U.S. patent application number 14/338497 was filed with the patent office on 2014-11-13 for gene signature for the prediction of radiation therapy response.
The applicant listed for this patent is University of South Florida. Invention is credited to Steven Eschrich, Javier F. Torres-Roca.
Application Number | 20140336945 14/338497 |
Document ID | / |
Family ID | 40455471 |
Filed Date | 2014-11-13 |
United States Patent
Application |
20140336945 |
Kind Code |
A1 |
Torres-Roca; Javier F. ; et
al. |
November 13, 2014 |
GENE SIGNATURE FOR THE PREDICTION OF RADIATION THERAPY RESPONSE
Abstract
Described are mathematical models and method, e.g.,
computer-implemented methods, for predicting tumor sensitivity to
radiation therapy, which can be used, e.g., for selecting a
treatment for a subject who has a tumor.
Inventors: |
Torres-Roca; Javier F.; (St.
Petersburg, FL) ; Eschrich; Steven; (Lakeland,
FL) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
University of South Florida |
Tampa |
FL |
US |
|
|
Family ID: |
40455471 |
Appl. No.: |
14/338497 |
Filed: |
July 23, 2014 |
Related U.S. Patent Documents
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Application
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Filing Date |
Patent Number |
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14012029 |
Aug 28, 2013 |
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14338497 |
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13037153 |
Feb 28, 2011 |
8660801 |
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14012029 |
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12210135 |
Sep 12, 2008 |
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13037153 |
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12053796 |
Mar 24, 2008 |
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12210135 |
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60972544 |
Sep 14, 2007 |
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60896550 |
Mar 23, 2007 |
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60896350 |
Mar 22, 2007 |
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Current U.S.
Class: |
702/19 |
Current CPC
Class: |
G16B 25/00 20190201;
G16B 40/00 20190201; G16B 5/00 20190201; G16H 50/50 20180101; C12Q
1/6883 20130101 |
Class at
Publication: |
702/19 |
International
Class: |
G06F 19/12 20060101
G06F019/12; G06F 19/00 20060101 G06F019/00; G06F 19/20 20060101
G06F019/20 |
Goverment Interests
FEDERALLY SPONSORED RESEARCH OR DEVELOPMENT
[0002] This invention was made with Government support under Grant
Nos. 5 K08 CA108926-03 and NCl Grant R21CA101355 awarded by the
National Institutes of Health, and National Functional Genomics
Center Grant No. DAMD 17-02-2-0051 awarded by the Department of
Defense. The Government has certain rights in the invention.
Claims
1. (canceled)
2. A method of selecting a treatment regimen for a subject having a
solid tumor, the method comprising: receiving, by a computing
device, information regarding expression levels of signature genes
comprising Androgen receptor (AR); Jun oncogene (c-Jun); Signal
transducer and activator of transcription 1 (STAT1); Protein kinase
C, beta (PRKCB or PKC); V-rel reticuloendotheliosis viral oncogene
homolog A (avian) (RELA or p65); c-Abl oncogene 1, receptor
tyrosine kinase (ABL1 or c-Abl); SMT3 suppressor of mif two 3
homolog 1 (S. cerevisiae) (SUMO1); PAK2; Histone deacetylase 1
(HDAC1); and Interferon regulatory factor 1 (IRF1) in a cell from
the solid tumor; calculating, by a computing device, a radiation
sensitivity index for the solid tumor based on expression levels of
the signature genes; and selecting, by a computing device, a
treatment regimen for the subject based on the radiation
sensitivity index, thereby providing information to select a
treatment regimen for the subject.
3. The method of claim 2, wherein a radiation sensitivity index
below a threshold indicates that radiation therapy is likely to be
effective in treating the tumor, and the method comprises selecting
a treatment regimen including radiation therapy; and wherein a
radiation sensitivity index above a threshold indicates that
radiation therapy is not likely to be effective in treating the
tumor, and the method comprises selecting a treatment regimen
excluding radiation therapy, or a treatment regime including a high
dose of radiation therapy.
4. The method of claim 2, wherein the radiation sensitivity index
is calculated based on a preselected dose of radiation, and the
method comprises selecting a dose of radiation that is greater than
the preselected dose of radiation for a subject who has a radiation
sensitivity index that is above a threshold.
5. The method of claim 2, wherein calculating a radiation
sensitivity index comprises applying a linear regression model to
the gene expression levels.
6. The method of claim 5, wherein the model is a rank-based linear
regression model.
7. The method of claim 6, wherein the linear regression model is
represented by the following algorithm:
RSI=k.sub.1*AR+k.sub.2*c-jun+k.sub.3*STAT1+k.sub.4*PKC+k.sub.5*RelA+k.sub-
.6*cAbl+ k.sub.7*SUMO1+k.sub.8*PAK2+k.sub.9*HDAC1+k.sub.10*IRF1.
I
8. The method of claim 2, wherein two or more signature genes are
weighted.
9. The method of claim 2, wherein the solid tumor originates from a
carcinoma of the breast, head and neck, lung, prostate, colon,
liver, brain, rectum, ovary, oral cavity, esophagus, cervix, or
bone.
10. The method of claim 2, wherein the method further comprises
administering the selected treatment to the subject.
11. A non-transitory computer readable medium storing instructions
for causing a computing system to: calculate a radiation
sensitivity index for a subject having a solid tumor based on
expression levels of signature genes comprising Androgen receptor
(AR); Jun oncogene (c-Jun); Signal transducer and activator of
transcription 1 (STAT1); Protein kinase C, beta (PRKCB or PKC);
V-rel reticuloendotheliosis viral oncogene homolog A (avian) (RELA
or p65); c-Abl oncogene 1, receptor tyrosine kinase (ABL1 or
c-Abl); SMT3 suppressor of mif two 3 homolog 1 (S. cerevisiae)
(SUMO1); PAK2; Histone deacetylase 1 (HDAC1); and Interferon
regulatory factor 1 (IRF1) in a cell from the solid tumor; and
select a treatment regimen for the subject based on the radiation
sensitivity index.
12. The non-transitory computer readable medium of claim 11,
wherein the medium comprises instructions for causing a computing
system to select a dose of radiation that is greater than a
preselected dose of radiation for a subject who has a radiation
sensitivity index that is above a threshold.
13. The non-transitory computer readable medium of claim 11,
wherein the radiation sensitivity index is calculated by applying a
linear regression model to the gene expression levels.
14. The non-transitory computer readable medium of claim 13,
wherein the model is a rank-based linear regression model.
15. The non-transitory computer readable medium of claim 14,
wherein the linear regression model is represented by the following
algorithm:
RSI=k.sub.1*AR+k.sub.2*c-jun+k.sub.3*STAT1+k.sub.4*PKC+k.sub.5*RelA+k.sub-
.6*cAbl+ k.sub.7*SUMO1+k.sub.8*PAK2+k.sub.9*HDAC1+k.sub.10*IRF1.
I
16. The non-transitory computer readable medium of claim 11,
wherein two or more signature genes are weighted.
17. The non-transitory computer readable medium of claim 11,
wherein the solid tumor originates from a carcinoma of the breast,
head and neck, lung, prostate, colon, liver, brain, rectum, ovary,
oral cavity, esophagus, cervix, or bone.
Description
CLAIM OF PRIORITY
[0001] This application is a continuation of U.S. patent
application Ser. No. 14/012,029, filed on Aug. 28, 2013, which is a
continuation of U.S. patent application Ser. No. 13/037,153, filed
on Feb. 28, 2011, now U.S. Pat. No. 8,660,801, which is a
continuation of U.S. patent application Ser. No. 12/210,135, filed
on Sep. 12, 2008, now abandoned, which claims the benefit of U.S.
Provisional Patent Application No. 60/972,544, filed on Sep. 14,
2007, and is a continuation in part of U.S. patent application Ser.
No. 12/053,796, filed on Mar. 24, 2008, now abandoned, which claims
the benefit of U.S. Provisional Patent Application No. 60/896,550,
filed on Mar. 23, 2007, and U.S. Provisional Patent Application No.
60/896,350, filed on Mar. 22, 2007. The entire contents of the
foregoing are hereby incorporated by reference.
TECHNICAL FIELD
[0003] This invention relates to mathematical models and methods
for predicting tumor sensitivity to radiation therapy using
biological assay data, which can be used, e.g., for selecting a
treatment for a subject who has a tumor.
BACKGROUND
[0004] Personalized medicine holds the promise that the diagnosis,
prevention and treatment of cancer will be based on individual
assessment of risk (Dalton and Friend, Science 2006;
312(5777):1165-8). The delivery of this promise in radiation
oncology is dependent on the ability to define the variables that
define response to clinical radiotherapy. Although most strategies
in personalized medicine have focused on specific disease sites
and/or drug therapies (van 't Veer et al., Nature 2002;
415(6871):530-6; Beer et al., Nat Med 2002; 8(8):816-24; Chung et
al., Cancer Cell 2004; 5(5):489-500; Eschrich et al., J Clin Oncol
2005; 23(15):3526-35; Giles et al., Semin Onco12008; 35 (1 Suppl
1):S1-17), the impact of individualizing radiation therapy is
significant. Approximately 60% of cancer patients are treated with
radiation therapy during their diagnosis (Perez, Principles and
Management of Radiation Therapy. Philadelphia-New York:
Lippincott-Raven; 1998). Thus, radiation therapy provides a common
denominator in cancer therapeutics.
[0005] Significant advances towards personalized radiation therapy
have been largely achieved by physical advances in radiotherapy
treatment planning and delivery (Bucci et al., CA Cancer J Clin
2005; 55(2):117-34). In contrast, the efforts in understanding the
biological parameters that define intrinsic radiosensitivity have
not met the same success. Thus, radiotherapy is prescribed without
considering the potential individual differences in tumor and
patient radiosensitivity. However there is evidence to suggest that
differences in intrinsic radiosensitivity exist (Zelefsky et al.,
J. Urology 2001; 166(3):876-81) and understanding their biological
basis could significantly impact clinical practice. Thus, a
successful radiosensitivity predictive assay would be central to
the development of biologically-guided personalized treatment
strategies in radiation oncology. However, although a number of
promising approaches have been developed in the past (e.g.,
determination of ex-vivo tumor SF2, (Bjork-Eriksson et al., Int J
Radiat Oncol Biol Phys 2000; 46(1):13-9; Buffa et al., Int J Radiat
Oncol Biol Phys 2001; 50(5):1113-22; Eschwege et al., Int J Radiat
Oncol Biol Phys 1997; 39(4):849-53; Taghian et al., Int J Radiat
Oncol Biol Phys 1993; 25(2):243-9; West et al., British Journal of
Cancer 1997; 76(9):1184-90; West et al., Br J Cancer 1993;
68(4):819-23); the use of electrodes to measure tumor hypoxia
(Fyles et al., J Clin Oncol 2002; 20(3):680-7; Movsas et al.,
Urology 2002; 60(4):634-9); and determination of tumor
proliferative potential (Tpot) (Begg et al., Radiother Oncol 1999;
50(1):13-23; Bourhis et al., Int J Radiat Oncol Biol Phys 1996;
35(3):471-6; Corvo et al., J Clin Oncol 1995; 13(8):1843-50), none
has become routine in the clinic.
SUMMARY
[0006] At least in part, the inventions described herein are based
on the development of methods and models that predict intrinsic
sensitivity of a tumor to radiation therapy based on a gene
expression profile.
[0007] In one aspect, the invention provides methods, e.g.,
computer-implemented methods, for predicting the sensitivity of a
cell, i.e., a living cell, e.g., a tumor cell or a normal
(non-tumor) cell, or a cultured cell, to a selected dose of
radiation therapy. The methods include assigning a radiation
sensitivity index to the cell based on expression levels of two or
more signature genes in the cell, wherein the radiation sensitivity
index indicates whether the cell is sensitive to radiation
therapy.
[0008] In an additional aspect, the invention provides methods for
predicting the effect of radiation therapy on a tumor. The methods
include assigning a radiation sensitivity index to the tumor based
on expression levels of two or more signature genes in a cell from
the tumor, wherein the radiation sensitivity index indicates
whether the radiation therapy is likely to be effective.
[0009] In yet another aspect, the invention provides methods for
assessing a tumor in a subject for a radiation therapy regimen. The
methods include assigning a radiation sensitivity index to the
tumor based on expression levels of two or more signature genes in
a cell from the tumor, wherein the radiation sensitivity index
indicates whether the tumor in the subject should be treated with
radiation therapy.
[0010] In a further aspect, the invention provides methods for
selecting a treatment regimen for a subject having a tumor. The
methods include assigning a radiation sensitivity index to the
tumor based on expression levels of two or more signature genes in
a cell from the tumor, and selecting a treatment regimen for the
subject based on the radiation sensitivity index. In general, a
radiation sensitivity index below a threshold indicates that
radiation therapy is likely to be effecting in treating the tumor,
and the method includes selecting a treatment regimen including
radiation therapy. Conversely, a radiation sensitivity index above
a threshold indicates that radiation therapy is not likely to be
effecting in treating the tumor, and the method includes selecting
a treatment regimen excluding radiation therapy, or a treatment
regime including a high dose of radiation therapy.
[0011] In another aspect, the invention provides methods for
selecting a dose of radiation to be administered to a subject
having a tumor. The methods include assigning to the tumor a
radiation sensitivity index for a preselected dose of radiation
based on expression levels of two or more signature genes in a cell
from the tumor, and selecting a dose of radiation for the subject
based on the radiation sensitivity index at the preselected dose of
radiation. In some embodiments, the methods include selecting a
dose of radiation that is the same as or less than the preselected
dose of radiation, if the radiation sensitivity index is below a
threshold. In some embodiments, the methods include selecting a
dose of radiation that is the greater than the preselected dose of
radiation, if the radiation sensitivity index is above a
threshold.
[0012] In some embodiments of the methods described herein,
assigning a radiation sensitivity index comprises applying a linear
regression model to the gene expression levels, e.g., a rank-based
linear regression model. In some embodiments, the expression levels
of the two or more signature genes are weighted. In some
embodiments, the linear regression model is represented by the
following algorithm:
RSI=K.sub.1*AR+K.sub.2*c-jun+k.sub.3*STAT1+k.sub.4*PKC+k.sub.5*RelA+k.su-
b.6*cAbl+
k.sub.7*SUMO1+k.sub.8*CDK1+k.sub.9*HDAC+k.sub.10*IRF1. I
[0013] In some embodiments of the methods described herein,
assigning a radiation sensitivity index can include determining a
level of expression of two or more signature genes in a cell.
[0014] In some embodiments of the methods described herein, the
signature genes are selected from the group consisting of Androgen
receptor (AR); Jun oncogene (c-Jun); Signal transducer and
activator of transcription 1 (STAT1); Protein kinase C, beta (PRKCB
or PKC); V-rel reticuloendotheliosis viral oncogene homolog A
(avian) (RELA or p65); c-Abl oncogene 1, receptor tyrosine kinase
(ABL1 or c-Abl); SMT3 suppressor of mif two 3 homolog 1 (S.
cerevisiae) (SUMO1); CDK1 (p34); Histone deacetylase 1 (HDAC 1);
and Interferon regulatory factor 1 (IRF1). In some embodiments, the
signature genes comprise a subset of the genes as listed in Table
10, 11, or 12. Optionally, assigning a radiation sensitivity index
can also include using gene expression levels of one or more genes
listed in FIGS. 6A-6P.
[0015] In another aspect, the invention provides
computer-implemented method of identifying genes associated with
radiation sensitivity. The methods include assigning a radiation
sensitivity value to one or more populations of cells, wherein the
radiation sensitivity value represents the sensitivity of the cells
to a selected dose of radiation; determining a level of gene
expression in the one or more populations of cells for each of a
plurality of genes; and identifying a subset comprising two or more
genes, the expression of which is correlated with the radiation
sensitivity value. In some embodiments, the subset of the genes is
identified by a method including applying a model representing gene
expression and radiosensitivity, e.g., a multivariate linear
regression model. The model can include, e.g., at least one
coefficient representing one, two, or all three of: tissue of
origin, ras status, or p53 status. In addition, the model can
include information regarding the dose of radiation administered
and the presence of any additional treatment or factors relevant to
the cell.
[0016] In some embodiments, the methods further include associating
a classifier representing biological importance with each gene in
the subset of genes, expression of which is correlated with the
radiation sensitivity value, and selecting a second subset
comprising two or more genes wherein the classifier representing
biological importance is above a preselected threshold, thereby
selecting a subset of biologically important genes. In some
embodiments, the classifier representing biological importance is
based on a review of relevant scientific literature. In some
embodiments, the model also includes a variable representing an
effect of administration of a treatment on expression of each gene
in the subset of biologically important genes.
[0017] In some embodiments, the methods further include selecting a
third subset comprising one or more genes based on the effect of
administration of the treatment, thereby identifying a subset of
therapeutic target genes, e.g., genes the expression of which may
be usefully manipulated to alter (i.e., increase or decrease)
sensitivity to radiation. In some embodiments, the effect of the
treatment is an increase or decrease in radiosensitivity. In some
embodiments, the methods further include selecting a treatment that
has an effect on radiosensitivity in the model.
[0018] In yet another aspect, the invention provides databases
including a plurality of records, wherein each record includes data
on the expression of at least two signature genes in a cell, and a
value representing sensitivity of the cell to a selected dose of
radiation. In some embodiments, the database also includes data
regarding the administration of a treatment, e.g., chemotherapy, to
the cell. In some embodiments, the database is in computer readable
form.
[0019] Also provided herein are microarrays including a substrate
and a plurality of individually addressable hybridisable array
elements arranged thereon, wherein the individually addressable
hybridisable array elements are selective for at least two
signature genes, and optionally at least one hybridisable array
element selective for an internal normalization control gene. In
some embodiments, the plurality of hybridisable array elements
consists of at least one element selective for each of AR; c-Jun;
STAT1; PKC; RelA (p65); c-Abl; SUMO-1; CDK1 (p34); HDAC1; and
IRF1.
[0020] In an additional aspect, the invention provides microfluidic
devices including a substrate and a plurality of reaction chambers
with reagents for selective quantification of at least two
signature genes; and optionally at least one reaction chamber
comprising reagents for selective quantification of an internal
normalization control gene. In some embodiments, the devices
include duplicate sets of the reaction chambers, e.g., to allow
processing of multiple samples simultaneously.
[0021] In a further aspect, the invention provides a medium, e.g.,
computer-readable medium, bearing instructions to cause a computer
to perform a method described herein. For example, the medium can
bear instructions to cause a computer to assign a radiation
sensitivity index to a cell based on expression levels of two or
more signature genes in the cell. In some embodiments, assigning a
radiation sensitivity index comprises applying a linear regression
model to the gene expression levels, e.g., a rank-based linear
regression model, e.g., wherein the two or more signature genes are
weighted.
[0022] In an additional aspect, the invention provides a medium,
e.g., computer-readable medium, bearing instructions to cause a
computer to assign a radiation sensitivity value to one or more
populations of cells, wherein the radiation sensitivity value
represents the sensitivity of the cells to a selected dose of
radiation; assign a level of gene expression in the one or more
populations of cells for each of a plurality of genes; and identify
a subset comprising two or more genes, the expression of which is
correlated with the radiation sensitivity value. In some
embodiments, identifying a subset of the genes comprises applying a
model representing gene expression and radiosensitivity, e.g., a
multivariate linear regression model. In some embodiments, the
model includes at least one coefficient representing one, two, or
all three of: tissue of origin, ras status, or p53 status.
[0023] In some embodiments, the medium further comprises
instructions to cause a computer to associate a classifier
representing biological importance with each gene in the subset of
genes, expression of which is correlated with the radiation
sensitivity value, and selecting a second subset comprising two or
more genes wherein the classifier representing biological
importance is above a preselected threshold, thereby selecting a
subset of biologically important genes.
[0024] In some embodiments, the model further comprises a variable
representing an effect of administration of a treatment on
expression of each gene in the subset of biologically important
genes. In some embodiments, the medium further includes
instructions to cause a computer to select a third subset
comprising two or more genes based on the effect of administration
of the treatment, thereby identifying a subset of therapeutic
target genes.
[0025] Also provided by the present invention are kits including
reagents for the specific quantification of gene expression levels
of two or more signature genes in a cell, and instructions for
carrying out a method as described herein. In some embodiments, the
kits include a medium as described herein.
[0026] The present invention has a number of advantages. The models
and methods described herein provide an opportunity to
individualize radiation dose parameters based on intrinsic
radiosensitivity. Since higher doses of radiation therapy are
associated with higher toxicity rate (Peeters et al., Int J Radiat
Oncol Biol Phys 2005; 61(4):1019-34), dose personalization would
result in a therapeutic ratio benefit. In addition the model may
provide a unique framework to understand the differences between
responders and non-responders that share a predicted radioresistant
phenotype. This may allow the accurate identification of patients
that benefit from the addition of concurrent chemotherapy.
[0027] Unless otherwise defined, all technical and scientific terms
used herein have the same meaning as commonly understood by one of
ordinary skill in the art to which this invention belongs. Methods
and materials are described herein for use in the present
invention; other, suitable methods and materials known in the art
can also be used. The materials, methods, and examples are
illustrative only and not intended to be limiting. All
publications, patent applications, patents, sequences, database
entries, and other references mentioned herein are incorporated by
reference in their entirety. In case of conflict, the present
specification, including definitions, will control.
[0028] Other features and advantages of the invention will be
apparent from the following detailed description and figures, and
from the claims.
DESCRIPTION OF DRAWINGS
[0029] FIGS. 1A and B are bar graphs showing that the predicted
tumor radiosensitivity is correlated with clinical response to
concurrent radiochemotherapy in rectal and esophageal cancer
patients. Predicted radiosensitivity indices (RSI) for each patient
were generated using a ten-gene, rank-based linear regression model
built from the cell line data as described herein. Statistical
significance was determined using a one-sided Mann-Whitney test for
differences. (1A) The mean predicted RSI of responders is
significantly lower than in non-responders in both clinical cohorts
(esophageal: p=0.05, rectal: p=0.03). (1B) Predicted RSI of each
individual patient in both cohorts is significantly different
relative to response (combined: p=0.001511).
[0030] FIG. 2 is a ROC curve that was generated using the predicted
RSI values to determine the sensitivity and specificity of the
radiosensitivity predictor. Using a threshold RSI value of
0.4619592, the predictor has an 80% sensitivity and 82%
specificity, with a positive predictive value (PPV) of 86%. The
estimated area under the curve (AUC) is 0.84.
[0031] FIG. 3 is a line graph demonstrating that predicted
radiosensitivity distinguishes clinical populations with different
disease related outcomes in head and neck cancer. Radiosensitivity
predictions were generated with the gene expression model as
described in 92 patients treated with definitive concurrent
radiochemotherapy at the Netherlands Cancer Institute. Using the
25th percentile as cutpoint (RSI<0.023), there is a superior 2
year Recurrence Free Survival (RFS) in the predicted radiosensitive
group (86% vs. 62%, p=0.06).
[0032] FIGS. 4A-4G is a list setting forth gene combinations
(profiles) that were evaluated and demonstrated significant
association with radiosensitivity in the Rectal+Esophagus cohorts
described herein. The gene symbols are joined by `_` and the
p-values from tests of significance between responders and
non-responders are also given.
[0033] FIG. 5 is a block diagram of computing devices and
systems.
[0034] FIGS. 6A-6P is a list setting forth 500 genes identified as
being correlated with radiosensitivity using a systems network
model as described herein.
[0035] FIG. 7 is a list of exemplary control genes useful in the
methods described herein.
DETAILED DESCRIPTION
[0036] The advent of high dimensional and high-throughput
technologies has provided an opportunity to address the development
of biomarkers from a different perspective. For example, gene
expression signatures have been shown to be prognostic in breast
(van 't Veer et al., Nature 2002; 415(6871):530-6), lung (Beer et
al., Nat Med 2002; 8(8):816-24), head and neck (Chung et al.,
Cancer Cell 2004; 5(5):489-500) and colon cancer (Eschrich et al.,
J Clin Oncol 2005; 23(15):3526-35). Recent studies have identified
biomarkers predictive of patient response to drug treatment,
including response to Gleevec in Chronic Myelogenous Leukemia (CML)
(Giles et al., Semin Oncol 2008; 35 (1 Suppl 1):S1-17). In
addition, gene expression can predict cellular intrinsic
radiosensitivity (Torres-Roca et al., Cancer Res. 65(16):7169-76
(2005)). The present inventors have developed a gene expression
model to predict radiosensitivity in patients.
[0037] Described herein is a novel multi-gene expression model of
intrinsic tumor radiosensitivity in a database of 48 human cancer
cell lines. The model is based on the expression of sets of
signature genes, which predicts a radiosensitivity index (RSI) that
is directly proportional to tumor radioresistance. The model was
clinically validated as a predictive factor of pathological
response in two independent cohorts of esophageal (n=12) and rectal
(n=14) cancer patients treated with preoperative concurrent
chemoradiation in prospective clinical trials at Moffitt Cancer
Center. In addition, RSI calculated by a method described herein
was of prognostic value in a third external dataset of head and
neck cancer patients (n=92) treated with definitive concurrent
chemoradiation within Phase 2 and 3 clinical trials at the
Netherlands Cancer Institute. Thus this model can be used to
individualize therapy in clinical radiation oncology. For example,
the model provides an opportunity to individualize radiation dose
parameters based on intrinsic radiosensitivity. Since higher doses
of radiation therapy are associated with higher toxicity rate, dose
personalization would result in a therapeutic ratio benefit. In
addition the model provides a unique framework to understand the
differences between responders and non-responders that share a
predicted radioresistant phenotype. This allows more accurate
identification of patients that benefit from the addition of
concurrent chemotherapy.
[0038] In the molecular medicine era, high-throughput technologies
(e.g., microarrays and proteomics) have led to the identification
of numerous molecular signatures of prognostic and/or predictive
significance (van 't Veer et al., Nature 2002; 415(6871):530-6;
Beer et al., Nat Med 2002; 8(8):816-24; Chung et al., Cancer Cell
2004; 5(5):489-500; Eschrich et al., J Clin Oncol 2005;
23(15):3526-35; Alizadeh et al., Nature 2000; 403(6769):503-11;
Bild et al., Nature 2006; 439(7074):353-7; van de Vijver et al.,
New Eng. J. Med. 2002; 347(25):1999-2009; Shedden et al., Nat Med
2008; 14(8):822-7). However the initial enthusiasm that these
signatures would lead to personalized medicine has been dampened by
their frequent lack of robustness (Simon et al., J Natl Cancer Inst
2003; 95(1):14-8).
[0039] The robustness of the radiosensitivity model described
herein is supported by several lines of evidence. First, the
algorithm was validated in three independent prospectively
collected datasets in three different diseases. Second, the model
was valid across different gene expression platforms. Gene
expression data in the esophageal and rectal cancer cohorts were
derived from Affymetrix U-133 Plus microarrays. However, gene
expression in the head and neck dataset was derived from NKI
arrays, which is a two channel based cDNA microarray platform. The
observation that the algorithm is transferable across platforms is
important as it demonstrates transferability to other clinical
platforms (e.g., using RT-PCR/Formalin-fixed tissue). Third, all
patients in the validating clinical cohorts were treated with
concurrent chemoradiation, since we were unable to obtain a dataset
of patients treated with radiation alone. However the algorithm was
based on cellular radiosensitivity. Thus, in spite of this
potential source of inaccuracy, the model was still validated.
Finally, the model showed both predictive and prognostic value.
[0040] The model described herein is designed to predict tumor
radiosensitivity. RSI was prognostic in the head and neck cancer
dataset, suggesting that the biological factors that determine
radiosensitivity are related to disease prognosis after treatment.
This is consistent with the observation that complete pathological
response in both esophageal and rectal cancer has strong prognostic
significance in several studies (Janjan et al., Am J Clin Oncol
2001; 24(2):107-12; Chirieac et al., Cancer 2005; 103(7):1347-55;
Gavioli et al., Dis Colon Rectum 2005; 48(10):1851-7; Capirci et
al., Int J Radiat Oncol Biol Phys. 2008 Sep. 1; 72(1):99-107. Epub
2008 Apr. 11). Thus a model that can identify complete responders
would indeed be desirable. Using the current model, 6/8 complete
responders fall below the threshold suggested by the ROC analysis
(SF2=0.46) suggesting that this model successfully identifies this
population.
[0041] In addition, there is a role for identifying patients that
are likely to be downstaged, particularly in rectal cancer. For
example, this knowledge might lead to better counseling of patients
with low-lying rectal tumors where sphincter-sparing surgery is
being considered. Patients that have low lying rectal cancer are
generally patients that have a tumor within 5 cm of the anal
sphincter. Classically, these patients when operated were treated
with an abdomino-perineal resection (APR), which removes the anal
sphincter and thus requires the patient to have a permanent
colostomy bag, which generally adversely affects the patient's
quality of life. In order to address this, protocols were developed
about 10-15 years ago to test whether using a course of
preoperative radiation or chemoradiation would improve the ability
of the surgeon to spare the sphincter; shrinking the tumor improves
the chances that the surgeon can remove the cancer and reconnect
the rectum and keep the normal sphincter mechanism intact. This
approach has been successful, but the likelihood of
sphincter-sparing surgery is related to the amount of downstaging
achieved by chemoradiation. Thus, the model described herein can
improve patient counseling before a treatment decision is made. For
example, if the patient is likely to respond to preoperative
treatment because the model determines that the rectal tumor is
radiosensitive, then the chances of success are high. However, if
the tumor is radioresistant, success is unlikely, and the patient
can be counseled to go directly to surgery and thereby be spared
the side effects of a treatment that is unlikely to be successful,
or if preoperative treatment is still pursued then higher doses of
radiation therapy could be prescribed to increase the chances of
success.
[0042] Determination of Radiosensitivity Index (RSI) of a Tumor
[0043] The methods described herein use a rank-based linear
algorithm to assign a radiosensitivity index (RSI) to a cell, i.e.,
a living cell, e.g., a tumor cell from a patient, a normal cell
from a patient, or a cultured cell. In general, the methods are
applicable to any mammal, particularly humans. The methods include
determining expression levels of signature genes in a cell or cells
of the tumor, and determining a RSI based on the expression levels.
In some embodiments, the methods include the use of two or more,
e.g., three, four, five, six, seven, eight, nine, or all ten
signature genes, as follows: Androgen receptor (AR); Jun oncogene
(c-Jun); Signal transducer and activator of transcription 1
(STAT1); Protein kinase C, beta (PRKCB or PKC); V-rel
reticuloendotheliosis viral oncogene homolog A (avian) (RELA or
p65); c-Abl oncogene 1, receptor tyrosine kinase (ABL1 or c-Abl);
SMT3 suppressor of mif two 3 homolog 1 (S. cerevisiae) (SUMO1);
CDK1 (p34); Histone deacetylase 1 (HDAC1); and Interferon
regulatory factor 1 (IRF1).
TABLE-US-00001 TABLE A Exemplary Sequences of Signature Genes -
Human Probe Gene Refseq Design Gene Name Symbol Identifiers
Identifier Androgen Receptor AR NM_000044.2 M23263.1 NM_001011645.1
Jun oncogene NM_002228.3 J04111.1 Signal transducer and STAT1
NM_007315.2 M97935.1 activator of NM_139266.1 transcription 1
Protein kinase C, beta PRKCB NM_002738.5 X06318.1 PKC NM_212535.1
v-rel reticulo- RELA NM_021975.2 U33838.1 endotheliosis P65 viral
oncogene homolog A (avian) c-Abl oncogene 1, ABL1 NM_007313.2
X16416.1 receptor tyrosine kinase c-Abl NM_005157.3 SMT3 suppressor
of SUMO1 NM_001005781.1 U83117.1 mif two 3 homolog 1 NM_001005782.1
(S. cerevisiae) NM_003352.4 P21 protein PAK2 NM_002577.3 U24153.1
(Cdc42/Rac)- activated CDK1(p34) kinase 2 Histone deacetylase 1
HDAC1 NM_004964.2 D50405.1 HDAC Interferon regulatory IRF1
NM_002198.2 L05072.1 factor 1
Although the exemplary gene sequences set forth above are for the
human genes, and thus are best suited for use in human cells, one
of skill in the art could readily identify mammalian homologs using
database searches (for known sequences) or routine molecular
biological techniques (to identify additional sequences). In
general, genes are considered homologs if they show at least 80%,
e.g., 90%, 95%, or more, identity in conserved regions (e.g.,
biologically important regions).
[0044] In some embodiments, the profile includes the signature
genes listed in a profile shown in Table 10, Table 11, Table 12, or
FIGS. 4A-4G. In some embodiments, the profile includes at least
c-jun, STAT1, cAbl, and IRF1. In some embodiments, the profile
includes at least IRF1.
[0045] A linear regression model useful in the methods described
herein includes gene expression levels and coefficients, or
weights, for combining expression levels. The coefficients can be
calculated using a least-squares fit of the proposed model to a
measure of cellular radiation sensitivity. One example described
herein used the survival fraction at 2 Gy (SF2) although other
measures at other dose levels (e.g., SF8) can be considered with
different coefficients being determined from each. The functional
form of the algorithm is given below, wherein each of the k.sub.i
coefficients will be determined by fitting expression levels to a
particular RSI measure.
RSI=k.sub.1*AR+k.sub.2*c-jun+k.sub.3*STAT1+k.sub.4*PKC+k.sub.5*RelA+k.su-
b.6*cAbl+
k.sub.7*SUMO1+k.sub.8*CDK1+k.sub.9*HDAC+k.sub.10*IRF1 I
[0046] In some embodiments, the methods include applying an
algorithm to expression level data determined in a cell; e.g., a
rank-based linear regression algorithm as described herein. In some
embodiments, the algorithm includes weighting coefficients for each
of the genes.
[0047] Methods of Use
[0048] The methods described herein can be used to identify a
radiation sensitivity index to a selected dose of radiation for any
solid tumor in a subject. A solid tumor is an abnormal mass of
hyperproliferative or neoplastic cells from a tissue other than
blood, bone marrow, or the lymphatic system, which may be benign or
cancerous. In general, the tumors treated by the methods described
herein are cancerous. As used herein, the terms
"hyperproliferative" and "neoplastic" refer to cells having the
capacity for autonomous growth, i.e., an abnormal state or
condition characterized by rapidly proliferating cell growth.
Hyperproliferative and neoplastic disease states may be categorized
as pathologic, i.e., characterizing or constituting a disease
state, or may be categorized as non-pathologic, i.e., a deviation
from normal but not associated with a disease state. The term is
meant to include all types of solid cancerous growths, metastatic
tissues or malignantly transformed cells, tissues, or organs,
irrespective of histopathologic type or stage of invasiveness.
"Pathologic hyperproliferative" cells occur in disease states
characterized by malignant tumor growth. Examples of non-pathologic
hyperproliferative cells include proliferation of cells associated
with wound repair. Examples of solid tumors are sarcomas,
carcinomas, and lymphomas. Leukemias (cancers of the blood)
generally do not form solid tumors.
[0049] The term "carcinoma" is art recognized and refers to
malignancies of epithelial or endocrine tissues including
respiratory system carcinomas, gastrointestinal system carcinomas,
genitourinary system carcinomas, testicular carcinomas, breast
carcinomas, prostatic carcinomas, endocrine system carcinomas, and
melanomas. In some embodiments, the disease is lung carcinoma,
rectal carcinoma, colon carcinoma, esophageal carcinoma, prostate
carcinoma, head and neck carcinoma, or melanoma. Exemplary
carcinomas include those forming from tissue of the cervix, lung,
prostate, breast, head and neck, colon and ovary. The term also
includes carcinosarcomas, e.g., which include malignant tumors
composed of carcinomatous and sarcomatous tissues. An
"adenocarcinoma" refers to a carcinoma derived from glandular
tissue or in which the tumor cells form recognizable glandular
structures.
[0050] The term "sarcoma" is art recognized and refers to malignant
tumors of mesenchymal derivation.
[0051] In some embodiments, the tumors treated by a method
described herein are of epithelial cell origin. In some
embodiments, the tumors originate from lung, colon, rectal,
esophageal, prostate, or head/neck tissues (e.g., originating from
the upper aerodigestive tract, including the lip, oral cavity,
nasal cavity, paranasal sinuses, pharynx, and larynx, e.g.,
squamous cell carcinomas originating from the mucosal lining
(epithelium)). In some embodiments, the tumors are metastatic, and
originate from an epithelial tissue (and are thus epithelial in
origin) but have spread to another tissue, e.g., epithelial-origin
prostate cancer that has spread to the bones of the pelvis, spine
and/or ribs, or lung carcinoma that has metastasized to the adrenal
glands, liver, brain, or bones.
[0052] The methods described herein can identify tumors that are
sensitive to radiation therapy, and thereby identify subjects who
would benefit from administration of radiation therapy having, or
who would benefit from concurrent administration of radiation
therapy and radiation sensitizing chemotherapy. For example, once a
RSI has been determined for a tumor, if the RSI is low and thus
indicates that the tumor is sensitive to radiation (and thus is
likely to be effectively treated with radiation), then a course of
radiation alone can be prescribed for the patient, or radiation and
possibly less invasive surgical removal methods, e.g., laparoscopic
methods. Alternatively, if the RSI is high and thus indicates that
the tumor is less sensitive or is not sensitive to radiation
therapy, then a course of chemotherapy, e.g., radiation sensitizing
chemotherapy, can be prescribed in combination with radiation
therapy, and optionally more invasive or radical surgical
resection. Thus the methods can be used to predict a subject's
response to radiation therapy. In some embodiments, the threshold
for sensitivity using an RSI as defined in esophageal and rectal
cancer as 0.46.
[0053] As one example, the methods can be used for identifying
patients that are likely to be downstaged, particularly in rectal
cancer. For example, this knowledge might lead to better counseling
of patients with low-lying rectal tumors where sphincter-sparing
surgery is being considered, as described above.
[0054] In some embodiments, a subject having a tumor is identified
(methods for diagnosing the presence of a tumor are well known in
the art and need not be repeated herein). A test sample is obtained
from the tumor and the level of signature protein or nucleic acid
(e.g., mRNA) is evaluated, wherein the level of signature protein
or nucleic acid is indicative of the sensitivity of the tumor to
radiation therapy. As used herein, a "test sample" refers to a
biological sample obtained from a subject of interest including a
cell or cells, e.g., tissue, from the tumor.
[0055] The assays described herein can also be used to determine
whether a subject should be administered one or more of radiation
therapy, chemotherapy, or surgical resection to treat a solid
tumor, e.g., to select a therapy or therapeutic regime for a
subject. For example, such methods can be used to determine whether
a subject can be effectively treated with radiation therapy alone
or radiation therapy with a second, non-radiation treatment
modality, e.g., surgery or chemotherapy, or will need radiation,
surgery, and chemotherapy.
[0056] In addition, the methods described herein can be used on
normal cells, i.e., non-tumor cells, to determine their sensitivity
to radiation therapy. This allows the use of the model to predict
the likelihood of radiation therapy-related toxicity or other side
effects.
[0057] The network system models described herein can also be used
to select genes to target for agents, e.g., radiosensitizing or
radioprotective agents. In these methods, the network models are
modified to model the effects of modulating various genes. One such
approach is to simulate the effects of biological targeting of one
or more of the identified network hubs. This type of in silico
perturbation of the developed model can provide additional
information on the hubs most likely to effect radiation phenotype.
The model can be perturbed by systematically reducing (using
computer simulations) the rank or weight of each hub gene to the
lowest possible value, in effect "knocking" the gene out. The
altered expression pattern will be used to predict patient
radiosensitivity using the same model previously constructed.
[0058] Differences from the unaltered SF2 predictions will be
recorded. These changes in SF2 will be examined and averaged over
the entire patient cohort to estimate the impact of individual gene
knockout.
[0059] Radiation therapies, chemotherapies, surgical resection
techniques, and methods that can be used to select specific
therapies appropriate for a given tumor, are known in the art, see,
e.g., "Practical Radiotherapy Planning," Dobbs, Barrett, and Ash
(1999) Arnold; "Walter & Miller's Textbook of Radiotherapy,"
Bomford and Kunkler (2002) Churchill Livingstone; "Cancer
Chemotherapy and Biotherapy: Principles and Practice," Chabner and
Longo (2005) Lippincott Williams & Wilkins; "Regional
Chemotherapy: Theory and Practice," Kerr and McArdle (2000) Informa
Healthcare; and "Textbook of Surgery," Tjandra et al. (2006)
Wiley-Blackwell.
[0060] Assays for Determining Expression Levels
[0061] Any method known in the art for obtaining a sample
comprising at least one living cell (preferably a plurality of
cells), e.g., a cell from a tumor (e.g., from a biopsy), or a
normal cell, or a cultured cell, can be used. Commonly used methods
to obtain tumor cells include surgical (the use of tissue taken
from the tumor after removal of all or part of the tumor) and
needle biopsies. The samples should be treated in any way that
preserves intact the gene expression levels of the living cells as
much as possible, e.g., flash freezing or chemical fixation, e.g.,
formalin fixation.
[0062] Any method known in the art can be used to extract material,
e.g., protein or nucleic acid (e.g., mRNA) from the sample. For
example, mechanical or enzymatic cell disruption can be used,
followed by a solid phase method (e.g., using a column) or
phenol-chloroform extraction, e.g., guanidinium
thiocyanate-phenol-chloroform extraction of the RNA. A number of
kits are commercially available for use in isolation of mRNA.
Purification can also be used if desired. See, e.g., Peirson and
Butler, Methods Mol. Biol. 2007; 362:315-27. A number of methods
are also known in the art to obtain proteins from cells, see, e.g.,
"Protein Methods," 2nd Edition by Bollag et al., Wiley Pub. (1996).
Optionally, cDNA can be transcribed from the mRNA.
[0063] Gene expression levels can be determined in many different
ways, including the quantification of fluorescence of hybridized
mRNA on glass slides, Northern blot analysis, real-time reverse
transcription PCR(RT-PCR) or other measures of gene expression
abundance. Each of these ways provides a different scale, however
each approach is proportional to the abundance of a particular mRNA
transcript.
[0064] A number of assays suitable for the determination of
expression levels of the signature genes in a biological sample are
known in the art. For example, expression levels can be evaluated
by obtaining a biological sample from tumor of a test subject and
contacting the biological sample with a compound or an agent
capable of detecting mRNA for the signature genes, or protein
encoded by the signature genes, such that the level of the protein
or nucleic acid is detected in the biological sample. The term
"biological sample" includes tissues, cells and fluids comprising
cells or tissues isolated from tumor of a subject, as well as
tissues and cells and fluids present within a subject. A preferred
biological sample is a biopsy sample taken from the tumor. The
level of expression of the signature genes can be measured in a
number of ways, including, but not limited to: measuring the mRNA
encoded by the signature genes; measuring the amount of protein
encoded by the signature genes; or measuring the activity of the
protein encoded by the signature genes.
[0065] The level of mRNA corresponding to the signature gene in a
cell can be determined both by in situ and by in vitro formats.
[0066] The isolated mRNA can be used in hybridization or
amplification assays that include, but are not limited to, Southern
or Northern analyses, polymerase chain reaction analyses and probe
arrays. One exemplary diagnostic method for the detection of mRNA
levels involves contacting the isolated mRNA with a nucleic acid
molecule (probe) that can hybridize to the mRNA encoded by the gene
being detected. The nucleic acid probe can be, for example, a
full-length nucleic acid or an oligonucleotide of at least 7, 15,
30, 50, 100, 250 or 500 nucleotides in length and sufficient to
specifically hybridize under stringent conditions to mRNA for a
signature gene. Other suitable probes for use in the diagnostic
assays are known in the art.
[0067] In one format, mRNA (or cDNA) from the sample is immobilized
on a surface and contacted with the probes, for example by running
the isolated mRNA on an agarose gel and transferring the mRNA from
the gel to a membrane, such as nitrocellulose. In an alternative
format, the probes are immobilized on a surface and the mRNA (or
cDNA) from the sample is contacted with the probes, for example, in
a two-dimensional gene chip array. A skilled artisan can adapt
known mRNA detection methods for use in detecting the level of mRNA
encoded by the signature genes.
[0068] The level of mRNA in a sample that is encoded by one of
signature can be evaluated with nucleic acid amplification, e.g.,
by rtPCR (Mullis (1987) U.S. Pat. No. 4,683,202), ligase chain
reaction (Barany (1991) Proc. Natl. Acad. Sci. USA 88:189-193),
self sustained sequence replication (Guatelli et al., (1990) Proc.
Natl. Acad. Sci. USA 87:1874-1878), transcriptional amplification
system (Kwoh et al., (1989), Proc. Natl. Acad. Sci. USA
86:1173-1177), Q-Beta Replicase (Lizardi et al., (1988)
Bio/Technology 6:1197), rolling circle replication (Lizardi et al.,
U.S. Pat. No. 5,854,033) or any other nucleic acid amplification
method, followed by the detection of the amplified molecules using
techniques known in the art. As used herein, amplification primers
are defined as being a pair of nucleic acid molecules that can
anneal to 5' or 3' regions of a gene (plus and minus strands,
respectively, or vice-versa) and contain a short region in between.
In general, amplification primers are from about 10 to 30
nucleotides in length and flank a region from about 50 to 200
nucleotides in length. Under appropriate conditions and with
appropriate reagents, such primers permit the amplification of a
nucleic acid molecule comprising the nucleotide sequence flanked by
the primers.
[0069] A preferred method is the use of microfluidic devices, e.g.,
for high-throughput real time-polymerase chain reaction (RT-PCR),
e.g., as described herein.
[0070] For in situ methods, a cell or tissue sample can be
prepared/processed and immobilized on a support, typically a glass
slide, and then contacted with a probe that can hybridize to mRNA
that encodes the signature gene being analyzed.
[0071] In another embodiment, the methods further contacting a
control sample with a compound or agent capable of detecting
signature mRNA, and comparing the presence of signature mRNA in the
control sample with the presence of signature mRNA in the test
sample.
[0072] A variety of methods can be used to determine the levels of
proteins encoded by the selected signature genes. In general, these
methods include contacting an agent that selectively binds to the
protein, such as an antibody, with a sample, and evaluating the
level of protein in the sample. In a preferred embodiment, the
antibody bears a detectable label. Antibodies can be polyclonal, or
more preferably, monoclonal. An intact antibody, or a fragment
thereof (e.g., Fab or F(ab')2) can be used. The term "labeled,"
with regard to the probe or antibody, is intended to encompass
direct labeling of the probe or antibody by coupling (i.e.,
physically linking) a detectable substance to the probe or
antibody, as well as indirect labeling of the probe or antibody by
reactivity with a detectable substance. Examples of detectable
substances are known in the art, as are methods of quantifying
levels of proteins detected thereby.
[0073] The detection methods can be used to detect signature
protein in a biological sample in vitro as well as in vivo. In
vitro techniques for detection of signature protein include enzyme
linked immunosorbent assays (ELISAs), immunoprecipitations,
immunofluorescence, enzyme immunoassay (EIA), radioimmunoassay
(RIA), and Western blot analysis. In vivo techniques for detection
of signature protein include introducing into a subject a labeled
anti-signature antibody. For example, the antibody can be labeled
with a radioactive marker whose presence and location in a subject
can be detected by standard imaging techniques.
[0074] In another embodiment, the methods further include
contacting a control sample with a compound or agent capable of
detecting signature protein, quantifying the level of signature
protein, and comparing the level of signature protein in the
control sample with the level of signature protein in the test
sample.
[0075] In some embodiments, the sensitivity of a tumor to radiation
therapy can be predicted by determining a gene expression profile
including expression levels for two or more of the signature genes
described herein, and comparing that expression profile to a
reference profile, e.g., a reference profile representing a tumor
that is sensitive to radiation; in that case, substantial
similarity between the reference profile and the profile of
expression from the tumor would indicate that the tumor was
sensitive to radiation. Methods for performing such methods are
known in the art, e.g., as described in U.S. Pat. No.
7,148,008.
[0076] Kits
[0077] The invention also includes kits for detecting and
quantifying the selected signature genes (e.g., mRNA or protein
corresponding to the signature genes) in a biological sample. For
example, the kit can include a compound or agent capable of
detecting mRNA or protein corresponding to the signature genes in a
biological sample; and a standard; and optionally one or more
reagents necessary for performing detection, quantification, or
amplification. The compounds, agents, and/or reagents can be
packaged in a suitable container. The kit can further comprise
instructions for using the kit to detect and quantify signature
protein or nucleic acid.
[0078] For antibody-based kits, the kit can include: (1) a first
antibody (e.g., attached to a solid support) which binds to a
polypeptide corresponding to a signature gene; and, optionally, (2)
a second, different antibody which binds to either the polypeptide
or the first antibody and is conjugated to a detectable agent.
[0079] For oligonucleotide-based kits, the kit can include: (1) an
oligonucleotide, e.g., a detectably labeled oligonucleotide, which
hybridizes to a nucleic acid sequence corresponding to a signature
gene or (2) a pair of primers useful for amplifying a nucleic acid
molecule corresponding to a signature gene. The kit can also
includes a buffering agent, a preservative, and/or a protein
stabilizing agent. The kit can also include components necessary
for detecting the detectable agent (e.g., an enzyme or a
substrate). The kit can also contain a control sample or a series
of control samples which can be assayed and compared to the test
sample contained. Each component of the kit can be enclosed within
an individual container and all of the various containers can be
within a single package, along with instructions for interpreting
the results of the assays performed using the kit.
[0080] In some embodiments, the kits include reagents specific for
the quantification of the signature genes listed in a profile shown
in Table 10, Table 11, Table 12, or FIGS. 4A-4G. In some
embodiments, the kits include primers or antibodies selective for
at least c-jun, STAT1, cAbl, and IRF1. In some embodiments, the
kits include primers or antibodies selective for at least IRF1 and
one additional signature gene. microfluidic devices for RT-PCR. In
some embodiments, the kits also include primers or antibodies
selective for a housekeeping or control gene, e.g., as listed in
FIG. 7.
[0081] Microarrays/Microfluidic Devices
[0082] Also described herein are microarrays useful for detecting
and quantifying levels of mRNA or protein corresponding to the
signature genes. The microarray comprises a substrate and
hybridisable array elements. For the detection of mRNA, the
microarray will include a plurality of individually addressable
areas including hybridizable array elements selective for the
selected signature genes. For the detection of protein, the
microarray will include a plurality of individually addressable
areas including reagents for the detection of one or more proteins
encoded by the signature genes, e.g., antibodies.
[0083] In some embodiments, the microarrays include hybridisable
array elements selective for the signature genes listed in a
profile shown in Table 10, Table 11, Table 12, or FIGS. 4A-4G. In
some embodiments, the microarrays include hybridisable array
elements selective for at least c-jun, STAT1, cAbl, and IRF1. In
some embodiments, the microarrays include hybridisable array
elements selective for at least IRF1 and one additional signature
gene.
[0084] The term "microarray" refers to a substrate having an
ordered arrangement of hybridisable array elements arranged
thereon. In some embodiments, the array elements are arranged so
that there are preferably at least about 10 different array
elements, on a 1 cm.sup.2 substrate surface. The maximum number of
array elements is unlimited, but can be upwards of at least 100,000
array elements. Furthermore, a hybridization signal from each of
the array elements is individually distinguishable. In a preferred
embodiment, the array elements comprise polynucleotide probes.
[0085] Hybridization causes a denatured polynucleotide probe and a
denatured complementary target to form a stable duplex through base
pairing. Hybridization methods are well known to those skilled in
the art (See, e.g., Laboratory Techniques in Biochemistry and
Molecular Biology, Vol. 24: Hybridization With Nucleic Acid Probes,
P. Tijssen, ed. Elsevier Science, New York, N.Y. (1993)).
Conditions can be selected for hybridization where exactly
complementary target and polynucleotide probe can hybridize, i.e.,
each base pair must interact with its complementary base pair.
Alternatively, conditions can be selected where target and
polynucleotide probes have mismatches but are still able to
hybridize. Suitable conditions can be selected, for example, by
varying the concentrations of salt or formamide in the
prehybridization, hybridization and wash solutions, or by varying
the hybridization and wash temperatures.
[0086] Hybridization can be performed at low stringency with
buffers, such as 6.times.SSPE with 0.005% Triton X-100 at
37.degree. C., which permits hybridization between target and
polynucleotide probes that contain some mismatches to form target
polynucleotide/probe complexes. Subsequent washes are performed at
higher stringency with buffers, such as 0.5.times.SSPE with 0.005%
Triton X-100 at 50.degree. C., to retain hybridization of only
those target/probe complexes that contain exactly complementary
sequences. Alternatively, hybridization can be performed with
buffers, such as 5.times.SSC/0.2% SDS at 60.degree. C. and washes
are performed in 2.times.SSC/0.2% SDS and then in 0.1.times.SSC.
Stringency can also be increased by adding agents such as
formamide. Background signals can be reduced by the use of
detergent, such as sodium dodecyl sulfate, Sarcosyl or Triton
X-100, or a blocking agent, such as sperm DNA.
[0087] Hybridization specificity can be evaluated by comparing the
hybridization of specificity-control polynucleotide probes to
specificity-control target polynucleotides that are added to a
sample in a known amount. The specificity-control target
polynucleotides may have one or more sequence mismatches compared
with the corresponding polynucleotide probes. In this manner,
whether only complementary target polynucleotides are hybridizing
to the polynucleotide probes or whether mismatched hybrid duplexes
are forming is determined.
[0088] After hybridization, the microarray is washed to remove
non-hybridized nucleic acids and complex formation between the
hybridisable array elements and the target polynucleotides is
detected.
[0089] Methods for detecting complex formation are known in the
art. In some embodiments, the target polynucleotides are labeled
with a fluorescent label and measurement of levels and patterns of
fluorescence indicative of complex formation is accomplished by
fluorescence microscopy, preferably confocal fluorescence
microscopy.
[0090] An argon ion laser excites the fluorescent label, emissions
are directed to a photomultiplier and the amount of emitted light
detected and quantitated. The detected signal should be
proportional to the amount of probe/target polynucleotide complex
at each position of the microarray. The fluorescence microscope can
be associated with a computer-driven scanner device to generate a
quantitative two-dimensional image of hybridization intensity. The
scanned image is examined to determine the abundance/expression
level of each hybridized target polynucleotide.
[0091] Typically, microarray fluorescence intensities can be
normalized to take into account variations in hybridization
intensities when more than one microarray is used under similar
test conditions. In a preferred embodiment, individual
polynucleotide probe/target complex hybridization intensities are
normalized using the intensities derived from internal
normalization controls contained on each microarray, e.g., control
genes, e.g., ubiquitin C; hydroxymethylbilane synthase; tyrosine
3-monooxygenase/tryptophan 5-monooxygenase activation protein, zeta
polypeptide; polymerase (RNA) II (DNA directed) polypeptide A, 220
kDa; importin 8; hypoxanthine phosphoribosyltransferase 1
(Lesch-Nyhan syndrome); ribosomal protein, large PO (RLP0);
peptidylprolyl isomerase A (cyclophilin A); beta actin; beta
glucuronidase; beta-2-microglobulin; phosphoglycerate kinase 1;
glyceraldehyde-3-phosphate dehydrogenase; transferrin receptor
(p90; CD71); TATA box binding protein; subunit A of the succinate
dehydrogenase complex; and the 18s ribosomal RNA (see FIG. 7).
[0092] The microarrays described herein include (or consist of)
individually addressable hybridisable array elements selective for
the signature genes as described herein, or a subset thereof listed
in any of Tables 10-12 or FIGS. 4A-4G. In some embodiments, the
microarrays also include one or more hybridisable array elements
selective for an internal normalization control, e.g., as described
herein. In some embodiments, the microarrays do not include
hybridisable array elements selective for other genes.
[0093] In some embodiments, a microfluidic
RT-PCR/paraffin-preserved tissue platform can be used. There are
several advantages to the use of this platform. First, this
platform is practical to use for routine diagnostic application.
For example, OncotypeDX, a multi-gene model for risk assessment in
breast cancer is in an RT-PCR/paraffin-preserved tissue platform.
Further, the RT-PCR approach is cost-efficient. In addition, a
standardized and optimized test could readily be tested in banked
tissue (paraffin-preserved), e.g., from cooperative group trials
(e.g., the Radiation Therapy Oncology Group (RTOG)), for
development into routine clinical use.
[0094] Custom-design microfluidics cards can be obtained, e.g.,
from Applied Biosystems (ABI), that include all 10 genes in the hub
model, or a subset thereof (e.g., as described herein), along with
the standard 16 housekeeping genes recommended by ABI, or a subset
thereof. Additional genes, e.g., 20, 30, 40, 50, 60, 70, 80, 90, or
100 genes, e.g., genes selected from the larger network model
consisting of 500 genes (listed in FIGS. 6A-6P), can also be
included in the cards. In some embodiments, all genes will be
present at least in duplicate, e.g., in duplicate or triplicate,
enabling the analysis of two or more samples per card. As one
example, if an ABI card is used, the microarray probeset targets
identified using a method described herein will be sequence-aligned
with the ABI probeset to determine the ABI probes closest to the
microarray target.
[0095] As one example, the microfluidics assays can be performed
substantially as follows. Briefly, THE ABI TAQMAN LOW DENSITY ARRAY
(TLDA) cards profile gene expression using the Comparative CT
Method of relative quantification. Each card consists of a series
of 384 interconnected wells divided into eight sets of assays. Each
well contains dried Applied Biosystems TaqMan primers and probes
for one mRNA target. Each of the 8 ports of the card is loaded with
100 .mu.L of sample-specific PCR mix (Each 100-.mu.L PCR mix should
contain 1 ng to 100 ng of total RNA converted to cDNA. Once the
card is loaded with samples, it is centrifuged to distribute the
PCR mix throughout the 48 wells of each port. The TLDA card is run
in the AB 7900HT using relative quantification analysis.
[0096] General methods for making and using microfluidic devices
are known in the art, see, e.g., U.S. Pat. Nos. 6,960,437 and
7,250,260.
[0097] Databases
[0098] In another aspect, the invention features a database
comprising a plurality of records. Each record includes data on the
expression of at least two signature genes in a tumor cell, and at
least one, two or preferably all of the following: data on tissue
of origin of the cell; data on ras status of the cell; and data on
p53 status of the cell, and optionally data on a preselected factor
relating to a subject who has the tumor. In some embodiments, the
preselected factor can be one or more of: the presence of a
treatment (e.g., the administration of a compound, e.g., a drug
(e.g., chemotherapy), vitamin, food or dietary supplement); the
presence of an environmental factor (e.g., the presence of a
substance in the environment); the presence of a genetic factor or
physical factor such as age.
[0099] In some embodiments, the database includes at least two
records, and the preselected factor in each of the records differs
form the other record. For example, in one embodiment, the
preselected factor can be administration of a compound and in one
record the preselected factor includes administration of the
compound and in the other record the compound is not administered,
is administered at a different dose and/or a different compound is
administered. In another embodiment, the preselected factor can be
an environmental factor and in one record the factor is present and
in the other record the environmental factor is not present or is
present at a different level. In yet another embodiment, the
preselected factor can be a physical factor such as age and the age
in one record varies from the age in the other record, e.g., a
difference in age of at least 5, 10, 15, 20 years or more.
[0100] In some embodiments, each record of the database includes
data on at least two preselected factors relating to the subject.
In one embodiment, the database includes at least two records, and
at least one preselected factor in each of the records differs from
the other record. Preferably, the database includes at least two
records and at least one preselected factor in the records differ
and at least one of the other preselected factors is the same. In
other embodiments, the database can include at least two records
and each record includes at least one preselected factor and at
least one preselected condition.
[0101] In some embodiments, the database includes at least two
records, wherein each record includes information regarding a cell
including expression levels of a subset or all of the 10 signature
genes as described herein, dose of radiation administered, and
survival fraction in response to that dose of radiation (e.g., for
a dose of 2 Gy, the survival fraction is referred to as SF2).
[0102] The database can be any kind of storage system capable of
storing the various data for each of the records as described
herein. For example, the database may be a flat file, a relational
database, a table in a database, an object in a computer readable
volatile or non-volatile memory, data accessible by computer
program, such as data stored in a resource fork of an application
program file on a computer readable storage medium. Preferably, the
database is in a computer readable medium (e.g., a computer memory
or storage device).
[0103] In some embodiments, each record can further include data on
the expression of at least one internal control gene, e.g., as
listed in FIG. 7.
[0104] The information obtained by evaluating the efficacy of
radiation therapy in treating a tumor can also be used to evaluate
the effects that various factors and conditions, e.g.,
environmental conditions, can have on tumor treatment. In some
embodiments, the information can be stored in a database as
described herein.
[0105] In another aspect, the invention features a method of
evaluating the likelihood that radiation therapy will be effective
in treating a tumor, using a database as described herein.
[0106] The database can be any kind of storage system capable of
storing various data for each of the records as described herein.
In preferred embodiments, the database is a computer medium having
a plurality of digitally encoded data records. The data record can
be structured as a table, e.g., a table that is part of a database
such as a relational database (e.g., a SQL database of the Oracle
or Sybase database environments).
[0107] As used herein, "machine-readable media" refers to any
medium that can be read and accessed directly by a machine, e.g., a
digital computer or analogue computer. Non-limiting examples of a
computer include a desktop PC, laptop, mainframe, server (e.g., a
web server, network server, or server farm), handheld digital
assistant, pager, mobile telephone, and the like. The computer can
be stand-alone or connected to a communications network, e.g., a
local area network (such as a VPN or intranet), a wide area network
(e.g., an Extranet or the Internet), or a telephone network (e.g.,
a wireless, DSL, or ISDN network). Machine-readable media include,
but are not limited to:
[0108] magnetic storage media, such as floppy discs, hard disc
storage medium, and magnetic tape; optical storage media such as
CD-ROM; electrical storage media such as RAM, ROM, EPROM, EEPROM,
flash memory, and the like; and hybrids of these categories such as
magnetic/optical storage media.
[0109] A variety of data storage structures are available to a
skilled artisan for creating a machine-readable medium having
recorded thereon the data described herein. The choice of the data
storage structure will generally be based on the means chosen to
access the stored information. In addition, a variety of data
processor programs and formats can be used to store the information
of the present invention on computer readable medium.
[0110] Computer Software/Hardware
[0111] FIG. 5 is a block diagram of computing devices and systems
700, 750 that may be used and implemented to perform operations
associated with the audio file toolbox 404. Computing device 700 is
intended to represent various forms of digital computers, such as
laptops, desktops, workstations, personal digital assistants,
servers, blade servers, mainframes, and other appropriate
computers. Computing device 750 is intended to represent various
forms of mobile devices, such as personal digital assistants,
cellular telephones, smartphones, and other similar computing
devices. The components shown here, their connections and
relationships, and their functions, are meant to be exemplary only,
and are not meant to limit implementations of the inventions
described and/or claimed in this document.
[0112] Computing device 700 includes a processor 702, memory 704, a
storage device 706, a high-speed interface 708 connecting to memory
704 and high-speed expansion ports 710, and a low speed interface
712 connecting to low speed bus 714 and storage device 706. Each of
the components 702, 704, 706, 708, 710, and 712, are interconnected
using various busses, and can be mounted on a common motherboard or
in other manners as appropriate. The processor 702 can process
instructions for execution within the computing device 700,
including instructions stored in the memory 704 or on the storage
device 706 to display graphical information for a GUI on an
external input/output device, such as display 716 coupled to high
speed interface 708. In other implementations, multiple processors
and/or multiple buses can be used, as appropriate, along with
multiple memories and types of memory. Also, multiple computing
devices 700 can be connected, with each device providing portions
of the operations (e.g., as a server bank, a group of blade
servers, or a multi-processor system).
[0113] The memory 704 stores information within the computing
device 700. In one implementation, the memory 704 is a
computer-readable medium. In one implementation, the memory 704 is
a volatile memory unit or units. In another implementation, the
memory 704 is a non-volatile memory unit or units.
[0114] The storage device 706 is capable of providing mass storage
for the computing device 700. In one implementation, the storage
device 706 is a computer-readable medium. In various different
implementations, the storage device 706 can be a floppy disk
device, a hard disk device, an optical disk device, or a tape
device, a flash memory or other similar solid state memory device,
or an array of devices, including devices in a storage area network
or other configurations. In one implementation, a computer program
product is tangibly embodied in an information carrier. The
computer program product contains instructions that, when executed,
perform one or more methods, such as those described above. The
information carrier is a computer- or machine-readable medium, such
as the memory 704, the storage device 706, memory on processor 702,
or a propagated signal.
[0115] The high speed controller 708 manages bandwidth-intensive
operations for the computing device 700, while the low speed
controller 712 manages lower bandwidth-intensive operations. Such
allocation of duties is exemplary only. In one implementation, the
high-speed controller 708 is coupled to memory 707, display 716
(e.g., through a graphics processor or accelerator), and to
high-speed expansion ports 710, which can accept various expansion
cards (not shown). In the implementation, low-speed controller 712
is coupled to storage device 706 and low-speed expansion port 714.
The low-speed expansion port, which can include various
communication ports (e.g., USB, Bluetooth, Ethernet, wireless
Ethernet) can be coupled to one or more input/output devices, such
as a keyboard, a pointing device, a scanner, or a networking device
such as a switch or router, e.g., through a network adapter.
[0116] The computing device 700 can be implemented in a number of
different forms, as shown in the figure. For example, it can be
implemented as a standard server 720, or multiple times in a group
of such servers. It can also be implemented as part of a rack
server system 724. In addition, it can be implemented in a personal
computer such as a laptop computer 722. Alternatively, components
from computing device 700 can be combined with other components in
a mobile device (not shown), such as device 750. Each of such
devices can contain one or more of computing device 700, 750, and
an entire system can be made up of multiple computing devices 700,
750 communicating with each other.
[0117] Computing device 750 includes a processor 752, memory 764,
an input/output device such as a display 754, a communication
interface 766, and a transceiver 768, among other components. The
device 750 can also be provided with a storage device, such as a
microdrive or other device, to provide additional storage. Each of
the components 750, 752, 764, 754, 766, and 768, are interconnected
using various buses, and several of the components can be mounted
on a common motherboard or in other manners as appropriate.
[0118] The processor 752 can process instructions for execution
within the computing device 750, including instructions stored in
the memory 764. The processor can also include separate analog and
digital processors. The processor can provide, for example, for
coordination of the other components of the device 750, such as
control of user interfaces, applications run by device 750, and
wireless communication by device 750.
[0119] Processor 752 can communicate with a user through control
interface 758 and display interface 756 coupled to a display 754.
The display 754 can be, for example, a TFT LCD display or an OLED
display, or other appropriate display technology. The display
interface 756 can comprise appropriate circuitry for driving the
display 754 to present graphical and other information to a user.
The control interface 758 can receive commands from a user and
convert them for submission to the processor 752. In addition, an
external interface 762 can be provide in communication with
processor 752, so as to enable near area communication of device
750 with other devices. External interface 762 can provide, for
example, for wired communication (e.g., via a docking procedure) or
for wireless communication (e.g., via Bluetooth or other such
technologies).
[0120] The memory 764 stores information within the computing
device 750. In one implementation, the memory 764 is a
computer-readable medium. In one implementation, the memory 764 is
a volatile memory unit or units. In another implementation, the
memory 764 is a non-volatile memory unit or units. Expansion memory
774 can also be provided and connected to device 750 through
expansion interface 772, which can include, for example, a SIMM
card interface. Such expansion memory 774 can provide extra storage
space for device 750, or can also store applications or other
information for device 750. Specifically, expansion memory 774 can
include instructions to carry out or supplement the processes
described above, and can include secure information also. Thus, for
example, expansion memory 774 can be provide as a security module
for device 750, and can be programmed with instructions that permit
secure use of device 750. In addition, secure applications can be
provided via the SIMM cards, along with additional information,
such as placing identifying information on the SIMM card in a
non-hackable manner.
[0121] The memory can include for example, flash memory and/or MRAM
memory, as discussed below. In one implementation, a computer
program product is tangibly embodied in an information carrier. The
computer program product contains instructions that, when executed,
perform one or more methods, such as those described above. The
information carrier is a computer- or machine-readable medium, such
as the memory 764, expansion memory 774, memory on processor 752,
or a propagated signal.
[0122] Device 750 can communicate wirelessly through communication
interface 766, which can include digital signal processing
circuitry where necessary. Communication interface 766 can provide
for communications under various modes or protocols, such as GSM
voice calls, SMS, EMS, or MMS messaging, CDMA, TDMA, PDC, WCDMA,
CDMA2000, or GPRS, among others. Such communication can occur, for
example, through radio-frequency transceiver 768. In addition,
short-range communication can occur, such as using a Bluetooth,
WiFi, or other such transceiver (not shown). In addition, GPS
receiver module 770 can provide additional wireless data to device
750, which can be used as appropriate by applications running on
device 750.
[0123] Device 750 can also communication audibly using audio codec
760, which can receive spoken information from a user and convert
it to usable digital information. Audio codex 760 can likewise
generate audible sound for a user, such as through a speaker, e.g.,
in a handset of device 750. Such sound can include sound from voice
telephone calls, can include recorded sound (e.g., voice messages,
music files, etc.) and can also include sound generated by
applications operating on device 750.
[0124] The computing device 750 can be implemented in a number of
different forms, as shown in the figure. For example, it can be
implemented as a cellular telephone 780. It can also be implemented
as part of a smartphone 782, personal digital assistant, or other
similar mobile device.
[0125] Where appropriate, the systems and the functional operations
described in this specification can be implemented in digital
electronic circuitry, or in computer software, firmware, or
hardware, including the structural means disclosed in this
specification and structural equivalents thereof, or in
combinations of them. The techniques can be implemented as one or
more computer program products, i.e., one or more computer programs
tangibly embodied in an information carrier, e.g., in a machine
readable storage device or in a propagated signal, for execution
by, or to control the operation of, data processing apparatus,
e.g., a programmable processor, a computer, or multiple computers.
A computer program (also known as a program, software, software
application, or code) can be written in any form of programming
language, including compiled or interpreted languages, and it can
be deployed in any form, including as a stand alone program or as a
module, component, subroutine, or other unit suitable for use in a
computing environment. A computer program does not necessarily
correspond to a file. A program can be stored in a portion of a
file that holds other programs or data, in a single file dedicated
to the program in question, or in multiple coordinated files (e.g.,
files that store one or more modules, sub programs, or portions of
code). A computer program can be deployed to be executed on one
computer or on multiple computers at one site or distributed across
multiple sites and interconnected by a communication network.
[0126] The processes and logic flows described in this
specification can be performed by one or more programmable
processors executing one or more computer programs to perform the
described functions by operating on input data and generating
output. The processes and logic flows can also be performed by, and
apparatus can be implemented as, special purpose logic circuitry,
e.g., an FPGA (field programmable gate array) or an ASIC
(application specific integrated circuit).
[0127] Processors suitable for the execution of a computer program
include, by way of example, both general and special purpose
microprocessors, and any one or more processors of any kind of
digital computer. Generally, the processor will receive
instructions and data from a read only memory or a random access
memory or both. The essential elements of a computer are a
processor for executing instructions and one or more memory devices
for storing instructions and data. Generally, a computer will also
include, or be operatively coupled to receive data from or transfer
data to, or both, one or more mass storage devices for storing
data, e.g., magnetic, magneto optical disks, or optical disks.
Information carriers suitable for embodying computer program
instructions and data include all forms of non volatile memory,
including by way of example semiconductor memory devices, e.g.,
EPROM, EEPROM, and flash memory devices; magnetic disks, e.g.,
internal hard disks or removable disks; magneto optical disks; and
CD ROM and DVD-ROM disks. The processor and the memory can be
supplemented by, or incorporated in, special purpose logic
circuitry.
[0128] To provide for interaction with a user, aspects of the
described techniques can be implemented on a computer having a
display device, e.g., a CRT (cathode ray tube) or LCD (liquid
crystal display) monitor, for displaying information to the user
and a keyboard and a pointing device, e.g., a mouse or a trackball,
by which the user can provide input to the computer. Other kinds of
devices can be used to provide for interaction with a user as well;
for example, feedback provided to the user can be any form of
sensory feedback, e.g., visual feedback, auditory feedback, or
tactile feedback; and input from the user can be received in any
form, including acoustic, speech, or tactile input.
[0129] The techniques can be implemented in a computing system that
includes a back-end component, e.g., as a data server, or that
includes a middleware component, e.g., an application server, or
that includes a front-end component, e.g., a client computer having
a graphical user interface or a Web browser through which a user
can interact with an implementation, or any combination of such
back-end, middleware, or front-end components. The components of
the system can be interconnected by any form or medium of digital
data communication, e.g., a communication network. Examples of
communication networks include a local area network ("LAN") and a
wide area network ("WAN"), e.g., the Internet.
[0130] The computing system can include clients and servers. A
client and server are generally remote from each other and
typically interact through a communication network. The
relationship of client and server arises by virtue of computer
programs running on the respective computers and having a
client-server relationship to each other. One computer-implemented
modeling algorithm is described herein (namely, the linear and
quadratic analysis), although such algorithms themselves are
generally outside the scope of the present invention. Other
software-based modeling algorithms can also be utilized, alone or
in combination, such as the classification or decision trees,
support vector machines or neural networks.
EXAMPLES
[0131] The invention is further described in the following
examples, which do not limit the scope of the invention described
in the claims.
Example 1
A Radiosensitivity Systems Model Captures Central Regulatory
Pathways in Radiation Response
[0132] The model used in the methods described herein was developed
in 48 cancer cell lines from the NCl panel of 60 (listed in Table
1). Radiosensitivity measurements (as determined by clonogenic
survival at 2 Gy, SF2) were either determined using known methods
(Gupta et al., Cancer Res 2001; 61:4278-82; Torres-Roca et al.,
Cancer Res 2005; 65(16):7169-76) (25 cell lines) or obtained from
the literature (23 cell lines). SF2 results for each cell line are
presented in Table 1.
TABLE-US-00002 TABLE 1 48 cell lines and measured SF2 values. Cell
Line Recorded SF2 BREAST_HS578T 0.79 BREAST_MDAMB231 0.82
COLON_HCT116 0.38 COLON_HCT15 0.4 COLON_SW620 0.62 LEUK_CCRFCEM
0.185 LEUK_HL60 0.315 LEUK_MOLT4 0.05 MELAN_SKMEL2 0.66
NSCLC_A549ATCC 0.61 NSCLC_H460 0.84 NSCLC_HOP62 0.164 NSCLC_NCIH23
0.086 OVAR_OVCAR5 0.408 RENAL_SN12C 0.62 BREAST_BT549 0.632
BREAST_MCF7 0.576 BREAST_MDAMB435 0.1795 BREAST_T47D 0.52 CNS_SF268
0.45 CNS_SF539 0.82 CNS_SNB19 0.43 CNS_SNB75 0.55 CNS_U251 0.57
COLON_COLO205 0.69 COLON_HCC-2998 0.44 COLON_HT29 0.79 COLON_KM12
0.42 MELAN_LOXIMVI 0.68 MELAN_M14 0.42 MELAN_MALME3M 0.8
MELAN_SKMEL28 0.74 MELAN_SKMEL5 0.72 MELAN_UACC257 0.48
MELAN_UACC62 0.52 NSCLC_EKVX 0.7 NSCLC_HOP92 0.43 OVAR_OVCAR3 0.55
OVAR_OVCAR4 0.29 OVAR_OVCAR8 0.6 OVAR_SKOV3 0.9 PROSTATE_DU145 0.52
PROSTATE_PC3 0.484 RENAL_7860 0.66 RENAL_A498 0.61 RENAL_ACHN 0.72
RENAL_CAKI1 0.37 RENAL_UO31 0.62
[0133] Gene expression profiles for all cell lines at baseline were
from Affymetrix HU6800 chips (7,129 genes) from a previously
published study (Staunton et al., Proc Natl Acad Sci USA 2001;
98(19):10787-92). These are publicly available as supplemental data
to the published study (Staunton et al., 2001). The gene expression
data had been previously preprocessed using the Affymetrix MAS 4.0
algorithm in average difference units. Negative expression values
were set to zero and the chips were normalized to the same mean
intensity.
[0134] From the total of 7,129, a subset of genes of interest was
selected by a linear regression algorithm where radiosensitivity
was modeled based on survival fraction at 2 Gy (SF2) in the 48
cancer cell line database. Gene expression profiles and SF2 for all
cell lines in the database had been previously determined, as
described above.
[0135] A general linear model was created for each gene in the cell
line dataset to model the SF2 values. Independent variables used
within the linear model were gene expression, p53 mutation status
(17 lines were wt, 31 were mutant), ras mutation status (33 wt, 15
mutant) and tissue of origin (TO), see Tables 2 and 3 for
additional details on the numbers of cell lines within each
category. Tissue of origin, p53 mutation and ras mutation were
coded using "dummy" variables (0/1).
TABLE-US-00003 TABLE 2 Cell line characteristics for TO (Tissue of
Origin) Number of Tissue of Origin Cell lines Melanoma 8 Colon 7
Breast 6 Renal 6 Non-Small Cell Carcinoma (NSCLC) 6 CNS 5 Ovarian 5
Leukemia 3 Prostate 2 TOTAL 48
TABLE-US-00004 TABLE 3 Cell line characteristics by each biological
variable. Tissue of Origin ras wt ras mut p53 wt Renal 5 0 Breast 0
0 CNS 0 0 Colon 0 0 Leukemia 0 0 Melanoma 4 0 NSCLC 2 0 Ovarian 4 0
Prostate 2 0 P53 mut Renal 0 1 Breast 4 2 CNS 5 0 Colon 4 3
Leukemia 0 3 Melanoma 3 1 NSCLC 0 4 Ovarian 0 1 Prostate 0 0
[0136] The linear model format initially considered all terms (9
TO, ras wt/mut, p53 wt/mut) and 2-, 3- and 4-way interactions among
these terms. Without accounting for linearly dependent terms, there
are 180 terms total, far more than the number of observations
(i.e., 48). These include an intercept, 14 terms involving a single
variable (gene expression, 9 TO, 2 p53, 2 ras), 53 paired terms, 76
triples and 36 terms with four variables interacting. However, the
number of non-singular terms was far less due to the sample size
(Table 3) and linearly dependent variables (typically interactions
with no effect) are dropped from the model. Interactions of larger
numbers of variables were dropped in favor of fewer in the case of
linearly dependent variables. Thus there are only 29 terms in the
linear model (an intercept, gene expression, 9 TO, p53, ras, 15
two-way interactions and 2 three-way interactions). When
considering biological states, the intercept was not used thus
producing 28 biological states. This model is expected to overfit
the data significantly; however the model was used to describe the
relationships in the data in an exploratory fashion as opposed to
statistically determining a significant relationship.
TABLE-US-00005 TABLE 4 Terms used in linear modeling. The term (y)
represents gene expression. The operator x represents an
interaction term between two or more variables. Terms y (Gene
expression ) TissueTypeBREAST TissueTypeCNS TissueTypeCOLON
TissueTypeLEUK TissueTypeMELAN TissueTypeNSCLC TissueTypeOVAR
TissueTypePROSTATE RASmut P53mut y x TissueTypeBREAST y x
TissueTypeCNS y x TissueTypeCOLON y x TissueTypeLEUK y x
TissueTypeMELAN y x TissueTypeNSCLC y x TissueTypeOVAR y x
TissueTypePROSTATE y x RASmut y x P53mut TissueTypeBREAST x RASmut
TissueTypeCOLON x RASmut TissueTypeMELAN x RASmut TissueTypeNSCLC x
RASmut TissueTypeOVAR x RASmut y x TissueTypeBREAST x RASmut y x
TissueTypeCOLON x RASmut
[0137] A model based on the description above was constructed for
each gene in the dataset using a least-squares fit. The best
fitting genes were selected, as measured by the sum of squares of
residuals. The gene-based linear models were compared to the fit
expected simply from the biological characteristics (tissue of
origin, ras status (mut vs. wild-type) and p53 status (mut vs.
wild-type)). This simpler model used 28 terms and resulted in a sum
of squared error of residuals of 1.208211.
[0138] The Resulting Model:
SF2=k.sub.0+k.sub.1(y.sub.x)+k.sub.2(TO)+k.sub.3(ras
status)+k.sub.4(p53
status)+k.sub.5(y.sub.x)(TO)+k.sub.6(y.sub.x)(ras
status)+k.sub.7(TO)(ras status)+k.sub.8(y.sub.x)(p53
status)+k.sub.9(TO)(p53)+k.sub.10(ras status)(p53
status)+k.sub.11(y.sub.x)(TO)(ras status)+k.sub.12(y.sub.x)(ras
status)(p53 status)+k.sub.13(TO)(ras status)(p53
status)+k.sub.14(y.sub.x)(TO)(ras status)(p53 status)
[0139] 500 gene-based models were chosen (threshold ssq=0.5416959)
corresponding to at most 45% of the sum squared error from the
biological characteristics model. The 500 genes, which are listed
in FIGS. 6A-6P, represent 7% of the total number of probesets on
the chip.
[0140] Next, pathway analysis was performed to examine the
biological significance of the genes identified. 500 probesets
representing these genes were loaded into GeneGO MetaCore software
(GeneGO, Encinitas, Calif.) and analyzed for significant
over-representation in various pathways; the primary edges
(interconnections) were plotted using literature-based annotations
and the model was reduced by identifying all genes (network hubs)
with more than 5 edges and less than 50% of edges hidden within the
network. 485 probesets were recognized in GeneGO.
[0141] Hubs within a gene network were defined using the GeneGO.TM.
software as nodes consisting of more than 5 connections and less
than 50% of the edges hidden within the network. Table 5 details
all defined hubs within the radiation response network together
with the number of edges and the number of hidden edges (along with
the probesets used on each platform for each of the hubs).
TABLE-US-00006 TABLE 5 Radiation network hub genes. Gene Number of
Number of HU6800 U133Plus Gene Name Number Edges Hidden Edges
Probeset Probeset Androgen 1. 19 0 M23263_at 211110_s_at receptor
c-Jun 2. 19 4 J04111_at 201466_s_at STAT1 3. 15 1 AFFX- AFFX-
HUMISGF3A/ HUMISGF3A/ M97935_MA_at M97935_MA_at PKC 4. 14 4
X06318_at 207957_s_at RelA (p65) 5. 14 2 U33838_at 201783_s_at
c-Abl 6. 13 0 X16416_at 202123_s_at SUMO-1 7. 13 0 U83117_at
208762_at CDK1 (p34) 8. 11 3 U24153_at 205962_at HDAC 9. 10 0
D50405_at 201209_at Integrin 10. 7 4 IRF1 11. 7 0 L05072_s_at
202531_at PKC-beta 12. 6 5 Caspase-8 13. 5 0 CDC25C 14. 5 4 Cyclin
D1 15. 5 0 FasR (CD95) 16. 5 0 Galpha(g)-specific 17. 5 5 peptide
GPCRs HES1 18. 5 0
[0142] The Gather program (Gene Annotation Tool to Help Explain
Relationships (Chang and Nevins, Bioinformatics 22(23):2926-2933,
2006) tool was used to identify significant relationships of terms
from the 10 genes. A threshold of p<0.005 was used as a
cutoff.
[0143] All hubs with more than 5 connections and less than 50% of
edges hidden within the network were selected as the major hubs for
classification purposes. Genes listed in Table 6 were selected. The
probes used on each platform (Affymetrix HU6800, HG U133Plus 2.0
and NKI cDNA arrays) are also listed in Table 3. Matches were
identified via sequence similarity to the original HU6800
platform.
[0144] Table 6 shows the ten "hub" genes on whose expression the
radiosensitivity model is built. These genes are also referred to
herein as "signature" genes.
TABLE-US-00007 TABLE 6 Radiation network hub (signature) genes.
HU6800 U133Plus NKI Gene Name Probeset Probeset Reporter Androgen
receptor M23263_at 211110_s_at 324293 c-Jun J04111_at 201466_s_at
329987 STAT1 AFFX- AFFX- 308421 HUMISGF3A/ HUMISGF3A/ M97935_MA_at
M97935_MA_at PKC X06318_at 207957_s_at 322907 RelA (p65) U33838_at
201783_s_at 326475 c-Abl X16416_at 202123_s_at 304192 SUMO-1
U83117_at 208762_at 308596 CDK1 (p34) U24153_at 205962_at 332859
HDAC1 D50405_at 201209_at 308690 IRF1 L05072_s_at 202531_at
310653
[0145] The selected genes are biologically important, as they have
been reported to be involved in regulating radiation signaling
(Deng et al., Nat Genet. 2004; 36(8):906-12; Hallahan et al.,
International Journal of Radiation Oncology*Biology*Physics 1996;
36(2):355-60; Kao et al., J Biol Chem 1999; 274(49):34779-84; Li
and Karin, PNAS 1998; 95(22):13012-7; Liu et al., Molecular Cell
2006; 21(4):467-80; Marten Fryknas et al., International Journal of
Cancer 2007; 120(1):189-95; Nakajima et al., Radiat Res 2004;
161(5):528-34; Pamment et al., Oncogene 2002; 21(51):7776-85;
Terzoudi et al., Int Radiat Biol 2000; 76(5):607-15; Wang et al.,
Nucleic Acids Res 2005; 33(13):4023-34). In addition, 7/10 (HDAC1,
PKC-beta, NFKB, c-Abl, STAT1, AR, CDK1) have been studied as
targets for radiosensitizer development (Wang et al., Nucleic Acids
Res 2005; 33(13):4023-34; Russell et al., Cancer Res 2003;
63(21):7377-83; Ma et al., J Clin Oncol 2003; 21(14):2760-76; Cerna
et al., Current topics in developmental biology 2006; 73:173-204;
Milas et al., Head & Neck 2003; 25(2):152-67, Kaminski et al.,
Int J Radiat Oncol Biol Phys 2003; 57(1):24-8). Furthermore, the
Gene Ontology (GO) terms captured by the 10 gene systems model,
include DNA damage response, histone deacetylation, regulation of
cell cycle, apoptosis and proliferation, all of which play an
important role in radiation response (Marples et al., Int J Radiat
Oncol Biol Phys 2008; 70(5):1310-8; Chinnaiyan et al., Int J Radiat
Oncol Biol Phys 2005; 62(1):223-9) Lindsay et al., Br J. Radiol.
2007 September; 80 Spec No 1:S2-6; Ma et al., 2003, supra). One
notable exception includes hypoxia (Moeller et al., Cancer
Metastasis Rev 2007; 26(2):241-8). However, since the analysis is
based on data generated in normoxic conditions, we would not expect
the model to capture hypoxia-related genes. In summary, the systems
model captures central pathways and genes involved in regulating
radiosensitivity.
[0146] Eight different cell lines were used to validate the
importance of c-jun in the systems model. In each experiment, a
pooled siRNA and c-jun siRNA experiment were performed several
times (i.e. replicates). One estimate of the impact, per cell line,
of c-Jun knockdown is a Wilcoxon signed-rank test between the
experiment and control that were run together. Table 7 represents
the characteristics of the experiments performed, including the
tissue of origin, number of times the experiment was performed,
mean values (with standard deviation) and a p-value testing
differences between mean values (Wilcoxon signed-rank test for cell
line replicate experiments).
TABLE-US-00008 TABLE 7 Individual cell line siRNA experiments. SF2
(Mean .+-. standard Tissue deviation) of Origin Cell Line n siRNA
pool vs. c-Jun siRNA p-value Lung A549 5 0.52 .+-. 0.13; 0.71 .+-.
0.11 0.062 Cancer H460 9 0.50 .+-. 0.06; 0.60 .+-. 0.08 0.004 Hop62
8 0.41 .+-. 0.16; 0.50 .+-. 0.18 0.039 Colon HCT116 7 0.23 .+-.
0.05; 0.30 .+-. 0.06 0.016 Cancer HCT15 7 0.59 .+-. 0.09; 0.66 .+-.
0.13 0.156 HT29 5 0.85 .+-. 0.21; 0.96 .+-. 0.31 0.312 Breast Hs578
10 0.62 .+-. 0.09; 0.61 .+-. 0.07 0.770 Cancer MDA231 6 0.61 .+-.
0.09; 0.67 .+-. 0.11 0.156
[0147] These results demonstrate that at least one of the genes in
the model, c-jun, is mechanistically involved in the cellular
response to radiation.
Example 2
Development of a Radiosensitivity Predictive Model Based on the
Systems Model
[0148] A linear regression algorithm to predict radiosensitivity
was developed and optimized using gene expression of the 10 genes
in the systems model.
[0149] Translation of the model to other datasets was an important
requirement, therefore the hubs were assigned ranks by expression
(gene expression data for the 10 identified genes were rank
ordered, so that lowest expression among the ten genes was ranked
1; HDAC gene expression was generally the highest of the ten genes
and therefore was often ranked tenth) and the linear regression
model was built from ranks (instead of absolute expression) (Xu et
al., BMC Bioinformatics 2008; 9:125) based on expression levels in
the 48 cell lines using the R statistical software package
(available on the world wide web at r-project.org). The model
predicts a continuous radiosensitivity index (RSI) that is based on
the survival fraction at a given dose, measured for the cell lines
in the database. Thus, the radiosensitivity index is directly
proportional to radioresistance (high index=radioresistance). Since
the 10 hubs were selected from the cell line data, cross-validation
of this linear regression model would yield optimistically-biased
estimates of accuracy. As a result, additional datasets were used
for validation.
[0150] A general model based on expression of all 10 hub genes is
as follows, including the weighting coefficient for each term
representing expression levels of the recited gene:
RSI=k.sub.1*AR+k.sub.2*c-jun+k.sub.3*STAT1+k.sub.4*PKC+k.sub.5*RelA+k.su-
b.6*cAbl+
k.sub.7*SUMO1+k.sub.8*CDK1+k.sub.9*HDAC+k.sub.10*IRF1 I
[0151] The rank-based linear regression equation for determining
RSI at a dose of 2 Gy identified using the present methods was the
following:
RSI=-0.0098009*AR+0.0128283*c-jun+0.0254552*STAT1-0.0017589*PKC-0.003817-
1*Re1A+0.1070213*cABL-0.0002509*SUMO1-0.0092431*CDK1-0.0204469*HDAC-0.0441-
683*IRF1 II
Example 3
The Radiosensitivity Model Predicts Pathological Response to
Chemoradiation in Rectal and Esophageal Cancer
[0152] The regression model developed as described in Examples 1-2
was then applied to similarly rank-ordered patient data to generate
a Radiation Sensitivity Index (RSI).
[0153] The model was applied to the prediction of clinical response
to concurrent radiochemotherapy in two independent
prospectively-collected pilot cohorts of patients with rectal
(n=14) and esophageal cancer (n=12). Pathological response was
defined by T stage criteria (see methods).
[0154] The Rectal Cancer Cohort consisted of 14 patients enrolled
in an IRB-approved prospective Phase 1 trial evaluating escalating
doses of oral topotecan as a radiosensitizing agent in patients
with rectal cancer. Informed consent was obtained for all patients
prior to enrollment. The eligibility criteria included patients
with histologically-confirmed rectal cancer with a primary tumor at
least 3 cm in size and a clinical stage of T-T.sub.4. An ECOG
performance status of 2 or less was required as well as a life
expectancy of more than 3 months. The diagnosis could not be more
than 90 days from initial clinic visit or from the start of
therapy. All study subjects were treated at the H Lee Moffitt
Cancer Center and Research Institute. Subjects were
clinically-staged by endoscopic ultrasound (EUS). Tumor and
adjacent normal mucosa biopsies (a minimum of 5 core biopsies) were
obtained for microarray analysis before initiation of therapy and
between day 10 and 14 of preoperative radiochemotherapy. For the
purposes of this study only the pretreatment tumor tissue
microarray was utilized. Biopsies were snap frozen in liquid
nitrogen. No macro or microdissection of the biopsies was
performed.
[0155] All study subjects were treated with preoperative concurrent
radiochemotherapy and underwent surgical resection (APR or LAR in
13/14) within 8 weeks of completion of preoperative treatment. The
starting dose of oral Topotecan was 0.25 mg/m.sup.2 and it was
administered at least 3 hours before XRT on a daily basis. Patients
were treated with 45 Gy per day (prescribed to the isocenter) to a
standard pelvic field with either a three field or four field 3-D
conformal technique. Table 8 shows a summary of the clinical
characteristics of this cohort.
TABLE-US-00009 TABLE 8 Clinical Characteristics for Rectal Cancer
Trial Sex Male 10 Female 4 Age (y) Mean 69.4 Median (range) 72
(50-90) Chemotherapy Dose 0.25 mg/m.sup.2/day 3 (21) 0.4 mg/m2/day
5 (36) 0.55 mg/m2/day 6 (43) Ultrasound Tumor Stage T3 14 (100)
Pathological Tumor Stage T0 2 (14.3) Tis 1 (7) T1 2 (14.3) T2 3
(21.4) T3 5 (36) T4 1 (7) Downstaging Yes 8 (57) No 6 (43) Values
are number (percentage) unless otherwise noted.
[0156] Pathological response in the rectal cancer cohort was
defined by at least a decrease of one T stage in the primary tumor
between the pretreatment EUS and the pathological evaluation of the
specimen (Janjan et al., Int. J. Rad. Oncol. Biol. Phys. 1999;
44(5):1027-38; Janjan et al., Am J Clin Oncol 2001; 24(2):107-12).
Pathological complete response was defined as no evidence of tumor
in the surgical specimen (primary and nodes). Based on this
definition, 57% (8/14) of the patients were considered
responders.
[0157] The Esophageal Cancer Cohort consisted of 12 patients
enrolled in an IRB-approved prospective tissue collection trial,
aimed at defining molecular signatures of prognostic value in
esophageal cancer. Clinical management was not dictated by the
protocol and left to the clinical judgment of the treating
physicians. Treatment details are presented in table 9. Eligibility
criteria included a histological diagnosis of esophageal cancer,
deemed a reasonable candidate for preoperative radiochemotherapy
and/or esophagectomy by the evaluating physician. An ECOG
performance below 2 was required. In addition, patients were
required to be chemotherapy naive. Study subjects were clinically
staged by EUS. Biopsies of the tumor and normal mucosa were snap
frozen in liquid nitrogen for microarray analysis.
[0158] All subjects in this cohort were treated with concurrent
radiochemotherapy to be followed by planned esophagectomy. 9/12
underwent planned esophagectomy. Three patients completed
concurrent radiochemotherapy but were not operated because of
patient or physician preference (2 patients) or progressive disease
(one patient). The clinical characteristics of this cohort is
summarized in Table 9.
TABLE-US-00010 TABLE 9 Clinical Characteristics for Esophageal
Trial Sex Male 7 (58.3) Female 5 (41.7) Age (y) Mean 67.08 Median
(range) 66 (51-80) Chemotherapy Regimen CDDP + 5-FU 3 (25) 5-FU 2
(16.7) Carbo/Tax + 5-FU 1 (8.3) NA 6 (50) Radiation Dose 45 1 (8.3)
50.4 4 (33.3) 54 2 (16.7) 61.2 1 (8.3) NA 4 (33.3) Clinical Tumor
Stage T2N1 1 (8.3) T3N0 1 (8.3) T3N1 7 (58.4) T4N1 3 (25)
Pathological Tumor Stage T0N0 4 (33.3) T0N1 1 (8.3) T1aN0 1 (8.3)
T1N1 2 (16.7) T2bN1 1 (8.3) T2N1 1 (8.3) Progressive Dx 2 (16.7)
Downstaging Yes 7 (58.3) No 5 (41.7) Values are number (percentage)
unless otherwise noted.
[0159] Clinical response in the esophageal cancer cohort was
defined as a decrease of at least two T stages between the
pretreatment EUS evaluation and the pathological evaluation of the
specimen (Chirieac et al., Cancer 2005; 103(7):1347-55). Three
patients in our cohort did not undergo esophagectomy. One had
progressive disease during preoperative therapy; the other two
experienced clinical complete responses (documented by PET and/or
EUS and biopsy) and had no evidence of disease at least one year
after completion of treatment. Based on this definition 50% (6/12)
of the patients were considered responders.
[0160] The specimen sampled was flash frozen within 15 minutes of
resection and the RNA was extracted. Total RNA from the excised
tissue was isolated using the TRIZOL.TM. Reagent (Invitrogen,
Carlsbad, Calif.) and the manufacturer's protocol. The aqueous
phase containing the RNA separated from the TRIZOL.TM. reagent was
further purified using the RNeasy cleanup procedure (Qiagen Inc.,
Valencia, Calif.). The quality of total RNA was then assessed by
agarose gel electrophoresis and A.sub.260/280 ratio or by analysis
on the Agilent 2100 Bioanalyzer. Five micrograms of total RNA from
each sample was processed for microarray analysis. The poly(A) RNA
was specifically converted to cDNA and then amplified and labeled
with biotin following the procedure initially described by Van
Gelder et al. (Proc Natl Acad Sci USA 1990; 87(5):1663-7).
Hybridization with the biotin labeled RNA, staining, and scanning
of the chips followed the prescribed procedure outlined in the
Affymetrix technical manual and has been previously described
(Dobbin et al., Clin Cancer Res 2005; 11(2 Pt 1):565-72).
[0161] The oligonucleotide probe arrays used were the Affymetrix
U133A 2.0 plus chips. Since the original cell line data was created
on the HU6800 GeneChip, while the newer patient expression data was
generated on HG-U133Plus chips, it was necessary to translate the
hub probesets in Table 6. This was done using the blast program to
find the best U133+ probeset match to the consensus sequence from
which the 6800 probeset was designed. The Affymetrix NetAffx
software was also used for this translation. Scanned output files
were visually inspected for hybridization artifacts and then
analyzed using the robust multi-array analysis method (RMA)
(Irizarry et al., Nucleic Acids Res 2003; 31(4):e15(27)).
Statistical testing for patient cohorts was determined from
predicted RSI values using a one-sided Mann-Whitney test. The test
was used to determine if the predicted RSI was significantly higher
for non-responders. Bar-charts of patient response were graphed
using mean and standard error values for each response group in
both the rectal cancer and esophageal cancer data. Relapse-free
survival differences between low and high RSI values were
calculated using a log-rank test of censored survival times.
[0162] As shown in FIG. 1, the model significantly separated
responders (R) from non-responders (NR) in the pilot clinical
cohort (all patients, mean predicted radiosensitivity index, R vs.
NR 0.34 vs. 0.48, p=0.002). Importantly, the model was accurate in
both disease cohorts in spite of the small number of patients
(rectal cancer patients, mean predicted radiosensitivity index, R
vs. NR 0.32 vs. 0.46, p=0.03) (esophageal cancer patients, mean
predicted radiosensitivity index, R vs. NR 0.37 vs. 0.50,
p=0.05).
[0163] To further describe the model, an ROC curve (FIG. 2) was
generated using the predicted radiosensitivity index values to
determine the sensitivity and specificity of the predictor. Using a
threshold RSI value of 0.46, the model has a sensitivity and
specificity of 80 and 82% respectively, with a positive predictive
value (PPV) of 86%. In addition, there were 8 patients that
experienced a complete pathological response in the two cohorts.
6/8 complete responders had a predicted radiosensitivity index
below the threshold. These numbers are encouraging since the
predictor was not developed to account for the radiosensitizing
effect of chemotherapy and the inclusion of chemotherapy was
expected to account for prediction inaccuracies.
[0164] These results show that RSI when analyzed as a continuous
variable is correlated with pathological response in rectal and
esophageal cancer patients treated with preoperative concurrent
chemoradiation.
[0165] It is important to note that false negatives (predicted
radioresistant that responded) were the main inaccuracy when the
model was dichotomized in the esophageal and rectal datasets. This
population represented 60% of the misclassified cases in the
esophageal and rectal cancer cohorts. It is possible that this
inaccuracy is due to the radiosensitization effect of chemotherapy.
The proportion of individuals within the rectal and esophageal
dataset that are classified in this group (11.5%) is consistent
with the observed improvement in clinical responses with concurrent
chemotherapy over radiotherapy alone (Herskovic et al., The New
England Journal of Medicine 1992; 326(24):1593-8; Al-Sarraf et al.,
J Clin Oncol 1997; 15(1):277-84 (published erratum appears in J
Clin Oncol 1997 February; 15(2):866); Bosset et al., The New
England journal of medicine 2006; 355(11):1114-23). Therefore, it
is possible that this effect can be addressed by analyzing
differences between responders and non-responders that share a
predicted radioresistant phenotype.
Example 4
The Radiosensitivity Predictive Model is of Prognostic Value in
Head and Neck Cancer
[0166] The model was further tested as a prognostic marker in
locally-advanced head and neck cancer patients treated with
definitive concurrent radiochemotherapy. The Head and Neck Cancer
Cohort consisted of 92 patients with head and neck cancer treated
within prospective randomized Phase II-III trials at The
Netherlands Cancer Institute. The majority of tumors were
locally-advanced advanced (94% T3 and above, 74% N1 and above). The
full clinical details of this cohort were previously published
(Pramana et al., Int J Radiat Oncol Biol Phys 2007; 69(5):1544-52).
All patients were treated with concurrent radiochemotherapy with
cisplatin-based chemotherapy. Total radiation dose was 70 Gy in 2
Gy daily fractions in all cases. Two different schedules of
cisplatin were given: 1. (high dose) 100 mg/m.sup.2 IV three times
during radiotherapy or 150 mg/m.sup.2 given intra-arterially four
times during radiotherapy; or 2. (low dose) 20.times.6 mg/m.sup.2
daily. No disease outcome differences were found between
chemotherapy schedules.
[0167] Gene expression profiles for all patients were generated
using the NKI array. These methods were previously published, see
Pramana et al., Int J Radiat Oncol Biol Phys 2007; 69(5):1544-52.
Probes were mapped from the HU6800 platform to the HG-U133 Plus 2.0
platform and NKI array format by mapping the probe sequences onto a
corresponding NCBI Refseq ID or genomic region, then identifying
the closest probe match on the new microarray platform.
[0168] Using the same algorithm developed in cell lines and tested
in the rectal and esophageal cohorts, radiosensitivity predictions
were generated for this dataset. The average radiosensitivity index
prediction was lower in this disease site when compared with rectal
and esophagus (predicted radiosensitivity index, head and neck vs.
esophagus vs. rectal 0.06 vs.0.43 vs.0.39). Although this could be
partly a function of radiosensitivity differences between these
diseases, it could also be due to platform differences (Affymetrix
U133 Plus vs. NKI array). In spite of these differences, the
radiosensitivity index was still of prognostic value within the
head and neck dataset. The predicted radiosensitive group had an
improved 2 year Relapse-Free survival (2 yr RFS 86% vs. 62%,
p=0.06), thus arguing that the model is capturing biological
commonalities that determine tumor radiosensitivity across disease
sites (FIG. 3).
[0169] These results show that RSI is of prognostic significance in
a cohort of 92 patients with locally-advanced head and neck cancer.
The applicability of the model in three different disease sites
strongly suggests that the model captures commonalities that define
radiosensitivity across disease sites. Therefore the model should
be generally applicable to other disease sites (e.g., lung,
prostate, or cervical cancer).
[0170] In addition, as noted above the gene expression in the head
and neck dataset was derived from NKI arrays, which is a two
channel based cDNA microarray platform, while the gene expression
data in the esophageal and rectal cancer cohorts were derived from
Affymetrix U-133 Plus microarrays. This indicates that the
algorithm is transferable across platforms.
Example 5
Identification of Subsets of Genes Significantly Associated with
Radiation Sensitivity
[0171] To determine whether all 10 of the above-described signature
genes were necessary for a robust prediction, subset analysis was
performed using the methods described herein.
[0172] Considering the 10 signature genes, subsets of these genes
were selected and tested for statistical significance in the
patient cohorts described earlier. For each subset, the gene
expression data was rank ordered and a linear regression model was
built. The coefficients and ranks of these models differ from the
10-gene model. Each new model was evaluated by generating RSI
predictions on the esophageal and rectal cancer patient cohort and
using a one-sided Wilcox test for significant difference in RSI
between responders and non-responders. In addition, a one-sided
Student's t test was also used to assess statistical
significance.
[0173] Likewise, the RSI predictions were generated for the head
and neck cancer patient set. Here, the 25th percentile of predicted
RSI was used as described above as a threshold for calling a
patient's tumor radiosensitive or radioresistant (for the purposes
of time to local recurrence). A log-rank statistical test was
performed on recurrence free survival times between the predicted
radiosensitive and radioresistant groups to assess statistical
significance. In addition, the mean predicted RSI was also used a
threshold and evaluated.
[0174] The rank-based approach to prediction does not allow single
genes to be used. In addition, some subsets of two genes lead to
identical ranking for all cell lines, thereby limiting the number
of possible subsets to be evaluated.
[0175] All of the potential gene combinations were evaluated in
each of the patient cohorts described above. Table 10 and 11 show
results for statistical significant subsets of genes (gene symbols
joined by `_`) and p-values from tests of significance between
responders and non-responders (in the manner described earlier).
Many significant subsets were identified, ranging from subsets of 2
genes to 10 genes.
[0176] In the head & neck trial there were 12 significant
subsets of genes when considering the difference in recurrence free
survival split at the 25.sup.th percentile of predicted RSI, which
are listed in Table 10. Using the median of predicted RSI
identified the gene subsets shown in Table 11.
TABLE-US-00011 TABLE 10 Subsets Significant in Head & Neck
Cancer Cohort by 25.sup.th Percentile Number in Subset Combination
25th Percentile 4 STAT1_SUMO1_HDAC_IRF1 0.053506572 5
AR_STAT1_SUMO1_HDAC_IRF1 0.050918175 5 c-jun_STAT1_RelA_SUMO1_IRF1
0.059557472 6 AR_c-jun_STAT1_RelA_SUMO1_IRF1 0.042062687 6
AR_c-jun_STAT1_cAbl_SUMO1_IRF1 0.046890608 6
AR_c-jun_STAT1_SUMO1_HDAC_IRF1 0.053117262 6
c-jun_STAT1_RelA_cAbl_SUMO1_IRF1 0.059557472 6
c-jun_STAT1_RelA_SUMO1_CDK1_IRF1 0.059557472 7
AR_c-jun_STAT1_RelA_SUMO1_CDK1_IRF1 0.042062687 7
c-jun_STAT1_PKC_RelA_cAbl_HDAC_IRF1 0.046900514 8
AR_c-jun_STAT1_PKC_RelA_cAbl_HDAC_IRF1 0.032529876 10
AR_c-jun_STAT1_PKC_RelA_cAbl_SUMO1_CDK1_HDAC_IRF1 0.062801635
TABLE-US-00012 TABLE 11 Subsets Significant in Head & Neck
Cancer Cohort by Median Number in Subset Combination Median 5
AR_PKC_cAbl_SUMO1_IRF1 0.039215827 5 c-jun_STAT1_RelA_SUMO1_IRF1
0.059557472 6 AR_c-jun_STAT1_RelA_SUMO1_IRF1 0.046890608 6
AR_c-jun_STAT1_cAbl_SUMO1_IRF1 0.046890608 6
c-jun_STAT1_RelA_cAbl_SUMO1_IRF1 0.059557472 6
c-jun_STAT1_RelA_SUMO1_CDK1_IRF1 0.059557472 7
AR_c-jun_STAT1_RelA_SUMO1_CDK1_IRF1 0.046890608 7
AR_c-jun_STAT1_RelA_cAbl_SUMO1_IRF1 0.046890608 8
AR_c-jun_STAT1_PKC_RelA_cAbl_SUMO1_IRF1 0.043331767
[0177] The Rectal & Esophageal trial had 259 significant
subsets, ranging from two hubs to all ten; FIGS. 4A-4G present a
list of those subsets.
[0178] There were five gene subsets that generated RSI predictions
that were of statistical significance in both the head/neck and
rectal and esophageal cancer patient cohorts. All included c-jun,
STAT1, cAbl, IRF1, and are listed in Table 12:
TABLE-US-00013 TABLE 12 Subsets Significant in Head & Neck,
Esophageal, and Rectal Cancer Cohorts Number of Genes in Profile
Genes in Profile 6 AR + c-jun + STAT1 + cAbl + SUMO1 + IRF1 6 c-jun
+ STAT1 + RelA + cAbl + SUMO1 + IRF1 7 c-jun + STAT1 + PKC + RelA +
cAbl + HDAC + IRF1 8 AR + c-jun + STAT1 + PKC + RelA + cAbl + HDAC
+ IRF1 10 AR + c-jun + STAT1 + PKC + RelA + cAbl + SUMO1 + CDK1 +
HDAC + IRF1
This indicates that these subsets of genes can be used in the
present methods in place of the ten signature gene profile.
Other Embodiments
[0179] It is to be understood that while the invention has been
described in conjunction with the detailed description thereof, the
foregoing description is intended to illustrate and not limit the
scope of the invention, which is defined by the scope of the
appended claims. Other aspects, advantages, and modifications are
within the scope of the following claims.
* * * * *