U.S. patent application number 16/534347 was filed with the patent office on 2020-01-30 for biomarker panel for diagnosing cancer.
The applicant listed for this patent is DEUTSCHES KREBSFORSCHUNGSZENTRUM STIFTUNG DES OFFENTLICHEN RECHTS. Invention is credited to Hermann Brenner, Hongda Chen.
Application Number | 20200033352 16/534347 |
Document ID | / |
Family ID | 52784968 |
Filed Date | 2020-01-30 |
United States Patent
Application |
20200033352 |
Kind Code |
A1 |
Brenner; Hermann ; et
al. |
January 30, 2020 |
Biomarker Panel For Diagnosing Cancer
Abstract
The present invention pertains to a new method for the
diagnosis, prognosis, stratification and/or monitoring of a
therapy, of cancer in a patient. The method is based on the
determination of the level of a panel of biomarkers selected from
CEA, AREG, IL-6, GDF-15, HGF-receptor, CXCL9, ErbB4-Her4, CXCL10,
Flt3L, VEGFR-2, CD69, CXCL5, PSA, EMMPRIN, Cathepsin-D, Caspase-3,
TNF-alpha, and INF-gamma. The new biomarker panel of the invention
allows diagnosing and even stratifying various cancer diseases.
Furthermore provided are diagnostic kits for performing the
non-invasive methods of the invention.
Inventors: |
Brenner; Hermann; (Wiesenba,
DE) ; Chen; Hongda; (Mannheim, DE) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
DEUTSCHES KREBSFORSCHUNGSZENTRUM STIFTUNG DES OFFENTLICHEN
RECHTS |
Heidelberg |
|
DE |
|
|
Family ID: |
52784968 |
Appl. No.: |
16/534347 |
Filed: |
August 7, 2019 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
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15561755 |
Sep 26, 2017 |
10408839 |
|
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PCT/EP2016/056314 |
Mar 23, 2016 |
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16534347 |
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Current U.S.
Class: |
1/1 |
Current CPC
Class: |
G01N 2333/525 20130101;
G01N 2333/521 20130101; G01N 2800/7028 20130101; G01N 2333/5412
20130101; G01N 2333/71 20130101; G01N 33/57446 20130101; G01N
2333/95 20130101; G01N 2333/485 20130101; G01N 2333/912 20130101;
G01N 33/57488 20130101; G01N 2333/495 20130101; G01N 2800/52
20130101; G01N 33/57419 20130101; G01N 33/57438 20130101; G01N
2800/60 20130101; G01N 2333/70596 20130101; G01N 2333/57
20130101 |
International
Class: |
G01N 33/574 20060101
G01N033/574 |
Foreign Application Data
Date |
Code |
Application Number |
Mar 27, 2015 |
EP |
15161465.8 |
Claims
1. A non-invasive method for the diagnosis, prognosis,
stratification and/or monitoring of a therapy, of a cancer disease
in a subject, comprising the steps of: (a) Providing a biological
sample from the subject, (b) Determining the level of at least two
or more biomarker selected from the group consisting of Flt3L AREG,
CEA, IL-6, GDF-15, HGF-receptor, CXCL9, ErbB4-Her4, CXCL10,
VEGFR-2, CD69, CXCL5, PSA, EMMPRIN, Cathepsin-D, Caspase-3,
TNF-alpha, and INF-gamma, in the biological sample, wherein a
differential level of the at least two or more biomarkers in the
biological sample from the subject as determined in step (b)
compared to a healthy control or a reference value is indicative
for the presence of a cancer disease in the subject.
2. The method according to claim 1, wherein step (b) comprises
determining the level of at least Flt3L or AREG in the biological
sample.
3. The non-invasive method according to claim 1, wherein step (b)
comprises determining the level of the Flt3L or AREG, in the
biological sample.
4. The method according to claim 1, wherein the biological sample
is a tissue sample or body liquid sample, preferably a blood
sample, most preferably a plasma sample.
5. The method according to claim 1, wherein the biomarker is a
protein biomarker.
6. The method according to claim 1, wherein the method is a
screening method for establishing a first diagnosis of cancer in
the subject.
7. The method according to claim 1, wherein the cancer is
colorectal cancer, pancreatic cancer, gastric cancer, breast
cancer, lung cancer, prostate cancer, hepatocellular cancer,
cervical cancer, ovarian cancer, liver cancer, bladder cancer,
cancer of the urinary tract, thyroid cancer, renal cancer,
carcinoma, melanoma, leukemia or brain cancer.
8. The method according to claim 7, wherein the cancer is
colorectal cancer, gastric cancer or pancreatic cancer.
9. The method according to claim 1, wherein a differential level of
a biomarker selected from CEA, GDF-15, AREG, IL-6, CXCL10, CXCL9,
PSA, TNF-alpha, and Cathepsin-D, is a level higher than the healthy
control or the reference value.
10. The method according to claim 1, wherein a differential level
of a biomarker selected from HGF-receptor, ErbB4-Heer4, CXCL5,
Flt3L, EMMPRIN, VEGFR-2, CD69 and Caspase-3, is a level lower than
the healthy control or the reference value.
11. The method according to claim 1, wherein the biomarker is
detected using one or more antibodies, preferably wherein the
biomarker is detected by western blot, ELISA, Proximity Extension
Assay, or mass-spectrometrically.
12. The A diagnostic kit for performing a method according to claim
1.
13. The diagnostic kit of claim 12, comprising one or more
antibodies for the detection of the biomarkers Flt3L and AREG.
Description
RELATED APPLICATIONS
[0001] This application is a continuation of U.S. application Ser.
No. 15/561,755, filed Sep. 26, 2017, which is a U.S. National Stage
of International Application No. PCT/EP2016/056314, filed Mar. 23,
2016, which designates the U.S., published in English, and claims
priority under 35 U.S.C. .sctn..sctn. 119 or 365(c) to European
Application No. 15161465.8, filed on Mar. 27, 2015. The entire
teachings of the above applications are incorporated herein by
reference.
FIELD OF THE INVENTION
[0002] The present invention pertains to a new method for the
diagnosis, prognosis, stratification and/or monitoring of a
therapy, of cancer in a patient. The method is based on the
determination of the level of a panel of biomarkers selected from
CEA, AREG, IL-6, GDF-15, HGF-receptor, CXCL9, ErbB4-Her4, CXCL10,
Flt3L, VEGFR-2, CD69, CXCL5, PSA, EMMPRIN, Cathepsin-D, Caspase-3,
TNF-alpha, and INF-gamma. The new biomarker panel of the invention
allows diagnosing and even stratifying various cancer diseases.
Furthermore provided are diagnostic kits for performing the
non-invasive methods of the invention.
DESCRIPTION
[0003] A major step in many aspects of research related to diseases
such as cancer is the identification of specific and sensitive
biomarkers suitable for the development of effective and improved
diagnostic, prognostic and therapeutic modalities. An aim of the
present invention is to provide novel biomarkers and biomarker
panels for use as novel diagnostic and/or prognostic markers and/or
for use in the development of novel therapeutics. Whilst mass
spectrometry, shot gun proteomics and DNA/RNA microarray analyses,
and deep sequencing have resulted in an increasing list of reported
potential tumor biomarkers, very few have found their way into the
clinical validation phase and even fewer are used as reliable
therapeutic targets or diagnostic markers.
[0004] With more than 1.2 million new colorectal cancer (CRC) cases
and 600,000 deaths occurring every year, CRC is the third most
commonly diagnosed cancer and the fourth most common cancer cause
of death worldwide. Due to the slow progression from precancerous
lesions to CRC, early detection could strongly reduce the burden of
this disease. However, sigmoidoscopy and colonoscopy, the current
gold standards for detection of CRC in the distal and total
colorectum, respectively, are limited by several disadvantages,
such as high costs, limited resources and low compliance.
Established non-invasive screening tests are based on stool
testing, such as guaiac based faecal occult blood tests (gFOBTs)
and faecal immunochemical tests (FITs). However, gFOBTs are limited
by low sensitivity and both gFOBTs and FITs face limitations in
adherence related to the need of stool collection.
[0005] Due to their non-invasive nature and ease of application in
routine medical practice, blood-based tests could ensure high
levels of adherence when applied as primary screening tools in
population-based CRC screening, especially for individuals who
don't prefer stool sampling, and search for blood-based screening
tests is a very active research area. However, most previous
studies aiming to discover and validate novel blood-based screening
markers recruited participants directly from hospitals. In such
clinical settings, the CRC cases typically include a higher
proportion of cases in advanced tumor stage than in screening
settings. Furthermore, cases may have undertaken some diagnostic or
early therapeutic procedures, which may influence potential
biomarkers and might lead to overestimation of differences from
biomarker levels in healthy controls and hence of diagnostic
performance. Additionally, confounding may result from
non-comparability of cases and controls with respect to other
factors, such as other medical conditions, setting of recruitment,
or pre-analytical handling of blood samples. Therefore, it is a
critical issue to identify biomarkers and to evaluate their
diagnostic performance in a true screening setting.
[0006] Even though different blood biomarkers, such as Septin 9
have been evaluated in both clinical and screening settings, direct
comparative analyses of a large number of biomarkers in the same
study are still sparse, which makes reported differences in
diagnostic performance from different studies difficult to
interpret and therefore calls for head-to-head comparisons of a
large number of biomarkers in the same study. Novel laboratory
techniques allow for such evaluation as well as for evaluation of
combinations of the most promising markers, but a very critical
issue in the evaluation of such high-dimensional data is rigorous
adjustment for potential overoptimism resulting from
overfitting.
[0007] Due to the continuing need for quick, but sensitive and
specific cancer diagnostics the present invention seeks to provide
a novel approach for a simple and minimal invasive but specific and
sensitive test system for the diagnosis or monitoring various
cancer diseases.
[0008] The above problem is solved in a first aspect by a
non-invasive method for the diagnosis, prognosis, stratification
and/or monitoring of a therapy, of a cancer disease in a subject,
comprising the steps of: [0009] (a) Providing a biological sample
from the subject, [0010] (b) Determining the level of one or more
biomarkers selected from the group consisting of CEA, AREG, IL-6,
GDF-15, HGF-receptor, CXCL9, ErbB4-Her4, CXCL10, Flt3L, VEGFR-2,
CD69, CXCL5, PSA, EMMPRIN, Cathepsin-D, Caspase-3, TNF-alpha, and
INF-gamma, in the biological sample, wherein a differential level
of one or more of the biomarkers in the biological sample from the
subject as determined in step (b) compared to a healthy control or
reference value is indicative for the presence of a cancer disease
in the subject.
[0011] A "diagnosis" or the term "diagnostic" in context of the
present invention means identifying the presence or nature of a
pathologic condition. Diagnostic methods differ in their
sensitivity and specificity. The "sensitivity" of a diagnostic
assay is the percentage of diseased individuals who test positive
(percent of "true positives"). Diseased individuals not detected by
the assay are "false negatives." Subjects who are not diseased and
who test negative in the assay, are termed "true negatives." The
"specificity" of a diagnostic assay is 1 minus the false positive
rate, where the "false positive" rate is defined as the proportion
of those without the disease who test positive. While a particular
diagnostic method may not provide a definitive diagnosis of a
condition, it suffices if the method provides a positive indication
that aids in diagnosis.
[0012] The term "prognosis" refers to a forecast as to the probable
outcome of the disease as well as the prospect of recovery from the
disease as indicated by the nature and symptoms of the case.
Accordingly, a negative or poor prognosis is defined by a lower
post-treatment survival term or survival rate. Conversely, a
positive or good prognosis is defined by an elevated post-treatment
survival term or survival rate. Usually prognosis is provided as
the time of progression free survival or overall survival.
[0013] The term "stratification" for the purposes of this invention
refers to the advantage that the method according to the invention
renders possible decisions for the treatment and therapy of the
patient, whether it is the hospitalization of the patient, the use,
effect and/or dosage of one or more drugs, a therapeutic measure or
the monitoring of a course of the disease and the course of therapy
or etiology or classification of a disease, e.g., into a new or
existing subtype or the differentiation of diseases and the
patients thereof. Particularly with regard to colorectal cancer,
"stratification" means in this context a classification of a
colorectal cancer as early or late stage colorectal cancer.
[0014] The term "monitoring a therapy" means for the purpose of the
present invention to observe disease progression in a subject who
receives a cancer therapy. In other words, the subject during the
therapy is regularly monitored for the effect of the applied
therapy, which allows the medical practitioner to estimate at an
early stage during the therapy whether the prescribed treatment is
effective or not, and therefore to adjust the treatment regime
accordingly.
[0015] As used herein, the term "subject" or "patient" refers to
any animal (e.g., a mammal), including, but not limited to, humans,
non-human primates, rodents, and the like, which is to be the
recipient of a particular treatment. Typically, the terms "subject"
and "patient" are used interchangeably herein in reference to a
human subject. As used herein, the term "subject suspected of
having cancer" refers to a subject that presents one or more
symptoms indicative of a cancer (e.g., a noticeable lump or mass).
A subject suspected of having cancer may also have one or more risk
factors. A subject suspected of having cancer has generally not
been tested for cancer. However, a "subject suspected of having
cancer" encompasses an individual who has received an initial
diagnosis (e.g., a CT scan showing a mass) but for whom the
sub-type or stage of cancer is not known. The term further includes
people who once had cancer (e.g., an individual in remission), and
people who have cancer and are suspected to have a metastatic
spread of the primary tumor. In this regard the present invention
is also applicable as follow-up care for monitoring a subject for a
reoccurrence of the cancer.
[0016] The term "cancer" and "cancer cells" refers to any cells
that exhibit uncontrolled growth in a tissue or organ of a
multicellular organism. Particular preferred cancers in context of
the present invention are selected from colorectal cancer,
pancreatic cancer, gastric cancer, breast cancer, lung cancer,
prostate cancer, hepatocellular cancer, cervical cancer, ovarian
cancer, liver cancer, bladder cancer, cancer of the urinary tract,
thyroid cancer, renal cancer, carcinoma, melanoma, leukemia or
brain cancer.
[0017] As used herein, the term "colorectal cancer" includes the
well-accepted medical definition that defines colorectal cancer as
a medical condition characterized by cancer of cells of the
intestinal tract below the small intestine (i.e., the large
intestine (colon), including the cecum, ascending colon, transverse
colon, descending colon, sigmoid colon, and rectum). Additionally,
as used herein, the term "colorectal cancer" also further includes
medical conditions, which are characterized by cancer of cells of
the duodenum and small intestine (jejunum and ileum).
[0018] As used herein, the terms "gastric cancer" or "stomach
cancer" refer to cancers of the stomach. The most common types of
gastric cancer are carcinomas, such as but not limited to,
adenocarcinomas, affecting the epithelial cells of the stomach.
Stomach cancers may additionally include, for example, sarcomas
affecting the connective tissue of the stomach and blastomas
affecting the blast tissue of the stomach.
[0019] The term "pancreatic cancer" encompasses benign or malignant
forms of pancreatic cancer, as well as any particular type of
cancer arising from cells of the pancreas (e.g., duct cell
carcinoma, acinar cell carcinoma, papillary carcinoma,
adenosquamous carcinoma, undifferentiated carcinoma, mucinous
carcinoma, giant cell carcinoma, mixed type pancreatic cancer,
small cell carcinoma, cystadenocarcinoma, unclassified pancreatic
cancers, pancreatoblastoma, and papillary-cystic neoplasm, and the
like.).
[0020] The term "biological sample" as used herein refers to a
sample that was obtained and may be assayed for any one of the
biomarkers as disclosed with the present invention, or their gene
expression. The biological sample can include a biological fluid
(e.g., blood, cerebrospinal fluid, urine, plasma, serum), tissue
biopsy, and the like. In some embodiments, the sample is a tissue
sample, for example, tumor tissue, and may be fresh, frozen, or
archival paraffin embedded tissue. Preferred samples for the
purposes of the present invention are bodily fluids, in particular
blood or plasma samples.
[0021] A "biomarker" or "marker" in the context of the present
invention refers to an organic biomolecule, particularly a
polypeptide, which is differentially present in a sample taken from
subjects having a certain condition as compared to a comparable
sample taken from subjects who do not have said condition (e.g.,
negative diagnosis, normal or healthy subject, or non-cancer
patients, depending on whether the patient is tested for cancer, or
metastatic cancer). For examples, a marker can be a polypeptide or
polysaccharide (having a particular apparent molecular weight)
which is present at an elevated or decreased level in samples of
cancer patients compared to samples of patients with a negative
diagnosis.
[0022] The term "determining the level of" a biomarker in a sample,
control or reference, as described herein shall refer to the
quantification of the presence of said biomarkers in the testes
sample. For example the concentration of the biomarkers in said
samples may be directly quantified via measuring the amount of
protein/polypeptide/polysaccharide as present in the tested sample.
However, also possible is to quantify the amount of biomarker
indirectly via assessing the gene expression of the encoding gene
of the biomarker, for example by quantification of the expressed
mRNA encoding for the respective biomarker. The present invention
shall not be restricted to any particular method for determining
the level of a given biomarker, but shall encompass all means that
allow for a quantification, or estimation, of the level of said
biomarker, either directly or indirectly. "Level" in the context of
the present invention is therefore a parameter describing the
absolute amount of a biomarker in a given sample, for example as
absolute weight, volume, or molar amounts; or alternatively "level"
pertains to the relative amounts, for example and preferably the
concentration of said biomarker in the tested sample, for example
mol/l, g/l, g/mol etc. In preferred embodiments the "level" refers
to the concentration of the tested biomarkers in g/l.
[0023] "Increase" of the level of a biomarker in a sample compared
to a control shall in preferred embodiments refer to statistically
significant increase in preferred aspects of the invention.
[0024] In alternative embodiments of the invention, certain
biomarkers as disclosed herein may also be significantly decreased
in the event of a cancer disease in a subject.
[0025] In course of the present invention plasma levels of 92
tumor-associated proteins were measured in all available 35
carriers of colorectal cancer (CRC) and a representative sample of
54 controls free of neoplasm recruited from 5516 participants of
screening colonoscopy in 2005-2012. The inventors aimed for a
head-to head comparison of the diagnostic performance of these 92
biomarkers and to derive and validate an algorithm based on a
combination of the most promising markers for early detection of
CRC, paying particular attention to rigorous adjustment for
potential overestimation of diagnostic performance. Results were
further validated in an independent sample of 54 CRC cases and 38
controls, as well as in other cancer diseases such as gastric
cancer or pancreatic cancer.
[0026] In a preferred embodiment the method of the herein disclosed
invention is performed in vitro or ex vivo. Since the herein
described diagnostic methods are non-invasive the term "providing a
biological" sample shall preferably not be interpreted to include a
surgical procedure conducted at the subject.
[0027] Preferred embodiments of the present invention pertain to
panels of a plurality of biomarkers as identified herein for the
diagnostic purposes as described. The advantage of combing the
biomarkers disclosed herein is an increased sensitivity and/or
specificity of the disclosed assays. Hence a preferred embodiment
of the invention pertains to the herein disclosed method wherein
step (b) comprises determining the level of at least two, three,
four, five, six, seven or eight biomarkers in the biological
sample. Most preferred is that at least four biomarkers are used.
More preferred is that at least 5 biomarkers are used. More
preferred is that at least 6 biomarkers are used. More preferred is
that at least 7 biomarkers are used. Most preferred is that at
least 8 biomarkers are used.
[0028] In one embodiment of the herein disclosed invention the
level of at least CEA, AREG, and GDF-15, in the biological sample,
is determined. In addition one of the following biomarkers may be,
if desired, be added to the panel for testing: IL-6, INF-gamma,
EMMPRIN, ErbB4-Her4, PSA, CD69, and, HGF-receptor.
[0029] One specifically preferred panel for use in context of the
herein disclosed invention comprises the selected of at least 4, 5,
6, 7 or 8 biomarkers selected from the group of INF-gamma, EMMPRIN,
ErbB4-Her4, PSA, CD69, AREG, HGF-receptor and CEA, in the
biological sample. Most preferred in this aspect is that at least
the biomarkers CEA and AREG, optionally any of the remaining
biomarkers is included in this panel. The most preferred embodiment
of the invention however relates to the application of a panel of
all 8 biomarkers, optionally wherein 1 or 2 biomarkers are
substituted with others, or omitted. The complete set of all 8
preferred biomarkers is however the most preferred panel of the
invention.
[0030] In this regard it is preferred that the analysis of the
marker panel in step (b) of the diagnostic method of the invention
is characterized in that the tested marker panel has an apparent
area under the curve (AUC) at 95% confidence interval (CI) of at
least 60%, preferably at least 65% or more preferably at least 70%.
How to determine the AUC is known to the skilled artisan.
Alternatively or additionally the panel of the invention may be
characterized by a sensitivity of at least 75%, preferably at least
80%, and a specificity of at least 40%, preferably at least 50%,
more preferably at least 60%.
[0031] To date, no single blood biomarker qualifying for mass
screening has been identified. The combination of multiple markers
might be a more promising approach to achieve the necessary
sensitivity and specificity for application in mass screening.
Although other marker panels were tested in the prior art, the
apparent differences to the panel as provided herein can be
explained by the fact that those prior art studies were done in a
clinical setting and did not apply any adjustment for over-optimism
(not doing so would have yielded an even higher AUC in our study).
The above mentioned limitations were also shared by many other
studies regarding blood biomarkers for CRC detection. For reasons
outlined in detail in the introduction, it is a critical issue to
identify for and evaluate biomarkers in samples from screening
settings in order to obtain valid performance characteristics under
screening conditions. Furthermore, as demonstrated herein,
correction for overfitting (cross-validation, bootstrap techniques)
and/or external validation are also indispensable to adjust for
potential overestimation of diagnostic performance. Hence, the
marker panel of the present invention is advantageous over previous
prior art panels.
[0032] The biomarkers of the invention are preferably protein
biomarkers.
[0033] The biomarker panel as disclosed herein is particular useful
in a cancer screening setting. Cancer screening in the herein
disclosed invention shall refer to a procedure where a subject is
for which not diagnosis was established is tested for the presence
of the cancer disease. This shall not be interpreted to exclude the
use of the biomarker of the invention for a diagnostic of a subject
that was already diagnosed to suffer from a cancer disease. Non
limiting examples for such an application are confirmation of a
diagnosis, monitoring or treatment success or monitoring
reoccurrence of a cancer in a subject that already received a
treatment and wherein cancer is in remission or was cured.
[0034] In context of the herein disclosed invention several
biomarkers where found to be either differentially up regulated or
down regulated in a cancer diagnosis compared to healthy subjects
(see table 2). Hence in context of the herein disclosed invention a
differential level of a biomarker selected from CEA, GDF-15, AREG,
IL-6, CXCL10, CXCL9, PSA, TNF-alpha, and Cathepsin-D, is a higher
level of that biomarker in a positive diagnosis. On the other hand,
a differential level of a biomarker selected from HGF-receptor,
ErbB4-Heer4, CXCL5, Flt3L, EMMPRIN, VEGFR-2, CD69 and Caspase-3, is
a lower level, in a positive diagnosis.
[0035] The skilled artisan will understand that numerous methods
may be used to select a threshold or reference value for a
particular marker or a plurality of markers. In diagnostic aspects,
a threshold value may be obtained by performing the assay method on
samples obtained from a population of patients having a certain
type of cancer, and from a second population of subjects that do
not have cancer. For prognostic or treatment monitoring
applications, a population of patients, all of which have, for
example, ovarian cancer, may be followed for the time period of
interest (e.g., six months following diagnosis or treatment,
respectively), and then dividing the population into two groups: a
first group of subjects that progress to an endpoint (e.g.,
recurrence of disease, death); and a second group of subjects that
did not progress to the end point. These are used to establish "low
risk" and "high risk" population values for the marker(s) measured,
respectively. Other suitable endpoints include, but are not limited
to, 5-year mortality rates or progression to metastatic
disease.
[0036] Once these groups are established, one or more thresholds
may be selected that provide an acceptable ability to predict
diagnosis, prognostic risk, treatment success, etc. In practice,
Receiver Operating Characteristic curves, or "ROC" curves, are
typically calculated by plotting the value of a variable versus its
relative frequency in two populations (called arbitrarily "disease"
and "normal" or "low risk" and "high risk" for example). For any
particular marker, a distribution of marker levels for subjects
with and without a disease may overlap. Under such conditions, a
test does not absolutely distinguish "disease" and "normal" with
100% accuracy, and the area of overlap indicates where the test
cannot distinguish "disease" and "normal." A threshold is selected,
above which (or below which, depending on how a marker changes with
the disease) the test is considered to be "positive" and below
which the test is considered to be "negative." The area under the
ROC curve (AUC) is a measure of the probability that the perceived
measurement may allow correct identification of a condition.
[0037] Additionally, thresholds may be established by obtaining an
earlier marker result from the same patient, to which later results
may be compared. In some aspects, the individuals act as their own
"control group." In markers that increase with disease severity or
prognostic risk, an increase over time in the same patient can
indicate a worsening of disease or a failure of a treatment
regimen, while a decrease over time can indicate remission of
disease or success of a treatment regimen.
[0038] In some embodiments, multiple thresholds or reference values
may be determined. This can be the case in so-called "tertile,"
"quartile," or "quintile" analyses. In these methods, the "disease"
and "normal" groups (or "low risk" and "high risk") groups can be
considered together as a single population, and are divided into 3,
4, or 5 (or more) "bins" having equal numbers of individuals. The
boundary between two of these "bins" may be considered
"thresholds." A risk (of a particular diagnosis or prognosis for
example) can be assigned based on which "bin" a test subject falls
into.
[0039] All numeric values are herein assumed to be modified by the
term "about," whether or not explicitly indicated. The term "about"
generally refers to a range of numbers that one of skill in the art
would consider equivalent to the recited value (i.e., having the
same function or result). In many instances, the terms "about" may
include numbers that are rounded to the nearest significant figure.
In particularly preferred embodiments of the invention the term
"about" may refer to a deviation of the respective numeric value of
a maximum of 20% of the numerical value, however more preferred is
15%, 10%, 5% and most preferred is 4%, 3%, 2%, and most preferred
is 1%.
[0040] In a preferred embodiment said sample is selected from the
group consisting of body fluids or tissue, preferably wherein said
body fluid sample is a blood sample, more preferably a plasma or
serum sample.
[0041] In all aspects and embodiments of the present invention in
may be preferred that the level of said at least one biomarker in
said sample is determined by means of a nucleic acid detection
method or a protein detection method. However, nucleic acid
detection methods are only applicable where an expressed protein is
the biomarker. Generally all means shall be comprised by the
present invention which allow for a quantification of the
expression of any one of the herein disclosed biomarker. Therefore
also promoter analysis and procedures assessing the epigenetic
status of a gene locus encoding a protein biomarker of the
invention are comprised by the herein described invention.
[0042] Detection methods that are preferred in context of the
herein described invention the level of said at least one biomarker
in said sample is determined by means of a detection method
selected from the group consisting of mass spectrometry, mass
spectrometry immunoassay (MSIA), antibody-based protein chips,
2-dimensional gel electrophoresis, stable isotope standard capture
with anti-peptide antibodies (SISCAPA), high-performance liquid
chromatography (HPLC), western blot, cytometry bead array (CBA),
protein immuno-precipitation, radio immunoassay, ligand binding
assay, and enzyme-linked immunosorbent assay (ELISA), preferably
wherein said protein detection method is ELISA. Suitable
alternative detection methods for quantification of a biomarker of
the invention are known to the skilled artisan.
[0043] In yet another aspect, the invention provides kits for
aiding a diagnosis of cancer, wherein the kits can be used to
detect the biomarkers of the present invention. For example, the
kits can be used to detect any one or combination of biomarkers
described above, which biomarkers are differentially present in
samples of a patient having the cancer and healthy patients. The
kits of the invention have many applications. For example, the kits
can be used to differentiate if a subject has the cancer, or has a
negative diagnosis, thus aiding a cancer diagnosis. In another
example, the kits can be used to identify compounds that modulate
expression of the biomarkers in in vitro cancer cells or in vivo
animal models for cancer.
[0044] Optionally, the kit can further comprise instructions for
suitable operational parameters in the form of a label or a
separate insert. For example, the kit may have standard
instructions informing a consumer how to wash the probe after a
sample of plasma is contacted on the probe.
[0045] In another embodiment, a kit comprises (a) an antibody that
specifically binds to a marker; and (b) a detection reagent. Such
kits can be prepared from the materials, and the previous
discussion regarding the materials (e.g., antibodies, detection
reagents, immobilized supports, etc.) is fully applicable to this
section and need not be repeated.
[0046] In either embodiment, the kit may optionally further
comprise a standard or control information so that the test sample
can be compared with the control information standard to determine
if the test amount of a marker detected in a sample is a diagnostic
amount consistent with a diagnosis of cancer.
[0047] Preferably the kit of the invention is a diagnostic kit for
performing a method in accordance with the present invention
comprising means for quantifying the level of said at least one
biomarker. Preferably the kit of the invention comprises means for
quantifying a biomarker selected from CEA, AREG, IL-6, GDF-15,
HGF-receptor, CXCL9, ErbB4-Her4, CXCL10, Flt3L, VEGFR-2, CD69,
CXCL5, PSA, EMMPRIN, Cathepsin-D, Caspase-3, TNF-alpha, and
INF-gamma. Such means for quantifying is for example at least one
antibody, preferably wherein the antibody is a monoclonal antibody,
such as a monoclonal antibody that specifically binds to any of the
aforementioned biomarkers. Such antibodies are known in the art and
commercially available.
[0048] The diagnostic kit of the invention in another embodiment
comprises at least 8 antibodies which each specifically bind to
INF-gamma, EMMPRIN, ErbB4-Her4, PSA, CD69, AREG, HGF-receptor and
CEA, preferably wherein said antibodies are monoclonal
antibodies.
[0049] The present invention will now be further described in the
following examples with reference to the accompanying figures and
sequences, nevertheless, without being limited thereto. For the
purposes of the present invention, all references as cited herein
are incorporated by reference in their entireties. In the Figures
and Sequences:
[0050] FIG. 1: STAndards for the Reporting of Diagnostic accuracy
studies (STARD) diagram of the participants in the BliTz study
(2005-2012).
[0051] FIG. 2: Box plots of plasma levels for 17 protein
biomarkers: (a) between CRC cases and controls; (b) early stages
(I/II) and advanced stage (III/IV) CRC. The bottom and top of the
box indicate the first (Q1) and third (Q3) quartiles, and the
middle line in the box is the median; the upper-limit equals Q3
plus 1.5 times interquartile range (IQR), and the lower-limit
equals Q1 minus 1.5 times IQR.
[0052] FIG. 3: Comparison of receiver operating characteristic
curve for the eight-marker algorithm: (a) between the training set
and the independent validation set; (b) between different subgroups
in the independent validation set (i.e., all CRC cases, tumor stage
I/II and tumor stage III/IV).
[0053] FIG. 4: Comparison of receiver operating characteristic
curve for the eight-marker algorithm between the colorectal cancer
training set, the colorectal cancer independent validation set, the
gastric cancer set and the pancreatic cancer set.
EXAMPLES
Materials and Methods
1. Study Design and Study Population
[0054] The analysis was conducted in the context of the BliTz study
("Begleitende Evaluierung innovativer Testverfahren zur
Darmkrebsfruherkennung"). Briefly, BliTz is an ongoing study among
participants of screening colonoscopy conducted in cooperation with
20 gastroenterology practices in South-western Germany since
November 2005, which aims to evaluate novel promising biomarkers
for early detection of CRC. Participants are recruited, and blood
samples are taken in the practices at a preparatory visit,
typically about one week prior to the screening colonoscopy.
[0055] For this analysis, the following exclusion criteria were
applied to exclude participants without adequate blood samples,
participants who do not represent a true screening setting, and
participants with potentially false negative results at screening
colonoscopy: blood samples taken after screening colonoscopy or
blood samples with unknown date of blood withdrawal, history of CRC
or inflammatory bowel disease, previous colonoscopy history in the
last five years or unknown colonoscopy history, incomplete
colonoscopy or insufficient bowel preparation (latter two criteria
only for controls). From the remaining participants of the BliTz
study recruited in 2005-2012 (N=4345), all 35 available cases with
newly detected CRC were included in the analysis. For comparison,
the inventors included a representative sample of 54 controls free
of colorectal neoplasms. Because this study was conducted in a true
screening population in which patients with CRC are expected to be
on average slightly older and to include a somewhat large
proportion of men, the inventors did not match for these factors as
this might lead to biased estimates of specificity in such a
setting.
[0056] For an independent validation, the inventors also included
54 additional CRC cases (recruited at four hospitals in and around
the city of Heidelberg after diagnosis but before initiation of
treatment) and 38 additional randomly selected controls free of
neoplasm from the BliTz study.
[0057] Colonoscopy and histology reports (BliTz study) and hospital
records (54 CRC cases for the independent validation set) were
collected from all participants. Relevant information was extracted
by two research assistants independently who were blind to the
blood test results. Tumor stages were classified according to the
UICC TNM classification.
2. Laboratory Procedures 2.1. Sample Preparation
[0058] Blood samples from participants giving informed consent were
to be collected before bowel preparation for colonoscopy (BliTz
study) or prior to large bowel surgery or neoadjuvant chemotherapy
(54 CRC cases from the clinical setting) in EDTA tubes. The blood
samples were immediately centrifuged at 2123 g for 10 minutes at
4.degree. C. and the supernatant was transferred into new tubes,
and transported to the biobank at DKFZ in a cool chain, where
plasma samples were stored at -80.degree. C. until analyses.
2.2. Laboratory Measurements
[0059] Protein profiling was performed using Proseek Multiplex
Oncology I.sup.96x96 (Olink Bioscience, Uppsala, Sweden) which
enables quantification of 92 human tumor-associated protein
biomarkers (full marker list in Supplementary Table 51). The panel
of 92 protein biomarkers reflects various biological mechanisms
involved in carcinogenesis, such as angiogenesis, cell-cell
signaling, growth control and inflammation. All laboratory
operations were conducted according to the Proseek Multiplex
Oncology I.sup.96x96 User Manual in the TATAA Biocenter (Goteborg,
Sweden). In short, the Prossek reagents are based on the Proximity
Extension Assay (PEA) technology, where 92 oligonucleotide labeled
antibody probe pairs are allowed to bind to their respective target
present in the sample. A PCR reporter sequence is formed by a
proximity dependent DNA polymerization event and is subsequently
detected and quantified using real-time PCR. Four internal controls
(including two incubation controls, one extension control and one
detection control) were included in the assay. In addition, there
were three replicates of negative controls which were used to
calculate the lower limit of detection (LOD) for each protein. All
information regarding the study population was blind to the
laboratory operators.
3. Data Normalization and Statistical Analyses
3.1 Data Normalization
[0060] Normalization of raw data followed the standard protocol
from the manufacturer and was conducted through the Olink Wizard of
GenEx software (MultiD, Goteborg, Sweden). For each data point, the
raw Cq-value (in log.sub.e scale) was exported from the Fluidigm
Real-Time PCR Analysis Software. The first step of normalization is
to subtract the raw Cq-value for the extension control for the
corresponding sample in order to correct for technical variation.
The calculated Cq-values (dCq-value) were further normalized
against the negative control determined in the measurement, which
yielded ddCq-values (hereafter: Cq-value, in log.sub.2 scale) and
could be used for further analyses. LOD was defined as the mean
value of the three negative controls plus 3 calculated standard
deviations. Missing data and data with a value lower than LOD were
replaced with LOD in the following statistical analyses.
3.2 Statistical Analyses
[0061] The plasma protein levels (Cq-value) were first compared
between CRC cases and neoplasm-free controls using Wilcoxon Rank
Sum Test (hereafter: Wilcoxon test), and Benjamini & Hochberg
method was additionally employed for multiple testing. The
following diagnosis-related indicators were used for evaluating the
diagnostic performance of each protein biomarker: sensitivity (true
positive rate), specificity (true negative rate), receiver
operating characteristics (ROC) curve, and area under the ROC curve
(AUC). For each individual protein biomarker, a logistic regression
model was used to construct the prediction model. Based on the
predicted possibilities from the prediction model, the AUCs and
their 95% confidence intervals (95% CIs, calculated based on 2000
bootstrap samples) were derived. Moreover, sensitivities of each
individual biomarker at cutoffs yielding 80% and 90% specificity
were calculated. In addition to direct estimates of the diagnosis
related indicators, the 0.632+ bootstrap method (1000 bootstrap
samples with replacement) was applied to adjust for potential
overestimation of diagnostic performance. Furthermore, for the
biomarkers which were identified to have significantly different
plasma levels between CRC cases and controls, stage-specific AUCs
(apparent and 0.632+ adjusted AUCs) were also calculated and Delong
test was employed to compare the differences of apparent AUCs
between early stages (i.e., tumor stage I/II) and advanced stages
(i.e., tumor stage III/IV).
[0062] A multi-marker algorithm was derived by applying the Lasso
logistic regression model based on all 92 protein markers. With the
purpose of adjusting for potential overfitting of the prediction
algorithm, a "0.632+ bootstrap subsampling approach" was conducted
in the following way: i) generate 1000 bootstrap samples
(subsampling method, bootstrap without replacement); ii) for each
bootstrap sample set, apply the Lasso logistic regression procedure
to select variables and to construct a prediction algorithm; iii)
apply this algorithm on those patients not included in the
bootstrap sample to obtain bootstrap estimates of prediction errors
for each bootstrap sample; iv) further adjust these results using
the 0.632+ method to obtain a nearly unbiased estimate of the
prognostic AUC of the original algorithm. Construction of the
algorithm was done including all CRC cases. Evaluation was likewise
performed for all CRC cases and, in addition separately for CRC
cases at early and advanced tumor stages. Finally, AUC and
sensitivity at cutoffs yielding 80% and 90% specificity,
respectively, and their 95% CIs of the multi-marker algorithm were
determined in the independent validation sample.
[0063] Statistical analyses were performed with the statistical
software R version 3.0.3. R package "Daim" was used to conduct
0.632+ bootstrap analyses for single markers R package "glmnet" was
employed to perform the Lasso logistic regression analysis for
multi-marker analyses. Additionally, R packages "peperr" and "c060"
were applied to conduct the "632+ bootstrap subsampling approach"
described above. All tests were two-sided and p-values of 0.05 or
less were considered to be statistically significant.
Example 1: Identification of 17 Biomarkers
[0064] FIG. 1 provides the STAandards for the Reporting of
Diagnostic accuracy studies (STARD) diagram which shows the
selection of study participants from all subjects enrolled in the
BliTz study in 2005-2012. The final study sample included 35 CRC
patients who were compared to a representative sample of 54
controls free of colorectal neoplasms. Latter included 6
participants with hyperplastic polyps and 48 participants without
colorectal polyps.
[0065] Table 1 presents the distribution of socio-demographic
characteristics in the CRC case group and the control group. The
controls were on average slightly younger than cases
(mean.+-.standard deviation: 62.8.+-.7.0 versus 66.9.+-.6.5 years).
71.4% of the patients with CRC were men, compared with 50.0% of
those free of colorectal neoplasms. Approximately equal proportions
of patients were diagnosed in early (stage I/II) and advanced stage
(stage III/IV), and there were equal numbers of patients with colon
and rectum cancer.
[0066] Overall, there were 17 protein biomarkers showing
significantly different plasma levels between CRC cases and
controls (Table 2). When using 25% false positive rate (FDR) as the
cutoff level for multiple testing, all the 17 biomarkers were still
statistically significant.
TABLE-US-00001 TABLE 1 Characteristics of the study population
Variable CRC cases (%) Controls.sup.a (%) Age (years) <60 5
(14.3) 24 (44.4) 60-64 9 (25.7) 9 (16.7) 65-69 8 (22.9) 8 (14.8)
.gtoreq.70 13 (37.1) 13 (24.1) Mean .+-. SD 66.9 .+-. 6.5 62.8 .+-.
7.0 Sex Male 25 (71.4) 27 (50.0) Female 10 (28.6) 27 (50.0) UICC
tumor stage I 13 (37.1) II 4 (11.4) III 16 (45.7) IV 2 (5.7) CRC
location Colon 17 (48.6) Rectum 17 (48.6) Unkown 1 (2.8) Total 35
(100.0) 54 (100.0) .sup.aControls included 6 participants with
hyperplastic polyps and 48 participants without any finding at
colonoscopy.
TABLE-US-00002 TABLE 2 Diagnostic performance of protein biomarkers
showing significant differences between CRC case and controls .632+
sens..sup.c Median Cq Adjusted Apparent AUC .632+ AUC at 80% at 90%
Marker CRC Controls p-value.sup.a p-value.sup.b [95% CI] [95% CI]
spec. spec. CEA 1.20 0.49 <0.001 0.015 0.73 [0.63-0.84] 0.69
[0.57-0.88] 52% 27% GDF-15 5.34 4.68 <0.001 0.016 0.72
[0.62-0.83] 0.69 [0.58-0.87] 43% 18% AREG 2.73 2.41 0.001 0.016
0.72 [0.61-0.83] 0.70 [0.57-0.86] 46% 36% IL-6 4.23 3.59 0.003
0.063 0.69 [0.58-0.80] 0.65 [0.54-0.84] 42% 16% CXCL10 6.84 6.20
0.013 0.184 0.66 [0.54-0.77] 0.60 [0.46-0.80] 27% 12% HGF-receptor
7.25 7.32 0.013 0.184 0.66 [0.54-0.77] 0.62 [0.48-0.81] 31% 18%
CXCL9 5.78 5.23 0.014 0.184 0.66 [0.54-0.77] 0.59 [0.45-0.81] 28%
13% ErbB4-Her4 6.67 6.76 0.017 0.198 0.65 [0.54-0.77] 0.60
[0.49-0.79] 32% 16% CXCL5 5.74 6.32 0.030 0.244 0.64 [0.52-0.76]
0.59 [0.44-0.79] 35% 22% Flt3L 6.95 7.17 0.030 0.244 0.64
[0.52-0.75] 0.59 [0.48-0.78] 30% 14% EMMPRIN 7.09 7.19 0.033 0.244
0.63 [0.52-0.75] 0.59 [0.46-0.79] 28% 13% PSA 2.24 1.20 0.041 0.244
0.63 [0.50-0.75] 0.59 [0.44-0.79] 33% 18% TNF-alpha -0.52 -0.78
0.042 0.244 0.63 [0.51-0.75] 0.57 [0.44-0.79] 27% 18% VEGFR-2 2.57
2.70 0.043 0.244 0.63 [0.51-0.75] 0.58 [0.43-0.78] 30% 17% CD69
6.67 7.19 0.044 0.244 0.63 [0.51-0.75] 0.59 [0.45-0.79] 29% 16%
Cathepsin-D 2.48 2.31 0.045 0.244 0.63 [0.51-0.74] 0.55 [0.34-0.77]
25% 12% Caspase-3 10.28 10.70 0.045 0.244 0.63 [0.51-0.75] 0.57
[0.43-0.78] 28% 15% .sup.aWilcoxon Rank Sum Test to compare the
protein expression differences between CRC cases and controls.
.sup.bThe p-value was adjusted for multiple testing by Benjamini
& Hochberg method. .sup.cSensitivities were adjusted by using
the .632+ bootstrap method. Abbreviations: AUC, area under the
receiver operating characteristic curve; CI, confidence
interval.
[0067] Carcinoembryonic antigen (CEA), growth differentiation
factor 15 (GDF-15) and amphiregulin (AREG) met a FDR threshold of
5%. Apart from prostate specific antigen (PSA), for which
statistically significantly higher plasma levels in men than in
women were found, all the other 16 biomarkers did not show any
statistically significant relationship with sex or age within the
group of controls free of colorectal neoplasms (p-values>0.05).
Additionally, sensitivity analyses excluding four participants
reporting to have had any cancer diagnosis in the past in
self-administrated questionnaires were also conducted, and yielded
almost identical results.
TABLE-US-00003 TABLE 3 Stage specific performance of specific
protein markers for detection of CRC Tumor stages I and II Tumer
stages III and IV Apparent AUC .632+ AUC Apparent AUC .632+ AUC
Marker [95% CI] [95% CI] [95% CI] [95% CI] p-value.sup.a AREG
0.79[0.67-0.91] 0.76[0.61-0.95] 0.65[0.50-0.80] 0.60[0.39-0.87]
0.168 IL-6 0.78[0.67-0.90] 0.74[0.62-0.94] 0.60[0.45-0.75]
0.49[0.23-0.77] 0.064 GDF-15 0.78[0.67-0.89] 0.72[0.61-0.91]
0.67[0.52-0.82] 0.61[0.40-0.87] 0.270 HGF-receptor 0.70[0.55-0.85]
0.65[0.44-0.91] 0.62[0.48-0.75] 0.54[0.40-0.78] 0.411 CXCL9
0.70[0.55-0.85] 0.64[0.46-0.89] 0.61[0.47-0.76] 0.48[0.24-0.75]
0.421 ErbB4-Her4 0.70[0.56-0.83] 0.63[0.50-0.88] 0.61[0.46-0.75]
0.51[0.25-0.78] 0.385 CXCL10 0.70[0.55-0.84] 0.62[0.45-0.88]
0.62[0.47-0.76] 0.49[0.23-0.77] 0.445 Flt3L 0.69[0.55-0.83]
0.62[0.45-0.88] 0.59[0.43-0.74] 0.50[0.26-0.76] 0.320 VEGFR-2
0.67[0.51-0.83] 0.61[0.37-0.91] 0.59[0.44-0.75] 0.49[0.25-0.77]
0.505 CD69 0.66[0.50-0.82] 0.60[0.41-0.90] 0.59[0.44-0.75]
0.51[0.25-0.78] 0.546 CXCL5 0.64[0.48-0.81] 0.58[0.29-0.85]
0.63[0.49-0.78] 0.55[0.30-0.82] 0.937 CEA 0.68[0.54-0.82]
0.58[0.28-0.87] 0.79[0.66-0.92] 0.75[0.60-0.95] 0.252 PSA
0.63[0.46-0.80] 0.58[0.27-0.85] 0.63[0.47-0.78] 0.56[0.26-0.81]
0.976 EMMPRIN 0.64[0.48-0.80] 0.55[0.26-0.83] 0.63[0.48-0.77]
0.55[0.37-0.81] 0.898 Cathepsin-D 0.65[0.50-0.80] 0.54[0.21-0.83]
0.61[0.46-0.75] 0.49[0.24-0.75] 0.688 Caspase-3 0.62[0.47-0.78]
0.52[0.28-0.82] 0.63[0.48-0.79] 0.55[0.27-0.85] 0.923 TNF-alpha
0.59[0.43-0.74] 0.48[0.22-0.76] 0.67[0.51-0.82] 0.60[0.37-0.88]
0.480 .sup.aDelong test was employed to test the differences of
AUCs between CRC at early stage and advanced stage. Abbreviations:
AUC, area under the receiver operating characteristic curve; CI,
confidence interval.
[0068] Among these 17 protein markers, 9 protein markers were
over-expressed and 8 protein markers showed lower levels in CRC
cases compared with controls (Table 2). The 0.632+ adjusted AUCs of
these 17 markers ranged from 0.70 to 0.55. Four markers, including
AREG, CEA, GDF-15 and interleukin 6 (IL-6), yielded substantially
better diagnostic performances than the others, with 0.632+
adjusted AUCs no less than 0.65. When the cutoff values were set to
yield 80% specificity, the highest 0.632+ adjusted sensitivity was
observed for CEA (52%). With cut-off values set to yield 90%
specificity, the highest 0.632+ adjusted sensitivity was observed
for AREG (36%).
[0069] FIG. 2 shows the distribution of plasma levels for the 17
protein markers for CRC patients in early tumor stages and advanced
tumor stages. 7 protein markers (IL-6, CXCL9, CXCL10, PSA,
cathepsin-D, caspase-3 and AREG) showed higher levels in early
tumor stages than in advanced ones. However, only the result for
IL-6 was statistically significant (p-value<0.05). Table 3 shows
the comparison of ROC analysis for these 17 markers between CRC
patients at early and advanced stages. Most markers (13/17) showed
higher adjusted AUCs in CRC patients at early tumor stages than at
advanced ones. However, none of the differences was statistically
significant. For three markers (AREG, IL-6 and GDF-15) the 0.632+
adjusted AUCs for early tumor stage CRC were higher than 0.70
(i.e., 0.76, 0.74, and 0.72, respectively). By contrast, CEA showed
the highest 0.632+ adjusted AUC for advanced stage CRC (0.75).
Example 2: Development of a Colorectal Cancer Diagnostic Panel of 8
Biomarkers
[0070] The inventors used the Lasso Logistic regression model to
construct a multi-marker prediction algorithm based on all 92
protein biomarkers. The following 8 markers were selected for
inclusion in the algorithm: IFN-gamma, EMMPRIN, ErbB4-Her4, PSA,
CD69, AREG, HGF-receptor and CEA (algorithm is shown in Table 4).
The apparent AUC was 0.88 (95% CI, 0.81-0.95). Through the "0.632+
bootstrap subsampling approach", the adjusted AUC of this algorithm
was 0.77 (95% CI, 0.59-0.91). Of note, this algorithm showed a
similar diagnostic value for early stage CRC and advanced stage CRC
(0.632+ adjusted AUC: 0.79 versus 0.75, respectively).
TABLE-US-00004 TABLE 4 Eight-marker algorithm derived through the
Lasso logistic regression model: intercept and marker coefficients
EMM-PRINErbB4- Variable Intercept INF.gamma. Her4 PSA CD69 AREG
HGFR CEA Coeff. 7.57 0.0259 -0.0887 -0.8138 0.0642-0.1793 0.9605
-0.5173 0.4450
[0071] Finally, the inventors also validated this eight-marker
algorithm in the independent validation set, which included 54 CRC
cases and 38 controls free of colorectal neoplasms. The age
distribution of this validation set was similar to the sage
distribution in the main study from the screening setting, even
though both cases and controls included somewhat lower proportions
of men. The tumor stage distribution of cases in the independent
validation set was similar to the stage distribution of CRC cases
detected at screening colonoscopy according to the German screening
colonoscopy registry. Table 5 and FIG. 3 show the diagnostic
performance of the eight-marker algorithm for CRC prediction in the
independent validation set. The AUC was 0.76 (95% CI, 0.65-0.85),
and sensitivities at cutoffs yielding 80% and 90% specificities
were 65% (95% CI, 41-80%) and 44% (95% CI, 24-72%), respectively.
In this independent validation set, diagnostic performance was
better for advanced stage than for early stage disease (AUC: 0.84
versus 0.72, respectively).
TABLE-US-00005 TABLE 5 The diagnostic performance of the
eight-marker algorithm for CRC detection in an independent
validation set Sensitivity [95% CI] at 80% at 90% CRC group AUC
[95% CI] specificity specificity All CRC cases 0.76 [0.65-0.85] 65%
[41-80%] 44% [24-72%] CRC at Stage I/II 0.72 [0.60-0.84] 61%
[34-79%] 34% [13-68%] CRC at Stage III/IV 0.84 [0.68-0.96] 75%
[50-94%] 69% [44-94%] Abbreviations: AUC, area under the receiver
operating characteristic curve; CI, confidence interval.
Example 3: Validation of the Diagnostic Panel of 8 Biomarkers in
the Diagnosis of qGastric Cancer and Pancreatic Cancer
[0072] The diagnostic value of the 8 biomarker panel of the
invention could also be validated for both pancreatic cancer and
gastric cancer (FIG. 4), indicating the general applicability of
the 8 biomarker panel for the diagnosis of cancers, not only
colorectal cancer.
* * * * *