U.S. patent application number 16/793347 was filed with the patent office on 2020-08-13 for predictive biomarkers for ovarian cancer.
This patent application is currently assigned to VERMILLION, INC.. The applicant listed for this patent is VERMILLION, INC.. Invention is credited to SURAJ AMONKAR, GREG P. BERTENSHAW, BRIAN C. MANSFIELD, PING F. YIP.
Application Number | 20200256874 16/793347 |
Document ID | 20200256874 / US20200256874 |
Family ID | 1000004794559 |
Filed Date | 2020-08-13 |
Patent Application | download [pdf] |
United States Patent
Application |
20200256874 |
Kind Code |
A1 |
MANSFIELD; BRIAN C. ; et
al. |
August 13, 2020 |
PREDICTIVE BIOMARKERS FOR OVARIAN CANCER
Abstract
Methods are provided for predicting the presence, subtype and
stage of ovarian cancer, as well as for assessing the therapeutic
efficacy of a cancer treatment and determining whether a subject
potentially is developing cancer. Associated test kits, computer
and analytical systems as well as software and diagnostic models
are also provided.
Inventors: |
MANSFIELD; BRIAN C.;
(AUSTIN, TX) ; YIP; PING F.; (AUSTIN, TX) ;
AMONKAR; SURAJ; (AUSTIN, TX) ; BERTENSHAW; GREG
P.; (AUSTIN, TX) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
VERMILLION, INC. |
Austin |
TX |
US |
|
|
Assignee: |
VERMILLION, INC.
AUSTIN
TX
|
Family ID: |
1000004794559 |
Appl. No.: |
16/793347 |
Filed: |
February 18, 2020 |
Related U.S. Patent Documents
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Application
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Patent Number |
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15815085 |
Nov 16, 2017 |
10605811 |
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16793347 |
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15041754 |
Feb 11, 2016 |
9846158 |
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15815085 |
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14172237 |
Feb 4, 2014 |
9274118 |
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15041754 |
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12165240 |
Jun 30, 2008 |
8664358 |
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14172237 |
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61037946 |
Mar 19, 2008 |
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60947253 |
Jun 29, 2007 |
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Current U.S.
Class: |
1/1 |
Current CPC
Class: |
G01N 2800/52 20130101;
Y02A 90/10 20180101; G01N 2800/50 20130101; G01N 33/57449 20130101;
G01N 2800/60 20130101 |
International
Class: |
G01N 33/574 20060101
G01N033/574 |
Claims
1. A set of reagents to measure the levels of biomarkers in a
specimen, wherein the biomarkers comprise a panel of biomarkers and
their measurable fragments selected from one of the following
panels: (a) CA125, Apo A1, HE4, and FSH, and one or more of the
following: CD 40 Antigen (CD40), Eotaxin 1 (CCL11), EN-RAGE
(S100A12), Ferritin (FTL), Growth Hormone (GH, somatotropin, human
growth hormone, HGH), Haptoglobin (Hp), Insulin like Growth Factor
I (IGF-I), Interleukin 8 (IL-8, CXCL8), Leptin (LEP), Macrophage
Derived Chemokine (MDC, CCL22), Macrophage Inflammatory Protein 1
alpha (MIP-1.alpha., CCL3), Myoglobin (Mb), Stem Cell Factor (SCF),
Tumor Necrosis Factor alpha (TNF.alpha., TNF), and von Willebrand
Factor (VWF); (b) CA125, Apo A1, HE4, FSH, and transferrin; and one
or more of the following: CD 40 Antigen (CD40), Eotaxin 1 (CCL11),
EN-RAGE (S100A12), Ferritin (FTL), Growth Hormone (GH,
somatotropin, human growth hormone, HGH), Haptoglobin (Hp), Insulin
like Growth Factor I (IGF-I), Interleukin 8 (IL-8, CXCL8), Leptin
(LEP), Macrophage Derived Chemokine (MDC, CCL22), Macrophage
Inflammatory Protein 1 alpha (MIP-1.alpha., CCL3), Myoglobin (Mb),
Stem Cell Factor (SCF), Tumor Necrosis Factor alpha (TNF.alpha.,
TNF), and von Willebrand Factor (VWF); (c) CA125, Apo A1, HE4, FSH,
and transthyretin; and one or more of the following: CD 40 Antigen
(CD40), Eotaxin 1 (CCL11), EN-RAGE (S100A12), Ferritin (FTL),
Growth Hormone (GH, somatotropin, human growth hormone, HGH),
Haptoglobin (Hp), Insulin like Growth Factor I (IGF-I), Interleukin
8 (IL-8, CXCL8), Leptin (LEP), Macrophage Derived Chemokine (MDC,
CCL22), Macrophage Inflammatory Protein 1 alpha (MIP-1.alpha.,
CCL3), Myoglobin (Mb), Stem Cell Factor (SCF), Tumor Necrosis
Factor alpha (TNF.alpha., TNF), and von Willebrand Factor (VWF);
(d) CA125, Apo A1, Beta-2 Microglobulin, and FSH; and one or more
of the following: CD 40 Antigen (CD40), Eotaxin 1 (CCL11), EN-RAGE
(S100A12), Ferritin (FTL), Growth Hormone (GH, somatotropin, human
growth hormone, HGH), Haptoglobin (Hp), Insulin like Growth Factor
I (IGF-I), Interleukin 8 (IL-8, CXCL8), Leptin (LEP), Macrophage
Derived Chemokine (MDC, CCL22), Macrophage Inflammatory Protein 1
alpha (MIP-1.alpha., CCL3), Myoglobin (Mb), Stem Cell Factor (SCF),
Tumor Necrosis Factor alpha (TNF.alpha., TNF), and von Willebrand
Factor (VWF); (e) CA125, Apo A1, transferrin, and FSH; and one or
more of the following: CD 40 Antigen (CD40), Eotaxin 1 (CCL11),
EN-RAGE (S100A12), Ferritin (FTL), Growth Hormone (GH,
somatotropin, human growth hormone, HGH), Haptoglobin (Hp), Insulin
like Growth Factor I (IGF-I), Interleukin 8 (IL-8, CXCL8), Leptin
(LEP), Macrophage Derived Chemokine (MDC, CCL22), Macrophage
Inflammatory Protein 1 alpha (MIP-1.alpha., CCL3), Myoglobin (Mb),
Stem Cell Factor (SCF), Tumor Necrosis Factor alpha (TNF.alpha.,
TNF), and von Willebrand Factor (VWF); (f) CA125, Apo A1,
transthyretin, and FSH; and one or more of the following: CD 40
Antigen (CD40), Eotaxin 1 (CCL11), EN-RAGE (S100A12), Ferritin
(FTL), Growth Hormone (GH, somatotropin, human growth hormone,
HGH), Haptoglobin (Hp), Insulin like Growth Factor I (IGF-I),
Interleukin 8 (IL-8, CXCL8), Leptin (LEP), Macrophage Derived
Chemokine (MDC, CCL22), Macrophage Inflammatory Protein 1 alpha
(MIP-1.alpha., CCL3), Myoglobin (Mb), Stem Cell Factor (SCF), Tumor
Necrosis Factor alpha (TNF.alpha., TNF), and von Willebrand Factor
(VWF); and (g) CA125, Apo A1, Beta-2 Microglobulin, transferrin,
transthyretin, and FSH; and one or more of the following: CD 40
Antigen (CD40), Eotaxin 1 (CCL11), EN-RAGE (S100A12), Ferritin
(FTL), Growth Hormone (GH, somatotropin, human growth hormone,
HGH), Haptoglobin (Hp), Insulin like Growth Factor I (IGF-I),
Interleukin 8 (IL-8, CXCL8), Leptin (LEP), Macrophage Derived
Chemokine (MDC, CCL22), Macrophage Inflammatory Protein 1 alpha
(MIP-1.alpha., CCL3), Myoglobin (Mb), Stem Cell Factor (SCF), Tumor
Necrosis Factor alpha (TNF.alpha., TNF), and von Willebrand Factor
(VWF).
2. (canceled)
3. The set of reagents of claim 1, wherein the reagents are binding
molecules.
4. The set of reagents of claim 3, wherein the binding molecules
are antibodies.
5. A test kit comprising the set of reagents of claim 1.
6. A method of predicting the likelihood of cancer in a subject,
comprising: detecting the levels of biomarkers in a specimen using
the set of reagents of claim 1, wherein a change in the levels of
the biomarkers, as compared with a control group of patients who do
not have cancer, is predictive of cancer in that subject.
7. The method of claim 6, wherein the cancer is ovarian cancer.
8. The method of claim 7, wherein a change in the relative levels
of the biomarkers is determined.
9. The method of claim 7, wherein the specimen is selected from the
group consisting of blood, serum, plasma, lymph, cerebrospinal
fluid, ascites, urine and tissue biopsy.
10. The method of claim 7, wherein the ovarian cancer is selected
from the group consisting of serous, endometrioid, mucinous, and
clear cell cancer.
11. The method of claim 7, wherein the prediction of ovarian cancer
includes a stage selected from the group consisting of Stage IA,
IB, IC, II, III and IV tumors.
12. The method of claim 7, further comprising creating a report of
the relative levels of the biomarkers.
13. The method of claim 12, wherein the report includes the
prediction as to the presence or absence of ovarian cancer in the
subject or the stratified risk of ovarian cancer for the subject,
optionally by stage of cancer.
14. The method of claim 7, wherein the sample is taken from a
subject selected from the group consisting of subjects who are
symptomatic for ovarian cancer and subjects who are at high risk
for ovarian cancer.
15. The method of claim 7, wherein the method has a sensitivity of
at least about 85 per cent and a specificity of at least about 85
per cent.
16. The method of claim 15, wherein the sensitivity and specificity
are determined for a population of women who are symptomatic for
ovarian cancer and have ovarian cancer as compared with a control
group of women who are symptomatic for ovarian cancer but who do
not have ovarian cancer.
17. A predictive or diagnostic model based on levels of the panels
of biomarkers of claim 1.
18. A multianalyte panel assay comprising the set of reagents of
claim 1.
19. A method to assess the therapeutic efficacy of a cancer
treatment, comprising: comparing the biomarker profiles in
specimens taken from a subject before and after the treatment or
during the course of treatment with a set of reagents according to
claim 1, wherein a change in the biomarker profile over time toward
a non-cancer profile or to a stable profile is interpreted as
efficacy.
20. A method for determining whether a subject potentially is
developing cancer, comprising: comparing the biomarker profiles in
specimens taken from a subject at two or more points in time with a
set of reagents according to claim 1, wherein a change in the
biomarker profile toward a cancer profile, is interpreted as a
progression toward developing cancer.
Description
STATEMENT OF PRIORITY
[0001] This application claims priority under 35 USC Section 119 to
Provisional Patent Applications Ser. Nos. 60/947,253 filed Jun. 29,
2007 and 61/037,946 filed Mar. 19, 2008, the disclosures of which
are hereby incorporated by reference in their entireties.
FIELD OF THE INVENTION
[0002] This invention provides methods for predicting and
diagnosing ovarian cancer, particularly epithelial ovarian cancer,
and it further provides associated analytical reagents, diagnostic
models, test kits and clinical reports.
BACKGROUND
[0003] The American Cancer Society estimates that ovarian cancer
will strike 22,430 women and take the lives of 15,280 women in 2007
in the United States. Ovarian cancer is not a single disease,
however, and there are actually more than 30 types and subtypes of
ovarian malignancies, each with its own pathology and clinical
behavior. Most experts therefore group ovarian cancers within three
major categories, according to the kind of cells from which they
were formed: epithelial tumors arise from cells that line or cover
the ovaries; germ cell tumors originate from cells that are
destined to form eggs within the ovaries; and sex cord-stromal cell
tumors begin in the connective cells that hold the ovaries together
and produce female hormones.
[0004] Common epithelial tumors begin in the surface epithelium of
the ovaries and account for about 90 per cent of all ovarian
cancers in the U.S. (and the following percentages reflect U.S.
prevalence of these cancers). They are further divided into a
number of subtypes--including serous, endometrioid, mucinous, and
clear cell tumors--that can be further subclassified as benign or
malignant tumors. Serous tumors are the most widespread forms of
ovarian cancer. They account for 40 per cent of common epithelial
tumors. About 50 per cent of these serous tumors are malignant, 33
per cent are benign, and 17 per cent are of borderline malignancy.
Serous tumors occur most often in women who are between 40 and 60
years of age.
[0005] Endometrioid tumors represent approximately 20 per cent of
common epithelial tumors. In about 20 per cent of individuals,
these cancers are associated with endometrial carcinoma (cancer of
the womb lining). In 5 per cent of cases, they also are linked with
endometriosis, an abnormal occurrence of endometrium (womb lining
tissue) within the pelvic cavity. The majority (about 80 per cent)
of these tumors are malignant, and the remainder (roughly 20 per
cent) usually is borderline malignancies. Endometrioid tumors occur
primarily in women who are between 50 and 70 years of age.
[0006] Clear cell tumors account for about 6 per cent of common
epithelial tumors. Nearly all of these tumors are malignant.
Approximately one-half of all clear cell tumors are associated with
endometriosis. Most patients with clear cell tumors are between 40
and 80 years of age.
[0007] Mucinous tumors make up about 1 per cent of all common
epithelial tumors. Most (approximately 80 per cent) of these tumors
are benign, 15 per cent are of borderline malignancy, and only 5
per cent are malignant. Mucinous tumors appear most often in women
between 30 to 50 years of age.
[0008] Ovarian cancer is by far the most deadly of gynecologic
cancers, accounting for more than 55 percent of all gynecologic
cancer deaths. But ovarian cancer is also among the most
treatable--if it is caught early. When ovarian cancer is caught
early and appropriately treated, the 5-year survival rate is 93
percent. See, for example, Luce et al, "Early Diagnosis Key to
Epithelial Ovarian Cancer Detection," The Nurse Practitioner,
December 2003 at p. 41. Extensive background information about
ovarian cancer is readily available on the interne, for example,
from the "Overview: Ovarian Cancer" of the Cancer Reference
Information provided by the American Cancer Society
(www.cancer.org) and the NCCN Clinical Practice Guidelines in
Oncology.TM. Ovarian Cancer V.1.2007 (www.nccn.org).
[0009] The current reality for the diagnosis of ovarian cancer is
that most cases--81 percent of all cases of ovarian cancer--are not
caught in earliest stage. This is because early stage ovarian
cancer is very difficult to diagnose. Its symptoms may not appear
or be noticed at this point. Or, symptoms--such as bloating,
indigestion, diarrhea, constipation and others--may be vague and
associated with many common and less serious conditions. Most
importantly, there has been no effective test for early detection.
An effective tool for early and accurate detection of ovarian
cancer is a critical unmet medical need.
Biomarkers for Ovarian Cancer
[0010] A variety of biomarkers to diagnose ovarian cancer have been
proposed, and elucidated through a variety of technology platforms
and data analysis tools. An interesting compilation of 1,261
potential protein biomarkers for various pathologies was presented
by N. Leigh Anderson et al., "A Target List of Candidate Biomarkers
for Targeted Proteomics," Biomarker Insights 2:1-48 (2006). A
spreadsheet listing the markers discussed in this paper can be
found at the website of the Plasma Proteome Institute
(http://www.plasmaproteome.org). Several published studies are
described immediately below and a number of other studies are
listed as references at the end of this specification. All of these
studies, all other documents cited in this specification, and
related provisional patent applications Ser. Nos. 60/947,253 filed
Jun. 29, 2007 and 61/037,946 filed Mar. 19, 2008, are hereby
incorporated by reference in their entireties.
[0011] For example, Cole, "Methods for detecting the onset,
progression and regression of gynecologic cancers." U.S. Pat. No.
5,356,817 (Oct. 18, 1994) described a method for detecting the
presence of a gynecologic cancer in a female, said cancer selected
from the group consisting of cervical cancer, ovarian cancer,
endometrial cancer, uterine cancer and vulva cancer, the method
comprising the steps of: (a) assaying a plasma or tissue sample
from the patient for the presence of CA 125, and at or about the
same time; and (b) assaying a bodily non-blood sample from said
patient for the presence of human chorionic gonadotropin
beta-subunit core fragment, wherein the detection of both CA 125
and human chorionic gonadotropin beta-subunit core fragment is an
indication of the presence of a gynecological cancer in the female.
A measurement of the human chorionic gonadotropin beta-subunit core
fragment alone was stated to be useful in monitoring progression
and regression of such cancers.
[0012] Fung et al, "Biomarker for ovarian and endometrial cancer:
hepcidin," U. S. Patent Application 20070054329, published Mar. 8,
2007, describes a method for qualifying ovarian and endometrial
cancer status based on measuring hepcidin as a single biomarker,
and based on panels of markers including hepcidin plus
transthyretin, and those two markers plus at least one biomarker
selected from the group consisting of: Apo A1, transferrin,
CTAP-III and ITIH4 fragment. An additional panel further includes
beta-2 microglobulin. These biomarkers were measured by mass
spectrometry, particularly SELDI-MS or by immunoassay. And data was
analyzed by ROC curve analysis.
[0013] Fung et al. also described the use of hepcidin levels used
in combination with other biomarkers, and concluded that the
predictive power of the test was improved. More specifically,
increased levels of hepcidin together with decreased levels
transthyretin were correlated with ovarian cancer. Increased levels
of hepcidin together with decreased levels of transthyretin,
together with levels of one or more of Apo A1 (decreased level),
transferrin (decreased level), CTAP-III (elevated level) and an
internal fragment of ITIH4 (elevated level) were also correlated
with ovarian cancer. The foregoing biomarkers were to further be
combined with beta-2 microglobulin (elevated level), CA125
(elevated level) and/or other known ovarian cancer biomarkers for
use in the disclosed diagnostic test. And hepcidin was said to be
hepcidin-25, transthyretin was said to be cysteinylated
transthyretin, and/or ITIH4 fragment perhaps being the ITIH4
fragment 1.
[0014] Diamandis, "Multiple marker assay for detection of ovarian
cancer," U. S. Patent Application 20060134120 published Jun. 22,
2006, described a method for detecting a plurality of kallikrein
markers associated with ovarian cancer and optionally CA125,
wherein the kallikrein markers comprise or are selected from the
group consisting of kallikrein 5, kallikrein 6, kallikrein 7,
kallikrein 8, kallikrein 10, and kallikrein 11. His patent
application explained that a significant difference in levels of
these kallikreins, which are a subgroup of secreted serine
proteases markers, and optionally that also of CA125, relative to
the corresponding normal levels, was indicative of ovarian cancer.
By repeatedly sampling these markers in the same patient over time,
Diamandis also found that a significant difference between the
levels of the kallikrein markers, and optionally CA125, in a later
sample, relative to an earlier sample, is an indication that a
patient's therapy is efficacious for inhibiting ovarian cancer.
Samples were evaluated by protein binding techniques, for example,
immunoassays, and by nucleotide array, PCR and the like
techniques.
[0015] Gorelik et al, Multiplexed Immunobead-Based Cytokine
Profiling for Early Detection of Ovarian Cancer" in Cancer
Epidemiol Biomarkers Prev. 2005:14(4) 981-7 (April 2005) reported
that a panel of multiple cytokines that separately may not show
strong correlation with the disease provide diagnostic potential. A
related patent application appears to be Lokshin et al.,
"Multifactorial assay for cancer detection," U. S. Patent
Application 20050069963 published Mar. 31, 2005. According to the
journal article, a novel multianalyte LabMAP profiling technology
was employed that allowed simultaneous measurement of multiple
markers. Various concentrations of 24 cytokines
(cytokinesichemokines, growth, and angiogenic factors) in
combination with CA-125 were measured in the blood sera of 44
patients with early-stage ovarian cancer, 45 healthy women, and 37
patients with benign pelvic tumors.
[0016] Of the cytokines discussed by Gorelik et al., six markers,
specifically interleukin (IL)-6, IL-8, epidermal growth factor
(EGF), vascular endothelial growth factor (VEGF), monocyte
chemoattractant protein-1 (MCP-1), together with CA-125, showed
significant differences in serum concentrations between ovarian
cancer and control groups. Out of those markers, IL-6, IL-8, VEGF,
EGF, and CA-125, were used in a classification tree analysis that
reportedly resulted in 84% sensitivity at 95% specificity. The
receiver operator characteristic curve (ROC) described using the
combination of markers produced sensitivities between 90% and 100%
and specificities of 80% to 90%. Interestingly, the receiver
operator characteristic curve for CA-125 alone resulted in
sensitivities of 70% to 80%. The classification tree analysis
described in the paper for discrimination of benign condition from
ovarian cancer used CA-125, granulocyte colony-stimulating factor
(G-CSF), IL-6, EGF, and VEGF which resulted in 86.5% sensitivity
and 93.0% specificity. The authors concluded that simultaneous
testing of a panel of serum cytokines and CA-125 using LabMAP
technology presented a promising approach for ovarian cancer
detection.
[0017] A related patent application by Lokshin, "Enhanced
diagnostic multimarker serological profiling," U. S. Patent
Application 20070042405 published Feb. 22, 2007 describes various
biomarker panels and associated methods for diagnosis of ovarian
cancer. One method involves determining the levels of at least four
markers in the blood of a patient, where at least two different
markers are selected from CA-125, prolactin, HE4 (human epididymis
protein 4), sV-CAM and TSH; and where a third marker and a fourth
marker are selected from CA-125, prolactin, HE4, sV-CAM. TSH,
cytokeratin, sI-CAM, IGFBP-1, eotaxin and FSH, where each of the
third marker and fourth marker selected from the above listed
markers is different from each other and different from either of
the first and second markers, and where dysregulation of at least
the four markers indicates high specificity and sensitivity for a
diagnosis of ovarian cancer. Another panel includes at least eight
markers in the blood of a patient, wherein at least four different
markers are selected from the group consisting of CA-125,
prolactin, HE4, sV-CAM, and TSH and wherein a fifth marker, a sixth
marker, a seventh marker and an eighth marker are selected from the
group consisting of CA-125, prolactin, HE4, sV-CAM, TSH,
cytokeratin, sI-CAM, IGFBP-1, eotaxin and FSH, and further wherein
each of said fifth marker, said sixth marker, said seventh marker
and said eighth marker is different from the other and is different
from any of said at least four markers, wherein dysregulation of
said at least eight markers indicates high specificity and
sensitivity for a diagnosis of ovarian cancer.
[0018] The Lokshin (2007) patent application also describes a blood
marker panel comprising two or more of EGF (epidermal growth
factor), G-CSF (granulocyte colony stimulating factor), IL-6, IL-8,
CA-125 (Cancer Antigen 125), VEGF (vascular endothelial growth
factor), MCP-I (monocyte chemoattractant protein-1), anti-IL6,
anti-TL8, anti-CA-125, anti-c-myc, anti-p53, anti-CEA, anti-CA
15-3, anti-MUC-1, anti-survivin, anti-bHCG, anti-osteopontin,
anti-PDGF, anti-Her2/neu, anti-Akt1, anti-cytokeratin 19,
cytokeratin 19, EGFR, CEA, kallikrein-8, M-CSF, FasL, ErbB2 and
Her2/neu in a sample of the patients blood, where the presence of
two or more of the following conditions indicated the presence of
ovarian cancer in the patient: EGF (low), G-CSF (high), IL-6
(high), IL-8 (high), VEGF (high), MCP-1 (low), anti-IL-6 (high),
anti-IL-8 (high), anti-CA-125 (high), anti-c-myc (high),
anti-p.sup.53 (high), anti-CEA (high), anti-CA 15-3 (high),
anti-MUC-1 (high), anti-survivin (high), anti-bHCG (high),
anti-osteopontin (high), anti-Her2/neu (high), anti-Akt1 (high),
anti-cytokeratin 19 (high), anti-PDGF (high), CA-125 (high),
cytokeratin 19 (high), EGFR (low, Her2/neu (low), CEA (high). FasL
(high), kallikrein-8 (low), ErbB2 (low) and M-CSF (low). Exemplary
panels include, without limitation: CA-125, cytokeratin-19, FasL,
M-CSF; cytokeratin-19, CEA, Fas, EGFR, kallikrein-8; CEA, Fas,
M-CSF, EGFR, CA-125; cytokeratin 19, kallikrein 8, CEA, CA 125,
M-CSF; kallikrein-8, EGFR, CA-125; cytokeratin-19, CEA, CA-125,
M-CSF, EGFR; cytokeratin-19, kallikrein-8, CA-125, M-CSF, FasL;
cytokeratin-19, kallikrein-8, CEA, M-CSF; cytokeratin-19,
kallikrein-8, CEA, CA-125; CA 125, cytokeratin 19, ErbB2; EGF,
G-CSF, IL-6, IL-8, VEGF and MCP-1; anti-CA 15-3, anti-IL-8,
anti-survivin, anti-p53 and anti c-myc; anti-CA 15-3, anti-IL-8,
anti-survivin, anti-p53, anti c-myc, anti-CEA, anti-IL-6, anti-EGF;
and anti-bHCG.
[0019] Chan, et al., "Use of biomarkers for detecting ovarian
cancer," U.S. Published Patent Application 20050059013, published
Mar. 17, 2005 describes a method of qualifying ovarian cancer
status in a subject comprising: (a) measuring at least one
biomarker in a sample from the subject, wherein the biomarker is
selected from the group consisting of ApoA1,
transthyretin.DELTA.N10, IAIH4 fragment, and combinations thereof,
and (b) correlating the measurement with ovarian cancer status.
[0020] Another embodiment in the Chan application described an
additional biomarker selected from CA125, CA125 II, CA15-3, CA19-9,
CA72-4, CA 195, tumor associated trypsin inhibitor (TATI), CEA,
placental alkaline phosphatase (PLAP), Sialyl TN,
galactosyltransferase, macrophage colony stimulating factor (M-CSF,
CSF-1), lysophosphatidic acid (LPA), 110 kD component of the
extracellular domain of the epidermal growth factor receptor
(p110EGFR), tissue kallikreins, for example, kallikrein 6 and
kallikrein 10 (NES-1), prostasin, HE4, creatine kinase B (CKB),
LASA, HER-2/neu, urinary gonadotropin peptide, Dianon NB 70/K,
Tissue peptide antigen (TPA), osteopontin and haptoglobin, and
protein variants (e.g., cleavage forms, isoforms) of the
markers.
[0021] An ELISA-based blood serum test described the evaluation of
four proteins useful in the early diagnosis of epithelial ovarian
cancer (leptin, prolactin, osteopontin and insulin-like growth
factor). The authors reported that no single protein could
completely distinguish the cancer group from the healthy control
group. However, the combination of these four proteins provided
sensitivity 95%, positive predictive value (PPV) 95%, specificity
95%, and negative predictive value (NPV) 94%, which was said to be
a considerable improvement on current methodology. Mor et al.,
"Serum protein markers for early detection of ovarian cancer," PNAS
(102:21) 7677-7682 (2005).
[0022] A related patent application by Mor et al. "Identification
of Cancer Protein Biomarkers Using Proteomic Techniques," U.S.
Patent Application 2005/0214826. published Sep. 29, 2005 describes
biomarkers identified by using a novel screening method. The
biomarkers are stated to discriminate between cancer and healthy
subjects as well as being useful in the prognosis and monitoring of
cancer. Specifically, the abstract of the patent application
relates to the use of leptin, prolactin, OPN and IGF-II for these
purposes. The disclosed invention is somewhat more generally
characterized as involving the comparison of expression of one or
more biomarkers in a sample that are selected from the group
consisting of: 6Ckine, ACE, BDNF, CA125, E-Selectin, EGF, Eot2,
ErbB1, follistatin, HCC4, HVEM, IGF-II, IGFBP-1, IL-17, IL-1sRII,
IL-2sRa, leptin, M-CSF R, MIF, MIP-1a, MIP3b, MMP-8, MMP7, MPIF-1,
OPN, PARC, PDGF Rb, prolactin, ProteinC, TGF-b RIII, TNF-R1, TNF-a,
VAP-1, VEGF R2 and VEGF R3. A significant difference in the
expression of these one or more biomarkers in the sample as
compared to a predetermined standard of each is said to diagnose or
aid in the diagnosis of cancer.
[0023] A patent application by Le Page et al. "Methods of
Diagnosing Ovarian Cancer and Kits Therefor," WO2007/030949,
published Mar. 22, 2007 describes a method for determining whether
a subject is affected by ovarian cancer by detecting the expression
levels of FGF-2 and CA125 and, optionally, IL-18.
[0024] Other approaches described in the patent and scientific
literature include the analysis of expression of particular gene
transcripts in blood cells. See, for example, Liew, "Method for the
Detection of Cancer Related Gene Transcripts in Blood," U.S.
Published Patent Application 2006/0134637, Jun. 22, 2006. Although
gene transcripts specific for ovarian cancer are not identified,
transcripts from Tables 3J, 3K and 3X are said to indicate the
presence of cancer. See also, Tchagang et al., "Early Detection of
Ovarian Cancer Using Group Biomarkers," Mol. Cancer Ther. (1):7
(2008).
[0025] Another diagnostic approach involves detecting circulating
antibodies directed against tumor-associated antigens. See, Nelson
et al. "Antigen Panels and Methods of Using the Same," U.S. Patent
Application 2005/0221305, published Oct. 6, 2005; and Robertson
"Cancer Detection Methods and Regents," U.S. Patent Application
2003/0232399, published Dec. 18, 2003.
[0026] What has been urgently needed in the field of gynecologic
oncology is a minimally invasive (preferably serum-based) clinical
test for assessing and predicting the presence of ovarian cancer
that is based on a robust set of biomarkers and sample features
identified from a large and diverse set of samples, together with
methods and associated computer systems and software tools to
predict, diagnose and monitor ovarian cancer with high accuracy at
its various stages.
SUMMARY OF THE INVENTION
[0027] The present invention generally relates to cancer biomarkers
and particularly to biomarkers associated with ovarian cancer. It
provides methods to predict, evaluate diagnose, and monitor cancer,
particularly ovarian cancer, by measuring certain biomarkers, and
further provides a set or array of reagents to evaluate the
expression levels of biomarkers that are associated with ovarian
cancer. A preferred set of biomarkers provides a detectable
molecular signature of ovarian cancer in a subject. The invention
provides a predictive or diagnostic test for ovarian cancer,
particularly for epithelial ovarian cancer and more particularly
for early-stage ovarian cancer (that is Stage I, Stage II or Stage
I and II together).
[0028] More specifically, predictive tests and associated methods
and products also provide useful clinical information regarding the
stage of ovarian cancer progression, that is: Stage I, Stage II,
Stage III and Stage IV and an advanced stage which reflects
relatively advanced tumors that cannot readily be classified as
either Stage III or Stage IV. Overall, the invention also relates
to newly discovered correlations between the relative levels of
expression of certain groups of markers in bodily fluids,
preferably blood serum and plasma, and a subject's ovarian cancer
status.
[0029] In one embodiment, the invention provides a set of reagents
to measure the expression levels of a panel or set of biomarkers in
a fluid sample drawn from a patient, such as blood, serum, plasma,
lymph, cerebrospinal fluid, ascites or urine. The reagents in a
further embodiment are a multianalyte panel assay comprising
reagents to evaluate the expression levels of these biomarker
panels.
[0030] In embodiments of the invention, a subject's sample is
prepared from tissue samples such a tissue biopsy or from primary
cell cultures or culture fluid. In a further embodiment, the
expression of the biomarkers is determined at the polypeptide
level. Related embodiments utilize immunoassays, enzyme-linked
immunosorbent assays and multiplexed immunoassays for this
purpose.
[0031] Preferred panels of biomarkers are selected from the group
consisting of the following sets of molecules and their measurable
fragments: (a) myoglobin, CRP (C reactive protein), FGF basic
protein and CA 19-9; (b) Hepatitis C NS4, Ribosomal P Antibody and
CRP; (c) CA 19-9. TGF alpha, EN-RAGE, EGF and HSP 90 alpha
antibody, (d) EN-RAGE, EGF, CA 125, Fibrinogen, Apolipoprotein
CIII, EGF, Cholera Toxin and CA 19-9; (e) Proteinase 3 (cANCA)
antibody, Fibrinogen, CA 125, EGF, CD40, TSH, Leptin, CA 19-9 and
lymphotactin; (f) CA125, EGFR, CRP, IL-18, Apolipoprotein CIII,
Tenascin C and Apolipoprotein A1; (g) CA125, Beta-2 Microglobulin,
CRP, Ferritin, TIMP-1, Creatine Kinase-MB and IL-8; (h) CA125,
EGFR, IL-10, Haptoglobin, CRP, Insulin, TIMP-1, Ferritin, Alpha-2
Macroglobulin, Leptin, IL-8, CTGF, EN-RAGE, Lymphotactin,
TNF-alpha, IGF-1, TNF RII, von Willebrand Factor and MDC; (i)
CA-125, CRP, EGF-R, CA-19-9, Apo-AI, Apo-CIII, IL-6, IL-I8, MIP-1a,
Tenascin C and Myoglobin; (j) CA-125, CRP, EGF-R, CA-19-9, Apo-AI,
Apo-CIII, IL-6, MIP-1 a, Tenascin C and Myoglobin; and (k) any of
the biomarker panels presented in Table II and Table III.
[0032] In another embodiment, the reagents that measure such
biomarkers may measure other molecular species that are found
upstream or downstream in a biochemical pathway or measure
fragments of such biomarkers and molecular species. In some
instances, the same reagent may accurately measure a biomarker and
its fragments.
[0033] Another embodiment of the present invention relates to
binding molecules (or binding reagents) to measure the biomarkers
and related molecules and fragments. Contemplated binding molecules
includes antibodies, both monoclonal and polyclonal, aptamers and
the like.
[0034] Other embodiments include such binding reagents provided in
the form of a test kit, optionally together with written
instructions for performing an evaluation of biomarkers to predict
the likelihood of ovarian cancer in a subject.
[0035] In other of its embodiments, the present invention provides
methods of predicting the likelihood of ovarian cancer in a subject
based on detecting or measuring the levels in a specimen or
biological sample from the subject of the foregoing biomarkers. As
described in this specification, a change in the expression levels
of these biomarkers, particularly their relative expression levels,
as compared with a control group of patients who do not have
ovarian cancer, is predictive of ovarian cancer in that
subject.
[0036] In other of its aspects, the type of ovarian cancer that is
predicted is serous, endometrioid, mucinous, and clear cell tumors.
And prediction of ovarian cancer includes the prediction of a
specific stage of the disease such as Stage I (IA, IB or IC), II,
III and IV tumors.
[0037] In yet another embodiment, the invention relates to creating
a report for a physician of the relative levels of the biomarkers
and to transmitting such a report by mail, fax, email or otherwise.
In an embodiment, a data stream is transmitted via the internet
that contains the reports of the biomarker evaluations. In a
further embodiment, the report includes the prediction as to the
presence or absence of ovarian cancer in the subject or the
stratified risk of ovarian cancer for the subject, optionally by
subtype or stage of cancer.
[0038] According to another aspect of the invention, the foregoing
evaluation of biomarker expression levels is combined for
diagnostic purposes with other diagnostic procedures such as
gastrointestinal tract evaluation, chest x-ray, HE4 test, CA-125
test, complete blood count, ultrasound or abdominal/pelvic
computerized tomography, blood chemistry profile and liver function
tests.
[0039] Yet other embodiments of the invention relate to the
evaluation of samples drawn from a subject who is symptomatic for
ovarian cancer or is at high risk for ovarian cancer. Other
embodiments relate to subjects who are asymptomatic of ovarian
cancer. Symptomatic subjects have one or more of the following:
pelvic mass; ascites; abdominal distention; general abdominal
discomfort and/or pain (gas, indigestion, pressure, swelling,
bloating, cramps); nausea, diarrhea, constipation, or frequent
urination; loss of appetite; feeling of fullness even after a light
meal; weight gain or loss with no known reason; and abnormal
bleeding from the vagina. The levels of biomarkers may be combined
with the findings of such symptoms for a diagnosis of ovarian
cancer.
[0040] Embodiments of the invention are highly accurate for
determining the presence of ovarian cancer. By "highly accurate" is
meant a sensitivity and a specificity each at least about 85 per
cent or higher, more preferably at least about 90 per cent or 92
per cent and most preferably at least about 95 per cent or 97 per
cent accurate. Embodiments of the invention further include methods
having a sensitivity of at least about 85 per cent, 90 per cent or
95 per cent and a specificity of at least about 55 per cent, 65 per
cent, 75 per cent, 85 per cent or 90 per cent or higher. Other
embodiments include methods having a specificity of at least about
85 per cent, 90 per cent or 95 per cent, and a sensitivity of at
least about 55 per cent, 65 per cent, 75 per cent, 85 per cent or
90 per cent or higher.
[0041] Embodiments of the invention relating sensitivity and
specificity are determined for a population of subjects who are
symptomatic for ovarian cancer and have ovarian cancer as compared
with a control group of subjects who are symptomatic for ovarian
cancer but who do not have ovarian cancer. In another embodiment,
sensitivity and specificity are determined for a population of
subjects who are at increased risk for ovarian cancer and have
ovarian cancer as compared with a control group of subjects who are
at increased risk for ovarian cancer but who do not have ovarian
cancer. And in another embodiment, sensitivity and specificity are
determined for a population of subjects who are symptomatic for
ovarian cancer and have ovarian cancer as compared with a control
group of subjects who are not symptomatic for ovarian cancer but
who do not have ovarian cancer.
[0042] In other aspects, the levels of the biomarkers are evaluated
by applying a statistical method such as knowledge discovery engine
(KDE.TM.), regression analysis, discriminant analysis,
classification tree analysis, random forests, ProteomeQuest.RTM.,
support vector machine, One R, kNN and heuristic naive Bayes
analysis, neural nets and variants thereof.
[0043] In another embodiment, a predictive or diagnostic model
based on the expression levels of the biomarkers is provided. The
model may be in the form of software code, computer readable format
or in the form of written instructions for evaluating the relative
expression of the biomarkers.
[0044] A patient's physician can utilize a report of the biomarker
evaluation, in a broader diagnostic context, in order to develop a
relatively more complete assessment of the risk that a given
patient has ovarian cancer. In making this assessment, a physician
will consider the clinical presentation of a patient, which
includes symptoms such as a suspicious pelvic mass and/or ascites,
abdominal distention and other symptoms without another obvious
source of malignancy. The general lab workup for symptomatic
patients currently includes a GI evaluation if clinically
indicated, chest x-ray, CA-125 test, CBC, ultrasound or
abdominal/pelvic CT if clinically indicated, chemistry profile with
LFTs and may include a family history evaluation along with genetic
marker tests such as BRCA-1 and BRCA-2. (See, generally, the NCCN
Clinical Practice Guidelines in Oncology.TM. for Ovarian Cancer,
V.1.2007.)
[0045] The present invention provides a novel and important
additional source of information to assist a physician in
stratifying a patient's risk of having ovarian cancer and in
planning the next diagnostic steps to take. The present invention
is also similarly useful in assessing the risk of ovarian cancer in
non-symptomatic, high-risk subjects as well as for the general
population as a screening tool. It is contemplated that the methods
of the present invention may be used by clinicians as part of an
overall assessment of other predictive and diagnostic
indicators.
[0046] The present invention also provides methods to assess the
therapeutic efficacy of existing and candidate chemotherapeutic
agents and other types of cancer treatments. As will be appreciated
by persons skilled in the art, the relative expression levels of
the biomarker panels--or biomarker profiles--are determined as
described above, in specimens taken from a subject prior to and
again after treatment or, optionally, at progressive stages during
treatment. A change in the relative expression of these biomarkers
to a non-cancer profile of expression levels (or to a more nearly
non-cancer expression profile) or to a stable, non-changing profile
of relative biomarker expression levels is interpreted as
therapeutic efficacy. Persons skilled in the art will readily
understand that a profile of such expressions levels may become
diagnostic for cancer or a pre-cancer, pre-malignant condition or
simply move toward such a diagnostic profile as the relative ratios
of the biomarkers become more like a cancer-related profile than
previously.
[0047] In another embodiment, the invention provides a method for
determining whether a subject potentially is developing cancer. The
relative levels of expression of the biomarkers are determined in
specimens taken from a subject over time, whereby a change in the
biomarker expression profile toward a cancer profile is interpreted
as a progression toward developing cancer.
[0048] The expression levels of the biomarkers of a specimen may be
stored electronically once a subject's analysis is completed and
recalled for such comparison purposes at a future time.
[0049] The present invention further provides methods, software
products, computer systems and networks, and associated instruments
that provide a highly accurate test for ovarian cancer.
[0050] The combinations of markers described in this specification
provide sensitive, specific and accurate methods for predicting the
presence of or detecting ovarian cancer at various stages of its
progression. The evaluation of samples as described may also
correlate with the presence of a pre-malignant or a pre-clinical
condition in a patient. Thus, it is contemplated that the disclosed
methods are useful for predicting or detecting the presence of
ovarian cancer in a sample, the absence of ovarian cancer in a
sample drawn from a subject, the stage of an ovarian cancer, the
grade of an ovarian cancer, the benign or malignant nature of an
ovarian cancer, the metastatic potential of an ovarian cancer, the
histological type of neoplasm associated with the ovarian cancer,
the indolence or aggressiveness of the cancer, and other
characteristics of ovarian cancer that are relevant to prevention,
diagnosis, characterization, and therapy of ovarian cancer in a
patient.
[0051] It is further contemplated that the methods disclosed are
also useful for assessing the efficacy of one or more test agents
for inhibiting ovarian cancer, assessing the efficacy of a therapy
for ovarian cancer, monitoring the progression of ovarian cancer,
selecting an agent or therapy for inhibiting ovarian cancer,
monitoring the treatment of a patient afflicted with ovarian
cancer, monitoring the inhibition of ovarian cancer in a patient,
and assessing the carcinogenic potential of a test compound by
evaluating biomarkers of test animals following exposure.
DETAILED DESCRIPTION
[0052] The biomarker panels and associated methods and products
were identified through the analysis of analyte levels of various
molecular species in human blood serum drawn from subjects having
ovarian cancer of various stages and subtypes, subjects having
non-cancer gynecological disorders and normal subjects. The
immunoassays described below were courteously performed by our
colleagues at Rules-Based Medicine of Austin, Tex. using their
Multi-Analyte Profile (MAP) Luminex.RTM. platform
(www.rulesbasedmedicine.com).
[0053] While a preferred sample is blood scrum, it is contemplated
that an appropriate sample can be derived from any biological
source or sample, such as tissues, extracts, cell cultures,
including cells (for example, tumor cells), cell lysates, and
physiological fluids, such as, for example, whole blood, plasma,
serum, saliva, ductal lavage, ocular lens fluid, cerebral spinal
fluid, sweat, urine, milk, ascites fluid, synovial fluid,
peritoneal fluid and the like. The sample can be obtained from
animals, preferably mammals, more preferably primates, and most
preferably humans using species specific binding agents that are
equivalent to those discussed below in the context of human sample
analysis. It is further contemplated that these techniques and
marker panels may be used to evaluate drug therapy in rodents and
other animals, including transgenic animals, relevant to the
development of human and veterinary therapeutics.
[0054] The sample can be treated prior to use by conventional
techniques, such as preparing plasma from blood, diluting viscous
fluids, and the like. Methods of sample treatment can involve
filtration, distillation, extraction, concentration, inactivation
of interfering components, addition of chaotropes, the addition of
reagents, and the like. Nucleic acids (including silencer,
regulatory and interfering RNA) may be isolated and their levels of
expression for the analytes described below also used in the
methods of the invention.
Samples and Analytical Platform
[0055] The set of blood serum samples that was analyzed to generate
most of the data discussed below contained 150 ovarian cancer
samples and 150 non-ovarian cancer samples. Subsets of these
samples were used as described. The ovarian cancer sample samples
further comprised the following epithelial ovarian cancer subtypes:
serous (64), clear cell (22). endometrioid (35), mucinous (15),
mixed, that is, consisting of more than one subtype (14). The stage
distribution of the ovarian cancer samples was: Stage I (41), Stage
II (23), Stage III (68), Stage IV (12) and unknown stage (6).
[0056] The non-ovarian cancer sample set includes the following
ovarian conditions: benign (104), normal ovary (29) and "low
malignant potential/borderline (3). The sample set also includes
serum from patients with other cancers: cervical cancer (7),
endometrial cancer (6) and uterine cancer (1).
[0057] Analyte levels in the samples discussed in this
specification were measured using a high-throughput, multi-analyte
immunoassay platform. A preferred platform is the Luminex.RTM. MAP
system as developed by Rules-Based Medicine, Inc. in Austin, Tex.
It is described on the company's website and also, for example, in
publications such as Chandler et al., "Methods and kits for the
diagnosis of acute coronary syndrome, U. S. Patent Application
2007/0003981, published Jan. 4, 2007, and a related application of
Spain et al., "Universal Shotgun Assay," U. S. Patent Application
2005/0221363, published Oct. 6, 2005. This platform has previously
been described in Lokshin (2007) and generated data used in other
analyses of ovarian cancer biomarkers. However, any immunoassay
platform or system may be used.
[0058] In brief, to describe a preferred analyte measurement
system, the MAP platform incorporates polystyrene microspheres that
are dyed internally with two spectrally distinct fluorochromes. By
using accurate ratios of the fluorochromes, an array is created
consisting of 100 different microsphere sets with specific spectral
addresses. Each microsphere set can display a different surface
reactant. Because microsphere sets can be distinguished by their
spectral addresses, they can be combined, allowing up to 100
different analytes to be measured simultaneously in a single
reaction vessel. A third fluorochrome coupled to a reporter
molecule quantifies the biomolecular interaction that has occurred
at the microsphere surface. Microspheres are interrogated
individually in a rapidly flowing fluid stream as they pass by two
separate lasers in the Luminex.RTM. analyzer. High-speed digital
signal processing classifies the microsphere based on its spectral
address and quantifies the reaction nil the surface in a few
seconds per sample.
[0059] Skilled artisans will recognize that a wide variety of
analytical techniques may be used to determine the levels of
biomarkers in a sample as is described and claimed in this
specification. Other types of binding reagents available to persons
skilled in the art may be utilized to measure the levels of the
indicated analytes in a sample. For example, a variety of binding
agents or binding reagents appropriate to evaluate the levels of a
given analyte may readily be identified in the scientific
literature. Generally, an appropriate binding agent will bind
specifically to an analyte, in other words, it reacts at a
detectable level with the analyte but does not react detectably (or
reacts with limited cross-reactivity) with other or unrelated
analytes. It is contemplated that appropriate binding agents
include polyclonal and monoclonal antibodies, aptamers, RNA
molecules and the like. Spectrometric methods also may be used to
measure the levels of analytes, including immunofluorescence, mass
spectrometry, nuclear magnetic resonance and optical spectrometric
methods. Depending on the binding agent to be utilized, the samples
may be processed, for example, by dilution, purification,
denaturation, digestion, fragmentation and the like before analysis
as would be known to persons skilled in the art. Also, gene
expression, for example, in a tumor cell or lymphocyte also may be
determined.
[0060] It is also contemplated that the identified biomarkers may
have multiple epitopes for immunassays and/or binding sites for
other types of binding agents. Thus, it is contemplated that
peptide fragments or other epitopes of the identified biomarkers,
isoforms of specific proteins and even compounds upstream or
downstream in a biological pathway or that have been
post-translationaily modified may be substituted for the identified
analytes or biomarkers so long as the relevant and relative
stoichiometries are taken into account appropriately. Skilled
artisans will recognize that alternative antibodies and binding
agents can be used to determine the levels of any particular
analyte, so long as their various specificities and binding
affinities are factored into the analysis.
[0061] A variety of algorithms may be used to measure or determine
the levels of expression of the analytes or biomarkers used in the
methods and test kits of the present invention. It is generally
contemplated that such algorithms will be capable of measuring
analyte levels beyond the measurement of simple cut-off values.
Thus, it is contemplated that the results of such algorithms will
generically be classified as multivariate index analyses by the
U.S. Food and Drug Administration. Specific types of algorithms
include: knowledge discovery engine (KDE.TM.), regression analysis,
discriminant analysis, classification tree analysis, random
forests, ProteomeQuest.RTM., support vector machine, One R, kNN and
heuristic naive Bayes analysis, neural nets and variants
thereof.
ANALYLSIS AND EXAMPLES
[0062] The following discussion and examples are provided to
describe and illustrate the present invention. As such, they should
not be construed to limit the scope of the invention. Those skilled
in the art will well appreciate that many other embodiments also
fall within the scope of the invention, as it is described in this
specification and the claims.
Analysis of Data To Find Informative Biomarker Panels Using the
KDE.TM.
[0063] Correlogic has described the use of evolutionary and pattern
recognition algorithms in evaluating complex data sets, including
the Knowledge Discovery Engine (KDE.TM.) and ProteomeQuest.RTM..
See, for example, Hitt et al., U.S. Pat. No. 6,925,389, "Process
for Discriminating Between Biological States Based on Hidden
Patterns From Biological Data" (issued Aug. 2, 2005); Hitt, U.S.
Pat. No. 7,096,206, "Heuristic Method of Classification," (issued
Aug. 22, 2006) and Hitt, U.S. Pat. No. 7,240,038, "Heuristic Method
of Classification," (to be issued Jul. 3, 2007). The use of this
technology to evaluate mass spectral data derived from ovarian
cancer samples is further elucidated in Hitt et al., "Multiple
high-resolution serum proteomic features for ovarian cancer
detection." U. S. Published Patent Application 2006/0064253.
published Mar. 23, 2006.
[0064] When analyzing the data set by Correlogic's Knowledge
Discovery Engine, the following five-biomarker panels were found to
provide sensitivities and specificities for various stages of
ovarian cancer as set forth in Table 1. Specifically, KDE Model 1
[2_0008_20] returned a relatively high accuracy for Stage I ovarian
cancer and included these markers: Cancer Antigen 19-9 (CA19-9,
Swiss-Prot Accession Number: Q9BXJ9), C Reactive Protein (CRP,
Swiss-Prot Accession Number: P02741), Fibroblast Growth
Factor-basic Protein (FGF-basic, Swiss-Prot Accession Number:
P09038) and Myoglobin (Swiss-Prot Accession Number: P02144). KDE
Model 2 [4_0002-10] returned a relatively high accuracy for Stage
III, IV and "advanced" ovarian cancer and included these markers:
Hepatitis C NS4 Antibody (Hep C NS4 Ab), Ribosomal P Antibody and
CRP. KDE Model 3 [4_0009_140] returned a relatively high accuracy
for Stage I and included these markers: CA 19-9, TGF alpha, EN-RAGE
(Swiss-Prot Accession Number: P80511), Epidermal Growth Factor
(EGF, Swiss-Prot Accession Number: P01133) and HSP 90 alpha
antibody. KDE Model 4 [4_0026_100] returned a relatively high
accuracy for Stage II and Stages III, IV and "advanced" ovarian
cancers and included these markers: EN-RAGE, EGF, Cancer Antigen
125 (CA125, Swiss-Prot Accession Number: Q14596), Fibrinogen
(Swiss-Prot Accession Number: Alpha chain P02671; Beta chain
P02675; Gamma chain P02679), Apolipoprotein CIII (ApoCIII,
Swiss-Prot Accession Number: P02656), Cholera Toxin and CA 19-9.
KDE Model 5 [4_0027_20] also returned a relatively high accuracy
for Stage II and Stages III, IV and "advanced" ovarian cancers and
included these markers: Proteinase 3 (cANCA) antibody. Fibrinogen,
CA 125, EGF, CD40 (Swiss-Prot Accession Number: Q6P2H9), Thyroid
Stimulating Hormone (TSH, Swiss-Prot Accession Number: Alpha
P01215; Beta P01222 P02679, Leptin (Swiss-Prot Accession Number:
P41159), CA 19-9 and Lymphotactin (Swiss-Prot Accession Number:
P47992). It is contemplated that skilled artisans could use the KDE
analytical tools to identify other, potentially useful sets of
biomarkers for predictive or diagnostic value based on the levels
of selected analytes. Note that the KDE algorithm may select and
utilize various markers based on their relative abundances; and
that a given marker, for example the level of cholera toxin in
Model IV may be zero but is relevant in combination with the other
markers selected in a particular grouping.
[0065] Skilled artisans will recognize that a limited size data set
as was used in this specification may lead to different results,
for example, different panels of markers and varying accuracies
when comparing the relative performance of KDE with other
analytical techniques to identify informative panels of biomarkers.
These particular KDE models were built on a relatively small data
set using 40 stage I ovarian cancers and 40 normal/benigns and were
tested blindly on the balance of the stage II, III/IV described
above. Thus, the specificity is of the stage I samples reflects
sample set size and potential overfitting. The drop in specificity
for the balance of the non-ovarian cancer samples also is expected
given the relatively larger size of the testing set relative to the
training set. Overall, the biomarker panel developed for the stage
I samples also provides potentially useful predictive and
diagnostic assays for later stages of ovarian cancer given the high
sensitivity values.
[0066] However, these examples of biomarker panels illustrate that
there are a number of parameters that can be adjusted to impact
model performance. For instance in these cases a variety of
different numbers of features are combined together, a variety of
match values are used, a variety of different lengths of evolution
of the genetic algorithm are used and models differing in the
number of nodes are generated. By routine experimentation apparent
to one skilled in the art, combinations of these parameters can be
used to generate other predictive models based on biomarker panels
having clinically relevant performance.
TABLE-US-00001 TABLE I Results of Analysis Using Knowledge
Discovery Engine to develop a stage I specific classification
model. Sensitivity Specificity Accuracy Sensitivity Sensitivity
Model Name Feature Match Generation Node Stage I Stage I Stage I
Stage II Stage III-IV Specificity 2_0008_20 4 0.9 20 12 75 100 87.5
60.9 46.5 82.0 4_0002_10 3 0.7 10 4 75 100 87.5 69.6 82.6 56
4_0009_140 5 0.6 140 5 75 100 87.5 43.5 39.5 71.6 4_0026_100 9 0.7
100 5 87.5 100 93.8 78.3 84.9 67 4_0027_20 9 0.8 20 5 87.5 100 93.8
78.3 84.9 60.6
Methods and Analysis To Find Informative Biomarker Panels Using
Random Forests
[0067] A preferred analytical technique, known to skilled artisans,
is that of Breiman, Random Forests. Machine Learning, 2001.
45:5-32; as further described by Segel, Machine Learning Benchmarks
and Random Forest Regression, 2004; and Robnik-Sikonja, Improving
Random Forests, in Machine Learning, ECML, 2004 Proceedings, J. F.
B. e. al., Editor, 2004, Springer: Berlin. Other variants of Random
Forests are also useful and contemplated for the methods of the
present invention, for example, Regression Forests, Survival
Forests, and weighted population Random Forests.
[0068] A modeling set of samples was used as described above for
diagnostic models built with the KDE algorithm. Since each of the
analyte assays is an independent measurement of a variable, under
some circumstances, known to those skilled in the art, it is
appropriate to scale the data to adjust for the differing variances
of each assay. In such cases, biweight, MAD or equivalent scaling
would be appropriate, although in some cases, scaling would not be
expected to have a significant impact. A bootstrap layer on top of
the Random Forests was used in obtaining the results discussed
below.
[0069] In preferred embodiments of the present invention,
contemplated panels of biomarkers are:
[0070] a. Cancer Antigen 125 (CA125, Swiss-Prot Accession Number:
Q14596) and Epidermal Growth Factor Receptor (EGF-R, Swiss-Prot
Accession Number: P00533).
[0071] b. CA125 and C Reactive Protein (CRP, Swiss-Prot Accession
Number: P02741).
[0072] c. CA125, CRP and EGF-R.
[0073] d. Any one or more of CA125, CRP and EGF-R, plus any one or
more of Ferritin (Swiss-Prot Accession Number: Heavy chain P02794;
Light chain P02792), Interleukin-8 (IL-8, Swiss-Prot Accession
Number: P10145), and Tissue Inhibitor of Metalloproteinases 1
(TIMP-1, Swiss-Prot Accession Number: P01033),
[0074] e. Any one of the biomarker panels presented in Table II and
Table III.
[0075] f. Any of the foregoing panels of biomarkers (a-e) plus any
one or more of the other biomarkers in the following list if not
previously included in the foregoing panels (a-e). These additional
biomarkers were identified empirically or by a literature review:
Alpha-2 Macroglobulin (A2M, Swiss-Prot Accession Number: P01023),
Apolipoprotein A1-1 (ApoA1, Swiss-Prot Accession Number: P02647),
Apolipoprotein C-III (ApoCIII, Swiss-Prot Accession Number:
P02656), Apolipoprotein H (ApoH, Swiss-Prot Accession Number:
P02749), Beta-2 Microglobulin (B2M, Swiss-Prot Accession Number:
P23560), Betacellulin (Swiss-Prot Accession Number: P35070), C
Reactive Protein (CRP, Swiss-Prot Accession Number: P02741). Cancer
Antigen 19-9 (CA19-9, Swiss-Prot Accession Number: Q9BXJ9), Cancer
Antigen 125 (CA125, Swiss-Prot Accession Number: Q14596), Collagen
Type 2 Antibody, Creatine Kinase-MB (CK-MB, Swiss-Prot Accession
Number: Brain P12277; Muscle P06732), C Reactive Protein (CRP,
Swiss-Prot Accession Number: P02741), Connective Tissue Growth
Factor (CTGF, Swiss-Prot Accession Number: P29279), Double Stranded
DNA Antibody (dsDNA Ab), EN-RAGE (Swiss-Prot Accession Number:
P80511), Eotaxin (C--C motif chemokine 11, small-inducible cytokine
A11 and Eosinophil chemotactic protein, Swiss-Prot Accession
Number: P51671), Epidermal Growth Factor Receptor (EGF-R,
Swiss-Prot Accession Number: P00533), Ferritin (Swiss-Prot
Accession Number: Heavy chain P02794; Light chain P02792),
Follicle-stimulating hormone (FSH, Follicle-stimulating hormone
beta subunit, FSH-beta, FSH-B, Follitropin beta chain, Follitropin
subunit beta, Swiss-Prot Accession Number: P01225), Haptoglobin
(Swiss-Prot Accession Number: P00738), HE4 (Major
epididymis-specific protein E4, Epididymal secretory protein E4,
Putative protease inhibitor WAP5 and WAP four-disulfide core domain
protein 2, Swiss-Prot Accession Number: Q14508), Insulin
(Swiss-Prot Accession Number: P01308), Insulin-like Growth Factor 1
(IGF-1, Swiss-Prot Accession Number: P01343), Insulin like growth
factor II (IGF-II, Somatomedin-A, Swiss-Prot Accession Number:
P01344), Insulin Factor VII (Swiss-Prot Accession Number: P08709),
Interleukin-6 (IL-6, Swiss-Prot Accession Number: P05231),
Interleukin-8 (IL-8, Swiss-Prot Accession Number: P10145),
Interleukin-10 (IL-10, Swiss-Prot Accession Number: P22301),
Interleukin-18 (IL-18, Swiss-Prot Accession Number: Q14116), Leptin
(Swiss-Prot Accession Number: P41159), Lymphotactin (Swiss-Prot
Accession Number: P47992), Macrophage-derived Chemokine (MDC,
Swiss-Prot Accession Number: O00626), Macrophage Inhibitory Factor
(SWISS PROT), Macrophage Inflammatory Protein 1 alpha (MIP-1alpha,
Swiss-Prot Accession Number: P10147), Macrophage migration
inhibitory factor (MIF, Phenylpyruvate tautomerase,
Glycosylation-inhibiting factor, GIF, Swiss-Prot Accession Number:
P14174), Myoglobin (Swiss-Prot Accession Number: P02144),
Ostopontin (Bone sialoprotein 1, Secreted phosphoprotein 1, SPP-1,
Urinary stone protein, Nephropontin, Uropontin, Swiss-Prot
Accession Number: P10451), Pancreatic Islet Cells (GAD) Antibody,
Prolactin (Swiss-Prot Accession Number: P01236), Stem Cell Factor
(SCF, Swiss-Prot Accession Number: P21583), Tenascin C (Swiss-Prot
Accession Number: P24821), Tissue Inhibitor of Metalloproteinases 1
(TIMP-1, Swiss-Prot Accession Number: P01033), Tumor Necrosis
Factor-alpha (TNF-alpha, Swiss-Prot Accession Number: P01375),
Tumor Necrosis Factor RII (TNF-RII, Swiss-Prot Accession Number:
Q92956), von Willebrand Factor (vWF, Swiss-Prot Accession Number:
P04275) and the other biomarkers identified as being informative
for cancer in the references cited in this specification.
[0076] Using the Random Forests analytical approach, a preferred
seven biomarker panel was identified that has a high predictive
value for Stage I ovarian cancer. It includes: ApoA1, ApoCIII,
CA125, CRP, EGF-R, IL-18 and Tenascin. In the course of building
and selecting the relatively more accurate models for Stage I
cancers generated by Random Forests using these biomarkers, the
sensitivity for Stage I ovarian cancers ranged from about 80% to
about 85%. Sensitivity was also about 95 for Stage II and about 94%
sensitive for Stage III/IV. The overall specificity was about
70%.
[0077] Similarly, a preferred seven biomarker panel was identified
that has a high predictive value for Stage II. It includes: B2M,
CA125, CK-MB, CRP, Ferritin, IL-8 and TIMP1. A preferred model for
Stage II had a sensitivity of about 82% and a specificity of about
88%.
[0078] For Stage III, Stage IV and advanced ovarian cancer, the
following 19 biomarker panel was identified: A2M, CA125, CRP, CTGF,
EGF-R, EN-RAGE, Ferritin, Haptoglobin, IGF-1, IL-8, IL-10, Insulin,
Leptin, Lymphotactin, MDC, TIMP-1, TNF-alpha, TNF-RII, vWF. A
preferred model for Stage III/IV had a sensitivity of about 86% and
a specificity of about 89%.
[0079] Other preferred biomarker or analyte panels for detecting,
diagnosing and monitoring ovarian cancer are shown in Table II and
in Table III. These panels include CA-125, CRP and EGF-R and, in
most cases, CA19-9. In Table II, 20 such panels of seven analytes
each selected from 20 preferred analytes are displayed in columns
numbered 1 through 20. In Table III, another 20 such panels of
seven analytes each selected from 23 preferred analytes are
displayed in columns numbered 1 through 20.
TABLE-US-00002 TABLE II Additional Biomarker Panels 1 2 3 4 5 6 7 8
9 10 11 12 13 14 15 16 17 18 19 20 CA125 x x x x x x x x x x x x x
x x x x x x x CRP x x x x x x x x x x x x x x x x x x x x EGF-R x x
x x x x x x x x x x x x x x x x x x CA19-9 x x x x x x x x x x x x
x x x x x x x Haptoglobin Serum Amyloid P x x x Apo AI x x IL-6 x x
x x x x Myoglobin x x x x x x x x x x x MIP-1.quadrature. x x x x x
x x x x x x x EN-RAGE CK-MB vWF x x x Leptin x x Apo CIII x x x
Growth Hormone x x x x x x IL-10 IL-18 x x x x x x x x
Myeloperoxidase x x VCAM-1 x x x
TABLE-US-00003 TABLE III Additional Biomarker Panels 1 2 3 4 5 6 7
8 9 10 11 12 13 14 15 16 17 18 19 20 CA125 x x x x x x x x x x x x
x x x x x x x x CRP x x x x x x x x x x x x x x x x x x x x EGF-R x
x x x x x x x x x x x x x x x x x x x CA19-9 x x x x x x x x x x x
x x x x x x x x Haptoglobin Serum Amyloid P x x x Apo AI x x IL-6 x
x x x x x Myoglobin x x x x x x x x x x MIP-1.quadrature. x x x x x
x x x x x x x x x EN-RAGE CK-MB x vWF x x x x Leptin x x x Apo CIII
x x x x x x Growth Hormone IL-10 x x IL-18 Myeloperoxidase x x x
VCAM-1 Insulin x Ferritin x x x x x Haptoglobin x
[0080] Other preferred biomarker panels (or models) for all stages
of ovarian cancer include: (a) CA-125, CRP, EGF-R, CA-19-9, Apo-AI,
Apo-CIII, IL-6, IL-18, MIP-1a, Tenascin C and Myoglobin; (b) CA125,
CRP, CA19-9, EGF-R, Myoglobin, IL-18, Apo CIII; and (e) CA125, CRP,
EGF-R, CA19-9, Apo CIII, MTP-1a, Myoglobin, IL-18, IL-6, Apo AI,
Tenascin C, vWF, Haptoglobin, IL-10. Optionally, any one or more of
the following biomarkers may be added to these or to any of the
other biomarker panels disclosed above in text or tables (to the
extent that any such panels are not already specifically identified
therein): vWF, Haptoglobin, IL-10, IGF-I, IGF-II, Prolactin, HE4,
ACE, ASP and Resistin.
[0081] Any two or more of the preferred biomarkers described above
will have predictive value, however, adding one or more of the
other preferred markers to any of the analytical panels described
herein may increase the panel's predictive value for clinical
purposes. For example, adding one or more of the different
biomarkers listed above or otherwise identified in the references
cited in this specification may also increase the biomarker panel's
predictive value and are therefore expressly contemplated. Skilled
artisans can readily assess the utility of such additional
biomarkers. It is contemplated that additional biomarker
appropriate for addition to the sets (or panels) of biomarkers
disclosed or claimed in this specification will not result in a
decrease in either sensitivity or specificity without a
corresponding increase in either sensitivity or specificity or
without a corresponding increase in robustness of the biomarker
panel overall. A sensitivity and/or specificity of at least about
80% or higher are preferred, more preferably at least about 85% or
higher, and most preferably at least about 90% or 95% or
higher.
[0082] To practice the methods of the present invention,
appropriate cut-off levels for each of the biomarker analytes must
be determined for cancer samples in comparison with control
samples. As discussed above, it is preferred that at least about 40
cancer samples and 40 benign samples (including benign,
non-malignant disease and normal subjects) be used for this
purpose. preferably case matched by age, sex and gender. Larger
sample sets are preferred. A person skilled in the art would
measure the level of each biomarker in the selected biomarker panel
and then use an algorithm, preferably such as Random Forest, to
compare the level of analytes in the cancer samples with the level
of analytes in the control samples. In this way, a predictive
profile can be prepared based on informative cutoffs for the
relevant disease type. The use of a separate validation set of
samples is preferred to confirm the cut-off values so determined.
Case and control samples can be obtained by obtaining consented (or
anonymized) samples in a clinical trial or from repositories like
the Screening Study for Prostate, Lung, Colorectal, and Ovarian
Cancer--PLCO Trial sponsored by the National Cancer Institute
(http://www.cancer.gov/clinicaltrials/PLCO-1) or The Gynecologic
Oncology Group (http://www.gog.org/). Samples obtained in multiple
sites are also preferred.
[0083] The results of analysis of patients' specimens using the
disclosed predictive biomarker panels may be output for the benefit
of the user or diagnostician, or may otherwise be displayed on a
medium such as, but not limited to, a computer screen, a computer
readable medium, a piece of paper, or any other visible medium.
[0084] The foregoing embodiments and advantages of this invention
are set forth, in part, in the preceding description and examples
and, in part, will be apparent to persons skilled in the art from
this description and examples and may be further realized from
practicing the invention as disclosed herein. For example, the
techniques of the present invention are readily applicable to
monitoring the progression of ovarian cancer in an individual, by
evaluating a specimen or biological sample as described above and
then repeating the evaluation at one or more later points in time,
such that a difference in the expression or disregulation of the
relevant biomarkers over time is indicative of the progression of
the ovarian cancer in that individual or the responsiveness to
therapy. All references, patents, journal articles, web pages and
other documents identified in this patent application are hereby
incorporated by reference in their entireties.
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References