(Nichirei Bioscience Inc, Japan) in combination with the DABMap Kit
chromogen system, omitting the SA-HRP solution (Ventana/Roche
Diagnostics GmbH, Mannheim, Germany). Antigen retrieval was done by
using Cell Conditioning ULTRA.RTM. (Ventana/Roche Diagnostics GmbH,
Mannheim, Germany) at mild (95.degree. C. for 8 min+100.degree. C.
for 20 min) conditions. Mouse cross reactivities were blocked by
using Blocking Reagents A and B from the N-Histofine Mousestain
Kit.RTM. (Nichirei Bioscience Inc, Japan) before and after primary
antibody incubation, following the manufacturer instructions. The
primary antibody was applied manually at the desired dilution in
Dako antibody diluent (AbD), followed by incubation for 1 hour at
ambient temperature. Corresponding negative controls were incubated
with AbD only. Sections were subsequently stained using the labeled
polymer system Simple Stain Mouse MAX PO (M) from the N-Histofine
Mousestain Kit.RTM. (Nichirei) and DAB substrate from the DABMap
Kit (Ventana/Roche Diagnostics). Counterstaining of sections was
done using hematoxylin (Ventana/Roche Diagnostics). After the
automated staining run, slides were dehydrated in a graded series
of ethanol, cleared in xylene and mounted with Pertex.RTM. mounting
medium.
Example 5
Prediction of MDM2i(2) Sensitivity in Human Primary Tumor Mouse
Xenograft Models and in Human Primary Tumors
[0217] The biomarkers in Table 2, were used in association with a
naive Bayes predictive model to predict MDM2i sensitivity in a
collection of human primary tumor samples and xenograft models, to
demonstrate whether the biomarkers, and their associated predictive
power, exists outside of in vitro cell line systems.
[0218] The gene-level expression values of all the biomarkers of
Table 2 were used as the feature basis for naive Bayes
probabilistic modeling. They were generated as described in Example
3, the only difference being in the RMA summarization step where
the normalization was targeted to a reference set of normal &
tumor samples. The naive Bayes modeling is conducted as described
in Example 3.
[0219] The human primary tumor samples and xenograft models
submitted for sensitivity prediction were a collection of about
18,000 and 503 samples, respectively, for which gene expression
profiles, generated with Affymetrix technology (Human Genome U133
plus 2.0 array), are available. The samples of the collection were
internally annotated with controlled vocabulary for sample ontology
including pathology, histology and primary site. The associated
gene chip data was gathered from both public and internal sources,
and normalized as described above to the same reference sample set
for consistency.
[0220] Ratios of MDM2i predicted sensitive samples from the
collection were compared to the proportions of sensitive cell
lines, as given by the MDM2i(2) chemical sensitivity data described
in Example 3 above. A good correlation is expected to demonstrate
the ability of the biomarkers disclosed in FIG. 3 to be predictive
for MDM2i sensitivity in human primary tumor samples. For clarity,
as well as to potentially identify lineages in which sensitive cell
line proportions are underestimated in the cell line chemical
sensitivity data, sensitive prediction ratio to sensitive cell line
ratio comparison is broken down by tissue of origin.
[0221] FIG. 9 (left panel) shows a correlation between predicted
sensitive human primary tumor samples from the collection and the
sensitive cell lines from MDM2i chemical sensitivity data. It
indicates the biomarkers disclosed in Table 2 and its use for
predicting sensitivity outside of in vitro cell line samples is
valid. It also indicates that the biomarkers disclosed in Table 2
can be used to predict MDM2i chemical sensitivity in human primary
tumor samples. It reveals new tumor indications which have not been
investigated previously and confirms results found in the current
study The new indications, for example, liver (hepatocellular
carcinoma) and kidney (renal cell carcinoma), represent potentially
new disease indications to be pre-clinically and clinically
evaluated with the biomarkers disclosed in Table 2 for treatment
with an MDM2i. FIG. 9A also indicates that the biomarkers disclosed
in Table 2 can be used to predict MDM2i chemical sensitivity in
primary melanoma tumors, consistent with the results found in the
current study.
[0222] FIG. 9 (right panel) shows a correlation between the
fractions of predicted sensitive human primary tumor samples and
the predicted sensitive ratios in the primary tumor xenograft
collection. The tumor samples/xenografts/cell lines are organized
by lineage. The dashed line in both panels is the identity line. It
shows the data generated from the in vivo mouse xenograft models,
in which the exemplified signature and associated predictive
classifier can be studied and validated, is in line with the data
from the rest of the in vivo collection samples. It confirms the
mouse xenograft models as a source of material to validate the p53
downstream target gene based classifier approach to predict
clinical outcome of cancer patients and diseases indications, in an
in vivo pre-clinical setting.
Example 6
Single Biomarkers and any Combinations of the Identified Thirteen
Biomarkers Predict Chemical Sensitivity to MDM2i
[0223] The thirteen biomarkers depicted in Table 2, when used in
association with a naive Bayes predictive modeling framework,
predict MDM2i sensitivity in both in vitro systems and in vivo, as
exemplified in Examples 3 and 5. To investigate whether subsets of
these thirteen biomarkers would also predict for MDM2i sensitivity,
single biomarkers and multiple combinations of them are employed as
feature basis for predictive modeling. Their prediction
performances are then compared to the ones achieved with either the
full thirteen biomarkers or with p53 mutation status when used as a
predictive feature for MDM2i sensitivity prediction.
[0224] Two instances of p53 mutation status are considered in
Example 6. These two instances are defined from the Exome Capture
Sequencing data of the CCLE cell lines, as mentioned in Example 2,
and are meant to be surrogates of clinical settings where the p53
gene is sequenced for stratification or clinical annotation of
patients.
[0225] The first instance of p53 mutation status is defined from
the mutations spanning exons 5 to 8 of p53. Exons 5 to 8 encompass
the DNA binding domain of p53, which contains the majority of
described p53 mutations, and are the p53 exons commonly targeted
for sequencing in clinical settings (for example, Rapid sequencing
of the p53 gene with a new automated DNA sequencer. Bharaj B.,
Angelopoulou K., and Diamandis E., Clinical Chemistry 44:7
1397-1403, 1998). The second instance considers the complete open
reading frame of the main p53 transcript, and is therefore defined
from all coding exon mutations.
[0226] Multiple biomarker combinations can be generated from the
list of 13 biomarkers disclosed in Table 2. All combination types
from 2 to 12 biomarkers are evaluated as feature basis for
predictive modeling of MDM2i chemical sensitivity. When more than
50 different combinations exist for a given combination type, the
number of evaluated combinations is restricted to 50. All 50
combinations in each combination type were randomly picked.
[0227] All predictive models associated to the above described
feature sets (single biomarkers, 2-to-12 biomarker combinations,
p53 mutation status instances) were trained and evaluated mostly as
described in Example 3. What differs from Example 3 is as follows:
The training data was slightly larger than the one used is Example
3 and encompasses 264 cell lines (47 from the sensitive class, and
217 from the insensitive class); A p-value threshold of 0.5 was
used upon the naive Bayes probabilistic modeling to call a cell
line either sensitive or insensitive in the 5-fold cross-validation
scheme. Moreover, all sample strata generated by the
cross-validation processes were randomly selected and independent
from one-another. The performances of the combinatorial predictive
models and their comparisons to the p53 mutation status instance
and 13 biomarker models are shown in FIGS. 10 to 12.
[0228] FIG. 10 depicts the positive predicted values (PPV) achieved
by the single biomarker, the combinatorial, the thirteen biomarker
and the p53 mutation models. The PPV is an estimate of the clinical
efficacy one would expect in a clinical trial upon patient
selection with the considered modeling process. For convenience,
the data is depicted as box-and-whisker plots when there are more
than five data points to plot per feature set.
[0229] FIG. 10 shows that combinations from as few as two and three
biomarkers outperform exon 5-to-8 p53 mutation ("ex5to8mt") and
all-exon p53 mutation features ("allExMt"), respectively. Indeed,
the upper and lower boundaries defined by the ends of the whiskers
encompass about 99% of the data points, assuming a normal
distribution of the data. Therefore, the majority of the evaluated
2- and 3-biomarker combinations show a higher PPV than the ones
achieved by the p53 mutation status instances. Moreover, even if
all single gene models do not outperform the two p53 mutations
instances, a majority of them (around 75%, the box plus the upper
whisker) outperforms the p53 all-exon mutations. Noteworthy, all
single gene models give rise to PPVs that are higher than the
sensitive cell line ratio (.about.18%) in the considered sample
population, indicating that MDM2i sensitivity prediction models,
built from as few as one biomarker, are capable of enriching the
selected samples in sensitive ones.
[0230] Additionally, under this modeling exercise, as plotted in
FIG. 10, the PPV given by the p53 exons 5-to-8 mutation status
model, averaged over the 5 cross-validation repeats, is 48%. It is
significantly lower than the p53 mutation PPV disclosed in Example
3 (56%). This indicates that, in a clinical setting where p53 exon
5-to-8 sequencing is employed for patient selection, which is
common practice, the exemplified 13 biomarker-based patient
selection has even higher added value than anticipated from Example
3.
[0231] FIG. 11 shows the specificities achieved by the several
evaluated models. As for PPV in FIG. 10, every combination made
from as few as 2 biomarkers is sufficient to achieve specificity
higher than the ones obtained from the mutations instances only.
All single biomarker models outperform the mutations, when
specificity is used to monitor the model performances.
[0232] FIG. 12 shows the sensitivities. Sensitivity is also called
recall, and is an estimate of the truly sensitive patient
population retained upon patient selection. Combinations of 9
biomarkers as the feature basis for MDM2i sensitivity prediction
models are sufficient to obtain sensitivities comparable to the one
achieved the full 13 biomarker list. However, only a few
9-biomarker combinations would achieve sensitivities higher than
the ones given by the 2 p53 mutation status predictive models. But
noteworthy, all evaluated combinations, from as few as 2
biomarkers, and a majority of single biomarker models, display
sensitivities higher than the one which is expected by chance upon
random classification (.about.18%).
[0233] In conclusion from FIGS. 10, 11 and 12, single biomarkers,
when used as feature basis in models predicting chemical
sensitivity to MDM2i, are sufficient to achieve sample sensitivity
predictions that would result in a significant enrichment of
potentially MDM2i responding patient in a clinical setting.
Furthermore, combinations made from any 2 biomarkers, or more,
increase the expected clinical efficacy with respect to the one
obtained with p53 mutation-based patient selection. Assembling 8
biomarkers, from any of the biomarkers listed in Table 2, are
sufficient to achieve a patient recall equivalent to the one given
by the 13 biomarker model. The predictive model performance
metrics, obtained for the 13 gene signature and associated
combinations, can be further optimized by optimizing the class
assignment p-value threshold used in the naive Bayes probabilistic
step of the model, as it was done in Example 3.
[0234] In a further embodiment, it is investigated whether the
biomarkers depicted in Table 2 could predict MDM2i chemical
sensitivity in collaboration with p53 mutation status. Single
biomarkers and combinations of 2 biomarkers and above are combined
with p53 mutation status in feature lists. The feature lists are
then utilized as basis for sensitivity predictive modeling, as
previously and described above. The performances of the multiple
resulting models are evaluated as described above.
[0235] The p53 mutation status instance which is used as an example
is the p53 exon 5-to-8 mutations. FIGS. 13, 14 and 15 depict the
PPVs, specificities and sensitivities of those models combining p53
mutation with biomarkers, respectively, and are compared to the
results given by mutation only models and the full 13-biomarker
model.
[0236] FIG. 13 shows that at least a single biomarker from the list
of 13, in collaboration with p53 mutation status, is sufficient to
achieve a PPV higher that the basal sensitivity rate (18%) in the
data. It also shows that a single biomarker at minimum, still in
combination with p53 mutation status, achieves a higher PPV than
the ones obtained with the two above mentioned p53 mutation status
instances, when employed as features in a predictive model. And
finally, any 5 biomarkers in combination with p53 mutation
recapitulate the PPV which is achieved by the 13 biomarker
model.
[0237] The same conclusions are drawn from FIGS. 14 and 15 when
specificities and sensitivities are taken into account as model
performance metrics. Noteworthy from FIG. 15, a high sensitivity
can be obtained from as few as one biomarker, when modeled along
with p53 mutation.
[0238] In conclusion, combining a single or multiple biomarkers
from Table 2 with p53 mutation status enables the prediction of
MDM2i sensitivity, and would result, when applied for patient
selection in a therapeutic or clinical setting, in a significant
enrichment with a limited loss of potential MDM2i responding
patients.
Example 7
The Identified Thirteen Biomarkers Predict Chemical Sensitivity to
MDM2i In Vivo and in Pre-Selected Wild-Type p53 Human Primary
Xenograft Models
[0239] To further validate the predictive biomarkers disclosed in
FIG. 3, human primary tumors were directly transplanted and grown
subcutaneously in mice and then assessed for MDM2i sensitivity. All
the animals were allowed to adapt for 4 days and housed in a
pathogen-controlled environment (5 mice/Type III cage) with access
to food and water ad libitum. Animals were identified with
transponders. Studies were performed according to procedures
covered by permit number 1975 issued by the Kantonales Veterinaramt
Basel-Stadt and strictly adhered to the Eidgenossisches
Tierschutzgesetz and the Eidgenossische Tierschutzverordnung.
Subcutaneous tumors were induced by transplanting tumor fragments
(3.times.3.times.3 mm.sup.3) of human patient tumor in the right
flank of Harlan nude mice. The administration of MDM2i(1) began
when tumors were at the size of 150-200 mm.sup.3 for a treatment
period up to 4 weeks. MDM2i(1) was made-up fresh for each
administration. MDM2i(1) was dissolved in 0.5% HPMC
(hydroxypropylmethylcellulose) and injected daily (q24h) at 100
mg/kg. Tumor volumes (TVol), determined from caliper measurements
(using the formula l.times.w.times.h.times..pi./6), were measured
three times per week. Tumor response was quantified by the
percentage of change in tumor volume, i.e.
( .DELTA. TVol drug TVol Day 0 .times. 100 ) . ##EQU00003##
The body-weight (BW) of the mice was measured three times per week
allowing calculation at any particular time-point relative to the
day of initiation of treatment (day 0) of both the percentage
change in BW (A % BW).
[0240] The cut-offs used for sensitivity to MDM2i were based on
RECIST adapted for full tumor volume measurement (instead of the
sum of longest diameters), i.e. models considered to be sensitive
or responsive either displayed a complete response (full
regression), or a partial response (>50% decrease in tumor
volume) or a stable disease (between 50% decrease and 35% increase
in tumor volume). In vivo models showing a progressive disease
(>35% increase in tumor volume) were considered as
non-responsive to the MDM2i treatment. The sensitivity call was
made at the maximum effect time-point during the treatment period,
to avoid any misleading call that could be due, for example, to the
appearance of resistance mechanism(s) following a sustained
treatment period with MDM2i. The human primary xenograft tumor
models used for the study are from a variety of tumors, and lineage
composition was chosen according to FIG. 9, for example, melanoma
(19), colorectal cancer (17), liposarcoma (3), renal cell carcinoma
(3), hepatic cancer (7), breast cancer (1), pancreatic cancer (2)
and lung cancer (3). Predictions of sensitivity for every human
primary xenograft tumor model were performed using all the
biomarkers disclosed in Table 2 as a single set and a predictive
model mostly as exemplified in Examples 3 and 5. In this particular
example, the naive Bayes probabilistic rule was trained by
comparing 77 MDM2i(2) sensitive cell lines to 557 insensitive cell
lines, and the naive Bayes predictive model probability threshold,
above which a xenograft tumor model is predicted as sensitive to
MDM2 inhibition, was set to 0.2. Additionally, the chemical
sensitivity assignment of the training cell lines into sensitive
and insensitive classes was done as exemplified in Example 2, with
the only difference being, when the pharmacological
characterization was replicated for a given cell line, the median
value of the cell line sensitivity metric was used for
summarization of the replicated sensitivities.
[0241] As shown in FIG. 16, 27/55 human primary xenograft tumor
models were predicted to be sensitive to an MDM2i, and of these 27
in vivo models, 19 were experimentally confirmed as sensitive,
resulting in a positive predictive value of 70.5%, improving the
basal response rate of 49%. Moreover, 28/55 in vivo models were
predicted to be insensitive to an MDM2i, and 20/28 were
experimentally confirmed as insensitive, leading to a significant
negative predictive value (NPV) of 71.5%. Overall, these data are
comparable to the predictive model performances described in FIG.
5. The assessment of human primary xenograft tumor model response
to MDM2i treatment allowed the validation of the MDM2i chemical
sensitivity predictive model in vivo, hence confirming the validity
of the biomarkers disclosed in Table 2, using patient-derived
xenografted tumors in mice (FIG. 16).
[0242] The p53 mutation call is available for all 55 human primary
xenograft tumor models used in this study. Thirty four of them are
wild-type p53. As shown in FIG. 17, 22/34 wild-type p53 human
primary xenograft tumor models were predicted to be sensitive to an
MDM2i, and of these 22 in vivo models, 18 were experimentally
confirmed as sensitive, resulting in a positive predictive value of
82%, again significantly improving the basal response rate of 65%.
Twelve out of 34 models were predicted to be insensitive to an
MDM2i, and 8/12 were experimentally confirmed as insensitive,
leading to a significant negative predictive value (NPV) of 66.5%.
Overall, these data are equally comparable to the predictive model
performances described in FIG. 5, again validating the biomarkers
disclosed in Table 2, using wild-type p53 selected patient-derived
xenografted tumors in mice (FIG. 17).
[0243] In conclusion, the in vitro predictive model performances
described in FIG. 5 were confirmed using human primary xenograft
tumor models, further validating the biomarkers disclosed in Table
2 in an in vivo setting. Importantly, the findings described here
support the use of the MDM2i chemical sensitivity predictive model
in either non-selected cancer patient or wild-type p53 pre-selected
cancer patient populations
* * * * *