U.S. patent application number 13/208903 was filed with the patent office on 2012-02-16 for pancreatic cancer biomarkers and uses thereof.
This patent application is currently assigned to SOMALOGIC, INC.. Invention is credited to Rachel M. Ostroff, Michael Riel-Mehan, Stephen Alaric Williams.
Application Number | 20120040861 13/208903 |
Document ID | / |
Family ID | 45565264 |
Filed Date | 2012-02-16 |
United States Patent
Application |
20120040861 |
Kind Code |
A1 |
Williams; Stephen Alaric ;
et al. |
February 16, 2012 |
Pancreatic Cancer Biomarkers and Uses Thereof
Abstract
The present disclosure includes biomarkers, methods, devices,
reagents, systems, and kits for the detection and diagnosis of
cancer generally and pancreatic cancer specifically. In one aspect,
the disclosure provides biomarkers that can be used alone or in
various combinations to diagnose cancer generally or pancreatic
cancer specifically. In another aspect, methods are provided for
diagnosing pancreatic cancer in an individual, where the methods
include detecting, in a biological sample from an individual, at
least one biomarker value corresponding to at least one biomarker
selected from the group of biomarkers provided in Table 1, wherein
the individual is classified as having pancreatic cancer, or the
likelihood of the individual having pancreatic cancer is
determined, based on the at least one biomarker value. In a further
aspect, methods are provided for diagnosing cancer generally in an
individual, where the methods include detecting, in a biological
sample from an individual, at least one biomarker value
corresponding to at least one biomarker selected from the group of
biomarkers provided in Table 19, wherein the individual is
classified as having cancer generally, or the likelihood of the
individual having cancer is determined, based on the at least one
biomarker value.
Inventors: |
Williams; Stephen Alaric;
(Boulder, CO) ; Riel-Mehan; Michael; (Louisville,
CO) ; Ostroff; Rachel M.; (Westminster, CO) |
Assignee: |
SOMALOGIC, INC.
Boulder
CO
|
Family ID: |
45565264 |
Appl. No.: |
13/208903 |
Filed: |
August 12, 2011 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
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61373687 |
Aug 13, 2010 |
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61418689 |
Dec 1, 2010 |
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61482347 |
May 4, 2011 |
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61482480 |
May 4, 2011 |
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Current U.S.
Class: |
506/9 ; 702/19;
901/41 |
Current CPC
Class: |
G01N 33/6893 20130101;
G16B 20/00 20190201; G01N 33/57438 20130101 |
Class at
Publication: |
506/9 ; 702/19;
901/41 |
International
Class: |
C40B 30/04 20060101
C40B030/04; G06F 19/00 20110101 G06F019/00 |
Claims
1. A method for diagnosing that an individual does or does not have
pancreatic cancer, the method comprising: detecting, in a
biological sample from an individual, biomarker values that each
correspond to one of at least N biomarkers selected from Table 1,
wherein said individual is classified as having or not having
pancreatic cancer, or the likelihood of the individual having
pancreatic cancer is determined, based on said biomarker values,
and wherein N=2-65.
2. The method of claim 1, wherein the diagnosis comprises the
differential diagnosis of pancreatic cancer from benign conditions
such as pancreatitis or a gastrointestinal disorder.
3. The method of claim 1 wherein the individual has an abdominal
mass.
4. The method of claim 1, wherein detecting the biomarker values
comprises performing an in vitro assay.
5. The method of claim 4, wherein said in vitro assay comprises at
least one capture reagent corresponding to each of said biomarkers,
and further comprising selecting said at least one capture reagent
from the group consisting of aptamers, antibodies, and a nucleic
acid probe.
6. The method of claim 5, wherein said at least one capture reagent
is an aptamer.
7. The method of claim 4, wherein the in vitro assay is selected
from the group consisting of an immunoassay, an aptamer-based
assay, a histological or cytological assay, and an mRNA expression
level assay.
8. The method of claim 1, wherein the biological sample is selected
from the group consisting of whole blood, plasma, serum and
pancreatic fluid.
9. The method of claim 8, wherein the biological sample is
plasma.
10. The method claim 1, wherein the biological sample is pancreatic
tissue and wherein the biomarker values derive from a histological
or cytological analysis of said pancreatic tissue.
11. The method of claim 1, wherein the individual is a human.
12. The method of claim 1, wherein N=3-10.
13. The method of claim 1, wherein N=3-15.
14. The method of claim 1, wherein N=2-10.
15. The method of claim 1, wherein N=4-10.
16. The method of claim 1, wherein N=5-10.
17. The method of claim 1, wherein the individual is high risk for
pancreatic cancer due to smoking, alcohol consumption or family
history of pancreatic cancer.
18. The method of claim 1, wherein the biomarkers are selected from
Table 18.
19. The method of claim 1, further comprising the biomarker
CA19-9.
20. A computer-implemented method for indicating a likelihood of
pancreatic cancer, the method comprising: retrieving on a computer
biomarker information for an individual, wherein the biomarker
information comprises biomarker values that each correspond to one
of at least N biomarkers selected from Table 1; performing with the
computer a classification of each of said biomarker values; and
indicating a likelihood that said individual has pancreatic cancer
based upon a plurality of classifications, and wherein N=2-65.
21. The method of claim 20, wherein indicating the likelihood that
the individual has pancreatic cancer comprises displaying the
likelihood on a computer display.
22. A computer program product for indicating a likelihood of
pancreatic cancer, the computer program product comprising: a
computer readable medium embodying program code executable by a
processor of a computing device or system, the program code
comprising: code that retrieves data attributed to a biological
sample from an individual, wherein the data comprises biomarker
values that each correspond to one of at least N biomarkers
selected from Table 1, wherein said biomarkers were detected in the
biological sample; and code that executes a classification method
that indicates a pancreatic disease status of the individual as a
function of said biomarker values; and wherein N=2-65.
23. The computer program product of claim 22, wherein said
classification method uses a probability density function.
24. The computer program product of claim 23, wherein said
classification method uses two or more classes.
25. A method for screening an asymptomatic high risk individual for
pancreatic cancer, the method comprising: detecting, in a
biological sample from an individual, biomarker values that each
correspond to one of at least N biomarkers selected from Table 1,
wherein said individual is classified as having or not having
pancreatic cancer, or the likelihood of the individual having
pancreatic cancer is determined, based on said biomarker values,
and wherein N=2-65.
26-41. (canceled)
42-46. (canceled)
47. A method for diagnosing that an individual does or does not
have cancer, the method comprising: detecting, in a biological
sample from an individual, biomarker values that each correspond to
one of at least N biomarkers selected from Table 19, wherein said
individual is classified as having or not having cancer, or the
likelihood of the individual having pancreatic cancer is
determined, based on said biomarker values, and wherein N=2-21.
48-60. (canceled)
61. A computer-implemented method for indicating a likelihood of
cancer, the method comprising: retrieving on a computer biomarker
information for an individual, wherein the biomarker information
comprises biomarker values that each correspond to one of at least
N biomarkers selected from Table 19; performing with the computer a
classification of each of said biomarker values; and indicating a
likelihood that said individual has cancer based upon a plurality
of classifications, and wherein N=2-21.
62. (canceled)
63. A computer program product for indicating a likelihood of
cancer, the computer program product comprising: a computer
readable medium embodying program code executable by a processor of
a computing device or system, the program code comprising: code
that retrieves data attributed to a biological sample from an
individual, wherein the data comprises biomarker values that each
correspond to one of at least N biomarkers selected from Table 19,
wherein said biomarkers were detected in the biological sample; and
code that executes a classification method that indicates a cancer
status of the individual as a function of said biomarker values;
and wherein N=2-21.
64-65. (canceled)
66. The method according to claim 1, wherein said individual is
classified as having or not having pancreatic cancer, or the
likelihood of the individual having pancreatic cancer is
determined, based on said biomarker values and at least one item of
additional biomedical information corresponding to said
individual.
67. The method according to claim 47, wherein said individual is
classified as having or not having pancreatic cancer, or the
likelihood of the individual having pancreatic cancer is
determined, based on said biomarker values and at least one item of
additional biomedical information corresponding to said
individual.
68. The method of claim 66, wherein said at least one item of
additional biomedical information is independently selected from
the group consisting of (a) information corresponding to the
presence or absence of a pancreatic mass or other abdominal mass,
(b) information corresponding to physical descriptors of said
individual, (c) information corresponding to a change in weight of
said individual, (d) information corresponding to the ethnicity of
said individual, (e) information corresponding to the gender of
said individual, (f) information corresponding to said individual's
smoking history, (g) information corresponding to said individual's
alcohol use history, (h) information corresponding to said
individual's occupational history, (i) information corresponding to
said individual's family history of pancreatic cancer or other
cancer, (j) information corresponding to the presence or absence in
said individual of at least one genetic marker correlating with a
higher risk of pancreatic cancer or cancer in said individual or a
family member of said individual, (k) information corresponding to
clinical symptoms of said individual, (l) information corresponding
to other laboratory tests, (m) information corresponding to gene
expression values of said individual, and (n) information
corresponding to said individual's exposure to known
carcinogens.
69. A classifier comprising the biomarkers of Table 18.
70. The method according to claim 25, wherein said individual is
classified as having or not having pancreatic cancer, or the
likelihood of the individual having pancreatic cancer is
determined, based on said biomarker values and at least one item of
additional biomedical information corresponding to said
individual.
71. The method of claim 70, wherein said at least one item of
additional biomedical information is independently selected from
the group consisting of (a) information corresponding to the
presence or absence of a pancreatic mass or other abdominal mass,
(b) information corresponding to physical descriptors of said
individual, (c) information corresponding to a change in weight of
said individual, (d) information corresponding to the ethnicity of
said individual, (e) information corresponding to the gender of
said individual, (f) information corresponding to said individual's
smoking history, (g) information corresponding to said individual's
alcohol use history, (h) information corresponding to said
individual's occupational history, (i) information corresponding to
said individual's family history of pancreatic cancer or other
cancer, (j) information corresponding to the presence or absence in
said individual of at least one genetic marker correlating with a
higher risk of pancreatic cancer or cancer in said individual or a
family member of said individual, (k) information corresponding to
clinical symptoms of said individual, (l) information corresponding
to other laboratory tests, (m) information corresponding to gene
expression values of said individual, and (n) information
corresponding to said individual's exposure to known
carcinogens.
72. The method of claim 67, wherein said at least one item of
additional biomedical information is independently selected from
the group consisting of (a) information corresponding to the
presence or absence of a pancreatic mass or other abdominal mass,
(b) information corresponding to physical descriptors of said
individual, (c) information corresponding to a change in weight of
said individual, (d) information corresponding to the ethnicity of
said individual, (e) information corresponding to the gender of
said individual, (f) information corresponding to said individual's
smoking history, (g) information corresponding to said individual's
alcohol use history, (h) information corresponding to said
individual's occupational history, (i) information corresponding to
said individual's family history of pancreatic cancer or other
cancer, (j) information corresponding to the presence or absence in
said individual of at least one genetic marker correlating with a
higher risk of pancreatic cancer or cancer in said individual or a
family member of said individual, (k) information corresponding to
clinical symptoms of said individual, (l) information corresponding
to other laboratory tests, (m) information corresponding to gene
expression values of said individual, and (n) information
corresponding to said individual's exposure to known carcinogens.
Description
RELATED APPLICATIONS
[0001] This application claims the benefit of U.S. Provisional
Application Ser. No. 61/373,687, filed Aug. 13, 2010, U.S.
Provisional Application Ser. No. 61/418,689, filed Dec. 1, 2010,
U.S. Provisional Application Ser. No. 61/482,347, filed May 4,
2011, and U.S. Provisional Application Ser. No. 61/482,480, filed
May 4, 2011, each of which is incorporated herein by reference in
its entirety.
FIELD OF THE INVENTION
[0002] The present application relates generally to the detection
of biomarkers and the diagnosis of cancer in an individual and,
more specifically, to one or more biomarkers, methods, devices,
reagents, systems, and kits for diagnosing cancer, more
particularly pancreatic cancer, in an individual.
BACKGROUND
[0003] The following description provides a summary of information
relevant to the present application and is not an admission that
any of the information provided or publications referenced herein
is prior art to the present application.
[0004] Pancreatic cancer is the fourth leading cause of
cancer-related death in the USA. While the 5-year survival is only
5%, this has been shown to increase with early surgical
intervention: in the 20% of subjects eligible for a "curative"
resection the survival increases to 15-20%. At the time of
diagnosis, more than half the patients have distant disease and
another 25% have regional spread. This is because the disease is
notoriously difficult to diagnose in its early stages. About 20
percent of patients with "operable" disease [stage IIb or less]
undergo a "curative" resection and the 5-year survival increases
from less than 5% to 15-20%.
[0005] Pancreatic cancers can arise from both the exocrine and
endocrine portions of the pancreas. Of pancreatic tumors, 95%
develop from the exocrine portion of the pancreas, including the
ductal epithelium, acinar cells, connective tissue, and lymphatic
tissue. Approximately 75% of all pancreatic carcinomas occur within
the head or neck of the pancreas, 15-20% occur in the body of the
pancreas, and 5-10% occur in the tail.
[0006] Recurrence can be local (in or near the same place it
started) or distant (spread to organs such as the liver, lungs, or
bone). When pancreatic exocrine cancer recurs, it is essentially
treated the same way as metastatic cancer, and is likely to include
chemotherapy if the patient can tolerate it. Typically, pancreatic
cancer first metastasizes to regional lymph nodes, then to the
liver, and, less commonly, to the lungs. It can also directly
invade surrounding visceral organs such as the duodenum, stomach,
and colon or metastasize to any surface in the abdominal cavity via
peritoneal spread. Ascites may result, and this has an ominous
prognosis. Pancreatic cancer may spread to the skin as painful
nodular metastases. Pancreatic cancer uncommonly metastasizes to
bone.
[0007] Two clinical applications for a blood based pancreatic
cancer test are for preclinical diagnosis in the asymptomatic,
high-risk population and differential diagnosis in the symptomatic
population. The clinical utility for both of these indications is
outlined below.
[0008] Screening in an asymptomatic, high risk population: There
were an estimated 43,140 new cases of pancreatic cancer in the USA
in 2010, and 36,800 deaths. Genetics, family history, chronic
pancreatitis, smoking and high alcohol consumption increase the
risk of pancreatic cancer, as does cystic fibrosis. The increase in
risk has been reported as:
[0009] Cigarette smoking: <25 per day is 2.times. risk, >25
per day is 3.times. risk
[0010] Alcohol: more than 3 drinks per day gives a 1.6 fold risk
increase
[0011] Family history: a first degree relative with the disease
gives a 5.times. increase
[0012] Adults with cystic fibrosis: 31.times. risk
[0013] BRCA2 genetic mutations: 10.times. risk
[0014] In the asymptomatic but at-risk population in the absence of
an effective screening paradigm the cancer is simply detected at
the time of symptomatic presentation. This is likely to be late.
The existence of an early detection test would increase the
proportion of patients eligible for curative surgery. The current
cure rate of 20% in the 20% of early detection subjects is only 4%
of the total population. If the eligibility for curative surgery
increased--by early detection in the asymptomatic population--from
the current 20%, then the curable total would increase, as would
the number of lives saved per year. Since pancreatic cancer is a
low prevalence disease, even in this high-risk population, high
specificity is an important attribute of a screening test. A low
false positive rate is essential to reduce the cost incurred by
unnecessary follow-up procedures and reduce anxiety for the
patient.
[0015] Differential diagnosis in the symptomatic patient.
Pancreatic cancer may be difficult to distinguish from benign
conditions such as pancreatitis or gastro-intestinal disorders. The
differential diagnosis of a primary exocrine pancreatic cancer
includes chronic pancreatitis, pancreatic endocrine tumors,
autoimmune pancreatitis, lymphoma, and a variety of other rare
conditions. Common but non-specific symptoms associated with
pancreatic cancer include:
[0016] Abdominal pain--particularly when radiating to the back
[0017] Obstructive jaundice
[0018] Sudden unexplained diabetes
[0019] Weight loss
[0020] Anorexia, fatigue
[0021] Nausea, vomiting
[0022] Acute or chronic pancreatitis
[0023] The table below shows the numbers of patients presenting to
emergency rooms and to hospitals with at least two of these
relevant symptoms; the first symptom is any one of the listed
symptoms, and the second symptom is the one listed in the table.
The emergency room data was from: (http://hcupnet.ahrq.gov/) while
the ambulatory data was from the CDC 2008 National Ambulatory
Medical Care Survey 2006 (number 8).
TABLE-US-00001 Relevant Symptoms by Age Emergency Dept All
Ambulatory Percentage 64-84 45-84 64-84 45-84 Abdominal 10.6%
39,922 142,188 106,458 379,168 pain Jaundice 11.0% 1,405 3,429
3,746 9,145 Weight loss 11.0% 5,465 9,860 14,574 26,294 Malaise and
4.1% 18,200 32,241 48,533 85,977 fatigue Acute 18.6% 9,807 31,080
26,152 82,879 pancreatitis Chronic 43.2% 1,061 7,966 2,829 21,242
pancreatitis All 75,860 226,765 202,293 604,705
[0024] Sensitive detection of resectable disease is essential for
the clinical utility of this indication. Prompt detection of
pancreatic cancer increases the chances of diagnosis of curable
disease. The diagnosis of pancreatic cancer is typically made
radiographically by the finding of a mass within the pancreas,
which often obstructs the pancreatic duct or biliary tree. However,
imaging can be invasive and costly. A blood test that determines
which patients require follow-up, including diagnostic imaging,
would benefit the patients and simplify the diagnosis.
[0025] Biomarker selection for a specific disease state involves
first the identification of markers that have a measurable and
statistically significant difference in a disease population
compared to a control population for a specific medical
application. Biomarkers can include secreted or shed molecules that
parallel disease development or progression and readily diffuse
into the blood stream from pancreatic cancer tissue or from
surrounding tissues and circulating cells in response to a tumor.
The biomarker or set of biomarkers identified are generally
clinically validated or shown to be a reliable indicator for the
original intended use for which it was selected. Biomarkers can
include small molecules, peptides, proteins, and nucleic acids.
Some of the key issues that affect the identification of biomarkers
include over-fitting of the available data and bias in the
data.
[0026] A variety of methods have been utilized in an attempt to
identify biomarkers and diagnose disease. For protein-based
markers, these include two-dimensional electrophoresis, mass
spectrometry, and immunoassay methods. For nucleic acid markers,
these include mRNA expression profiles, microRNA profiles, FISH,
serial analysis of gene expression (SAGE), and large scale gene
expression arrays.
[0027] The utility of two-dimensional electrophoresis is limited by
low detection sensitivity; issues with protein solubility, charge,
and hydrophobicity; gel reproducibility; and the possibility of a
single spot representing multiple proteins. For mass spectrometry,
depending on the format used, limitations revolve around the sample
processing and separation, sensitivity to low abundance proteins,
signal to noise considerations, and inability to immediately
identify the detected protein. Limitations in immunoassay
approaches to biomarker discovery are centered on the inability of
antibody-based multiplex assays to measure a large number of
analytes. One might simply print an array of high-quality
antibodies and, without sandwiches, measure the analytes bound to
those antibodies. (This would be the formal equivalent of using a
whole genome of nucleic acid sequences to measure by hybridization
all DNA or RNA sequences in an organism or a cell. The
hybridization experiment works because hybridization can be a
stringent test for identity. Even very good antibodies are not
stringent enough in selecting their binding partners to work in the
context of blood or even cell extracts because the protein ensemble
in those matrices have extremely different abundances.) Thus, one
must use a different approach with immunoassay-based approaches to
biomarker discovery--one would need to use multiplexed ELISA assays
(that is, sandwiches) to get sufficient stringency to measure many
analytes simultaneously to decide which analytes are indeed
biomarkers. Sandwich immunoassays do not scale to high content, and
thus biomarker discovery using stringent sandwich immunoassays is
not possible using standard array formats. Lastly, antibody
reagents are subject to substantial lot variability and reagent
instability. The instant platform for protein biomarker discovery
overcomes this problem.
[0028] Many of these methods rely on or require some type of sample
fractionation prior to the analysis. Thus the sample preparation
required to run a sufficiently powered study designed to identify
and discover statistically relevant biomarkers in a series of
well-defined sample populations is extremely difficult, costly, and
time consuming. During fractionation, a wide range of variability
can be introduced into the various samples. For example, a
potential marker could be unstable to the process, the
concentration of the marker could be changed, inappropriate
aggregation or disaggregation could occur, and inadvertent sample
contamination could occur and thus obscure the subtle changes
anticipated in early disease.
[0029] It is widely accepted that biomarker discovery and detection
methods using these technologies have serious limitations for the
identification of diagnostic biomarkers. These limitations include
an inability to detect low-abundance biomarkers, an inability to
consistently cover the entire dynamic range of the proteome,
irreproducibility in sample processing and fractionation, and
overall irreproducibility and lack of robustness of the method.
Further, these studies have introduced biases into the data and not
adequately addressed the complexity of the sample populations,
including appropriate controls, in terms of the distribution and
randomization required to identify and validate biomarkers within a
target disease population.
[0030] Although efforts aimed at the discovery of new and effective
biomarkers have gone on for several decades, the efforts have been
largely unsuccessful. Biomarkers for various diseases typically
have been identified in academic laboratories, usually through an
accidental discovery while doing basic research on some disease
process. Based on the discovery and with small amounts of clinical
data, papers were published that suggested the identification of a
new biomarker. Most of these proposed biomarkers, however, have not
been confirmed as real or useful biomarkers, primarily because the
small number of clinical samples tested provide only weak
statistical proof that an effective biomarker has in fact been
found. That is, the initial identification was not rigorous with
respect to the basic elements of statistics. In each of the years
1994 through 2003, a search of the scientific literature shows that
thousands of references directed to biomarkers were published.
During that same time frame, however, the FDA approved for
diagnostic use, at most, three new protein biomarkers a year, and
in several years no new protein biomarkers were approved.
[0031] Based on the history of failed biomarker discovery efforts,
mathematical theories have been proposed that further promote the
general understanding that biomarkers for disease are rare and
difficult to find. Biomarker research based on 2D gels or mass
spectrometry supports these notions. Very few useful biomarkers
have been identified through these approaches. However, it is
usually overlooked that 2D gel and mass spectrometry measure
proteins that are present in blood at approximately 1 nM
concentrations and higher, and that this ensemble of proteins may
well be the least likely to change with disease. Other than the
instant biomarker discovery platform, proteomic biomarker discovery
platforms that are able to accurately measure protein expression
levels at much lower concentrations do not exist.
[0032] Much is known about biochemical pathways for complex human
biology. Many biochemical pathways culminate in or are started by
secreted proteins that work locally within the pathology, for
example growth factors are secreted to stimulate the replication of
other cells in the pathology, and other factors are secreted to
ward off the immune system, and so on. While many of these secreted
proteins work in a paracrine fashion, some operate distally in the
body. One skilled in the art with a basic understanding of
biochemical pathways would understand that many pathology-specific
proteins ought to exist in blood at concentrations below (even far
below) the detection limits of 2D gels and mass spectrometry. What
must precede the identification of this relatively abundant number
of disease biomarkers is a proteomic platform that can analyze
proteins at concentrations below those detectable by 2D gels or
mass spectrometry.
[0033] Accordingly, a need exists for biomarkers, methods, devices,
reagents, systems, and kits that enable (a) the differentiation of
pancreatic cancer from benign conditions; (b) screening of
asymptomatic, high risk individuals for pancreatic cancer; (c) the
detection of pancreatic cancer biomarkers; and (d) the diagnosis of
pancreatic cancer.
SUMMARY
[0034] The present application includes biomarkers, methods,
reagents, devices, systems, and kits for the detection and
diagnosis of cancer and more particularly, pancreatic cancer. The
biomarkers of the present application were identified using a
multiplex aptamer-based assay which is described in detail in
Example 1. By using the aptamer-based biomarker identification
method described herein, this application describes a surprisingly
large number of pancreatic cancer biomarkers that are useful for
the detection and diagnosis of pancreatic cancer as well as a large
number of cancer biomarkers that are useful for the detection and
diagnosis of cancer more generally. In identifying these
biomarkers, over 800 proteins from hundreds of individual samples
were measured, some of which were at concentrations in the low
femtomolar range. This is about four orders of magnitude lower than
biomarker discovery experiments done with 2D gels and/or mass
spectrometry.
[0035] While certain of the described pancreatic cancer biomarkers
are useful alone for detecting and diagnosing pancreatic cancer,
methods are described herein for the grouping of multiple subsets
of the pancreatic cancer biomarkers that are useful as a panel of
biomarkers. Once an individual biomarker or subset of biomarkers
has been identified, the detection or diagnosis of pancreatic
cancer in an individual can be accomplished using any assay
platform or format that is capable of measuring differences in the
levels of the selected biomarker or biomarkers in a biological
sample.
[0036] However, it was only by using the aptamer-based biomarker
identification method described herein, wherein over 800 separate
potential biomarker values were individually screened from a large
number of individuals having previously been diagnosed either as
having or not having pancreatic cancer that it was possible to
identify the pancreatic cancer biomarkers disclosed herein. This
discovery approach is in stark contrast to biomarker discovery from
conditioned media or lysed cells as it queries a more
patient-relevant system that requires no translation to human
pathology.
[0037] Thus, in one aspect of the instant application, one or more
biomarkers are provided for use either alone or in various
combinations to diagnose pancreatic cancer or permit the
differential diagnosis of pancreatic cancer from benign
gastrointestinal (GI) conditions such as acute or chronic
pancreatitis (or both), pancreatic obstruction, GERD, gallstones,
or abnormal imaging later found to be benign. Exemplary embodiments
include the biomarkers provided in Table 1, Col. 2, which as noted
above, were identified using a multiplex aptamer-based assay, as
described generally in Example 1 and more specifically in Example
2. The markers provided in Table 1 are useful in diagnosing
pancreatic cancer in a high risk, asymptomatic population and for
distinguishing acute or chronic pancreatitis (or both), pancreatic
obstruction, GERD, gallstones, or abnormal imaging later found to
be benign from pancreatic cancer.
[0038] While certain of the described pancreatic cancer biomarkers
are useful alone for detecting and diagnosing pancreatic cancer,
methods are also described herein for the grouping of multiple
subsets of the pancreatic cancer biomarkers that are each useful as
a panel of two or more biomarkers. Thus, various embodiments of the
instant application provide combinations comprising N biomarkers,
wherein N is at least two biomarkers. In other embodiments, N is
selected to be any number from 2-65 biomarkers.
[0039] In yet other embodiments, N is selected to be any number
from 2-7, 2-10, 2-15, 2-20, 2-25, 2-30, 2-35, 2-40, 2-45, 2-50,
2-55, or 2-65. In other embodiments, N is selected to be any number
from 3-7, 3-10, 3-15, 3-20, 3-25, 3-30, 3-35, 3-40, 3-45, 3-50,
3-55, or 3-65. In other embodiments, N is selected to be any number
from 4-7, 4-10, 4-15, 4-20, 4-25, 4-30, 4-35, 4-40, 4-45, 4-50,
4-55, or 4-65. In other embodiments, N is selected to be any number
from 5-7, 5-10, 5-15, 5-20, 5-25, 5-30, 5-35, 5-40, 5-45, 5-50,
5-55, or 5-65. In other embodiments, N is selected to be any number
from 6-10, 6-15, 6-20, 6-25, 6-30, 6-35, 6-40, 6-45, 6-50, 6-55, or
6-65. In other embodiments, N is selected to be any number from
7-10, 7-15, 7-20, 7-25, 7-30, 7-35, 7-40, 7-45, 7-50, 7-55, or
7-65. In other embodiments, N is selected to be any number from
8-10, 8-15, 8-20, 8-25, 8-30, 8-35, 8-40, 8-45, 8-50, 8-55, or
8-65. In other embodiments, N is selected to be any number from
9-15, 9-20, 9-25, 9-30, 9-35, 9-40, 9-45, 9-50, 9-55, or 9-65. In
other embodiments, N is selected to be any number from 10-15,
10-20, 10-25, 10-30, 10-35, 10-40, 10-45, 10-50, 10-55, or 10-65.
It will be appreciated that N can be selected to encompass similar,
but higher order, ranges.
[0040] In another aspect, a method is provided for diagnosing
pancreatic cancer in an individual, the method including detecting,
in a biological sample from an individual, at least one biomarker
value corresponding to at least one biomarker selected from the
group of biomarkers provided in Table 1, Col. 2, wherein the
individual is classified as having pancreatic cancer based on the
at least one biomarker value.
[0041] In another aspect, a method is provided for diagnosing
pancreatic cancer in an individual, the method including detecting,
in a biological sample from an individual, biomarker values that
each correspond to one of at least N biomarkers selected from the
group of biomarkers set forth in Table 1, Col. 2, wherein the
likelihood of the individual having pancreatic cancer is determined
based on the biomarker values.
[0042] In another aspect, a method is provided for diagnosing
pancreatic cancer in an individual, the method including detecting,
in a biological sample from an individual, biomarker values that
each correspond to one of at least N biomarkers selected from the
group of biomarkers set forth in Table 1, Col. 2, wherein the
individual is classified as having pancreatic cancer based on the
biomarker values, and wherein N=2-10.
[0043] In another aspect, a method is provided for diagnosing
pancreatic cancer in an individual, the method including detecting,
in a biological sample from an individual, biomarker values that
each correspond to one of at least N biomarkers selected from the
group of biomarkers set forth in Table 1, Col. 2, wherein the
likelihood of the individual having pancreatic cancer is determined
based on the biomarker values, and wherein N=2-10.
[0044] In another aspect, a method is provided for diagnosing that
an individual does not have pancreatic cancer, the method including
detecting, in a biological sample from an individual, at least one
biomarker value corresponding to at least one biomarker selected
from the group of biomarkers set forth in Table 1, Col. 2, wherein
the individual is classified as not having pancreatic cancer based
on the at least one biomarker value.
[0045] In another aspect, a method is provided for diagnosing that
an individual does not have pancreatic cancer, the method including
detecting, in a biological sample from an individual, biomarker
values that each corresponding to one of at least N biomarkers
selected from the group of biomarkers set forth in Table 1, Col. 2,
wherein the individual is classified as not having pancreatic
cancer based on the biomarker values, and wherein N=2-10.
[0046] In another aspect, a method is provided for diagnosing
pancreatic cancer, the method including detecting, in a biological
sample from an individual, biomarker values that each correspond to
a biomarker on a panel of N biomarkers, wherein the biomarkers are
selected from the group of biomarkers set forth in Table 1, Col. 2,
wherein a classification of the biomarker values indicates that the
individual has pancreatic cancer, and wherein N=3-10.
[0047] In another aspect, a method is provided for diagnosing
pancreatic cancer, the method including detecting, in a biological
sample from an individual, biomarker values that each correspond to
a biomarker on a panel of N biomarkers, wherein the biomarkers are
selected from the group of biomarkers set forth in Table 1, Col. 2,
wherein a classification of the biomarker values indicates that the
individual has pancreatic cancer, and wherein N=3-10.
[0048] In another aspect, a method is provided for diagnosing
pancreatic cancer, the method including detecting, in a biological
sample from an individual, biomarker values that each correspond to
a biomarker on a panel of biomarkers selected from the group of
panels set forth in Tables 2-11, wherein a classification of the
biomarker values indicates that the individual has pancreatic
cancer.
[0049] In another aspect, a method is provided for diagnosing an
absence of pancreatic cancer, the method including detecting, in a
biological sample from an individual, biomarker values that each
correspond to a biomarker on a panel of N biomarkers, wherein the
biomarkers are selected from the group of biomarkers set forth in
Table 1, Col. 2, wherein a classification of the biomarker values
indicates an absence of pancreatic cancer in the individual, and
wherein N=3-10.
[0050] In another aspect, a method is provided for diagnosing an
absence of pancreatic cancer, the method including detecting, in a
biological sample from an individual, biomarker values that each
correspond to a biomarker on a panel of N biomarkers, wherein the
biomarkers are selected from the group of biomarkers set forth in
Table 1, Col. 2, wherein a classification of the biomarker values
indicates an absence of pancreatic cancer in the individual, and
wherein N=3-10.
[0051] In another aspect, a method is provided for diagnosing an
absence of pancreatic cancer, the method including detecting, in a
biological sample from an individual, biomarker values that each
correspond to a biomarker on a panel of biomarkers selected from
the group of panels provided in Tables 2-11, wherein a
classification of the biomarker values indicates an absence of
pancreatic cancer in the individual.
[0052] In another aspect, a method is provided for diagnosing
pancreatic cancer in an individual, the method including detecting,
in a biological sample from an individual, biomarker values that
correspond to one of at least N biomarkers selected from the group
of biomarkers set forth in Table 1, Col. 2, wherein the individual
is classified as having pancreatic cancer based on a classification
score that deviates from a predetermined threshold, and wherein
N=2-10.
[0053] In another aspect, a method is provided for diagnosing an
absence of pancreatic cancer in an individual, the method including
detecting, in a biological sample from an individual, biomarker
values that correspond to one of at least N biomarkers selected
from the group of biomarkers set forth in Table 1, Col. 2, wherein
said individual is classified as not having pancreatic cancer based
on a classification score that deviates from a predetermined
threshold, and wherein N=2-10.
[0054] In another aspect, a computer-implemented method is provided
for indicating a likelihood of pancreatic cancer. The method
comprises: retrieving on a computer biomarker information for an
individual, wherein the biomarker information comprises biomarker
values that each correspond to one of at least N biomarkers,
wherein N is as defined above, selected from the group of
biomarkers set forth in Table 1, Col. 2; performing with the
computer a classification of each of the biomarker values; and
indicating a likelihood that the individual has pancreatic cancer
based upon a plurality of classifications.
[0055] In another aspect, a computer-implemented method is provided
for classifying an individual as either having or not having
pancreatic cancer. The method comprises: retrieving on a computer
biomarker information for an individual, wherein the biomarker
information comprises biomarker values that each correspond to one
of at least N biomarkers selected from the group of biomarkers
provided in Table 1, Col. 2; performing with the computer a
classification of each of the biomarker values; and indicating
whether the individual has pancreatic cancer based upon a plurality
of classifications.
[0056] In another aspect, a computer program product is provided
for indicating a likelihood of pancreatic cancer. The computer
program product includes a computer readable medium embodying
program code executable by a processor of a computing device or
system, the program code comprising: code that retrieves data
attributed to a biological sample from an individual, wherein the
data comprises biomarker values that each correspond to one of at
least N biomarkers, wherein N is as defined above, in the
biological sample selected from the group of biomarkers set forth
in Table 1, Col. 2; and code that executes a classification method
that indicates a likelihood that the individual has pancreatic
cancer as a function of the biomarker values.
[0057] In another aspect, a computer program product is provided
for indicating a pancreatic cancer status of an individual. The
computer program product includes a computer readable medium
embodying program code executable by a processor of a computing
device or system, the program code comprising: code that retrieves
data attributed to a biological sample from an individual, wherein
the data comprises biomarker values that each correspond to one of
at least N biomarkers in the biological sample selected from the
group of biomarkers provided in Table 1, Col. 2; and code that
executes a classification method that indicates a pancreatic cancer
status of the individual as a function of the biomarker values.
[0058] In another aspect, a computer-implemented method is provided
for indicating a likelihood of pancreatic cancer. The method
comprises retrieving on a computer biomarker information for an
individual, wherein the biomarker information comprises a biomarker
value corresponding to a biomarker selected from the group of
biomarkers set forth in Table 1, Col. 2; performing with the
computer a classification of the biomarker value; and indicating a
likelihood that the individual has pancreatic cancer based upon the
classification.
[0059] In another aspect, a computer-implemented method is provided
for classifying an individual as either having or not having
pancreatic cancer. The method comprises retrieving from a computer
biomarker information for an individual, wherein the biomarker
information comprises a biomarker value corresponding to a
biomarker selected from the group of biomarkers provided in Table
1, Col. 2; performing with the computer a classification of the
biomarker value; and indicating whether the individual has
pancreatic cancer based upon the classification.
[0060] In still another aspect, a computer program product is
provided for indicating a likelihood of pancreatic cancer. The
computer program product includes a computer readable medium
embodying program code executable by a processor of a computing
device or system, the program code comprising: code that retrieves
data attributed to a biological sample from an individual, wherein
the data comprises a biomarker value corresponding to a biomarker
in the biological sample selected from the group of biomarkers set
forth in Table 1, Col. 2; and code that executes a classification
method that indicates a likelihood that the individual has
pancreatic cancer as a function of the biomarker value.
[0061] In still another aspect, a computer program product is
provided for indicating a pancreatic cancer status of an
individual. The computer program product includes a computer
readable medium embodying program code executable by a processor of
a computing device or system, the program code comprising: code
that retrieves data attributed to a biological sample from an
individual, wherein the data comprises a biomarker value
corresponding to a biomarker in the biological sample selected from
the group of biomarkers provided in Table 1, Col. 2; and code that
executes a classification method that indicates a pancreatic cancer
status of the individual as a function of the biomarker value.
[0062] While certain of the described cancer biomarkers are useful
alone for detecting and diagnosing cancer, methods are described
herein for the grouping of multiple subsets of the cancer
biomarkers that are useful as a panel of biomarkers. Once an
individual biomarker or subset of biomarkers has been identified,
the detection or diagnosis of cancer in an individual can be
accomplished using any assay platform or format that is capable of
measuring differences in the levels of the selected biomarker or
biomarkers in a biological sample.
[0063] However, it was only by using the aptamer-based biomarker
identification method described herein, wherein over 800 separate
potential biomarker values were individually screened from a large
number of individuals having previously been diagnosed either as
having or not having cancer that it was possible to identify the
cancer biomarkers disclosed herein. This discovery approach is in
stark contrast to biomarker discovery from conditioned media or
lysed cells as it queries a more patient-relevant system that
requires no translation to human pathology.
[0064] Thus, in one aspect of the instant application, one or more
biomarkers are provided for use either alone or in various
combinations to diagnose cancer. Exemplary embodiments include the
biomarkers provided in Table 19, which were identified using a
multiplex aptamer-based assay, as described generally in Example 1
and more specifically in Example 7. The markers provided in Table
19 are useful in distinguishing individuals who have cancer from
those who do not have cancer.
[0065] While certain of the described cancer biomarkers are useful
alone for detecting and diagnosing cancer, methods are also
described herein for the grouping of multiple subsets of the cancer
biomarkers that are each useful as a panel of three or more
biomarkers. Thus, various embodiments of the instant application
provide combinations comprising N biomarkers, wherein N is at least
three biomarkers. In other embodiments, N is selected to be any
number from 3-65 biomarkers.
[0066] In yet other embodiments, N is selected to be any number
from 3-7, 3-10, 3-15, 3-20, 3-25, 3-30, 3-35, 3-40, 3-45, 3-50,
3-55, 3-60, or 3-65. In other embodiments, N is selected to be any
number from 4-7, 4-10, 4-15, 4-20, 4-25, 4-30, 4-35, 4-40, 4-45,
4-50, 4-55, 4-60, or 4-65. In other embodiments, N is selected to
be any number from 5-7, 5-10, 5-15, 5-20, 5-25, 5-30, 5-35, 5-40,
5-45, 5-50, 5-55, 5-60, or 5-65. In other embodiments, N is
selected to be any number from 6-10, 6-15, 6-20, 6-25, 6-30, 6-35,
6-40, 6-45, 6-50, 6-55, 6-60, or 6-65. In other embodiments, N is
selected to be any number from 7-10, 7-15, 7-20, 7-25, 7-30, 7-35
7-40, 7-45, 7-50, 7-55, 7-60, or 7-65. In other embodiments, N is
selected to be any number from 8-10, 8-15, 8-20, 8-25, 8-30, 8-35,
8-40, 8-45, 8-50, 8-55, 8-60, or 8-65. In other embodiments, N is
selected to be any number from 9-15, 9-20, 9-25, 9-30, 9-35, 9-40,
9-45, 9-50, 9-55, 9-60, or 9-65. In other embodiments, N is
selected to be any number from 10-15, 10-20, 10-25, 10-30, 10-35,
10-40, 10-45, 10-50, 10-55, 10-60, or 10-65. It will be appreciated
that N can be selected to encompass similar, but higher order,
ranges.
[0067] In another aspect, a method is provided for diagnosing
cancer in an individual, the method including detecting, in a
biological sample from an individual, at least one biomarker value
corresponding to at least one biomarker selected from the group of
biomarkers provided in Table 19, wherein the individual is
classified as having cancer based on the at least one biomarker
value.
[0068] In another aspect, a method is provided for diagnosing
cancer in an individual, the method including detecting, in a
biological sample from an individual, biomarker values that each
correspond to one of at least N biomarkers selected from the group
of biomarkers set forth in Table 19, wherein the likelihood of the
individual having cancer is determined based on the biomarker
values.
[0069] In another aspect, a method is provided for diagnosing
cancer in an individual, the method including detecting, in a
biological sample from an individual, biomarker values that each
correspond to one of at least N biomarkers selected from the group
of biomarkers set forth in Table 19, wherein the individual is
classified as having cancer based on the biomarker values, and
wherein N=3-10.
[0070] In another aspect, a method is provided for diagnosing
cancer in an individual, the method including detecting, in a
biological sample from an individual, biomarker values that each
correspond to one of at least N biomarkers selected from the group
of biomarkers set forth in Table 19, wherein the likelihood of the
individual having cancer is determined based on the biomarker
values, and wherein N=3-10.
[0071] In another aspect, a method is provided for diagnosing that
an individual does not have cancer, the method including detecting,
in a biological sample from an individual, at least one biomarker
value corresponding to at least one biomarker selected from the
group of biomarkers set forth in Table 19, wherein the individual
is classified as not having cancer based on the at least one
biomarker value.
[0072] In another aspect, a method is provided for diagnosing that
an individual does not have cancer, the method including detecting,
in a biological sample from an individual, biomarker values that
each corresponding to one of at least N biomarkers selected from
the group of biomarkers set forth in Table 19, wherein the
individual is classified as not having cancer based on the
biomarker values, and wherein N=3-10.
[0073] In another aspect, a method is provided for diagnosing
cancer, the method including detecting, in a biological sample from
an individual, biomarker values that each correspond to a biomarker
on a panel of N biomarkers, wherein the biomarkers are selected
from the group of biomarkers set forth in Table 19, wherein a
classification of the biomarker values indicates that the
individual has cancer, and wherein N=3-10.
[0074] In another aspect, a method is provided for diagnosing
cancer, the method including detecting, in a biological sample from
an individual, biomarker values that each correspond to a biomarker
on a panel of N biomarkers, wherein the biomarkers are selected
from the group of biomarkers set forth in Table 19, wherein a
classification of the biomarker values indicates that the
individual has cancer, and wherein N=3-10.
[0075] In another aspect, a method is provided for diagnosing
cancer, the method including detecting, in a biological sample from
an individual, biomarker values that each correspond to a biomarker
on a panel of biomarkers selected from the group of panels set
forth in Tables 20-29 wherein a classification of the biomarker
values indicates that the individual has cancer.
[0076] In another aspect, a method is provided for diagnosing an
absence of cancer, the method including detecting, in a biological
sample from an individual, biomarker values that each correspond to
a biomarker on a panel of N biomarkers, wherein the biomarkers are
selected from the group of biomarkers set forth in Table 19,
wherein a classification of the biomarker values indicates an
absence of cancer in the individual, and wherein N=3-10.
[0077] In another aspect, a method is provided for diagnosing an
absence of cancer, the method including detecting, in a biological
sample from an individual, biomarker values that each correspond to
a biomarker on a panel of N biomarkers, wherein the biomarkers are
selected from the group of biomarkers set forth in Table 19,
wherein a classification of the biomarker values indicates an
absence of cancer in the individual, and wherein N=3-10.
[0078] In another aspect, a method is provided for diagnosing an
absence of cancer, the method including detecting, in a biological
sample from an individual, biomarker values that each correspond to
a biomarker on a panel of biomarkers selected from the group of
panels provided in Tables 20-29, wherein a classification of the
biomarker values indicates an absence of cancer in the
individual.
[0079] In another aspect, a method is provided for diagnosing
cancer in an individual, the method including detecting, in a
biological sample from an individual, biomarker values that
correspond to one of at least N biomarkers selected from the group
of biomarkers set forth in Table 19, wherein the individual is
classified as having cancer based on a classification score that
deviates from a predetermined threshold, and wherein N=3-10.
[0080] In another aspect, a method is provided for diagnosing an
absence of cancer in an individual, the method including detecting,
in a biological sample from an individual, biomarker values that
correspond to one of at least N biomarkers selected from the group
of biomarkers set forth in Table 19, wherein said individual is
classified as not having cancer based on a classification score
that deviates from a predetermined threshold, and wherein
N=3-10.
[0081] In another aspect, a computer-implemented method is provided
for indicating a likelihood of cancer. The method comprises:
retrieving on a computer biomarker information for an individual,
wherein the biomarker information comprises biomarker values that
each correspond to one of at least N biomarkers, wherein N is as
defined above, selected from the group of biomarkers set forth in
Table 19; performing with the computer a classification of each of
the biomarker values; and indicating a likelihood that the
individual has cancer based upon a plurality of
classifications.
[0082] In another aspect, a computer-implemented method is provided
for classifying an individual as either having or not having
cancer. The method comprises: retrieving on a computer biomarker
information for an individual, wherein the biomarker information
comprises biomarker values that each correspond to one of at least
N biomarkers selected from the group of biomarkers provided in
Table 19; performing with the computer a classification of each of
the biomarker values; and indicating whether the individual has
cancer based upon a plurality of classifications.
[0083] In another aspect, a computer program product is provided
for indicating a likelihood of cancer. The computer program product
includes a computer readable medium embodying program code
executable by a processor of a computing device or system, the
program code comprising: code that retrieves data attributed to a
biological sample from an individual, wherein the data comprises
biomarker values that each correspond to one of at least N
biomarkers, wherein N is as defined above, in the biological sample
selected from the group of biomarkers set forth in Table 19; and
code that executes a classification method that indicates a
likelihood that the individual has cancer as a function of the
biomarker values.
[0084] In another aspect, a computer program product is provided
for indicating a cancer status of an individual. The computer
program product includes a computer readable medium embodying
program code executable by a processor of a computing device or
system, the program code comprising: code that retrieves data
attributed to a biological sample from an individual, wherein the
data comprises biomarker values that each correspond to one of at
least N biomarkers in the biological sample selected from the group
of biomarkers provided in Table 19; and code that executes a
classification method that indicates a cancer status of the
individual as a function of the biomarker values.
[0085] In another aspect, a computer-implemented method is provided
for indicating a likelihood of cancer. The method comprises
retrieving on a computer biomarker information for an individual,
wherein the biomarker information comprises a biomarker value
corresponding to a biomarker selected from the group of biomarkers
set forth in Table 19; performing with the computer a
classification of the biomarker value; and indicating a likelihood
that the individual has cancer based upon the classification.
[0086] In another aspect, a computer-implemented method is provided
for classifying an individual as either having or not having
cancer. The method comprises retrieving from a computer biomarker
information for an individual, wherein the biomarker information
comprises a biomarker value corresponding to a biomarker selected
from the group of biomarkers provided in Table 19; performing with
the computer a classification of the biomarker value; and
indicating whether the individual has cancer based upon the
classification.
[0087] In still another aspect, a computer program product is
provided for indicating a likelihood of cancer. The computer
program product includes a computer readable medium embodying
program code executable by a processor of a computing device or
system, the program code comprising: code that retrieves data
attributed to a biological sample from an individual, wherein the
data comprises a biomarker value corresponding to a biomarker in
the biological sample selected from the group of biomarkers set
forth in Table 19; and code that executes a classification method
that indicates a likelihood that the individual has cancer as a
function of the biomarker value.
[0088] In still another aspect, a computer program product is
provided for indicating a cancer status of an individual. The
computer program product includes a computer readable medium
embodying program code executable by a processor of a computing
device or system, the program code comprising: code that retrieves
data attributed to a biological sample from an individual, wherein
the data comprises a biomarker value corresponding to a biomarker
in the biological sample selected from the group of biomarkers
provided in Table 19; and code that executes a classification
method that indicates a cancer status of the individual as a
function of the biomarker value.
[0089] In still another aspect, a method is provided for diagnosing
pancreatic cancer, the method including detecting, in a biological
sample from an individual, the tumor marker CA 19-9 in addition to
biomarker values that each correspond to a biomarker on a panel of
biomarkers selected from the group of panels set forth in Table 1
wherein a classification of the combined CA 19-9 and biomarker
values indicates that the individual has pancreatic cancer.
BRIEF DESCRIPTION OF THE DRAWINGS
[0090] FIG. 1A is a flowchart for an exemplary method for detecting
pancreatic cancer in a biological sample.
[0091] FIG. 1B is a flowchart for an exemplary method for detecting
pancreatic cancer in a biological sample using a naive Bayes
classification method.
[0092] FIG. 2 shows a ROC curve for a single biomarker, CTSB, using
a naive Bayes classifier for a test that detects pancreatic
cancer.
[0093] FIG. 3 shows ROC curves for biomarker panels of from two to
ten biomarkers using naive Bayes classifiers for a test that
detects pancreatic cancer.
[0094] FIG. 4 illustrates the increase in the classification score
(AUC) as the number of biomarkers is increased from one to ten
using naive Bayes classification for a pancreatic cancer panel.
[0095] FIG. 5 shows the measured biomarker distributions for CTSB
as a cumulative distribution function (cdf) in log-transformed RFU
for the GI and normal controls combined (solid line) and the
pancreatic cancer disease group (dotted line) along with their
curve fits to a normal cdf (dashed lines) used to train the naive
Bayes classifiers.
[0096] FIG. 6 illustrates an exemplary computer system for use with
various computer-implemented methods described herein.
[0097] FIG. 7 is a flowchart for a method of indicating the
likelihood that an individual has pancreatic cancer in accordance
with one embodiment.
[0098] FIG. 8 is a flowchart for a method of indicating the
likelihood that an individual has pancreatic cancer in accordance
with one embodiment.
[0099] FIG. 9 illustrates an exemplary aptamer assay that can be
used to detect one or more pancreatic cancer biomarkers in a
biological sample.
[0100] FIG. 10 shows a histogram of frequencies for which
biomarkers were used in building classifiers to distinguish between
pancreatic cancer and the GI and normal controls from an aggregated
set of potential biomarkers.
[0101] FIG. 11A shows a pair of histograms summarizing all possible
single protein naive Bayes classifier scores (AUC) using the
biomarkers set forth in Table 1 (solid) and a set of random markers
(dotted).
[0102] FIG. 11B shows a pair of histograms summarizing all possible
two-protein protein naive Bayes classifier scores (AUC) using the
biomarkers set forth in Table 1 (solid) and a set of random markers
(dotted).
[0103] FIG. 11C shows a pair of histograms summarizing all possible
three-protein naive Bayes classifier scores (AUC) using the
biomarkers set forth in Table 1 (solid) and a set of random markers
(dotted).
[0104] FIG. 12 shows the AUC for naive Bayes classifiers using from
2-10 markers selected from the full panel (diamond) and the scores
obtained by dropping the best 5, 10, and 15 markers during
classifier generation.
[0105] FIG. 13 shows the performance of three different
classifiers: CA19-9 alone, the SOMAmer panel and the combination of
SOMAmers and CA19-9.
[0106] FIG. 14 shows the performance of CA19-9 plus one (HAMP) or
two (HAMP and CTSB) SOMAmer biomarkers.
[0107] FIG. 15 shows the performance of the 10 marker random forest
classifier.
[0108] FIG. 16A shows a set of ROC curves modeled from the data in
Table 14 for panels of from one to five markers.
[0109] FIG. 16B shows a set of ROC curves computed from the
training data for panels of from one to five markers as in FIG.
12A.
[0110] FIGS. 17A and 17B show a comparison of performance between
ten biomarkers selected by a greedy selection procedure (Table 19)
and 1,000 randomly sampled sets of ten "non marker" biomarkers. The
mean AUC for the ten biomarkers in Table 19 is shown as a dotted
vertical line. In FIG. 17A, sets of ten biomarkers were randomly
selected from all 10 analytes present in all 3 cancer studies that
were not selected by the greedy procedure. In FIG. 17B, the same
procedure as 17A was used; however, the sampling was restricted to
the remaining 55 biomarkers from Table 1 that were not selected by
the greedy procedure.
[0111] FIG. 18 shows receiver operating characteristic (ROC) curves
for the 3 naive Bayes classifiers set forth in Table 19. For each
study, the area under the curve (AUC) is also displayed next to the
legend.
DETAILED DESCRIPTION
[0112] Reference will now be made in detail to representative
embodiments of the invention. While the invention will be described
in conjunction with the enumerated embodiments, it will be
understood that the invention is not intended to be limited to
those embodiments. On the contrary, the invention is intended to
cover all alternatives, modifications, and equivalents that may be
included within the scope of the present invention as defined by
the claims.
[0113] One skilled in the art will recognize many methods and
materials similar or equivalent to those described herein, which
could be used in and are within the scope of the practice of the
present invention. The present invention is in no way limited to
the methods and materials described.
[0114] Unless defined otherwise, 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. Although
any methods, devices, and materials similar or equivalent to those
described herein can be used in the practice or testing of the
invention, the preferred methods, devices and materials are now
described.
[0115] All publications, published patent documents, and patent
applications cited in this application are indicative of the level
of skill in the art(s) to which the application pertains. All
publications, published patent documents, and patent applications
cited herein are hereby incorporated by reference to the same
extent as though each individual publication, published patent
document, or patent application was specifically and individually
indicated as being incorporated by reference.
[0116] As used in this application, including the appended claims,
the singular forms "a," "an," and "the" include plural references,
unless the content clearly dictates otherwise, and are used
interchangeably with "at least one" and "one or more." Thus,
reference to "an aptamer" includes mixtures of aptamers, reference
to "a probe" includes mixtures of probes, and the like.
[0117] As used herein, the term "about" represents an insignificant
modification or variation of the numerical value such that the
basic function of the item to which the numerical value relates is
unchanged.
[0118] As used herein, the terms "comprises," "comprising,"
"includes," "including," "contains," "containing," and any
variations thereof, are intended to cover a non-exclusive
inclusion, such that a process, method, product-by-process, or
composition of matter that comprises, includes, or contains an
element or list of elements does not include only those elements
but may include other elements not expressly listed or inherent to
such process, method, product-by-process, or composition of
matter.
[0119] The present application includes biomarkers, methods,
devices, reagents, systems, and kits for the detection and
diagnosis of pancreatic cancer and cancer more generally.
[0120] In one aspect, one or more biomarkers are provided for use
either alone or in various combinations to diagnose pancreatic
cancer, permit the differential diagnosis of pancreatic cancer from
non-malignant GI conditions including acute or chronic pancreatitis
(or both), pancreatic obstruction, GERD, gallstones, or abnormal
imaging later found to be benign, monitor pancreatic cancer
recurrence, or address other clinical indications. As described in
detail below, exemplary embodiments include the biomarkers provided
in Table 1, Col. 2, which were identified using a multiplex
aptamer-based assay that is described generally in Example 1 and
more specifically in Example 2.
[0121] Table 1, Col. 2 sets forth the findings obtained from
analyzing hundreds of individual blood samples from pancreatic
cancer cases, and hundreds of equivalent individual blood samples
from GI and normal controls. The GI and normal controls group was
designed to match the populations with which a pancreatic cancer
diagnostic test can have the most benefit, including asymptomatic
individuals and symptomatic individuals. The normal control group
represents asymptomatic individuals with a high risk of pancreatic
cancer. High risk for pancreatic cancer includes family history of
pancreatic cancer, obesity, smoking, diabetes, cystic fibrosis,
chronic or hereditary pancreatitis, BRCA mutation carrier, p16
mutation, and Peutz-Jeghers syndrome (Brand E et al. Gut
2007:56:1460). The GI control group includes nonspecific abdominal
symptoms such as acute or chronic pancreatitis (or both),
pancreatic obstruction, GERD, gallstones, or abnormal imaging later
found to be benign. Samples from the normal controls were combined
with the GI controls to discover biomarkers useful for both
screening high risk asymptomatic individuals and differential
diagnosis in symptomatic individuals. The potential biomarkers were
measured in individual samples rather than pooling the disease and
control blood; this allowed a better understanding of the
individual and group variations in the phenotypes associated with
the presence and absence of disease (in this case pancreatic
cancer). Since 823 protein measurements were made on each sample,
and several hundred samples from each of the disease and the
control populations were individually measured, Table 1, Col. 2
resulted from an analysis of an uncommonly large set of data. The
measurements were analyzed using the methods described in the
section, "Classification of Biomarkers and Calculation of Disease
Scores" herein. Table 1, Col. 2 lists the 65 biomarkers found to be
useful in distinguishing samples obtained from individuals with
pancreatic cancer from "control" samples obtained from GI and
normal controls. GI controls include subjects with acute or chronic
pancreatitis (or both), pancreatic obstruction, GERD, gallstones,
or abnormal imaging later found to be benign.
[0122] While certain of the described pancreatic cancer biomarkers
are useful alone for detecting and diagnosing pancreatic cancer,
methods are also described herein for the grouping of multiple
subsets of the pancreatic cancer biomarkers, where each grouping or
subset selection is useful as a panel of three or more biomarkers,
interchangeably referred to herein as a "biomarker panel" and a
panel. Thus, various embodiments of the instant application provide
combinations comprising N biomarkers, wherein N is at least two
biomarkers. In other embodiments, N is selected from 2-65
biomarkers.
[0123] In yet other embodiments, N is selected to be any number
from 2-7, 2-10, 2-15, 2-20, 2-25, 2-30, 2-35, 2-40, 2-45, 2-50,
2-55, or 2-65. In other embodiments, N is selected to be any number
from 3-7, 3-10, 3-15, 3-20, 3-25, 3-30, 3-35, 3-40, 3-45, 3-50,
3-55, or 3-65. In other embodiments, N is selected to be any number
from 4-7, 4-10, 4-15, 4-20, 4-25, 4-30, 4-35, 4-40, 4-45, 4-50,
4-55, or 4-65. In other embodiments, N is selected to be any number
from 5-7, 5-10, 5-15, 5-20, 5-25, 5-30, 5-35, 5-40, 5-45, 5-50,
5-55, or 5-65. In other embodiments, N is selected to be any number
from 6-10, 6-15, 6-20, 6-25, 6-30, 6-35, 6-40, 6-45, 6-50, 6-55, or
6-65. In other embodiments, N is selected to be any number from
7-10, 7-15, 7-20, 7-25, 7-30, 7-35, 7-40, 7-45, 7-50, 7-55, or
7-65. In other embodiments, N is selected to be any number from
8-10, 8-15, 8-20, 8-25, 8-30, 8-35, 8-40, 8-45, 8-50, 8-55, or
8-65. In other embodiments, N is selected to be any number from
9-15, 9-20, 9-25, 9-30, 9-35, 9-40, 9-45, 9-50, 9-55, or 9-65. In
other embodiments, N is selected to be any number from 10-15,
10-20, 10-25, 10-30, 10-35, 10-40, 10-45, 10-50, 10-55, or 10-65.
It will be appreciated that N can be selected to encompass similar,
but higher order, ranges.
[0124] In one embodiment, the number of biomarkers useful for a
biomarker subset or panel is based on the sensitivity and
specificity value for the particular combination of biomarker
values. The terms "sensitivity" and "specificity" are used herein
with respect to the ability to correctly classify an individual,
based on one or more biomarker values detected in their biological
sample, as having pancreatic cancer or not having pancreatic
cancer. "Sensitivity" indicates the performance of the biomarker(s)
with respect to correctly classifying individuals that have
pancreatic cancer. "Specificity" indicates the performance of the
biomarker(s) with respect to correctly classifying individuals who
do not have pancreatic cancer. For example, 85% specificity and 90%
sensitivity for a panel of markers used to test a set of control
samples and pancreatic cancer samples indicates that 85% of the
control samples were correctly classified as control samples by the
panel, and 90% of the pancreatic cancer samples were correctly
classified as pancreatic cancer samples by the panel. The desired
or preferred minimum value can be determined as described in
Example 3. Representative panels are set forth in Tables 4-11,
which set forth a series of 100 different panels of 3-10
biomarkers, which have the indicated levels of specificity and
sensitivity for each panel. The total number of occurrences of each
marker in each of these panels is indicated at the bottom of each
Table.
[0125] In one aspect, pancreatic cancer is detected or diagnosed in
an individual by conducting an assay on a biological sample from
the individual and detecting biomarker values that each correspond
to at least one of the biomarkers CTSB, C5a or C5 and at least N
additional biomarkers selected from the list of biomarkers in Table
1, Col. 2, wherein N equals 2, 3, 4, 5, 6, 7, 8, or 9. In a further
aspect, pancreatic cancer is detected or diagnosed in an individual
by conducting an assay on a biological sample from the individual
and detecting biomarker values that each correspond to the
biomarkers CTSB, C5a or C5 and one of at least N additional
biomarkers selected from the list of biomarkers in Table 1, Col. 2,
wherein N equals 1, 2, 3, 4, 5, 6, or 7. In a further aspect,
pancreatic cancer is detected or diagnosed in an individual by
conducting an assay on a biological sample from the individual and
detecting biomarker values that each correspond to the biomarker
CTSB and one of at least N additional biomarkers selected from the
list of biomarkers in Table 1, Col. 2, wherein N equals 2, 3, 4, 5,
6, 7, 8, or 9. In a further aspect, pancreatic cancer is detected
or diagnosed in an individual by conducting an assay on a
biological sample from the individual and detecting biomarker
values that each correspond to the biomarker C5a and one of at
least N additional biomarkers selected from the list of biomarkers
in Table 1, Col. 2, wherein N equals 2, 3, 4, 5, 6, 7, 8, or 9. In
a further aspect, pancreatic cancer is detected or diagnosed in an
individual by conducting an assay on a biological sample from the
individual and detecting biomarker values that each correspond to
the biomarker C5 and one of at least N additional biomarkers
selected from the list of biomarkers in Table 1, Col. 2, wherein N
equals 2, 3, 4, 5, 6, 7, 8, or 9.
[0126] The pancreatic cancer biomarkers identified herein represent
a relatively large number of choices for subsets or panels of
biomarkers that can be used to effectively detect or diagnose
pancreatic cancer. Selection of the desired number of such
biomarkers depends on the specific combination of biomarkers
chosen. It is important to remember that panels of biomarkers for
detecting or diagnosing pancreatic cancer may also include
biomarkers not found in Table 1, Col. 2, and that the inclusion of
additional biomarkers not found in Table 1, Col. 2 may reduce the
number of biomarkers in the particular subset or panel that is
selected from Table 1, Col. 2. The number of biomarkers from Table
1, Col. 2 used in a subset or panel may also be reduced if
additional biomedical information is used in conjunction with the
biomarker values to establish acceptable sensitivity and
specificity values for a given assay.
[0127] Another factor that can affect the number of biomarkers to
be used in a subset or panel of biomarkers is the procedures used
to obtain biological samples from individuals who are being
diagnosed for pancreatic cancer. In a carefully controlled sample
procurement environment, the number of biomarkers necessary to meet
desired sensitivity and specificity values will be lower than in a
situation where there can be more variation in sample collection,
handling and storage. In developing the list of biomarkers set
forth in Table 1, Col. 2, multiple sample collection sites were
utilized to collect data for classifier training. This provides for
more robust biomarkers that are less sensitive to variations in
sample collection, handling and storage, but can also require that
the number of biomarkers in a subset or panel be larger than if the
training data were all obtained under very similar conditions.
[0128] One aspect of the instant application can be described
generally with reference to FIGS. 1A and 1B. A biological sample is
obtained from an individual or individuals of interest. The
biological sample is then assayed to detect the presence of one or
more (N) biomarkers of interest and to determine a biomarker value
for each of said N biomarkers (referred to in FIG. 1B as marker
RFU). Once a biomarker has been detected and a biomarker value
assigned each marker is scored or classified as described in detail
herein. The marker scores are then combined to provide a total
diagnostic score, which indicates the likelihood that the
individual from whom the sample was obtained has pancreatic
cancer.
[0129] "Biological sample", "sample", and "test sample" are used
interchangeably herein to refer to any material, biological fluid,
tissue, or cell obtained or otherwise derived from an individual.
This includes blood (including whole blood, leukocytes, peripheral
blood mononuclear cells, buffy coat, plasma, and serum), sputum,
tears, mucus, nasal washes, nasal aspirate, breath, urine, semen,
saliva, peritoneal washings, ascites, cystic fluid, meningeal
fluid, amniotic fluid, glandular fluid, pancreatic fluid, lymph
fluid, pleural fluid, nipple aspirate, bronchial aspirate,
bronchial brushing, synovial fluid, joint aspirate, organ
secretions, cells, a cellular extract, and cerebrospinal fluid.
This also includes experimentally separated fractions of all of the
preceding. For example, a blood sample can be fractionated into
serum, plasma or into fractions containing particular types of
blood cells, such as red blood cells or white blood cells
(leukocytes). If desired, a sample can be a combination of samples
from an individual, such as a combination of a tissue and fluid
sample. The term "biological sample" also includes materials
containing homogenized solid material, such as from a stool sample,
a tissue sample, or a tissue biopsy, for example. The term
"biological sample" also includes materials derived from a tissue
culture or a cell culture. Any suitable methods for obtaining a
biological sample can be employed; exemplary methods include, e.g.,
phlebotomy, swab (e.g., buccal swab), and a fine needle aspirate
biopsy procedure. Exemplary tissues susceptible to fine needle
aspiration include lymph node, lung, lung washes, BAL
(bronchoalveolar lavage), thyroid, breast, pancreas and liver.
Samples can also be collected, e.g., by micro dissection (e.g.,
laser capture micro dissection (LCM) or laser micro dissection
(LMD)), bladder wash, smear (e.g., a PAP smear), or ductal lavage.
A "biological sample" obtained or derived from an individual
includes any such sample that has been processed in any suitable
manner after being obtained from the individual.
[0130] Further, it should be realized that a biological sample can
be derived by taking biological samples from a number of
individuals and pooling them or pooling an aliquot of each
individual's biological sample. The pooled sample can be treated as
a sample from a single individual and if the presence of cancer is
established in the pooled sample, then each individual biological
sample can be re-tested to determine which individual/s have
pancreatic cancer.
[0131] For purposes of this specification, the phrase "data
attributed to a biological sample from an individual" is intended
to mean that the data in some form derived from, or were generated
using, the biological sample of the individual. The data may have
been reformatted, revised, or mathematically altered to some degree
after having been generated, such as by conversion from units in
one measurement system to units in another measurement system; but,
the data are understood to have been derived from, or were
generated using, the biological sample.
[0132] "Target", "target molecule", and "analyte" are used
interchangeably herein to refer to any molecule of interest that
may be present in a biological sample. A "molecule of interest"
includes any minor variation of a particular molecule, such as, in
the case of a protein, for example, minor variations in amino acid
sequence, disulfide bond formation, glycosylation, lipidation,
acetylation, phosphorylation, or any other manipulation or
modification, such as conjugation with a labeling component, which
does not substantially alter the identity of the molecule. A
"target molecule", "target", or "analyte" is a set of copies of one
type or species of molecule or multi-molecular structure. "Target
molecules", "targets", and "analytes" refer to more than one such
set of molecules. Exemplary target molecules include proteins,
polypeptides, nucleic acids, carbohydrates, lipids,
polysaccharides, glycoproteins, hormones, receptors, antigens,
antibodies, affybodies, antibody mimics, viruses, pathogens, toxic
substances, substrates, metabolites, transition state analogs,
cofactors, inhibitors, drugs, dyes, nutrients, growth factors,
cells, tissues, and any fragment or portion of any of the
foregoing.
[0133] As used herein, "polypeptide," "peptide," and "protein" are
used interchangeably herein to refer to polymers of amino acids of
any length. The polymer may be linear or branched, it may comprise
modified amino acids, and it may be interrupted by non-amino acids.
The terms also encompass an amino acid polymer that has been
modified naturally or by intervention; for example, disulfide bond
formation, glycosylation, lipidation, acetylation, phosphorylation,
or any other manipulation or modification, such as conjugation with
a labeling component. Also included within the definition are, for
example, polypeptides containing one or more analogs of an amino
acid (including, for example, unnatural amino acids, etc.), as well
as other modifications known in the art. Polypeptides can be single
chains or associated chains. Also included within the definition
are preproteins and intact mature proteins; peptides or
polypeptides derived from a mature protein; fragments of a protein;
splice variants; recombinant forms of a protein; protein variants
with amino acid modifications, deletions, or substitutions;
digests; and post-translational modifications, such as
glycosylation, acetylation, phosphorylation, and the like.
[0134] As used herein, "marker" and "biomarker" are used
interchangeably to refer to a target molecule that indicates or is
a sign of a normal or abnormal process in an individual or of a
disease or other condition in an individual. More specifically, a
"marker" or "biomarker" is an anatomic, physiologic, biochemical,
or molecular parameter associated with the presence of a specific
physiological state or process, whether normal or abnormal, and, if
abnormal, whether chronic or acute. Biomarkers are detectable and
measurable by a variety of methods including laboratory assays and
medical imaging. When a biomarker is a protein, it is also possible
to use the expression of the corresponding gene as a surrogate
measure of the amount or presence or absence of the corresponding
protein biomarker in a biological sample or methylation state of
the gene encoding the biomarker or proteins that control expression
of the biomarker.
[0135] As used herein, "biomarker value", "value", "biomarker
level", and "level" are used interchangeably to refer to a
measurement that is made using any analytical method for detecting
the biomarker in a biological sample and that indicates the
presence, absence, absolute amount or concentration, relative
amount or concentration, titer, a level, an expression level, a
ratio of measured levels, or the like, of, for, or corresponding to
the biomarker in the biological sample. The exact nature of the
"value" or "level" depends on the specific design and components of
the particular analytical method employed to detect the
biomarker.
[0136] When a biomarker indicates or is a sign of an abnormal
process or a disease or other condition in an individual, that
biomarker is generally described as being either over-expressed or
under-expressed as compared to an expression level or value of the
biomarker that indicates or is a sign of a normal process or an
absence of a disease or other condition in an individual.
"Up-regulation", "up-regulated", "over-expression",
"over-expressed", and any variations thereof are used
interchangeably to refer to a value or level of a biomarker in a
biological sample that is greater than a value or level (or range
of values or levels) of the biomarker that is typically detected in
similar biological samples from healthy or normal individuals. The
terms may also refer to a value or level of a biomarker in a
biological sample that is greater than a value or level (or range
of values or levels) of the biomarker that may be detected at a
different stage of a particular disease.
[0137] "Down-regulation", "down-regulated", "under-expression",
"under-expressed", and any variations thereof are used
interchangeably to refer to a value or level of a biomarker in a
biological sample that is less than a value or level (or range of
values or levels) of the biomarker that is typically detected in
similar biological samples from healthy or normal individuals. The
terms may also refer to a value or level of a biomarker in a
biological sample that is less than a value or level (or range of
values or levels) of the biomarker that may be detected at a
different stage of a particular disease.
[0138] Further, a biomarker that is either over-expressed or
under-expressed can also be referred to as being "differentially
expressed" or as having a "differential level" or "differential
value" as compared to a "normal" expression level or value of the
biomarker that indicates or is a sign of a normal process or an
absence of a disease or other condition in an individual. Thus,
"differential expression" of a biomarker can also be referred to as
a variation from a "normal" expression level of the biomarker.
[0139] The term "differential gene expression" and "differential
expression" are used interchangeably to refer to a gene (or its
corresponding protein expression product) whose expression is
activated to a higher or lower level in a subject suffering from a
specific disease, relative to its expression in a normal or control
subject. The terms also include genes (or the corresponding protein
expression products) whose expression is activated to a higher or
lower level at different stages of the same disease. It is also
understood that a differentially expressed gene may be either
activated or inhibited at the nucleic acid level or protein level,
or may be subject to alternative splicing to result in a different
polypeptide product. Such differences may be evidenced by a variety
of changes including mRNA levels, surface expression, secretion or
other partitioning of a polypeptide. Differential gene expression
may include a comparison of expression between two or more genes or
their gene products; or a comparison of the ratios of the
expression between two or more genes or their gene products; or
even a comparison of two differently processed products of the same
gene, which differ between normal subjects and subjects suffering
from a disease; or between various stages of the same disease.
Differential expression includes both quantitative, as well as
qualitative, differences in the temporal or cellular expression
pattern in a gene or its expression products among, for example,
normal and diseased cells, or among cells which have undergone
different disease events or disease stages.
[0140] As used herein, "individual" refers to a test subject or
patient. The individual can be a mammal or a non-mammal. In various
embodiments, the individual is a mammal. A mammalian individual can
be a human or non-human. In various embodiments, the individual is
a human. A healthy or normal individual is an individual in which
the disease or condition of interest (including, for example,
pancreatic diseases, pancreatic-associated diseases, or other
pancreatic conditions) is not detectable by conventional diagnostic
methods.
[0141] "Diagnose", "diagnosing", "diagnosis", and variations
thereof refer to the detection, determination, or recognition of a
health status or condition of an individual on the basis of one or
more signs, symptoms, data, or other information pertaining to that
individual. The health status of an individual can be diagnosed as
healthy/normal (i.e., a diagnosis of the absence of a disease or
condition) or diagnosed as ill/abnormal (i.e., a diagnosis of the
presence, or an assessment of the characteristics, of a disease or
condition). The terms "diagnose", "diagnosing", "diagnosis", etc.,
encompass, with respect to a particular disease or condition, the
initial detection of the disease; the characterization or
classification of the disease; the detection of the progression,
remission, or recurrence of the disease; and the detection of
disease response after the administration of a treatment or therapy
to the individual. The diagnosis of pancreatic cancer includes
distinguishing individuals who have cancer from individuals who do
not. It further includes distinguishing GI and normal controls from
pancreatic cancer.
[0142] "Prognose", "prognosing", "prognosis", and variations
thereof refer to the prediction of a future course of a disease or
condition in an individual who has the disease or condition (e.g.,
predicting patient survival), and such terms encompass the
evaluation of disease response after the administration of a
treatment or therapy to the individual.
[0143] "Evaluate", "evaluating", "evaluation", and variations
thereof encompass both "diagnose" and "prognose" and also encompass
determinations or predictions about the future course of a disease
or condition in an individual who does not have the disease as well
as determinations or predictions regarding the likelihood that a
disease or condition will recur in an individual who apparently has
been cured of the disease. The term "evaluate" also encompasses
assessing an individual's response to a therapy, such as, for
example, predicting whether an individual is likely to respond
favorably to a therapeutic agent or is unlikely to respond to a
therapeutic agent (or will experience toxic or other undesirable
side effects, for example), selecting a therapeutic agent for
administration to an individual, or monitoring or determining an
individual's response to a therapy that has been administered to
the individual. Thus, "evaluating" pancreatic cancer can include,
for example, any of the following: prognosing the future course of
pancreatic cancer in an individual; predicting the recurrence of
pancreatic cancer in an individual who apparently has been cured of
pancreatic cancer; or determining or predicting an individual's
response to a pancreatic cancer treatment or selecting a pancreatic
cancer treatment to administer to an individual based upon a
determination of the biomarker values derived from the individual's
biological sample.
[0144] Any of the following examples may be referred to as either
"diagnosing" or "evaluating" pancreatic cancer: initially detecting
the presence or absence of pancreatic cancer; determining a
specific stage, type or sub-type, or other classification or
characteristic of pancreatic cancer; determining whether a
suspicious mass is a benign lesion or a malignant pancreatic tumor;
or detecting/monitoring pancreatic cancer progression (e.g.,
monitoring tumor growth or metastatic spread), remission, or
recurrence.
[0145] As used herein, "additional biomedical information" refers
to one or more evaluations of an individual, other than using any
of the biomarkers described herein, that are associated with cancer
risk or, more specifically, pancreatic cancer risk. "Additional
biomedical information" includes any of the following: physical
descriptors of an individual, including a pancreatic mass observed
by any of contrast-enhanced multislice (multidetector) helical
computed tomography (CT) scanning with three dimensional
reconstruction, transcutaneous or endoscopic ultrasound (US or
EUS), endoscopic retrograde cholangiopancreatography (ERCP),
magnetic resonance imaging (MRI), MR cholangiopancreatography
(MRCP), or abdominal ultrasound; the height and/or weight of an
individual; change in weight; the ethnicity of an individual;
occupational history; family history of pancreatic cancer (or other
cancer); the presence of a genetic marker(s) correlating with a
higher risk of pancreatic cancer (or other cancer) in the
individual or a family member; the presence or absence of a
pancreatic mass or other abdominal mass; size of mass; location of
mass; morphology of mass and associated abdominal region (e.g., as
observed through imaging); clinical symptoms such as abdominal
pain, weight loss, anorexia, early satiety, diarrhea, or
steatorrhea, jaundice, recent onset of atypical diabetes mellitus,
a history of recent but unexplained thrombophlebitis, or previous
attack of pancreatitis, and the like; gene expression values;
physical descriptors of an individual, including physical
descriptors observed by radiologic imaging; the height and/or
weight of an individual; the gender of an individual; the ethnicity
of an individual; smoking history; alcohol use history;
occupational history; exposure to known carcinogens (e.g., exposure
to any of asbestos, radon gas, chemicals, smoke from fires, and air
pollution, which can include emissions from stationary or mobile
sources such as industrial/factory or auto/marine/aircraft
emissions); exposure to second-hand smoke; and family history of
pancreatic cancer or other cancer. Testing of biomarker levels in
combination with an evaluation of any additional biomedical
information, including other laboratory tests (e.g., CA 19-9
testing, serum bilirubin concentration, alkaline phosphatase
activity, presence of anemia), may, for example, improve
sensitivity, specificity, and/or AUC for detecting pancreatic
cancer (or other pancreatic cancer-related uses) as compared to
biomarker testing alone or evaluating any particular item of
additional biomedical information alone (e.g., ultrasound imaging
alone). Additional biomedical information can be obtained from an
individual using routine techniques known in the art, such as from
the individual themselves by use of a routine patient questionnaire
or health history questionnaire, etc., or from a medical
practitioner, etc. Testing of biomarker levels in combination with
an evaluation of any additional biomedical information may, for
example, improve sensitivity, specificity, and/or AUC for detecting
pancreatic cancer (or other pancreatic cancer-related uses) as
compared to biomarker testing alone or evaluating any particular
item of additional biomedical information alone (e.g., CT imaging
alone).
[0146] Cancer associated antigen 19-9 (CA 19-9) is a known blood
marker for pancreatic cancer. The reported sensitivity and
specificity of CA 19-9 for pancreatic cancer are 80 to 90 percent,
respectively. However, these values are closely related to tumor
size. The accuracy of CA 19-9 to identify patients with small
surgically resectable cancers is limited. CA 19-9 requires the
presence of the Lewis blood group antigen (a glycosyl transferase)
to be expressed. Among individuals with a Lewis-negative phenotype
(an estimated 5 to 10 percent of the population), CA 19-9 levels
are not a useful tumor marker. The specificity of CA 19-9 is also
limited. CA 19-9 is frequently elevated in patients with various
benign pancreaticobiliary disorders. The degree of elevation of CA
19-9 (both at initial presentation and in the postoperative
setting) is associated with long-term prognosis. Furthermore, in
patients who appear to have potentially resectable disease, the
magnitude of the CA 19-9 level can also help to predict the
presence of radiographically occult metastatic disease as well.
Serial monitoring of CA 19-9 levels is useful to follow patients
after potentially curative surgery and for those who are receiving
chemotherapy for advanced disease. Rising CA 19-9 levels usually
precede the radiographic appearance of recurrent disease, but
confirmation of disease progression should be pursued with imaging
studies and/or biopsy. Testing of biomarker levels in combination
with CA 19-9 may, for example, improve improve sensitivity,
specificity, and/or AUC for detecting pancreatic cancer (or other
pancreatic cancer-related uses) as compared to CA 19-9 alone.
[0147] The term "area under the curve" or "AUC" refers to the area
under the curve of a receiver operating characteristic (ROC) curve,
both of which are well known in the art. AUC measures are useful
for comparing the accuracy of a classifier across the complete data
range. Classifiers with a greater AUC have a greater capacity to
classify unknowns correctly between two groups of interest (e.g.,
pancreatic cancer samples and normal or control samples). ROC
curves are useful for plotting the performance of a particular
feature (e.g., any of the biomarkers described herein and/or any
item of additional biomedical information) in distinguishing
between two populations (e.g., cases having pancreatic cancer and
controls without pancreatic cancer). Typically, the feature data
across the entire population (e.g., the cases and controls) are
sorted in ascending order based on the value of a single feature.
Then, for each value for that feature, the true positive and false
positive rates for the data are calculated. The true positive rate
is determined by counting the number of cases above the value for
that feature and then dividing by the total number of cases. The
false positive rate is determined by counting the number of
controls above the value for that feature and then dividing by the
total number of controls. Although this definition refers to
scenarios in which a feature is elevated in cases compared to
controls, this definition also applies to scenarios in which a
feature is lower in cases compared to the controls (in such a
scenario, samples below the value for that feature would be
counted). ROC curves can be generated for a single feature as well
as for other single outputs, for example, a combination of two or
more features can be mathematically combined (e.g., added,
subtracted, multiplied, etc.) to provide a single sum value, and
this single sum value can be plotted in a ROC curve. Additionally,
any combination of multiple features, in which the combination
derives a single output value, can be plotted in a ROC curve. These
combinations of features may comprise a test. The ROC curve is the
plot of the true positive rate (sensitivity) of a test against the
false positive rate (1-specificity) of the test.
[0148] As used herein, "detecting" or "determining" with respect to
a biomarker value includes the use of both the instrument required
to observe and record a signal corresponding to a biomarker value
and the material/s required to generate that signal. In various
embodiments, the biomarker value is detected using any suitable
method, including fluorescence, chemiluminescence, surface plasmon
resonance, surface acoustic waves, mass spectrometry, infrared
spectroscopy, Raman spectroscopy, atomic force microscopy, scanning
tunneling microscopy, electrochemical detection methods, nuclear
magnetic resonance, quantum dots, and the like.
[0149] "Solid support" refers herein to any substrate having a
surface to which molecules may be attached, directly or indirectly,
through either covalent or non-covalent bonds. A "solid support"
can have a variety of physical formats, which can include, for
example, a membrane; a chip (e.g., a protein chip); a slide (e.g.,
a glass slide or coverslip); a column; a hollow, solid, semi-solid,
pore- or cavity-containing particle, such as, for example, a bead;
a gel; a fiber, including a fiber optic material; a matrix; and a
sample receptacle. Exemplary sample receptacles include sample
wells, tubes, capillaries, vials, and any other vessel, groove or
indentation capable of holding a sample. A sample receptacle can be
contained on a multi-sample platform, such as a microtiter plate,
slide, microfluidics device, and the like. A support can be
composed of a natural or synthetic material, an organic or
inorganic material. The composition of the solid support on which
capture reagents are attached generally depends on the method of
attachment (e.g., covalent attachment). Other exemplary receptacles
include microdroplets and microfluidic controlled or bulk
oil/aqueous emulsions within which assays and related manipulations
can occur. Suitable solid supports include, for example, plastics,
resins, polysaccharides, silica or silica-based materials,
functionalized glass, modified silicon, carbon, metals, inorganic
glasses, membranes, nylon, natural fibers (such as, for example,
silk, wool and cotton), polymers, and the like. The material
composing the solid support can include reactive groups such as,
for example, carboxy, amino, or hydroxyl groups, which are used for
attachment of the capture reagents. Polymeric solid supports can
include, e.g., polystyrene, polyethylene glycol tetraphthalate,
polyvinyl acetate, polyvinyl chloride, polyvinyl pyrrolidone,
polyacrylonitrile, polymethyl methacrylate,
polytetrafluoroethylene, butyl rubber, styrenebutadiene rubber,
natural rubber, polyethylene, polypropylene,
(poly)tetrafluoroethylene, (poly)vinylidenefluoride, polycarbonate,
and polymethylpentene. Suitable solid support particles that can be
used include, e.g., encoded particles, such as Luminex-type encoded
particles, magnetic particles, and glass particles.
Exemplary Uses of Biomarkers
[0150] In various exemplary embodiments, methods are provided for
diagnosing pancreatic cancer in an individual by detecting one or
more biomarker values corresponding to one or more biomarkers that
are present in the circulation of an individual, such as in serum
or plasma, by any number of analytical methods, including any of
the analytical methods described herein. These biomarkers are, for
example, differentially expressed in individuals with pancreatic
cancer as compared to individuals without pancreatic cancer.
Detection of the differential expression of a biomarker in an
individual can be used, for example, to permit the early diagnosis
of pancreatic cancer, to distinguish between a benign and malignant
mass (such as, for example, a mass observed on a computed
tomography (CT) scan, MRI or ultrasound), to monitor pancreatic
cancer recurrence, or for differential diagnosis from other
clinical conditions such as acute or chronic pancreatitis (or
both), pancreatic obstruction, GERD, gallstones, or abnormal
imaging later found to be benign.
[0151] Any of the biomarkers described herein may be used in a
variety of clinical indications for pancreatic cancer, including
any of the following: detection of pancreatic cancer (such as in a
high-risk individual or population); characterizing pancreatic
cancer (e.g., determining pancreatic cancer type, sub-type, or
stage), such as by distinguishing between pancreatic cancer
(pancreatic cancer) and acute or chronic pancreatitis (or both),
pancreatic obstruction, GERD, gallstones, or abnormal imaging later
found to be benign and/or between adenocarcinoma and other
malignant cell types (or otherwise facilitating histopathology);
determining whether a pancreatic mass is benign or a malignant
pancreatic tumor; determining pancreatic cancer prognosis;
monitoring pancreatic cancer progression or remission; monitoring
for pancreatic cancer recurrence; monitoring metastasis; treatment
selection; monitoring response to a therapeutic agent or other
treatment; stratification of individuals for endoscopic ultrasound
(EUS) screening (e.g., identifying those individuals at greater
risk of pancreatic cancer and thereby most likely to benefit from
radiologic screening, thus increasing the positive predictive value
of EUS); combining biomarker testing with additional biomedical
information, such as smoking or alcohol history, etc., or CA 19-9
level, the presence of a genetic marker(s) indicating a higher risk
for pancreatic cancer, etc., or with mass size, morphology,
presence of ascites, etc. (such as to provide an assay with
increased diagnostic performance compared to CA 19-9 testing or
other biomarker testing or with mass size, morphology, etc.);
facilitating the diagnosis of an abdominal mass as malignant or
benign; facilitating clinical decision making once an abdominal
mass is observed on CT, MRI, PET or EUS (e.g., ordering repeat
radiologic scans if the abdominal mass is deemed to be low risk,
such as if a biomarker-based test is negative, with or without
categorization of mass size, or considering biopsy if the mass is
deemed medium to high risk, such as if a biomarker-based test is
positive, with or without categorization of mass size or extent of
tissue invasion); and facilitating decisions regarding clinical
follow-up (e.g., whether to implement repeat radiologic imaging
scans, fine needle biopsy, or surgery after observing an abdominal
mass on imaging). Biomarker testing may improve positive predictive
value (PPV) over EUS screening of high risk individuals alone. In
addition to their utilities in conjunction with EUS screening, the
biomarkers described herein can also be used in conjunction with
any other imaging modalities used for pancreatic cancer, such as
CT, MRI or PET scan. Furthermore, the described biomarkers may also
be useful in permitting certain of these uses before indications of
pancreatic cancer are detected by imaging modalities or other
clinical correlates, or before symptoms appear. It further includes
distinguishing acute or chronic pancreatitis (or both), pancreatic
obstruction, GERD, gallstones, or abnormal imaging later found to
be benign from pancreatic cancer.
[0152] As an example of the manner in which any of the biomarkers
described herein can be used to diagnose pancreatic cancer,
differential expression of one or more of the described biomarkers
in an individual who is not known to have pancreatic cancer may
indicate that the individual has pancreatic cancer, thereby
enabling detection of pancreatic cancer at an early stage of the
disease when treatment is most effective, perhaps before the
pancreatic cancer is detected by other means or before symptoms
appear. Over-expression of one or more of the biomarkers during the
course of pancreatic cancer may be indicative of pancreatic cancer
progression, e.g., a pancreatic tumor is growing and/or
metastasizing (and thus indicate a poor prognosis), whereas a
decrease in the degree to which one or more of the biomarkers is
differentially expressed (i.e., in subsequent biomarker tests, the
expression level in the individual is moving toward or approaching
a "normal" expression level) may be indicative of pancreatic cancer
remission, e.g., a pancreatic tumor is shrinking (and thus indicate
a good or better prognosis). Similarly, an increase in the degree
to which one or more of the biomarkers is differentially expressed
(i.e., in subsequent biomarker tests, the expression level in the
individual is moving further away from a "normal" expression level)
during the course of pancreatic cancer treatment may indicate that
the pancreatic cancer is progressing and therefore indicate that
the treatment is ineffective, whereas a decrease in differential
expression of one or more of the biomarkers during the course of
pancreatic cancer treatment may be indicative of pancreatic cancer
remission and therefore indicate that the treatment is working
successfully. Additionally, an increase or decrease in the
differential expression of one or more of the biomarkers after an
individual has apparently been cured of pancreatic cancer may be
indicative of pancreatic cancer recurrence. In a situation such as
this, for example, the individual can be re-started on therapy (or
the therapeutic regimen modified such as to increase dosage amount
and/or frequency, if the individual has maintained therapy) at an
earlier stage than if the recurrence of pancreatic cancer was not
detected until later. Furthermore, a differential expression level
of one or more of the biomarkers in an individual may be predictive
of the individual's response to a particular therapeutic agent. In
monitoring for pancreatic cancer recurrence or progression, changes
in the biomarker expression levels may indicate the need for repeat
imaging (e.g., repeat EUS), such as to determine pancreatic cancer
activity or to determine the need for changes in treatment.
[0153] Detection of any of the biomarkers described herein may be
particularly useful following, or in conjunction with, pancreatic
cancer treatment, such as to evaluate the success of the treatment
or to monitor pancreatic cancer remission, recurrence, and/or
progression (including metastasis) following treatment. Pancreatic
cancer treatment may include, for example, administration of a
therapeutic agent to the individual, performance of surgery (e.g.,
surgical resection of at least a portion of a pancreatic tumor or
removal of pancreatic and surrounding tissue), administration of
radiation therapy, or any other type of pancreatic cancer treatment
used in the art, and any combination of these treatments. For
example, any of the biomarkers may be detected at least once after
treatment or may be detected multiple times after treatment (such
as at periodic intervals), or may be detected both before and after
treatment. Differential expression levels of any of the biomarkers
in an individual over time may be indicative of pancreatic cancer
progression, remission, or recurrence, examples of which include
any of the following: an increase or decrease in the expression
level of the biomarkers after treatment compared with the
expression level of the biomarker before treatment; an increase or
decrease in the expression level of the biomarker at a later time
point after treatment compared with the expression level of the
biomarker at an earlier time point after treatment; and a
differential expression level of the biomarker at a single time
point after treatment compared with normal levels of the
biomarker.
[0154] As a specific example, the biomarker levels for any of the
biomarkers described herein can be determined in pre-surgery and
post-surgery (e.g., 2-8 weeks after surgery) serum or plasma
samples. An increase in the biomarker expression level(s) in the
post-surgery sample compared with the pre-surgery sample can
indicate progression of pancreatic cancer (e.g., unsuccessful
surgery), whereas a decrease in the biomarker expression level(s)
in the post-surgery sample compared with the pre-surgery sample can
indicate regression of pancreatic cancer (e.g., the surgery
successfully removed the pancreatic tumor). Similar analyses of the
biomarker levels can be carried out before and after other forms of
treatment, such as before and after radiation therapy or
administration of a therapeutic agent or cancer vaccine.
[0155] In addition to testing biomarker levels as a stand-alone
diagnostic test, biomarker levels can also be done in conjunction
with determination of SNPs or other genetic lesions or variability
that are indicative of increased risk of susceptibility of disease.
(See, e.g., Amos et al., Nature Genetics 40, 616-622 (2009)).
[0156] In addition to testing biomarker levels as a stand-alone
diagnostic test, biomarker levels can also be done in conjunction
with radiologic screening. In addition to testing biomarker levels
as a stand-alone diagnostic test, biomarker levels can also be done
in conjunction with relevant symptoms or genetic testing. Detection
of any of the biomarkers described herein may be useful after
pancreatic mass has been observed through imaging to aid in the
diagnosis of pancreatic cancer and guide appropriate clinical care
of the individual, including care by an appropriate surgical
specialist or by palliative therapy in the unresectable patient. In
addition to testing biomarker levels in conjunction with relevant
symptoms or risk factors, information regarding the biomarkers can
also be evaluated in conjunction with other types of data,
particularly data that indicates an individual's risk for
pancreatic cancer (e.g., patient clinical history, symptoms, family
history of pancreatic cancer, history of smoking or alcohol use,
sudden onset of diabetes mellitus, jaundice, risk factors such as
the presence of a genetic marker(s), and/or status of other
biomarkers, etc.). These various data can be assessed by automated
methods, such as a computer program/software, which can be embodied
in a computer or other apparatus/device.
[0157] In addition to testing biomarker levels in conjunction with
radiologic screening in high risk individuals (e.g., assessing
biomarker levels in conjunction with size or other characteristics
of a pancreatic mass observed on an imaging scan), information
regarding the biomarkers can also be evaluated in conjunction with
other types of data, particularly data that indicates an
individual's risk for pancreatic cancer (e.g., patient clinical
history, symptoms, family history of cancer, risk factors such as
whether or not the individual is a smoker, heavy alcohol user
and/or status of other biomarkers, etc.). These various data can be
assessed by automated methods, such as a computer program/software,
which can be embodied in a computer or other apparatus/device.
[0158] Any of the described biomarkers may also be used in imaging
tests. For example, an imaging agent can be coupled to any of the
described biomarkers, which can be used to aid in pancreatic cancer
diagnosis, to monitor disease progression/remission or metastasis,
to monitor for disease recurrence, or to monitor response to
therapy, among other uses.
Detection and Determination of Biomarkers and Biomarker Values
[0159] A biomarker value for the biomarkers described herein can be
detected using any of a variety of known analytical methods. In one
embodiment, a biomarker value is detected using a capture reagent.
As used herein, a "capture agent" or "capture reagent" refers to a
molecule that is capable of binding specifically to a biomarker. In
various embodiments, the capture reagent can be exposed to the
biomarker in solution or can be exposed to the biomarker while the
capture reagent is immobilized on a solid support. In other
embodiments, the capture reagent contains a feature that is
reactive with a secondary feature on a solid support. In these
embodiments, the capture reagent can be exposed to the biomarker in
solution, and then the feature on the capture reagent can be used
in conjunction with the secondary feature on the solid support to
immobilize the biomarker on the solid support. The capture reagent
is selected based on the type of analysis to be conducted. Capture
reagents include but are not limited to aptamers, antibodies,
adnectins, ankyrins, other antibody mimetics and other protein
scaffolds, autoantibodies, chimeras, small molecules, an
F(ab').sub.2 fragment, a single chain antibody fragment, an Fv
fragment, a single chain Fv fragment, a nucleic acid, a lectin, a
ligand-binding receptor, affybodies, nanobodies, imprinted
polymers, avimers, peptidomimetics, a hormone receptor, a cytokine
receptor, and synthetic receptors, and modifications and fragments
of these.
[0160] In some embodiments, a biomarker value is detected using a
biomarker/capture reagent complex.
[0161] In other embodiments, the biomarker value is derived from
the biomarker/capture reagent complex and is detected indirectly,
such as, for example, as a result of a reaction that is subsequent
to the biomarker/capture reagent interaction, but is dependent on
the formation of the biomarker/capture reagent complex.
[0162] In some embodiments, the biomarker value is detected
directly from the biomarker in a biological sample.
[0163] In one embodiment, the biomarkers are detected using a
multiplexed format that allows for the simultaneous detection of
two or more biomarkers in a biological sample. In one embodiment of
the multiplexed format, capture reagents are immobilized, directly
or indirectly, covalently or non-covalently, in discrete locations
on a solid support. In another embodiment, a multiplexed format
uses discrete solid supports where each solid support has a unique
capture reagent associated with that solid support, such as, for
example quantum dots. In another embodiment, an individual device
is used for the detection of each one of multiple biomarkers to be
detected in a biological sample. Individual devices can be
configured to permit each biomarker in the biological sample to be
processed simultaneously. For example, a microtiter plate can be
used such that each well in the plate is used to uniquely analyze
one of multiple biomarkers to be detected in a biological
sample.
[0164] In one or more of the foregoing embodiments, a fluorescent
tag can be used to label a component of the biomarker/capture
complex to enable the detection of the biomarker value. In various
embodiments, the fluorescent label can be conjugated to a capture
reagent specific to any of the biomarkers described herein using
known techniques, and the fluorescent label can then be used to
detect the corresponding biomarker value. Suitable fluorescent
labels include rare earth chelates, fluorescein and its
derivatives, rhodamine and its derivatives, dansyl,
allophycocyanin, PBXL-3, Qdot 605, Lissamine, phycoerythrin, Texas
Red, and other such compounds.
[0165] In one embodiment, the fluorescent label is a fluorescent
dye molecule. In some embodiments, the fluorescent dye molecule
includes at least one substituted indolium ring system in which the
substituent on the 3-carbon of the indolium ring contains a
chemically reactive group or a conjugated substance. In some
embodiments, the dye molecule includes an AlexFluor molecule, such
as, for example, AlexaFluor 488, AlexaFluor 532, AlexaFluor 647,
AlexaFluor 680, or AlexaFluor 700. In other embodiments, the dye
molecule includes a first type and a second type of dye molecule,
such as, e.g., two different AlexaFluor molecules. In other
embodiments, the dye molecule includes a first type and a second
type of dye molecule, and the two dye molecules have different
emission spectra.
[0166] Fluorescence can be measured with a variety of
instrumentation compatible with a wide range of assay formats. For
example, spectrofluorimeters have been designed to analyze
microtiter plates, microscope slides, printed arrays, cuvettes,
etc. See Principles of Fluorescence Spectroscopy, by J. R.
Lakowicz, Springer Science+Business Media, Inc., 2004. See
Bioluminescence & Chemiluminescence: Progress & Current
Applications; Philip E. Stanley and Larry J. Kricka editors, World
Scientific Publishing Company, January 2002.
[0167] In one or more of the foregoing embodiments, a
chemiluminescence tag can optionally be used to label a component
of the biomarker/capture complex to enable the detection of a
biomarker value. Suitable chemiluminescent materials include any of
oxalyl chloride, Rodamin 6G, Ru(bipy)32+, TMAE
(tetrakis(dimethylamino)ethylene), Pyrogallol
(1,2,3-trihydroxibenzene), Lucigenin, peroxyoxalates, Aryl
oxalates, Acridinium esters, dioxetanes, and others.
[0168] In yet other embodiments, the detection method includes an
enzyme/substrate combination that generates a detectable signal
that corresponds to the biomarker value. Generally, the enzyme
catalyzes a chemical alteration of the chromogenic substrate which
can be measured using various techniques, including
spectrophotometry, fluorescence, and chemiluminescence. Suitable
enzymes include, for example, luciferases, luciferin, malate
dehydrogenase, urease, horseradish peroxidase (HRPO), alkaline
phosphatase, beta-galactosidase, glucoamylase, lysozyme, glucose
oxidase, galactose oxidase, and glucose-6-phosphate dehydrogenase,
uricase, xanthine oxidase, lactoperoxidase, microperoxidase, and
the like.
[0169] In yet other embodiments, the detection method can be a
combination of fluorescence, chemiluminescence, radionuclide or
enzyme/substrate combinations that generate a measurable signal.
Multimodal signaling could have unique and advantageous
characteristics in biomarker assay formats.
[0170] More specifically, the biomarker values for the biomarkers
described herein can be detected using known analytical methods
including, singleplex aptamer assays, multiplexed aptamer assays,
singleplex or multiplexed immunoassays, mRNA expression profiling,
miRNA expression profiling, mass spectrometric analysis,
histological/cytological methods, etc. as detailed below.
Determination of Biomarker Values Using Aptamer-Based Assays
[0171] Assays directed to the detection and quantification of
physiologically significant molecules in biological samples and
other samples are important tools in scientific research and in the
health care field. One class of such assays involves the use of a
microarray that includes one or more aptamers immobilized on a
solid support. The aptamers are each capable of binding to a target
molecule in a highly specific manner and with very high affinity.
See, e.g., U.S. Pat. No. 5,475,096 entitled "Nucleic Acid Ligands";
see also, e.g., U.S. Pat. No. 6,242,246, U.S. Pat. No. 6,458,543,
and U.S. Pat. No. 6,503,715, each of which is entitled "Nucleic
Acid Ligand Diagnostic Biochip". Once the microarray is contacted
with a sample, the aptamers bind to their respective target
molecules present in the sample and thereby enable a determination
of a biomarker value corresponding to a biomarker.
[0172] As used herein, an "aptamer" refers to a nucleic acid that
has a specific binding affinity for a target molecule. It is
recognized that affinity interactions are a matter of degree;
however, in this context, the "specific binding affinity" of an
aptamer for its target means that the aptamer binds to its target
generally with a much higher degree of affinity than it binds to
other components in a test sample. An "aptamer" is a set of copies
of one type or species of nucleic acid molecule that has a
particular nucleotide sequence. An aptamer can include any suitable
number of nucleotides, including any number of chemically modified
nucleotides. "Aptamers" refers to more than one such set of
molecules. Different aptamers can have either the same or different
numbers of nucleotides. Aptamers can be DNA or RNA or chemically
modified nucleic acids and can be single stranded, double stranded,
or contain double stranded regions, and can include higher ordered
structures. An aptamer can also be a photoaptamer, where a
photoreactive or chemically reactive functional group is included
in the aptamer to allow it to be covalently linked to its
corresponding target. Any of the aptamer methods disclosed herein
can include the use of two or more aptamers that specifically bind
the same target molecule. As further described below, an aptamer
may include a tag. If an aptamer includes a tag, all copies of the
aptamer need not have the same tag. Moreover, if different aptamers
each include a tag, these different aptamers can have either the
same tag or a different tag.
[0173] An aptamer can be identified using any known method,
including the SELEX process. Once identified, an aptamer can be
prepared or synthesized in accordance with any known method,
including chemical synthetic methods and enzymatic synthetic
methods.
[0174] As used herein, a "SOMAmer" or Slow Off-Rate Modified
Aptamer refers to an aptamer having improved off-rate
characteristics. SOMAmers can be generated using the improved SELEX
methods described in U.S. Publication No. 2009/0004667, entitled
"Method for Generating Aptamers with Improved Off-Rates."
[0175] The terms "SELEX" and "SELEX process" are used
interchangeably herein to refer generally to a combination of (1)
the selection of aptamers that interact with a target molecule in a
desirable manner, for example binding with high affinity to a
protein, with (2) the amplification of those selected nucleic
acids. The SELEX process can be used to identify aptamers with high
affinity to a specific target or biomarker.
[0176] SELEX generally includes preparing a candidate mixture of
nucleic acids, binding of the candidate mixture to the desired
target molecule to form an affinity complex, separating the
affinity complexes from the unbound candidate nucleic acids,
separating and isolating the nucleic acid from the affinity
complex, purifying the nucleic acid, and identifying a specific
aptamer sequence. The process may include multiple rounds to
further refine the affinity of the selected aptamer. The process
can include amplification steps at one or more points in the
process. See, e.g., U.S. Pat. No. 5,475,096, entitled "Nucleic Acid
Ligands". The SELEX process can be used to generate an aptamer that
covalently binds its target as well as an aptamer that
non-covalently binds its target. See, e.g., U.S. Pat. No. 5,705,337
entitled "Systematic Evolution of Nucleic Acid Ligands by
Exponential Enrichment: Chemi-SELEX."
[0177] The SELEX process can be used to identify high-affinity
aptamers containing modified nucleotides that confer improved
characteristics on the aptamer, such as, for example, improved in
vivo stability or improved delivery characteristics. Examples of
such modifications include chemical substitutions at the ribose
and/or phosphate and/or base positions. SELEX process-identified
aptamers containing modified nucleotides are described in U.S. Pat.
No. 5,660,985, entitled "High Affinity Nucleic Acid Ligands
Containing Modified Nucleotides", which describes oligonucleotides
containing nucleotide derivatives chemically modified at the 5'-
and 2'-positions of pyrimidines. U.S. Pat. No. 5,580,737, see
supra, describes highly specific aptamers containing one or more
nucleotides modified with 2'-amino (2'-NH2), 2'-fluoro (2'-F),
and/or 2'-O-methyl (2'-OMe). See also, U.S. Patent Application
Publication 20090098549, entitled "SELEX and PHOTOSELEX", which
describes nucleic acid libraries having expanded physical and
chemical properties and their use in SELEX and photoSELEX.
[0178] SELEX can also be used to identify aptamers that have
desirable off-rate characteristics. See U.S. Patent Application
Publication 20090004667, entitled "Method for Generating Aptamers
with Improved Off-Rates", which describes improved SELEX methods
for generating aptamers that can bind to target molecules. Methods
for producing aptamers and photoaptamers having slower rates of
dissociation from their respective target molecules are described.
The methods involve contacting the candidate mixture with the
target molecule, allowing the formation of nucleic acid-target
complexes to occur, and performing a slow off-rate enrichment
process wherein nucleic acid-target complexes with fast
dissociation rates will dissociate and not reform, while complexes
with slow dissociation rates will remain intact. Additionally, the
methods include the use of modified nucleotides in the production
of candidate nucleic acid mixtures to generate aptamers with
improved off-rate performance.
[0179] A variation of this assay employs aptamers that include
photoreactive functional groups that enable the aptamers to
covalently bind or "photocrosslink" their target molecules. See,
e.g., U.S. Pat. No. 6,544,776 entitled "Nucleic Acid Ligand
Diagnostic Biochip". These photoreactive aptamers are also referred
to as photoaptamers. See, e.g., U.S. Pat. No. 5,763,177, U.S. Pat.
No. 6,001,577, and U.S. Pat. No. 6,291,184, each of which is
entitled "Systematic Evolution of Nucleic Acid Ligands by
Exponential Enrichment: Photoselection of Nucleic Acid Ligands and
Solution SELEX"; see also, e.g., U.S. Pat. No. 6,458,539, entitled
"Photoselection of Nucleic Acid Ligands". After the microarray is
contacted with the sample and the photoaptamers have had an
opportunity to bind to their target molecules, the photoaptamers
are photoactivated, and the solid support is washed to remove any
non-specifically bound molecules. Harsh wash conditions may be
used, since target molecules that are bound to the photoaptamers
are generally not removed, due to the covalent bonds created by the
photoactivated functional group(s) on the photoaptamers. In this
manner, the assay enables the detection of a biomarker value
corresponding to a biomarker in the test sample.
[0180] In both of these assay formats, the aptamers are immobilized
on the solid support prior to being contacted with the sample.
Under certain circumstances, however, immobilization of the
aptamers prior to contact with the sample may not provide an
optimal assay. For example, pre-immobilization of the aptamers may
result in inefficient mixing of the aptamers with the target
molecules on the surface of the solid support, perhaps leading to
lengthy reaction times and, therefore, extended incubation periods
to permit efficient binding of the aptamers to their target
molecules. Further, when photoaptamers are employed in the assay
and depending upon the material utilized as a solid support, the
solid support may tend to scatter or absorb the light used to
effect the formation of covalent bonds between the photoaptamers
and their target molecules. Moreover, depending upon the method
employed, detection of target molecules bound to their aptamers can
be subject to imprecision, since the surface of the solid support
may also be exposed to and affected by any labeling agents that are
used. Finally, immobilization of the aptamers on the solid support
generally involves an aptamer-preparation step (i.e., the
immobilization) prior to exposure of the aptamers to the sample,
and this preparation step may affect the activity or functionality
of the aptamers.
[0181] Aptamer assays that permit an aptamer to capture its target
in solution and then employ separation steps that are designed to
remove specific components of the aptamer-target mixture prior to
detection have also been described (see U.S. Patent Application
Publication 20090042206, entitled "Multiplexed Analyses of Test
Samples"). The described aptamer assay methods enable the detection
and quantification of a non-nucleic acid target (e.g., a protein
target) in a test sample by detecting and quantifying a nucleic
acid (i.e., an aptamer). The described methods create a nucleic
acid surrogate (i.e, the aptamer) for detecting and quantifying a
non-nucleic acid target, thus allowing the wide variety of nucleic
acid technologies, including amplification, to be applied to a
broader range of desired targets, including protein targets.
[0182] Aptamers can be constructed to facilitate the separation of
the assay components from an aptamer biomarker complex (or
photoaptamer biomarker covalent complex) and permit isolation of
the aptamer for detection and/or quantification. In one embodiment,
these constructs can include a cleavable or releasable element
within the aptamer sequence. In other embodiments, additional
functionality can be introduced into the aptamer, for example, a
labeled or detectable component, a spacer component, or a specific
binding tag or immobilization element. For example, the aptamer can
include a tag connected to the aptamer via a cleavable moiety, a
label, a spacer component separating the label, and the cleavable
moiety. In one embodiment, a cleavable element is a photocleavable
linker. The photocleavable linker can be attached to a biotin
moiety and a spacer section, can include an NHS group for
derivatization of amines, and can be used to introduce a biotin
group to an aptamer, thereby allowing for the release of the
aptamer later in an assay method.
[0183] Homogenous assays, done with all assay components in
solution, do not require separation of sample and reagents prior to
the detection of signal. These methods are rapid and easy to use.
These methods generate signal based on a molecular capture or
binding reagent that reacts with its specific target. For
pancreatic cancer, the molecular capture reagents would be an
aptamer or an antibody or the like and the specific target would be
a pancreatic cancer biomarker of Table 1, Col. 2.
[0184] In one embodiment, a method for signal generation takes
advantage of anisotropy signal change due to the interaction of a
fluorophore-labeled capture reagent with its specific biomarker
target. When the labeled capture reacts with its target, the
increased molecular weight causes the rotational motion of the
fluorophore attached to the complex to become much slower changing
the anisotropy value. By monitoring the anisotropy change, binding
events may be used to quantitatively measure the biomarkers in
solutions. Other methods include fluorescence polarization assays,
molecular beacon methods, time resolved fluorescence quenching,
chemiluminescence, fluorescence resonance energy transfer, and the
like.
[0185] An exemplary solution-based aptamer assay that can be used
to detect a biomarker value corresponding to a biomarker in a
biological sample includes the following: (a) preparing a mixture
by contacting the biological sample with an aptamer that includes a
first tag and has a specific affinity for the biomarker, wherein an
aptamer affinity complex is formed when the biomarker is present in
the sample; (b) exposing the mixture to a first solid support
including a first capture element, and allowing the first tag to
associate with the first capture element; (c) removing any
components of the mixture not associated with the first solid
support; (d) attaching a second tag to the biomarker component of
the aptamer affinity complex; (e) releasing the aptamer affinity
complex from the first solid support; (f) exposing the released
aptamer affinity complex to a second solid support that includes a
second capture element and allowing the second tag to associate
with the second capture element; (g) removing any non-complexed
aptamer from the mixture by partitioning the non-complexed aptamer
from the aptamer affinity complex; (h) eluting the aptamer from the
solid support; and (i) detecting the biomarker by detecting the
aptamer component of the aptamer affinity complex.
[0186] Any means known in the art can be used to detect a biomarker
value by detecting the aptamer component of an aptamer affinity
complex. A number of different detection methods can be used to
detect the aptamer component of an affinity complex, such as, for
example, hybridization assays, mass spectroscopy, or QPCR. In some
embodiments, nucleic acid sequencing methods can be used to detect
the aptamer component of an aptamer affinity complex and thereby
detect a biomarker value. Briefly, a test sample can be subjected
to any kind of nucleic acid sequencing method to identify and
quantify the sequence or sequences of one or more aptamers present
in the test sample. In some embodiments, the sequence includes the
entire aptamer molecule or any portion of the molecule that may be
used to uniquely identify the molecule. In other embodiments, the
identifying sequencing is a specific sequence added to the aptamer;
such sequences are often referred to as "tags," "barcodes," or
"zipcodes." In some embodiments, the sequencing method includes
enzymatic steps to amplify the aptamer sequence or to convert any
kind of nucleic acid, including RNA and DNA that contain chemical
modifications to any position, to any other kind of nucleic acid
appropriate for sequencing.
[0187] In some embodiments, the sequencing method includes one or
more cloning steps. In other embodiments the sequencing method
includes a direct sequencing method without cloning.
[0188] In some embodiments, the sequencing method includes a
directed approach with specific primers that target one or more
aptamers in the test sample. In other embodiments, the sequencing
method includes a shotgun approach that targets all aptamers in the
test sample.
[0189] In some embodiments, the sequencing method includes
enzymatic steps to amplify the molecule targeted for sequencing. In
other embodiments, the sequencing method directly sequences single
molecules. An exemplary nucleic acid sequencing-based method that
can be used to detect a biomarker value corresponding to a
biomarker in a biological sample includes the following: (a)
converting a mixture of aptamers that contain chemically modified
nucleotides to unmodified nucleic acids with an enzymatic step; (b)
shotgun sequencing the resulting unmodified nucleic acids with a
massively parallel sequencing platform such as, for example, the
454 Sequencing System (454 Life Sciences/Roche), the Illumina
Sequencing System (Illumina), the ABI SOLID Sequencing System
(Applied Biosystems), the HeliScope Single Molecule Sequencer
(Helicon Biosciences), or the Pacific Biosciences Real Time
Single-Molecule Sequencing System (Pacific BioSciences) or the
Polonator G Sequencing System (Dover Systems); and (c) identifying
and quantifying the aptamers present in the mixture by specific
sequence and sequence count.
Determination of Biomarker Values Using Immunoassays
[0190] Immunoassay methods are based on the reaction of an antibody
to its corresponding target or analyte and can detect the analyte
in a sample depending on the specific assay format. To improve
specificity and sensitivity of an assay method based on
immuno-reactivity, monoclonal antibodies are often used because of
their specific epitope recognition. Polyclonal antibodies have also
been successfully used in various immunoassays because of their
increased affinity for the target as compared to monoclonal
antibodies. Immunoassays have been designed for use with a wide
range of biological sample matrices. Immunoassay formats have been
designed to provide qualitative, semi-quantitative, and
quantitative results.
[0191] Quantitative results are generated through the use of a
standard curve created with known concentrations of the specific
analyte to be detected. The response or signal from an unknown
sample is plotted onto the standard curve, and a quantity or value
corresponding to the target in the unknown sample is
established.
[0192] Numerous immunoassay formats have been designed. ELISA or
EIA can be quantitative for the detection of an analyte. This
method relies on attachment of a label to either the analyte or the
antibody and the label component includes, either directly or
indirectly, an enzyme. ELISA tests may be formatted for direct,
indirect, competitive, or sandwich detection of the analyte. Other
methods rely on labels such as, for example, radioisotopes (I125)
or fluorescence. Additional techniques include, for example,
agglutination, nephelometry, turbidimetry, Western blot,
immunoprecipitation, immunocytochemistry, immunohistochemistry,
flow cytometry, Luminex assay, and others (see ImmunoAssay: A
Practical Guide, edited by Brian Law, published by Taylor &
Francis, Ltd., 2005 edition).
[0193] Exemplary assay formats include enzyme-linked immunosorbent
assay (ELISA), radioimmunoassay, fluorescent, chemiluminescence,
and fluorescence resonance energy transfer (FRET) or time
resolved-FRET (TR-FRET) immunoassays. Examples of procedures for
detecting biomarkers include biomarker immunoprecipitation followed
by quantitative methods that allow size and peptide level
discrimination, such as gel electrophoresis, capillary
electrophoresis, planar electrochromatography, and the like.
[0194] Methods of detecting and/or quantifying a detectable label
or signal generating material depend on the nature of the label.
The products of reactions catalyzed by appropriate enzymes (where
the detectable label is an enzyme; see above) can be, without
limitation, fluorescent, luminescent, or radioactive or they may
absorb visible or ultraviolet light. Examples of detectors suitable
for detecting such detectable labels include, without limitation,
x-ray film, radioactivity counters, scintillation counters,
spectrophotometers, colorimeters, fluorometers, luminometers, and
densitometers.
[0195] Any of the methods for detection can be performed in any
format that allows for any suitable preparation, processing, and
analysis of the reactions. This can be, for example, in multi-well
assay plates (e.g., 96 wells or 384 wells) or using any suitable
array or microarray. Stock solutions for various agents can be made
manually or robotically, and all subsequent pipetting, diluting,
mixing, distribution, washing, incubating, sample readout, data
collection and analysis can be done robotically using commercially
available analysis software, robotics, and detection
instrumentation capable of detecting a detectable label.
Determination of Biomarker Values Using Gene Expression
Profiling
[0196] Measuring mRNA in a biological sample may be used as a
surrogate for detection of the level of the corresponding protein
in the biological sample. Thus, any of the biomarkers or biomarker
panels described herein can also be detected by detecting the
appropriate RNA.
[0197] mRNA expression levels are measured by reverse transcription
quantitative polymerase chain reaction (RT-PCR followed with qPCR).
RT-PCR is used to create a cDNA from the mRNA. The cDNA may be used
in a qPCR assay to produce fluorescence as the DNA amplification
process progresses. By comparison to a standard curve, qPCR can
produce an absolute measurement such as number of copies of mRNA
per cell. Northern blots, microarrays, Invader assays, and RT-PCR
combined with capillary electrophoresis have all been used to
measure expression levels of mRNA in a sample. See Gene Expression
Profiling: Methods and Protocols, Richard A. Shimkets, editor,
Humana Press, 2004.
[0198] miRNA molecules are small RNAs that are non-coding but may
regulate gene expression. Any of the methods suited to the
measurement of mRNA expression levels can also be used for the
corresponding miRNA. Recently many laboratories have investigated
the use of miRNAs as biomarkers for disease. Many diseases involve
wide-spread transcriptional regulation, and it is not surprising
that miRNAs might find a role as biomarkers. The connection between
miRNA concentrations and disease is often even less clear than the
connections between protein levels and disease, yet the value of
miRNA biomarkers might be substantial. Of course, as with any RNA
expressed differentially during disease, the problems facing the
development of an in vitro diagnostic product will include the
requirement that the miRNAs survive in the diseased cell and are
easily extracted for analysis, or that the miRNAs are released into
blood or other matrices where they must survive long enough to be
measured. Protein biomarkers have similar requirements, although
many potential protein biomarkers are secreted intentionally at the
site of pathology and function, during disease, in a paracrine
fashion. Many potential protein biomarkers are designed to function
outside the cells within which those proteins are synthesized.
Detection of Biomarkers Using In Vivo Molecular Imaging
Technologies
[0199] Any of the described biomarkers (see Table 1, Col. 2) may
also be used in molecular imaging tests. For example, an imaging
agent can be coupled to any of the described biomarkers, which can
be used to aid in pancreatic cancer diagnosis, to monitor disease
progression/remission or metastasis, to monitor for disease
recurrence, or to monitor response to therapy, among other
uses.
[0200] In vivo imaging technologies provide non-invasive methods
for determining the state of a particular disease in the body of an
individual. For example, entire portions of the body, or even the
entire body, may be viewed as a three dimensional image, thereby
providing valuable information concerning morphology and structures
in the body. Such technologies may be combined with the detection
of the biomarkers described herein to provide information
concerning the cancer status, in particular the pancreatic cancer
status, of an individual.
[0201] The use of in vivo molecular imaging technologies is
expanding due to various advances in technology. These advances
include the development of new contrast agents or labels, such as
radiolabels and/or fluorescent labels, which can provide strong
signals within the body; and the development of powerful new
imaging technology, which can detect and analyze these signals from
outside the body, with sufficient sensitivity and accuracy to
provide useful information. The contrast agent can be visualized in
an appropriate imaging system, thereby providing an image of the
portion or portions of the body in which the contrast agent is
located. The contrast agent may be bound to or associated with a
capture reagent, such as an aptamer or an antibody, for example,
and/or with a peptide or protein, or an oligonucleotide (for
example, for the detection of gene expression), or a complex
containing any of these with one or more macromolecules and/or
other particulate forms.
[0202] The contrast agent may also feature a radioactive atom that
is useful in imaging. Suitable radioactive atoms include
technetium-99m or iodine-123 for scintigraphic studies. Other
readily detectable moieties include, for example, spin labels for
magnetic resonance imaging (MRI) such as, for example, iodine-123
again, iodine-131, indium-111, fluorine-19, carbon-13, nitrogen-15,
oxygen-17, gadolinium, manganese or iron. Such labels are well
known in the art and could easily be selected by one of ordinary
skill in the art.
[0203] Standard imaging techniques include but are not limited to
magnetic resonance imaging, computed tomography scanning, positron
emission tomography (PET), single photon emission computed
tomography (SPECT), and the like. For diagnostic in vivo imaging,
the type of detection instrument available is a major factor in
selecting a given contrast agent, such as a given radionuclide and
the particular biomarker that it is used to target (protein, mRNA,
and the like). The radionuclide chosen typically has a type of
decay that is detectable by a given type of instrument. Also, when
selecting a radionuclide for in vivo diagnosis, its half-life
should be long enough to enable detection at the time of maximum
uptake by the target tissue but short enough that deleterious
radiation of the host is minimized.
[0204] Exemplary imaging techniques include but are not limited to
PET and SPECT, which are imaging techniques in which a radionuclide
is synthetically or locally administered to an individual. The
subsequent uptake of the radiotracer is measured over time and used
to obtain information about the targeted tissue and the biomarker.
Because of the high-energy (gamma-ray) emissions of the specific
isotopes employed and the sensitivity and sophistication of the
instruments used to detect them, the two-dimensional distribution
of radioactivity may be inferred from outside of the body.
[0205] Commonly used positron-emitting nuclides in PET include, for
example, carbon-11, nitrogen-13, oxygen-15, and fluorine-18.
Isotopes that decay by electron capture and/or gamma-emission are
used in SPECT and include, for example iodine-123 and
technetium-99m. An exemplary method for labeling amino acids with
technetium-99m is the reduction of pertechnetate ion in the
presence of a chelating precursor to form the labile
technetium-99m-precursor complex, which, in turn, reacts with the
metal binding group of a bifunctionally modified chemotactic
peptide to form a technetium-99m-chemotactic peptide conjugate.
[0206] Antibodies are frequently used for such in vivo imaging
diagnostic methods. The preparation and use of antibodies for in
vivo diagnosis is well known in the art. Labeled antibodies which
specifically bind any of the biomarkers in Table 1, Col. 2 can be
injected into an individual suspected of having a certain type of
cancer (e.g., pancreatic cancer), detectable according to the
particular biomarker used, for the purpose of diagnosing or
evaluating the disease status of the individual. The label used
will be selected in accordance with the imaging modality to be
used, as previously described. Localization of the label permits
determination of the spread of the cancer. The amount of label
within an organ or tissue also allows determination of the presence
or absence of cancer in that organ or tissue.
[0207] Similarly, aptamers may be used for such in vivo imaging
diagnostic methods. For example, an aptamer that was used to
identify a particular biomarker described in Table 1, Col. 2 (and
therefore binds specifically to that particular biomarker) may be
appropriately labeled and injected into an individual suspected of
having pancreatic cancer, detectable according to the particular
biomarker, for the purpose of diagnosing or evaluating the
pancreatic cancer status of the individual. The label used will be
selected in accordance with the imaging modality to be used, as
previously described. Localization of the label permits
determination of the spread of the cancer. The amount of label
within an organ or tissue also allows determination of the presence
or absence of cancer in that organ or tissue. Aptamer-directed
imaging agents could have unique and advantageous characteristics
relating to tissue penetration, tissue distribution, kinetics,
elimination, potency, and selectivity as compared to other imaging
agents.
[0208] Such techniques may also optionally be performed with
labeled oligonucleotides, for example, for detection of gene
expression through imaging with antisense oligonucleotides. These
methods are used for in situ hybridization, for example, with
fluorescent molecules or radionuclides as the label. Other methods
for detection of gene expression include, for example, detection of
the activity of a reporter gene.
[0209] Another general type of imaging technology is optical
imaging, in which fluorescent signals within the subject are
detected by an optical device that is external to the subject.
These signals may be due to actual fluorescence and/or to
bioluminescence. Improvements in the sensitivity of optical
detection devices have increased the usefulness of optical imaging
for in vivo diagnostic assays.
[0210] The use of in vivo molecular biomarker imaging is
increasing, including for clinical trials, for example, to more
rapidly measure clinical efficacy in trials for new cancer
therapies and/or to avoid prolonged treatment with a placebo for
those diseases, such as multiple sclerosis, in which such prolonged
treatment may be considered to be ethically questionable.
[0211] For a review of other techniques, see N. Blow, Nature
Methods, 6, 465-469, 2009.
Determination of Biomarker Values Using Histology/Cytology
Methods
[0212] For evaluation of pancreatic cancer, a variety of tissue
samples may be used in histological or cytological methods. Sample
selection depends on the primary tumor location and sites of
metastases. For example, tissue samples (forceps biopsy, fine
needle aspiration (FNA), and/or brush cytology) collected at the
time of endoscopic retrograde cholangiopancreatography (ERCP), or
endoscopic ultrasound (EUS)-guided FNA can be used for histology.
Ascites or peritoneal washings or pancreatic fluid can be used for
cyotology. Any of the biomarkers identified herein that were shown
to be up-regulated (ee Table 1, Col. 6) in the individuals with
pancreatic can be used to stain a histological specimen as an
indication of disease.
[0213] In one embodiment, one or more capture reagent/s specific to
the corresponding biomarker/s are used in a cytological evaluation
of a pancreatic cell sample and may include one or more of the
following: collecting a cell sample, fixing the cell sample,
dehydrating, clearing, immobilizing the cell sample on a microscope
slide, permeabilizing the cell sample, treating for analyte
retrieval, staining, destaining, washing, blocking, and reacting
with one or more capture reagent/s in a buffered solution. In
another embodiment, the cell sample is produced from a cell
block.
[0214] In another embodiment, one or more capture reagent/s
specific to the corresponding biomarkers are used in a histological
evaluation of a pancreatic tissue sample and may include one or
more of the following: collecting a tissue specimen, fixing the
tissue sample, dehydrating, clearing, immobilizing the tissue
sample on a microscope slide, permeabilizing the tissue sample,
treating for analyte retrieval, staining, destaining, washing,
blocking, rehydrating, and reacting with capture reagent/s in a
buffered solution. In another embodiment, fixing and dehydrating
are replaced with freezing.
[0215] In another embodiment, the one or more aptamer/s specific to
the corresponding biomarker/s are reacted with the histological or
cytological sample and can serve as the nucleic acid target in a
nucleic acid amplification method. Suitable nucleic acid
amplification methods include, for example, PCR, q-beta replicase,
rolling circle amplification, strand displacement, helicase
dependent amplification, loop mediated isothermal amplification,
ligase chain reaction, and restriction and circularization aided
rolling circle amplification.
[0216] In one embodiment, the one or more capture reagent/s
specific to the corresponding biomarkers for use in the
histological or cytological evaluation are mixed in a buffered
solution that can include any of the following: blocking materials,
competitors, detergents, stabilizers, carrier nucleic acid,
polyanionic materials, etc.
[0217] A "cytology protocol" generally includes sample collection,
sample fixation, sample immobilization, and staining. "Cell
preparation" can include several processing steps after sample
collection, including the use of one or more slow off-rate aptamers
for the staining of the prepared cells.
[0218] Sample collection can include directly placing the sample in
an untreated transport container, placing the sample in a transport
container containing some type of media, or placing the sample
directly onto a slide (immobilization) without any treatment or
fixation.
[0219] Sample immobilization can be improved by applying a portion
of the collected specimen to a glass slide that is treated with
polylysine, gelatin, or a silane. Slides can be prepared by
smearing a thin and even layer of cells across the slide. Care is
generally taken to minimize mechanical distortion and drying
artifacts. Liquid specimens can be processed in a cell block
method. Or, alternatively, liquid specimens can be mixed 1:1 with
the fixative solution for about 10 minutes at room temperature.
[0220] Cell blocks can be prepared from residual effusions, sputum,
urine sediments, gastrointestinal fluids, cell scraping, or fine
needle aspirates. Cells are concentrated or packed by
centrifugation or membrane filtration. A number of methods for cell
block preparation have been developed. Representative procedures
include the fixed sediment, bacterial agar, or membrane filtration
methods. In the fixed sediment method, the cell sediment is mixed
with a fixative like Bouins, picric acid, or buffered formalin and
then the mixture is centrifuged to pellet the fixed cells. The
supernatant is removed, drying the cell pellet as completely as
possible. The pellet is collected and wrapped in lens paper and
then placed in a tissue cassette. The tissue cassette is placed in
a jar with additional fixative and processed as a tissue sample.
Agar method is very similar but the pellet is removed and dried on
paper towel and then cut in half. The cut side is placed in a drop
of melted agar on a glass slide and then the pellet is covered with
agar making sure that no bubbles form in the agar. The agar is
allowed to harden and then any excess agar is trimmed away. This is
placed in a tissue cassette and the tissue process completed.
Alternatively, the pellet may be directly suspended in 2% liquid
agar at 65.degree. C. and the sample centrifuged. The agar cell
pellet is allowed to solidify for an hour at 4.degree. C. The solid
agar may be removed from the centrifuge tube and sliced in half.
The agar is wrapped in filter paper and then the tissue cassette.
Processing from this point forward is as described above.
Centrifugation can be replaced in any these procedures with
membrane filtration. Any of these processes may be used to generate
a "cell block sample".
[0221] Cell blocks can be prepared using specialized resin
including Lowicryl resins, LR White, LR Gold, Unicryl, and
MonoStep. These resins have low viscosity and can be polymerized at
low temperatures and with ultra violet (UV) light. The embedding
process relies on progressively cooling the sample during
dehydration, transferring the sample to the resin, and polymerizing
a block at the final low temperature at the appropriate UV
wavelength.
[0222] Cell block sections can be stained with hematoxylin-eosin
for cytomorphological examination while additional sections are
used for examination for specific markers.
[0223] Whether the process is cytologoical or histological, the
sample may be fixed prior to additional processing to prevent
sample degradation. This process is called "fixation" and describes
a wide range of materials and procedures that may be used
interchangeably. The sample fixation protocol and reagents are best
selected empirically based on the targets to be detected and the
specific cell/tissue type to be analyzed. Sample fixation relies on
reagents such as ethanol, polyethylene glycol, methanol, formalin,
or isopropanol. The samples should be fixed as soon after
collection and affixation to the slide as possible. However, the
fixative selected can introduce structural changes into various
molecular targets making their subsequent detection more difficult.
The fixation and immobilization processes and their sequence can
modify the appearance of the cell and these changes must be
anticipated and recognized by the cytotechnologist. Fixatives can
cause shrinkage of certain cell types and cause the cytoplasm to
appear granular or reticular. Many fixatives function by
crosslinking cellular components. This can damage or modify
specific epitopes, generate new epitopes, cause molecular
associations, and reduce membrane permeability. Formalin fixation
is one of the most common cytological/histological approaches.
Formalin forms methyl bridges between neighboring proteins or
within proteins. Precipitation or coagulation is also used for
fixation and ethanol is frequently used in this type of fixation. A
combination of crosslinking and precipitation can also be used for
fixation. A strong fixation process is best at preserving
morphological information while a weaker fixation process is best
for the preservation of molecular targets.
[0224] A representative fixative is 50% absolute ethanol, 2 mM
polyethylene glycol (PEG), 1.85% formaldehyde. Variations on this
formulation include ethanol (50% to 95%), methanol (20%-50%), and
formalin (formaldehyde) only. Another common fixative is 2% PEG
1500, 50% ethanol, and 3% methanol. Slides are place in the
fixative for about 10 to 15 minutes at room temperature and then
removed and allowed to dry. Once slides are fixed they can be
rinsed with a buffered solution like PBS.
[0225] A wide range of dyes can be used to differentially highlight
and contrast or "stain" cellular, sub-cellular, and tissue features
or morphological structures. Hematoylin is used to stain nuclei a
blue or black color. Orange G-6 and Eosin Azure both stain the
cell's cytoplasm. Orange G stains keratin and glycogen containing
cells yellow. Eosin Y is used to stain nucleoli, cilia, red blood
cells, and superficial epithelial squamous cells. Romanowsky stains
are used for air dried slides and are useful in enhancing
pleomorphism and distinguishing extracellular from intracytoplasmic
material.
[0226] The staining process can include a treatment to increase the
permeability of the cells to the stain. Treatment of the cells with
a detergent can be used to increase permeability. To increase cell
and tissue permeability, fixed samples can be further treated with
solvents, saponins, or non-ionic detergents. Enzymatic digestion
can also improve the accessibility of specific targets in a tissue
sample.
[0227] After staining, the sample is dehydrated using a succession
of alcohol rinses with increasing alcohol concentration. The final
wash is done with xylene or a xylene substitute, such as a citrus
terpene, that has a refractive index close to that of the coverslip
to be applied to the slide. This final step is referred to as
clearing. Once the sample is dehydrated and cleared, a mounting
medium is applied. The mounting medium is selected to have a
refractive index close to the glass and is capable of bonding the
coverslip to the slide. It will also inhibit the additional drying,
shrinking, or fading of the cell sample.
[0228] Regardless of the stains or processing used, the final
evaluation of the pancreatic cytological specimen is made by some
type of microscopy to permit a visual inspection of the morphology
and a determination of the marker's presence or absence. Exemplary
microscopic methods include brightfield, phase contrast,
fluorescence, and differential interference contrast.
[0229] If secondary tests are required on the sample after
examination, the coverslip may be removed and the slide destained.
Destaining involves using the original solvent systems used in
staining the slide originally without the added dye and in a
reverse order to the original staining procedure. Destaining may
also be completed by soaking the slide in an acid alcohol until the
cells are colorless. Once colorless the slides are rinsed well in a
water bath and the second staining procedure applied.
[0230] In addition, specific molecular differentiation may be
possible in conjunction with the cellular morphological analysis
through the use of specific molecular reagents such as antibodies
or nucleic acid probes or aptamers. This improves the accuracy of
diagnostic cytology. Micro-dissection can be used to isolate a
subset of cells for additional evaluation, in particular, for
genetic evaluation of abnormal chromosomes, gene expression, or
mutations.
[0231] Preparation of a tissue sample for histological evaluation
involves fixation, dehydration, infiltration, embedding, and
sectioning. The fixation reagents used in histology are very
similar or identical to those used in cytology and have the same
issues of preserving morphological features at the expense of
molecular ones such as individual proteins. Time can be saved if
the tissue sample is not fixed and dehydrated but instead is frozen
and then sectioned while frozen. This is a more gentle processing
procedure and can preserve more individual markers. However,
freezing is not acceptable for long term storage of a tissue sample
as subcellular information is lost due to the introduction of ice
crystals. Ice in the frozen tissue sample also prevents the
sectioning process from producing a very thin slice and thus some
microscopic resolution and imaging of subcellular structures can be
lost. In addition to formalin fixation, osmium tetroxide is used to
fix and stain phospholipids (membranes).
[0232] Dehydration of tissues is accomplished with successive
washes of increasing alcohol concentration. Clearing employs a
material that is miscible with alcohol and the embedding material
and involves a stepwise process starting at 50:50 alcohol:clearing
reagent and then 100% clearing agent (xylene or xylene substitute).
Infiltration involves incubating the tissue with a liquid form of
the embedding agent (warm wax, nitrocellulose solution) first at
50:50 embedding agent: clearing agent and the 100% embedding agent.
Embedding is completed by placing the tissue in a mold or cassette
and filling with melted embedding agent such as wax, agar, or
gelatin. The embedding agent is allowed to harden. The hardened
tissue sample may then be sliced into thin section for staining and
subsequent examination.
[0233] Prior to staining, the tissue section is dewaxed and
rehydrated. Xylene is used to dewax the section, one or more
changes of xylene may be used, and the tissue is rehydrated by
successive washes in alcohol of decreasing concentration. Prior to
dewax, the tissue section may be heat immobilized to a glass slide
at about 80.degree. C. for about 20 minutes.
[0234] Laser capture micro-dissection allows the isolation of a
subset of cells for further analysis from a tissue section.
[0235] As in cytology, to enhance the visualization of the
microscopic features, the tissue section or slice can be stained
with a variety of stains. A large menu of commercially available
stains can be used to enhance or identify specific features.
[0236] To further increase the interaction of molecular reagents
with cytological/histological samples, a number of techniques for
"analyte retrieval" have been developed. The first such technique
uses high temperature heating of a fixed sample. This method is
also referred to as heat-induced epitope retrieval or HIER. A
variety of heating techniques have been used, including steam
heating, microwaving, autoclaving, water baths, and pressure
cooking or a combination of these methods of heating. Analyte
retrieval solutions include, for example, water, citrate, and
normal saline buffers. The key to analyte retrieval is the time at
high temperature but lower temperatures for longer times have also
been successfully used. Another key to analyte retrieval is the pH
of the heating solution. Low pH has been found to provide the best
immunostaining but also gives rise to backgrounds that frequently
require the use of a second tissue section as a negative control.
The most consistent benefit (increased immunostaining without
increase in background) is generally obtained with a high pH
solution regardless of the buffer composition. The analyte
retrieval process for a specific target is empirically optimized
for the target using heat, time, pH, and buffer composition as
variables for process optimization. Using the microwave analyte
retrieval method allows for sequential staining of different
targets with antibody reagents. But the time required to achieve
antibody and enzyme complexes between staining steps has also been
shown to degrade cell membrane analytes. Microwave heating methods
have improved in situ hybridization methods as well.
[0237] To initiate the analyte retrieval process, the section is
first dewaxed and hydrated. The slide is then placed in 10 mM
sodium citrate buffer pH 6.0 in a dish or jar. A representative
procedure uses an 1100 W microwave and microwaves the slide at 100%
power for 2 minutes followed by microwaving the slides using 20%
power for 18 minutes after checking to be sure the slide remains
covered in liquid. The slide is then allowed to cool in the
uncovered container and then rinsed with distilled water. HIER may
be used in combination with an enzymatic digestion to improve the
reactivity of the target to immunochemical reagents.
[0238] One such enzymatic digestion protocol uses proteinase K. A
20 g/ml concentration of proteinase K is prepared in 50 mM Tris
Base, 1 mM EDTA, 0.5% Triton X-100, pH 8.0 buffer. The process
first involves dewaxing sections in 2 changes of xylene, 5 minutes
each. Then the sample is hydrated in 2 changes of 100% ethanol for
3 minutes each, 95% and 80% ethanol for 1 minute each, and then
rinsed in distilled water. Sections are covered with Proteinase K
working solution and incubated 10-20 minutes at 37 C in humidified
chamber (optimal incubation time may vary depending on tissue type
and degree of fixation). The sections are cooled at room
temperature for 10 minutes and then rinsed in PBS Tween 20 for
2.times.2 min. If desired, sections can be blocked to eliminate
potential interference from endogenous compounds and enzymes. The
section is then incubated with primary antibody at appropriate
dilution in primary antibody dilution buffer for 1 hour at room
temperature or overnight at 4 C. The section is then rinsed with
PBS Tween 20 for 2.times.2 min. Additional blocking can be
performed, if required for the specific application, followed by
additional rinsing with PBS Tween 20 for 3.times.2 min and then
finally the immunostaining protocol completed.
[0239] A simple treatment with 1% SDS at room temperature has also
been demonstrated to improve immunohistochemical staining. Analyte
retrieval methods have been applied to slide mounted sections as
well as free floating sections. Another treatment option is to
place the slide in a jar containing citric acid and 0.1 Nonident
P40 at pH 6.0 and heating to 95.degree. C. The slide is then washed
with a buffer solution like PBS.
[0240] For immunological staining of tissues it may be useful to
block non-specific association of the antibody with tissue proteins
by soaking the section in a protein solution like serum or non-fat
dry milk.
[0241] Blocking reactions may include the need to reduce the level
of endogenous biotin; eliminate endogenous charge effects;
inactivate endogenous nucleases; and/or inactivate endogenous
enzymes like peroxidase and alkaline phosphatase. Endogenous
nucleases may be inactivated by degradation with proteinase K, by
heat treatment, use of a chelating agent such as EDTA or EGTA, the
introduction of carrier DNA or RNA, treatment with a chaotrope such
as urea, thiourea, guanidine hydrochloride, guanidine thiocyanate,
lithium perchlorate, etc, or diethyl pyrocarbonate. Alkaline
phosphatase may be inactivated by treated with 0.1N HCl for 5
minutes at room temperature or treatment with 1 mM levamisole.
Peroxidase activity may be eliminated by treatment with 0.03%
hydrogen peroxide. Endogenous biotin may be blocked by soaking the
slide or section in an avidin (streptavidin, neutravidin may be
substituted) solution for at least 15 minutes at room temperature.
The slide or section is then washed for at least 10 minutes in
buffer. This may be repeated at least three times. Then the slide
or section is soaked in a biotin solution for 10 minutes. This may
be repeated at least three times with a fresh biotin solution each
time. The buffer wash procedure is repeated. Blocking protocols
should be minimized to prevent damaging either the cell or tissue
structure or the target or targets of interest but one or more of
these protocols could be combined to "block" a slide or section
prior to reaction with one or more slow off-rate aptamers. See
Basic Medical Histology: the Biology of Cells, Tissues and Organs,
authored by Richard G. Kessel, Oxford University Press, 1998.
Determination of Biomarker Values Using Mass Spectrometry
Methods
[0242] A variety of configurations of mass spectrometers can be
used to detect biomarker values. Several types of mass
spectrometers are available or can be produced with various
configurations. In general, a mass spectrometer has the following
major components: a sample inlet, an ion source, a mass analyzer, a
detector, a vacuum system, and instrument-control system, and a
data system. Difference in the sample inlet, ion source, and mass
analyzer generally define the type of instrument and its
capabilities. For example, an inlet can be a capillary-column
liquid chromatography source or can be a direct probe or stage such
as used in matrix-assisted laser desorption. Common ion sources
are, for example, electrospray, including nanospray and microspray
or matrix-assisted laser desorption. Common mass analyzers include
a quadrupole mass filter, ion trap mass analyzer and time-of-flight
mass analyzer. Additional mass spectrometry methods are well known
in the art (see Burlingame et al. Anal. Chem. 70:647 R-716R (1998);
Kinter and Sherman, New York (2000)).
[0243] Protein biomarkers and biomarker values can be detected and
measured by any of the following: electrospray ionization mass
spectrometry (ESI-MS), ESI-MS/MS, ESI-MS/(MS)n, matrix-assisted
laser desorption ionization time-of-flight mass spectrometry
(MALDI-TOF-MS), surface-enhanced laser desorption/ionization
time-of-flight mass spectrometry (SELDI-TOF-MS),
desorption/ionization on silicon (DIOS), secondary ion mass
spectrometry (SIMS), quadrupole time-of-flight (Q-TOF), tandem
time-of-flight (TOF/TOF) technology, called ultraflex III TOF/TOF,
atmospheric pressure chemical ionization mass spectrometry
(APCI-MS), APCI-MS/MS, APCI-(MS)N, atmospheric pressure
photoionization mass spectrometry (APPI-MS), APPI-MS/MS, and
APPI-(MS)N, quadrupole mass spectrometry, Fourier trans-form mass
spectrometry (FTMS), quantitative mass spectrometry, and ion trap
mass spectrometry.
[0244] Sample preparation strategies are used to label and enrich
samples before mass spectroscopic characterization of protein
biomarkers and determination biomarker values. Labeling methods
include but are not limited to isobaric tag for relative and
absolute quantitation (iTRAQ) and stable isotope labeling with
amino acids in cell culture (SILAC). Capture reagents used to
selectively enrich samples for candidate biomarker proteins prior
to mass spectroscopic analysis include but are not limited to
aptamers, antibodies, nucleic acid probes, chimeras, small
molecules, an F(ab')2 fragment, a single chain antibody fragment,
an Fv fragment, a single chain Fv fragment, a nucleic acid, a
lectin, a ligand-binding receptor, affybodies, nanobodies,
ankyrins, domain antibodies, alternative antibody scaffolds (e.g.
diabodies etc) imprinted polymers, avimers, peptidomimetics,
peptoids, peptide nucleic acids, threose nucleic acid, a hormone
receptor, a cytokine receptor, and synthetic receptors, and
modifications and fragments of these.
Determination of Biomarker Values Using a Proximity Ligation
Assay
[0245] A proximity ligation assay can be used to determine
biomarker values. Briefly, a test sample is contacted with a pair
of affinity probes that may be a pair of antibodies or a pair of
aptamers, with each member of the pair extended with an
oligonucleotide. The targets for the pair of affinity probes may be
two distinct determinates on one protein or one determinate on each
of two different proteins, which may exist as homo- or
hetero-multimeric complexes. When probes bind to the target
determinates, the free ends of the oligonucleotide extensions are
brought into sufficiently close proximity to hybridize together.
The hybridization of the oligonucleotide extensions is facilitated
by a common connector oligonucleotide which serves to bridge
together the oligonucleotide extensions when they are positioned in
sufficient proximity. Once the oligonucleotide extensions of the
probes are hybridized, the ends of the extensions are joined
together by enzymatic DNA ligation.
[0246] Each oligonucleotide extension comprises a primer site for
PCR amplification. Once the oligonucleotide extensions are ligated
together, the oligonucleotides form a continuous DNA sequence
which, through PCR amplification, reveals information regarding the
identity and amount of the target protein, as well as, information
regarding protein-protein interactions where the target
determinates are on two different proteins. Proximity ligation can
provide a highly sensitive and specific assay for real-time protein
concentration and interaction information through use of real-time
PCR. Probes that do not bind the determinates of interest do not
have the corresponding oligonucleotide extensions brought into
proximity and no ligation or PCR amplification can proceed,
resulting in no signal being produced.
[0247] The foregoing assays enable the detection of biomarker
values that are useful in methods for diagnosing pancreatic cancer,
where the methods comprise detecting, in a biological sample from
an individual, at least N biomarker values that each correspond to
a biomarker selected from the group consisting of the biomarkers
provided in Table 1, Col. 2, wherein a classification, as described
in detail below, using the biomarker values indicates whether the
individual has pancreatic cancer. While certain of the described
pancreatic cancer biomarkers are useful alone for detecting and
diagnosing pancreatic cancer, methods are also described herein for
the grouping of multiple subsets of the pancreatic cancer
biomarkers that are each useful as a panel of three or more
biomarkers. Thus, various embodiments of the instant application
provide combinations comprising N biomarkers, wherein N is at least
three biomarkers. In other embodiments, N is selected to be any
number from 2-65 biomarkers. It will be appreciated that N can be
selected to be any number from any of the above described ranges,
as well as similar, but higher order, ranges. In accordance with
any of the methods described herein, biomarker values can be
detected and classified individually or they can be detected and
classified collectively, as for example in a multiplex assay
format.
[0248] In another aspect, methods are provided for detecting an
absence of pancreatic cancer, the methods comprising detecting, in
a biological sample from an individual, at least N biomarker values
that each correspond to a biomarker selected from the group
consisting of the biomarkers provided in Table 1, Col. 2, wherein a
classification, as described in detail below, of the biomarker
values indicates an absence of pancreatic cancer in the individual.
While certain of the described pancreatic cancer biomarkers are
useful alone for detecting and diagnosing the absence of pancreatic
cancer, methods are also described herein for the grouping of
multiple subsets of the pancreatic cancer biomarkers that are each
useful as a panel of three or more biomarkers. Thus, various
embodiments of the instant application provide combinations
comprising N biomarkers, wherein N is at least three biomarkers. In
other embodiments, N is selected to be any number from 2-65
biomarkers. It will be appreciated that N can be selected to be any
number from any of the above described ranges, as well as similar,
but higher order, ranges. In accordance with any of the methods
described herein, biomarker values can be detected and classified
individually or they can be detected and classified collectively,
as for example in a multiplex assay format.
Classification of Biomarkers and Calculation of Disease Scores
[0249] A biomarker "signature" for a given diagnostic test contains
a set of markers, each marker having different levels in the
populations of interest. Different levels, in this context, may
refer to different means of the marker levels for the individuals
in two or more groups, or different variances in the two or more
groups, or a combination of both. For the simplest form of a
diagnostic test, these markers can be used to assign an unknown
sample from an individual into one of two groups, either diseased
or not diseased. The assignment of a sample into one of two or more
groups is known as classification, and the procedure used to
accomplish this assignment is known as a classifier or a
classification method. Classification methods may also be referred
to as scoring methods. There are many classification methods that
can be used to construct a diagnostic classifier from a set of
biomarker values. In general, classification methods are most
easily performed using supervised learning techniques where a data
set is collected using samples obtained from individuals within two
(or more, for multiple classification states) distinct groups one
wishes to distinguish. Since the class (group or population) to
which each sample belongs is known in advance for each sample, the
classification method can be trained to give the desired
classification response. It is also possible to use unsupervised
learning techniques to produce a diagnostic classifier.
[0250] Common approaches for developing diagnostic classifiers
include decision trees; bagging, boosting, forests and random
forests; rule inference based learning; Parzen Windows; linear
models; logistic; neural network methods; unsupervised clustering;
K-means; hierarchical ascending/descending; semi-supervised
learning; prototype methods; nearest neighbor; kernel density
estimation; support vector machines; hidden Markov models;
Boltzmann Learning; and classifiers may be combined either simply
or in ways which minimize particular objective functions. For a
review, see, e.g., Pattern Classification, R. O. Duda, et al.,
editors, John Wiley & Sons, 2nd edition, 2001; see also, The
Elements of Statistical Learning--Data Mining, Inference, and
Prediction, T. Hastie, et al., editors, Springer Science+Business
Media, LLC, 2nd edition, 2009; each of which is incorporated by
reference in its entirety.
[0251] To produce a classifier using supervised learning
techniques, a set of samples called training data are obtained. In
the context of diagnostic tests, training data includes samples
from the distinct groups (classes) to which unknown samples will
later be assigned. For example, samples collected from individuals
in a control population and individuals in a particular disease
population can constitute training data to develop a classifier
that can classify unknown samples (or, more particularly, the
individuals from whom the samples were obtained) as either having
the disease or being free from the disease. The development of the
classifier from the training data is known as training the
classifier. Specific details on classifier training depend on the
nature of the supervised learning technique. For purposes of
illustration, an example of training a naive Bayesian classifier
will be described below (see, e.g., Pattern Classification, R. O.
Duda, et al., editors, John Wiley & Sons, 2nd edition, 2001;
see also, The Elements of Statistical Learning--Data Mining,
Inference, and Prediction, T. Hastie, et al., editors, Springer
Science+Business Media, LLC, 2nd edition, 2009).
[0252] Since typically there are many more potential biomarker
values than samples in a training set, care must be used to avoid
over-fitting. Over-fitting occurs when a statistical model
describes random error or noise instead of the underlying
relationship. Over-fitting can be avoided in a variety of way,
including, for example, by limiting the number of markers used in
developing the classifier, by assuming that the marker responses
are independent of one another, by limiting the complexity of the
underlying statistical model employed, and by ensuring that the
underlying statistical model conforms to the data.
[0253] An illustrative example of the development of a diagnostic
test using a set of biomarkers includes the application of a naive
Bayes classifier, a simple probabilistic classifier based on Bayes
theorem with strict independent treatment of the biomarkers. Each
biomarker is described by a class-dependent probability density
function (pdf) for the measured RFU values or log RFU (relative
fluorescence units) values in each class. The joint pdfs for the
set of markers in one class is assumed to be the product of the
individual class-dependent pdfs for each biomarker. Training a
naive Bayes classifier in this context amounts to assigning
parameters ("parameterization") to characterize the class dependent
pdfs. Any underlying model for the class-dependent pdfs may be
used, but the model should generally conform to the data observed
in the training set.
[0254] Specifically, the class-dependent probability of measuring a
value x.sub.i for biomarker i in the disease class is written as
p(x.sub.i|d) and the overall naive Bayes probability of observing n
markers with values {tilde over (x)}=(x.sub.1, x.sub.2, . . .
x.sub.n) is written as p(x.sub.i|d)=.PI..sub.i=1.sup.n p(x.sub.i|d)
where the individual x.sub.is are the measured biomarker levels in
RFU or log RFU. The classification assignment for an unknown is
facilitated by calculating the probability of being diseased
p(d|{tilde over (x)}) having measured {tilde over (x)} compared to
the probability of being disease free (control) p(c|{tilde over
(x)}) for the same measured values. The ratio of these
probabilities is computed from the class-dependent pdfs by
application of Bayes theorem, i.e.,
p ( d x ~ ) p ( c x ~ ) = p ( x ~ d ) p ( d ) p ( x ~ c ) ( 1 - p (
d ) ) ##EQU00001##
where p(d) is the prevalence of the disease in the population
appropriate to the test. Taking the logarithm of both sides of this
ratio and substituting the naive Bayes class-dependent
probabilities from above gives
ln ( p ( d x ~ ) p ( c x ~ ) ) = i = 1 n ln ( p ( x i d ) p ( x i c
) ) + ln ( p ( d ) 1 - p ( d ) ) . ##EQU00002##
This form is known as the log likelihood ratio and simply states
that the log likelihood of being free of the particular disease
versus having the disease and is primarily composed of the sum of
individual log likelihood ratios of the n individual biomarkers. In
its simplest form, an unknown sample (or, more particularly, the
individual from whom the sample was obtained) is classified as
being free of the disease if the above ratio is greater than zero
and having the disease if the ratio is less than zero.
[0255] In one exemplary embodiment, the class-dependent biomarker
pdfs p(x.sub.i|c) and p(x.sub.i|d) are assumed to be normal or
log-normal distributions in the measured RFU values x.sub.i,
i.e.
p ( x i c ) = 1 2 .pi. .sigma. c , i exp ( - ( x i - .mu. c , i ) 2
2 .sigma. c , i 2 ) , ##EQU00003##
with a similar expression for with and. Parameterization of the
model requires estimation of two parameters for each
class-dependent pdf, a mean .mu. and a variance .sigma..sup.2, from
the training data. This may be accomplished in a number of ways,
including, for example, by maximum likelihood estimates, by
least-squares, and by any other methods known to one skilled in the
art. Substituting the normal distributions for and into the
log-likelihood ratio defined above gives the following
expression:
ln ( p ( d x ~ ) p ( c x ~ ) ) = i = 1 n ln ( .sigma. c , i .sigma.
d i ) - 1 2 i = 1 n [ ( x i - .mu. d , i .sigma. d , i ) 2 - ( x i
- .mu. c , i .sigma. c , i ) 2 ] + ln ( p ( d ) 1 - p ( d ) )
##EQU00004##
[0256] Once a set of .mu.s and .sigma..sup.2s have been defined for
each pdf in each class from the training data and the disease
prevalence in the population is specified, the Bayes classifier is
fully determined and may be used to classify unknown samples with
measured values {tilde over (x)}.
[0257] The performance of the naive Bayes classifier is dependent
upon the number and quality of the biomarkers used to construct and
train the classifier. A single biomarker will perform in accordance
with its KS-distance (Kolmogorov-Smirnov), as defined in Example 3,
below. If a classifier performance metric is defined as the area
under the receiver operator characteristic curve (AUC), a perfect
classifier will have a score of 1 and a random classifier, on
average, will have a score of 0.5. The definition of the
KS-distance between two sets A and B of sizes n and m is the value,
D.sub.n,m=sup.sub.x|F.sub.A,n(x)-F.sub.B,m(x)|, which is the
largest difference between two empirical cumulative distribution
functions (cdf). The empirical cdf for a set A of n observations
X.sub.i is defined as,
F A , n ( x ) = 1 n i = 1 n I X i .ltoreq. x , ##EQU00005##
where I.sub.X.sub.i.sub.<x is the indicator function which is
equal to 1 if X.sub.i<x and is otherwise equal to 0. By
definition, this value is bounded between 0 and 1, where a
KS-distance of 1 indicates that the emperical distributions do not
overlap.
[0258] The addition of subsequent markers with good KS distances
(>0.3, for example) will, in general, improve the classification
performance if the subsequently added markers are independent of
the first marker. Using the sensitivity plus specificity as a
classifier score, it is straightforward to generate many high
scoring classifiers with a variation of a greedy algorithm. (A
greedy algorithm is any algorithm that follows the problem solving
metaheuristic of making the locally optimal choice at each stage
with the hope of finding the global optimum.)
[0259] The algorithm approach used here is described in detail in
Example 4. Briefly, all single analyte classifiers are generated
from a table of potential biomarkers and added to a list. Next, all
possible additions of a second analyte to each of the stored single
analyte classifiers is then performed, saving a predetermined
number of the best scoring pairs, say, for example, a thousand, on
a new list. All possible three marker classifiers are explored
using this new list of the best two-marker classifiers, again
saving the best thousand of these. This process continues until the
score either plateaus or begins to deteriorate as additional
markers are added. Those high scoring classifiers that remain after
convergence can be evaluated for the desired performance for an
intended use. For example, in one diagnostic application,
classifiers with a high sensitivity and modest specificity may be
more desirable than modest sensitivity and high specificity. In
another diagnostic application, classifiers with a high specificity
and a modest sensitivity may be more desirable. The desired level
of performance is generally selected based upon a trade-off that
must be made between the number of false positives and false
negatives that can each be tolerated for the particular diagnostic
application. Such trade-offs generally depend on the medical
consequences of an error, either false positive or false
negative.
[0260] Various other techniques are known in the art and may be
employed to generate many potential classifiers from a list of
biomarkers using a naive Bayes classifier. In one embodiment, what
is referred to as a genetic algorithm can be used to combine
different markers using the fitness score as defined above. Genetic
algorithms are particularly well suited to exploring a large
diverse population of potential classifiers. In another embodiment,
so-called ant colony optimization can be used to generate sets of
classifiers. Other strategies that are known in the art can also be
employed, including, for example, other evolutionary strategies as
well as simulated annealing and other stochastic search methods.
Metaheuristic methods, such as, for example, harmony search may
also be employed.
[0261] Exemplary embodiments use any number of the pancreatic
cancer biomarkers listed in Table 1, Col. 2 in various combinations
to produce diagnostic tests for detecting pancreatic cancer (see
Example 2 for a detailed description of how these biomarkers were
identified). In one embodiment, a method for diagnosing pancreatic
cancer uses a naive Bayes classification method in conjunction with
any number of the pancreatic cancer biomarkers listed in Table 1,
Col. 2. In an illustrative example (Example 3), the simplest test
for detecting pancreatic cancer from a population of GI and normal
controls can be constructed using a single biomarker, for example,
CTSB which is differentially expressed in pancreatic cancer with a
KS-distance of 0.52. Using the parameters, .mu..sub.c,i,
.sigma..sub.c,i, .mu..sub.,d,i, and, .sigma..sub.d,i for CTSB from
Table 16 and the equation for the log-likelihood described above, a
diagnostic test with an AUC of 0.79 can be derived, see Table 15.
The ROC curve for this test is displayed in FIG. 2.
[0262] Addition of biomarker C5a, for example, with a KS-distance
of 0.40, significantly improves the classifier performance to an
AUC of 0.85. Note that the score for a classifier constructed of
two biomarkers is not a simple sum of the KS-distances;
KS-distances are not additive when combining biomarkers and it
takes many more weak markers to achieve the same level of
performance as a strong marker. Adding a third marker, C5, for
example, boosts the classifier performance to an AUC of 0.88.
Adding additional biomarkers, such as, for example, CCL18, CSF1R,
KLK7, ETHE1, C5-C6, KLK8, and VEGFA, produces a series of
pancreatic cancer tests summarized in Table 15 and displayed as a
series of ROC curves in FIG. 3. The score of the classifiers as a
function of the number of analytes used in classifier construction
is displayed in FIG. 4. The AUC of this exemplary ten-marker
classifier is 0.91.
[0263] The markers listed in Table 1, Col. 2 can be combined in
many ways to produce classifiers for diagnosing pancreatic cancer.
In some embodiments, panels of biomarkers are comprised of
different numbers of analytes depending on a specific diagnostic
performance criterion that is selected. For example, certain
combinations of biomarkers will produce tests that are more
sensitive (or more specific) than other combinations.
[0264] Once a panel is defined to include a particular set of
biomarkers from Table 1, Col. 2 and a classifier is constructed
from a set of training data, the definition of the diagnostic test
is complete. In one embodiment, the procedure used to classify an
unknown sample is outlined in FIG. 1A. In another embodiment the
procedure used to classify an unknown sample is outlined in FIG.
1B. The biological sample is appropriately diluted and then run in
one or more assays to produce the relevant quantitative biomarker
levels used for classification. The measured biomarker levels are
used as input for the classification method that outputs a
classification and an optional score for the sample that reflects
the confidence of the class assignment.
[0265] Table 1 identifies 65 biomarkers that are useful for
diagnosing pancreatic cancer. This is a surprisingly larger number
than expected when compared to what is typically found during
biomarker discovery efforts and may be attributable to the scale of
the described study, which encompassed over 800 proteins measured
in hundreds of individual samples, in some cases at concentrations
in the low femtomolar range. Presumably, the large number of
discovered biomarkers reflects the diverse biochemical pathways
implicated in both tumor biology and the body's response to the
tumor's presence; each pathway and process involves many proteins.
The results show that no single protein of a small group of
proteins is uniquely informative about such complex processes;
rather, that multiple proteins are involved in relevant processes,
such as apoptosis or extracellular matrix repair, for example.
[0266] Given the numerous biomarkers identified during the
described study, one would expect to be able to derive large
numbers of high-performing classifiers that can be used in various
diagnostic methods. To test this notion, tens of thousands of
classifiers were evaluated using the biomarkers in Table 1. As
described in Example 4, many subsets of the biomarkers presented in
Table 1 can be combined to generate useful classifiers. By way of
example, descriptions are provided for classifiers containing 1, 2,
and 3 biomarkers for detection of pancreatic cancer. As described
in Example 4, all classifiers that were built using the biomarkers
in Table 1 perform distinctly better than classifiers that were
built using "non-markers".
[0267] The performance of classifiers obtained by randomly
excluding some of the markers in Table 1, which resulted in smaller
subsets from which to build the classifiers, was also tested. As
described in Example 4, Part 3, the classifiers that were built
from random subsets of the markers in Table 1 performed similarly
to optimal classifiers that were built using the full list of
markers in Table 1.
[0268] The performance of ten-marker classifiers obtained by
excluding the "best" individual markers from the ten-marker
aggregation was also tested. As described in Example 4, Part 3,
classifiers constructed without the "best" markers in Table 1 also
performed well. Many subsets of the biomarkers listed in Table 1
performed close to optimally, even after removing the top 15 of the
markers listed in the Table. This implies that the performance
characteristics of any particular classifier are likely not due to
some small core group of biomarkers and that the disease process
likely impacts numerous biochemical pathways, which alters the
expression level of many proteins.
[0269] The results from Example 4 suggest certain possible
conclusions: First, the identification of a large number of
biomarkers enables their aggregation into a vast number of
classifiers that offer similarly high performance. Second,
classifiers can be constructed such that particular biomarkers may
be substituted for other biomarkers in a manner that reflects the
redundancies that undoubtedly pervade the complexities of the
underlying disease processes. That is to say, the information about
the disease contributed by any individual biomarker identified in
Table 1 overlaps with the information contributed by other
biomarkers, such that it may be that no particular biomarker or
small group of biomarkers in Table 1 must be included in any
classifier.
[0270] Exemplary embodiments use naive Bayes classifiers
constructed from the data in Table 16 to classify an unknown
sample. The procedure is outlined in FIGS. 1A and 1B. In one
embodiment, the biological sample is optionally diluted and run in
a multiplexed aptamer assay. The data from the assay are normalized
and calibrated as outlined in Example 3, and the resulting
biomarker levels are used as input to a Bayes classification
scheme. The log-likelihood ratio is computed for each measured
biomarker individually and then summed to produce a final
classification score, which is also referred to as a diagnostic
score. The resulting assignment as well as the overall
classification score can be reported. Optionally, the individual
log-likelihood risk factors computed for each biomarker level can
be reported as well. The details of the classification score
calculation are presented in Example 3.
Kits
[0271] Any combination of the biomarkers of Table 1, Col. 2 (as
well as additional biomedical information) can be detected using a
suitable kit, such as for use in performing the methods disclosed
herein. Furthermore, any kit can contain one or more detectable
labels as described herein, such as a fluorescent moiety, etc.
[0272] In one embodiment, a kit includes (a) one or more capture
reagents (such as, for example, at least one aptamer or antibody)
for detecting one or more biomarkers in a biological sample,
wherein the biomarkers include any of the biomarkers set forth in
Table 1, Col. 2, and optionally (b) one or more software or
computer program products for classifying the individual from whom
the biological sample was obtained as either having or not having
pancreatic cancer or for determining the likelihood that the
individual has pancreatic cancer, as further described herein.
Alternatively, rather than one or more computer program products,
one or more instructions for manually performing the above steps by
a human can be provided.
[0273] The combination of a solid support with a corresponding
capture reagent and a signal generating material is referred to
herein as a "detection device" or "kit". The kit can also include
instructions for using the devices and reagents, handling the
sample, and analyzing the data. Further the kit may be used with a
computer system or software to analyze and report the result of the
analysis of the biological sample.
[0274] The kits can also contain one or more reagents (e.g.,
solubilization buffers, detergents, washes, or buffers) for
processing a biological sample. Any of the kits described herein
can also include, e.g., buffers, blocking agents, mass spectrometry
matrix materials, antibody capture agents, positive control
samples, negative control samples, software and information such as
protocols, guidance and reference data.
[0275] In one aspect, the invention provides kits for the analysis
of pancreatic cancer status. The kits include PCR primers for one
or more biomarkers selected from Table 1, Col. 2. The kit may
further include instructions for use and correlation of the
biomarkers with pancreatic cancer. The kit may also include a DNA
array containing the complement of one or more of the biomarkers
selected from Table 1, Col. 2, reagents, and/or enzymes for
amplifying or isolating sample DNA. The kits may include reagents
for real-time PCR, for example, TaqMan probes and/or primers, and
enzymes.
[0276] For example, a kit can comprise (a) reagents comprising at
least capture reagent for quantifying one or more biomarkers in a
test sample, wherein said biomarkers comprise the biomarkers set
forth in Table 1, Col. 2, or any other biomarkers or biomarkers
panels described herein, and optionally (b) one or more algorithms
or computer programs for performing the steps of comparing the
amount of each biomarker quantified in the test sample to one or
more predetermined cutoffs and assigning a score for each biomarker
quantified based on said comparison, combining the assigned scores
for each biomarker quantified to obtain a total score, comparing
the total score with a predetermined score, and using said
comparison to determine whether an individual has pancreatic
cancer. Alternatively, rather than one or more algorithms or
computer programs, one or more instructions for manually performing
the above steps by a human can be provided.
Computer Methods and Software
[0277] Once a biomarker or biomarker panel is selected, a method
for diagnosing an individual can comprise the following: 1) collect
or otherwise obtain a biological sample; 2) perform an analytical
method to detect and measure the biomarker or biomarkers in the
panel in the biological sample; 3) perform any data normalization
or standardization required for the method used to collect
biomarker values; 4) calculate the marker score; 5) combine the
marker scores to obtain a total diagnostic score; and 6) report the
individual's diagnostic score. In this approach, the diagnostic
score may be a single number determined from the sum of all the
marker calculations that is compared to a preset threshold value
that is an indication of the presence or absence of disease. Or the
diagnostic score may be a series of bars that each represent a
biomarker value and the pattern of the responses may be compared to
a pre-set pattern for determination of the presence or absence of
disease.
[0278] At least some embodiments of the methods described herein
can be implemented with the use of a computer. An example of a
computer system 100 is shown in FIG. 6. With reference to FIG. 6,
system 100 is shown comprised of hardware elements that are
electrically coupled via bus 108, including a processor 101, input
device 102, output device 103, storage device 104,
computer-readable storage media reader 105a, communications system
106 processing acceleration (e.g., DSP or special-purpose
processors) 107 and memory 109. Computer-readable storage media
reader 105a is further coupled to computer-readable storage media
105b, the combination comprehensively representing remote, local,
fixed and/or removable storage devices plus storage media, memory,
etc. for temporarily and/or more permanently containing
computer-readable information, which can include storage device
104, memory 109 and/or any other such accessible system 100
resource. System 100 also comprises software elements (shown as
being currently located within working memory 191) including an
operating system 192 and other code 193, such as programs, data and
the like.
[0279] With respect to FIG. 6, system 100 has extensive flexibility
and configurability. Thus, for example, a single architecture might
be utilized to implement one or more servers that can be further
configured in accordance with currently desirable protocols,
protocol variations, extensions, etc. However, it will be apparent
to those skilled in the art that embodiments may well be utilized
in accordance with more specific application requirements. For
example, one or more system elements might be implemented as
sub-elements within a system 100 component (e.g., within
communications system 106). Customized hardware might also be
utilized and/or particular elements might be implemented in
hardware, software or both. Further, while connection to other
computing devices such as network input/output devices (not shown)
may be employed, it is to be understood that wired, wireless,
modem, and/or other connection or connections to other computing
devices might also be utilized.
[0280] In one aspect, the system can comprise a database containing
features of biomarkers characteristic of pancreatic cancer. The
biomarker data (or biomarker information) can be utilized as an
input to the computer for use as part of a computer implemented
method. The biomarker data can include the data as described
herein.
[0281] In one aspect, the system further comprises one or more
devices for providing input data to the one or more processors.
[0282] The system further comprises a memory for storing a data set
of ranked data elements.
[0283] In another aspect, the device for providing input data
comprises a detector for detecting the characteristic of the data
element, e.g., such as a mass spectrometer or gene chip reader.
[0284] The system additionally may comprise a database management
system. User requests or queries can be formatted in an appropriate
language understood by the database management system that
processes the query to extract the relevant information from the
database of training sets.
[0285] The system may be connectable to a network to which a
network server and one or more clients are connected. The network
may be a local area network (LAN) or a wide area network (WAN), as
is known in the art. Preferably, the server includes the hardware
necessary for running computer program products (e.g., software) to
access database data for processing user requests.
[0286] The system may include an operating system (e.g., UNIX or
Linux) for executing instructions from a database management
system. In one aspect, the operating system can operate on a global
communications network, such as the Internet, and utilize a global
communications network server to connect to such a network.
[0287] The system may include one or more devices that comprise a
graphical display interface comprising interface elements such as
buttons, pull down menus, scroll bars, fields for entering text,
and the like as are routinely found in graphical user interfaces
known in the art. Requests entered on a user interface can be
transmitted to an application program in the system for formatting
to search for relevant information in one or more of the system
databases. Requests or queries entered by a user may be constructed
in any suitable database language.
[0288] The graphical user interface may be generated by a graphical
user interface code as part of the operating system and can be used
to input data and/or to display inputted data. The result of
processed data can be displayed in the interface, printed on a
printer in communication with the system, saved in a memory device,
and/or transmitted over the network or can be provided in the form
of the computer readable medium.
[0289] The system can be in communication with an input device for
providing data regarding data elements to the system (e.g.,
expression values). In one aspect, the input device can include a
gene expression profiling system including, e.g., a mass
spectrometer, gene chip or array reader, and the like.
[0290] The methods and apparatus for analyzing pancreatic cancer
biomarker information according to various embodiments may be
implemented in any suitable manner, for example, using a computer
program operating on a computer system. A conventional computer
system comprising a processor and a random access memory, such as a
remotely-accessible application server, network server, personal
computer or workstation may be used. Additional computer system
components may include memory devices or information storage
systems, such as a mass storage system and a user interface, for
example a conventional monitor, keyboard and tracking device. The
computer system may be a stand-alone system or part of a network of
computers including a server and one or more databases.
[0291] The pancreatic cancer biomarker analysis system can provide
functions and operations to complete data analysis, such as data
gathering, processing, analysis, reporting and/or diagnosis. For
example, in one embodiment, the computer system can execute the
computer program that may receive, store, search, analyze, and
report information relating to the pancreatic cancer biomarkers.
The computer program may comprise multiple modules performing
various functions or operations, such as a processing module for
processing raw data and generating supplemental data and an
analysis module for analyzing raw data and supplemental data to
generate a pancreatic cancer status and/or diagnosis. Diagnosing
pancreatic cancer status may comprise generating or collecting any
other information, including additional biomedical information,
regarding the condition of the individual relative to the disease,
identifying whether further tests may be desirable, or otherwise
evaluating the health status of the individual.
[0292] Referring now to FIG. 7, an example of a method of utilizing
a computer in accordance with principles of a disclosed embodiment
can be seen. In FIG. 7, a flowchart 3000 is shown. In block 3004,
biomarker information can be retrieved for an individual. The
biomarker information can be retrieved from a computer database,
for example, after testing of the individual's biological sample is
performed. The biomarker information can comprise biomarker values
that each correspond to one of at least N biomarkers selected from
a group consisting of the biomarkers provided in Table 1, Col. 2,
wherein N=2-65. In block 3008, a computer can be utilized to
classify each of the biomarker values. And, in block 3012, a
determination can be made as to the likelihood that an individual
has pancreatic cancer based upon a plurality of classifications.
The indication can be output to a display or other indicating
device so that it is viewable by a person. Thus, for example, it
can be displayed on a display screen of a computer or other output
device.
[0293] Referring now to FIG. 8, an alternative method of utilizing
a computer in accordance with another embodiment can be illustrated
via flowchart 3200. In block 3204, a computer can be utilized to
retrieve biomarker information for an individual. The biomarker
information comprises a biomarker value corresponding to a
biomarker selected from the group of biomarkers provided in Table
1, Col. 2. In block 3208, a classification of the biomarker value
can be performed with the computer. And, in block 3212, an
indication can be made as to the likelihood that the individual has
pancreatic cancer based upon the classification. The indication can
be output to a display or other indicating device so that it is
viewable by a person. Thus, for example, it can be displayed on a
display screen of a computer or other output device.
[0294] Some embodiments described herein can be implemented so as
to include a computer program product. A computer program product
may include a computer readable medium having computer readable
program code embodied in the medium for causing an application
program to execute on a computer with a database.
[0295] As used herein, a "computer program product" refers to an
organized set of instructions in the form of natural or programming
language statements that are contained on a physical media of any
nature (e.g., written, electronic, magnetic, optical or otherwise)
and that may be used with a computer or other automated data
processing system. Such programming language statements, when
executed by a computer or data processing system, cause the
computer or data processing system to act in accordance with the
particular content of the statements. Computer program products
include without limitation: programs in source and object code
and/or test or data libraries embedded in a computer readable
medium. Furthermore, the computer program product that enables a
computer system or data processing equipment device to act in
pre-selected ways may be provided in a number of forms, including,
but not limited to, original source code, assembly code, object
code, machine language, encrypted or compressed versions of the
foregoing and any and all equivalents.
[0296] In one aspect, a computer program product is provided for
indicating a likelihood of pancreatic cancer. The computer program
product includes a computer readable medium embodying program code
executable by a processor of a computing device or system, the
program code comprising: code that retrieves data attributed to a
biological sample from an individual, wherein the data comprises
biomarker values that each correspond to one of at least N
biomarkers in the biological sample selected from the group of
biomarkers provided in Table 1, Col. 2, wherein N=2-65; and code
that executes a classification method that indicates a pancreatic
cancer status of the individual as a function of the biomarker
values.
[0297] In still another aspect, a computer program product is
provided for indicating a likelihood of pancreatic cancer. The
computer program product includes a computer readable medium
embodying program code executable by a processor of a computing
device or system, the program code comprising: code that retrieves
data attributed to a biological sample from an individual, wherein
the data comprises a biomarker value corresponding to a biomarker
in the biological sample selected from the group of biomarkers
provided in Table 1, Col. 2; and code that executes a
classification method that indicates a pancreatic cancer status of
the individual as a function of the biomarker value.
[0298] While various embodiments have been described as methods or
apparatuses, it should be understood that embodiments can be
implemented through code coupled with a computer, e.g., code
resident on a computer or accessible by the computer. For example,
software and databases could be utilized to implement many of the
methods discussed above. Thus, in addition to embodiments
accomplished by hardware, it is also noted that these embodiments
can be accomplished through the use of an article of manufacture
comprised of a computer usable medium having a computer readable
program code embodied therein, which causes the enablement of the
functions disclosed in this description. Therefore, it is desired
that embodiments also be considered protected by this patent in
their program code means as well. Furthermore, the embodiments may
be embodied as code stored in a computer-readable memory of
virtually any kind including, without limitation, RAM, ROM,
magnetic media, optical media, or magneto-optical media. Even more
generally, the embodiments could be implemented in software, or in
hardware, or any combination thereof including, but not limited to,
software running on a general purpose processor, microcode, PLAs,
or ASICs.
[0299] It is also envisioned that embodiments could be accomplished
as computer signals embodied in a carrier wave, as well as signals
(e.g., electrical and optical) propagated through a transmission
medium. Thus, the various types of information discussed above
could be formatted in a structure, such as a data structure, and
transmitted as an electrical signal through a transmission medium
or stored on a computer readable medium.
[0300] It is also noted that many of the structures, materials, and
acts recited herein can be recited as means for performing a
function or step for performing a function. Therefore, it should be
understood that such language is entitled to cover all such
structures, materials, or acts disclosed within this specification
and their equivalents, including the matter incorporated by
reference.
[0301] The biomarker identification process, the utilization of the
biomarkers disclosed herein, and the various methods for
determining biomarker values are described in detail above with
respect to pancreatic cancer. However, the application of the
process, the use of identified biomarkers, and the methods for
determining biomarker values are fully applicable to other specific
types of cancer, to cancer generally, to any other disease or
medical condition, or to the identification of individuals who may
or may not be benefited by an ancillary medical treatment. Except
when referring to specific results related to pancreatic cancer, as
is clear from the context, references herein to pancreatic cancer
may be understood to include other types of cancer, cancer
generally, or any other disease or medical condition.
EXAMPLES
[0302] The following examples are provided for illustrative
purposes only and are not intended to limit the scope of the
application as defined by the appended claims. All examples
described herein were carried out using standard techniques, which
are well known and routine to those of skill in the art. Routine
molecular biology techniques described in the following examples
can be carried out as described in standard laboratory manuals,
such as Sambrook et al., Molecular Cloning: A Laboratory Manual,
3rd. ed., Cold Spring Harbor Laboratory Press, Cold Spring Harbor,
N.Y., (2001).
Example 1
Multiplexed Aptamer Analysis of Samples
[0303] This example describes the multiplex aptamer assay used to
analyze the samples and controls for the identification of the
biomarkers set forth in Table 1, Col. 2 (see FIG. 9) and the
identification of the cancer biomarkers set forth in Table 19. For
the pancreatic cancer, lung cancer, and mesothelioma studies, the
multiplexed analysis utilized 823 aptamers, each unique to a
specific target.
[0304] In this method, pipette tips were changed for each solution
addition.
[0305] Also, unless otherwise indicated, most solution transfers
and wash additions used the 96-well head of a Beckman Biomek FxP.
Method steps manually pipetted used a twelve channel P200
Pipetteman (Rainin Instruments, LLC, Oakland, Calif.), unless
otherwise indicated. A custom buffer referred to as SB17 was
prepared in-house, comprising 40 mM HEPES, 100 mM NaCl, 5 mM KCl, 5
mM MgCl2, 1 mM EDTA at pH 7.5. A custom buffer referred to as 5B18
was prepared in-house, comprising 40 mM HEPES, 100 mM NaCl, 5 mM
KCl, 5 mM MgCl2 at pH 7.5. All steps were performed at room
temperature unless otherwise indicated.
[0306] 1. Preparation of Aptamer Stock Solution
[0307] For aptamers without a photo-cleavable biotin linker, custom
stock aptamer solutions for 10%, 1% and 0.03% plasma were prepared
at 8.times. concentration in 1.times.5B17, 0.05% Tween-20 with
appropriate photo-cleavable, biotinylated primers, where the
resultant primer concentration was 3 times the relevant aptamer
concentration. The primers hybridized to all or part of the
corresponding aptamer.
[0308] Each of the 3, 8.times. aptamer solutions were diluted
separately 1:4 into 1.times. SB17, 0.05% Tween-20 (1500 .mu.L of
8.times. stock into 4500 .mu.L of 1.times. SB17, 0.05% Tween-20) to
achieve a 2.times. concentration. Each diluted aptamer master mix
was then split, 1500 .mu.L each, into 4 2 mL screw cap tubes and
brought to 95.degree. C. for 5 minutes, followed by a 37.degree. C.
incubation for 15 minutes. After incubation the 4 2 mL tubes
corresponding to a particular aptamer master mix were combined into
a reagent trough, and 55 .mu.L of a 2.times. aptamer mix (for all
three mixes) was manually pipetted into a 96-well Hybaid plate and
the plate foil sealed. The final result was 3, 96-well, foil-sealed
Hybaid plates. The individual aptamer concentration was 0.5 nM.
[0309] 2. Assay Sample Preparation
[0310] Frozen aliquots of 100% plasma, stored at -80.degree. C.,
were placed in 25.degree. C. water bath for 10 minutes. Thawed
samples were placed on ice, gently vortexed (set on 4) for 8
seconds and then replaced on ice.
[0311] A 20% sample solution was prepared by transferring 16 .mu.L
of sample using a 50 .mu.L 8-channel spanning pipettor into 96-well
Hybaid plates, each well containing 64 .mu.L of the appropriate
sample diluent at 4.degree. C. (0.8.times.5B18, 0.05% Tween-20, 2
.mu.M Z-block.sub.--2, 0.6 mM MgCl2 for plasma). This plate was
stored on ice until the next sample dilution steps were
initiated.
[0312] To commence sample and aptamer equilibration, the 20% sample
plate was briefly centrifuged and placed on the Beckman FX where it
was mixed by pipetting up and down with the 96-well pipettor. A 2%
sample was then prepared by diluting 10 .mu.L of the 20% sample
into 90 .mu.L of 1.times. SB17, 0.05% Tween-20. Next, dilution of 6
.mu.L of the resultant 2% sample into 194 .mu.L of 1.times. SB17,
0.05% Tween-20 made a 0.06% sample plate. Dilutions were done on
the Beckman Biomek FxP. After each transfer, the solutions were
mixed by pipetting up and down. The 3 sample dilution plates were
then transferred to their respective aptamer solutions by adding 55
.mu.L of the sample to 55 .mu.L of the appropriate 2.times. aptamer
mix. The sample and aptamer solutions were mixed on the robot by
pipetting up and down.
[0313] 3. Sample Equilibration Binding
[0314] The sample/aptamer plates were foil sealed and placed into a
37.degree. C. incubator for 3.5 hours before proceeding to the
Catch 1 step.
[0315] 4. Preparation of Catch 2 Bead Plate
[0316] An 5.5 mL aliquot of MyOne (Invitrogen Corp., Carlsbad,
Calif.) Streptavidin C1 beads (10 mg/mL) was washed 2 times with
equal volumes of 20 mM NaOH (5 minute incubation for each wash), 3
times with equal volumes of 1.times.5B17, 0.05% Tween-20 and
resuspended in 5.5 mL 1.times.5B17, 0.05% Tween-20. Using a 12-span
multichannel pipettor, 50 .mu.L of this solution was manually
pipetted into each well of a 96-well Hybaid plate. The plate was
then covered with foil and stored at 4.degree. C. for use in the
assay.
[0317] 5. Preparation of Catch 1 Bead Plates
[0318] Three 0.45 .mu.m Millipore HV plates (Durapore membrane,
Cat# MAHVN4550) were equilibrated with 100 .mu.L of 1.times. SB17,
0.05% Tween-20 for at least 10 minutes. The equilibration buffer
was then filtered through the plate and 133.3 .mu.L of a 7.5%
streptavidin-agarose bead slurry (in 1.times. SB17, 0.05% Tween-20)
was added into each well. To keep the streptavidin-agarose beads
suspended while transferring them into the filter plate, the bead
solution was manually mixed with a 200 .mu.L, 12-channel pipettor,
15 times. After the beads were distributed across the 3 filter
plates, a vacuum was applied to remove the bead supernatant.
Finally, the beads were washed in the filter plates with 200 .mu.L
1.times. SB17, 0.05% Tween-20 and then resuspended in 200 .mu.L
1.times. SB17, 0.05% Tween-20. The bottoms of the filter plates
were blotted and the plates stored for use in the assay.
[0319] 6. Loading the Cytomat
[0320] The cytomat was loaded with all tips, plates, all reagents
in troughs (except NHS-biotin reagent which was prepared fresh
right before addition to the plates), 3 prepared Catch 1 filter
plates and 1 prepared MyOne plate.
[0321] 7. Catch 1
[0322] After a 3.5 hour equilibration time, the sample/aptamer
plates were removed from the incubator, centrifuged for about 1
minute, foil removed, and placed on the deck of the Beckman Biomek
FxP. The Beckman Biomek FxP program was initiated. All subsequent
steps in Catch 1 were performed by the Beckman Biomek FxP robot
unless otherwise noted. Within the program, the vacuum was applied
to the Catch 1 filter plates to remove the bead supernatant. One
hundred microlitres of each of the 10%, 1% and 0.03% equilibration
binding reactions were added to their respective Catch 1 filtration
plates, and each plate was mixed using an on-deck orbital shaker at
800 rpm for 10 minutes.
[0323] Unbound solution was removed via vacuum filtration. The
Catch 1 beads were washed with 190 .mu.L of 100 .mu.M biotin in
1.times. SB17, 0.05% Tween-20 followed by 190 .mu.L of 1.times.
SB17, 0.05% Tween-20 by dispensing the solution and immediately
drawing a vacuum to filter the solution through the plate.
[0324] Next, 190 .mu.L 1.times. SB17, 0.05% Tween-20 was added to
the Catch 1 plates. Plates were blotted to remove droplets using an
on-deck blot station and then incubated with orbital shakers at 800
rpm for 10 minutes at 25.degree. C.
[0325] The robot removed this wash via vacuum filtration and
blotted the bottom of the filter plate to remove droplets using the
on-deck blot station.
[0326] 8. Tagging
[0327] A NHS-PEO4-biotin aliquot was thawed at 37.degree. C. for 6
minutes and then diluted 1:100 with tagging buffer (SB17 at pH 7.25
0.05% Tween-20). The NHS-PEO4-biotin reagent was dissolved at 100
mM concentration in anhydrous DMSO and had been stored frozen at
-20.degree. C. Upon a robot prompt, the diluted NHS-PEO4-biotin
reagent was manually added to an on-deck trough and the robot
program was manually re-initiated to dispense 100 .mu.L of the
NHS-PEO4-biotin into each well of each Catch 1 filter plate. This
solution was allowed to incubate with Catch 1 beads shaking at 800
rpm for 5 minutes on the orbital shakers.
[0328] 9. Kinetic Challenge and Photo-Cleavage
[0329] The tagging reaction was quenched by the addition of 150
.mu.L of 20 mM glycine in 1.times. SB17, 0.05% Tween-20 to the
Catch 1 plates while still containing the NHS tag. The plates were
then incubated for 1 minute on orbital shakers at 800 rpm. The
NHS-tag/glycine solution was removed via vacuum filtration. Next,
190 .mu.L 20 mM glycine (1.times. SB17, 0.05% Tween-20) was added
to each plate and incubated for 1 minute on orbital shakers at 800
rpm before removal by vacuum filtration.
[0330] 190 .mu.L of 1.times. SB17, 0.05% Tween-20 was added to each
plate and removed by vacuum filtration.
[0331] The wells of the Catch 1 plates were subsequently washed
three times by adding 190 .mu.L 1.times. SB17, 0.05% Tween-20,
placing the plates on orbital shakers for 1 minute at 800 rpm
followed by vacuum filtration. After the last wash the plates were
placed on top of a 1 mL deep-well plate and removed from the deck.
The Catch 1 plates were centrifuged at 1000 rpm for 1 minute to
remove as much extraneous volume from the agarose beads before
elution as possible.
[0332] The plates were placed back onto the Beckman Biomek FxP and
85 .mu.L of 10 mM DxSO4 in 1.times. SB17, 0.05% Tween-20 was added
to each well of the filter plates.
[0333] The filter plates were removed from the deck, placed onto a
Variomag Thermoshaker (Thermo Fisher Scientific, Inc., Waltham,
Mass.) under the BlackRay (Ted Pella, Inc., Redding, Calif.) light
sources, and irradiated for 10 minutes while shaking at 800
rpm.
[0334] The photocleaved solutions were sequentially eluted from
each Catch 1 plate into a common deep well plate by first placing
the 10% Catch 1 filter plate on top of a 1 mL deep-well plate and
centrifuging at 1000 rpm for 1 minute. The 1% and 0.03% Catch 1
plates were then sequentially centrifuged into the same deep well
plate.
[0335] 10. Catch 2 Bead Capture
[0336] The 1 mL deep well block containing the combined eluates of
Catch 1 was placed on the deck of the Beckman Biomek FxP for Catch
2.
[0337] The robot transferred all of the photo-cleaved eluate from
the 1 mL deep-well plate onto the Hybaid plate containing the
previously prepared Catch 2 MyOne magnetic beads (after removal of
the MyOne buffer via magnetic separation).
[0338] The solution was incubated while shaking at 1350 rpm for 5
minutes at 25.degree. C. on a Variomag Thermoshaker (Thermo Fisher
Scientific, Inc., Waltham, Mass.).
[0339] The robot transferred the plate to the on deck magnetic
separator station. The plate was incubated on the magnet for 90
seconds before removal and discarding of the supernatant.
[0340] 11. 37.degree. C. 30% Glycerol Washes
[0341] The Catch 2 plate was moved to the on-deck thermal shaker
and 75 .mu.L of 1.times. SB17, 0.05% Tween-20 was transferred to
each well. The plate was mixed for 1 minute at 1350 rpm and
37.degree. C. to resuspend and warm the beads. To each well of the
Catch 2 plate, 75 .mu.L of 60% glycerol at 37.degree. C. was
transferred and the plate continued to mix for another minute at
1350 rpm and 37.degree. C. The robot transferred the plate to the
37.degree. C. magnetic separator where it was incubated on the
magnet for 2 minutes and then the robot removed and discarded the
supernatant. These washes were repeated two more times.
[0342] After removal of the third 30% glycerol wash from the Catch
2 beads, 150 .mu.L of 1.times. SB17, 0.05% Tween-20 was added to
each well and incubated at 37.degree. C., shaking at 1350 rpm for 1
minute, before removal by magnetic separation on the 37.degree. C.
magnet.
[0343] The Catch 2 beads were washed a final time using 150 .mu.L
1.times. SB19, 0.05% Tween-20 with incubation for 1 minute while
shaking at 1350 rpm at 25.degree. C. prior to magnetic
separation.
[0344] 12. Catch 2 Bead Elution and Neutralization
[0345] The aptamers were eluted from Catch 2 beads by adding 105
.mu.L of 100 mM CAPSO with 1M NaCl, 0.05% Tween-20 to each well.
The beads were incubated with this solution with shaking at 1350
rpm for 5 minutes.
[0346] The Catch 2 plate was then placed onto the magnetic
separator for 90 seconds prior to transferring 90 .mu.L of the
eluate to a new 96-well plate containing 10 .mu.L of 500 mM HCl,
500 mM HEPES, 0.05% Tween-20 in each well. After transfer, the
solution was mixed robotically by pipetting 90 .mu.L up and down
five times.
[0347] 13. Hybridization
[0348] The Beckman Biomek FxP transferred 20 .mu.L of the
neutralized Catch 2 eluate to a fresh Hybaid plate, and 5 .mu.L of
10.times. Agilent Block, containing a 10.times. spike of
hybridization controls, was added to each well. Next, 25 .mu.L of
2.times. Agilent Hybridization buffer was manually pipetted to the
each well of the plate containing the neutralized samples and
blocking buffer and the solution was mixed by manually pipetting 25
.mu.L up and down 15 times slowly to avoid extensive bubble
formation. The plate was spun at 1000 rpm for 1 minute.
[0349] A gasket slide was placed into an Agilent hybridization
chamber and 40 .mu.L of each of the samples containing
hybridization and blocking solution was manually pipetted into each
gasket. An 8-channel variable spanning pipettor was used in a
manner intended to minimize bubble formation. Custom Agilent
microarray slides (Agilent Technologies, Inc., Santa Clara,
Calif.), with their Number Barcode facing up, were then slowly
lowered onto the gasket slides (see Agilent manual for detailed
description).
[0350] The top of the hybridization chambers were placed onto the
slide/backing sandwich and clamping brackets slid over the whole
assembly. These assemblies were tightly clamped by turning the
screws securely.
[0351] Each slide/backing slide sandwich was visually inspected to
assure the solution bubble could move freely within the sample. If
the bubble did not move freely the hybridization chamber assembly
was gently tapped to disengage bubbles lodged near the gasket.
[0352] The assembled hybridization chambers were incubated in an
Agilent hybridization oven for 19 hours at 60.degree. C. rotating
at 20 rpm.
[0353] 14. Post Hybridization Washing
[0354] Approximately 400 mL Agilent Wash Buffer 1 was placed into
each of two separate glass staining dishes. One of the staining
dishes was placed on a magnetic stir plate and a slide rack and
stir bar were placed into the buffer.
[0355] A staining dish for Agilent Wash 2 was prepared by placing a
stir bar into an empty glass staining dish.
[0356] A fourth glass staining dish was set aside for the final
acetonitrile wash.
[0357] Each of six hybridization chambers was disassembled.
One-by-one, the slide/backing sandwich was removed from its
hybridization chamber and submerged into the staining dish
containing Wash 1. The slide/backing sandwich was pried apart using
a pair of tweezers, while still submerging the microarray slide.
The slide was quickly transferred into the slide rack in the Wash 1
staining dish on the magnetic stir plate.
[0358] The slide rack was gently raised and lowered 5 times. The
magnetic stirrer was turned on at a low setting and the slides
incubated for 5 minutes.
[0359] When one minute was remaining for Wash 1, Wash Buffer 2
pre-warmed to 37.degree. C. in an incubator was added to the second
prepared staining dish. The slide rack was quickly transferred to
Wash Buffer 2 and any excess buffer on the bottom of the rack was
removed by scraping it on the top of the stain dish. The slide rack
was gently raised and lowered 5 times. The magnetic stirrer was
turned on at a low setting and the slides incubated for 5
minutes.
[0360] The slide rack was slowly pulled out of Wash 2, taking
approximately 15 seconds to remove the slides from the
solution.
[0361] With one minute remaining in Wash 2 acetonitrile (ACN) was
added to the fourth staining dish. The slide rack was transferred
to the acetonitrile stain dish. The slide rack was gently raised
and lowered 5 times. The magnetic stirrer was turned on at a low
setting and the slides incubated for 5 minutes.
[0362] The slide rack was slowly pulled out of the ACN stain dish
and placed on an absorbent towel. The bottom edges of the slides
were quickly dried and the slide was placed into a clean slide
box.
[0363] 15. Microarray Imaging
[0364] The microarray slides were placed into Agilent scanner slide
holders and loaded into the Agilent Microarray scanner according to
the manufacturers instructions.
[0365] The slides were imaged in the Cy3-channel at 5 .mu.m
resolution at the 100% PMT setting and the XRD option enabled at
0.05. The resulting tiff images were processed using Agilent
feature extraction software version 10.5.
Example 2
Biomarker Identification
[0366] The identification of potential pancreatic cancer biomarkers
was performed for diagnosis of pancreatic cancer in asymptomatic
individuals and symptomatic individuals with acute or chronic
pancreatitis (or both), pancreatic obstruction, GERD, gallstones,
or abnormal imaging later found to be benign, collectively the GI
and normal controls. Enrollment criteria for this study were age 18
or older, able to give informed consent, and plasma sample and
documented diagnosis of pancreatic cancer or benign findings. For
cases, blood samples collected prior to treatment or surgery and
subsequently diagnosed with pancreatic cancer. Exclusion criteria
included prior diagnosis or treatment of cancer (excluding squamous
cell carcinoma of the skin) within 5 years of the blood draw.
Plasma samples were collected from 2 different sites and included
143 pancreatic cancer samples and 115 control group samples. The
multiplexed aptamer affinity assay as described in Example 1 was
used to measure and report the RFU value for 823 analytes in each
of these 258 samples. Since the plasma samples were obtained from 2
independent studies and sites under similar protocols, an
examination of site differences prior to the analysis for
biomarkers discovery was performed.
[0367] Each of the case and control populations were separately
compared by generating class-dependent cumulative distribution
functions (cdfs) for each of the 823 analytes. The KS-distance
(Kolmogorov-Smirnov statistic) between values from two sets of
samples is a non parametric measurement of the extent to which the
empirical distribution of the values from one set (Set A) differs
from the distribution of values from the other set (Set B). For any
value of a threshold T some proportion of the values from Set A
will be less than T, and some proportion of the values from Set B
will be less than T. The KS-distance measures the maximum
(unsigned) difference between the proportion of the values from the
two sets for any choice of T.
[0368] This set of potential biomarkers can be used to build
classifiers that assign samples to either a control or disease
group. In fact, many such classifiers were produced from these sets
of biomarkers and the frequency with which any biomarker was used
in good scoring classifiers determined. Those biomarkers that
occurred most frequently among the top scoring classifiers were the
most useful for creating a diagnostic test. In this example,
Bayesian classifiers were used to explore the classification space
but many other supervised learning techniques may be employed for
this purpose. The scoring fitness of any individual classifier was
gauged by the area under the receiver operating characteristic
curve (AUC of the ROC) of the classifier at the Bayesian surface
assuming a disease prevalence of 0.5. This scoring metric varies
from zero to one, with one being an error-free classifier. The
details of constructing a Bayesian classifier from biomarker
population measurements are described in Example 3.
[0369] Using the 65 analytes in Table 1, a total of 973 10-analyte
classifiers were found with an AUC of 0.90 for diagnosing
pancreatic cancer from the control group. From this set of
classifiers, a total of 11 biomarkers were found to be present in
30% or more of the high scoring classifiers. Table 13 provides a
list of these potential biomarkers and FIG. 10 is a frequency plot
for the identified biomarkers.
Example 3
Naive Bayesian Classification for Pancreatic Cancer
[0370] From the list of biomarkers identified as useful for
discriminating between pancreatic cancer and controls, a panel of
ten biomarkers was selected and a naive Bayes classifier was
constructed, see Table 16. The class-dependent probability density
functions (pdfs), p(x.sub.i|c) and p(x.sub.i|d), where x.sub.i is
the log of the measured RFU value for biomarker i, and c and d
refer to the control and disease populations, were modeled as
log-normal distribution functions characterized by a mean .mu. and
variance .sigma..sup.2. The parameters for pdfs of the ten
biomarkers are listed in Table 16 and an example of the raw data
along with the model fit to a normal pdf is displayed in FIG. 5.
The underlying assumption appears to fit the data quite well as
evidenced by FIG. 5.
[0371] The naive Bayes classification for such a model is given by
the following equation, where p(d) is the prevalence of the disease
in the population.
ln ( p ( d x ~ ) p ( c x ~ ) ) = i = 1 n ln ( .sigma. c , i .sigma.
d i ) - 1 2 i = 1 n [ ( x i - .mu. d , i .sigma. d , i ) 2 - ( x i
- .mu. c , i .sigma. c , i ) 2 ] + ln ( p ( d ) 1 - p ( d ) ) ,
##EQU00006##
appropriate to the test and n=10. Each of the terms in the
summation is a log-likelihood ratio for an individual marker and
the total log-likelihood ratio of a sample {tilde over (x)} being
free from the disease of interest (i.e. in this case, pancreatic
cancer) versus having the disease is simply the sum of these
individual terms plus a term that accounts for the prevalence of
the disease. For simplicity, we assume p(d)=0.5 so that
ln ( p ( d ) 1 - p ( d ) ) = 0. ##EQU00007##
[0372] Given an unknown sample measurement in log(RFU) for each of
the ten biomarkers of 6.3, 9.3, 8.7, 10.8, 7.4, 11.4, 11.7, 9.0,
8.0, 7.3, the calculation of the classification is detailed in
Table 16. The individual components comprising the log likelihood
ratio for disease versus control class are tabulated and can be
computed from the parameters in Table 16 and the values of {tilde
over (x)}. The sum of the individual log likelihood ratios is
-3.044, or a likelihood of being free from the disease versus
having the disease of 21, where likelihood e.sup.3.044=21. The
first 3 biomarker values have likelihoods more consistent with the
disease group (log likelihood >0) but the remaining 7 biomarkers
are all consistently found to favor the control group. Multiplying
the likelihoods together gives the same results as that shown
above; a likelihood of 21 that the unknown sample is free from the
disease. In fact, this sample came from the control population in
the training set.
Example 4
Greedy Algorithm for Selecting Biomarker Panels for Classifiers
[0373] This example describes the selection of biomarkers from
Table 1 to form panels that can be used as classifiers in any of
the methods described herein. Subsets of the biomarkers in Table 1
were selected to construct classifiers with good performance. This
method was also used to determine which potential markers were
included as biomarkers in Example 2.
[0374] The measure of classifier performance used here is the AUC;
a performance of 0.5 is the baseline expectation for a random (coin
toss) classifier, a classifier worse than random would score
between 0.0 and 0.5, a classifier with better than random
performance would score between 0.5 and 1.0. A perfect classifier
with no errors would have a sensitivity of 1.0 and a specificity of
1.0. One can apply the methods described in Example 4 to other
common measures of performance such as the F-measure, the sum of
sensitivity and specificity, or the product of sensitivity and
specificity. Specifically one might want to treat sensitivity and
specificity with differing weight, so as to select those
classifiers which perform with higher specificity at the expense of
some sensitivity, or to select those classifiers which perform with
higher sensitivity at the expense of some specificity. Since the
method described here only involves a measure of "performance", any
weighting scheme which results in a single performance measure can
be used. Different applications will have different benefits for
true positive and true negative findings, and also different costs
associated with false positive findings from false negative
findings. For example, screening asymptomatic high risk individuals
and the differential diagnosis of pancreatic cancer from benign GI
symptoms will not in general have the same optimal trade-off
between specificity and sensitivity. The different demands of the
two tests will in general require setting different weighting to
positive and negative misclassifications, reflected in the
performance measure. Changing the performance measure will in
general change the exact subset of markers selected from Table 1,
Col. 2 for a given set of data.
[0375] For the Bayesian approach to the discrimination of
pancreatic cancer samples from control samples described in Example
3, the classifier was completely parameterized by the distributions
of biomarkers in the disease and benign training samples, and the
list of biomarkers was chosen from Table 1; that is to say, the
subset of markers chosen for inclusion determined a classifier in a
one-to-one manner given a set of training data.
[0376] The greedy method employed here was used to search for the
optimal subset of markers from Table 1. For small numbers of
markers or classifiers with relatively few markers, every possible
subset of markers was enumerated and evaluated in terms of the
performance of the classifier constructed with that particular set
of markers (see Example 4, Part 2). (This approach is well known in
the field of statistics as "best subset selection"; see, e.g.,
Hastie et al). However, for the classifiers described herein, the
number of combinations of multiple markers can be very large, and
it was not feasible to evaluate every possible set of 10 markers,
as there are 30,045,015 possible combinations that can be generated
from a list of only 30 total analytes. Because of the
impracticality of searching through every subset of markers, the
single optimal subset may not be found; however, by using this
approach, many excellent subsets were found, and, in many cases,
any of these subsets may represent an optimal one.
[0377] Instead of evaluating every possible set of markers, a
"greedy" forward stepwise approach may be followed (see, e.g.,
Dabney A R, Storey J D (2007) Optimality Driven Nearest Centroid
Classification from Genomic Data.
PLoS ONE 2(10): e1002. doi:10.1371/journal.pone.0001002). Using
this method, a classifier is started with the best single marker
(based on KS-distance for the individual markers) and is grown at
each step by trying, in turn, each member of a marker list that is
not currently a member of the set of markers in the classifier. The
one marker which scores best in combination with the existing
classifier is added to the classifier. This is repeated until no
further improvement in performance is achieved. Unfortunately, this
approach may miss valuable combinations of markers for which some
of the individual markers are not all chosen before the process
stops.
[0378] The greedy procedure used here was an elaboration of the
preceding forward stepwise approach, in that, to broaden the
search, rather than keeping just a single candidate classifier
(marker subset) at each step, a list of candidate classifiers was
kept. The list was seeded with every single marker subset (using
every marker in the table on its own). The list was expanded in
steps by deriving new classifiers (marker subsets) from the ones
currently on the list and adding them to the list. Each marker
subset currently on the list was extended by adding any marker from
Table 1 not already part of that classifier, and which would not,
on its addition to the subset, duplicate an existing subset (these
are termed "permissible markers"). Every existing marker subset was
extended by every permissible marker from the list. Clearly, such a
process would eventually generate every possible subset, and the
list would run out of space. Therefore, all the generated
classifiers were kept only while the list was less than some
predetermined size (often enough to hold all three marker subsets).
Once the list reached the predetermined size limit, it became
elitist; that is, only those classifiers which showed a certain
level of performance were kept on the list, and the others fell off
the end of the list and were lost. This was achieved by keeping the
list sorted in order of classifier performance; new classifiers
which were at least as good as the worst classifier currently on
the list were inserted, forcing the expulsion of the current bottom
underachiever. One further implementation detail is that the list
was completely replaced on each generational step; therefore, every
classifier on the list had the same number of markers, and at each
step the number of markers per classifier grew by one.
[0379] Since this method produced a list of candidate classifiers
using different combinations of markers, one may ask if the
classifiers can be combined in order to avoid errors which might be
made by the best single classifier, or by minority groups of the
best classifiers. Such "ensemble" and "committee of experts"
methods are well known in the fields of statistical and machine
learning and include, for example, "Averaging", "Voting",
"Stacking", "Bagging" and "Boosting" (see, e.g., Hastie et al.).
These combinations of simple classifiers provide a method for
reducing the variance in the classifications due to noise in any
particular set of markers by including several different
classifiers and therefore information from a larger set of the
markers from the biomarker table, effectively averaging between the
classifiers. An example of the usefulness of this approach is that
it can prevent outliers in a single marker from adversely affecting
the classification of a single sample. The requirement to measure a
larger number of signals may be impractical in conventional "one
marker at a time" antibody assays but has no downside for a fully
multiplexed aptamer assay. Techniques such as these benefit from a
more extensive table of biomarkers and use the multiple sources of
information concerning the disease processes to provide a more
robust classification.
[0380] The biomarkers selected in Table 1 gave rise to classifiers
which perform better than classifiers built with "non-markers"
(i.e., proteins having signals that did not meet the criteria for
inclusion in Table 1 (as described in Example 2)).
[0381] For classifiers containing only one, two, and three markers,
all possible classifiers obtained using the biomarkers in Table 1
were enumerated and examined for the distribution of performance
compared to classifiers built from a similar table of randomly
selected non-markers signals.
[0382] In FIG. 11, the AUC was used as the measure of performance;
a performance of 0.5 is the baseline expectation for a random (coin
toss) classifier. The histogram of classifier performance was
compared with the histogram of performance from a similar
exhaustive enumeration of classifiers built from a "non-marker"
table of 65 non-marker signals; the 65 signals were randomly chosen
from aptamers that did not demonstrate differential signaling
between control and disease populations.
[0383] FIG. 11 shows histograms of the performance of all possible
one, two, and three-marker classifiers built from the biomarker
parameters in Table 14 for biomarkers that can discriminate between
the control group and pancreatic cancer and compares these
classifiers with all possible one, two, and three-marker
classifiers built using the 65 "non-marker" aptamer RFU signals.
FIG. 11A shows the histograms of single marker classifier
performance, FIG. 11B shows the histogram of two marker classifier
performance, and FIG. 11C shows the histogram of three marker
classifier performance.
[0384] In FIG. 11, the solid lines represent the histograms of the
classifier performance of all one, two, and three-marker
classifiers using the biomarker data for GI and normal controls and
pancreatic cancer in Table 14. The dotted lines are the histograms
of the classifier performance of all one, two, and three-marker
classifiers using the data for controls and pancreatic cancer but
using the set of random non-marker signals.
[0385] The classifiers built from the markers listed in Table 1
form a distinct histogram, well separated from the classifiers
built with signals from the "non-markers" for all one-marker,
two-marker, and three-marker comparisons. The performance and AUC
score of the classifiers built from the biomarkers in Table 1 also
increase faster with the number of markers than do the classifiers
built from the non-markers, the separation increases between the
marker and non-marker classifiers as the number of markers per
classifier increases. All classifiers built using the biomarkers
listed in Table 14 perform distinctly better than classifiers built
using the "non-markers".
[0386] The distributions of classifier performance show that there
are many possible multiple-marker classifiers that can be derived
from the set of analytes in Table 1. Although some biomarkers are
better than others on their own, as evidenced by the distribution
of classifier scores and AUCs for single analytes, it was desirable
to determine whether such biomarkers are required to construct high
performing classifiers. To make this determination, the behavior of
classifier performance was examined by leaving out some number of
the best biomarkers. FIG. 12 compares the performance of
classifiers built with the full list of biomarkers in Table 1 with
the performance of classifiers built with subsets of biomarkers
from Table 1 that excluded top-ranked markers.
[0387] FIG. 12 demonstrates that classifiers constructed without
the best markers perform well, implying that the performance of the
classifiers was not due to some small core group of markers and
that the changes in the underlying processes associated with
disease are reflected in the activities of many proteins. Many
subsets of the biomarkers in Table 1 performed close to optimally,
even after removing the top 15 of the 65 markers from Table 1.
After dropping the 15 top-ranked markers (ranked by KS-distance)
from Table 1, the classifier performance increased with the number
of markers selected from the table to reach an AUC of almost 0.87,
close to the performance of the optimal classifier score of 0.91
selected from the full list of biomarkers.
[0388] Finally, FIG. 16 shows how the ROC performance of typical
classifiers constructed from the list of parameters in Table 14
according to Example 3. A five analyte classifier was constructed
with CTSB, C5a, C5, CCL18, and CSF1R. FIG. 16A shows the
performance of the model, assuming independence of these markers,
as in Example 3, and FIG. 16B shows the empirical ROC curves
generated from the study data set used to define the parameters in
Table 14. It can be seen that the performance for a given number of
selected markers was qualitatively in agreement, and that
quantitative agreement was generally quite good, as evidenced by
the AUCs, although the model calculation tends to overestimate
classifier performance. This is consistent with the notion that the
information contributed by any particular biomarker concerning the
disease processes is redundant with the information contributed by
other biomarkers provided in Table 1 while the model calculation
assumes complete independence. FIG. 16 thus demonstrates that Table
1 in combination with the methods described in Example 3 enable the
construction and evaluation of a great many classifiers useful for
the discrimination of pancreatic cancer from the control group.
Example 5
Incorporating CA19-9
[0389] Cancer associated antigen 19-9 (CA 19-9) is a known serum
marker for pancreatic cancer. The reported sensitivity and
specificity of CA 19-9 for pancreatic cancer are 80 to 90 percent,
respectively. However, the accuracy of CA 19-9 to identify patients
with small surgically resectable cancers is limited. The
specificity of CA 19-9 is also limited; CA 19-9 is frequently
elevated in patients with various benign pancreaticobiliary
disorders.
[0390] The degree of elevation of CA 19-9 in pancreatic cancer is
associated with long-term prognosis. Furthermore, in patients who
appear to have potentially resectable disease, the magnitude of the
CA 19-9 level can also help to predict the presence of
radiographically occult metastatic disease. Serial monitoring of CA
19-9 levels is useful to follow patients after potentially curative
surgery and for those who are receiving chemotherapy for advanced
disease. Rising CA 19-9 levels usually precede the radiographic
appearance of recurrent disease, but confirmation of disease
progression should be pursued with imaging studies and/or biopsy.
Testing of biomarker levels in combination with CA 19-9 may improve
sensitivity, specificity, and/or AUC for detecting pancreatic
cancer (or other pancreatic cancer-related uses) as compared to CA
19-9 alone.
[0391] An elevated level of CA19-9 is considered to be 35-40 U/ml
in serum.
[0392] We received clinical CA19-9 measurements for a subset of the
training samples. Of the original 100 cases and 69 controls, we had
CA19-9 measurements for 99 cases and 52 controls. Therefore, we
trained a new set of random forest models on this subset of samples
using subsets of the SOMAmers in Table 1. We also trained new
classifiers which incorporated the CA19-9 measurement with our
SOMAmer panel (combined panel).
[0393] The classifier performance of the three different approaches
(SOMAmer, CA19-9, and a combined panel) is shown in FIG. 13. The
SOMAmer panel and CA19-9 perform similarly, however when the two
are combined into a single classifier the performance improves
dramatically. For a specificity of 100%, the SOMAmer panel and
CA19-9 have a sensitivity just under 50%, whereas the combined
classifier has a sensitivity of around 75%.
[0394] Further analysis revealed that when CA19-9 is included in
the classifier, the number of SOMAmers required for the same
relative performance is reduced. FIG. 14 shows the performance of
random forest classifiers that use CA19-9 and either one or two
additional SOMAmers. The left panel shows the performance of a
model trained using CA19-9 and HAMP and the right panel shows the
performance of CA19-9, HAMP, and CTSB.
Example 6
Clinical Biomarker Panel
[0395] A random forest classifier was built from a panel of
biomarkers selected that may be the most appropriate for use in a
clinical diagnostic test. Unlike the models selected by the naive
Bayes greedy forward algorithm, the random forest classifier does
not assume that the biomarker measurements are randomly
distributed. Therefore this model can utilize biomarkers from Table
1 that are not effective in the naive Bayes classifier.
[0396] The panel was selected using a backward elimination
procedure that utilized the gini importance measure provided by the
random forest classifier. The gini importance is a measure of the
effectiveness of a biomarker at correctly classifying samples in
the training set. This measure of biomarker importance can be used
to eliminate markers that are less vital to the performance of the
classifier. The backward elimination procedure was initiated by
building a random forest classifier that included all 65 in Table
1. The least important biomarker was then eliminated and a new
model was built with the remaining biomakers. This procedure
continued until only single biomarker remained.
[0397] The final panel that was selected provided the best balance
between the greatest AUC and the lowest number of markers in the
model. The panel of ten biomarkers that satisfied these criteria is
composed of the following analytes, APOA1, CTSB, C2, MMP7, HAMP,
TFP1, C5, c5a, SFRP1, and ETHE1. A plot of the ROC curve for this
biomarker panel is shown in FIG. 15. The figure indicates two
possible decision cutoffs illustrated by arrows: a symptomatic
cutoff where a sensitivity of 84% or more can be obtained with at
least 80% specificity; and an asymptomatic cutoff where a
specificity of 97.5% can be obtained with at least 60%
sensitivity.
Example 7
Biomarkers for the Diagnosis of Cancer
[0398] The identification of potential biomarkers for the general
diagnosis of cancer was performed. Both case and control samples
were evaluated from 3 different types of cancer (pancreatic cancer,
lung cancer, and mesothelioma). Across the collection sites,
inclusion criteria were at least 18 years old with signed informed
consent. Both cases and controls were excluded for known malignancy
other than the cancer in question.
[0399] Pancreatic Cancer. Case and control samples were obtained as
described in Example 2.
[0400] Lung Cancer. Case and control samples were obtained from
three academic cancer center biorepositories and one commercial
biorepository to identify potential markers for the differential
diagnosis of non-small cell lung cancer (NSCLC) from a control
group of high risk smokers and individuals with benign pulmonary
nodules. The study was composed of 978 samples collected from
smokers and patients with benign nodules as well as 320 individuals
diagnosed with NSCLC.
[0401] Pleural Mesothelioma. Case and control samples were obtained
from an academic cancer center biorepository to identify potential
markers for the differential diagnosis of malignant pleural
mesothelioma from individuals with a history of asbestos exposure
or benign lung disease, including suspicious radiology findings
that were later diagnosed as non-malignant. The study was composed
of 30 samples collected from asbestos exposed individuals and 41
samples collected from mesothelioma patients.
[0402] A final list of cancer biomarkers was identified by
combining the sets of biomarkers considered for each of the 3
different cancer studies. Bayesian classifiers that used biomarker
sets of increasing size were successively constructed using a
greedy algorithm (as described in greater detail in Section 7.2 of
this Example). The sets (or panels) of biomarkers that were useful
for diagnosing cancer in general among the types of cancer were
compiled as a function of set (or panel) size and analyzed for
their performance. This analysis resulted in the list of 10 cancer
biomarkers shown in Table 19, each of which was present in at least
one of these successive marker sets, which ranged in size from
three to ten markers. As an illustrative example, we describe the
generation of a specific panel composed of ten cancer biomarkers,
which is shown in Table 32.
[0403] 7.1 Naive Bayesian Classification for Cancer
[0404] From the list of biomarkers in Table 1, a panel of ten
potential cancer biomarkers was selected using a greedy algorithm
for biomarker selection, as outlined in Section 7.2 of this
Example. A distinct naive Bayes classifier was constructed for each
of the 3. The class-dependent probability density functions (pdfs),
p(x.sub.i|c) and p(x.sub.i|d), where x.sub.i is the log of the
measured RFU value for biomarker i, and c and d refer to the
control and disease populations, were modeled as log-normal
distribution functions characterized by a mean .mu. and variance
.sigma..sup.2. The parameters for pdfs of the 3 models composed of
the ten potential biomarkers are listed in Table 31.
[0405] The naive Bayes classification for such a model is given by
the following equation, where p(d) is the prevalence of the disease
in the population,
ln ( p ( d x ~ ) p ( c x ~ ) ) = i = 1 n ln ( .sigma. c , i .sigma.
d i ) - 1 2 i = 1 n [ ( x i - .mu. d , i .sigma. d , i ) 2 - ( x i
- .mu. c , i .sigma. c , i ) 2 ] + ln ( p ( d ) 1 - p ( d ) ) ,
##EQU00008##
appropriate to the test and n=10. Each of the terms in the
summation is a log-likelihood ratio for an individual marker and
the total log-likelihood ratio of a sample {tilde over (x)} being
free from the disease interest (i.e., in this case, each particular
disease from the 3 different cancer types) versus having the
disease is simply the sum of these individual terms plus a term
that accounts for the prevalence of the disease. For simplicity, we
assume p(d)=0.5 so that
ln ( p ( d ) 1 - p ( d ) ) = 0. ##EQU00009##
[0406] Given an unknown sample measurement in log(RFU) for each of
the ten biomarkers of 10.1, 8.9, 8.8, 8.8, 9.1, 7.3, 8.2, 9.5, 6.7,
7.7, the calculation of the classification is detailed in Table 32.
The individual components comprising the log likelihood ratio for
disease versus control class are tabulated and can be computed from
the parameters in Table 31 and the values of {tilde over (x)}. The
sum of the individual log likelihood ratios is -4.568, or a
likelihood of being free from the disease versus having the disease
of 96, where likelihood e.sup.4.568=96. Only 1 of the biomarker
values have likelihoods more consistent with the disease group (log
likelihood >0) but the remaining 9 biomarkers are all
consistently found to favor the control group. Multiplying the
likelihoods together gives the same results as that shown above; a
likelihood of 96 that the unknown sample is free from the disease.
In fact, this sample came from the control population in the NSCLC
training set.
7.2 Greedy Algorithm for Selecting Cancer Biomarker Panels for
Classifiers
Part 1
[0407] Subsets of the biomarkers in Table 1 were selected to
construct potential classifiers that could be used to determine
which of the markers could be used as general cancer biomarkers to
detect cancer.
[0408] Given a set of markers, a distinct model was trained for
each of the 3 cancer studies, so a global measure of performance
was required to select a set of biomarkers that was able to
classify simultaneously many different types of cancer. The measure
of classifier performance used here was the mean of the area under
ROC curve across all naive Bayes classifiers. The ROC curve is a
plot of a single classifier true positive rate (sensitivity) versus
the false positive rate (1-specificity). The area under the ROC
curve (AUC) ranges from 0 to 1.0, where an AUC of 1.0 corresponds
to perfect classification and an AUC of 0.5 corresponds to random
(coin toss) classifier. One can apply other common measures of
performance such as the F-measure or the sum or product of
sensitivity and specificity. Specifically, one might want to treat
sensitivity and specificity with differing weight, in order to
select those classifiers that perform with higher specificity at
the expense of some sensitivity, or to select those classifiers
which perform with higher sensitivity at the expense of
specificity. We chose to use the AUC because it encompasses all
combinations of sensitivity and specificity in a single measure.
Different applications will have different benefits for true
positive and true negative findings, and will have different costs
associated with false positive findings from false negative
findings. Changing the performance measure may change the exact
subset of markers selected for a given set of data.
[0409] For the Bayesian approach to the discrimination of cancer
samples from control samples described in Section 7.1 of this
Example, the classifier was completely parameterized by the
distributions of biomarkers in each of the 3 cancer studies, and
the list of biomarkers was chosen from Table 19. That is to say,
the subset of markers chosen for inclusion determined a classifier
in a one-to-one manner given a set of training data.
[0410] The greedy method employed here was used to search for the
optimal subset of markers from Table 1. For small numbers of
markers or classifiers with relatively few markers, every possible
subset of markers was enumerated and evaluated in terms of the
performance of the classifier constructed with that particular set
of markers (see Example 4, Part 2). (This approach is well known in
the field of statistics as "best subset selection"; see, e.g.,
Hastie et al). However, for the classifiers described herein, the
number of combinations of multiple markers can be very large, and
it was not feasible to evaluate every possible set of 10 markers,
as there are 30,045,015 possible combinations that can be generated
from a list of only 30 total analytes. Because of the
impracticality of searching through every subset of markers, the
single optimal subset may not be found; however, by using this
approach, many excellent subsets were found, and, in many cases,
any of these subsets may represent an optimal one.
[0411] Instead of evaluating every possible set of markers, a
"greedy" forward stepwise approach may be followed (see, e.g.,
Dabney A R, Storey J D (2007) Optimality Driven Nearest Centroid
Classification from Genomic Data.
PLoS ONE 2(10): e1002. doi:10.1371/journal.pone.0001002). Using
this method, a classifier is started with the best single marker
(based on KS-distance for the individual markers) and is grown at
each step by trying, in turn, each member of a marker list that is
not currently a member of the set of markers in the classifier. The
one marker that scores the best in combination with the existing
classifier is added to the classifier. This is repeated until no
further improvement in performance is achieved. Unfortunately, this
approach may miss valuable combinations of markers for which some
of the individual markers are not all chosen before the process
stops.
[0412] The greedy procedure used here was an elaboration of the
preceding forward stepwise approach, in that, to broaden the
search, rather than keeping just a single marker subset at each
step, a list of candidate marker sets was kept. The list was seeded
with a list of single markers. The list was expanded in steps by
deriving new marker subsets from the ones currently on the list and
adding them to the list. Each marker subset currently on the list
was extended by adding any marker from Table 1 not already part of
that classifier, and which would not, on its addition to the
subset, duplicate an existing subset (these are termed "permissible
markers"). Each time a new set of markers was defined, a set of
classifiers composed of one for each cancer study was trained using
these markers, and the global performance was measured via the mean
AUC across all 3 studies. To avoid potential over fitting, the AUC
for each cancer study model was calculated via a ten-fold cross
validation procedure. Every existing marker subset was extended by
every permissible marker from the list. Clearly, such a process
would eventually generate every possible subset, and the list would
run out of space. Therefore, all the generated marker sets were
kept only while the list was less than some predetermined size.
Once the list reached the predetermined size limit, it became
elitist; that is, only those classifier sets which showed a certain
level of performance were kept on the list, and the others fell off
the end of the list and were lost. This was achieved by keeping the
list sorted in order of classifier set performance; new marker sets
whose classifiers were globally at least as good as the worst set
of classifiers currently on the list were inserted, forcing the
expulsion of the current bottom underachieving classifier sets. One
further implementation detail is that the list was completely
replaced on each generational step; therefore, every marker set on
the list had the same number of markers, and at each step the
number of markers per classifier grew by one.
[0413] In one embodiment, the set (or panel) of biomarkers useful
for constructing classifiers for diagnosing general cancer from
non-cancer is based on the mean AUC for the particular combination
of biomarkers used in the classification scheme. We identified many
combinations of biomarkers derived from the markers in Table 19
that were able to effectively classify different cancer samples
from controls. Representative panels are set forth in Tables 22-29,
which set forth a series of 100 different panels of 3-10
biomarkers, which have the indicated mean cross validation (CV) AUC
for each panel. The total number of occurrences of each marker in
each of these panels is indicated at the bottom of each table.
[0414] The biomarkers selected in Table 19 gave rise to classifiers
that perform better than classifiers built with "non-markers." In
FIG. 17, we display the performance of our ten biomarker
classifiers compared to the performance of other possible
classifiers.
[0415] FIG. 17A shows the distribution of mean AUCs for classifiers
built from randomly sampled sets of ten "non-markers" taken from
the entire set of 10 present in all 3 studies, excluding the ten
markers in Table 19. The performance of the ten potential cancer
biomarkers is displayed as a vertical dashed line. This plot
clearly shows that the performance of the ten potential biomarkers
is well beyond the distribution of other marker combinations.
[0416] FIG. 17B displays a similar distribution as FIG. 17A,
however the randomly sampled sets were restricted to the 55
biomarkers from Table 1 that were not selected by the greedy
biomarker selection procedure for ten analyte classifiers. This
plot demonstrates that the ten markers chosen by the greedy
algorithm represent a subset of biomarkers that generalize to other
types of cancer far better than classifiers built with the
remaining 55 biomarkers.
[0417] Finally, FIG. 18 shows the classifier ROC curve for each of
the 3 cancer studies classifiers. The foregoing embodiments and
examples are intended only as examples. No particular embodiment,
example, or element of a particular embodiment or example is to be
construed as a critical, required, or essential element or feature
of any of the claims. Further, no element described herein is
required for the practice of the appended claims unless expressly
described as "essential" or "critical." Various alterations,
modifications, substitutions, and other variations can be made to
the disclosed embodiments without departing from the scope of the
present application, which is defined by the appended claims. The
specification, including the figures and examples, is to be
regarded in an illustrative manner, rather than a restrictive one,
and all such modifications and substitutions are intended to be
included within the scope of the application. Accordingly, the
scope of the application should be determined by the appended
claims and their legal equivalents, rather than by the examples
given above. For example, steps recited in any of the method or
process claims may be executed in any feasible order and are not
limited to an order presented in any of the embodiments, the
examples, or the claims. Further, in any of the aforementioned
methods, one or more biomarkers of Table 1 or Table 19 can be
specifically excluded either as an individual biomarker or as a
biomarker from any panel.
TABLE-US-00002 TABLE 1 Cancer Biomarkers Column #2 Column #1
Biomarker Designation Column #3 Column #4 Column #5 Column #6
Biomarker # Entrez Gene Symbol(s) Entrez Gene ID SwissProt ID
Public Name Direction 1 ACP5 54 P13686 TrATPase Up 2 ACY1 95 Q03154
Aminoacylase-1 Up 3 AHSG 197 P02765 .alpha.2-HS-Glycoprotein Down 4
ALPL 249 P05186 Alkaline phosphatase, Down bone 5 APOA1 335 P02647
Apo A-I Down 6 APOE 348 P02649 Apo E2 Up 7 BMP6 654 P22004 BMP-6 Up
8 C2 717 P06681 C2 Up 9 C5 727 P01031 C5 Up 10 C5 727 P01031 C5a Up
11 C5-C6 727; 729 P01031; P13671 C5b, 6 Complex Up 12 C9 735 P02748
C9 Up 13 CCL18 6362 P55774 MIP-4 Up 14 CCL23 6368 P55773 MPIF-1 Up
15 CCL23 6368 P55773 Ck-.beta.-8-1 Up 16 CDK5-CDK5R1 1020; 1775
Q00535; Q15078 CDK5/p35 Up 17 CKB-CKM- 1152; 1158 P12277; P06732
CK-MB Down 18 CKM 1158 P06732 CK-MM Down 19 CRP 1401 P02741 CRP Up
20 CSF1R 1436 P07333 M-CSF R Up 21 CTSB 1508 P07858 Cathepsin B Up
22 ENTPD1 953 P49961 CD39 Up 23 ESM1 11082 Q9NQ30 Endocan Up 24
ETHE1 23474 O95571 ETHE1 Up 25 FCGR3B 2215 O75015 FC.gamma.3B Up 26
FGFR3 2261 P22607 FGFR-3 Up 27 FSTL3 10272 O95633 FSTL3 Up 28 GDF11
10220 O95390 GDF-11 Down 29 GFRA1 2674 P56159 GFR .alpha.-1 Up 30
HAMP 57817 P81172 Hepcidin-25 Up 31 HINT1 3094 P49773 HINT1 Down 32
IDUA 3425 P35475 IDUA Up 33 IL11RA 3590 Q14626 IL-11 R.alpha. Down
34 IL12A-IL12B 3592; 3593 P29459; P29460 IL-12 Down 35 IL18R1 8809
Q13478 IL-18 R.alpha. Up 36 IL1RL1 9173 Q01638 IL-1 R4 Up 37 INSR
3643 P06213 IR Up 38 KIT 3815 P10721 SCF sR Down 39 KLK3-SERPINA3
354; 12 P07288; P01011 PSA-ACT Up 40 KLK7 5650 P49862 Kallikrein 7
Down 41 KLK8 11202 O60259 Kallikrein 8 Up 42 KLKB1 3818 P03952
Prekallikrein Down 43 LBP 3929 P18428 LBP Up 44 LTF 4057 P02788
Lactoferrin Down 45 MCM2 4171 P49736 MCM2 Up 46 MDK 4192 P21741
Midkine Up 47 MMP7 4316 P09237 MMP-7 Up 48 MRC1 4360 P22897
Macrophage mannose Up receptor 49 NID1 4811 P14543 Nidogen Up 50
NID2 22795 Q14112 Nidogen-2 Up 51 NRP1 8829 O14786 NRP1 Up 52 PLAT
5327 P00750 tPA Up 53 SERPINA5 5104 P05154 Protein C Inhibitor Down
54 SERPINF2 5345 P08697 .alpha.2-Antiplasmin Down 55 SFRP1 6422
Q8N474 FRP-1, soluble Up 56 SGTA 6449 O43765 SGT.alpha. Down 57
TFPI 7035 P10646 TFPI Up 58 THBS2 7058 P35442 Thrombospondin-2 Up
59 THBS4 7060 P35443 Thrombospondin-4 Down 60 TIMP1 7076 P01033
TIMP-1 Up 61 TNFRSF18 8784 Q9Y5U5 GITR/TNFRSF18 Down 62 TNFRSF1B
7133 P20333 TNF sR-II Up 63 TOP1 7150 P11387 Topoisomerase I Down
64 VEGFA 7422 P15692 VEGF Down 65 VEGFC 7424 P49767 VEGF-C Up
TABLE-US-00003 TABLE 2 Panels of 1 Biomarker Markers CV AUC 1 CTSB
0.780 2 C2 0.771 3 APOA1 0.754 4 C5 0.745 5 TFPI 0.739 6 C5a 0.724
7 TIMP1 0.720 8 FCGR3B 0.719 9 HAMP 0.718 10 CRP 0.717 11 NRP1
0.716 12 THBS2 0.715 13 MMP7 0.711 14 CCL18 0.709 15 CSF1R 0.705 16
ACP5 0.704 17 LBP 0.703 18 MRC1 0.699 19 PLAT 0.699 20 GFRA1 0.698
21 CCL23 0.696 22 KLK7 0.696 23 MDK 0.694 24 CKB-CKM 0.694 25
KLK3-SERPINA3 0.693 26 CKM 0.693 27 GDF11 0.692 28 IL11RA 0.690 29
IL1RL1 0.690 30 ETHE1 0.684 31 FSTL3 0.681 32 KIT 0.680 33 FGFR3
0.677 34 KLKB1 0.677 35 THBS4 0.669 36 ACY1 0.666 37 C5-C6 0.664 38
INSR 0.663 39 IL18R1 0.663 40 BMP6 0.663 41 TNFRSF1B 0.660 42 C9
0.657 43 SERPINA5 0.655 44 IL12A-IL12B 0.655 45 NID2 0.649 46 TOP1
0.647 47 NID1 0.642 48 CCL23 0.641 49 MCM2 0.641 50 AHSG 0.638 51
VEGFC 0.637 52 ENTPD1 0.637 53 HINT1 0.637 54 ALPL 0.635 55 LTF
0.632 56 ESM1 0.625 57 SERPINF2 0.624 58 CDK5-CDK5R1 0.623 59 SGTA
0.603 60 KLK8 0.597 61 IDUA 0.594 62 SFRP1 0.586 63 VEGFA 0.585 64
APOE 0.574 65 TNFRSF18 0.527
TABLE-US-00004 TABLE 3 Panels of 2 Biomarkers Markers CV AUC 1 C5
CTSB 0.848 2 C5a CTSB 0.841 3 CTSB ETHE1 0.833 4 CTSB HAMP 0.830 5
CTSB THBS4 0.830 6 KIT CTSB 0.829 7 C9 CTSB 0.828 8 CTSB KLK7 0.826
9 CTSB C2 0.821 10 C5 APOA1 0.820 11 CTSB CRP 0.818 12 CCL23 CTSB
0.817 13 C5-C6 CTSB 0.814 14 CTSB IL11RA 0.812 15 CCL18 CTSB 0.811
16 APOA1 CTSB 0.809 17 C5 CSF1R 0.808 18 GDF11 CTSB 0.807 19 C5 C2
0.806 20 C2 TFPI 0.806 21 C5 CCL18 0.804 22 C2 IL11RA 0.803 23
CCL23 CTSB 0.802 24 TIMP1 C5 0.801 25 CTSB LBP 0.799 26 CTSB TFPI
0.799 27 PLAT C5 0.799 28 CTSB AHSG 0.799 29 CCL18 C2 0.799 30 C5
MRC1 0.799 31 APOA1 C2 0.798 32 C5 FCGR3B 0.797 33 C5 TFPI 0.797 34
ALPL CTSB 0.796 35 C5 MMP7 0.796 36 CTSB KLK3-SERPINA3 0.795 37
MMP7 CTSB 0.795 38 MMP7 C2 0.794 39 TFPI HAMP 0.793 40 CCL18 ETHE1
0.793 41 C2 HAMP 0.792 42 PLAT C2 0.792 43 CTSB NRP1 0.792 44 LTF
CTSB 0.791 45 C5 ACP5 0.790 46 APOA1 TFPI 0.790 47 C5a TNFRSF1B
0.789 48 C5a CCL18 0.789 49 CKM CTSB 0.789 50 C5 THBS2 0.789 51 C2
CRP 0.788 52 C5a KLK7 0.788 53 C2 THBS4 0.788 54 CSF1R THBS4 0.788
55 C5a FSTL3 0.788 56 C5 NRP1 0.788 57 C5a C2 0.788 58 C5a TFPI
0.787 59 CTSB CKB-CKM 0.787 60 C5a NRP1 0.787 61 CSF1R APOA1 0.787
62 TFPI CRP 0.787 63 MMP7 KLK7 0.787 64 C5a FCGR3B 0.787 65 C2
ETHE1 0.786 66 CCL23 C2 0.786 67 PLAT CTSB 0.786 68 CCL18 TFPI
0.786 69 ACP5 CRP 0.785 70 C2 KLK7 0.785 71 C5 CCL23 0.784 72 MMP7
C5a 0.784 73 APOA1 KLK7 0.784 74 C5 GFRA1 0.784 75 C5 HAMP 0.784 76
C5 C5a 0.784 77 NRP1 CRP 0.783 78 KIT C2 0.783 79 C5 IL1RL1 0.783
80 APOA1 ETHE1 0.783 81 CTSB CDK5-CDK5R1 0.782 82 CSF1R CRP 0.782
83 TIMP1 CTSB 0.782 84 IL1RL1 CTSB 0.782 85 CSF1R C5a 0.782 86
TIMP1 C5a 0.781 87 TFPI KLK7 0.781 88 C5 KLKB1 0.781 89 CTSB FCGR3B
0.781 90 APOA1 MMP7 0.781 91 IL12A-IL12B CTSB 0.781 92 C5 MDK 0.780
93 MDK CTSB 0.780 94 C5 TNFRSF1B 0.780 95 C2 ACP5 0.780 96
IL12A-IL12B C2 0.780 97 NRP1 TFPI 0.780 98 C5 KIT 0.779 99 FCGR3B
ETHE1 0.779 100 C5-C6 C2 0.779
TABLE-US-00005 TABLE 4 Panels of 3 Biomarkers Markers CV AUC 1 C5
C5a CTSB 0.871 2 C5 CTSB ETHE1 0.870 3 C5 CTSB HAMP 0.866 4 C5
CCL18 CTSB 0.865 5 C5 KIT CTSB 0.862 6 C5 CTSB THBS4 0.861 7 KIT
C5a CTSB 0.861 8 C5a CCL18 CTSB 0.859 9 CTSB HAMP ETHE1 0.859 10
CTSB KLK7 HAMP 0.859 11 C5a CTSB KLK7 0.859 12 C9 C5 CTSB 0.859 13
C5-C6 C5a CTSB 0.858 14 C5a CTSB ETHE1 0.858 15 C5 ALPL CTSB 0.857
16 C5a CTSB HAMP 0.856 17 KIT CTSB HAMP 0.856 18 KIT CTSB ETHE1
0.854 19 C5 LTF CTSB 0.854 20 C5a CTSB THBS4 0.854 21 C5 CCL23 CTSB
0.854 22 C5 APOA1 CTSB 0.854 23 CCL18 CTSB ETHE1 0.853 24 C5 CTSB
IL11RA 0.853 25 C5 CTSB C2 0.852 26 C5a CTSB C2 0.852 27 CTSB THBS4
ETHE1 0.852 28 CTSB KLK7 ETHE1 0.851 29 C5-C6 CTSB HAMP 0.851 30
C5a CCL23 CTSB 0.850 31 C9 CTSB ETHE1 0.850 32 C5 CTSB KLK7 0.849
33 C5-C6 CTSB ETHE1 0.849 34 GDF11 C5a CTSB 0.849 35 CTSB THBS4
HAMP 0.848 36 CCL23 CTSB ETHE1 0.848 37 PLAT C5 CTSB 0.848 38 C5a
CTSB IL11RA 0.848 39 PLAT C5a CTSB 0.848 40 C9 KIT CTSB 0.847 41 C5
VEGFA CTSB 0.847 42 CTSB C2 HAMP 0.847 43 C5 CCL23 CTSB 0.847 44
CTSB C2 ETHE1 0.847 45 C5 CSF1R APOA1 0.846 46 CCL18 CTSB HAMP
0.846 47 CTSB C2 IL11RA 0.846 48 C5a CCL23 CTSB 0.846 49 GDF11 CTSB
HAMP 0.846 50 C9 CTSB THBS4 0.845 51 C5a CTSB CDK5-CDK5R1 0.845 52
APOA1 CTSB ETHE1 0.845 53 C9 C5a CTSB 0.845 54 C9 CCL18 CTSB 0.844
55 C5 CTSB TFPI 0.844 56 CCL18 CTSB THBS4 0.844 57 KIT CTSB THBS4
0.844 58 C9 CTSB KLK7 0.844 59 CTSB C2 THBS4 0.844 60 C5-C6 CCL18
CTSB 0.843 61 C5 CSF1R CTSB 0.843 62 C9 CTSB HAMP 0.843 63 C5 CTSB
ACP5 0.843 64 C5 CSF1R CCL18 0.842 65 C5-C6 KIT CTSB 0.842 66 C5
MMP7 CTSB 0.842 67 PLAT C9 CTSB 0.842 68 C5 CTSB NRP1 0.842 69 MMP7
C5a CTSB 0.842 70 C5 CTSB LBP 0.841 71 CSF1R C5a CTSB 0.841 72
C5-C6 C9 CTSB 0.841 73 C5 GDF11 CTSB 0.841 74 KIT CCL18 CTSB 0.840
75 CTSB THBS4 CRP 0.840 76 C5 CTSB AHSG 0.840 77 C9 CTSB C2 0.840
78 LTF C5a CTSB 0.840 79 C5a CTSB TFPI 0.840 80 C5a CTSB TNFRSF1B
0.839 81 ALPL C5a CTSB 0.839 82 C5a CTSB NRP1 0.839 83 APOA1 C5a
CTSB 0.839 84 CCL23 CTSB ETHE1 0.839 85 C5a CTSB FCGR3B 0.838 86
CTSB TFPI ETHE1 0.838 87 C5 KLK8 CTSB 0.838 88 C5-C6 C5 CTSB 0.838
89 C5a CTSB KLK3-SERPINA3 0.838 90 CTSB LBP ETHE1 0.838 91 CTSB
IL11RA ETHE1 0.838 92 CTSB HAMP IL11RA 0.838 93 ALPL CTSB KLK7
0.838 94 CCL18 CTSB KLK7 0.838 95 C5a CTSB INSR 0.838 96 CTSB C2
KLK7 0.838 97 CTSB AHSG ETHE1 0.838 98 C5-C6 CTSB THBS4 0.838 99 C5
CTSB KLKB1 0.837 100 C5a CTSB LBP 0.837
TABLE-US-00006 TABLE 5 Panels of 4 Biomarkers Markers CV AUC 1 C5
CCL18 CTSB ETHE1 0.882 2 C5 C5a CCL18 CTSB 0.880 3 C5 CTSB HAMP
ETHE1 0.880 4 C5 KIT C5a CTSB 0.880 5 C5 C5a CTSB ETHE1 0.880 6 C5
CTSB THBS4 ETHE1 0.878 7 C5 LTF CTSB ETHE1 0.877 8 C5 CSF1R C5a
CTSB 0.877 9 PLAT C5 C5a CTSB 0.876 10 C5 ALPL CTSB ETHE1 0.876 11
C5 KIT CTSB ETHE1 0.875 12 C5a CTSB KLK7 HAMP 0.875 13 C5 KIT CTSB
HAMP 0.875 14 C5 CCL23 CTSB ETHE1 0.875 15 C5a CTSB KLK7 ETHE1
0.875 16 PLAT C5 CTSB ETHE1 0.875 17 C5 C5a CTSB HAMP 0.874 18 CTSB
KLK7 HAMP ETHE1 0.874 19 C5 KIT CCL18 CTSB 0.874 20 C9 C5 CTSB
ETHE1 0.874 21 C5 CCL18 CTSB THBS4 0.874 22 C5-C6 CCL18 CTSB ETHE1
0.874 23 C5 LTF C5a CTSB 0.874 24 C5 ALPL C5a CTSB 0.873 25 KIT
CTSB HAMP ETHE1 0.873 26 C5 C5a CTSB KLK7 0.873 27 C5 C5a CTSB
THBS4 0.872 28 C5-C6 C5a CCL18 CTSB 0.872 29 C5 CTSB KLK7 HAMP
0.872 30 C5 C5a CTSB ACP5 0.872 31 C5 C5a CTSB IL11RA 0.872 32 C5
C5a CCL23 CTSB 0.872 33 C5 CCL18 CTSB HAMP 0.872 34 C5a CCL18 CTSB
KLK7 0.872 35 C5 KIT VEGFA CTSB 0.871 36 KIT C5a CCL18 CTSB 0.871
37 C5 APOA1 CTSB ETHE1 0.871 38 C5a CCL18 CTSB ETHE1 0.871 39 KIT
C5a CTSB ETHE1 0.871 40 PLAT C9 C5 CTSB 0.871 41 C5 ALPL CTSB THBS4
0.870 42 C5 ALPL CCL18 CTSB 0.870 43 C5 CSF1R CTSB ETHE1 0.870 44
C5 CTSB THBS4 HAMP 0.870 45 C5a CCL18 CTSB THBS4 0.870 46 C5 CTSB
KLK7 ETHE1 0.869 47 ALPL C5a CTSB KLK7 0.869 48 MMP7 C5a CTSB KLK7
0.869 49 C5 LTF CCL18 CTSB 0.869 50 C9 C5 KIT CTSB 0.869 51 C5-C6
CTSB HAMP ETHE1 0.869 52 C5 ALPL CTSB HAMP 0.869 53 LTF C5a CTSB
KLK7 0.869 54 C5-C6 KIT C5a CTSB 0.869 55 C5 ALPL CTSB IL11RA 0.868
56 KIT C5a CTSB HAMP 0.868 57 C9 C5 CCL18 CTSB 0.868 58 C5 LTF CTSB
THBS4 0.868 59 C5 CTSB ACP5 ETHE1 0.868 60 C5 CCL18 CTSB IL11RA
0.868 61 CCL18 CTSB HAMP ETHE1 0.868 62 PLAT KIT C5a CTSB 0.868 63
C5-C6 C5a CTSB ETHE1 0.868 64 C5 C5a CTSB C2 0.868 65 C9 C5 ALPL
CTSB 0.868 66 C5 CTSB IL11RA ETHE1 0.868 67 CCL18 CTSB KLK7 ETHE1
0.868 68 C5 CTSB C2 ETHE1 0.868 69 C5 KIT CTSB THBS4 0.867 70 CCL18
CTSB THBS4 ETHE1 0.867 71 CCL18 CTSB KLK7 HAMP 0.867 72 C5 CSF1R
CCL18 ETHE1 0.867 73 KIT CCL18 CTSB ETHE1 0.867 74 C5 C5a CTSB
CDK5-CDK5R1 0.867 75 C5 C5a CCL23 CTSB 0.867 76 C5 KIT ALPL CTSB
0.867 77 KIT CSF1R C5a CTSB 0.867 78 C5 KIT LTF CTSB 0.867 79 C5
LTF CTSB IL11RA 0.867 80 C9 C5 CSF1R CTSB 0.866 81 C5-C6 C5a CTSB
KLK7 0.866 82 C5 C5a CTSB INSR 0.866 83 C5a CCL23 CTSB KLK7 0.866
84 C5 GDF11 CTSB HAMP 0.866 85 C5 GDF11 C5a CTSB 0.866 86 C5 CSF1R
CTSB HAMP 0.866 87 C5 C5a CTSB TNFRSF1B 0.866 88 C5 CCL23 CTSB
ETHE1 0.866 89 C9 C5 LTF CTSB 0.866 90 C9 C5 CTSB HAMP 0.866 91 C9
C5 CTSB THBS4 0.866 92 C5 LTF CTSB HAMP 0.866 93 C5-C6 C5 C5a CTSB
0.865 94 C5 KLK8 C5a CTSB 0.865 95 C5 VEGFA CTSB ETHE1 0.865 96 C5a
CTSB HAMP ETHE1 0.865 97 C5 MMP7 C5a CTSB 0.865 98 C5 C5a CTSB ESM1
0.865 99 C5a CCL18 CTSB IL11RA 0.865 100 C5a CTSB C2 ETHE1
0.865
TABLE-US-00007 TABLE 6 Panels of 5 Biomarkers Markers CV AUC 1 C5
CSF1R C5a CTSB ETHE1 0.892 2 C5 C5a CCL18 CTSB ETHE1 0.889 3 C5
CCL18 CTSB THBS4 ETHE1 0.888 4 C5 CSF1R CCL18 CTSB ETHE1 0.887 5
PLAT C5 C5a CTSB ETHE1 0.886 6 C5 KIT CSF1R C5a CTSB 0.886 7 C5 KIT
CCL18 CTSB ETHE1 0.886 8 C5 KIT C5a CCL18 CTSB 0.886 9 C5 LTF CCL18
CTSB ETHE1 0.886 10 C5 KIT C5a CTSB ETHE1 0.885 11 C5a CCL18 CTSB
KLK7 ETHE1 0.885 12 C5 CSF1R C5a CCL18 CTSB 0.885 13 C5 C5a CCL18
CTSB THBS4 0.885 14 C5 ALPL CCL18 CTSB ETHE1 0.884 15 C5 C5a CTSB
KLK7 ETHE1 0.884 16 C5 ALPL CTSB THBS4 ETHE1 0.884 17 C5a CTSB KLK7
HAMP ETHE1 0.884 18 C5 CTSB KLK7 HAMP ETHE1 0.884 19 C5 CCL18 CTSB
HAMP ETHE1 0.884 20 C5 KIT CTSB HAMP ETHE1 0.884 21 C5 CSF1R C5a
CTSB THBS4 0.884 22 C5 LTF CTSB THBS4 ETHE1 0.884 23 C5 ALPL C5a
CTSB ETHE1 0.883 24 CCL18 CTSB KLK7 HAMP ETHE1 0.883 25 C5 KIT LTF
C5a CTSB 0.883 26 PLAT C5 KIT C5a CTSB 0.883 27 PLAT C9 C5 CTSB
ETHE1 0.883 28 C5 LTF C5a CTSB ETHE1 0.883 29 C5 CSF1R CTSB HAMP
ETHE1 0.883 30 C9 C5 CSF1R CTSB ETHE1 0.882 31 PLAT C5 CTSB THBS4
ETHE1 0.882 32 C5 C5a CCL23 CTSB ETHE1 0.882 33 C5 C5a CCL18 CTSB
KLK7 0.882 34 C5-C6 C5a CCL18 CTSB ETHE1 0.882 35 C5 CSF1R CTSB
THBS4 ETHE1 0.882 36 C5 C5a CTSB KLK7 HAMP 0.882 37 C5 LTF C5a
CCL18 CTSB 0.882 38 C5 KIT ALPL C5a CTSB 0.882 39 C5 CCL23 CCL18
CTSB ETHE1 0.881 40 PLAT C5 CCL18 CTSB ETHE1 0.881 41 C5 C5a CTSB
THBS4 ETHE1 0.881 42 C5 KLK8 CCL18 CTSB ETHE1 0.881 43 C5 C5a CCL18
CTSB IL11RA 0.881 44 C5 KIT ALPL CCL18 CTSB 0.881 45 PLAT C5 C5a
CCL18 CTSB 0.881 46 C5 CCL18 CTSB IL11RA ETHE1 0.881 47 C5 C5a CTSB
INSR ETHE1 0.881 48 C5 KIT C5a CTSB HAMP 0.881 49 C5 C5a CTSB ACP5
ETHE1 0.881 50 C5-C6 CCL18 CTSB THBS4 ETHE1 0.880 51 C5 LTF C5a
CTSB KLK7 0.880 52 C5 C5a CTSB HAMP ETHE1 0.880 53 C5 KIT ALPL CTSB
ETHE1 0.880 54 C5 KIT LTF CTSB ETHE1 0.880 55 C5 ALPL C5a CTSB KLK7
0.880 56 C5-C6 KIT C5a CCL18 CTSB 0.880 57 C5 CTSB THBS4 HAMP ETHE1
0.880 58 C5a CCL18 CTSB KLK7 HAMP 0.880 59 C5 CCL18 CTSB KLK7 ETHE1
0.880 60 C5 CCL18 CTSB ACP5 ETHE1 0.880 61 C5 KIT LTF CCL18 CTSB
0.880 62 MMP7 C5a CTSB KLK7 HAMP 0.880 63 C5 KIT C5a CCL23 CTSB
0.880 64 C9 C5 CCL18 CTSB ETHE1 0.880 65 C5 CSF1R C5a CTSB HAMP
0.880 66 C5 ALPL C5a CCL18 CTSB 0.880 67 C5 C5a CCL18 CTSB HAMP
0.880 68 C5 CSF1R C5a CTSB IL11RA 0.880 69 C5a CCL23 CTSB KLK7
ETHE1 0.880 70 C5-C6 KIT CCL18 CTSB ETHE1 0.879 71 C5 CSF1R CCL23
CTSB ETHE1 0.879 72 C5 LTF CCL23 CTSB ETHE1 0.879 73 C5 KLK8 C5a
CCL18 CTSB 0.879 74 LTF C5a CCL18 CTSB KLK7 0.879 75 PLAT C5 CCL23
CTSB ETHE1 0.879 76 C5 KIT CSF1R CTSB ETHE1 0.879 77 ALPL C5a CCL18
CTSB KLK7 0.879 78 KIT C5a CCL18 CTSB ETHE1 0.879 79 CSF1R C5a
CCL18 CTSB ETHE1 0.879 80 C5 KIT C5a CTSB THBS4 0.879 81 C5 VEGFA
CCL18 CTSB ETHE1 0.879 82 C5 CSF1R C5a CTSB KLK7 0.879 83 CSF1R C5a
CTSB KLK7 ETHE1 0.879 84 C5-C6 C5a CTSB KLK7 ETHE1 0.879 85 MMP7
C5a CCL18 CTSB KLK7 0.879 86 C5-C6 C5 CCL18 CTSB ETHE1 0.879 87 C5
ALPL CTSB HAMP ETHE1 0.879 88 C5 KIT VEGFA CTSB ETHE1 0.879 89 C5
CCL18 CTSB C2 ETHE1 0.879 90 C5 KIT CCL18 CTSB HAMP 0.879 91 C5
CCL23 CTSB THBS4 ETHE1 0.879 92 C5 ALPL C5a CTSB THBS4 0.879 93 C5
VEGFA CTSB THBS4 ETHE1 0.879 94 C5 LTF CCL18 CTSB THBS4 0.879 95 C5
LTF CTSB IL11RA ETHE1 0.878 96 MMP7 C5a CTSB KLK7 ETHE1 0.878 97 C5
KIT VEGFA CCL18 CTSB 0.878 98 C5 ALPL CCL18 CTSB THBS4 0.878 99
C5-C6 C5a CCL18 CTSB KLK7 0.878 100 C5 APOA1 CCL18 CTSB ETHE1
0.878
TABLE-US-00008 TABLE 7 Panels of 6 Biomarkers Markers CV AUC 1 C5
CSF1R C5a CCL18 CTSB 0.898 ETHE1 2 C5 CSF1R C5a CTSB THBS4 0.896
ETHE1 3 C5 KIT CSF1R C5a CTSB 0.895 ETHE1 4 C5 KIT C5a CCL18 CTSB
0.894 ETHE1 5 PLAT C5 CSF1R C5a CTSB 0.893 ETHE1 6 C5 ALPL CCL18
CTSB THBS4 0.893 ETHE1 7 C5 CSF1R CCL18 CTSB THBS4 0.892 ETHE1 8 C5
CSF1R C5a CTSB KLK7 0.892 ETHE1 9 C5 LTF CCL18 CTSB THBS4 0.892
ETHE1 10 C5 C5a CCL18 CTSB KLK7 0.892 ETHE1 11 C5 CCL18 CTSB KLK7
HAMP 0.892 ETHE1 12 C5 LTF C5a CCL18 CTSB 0.892 ETHE1 13 PLAT C5
C5a CCL18 CTSB 0.891 ETHE1 14 C5 KIT CSF1R C5a CCL18 0.891 CTSB 15
C5 CSF1R C5a CCL18 CTSB 0.891 THBS4 16 PLAT C5 KIT C5a CTSB 0.891
ETHE1 17 C5 KIT LTF CCL18 CTSB 0.891 ETHE1 18 C5-C6 C5a CCL18 CTSB
KLK7 0.890 ETHE1 19 C5 CSF1R C5a CCL23 CTSB 0.890 ETHE1 20 C5 ALPL
C5a CCL18 CTSB 0.890 ETHE1 21 C5 LTF CCL18 CTSB KLK7 0.890 ETHE1 22
C5 KIT CCL18 CTSB THBS4 0.890 ETHE1 23 C5 C5a CCL18 CTSB THBS4
0.890 ETHE1 24 C5 KIT CCL18 CTSB HAMP 0.890 ETHE1 25 C5 CSF1R C5a
CTSB IL11RA 0.890 ETHE1 26 C5 LTF C5a CCL18 CTSB 0.890 KLK7 27 C5
KIT CSF1R CCL18 CTSB 0.890 ETHE1 28 C5 KIT VEGFA CCL18 CTSB 0.889
ETHE1 29 C5 KIT ALPL CCL18 CTSB 0.889 ETHE1 30 CSF1R C5a CTSB KLK7
HAMP 0.889 ETHE1 31 PLAT C5 C5a CTSB KLK7 0.889 ETHE1 32 C5 KIT LTF
C5a CCL18 0.889 CTSB 33 C5 C5a CTSB KLK7 HAMP 0.889 ETHE1 34 PLAT
C9 C5 CSF1R CTSB 0.889 ETHE1 35 C5 KIT LTF C5a CTSB 0.889 ETHE1 36
C5 LTF CCL18 CTSB IL11RA 0.889 ETHE1 37 LTF C5a CCL18 CTSB KLK7
0.889 ETHE1 38 C5 CSF1R CTSB KLK7 HAMP 0.889 ETHE1 39 C5-C6 CSF1R
C5a CCL18 CTSB 0.888 ETHE1 40 C5 KIT CSF1R C5a CTSB 0.888 THBS4 41
C5 KIT C5a CCL18 CTSB 0.888 THBS4 42 C5 KIT ALPL C5a CTSB 0.888
ETHE1 43 C5 CSF1R C5a CCL23 CTSB 0.888 ETHE1 44 C5a CCL18 CTSB KLK7
HAMP 0.888 ETHE1 45 C5 ALPL CTSB KLK7 HAMP 0.888 ETHE1 46 C5 ALPL
C5a CCL18 CTSB 0.888 KLK7 47 C5 ALPL CCL18 CTSB KLK7 0.888 ETHE1 48
C5 ALPL C5a CTSB KLK7 0.888 ETHE1 49 C5 ALPL CCL18 CTSB IL11RA
0.888 ETHE1 50 C5 KIT CSF1R CTSB HAMP 0.888 ETHE1 51 C5 CSF1R CCL23
CCL18 CTSB 0.888 ETHE1 52 CSF1R C5a CCL18 CTSB THBS4 0.888 ETHE1 53
C5 CSF1R C5a CCL18 CTSB 0.888 IL11RA 54 C5 C5a CCL18 CTSB ACP5
0.888 ETHE1 55 CSF1R CCL18 CTSB KLK7 HAMP 0.888 ETHE1 56 C9 C5
CSF1R CCL18 CTSB 0.888 ETHE1 57 PLAT C5 C5a CCL23 CTSB 0.888 ETHE1
58 PLAT C5 KIT CSF1R C5a 0.888 CTSB 59 C5 LTF C5a CTSB KLK7 0.888
ETHE1 60 C5-C6 CCL18 CTSB KLK7 HAMP 0.888 ETHE1 61 C5 CSF1R C5a
CTSB ACP5 0.888 ETHE1 62 C5 CSF1R C5a CTSB KLK7 0.888 HAMP 63 C5
LTF C5a CCL18 CTSB 0.888 IL11RA 64 C5 CSF1R ALPL C5a CTSB 0.888
ETHE1 65 C5 CSF1R C5a CTSB HAMP 0.888 ETHE1 66 C5 C5a CCL23 CTSB
KLK7 0.888 ETHE1 67 CSF1R C5a CCL18 CTSB KLK7 0.888 ETHE1 68 C5 KIT
KLK8 CCL18 CTSB 0.888 ETHE1 69 C5 C5a CCL18 CTSB INSR 0.887 ETHE1
70 C5 LTF CTSB KLK7 HAMP 0.887 ETHE1 71 C5 C5a CCL18 CTSB HAMP
0.887 ETHE1 72 C5-C6 KIT CSF1R C5a CTSB 0.887 ETHE1 73 C5-C6 KIT
C5a CCL18 CTSB 0.887 ETHE1 74 ALPL C5a CCL18 CTSB KLK7 0.887 ETHE1
75 C5 CSF1R CCL18 CTSB IL11RA 0.887 ETHE1 76 C5 C5a CCL18 CTSB
IL11RA 0.887 ETHE1 77 C5-C6 C5a CCL18 CTSB THBS4 0.887 ETHE1 78 C5a
CCL18 CTSB KLK7 INSR 0.887 ETHE1 79 C9 C5 CSF1R CTSB THBS4 0.887
ETHE1 80 C5 CSF1R LTF C5a CTSB 0.887 ETHE1 81 C5 ALPL C5a CCL18
CTSB 0.887 THBS4 82 C5 KIT C5a CCL23 CTSB 0.887 ETHE1 83 C5 C5a
CCL18 CTSB KLK7 0.887 HAMP 84 MMP7 C5a CTSB KLK7 HAMP 0.887 ETHE1
85 C5 C5a CTSB ACP5 KLK7 0.887 ETHE1 86 C5 MMP7 C5a CTSB KLK7 0.887
ETHE1 87 C5 CCL18 CTSB THBS4 HAMP 0.887 ETHE1 88 C5-C6 LTF C5a
CCL18 CTSB 0.887 KLK7 89 C5 CSF1R ALPL CTSB THBS4 0.887 ETHE1 90 C5
CSF1R CCL18 CTSB HAMP 0.887 ETHE1 91 C5 CSF1R C5a CCL18 CTSB 0.887
KLK7 92 C5-C6 KIT CCL18 CTSB HAMP 0.887 ETHE1 93 C5 KIT C5a CTSB
ACP5 0.887 ETHE1 94 PLAT C5 C5a CTSB THBS4 0.887 ETHE1 95 C5 LTF
C5a CCL18 CTSB 0.887 THBS4 96 C5 KIT ALPL C5a CCL18 0.886 CTSB 97
KLK8 C5a CCL18 CTSB KLK7 0.886 ETHE1 98 C5 VEGFA CCL18 CTSB THBS4
0.886 ETHE1 99 C5 KIT C5a CTSB HAMP 0.886 ETHE1 100 C5 LTF C5a
CCL23 CTSB 0.886 ETHE1
TABLE-US-00009 TABLE 8 Panels of 7 Biomarkers Markers CV AUC 1 C5
CSF1R C5a CCL18 CTSB 0.900 THBS4 ETHE1 2 C5 KIT CSF1R C5a CCL18
0.900 CTSB ETHE1 3 PLAT C5 KIT CSF1R C5a 0.899 CTSB ETHE1 4 C5
CSF1R C5a CCL18 CTSB 0.898 IL11RA ETHE1 5 C5 CSF1R C5a CCL18 CTSB
0.898 KLK7 ETHE1 6 C5 LTF C5a CCL18 CTSB 0.897 KLK7 ETHE1 7 C5-C6
CSF1R C5a CCL18 CTSB 0.897 THBS4 ETHE1 8 C5 CSF1R C5a CTSB KLK7
0.896 HAMP ETHE1 9 C5 KIT CSF1R C5a CTSB 0.896 THBS4 ETHE1 10 C5
ALPL C5a CCL18 CTSB 0.896 KLK7 ETHE1 11 C5 KIT VEGFA CSF1R CCL18
0.896 CTSB ETHE1 12 PLAT C5 CSF1R C5a CTSB 0.896 THBS4 ETHE1 13 C5
CSF1R ALPL CCL18 CTSB 0.895 THBS4 ETHE1 14 PLAT C5 CSF1R C5a CCL18
0.895 CTSB ETHE1 15 C5 KIT CSF1R C5a CCL18 0.895 CTSB THBS4 16 C5
CSF1R C5a CCL23 CTSB 0.895 THBS4 ETHE1 17 C5 KIT VEGFA CSF1R C5a
0.895 CTSB ETHE1 18 C5 VEGFA CSF1R CCL18 CTSB 0.895 THBS4 ETHE1 19
C5 LTF C5a CCL23 CTSB 0.895 KLK7 ETHE1 20 C5 CSF1R KLK8 C5a CCL18
0.894 CTSB ETHE1 21 C5-C6 KIT CSF1R C5a CCL18 0.894 CTSB ETHE1 22
C5-C6 C5 CSF1R C5a CCL18 0.894 CTSB ETHE1 23 C5 KIT LTF C5a CCL18
0.894 CTSB ETHE1 24 C5-C6 CSF1R C5a CCL18 CTSB 0.894 KLK7 ETHE1 25
C5 KIT CSF1R LTF C5a 0.894 CTSB ETHE1 26 PLAT C5 KIT C5a CCL18
0.894 CTSB ETHE1 27 C5 KIT CSF1R ALPL C5a 0.894 CTSB ETHE1 28 C5
KLK8 C5a CCL18 CTSB 0.894 KLK7 ETHE1 29 C5 CSF1R ALPL C5a CCL18
0.894 CTSB ETHE1 30 C5 CSF1R LTF C5a CCL18 0.894 CTSB ETHE1 31
C5-C6 LTF C5a CCL18 CTSB 0.894 KLK7 ETHE1 32 C5 CSF1R C5a CCL18
CTSB 0.894 CDK5- ETHE1 CDK5R1 33 PLAT C5 CSF1R C5a CCL23 0.894 CTSB
ETHE1 34 C5 C5a CCL18 CTSB KLK7 0.894 HAMP ETHE1 35 C5 CSF1R CCL18
CTSB KLK7 0.894 HAMP ETHE1 36 C5 KIT KLK8 C5a CCL18 0.894 CTSB
ETHE1 37 C5 KIT CSF1R C5a CCL23 0.894 CTSB ETHE1 38 C5 ALPL C5a
CCL18 CTSB 0.894 THBS4 ETHE1 39 C5 C5a CCL23 CCL18 CTSB 0.894 KLK7
ETHE1 40 C5 KIT CSF1R CCL18 CTSB 0.894 HAMP ETHE1 41 PLAT C5 CSF1R
C5a CTSB 0.894 KLK7 ETHE1 42 C5 KIT CSF1R CCL18 CTSB 0.894 THBS4
ETHE1 43 C5 KIT C5a CCL18 CTSB 0.894 THBS4 ETHE1 44 C5 KIT LTF
CCL18 CTSB 0.894 THBS4 ETHE1 45 C5 CSF1R MMP7 C5a CTSB 0.894 KLK7
ETHE1 46 C5 CSF1R LTF CCL18 CTSB 0.894 THBS4 ETHE1 47 C5 KIT CSF1R
C5a CTSB 0.894 ACP5 ETHE1 48 C5 KIT ALPL C5a CCL18 0.894 CTSB ETHE1
49 C5 C5a CCL18 CTSB ACP5 0.893 KLK7 ETHE1 50 C5 CSF1R ALPL C5a
CTSB 0.893 KLK7 ETHE1 51 C5 KIT ALPL CCL18 CTSB 0.893 THBS4 ETHE1
52 C5-C6 CSF1R C5a CTSB KLK7 0.893 HAMP ETHE1 53 C5 CSF1R LTF C5a
CTSB 0.893 KLK7 ETHE1 54 C5 CSF1R ALPL C5a CTSB 0.893 THBS4 ETHE1
55 C5 CSF1R C5a CCL18 CTSB 0.893 ACP5 ETHE1 56 C5 CSF1R C5a CCL18
CTSB 0.893 TFPI ETHE1 57 C5 ALPL CCL18 CTSB KLK7 0.893 HAMP ETHE1
58 C5 CSF1R C5a CTSB ACP5 0.893 KLK7 ETHE1 59 PLAT C9 C5 CSF1R CTSB
0.893 THBS4 ETHE1 60 C5 KIT LTF C5a CCL23 0.893 CTSB ETHE1 61 C5
CSF1R C5a CCL18 CTSB 0.893 FGFR3 ETHE1 62 PLAT C5 C5a CCL18 CTSB
0.893 KLK7 ETHE1 63 PLAT C5 CSF1R C5a CTSB 0.893 IL11RA ETHE1 64 C5
CSF1R C5a CCL23 CCL18 0.893 CTSB ETHE1 65 C5 KIT CSF1R C5a CTSB
0.893 HAMP ETHE1 66 C5 CSF1R ALPL C5a CCL18 0.893 CTSB KLK7 67 C5
LTF CCL18 CTSB KLK7 0.893 HAMP ETHE1 68 C5 KIT CSF1R LTF CCL18
0.893 CTSB ETHE1 69 PLAT C5 CSF1R C5a CCL23 0.893 CTSB ETHE1 70 C5
KIT CSF1R ALPL C5a 0.893 CCL18 CTSB 71 CSF1R MMP7 C5a CTSB KLK7
0.893 HAMP ETHE1 72 C5 CSF1R C5a CCL23 CTSB 0.892 KLK7 ETHE1 73 C5
CSF1R ALPL CCL18 CTSB 0.892 IL11RA ETHE1 74 C5 MMP7 C5a CCL18 CTSB
0.892 KLK7 ETHE1 75 PLAT C9 C5 CSF1R C5a 0.892 CTSB ETHE1 76 C5 KIT
CSF1R LTF C5a 0.892 CCL18 CTSB 77 C5 KIT CSF1R MMP7 C5a 0.892 CTSB
ETHE1 78 C5 CSF1R LTF C5a CCL18 0.892 CTSB KLK7 79 C9 C5 CSF1R
CCL18 CTSB 0.892 THBS4 ETHE1 80 C5 CSF1R GDF11 C5a CCL18 0.892 CTSB
ETHE1 81 C5 LTF C5a CCL18 CTSB 0.892 THBS4 ETHE1 82 PLAT C9 C5
CSF1R CCL18 0.892 CTSB ETHE1 83 C5 KIT VEGFA KLK8 CCL18 0.892 CTSB
ETHE1 84 C5 CSF1R LTF C5a CTSB 0.892 THBS4 ETHE1 85 CSF1R C5a CCL18
CTSB KLK7 0.892 HAMP ETHE1 86 C5 CSF1R LTF C5a CCL23 0.892 CTSB
ETHE1 87 C5-C6 ALPL C5a CCL18 CTSB 0.892 KLK7 ETHE1 88 KIT CSF1R
C5a CCL18 CTSB 0.892 THBS4 ETHE1 89 C5 CSF1R ALPL C5a CCL18 0.892
CTSB THBS4 90 C5 C5a CCL18 CTSB KLK7 0.892 INSR ETHE1 91 C5 CSF1R
LTF C5a CCL18 0.892 CTSB IL11RA 92 C5 CSF1R C5a CCL18 CTSB 0.892
ESM1 ETHE1 93 C5-C6 C5a CCL18 CTSB KLK7 0.892 HAMP ETHE1 94 PLAT C5
C5a CCL18 CTSB 0.892 THBS4 ETHE1 95 C5 ALPL C5a CTSB KLK7 0.892
HAMP ETHE1 96 C5 LTF C5a CCL18 CTSB 0.892 IL11RA ETHE1 97 C5 VEGFA
CSF1R C5a CCL18 0.892 CTSB ETHE1 98 C5 LTF C5a CTSB KLK7 0.892 HAMP
ETHE1 99 C5 ALPL C5a CCL23 CTSB 0.892 KLK7 ETHE1 100 C5 KIT LTF C5a
CCL18 0.892 CTSB THBS4
TABLE-US-00010 TABLE 9 Panels of 8 Biomarkers Markers CV AUC 1 C5
KIT CSF1R C5a CCL18 0.902 CTSB THBS4 ETHE1 2 C5 CSF1R LTF C5a CCL18
0.902 CTSB KLK7 ETHE1 3 PLAT C5 KIT CSF1R C5a 0.901 CCL18 CTSB
ETHE1 4 C5 KIT CSF1R LTF C5a 0.901 CCL18 CTSB ETHE1 5 C5 CSF1R ALPL
C5a CCL18 0.901 CTSB KLK7 ETHE1 6 C5-C6 VEGFA CSF1R C5a CCL18 0.900
CTSB KLK7 ETHE1 7 C5-C6 KIT CSF1R C5a CCL18 0.900 CTSB THBS4 ETHE1
8 C5 VEGFA CSF1R C5a CCL18 0.899 CTSB KLK7 ETHE1 9 C5 KIT CSF1R
ALPL C5a 0.899 CCL18 CTSB ETHE1 10 C5 CSF1R C5a CCL18 CTSB 0.899
KLK7 HAMP ETHE1 11 C5 KIT VEGFA CSF1R CCL18 0.899 CTSB THBS4 ETHE1
12 C5 KIT CSF1R KLK8 C5a 0.899 CCL18 CTSB ETHE1 13 C5 CSF1R LTF C5a
CCL18 0.899 CTSB THBS4 ETHE1 14 C5 KIT VEGFA CSF1R C5a 0.899 CCL18
CTSB ETHE1 15 C5 CSF1R ALPL C5a CCL18 0.899 CTSB THBS4 ETHE1 16
C5-C6 C5 CSF1R C5a CCL18 0.899 CTSB THBS4 ETHE1 17 C5-C6 CSF1R LTF
C5a CCL18 0.899 CTSB KLK7 ETHE1 18 C5 KIT VEGFA CSF1R KLK8 0.898
CCL18 CTSB ETHE1 19 PLAT C5 CSF1R C5a CCL18 0.898 CTSB THBS4 ETHE1
20 C5 CSF1R ALPL C5a CCL18 0.898 CTSB IL11RA ETHE1 21 C5 LTF C5a
CCL23 CCL18 0.898 CTSB KLK7 ETHE1 22 C5-C6 C5 KIT CSF1R C5a 0.898
CCL18 CTSB ETHE1 23 C5 CSF1R LTF C5a CCL18 0.898 CTSB IL11RA ETHE1
24 C5 CSF1R MMP7 C5a CCL18 0.898 CTSB KLK7 ETHE1 25 C5 KIT CSF1R
LTF C5a 0.898 CCL23 CTSB ETHE1 26 C5 ALPL KLK8 C5a CCL18 0.898 CTSB
KLK7 ETHE1 27 C5 LTF KLK8 C5a CCL18 0.898 CTSB KLK7 ETHE1 28 PLAT
C5 KIT CSF1R C5a 0.898 CCL23 CTSB ETHE1 29 C5 KIT CSF1R C5a CCL18
0.898 CTSB ACP5 ETHE1 30 C5 KIT CSF1R C5a CCL23 0.898 CCL18 CTSB
ETHE1 31 C5-C6 CSF1R C5a CCL18 CTSB 0.898 KLK7 HAMP ETHE1 32 C5
CSF1R KLK8 C5a CCL18 0.898 CTSB KLK7 ETHE1 33 PLAT C5 KIT CSF1R C5a
0.898 CTSB THBS4 ETHE1 34 C5 KIT CSF1R ALPL CCL18 0.898 CTSB THBS4
ETHE1 35 C5 KIT CSF1R LTF CCL18 0.898 CTSB THBS4 ETHE1 36 C5 CSF1R
C5a CCL23 CCL18 0.898 CTSB THBS4 ETHE1 37 C5 CSF1R LTF C5a CCL23
0.898 CTSB KLK7 ETHE1 38 C5 CSF1R C5a CCL18 CTSB 0.898 THBS4 KLK7
ETHE1 39 C5 CSF1R KLK8 C5a CCL18 0.898 CTSB THBS4 ETHE1 40 C5 VEGFA
CSF1R C5a CCL18 0.898 CTSB THBS4 ETHE1 41 C5-C6 CSF1R MMP7 C5a
CCL18 0.897 CTSB KLK7 ETHE1 42 PLAT C5 CSF1R C5a CCL18 0.897 CTSB
KLK7 ETHE1 43 C5 KIT CSF1R C5a CCL18 0.897 CTSB TFPI ETHE1 44 C5
CSF1R GDF11 LTF C5a 0.897 CCL18 CTSB ETHE1 45 C5 LTF C5a CCL18 CTSB
0.897 THBS4 KLK7 ETHE1 46 C5 KIT CSF1R C5a CTSB 0.897 KLK7 HAMP
ETHE1 47 C5 KIT CSF1R LTF C5a 0.897 CTSB THBS4 ETHE1 48 PLAT C5
CSF1R C5a CCL18 0.897 CTSB IL11RA ETHE1 49 C5 LTF MMP7 C5a CCL18
0.897 CTSB KLK7 ETHE1 50 C5-C6 KIT VEGFA CSF1R C5a 0.897 CCL18 CTSB
ETHE1 51 C5 KIT CSF1R ALPL C5a 0.897 CTSB THBS4 ETHE1 52 C5
IL12A-IL12B CSF1R C5a CCL18 0.896 CTSB THBS4 ETHE1 53 C5 CSF1R C5a
CCL23 CCL18 0.896 CTSB KLK7 ETHE1 54 PLAT C5 CSF1R MMP7 C5a 0.896
CTSB KLK7 ETHE1 55 C5 CSF1R MMP7 C5a CTSB 0.896 KLK7 HAMP ETHE1 56
C5 KIT LTF C5a CCL18 0.896 CTSB KLK7 ETHE1 57 C5 KIT CSF1R C5a
CCL18 0.896 CTSB IL11RA ETHE1 58 C5 VEGFA CSF1R MMP7 C5a 0.896 CTSB
KLK7 ETHE1 59 C5 CSF1R LTF C5a CCL18 0.896 CTSB THBS4 KLK7 60 C5
CSF1R C5a CCL18 CTSB 0.896 THBS4 INSR ETHE1 61 C5-C6 KIT CSF1R C5a
CCL23 0.896 CCL18 CTSB ETHE1 62 C5 KIT C5a CCL18 CTSB 0.896 KLK7
HAMP ETHE1 63 C5 LTF C5a CCL18 CTSB 0.896 KLK7 INSR ETHE1 64 C5 KIT
CSF1R LTF C5a 0.896 CCL18 CTSB THBS4 65 C5 CSF1R LTF C5a CCL23
0.896 CTSB THBS4 ETHE1 66 C5-C6 CSF1R ALPL C5a CCL18 0.896 CTSB
KLK7 ETHE1 67 C5 VEGFA CSF1R C5a CCL18 0.896 CTSB IL11RA ETHE1 68
C5 CSF1R C5a CCL18 CTSB 0.896 ACP5 KLK7 ETHE1 69 PLAT C9 C5 CSF1R
CCL18 0.896 CTSB THBS4 ETHE1 70 PLAT C5 KIT CSF1R ALPL 0.896 C5a
CTSB ETHE1 71 C5 KIT LTF C5a CCL18 0.896 CTSB THBS4 ETHE1 72 C5-C6
LTF C5a CCL23 CCL18 0.896 CTSB KLK7 ETHE1 73 C5 KIT CSF1R CCL18
CTSB 0.896 THBS4 HAMP ETHE1 74 PLAT C5 CSF1R C5a CTSB 0.896 KLK7
HAMP ETHE1 75 C5 LTF C5a CCL18 CTSB 0.896 KLK7 HAMP ETHE1 76 C5 KIT
CSF1R LTF C5a 0.896 CCL18 CTSB IL11RA 77 PLAT C5 KIT VEGFA CSF1R
0.896 C5a CTSB ETHE1 78 C5 KIT ALPL C5a CCL18 0.896 CTSB THBS4
ETHE1 79 KIT CSF1R C5a CCL18 CTSB 0.896 KLK7 HAMP ETHE1 80 C5 ALPL
C5a CCL23 CCL18 0.896 CTSB KLK7 ETHE1 81 C5 CSF1R C5a CCL18 CTSB
0.896 THBS4 ESM1 ETHE1 82 C5 CSF1R C5a CCL18 CTSB 0.896 TFPI THBS4
ETHE1 83 C5 KIT KLK8 C5a CCL18 0.896 CTSB THBS4 ETHE1 84 C5-C6
CSF1R C5a CCL18 CTSB 0.896 THBS4 KLK7 ETHE1 85 PLAT C5 CSF1R C5a
CCL23 0.896 CTSB KLK7 ETHE1 86 PLAT C5 KIT CSF1R KLK8 0.896 C5a
CTSB ETHE1 87 C5 CSF1R C5a CCL18 CTSB 0.896 TFPI KLK7 ETHE1 88 C5
KIT CSF1R C5a CCL23 0.896 CTSB THBS4 ETHE1 89 C5 VEGFA CSF1R C5a
CCL23 0.896 CTSB KLK7 ETHE1 90 PLAT C5 ALPL C5a CCL18 0.896 CTSB
KLK7 ETHE1 91 C5 KIT CSF1R ALPL C5a 0.896 CCL18 CTSB THBS4 92 PLAT
C5 LTF C5a CCL18 0.896 CTSB KLK7 ETHE1 93 PLAT C5 KIT CSF1R C5a
0.896 CCL23 CTSB ETHE1 94 PLAT C9 C5 KIT CSF1R 0.896 CCL18 CTSB
ETHE1 95 C5 CSF1R LTF C5a CTSB 0.895 KLK7 HAMP ETHE1 96 C5-C6 KIT
VEGFA CSF1R CCL18 0.895 CTSB THBS4 ETHE1 97 C5 ALPL C5a CCL18 CTSB
0.895 THBS4 KLK7 ETHE1 98 CSF1R LTF C5a CCL23 CCL18 0.895 CTSB KLK7
ETHE1 99 C5 KIT VEGFA CSF1R C5a 0.895 CTSB THBS4 ETHE1 100 C5 CSF1R
C5a CCL18 CTSB 0.895 THBS4 CDK5- ETHE1 CDK5R1
TABLE-US-00011 TABLE 10 Panels of 9 Biomarkers Markers CV AUC 1 C5
CSF1R LTF C5a CCL23 0.903 CCL18 CTSB KLK7 ETHE1 2 C5 VEGFA CSF1R
KLK8 C5a 0.902 CCL18 CTSB KLK7 ETHE1 3 C5-C6 C5 VEGFA CSF1R C5a
0.902 CCL18 CTSB KLK7 ETHE1 4 C5 VEGFA CSF1R MMP7 C5a 0.902 CCL18
CTSB KLK7 ETHE1 5 C5-C6 VEGFA CSF1R MMP7 C5a 0.902 CCL18 CTSB KLK7
ETHE1 6 C5 KIT CSF1R LTF C5a 0.902 CCL18 CTSB KLK7 ETHE1 7 C5 KIT
CSF1R ALPL C5a 0.902 CCL18 CTSB THBS4 ETHE1 8 C5 KIT CSF1R LTF C5a
0.902 CCL18 CTSB THBS4 ETHE1 9 C5 CSF1R ALPL KLK8 C5a 0.901 CCL18
CTSB KLK7 ETHE1 10 PLAT C5 KIT CSF1R C5a 0.901 CCL18 CTSB THBS4
ETHE1 11 C5 CSF1R ALPL C5a CCL18 0.901 CTSB THBS4 KLK7 ETHE1 12 C5
KIT CSF1R LTF C5a 0.901 CCL18 CTSB IL11RA ETHE1 13 C5-C6 C5 CSF1R
LTF C5a 0.901 CCL18 CTSB KLK7 ETHE1 14 C5 KIT CSF1R KLK8 C5a 0.901
CCL18 CTSB THBS4 ETHE1 15 C5-C6 C5 KIT VEGFA CSF1R 0.901 C5a CCL18
CTSB ETHE1 16 C5 CSF1R LTF C5a CCL18 0.901 CTSB THBS4 KLK7 ETHE1 17
C5 KIT VEGFA CSF1R KLK8 0.901 CCL18 CTSB THBS4 ETHE1 18 C5 VEGFA
CSF1R C5a CCL18 0.901 CTSB THBS4 KLK7 ETHE1 19 C5-C6 VEGFA CSF1R
C5a CCL18 0.901 CTSB THBS4 KLK7 ETHE1 20 C5 CSF1R ALPL C5a CCL18
0.900 CTSB TFPI KLK7 ETHE1 21 C5 KIT VEGFA CSF1R C5a 0.900 CCL18
CTSB THBS4 ETHE1 22 C5-C6 KIT VEGFA CSF1R C5a 0.900 CCL18 CTSB KLK7
ETHE1 23 C5-C6 CSF1R LTF C5a CCL23 0.900 CCL18 CTSB KLK7 ETHE1 24
PLAT C5 KIT CSF1R KLK8 0.900 C5a CCL18 CTSB ETHE1 25 C5 CSF1R LTF
C5a CCL18 0.900 CTSB TFPI KLK7 ETHE1 26 C5 CSF1R LTF KLK8 C5a 0.900
CCL18 CTSB KLK7 ETHE1 27 C5 KIT CSF1R LTF C5a 0.900 CCL23 CTSB
THBS4 ETHE1 28 C5-C6 C5 VEGFA CSF1R C5a 0.900 CCL18 CTSB THBS4
ETHE1 29 C5-C6 C5 KIT CSF1R C5a 0.900 CCL18 CTSB THBS4 ETHE1 30 C5
CSF1R LTF C5a CCL18 0.900 CTSB KLK7 IL11RA ETHE1 31 C5-C6 C5 KIT
CSF1R LTF 0.900 C5a CCL18 CTSB ETHE1 32 C5 CSF1R LTF C5a CCL18
0.900 CTSB KLK7 HAMP ETHE1 33 C5 KIT CSF1R ALPL C5a 0.900 CCL18
CTSB KLK7 ETHE1 34 C5 CSF1R LTF MMP7 C5a 0.900 CCL18 CTSB KLK7
ETHE1 35 C5-C6 KIT VEGFA CSF1R C5a 0.900 CCL18 CTSB THBS4 ETHE1 36
C5 KIT CSF1R LTF C5a 0.900 CCL23 CCL18 CTSB ETHE1 37 C5 KIT VEGFA
CSF1R KLK8 0.900 C5a CCL18 CTSB ETHE1 38 C5 LTF C5a CCL23 CCL18
0.900 CTSB THBS4 KLK7 ETHE1 39 C5-C6 VEGFA CSF1R KLK8 C5a 0.900
CCL18 CTSB KLK7 ETHE1 40 PLAT C5 CSF1R ALPL C5a 0.900 CCL18 CTSB
KLK7 ETHE1 41 C5-C6 CSF1R LTF C5a CCL18 0.900 CTSB THBS4 KLK7 ETHE1
42 C5 KIT LTF C5a CCL18 0.899 CTSB KLK7 HAMP ETHE1 43 C5 KIT CSF1R
C5a CCL18 0.899 CTSB THBS4 INSR ETHE1 44 C5 KIT VEGFA CSF1R C5a
0.899 CCL18 CTSB KLK7 ETHE1 45 C5 KIT CSF1R ALPL C5a 0.899 CCL18
CTSB IL11RA ETHE1 46 C5 CSF1R ALPL C5a CCL23 0.899 CCL18 CTSB KLK7
ETHE1 47 C5 CSF1R C5a CCL23 CCL18 0.899 CTSB THBS4 KLK7 ETHE1 48 C5
CSF1R C5a CCL18 CTSB 0.899 THBS4 KLK7 HAMP ETHE1 49 C5 CSF1R ALPL
C5a CCL18 0.899 CTSB KLK7 HAMP ETHE1 50 C5 KIT CSF1R LTF CCL23
0.899 CCL18 CTSB THBS4 ETHE1 51 C5 KIT LTF KLK8 C5a 0.899 CCL18
CTSB KLK7 ETHE1 52 C5-C6 C5 CSF1R ALPL C5a 0.899 CCL18 CTSB KLK7
ETHE1 53 C5 CSF1R ALPL C5a CCL18 0.899 CTSB KLK7 IL11RA ETHE1 54 C5
VEGFA CSF1R C5a CCL23 0.899 CCL18 CTSB KLK7 ETHE1 55 C5 CSF1R LTF
C5a CCL23 0.899 CCL18 CTSB THBS4 ETHE1 56 C5-C6 CSF1R MMP7 C5a
CCL18 0.899 CTSB KLK7 HAMP ETHE1 57 C5 CSF1R MMP7 C5a CCL18 0.899
CTSB THBS4 KLK7 ETHE1 58 C5 KIT CSF1R C5a CCL18 0.899 CTSB ACP5
THBS4 ETHE1 59 C5 KIT CSF1R MMP7 C5a 0.899 CCL18 CTSB THBS4 ETHE1
60 C5 KIT CSF1R LTF C5a 0.899 CCL18 CTSB TFPI ETHE1 61 C5 KIT LTF
C5a CCL23 0.899 CCL18 CTSB THBS4 ETHE1 62 C5-C6 PLAT C5 KIT CSF1R
0.899 C5a CCL18 CTSB ETHE1 63 PLAT C5 CSF1R LTF C5a 0.899 CCL18
CTSB KLK7 ETHE1 64 C5 CSF1R MMP7 C5a CCL18 0.899 CTSB KLK7 HAMP
ETHE1 65 C5 CSF1R KLK8 C5a CCL23 0.899 CCL18 CTSB KLK7 ETHE1 66 C9
C5 CSF1R LTF C5a 0.899 CCL18 CTSB KLK7 ETHE1 67 C5 VEGFA CSF1R MMP7
C5a 0.899 CTSB KLK7 IL11RA ETHE1 68 C5 KIT CSF1R LTF KLK8 0.899 C5a
CCL18 CTSB ETHE1 69 PLAT C5 KIT VEGFA CSF1R 0.899 C5a CCL23 CTSB
ETHE1 70 C5 KIT CSF1R C5a CCL18 0.899 CTSB TFPI THBS4 ETHE1 71
C5-C6 KIT CSF1R C5a CCL23 0.899 CCL18 CTSB THBS4 ETHE1 72 PLAT C5
KIT CSF1R LTF 0.899 C5a CCL18 CTSB ETHE1 73 C5-C6 PLAT C5 CSF1R C5a
0.899 CCL18 CTSB THBS4 ETHE1 74 C5 CSF1R MMP7 ALPL C5a 0.899 CCL18
CTSB KLK7 ETHE1 75 C5 KIT CSF1R C5a CCL18 0.899 CTSB KLK7 HAMP
ETHE1 76 C5 KIT CSF1R LTF CCL18 0.899 CTSB TFPI THBS4 ETHE1 77 C5
KIT VEGFA CSF1R C5a 0.899 CCL18 CTSB IL11RA ETHE1 78 C5 CSF1R MMP7
KLK8 C5a 0.899 CCL18 CTSB KLK7 ETHE1 79 PLAT C5 CSF1R C5a CCL23
0.899 CCL18 CTSB KLK7 ETHE1 80 C5 ALPL KLK8 C5a CCL18 0.899 CTSB
THBS4 KLK7 ETHE1 81 C5-C6 KIT CSF1R LTF C5a 0.899 CCL18 CTSB KLK7
ETHE1 82 PLAT C5 CSF1R MMP7 C5a 0.899 CCL18 CTSB KLK7 ETHE1 83 C5
IL12A-IL12B CSF1R LTF C5a 0.899 CCL18 CTSB KLK7 ETHE1 84 C5 KIT
CSF1R ALPL C5a 0.898 CCL18 CTSB TFPI ETHE1 85 C5 KIT VEGFA CSF1R
C5a 0.898 CTSB KLK7 HAMP ETHE1 86 C5 KIT CSF1R ALPL CCL18 0.898
CTSB TFPI THBS4 ETHE1 87 PLAT C5 KIT CSF1R LTF 0.898 C5a CCL23 CTSB
ETHE1 88 C5-C6 KIT CSF1R C5a CCL18 0.898 CTSB TFPI THBS4 ETHE1 89
C5 VEGFA CSF1R C5a CCL18 0.898 CTSB ACP5 KLK7 ETHE1 90 C5 VEGFA
CSF1R LTF C5a 0.898 CCL18 CTSB KLK7 ETHE1 91 C5 CSF1R ALPL C5a
CCL18 0.898 CTSB TFPI THBS4 ETHE1 92 C5 CSF1R KLK8 C5a CCL18 0.898
CTSB THBS4 KLK7 ETHE1 93 C5 KIT LTF C5a CCL23 0.898 CCL18 CTSB KLK7
ETHE1 94 PLAT C5 KIT CSF1R C5a 0.898 CCL23 CCL18 CTSB ETHE1 95 C5
VEGFA CSF1R C5a CCL18 0.898 CTSB KLK7 ESM1 ETHE1 96 C5 KIT CSF1R
C5a CCL23 0.898 CCL18 CTSB THBS4 ETHE1 97 C5 VEGFA CSF1R C5a CCL18
0.898 CTSB KLK7 IL11RA ETHE1 98 C5 VEGFA CSF1R C5a CCL23 0.898 CTSB
THBS4 KLK7 ETHE1 99 C5-C6 KIT CSF1R C5a CCL18 0.898 CTSB KLK7 HAMP
ETHE1 100 C5-C6 CSF1R LTF MMP7 C5a 0.898 CCL18 CTSB KLK7 ETHE1
TABLE-US-00012 TABLE 11 Panels of 10 Biomarkers Markers CV AUC 1
C5-C6 C5 VEGFA CSF1R KLK8 0.905 C5a CCL18 CTSB KLK7 ETHE1 2 C5
CSF1R LTF C5a CCL23 0.904 CCL18 CTSB THBS4 KLK7 ETHE1 3 C5 KIT
VEGFA CSF1R KLK8 0.904 C5a CCL18 CTSB KLK7 ETHE1 4 C5-C6 C5 VEGFA
CSF1R LTF 0.904 C5a CCL18 CTSB KLK7 ETHE1 5 C5 VEGFA CSF1R MMP7
KLK8 0.904 C5a CCL18 CTSB KLK7 ETHE1 6 C5-C6 KIT VEGFA CSF1R MMP7
0.904 C5a CCL18 CTSB KLK7 ETHE1 7 C5-C6 KIT VEGFA CSF1R KLK8 0.903
C5a CCL18 CTSB KLK7 ETHE1 8 C5-C6 VEGFA CSF1R MMP7 C5a 0.903 CCL18
CTSB KLK7 IL11RA ETHE1 9 C5 VEGFA CSF1R KLK8 C5a 0.903 CCL18 CTSB
THBS4 KLK7 ETHE1 10 C5 KIT VEGFA CSF1R MMP7 0.903 C5a CCL18 CTSB
KLK7 ETHE1 11 C5 VEGFA CSF1R MMP7 C5a 0.903 CCL18 CTSB THBS4 KLK7
ETHE1 12 C5 KIT CSF1R LTF C5a 0.903 CCL23 CCL18 CTSB THBS4 ETHE1 13
C5 CSF1R ALPL KLK8 C5a 0.903 CCL18 CTSB THBS4 KLK7 ETHE1 14 C5-C6
KIT VEGFA CSF1R LTF 0.903 C5a CCL18 CTSB KLK7 ETHE1 15 C5-C6 VEGFA
CSF1R MMP7 C5a 0.903 CCL18 CTSB THBS4 KLK7 ETHE1 16 C5 KIT CSF1R
LTF C5a 0.903 CCL23 CCL18 CTSB KLK7 ETHE1 17 C5-C6 C5 VEGFA CSF1R
C5a 0.903 CCL23 CCL18 CTSB KLK7 ETHE1 18 C5 VEGFA CSF1R MMP7 C5a
0.903 CCL18 CTSB KLK7 IL11RA ETHE1 19 C5-C6 C5 KIT VEGFA CSF1R
0.902 C5a CCL18 CTSB THBS4 ETHE1 20 C5-C6 CSF1R LTF C5a CCL23 0.902
CCL18 CTSB THBS4 KLK7 ETHE1 21 C5 KIT CSF1R LTF KLK8 0.902 C5a
CCL18 CTSB THBS4 ETHE1 22 C5 KIT VEGFA CSF1R KLK8 0.902 C5a CCL18
CTSB THBS4 ETHE1 23 C5 KIT CSF1R ALPL KLK8 0.902 C5a CCL18 CTSB
THBS4 ETHE1 24 C5-C6 C5 VEGFA CSF1R ALPL 0.902 C5a CCL18 CTSB KLK7
ETHE1 25 C5 KIT CSF1R LTF KLK8 0.902 C5a CCL18 CTSB KLK7 ETHE1 26
C5 CSF1R LTF KLK8 C5a 0.902 CCL18 CTSB THBS4 KLK7 ETHE1 27 C5 CSF1R
ALPL C5a CCL23 0.902 CCL18 CTSB THBS4 KLK7 ETHE1 28 C5-C6 VEGFA
CSF1R MMP7 KLK8 0.902 C5a CCL18 CTSB KLK7 ETHE1 29 C5 KIT VEGFA
CSF1R C5a 0.902 CCL23 CCL18 CTSB KLK7 ETHE1 30 C5 KIT CSF1R LTF C5a
0.902 CCL18 CTSB KLK7 HAMP ETHE1 31 C5 KIT CSF1R LTF C5a 0.902
CCL18 CTSB TFPI THBS4 ETHE1 32 C5-C6 C5 VEGFA CSF1R C5a 0.902 CCL18
CTSB THBS4 KLK7 ETHE1 33 C5 VEGFA CSF1R KLK8 C5a 0.902 CCL18 CTSB
KLK7 ESM1 ETHE1 34 PLAT C5 VEGFA CSF1R KLK8 0.902 C5a CCL18 CTSB
KLK7 ETHE1 35 C5-C6 C5 CSF1R LTF C5a 0.902 CCL18 CTSB THBS4 KLK7
ETHE1 36 C5 VEGFA CSF1R C5a CCL23 0.902 CCL18 CTSB THBS4 KLK7 ETHE1
37 C5 KIT VEGFA CSF1R MMP7 0.902 C5a CCL18 CTSB IL11RA ETHE1 38 C5
CSF1R LTF KLK8 C5a 0.902 CCL23 CCL18 CTSB KLK7 ETHE1 39 C5 KIT
VEGFA CSF1R KLK8 0.902 C5a CCL18 CTSB IL11RA ETHE1 40 C5-C6 C5 KIT
CSF1R LTF 0.902 C5a CCL18 CTSB THBS4 ETHE1 41 C5 CSF1R LTF MMP7 C5a
0.901 CCL18 CTSB KLK7 IL11RA ETHE1 42 C5 VEGFA CSF1R KLK8 C5a 0.901
CCL23 CCL18 CTSB KLK7 ETHE1 43 C5 KIT CSF1R ALPL KLK8 0.901 C5a
CCL18 CTSB KLK7 ETHE1 44 KIT VEGFA CSF1R MMP7 KLK8 0.901 C5a CCL18
CTSB KLK7 ETHE1 45 C5 KIT CSF1R LTF KLK8 0.901 C5a CCL18 CTSB
IL11RA ETHE1 46 C5-C6 C5 CSF1R LTF C5a 0.901 CCL23 CCL18 CTSB KLK7
ETHE1 47 C5-C6 KIT CSF1R LTF C5a 0.901 CCL23 CCL18 CTSB THBS4 ETHE1
48 C5-C6 VEGFA CSF1R KLK8 C5a 0.901 CCL18 CTSB THBS4 KLK7 ETHE1 49
PLAT C5 KIT VEGFA CSF1R 0.901 KLK8 C5a CCL18 CTSB ETHE1 50 C5 VEGFA
CSF1R LTF C5a 0.901 CCL18 CTSB KLK7 IL11RA ETHE1 51 C5 KIT CSF1R
ALPL C5a 0.901 CCL18 CTSB TFPI THBS4 ETHE1 52 C5 CSF1R MMP7 KLK8
C5a 0.901 CCL18 CTSB THBS4 KLK7 ETHE1 53 C5 CSF1R LTF KLK8 C5a
0.901 CCL18 CTSB TFPI KLK7 ETHE1 54 C5 CSF1R LTF MMP7 C5a 0.901
CCL18 CTSB THBS4 KLK7 ETHE1 55 C5 CSF1R ALPL KLK8 C5a 0.901 CCL18
CTSB TFPI KLK7 ETHE1 56 C5 KIT VEGFA CSF1R LTF 0.901 C5a CCL18 CTSB
KLK7 ETHE1 57 C5-C6 C5 VEGFA CSF1R C5a 0.901 CCL18 CTSB ACP5 KLK7
ETHE1 58 PLAT C5 KIT CSF1R KLK8 0.901 C5a CCL18 CTSB THBS4 ETHE1 59
C5 CSF1R MMP7 C5a CCL18 0.901 CTSB THBS4 KLK7 HAMP ETHE1 60 C5-C6
KIT CSF1R LTF C5a 0.901 CCL18 CTSB KLK7 HAMP ETHE1 61 PLAT C5 KIT
VEGFA CSF1R 0.901 C5a CCL18 CTSB THBS4 ETHE1 62 C5-C6 VEGFA CSF1R
LTF C5a 0.901 CCL18 CTSB THBS4 KLK7 ETHE1 63 C5 VEGFA CSF1R LTF C5a
0.901 CCL23 CCL18 CTSB KLK7 ETHE1 64 C5 CSF1R ALPL C5a CCL18 0.901
CTSB TFPI THBS4 KLK7 ETHE1 65 C5-C6 KIT VEGFA CSF1R C5a 0.901 CCL18
CTSB THBS4 KLK7 ETHE1 66 C5-C6 C5 VEGFA CSF1R MMP7 0.901 C5a CCL18
CTSB KLK7 ETHE1 67 C5 KIT CSF1R LTF MMP7 0.901 C5a CCL18 CTSB KLK7
ETHE1 68 C5-C6 C5 VEGFA CSF1R C5a 0.901 CCL23 CCL18 CTSB THBS4
ETHE1 69 PLAT C9 C5 KIT VEGFA 0.901 CSF1R KLK8 CCL18 CTSB ETHE1 70
C5-C6 PLAT C5 VEGFA CSF1R 0.901 C5a CCL18 CTSB KLK7 ETHE1 71 PLAT
C5 VEGFA CSF1R MMP7 0.901 C5a CCL18 CTSB KLK7 ETHE1 72 C5-C6 C5
CSF1R LTF KLK8 0.901 C5a CCL18 CTSB KLK7 ETHE1 73 C5-C6 C5 KIT
VEGFA CSF1R 0.901 C5a CCL18 CTSB KLK7 ETHE1 74 C5-C6 C5 KIT VEGFA
CSF1R 0.901 C5a CCL23 CCL18 CTSB ETHE1 75 C5-C6 C5 VEGFA CSF1R KLK8
0.901 C5a CCL18 CTSB THBS4 ETHE1 76 C5-C6 KIT VEGFA CSF1R C5a 0.901
CCL23 CCL18 CTSB THBS4 ETHE1 77 C5 VEGFA CSF1R LTF KLK8 0.901 C5a
CCL18 CTSB KLK7 ETHE1 78 C5 CSF1R LTF C5a CCL18 0.901 CTSB TFPI
THBS4 KLK7 ETHE1 79 C5 KIT CSF1R LTF C5a 0.901 CCL18 CTSB THBS4
KLK7 ETHE1 80 C5-C6 C5 KIT VEGFA CSF1R 0.901 KLK8 C5a CCL18 CTSB
ETHE1 81 C5 KIT CSF1R MMP7 C5a 0.901 CCL18 CTSB KLK7 HAMP ETHE1 82
C5 CSF1R LTF MMP7 C5a 0.901 CCL18 CTSB KLK7 HAMP ETHE1 83 C5 KIT
VEGFA CSF1R C5a 0.901 CCL18 CTSB KLK7 HAMP ETHE1 84 C5 VEGFA CSF1R
ALPL KLK8 0.901 C5a CCL18 CTSB KLK7 ETHE1 85 C5 CSF1R LTF MMP7 C5a
0.900 CCL23 CCL18 CTSB KLK7 ETHE1 86 C5-C6 KIT VEGFA CSF1R C5a
0.900 CCL18 CTSB KLK7 HAMP ETHE1 87 C5-C6 KIT CSF1R LTF C5a 0.900
CCL23 CCL18 CTSB KLK7 ETHE1 88 C5-C6 PLAT C5 KIT VEGFA 0.900 CSF1R
C5a CCL18 CTSB ETHE1 89 C5 VEGFA CSF1R KLK8 C5a 0.900 CCL18 CTSB
KLK7 IL11RA ETHE1 90 C5 CSF1R ALPL KLK8 C5a 0.900 CCL23 CCL18 CTSB
KLK7 ETHE1 91 C5 KIT VEGFA CSF1R KLK8 0.900 C5a CCL18 CTSB TFPI
ETHE1 92 C5 KIT VEGFA CSF1R C5a 0.900 CCL18 CTSB ACP5 KLK7 ETHE1 93
PLAT C5 CSF1R ALPL C5a 0.900 CCL18 CTSB THBS4 KLK7 ETHE1 94 C5 KIT
VEGFA CSF1R C5a 0.900 CCL23 CCL18 CTSB THBS4 ETHE1 95 C5 VEGFA
CSF1R MMP7 ALPL 0.900 C5a CCL18 CTSB KLK7 IL11RA 96 PLAT C5 CSF1R
LTF C5a 0.900 CCL23 CCL18 CTSB KLK7 ETHE1 97 C5 VEGFA CSF1R LTF
MMP7 0.900 C5a CCL18 CTSB KLK7 IL11RA 98 C5 CSF1R LTF C5a CCL18
0.900 CTSB KLK7 HAMP IL11RA ETHE1 99 C5-C6 KIT VEGFA CSF1R MMP7
0.900 C5a CCL18 CTSB IL11RA ETHE1 100 C5-C6 C5 KIT CSF1R LTF 0.900
C5a CCL18 CTSB KLK7 ETHE1
TABLE-US-00013 TABLE 12 Counts of markers in biomarker panels Panel
Size Biomarker 3 4 5 6 7 8 9 10 ACP5 35 35 40 54 58 60 54 59 ACY1 7
5 2 0 1 0 0 0 AHSG 18 15 3 0 0 0 0 0 ALPL 28 69 109 146 171 179 178
190 APOA1 89 48 22 13 5 3 7 9 APOE 5 1 0 0 0 0 0 0 BMP6 5 1 0 0 0 0
0 0 C2 156 106 100 64 48 41 47 66 C5 256 373 600 731 777 806 819
808 C5a 153 370 552 641 751 862 920 958 C5-C6 45 92 103 114 134 175
217 287 C9 58 119 109 91 73 74 67 71 CCL18 119 157 284 459 605 694
807 893 CCL23 23 35 30 26 26 23 17 17 CCL23 45 57 65 94 115 152 158
182 CDK5- 12 19 19 12 11 14 16 16 CDK5R1 CKB-CKM 14 7 1 1 0 0 0 0
CKM 6 0 0 0 0 0 0 0 CRP 65 0 0 0 0 0 0 0 CSF1R 98 74 131 266 442
671 810 913 CTSB 586 963 990 995 999 1000 1000 1000 ENTPD1 4 6 2 0
0 0 0 0 ESM1 14 13 16 17 14 17 27 30 ETHE1 118 237 403 613 778 870
923 955 FCGR3B 34 14 5 2 1 0 0 0 FGFR3 13 10 11 14 10 16 11 8 FSTL3
9 4 1 0 0 0 0 0 GDF11 29 43 31 20 17 20 19 20 GFRA1 10 4 0 0 0 0 0
0 HAMP 93 202 239 218 193 166 154 131 HINT1 14 19 14 14 14 10 8 10
IDUA 4 3 1 0 0 0 0 0 IL11RA 58 78 69 68 73 77 109 150 IL12A-IL12B
18 15 10 13 10 11 13 13 IL18R1 7 1 0 0 0 0 0 0 IL1RL1 17 10 3 0 0 0
0 0 INSR 21 26 24 27 44 42 49 44 KIT 63 142 202 251 306 348 392 445
KLK3- 18 12 2 0 0 0 0 0 SERPINA3 KLK7 89 127 231 317 410 511 606
714 KLK8 19 27 40 59 90 137 205 294 KLKB1 12 7 1 0 0 0 0 0 LBP 22
21 5 0 0 0 0 0 LTF 30 66 106 161 202 252 310 347 MCM2 6 4 0 0 0 0 0
0 MDK 14 3 0 0 0 0 0 0 MMP7 56 42 56 73 97 130 194 270 MRC1 19 4 3
1 0 0 1 0 NID1 7 2 0 0 0 0 0 0 NID2 7 0 0 0 0 0 0 0 NRP1 44 20 12 5
2 2 2 1 PLAT 48 54 92 123 143 145 165 177 SERPINA5 5 0 0 0 0 0 0 0
SERPINF2 5 3 3 0 0 0 0 0 SGTA 3 2 1 0 0 0 0 0 TFPI 100 60 51 46 57
70 91 111 THBS2 4 0 0 0 0 0 0 0 THBS4 66 110 146 193 243 276 334
354 TIMP1 22 2 0 0 0 0 0 0 TNFRSF18 8 9 3 0 1 3 2 2 TNFRSF1B 20 12
8 6 4 1 0 0 TOP1 6 3 0 0 0 0 0 0 VEGFA 16 33 47 51 75 142 268 455
VEGFC 5 4 2 1 0 0 0 0
TABLE-US-00014 TABLE 13 Analytes in ten marker classifiers CTSB C5a
ETHE1 CSF1R CCL18 C5 KLK7 VEGFA KIT THBS4 LTF
TABLE-US-00015 TABLE 14 Parameters derived from training set for
naive Bayes classifier. Biomarker .mu..sub.c .mu..sub.d
.sigma..sub.c .sigma..sub.d CSF1R 10.712 10.995 0.398 0.399 CTSB
8.836 9.398 0.287 0.621 IL1RL1 9.702 10.189 0.533 0.780 GDF11 8.889
8.578 0.291 0.379 ETHE1 7.373 7.443 0.119 0.121 CCL23 8.795 8.975
0.312 0.329 FGFR3 6.992 7.166 0.178 0.225 KIT 9.770 9.623 0.287
0.318 FSTL3 8.787 9.029 0.290 0.374 THBS2 7.481 7.922 0.270 0.633
SERPINF2 9.264 9.175 0.115 0.162 TNFRSF1B 10.748 11.028 0.380 0.452
TNFRSF18 12.308 12.279 0.139 0.168 BMP6 7.958 8.138 0.142 0.239
GFRA1 7.324 7.465 0.182 0.200 CRP 11.965 12.304 0.735 0.233
SERPINA5 10.309 10.101 0.300 0.419 KLKB1 11.802 11.666 0.159 0.211
APOE 8.081 8.314 0.406 0.656 SFRP1 7.096 7.219 0.221 0.309 C2
11.506 11.611 0.100 0.132 CKM 7.313 7.192 0.154 0.116 TFPI 10.179
10.490 0.261 0.352 INSR 8.480 8.633 0.224 0.255 NID2 8.595 8.806
0.213 0.384 HAMP 10.424 11.079 0.788 0.617 MDK 8.034 8.495 0.570
0.578 CDK5-CDK5R1 6.937 6.994 0.108 0.111 NID1 9.771 9.941 0.213
0.357 VEGFC 7.454 7.540 0.118 0.126 C9 11.911 12.076 0.234 0.233
LTF 10.120 9.870 0.442 0.419 IL12A-IL12B 7.311 7.273 0.052 0.057 C5
9.485 9.603 0.119 0.143 IL18R1 7.643 7.845 0.186 0.475 CCL18 11.320
11.616 0.477 0.398 VEGFA 8.532 8.601 0.170 0.134 IDUA 8.428 8.694
0.366 0.558 TOP1 6.892 6.842 0.088 0.091 C5-C6 6.506 6.593 0.133
0.144 TIMP1 9.815 10.148 0.264 0.430 C5a 11.354 11.606 0.254 0.246
THBS4 10.013 9.794 0.359 0.400 ENTPD1 7.225 7.299 0.110 0.103 LBP
9.102 9.489 0.439 0.548 KLK3-SERPINA3 9.034 9.287 0.353 0.422 MCM2
7.794 7.975 0.226 0.359 SGTA 5.920 5.883 0.060 0.079 ESM1 9.715
9.919 0.330 0.476 PLAT 8.517 8.838 0.461 0.502 KLK7 8.322 7.989
0.321 0.391 CCL23 7.909 8.097 0.227 0.267 ACP5 10.198 10.436 0.292
0.343 NRP1 8.832 9.047 0.243 0.256 MMP7 9.084 9.574 0.437 0.706
ACY1 9.898 10.411 0.628 0.919 ALPL 10.577 10.290 0.377 0.417 IL11RA
7.312 7.213 0.110 0.107 APOA1 9.701 9.480 0.171 0.295 CKB-CKM 7.506
7.025 0.653 0.479 KLK8 7.361 7.421 0.100 0.178 AHSG 11.914 11.826
0.133 0.167 HINT1 5.835 5.793 0.086 0.104 MRC1 9.628 9.995 0.370
0.490 FCGR3B 10.920 11.145 0.255 0.269
TABLE-US-00016 TABLE 15 AUC for exemplary combinations of
biomarkers # AUC 1 CTSB 0.791 2 CTSB C5a 0.853 3 CTSB C5a C5 0.880
4 CTSB C5a C5 CCL18 0.890 5 CTSB C5a C5 CCL18 CSF1R 0.895 6 CTSB
C5a C5 CCL18 CSF1R KLK7 0.895 7 CTSB C5a C5 CCL18 CSF1R KLK7 ETHE1
0.906 8 CTSB C5a C5 CCL18 CSF1R KLK7 ETHE1 C5-C6 0.902 9 CTSB C5a
C5 CCL18 CSF1R KLK7 ETHE1 C5-C6 KLK8 0.903 10 CTSB C5a C5 CCL18
CSF1R KLK7 ETHE1 C5-C6 KLK8 VEGFA 0.913
TABLE-US-00017 TABLE 16 Calculations derived from training set for
naive Bayes classifier. Biomarker .mu..sub.c .mu..sub.d
.sigma..sub.c .sigma..sub.d {tilde over (x)} p(c|{tilde over (x)})
p(d|{tilde over (x)}) ln(p(d|{tilde over (x)})/p(c|{tilde over
(x)})) CSF1R 10.712 10.995 0.398 0.399 10.751 0.997 0.831 -0.182
CTSB 8.836 9.398 0.287 0.621 9.036 1.091 0.542 -0.700 CCL18 11.320
11.616 0.477 0.398 11.658 0.651 0.996 0.425 KLK7 8.322 7.989 0.321
0.391 8.048 0.862 1.009 0.158 VEGFA 8.532 8.601 0.170 0.134 8.687
1.554 2.425 0.445 ETHE1 7.373 7.443 0.119 0.121 7.313 2.932 1.845
-0.463 C5-C6 6.506 6.593 0.133 0.144 6.349 1.490 0.662 -0.811 C5a
11.354 11.606 0.254 0.246 11.400 1.547 1.139 -0.306 KLK8 7.361
7.421 0.100 0.178 7.420 3.344 2.237 -0.402 C5 9.485 9.603 0.119
0.143 9.306 1.084 0.324 -1.207
TABLE-US-00018 TABLE 17 Clinical characteristics of the training
set Meta Data Levels Control Pancreatic Cancer p-value Samples 115
143 GENDER F 59 70 M 56 73 8.02e-01 AGE Mean 57.6 68.6 SD 13.7 9.7
8.98e-12
TABLE-US-00019 TABLE 18 Ten biomarker classifier proteins UniProt
Biomarker ID Direction* Biological Process (GO) C5-C6 P01031 Up
immune system process P13671 regulation of immune system process
proteolysis response to stress regulation of cell death signaling
regulation of signaling pathway C5 P01031 Up immune system process
regulation of immune system process proteolysis response to stress
signaling regulation of signaling pathway VEGFA P15692 Down immune
system process regulation of immune system process response to
stress regulation of cell death signaling regulation of signaling
pathway CSF1R P07333 Up cell proliferation signaling process
signaling KLK8 O60259 Up proteolysis response to stress cell
proliferation C5a P01031 Up immune system process regulation of
immune system process proteolysis response to stress signaling
regulation of signaling pathway CCL18 P55774 Up immune system
process response to stress cell communication signaling process
signaling CTSB P07858 Up proteolysis response to stress regulation
of cell death KLK7 P49862 Down proteolysis ETHE1 O95571 Up
TABLE-US-00020 TABLE 19 Biomarkers of general cancer ACY1 APOA1 C5
CCL23 CKB-CKM CKM ENTPD1 GDF11 HAMP HINT1 KIT KLK3-SERPINA3 LBP
SERPINF2 THBS2 TIMP1 C9 FSTL3 IL12A-IL12B CDK5-CDK5R1 CCL23
TABLE-US-00021 TABLE 20 Panels of 1 Biomarker Markers Mean CV AUC 1
KIT 0.753 2 CKB-CKM 0.750 3 C9 0.740 4 APOA1 0.740 5 KLK3-SERPINA3
0.732 6 CKM 0.730 7 CCL23 0.713 8 CCL23 0.705 9 TIMP1 0.695 10 LBP
0.691 11 C5 0.690 12 ACY1 0.676 13 HAMP 0.670 14 CDK5-CDK5R1 0.670
15 HINT1 0.669 16 SERPINF2 0.663 17 GDF11 0.656 18 ENTPD1 0.651 19
THBS2 0.650 20 FSTL3 0.643 21 IL12A-IL12B 0.640
TABLE-US-00022 TABLE 21 Panels of 2 Biomarkers Markers Mean CV AUC
1 KIT APOA1 0.808 2 APOA1 CKB-CKM 0.801 3 KIT CCL23 0.791 4 C9 KIT
0.791 5 KIT CKB-CKM 0.790 6 C9 CKB-CKM 0.789 7 C9 APOA1 0.789 8 KIT
LBP 0.787 9 TIMP1 KIT 0.787 10 C5 KIT 0.787 11 C9 CKM 0.786 12 CKM
APOA1 0.786 13 C5 APOA1 0.785 14 KIT CCL23 0.784 15 TIMP1 CKB-CKM
0.782 16 CCL23 CKB-CKM 0.781 17 KIT CKM 0.780 18 KIT ACY1 0.780 19
KIT KLK3-SERPINA3 0.780 20 CKM CCL23 0.778 21 CKB-CKM HINT1 0.778
22 KIT CDK5-CDK5R1 0.777 23 SERPINF2 CKB-CKM 0.777 24 APOA1 ACY1
0.777 25 KIT SERPINF2 0.776 26 LBP CKB-CKM 0.776 27 CKB-CKM
KLK3-SERPINA3 0.776 28 APOA1 KLK3-SERPINA3 0.776 29 APOA1 CCL23
0.775 30 TIMP1 CKM 0.774 31 C9 ACY1 0.774 32 CDK5-CDK5R1 CKB-CKM
0.773 33 IL12A-IL12B CKB-CKM 0.773 34 TIMP1 C9 0.773 35 APOA1 HINT1
0.773 36 C5 CCL23 0.772 37 KIT HINT1 0.772 38 IL12A-IL12B KIT 0.771
39 CKM SERPINF2 0.771 40 ACY1 CKB-CKM 0.770 41 APOA1 CCL23 0.770 42
C9 CDK5-CDK5R1 0.769 43 C5 CKB-CKM 0.769 44 C9 HINT1 0.769 45 CKM
CCL23 0.767 46 CCL23 KLK3-SERPINA3 0.767 47 CKM KLK3-SERPINA3 0.767
48 C9 FSTL3 0.767 49 APOA1 LBP 0.766 50 C9 SERPINF2 0.766 51 C9
CCL23 0.765 52 CKM LBP 0.765 53 CCL23 CKB-CKM 0.764 54 KIT ENTPD1
0.764 55 CKM HINT1 0.764 56 C9 LBP 0.764 57 C9 C5 0.764 58 KIT HAMP
0.764 59 FSTL3 CKB-CKM 0.763 60 KIT FSTL3 0.763 61 CKM CKB-CKM
0.762 62 HAMP CKB-CKM 0.762 63 CKM ACY1 0.762 64 TIMP1 APOA1 0.762
65 APOA1 CDK5-CDK5R1 0.761 66 C5 KLK3-SERPINA3 0.761 67 C5 HINT1
0.760 68 C9 GDF11 0.760 69 C9 THBS2 0.760 70 CKM CDK5-CDK5R1 0.760
71 ENTPD1 CKB-CKM 0.759 72 C5 CCL23 0.759 73 CCL23 ACY1 0.758 74
CCL23 ACY1 0.758 75 C5 CDK5-CDK5R1 0.757 76 C5 CKM 0.757 77 TIMP1
KLK3-SERPINA3 0.757 78 CKM HAMP 0.757 79 C9 HAMP 0.757 80 C9 CCL23
0.757 81 C9 IL12A-IL12B 0.756 82 LBP ACY1 0.756 83 C9 ENTPD1 0.754
84 CKM ENTPD1 0.754 85 APOA1 SERPINF2 0.754 86 LBP HINT1 0.754 87
CDK5-CDK5R1 KLK3-SERPINA3 0.754 88 APOA1 ENTPD1 0.753 89 TIMP1
CCL23 0.753 90 KIT GDF11 0.753 91 GDF11 KLK3-SERPINA3 0.753 92
IL12A-IL12B CKM 0.753 93 C5 SERPINF2 0.752 94 APOA1 GDF11 0.752 95
CCL23 KLK3-SERPINA3 0.751 96 CCL23 CDK5-CDK5R1 0.751 97 ACY1
KLK3-SERPINA3 0.749 98 C9 KLK3-SERPINA3 0.749 99 LBP CDK5-CDK5R1
0.749 100 APOA1 HAMP 0.748
TABLE-US-00023 TABLE 22 Panels of 3 Biomarkers Mean CV Markers AUC
1 C5 KIT APOA1 0.830 2 KIT APOA1 CKB-CKM 0.826 3 C9 KIT APOA1 0.822
4 KIT APOA1 ACY1 0.820 5 KIT APOA1 CDK5-CDK5R1 0.819 6 APOA1 CCL23
CKB-CKM 0.819 7 APOA1 ACY1 CKB-CKM 0.818 8 C9 KIT CKB-CKM 0.817 9
C9 KIT ACY1 0.816 10 KIT APOA1 LBP 0.816 11 C5 KIT CCL23 0.816 12
C5 APOA1 CKB-CKM 0.816 13 TIMP1 C9 KIT 0.815 14 C9 APOA1 CKB-CKM
0.815 15 C5 KIT CDK5-CDK5R1 0.815 16 APOA1 CKB-CKM HINT1 0.815 17
KIT CKM APOA1 0.815 18 KIT APOA1 CCL23 0.815 19 TIMP1 KIT APOA1
0.814 20 APOA1 LBP CKB-CKM 0.813 21 C9 KIT CKM 0.813 22 APOA1
CDK5-CDK5R1 CKB-CKM 0.812 23 KIT APOA1 CCL23 0.812 24 C9 CKM APOA1
0.812 25 TIMP1 KIT CDK5-CDK5R1 0.812 26 C5 KIT CKB-CKM 0.812 27
TIMP1 APOA1 CKB-CKM 0.812 28 KIT APOA1 HINT1 0.812 29 C9 KIT HINT1
0.811 30 KIT CDK5-CDK5R1 CKB-CKM 0.811 31 KIT LBP CKB-CKM 0.811 32
IL12A-IL12B KIT APOA1 0.811 33 C9 C5 KIT 0.811 34 C5 KIT CCL23
0.811 35 C5 KIT HINT1 0.811 36 KIT CCL23 ACY1 0.809 37 C9 KIT CCL23
0.809 38 APOA1 CKB-CKM KLK3-SERPINA3 0.809 39 KIT CCL23 ACY1 0.809
40 APOA1 SERPINF2 CKB-CKM 0.808 41 C9 ACY1 CKB-CKM 0.808 42 TIMP1
KIT CKB-CKM 0.808 43 KIT APOA1 KLK3-SERPINA3 0.808 44 IL12A-IL12B
APOA1 CKB-CKM 0.808 45 KIT CCL23 CKB-CKM 0.807 46 C5 APOA1
CDK5-CDK5R1 0.807 47 C5 APOA1 CCL23 0.807 48 KIT ACY1 CKB-CKM 0.807
49 C5 KIT ACY1 0.807 50 TIMP1 C5 KIT 0.807 51 C9 C5 CKB-CKM 0.806
52 C5 APOA1 HINT1 0.806 53 C9 CDK5-CDK5R1 CKB-CKM 0.806 54 C9
CKB-CKM HINT1 0.806 55 C5 CCL23 CKB-CKM 0.806 56 C5 KIT SERPINF2
0.806 57 KIT CCL23 CKB-CKM 0.806 58 C5 CKM APOA1 0.806 59 CKM APOA1
CCL23 0.806 60 APOA1 CCL23 CKB-CKM 0.806 61 C5 CKB-CKM HINT1 0.806
62 APOA1 HAMP CKB-CKM 0.806 63 KIT LBP CDK5-CDK5R1 0.805 64 TIMP1
KIT CCL23 0.805 65 KIT APOA1 ENTPD1 0.805 66 TIMP1 C9 CKB-CKM 0.805
67 C5 APOA1 ACY1 0.804 68 C9 KIT CDK5-CDK5R1 0.804 69 TIMP1 KIT CKM
0.804 70 C9 APOA1 CDK5-CDK5R1 0.804 71 C9 CCL23 CKB-CKM 0.804 72
KIT CKB-CKM HINT1 0.804 73 TIMP1 CDK5-CDK5R1 CKB-CKM 0.804 74 KIT
APOA1 SERPINF2 0.804 75 KIT CKM LBP 0.803 76 CKM APOA1 ACY1 0.803
77 C5 CDK5-CDK5R1 CKB-CKM 0.803 78 KIT APOA1 HAMP 0.803 79 TIMP1 C9
CKM 0.803 80 KIT LBP ACY1 0.803 81 C9 CKM ACY1 0.803 82 C5
IL12A-IL12B KIT 0.803 83 LBP CKB-CKM HINT1 0.803 84 C9 CKM
CDK5-CDK5R1 0.803 85 C9 KIT FSTL3 0.802 86 LBP CDK5-CDK5R1 CKB-CKM
0.802 87 C9 KIT SERPINF2 0.802 88 APOA1 FSTL3 CKB-CKM 0.802 89 C5
KIT CKM 0.802 90 KIT CKM CDK5-CDK5R1 0.802 91 TIMP1 KIT ACY1 0.802
92 C9 IL12A-IL12B CKB-CKM 0.801 93 KIT CCL23 CDK5-CDK5R1 0.801 94
KIT CCL23 LBP 0.801 95 C9 KIT LBP 0.801 96 CCL23 CDK5-CDK5R1
CKB-CKM 0.801 97 KIT SERPINF2 LBP 0.801 98 C5 KIT ENTPD1 0.801 99
APOA1 ENTPD1 CKB-CKM 0.800 100 KIT CKM ACY1 0.800
TABLE-US-00024 TABLE 23 Panels of 4 Biomarkers Markers Mean CV AUC
1 C5 KIT APOA1 CDK5-CDK5R1 0.845 2 C5 KIT APOA1 CKB-CKM 0.839 3 KIT
APOA1 CDK5-CDK5R1 CKB-CKM 0.839 4 KIT APOA1 ACY1 CKB-CKM 0.838 5 C5
KIT APOA1 ACY1 0.837 6 C9 KIT APOA1 CKB-CKM 0.835 7 C5 KIT APOA1
HINT1 0.835 8 C5 APOA1 CKB-CKM HINT1 0.835 9 KIT APOA1 CCL23
CKB-CKM 0.834 10 C5 KIT APOA1 CCL23 0.834 11 KIT APOA1 CCL23
CKB-CKM 0.833 12 C9 KIT APOA1 ACY1 0.833 13 KIT APOA1 LBP CKB-CKM
0.833 14 C5 KIT APOA1 CCL23 0.833 15 C9 KIT CKM APOA1 0.833 16 C5
APOA1 CDK5-CDK5R1 CKB-CKM 0.833 17 IL12A-IL12B KIT APOA1 CKB-CKM
0.832 18 C5 KIT CDK5-CDK5R1 CKB-CKM 0.832 19 C5 KIT CKM APOA1 0.832
20 TIMP1 KIT CDK5-CDK5R1 CKB-CKM 0.832 21 C9 C5 KIT CKB-CKM 0.832
22 TIMP1 KIT APOA1 CKB-CKM 0.832 23 TIMP1 C5 KIT CDK5-CDK5R1 0.831
24 C9 KIT APOA1 CDK5-CDK5R1 0.831 25 C5 KIT CCL23 ACY1 0.831 26 C5
IL12A-IL12B KIT APOA1 0.831 27 C5 APOA1 ACY1 CKB-CKM 0.830 28 C9 C5
KIT APOA1 0.830 29 KIT APOA1 CKB-CKM HINT1 0.830 30 C9 KIT ACY1
CKB-CKM 0.830 31 C5 KIT CCL23 CKB-CKM 0.830 32 C5 APOA1 CCL23
CKB-CKM 0.829 33 C9 KIT CCL23 ACY1 0.829 34 C9 KIT APOA1 CCL23
0.829 35 C5 KIT CCL23 CKB-CKM 0.829 36 C5 KIT APOA1 SERPINF2 0.829
37 APOA1 CCL23 ACY1 CKB-CKM 0.829 38 C5 KIT APOA1 ENTPD1 0.829 39
KIT APOA1 LBP CDK5-CDK5R1 0.829 40 KIT APOA1 CCL23 ACY1 0.829 41 C9
KIT APOA1 HINT1 0.829 42 KIT LBP CDK5-CDK5R1 CKB-CKM 0.829 43 KIT
APOA1 ACY1 CDK5-CDK5R1 0.828 44 TIMP1 KIT APOA1 ACY1 0.828 45 C5
KIT CCL23 CDK5-CDK5R1 0.828 46 KIT APOA1 CCL23 ACY1 0.828 47 TIMP1
C9 KIT CDK5-CDK5R1 0.828 48 C5 APOA1 CCL23 CKB-CKM 0.828 49 KIT CKM
APOA1 ACY1 0.828 50 TIMP1 C5 KIT APOA1 0.828 51 C5 KIT CCL23 ACY1
0.828 52 C9 KIT CCL23 CKB-CKM 0.828 53 APOA1 LBP ACY1 CKB-CKM 0.827
54 TIMP1 C9 KIT CKB-CKM 0.827 55 C9 KIT CDK5-CDK5R1 CKB-CKM 0.827
56 KIT APOA1 LBP ACY1 0.827 57 KIT APOA1 SERPINF2 CKB-CKM 0.827 58
APOA1 ACY1 CDK5-CDK5R1 CKB-CKM 0.827 59 C5 KIT CCL23 CDK5-CDK5R1
0.827 60 C9 KIT CKM ACY1 0.827 61 TIMP1 APOA1 ACY1 CKB-CKM 0.827 62
C5 KIT APOA1 LBP 0.827 63 TIMP1 KIT APOA1 CDK5-CDK5R1 0.827 64 KIT
CKM APOA1 CDK5-CDK5R1 0.826 65 KIT APOA1 HAMP CKB-CKM 0.826 66 C5
KIT ACY1 CDK5-CDK5R1 0.826 67 TIMP1 C9 KIT APOA1 0.826 68 C9 KIT
CKB-CKM HINT1 0.826 69 APOA1 LBP CKB-CKM HINT1 0.826 70 C9 KIT ACY1
CDK5-CDK5R1 0.826 71 TIMP1 APOA1 CDK5-CDK5R1 CKB-CKM 0.826 72 KIT
APOA1 CCL23 CDK5-CDK5R1 0.826 73 C9 CKM APOA1 CDK5-CDK5R1 0.826 74
C5 KIT CKB-CKM HINT1 0.825 75 C5 KIT SERPINF2 CDK5-CDK5R1 0.825 76
C9 KIT CKM CDK5-CDK5R1 0.825 77 TIMP1 C9 KIT ACY1 0.825 78 C5 CCL23
CDK5-CDK5R1 CKB-CKM 0.825 79 KIT APOA1 ENTPD1 CKB-CKM 0.825 80 C9
KIT APOA1 LBP 0.825 81 C5 KIT APOA1 KLK3-SERPINA3 0.825 82 C9 KIT
CKM HINT1 0.825 83 C5 APOA1 LBP CKB-CKM 0.825 84 KIT LBP ACY1
CKB-CKM 0.825 85 APOA1 CCL23 CDK5-CDK5R1 CKB-CKM 0.825 86 C9 CKM
APOA1 ACY1 0.824 87 KIT CKM APOA1 LBP 0.824 88 C9 KIT CKM CCL23
0.824 89 TIMP1 C5 KIT ACY1 0.824 90 C9 APOA1 CDK5-CDK5R1 CKB-CKM
0.824 91 KIT ACY1 CDK5-CDK5R1 CKB-CKM 0.824 92 C5 KIT ACY1 CKB-CKM
0.824 93 KIT CCL23 CDK5-CDK5R1 CKB-CKM 0.824 94 APOA1 ACY1 FSTL3
CKB-CKM 0.824 95 C9 C5 KIT CKM 0.824 96 C5 KIT CKM CDK5-CDK5R1
0.824 97 KIT CCL23 CDK5-CDK5R1 CKB-CKM 0.824 98 C5 APOA1 SERPINF2
CKB-CKM 0.824 99 C5 KIT APOA1 HAMP 0.824 100 C9 APOA1 CKB-CKM HINT1
0.824
TABLE-US-00025 TABLE 24 Panels of 5 Biomarkers Markers Mean CV AUC
1 C5 KIT APOA1 CDK5-CDK5R1 CKB-CKM 0.854 2 C5 KIT APOA1 ACY1
CKB-CKM 0.851 3 C5 KIT APOA1 CCL23 CKB-CKM 0.849 4 C5 KIT APOA1
CCL23 CKB-CKM 0.847 5 KIT APOA1 ACY1 CDK5-CDK5R1 CKB-CKM 0.847 6
KIT APOA1 LBP ACY1 CKB-CKM 0.847 7 C5 KIT APOA1 CKB-CKM HINT1 0.847
8 TIMP1 KIT APOA1 ACY1 CKB-CKM 0.847 9 C5 KIT APOA1 ACY1
CDK5-CDK5R1 0.846 10 C5 KIT APOA1 CCL23 CDK5-CDK5R1 0.846 11 KIT
APOA1 LBP CDK5-CDK5R1 CKB-CKM 0.845 12 C5 KIT CCL23 CDK5-CDK5R1
CKB-CKM 0.845 13 C5 KIT APOA1 CCL23 CDK5-CDK5R1 0.845 14 TIMP1 C5
KIT APOA1 CDK5-CDK5R1 0.845 15 TIMP1 KIT APOA1 CDK5-CDK5R1 CKB-CKM
0.844 16 KIT APOA1 CCL23 ACY1 CKB-CKM 0.844 17 KIT APOA1 CCL23
CDK5-CDK5R1 CKB-CKM 0.844 18 C5 KIT CCL23 CDK5-CDK5R1 CKB-CKM 0.844
19 C5 KIT CKM APOA1 CDK5-CDK5R1 0.844 20 C5 KIT APOA1 LBP
CDK5-CDK5R1 0.844 21 C5 IL12A-IL12B KIT APOA1 CKB-CKM 0.844 22 KIT
APOA1 CCL23 ACY1 CKB-CKM 0.843 23 C5 KIT APOA1 CCL23 ACY1 0.843 24
KIT APOA1 CCL23 CDK5-CDK5R1 CKB-CKM 0.843 25 C9 KIT CKM APOA1
CDK5-CDK5R1 0.843 26 C5 KIT APOA1 CCL23 ACY1 0.843 27 C9 KIT APOA1
CDK5-CDK5R1 CKB-CKM 0.843 28 C5 KIT APOA1 LBP CKB-CKM 0.843 29
IL12A-IL12B KIT APOA1 CDK5-CDK5R1 CKB-CKM 0.843 30 C9 C5 KIT APOA1
CKB-CKM 0.843 31 C9 KIT APOA1 ACY1 CKB-CKM 0.843 32 TIMP1 C5 KIT
CDK5-CDK5R1 CKB-CKM 0.843 33 C5 APOA1 CCL23 CDK5-CDK5R1 CKB-CKM
0.843 34 C9 KIT CKM APOA1 ACY1 0.843 35 TIMP1 C5 KIT APOA1 ACY1
0.842 36 C5 KIT ACY1 CDK5-CDK5R1 CKB-CKM 0.842 37 C5 KIT APOA1
SERPINF2 CKB-CKM 0.842 38 C5 IL12A-IL12B KIT APOA1 CDK5-CDK5R1
0.841 39 KIT APOA1 CDK5-CDK5R1 HAMP CKB-CKM 0.841 40 C5 APOA1 CCL23
CKB-CKM HINT1 0.841 41 C5 APOA1 CDK5-CDK5R1 CKB-CKM HINT1 0.841 42
C9 KIT APOA1 CCL23 CKB-CKM 0.841 43 C5 APOA1 ACY1 CDK5-CDK5R1
CKB-CKM 0.841 44 TIMP1 C5 KIT APOA1 CKB-CKM 0.841 45 C9 KIT APOA1
CKB-CKM HINT1 0.841 46 C5 APOA1 ACY1 CKB-CKM HINT1 0.840 47 TIMP1
KIT ACY1 CDK5-CDK5R1 CKB-CKM 0.840 48 C9 IL12A-IL12B KIT APOA1
CKB-CKM 0.840 49 C5 KIT APOA1 CDK5-CDK5R1 FSTL3 0.840 50 C9 KIT CKM
APOA1 HINT1 0.840 51 C9 C5 KIT ACY1 CKB-CKM 0.840 52 C9 C5 KIT CKM
APOA1 0.840 53 C5 KIT CKM APOA1 ACY1 0.840 54 C5 KIT CCL23 ACY1
CKB-CKM 0.840 55 IL12A-IL12B KIT APOA1 LBP CKB-CKM 0.840 56 C5 KIT
CCL23 ACY1 CKB-CKM 0.840 57 C5 KIT APOA1 SERPINF2 CDK5-CDK5R1 0.840
58 C9 KIT CCL23 ACY1 CKB-CKM 0.839 59 KIT APOA1 LBP CKB-CKM HINT1
0.839 60 IL12A-IL12B KIT APOA1 ACY1 CKB-CKM 0.839 61 C5 KIT APOA1
CDK5-CDK5R1 HAMP 0.839 62 C5 KIT APOA1 CDK5-CDK5R1 HINT1 0.839 63
C9 KIT CKM APOA1 CCL23 0.839 64 C9 C5 KIT APOA1 CDK5-CDK5R1 0.839
65 C5 KIT LBP CDK5-CDK5R1 CKB-CKM 0.839 66 KIT APOA1 CCL23 LBP
CKB-CKM 0.839 67 KIT APOA1 CCL23 LBP CKB-CKM 0.839 68 KIT APOA1
SERPINF2 ACY1 CKB-CKM 0.838 69 C5 KIT APOA1 ACY1 FSTL3 0.838 70 C5
IL12A-IL12B KIT APOA1 CCL23 0.838 71 KIT APOA1 ACY1 FSTL3 CKB-CKM
0.838 72 C5 KIT APOA1 ENTPD1 CKB-CKM 0.838 73 C9 C5 KIT CCL23
CKB-CKM 0.838 74 C5 APOA1 SERPINF2 CDK5-CDK5R1 CKB-CKM 0.838 75 C9
C5 KIT CKB-CKM HINT1 0.838 76 KIT CKM APOA1 LBP CDK5-CDK5R1 0.838
77 C9 KIT APOA1 CCL23 ACY1 0.838 78 KIT APOA1 ACY1 CKB-CKM HINT1
0.838 79 TIMP1 C9 C5 KIT CKB-CKM 0.838 80 C5 KIT SERPINF2
CDK5-CDK5R1 CKB-CKM 0.838 81 C5 KIT CCL23 CKB-CKM HINT1 0.838 82 C5
KIT CCL23 ACY1 CDK5-CDK5R1 0.838 83 C5 KIT CCL23 ACY1 CDK5-CDK5R1
0.838 84 C5 KIT APOA1 LBP ACY1 0.838 85 TIMP1 C5 APOA1 CDK5-CDK5R1
CKB-CKM 0.838 86 IL12A-IL12B KIT APOA1 CCL23 CKB-CKM 0.838 87 TIMP1
C5 KIT ACY1 CDK5-CDK5R1 0.838 88 C9 KIT APOA1 LBP CKB-CKM 0.838 89
KIT APOA1 SERPINF2 CDK5-CDK5R1 CKB-CKM 0.838 90 TIMP1 KIT APOA1
CCL23 CKB-CKM 0.838 91 TIMP1 KIT LBP CDK5-CDK5R1 CKB-CKM 0.838 92
C5 KIT APOA1 CCL23 HINT1 0.838 93 KIT APOA1 CCL23 CKB-CKM HINT1
0.838 94 C9 C5 KIT APOA1 CCL23 0.837 95 C9 C5 KIT APOA1 ACY1 0.837
96 C5 KIT LBP ACY1 CKB-CKM 0.837 97 C5 KIT CKM APOA1 CCL23 0.837 98
C5 KIT CDK5-CDK5R1 CKB-CKM HINT1 0.837 99 C5 APOA1 CCL23 ACY1
CKB-CKM 0.837 100 C5 KIT APOA1 FSTL3 CKB-CKM 0.837
TABLE-US-00026 TABLE 25 Panels of 6 Biomarkers Markers Mean CV AUC
1 C5 KIT APOA1 ACY1 CDK5-CDK5R1 0.860 CKB-CKM 2 C5 KIT APOA1 CCL23
CDK5-CDK5R1 0.859 CKB-CKM 3 C5 KIT APOA1 CCL23 CDK5-CDK5R1 0.858
CKB-CKM 4 C5 KIT APOA1 CCL23 ACY1 0.857 CKB-CKM 5 C5 KIT APOA1
CCL23 ACY1 0.856 CKB-CKM 6 TIMP1 C5 KIT APOA1 CDK5-CDK5R1 0.856
CKB-CKM 7 C5 IL12A-IL12B KIT APOA1 CDK5-CDK5R1 0.855 CKB-CKM 8 C5
KIT APOA1 LBP CDK5-CDK5R1 0.855 CKB-CKM 9 C5 KIT APOA1 LBP ACY1
0.855 CKB-CKM 10 TIMP1 KIT APOA1 ACY1 CDK5-CDK5R1 0.854 CKB-CKM 11
C5 KIT APOA1 CDK5-CDK5R1 CKB-CKM 0.854 HINT1 12 C5 KIT APOA1 CCL23
CKB-CKM 0.853 HINT1 13 C5 KIT CKM APOA1 CCL23 0.853 CDK5-CDK5R1 14
C5 KIT APOA1 SERPINF2 CDK5-CDK5R1 0.853 CKB-CKM 15 C5 KIT APOA1
ACY1 CKB-CKM 0.852 HINT1 16 KIT APOA1 LBP ACY1 CDK5-CDK5R1 0.852
CKB-CKM 17 C5 IL12A-IL12B KIT APOA1 ACY1 0.852 CKB-CKM 18 C9 C5 KIT
CKM APOA1 0.852 CDK5-CDK5R1 19 C5 IL12A-IL12B KIT APOA1 CCL23 0.852
CKB-CKM 20 C5 KIT APOA1 SERPINF2 ACY1 0.851 CKB-CKM 21 C5 KIT APOA1
CDK5-CDK5R1 FSTL3 0.851 CKB-CKM 22 KIT APOA1 CCL23 ACY1 CDK5-CDK5R1
0.851 CKB-CKM 23 C9 KIT APOA1 CCL23 ACY1 0.851 CKB-CKM 24 TIMP1 C5
KIT APOA1 ACY1 0.851 CKB-CKM 25 C5 KIT APOA1 CCL23 ACY1 0.851
CDK5-CDK5R1 26 KIT APOA1 CCL23 ACY1 CDK5-CDK5R1 0.851 CKB-CKM 27 C5
KIT APOA1 CDK5-CDK5R1 HAMP 0.851 CKB-CKM 28 C9 KIT APOA1 ACY1
CDK5-CDK5R1 0.850 CKB-CKM 29 TIMP1 C5 KIT ACY1 CDK5-CDK5R1 0.850
CKB-CKM 30 C5 KIT APOA1 CCL23 LBP 0.850 CKB-CKM 31 C9 KIT CKM APOA1
CCL23 0.850 ACY1 32 TIMP1 C5 KIT APOA1 ACY1 0.850 CDK5-CDK5R1 33 C5
KIT APOA1 CCL23 ACY1 0.850 CDK5-CDK5R1 34 KIT APOA1 CCL23 LBP ACY1
0.850 CKB-CKM 35 C5 APOA1 CCL23 ACY1 CDK5-CDK5R1 0.850 CKB-CKM 36
C5 KIT CKM APOA1 CCL23 0.849 CDK5-CDK5R1 37 C5 KIT LBP ACY1
CDK5-CDK5R1 0.849 CKB-CKM 38 IL12A-IL12B KIT APOA1 ACY1 CDK5-CDK5R1
0.849 CKB-CKM 39 C5 KIT CCL23 ACY1 CDK5-CDK5R1 0.849 CKB-CKM 40
IL12A-IL12B KIT APOA1 LBP CDK5-CDK5R1 0.849 CKB-CKM 41 C5 KIT APOA1
ACY1 FSTL3 0.849 CKB-CKM 42 C9 KIT APOA1 CCL23 CDK5-CDK5R1 0.849
CKB-CKM 43 KIT APOA1 CCL23 LBP CDK5-CDK5R1 0.849 CKB-CKM 44 C9 C5
KIT APOA1 ACY1 0.849 CKB-CKM 45 C5 KIT CKM APOA1 LBP 0.849
CDK5-CDK5R1 46 C5 KIT CCL23 ACY1 CDK5-CDK5R1 0.849 CKB-CKM 47 C5
KIT APOA1 SERPINF2 CCL23 0.848 CKB-CKM 48 C9 C5 KIT APOA1
CDK5-CDK5R1 0.848 CKB-CKM 49 C5 KIT APOA1 LBP CKB-CKM 0.848 HINT1
50 C5 KIT CKM APOA1 SERPINF2 0.848 CDK5-CDK5R1 51 KIT APOA1 ACY1
CDK5-CDK5R1 HAMP 0.848 CKB-CKM 52 TIMP1 C5 KIT CCL23 CDK5-CDK5R1
0.848 CKB-CKM 53 C5 IL12A-IL12B KIT APOA1 LBP 0.848 CKB-CKM 54 C5
KIT APOA1 CCL23 LBP 0.848 CKB-CKM 55 TIMP1 C5 KIT APOA1 CKB-CKM
0.848 HINT1 56 C9 C5 KIT APOA1 CCL23 0.848 CKB-CKM 57 KIT APOA1
CCL23 LBP ACY1 0.848 CKB-CKM 58 KIT APOA1 SERPINF2 ACY1 CDK5-CDK5R1
0.848 CKB-CKM 59 TIMP1 C5 KIT CCL23 CDK5-CDK5R1 0.848 CKB-CKM 60 C9
KIT CKM APOA1 ACY1 0.848 CDK5-CDK5R1 61 C9 C5 KIT CCL23 ACY1 0.847
CKB-CKM 62 TIMP1 KIT APOA1 CCL23 CDK5-CDK5R1 0.847 CKB-CKM 63 KIT
APOA1 CCL23 LBP CDK5-CDK5R1 0.847 CKB-CKM 64 C5 IL12A-IL12B KIT
APOA1 CCL23 0.847 CKB-CKM 65 C5 IL12A-IL12B KIT CCL23 CDK5-CDK5R1
0.847 CKB-CKM 66 KIT APOA1 ACY1 CDK5-CDK5R1 FSTL3 0.847 CKB-CKM 67
C5 KIT APOA1 CCL23 CKB-CKM 0.847 HINT1 68 C5 KIT CKM APOA1 ACY1
0.847 CDK5-CDK5R1 69 C5 IL12A-IL12B KIT APOA1 CCL23 0.847
CDK5-CDK5R1 70 TIMP1 KIT APOA1 LBP CDK5-CDK5R1 0.847 CKB-CKM 71 C5
KIT APOA1 LBP ACY1 0.847 CDK5-CDK5R1 72 TIMP1 KIT APOA1 CDK5-CDK5R1
HAMP 0.847 CKB-CKM 73 TIMP1 IL12A-IL12B KIT APOA1 CDK5-CDK5R1 0.847
CKB-CKM 74 C5 KIT SERPINF2 ACY1 CDK5-CDK5R1 0.847 CKB-CKM 75 TIMP1
C5 KIT APOA1 CCL23 0.847 CDK5-CDK5R1 76 C5 KIT APOA1 CCL23 LBP
0.847 CDK5-CDK5R1 77 C9 KIT CKM APOA1 CCL23 0.847 CDK5-CDK5R1 78
TIMP1 KIT APOA1 LBP ACY1 0.847 CKB-CKM 79 C5 KIT CKM APOA1 CCL23
0.847 ACY1 80 TIMP1 C5 KIT APOA1 CCL23 0.847 CKB-CKM 81 C5 APOA1
CCL23 CDK5-CDK5R1 CKB-CKM 0.847 HINT1 82 C9 C5 KIT CKM APOA1 0.847
HINT1 83 TIMP1 C9 KIT APOA1 CDK5-CDK5R1 0.847 CKB-CKM 84 TIMP1 C5
KIT APOA1 CCL23 0.846 CDK5-CDK5R1 85 C9 KIT APOA1 CDK5-CDK5R1 HAMP
0.846 CKB-CKM 86 C5 KIT CCL23 LBP CDK5-CDK5R1 0.846 CKB-CKM 87
TIMP1 C5 KIT LBP CDK5-CDK5R1 0.846 CKB-CKM 88 IL12A-IL12B KIT APOA1
CCL23 CDK5-CDK5R1 0.846 CKB-CKM 89 TIMP1 C5 KIT CKM APOA1 0.846
CDK5-CDK5R1 90 C5 KIT APOA1 ACY1 HAMP 0.846 CKB-CKM 91 TIMP1 C5 KIT
APOA1 CCL23 0.846 CKB-CKM 92 C9 C5 KIT APOA1 CKB-CKM 0.846 HINT1 93
C5 IL12A-IL12B KIT APOA1 CKB-CKM 0.846 HINT1 94 C5 KIT APOA1 ACY1
CDK5-CDK5R1 0.846 FSTL3 95 C9 C5 KIT APOA1 CCL23 0.846 CDK5-CDK5R1
96 C5 KIT CKM APOA1 LBP 0.846 ACY1 97 C9 KIT CKM APOA1 LBP 0.846
CDK5-CDK5R1 98 KIT APOA1 LBP CDK5-CDK5R1 HAMP 0.846 CKB-CKM 99 C5
KIT APOA1 CCL23 SERPINF2 0.846 CKB-CKM 100 C9 C5 IL12A-IL12B KIT
APOA1 0.846 CKB-CKM
TABLE-US-00027 TABLE 26 Panels of 7 Biomarkers Markers Mean CV AUC
1 C5 KIT APOA1 CCL23 ACY1 0.864 CDK5-CDK5R1 CKB-CKM 2 C5 KIT APOA1
CCL23 ACY1 0.863 CDK5-CDK5R1 CKB-CKM 3 TIMP1 C5 KIT APOA1 ACY1
0.863 CDK5-CDK5R1 CKB-CKM 4 C5 KIT APOA1 ACY1 CDK5-CDK5R1 0.861
FSTL3 CKB-CKM 5 C5 KIT APOA1 LBP ACY1 0.861 CDK5-CDK5R1 CKB-CKM 6
C5 KIT APOA1 SERPINF2 ACY1 0.860 CDK5-CDK5R1 CKB-CKM 7 C5
IL12A-IL12B KIT APOA1 ACY1 0.860 CDK5-CDK5R1 CKB-CKM 8 C5
IL12A-IL12B KIT APOA1 CCL23 0.860 CDK5-CDK5R1 CKB-CKM 9 C5 KIT
APOA1 CCL23 LBP 0.859 CDK5-CDK5R1 CKB-CKM 10 C5 IL12A-IL12B KIT
APOA1 LBP 0.859 CDK5-CDK5R1 CKB-CKM 11 C5 KIT APOA1 CCL23 LBP 0.859
CDK5-CDK5R1 CKB-CKM 12 C5 KIT APOA1 CCL23 CDK5-CDK5R1 0.859 CKB-CKM
HINT1 13 TIMP1 C5 KIT APOA1 CCL23 0.859 CDK5-CDK5R1 CKB-CKM 14 C5
KIT APOA1 CCL23 LBP 0.859 ACY1 CKB-CKM 15 C5 IL12A-IL12B KIT APOA1
CCL23 0.858 CDK5-CDK5R1 CKB-CKM 16 TIMP1 C5 KIT APOA1 CCL23 0.858
CDK5-CDK5R1 CKB-CKM 17 C5 KIT APOA1 CCL23 ACY1 0.858 CKB-CKM HINT1
18 C5 IL12A-IL12B KIT APOA1 CCL23 0.858 ACY1 CKB-CKM 19 C5 KIT
APOA1 SERPINF2 CCL23 0.858 CDK5-CDK5R1 CKB-CKM 20 C5 KIT APOA1 ACY1
CDK5-CDK5R1 0.857 HAMP CKB-CKM 21 C9 KIT APOA1 CCL23 ACY1 0.857
CDK5-CDK5R1 CKB-CKM 22 C5 KIT CKM APOA1 CCL23 0.857 ACY1
CDK5-CDK5R1 23 TIMP1 C5 IL12A-IL12B KIT APOA1 0.856 CDK5-CDK5R1
CKB-CKM 24 TIMP1 C5 KIT APOA1 CCL23 0.856 ACY1 CKB-CKM 25 C5 KIT
APOA1 SERPINF2 CCL23 0.856 ACY1 CKB-CKM 26 C5 KIT APOA1 CCL23
SERPINF2 0.856 ACY1 CKB-CKM 27 C9 C5 KIT CKM APOA1 0.856 CCL23
CDK5-CDK5R1 28 IL12A-IL12B KIT APOA1 LBP ACY1 0.856 CDK5-CDK5R1
CKB-CKM 29 C5 IL12A-IL12B KIT APOA1 CCL23 0.856 ACY1 CKB-CKM 30 C5
KIT APOA1 CCL23 LBP 0.856 ACY1 CKB-CKM 31 C5 KIT APOA1 CCL23
SERPINF2 0.856 CDK5-CDK5R1 CKB-CKM 32 C5 KIT CKM APOA1 CCL23 0.856
ACY1 CDK5-CDK5R1 33 C5 KIT APOA1 ACY1 CDK5-CDK5R1 0.855 CKB-CKM
HINT1 34 C9 C5 KIT APOA1 CCL23 0.855 CDK5-CDK5R1 CKB-CKM 35 TIMP1
KIT APOA1 LBP ACY1 0.855 CDK5-CDK5R1 CKB-CKM 36 KIT APOA1 CCL23 LBP
ACY1 0.855 CDK5-CDK5R1 CKB-CKM 37 C5 KIT APOA1 SERPINF2 LBP 0.855
ACY1 CKB-CKM 38 C5 KIT APOA1 CCL23 CDK5-CDK5R1 0.855 CKB-CKM HINT1
39 C9 C5 KIT APOA1 CCL23 0.855 ACY1 CKB-CKM 40 C5 KIT APOA1 CCL23
CCL23 0.855 CDK5-CDK5R1 CKB-CKM 41 TIMP1 C5 KIT APOA1 SERPINF2
0.855 CDK5-CDK5R1 CKB-CKM 42 TIMP1 C5 KIT APOA1 LBP 0.854
CDK5-CDK5R1 CKB-CKM 43 C5 KIT APOA1 CCL23 CCL23 0.854 ACY1 CKB-CKM
44 C5 KIT APOA1 LBP CDK5-CDK5R1 0.854 CKB-CKM HINT1 45 C5 KIT APOA1
CCL23 LBP 0.854 CKB-CKM HINT1 46 C5 IL12A-IL12B KIT APOA1 LBP 0.854
ACY1 CKB-CKM 47 C9 KIT CKM APOA1 CCL23 0.854 ACY1 CDK5-CDK5R1 48 C5
KIT CKM APOA1 CCL23 0.854 SERPINF2 CDK5-CDK5R1 49 TIMP1 C5 KIT
APOA1 CCL23 0.854 ACY1 CKB-CKM 50 C5 KIT APOA1 CCL23 ACY1 0.854
FSTL3 CKB-CKM 51 C5 IL12A-IL12B KIT APOA1 CDK5-CDK5R1 0.854 HAMP
CKB-CKM 52 TIMP1 IL12A-IL12B KIT APOA1 ACY1 0.854 CDK5-CDK5R1
CKB-CKM 53 C5 KIT APOA1 CCL23 CDK5-CDK5R1 0.854 FSTL3 CKB-CKM 54 C5
KIT CKM APOA1 CCL23 0.854 LBP CDK5-CDK5R1 55 C5 KIT APOA1 CCL23
CDK5-CDK5R1 0.854 HAMP CKB-CKM 56 TIMP1 KIT APOA1 ACY1 CDK5-CDK5R1
0.854 HAMP CKB-CKM 57 TIMP1 KIT APOA1 CCL23 ACY1 0.854 CDK5-CDK5R1
CKB-CKM 58 C9 C5 KIT APOA1 ACY1 0.854 CDK5-CDK5R1 CKB-CKM 59 C5
IL12A-IL12B KIT APOA1 SERPINF2 0.854 CDK5-CDK5R1 CKB-CKM 60 C5 KIT
APOA1 CCL23 CDK5-CDK5R1 0.854 HAMP CKB-CKM 61 KIT APOA1 CCL23 LBP
ACY1 0.854 CDK5-CDK5R1 CKB-CKM 62 C9 C5 KIT CKM APOA1 0.854
CDK5-CDK5R1 HINT1 63 TIMP1 C5 KIT APOA1 CDK5-CDK5R1 0.854 CKB-CKM
HINT1 64 C5 KIT CKM APOA1 LBP 0.854 ACY1 CDK5-CDK5R1 65 TIMP1 C5
KIT CKM APOA1 0.853 CCL23 CDK5-CDK5R1 66 C5 KIT APOA1 LBP
CDK5-CDK5R1 0.853 FSTL3 CKB-CKM 67 C9 C5 KIT CKM APOA1 0.853 ACY1
CDK5-CDK5R1 68 C5 IL12A-IL12B KIT APOA1 CCL23 0.853 LBP CKB-CKM 69
C9 KIT APOA1 LBP ACY1 0.853 CDK5-CDK5R1 CKB-CKM 70 C5 KIT CCL23 LBP
ACY1 0.853 CDK5-CDK5R1 CKB-CKM 71 C5 KIT APOA1 LBP ACY1 0.853
CKB-CKM HINT1 72 KIT APOA1 LBP ACY1 CDK5-CDK5R1 0.853 FSTL3 CKB-CKM
73 C5 KIT CKM APOA1 LBP 0.853 CDK5-CDK5R1 HINT1 74 C9 C5 KIT APOA1
CDK5-CDK5R1 0.853 CKB-CKM HINT1 75 C5 KIT APOA1 SERPINF2 LBP 0.853
CDK5-CDK5R1 CKB-CKM 76 C9 C5 KIT CKM APOA1 0.853 CCL23 ACY1 77
IL12A-IL12B KIT APOA1 CCL23 ACY1 0.853 CDK5-CDK5R1 CKB-CKM 78 TIMP1
C5 KIT APOA1 CDK5-CDK5R1 0.853 HAMP CKB-CKM 79 C5 KIT APOA1 LBP
ACY1 0.853 FSTL3 CKB-CKM 80 C5 KIT CKM APOA1 SERPINF2 0.853 ACY1
CDK5-CDK5R1 81 TIMP1 C5 KIT APOA1 ACY1 0.853 CKB-CKM HINT1 82 KIT
APOA1 LBP ACY1 CDK5-CDK5R1 0.852 HAMP CKB-CKM 83 C5 IL12A-IL12B KIT
APOA1 CDK5-CDK5R1 0.852 FSTL3 CKB-CKM 84 C5 KIT CKM APOA1 SERPINF2
0.852 CCL23 CDK5-CDK5R1 85 C5 KIT CCL23 LBP ACY1 0.852 CDK5-CDK5R1
CKB-CKM 86 C9 C5 KIT CKM APOA1 0.852 CCL23 CDK5-CDK5R1 87 C5 KIT
APOA1 CCL23 ACY1 0.852 FSTL3 CKB-CKM 88 C5 KIT APOA1 CCL23
CDK5-CDK5R1 0.852 FSTL3 CKB-CKM 89 TIMP1 C5 KIT LBP ACY1 0.852
CDK5-CDK5R1 CKB-CKM 90 C5 KIT CKM APOA1 CCL23 0.852 LBP CDK5-CDK5R1
91 C5 KIT APOA1 SERPINF2 CDK5-CDK5R1 0.852 CKB-CKM HINT1 92
IL12A-IL12B KIT APOA1 CCL23 LBP 0.852 CDK5-CDK5R1 CKB-CKM 93 C5 KIT
APOA1 LBP CDK5-CDK5R1 0.852 HAMP CKB-CKM 94 IL12A-IL12B KIT APOA1
CCL23 ACY1 0.852 CDK5-CDK5R1 CKB-CKM 95 C5 KIT APOA1 CCL23 ACY1
0.852 CKB-CKM HINT1 96 C5 KIT CKM APOA1 CCL23 0.852 CDK5-CDK5R1
HINT1 97 C5 IL12A-IL12B KIT LBP ACY1 0.852 CDK5-CDK5R1 CKB-CKM 98
TIMP1 C5 KIT APOA1 LBP 0.852 ACY1 CKB-CKM 99 TIMP1 C5 KIT CKM APOA1
0.852 ACY1 CDK5-CDK5R1 100 TIMP1 KIT APOA1 CCL23 ACY1 0.852
CDK5-CDK5R1 CKB-CKM
TABLE-US-00028 TABLE 27 Panels of 8 Biomarkers Markers Mean CV AUC
1 C5 KIT APOA1 CCL23 LBP 0.864 ACY1 CDK5-CDK5R1 CKB-CKM 2 C5
IL12A-IL12B KIT APOA1 CCL23 0.864 ACY1 CDK5-CDK5R1 CKB-CKM 3 C5 KIT
APOA1 CCL23 LBP 0.864 ACY1 CDK5-CDK5R1 CKB-CKM 4 TIMP1 C5 KIT APOA1
CCL23 0.863 ACY1 CDK5-CDK5R1 CKB-CKM 5 TIMP1 C5 IL12A-IL12B KIT
APOA1 0.863 ACY1 CDK5-CDK5R1 CKB-CKM 6 TIMP1 C5 KIT APOA1 CCL23
0.863 ACY1 CDK5-CDK5R1 CKB-CKM 7 C5 IL12A-IL12B KIT APOA1 LBP 0.862
ACY1 CDK5-CDK5R1 CKB-CKM 8 TIMP1 C5 KIT APOA1 SERPINF2 0.862 ACY1
CDK5-CDK5R1 CKB-CKM 9 C5 IL12A-IL12B KIT APOA1 CCL23 0.862 ACY1
CDK5-CDK5R1 CKB-CKM 10 C5 KIT APOA1 SERPINF2 CCL23 0.862 ACY1
CDK5-CDK5R1 CKB-CKM 11 C5 KIT APOA1 CCL23 SERPINF2 0.861 ACY1
CDK5-CDK5R1 CKB-CKM 12 TIMP1 C5 KIT APOA1 LBP 0.861 ACY1
CDK5-CDK5R1 CKB-CKM 13 C5 KIT APOA1 LBP ACY1 0.861 CDK5-CDK5R1
FSTL3 CKB-CKM 14 C5 IL12A-IL12B KIT APOA1 CCL23 0.861 LBP
CDK5-CDK5R1 CKB-CKM 15 C5 KIT APOA1 CCL23 LBP 0.860 CDK5-CDK5R1
CKB-CKM HINT1 16 C5 KIT APOA1 CCL23 ACY1 0.860 CDK5-CDK5R1 CKB-CKM
HINT1 17 C5 IL12A-IL12B KIT APOA1 ACY1 0.860 CDK5-CDK5R1 FSTL3
CKB-CKM 18 C5 IL12A-IL12B KIT APOA1 SERPINF2 0.860 ACY1 CDK5-CDK5R1
CKB-CKM 19 C5 KIT CKM APOA1 CCL23 0.860 LBP ACY1 CDK5-CDK5R1 20 C5
KIT APOA1 CCL23 CCL23 0.859 ACY1 CDK5-CDK5R1 CKB-CKM 21 C5 KIT
APOA1 CCL23 ACY1 0.859 CDK5-CDK5R1 HAMP CKB-CKM 22 C5 KIT APOA1
SERPINF2 LBP 0.859 ACY1 CDK5-CDK5R1 CKB-CKM 23 C5 KIT APOA1 CCL23
ACY1 0.859 CDK5-CDK5R1 FSTL3 CKB-CKM 24 TIMP1 C5 IL12A-IL12B KIT
APOA1 0.859 CCL23 CDK5-CDK5R1 CKB-CKM 25 C5 KIT APOA1 CCL23 ACY1
0.859 CDK5-CDK5R1 HAMP CKB-CKM 26 C5 IL12A-IL12B KIT APOA1 CCL23
0.859 LBP CDK5-CDK5R1 CKB-CKM 27 C5 KIT APOA1 CCL23 ACY1 0.859
CDK5-CDK5R1 FSTL3 CKB-CKM 28 TIMP1 C5 KIT APOA1 ACY1 0.859
CDK5-CDK5R1 HAMP CKB-CKM 29 C5 IL12A-IL12B KIT APOA1 SERPINF2 0.859
CCL23 CDK5-CDK5R1 CKB-CKM 30 C5 KIT APOA1 CCL23 LBP 0.859 ACY1
CKB-CKM HINT1 31 C9 C5 KIT CKM APOA1 0.859 CCL23 ACY1 CDK5-CDK5R1
32 C9 KIT APOA1 CCL23 LBP 0.859 ACY1 CDK5-CDK5R1 CKB-CKM 33 C9 C5
KIT APOA1 CCL23 0.859 ACY1 CDK5-CDK5R1 CKB-CKM 34 C5 KIT APOA1 ACY1
CDK5-CDK5R1 0.858 FSTL3 HAMP CKB-CKM 35 TIMP1 C5 KIT APOA1 CCL23
0.858 LBP CDK5-CDK5R1 CKB-CKM 36 C5 KIT APOA1 LBP ACY1 0.858
CDK5-CDK5R1 CKB-CKM HINT1 37 C5 IL12A-IL12B KIT APOA1 ACY1 0.858
CDK5-CDK5R1 HAMP CKB-CKM 38 TIMP1 C5 KIT APOA1 SERPINF2 0.858 CCL23
CDK5-CDK5R1 CKB-CKM 39 TIMP1 C5 IL12A-IL12B KIT APOA1 0.858 CCL23
CDK5-CDK5R1 CKB-CKM 40 C5 KIT APOA1 SERPINF2 CCL23 0.858
CDK5-CDK5R1 CKB-CKM HINT1 41 TIMP1 C5 KIT APOA1 CCL23 0.858 LBP
CDK5-CDK5R1 CKB-CKM 42 C5 IL12A-IL12B KIT APOA1 CCL23 0.858 LBP
ACY1 CKB-CKM 43 C5 KIT APOA1 SERPINF2 CCL23 0.858 LBP CDK5-CDK5R1
CKB-CKM 44 C5 KIT CKM APOA1 SERPINF2 0.857 LBP ACY1 CDK5-CDK5R1 45
TIMP1 C5 KIT APOA1 CCL23 0.857 CDK5-CDK5R1 CKB-CKM HINT1 46 C5 KIT
APOA1 CCL23 SERPINF2 0.857 LBP ACY1 CKB-CKM 47 TIMP1 C5 KIT APOA1
ACY1 0.857 CDK5-CDK5R1 CKB-CKM HINT1 48 C5 IL12A-IL12B KIT APOA1
CCL23 0.857 CDK5-CDK5R1 CKB-CKM HINT1 49 C5 KIT APOA1 CCL23 ACY1
0.857 CDK5-CDK5R1 CKB-CKM HINT1 50 C5 KIT CKM APOA1 CCL23 0.857
SERPINF2 ACY1 CDK5-CDK5R1 51 C5 KIT APOA1 SERPINF2 ACY1 0.857
CDK5-CDK5R1 HAMP CKB-CKM 52 C5 KIT APOA1 GDF11 CCL23 0.857 ACY1
CDK5-CDK5R1 CKB-CKM 53 TIMP1 C5 IL12A-IL12B KIT APOA1 0.857 LBP
CDK5-CDK5R1 CKB-CKM 54 C9 C5 KIT APOA1 CCL23 0.857 ACY1 CKB-CKM
HINT1 55 TIMP1 C5 KIT APOA1 ACY1 0.857 CDK5-CDK5R1 FSTL3 CKB-CKM 56
TIMP1 C5 KIT APOA1 CCL23 0.857 LBP ACY1 CKB-CKM 57 C9 C5 KIT CKM
APOA1 0.857 CCL23 CDK5-CDK5R1 HINT1 58 C5 KIT APOA1 SERPINF2 ACY1
0.857 CDK5-CDK5R1 FSTL3 CKB-CKM 59 C5 KIT CKM APOA1 CCL23 0.857 LBP
ACY1 CDK5-CDK5R1 60 C5 KIT APOA1 LBP ACY1 0.857 CDK5-CDK5R1 HAMP
CKB-CKM 61 IL12A-IL12B KIT APOA1 CCL23 LBP 0.857 ACY1 CDK5-CDK5R1
CKB-CKM 62 C5 KIT APOA1 SERPINF2 CCL23 0.857 LBP ACY1 CKB-CKM 63 C9
C5 KIT APOA1 CCL23 0.857 LBP CDK5-CDK5R1 CKB-CKM 64 TIMP1 C9 KIT
APOA1 CCL23 0.856 ACY1 CDK5-CDK5R1 CKB-CKM 65 TIMP1 C5 KIT CKM
APOA1 0.856 CCL23 ACY1 CDK5-CDK5R1 66 C9 C5 KIT CKM APOA1 0.856
CCL23 CCL23 CDK5-CDK5R1 67 C5 IL12A-IL12B KIT APOA1 SERPINF2 0.856
LBP CDK5-CDK5R1 CKB-CKM 68 C5 IL12A-IL12B KIT APOA1 LBP 0.856
CDK5-CDK5R1 HAMP CKB-CKM 69 TIMP1 IL12A-IL12B KIT APOA1 LBP 0.856
ACY1 CDK5-CDK5R1 CKB-CKM 70 C5 IL12A-IL12B KIT APOA1 CCL23 0.856
CDK5-CDK5R1 HAMP CKB-CKM 71 TIMP1 C5 KIT APOA1 GDF11 0.856 ACY1
CDK5-CDK5R1 CKB-CKM 72 C9 C5 KIT CKM APOA1 0.856 CCL23 ACY1 HINT1
73 C9 C5 KIT CKM APOA1 0.856 CCL23 ACY1 CDK5-CDK5R1 74 TIMP1 C5 KIT
APOA1 CCL23 0.856 ACY1 CKB-CKM HINT1 75 C5 IL12A-IL12B KIT APOA1
LBP 0.856 CDK5-CDK5R1 FSTL3 CKB-CKM 76 TIMP1 KIT APOA1 CCL23 LBP
0.856 ACY1 CDK5-CDK5R1 CKB-CKM 77 TIMP1 C9 C5 KIT CKM 0.856 APOA1
CCL23 CDK5-CDK5R1 78 C5 KIT CKM APOA1 CCL23 0.856 LBP CDK5-CDK5R1
HINT1 79 C9 C5 KIT CKM APOA1 0.856 LBP ACY1 CDK5-CDK5R1 80 C5 KIT
CKM APOA1 CCL23 0.856 SERPINF2 LBP CDK5-CDK5R1 81 C9 KIT CKM APOA1
CCL23 0.856 LBP ACY1 CDK5-CDK5R1 82 C5 IL12A-IL12B KIT APOA1 CCL23
0.856 LBP ACY1 CKB-CKM 83 C5 KIT APOA1 CCL23 LBP 0.856 CDK5-CDK5R1
CKB-CKM HINT1 84 C5 KIT APOA1 CCL23 LBP 0.856 CDK5-CDK5R1 FSTL3
CKB-CKM 85 C5 IL12A-IL12B KIT APOA1 SERPINF2 0.856 CCL23 ACY1
CKB-CKM 86 TIMP1 C5 KIT APOA1 CCL23 0.856 CDK5-CDK5R1 CKB-CKM HINT1
87 IL12A-IL12B KIT APOA1 LBP ACY1 0.856 CDK5-CDK5R1 HAMP CKB-CKM 88
TIMP1 C9 KIT CKM APOA1 0.856 CCL23 ACY1 CDK5-CDK5R1 89 C9 C5 KIT
CKM APOA1 0.856 ACY1 CDK5-CDK5R1 HINT1 90 C5 IL12A-IL12B KIT CKM
APOA1 0.855 CCL23 ACY1 CDK5-CDK5R1 91 C9 C5 KIT APOA1 LBP 0.855
ACY1 CDK5-CDK5R1 CKB-CKM 92 C5 KIT CKM APOA1 SERPINF2 0.855 CCL23
ACY1 CDK5-CDK5R1 93 C9 C5 IL12A-IL12B KIT CKM 0.855 APOA1 CCL23
CDK5-CDK5R1 94 TIMP1 C5 KIT APOA1 CCL23 0.855 CDK5-CDK5R1 HAMP
CKB-CKM 95 C5 KIT CKM APOA1 CCL23 0.855 ACY1 CDK5-CDK5R1 FSTL3 96
TIMP1 C5 IL12A-IL12B KIT APOA1 0.855 CDK5-CDK5R1 HAMP CKB-CKM 97 C9
C5 KIT APOA1 CCL23 0.855 LBP ACY1 CKB-CKM 98 C9 C5 KIT APOA1 CCL23
0.855 ACY1 CDK5-CDK5R1 CKB-CKM 99 C9 C5 KIT CKM APOA1 0.855 CCL23
LBP CDK5-CDK5R1 100 C9 KIT APOA1 CCL23 ACY1 0.855 CDK5-CDK5R1 HAMP
CKB-CKM
TABLE-US-00029 TABLE 28 Panels of 9 Biomarkers Markers Mean CV AUC
1 C5 IL12A-IL12B KIT APOA1 CCL23 0.864 LBP ACY1 CDK5-CDK5R1 CKB-CKM
2 C5 IL12A-IL12B KIT APOA1 CCL23 0.864 LBP ACY1 CDK5-CDK5R1 CKB-CKM
3 C5 KIT APOA1 SERPINF2 CCL23 0.863 LBP ACY1 CDK5-CDK5R1 CKB-CKM 4
TIMP1 C5 KIT APOA1 CCL23 0.863 LBP ACY1 CDK5-CDK5R1 CKB-CKM 5 TIMP1
C5 IL12A-IL12B KIT APOA1 0.863 CCL23 ACY1 CDK5-CDK5R1 CKB-CKM 6
TIMP1 C5 IL12A-IL12B KIT APOA1 0.862 LBP ACY1 CDK5-CDK5R1 CKB-CKM 7
C5 KIT APOA1 CCL23 LBP 0.862 ACY1 CDK5-CDK5R1 CKB-CKM HINT1 8 C5
KIT APOA1 CCL23 SERPINF2 0.862 LBP ACY1 CDK5-CDK5R1 CKB-CKM 9 TIMP1
C5 IL12A-IL12B KIT APOA1 0.862 CCL23 ACY1 CDK5-CDK5R1 CKB-CKM 10
TIMP1 C5 KIT APOA1 SERPINF2 0.861 CCL23 ACY1 CDK5-CDK5R1 CKB-CKM 11
TIMP1 C5 KIT APOA1 SERPINF2 0.861 LBP ACY1 CDK5-CDK5R1 CKB-CKM 12
TIMP1 C5 IL12A-IL12B KIT APOA1 0.861 SERPINF2 ACY1 CDK5-CDK5R1
CKB-CKM 13 C5 IL12A-IL12B KIT APOA1 SERPINF2 0.861 CCL23 ACY1
CDK5-CDK5R1 CKB-CKM 14 TIMP1 C5 KIT APOA1 CCL23 0.861 LBP ACY1
CDK5-CDK5R1 CKB-CKM 15 TIMP1 C5 KIT APOA1 CCL23 0.861 ACY1
CDK5-CDK5R1 CKB-CKM HINT1 16 C9 C5 KIT APOA1 CCL23 0.861 LBP ACY1
CDK5-CDK5R1 CKB-CKM 17 C5 IL12A-IL12B KIT APOA1 SERPINF2 0.861
CCL23 LBP CDK5-CDK5R1 CKB-CKM 18 TIMP1 C5 IL12A-IL12B KIT APOA1
0.861 ACY1 CDK5-CDK5R1 HAMP CKB-CKM 19 C5 KIT APOA1 CCL23 LBP 0.861
ACY1 CDK5-CDK5R1 FSTL3 CKB-CKM 20 TIMP1 C5 KIT APOA1 CCL23 0.861
ACY1 CDK5-CDK5R1 HAMP CKB-CKM 21 TIMP1 C5 KIT APOA1 CCL23 0.860
SERPINF2 ACY1 CDK5-CDK5R1 CKB-CKM 22 C5 KIT CKM APOA1 CCL23 0.860
SERPINF2 LBP ACY1 CDK5-CDK5R1 23 C5 IL12A-IL12B KIT APOA1 LBP 0.860
ACY1 CDK5-CDK5R1 FSTL3 CKB-CKM 24 C5 KIT APOA1 CCL23 LBP 0.860 ACY1
CDK5-CDK5R1 HAMP CKB-CKM 25 C5 IL12A-IL12B KIT APOA1 SERPINF2 0.860
LBP ACY1 CDK5-CDK5R1 CKB-CKM 26 C5 IL12A-IL12B KIT APOA1 CCL23
0.860 SERPINF2 ACY1 CDK5-CDK5R1 CKB-CKM 27 C5 KIT APOA1 CCL23 LBP
0.860 ACY1 CDK5-CDK5R1 FSTL3 CKB-CKM 28 TIMP1 C5 KIT APOA1 CCL23
0.860 CCL23 ACY1 CDK5-CDK5R1 CKB-CKM 29 C5 KIT APOA1 CCL23 CCL23
0.860 LBP ACY1 CDK5-CDK5R1 CKB-CKM 30 TIMP1 C5 IL12A-IL12B KIT
APOA1 0.860 CCL23 LBP CDK5-CDK5R1 CKB-CKM 31 C9 C5 KIT CKM APOA1
0.860 CCL23 ACY1 CDK5-CDK5R1 HINT1 32 TIMP1 C5 KIT APOA1 SERPINF2
0.860 ACY1 CDK5-CDK5R1 HAMP CKB-CKM 33 C9 C5 KIT CKM APOA1 0.859
SERPINF2 CCL23 ACY1 CDK5-CDK5R1 34 TIMP1 C9 C5 KIT APOA1 0.859
CCL23 ACY1 CDK5-CDK5R1 CKB-CKM 35 C5 KIT APOA1 SERPINF2 CCL23 0.859
LBP CDK5-CDK5R1 CKB-CKM HINT1 36 TIMP1 C9 C5 KIT CKM 0.859 APOA1
CCL23 ACY1 CDK5-CDK5R1 37 C5 IL12A-IL12B KIT APOA1 CCL23 0.859 LBP
CDK5-CDK5R1 CKB-CKM HINT1 38 C5 KIT APOA1 CCL23 SERPINF2 0.859
CCL23 ACY1 CDK5-CDK5R1 CKB-CKM 39 C5 IL12A-IL12B KIT APOA1 CCL23
0.859 ACY1 CDK5-CDK5R1 HAMP CKB-CKM 40 TIMP1 C5 KIT APOA1 CCL23
0.859 LBP CDK5-CDK5R1 CKB-CKM HINT1 41 C5 KIT APOA1 SERPINF2 CCL23
0.859 ACY1 CDK5-CDK5R1 CKB-CKM HINT1 42 C5 KIT APOA1 GDF11 CCL23
0.859 LBP ACY1 CDK5-CDK5R1 CKB-CKM 43 C5 KIT APOA1 SERPINF2 LBP
0.859 ACY1 CDK5-CDK5R1 FSTL3 CKB-CKM 44 C5 KIT CKM APOA1 CCL23
0.859 LBP ACY1 CDK5-CDK5R1 HINT1 45 C5 IL12A-IL12B KIT APOA1 CCL23
0.859 ACY1 CDK5-CDK5R1 FSTL3 CKB-CKM 46 C5 KIT APOA1 CCL23 LBP
0.859 ACY1 CDK5-CDK5R1 CKB-CKM HINT1 47 C9 C5 KIT CKM APOA1 0.859
CCL23 LBP ACY1 CDK5-CDK5R1 48 C5 IL12A-IL12B KIT APOA1 CCL23 0.859
CCL23 ACY1 CDK5-CDK5R1 CKB-CKM 49 C9 C5 KIT APOA1 CCL23 0.858 ACY1
CDK5-CDK5R1 CKB-CKM HINT1 50 C9 C5 IL12A-IL12B KIT CKM 0.858 APOA1
CCL23 ACY1 CDK5-CDK5R1 51 C5 KIT APOA1 CCL23 ACY1 0.858 CDK5-CDK5R1
FSTL3 HAMP CKB-CKM 52 C5 IL12A-IL12B KIT APOA1 CCL23 0.858 ACY1
CDK5-CDK5R1 HAMP CKB-CKM 53 C5 KIT APOA1 CCL23 LBP 0.858 ACY1
CDK5-CDK5R1 HAMP CKB-CKM 54 C5 IL12A-IL12B KIT APOA1 CCL23 0.858
ACY1 CDK5-CDK5R1 FSTL3 CKB-CKM 55 TIMP1 C9 KIT APOA1 CCL23 0.858
LBP ACY1 CDK5-CDK5R1 CKB-CKM 56 C9 C5 KIT CKM APOA1 0.858 CCL23
CCL23 ACY1 CDK5-CDK5R1 57 TIMP1 C5 KIT APOA1 LBP 0.858 ACY1
CDK5-CDK5R1 FSTL3 CKB-CKM 58 C5 KIT APOA1 GDF11 CCL23 0.858
SERPINF2 ACY1 CDK5-CDK5R1 CKB-CKM 59 TIMP1 C5 IL12A-IL12B KIT APOA1
0.858 SERPINF2 CCL23 CDK5-CDK5R1 CKB-CKM 60 C5 KIT APOA1 SERPINF2
CCL23 0.858 ACY1 CDK5-CDK5R1 HAMP CKB-CKM 61 C5 IL12A-IL12B KIT CKM
APOA1 0.858 CCL23 LBP ACY1 CDK5-CDK5R1 62 TIMP1 C5 KIT APOA1 CCL23
0.858 ACY1 CDK5-CDK5R1 HAMP CKB-CKM 63 C5 KIT APOA1 SERPINF2 CCL23
0.858 ACY1 CDK5-CDK5R1 FSTL3 CKB-CKM 64 TIMP1 C5 KIT CKM APOA1
0.858 CCL23 SERPINF2 ACY1 CDK5-CDK5R1 65 C5 KIT APOA1 LBP ACY1
0.858 CDK5-CDK5R1 FSTL3 HAMP CKB-CKM 66 TIMP1 C5 KIT APOA1 CCL23
0.858 ACY1 CDK5-CDK5R1 CKB-CKM HINT1 67 TIMP1 C9 KIT APOA1 CCL23
0.858 ACY1 CDK5-CDK5R1 HAMP CKB-CKM 68 TIMP1 C5 IL12A-IL12B KIT
APOA1 0.858 CCL23 LBP CDK5-CDK5R1 CKB-CKM 69 C5 KIT APOA1 GDF11
CCL23 0.858 ACY1 CDK5-CDK5R1 CKB-CKM HINT1 70 C5 IL12A-IL12B KIT
APOA1 ACY1 0.858 CDK5-CDK5R1 FSTL3 HAMP CKB-CKM 71 TIMP1 C5 KIT
APOA1 LBP 0.858 ACY1 CDK5-CDK5R1 HAMP CKB-CKM 72 C9 C5 KIT APOA1
LBP 0.858 ACY1 CDK5-CDK5R1 CKB-CKM HINT1 73 TIMP1 C5 KIT APOA1
CCL23 0.858 ACY1 CDK5-CDK5R1 FSTL3 CKB-CKM 74 C5 IL12A-IL12B KIT
APOA1 LBP 0.858 ACY1 CDK5-CDK5R1 HAMP CKB-CKM 75 TIMP1 C5 KIT APOA1
LBP 0.858 ACY1 CDK5-CDK5R1 CKB-CKM HINT1 76 TIMP1 C5 KIT APOA1
GDF11 0.858 CCL23 ACY1 CDK5-CDK5R1 CKB-CKM 77 C9 C5 KIT APOA1 CCL23
0.858 LBP CDK5-CDK5R1 CKB-CKM HINT1 78 C5 KIT CKM APOA1 SERPINF2
0.858 CCL23 LBP ACY1 CDK5-CDK5R1 79 C9 IL12A-IL12B KIT APOA1 CCL23
0.858 LBP ACY1 CDK5-CDK5R1 CKB-CKM 80 TIMP1 C5 IL12A-IL12B KIT
APOA1 0.858 CCL23 CDK5-CDK5R1 HAMP CKB-CKM 81 C5 KIT APOA1 GDF11
CCL23 0.858 LBP ACY1 CDK5-CDK5R1 CKB-CKM 82 C9 C5 KIT CKM APOA1
0.858 CCL23 ACY1 CDK5-CDK5R1 HAMP 83 C5 IL12A-IL12B KIT APOA1 GDF11
0.858 CCL23 ACY1 CDK5-CDK5R1 CKB-CKM 84 C9 C5 KIT APOA1 CCL23 0.857
ACY1 CDK5-CDK5R1 HAMP CKB-CKM 85 C9 C5 KIT APOA1 SERPINF2 0.857
CCL23 ACY1 CDK5-CDK5R1 CKB-CKM 86 C5 IL12A-IL12B KIT APOA1 CCL23
0.857 LBP ACY1 CKB-CKM HINT1 87 C5 IL12A-IL12B KIT APOA1 SERPINF2
0.857 CCL23 LBP ACY1 CKB-CKM 88 C5 IL12A-IL12B KIT APOA1 CCL23
0.857 CCL23 LBP CDK5-CDK5R1 CKB-CKM 89 C5 KIT APOA1 CCL23 ACY1
0.857 CDK5-CDK5R1 FSTL3 CKB-CKM HINT1 90 TIMP1 IL12A-IL12B KIT
APOA1 LBP 0.857 ACY1 CDK5-CDK5R1 HAMP CKB-CKM 91 C5 IL12A-IL12B KIT
APOA1 SERPINF2 0.857 ACY1 CDK5-CDK5R1 FSTL3 CKB-CKM 92 TIMP1 C5 KIT
APOA1 GDF11 0.857 SERPINF2 ACY1 CDK5-CDK5R1 CKB-CKM 93 C9 KIT CKM
APOA1 CCL23 0.857 LBP ACY1 CDK5-CDK5R1 HINT1 94 TIMP1 C5 KIT APOA1
SERPINF2 0.857 CCL23 LBP CDK5-CDK5R1 CKB-CKM 95 TIMP1 C5
IL12A-IL12B KIT CKM 0.857 APOA1 CCL23 ACY1 CDK5-CDK5R1 96 C9 C5 KIT
CKM APOA1 0.857 LBP ACY1 CDK5-CDK5R1 HINT1 97 C5 KIT APOA1 CCL23
SERPINF2 0.857 ACY1 CDK5-CDK5R1 HAMP CKB-CKM 98 C5 IL12A-IL12B KIT
APOA1 GDF11 0.857 CCL23 ACY1 CDK5-CDK5R1 CKB-CKM 99 C9 C5 KIT CKM
APOA1 0.857 CCL23 LBP ACY1 CDK5-CDK5R1 100 TIMP1 C5 KIT APOA1 ACY1
0.857 CDK5-CDK5R1 FSTL3 HAMP CKB-CKM
TABLE-US-00030 TABLE 29 Panels of 10 Biomarkers Markers Mean CV AUC
1 C5 IL12A-IL12B KIT APOA1 SERPINF2 0.863 CCL23 LBP ACY1
CDK5-CDK5R1 CKB-CKM 2 TIMP1 C5 IL12A-IL12B KIT APOA1 0.863 CCL23
LBP ACY1 CDK5-CDK5R1 CKB-CKM 3 TIMP1 C5 IL12A-IL12B KIT APOA1 0.862
CCL23 LBP ACY1 CDK5-CDK5R1 CKB-CKM 4 TIMP1 C5 IL12A-IL12B KIT APOA1
0.862 SERPINF2 LBP ACY1 CDK5-CDK5R1 CKB-CKM 5 C5 KIT APOA1 SERPINF2
CCL23 0.862 LBP ACY1 CDK5-CDK5R1 CKB-CKM HINT1 6 C5 IL12A-IL12B KIT
APOA1 CCL23 0.862 SERPINF2 LBP ACY1 CDK5-CDK5R1 CKB-CKM 7 TIMP1 C5
KIT APOA1 CCL23 0.862 SERPINF2 LBP ACY1 CDK5-CDK5R1 CKB-CKM 8 TIMP1
C5 KIT APOA1 CCL23 0.862 LBP ACY1 CDK5-CDK5R1 CKB-CKM HINT1 9 TIMP1
C5 KIT APOA1 SERPINF2 0.861 CCL23 LBP ACY1 CDK5-CDK5R1 CKB-CKM 10
C9 C5 KIT APOA1 CCL23 0.861 LBP ACY1 CDK5-CDK5R1 HAMP CKB-CKM 11 C9
C5 KIT CKM APOA1 0.861 SERPINF2 CCL23 ACY1 CDK5-CDK5R1 HINT1 12 C5
IL12A-IL12B KIT APOA1 CCL23 0.861 CCL23 LBP ACY1 CDK5-CDK5R1
CKB-CKM 13 C5 IL12A-IL12B KIT APOA1 CCL23 0.861 LBP ACY1
CDK5-CDK5R1 CKB-CKM HINT1 14 C9 C5 KIT APOA1 CCL23 0.861 LBP ACY1
CDK5-CDK5R1 CKB-CKM HINT1 15 TIMP1 C5 IL12A-IL12B KIT APOA1 0.861
SERPINF2 CCL23 ACY1 CDK5-CDK5R1 CKB-CKM 16 TIMP1 C9 C5 KIT APOA1
0.860 CCL23 LBP ACY1 CDK5-CDK5R1 CKB-CKM 17 TIMP1 C5 IL12A-IL12B
KIT APOA1 0.860 CCL23 ACY1 CDK5-CDK5R1 HAMP CKB-CKM 18 C9 C5 KIT
CKM APOA1 0.860 SERPINF2 CCL23 LBP ACY1 CDK5-CDK5R1 19 TIMP1 C5 KIT
APOA1 SERPINF2 0.860 CCL23 ACY1 CDK5-CDK5R1 CKB-CKM HINT1 20 C5 KIT
APOA1 GDF11 CCL23 0.860 LBP ACY1 CDK5-CDK5R1 CKB-CKM HINT1 21 C5
IL12A-IL12B KIT APOA1 CCL23 0.860 LBP ACY1 CDK5-CDK5R1 FSTL3
CKB-CKM 22 C9 C5 KIT CKM APOA1 0.860 CCL23 LBP ACY1 CDK5-CDK5R1
HINT1 23 C9 C5 IL12A-IL12B KIT CKM 0.860 APOA1 CCL23 LBP ACY1
CDK5-CDK5R1 24 C5 IL12A-IL12B KIT APOA1 CCL23 0.860 LBP ACY1
CDK5-CDK5R1 HAMP CKB-CKM 25 C9 C5 KIT CKM APOA1 0.860 CCL23 ACY1
CDK5-CDK5R1 CKB-CKM HINT1 26 C5 IL12A-IL12B KIT APOA1 CCL23 0.860
LBP ACY1 CDK5-CDK5R1 HAMP CKB-CKM 27 C9 C5 KIT CKM APOA1 0.860
CCL23 LBP CDK5-CDK5R1 CKB-CKM HINT1 28 C5 KIT APOA1 SERPINF2 CCL23
0.860 LBP ACY1 CDK5-CDK5R1 HAMP CKB-CKM 29 TIMP1 C9 C5 KIT APOA1
0.860 CCL23 ACY1 CDK5-CDK5R1 CKB-CKM HINT1 30 TIMP1 C5 IL12A-IL12B
KIT APOA1 0.860 CCL23 ACY1 CDK5-CDK5R1 HAMP CKB-CKM 31 C9 C5 KIT
APOA1 SERPINF2 0.860 CCL23 LBP ACY1 CDK5-CDK5R1 CKB-CKM 32 TIMP1 C5
IL12A-IL12B KIT APOA1 0.860 SERPINF2 ACY1 CDK5-CDK5R1 HAMP CKB-CKM
33 C5 IL12A-IL12B KIT APOA1 CCL23 0.859 LBP ACY1 CDK5-CDK5R1 FSTL3
CKB-CKM 34 C5 KIT APOA1 CCL23 SERPINF2 0.859 CCL23 LBP ACY1
CDK5-CDK5R1 CKB-CKM 35 C9 C5 IL12A-IL12B KIT APOA1 0.859 CCL23 LBP
ACY1 CDK5-CDK5R1 CKB-CKM 36 TIMP1 C9 C5 KIT CKM 0.859 APOA1 CCL23
ACY1 CDK5-CDK5R1 HINT1 37 TIMP1 C5 IL12A-IL12B KIT APOA1 0.859 LBP
ACY1 CDK5-CDK5R1 HAMP CKB-CKM 38 TIMP1 C5 IL12A-IL12B KIT APOA1
0.859 CCL23 SERPINF2 ACY1 CDK5-CDK5R1 CKB-CKM 39 TIMP1 C5 KIT APOA1
GDF11 0.859 CCL23 LBP ACY1 CDK5-CDK5R1 CKB-CKM 40 C9 C5 KIT CKM
APOA1 0.859 CCL23 CCL23 LBP ACY1 CDK5-CDK5R1 41 TIMP1 C5 KIT APOA1
CCL23 0.859 LBP ACY1 CDK5-CDK5R1 HAMP CKB-CKM 42 C5 KIT APOA1 CCL23
LBP 0.859 ACY1 CDK5-CDK5R1 FSTL3 CKB-CKM HINT1 43 TIMP1 C5 KIT
APOA1 CCL23 0.859 LBP ACY1 CDK5-CDK5R1 CKB-CKM HINT1 44 C5 KIT
APOA1 SERPINF2 CCL23 0.859 LBP ACY1 CDK5-CDK5R1 FSTL3 CKB-CKM 45
TIMP1 C9 C5 KIT APOA1 0.859 SERPINF2 CCL23 ACY1 CDK5-CDK5R1 CKB-CKM
46 TIMP1 C9 C5 IL12A-IL12B KIT 0.859 CKM APOA1 CCL23 ACY1
CDK5-CDK5R1 47 TIMP1 C5 KIT APOA1 CCL23 0.859 SERPINF2 ACY1
CDK5-CDK5R1 HAMP CKB-CKM 48 C5 IL12A-IL12B KIT APOA1 SERPINF2 0.859
CCL23 LBP CDK5-CDK5R1 CKB-CKM HINT1 49 TIMP1 C5 KIT APOA1 CCL23
0.859 LBP ACY1 CDK5-CDK5R1 HAMP CKB-CKM 50 TIMP1 C5 KIT APOA1 CCL23
0.859 CCL23 LBP ACY1 CDK5-CDK5R1 CKB-CKM 51 C9 C5 KIT CKM APOA1
0.859 CCL23 LBP ACY1 CDK5-CDK5R1 HAMP 52 C5 KIT APOA1 CCL23
SERPINF2 0.859 LBP ACY1 CDK5-CDK5R1 FSTL3 CKB-CKM 53 TIMP1 C9 C5
KIT APOA1 0.859 CCL23 ACY1 CDK5-CDK5R1 HAMP CKB-CKM 54 TIMP1 C5
IL12A-IL12B KIT APOA1 0.859 CCL23 CCL23 ACY1 CDK5-CDK5R1 CKB-CKM 55
C9 C5 KIT CKM APOA1 0.859 CCL23 CCL23 ACY1 CDK5-CDK5R1 HINT1 56
TIMP1 C5 IL12A-IL12B KIT APOA1 0.859 GDF11 CCL23 ACY1 CDK5-CDK5R1
CKB-CKM 57 TIMP1 C5 KIT APOA1 SERPINF2 0.858 CCL23 LBP CDK5-CDK5R1
CKB-CKM HINT1 58 C5 IL12A-IL12B KIT APOA1 GDF11 0.858 CCL23 LBP
ACY1 CDK5-CDK5R1 CKB-CKM 59 C5 KIT APOA1 CCL23 LBP 0.858 ACY1
CDK5-CDK5R1 HAMP CKB-CKM HINT1 60 TIMP1 C5 KIT APOA1 GDF11 0.858
CCL23 ACY1 CDK5-CDK5R1 HAMP CKB-CKM 61 TIMP1 C9 C5 KIT CKM 0.858
APOA1 CCL23 LBP ACY1 CDK5-CDK5R1 62 C5 IL12A-IL12B KIT APOA1 CCL23
0.858 SERPINF2 CCL23 ACY1 CDK5-CDK5R1 CKB-CKM 63 C9 C5 KIT APOA1
CCL23 0.858 LBP ACY1 CDK5-CDK5R1 FSTL3 CKB-CKM 64 C9 C5 KIT CKM
APOA1 0.858 CCL23 LBP ACY1 CDK5-CDK5R1 CKB-CKM 65 TIMP1 C5 KIT
APOA1 CCL23 0.858 ACY1 CDK5-CDK5R1 HAMP CKB-CKM HINT1 66 C5 KIT
APOA1 CCL23 CCL23 0.858 LBP ACY1 CDK5-CDK5R1 CKB-CKM HINT1 67 C5
KIT APOA1 CCL23 SERPINF2 0.858 LBP ACY1 CDK5-CDK5R1 HAMP CKB-CKM 68
C5 KIT APOA1 GDF11 CCL23 0.858 LBP ACY1 CDK5-CDK5R1 HAMP CKB-CKM 69
TIMP1 C5 KIT APOA1 SERPINF2 0.858 LBP ACY1 CDK5-CDK5R1 HAMP CKB-CKM
70 C5 KIT APOA1 CCL23 SERPINF2 0.858 LBP ACY1 CDK5-CDK5R1 CKB-CKM
HINT1 71 C5 IL12A-IL12B KIT APOA1 GDF11 0.858 CCL23 ACY1
CDK5-CDK5R1 HAMP CKB-CKM 72 C5 KIT APOA1 GDF11 SERPINF2 0.858 CCL23
LBP ACY1 CDK5-CDK5R1 CKB-CKM 73 TIMP1 C5 IL12A-IL12B KIT APOA1
0.858 CCL23 ACY1 CDK5-CDK5R1 CKB-CKM HINT1 74 C9 C5 IL12A-IL12B KIT
CKM 0.858 APOA1 CCL23 ACY1 CDK5-CDK5R1 HINT1 75 TIMP1 C5 KIT APOA1
GDF11 0.858 CCL23 ACY1 CDK5-CDK5R1 CKB-CKM HINT1 76 TIMP1 C9 C5
IL12A-IL12B KIT 0.858 APOA1 CCL23 ACY1 CDK5-CDK5R1 CKB-CKM 77 TIMP1
C9 KIT APOA1 CCL23 0.858 LBP ACY1 CDK5-CDK5R1 HAMP CKB-CKM 78 TIMP1
C5 IL12A-IL12B KIT APOA1 0.858 LBP ACY1 CDK5-CDK5R1 FSTL3 CKB-CKM
79 C9 C5 KIT APOA1 CCL23 0.858 CCL23 LBP ACY1 CDK5-CDK5R1 CKB-CKM
80 TIMP1 C9 C5 KIT APOA1 0.858 CCL23 CCL23 ACY1 CDK5-CDK5R1 CKB-CKM
81 TIMP1 C9 IL12A-IL12B KIT APOA1 0.858 CCL23 LBP ACY1 CDK5-CDK5R1
CKB-CKM 82 C9 C5 KIT CKM APOA1 0.858 CCL23 SERPINF2 CCL23 ACY1
CDK5-CDK5R1 83 C5 KIT APOA1 GDF11 CCL23 0.858 SERPINF2 LBP ACY1
CDK5-CDK5R1 CKB-CKM 84 C9 KIT CKM APOA1 CCL23 0.858 LBP ACY1
CDK5-CDK5R1 CKB-CKM HINT1 85 C5 IL12A-IL12B KIT APOA1 GDF11 0.858
CCL23 LBP ACY1 CDK5-CDK5R1 CKB-CKM 86 C5 IL12A-IL12B KIT APOA1
SERPINF2 0.858 LBP ACY1 CDK5-CDK5R1 HAMP CKB-CKM 87 C5 IL12A-IL12B
KIT APOA1 SERPINF2 0.858 LBP ACY1 CDK5-CDK5R1 FSTL3 CKB-CKM 88 C5
KIT APOA1 SERPINF2 CCL23 0.858 ACY1 CDK5-CDK5R1 HAMP CKB-CKM HINT1
89 C5 KIT CKM APOA1 SERPINF2 0.858 CCL23 LBP ACY1 CDK5-CDK5R1 HINT1
90 TIMP1 C5 KIT APOA1 CCL23 0.858 CCL23 ACY1 CDK5-CDK5R1 CKB-CKM
HINT1 91 C5 KIT APOA1 CCL23 LBP 0.858 ACY1 CDK5-CDK5R1 FSTL3 HAMP
CKB-CKM 92 TIMP1 C9 C5 KIT CKM 0.858 APOA1 CCL23 ACY1 CDK5-CDK5R1
CKB-CKM 93 C9 C5 KIT CKM APOA1 0.858 CCL23 ACY1 CDK5-CDK5R1 HAMP
HINT1 94 TIMP1 C5 IL12A-IL12B KIT APOA1 0.858 GDF11 CCL23 ACY1
CDK5-CDK5R1 CKB-CKM 95 TIMP1 C5 IL12A-IL12B KIT APOA1 0.858
SERPINF2 CCL23 LBP CDK5-CDK5R1 CKB-CKM 96 C5 IL12A-IL12B KIT APOA1
SERPINF2 0.858 CCL23 ACY1 CDK5-CDK5R1 HAMP CKB-CKM 97 C5
IL12A-IL12B KIT APOA1 GDF11 0.858 ACY1 CDK5-CDK5R1 FSTL3 HAMP
CKB-CKM 98 C5 IL12A-IL12B KIT CKM APOA1 0.858 SERPINF2 CCL23 LBP
ACY1 CDK5-CDK5R1 99 TIMP1 C9 C5 KIT CKM 0.858 APOA1 SERPINF2 CCL23
ACY1 CDK5-CDK5R1 100 TIMP1 C5 KIT CKM APOA1 0.858 CCL23 LBP ACY1
CDK5-CDK5R1 HINT1
TABLE-US-00031 TABLE 30 Counts of markers in biomarker panels Panel
Size Biomarker 3 4 5 6 7 8 9 10 ACY1 141 192 308 399 489 590 658
759 APOA1 180 395 598 728 833 919 962 981 C5 163 285 437 559 644
693 773 834 C9 190 314 340 341 359 395 436 511 CCL23 151 168 191
202 238 273 308 363 CCL23 150 160 195 260 332 412 502 587 CDK5- 147
230 359 512 660 785 893 943 CDK5R1 CKB-CKM 187 391 473 563 623 654
680 685 CKM 174 227 224 254 298 350 407 476 ENTPD1 107 57 38 31 27
12 8 14 FSTL3 112 89 87 101 107 136 170 190 GDF11 112 62 52 53 73
116 156 228 HAMP 107 67 73 96 134 199 265 322 HINT1 129 156 182 205
240 276 336 421 IL12A-IL12B 116 120 132 169 208 268 320 355 KIT 188
523 728 862 928 977 995 999 KLK3- 166 71 40 28 23 22 21 13 SERPINA3
LBP 146 177 208 250 326 383 471 565 SERPINF2 126 134 139 161 206
241 300 351 THBS2 72 7 0 0 0 0 0 0 TIMP1 136 175 196 226 252 299
339 403
TABLE-US-00032 TABLE 31 Parameters derived from cancer datasets set
for naive Bayes classifiers Pancreatic Cancer NSCLC Mesothelioma
Con- Con- Con- trol Cancer trol Cancer trol Cancer ACY1 Mean 9.90
10.41 9.70 9.43 9.29 8.67 SD 0.63 0.92 0.45 0.46 0.57 0.65 APOA1
Mean 9.70 9.48 8.77 8.65 9.22 8.97 SD 0.17 0.30 0.21 0.23 0.13 0.24
C5 Mean 9.49 9.60 10.13 10.20 10.05 10.19 SD 0.12 0.14 0.12 0.14
0.11 0.16 CCL23 Mean 7.91 8.10 7.38 7.45 6.76 6.97 SD 0.23 0.27
0.15 0.20 0.08 0.23 CDK5- Mean 6.94 6.99 6.85 6.93 6.72 6.88 CDK5R1
SD 0.11 0.11 0.12 0.15 0.11 0.12 CKB-CKM Mean 7.51 7.02 7.45 7.06
8.25 7.41 SD 0.65 0.48 0.49 0.49 0.61 0.49 IL12A-IL12B Mean 7.31
7.27 8.86 8.80 7.76 7.71 SD 0.05 0.06 0.11 0.13 0.05 0.07 KIT Mean
9.77 9.62 8.67 8.46 8.62 8.34 SD 0.29 0.32 0.22 0.27 0.22 0.17 LBP
Mean 9.10 9.49 8.32 8.47 9.19 9.51 SD 0.44 0.55 0.32 0.50 0.26 0.68
SERPINF2 Mean 9.26 9.18 8.97 8.85 8.80 8.67 SD 0.12 0.16 0.21 0.19
0.21 0.26
TABLE-US-00033 TABLE 32 Calculations derived from training set for
naive Bayes classifier. Biomarker .mu..sub.c .mu..sub.d
.sigma..sub.c .sigma..sub.d {tilde over (x)} p(c|{tilde over (x)})
p(d|{tilde over (x)}) ln(p(d|{tilde over (x)})/p(c|{tilde over
(x)})) KIT 8.671 8.462 0.222 0.270 8.763 1.652 0.794 -0.732
SERPINF2 8.971 8.852 0.208 0.194 9.085 1.649 0.998 -0.503 CCL23
7.382 7.452 0.146 0.204 7.327 2.539 1.626 -0.445 IL12A-IL12B 8.857
8.798 0.115 0.131 8.863 3.478 2.691 -0.257 CDK5-CDK5R1 6.852 6.931
0.122 0.149 6.688 1.321 0.712 -0.618 ACY1 9.701 9.435 0.449 0.459
9.526 0.823 0.853 0.035 APOA1 8.772 8.648 0.210 0.230 8.805 1.875
1.378 -0.308 CKB-CKM 7.449 7.062 0.495 0.487 7.742 0.676 0.309
-0.782 LBP 8.322 8.472 0.317 0.504 8.215 1.187 0.695 -0.536 C5
10.127 10.201 0.123 0.144 10.086 3.077 2.017 -0.422
* * * * *
References