U.S. patent application number 17/033530 was filed with the patent office on 2021-04-29 for prediction of recurrence for bladder cancer by a protein signature in tissue samples.
This patent application is currently assigned to Deutsches Krebsforschungszentrum. The applicant listed for this patent is Deutsches Krebsforschungszentrum, Institut Curie. Invention is credited to Jorg HOHEISEL, Francois RADVANYI, Christoph SCHRODER, Harish SRINIVASAN.
Application Number | 20210123914 17/033530 |
Document ID | / |
Family ID | 1000005329005 |
Filed Date | 2021-04-29 |
United States Patent
Application |
20210123914 |
Kind Code |
A1 |
SCHRODER; Christoph ; et
al. |
April 29, 2021 |
PREDICTION OF RECURRENCE FOR BLADDER CANCER BY A PROTEIN SIGNATURE
IN TISSUE SAMPLES
Abstract
The present invention pertains to the field of cancer
prediction. Specifically, it relates to a method for predicting the
risk of recurrence of bladder cancer in a subject after treatment
of bladder cancer comprising the steps of determining the amount of
at least one biomarker selected from the biomarkers shown in Table,
and comparing the amount of said at least one biomarker with a
reference amount for said at least one biomarker, whereby the risk
of recurrence of bladder cancer is to be predicted. The present
invention also contemplates a method for identifying a subject
being in need of a further bladder cancer therapy. Encompassed are,
furthermore, diagnostic devices and kits for carrying out said
methods.
Inventors: |
SCHRODER; Christoph;
(Heidelberg, DE) ; SRINIVASAN; Harish;
(Heidelberg, DE) ; HOHEISEL; Jorg; (Wiesloch,
DE) ; RADVANYI; Francois; (Paris, FR) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
Deutsches Krebsforschungszentrum
Institut Curie |
Heidelberg
Paris Cedex 05 |
|
DE
FR |
|
|
Assignee: |
Deutsches
Krebsforschungszentrum
Heidelberg
DE
Institut Curie
Paris Cedex 05
FR
|
Family ID: |
1000005329005 |
Appl. No.: |
17/033530 |
Filed: |
September 25, 2020 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
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15589764 |
May 8, 2017 |
10809261 |
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17033530 |
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14124580 |
Feb 21, 2014 |
9678075 |
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PCT/EP2012/060876 |
Jun 8, 2012 |
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15589764 |
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Current U.S.
Class: |
1/1 |
Current CPC
Class: |
G01N 2800/52 20130101;
G01N 2800/56 20130101; G01N 33/57407 20130101; G01N 2800/54
20130101 |
International
Class: |
G01N 33/574 20060101
G01N033/574 |
Foreign Application Data
Date |
Code |
Application Number |
Jun 10, 2011 |
EP |
11 169 588.8 |
Claims
1-15. (canceled)
16. A method for predicting the risk of recurrence of bladder
cancer in a subject, comprising the steps of: (a) determining the
amount of at least one biomarker selected from the biomarkers shown
in Table 1 in a sample from the subject; and (a) comparing the
amount of the at least one biomarker with a reference amount for
the at least one biomarker, whereby the risk of recurrence of
bladder cancer is to be predicted.
17. The method of claim 16, wherein the at least one biomarker is
selected from the group of biomarkers listed in Table 2, and
wherein an increase in the at least one biomarker as compared to
the reference amount indicates that the subject is at risk of
recurrence of bladder cancer.
18. The method of claim 16, wherein the at least one biomarker is
selected from the group of biomarkers listed in Table 3, and
wherein a decrease in the at least one biomarker as compared to the
reference amount indicates that the subject is at risk of
recurrence of bladder cancer.
19. The method of claim 16, wherein the risk of recurrence of
bladder cancer is predicted after surgery comprising removal of a
tumor.
20. The method of claim 19, wherein the surgery is transurethral
resection, or radical cystectomy, or partial cystectomy.
21. The method of claim 16, wherein the sample is tumor tissue, or
a blood, serum or plasma sample.
22. The method of claim 16, wherein the reference amount is derived
from a subject or group of subjects known to be at risk for
recurrence of bladder cancer, or from a subject or group of
subjects known to not be at risk for recurrence of bladder
cancer.
23. A method for identifying a subject in need of a further bladder
cancer therapy after treatment of bladder cancer, comprising the
steps of: (a) determining the amount of at least one biomarker
selected from the biomarkers shown in Table 1 in a sample of a
subject; and (b) comparing the amount of the at least one biomarker
with a reference amount for the at least one biomarker, whereby a
subject in need of further bladder cancer therapy is to be
identified.
24. The method of claim 23, wherein the treatment of bladder cancer
is surgery comprising removal of a tumor, and/or wherein the
further bladder cancer therapy is adjuvant intravesical
therapy.
25. A method for predicting the risk of progression of bladder
cancer in a subject suffering from bladder cancer, comprising the
steps of: (a) determining the amount of at least one biomarker
selected from the biomarkers shown in Table 1 in a sample from the
subject; and (b) comparing the amount of the at least one biomarker
with a reference amount for the at least one biomarker, whereby the
risk of progression of bladder cancer is to be predicted.
Description
CROSS-REFERENCE TO RELATED APPLICATIONS
[0001] This application is a continuation of U.S. patent
application Ser. No. 15/589,764, filed May 8, 2017, which is a
divisional of U.S. patent application Ser. No. 14/124,580, filed
Feb. 21, 2014, now U.S. Pat. No. 9,678,075, which is the U.S.
National Phase of International Patent Application No.
PCT/EP2012/060876, filed Jun. 8, 2012, which claims priority from
European Patent Application No. 11169588.8, filed Jun. 10, 2011.
The contents of these applications are incorporated herein by
reference in their entirety.
[0002] The present invention pertains to the field of cancer
prediction. Specifically, it relates to a method for predicting the
risk of recurrence of bladder cancer in a subject after treatment
of bladder cancer comprising the steps of determining the amount of
at least one biomarker selected from the biomarkers shown in Table
1-3, and comparing the amount of said at least one biomarker with a
reference amount for said at least one biomarker, whereby the risk
of recurrence of bladder cancer is to be predicted. The present
invention also contemplates a method for identifying a subject
being in need of further bladder cancer therapy, a method for
predicting the risk of progression of bladder cancer, a method for
monitoring treatment of bladder cancer as well as method for
monitoring progression of bladder cancer. Encompassed are,
furthermore, diagnostic devices and kits for carrying out said
methods.
[0003] Bladder cancer is the fourth most common type of cancer in
men and the ninth most common cancer in women. Nonmuscle-invasive
bladder cancer has a high propensity for recurrence. Since it
usually requires life-long surveillance, it is one of the most
expensive cancers to treat.
[0004] Proteins, as the end product or the acting products of gene
expression play a vital role in all activities of a cell. Proteins,
as readily available through many body fluids such as urine, plasma
and tissue extracts provide the immediate option for clinical
analysis. Proteomic technologies are important for the discovery of
clinically relevant biomarkers in various types of cancers.
[0005] So far, only a few biomarkers have been described for
assessing recurrence of bladder cancer. Karam et al. (Lancet Oncol.
(2007); 8: 128-36) discloses that the apoptosis markers Bcl-2, P53,
caspase-3, and P53 can be combined for prediction of bladder cancer
recurrence and mortality after radical cystectomy. However, the
wide spread application of such biomarkers depends on the accuracy
of the detection methods for the individual mutations which are
rather inconvenient at present.
[0006] Thus, there is still a strong need for more reliable
biomarkers for predicting the risk of recurrence and progression of
bladder cancer. Moreover, diagnosis and further personalized
treatment of subjects with bladder cancer, in particular
nonmuscle-invasive bladder cancer, should be promoted.
[0007] Therefore, the present invention relates to a method for
predicting the risk of recurrence of bladder cancer in a subject,
comprising the steps of: [0008] a) determining the amount of at
least one biomarker selected from the biomarkers shown in Table 1
in sample from said subject, and [0009] b) comparing the amount of
said at least one biomarker with a reference amount for said at
least one biomarker, whereby the risk of recurrence of bladder
cancer is to be predicted.
[0010] The method as referred to in accordance with the present
invention includes a method which essentially consists of the
aforementioned steps or a method which includes further steps.
However, it is to be understood that the method, in a preferred
embodiment, is a method carried out ex vivo, i.e. not practised on
the human or animal body. The method, preferably, can be assisted
by automation.
[0011] The term "bladder cancer" as used herein refers to cancer of
the bladder. In particular, the term refers to urothelial cell
carcinoma (also known as "transitional cell carcinoma") which
account for 90 percent of bladder cancers in industrial countries.
The symptoms and implications accompanying bladder cancer are well
known from standard text books of medicine such as Stedmen or
Pschyrembl, like blood in the urine, pain during urination,
frequent urination or feeling the need to urinate without being
able to do so. In particular, the "bladder cancer" refers to
disease in which the cells lining the urinary bladder lose the
ability to regulate their growth resulting in a mass of cells that
form a tumor. Preferably, the term encompasses numerous types of
malignant growths of the urinary bladder. It is well known that
Bladder cancer carries a broad spectrum of aggressiveness and risk.
Usually, bladder cancer originates in the urothelium, a 3- to
7-cell mucosal layer within the muscular bladder. Preferably,
bladder cancer as used herein refers to invasive bladder cancer.
More preferable, the term refers to nonmuscle-invasive bladder
cancer. Invasive bladder cancer has at least penetrated the
muscular layer of the bladder wall, whereas nonmuscle-invasive
bladder cancer is limited to the innermost linings of the bladder
(known as the mucosa and lamina propria).
[0012] The most common staging system for bladder tumors is the TNM
system. This staging system takes into account how deep the tumor
has grown into the bladder, whether there is cancer in the lymph
nodes and whether the cancer has spread to any other part of the
body. According to the TNM (tumor, lymph node, and metastasis)
staging system which is a pathologic staging system, bladder cancer
can be also staged as follows: In bladder cancer stage 0, cancer
cells are confined to the mucosa. In bladder cancer stage I the
tumour invades the subepithelial connective tissue/lamina propria.
In bladder cancer stage II cancer cells have invaded the muscularis
propria but the tumour is still organ-confined. In bladder cancer
stage III cancer cells have extended through the bladder wall to
the perivesical tissue or to the Prostatic stroma, uterus or
vagina. In bladder cancer stage IV cancer cells have proliferated
to the lymph nodes, pelvic or abdominal wall, and/or other organs.
The "bladder cancer" in the context of the present invention, may
encompass any of the aforementioned stages. However, it is
particular envisaged that the bladder cancer is stage 0 (in
particular stage Ta and Tis) or stage I bladder cancer according to
the aforementioned staging system. Accordingly, bladder cancer, as
used herein, is a nonmuscle-invasive, in particular
non-muscle-invasive low stage bladder cancer.
[0013] Moreover, bladder cancer can be graded according the 1973
World Health Organization classification. The bladder cancer to be
assessed in the context of the method of the present invention, is
preferably, low grade bladder cancer, in particular grade 1 or
grade 2 according to this classification. Thus, the bladder cancer
is preferably low grade, low stage bladder cancer.
[0014] For more information on grading on staging of bladder cancer
see, Jacobs, Bruce L. (2010). Bladder Cancer in 2010. CA Cancer J
Clin. 60(4):244-72, which herewith is incorporated by reference
with respect to its entire disclosure content).
[0015] In accordance with the method of the present invention, the
risk of recurrence of bladder cancer shall be predicted, and, thus,
the risk of a subject to suffer from recurrent bladder cancer.
Recurrent bladder cancer is a cancer that reappears in the urinary
bladder (or in a nearby organ) after having being treated.
Accordingly, the "recurrence of bladder cancer" as used herein
refers to bladder cancer which recurs after treatment of bladder
cancer. Preferably, it is predicted whether bladder cancer recurs
within 1 year, 2 years, 3 years, 5 years, 10 years, 15 years, or 20
years, or any intermitting time range after said treatment.
Preferably, it is predicted whether bladder cancer recurs within 2
years, or, more preferably, within 4 years after said treatment.
The cancer recurrence may be a local recurrence or a distal
recurrence. Local recurrence refers to cancers that recur in
tissues or organs adjacent to or proximate to the urinary bladder,
whereas distal recurrence refers to cancers that recur distant from
the cancerous tissue or organ. Preferably, the cancer recurrence is
a local recurrence. Recurrence and progression of bladder cancer is
described, e.g., by Mansoor et al. in 2011 (J Coll Physicians Surg
Pak. 21(3):157-160).
[0016] The term "predicting the risk" as used herein, preferably,
refers to assessing the probability according to which bladder
cancer bladder cancer will recur in a subject. More preferably, the
risk/probability of recurrence of bladder cancer within a certain
time window is predicted. In a preferred embodiment of the present
invention, the predictive window, preferably, is an interval at
least 1 month, at least 3 month, at least 6 month, at least 9
month, at least 1 year, at least 2 years, at least 3 years, at
least 4 years, at least 5 years, at least 10 years, at least 15
years, or at least 20 years, or any intermitting time range. In a
particular preferred embodiment of the present invention, the
predictive window, preferably, is an interval of 2 years, or more
preferably, of 4 years. In another preferred embodiment of the
present invention, the predictive window will be the entire life
span of the subject. Preferably, said the predictive window is
calculated from the completion of treatment of bladder surgery.
More preferably, said predictive window is calculated from the time
point at which the sample to be tested has been obtained.
[0017] As will be understood by those skilled in the art, such a
prediction is usually not intended to be correct for 100% of the
subjects. The term, however, requires that prediction can be made
for a statistically significant portion of subjects in a proper and
correct manner. Whether a portion is statistically significant can
be determined without further ado by the person skilled in the art
using various well known statistic evaluation tools, e.g.,
determination of confidence intervals, p-value determination,
Student's t-test, Mann-Whitney test etc. Details are found in Dowdy
and Wearden, Statistics for Research, John Wiley & Sons, New
York 1983. Preferred confidence intervals are at least 90%, at
least 95%, at least 97%, at least 98%, or at least 99%. The
p-values are, preferably, 0.1, 0.05, 0.01, 0.005, or 0.0001.
Preferably, the probability envisaged by the present invention
allows that the prediction of an increased, normal or decreased
risk will be correct for at least 60%, at least 70%, at least 80%,
or at least 90% of the subjects of a given cohort or population.
The term, preferably, relates to predicting whether a subject is at
elevated risk or reduced risk as compared to the average risk for
the recurrence of bladder cancer in a population of subjects.
[0018] The term "predicting the risk of recurrence of bladder
cancer" as used herein means that the subject to be analyzed by the
method of the present invention is allocated either into the group
of subjects being at risk of recurrence of bladder cancer, or into
the group of subjects being not at risk of recurrence of bladder
cancer. A risk of recurrence of bladder cancer as referred to in
accordance with the present invention, preferably, means that the
risk of recurrence of bladder cancer is elevated (within the
predictive window). Preferably, said risk is elevated as compared
to the average risk in a cohort of subjects with bladder cancer
(i.e. a group of subjects having been subjected to bladder cancer
treatment).
[0019] If a subject is not at risk of recurrence of bladder cancer
as referred to in accordance with the present invention,
preferably, the risk of recurrence of bladder cancer shall be
reduced (within the predictive window). Preferably, said risk is
reduced as compared to the average risk in a cohort of subjects
with bladder cancer (i.e. a group of subjects having been subjected
to bladder cancer treatment). A subject who is at risk of
recurrence of bladder cancer preferably has a risk of 90% or
larger, or, more preferably of 75% or larger of recurrence of
bladder cancer, preferably, within a predictive window of 5 years.
A subject who is at not at risk of recurrence of bladder cancer
preferably has a risk of lower than 10%, more preferably of lower
than, 10% or lower of recurrence of bladder cancer, preferably,
within a predictive window of 5 years.
[0020] The term "subject" as used herein relates to animals,
preferably mammals, and, more preferably, humans. The subject to be
tested in the context of the method of the present invention shall
suffer or shall have suffered from bladder cancer. Preferably, the
method of the present invention shall be applied for subjects known
to suffer from bladder cancer. More preferably, the method of the
present invention is applied to a subject known to suffer from
bladder cancer, wherein said subject i) will be treated against
bladder cancer in the future, ii) is treated against bladder cancer
or iii) has been treated against bladder cancer at the time at
which the method is carried out (or to be more precise, at the time
at which the sample is obtained). Preferred treatments of bladder
cancer are disclosed elsewhere herein.
[0021] The term "biomarker" as used herein refers to a polypeptide
as shown in Table 1 (and table 2 and 3, respectively) or a fragment
or variant of such a polypeptide being associated to the recurrence
of bladder cancer to the same extent as the polypeptides recited in
Table 1 (and table 2 and 3, respectively). In the tables all
biomarkers are uniquely described by the Uniprot identifier, the
Uniprot accession ID, the respective gene code as defined by the
human genome nomenclature consortium (HGNC) and the official
protein name as provided by the Uniprot database. For more
information on the protein, see the UniProt Database, in
particular, the UniProt release 2011_06 of May 31, 2011, see also
The UniProt Consortium Ongoing and future developments at the
Universal Protein Resource Nucleic Acids Res. 39: D214-D219 (2011).
Variants of said polypeptide as shown in the aforementioned tables
include polypeptides which differ in their amino acid sequence due
to the presence of conservative amino acid substitutions.
Preferably, such variants have an amino acid sequence being at
least 70%, at least 80%, at least 90%, at least 95%, at least 98%,
or at least 99% identical over the entire sequence region to the
amino acid sequences of the aforementioned specific polypeptides.
Variants may be allelic variants, splice variants or any other
species specific homologs, paralogs, or orthologs. Preferably, the
percent identity can be determined by the algorithms of Needleman
and Wunsch or Smith and Waterman. Programs and algorithms to carry
out sequence alignments are well known by a skilled artisan. To
carry out the sequence alignments, the program PileUp (J. Mol.
Evolution., 25, 351-360, 1987, Higgins et al., CABIOS, 5 1989:
151-153) or the programs Gap and BestFit (Needleman 1970, J. Mol.
Biol. 48; 443-453 and Smith 1981, Adv. Appl. Math. 2; 482-489),
which are part of the GCG software packet (Genetics Computer Group,
575 Science Drive, Madison, Wis., USA 53711, Version 1991), are
preferably to be used. The sequence identity values recited above
in percent (%) are to be determined, preferably, using the program
GAP over the entire sequence region with the following settings:
Gap Weight: 50, Length Weight: 3, Average Match: 10.000 and Average
Mismatch: 0.000, which, unless otherwise specified, shall always be
used as standard settings for sequence alignments.
[0022] In the method according to the present invention, at least
one biomarker of the afore-mentioned group of biomarkers, and thus
of the human proteins as shown in Table 1, is to be determined.
However, more preferably, a group of biomarkers will be determined
in order to strengthen specificity and/or sensitivity of the
assessment. Such a group, preferably, comprises at least 2, at
least 3, at least 4, at least 5, at least 10 or up to all of the
said biomarkers shown in the Tables. In addition to the specific
biomarkers recited in the specification, other biomarkers may be,
preferably, determined as well in the methods of the present
invention. Preferred combinations of biomarkers are disclosed
herein elsewhere.
[0023] In a preferred embodiment of the method of the invention,
said at least one biomarker is selected from the group of
biomarkers listed in Table 2. An increase in such a biomarker in a
sample of a test subject as compared to the reference is indicative
for the risk of recurrence of bladder cancer. Moreover, a decrease
in such a biomarker as compared to the reference, preferably,
indicates that the subject is not a risk of recurrence of bladder
cancer.
[0024] In another preferred embodiment of the method of the
invention, said at least one biomarker is selected from the group
of biomarkers listed in Table 3. A decrease in such a biomarker as
compared to the reference amount is indicative for the risk of
recurrence of bladder cancer. Moreover, an increase in such a
biomarker as compared to the reference, preferably, indicates that
the subject is not a risk of recurrence of bladder cancer.
[0025] The term "treatment of bladder cancer" as used herein
encompasses any treatment regimen that aims to treat bladder
cancer. Such treatment regimens are well known in the art.
Preferably, the treatment of bladder cancer is selected from
surgery, radiation therapy, immunotherapy and chemotherapy. The
most preferred treatment of bladder cancer is surgery in which the
tumor is removed from the bladder. A particular preferred surgery
is transurethral resection of the tumor. Transurethral resection
is, preferably, carried out for low grade, low stage cancers. In
this surgery, the tumor is shaved off the bladder wall with a
heated wire and the area is treated with diathermy--a mild electric
current that reduces bleeding. Said resection may be carried out
with or without adjuvant intravesical therapy.
[0026] The term "intravesical therapy", as used herein, preferably,
refers to the instillation of a biological agent or a chemotherapy
drug directly into the bladder. Said instillation is done in order
to destroy any residual cancer cells. Intravesical therapy is a
form of local drug therapy whereby the treatment is targeted
directly at the site of the cancer (bladder) as opposed to systemic
drug therapy where a drug is injected into a vein or is given
orally and travels throughout the circulatory system in order to
reach the bladder.
[0027] The most preferred intravesical therapy is intravesical
immunotherapy, in particular immunotherapy with Bacillus
Calmette-Guerin (BCG). This therapy allows for boosting the body's
natural immune system to destroy the bladder cancer cells.
[0028] Also preferred is intravesical chemotherapy in which a
chemotherapeutic agent is administered. Preferred agents are
mitomycin C and thiotepa. Further preferred agents are pirarubicin
and epirubicin.
[0029] A further preferred surgery is cystectomy, in particular
radical or partial cystectomy. The term "cystectomy" refers to
removal of all (radical cystectomy) or part (partial cystectomy)
removal of the urinary bladder. This kind of surgery is usually
carried out in invasive, in particular muscle invasive, bladder
cancer.
[0030] The term "sample" as used herein refers to a sample of a
body fluid, to a sample of separated cells, or to a sample from a
tissue or an organ. Samples of body fluids can be obtained by
well-known techniques and include, preferably, samples of urine or
more preferably, samples of blood, plasma, serum. Tissue or organ
samples may be obtained from any tissue, in particular from tumor
tissue, of an organ, in particular from the bladder, by, e.g.,
biopsy. Separated cells may be obtained from the body fluids or the
tissues or organs by separating techniques such as centrifugation
or cell sorting. Preferably, cell-, tissue- or organ samples are
obtained from those cells, tissues or organs which express or
produce the polypeptides referred to herein.
[0031] As set forth above, it is particular preferred that the
sample is obtained from the bladder. Preferably, the sample is
bladder carcinoma tissue (and, thus, tumor tissue). How to obtain
such as sample is well known in the art (in particular the sample
can be obtained by biopsy or resection). More preferably, said
sample is bladder carcinoma tissue obtained during removal of said
tissue by surgery.
[0032] The sample to be analyzed in the context of the methods of
the present invention may be obtained prior, during or after
treatment of bladder cancer, in particularly prior, during, or
after the surgery as described herein. A sample obtained prior to
treatment is, in an increasing order of preference, obtained not
more than one year, not more than six, five, four, three or two
months, or one month prior to the initiation of said treatment, in
particular during the start of surgery. It is also contemplated to
obtain a sample not more than two weeks, or not more than one week
prior to said treatment. A sample obtained after treatment
preferably, can be obtained after the end of the treatment, e.g.
after completion of surgery. A sample obtained after treatment is,
in an increasing order of preference not more than three years,
obtained not more one year, not more than six, five, four, three or
two months, or one month after said treatment. It is also
contemplated to obtain a sample not more than two weeks, or not
more than one week after said treatment. As set forth above, it is
particularly envisaged that the sample has been obtained during the
treatment, in particular during surgery.
[0033] Determining the amount of the polypeptide biomarkers
referred to in this specification relates to measuring the amount
or concentration, preferably semi-quantitatively or quantitatively.
Measuring can be done directly or indirectly. Direct measuring
relates to measuring the amount or concentration of the polypeptide
based on a signal which is obtained from the polypeptide itself and
the intensity of which directly correlates with the number of
molecules of the polypeptide present in the sample. Such a
signal--sometimes referred to herein as intensity signal may be
obtained, e.g., by measuring an intensity value of a specific
physical or chemical property of the polypeptide. Indirect
measuring includes measuring of a signal obtained from a secondary
component (i.e. a component not being the polypeptide itself) or a
biological read out system, e.g., measurable cellular responses,
ligands, labels, or enzymatic reaction products.
[0034] In accordance with the present invention, determining the
amount of a polypeptide biomarker can be achieved by all known
means for determining the amount of a polypeptide in a sample.
[0035] Said means comprise immunoassay devices and methods which
may utilize labeled molecules in various sandwich, competition, or
other assay formats. Preferably, the immunoassay device is an
antibody array, in particular a planar antibody microarray, a bead
based array (e.g. provided by Luminex Corporation, Austin, USA).
Also preferred are stripe tests. Said assays will develop a signal
which is indicative for the presence or absence of the polypeptide
and, thus, the biomarker.
[0036] Moreover, the signal strength can, preferably, be correlated
directly or indirectly (e.g. reverse-proportional) to the amount of
polypeptide present in a sample. Further suitable methods comprise
measuring a physical or chemical property specific for the
polypeptide such as its precise molecular mass or NMR spectrum.
Said methods comprise, preferably, biosensors, optical devices
coupled to immunoassays, biochips, analytical devices such as
mass-spectrometers, NMR-analyzers, or chromatography devices.
Further, methods include micro-plate ELISA-based methods,
fully-automated or robotic immunoassays, CBA (an enzymatic Cobalt
Binding Assay), and latex agglutination assays.
[0037] Preferably, determining the amount of a polypeptide
biomarker comprises the steps of (a) contacting a cell capable of
eliciting a cellular response the intensity of which is indicative
of the amount of the polypeptide with the said polypeptide for an
adequate period of time, (b) measuring the cellular response. For
measuring cellular responses, the sample or processed sample is,
preferably, added to a cell culture and an internal or external
cellular response is measured. The cellular response may include
the measurable expression of a reporter gene or the secretion of a
substance, e.g. a peptide, polypeptide, or a small molecule. The
expression or substance shall generate an intensity signal which
correlates to the amount of the polypeptide.
[0038] Also preferably, determining the amount of a polypeptide
biomarker comprises the step of measuring a specific intensity
signal obtainable from the polypeptide in the sample. As described
above, such a signal may be the signal intensity observed at a mass
to charge (m/z) variable specific for the polypeptide observed in
mass spectra or a NMR spectrum specific for the polypeptide.
[0039] Determining the amount of a polypeptide biomarker may,
preferably, comprise the steps of (a) contacting the polypeptide
with a specific ligand, (b) removing non-bound ligand, (c)
measuring the amount of bound ligand. The bound ligand will
generate an intensity signal. Binding according to the present
invention includes both covalent and non-covalent binding. A ligand
according to the present invention can be any compound, e.g., a
peptide, polypeptide, nucleic acid, or small molecule, binding to
the polypeptide described herein. Preferred ligands include
antibodies, nucleic acids, peptides or polypeptides such as
receptors or binding partners for the polypeptide and fragments
thereof comprising the binding domains for the peptides, and
aptamers, e.g. nucleic acid or peptide aptamers. Methods to prepare
such ligands are well-known in the art. For example, identification
and production of suitable antibodies or aptamers is also offered
by commercial suppliers. The person skilled in the art is familiar
with methods to develop derivatives of such ligands with higher
affinity or specificity. For example, random mutations can be
introduced into the nucleic acids, peptides or polypeptides. These
derivatives can then be tested for binding according to screening
procedures known in the art, e.g. phage display. Antibodies as
referred to herein include both polyclonal and monoclonal
antibodies, as well as fragments thereof, such as Fv, Fab, scFv and
F(ab)2 fragments that are capable of binding antigen or hapten. The
present invention also includes single chain antibodies and
humanized hybrid antibodies wherein amino acid sequences of a
non-human donor antibody exhibiting a desired antigen-specificity
are combined with sequences of a human acceptor antibody.
Alternatively, chimeric mouse antibodies with rabbit Fc can be
used. The donor sequences will usually include at least the
antigen-binding amino acid residues of the donor but may comprise
other structurally and/or functionally relevant amino acid residues
of the donor antibody as well. Such hybrids can be prepared by
several methods well known in the art. Preferably, the ligand or
agent binds specifically to the polypeptide. Specific binding
according to the present invention means that the ligand or agent
should not bind substantially to ("cross-react" with) another
peptide, polypeptide or substance present in the sample to be
analyzed. Preferably, the specifically bound polypeptide should be
bound with at least 3 times higher, more preferably at least 10
times higher and even more preferably at least 50 times higher
affinity than any other relevant peptide or polypeptide.
Non-specific binding may be tolerable, if it can still be
distinguished and measured unequivocally, e.g. according to its
size on a Western Blot, or by its relatively higher abundance in
the sample. Binding of the ligand can be measured by any method
known in the art. Preferably, said method is semiquantitative or
quantitative. Suitable methods are described in the following.
First, binding of a ligand may be measured directly, e.g. by mass
spectroscopy, NMR or surface plasmon resonance. Second, if the
ligand also serves as a substrate of an enzymatic activity of the
polypeptide of interest, an enzymatic reaction product may be
measured (e.g. the amount of a protease can be measured by
measuring the amount of cleaved substrate, e.g. on a Western Blot).
Alternatively, the ligand may exhibit enzymatic properties itself
and the "ligand/polypeptide" complex or the ligand which was bound
by the polypeptide, respectively, may be contacted with a suitable
substrate allowing detection by the generation of an intensity
signal. For measurement of enzymatic reaction products, preferably
the amount of substrate is saturating. The substrate may also be
labeled with a detectable lable prior to the reaction. Preferably,
the sample is contacted with the substrate for an adequate period
of time. An adequate period of time refers to the time necessary
for a detectable, preferably measurable, amount of product to be
produced. Instead of measuring the amount of product, the time
necessary for appearance of a given (e.g. detectable) amount of
product can be measured. Third, the ligand may be coupled
covalently or non-covalently to a label allowing detection and
measurement of the ligand. Labeling may be done by direct or
indirect methods. Direct labeling involves coupling of the label
directly (covalently or non-covalently) to the ligand. Indirect
labeling involves binding (covalently or non-covalently) of a
secondary ligand to the first ligand. The secondary ligand should
specifically bind to the first ligand. Said secondary ligand may be
coupled with a suitable label and/or be the target (receptor) of a
tertiary ligand binding to the secondary ligand. The use of
secondary, tertiary or even higher order ligands is often used to
increase the signal. Suitable secondary and higher order ligands
may include antibodies, secondary antibodies, and the well-known
streptavidin-biotin system (Vector Laboratories, Inc.). The ligand
or substrate may also be "tagged" with one or more tags as known in
the art. Such tags may then be targets for higher order ligands.
Suitable tags include biotin, digoxygenin, His-Tag,
Glutathion-S-Transferase, FLAG, GFP, myc-tag, influenza A virus
haemagglutinin (HA), maltose binding protein, and the like. In the
case of a peptide or polypeptide, the tag is preferably at the
N-terminus and/or C-terminus. Suitable labels are any labels
detectable by an appropriate detection method. Typical labels
include gold particles, latex beads, acridan ester, luminol,
ruthenium, enzymatically active labels, radioactive labels,
magnetic labels ("e.g. magnetic beads", including paramagnetic and
superparamagnetic labels), and fluorescent labels. Enzymatically
active labels include e.g. horseradish peroxidase, alkaline
phosphatase, beta-Galactosidase, Luciferase, and derivatives
thereof. Suitable substrates for detection include
di-amino-benzidine (DAB), 3,3'-5,5'-tetramethylbenzidine, NBT-BCIP
(4-nitro blue tetrazolium chloride and
5-bromo-4-chloro-3-indolyl-phosphate, available as ready-made stock
solution from Roche Diagnostics), CDP-Star.TM. (Amersham
Biosciences), ECF.TM. (Amersham Biosciences). A suitable
enzyme-substrate combination may result in a colored reaction
product, fluorescence or chemo luminescence, which can be measured
according to methods known in the art (e.g. using a light-sensitive
film or a suitable camera system). As for measuring the enyzmatic
reaction, the criteria given above apply analogously. Typical
fluorescent labels include fluorescent proteins (such as GFP and
its derivatives), Cy3, Cy5, or Dy-547, Dy-549, Dy-647, Dy-649
(Dyomics, Jena, Germany) or Texas Red, Fluorescein, and the Alexa
dyes (e.g. Alexa 568). Further fluorescent labels are available
e.g. from Molecular Probes (Oregon). Also the use of quantum dots
as fluorescent labels is contemplated. Typical radioactive labels
include <35>S, <125>I, <32>P, <33>P and the
like. A radioactive label can be detected by any method known and
appropriate, e.g. a light-sensitive film or a phosphor imager.
Suitable measurement methods according the present invention also
include precipitation (particularly immunoprecipitation),
electrochemiluminescence (electro-generated chemiluminescence), RIA
(radioimmunoassay), ELISA (enzyme-linked immunosorbent assay),
sandwich enzyme immune tests, electrochemiluminescence sandwich
immunoassays (ECLIA), dissociation-enhanced lanthanide fluoro
immuno assay (DELFIA), scintillation proximity assay (SPA), FRET
based proximity assays (Anal Chem. 2005 Apr. 15; 77(8):2637-42.) or
Ligation proximity assays (Nature Biotechnology 20, 473-477 (2002),
turbidimetry, nephelometry, latex-enhanced turbidimetry or
nephelometry, or solid phase immune tests. Further methods known in
the art (such as gel electrophoresis, 2D gel electrophoresis, SDS
polyacrylamid gel electrophoresis (SDS-PAGE), Western Blotting, and
mass spectrometry), can be used alone or in combination with
labeling or other detection methods as described above.
[0040] The amount of a polypeptide biomarker may be, also
preferably, determined as follows: (a) contacting a solid support
comprising a ligand for the polypeptide as specified above with a
sample comprising the polypeptide and (b) measuring the amount of
polypeptide which is bound to the support. The ligand, preferably,
chosen from the group consisting of nucleic acids, peptides,
polypeptides, antibodies and aptamers, is preferably present on a
solid support in immobilized form. Materials for manufacturing
solid supports are well known in the art and include, inter alia,
commercially available column materials, polystyrene beads, latex
beads, magnetic beads, colloid metal particles, glass and/or
silicon chips and surfaces, nitrocellulose strips, membranes,
sheets, duracytes, wells and walls of reaction trays, plastic tubes
etc. The ligand or agent may be bound to many different carriers.
Examples of well-known carriers include glass, polystyrene,
polyvinyl chloride, polypropylene, polyethylene, polycarbonate,
dextran, nylon, amyloses, natural and modified celluloses,
polyacrylamides, agaroses, and magnetite. The nature of the carrier
can be either soluble or insoluble for the purposes of the
invention. Suitable methods for fixing/immobilizing said ligand are
well known and include, but are not limited to ionic, hydrophobic,
covalent interactions and the like. It is also contemplated to use
"suspension arrays" as arrays according to the present invention
(Nolan 2002, Trends Biotechnol. 20(1):9-12). In such suspension
arrays, the carrier, e.g. a microbead or microsphere, is present in
suspension. The array consists of different microbeads or
microspheres, possibly labeled, carrying different ligands. Methods
of producing such arrays, for example based on solid-phase
chemistry and photo-labile protective groups, are generally known,
see e.g., U.S. Pat. No. 5,744,305.
[0041] The term "amount" as used herein encompasses the absolute
amount of a biomarker, the relative amount or concentration of the
said biomarker as well as any value or parameter which correlates
thereto or can be derived therefrom. Such values or parameters
comprise intensity signal values from all specific physical or
chemical properties obtained from the said biomarker by direct
measurements, e.g., intensity values in mass spectra or NMR spectra
or surface Plasmon resonance spectra. Moreover, encompassed are all
values or parameters which are obtained by indirect measurements
specified elsewhere in this description, e.g., response levels
determined from biological read out systems in response to the
peptides or intensity signals obtained from specifically bound
ligands. It is to be understood that values correlating to the
aforementioned amounts or parameters can also be obtained by all
standard mathematical operations.
[0042] The term "comparing" as used herein encompasses comparing
the amount of the biomarker comprised by the sample to be analyzed
with an amount of a suitable reference source specified elsewhere
in this description. It is to be understood that comparing as used
herein refers to a comparison of corresponding parameters or
values, e.g., an absolute amount is compared to an absolute
reference amount, while a concentration is compared to a reference
concentration, or an intensity signal obtained from a test sample
is compared to the same type of intensity signal of a reference
sample. The comparison referred to in step (b) of the method of the
present invention may be carried out manually or computer assisted.
For a computer assisted comparison, the value of the determined
amount may be compared to values corresponding to suitable
references which are stored in a database by a computer program.
The computer program may further evaluate the result of the
comparison, i.e. automatically provide the desired assessment in a
suitable output format. Based on the comparison of the amount
determined in step a) and the reference amount, it is possible to
predict the risk of recurrence of bladder cancer in a subject after
treatment of bladder cancer.
[0043] The term "reference" as used herein refers to amounts of the
biomarker which allow for predicting whether a subject is at risk
of recurrence of bladder cancer, or not. Therefore, the reference
may either be derived from (i) a subject known to be at risk of
recurrence of bladder cancer (or from a group of said subjects) or
(ii) a subject known not to be at risk of recurrence of bladder
cancer. Preferably, said reference is derived from a sample of the
aforementioned subjects. More preferably, an increased amount of
the said at least one biomarker selected from the biomarkers shown
in Table 2 compared to the reference is indicative for a subject
being at risk of recurrence of bladder cancer, whereas a decreased
amount of the said at least one biomarker selected from the
biomarkers shown in Table 2 compared to the reference is indicative
for a subject not being at risk of recurrence of bladder cancer.
Also preferably, an increased amount of the said at least one
biomarker selected from the biomarkers shown in Table 3 compared to
the reference is indicative for a subject not being at risk of
recurrence of bladder cancer, whereas a decreased amount of the
said at least one biomarker selected from the biomarkers shown in
Table 3 compared to the reference is indicative for a subject being
at risk of recurrence of bladder cancer.
[0044] Preferably, the increases or decreases as referred to herein
are statistically significant. Whether an increase or decrease is
statistically significant can be determined by the skilled person
without further ado.
[0045] In the context of the methods of the present invention, the
amount of more than one biomarker may be determined. Of course,
the, thus, determined amounts shall be compared to various
reference amounts, i.e. to the reference amounts for the individual
biomarker tested.
[0046] Moreover, the references, preferably, define threshold
amounts or thresholds. Suitable reference amounts or threshold
amounts may be determined by the method of the present invention
from a reference sample to be analyzed together, i.e.
simultaneously or subsequently, with the test sample. A preferred
reference amount serving as a threshold may be derived from the
upper limit of normal (ULN), i.e. the upper limit of the
physiological amount to be found in a population of subjects (e.g.
patients enrolled for a clinical trial). The ULN for a given
population of subjects can be determined by various well known
techniques. A suitable technique may be to determine the median of
the population for the peptide or polypeptide amounts to be
determined in the method of the present invention. Suitable
threshold amounts can also be identified by ROC plots depicting the
overlap between the two distributions by plotting the sensitivity
versus 1-specificity for the complete range of decision thresholds.
On the y-axis is sensitivity, or the true-positive fraction,
defined as (number of true-positive test results)/(number of
true-positive+number of false-negative test results). This has also
been referred to as positivity in the presence of a given disease.
It is calculated solely from the affected subgroup. On the x-axis
is the false-positive fraction, or 1-specificity, defined as
(number of false-positive results)/(number of true-negative+number
of false-positive results). It is an index of specificity and is
calculated entirely from the unaffected subgroup. Because the true-
and false-positive fractions are calculated entirely separately, by
using the test results from two different subgroups, the ROC plot
is independent of the prevalence of disease in the sample. Each
point on the ROC plot represents a sensitivity/I-specificity pair
corresponding to a particular decision threshold. A test with
perfect discrimination (no overlap in the two distributions of
results) has an ROC plot that passes through the upper left corner,
where the true-positive fraction is 1.0, or 100% (perfect
sensitivity), and the false-positive fraction is 0 (perfect
specificity). The theoretical plot for a test with no
discrimination (identical distributions of results for the two
groups) is a 45 degrees diagonal line from the lower left corner to
the upper right corner. Most plots fall in between these two
extremes.
[0047] Further preferred are the following diagnostic
algorithms:
[0048] i) An essentially identical or an increased amount of the at
least one biomarker as compared to the reference amount indicates
that the subject is at risk of recurrence of bladder cancer, if the
at least one biomarker is selected from the biomarkers shown in
Table 2, and if the reference amount is derived from a subject
known to be at risk of recurrence of bladder cancer, and/or (ii) an
essentially identical or a decreased amount of the at least one
biomarker as compared to the reference amount indicates that the
subject is at not risk of recurrence of bladder cancer, if the at
least one biomarker is selected from the biomarkers shown in Table
2, and if the reference amount is derived from a subject known to
be not at risk of recurrence of bladder cancer.
[0049] ii) An essentially identical or a decreased amount of the at
least one biomarker as compared to the reference amount indicates
that the subject is at risk of recurrence of bladder cancer, if the
at least one biomarker is selected from the biomarkers shown in
Table 3, and if the reference amount is derived from a subject
known to be at risk of recurrence of bladder cancer, and/or an
essentially identical or an increased amount of the at least one
biomarker as compared to the reference amount indicates that the
subject is not at risk of recurrence of bladder cancer, if the at
least one biomarker is selected from the biomarkers shown in Table
3, and if the reference amount is derived from a subject known to
be not at risk of recurrence of bladder cancer.
[0050] Advantageously, it has been found in the study underlying
the present invention that the biomarkers listed in the Tables 1
are reliable markers for predicting the risk of recurrence of
bladder cancer in a subject treated against bladder cancer. Said
prediction is of high importance since bladder cancer, in
particular nonmuscle-invasive bladder cancer, has a high degree of
recurrence. Therefore, bladder cancer usually requires life-long
monitoring, resulting in high health care costs. The findings
underlying the aforementioned method also allow for an improved
clinical management of bladder cancer since subjects can be
identified which need intensive monitoring, or which do not need
intensive monitoring. Furthermore, said findings of said method of
the present invention also give hope to subjects being identified
to be not of risk of recurrence of bladder cancer and, therefore,
avoid misdirected and unnecessary treatment. Further, the success
of a therapy can be monitored. In the studies underlying this
invention, tissue samples from subjects after treatment of bladder
cancer were analyzed using antibody microarrays comprising 810
antibodies against 741 different polypeptides. It was assessed
whether there are differences between subjects in which bladder
cancer recurred and subjects in which bladder cancer did not recur
in the follow-up period. Differences in the polypeptide amounts
between subjects which turned out to be statistically significant
are shown in the Tables 1, 2 and 3 below and could be used as
biomarkers for predicting the risk of recurrence of bladder cancer.
Table 1 shows an overview of all biomarkers with modulated
expression with respect to bladder cancer recurrence. Table 2 shows
an overview on biomarkers which were increased in subjects in which
bladder cancer recurred after treatment. Table 3 shows an overview
on biomarkers which were decreased in subjects in which bladder
cancer recurred after treatment. Thus, increased amounts of the
biomarkers shown in table 2, and decreased amounts of the
biomarkers shown in table 3 are associated with bladder cancer
recurrence.
[0051] It is to be understood that a subject who is at risk of
bladder cancer recurrence requires closer monitoring, und thus,
shorter surveillance intervals as a subject who is not at risk of
bladder cancer recurrence.
[0052] Therefore, the aforementioned method, preferably, further
comprises the step of recommending the duration of surveillance
intervals for the subject suffering from bladder cancer.
Preferably, short surveillance intervals are recommended, if the
subject is at risk of bladder cancer recurrence. Preferably, long
surveillance intervals are recommended, if the subject is not at
risk of bladder cancer recurrence. A short surveillance interval
is, preferably, an interval of 5 months. More preferably, it is an
interval of 4 months. Most preferably, it is an interval of 3
months or less. A long surveillance interval is, preferably, an
interval of 9 months. More preferably, it is an interval of 1 year
or more.
[0053] The definitions and explanations given herein above apply
mutatis mutandis to the embodiments described herein below (except
stated otherwise).
[0054] Moreover, the present invention relates to a method
identifying a subject being in need of further bladder cancer
therapy, comprising the steps of: [0055] a. determining the amount
of at least one biomarker selected from the biomarkers shown in
Table 1 in a sample from the subject, and [0056] b. comparing the
amount of said at least one biomarker with a reference amount for
said at least one biomarker, whereby a subject being in need of
further bladder cancer therapy is to be identified.
[0057] The phrase "a subject in need of further bladder cancer
therapy" as used herein relates to a subject who is at risk of
recurrence of bladder cancer (as diagnosed by method described
above). It will be understood that further bladder cancer therapy
is at least beneficial for such subject. As discussed above, the
diagnostic method of the present invention already allows
identifying subjects being at risk of recurrence of bladder cancer
shortly after treatment. Accordingly, such subjects which may not
be unambiguously identifiable based on their clinical symptoms.
[0058] Preferred treatments of bladder cancer are described herein
above. Preferred further bladder cancers therapies are, preferably,
the described treatment regimens. More preferably, said further
bladder cancer therapy is adjuvant intravesical therapy,
preferably, immunotherapy or chemotherapy.
[0059] Preferably, the reference is derived from a subject or group
of subjects known to be in need of further bladder cancer therapy,
or from a subject or group of subjects known to be not in need of
further bladder cancer therapy.
[0060] Preferably, the said at least one biomarker is selected from
the group of biomarkers listed in Table 2, and wherein an increase
in the said at least one biomarker as compared to the reference
amount indicates that the subject is in need of further bladder
cancer therapy, and/or wherein a decrease indicates that the
subject is not in need of further bladder cancer therapy.
[0061] Preferably, the said at least one biomarker is selected from
the group of biomarkers listed in Table 3, wherein a decrease in
the said at least one biomarker as compared to the reference amount
indicates that the subject is in need of further bladder cancer
therapy and/or wherein an increase indicates that the subject is
not in need of further bladder cancer therapy.
[0062] The term "sample" has been described herein above.
Preferably, the sample to be tested has been obtained after
treatment of bladder cancer. More preferably, the sample has been
obtained during treatment of bladder cancer, in particular, during
surgery.
[0063] Moreover, the present invention relates to a method for
predicting the risk of progression of bladder cancer in a subject
suffering from bladder cancer, comprising the steps of the
aforementioned method of predicting the risk of recurrence of
bladder cancer, and the further step of predicting progression of
bladder cancer.
[0064] In particular, the present invention present invention
relates to a method for predicting the risk of progression of
bladder cancer in a subject suffering from bladder cancer,
comprising the steps of [0065] a) determining the amount of at
least one biomarker selected from the biomarkers shown in table 1,
2, 3 in a sample from said subject, and [0066] b) comparing the
amount of said at least one biomarker with a reference amount for
said at least one biomarker, whereby the risk of progression of
bladder cancer is to be predicted.
[0067] The definitions for the terms "bladder cancer", "amount",
"comparing", "subject", and "reference amount" given above apply
accordingly. However, in the context of the aforementioned method
is also contemplated that the subject suffering from bladder cancer
may be also untreated (with respect to bladder cancer). Therefore,
the sample to be used in the context of the aforementioned method
may be obtained at any time-point after the onset of bladder
cancer. In a more preferred embodiment, however, the sample is
obtained as set forth in connection with the method for predicting
the risk of recurrence of bladder cancer in a subject, in
particular in low grade, low stage bladder cancer.
[0068] Preferably, the at least one biomarker is selected from
table 2. More preferably, an increased amount of the said at least
one biomarker selected from the biomarkers shown in Table 2
compared to the reference is indicative for a subject being at risk
of progression of bladder cancers, whereas a decreased amount of
the said at least one biomarker selected from the biomarkers shown
in Table 2 compared to the reference is indicative for a subject
not being at risk of progression of bladder cancer.
[0069] Also preferably, the at least one biomarker is selected from
table 3. More preferably, a decreased amount of the said at least
one biomarker selected from the biomarkers shown in Table 3
compared to the reference is indicative for a subject being at risk
of progression of bladder cancers, whereas an increased amount of
the said at least one biomarker selected from the biomarkers shown
in Table 3 compared to the reference is indicative for a subject
not being at risk of progression. Preferably, the increases or
decreases as referred to herein are statistically significant.
Whether an increase or decrease is statistically significant can be
determined by the skilled person without further ado.
[0070] It is to be understood that a subject who is at risk of
progression of bladder cancer, preferably, shall be at increased
risk of progression of bladder cancer, whereas a subject who is not
at risk of progression of bladder cancer, preferably, is at
decreased risk of progression of bladder cancer.
[0071] Preferred references may be obtained from a subject or group
thereof known to be at risk of progression of bladder cancer, or
from a subject or group of subjects known to be not at risk of
recurrence of bladder cancer.
[0072] Moreover, the present invention relates to a method for
monitoring progression of bladder cancer in a subject suffering
from bladder cancer, comprising the steps of [0073] a) determining
the amount of at least one biomarker selected from the biomarkers
shown in table 1, 2 or 3 in a sample from said subject, [0074] b)
determining the amount of said at least one biomarker in a second
sample from said subject, said second sample being obtained after
said first sample, and [0075] c) comparing the amount of said at
least one biomarker in said first sample to the amount of said at
least one biomarker in said second sample.
[0076] The term "monitoring progression of bladder cancer" as
referred to above relates to keeping track of the status of the
disease, i.e. of bladder cancer. Monitoring includes comparing the
status of the disease as reflected by the amount of the biomarker
in a first sample taken at a first time point to the status of the
disease reflected by the amount of the biomarker in a second sample
taken at a second time point.
[0077] Preferably, if an amount of at least one biomarker as shown
in table 2 is determined, the following applies: The status of the
disease became worse and, thus, there was progression of the
disease, if the amount of the biomarker is increased in the second
sample as compared to the first sample, whereas there was
amelioration and, thus, improvement of the status of the disease if
the biomarker is decreased in the second sample as compared to the
first sample. If no change is observed, i.e. an essentially
identical amount is determined in the first and the second sample,
the status of the disease remained unchanged and the disease, thus,
was stagnating. An essentially identical amount is determined if no
statistically significant change in the amount is determined
between the first and the second sample. Whether the amounts are
essentially identical can be determined by the skilled artisan
without further ado.
[0078] Preferably, if the amount of the marker of the at least one
biomarker as shown in table 3 was determined, the following
applies: The status of the disease became worse and, thus, there
was progression of the disease, if the amount of the biomarker is
decreased in the second sample as compared to the first sample,
whereas there was amelioration and, thus, improvement of the status
of the disease if the biomarker is increased in the second sample
as compared to the first sample. If no change is observed, i.e. an
essentially identical amount is determined in the first and the
second sample, the status of the disease remained unchanged and the
disease, thus, was stagnating.
[0079] Accordingly, the following diagnostic algorithms are
particularly preferred:
[0080] Preferably, an increase of the amount of at least one
biomarker as shown in Table 2 in the second sample as compared to
the first sample is indicative for the diagnosis of progression of
bladder cancer. Preferably, a decrease of the amount of at least
one biomarker as shown in Table 2 in the second sample as compared
to the first sample is indicative for the diagnosis of amelioration
of bladder cancer. Preferably, an essentially identical amount in
the first and second sample is indicative for stagnation of bladder
cancer.
[0081] Preferably, a decrease of the amount of at least one
biomarker as shown in Table 3 in the second sample as compared to
the first sample is indicative for the diagnosis of progression of
bladder cancer. Preferably, an increase of the amount of at least
one biomarker as shown in Table 3 in the second sample as compared
to the first sample is indicative for the diagnosis of amelioration
of bladder cancer. Preferably, an essentially identical amount in
the first and second sample is indicative for stagnation of bladder
cancer.
[0082] Preferably, a change, i.e. increase or decrease is
significant if the amounts differ by at least about 5%, at least
about 10%, at least about 15%, at least about 20%, at least about
25% or at least about 50%.
[0083] The term "sample" has been described elsewhere herein. The
"first sample", in principle, can be obtained at any time point
after the onset or diagnosis of bladder cancer. Preferably,
however, it is obtained as in cancers being in stage 0 or 1
according to the TMN staging system and/or being graded as grade 1
or 2 according to the WHO classification system (see above). Also,
preferably, it may be obtained during surgery (for preferred
surgeries, see elsewhere herein). The "second sample" is,
preferably, understood as a sample which is obtained in order to
reflect a change of the amount of the at least biomarker as
compared to the amount of the respective marker in the first
sample. Thus, second sample shall be obtained after the first
sample. Preferably, the second sample is not obtained too early
after the first sample (in order to observe a sufficiently
significant change to allow for monitoring). In accordance with the
method of the present invention, the second sample is preferably
obtained within a period of 1 month to 2 years after the first
sample. Preferably, said second sample is obtained two years, or
one year after the first sample. More preferably, said second
sample is obtained 9 months after the first sample. Even more
preferably, said second sample is obtained 6 months after the first
sample. Most preferably, said second sample is obtained 3 months
after the first sample.
[0084] Further envisaged by the present invention is a method for
monitoring treatment of bladder cancer in a subject suffering from
bladder cancer, comprising the steps of [0085] a) determining the
amount of at least one biomarker selected from the biomarkers shown
in tables 1 to 3 in a from sample from said subject, [0086] b)
determining the amount of said at least one biomarker in a second
sample from said subject, said second sample being obtained after
said first sample, and [0087] c) comparing the amount of said at
least one biomarker in said first sample to the amount of said at
least one biomarker in said second sample, whereby treatment of
cancer is monitored.
[0088] The term "monitoring treatment of bladder cancer" as used
herein, preferably, relates to assessing the effects of treatment
of bladder cancer, i.e. to assess whether treatment of bladder
cancer is successful or not. Preferably, a treatment is considered
as successful, if the condition of the subject with respect to
bladder cancer did ameliorate. Preferably, a treatment is
considered as not successful, if the condition of the subject with
respect to bladder cancer worsened progress. Preferred methods of
treatments of bladder cancer are described elsewhere herein.
Preferably, the treatment is surgery and/or adjuvant intravesical
therapy (see explanations elsewhere).
[0089] The following diagnostic algorithms are particularly
preferred.
[0090] Preferably, the at least one biomarker is selected from the
group of biomarkers shown in Table 2. Preferably, a decrease of the
amount of at least one biomarker as shown in Table 2 in the second
sample as compared to the first sample is indicates that the
treatment is successful. Preferably, an increase of the amount of
at least one biomarker as shown in Table 2 in the second sample as
compared to the first sample indicates that the treatment is not
successful.
[0091] Preferably, the at least one biomarker is selected from the
group of biomarkers shown in Table 3. Preferably, an increase of
the amount of at least one biomarker as shown in Table 3 in the
second sample as compared to the first sample indicates that the
treatment is successful. Preferably, a decrease of the amount of at
least one biomarker as shown in Table 3 in the second sample as
compared to the first sample indicates that the treatment is not
successful.
[0092] Preferably, a change, i.e. increase or decrease is
statistically significant if the amounts differ by at least 5%, at
least 10/, at least 15%, at least 20%, at least 25% or at least 50%
(see also above).
[0093] Preferred samples have been described elsewhere herein. The
"first sample", in principle, can be obtained before or during
treatment of bladder cancer. If the treatment is surgery, the first
sample is preferably obtained during surgery. The "second sample"
is, preferably, understood as a sample which is obtained in order
to reflect a change of the amount of the at least biomarker as
compared to the amount of the respective marker in the first
sample. Thus, second sample shall be obtained after the first
sample. In principle, the second sample is obtained during or after
treatment of bladder cancer. Preferably, the second sample is not
obtained too early after the first sample (in order to observe a
sufficiently significant change to allow for monitoring). Thus,
said second sample is, preferably, obtained one year after the
first sample. More preferably, said second sample is obtained 9
months after the first sample. Even more preferably, said second
sample is obtained 6 months after the first sample. Most
preferably, said second sample is obtained 3 months after the first
sample. If the treatment is surgery, it is particularly
contemplated that the first sample is obtained before or during
surgery and that the second sample is obtained after surgery.
[0094] Moreover, the present invention relates to the use of at
least one biomarker selected from the group of biomarkers shown in
Table 1, 2 or 3, or of a detection agent for said at least one
biomarker for predicting the risk of recurrence of bladder cancer
in a subject after treatment of bladder cancer, for identifying a
subject being in need of further bladder cancer therapy, for
predicting the risk of progression of bladder cancer, for
monitoring progression of bladder cancer, or for monitoring
treatment of bladder cancer.
[0095] The term "detection agent" as used herein refers to an agent
which is capable of specifically recognizing and binding a
biomarker selected from the biomarkers shown in table 1. The agent
shall allow for direct or indirect detection of the complex formed
by the said agent and the biomarker. Direct detection can be
achieved by including into the agent a detectable label. Indirect
labelling may be achieved by a further agent which specifically
binds to the complex comprising the biomarker and the detection
agent wherein the said further agent is than capable of generating
a detectable signal. Suitable compounds which can be used as
detection agents are well known in the art. Preferably, the
detection agent is an antibody or aptamere which specifically binds
to the biomarker protein or a nucleic acid encoding the biomarker.
The term "antibody" as used herein includes both polyclonal and
monoclonal antibodies, as well as any modifications or fragments
thereof, such as Fv, Fab and F(ab) 2 fragments. The antibody shall
be capable of specifically binding a biomarker selected from the
biomarkers shown in table 1.
[0096] In an embodiment of the method of the present invention, the
amount of at least one biomarker selected from the group consisting
of FAS, IL-1B, IL-8, DEFB4, CTSS and IL-17B is determined in step
a) instead of the at least one biomarker shown in Table 1.
[0097] The present invention also relates to a device for
predicting recurrence of bladder cancer or for the prediction of
progression of bladder cancer in a sample of a subject comprising:
[0098] a. an analyzing unit for the said sample of the subject
comprising a detection agent for at least one biomarker as shown in
Table 1, Table 2 or Table 3, said detection agent allowing for the
determination of the amount of the said at least one biomarker in
the sample; and operatively linked thereto, and [0099] b. an
evaluation unit comprising a data processing unit and a data base,
said data base comprising a stored reference and said data
processing unit being capable of carrying out a comparison of the
amount of the at least one biomarker determined by the analyzing
unit and the stored reference thereby establishing the
prediction.
[0100] Preferred references are disclosed herein elsewhere.
[0101] The term "device" as used herein relates to a system of
means comprising at least the afore-mentioned analyzing unit and
the evaluation unit operatively linked to each other as to allow
the diagnosis. Preferred detection agents to be used for the device
of the present invention are disclosed above in connection with the
method of the invention. Preferably, detection agents are
antibodies or aptameres. How to link the units of the device in an
operating manner will depend on the type of units included into the
device. For example, where units for automatically determining the
amount of the biomarker are applied, the data obtained by said
automatically operating unit can be processed by, e.g., a computer
program in order to obtain the desired results. Preferably, the
units are comprised by a single device in such a case. The computer
unit, preferably, comprises a database including the stored
reference(s) as well as a computer-implemented algorithm for
carrying out a comparison of the determined amounts for the
polypeptide biomarkers with the stored reference of the database.
Computer-implemented as used herein refers to a computer-readable
program code tangibly included into the computer unit. The results
may be given as output of raw data which need interpretation by the
clinician. Preferably, the output of the device is, however,
processed, i.e. evaluated, raw data the interpretation of which
does not require a clinician.
[0102] In a preferred device of the invention, the detection agent,
preferably, an antibody, is immobilized on a solid support in an
array format. It will be understood that a device according to the
present invention can determine the amount of more than one
biomarker simultaneously. To this end, the detection agents may be
immobilized on a solid support and arranged in an array format,
e.g., in a so called "microarray".
[0103] The present invention also relates to a kit comprising a
detection agent for determining the amount of at least one
biomarker as shown in any one of Tables 1 to 3 and evaluation
instructions for establishing the diagnosis.
[0104] The term "kit" as used herein refers to a collection of the
aforementioned agent and the instructions provided in a
ready-to-use manner for determining the biomarker amount in a
sample. The agent and the instructions are, preferably, provided in
a single container. Preferably, the kit also comprises further
components which are necessary for carrying out the determination
of the amount of the biomarker. Such components may be auxiliary
agents which are required for the detection of the biomarker or
calibration standards. Moreover, the kit may, preferably, comprise
agents for the detection of more than one biomarker.
[0105] In the context of the present invention, it is particularly
envisaged to determine the amount of more than one biomarker, e.g.,
for predicting the risk of recurrence of bladder cancer. The
combined determination of biomarkers of is advantageous since it
allows for a higher specificity and sensitivity, e.g., when
predicting the risk of recurrence of bladder cancer.
[0106] The following combinations of biomarkers are particularly
preferred in accordance with the methods, kits, devices, and uses
of the present invention:
a combination of LMNA, YBOX1, and JUN a combination of LMNA, YBOX1,
JUN, AKT3, and SMAD3 a combination of LMNA, YBOX1, JUN, AKT3,
SMAD3, LYAM1, and PABP1. a combination of LMNA, YBOX1, JUN, AKT3,
SMAD3, LYAM1, PABP1, TIA1, CASP3, CDN1A, CASP9, and YETS2 a
combination of LMNA, YBOX1, JUN, AKT3, SMAD3, LYAM1, PABP1, TIA1,
CASP3, CDN1A, CASP9, YETS2, PO2F2, TOP2A, and RSSA. a combination
of LMNA, YBOX1, JUN, AKT3, SMAD3, LYAM1, PABP1, TIA1, CASP3, CDN1A,
CASP9, YETS2, PO2F2, TOP2A, RSSA, NFAC4, ZBT17, AKTIP, HSP7C, and
LIFR.
[0107] Whether the increased or decreased amounts of the various
biomarkers are indicative for a condition or risk as referred to
herein can be derived from Tables 2 and 3. A combination of the
aforementioned markers will increase sensitivity and specificity of
the diagnostic assay.
[0108] Further, it is envisaged to determine a combination of at
least three biomarkers as set forth in Table 1 in accordance with
the present invention. In particular, is it envisaged to determine
a combination of at least three biomarkers selected from the
aforementioned biomarkers, i.e. from LMNA, YBOX1, JUN, AKT3, SMAD3,
LYAM1, PABP1, TIA, CASP3, CDN1A, CASP9, YETS2, PO2F2, TOP2A, RSSA,
NFAC4, ZBT17, AKTIP, HSP7C, and LIFR. The phrase "at least three",
preferably, means three or more than three. In particular, it is
envisaged to determine at least 5, at least 8, at least 10, or at
least 15 biomarkers.
[0109] The present invention also envisages a composition, or a kit
comprising a detection agent which specifically binds to LMNA, a
detection agent which specifically binds to YBOX1, and a detection
agent which specifically binds to JUN. For a further explanation of
these biomarkers, see tables 1 to 3.
[0110] The present invention also envisages a composition, or a kit
comprising a detection agent which specifically binds to LMNA a
detection agent which specifically binds to YBOX1, a detection
agent which specifically binds to JUN, a detection agent which
specifically binds to AKT3, and a detection agent which
specifically binds to SMAD3. The present invention also envisages a
composition, or a kit, said composition or kit comprising a
detection agent which specifically binds to LMNA, a detection agent
which specifically binds to YBOX1, a detection agent which
specifically binds to JUN, a detection agent which specifically
binds to AKT3, a detection agent which specifically binds to SMAD3,
a detection agent which specifically binds to LYAM1, and a
detection agent which specifically binds to PABP1.
[0111] The present invention also envisages a composition, or a
kit, said composition or kit comprising a detection agent which
specifically binds to LMNA, a detection agent which specifically
binds to YBOX1, a detection agent which specifically binds to JUN,
a detection agent which specifically binds to AKT3, a detection
agent which specifically binds to SMAD3, a detection agent which
specifically binds to LYAM1, a detection agent which specifically
binds to PABP1, a detection agent which specifically binds to TIA1,
a detection agent which specifically binds to CASP3, and a
detection agent which specifically binds to CDN1A, and a detection
agent which specifically binds to CASP9, a detection agent which
specifically binds to YETS2.
[0112] The present invention also envisages a composition, or a
kit, said composition or kit comprising a detection agent which
specifically binds to LMNA, a detection agent which specifically
binds to YBOX1, a detection agent which specifically binds to JUN,
a detection agent which specifically binds to AKT3, a detection
agent which specifically binds to SMAD3, a detection agent which
specifically binds to LYAM1, a detection agent which specifically
binds to PABP1, a detection agent which specifically binds to TIA1,
a detection agent which specifically binds to CASP3, a detection
agent which specifically binds to CDN1A, a detection agent which
specifically binds to CASP9, a detection agent which specifically
binds to YETS2, a detection agent which specifically binds to
PO2F2, and a detection agent which specifically binds to TOP2A, a
detection agent which specifically binds to RSSA.
[0113] The present invention also envisages a composition, or a
kit, said composition or kit comprising a detection agent which
specifically binds to LMNA, a detection agent which specifically
binds to YBOX1, a detection agent which specifically binds to JUN,
a detection agent which specifically binds to AKT3, a detection
agent which specifically binds to SMAD3, a detection agent which
specifically binds to LYAM1, a detection agent which specifically
binds to PABP1, a detection agent which specifically binds to TIA,
a detection agent which specifically binds to CASP3, a detection
agent which specifically binds to CDN1A, a detection agent which
specifically binds to CASP9, a detection agent which specifically
binds to YETS2, a detection agent which specifically binds to
PO2F2, a detection agent which specifically binds to TOP2A, a
detection agent which specifically binds to RSSA, a detection agent
which specifically binds to NFAC4, a detection agent which
specifically binds to ZBT17, a detection agent which specifically
binds to AKTIP, and a detection agent which specifically binds to
HSP7C, and a detection agent which specifically binds to LIFR.
[0114] It is particularly envisaged that the detection agents,
preferably, an antibody or fragment thereof, comprised by the
aforementioned kits or compositions are immobilized on a solid
support in an array format. In particular, the detection agents may
be immobilized on a solid support and arranged in an array format,
e.g., in a so called "microarray". Accordingly, the present
invention also envisaged a microarray comprising the aforementioned
detection agents.
[0115] Preferably, the kit, the composition and the microarray is
used for predicting the risk of recurrence of bladder cancer in a
sample of a subject.
[0116] All references cited in this specification are herewith
incorporated by reference with respect to their entire disclosure
content and the disclosure content specifically mentioned in this
specification.
FIGURE LEGENDS
[0117] FIG. 1: With an random-forrest classifier applied to the
training set, all samples were classified correctly (dashed line)
corresponding to an area under the cureve (AUC) of 100%. A cross
validation with the Leave-One-Out method resulted in a good
discrimination of the samples with an AUC of 90.4% (see also
Examples).
[0118] The following Examples shall merely illustrate the
invention. They shall not be construed, whatsoever, to limit the
scope of the invention.
EXAMPLE 1: IDENTIFICATION OF POLYPEPTIDE BIOMARKER TO PREDICT THE
RISK OF RECURRENCE OF BLADDER CANCER
[0119] In order to identify polypeptides with differential
abundance in bladder cancer patients with recurrent tumours
compared to non-recurrent tumours a study was performed utilising
complex antibody microarrays. In this study the protein fraction of
the samples was directly labeled by a fluorescent dye, using
NHS-ester chemistry. A reference was established by pooling all
samples comprised in the study and labeled with a second
fluorescent dye. For incubation each sample was mixed with the
reference sample and incubated on an antibody microarray in a
competitive dual-colour approach.
[0120] For inclusion on the array specific target proteins were
selected based on the up- or downregulation in transcriptional
studies for different cancer entities. The antibody microarray
applied in this study comprised 810 antibodies that were directed
at 741 different proteins. All antibodies were immobilised at least
in duplicates. In addition, all incubations were performed in
duplicates. The study involved samples from 19 patients with
recurrent and six patients with non-recurrent bladder cancers. The
tumours were classified as stage 0 (Ta or Tis) and low grade.
[0121] After protein extraction from the tissue samples using T-PER
reagent (Thermo Fisher), the protein samples were labeled with
Dy-549 (Dyomics, Jena, Germany). Additionally, a common reference
was prepared by pooling of samples and subsequent labeling with
Dy-649 (Dyomics). All protein samples were labeled at a protein
concentration of 1 mg/mL with 0.1 mg/mL of the NHS-esters of the
fluorescent dyes in 100 mM sodium bicarbonate buffer (pH 9.0) on a
shaker at 4.degree. C. After 1 h, the reactions were stopped by
addition of 10% glycin. Unreacted dye was removed 30 min later and
the buffer changed to PBS using Zeba Desalt columns (Thermo
Scientific). Subsequently, Complete Protease Inhibitor Cocktail
tablets (Roche, Mannheim, Germany) were added as recommended by the
manufacturer.
[0122] Homemade incubation chambers were attached to the array
slides with Terostat-81 (Henkel, Dusseldorf, Germany). The inner
dimensions of the incubation chambers matched the area of the array
(9 mm.times.18 mm) with an additional border of 2 mm and a height
of 5 mm. Prior to adding the labeled protein samples, the arrays
were blocked with 10% skim milk powder and 0.05% Tween-20 in
phosphate buffered saline (PBS) on a Slidebooster instrument
(Advalytix, Munich, Germany) for 4 h. Incubation was performed with
labeled samples diluted 1:60 in blocking solution containing 0.1%
(w/v) Triton-X100 and Complete Protease Inhibitor Cocktail for 16 h
in a total volume of 600 .mu.L. Slides were thoroughly washed with
PBSTT prior and after detaching the incubation chambers. Finally,
the slides were rinsed with 0.1.times.PBS and distilled water and
dried in a stream of air.
[0123] Slide scanning was done on a ScanArray 5000 or 4000 XL unit
(Packard, Billerica, USA) using the identical instrument laser
power and PMT in each experiment. Spot segmentation was performed
with GenePix Pro 6.0 (Molecular Devices, Union City, USA).
Resulting data were analyzed using the LIMMA package of
R-Bioconductor after uploading the mean signal and median
background intensities. For normalization an invariant Lowess
normalization was applied (Sill M. et al. BMC Bioinformatics. 2010
11:556). For differential analyses of the depletion experiment a
one-factorial linear model was fitted with LIMMA resulting in a
two-sided t-test or F-test based on moderated statistics. All
presented p-values were adjusted for multiple testing by
controlling the false discovery rate according to Benjamini and
Hochberg.
[0124] Using LIMMA analysis, 100 proteins were identified with
differential abundance between recurrent and non-recurrent samples
at a highly significant level of adj. P<0.003. The results of
the aforementioned study are summarized in the following Tables. In
the tables the difference of protein abundance in the two sample
groups is given by the log fold change. The level of significance
is indicated by the p-value adjusted for multiple testing as
described above.
TABLE-US-00001 TABLE 1 Differentially regulated biomarkers (all)
Log fold Adjusted Uniprot HGNC Nr Uniprot Identifier change p-value
Accession Symbol Protein name 1 YBOX1_HUMAN 0.52 1.87E-08 P67809
YBX1 Nuclease-sensitive element-binding protein 1 2 LMNA_HUMAN 0.72
3.32E-09 P02545 LMNA Lamin A, Prelamin-A/C, Lamin-A/C 3 JUN_HUMAN
0.50 1.27E-07 P05412 JUN Transcription factor AP-1 4 PABP1_HUMAN
-0.36 2.13E-07 P11940 PABPC1 Polyadenylate-binding protein 1 5
SMAD3_HUMAN -0.59 2.13E-07 P84022 SMAD3 Mothers against
decapentaplegic homolog 3 6 TIA1_HUMAN -0.39 2.13E-07 P31483 TIA1
Nucleolysin TIA-1 isoform p40 7 AKT3_HUMAN 0.48 2.47E-07 Q9Y243
AKT3 RAC-gamma serine/threonine-protein kinase 8 CDN1A_HUMAN -0.52
3.38E-07 P38936 CDKN1A Cyclin-dependent kinase inhibitor 1 9
LYAM1_HUMAN -0.52 9.62E-07 P14151 SELL L-selectin 10 YETS2_HUMAN
0.32 5.28E-06 Q9ULM3 YEATS2 YEATS domain-containing protein 2 11
AKTIP_HUMAN -0.35 5.38E-06 Q9H8T0 AKTIP AKT-interacting protein 12
HSP7C_HUMAN -0.34 1.04E-05 P11142 HSPA8 Heat shock cognate 71 kDa
protein 13 PRI1_HUMAN -0.36 1.04E-05 P49642 PRIM1 DNA primase small
subunit 14 RSSA_HUMAN -0.35 1.07E-05 P08865 RPSA 40S ribosomal
protein SA 15 GRM1A_HUMAN 0.27 1.10E-05 Q96CP6 GRAMD1A GRAM
domain-containing protein 1A 16 TPA_HUMAN 0.23 1.14E-05 P00750 PLAT
Tissue-type plasminogen activator chain B 17 ZBT17_HUMAN -0.55
1.14E-05 Q13105 ZBTB17 Zinc finger and BTB domain-containing
protein 17 18 CADH1_HUMAN 0.33 1.15E-05 P12830 CDH1 E-Cad/CTF2 19
LAMP2_HUMAN 0.26 1.15E-05 P13473 LAMP2 Lysosome-associated membrane
glycoprotein 2 20 LIFR_HUMAN 0.30 2.17E-05 P42702 LIFR Leukemia
inhibitory factor receptor 21 TOP2A_HUMAN 0.48 2.17E-05 P11388
TOP2A DNA topoisomerase 2-alpha 22 SPS2L_HUMAN -0.23 2.20E-05
Q9NUQ6 SPATS2L SPATS2-like protein 23 NFAC4_HUMAN 0.35 2.47E-05
Q14934 NFATC4 Nuclear factor of activated T-cells, cytoplasmic 4 24
SF3B3_HUMAN 0.32 2.47E-05 Q15393 SF3B3 Splicing factor 3B subunit 3
25 UBIQ_HUMAN 0.24 2.47E-05 P62988 UBC Ubiquitin 26 2DMB_HUMAN
-0.35 3.41E-05 P28068 HLA-DMB HLA class II histocompatibility
antigen, DM beta chain 27 FAK1_HUMAN -0.36 3.93E-05 Q05397 PTK2
Focal adhesion kinase 1 28 IFNG_HUMAN -0.46 3.95E-05 P01579 IFNG
Interferon gamma 29 SP1_HUMAN -0.36 4.14E-05 P08047 SP1
Transcription factor Sp1 30 ACTN1_HUMAN -0.36 4.31E-05 P12814 ACTN1
Alpha-actinin-1 31 TIE1_HUMAN -0.30 6.17E-05 P35590 TIE1
Tyrosine-protein kinase receptor Tie-1 32 MMP13_HUMAN 0.25 6.32E-05
P45452 MMP13 Collagenase 3 33 TIMP1_HUMAN -0.33 6.32E-05 P01033
TIMP1 Metalloproteinase inhibitor 1 34 VTNC_HUMAN -0.51 6.76E-05
P04004 VTN Somatomedin-B 35 K1C17_HUMAN -0.20 1.70E-04 Q04695 KRT17
Keratin, type I cytoskeletal 17 36 NFKB1_HUMAN 0.32 1.76E-04 P19838
NFKB1 Nuclear factor NF-kappa-B p105 subunit 37 NAP1_HUMAN -0.27
1.91E-04 Q9BU70 C9orf156 Nef-associated protein 1 38 RL10_HUMAN
-0.28 1.91E-04 P27635 RPL10 60S ribosomal protein L10 39 KLF5_HUMAN
0.37 1.96E-04 Q13887 KLF5 Krueppel-like factor 5 40 MMP1_HUMAN
-0.26 2.27E-04 P03956 MMP1 27 kDa interstitial collagenase 41
CDKN3_HUMAN -0.33 2.36E-04 Q16667 CDKN3 Cyclin-dependent kinase
inhibitor 3 42 CD59_HUMAN -0.33 2.56E-04 P13987 CD59 CD59
glycoprotein 43 PO2F2_HUMAN -0.35 2.56E-04 P09086 POU2F2 POU
domain, class 2, transcription factor 2 44 MPIP2_HUMAN -0.28
2.76E-04 P30305 CDC25B M-phase inducer phosphatase 2 45 FRAP_HUMAN
-0.27 2.78E-04 P42345 FRAP1 Serine/threonine-protein kinase mTOR 46
IRS2_HUMAN -0.33 3.10E-04 Q9Y4H2 IRS2 Insulin receptor substrate 2
47 B2LA1_HUMAN 0.24 3.49E-04 Q16548 BCL2A1 Bcl-2-related protein A1
48 ERBB2_HUMAN -0.24 3.65E-04 P04626 ERBB2 Receptor
tyrosine-protein kinase erbB-2 49 CASP3_HUMAN 0.40 3.99E-04 P42574
CASP3 Caspase-3 subunit p17 50 FINC_HUMAN -0.31 3.99E-04 P02751 FN1
Ugl-Y2 51 LAC_HUMAN -0.26 4.00E-04 P01842 IGLC3 Ig lambda chain C
regions 52 AURKB_HUMAN -0.33 4.05E-04 Q96GD4 AURKB
Serine/threonine-protein kinase 12 53 MPP3_HUMAN -0.21 4.10E-04
Q13368 MPP3 MAGUK p55 subfamily member 3 54 CD2A2_HUMAN -0.32
4.34E-04 Q8N726 CDKN2A Cyclin-dependent kinase inhibitor 2A.
isoform 4 55 EPCAM_HUMAN -0.30 4.39E-04 P16422 EPCAM Epithelial
cell adhesion molecule 56 SOX9_HUMAN 0.23 4.39E-04 P48436 SOX9
Transcription factor SOX-9 57 TSP3_HUMAN -0.24 4.39E-04 P49746
THBS3 Thrombospondin-3 58 MUC5B_HUMAN 0.25 4.97E-04 Q9HC84 MUC5B
Mucin-5B 59 CP3A7_HUMAN -0.23 5.54E-04 P24462 CYP3A7 Cytochrome
P450 3A7 60 NMDE3_HUMAN -0.27 5.62E-04 Q14957 GRIN2C Glutamate
[NMDA] receptor subunit epsilon-3 61 THYG_HUMAN -0.34 5.62E-04
P01266 TG Thyroglobulin 62 AQP1_HUMAN 0.24 6.21E-04 P29972 AQP1
Aquaporin-1 63 IL15_HUMAN -0.80 6.21E-04 P40933 IL15 Interleukin-15
64 LAT1_HUMAN -0.24 6.21E-04 Q01650 SLC7A5 Large neutral amino
acids transporter small subunit 1 65 GSHB_HUMAN 0.17 6.35E-04
P48637 GSS Glutathione synthetase 66 RPB3_HUMAN -0.23 6.58E-04
P19387 POLR2C DNA-directed RNA polymerase II subunit RPB3 67
K1C19_HUMAN 0.21 7.26E-04 P08727 KRT19 Keratin, type I cytoskeletal
19 68 PAK2_HUMAN 0.20 7.46E-04 Q13177 PAK2 PAK-2p34 69 ZN593_HUMAN
0.24 7.81E-04 O00488 ZNF593 Zinc finger protein 593 70 MYD88_HUMAN
0.22 8.13E-04 Q99836 MYD88 Myeloid differentiation primary response
protein MyD88 71 IL8_HUMAN -0.23 9.48E-04 P10145 IL8 IL-8(7-77) 72
CUL2_HUMAN -0.24 9.76E-04 Q13617 CUL2 Cullin-2 73 SEP15_HUMAN -0.21
9.76E-04 O60613 SEP15 15 kDa selenoprotein 74 TNF13_HUMAN -0.24
9.88E-04 O75888 TNFSF13 Tumor necrosis factor ligand superfamily
member 13 75 APBA1_HUMAN 0.25 1.03E-03 Q02410 APBA1 Amyloid beta A4
precursor protein-binding family A member 1 76 EPHB3_HUMAN 0.21
1.03E-03 P54753 EPHB3 Ephrin type-B receptor 3 77 MK10_HUMAN -0.18
1.05E-03 P53779 MAPK10 Mitogen-activated protein kinase 10 78
GDN_HUMAN -0.26 1.17E-03 P07093 SERPINE2 Glia-derived nexin 79
HMMR_HUMAN -0.26 1.17E-03 O75330 HMMR Hyaluronan mediated motility
receptor 80 IL10_HUMAN 0.22 1.17E-03 P22301 IL10 Interleukin-10 81
OLFM4_HUMAN -0.23 1.19E-03 Q6UX06 OLFM4 Olfactomedin-4 82
CISY_HUMAN -0.30 1.30E-03 Q75390 CS Citrate synthase, mitochondrial
83 ID2_HUMAN -0.28 1.30E-03 Q02363 ID2 DNA-binding protein
inhibitor ID-2 84 MUTED_HUMAN -0.29 1.35E-03 Q8TDH9 MUTED Protein
Muted homolog 85 SEPR_HUMAN -0.25 1.42E-03 Q12884 FAP Seprase 86
TR10A_HUMAN -0.37 1.58E-03 O00220 TNFRSF10A Tumor necrosis factor
receptor superfamily member 10A 87 K2C8_HUMAN -0.19 1.58E-03 P05787
KRT8 Keratin, type II cytoskeletal 8 88 TNFB_HUMAN 0.20 1.58E-03
P01374 LTA Lymphotoxin-alpha 89 ANFB_HUMAN 0.19 1.58E-03 P16860
NPPB BNP(5-32) 90 CP1B1_HUMAN -0.30 1.72E-03 Q16678 CYP1B1
Cytochrome P450 1B1 91 BRPF3_HUMAN -0.18 1.74E-03 Q9ULD4 BRPF3
Bromodomain and PHD finger-containing protein 3 92 AP4B1_HUMAN
-0.21 1.81E-03 Q9Y6B7 AP4B1 AP-4 complex subunit beta-1 93
GBRB1_HUMAN -0.21 1.81E-03 P18505 GABRB1 Gamma-aminobutyric acid
receptor subunit beta-1 94 SIA7F_HUMAN 0.18 2.00E-03 Q969X2
ST6GALNAC6 Alpha-N-acetylgalactosaminide alpha-2,6-
sialyltransferase 6 95 HXC11_HUMAN -0.17 2.04E-03 O43248 HOXC11
Homeobox protein Hox-C11 96 PIGC_HUMAN -0.15 2.05E-03 Q92535 PIGC
Phosphatidylinositol N-acetylglucosaminyltransferase subunit C 97
TRI22_HUMAN -0.23 2.05E-03 Q8IYM9 TRIM22 Tripartite
motif-containing protein 22 98 OSTP_HUMAN -0.23 2.10E-03 P10451
SPP1 Osteopontin 99 ZO2_HUMAN 0.19 2.29E-03 Q9UDY2 TJP2 Tight
junction protein ZO-2 100 PO2F1_HUMAN -0.23 2.55E-03 P14859 POU2F1
POU domain, class 2, transcription factor 1
TABLE-US-00002 TABLE 3 Down-regulated biomarker Log fold Adjusted
Uniprot HGNC Nr Uniprot Identifier change p-value Accession Symbol
Protein name 4 PABP1_HUMAN -0.36 2.13E-07 P11940 PABPC1
Polyadenylate-binding protein 1 5 SMAD3_HUMAN -0.59 2.13E-07 P84022
SMAD3 Mothers against decapentaplegic homolog 3 6 TIA1_HUMAN -0.39
2.13E-07 P31483 TIA1 Nucleolysin TIA-1 isoform p40 8 CDN1A_HUMAN
-0.52 3.38E-07 P38936 CDKN1A Cyclin-dependent kinase inhibitor 1 9
LYAM1_HUMAN -0.52 9.62E-07 P14151 SELL L-selectin 11 AKTIP_HUMAN
-0.35 5.38E-06 Q9H8T0 AKTIP AKT-interacting protein 12 HSP7C_HUMAN
-0.34 1.04E-05 P11142 HSPA8 Heat shock cognate 71 kDa protein 13
PRI1_HUMAN -0.36 1.04E-05 P49642 PRIM1 DNA primase small subunit 14
RSSA_HUMAN -0.35 1.07E-05 P08865 RPSA 40S ribosomal protein SA 17
ZBT17_HUMAN -0.55 1.14E-05 Q13105 ZBTB17 Zinc finger and BTB
domain-containing protein 17 22 SPS2L_HUMAN -0.23 2.20E-05 Q9NUQ6
SPATS2L SPATS2-like protein 26 2DMB_HUMAN -0.35 3.41E-05 P28068
HLA-DMB HLA class II histocompatibility antigen, DM beta chain 27
FAK1_HUMAN -0.36 3.93E-05 Q05397 PTK2 Focal adhesion kinase 1 28
IFNG_HUMAN -0.46 3.95E-05 P01579 IFNG Interferon gamma 29 SP1_HUMAN
-0.36 4.14E-05 P08047 SP1 Transcription factor Sp1 30 ACTN1_HUMAN
-0.36 4.31E-05 P12814 ACTN1 Alpha-actinin-1 31 TIE1_HUMAN -0.30
6.17E-05 P35590 TIE1 Tyrosine-protein kinase receptor Tie-1 33
TIMP1_HUMAN -0.33 6.32E-05 P01033 TIMP1 Metalloproteinase inhibitor
1 34 VTNC_HUMAN -0.51 6.76E-05 P04004 VTN Somatomedin-B 35
K1C17_HUMAN -0.20 1.70E-04 Q04695 KRT17 Keratin, type I
cytoskeletal 17 37 NAP1_HUMAN -0.27 1.91E-04 Q9BU70 C9orf156
Nef-associated protein 1 38 RL10_HUMAN -0.28 1.91E-04 P27635 RPL10
60S ribosomal protein L10 40 MMP1_HUMAN -0.26 2.27E-04 P03956 MMP1
27 kDa interstitial collagenase 41 CDKN3_HUMAN -0.33 2.36E-04
Q16667 CDKN3 Cyclin-dependent kinase inhibitor 3 42 CD59_HUMAN
-0.33 2.56E-04 P13987 CD59 CD59 glycoprotein 43 PO2F2_HUMAN -0.35
2.56E-04 P09086 POU2F2 POU domain, class 2, transcription factor 2
44 MPIP2_HUMAN -0.28 2.76E-04 P30305 CDC25B M-phase inducer
phosphatase 2 45 FRAP_HUMAN -0.27 2.78E-04 P42345 FRAP1
Serine/threonine-protein kinase mTOR 46 IRS2_HUMAN -0.33 3.10E-04
Q9Y4H2 IRS2 Insulin receptor substrate 2 48 ERBB2_HUMAN -0.24
3.65E-04 P04626 ERBB2 Receptor tyrosine-protein kinase erbB-2 50
FINC_HUMAN -0.31 3.99E-04 P02751 FN1 Ugl-Y2 51 LAC_HUMAN -0.26
4.00E-04 P01842 IGLC3 Ig lambda chain C regions 52 AURKB_HUMAN
-0.33 4.05E-04 Q96GD4 AURKB Serine/threonine-protein kinase 12 53
MPP3_HUMAN -0.21 4.10E-04 Q13368 MPP3 MAGUK p55 subfamily member 3
54 CD2A2_HUMAN -0.32 4.34E-04 Q8N726 CDKN2A Cyclin-dependent kinase
inhibitor 2A, isoform 4 55 EPCAM_HUMAN -0.30 4.39E-04 P16422 EPCAM
Epithelial cell adhesion molecule 57 TSP3_HUMAN -0.24 4.39E-04
P49746 THBS3 Thrombospondin-3 59 CP3A7_HUMAN -0.23 5.54E-04 P24462
CYP3A7 Cytochrome P450 3A7 60 NMDE3_HUMAN -0.27 5.62E-04 Q14957
GRIN2C Glutamate [NMDA] receptor subunit epsilon-3 61 THYG_HUMAN
-0.34 5.62E-04 P01266 TG Thyroglobulin 63 IL15_HUMAN -0.80 6.21E-04
P40933 IL15 Interleukin-15 64 LAT1_HUMAN -0.24 6.21E-04 Q01650
SLC7A5 Large neutral amino acids transporter small subunit 1 66
RPB3_HUMAN -0.23 6.58E-04 P19387 POLR2C DNA-directed RNA polymerase
II subunit RPB3 71 IL8_HUMAN -0.23 9.48E-04 P10145 IL8 IL-8(7-77)
72 CUL2_HUMAN -0.24 9.76E-04 Q13617 CUL2 Cullin-2 73 SEP15_HUMAN
-0.21 9.76E-04 O60613 SEP15 15 kDa selenoprotein 74 TNF13_HUMAN
-0.24 9.88E-04 O75888 TNFSF13 Tumor necrosis factor ligand
superfamily member 13 77 MK10_HUMAN -0.18 1.05E-03 P53779 MAPK10
Mitogen-activated protein kinase 10 78 GDN_HUMAN -0.26 1.17E-03
P07093 SERPINE2 Glia-derived nexin 79 HMMR_HUMAN -0.26 1.17E-03
O75330 HMMR Hyaluronan mediated motility receptor 81 OLFM4_HUMAN
-0.23 1.19E-03 Q6UX06 OLFM4 Olfactomedin-4 82 CISY_HUMAN -0.30
1.30E-03 O75390 CS Citrate synthase, mitochondrial 83 ID2_HUMAN
-0.28 1.30E-03 Q02363 ID2 DNA-binding protein inhibitor ID-2 84
MUTED_HUMAN -0.29 1.35E-03 Q8TDH9 MUTED Protein Muted homolog 85
SEPR_HUMAN -0.25 1.42E-03 Q12884 FAP Seprase 86 TR10A_HUMAN -0.37
1.58E-03 O00220 TNFRSF10A Tumor necrosis factor receptor
superfamily member 10A 87 K2C8_HUMAN -0.19 1.58E-03 P05787 KRT8
Keratin, type II cytoskeletal 8 90 CP1B1_HUMAN -0.30 1.72E-03
Q16678 CYP1B1 Cytochrome P450 1B1 91 BRPF3_HUMAN -0.18 1.74E-03
Q9ULD4 BRPF3 Bromodomain and PHD finger-containing protein 3 92
AP4B1_HUMAN -0.21 1.81E-03 Q9Y6B7 AP4B1 AP-4 complex subunit beta-1
93 GBRB1_HUMAN -0.21 1.81E-03 P18505 GABRB1 Gamma-aminobutyric acid
receptor subunit beta-1 95 HXC11_HUMAN -0.17 2.04E-03 O43248 HOXC11
Homeobox protein Hox-C11 96 PIGC_HUMAN -0.15 2.05E-03 Q92535 PIGC
Phosphatidylinositol N-acetylglucosaminyltransferase subunit C 97
TRI22_HUMAN -0.23 2.05E-03 Q8IYM9 TRIM22 Tripartite
motif-containing protein 22 98 OSTP_HUMAN -0.23 2.10E-03 P10451
SPP1 Osteopontin 100 PO2F1_HUMAN -0.23 2.55E-03 P14859 POU2F1 POU
domain, class 2, transcription factor 1
TABLE-US-00003 TABLE 2 Up-regulated biomarker Log fold Adjusted
Uniprot HGNC Nr Uniprot Identifier change p-value Accession Symbol
Protein name 1 LMNA_HUMAN 0.72 3.32E-09 P02545 LMNA Lamin A,
Prelamin-A/C, Lamin-A/C 2 YBOX1_HUMAN 0.52 1.87E-08 P67809 YBX1
Nuclease-sensitive element-binding protein 1 3 JUN_HUMAN 0.50
1.27E-07 P05412 JUN Transcription factor AP-1 7 AKT3_HUMAN 0.48
2.47E-07 Q9Y243 AKT3 RAC-gamma serine/threonine-protein kinase 10
YETS2_HUMAN 0.32 5.28E-06 Q9ULM3 YEATS2 YEATS domain-containing
protein 2 15 GRM1A_HUMAN 0.27 1.10E-05 Q96CP6 GRAMD1A GRAM
domain-containing protein 1A 16 TPA_HUMAN 0.23 1.14E-05 P00750 PLAT
Tissue-type plasminogen activator chain B 18 CADH1_HUMAN 0.33
1.15E-05 P12830 CDH1 E-Cad/CTF2 19 LAMP2_HUMAN 0.26 1.15E-05 P13473
LAMP2 Lysosome-associated membrane glycoprotein 2 20 LIFR_HUMAN
0.30 2.17E-05 P42702 LIFR Leukemia inhibitory factor receptor 21
TOP2A_HUMAN 0.48 2.17E-05 P11388 TOP2A DNA topoisomerase 2-alpha 23
NFAC4_HUMAN 0.35 2.47E-05 Q14934 NFATC4 Nuclear factor of activated
T-cells, cytoplasmic 4 24 SF3B3_HUMAN 0.32 2.47E-05 Q15393 SF3B3
Splicing factor 3B subunit 3 25 UBIQ_HUMAN 0.24 2.47E-05 P62988 UBC
Ubiquitin 32 MMP13_HUMAN 0.25 6.32E-05 P45452 MMP13 Collagenase 3
36 NFKB1_HUMAN 0.32 1.76E-04 P19838 NFKB1 Nuclear factor NF-kappa-B
p105 subunit 39 KLF5_HUMAN 0.37 1.96E-04 Q13887 KLF5
E-Krueppel-like factor 5 47 B2LA1_HUMAN 0.24 3.49E-04 Q16548 BCL2A1
Bcl-2-related protein A1 49 CASP3_HUMAN 0.40 3.99E-04 P42574 CASP3
Caspase-3 subunit p17 56 SOX9_HUMAN 0.23 4.39E-04 P48436 SOX9
Transcription factor SOX-9 58 MUC5B_HUMAN 0.25 4.97E-04 Q9HC84
MUC5B Mucin-5B 62 AQP1_HUMAN 0.24 6.21E-04 P29972 AQP1 Aquaporin-1
65 GSHB_HUMAN 0.17 6.35E-04 P48637 GSS Glutathione synthetase 67
K1C19_HUMAN 0.21 7.26E-04 P08727 KRT19 Keratin, type I cytoskeletal
19 68 PAK2_HUMAN 0.20 7.46E-04 Q13177 PAK2 PAK-2p34 69 ZN593_HUMAN
0.24 7.81E-04 O00488 ZNF593 Zinc finger protein 593 70 MYD88_HUMAN
0.22 8.13E-04 Q99836 MYD88 Myeloid differentiation primary response
protein MyD88 75 APBA1_HUMAN 0.25 1.03E-03 Q02410 APBA1 Amyloid
beta A4 precursor protein-binding family A member 1 76 EPHB3_HUMAN
0.21 1.03E-03 P54753 EPHB3 Ephrin type-B receptor 3 80 IL10_HUMAN
0.22 1.17E-03 P22301 IL10 Interleukin-10 88 TNFB_HUMAN 0.20
1.58E-03 P01374 LTA Lymphotoxin-alpha 89 ANFB_HUMAN 0.19 1.58E-03
P16860 NPPB BNP(5-32) 94 SIA7F_HUMAN 0.18 2.00E-03 Q969X2
ST6GALNAC6 Alpha-N-acetylgalactosaminide alpha-2,6-
sialyltransferase 6 99 ZO2_HUMAN 0.19 2.29E-03 Q9UDY2 TJP2 Tight
junction protein ZO-2
EXAMPLE 2: CLASSIFICATION TEST
[0125] In addition, for the data multivariate classification rules
were constructed for discriminating between recurrent and
non-recurrent samples. Multivariate classifiers were built by
applying the nearest shrunken centroid classification method called
Prediction Analysis of Microarrays (PAM) which selects from the
full data set a subset of proteins capable of discriminating
between the classes based on their joint expression profiles
(Tibshirani R. et al., PNAS 99(10):6567-72.). Optimal PAM threshold
parameters were determined in an internal cross-validation step,
while the misclassification errors of the classifiers were
estimated by an outer 0.632 bootstrap loop incorporating 100
bootstrap samples.
[0126] This analysis led to an optimal discrimination of the sample
types with a classificator based on the expression of the proteins
LMNA, YBOX1, JUN, AKT3, SMAD3, LYAM1, PABP1, TIA1, CASP3, CDN1A,
CASP9, YETS2, PO2F2, TOP2A, RSSA, NFAC4, ZBT17, AKTIP, HSP7C, and
LIFR. The proteins are ordered by their selection frequency in the
different bootstrap samples.
[0127] With the classificator described above the following
classification of the sample set described in example 1 was
obtained:
TABLE-US-00004 Classified as Non-Recurrent Recurrent Sample
Non-Recurrent (n = 6) 6 0 type Recurrent (n = 19) 2 17
[0128] This corresponds to a sensitivity of 81% at a specificity of
100% for the prediction of recurrence. The overall accuracy of the
classification is 91%.
[0129] The proteins chosen for the classification match the
proteins of highest significance in the statistical LIMMA analysis
of example 1 and provided in the tables 1-3.
[0130] Besides of the complex algorithm, also a hierarchical
clustering based on the proteins selected by PAM resulted in a good
separation of the two groups (not shown).
[0131] In addition, we build a Random Forest classifier based on
the 20 most differentially regulated proteins from the LIMMA
analysis. For a classification on the training set all samples were
classified correct, corresponding to a sensitivity of 100% and a
specificity of 100%. To assess the transferability to other test
sets, we performed a leave-one-out cross validation. The
classification results for the different test sets in the cross
validation steps are summarized as a receiver operator curve (FIG.
1). The respective overall misclassification rate for the cross
validation was as low as 20% (SD 0.08) with an area under the curve
of 90.4%.
* * * * *