U.S. patent application number 16/868293 was filed with the patent office on 2020-12-24 for immune gene signatures in cancer.
The applicant listed for this patent is H. Lee Moffitt Cancer Center and Research Institute, Inc.. Invention is credited to Domenico Coppola, Audrey Loboda, James J. Mule, Michael V. Nebozhyn, Timothy J. Yeatman.
Application Number | 20200399706 16/868293 |
Document ID | / |
Family ID | 1000005066235 |
Filed Date | 2020-12-24 |
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United States Patent
Application |
20200399706 |
Kind Code |
A1 |
Mule; James J. ; et
al. |
December 24, 2020 |
Immune Gene Signatures in Cancer
Abstract
This invention relates to methods for selecting a treatment,
treating, and predicting survival time in subjects with cancer,
such as colorectal cancer, based on tumor expression levels of
chemokines, cytotoxic genes, and/or dendritic cell genes.
Inventors: |
Mule; James J.; (Odessa,
FL) ; Coppola; Domenico; (Tampa, FL) ;
Yeatman; Timothy J.; (Thonotosassa, FL) ; Loboda;
Audrey; (Philadelphia, PA) ; Nebozhyn; Michael
V.; (Yeadon, PA) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
H. Lee Moffitt Cancer Center and Research Institute, Inc. |
Tampa |
FL |
US |
|
|
Family ID: |
1000005066235 |
Appl. No.: |
16/868293 |
Filed: |
May 6, 2020 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
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16029499 |
Jul 6, 2018 |
10648040 |
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16868293 |
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15171713 |
Jun 2, 2016 |
10041129 |
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16029499 |
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13575354 |
Oct 23, 2012 |
9404926 |
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PCT/US2011/022845 |
Jan 28, 2011 |
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15171713 |
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61299798 |
Jan 29, 2010 |
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Current U.S.
Class: |
1/1 |
Current CPC
Class: |
C12Q 2600/106 20130101;
G01N 33/57415 20130101; C12Q 2600/118 20130101; G01N 33/574
20130101; C12Q 1/6886 20130101; C12Q 2600/158 20130101; G01N
33/57407 20130101 |
International
Class: |
C12Q 1/6886 20060101
C12Q001/6886; G01N 33/574 20060101 G01N033/574 |
Goverment Interests
FEDERALLY SPONSORED RESEARCH OR DEVELOPMENT
[0002] This invention was made with Government support under Grant
No. R01 CA148995-01 awarded by the National Institutes of Health.
The Government has certain rights in the invention.
Claims
1. A method of treating a subject who has a solid tumor, the method
comprising: obtaining cells from the tumor; determining gene
expression levels of chemokine (C-C motif) ligand 2 (CCL2), CCL3,
CCL4, CCL5, CCL8, chemokine (C-C motif) ligand 18 (pulmonary and
activation-regulated) (CCL18), CCL19, CCL21, chemokine (C-X-C
motif) ligand 9 (CXCL9), CXCL10, CXCL11, and CXCL13 in the tumor
cells; comparing the tumor gene expression levels to reference gene
expression levels; identifying a subject who has tumor gene
expression levels above the reference gene expression levels; and
administering an immunotherapy treatment for cancer to a subject
who has tumor gene expression levels above the reference gene
expression levels.
2. The method of claim 1, wherein determining gene expression
levels comprises determining protein levels or mRNA levels.
3. The method of claim 2, wherein determining mRNA levels comprises
using reverse transcription polymerase chain reaction or a gene
chip.
4. The method of claim 1, wherein the subject is a human.
5. The method of claim 1, wherein the tumor is non small cell lung
cancer.
6. The method of claim 1, wherein the treatment comprises
administering cyclophosphamide; anti-interleukin-2R immunotoxin;
and/or an anti-cancer antibody selected from the group consisting
of anti-CD137, anti-programmed death-1 receptor (PD1), and
anti-cytotoxic T-lymphocyte antigen-4 (CTLA-4).
7. The method of claim 6, wherein the antibody is anti-CD137.
8. The method of claim 6, wherein the antibody is anti-PD1.
9. The method of claim 6, wherein the antibody is anti-CTLA-4.
Description
CLAIM OF PRIORITY
[0001] This application is a continuation of U.S. application Ser.
No. 16/029,499, filed on Jul. 6, 2018, which is a continuation of
U.S. application Ser. No. 15/171,713, filed on Jun. 2, 2016, now
U.S. Pat. No. 10,041,129, which is a continuation of U.S.
application Ser. No. 13/575,354, filed on Jul. 26, 2012, now U.S.
Pat. No. 9,404,926, which is a U.S. National Phase Application of
International Patent Application No. PCT/US2011/022845, filed on
Jan. 28, 2011, which claims the benefit of U.S. Patent Application
No. 61/299,798, filed on Jan. 29, 2010. The entire contents of the
foregoing are hereby incorporated by reference.
TECHNICAL FIELD
[0003] This invention relates to methods for identifying tumors
associated with immune cell infiltration, and for making a
prognosis in subjects with cancer, such as colorectal cancer.
BACKGROUND
[0004] Tumor-induced, host immune response has been described in
breast (2-5), lung (6, 7), ovarian (8, 9), and CRC (10, 11) among
other solid tumor types. This response may include fibrosis,
lymphocytic or neutrophilic infiltration, and other reactive
changes within the tumor and/or in the surrounding tissue.
SUMMARY
[0005] The present invention is based, at least in part, on the
discovery of gene signatures that predict the presence of
infiltrating immune cells. Expression levels of these genes can be
used to optimize or select treatment and predict survival in
subjects with tumors.
[0006] Thus, in a first aspect, the invention provides methods for
predicting survival time for a subject who has a tumor. The methods
include obtaining cells from the tumor; determining one or more of:
[0007] (i) gene expression levels of chemokines CCL2, CCL3, CCL4,
CCL5, CCL8, CCL18, CCL19, CCL21, CXCL9, CXCL10, CXCL11, and CXCL13
in the tumor cells; [0008] (ii) gene expression levels of cytotoxic
cell genes cathepsin H (CTSH), CTSC, CTSD, CTSE, CTSO, CTSS, CTSZ,
granzyme A (GZMA), GZMB, GZMH, GZMK, Fcgamma receptor (FcgammaR)
type IIa (FCGR2A), FCGR2B, FCGR2C, Fcgamma receptor (FcgammaR) type
IIIa (FCGR3A), FCGR3B in the tumor cells; or [0009] (iii) gene
expression levels of dendritic cell genes S 100PBP, S100A10,
S100A6, S100A7L1, S100G, S100A1, S100A7, S100A14, S100A16, S100A2,
S100A11, S100P, S100Z, S100A3, S100A13, S100A12, S100B, S100A4,
S100A9, S100A8, and CD209 in the tumor cells; comparing the tumor
gene expression levels to reference gene expression levels; and
predicting longer survival time if tumor gene expression levels are
above the reference gene expression levels, or predicting shorter
survival time if tumor gene expression levels are below the
reference gene expression levels.
[0010] In another aspect, the invention provides methods for
monitoring an immunotherapy in a subject who has a tumor. The
methods include obtaining cells from the tumor; determining first
gene expression levels of one or more of: [0011] (i) gene
expression levels of chemokines CCL2, CCL3, CCL4, CCL5, CCL8,
CCL18, CCL19, CCL21, CXCL9, CXCL10, CXCL11, and CXCL13 in the tumor
cells; [0012] (ii) gene expression levels of cytotoxic cell genes
cathepsin H (CTSH), CTSC, CTSD, CTSE, CTSO, CTSS, CTSZ, granzyme A
(GZMA), GZMB, GZMH, GZMK, Fcgamma receptor (FcgammaR) type IIa
(FCGR2A), FCGR2B, FCGR2C, Fcgamma receptor (FcgammaR) type IIIa
(FCGR3A), FCGR3B in the tumor cells; or [0013] (iii) gene
expression levels of dendritic cell genes S 100PBP, S100A10,
S100A6, S100A7L1, S100G, S100A1, S100A7, S100A14, S100A16, S100A2,
S100A11, S100P, S100Z, S100A3, S100A13, S100A12, S100B, S100A4,
S100A9, S100A8, and CD209 in the tumor cells; administering one or
more doses of an immunotherapy to the subject; determining second
gene expression levels of the same genes in the tumor cells; and
comparing the first and second gene expression levels, wherein
second gene expression levels that are higher than the first gene
expression levels indicate that the treatment is effective, and
second gene expression levels that are the same as or lower that
the first gene expression levels indicate that the treatment is not
effective.
[0014] In another aspect, the invention provides methods for
treating a subject who has a tumor. The methods include obtaining
cells from the tumor; determining expression levels of one or more
of: [0015] (i) gene expression levels of chemokines CCL2, CCL3,
CCL4, CCL5, CCL8, CCL18, CCL19, CCL21, CXCL9, CXCL10, CXCL11, and
CXCL13 in the tumor cells; [0016] (ii) gene expression levels of
cytotoxic cell genes cathepsin H (CTSH), CTSC, CTSD, CTSE, CTSO,
CTSS, CTSZ, granzyme A (GZMA), GZMB, GZMH, GZMK, Fcgamma receptor
(FcgammaR) type IIa (FCGR2A), FCGR2B, FCGR2C, Fcgamma receptor
(FcgammaR) type IIIa (FCGR3A), FCGR3B in the tumor cells; or [0017]
(iii) gene expression levels of dendritic cell genes S100PBP,
S100A10, S100A6, S100A7L1, S100G, S100A1, S100A7, S100A14, S100A16,
S100A2, S100A11, S100P, S100Z, S100A3, S100A13, S100A12, S100B,
S100A4, S100A9, S100A8, and CD209 in the tumor cells; comparing the
tumor gene expression levels to reference gene expression levels;
and selecting for the subject a treatment comprising an
immunotherapy if tumor gene expression levels are above the
reference gene expression levels, or selecting for the subject a
treatment not comprising an immunotherapy if tumor gene expression
levels are below the reference gene expression levels.
[0018] In a further aspect, the invention provides methods for
selecting a treatment for a subject who has a tumor. The methods
include obtaining cells from the tumor; determining one or more of:
[0019] (i) gene expression levels of chemokines CCL2, CCL3, CCL4,
CCL5, CCL8, CCL18, CCL19, CCL21, CXCL9, CXCL10, CXCL11, and CXCL13
in the tumor cells; [0020] (ii) gene expression levels of cytotoxic
cell genes cathepsin H (CTSH), CTSC, CTSD, CTSE, CTSO, CTSS, CTSZ,
granzyme A (GZMA), GZMB, GZMH, GZMK, Fcgamma receptor (FcgammaR)
type IIa (FCGR2A), FCGR2B, FCGR2C, Fcgamma receptor (FcgammaR) type
IIIa (FCGR3A), FCGR3B in the tumor cells; and [0021] (iii) gene
expression levels of dendritic cell genes S100PBP, S100A10, S100A6,
S100A7L1, S100G, S100A1, S100A7, S100A14, S100A16, S100A2, S100A11,
S100P, S100Z, S100A3, S100A13, S100A12, S100B, S100A4, S100A9,
S100A8, and CD209 in the tumor cells; comparing the tumor gene
expression levels to reference gene expression levels; and
selecting for the subject a treatment comprising an immunotherapy
if tumor gene expression levels are above the reference gene
expression levels, or selecting for the subject a treatment not
comprising an immunotherapy if tumor gene expression levels are
below the reference gene expression levels.
[0022] In some embodiments of the methods described herein,
determining gene expression levels comprises determining protein
levels. In some embodiments of the methods described herein,
determining gene expression levels comprises determining mRNA
levels.
[0023] In some embodiments of the methods described herein, the
methods include determining chemokine gene expression levels. In
some embodiments of the methods described herein, the methods
include determining cytotoxic cell gene expression levels. In some
embodiments of the methods described herein, the methods include
determining dendritic cell gene expression levels. In some
embodiments of the methods described herein, the methods include
determining chemokine gene expression levels and cytotoxic cell
gene expression levels. In some embodiments of the methods
described herein, the methods include determining cytotoxic cell
gene expression levels and dendritic cell gene expression levels.
In some embodiments of the methods described herein, the methods
include determining chemokine gene expression levels and dendritic
cell gene expression levels. In some embodiments of the methods
described herein, the methods include determining chemokine gene
expression levels, cytotoxic cell gene expression levels, and
dendritic cell gene expression levels.
[0024] In some embodiments of the methods described herein, the
longer survival time is 2 years or more, and the shorter survival
time is less than 2 years.
[0025] In some embodiments of the methods described herein, the
methods further include communicating predicted survival time to
the subject or a health care provider. In some embodiments of the
methods described herein, the methods further include communicating
information regarding the effectiveness of a treatment to the
subject or a health care provider. In some embodiments of the
methods described herein, the methods further include communicating
information regarding treatment or selection of a treatment to the
subject or a health care provider.
[0026] In some embodiments of the methods described herein,
immunotherapy comprises administering to the subject dendritic
cells or peptides with adjuvant, a DNA-based vaccine, cytokines,
cyclophosphamide, anti-interleukin-2R immunotoxin, or an
anti-cancer antibody. In some embodiments, the antibody is
anti-CD137, anti-PD1, or anti-CTLA-4. In some embodiments, the
immunotherapy comprises administering to the subject tumor-pulsed
dendritic cells.
[0027] In some embodiments of the methods described herein, the
subject is a human.
[0028] In some embodiments of the methods described herein, the
tumor is a solid tumor.
[0029] In some embodiments of the methods described herein, the
tumor is colorectal cancer.
[0030] A "subject" as described herein can be any subject having a
proliferative disorder. For example, the subject can be any mammal,
such as a human, including a human cancer patient. Exemplary
nonhuman mammals include a nonhuman primate (such as a monkey or
ape), mouse, rat, goat, cattle, pig, horse, sheep, cat, and
dog.
[0031] 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.
[0032] Other features and advantages of the invention will be
apparent from the following detailed description and figures, and
from the claims.
DESCRIPTION OF DRAWINGS
[0033] FIG. 1A is a bar graph illustrating the observed range of
the immune response as represented by the metagene #1 score. The
barplot shows CRCs that have 11 highest and 10 lowest values of the
mean score of selected immune metagenes grouped by whether they had
the highest 11 or lowest 10 scores and sorted by the mean metagene
score.
[0034] FIG. 1B is a bar graph illustrating of the relationship
between patient overall survival and the immune response as
quantified by the score of metagene #1 on selected CRCs with known
status of ectopic lymph node-like structures confirmed by
immunohistochemistry. The barplot of CRCs without lymphoid
structures and CRCs with lymphoid structures segregated into two
groups: those with overall survival time less than 2 years (shown
on the left), and those with overall survival time greater than 2
years (shown on the right). Score for metagene #1 is plotted on the
Y-axis.
[0035] FIGS. 2A-J are a set of images showing the results of H
& E staining and immunohistochemistry analysis of primary CRCs.
All 10 of the lowest gene signature-scored CRCs revealed a lightly
dispersed or absent lymphocytic peritumoral host response, and low
to no appreciable expression of B cell (i.e., CD20; 2A) and T cell
(i.e., CD3; 2B) markers. All 11 of the highest gene
signature-scored CRCs, revealed a marked peritumoral lymphocytic
host response organized as ectopic lymph node-like structures by
hematoxylin and eosin staining (arrows; 2C) and by
immunohistochemistry (2D-I). CD20.sup.+ (2E, F) and CD79a.sup.+
(2G) B cells and CD21.sup.+ follicular dendritic cells (J) are
concentrated in the center of follicles with CD3.sup.+ (2D, H, I) T
cells appearing in the parafollicular cortex or marginal zones,
with some dispersion into the follicles. In some cases, a fibrous
stroma was observed to encapsulate a follicle (2I). In panels H and
I, T=T cells, B=B cells, and S=stroma. In panel J, T=tumor; the
follicle (arrow) is at the front edge of the invading colonic
adenocarcinoma.
[0036] FIGS. 3A-B show that chemokines are upregulated in tumors
with lymphocyte involvement. Hierarchical clustering of tumors with
and without lymphoid structures done on selected set of known
chemokines. For each gene, a single representative probe set with
the highest dynamic range across all profiled samples was picked up
from all probe sets that mapped to a given gene symbol. Genes are
clustered using Pearson correlation distance metric, tumors are
clustered using Euclidean distance metric. Ward linkage was applied
in both cases, for clustering tumors and genes. FIG. 3A is a
heatmap showing mean-centered intensities (averaged within each
probe set across all tumors shown). FIG. 3B is a bar graph
displaying chemokine score: the mean value of chemokines as
averaged across all probe sets shown in 3A.
[0037] FIG. 4A is a heat map showing 12 chemokines that correlate
with metagene #1. The heatmap of 326 colorectal tumors and 12 genes
comprising metagene #1 sorted by the metagene score (mean of
probesets that map to a given 12-gene gene set). The 11 samples
with the highest metagene score are at the top of the figure, and
10 samples with the lowest metagene score are at the bottom of this
figure.
[0038] FIG. 4B is a bar graph illustrating the relationship between
patient overall survival and the immune response as quantified by
the score of chemokine genes on selected CRCs with known status of
ectopic lymph node-like structures confirmed by
immunohistochemistry. The barplot of CRCs without lymphoid
structures and CRCs with lymphoid structures segregated into two
groups: those with overall survival time less than 2 years (shown
on the left), and those with overall survival time greater than 2
years (shown on the right). Scores for the chemokine genes are
plotted on the Y-axis.
[0039] FIGS. 5A-B show that cytotoxic genes are upregulated in
tumors with ectopic lymph node-like structures. Hierarchical
clustering of tumors with and without lymphoid structures were
performed on a selected set of known cytotoxic genes. For each
gene, a single representative probe set with the highest dynamic
range across all profiled samples was picked up from all probe sets
that mapped to a given gene symbol. Genes are clustered using
Pearson correlation distance metric; tumors are clustered using
Euclidean distance metric. Ward linkage was applied in both cases,
for clustering tumors and genes. FIG. 5A is a heatmap showing
mean-centered intensities (averaged within each probe set across
all tumors shown). FIG. 5B is a bar plot displaying the mean value
of cytotoxic genes as averaged across all probe sets.
[0040] FIGS. 6A-B shows that dendritic cell marker genes are
upregulated in CRCs with ectopic lymph node involvement.
Hierarchical clustering of tumors with and without lymphoid
structures was done on a selected set of known dendritic cell
marker genes. For each gene, a single representative probe set with
the highest dynamic range across all profiled samples was picked up
from all probe sets that mapped to a given gene symbol. Genes are
clustered using Pearson correlation distance metric, tumors are
sorted by the dendritic genes score (shown in 6B) computed as the
mean value for each tumor across all genes shown on the figure.
Ward linkage was applied for clustering genes. FIG. 6A is a heatmap
showing mean-centered intensities (averaged within each probe set
across all tumors shown). FIG. 6B is a bar plot displaying the mean
value of dendritic cell marker genes as averaged across all probe
sets.
[0041] FIGS. 7A-B are bar graphs illustrating the relationship
between patient overall survival and the immune response as
quantified by the score of cytotoxic cell--(7A) and dendritic
cell--(7B) related genes on selected CRCs with known status of
ectopic lymph node-like structures confirmed by
immunohistochemistry. The barplot of CRCs without lymphoid
structures and CRCs with lymphoid structures segregated into two
groups: those with overall survival time less than 2 years (shown
on the left), and those with overall survival time greater than 2
years (shown on the right). Scores for cytotoxic cell- and
dendritic cell-related genes are plotted on the Y-axis.
DETAILED DESCRIPTION
[0042] It has been shown that some growing, human solid tumors are
infiltrated by immune cells. Data characterizing the nature of this
host immune response in a wide variety of distinct tumor types have
been published in the recent literature (2-9), including primary
CRCs (14). As shown herein, profiling gene signatures predicts the
presence of infiltrating immune cells, and expression levels of
these genes can be used to assign a prognosis and select or
optimize treatment in subjects with tumors.
[0043] Methods of Assigning a Prognosis or Predicting Survival
[0044] The methods can be used to monitor a treatment (e.g., an
immunotherapy), or to select a treatment, e.g., to select a
treatment regime including an immunotherapy for a subject. In
addition, the methods described herein can be used for, e.g., to
assist in, assigning a prognosis or predicting survival in a
subject who has a tumor, e.g., a solid tumor.
[0045] As used herein, the term "cancer" refers 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. In general, a
cancer will be associated with the presence of one or more tumors,
i.e., abnormal cell masses. The term "tumor" is meant to include
all types of cancerous growths or oncogenic processes, 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. In general, the methods
described herein can be practiced on subjects with solid
tumors.
[0046] Tumors include malignancies of the various organ systems,
such as affecting lung, breast, thyroid, lymphoid,
gastrointestinal, and genito-urinary tract, as well as
adenocarcinomas which include malignancies such as most colon
cancers, renal-cell carcinoma, prostate cancer and/or testicular
tumors, non-small cell carcinoma of the lung, cancer of the small
intestine and cancer of the esophagus. 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 renal 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. The term "sarcoma" is art
recognized and refers to malignant tumors of mesenchymal
derivation.
[0047] In some embodiments, cancers evaluated by the methods
described herein include those that are particularly immunogenic,
e.g., neuroblastoma, melanoma, and renal cell cancer.
[0048] In some embodiments, cancers evaluated by the methods
described herein include epithelial cancers, such as a lung cancer
(e.g., non-small-cell lung cancer (NSCLC)), breast cancer,
colorectal cancer, head and neck cancer, or ovarian cancer.
Epithelial malignancies are cancers that affect epithelial
tissues.
[0049] Lymphoid Structures in Solid Tumors
[0050] Lymphoid structures have been described in solid tumors. As
examples, Coronella-Wood et al. (2, 3) have described breast
tumor-infiltrating lymphocytes composed of B cell aggregates
containing interdigitating CD21+ follicular dendritic cells. The
presence of ectopic, organized lymphoid tissue has also been
reported in ovarian (8, 9, 19); colon (20, 21); and lung tumors (6,
7), which has mostly focused on the presence of dendritic cell
subpopulations, the level of which predicted better prognosis in
some tumor types (7, 8, 22). A similar correlation was recently
reported for patients with CRC (10).
[0051] Colorectal Adenocarcinoma (CRC)
[0052] In some embodiments, the methods herein can be used to
select treatment or predict survival in a subject who has
colorectal adenocarcinoma (CRC). CRC is one of the most common
malignancies, accounting for approximately 15% of all
cancer-related deaths in the U.S. The prevalence of CRC increases
with age, the largest number of tumors occurring during the sixth
decade. The expected annual incidence of this tumor has risen over
the last decade and 149,000 new cases were estimated in 2009 (1).
If not diagnosed and treated early, this tumor spreads through the
entire bowel wall, extends to adjacent organs, and eventually
metastasizes to regional lymph nodes and distant sites. The
majority of deaths from CRC occur in patients with metastatic late
stage tumors, which are incurable most of the time.
[0053] CRC is known to elicit an inflammatory immune reaction
composed of acute and/or chronic inflammatory cells, including
lymphocytes, infiltrating the tumor as well as the surrounding
colonic wall. The lymphocytic component of this response has been
shown to include some antigen-specific T cells originating without
prior immunotherapy (12, 13). As described herein, immune
gene-related signatures predict the presence of unique histologic
features of lymphoid cell infiltrates in colorectal carcinoma
(CRCs) that correlate with clinical parameters.
[0054] Assays, References, and Samples
[0055] The methods described herein include determining levels of
selected immune-related genes, i.e., chemokines, cytotoxic cell
genes, and/or dendritic cell genes. In some embodiments, all of the
genes listed in the tables below are evaluated. In some
embodiments, two, three, four, five, six, seven, eight, nine, ten,
eleven, twelve, or more of the listed genes are evaluated. Although
the terminology "genes" is used herein, in some embodiments, the
methods include detecting levels of the proteins encoded by the
listed genes. In some embodiments, the methods include detecting
transcript (mRNA) levels.
[0056] Chemokines
[0057] Chemokines are secreted proteins involved in
immunoregulatory and inflammatory processes. The chemokines used in
the present methods are as follows:
TABLE-US-00001 Chemokines GenBank GenBank Gene Acc. No.: Acc. No.:
Symbol Gene Name Nucleic Acid Protein CCL2 chemokine (C-C motif)
ligand 2 NM_002982.3 NP_002973.1 CCL3 chemokine (C-C motif) ligand
3 NM_002983.2 NP_002974.1 CCL4 chemokine (C-C motif) ligand 4
NM_002984.2 NP_002975.1 CCL5 chemokine (C-C motif) ligand 5
NM_002985.2 NP_002976.2 CCL8 chemokine (C-C motif) ligand 8
NM_005623.2 NP_005614.2 CCL18 chemokine (C-C motif) ligand 18
NM_002988.2 NP_002979.1 (pulmonary and activation-regulated) CCL19
chemokine (C-C motif) ligand 19 NM_006274.2 NP_006265.1 CCL21
chemokine (C-C motif) ligand 21 NM_002989.2 NP_002980.1 CXCL9
chemokine (C-X-C motif) ligand 9 NM_002416.1 NP_002407.1 CXCL10
chemokine (C-X-C motif) ligand 10 NM_001565.2 NP_001556.2 CXCL11
chemokine (C-X-C motif) ligand 11 NM_005409.4 NP_005400.1 CXCL13
chemokine (C-X-C motif) ligand 13 NM_006419.2 NP_006410.1
[0058] Cytotoxic Cell Genes
[0059] The cytotoxic cell genes evaluated in the methods described
herein are those expressed by cytotoxic cells involved in the
immune response, e.g., lysosomal/proteolytic enzymes (cathepsins),
granzymes, and FcgR2s.
TABLE-US-00002 Cytotoxic Genes GenBank GenBank Gene Acc. No.: Acc.
No.: Symbol Gene Name Nucleic Acid Protein CTSH Cathepsin H
NM_004390.3 NP_004381.2 CTSC Cathepsin C NM_001814.4 NP_001805.3
(Dipeptidyl peptidase 1) CTSD Cathepsin D NM_001909.3 NP_001900.1
CTSE Cathepsin E NM_001910.2; NP_001901.1; NM_148964.1 NP_683865.1
CTSO Cathepsin O NM_001334.2 NP_001325.1 CTSS Cathepsin S
NM_004079.3 NP_004070.3 CTSZ Cathepsin Z NM_001336.3 NP_0013272
GZMA Granzyme A NM_006141.3 NP_006135.1 GZMB Granzyme B NM_004131.4
NP_004122.2 GZMH Granzyme H NM_033423.3 NP_219491.1 GZMK Granzyme K
NM_002104.2 NP_002095.1 FCGR2A Fcgamma receptor NM_001136219.1.;
NP_001129691.1; (FcgammaR) type IIa NM_021642.3 NP_067674.2 FCGR2B
Fcgamma receptor NM_001002273.2 NP_001002273.1 (FcgammaR) type IIb
NM_001002274.2 NP_001002274.1 NM_001002275.2 NP_001002275.1
NM_001190828.1 NP_001177757.1 NM_004001.4. NP_003992.3 FCGR2C
Fcgamma receptor NM_201563.4 NP_963857.3 (FcgammaR) type IIc FCGR3A
Fcgamma receptor NM_000569.6 NP_000560.5 (FcgammaR) type IIIa
NM_001127592.1 NP_001121064.1 NM_001127593.1 NP_001121065.1
NM_001127595.1 NP_001121067.1 NM_001127596.1. NP_001121068.1 FCGR3B
Fcgamma receptor NM_000570.3 NP_000561.3. (FcgammaR) type IIIb
[0060] Dendritic Cell Genes
[0061] Most of the dendritic cell genes evaluated in the methods
described herein belong to the S100 family of proteins, and contain
two EF-hand calcium-binding motifs. S100 proteins are involved in
the regulation of a number of cellular processes such as cell cycle
progression and differentiation. CD209 is a pathogen-recognition
receptor expressed on the surface of immature dendritic cells; it
is believed to be involved in initiation of the primary immune
response.
TABLE-US-00003 Dendritic Cell Genes GenBank GenBank Gene Acc. No.:
Acc. No.: Symbol Gene Name Nucleic Acid Protein S100PBP S100P
Binding Protein NM_022753.2 NP_073590.2 S100A10 S100
calcium-binding protein A10 NM_002966.2 NP_002957.1 S100A6 S100
calcium-binding protein A6 NM_014624.3 NP_055439.1 S100A7L1 S100
calcium-binding protein A7-like 1 NM_176823.3 NP_789793.1 S100G
S100 calcium-binding protein G NM_0040572 NP_004048.1 S100A1 S100
calcium-binding protein A1 NM_006271.1 NP_006262.1 S100A7 S100
calcium-binding protein A7 NM_002963.3 NP_002954.2 S100A14 S100
calcium-binding protein A14 NM_020672.1 NP_065723.1 S100A16 S100
calcium-binding protein A16 NM_080388.1 NP_525127.1 S100A2 S100
calcium-binding protein A2 NM_005978.3. NP_005969.1 S100A11 S100
calcium-binding protein A11 NM_005620.1 NP_005611.1 S100P S100
calcium-binding protein P NM_005980.2 NP_005971.1. S100Z S100
calcium-binding protein Z NM_130772.3 NP_570128.2 S100A3 S100
calcium-binding protein A3 NM_002960.1 NP_002951.1 S100A13 S100
calcium-binding protein A13 NM_001024210.1 NP_001019381.1
NM_001024211.1 NP_001019382.1 NM_001024212.1 NP_001019383.1
NM_001024213.1 NP_001019384.1 NM_005979.2 NP_005970.1 S100A12 S100
calcium-binding protein A12 NM_005621.1 NP_005612.1 S100B S100
calcium-binding protein B NM_006272.2 NP_006263.1 S100A4 S100
calcium-binding protein A4 NM_002961.2; NP_002952.1; NM_019554.2
NP_062427.1 S100A9 S100 calcium-binding protein A9 NM_002965.3
NP_002956.1 S100A8 S100 calcium-binding protein A8 NM_002964.3
NP_002955.2 CD209 CD209 antigen NM_001144893.1 NP_001138365.1
NM_001144894.1 NP_001138366.1 NM_001144895.1 NP_001138367.1
NM_001144896.1 NP_001138368.1 NM_001144897.1 NP_001138369.1
NM_021155.3 NP_066978.1
[0062] In some embodiments, the methods include assaying the
presence or levels of immune-related mRNA or proteins in the
sample. The presence and/or level of a protein can be evaluated
using methods known in the art, e.g., using quantitative
immunoassay methods. The presence and/or level of an mRNA can be
evaluated using methods known in the art, e.g., Northern blotting
or quantitative PCR methods, e.g., RT-PCR. In some embodiments,
high throughput methods, e.g., protein or gene chips as are known
in the art (see, e.g., Ch. 12, Genomics, in Griffiths et al., Eds.
Modern genetic Analysis, 1999, W. H. Freeman and Company; Ekins and
Chu, Trends in Biotechnology, 1999, 17:217-218; MacBeath and
Schreiber, Science 2000, 289(5485):1760-1763; Simpson, Proteins and
Proteomics: A Laboratory Manual, Cold Spring Harbor Laboratory
Press; 2002; Hardiman, Microarrays Methods and Applications: Nuts
&Bolts, DNA Press, 2003), can be used to detect the presence
and/or level of chemokine proteins as described herein.
[0063] In some embodiments, the methods include assaying levels of
one or more control genes or proteins, and comparing the level of
expression of the immune-related genes or proteins to the level of
the control genes or proteins, to normalize the levels of the
immune-related genes or proteins. Suitable endogenous control genes
includes a gene whose expression level should not differ between
samples, such as a housekeeping or maintenance gene, e.g., 18S
ribosomal RNA; beta Actin; Glyceraldehyde-3-phosphate
dehydrogenase; Phosphoglycerate kinase 1; Peptidylprolyl isomerase
A (cyclophilin A); Ribosomal protein L13a; large Ribosomal protein
P0; Beta-2-microglobulin; Tyrosine 3-monooxygenase/tryptophan
5-monooxygenase activation protein, zeta polypeptide; Succinate
dehydrogenase; Transferrin receptor (p90, CD71); Aminolevulinate,
delta-, synthase 1; Glucuronidase, beta; Hydroxymethyl-bilane
synthase; Hypoxanthine phosphoribosyltransferase 1; TATA box
binding protein; and/or Tubulin, beta polypeptide.
[0064] Generally speaking, the methods described herein can be
performed on cells from a tumor. The cells can be obtained by known
methods, e.g., during a biopsy (such as a core needle biopsy), or
during a surgical procedure to remove all or part of the tumor. The
cells can be used fresh, frozen, fixed, and/or preserved, so long
as the mRNA or protein that is to be assayed is maintained in a
sufficiently intact state to allow accurate analysis.
[0065] In some embodiments of the methods described herein, the
levels of the immune-related genes in the tumor sample can be
compared individually to levels in a reference. The reference
levels can represent levels in a subject who has a good prognosis,
or a long predicted survival time (e.g., 2 years or more).
Alternatively, reference levels can represent levels in a subject
who has a poor prognosis, or a shorter predicted survival time
(e.g., less than 2 years). In some embodiments, the reference
levels represent a threshold, and a level in the tumor that is
above the threshold reference level indicates that the subject has
a good prognosis, or a long predicted survival time (e.g., 2 years
or more), and levels below the threshold reference level indicates
that the subject has a poor prognosis, or a shorter predicted
survival time (e.g., less than 2 years).
[0066] In some embodiments, the reference levels can represent
levels in a subject who has lymphoid like structures present in the
tumor, or is predicted to respond to immunotherapy. Alternatively,
reference levels can represent levels in a subject who lacks tumor
lymphoid structures, or is predicted to have no or a poor response
to immunotherapy. In some embodiments, the reference levels
represent a threshold, and a level in the tumor that is above the
threshold reference level indicates that the subject has tumor
lymphoid structures, or is predicted to respond to immunotherapy,
and levels below the threshold reference level indicates that the
subject lacks lymphoid structures and is predicted to have no or
poor response to immunotherapy. In subjects who are predicted to
have tumor lymphoid structures, or who are predicted to respond to
immunotherapy, the methods can further include administering an
immunotherapy for those subjects, or selecting or recommending a
treatment including an immunotherapy for those subjects.
[0067] In some embodiments of the methods described herein, values
representing the levels of the immune-related genes can be summed
to produce a "tumor immune-related gene score" that can be compared
to a reference immune-related gene score, wherein a tumor
immune-related gene score that is above the reference
immune-related gene score indicates that the subject has a long
predicted survival time (e.g., 2 years or more) or is predicted to
have a positive response to immunotherapy, and an immune-related
gene score below the reference score indicates that the subject has
a shorter predicted survival time (e.g., less than 2 years), or is
predicted to have no or a poor response to immunotherapy.
[0068] For example, in some embodiments, the expression levels of
each of the evaluated genes can be assigned a value (e.g., a value
that represents the expression level of the gene, e.g., normalized
to an endogenous control gene as described herein). That value
(optionally weighted to increase or decrease its effect on the
final score) can be summed to produce an immune-related gene score.
One of skill in the art could optimize such a method to determine
an optimal algorithm for determining an immune-related gene
score.
[0069] The methods described herein can include determining levels
(or scores) for all of the 12 chemokines, 20 dendritic cell genes,
and 16 cytotoxic cell genes; or for the 12 chemokines and 20
dendritic cell genes; or for the 20 dendritic cell genes, and 16
cytotoxic cell genes; or for the 12 chemokines, and 16 cytotoxic
cell genes, or for any of the gene sets alone. In some embodiments
all of the genes in each set are evaluated, but in some embodiments
a subset of one or all of the sets is evaluated.
[0070] One of skill in the art will appreciate that references can
be determined using known epidemiological and statistical methods,
e.g., by determining an immune-related gene score, or
immune-related gene protein or mRNA levels, in tumors from an
appropriate cohort of subjects, e.g., subjects with the same type
of cancer as the test subject and a known prognosis (e.g., good or
poor) or predicted survival time (e.g., less than 2 years, or 2
years or more).
[0071] In some embodiments, the methods can be used to monitor the
efficacy of a treatment, e.g., an immunotherapy, e.g., methods
comprising administering to the subject therapies that promote
anti-cancer immunity, including administering one or more of:
dendritic cells or peptides with adjuvant, DNA-based vaccines,
cytokines (e.g., IL-2), cyclophosphamide, anti-interleukin-2R
immunotoxins, and/or antibodies such as anti-CD137, anti-PD1, or
anti-CTLA-4; see, e.g., Kruger et al., "Immune based therapies in
cancer," Histol Histopathol. 2007 June; 22(6):687-96; Eggermont et
al., "Anti-CTLA-4 antibody adjuvant therapy in melanoma," Semin
Oncol. 2010 October; 37(5):455-9; Klinke D J 2nd, "A multiscale
systems perspective on cancer, immunotherapy, and Interleukin-12,"
Mol Cancer. 2010 Sep. 15; 9:242; Alexandrescu et al.,
"Immunotherapy for melanoma: current status and perspectives," J
Immunother. 2010 July-August; 33(6):570-90; Moschella et al.,
"Combination strategies for enhancing the efficacy of immunotherapy
in cancer patients," Ann N Y Acad Sci. 2010 April; 1194:169-78;
Ganesan and Bakhshi, "Systemic therapy for melanoma," Natl Med J
India. 2010 January-February; 23(1):21-7; Golovina and Vonderheide,
"Regulatory T cells: overcoming suppression of T-cell immunity,"
Cancer J. 2010 July-August; 16(4):342-7. In some embodiments, the
methods include administering a composition comprising tumor-pulsed
dendritic cells, e.g., as described in WO2009/114547 and references
cited therein. The methods include determining levels of the
immune-related genes in a sample, then administering one or more
doses of the treatment, then determining levels of the
immune-related genes to determine whether the treatment has
increase immune infiltration of the tumor. An increase in
immune-related gene levels (or immune-related gene score, if
calculated) indicates that the treatment was effective.
EXAMPLES
[0072] The invention is further described in the following
examples, which do not limit the scope of the invention described
in the claims.
[0073] Statistical analysis for the following examples was
performed as follows. On the figure legends, the term
"significance" denoted p-value by Fisher exact test. Throughout the
paper, for each gene, a single probe set with highest standard
deviation across all samples, was selected among multiple probesets
available on the array that mapped to the same gene symbol.
Significance of linkages of gene profiles to patient survival was
analyzed statistically by both ANOVA and Wilcoxon Rank-Sum
tests.
Example 1. Immune Gene Profiling
[0074] Several metagenes--tightly correlated sets of genes
biologically related to inflammation and immune response--were
identified. Metagene analysis was performed on samples from the
Moffitt Cancer Center (MCC) CRC500 tumor bank and CRCs were sorted
by low versus high scores.
[0075] Selection of human tissues was performed as follows. The MCC
CRC500 gene profiling database was interrogated for the presence of
genes biologically related to inflammation and immune response. The
11 CRCs with the highest expression of these genes (2 of the CRCs,
denoted T2157A1 and T2157A3, are separate samples obtained from the
same patient) were selected and compared with the 10 CRCs with the
lowest or absent expression of the same genes. The histologic
slides corresponding to these cases, and prepared from the
mirror-image of the portion of tumor submitted for the mRNA
microarray analysis, were retrieved from the MCC Anatomic Pathology
Division's repository. All of the specimens were preserved in 10%
buffered formalin prior to embedding in paraffin. The slides were
reviewed to assess the presence of microscopically evident host
immune response. The final pathology report for each case was also
reviewed and the pathologic data were collected.
[0076] The tumors were staged according to both Dukes and TNM
systems. All tumors occurred in the absence of genetic cancer
syndromes such as human non-polyposis colon cancer syndrome
(HNPCC), familial adenomatous polyposis syndrome (FAP), among
others; also cancers arising in the background of ulcerative
colitis or Crohn's disease were excluded from the study. Linked,
annotated clinical follow-up data (e.g., survival) and treatments
received were also available in the database.
[0077] mRNA microarray analysis was performed as follows. Twelve
normal colorectal mucosa samples, 9 normal liver samples, and 326
colorectal adenocarcinoma tumor specimens (19 annotated as
metastasis, 265 as primary, and 42 as unknown) from human patients
were arrayed on Affymetrix HG-U133+ GeneChip microarrays (denoted
MCC CRC500). For the current study, this existing MCC CRC500
database was interrogated and the data were processed using RMA
normalization algorithm as implemented in Affymetrix Power Tools
software package (APT; which are a set of cross-platform command
line programs that implement algorithms for analyzing and working
with Affymetrix GeneChip arrays) using default settings. Obtained
probe set intensities were then converted to log 10. Probes were
selected for heatmaps by starting with all gene symbols from a
given family and then reducing to probes that showed a desired
correlation pattern.
[0078] Three hundred and twenty six "gene chipped" CRCs were
evaluated from this tumor bank. The chip contained 20,155 unique
genes. About 50 separate metagene groupings were derived. Among
them a metagene (Metagene #1) with overwhelming enrichment for
immune- and inflammation-related genes was identified. It is also
the largest metagene in terms of number of genes (comprising 320
unique immune gene symbols). FIG. 1A shows the 21 of 326 CRCs
selected by the 11 highest and 10 lowest values of the mean score
of metagene #1.
Example 2. Pathologic and Clinical Findings
[0079] The patients with 21 CRCs selected by the 11 highest and 10
lowest values of the mean score of metagene #1 had a median age of
69 years (range, 51-83). Thirteen were male and seven were female.
Most of the primary CRCs were moderately differentiated (n=16); of
these 8 were metagene #1 "high" and 8 were metagene #1 "low". Three
tumors were well differentiated; of these one was metagene #1
"high" and two were metagene #1 "low". One CRC was poorly
differentiated and it was metagene #1 "high". Thus, there was no
definable correlation between CRCs selected by metagene #1 and
their grade of differentiation.
[0080] The relationship between overall survival of the patients
and the immune response as quantified by the score of metagene #1
for the 21 selected CRCs was also examined. FIG. 1B shows the bar
plot of these two groups of metagene #1 selected CRCs: those with
overall survival time less than 2 years (shown on the left), and
those with overall survival time greater than 2 years (shown on the
right). Score for metagene #1 is plotted on the Y-axis. As can be
seen, there was a significant trend of increased overall survival
(>2 years) of patients with CRCs with the highest values of the
mean score of metagene #1.
Example 3. Histopathologic and Immunohistochemical Findings
[0081] Immunohistochemistry and analysis were performed as follows.
The tissues were stained using the avidin-biotin-complex method
with retrieval under high pH. Prediluted, monoclonal antibodies
(mAb) to CD3 (rabbit mAb, Ventana Medical Systems, Inc., Tucson,
Ariz.), CD20 (mouse mAb, Ventana), CD79a (mouse mAb, Ventana),
Ki-67 (rabbit mAb, Ventana), and CD21 (mouse mAb, Novocastra
Laboratories Ltd., Newcastle upon Tyne, UK) were used for the
analysis of lymphoid infiltrates. The slides were de-paraffinized
by heating at 56.degree. C. for 30 minutes and by three washes,
five minutes each, with xylene. Tissues were rehydrated by a series
of five-minute washes in 100%, 95%, and distilled water. After
blocking with universal blocking serum (Ventana) for 30 minutes,
the samples were incubated with each primary mAb at 37.degree. C.
for 32 minutes. The samples were then incubated with biotin-labeled
secondary mAb and streptavidin-horseradish peroxidase for 30
minutes each. The slides were developed with 3,3'-diaminobenzidine
tetrahydrochloride substrate (Ventana) and counterstained with
hematoxylin and bluing (Ventana). The tissue samples were
dehydrated and coverslipped. Appropriate cell conditioning
(following the Ventana recommendations) was used for antigen
retrieval for all antibodies. A negative control was included of
non immune mouse sera and omitting the primary antibody during the
primary antibody incubation step. The positive controls were
selected following the Ventana recommendations for CD3, CD20,
CD79a, and Ki-67, and the Novocastra recommendations for CD21.
[0082] The mAb stained tissue slides were blinded and examined by
two independent pathologists simultaneously. In case of discrepancy
a consensus was reached by the re-valuation of the slides. The
positivity of the stains was calculated semi-quantitatively by
estimating the percent of nuclear positivity in the lymphoid
cells.
[0083] Microscopically, all 10 of the lowest metagene #1-scored
CRCs revealed a minimally dispersed or absent lymphocytic
peritumoral host response, and low to no appreciable expression for
lymphocytic markers (FIGS. 2A, B). Conversely, all 11 of the
highest metagene #1-scored CRCs, revealed a marked peritumoral
lymphocytic host response organized, remarkably, as ectopic lymph
node-like structures by hematoxylin and eosin staining (FIG. 2C)
and by immunohistochemistry (FIGS. 2D-I), particularly at the
invasive edge of the tumors. Of note, there was no statistically
significant difference between the presence of ectopic lymph
node-like structures and gender (p>0.5), tumor grade (p>0.5),
tumor site location (p>0.5; Table 2), and tumor stage (p>0.5;
Table 2).
[0084] Ectopic lymph node-like structures were observed
intratumorally as well, occasionally accompanied by a lightly
diffuse pattern of lymphocytes within the tumor parenchyma. The
lymphoid structures were found to contain follicles. The majority
of the CD3.sup.+ T cells were located in parafollicular cortex-like
zones (FIGS. 2D, H, I); scattered CD3.sup.+ T cells were
occasionally seen within these tumors as well. CD20.sup.+ B cells
were present almost exclusively within the follicular structures
(FIGS. 2E, F). In every case, CD79a.sup.+ B cell precursors were
identified within the lymphoid follicles (FIG. 2G). CD21.sup.+
dendritic cells were present within the follicular germinal
centers, establishing the true follicular nature of the lymphoid
aggregates (FIG. 2J). The Ki-67 expression was found only in the
highest metagene #1-scored CRC cases, suggesting that these
germinal centers are in different stage of maturation. Lymphocytic
proliferation was found to contain both B and T lymphocytes, which
suggests newly formed and/or activated ectopic lymph node-like
structures. Together, these findings support the hypothesis that
these follicles represent secondary and/or tertiary ectopic lymph
node-like structures.
Example 4. Identification of a Chemokine Gene Signature
[0085] 326 colorectal tumors and 12 genes comprising metagene #1
were sorted by the metagene score (mean of probesets that map to a
given 12-gene, gene set). Again, the 11 samples with the highest
metagene score (shown on the previous Figures) are shown at the
top, and 10 samples with the lowest metagene score (shown on the
previous Figures) are shown at the bottom. Table 1 shows the
hierarchical clustering of 326 CRC tumors and the selected 12
chemokines (i.e., CCL2, CCL3, CCL4, CCL5, CCL8, CCL18, CCL19,
CCL21, CXCL9, CXCL10, CXCL11, and CXCL13) that were most correlated
with the metagene #1 score. For each chemokine, the label provides
gene symbol, numerical order on the corresponding probeset when
processed with APT package, as well as probeset identification.
Collectively, these data demonstrate that the chemokines, because
of their potent, biologic attraction of immune cell subtypes track
strongly with the formation or presence of ectopic lymph node-like
structures in CRC tumor masses.
TABLE-US-00004 TABLE 1 Chemokine Gene Expression in Human Colon
Tumors: A 12 Gene Signature Predicts Presence of Ectopic Lymph
Nodes * Colon500, Chemokine Correlation to Metagene#1 probe#
probeset symbol.sup..dagger. rho p-value 65 1405_i_at CCL5 83%
0E+00 17867 204655_at CCL5 83% 0E+00 52045 1555759_a_at CCL5 81%
0E+00 17288 205242_at CXCL13 72% 0E+00 18402 204103_at CCL4 72%
0E+00 18665 203915_at CXCL9 72% 0E+00 18007 204533_at CXCL10 66%
7E-42 12512 210072_at CCL19 61% 4E-34 235 32128_at CCL18 58% 2E-30
8658 214038_at CCL8 58% 1E-30 12636 209924_at CCL18 58% 8E-31 6122
216598_s_at CCL2 57% 4E-30 17425 205114_s_at CCL3 56% 2E-28 11482
211122_s_at CXCL11 55% 1E-27 12419 210163_at CXCL11 55% 1E-27 17905
204606_at CCL21 53% 5E-25 Table 1. * Hierarchical clustering of 326
CRCs and selected .sup..dagger.12 chemokines (CCL2, CCL3, CCL4,
CCL5, CCL8, CCL18, CCL19, CCL21, CXCL9, CXCL10, CXCL11, CXCL13)
that are most correlated with metagene #1 score. Tumors are sorted
by the metagene score, chemokines are clustered using Pearson
correlation distance metric and Ward linkage. For each chemokine,
the label provides gene symbol, numerical order on the
corresponding probeset when processed with APT package, as well as
probeset id. Rho, Pearson correlation coefficient.
[0086] Table 1. *Hierarchical clustering of 326 CRCs and selected
.sup..dagger.12 chemokines (CCL2, CCL3, CCL4, CCL5, CCL8, CCL18,
CCL19, CCL21, CXCL9, CXCL10, CXCL11, CXCL13) that are most
correlated with metagene #1 score. Tumors are sorted by the
metagene score, chemokines are clustered using Pearson correlation
distance metric and Ward linkage. For each chemokine, the label
provides gene symbol, numerical order on the corresponding probeset
when processed with APT package, as well as probeset id. Rho,
Pearson correlation coefficient.
[0087] There was a strong correlation of association between the
chemokine gene profile and the presence of ectopic lymph node-like
structures in CRCs (See FIG. 3A). Hierarchical clustering of tumors
with and without lymphoid structures was performed on a selected
set of known chemokine genes (shown in FIG. 3B). Strong
correspondence exists between score mean cutoffs for metagene #1
and the chemokine gene set. For each gene, a single representative
probe set with the highest dynamic range across all profiled
samples was picked from all probe sets that mapped to a given gene
symbol. Genes are clustered using Pearson correlation distance
metric; tumors are sorted by gene scores (shown in in FIG. 3B).
Example 5. Molecular Signatures and Clinical Parameters
[0088] Table 2 shows that metagene #1 and the 12-chemokine gene
signature that identify the presence of ectopic lymph node-like
structures in CRCs are independent of tumor staging (TNM and
Dukes), tumor site location, and treatment received (e.g., surgery
alone, surgery plus chemotherapy with or without external beam
radiation).
TABLE-US-00005 TABLE 2 Molecular Signatures that Identify the
Presence of Ectopic Lymph Node Structures are Independent of Tumor
Site Location, Stage and Patient Treatment Ectopic Lymph Tumor
Dukes/TNM Signatures* Tumor.sup..dagger. Nodes Site Stage Treatment
+ T2151 + Rectum D/IVA 5FU + T4555 + Left B/IIA None + T252 + Right
C/IIIC 5FU + radiation + T2638 + Rectum C/IIIB 5FU/Irinotecan +
T4948 + Sigmoid C/IIIB 5FU + radiation + T1923 + Left B/IIA 5FU +
T462 + Rectum B/IIA None + T568 + Left NA.sup..dagger-dbl.
5FU/mitomycin/Irinotecan + radiation + T3701 + Right C/IIIB 5FU +
T3395 + Sigmoid B/IIB None - T5108 - Right NA.sup..dagger-dbl. 5FU
- T2648 - Rectum --/0 None - T5029 - Sigmoid C/IIIB NA - T3138 -
Left NA.sup..dagger-dbl. 5FU/Irinotecan - T6190 - Sigmoid D/IVA
5FU/Irinotecan/Erbitux/Irinotecan - T2588 - Left B/IIC
5FU/Irinotecan/5- FUDR + mitomycin/Irinotecan + radiation -
T2157A1/A3 - Right D/IVA None - T4376 - Left B/IIC 5FU/Irinotecan -
T5162 - Left B/IIC 5FU + radiation - T2412 - Left C/IIIB
5FU/Irinotecan/Erbitux Table 2. Molecular Signatures are
Independent of Tumor Stage and Patient Treatment. *Metagene #1 and
12-chemokine signature. .sup..dagger.CRCs that have the 10 highest
and 10 lowest metagene #1 and 12-chemokine signature scores.
.sup..dagger-dbl.Not available.
[0089] The relationship between overall survival of the patients
and metagene #1 and the 12-chemokine gene signature was also
examined for the 21 selected CRCs. FIG. 4B shows the bar plots of
these groups of signature-selected CRCs: those with overall
survival time less than 2 years (shown on the left), and those with
overall survival time greater than 2 years (shown on the right).
Score for genes is plotted on the Y-axis. As can be seen, there was
a significant trend of increased overall survival (>2 years) of
patients with CRCs with the highest values of the mean score of the
12-chemokine gene signature.
Example 6. Identification of T Cell Activation-Related Gene
Signatures
[0090] The heatmaps of FIGS. 5A and 6A show strong correlations of
association between cytotoxic cell (lysosomal/proteolytic enzymes,
granzymes, FcgR2s) and dendritic cell (S100 family, CD209) gene
profiles, respectively, and the presence of ectopic lymph node-like
structures in CRCs. Hierarchical clustering of tumors with and
without ectopic lymph node-like structures were then performed on a
selected set of known cytotoxic- and dendritic cell-related genes
(shown in FIGS. 5B and 6B). Strong correspondence exists between
score mean cutoffs for metagene #1 and cytotoxic and dendritic
cell-related gene sets. For each gene, a single representative
probe set with the highest dynamic range across all profiled
samples was picked up from all probe sets that mapped to a given
gene symbol. Genes are clustered using Pearson correlation distance
metric; tumors are sorted by the genes score (shown in FIGS. 5 and
6) and are computed as the mean value for each tumor across all
genes shown. Collectively, the cytotoxic and dendritic cell genes
reflect an important immune cell composition of ectopic lymph
node-like structures in CRC.
[0091] FIGS. 7A-B show the bar plots of these groups of
signature-selected CRCs: those with overall survival time less than
2 years (shown on the left), and those with overall survival time
greater than 2 years (shown on the right). Score for genes is
plotted on the Y-axis. As can be seen, there was a significant
trend of increased overall survival (>2 years) of patients with
CRCs with the highest values of the mean score of the cytotoxic and
dendritic cell gene signatures.
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Other Embodiments
[0133] 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.
* * * * *