Note: Descriptions are shown in the official language in which they were submitted.
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BIOMARKERS FOR BLADDER CANCER
STATEMENT AS TO RIGHTS TO INVENTIONS MADE UNDER
FEDERALLY SPONSORED RESEARCH AND DEVELOPMENT
[00011 The present invention is supported by the Elsa U Pardee Research
Foundation, the National Cancer Institute Early Detection Research Network
(DA85067) and
the Virginia Prostate Center. The Government may have certain rights in the
invention.
BACKGROUND OF THE INVENTION
[0002] Bladder cancer is the second most common genitourinary malignancy
accounting for approximately 5% of all newly diagnosed cancers in the United
States (Klein
et al., Cancer 82 (2):49-354 (1998)). More than 90% are of the transitional
cell carcinoma
(TCC) histology (Stein et al., J. Urol. 160:645-659 (1998)). At present, the
most reliable way
of diagnosis and surveillance of bladder cancer is by cystoscopic examination
and bladder
biopsy for histologic confirmation. The invasive and labor-intensive nature of
this procedure
presents a challenge to develop better, less costly, and non-invasive
diagnostic tools. Urine
cytology has for many years been the 'gold standard' of the non-invasive
approaches. It has
high specificity and provides the advantage over biopsy of screening the
entire urothelium
(Klein et al., Cancer 82 (2): 49-354 (1998); Stein et al., J. Urol. 160:645-
659 (1998)).
However, its high false negative rate, particularly for low grade tumors, has
limited its use as
an adjunct to cystoscopy.
[0003] Many non-invasive molecular diagnostic tests have been-developed
based on an ever increasing knowledge about the molecular alterations
associated with
bladder cancer pathogenesis. The bladder tumor antigen (BTA) (Schamhart et
al., Eur. Urol.
34: 99-106 (1998)), the BTA stat (Sarosdy et al., Urology 50:349-53 (1997)),
the
fibrinogen/fibrin degradation products (FDP) (Schmnetter et al., J. Urol.
158:801-805 (1997))
and the nuclear matrix protein-22 (NP-22) (Soloway et al., J Urol. 156:363-367
(1996)) tests,
have been approved by the FDA to be used in conjunction with cystoscopy. See
Grossman et
al., Urol. Oncology 5:3-10 (2000) for review. Additional molecular assays
currently being
evaluated for their diagnostic/prognostic utility are the Telomerase (Hoshi et
al., Urol. Onc.
5:25-30 (2000)), Immunocyt (Fradet et al., Can. J. Urol. 1997, 4:400-5 (1997))
and
hyaluroriic acid/hyaluronidase (Pham et al., Cancer Research 57:778-783
(1997); Lokeshwar
et al., Cancer Research 57:773-777 (1997)) tests, microsatellite analysis
(Steiner et al., Nat.
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WO 2006/020302 PCT/US2005/025632
Aced. 6:621-624 (1997)), as well as assays detecting blood group antigens
(Golijanin et al.,
Urology 46(2):173-177 (1995)), carcinoembryonic antigen (Liu et al., J. Urol.
137:1258
(1987)), p53 and retinoblastoma proteins (Grossman et al., Urol. Oncology 5:3-
10 (2000)), E
cadherin (Banks et al., J. Clin. Pathol. 48:179-180 (1995); Protheroe et al.,
British J. Cancer
80(1/2):273-8 (1999)), andvarious growth factors (Halachmi et al., British J.
Urology
82:647-654 (1998)).
[0004] The effectiveness of any diagnostic test depends on its specificity and
selectivity, or the relative ratio of true positive, true negative, false
positive and false negative
diagnoses. Methods of increasing the percent of true positive and true
negative diagnoses for
any condition are desirable medical goals. In the case of bladder cancer, the
present
diagnostic tests are not completely satisfactory for the reasons described
above.
[0005] One of the recent technological advances in facilitating protein
profiling of complex biologic mixtures is the ProteinChip surface-enhanced
laser
desorption/ionization time of flight mass spectrometry (SELDI=TOF-MS) (Kuwata,
H., et al.,
Biochem. Biophys. Res. Commun. 245:764-773 (1998); Merchant, M. et al.,
Electrophoresis
21:1164-1177 (2000)). This technology utilizes protein chips coated with a
chemical to
affinity capture protein molecules from complex mixtures. The SELDI system is
a n
extremely sensitive and rapid method that analyzes complex mixtures of
proteins and
peptides. Applications of this technology show great potential for the early
detection of
prostate, breast, esophageal, ovarian, and hepatic cancers (Paweletz, C., et
al., Drug Dev. Res.
49:34-42 (2000); Wright, G., et al., Prostate Cancer and Prostate Diseases
2:264-276
(1999); Cazares, L.H., et al., Clin. Cancer Res. 8:2541-2552 (2002); Paweletz,
C., et al.,
Disease Markers 17:201-307 (2001)). Moreover, the analysis of SELDI data by
"artificial
intelligence" algorithms can lead to the identification of serum protein
"fingerprints" of
prostate, ovarian and breast cancers (Qu, Y., et al., Clin. Chem. 48(10):1835-
43 (2002);
Petricoin, E., et al., LANCET 359:572-577 (2002); Li, J., et al., Clin. Chem.
48(8):1296-304
(2002); Vlahou, A., et al., J. Biomed. and Biotechnol. 2003(5):308-314;
Vlahou, A., et aL,
Clin. Breast Cancer 4(3):203-9; Vlahou, A., et al., American J. Pathology
158(4):1491-1502
(2001)).
[0006] The identification and simultaneous analysis of a panel of biomarkers,
representative of the various biological characteristics of the cancer, has
greater potential for
improving the early detection/diagnosis of bladder cancer. Moreover, in an
economy-
conscious environment in which cost-effective medicine is an overriding
concern, physicians
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treating cancer patients need convenient, efficient methods to rapidly
diagnose bladder cancer
and to evaluate responses to therapy. The present invention meets this and
other goals.
BRIEF SUMMARY OF THE INVENTION
[0007] The present invention provides a method for qualifying bladder cancer
status in a subject, the method comprising: (a) measuring at least one
biomarker in a
biological sample from the subject, wherein the at least one biomarker is
selected from the
group consisting of Marker 1, Marker 2, Marker 3, Marker 4, Marker 5, Marker
6; Marker 7,
Marker 8, Marker 9, Marker 10, Marker 11, and Marker 12; and (b) correlating
the
measurement with bladder cancer status. The biological sample can be any
suitable sample,
such as urine or serum.
[0008] In one embodiment, a plurality of biomarkers is measured. The
plurality may comprise at least 3 biomarkers or at least 4 biomarkers.
[0009] In another embodiment, one or more biomarkers is also measured in
the subject: Marker 13, Marker 14, Marker 15, Marker 16, Marker 17, and Marker
18.
[0010] The invention further provides a method for qualifying bladder cancer
status in a subject comprising: (a) measuring a plurality of biomarkers in a
biological sample
from the subject, wherein at least one biomarker is selected from the group
consisting of
Marker 1, Marker 2, Marker 3, Marker 4, Marker 5, Marker 6, Marker 7, Marker
8, Marker 9,
Marker 10, Marker 11, and Marker 12 and at least one biomarker is selected
from the group
consisting of Marker 13, Marker 14, Marker 15, Marker 16, Marker 17, and
Marker 18; and
(b) correlating the measurement with bladder cancer status. The biological
sample can be
any suitable sample, such as urine or serum.
[0011] In another embodiment, the methods for qualifying bladder cancer
status comprise measuring the biomarkers by capttiring the biomarker on an
adsorbent
surface of a SELDI probe and detecting the captured biomarkers by laser
desorption-
ionization mass spectrometry. Any adsorbent surface can beused to capture the
biomarkers.
For example, the adsorbent on the substrate can be a cation exchange
adsorbent, a biospecific
adsorbent, etc.
[0012] In another embodiment, the methods for qualifying bladder cancer
status comprise measuring the biomarkers by immunoassay.
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[0013] In another embodiment, the bladder cancer status is selected from
bladder cancer and non-bladder cancer.
[0014] In another embodiment, the correlation is performed by a software
classification algorithm.
[0015] In another embodiment, the methods for qualifying bladder cancer
status comprise the additional steps of: (c) managing subject treatment based
on the status
and (d) measuring the at least one biomarker after subject management.
[0016] The invention further provides a method for measuring at least one
biomarker in a sample from a subject, wherein the at least one biomarker is
selected from the
group consisting of Marker 1, Marker 2, Marker 3, Marker 4, Marker 5, Marker
6, Marker 7,
Marker 8, Marker 9, Marker 10, Marker 11, and Marker 12. The biological sample
can be
any suitable sample, such as urine or serum.
[0017] In one embodiment, a plurality of biomarkers is measured. The
plurality may comprise at least 3 biomarkers or at least 4 biomarkers.
[0018] In another embodiment, one or more biomarkers is also measured in
the subject: Marker 13, Marker 14, Marker 15, Marker 16, Marker 17, and Marker
18.
[0019] The invention also provides a method comprising measuring a plurality
of biomarkers in a sample from a subject, wherein at least one biomarker is
selected from the
group consisting of Marker 1, Marker 2, Marker 3, Marker 4, Marker 5, Marker
6, Marker 7,
Marker 8, Marker 9, Marker 10, Marker 11, and Marker 12 and at least one
biomarker is
selected from the group consisting of Marker 13, Marker 14, Marker 15, Marker
16, Marker
17, and Marker 18. The biological sample can be any suitable sample, such as
urine or
serum.
[0020] In another embodiment, the methods of measuring biomarkers
comprise capturing the biomarker on an adsorbent surface of a SELDI probe and
detecting
the captured biomarkers by laser desorption-ionization mass spectrornetry. Any
adsorbent
surface can be used to capture the biomarkers. For example, the adsorbent on
the substrate
can be a cation exchange adsorbent, a biospecific adsorbent, etc.
[0021] The invention also provides kits comprising: (a) a solid support
comprising at least one capture reagent attached thereto, wherein the capture
reagent binds at
least one biomarker selected from the group consisting of Marker 1, Marker 2,
Marker 3,
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Marker 4, Marker 5, Marker 6, Marker 7, Marker 8, Marker 9, Marker 10, Marker
11, and
Marker 12; and (b) instructions for using the solid support to detect the at
least one
biomarker.
[0022] In another embodiment, the kits further comprise instructions for using
the solid support to detect one or more of the following biomarkers: Marker
13, Marker 14,
Marker 15, Marker 16, Marker 17, and Marker 18.
[0023] In some embodiments, the kits comprise instructions for using the solid
support to detect a plurality of biomarkers. The plurality may comprise at
least 3 biomarkers
or at least 4 biomarkers.
[0024] The invention further provides kits comprising: (a) a solid support
comprising at least one capture reagent attached thereto, wherein the capture
reagent binds a
plurality of biomarkers, wherein at least one biomarker is selected from the
group consisting
of Marker 1, Marker 2, Marker 3, Marker 4, Marker 5, Marker 6, Marker 7,
Marker 8, Marker
9, Marker 10, Marker 11, and Marker 12 and at least one biomarker is selected
from the
group consisting of Marker 13, Marker 14, Marker 15, Marker 16, Marker 17, and
Marker 18;
and (b) instructions for using the solid support to detect the plurality of
biomarkers.
[0025] In one embodiment, the solid support comprising a capture reagent is a
SELDI probe. In another embodiment, the capture reagent is a cation exchange
adsorbent.
[0026] In another embodiment, the kits additionally comprise (c) an anion
exchange chromatography adsorbent.
[0027] The invention also provides kits comprising: (a) a solid support
comprising at least one capture reagent attached thereto, wherein the capture
reagents bind at
least one biomarker selected from the group consisting of Marker 1, Marker 2,
Marker 3,
Marker 4, Marker S, Marker 6, Marker 7, Marker 8, Marker 9, Marker 10, Marker
11, and
Marker 12; and (b)a container containing at least one of the biomarkers.
[0028] In one embodiment, the,kits further comprise instructions for using the
. .,. .
solid support:to detect one or more of the following biomarkers: Marker 13,
Marker 14,
Marker 15, Marker 16, Marker 17, and Marker 18.
[0029] In some embodiments, the kits comprise a plurality of biomarkers.
The plurality may comprise at least 3 biomarkers or at least 4 biomarkers.
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[0030] The invention also provides kits comprising: (a) a solid support
comprising at least one capture reagent attached thereto, wherein the capture
reagents bind a
plurality of biomarkers, wherein at least one biomarker is selected from the
group consisting
of Marker 1, Marker 2, Marker 3, Marker 4, Marker 5, Marker 6, Marker 7,
Marker 8, Marker
9, Marker 10, Marker 11, and Marker 12, and at least one biomarker is selected
from the
group consisting of Marker 13, Marker 14, Marker 15, Marker 16, Marker 17, and
Marker 18;
and (b) a container containing at least one of the biomarkers.
[0031] In some embodiments, the container contains a plurality of biomarkers.
The plurality may comprise at least 3 biomarkers or at least 4 biomarkers.
[0032] In one embodiment, the solid support comprising a capture reagent is a
SELDI probe. In another embodiment, the capture reagent is a cation exchange
adsorbent.
[0033] In another embodiment, the kits additionally comprise (c) an anion
exchange chromatography adsorbent.
[0034] The invention further provides a software product comprising: (a) code
that accesses data attributed to a sample, the data comprising measurement of
at least one
biomarker in the sample, the biomarker selected from the group consisting of
Marker 1,
Marker 2, Marker 3, Marker 4, Marker 5, Marker 6, Marker 7, Marker 8, Marker
9, Marker
10, Marker 11, and Marker 12; and (b) code that executes a classification
algorithm that
classifies the bladder cancer status of the sample as a function of the
measurement.
[0035] In one embodiment, the classification algorithm classifies the bladder
cancer status of the sample further as a function of the measuremerit of one
or more of the
following biomarkers: Marker 13, Marker 14, Marker 15, Marker 16, Marker 17,
and Marker
18.
[0036] In some embodiments, the -classification algorithm classifies the
bladder cancer status, of the sample as a function of the measurement of a
plurality of
biomarkers. The plurality may comprise at least 3 biomarkers or at least 4
biomarkers.
[0037] The invention further provides a software product couiprising: (a) code
that accesses data attributed to a sample, the data comprising measurement of
a plurality of
biomarkers in the sample, and wherein at least one biomarker is selected from
the group.
consisting of Marker 1, Marker 2, Marker 3, Marker 4, Marker 5, Marker 6,
Marker 7,
Marker 8, Marker 9, Marker 10, Marker 11, and Marker 12 and at least one
biomarker is
selected from the group consisting of Marker 13, Marker 14, Marker 15, Marker
16, Marker
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17, and Marker 18; and (b) code that executes a classification algorithm that
classifies the
bladder cancer status of the sample as a function of the measurement.
[0038] The invention further provides purified biomolecules selected from the
group of biomarkers consisting of Marker 1, Marker 2, Marker 3, Marker 4,
Marker 5,
Marker 6, Marker 7, Marker 8, Marker 9, Marker 10, Marker 11, and Marker 12.
[0039] The invention further provides a method comprising detecting a
biomarker from the ground consisting of Marker 1, Marker 2, Marker 3, Marker
4, Marker 5,
Marker 6, Marker 7, Marker 8, Marker 9, Marker 10, Marker 11, and Marker 12 by
mass
spectrometry or immunoassay.
[0040] The invention further provides a method comprising detecting a
plurality of biomarkers by mass spectrometry or immunoassay, wherein at least
one
biomarker is selected from the group consisting of Marker 1, Marker 2, Marker
3, Marker 4,
Marker 5, Marker 6, Marker 7, Marker 8, Marker 9, Marker 10, Marker 11, and
Marker 12
and at least one biomarker is selected from the group consisting of Marker 13,
Marker 14,
Marker 15, Marker 16, Marker 17, and Marker 18.
[0041] The invention also provides a method comprising communicating to a
subject a diagnosis relating to bladder cancer status determined from the
correlation of
biomarkers in a sample from the subject, wherein at least one biomarker is
selected from the
group consisting of Marker 1,-Marker 2, Marker 3, Marker 4, Marker 5, Marker
6, Marker 7,
Marker 8, Marker 9, Marker 10, Marker 11; and Marker 12.
[0042] The invention also provides a method comprising communicating to a
subject a diagnosis relating to bladder cancer status determined from the
correlation of a
plurality of biomarkers in a sample from the subject, wherein at least one
biomarker is
selected from the group consisting of Marker 1, Marker 2, Marker 3, Marker 4,
Marker 5,
Marker 6, Marker 7, Marker 8, Marker 9, Marker 10, Marker 11, and Marker 12
and at least
one biomarker is selected from the group consisting of Marker 13, Marker 14,
Marker 15,
Marker 16, Marker 17, and Marker 18.
[0043] In one embodiment, the diagnosis is communicated to the subject via a
computer-generated medium.
[0044] The invention also provides a method for identifying a compound that
interacts with any of the biomarkers selected from the group consisting of
Marker 1, Marker
2, Marker 3, Marker 4, Marker 5, Marker 6, Marker 7, Marker 8, Marker 9,
Marker 10,
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Marker 11, and Marker 12, wherein the method comprises: (a) contacting the
biomarker with
a test compound; and (b) determining whether the test compound interacts with
the
biomarker.
[0045] The invention also provides a method for modulating the concentration
of a biomarker selected from the group consisting of Marker 1, Marker 2,
Marker 3, Marker
4, Marker 5, Marker 6, Marker 7, Marker 8, Marker 9, Marker 10, Marker 11,
Marker 12,
Marker 13, Marker 14, Marker 15, Marker 16, Marker 17, and Marker 18 in a
cell, wherein
the method comprises: (a) contacting the cell with a protease inhibitor,
wherein the protease
inhibitor prevents cleavage of the biomarker.
[0046] The invention further provides a method of treating a condition in a
subject, wherein the method comprises administering to a subject a
therapeutically effective
amount of a compound which modulates the expression or activity of a protease
which
cleaves a biomarker selected from the group consisting of Marker 1, Marker 2,
Marker 3,
Marker 4, Marker 5, Marker 6, Marker 7, Marker 8, Marker 9, Marker 10, Marker
11, Marker
12, Marker 13, Marker 14, Marker 15, Marker 16, Marker 17, and Marker 18. In
one
embodiment, the condition is bladder cancer.
BRIEF DESCRiPTTON OF THE DRAWINGS
[0047] FIG. 1 shows a decision tree for classifying a sample as bladder cancer
or non-bladder cancer using certain biomarkers of this invention, as utilized
in Example 1.
"C" represents bladder cancer patients and "B" represents "benign" patients
(those that are
normal or have benign or other cancers). The squares are the primary nodes and
the circles
indicate terminal nodes. The mass value in the root nodes (in kDa) are
followed by the
intensity cut-off levels of the splitter as well as the number of samples
involved.
[0048] FIG. 2 depicts mass spectra of the peaks (arrows) forming the main
splitters of the decision tree.
[0049] FIG. 3 depicts the intensity distribution of the peaks forming the main
splitters of the decision tree. Each square corresponds to a decision node of
the tree shown in
FIG. 1. The mass of the main, splitter (in kDa), its intensity value in the
cancer ("C") and
non-cancer (normal and benign, or "B") samples, and the intensity cut-off
values that form
the splitting rule are shown.
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DETAILED DESCRIPTION OF THE INVENTION
1. INTRODUCTION
[0050] A biomarker is an organic biomolecule, the presence of which in a
sample is used to determine the phenotypic status of the subject (e.g.,
bladder cancer patient
v. normal or non-bladder cancer patient). In a preferred embodiment, the
biomarker is
differentially present in a sample taken from a subject of one phenotypic
status (e.g., having a
disease) as compared with another phenotypic status (e.g., not having the
disease). A
biomarker is differentially present between different phenotypic statuses if
the mean or
median expression level of the biomarker in the different groups is calculated
to be
statistically significant. Common tests for statistical significance include,
among others, t-
test, ANOVA, Kraskal-Wallis, Wilcoxon, Mann-Whitney and odds ratio.
Biomarkers, alone
or in combination, provide measures of relative risk that a subject belongs to
one phenotypic
status or another. Therefore, they are useful as markers for disease
(diagnostics), therapeutic
effectiveness of a drug (theranostics) and drug toxicity.
H. BIOMARKERS FOR BLADDER CANCER
A. Biomarkers
[0051] This invention provides polypeptide-based biomarkers that are used to
distinguish subjects with bladder cancer from subjects that are normal or with
non-bladder
cancer. The biomarkers are preferably differentially present in subjects
having bladder
cancer, versus subjects who are normal or have non-bladder cancer. The
biomarkers are
characterized by mass-to-charge ratio as determined by mass spectrometry, by
the shape of
their spectral peak in time-of-flight mass spectrometry and by their binding
characteristics to
adsorbent surfaces. These characteristics provide one method to determine
whether a
particular detected biomolecule is a biomarker of this invention. These
characteristics
represent inherent characteristics of the biomolecules and not process
lirnmitations in the
manner in which the biomolecules are discriminated. In one aspect, this
invention provides
these biomarkers in isolated form.
[0052] The biomarkers were discovered using SELDI technology employing
ProteinChip arrays from Ciphergen Biosystems, Inc. (Fremont, CA)
("Ciphergen"). Urine
samples were collected from subjects diagnosed with bladder cancer and
subjects diagnosed
as normal. The samples were fractionated by anion exchange chromatography.
Fractionated
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samples were applied to SELDI biochips and spectra of polypeptides in the
samples were
generated by time-of-flight mass spectrometry on a Ciphergen PBSII mass
spectrometer. The
spectra thus obtained were analyzed by Ciphergen Express'm Data Manager
Software with
Biomarker Wizard and Biomarker Pattern Software from Ciphergen Biosystems,
Inc. The
mass spectra for each group were subjected to scatter plot analysis. A Mann-
Whitney test
analysis was employed to compare bladder cancer and control groups for each
protein cluster
in the scatter plot, and proteins were selected that differed significantly
(p<0.0001) between
the two groups. This method is described in more detail in the Example
Section.
[0053] The biomarkers thus discovered are presented in Tables 1 and 2. The
"ProteinChip assay" column of Table 2 refers to the type of biochip to which
the biomarker
binds and the wash conditions, as per the Example.
TABLE 1
Marker No. Mass (Da)
1 M2670.00
2 M4210.00
3 M5400.00
4 M5510.00
M5580.00
6 M5700.00
7 M8490.00
8 M9100.00
9 M10800.00
M17000.00
11 M56500.00
12 M67000.00
13 M3380.00
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Marker No. Mass (Da)
14 M3540.00
15 M4960.00
16 M5830.00
17 M7070.00
18 M9420.00
TABLE 2
Marker No. Mass (Da) P-Value Up or down ProteinChip assay
regulated in
bladder
cancer
1 M2670.00 0.006 down WCX, wash with 100 mM
Na acetate pH 4
2 M4210.00 0.042 up WCX, wash with 100 mM
Na acetate pH 4
4 ~ M5510.00 0.044 both WCX, wash with 100 mM
Na acetate pH 4
8 M9100.00 0.25 up WCX, wash with 100 mM
Na acetate pH 4
14 M3540.00 0.007 down WCX, wash with 100 mM
Na acetate pH 4
15 M4960.00 <0.0001 down WCX, wash with 100 mM
Na acetate pH 4
17 M7070.00 0.41 up WCX, wash with 100 mM
Na acetate pH 4
j00541 The biomarkers of this invention are characterized by their mass-to-
charge ratio as determined by mass spectrometry. The mass-to-charge ratio of
each
biomarker is provided in Table 1 after the "M." Thus, for example, M2670.00
has a
measured mass-to-charge ratio of 2670.00. The mass-to-charge ratios were
det6rmined from
mass spectra generated on a Ciphergen Biosystems, Inc. PBS TI mass
spectrometer. This
instrument has a mass accuracy of about +/- 0.3 percent. Additionally, the
instrument has a
mass resolution of about 400 to 1000 m/dm, where m is mass and dm is the mass
spectral
peak width at 0.5 peak height. The mass-to-charge ratio of the biomarkers was
determined
using Biomarker Wizard"' software (Ciphergen Biosystems, Inc.). Biomarker
Wizard assigns
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a mass-to-charge ratio to a biomarker by clustering the mass-to-charge ratios
of the same
peaks from all the spectra analyzed, as determined by the PBSII, taking the
maximum and
minimum mass-to-charge-ratio in the cluster, and dividing by two. Accordingly,
the masses
provided reflect these specifications.
[0055] The biomarkers of this invention are further characterized by the shape
of their spectral peak in time-of-flight mass spectrometry. Mass spectra
showing peaks
representing the biomarkers are presented in FIG. 2.
[0056] The biomarkers of this invention are further characterized by their
binding properties on chromatographic surfaces. Most of the biomarkers bind to
cation
exchange adsorbents (e.g., the Ciphergen WCX ProteinChip array) after
washing with
100 mM sodium acetate at pH 4.
[0057] Because the biomarkers of this invention are characterized by mass-to-
charge ratio, binding properties and spectral shape, they can be detected by
mass
spectrometry without knowing their specific identity. However, if desired,
biomarkers whose
identity is not determined can be identified by, for example, determining the
amino acid
sequence of the polypeptides. For example, a biomarker can be peptide-mapped
with a
number of enzymes, such as trypsin or V8 protease, and the molecular weights
of the
digestion fragments can be used to search databases for sequences that match
the molecular
weights of the digestion fragments generated by the various enzymes.
Alternatively, protein
biomarkers can be sequenced using tandem MS technology. In this method, the
protein is
isolated by, for example, gel electrophoresis. A band containing the biomarker
is cut out and
the protein is subject to protease digestion. Individual protein fragments are
separated by a
first mass spectrometer. The fragment is then subjected to collision-induced
cooling, which
fragments the peptide and produces a polypeptide ladder. A polypeptide ladder
is then
analyzed by the second mass spectrometer of the tandem MS. The difference in
masses of
the members of the polypeptide ladder identifies the amino acids in the
sequence. An entire
protein can be sequenced this way, or a sequence fragment can be subjected to
database
mining to fmd identity candidates.
[0058] The preferred biological source for detection of the biomarkers is
urine. However, in other embodiments, the biomarkers can be detected in serum.
[0059] The biomarkers of this invention are biomolecules. Accordingly, this
invention provides these biomolecules in isolated form. The biomarkers can be
isolated from
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biological fluids, such as urine or serum. They can be isolated by any method
known in the
art, based on both their mass and their binding characteristics. For example,
a sample
comprising the biomolecules can be subject to chromatographic fractionation,
as described
herein, and subject to further separation by, e.g., acrylamide gel
electrophoresis. Knowledge
of the identity of the biomarker also allows their isolation by immunoaffinity
chromatography.
B. Modified Forms Of Proteins As Biomarkers
[0060] It has been found that proteins frequently exist in a sample in a
plurality of different forms characterized by detectably different masses.
These forms can
result from pre- translational modifications, post-translational modifications
or both. Pre-
translational modified forms include allelic variants, splice variants and RNA
editing forms.
Post-translationally modified forms include forms resulting from, among other
things.
proteolytic cleavage (e.g., fragments of a parent protein), glycosylation,
phosphorylation,
lipidation, oxidation, methylation, cystinylation, sulphonation and
acetylation. The collection
of proteins including a specific protein and all modified forms of it is
referred to herein as a
"protein cluster." The collection of all modified forms of a specific protein,
excluding the
specific protein, itself, is referred to herein as a "modified protein
cluster." Modified forms
of any biomarker of this invention also may be used, themselves, as
biomarkers. In certain
cases the modified forms may exhibit better discriminatory power in diagnosis
thanthe
specific forms set forth herein.
[0061] Modified forms of a biomarker can be initially detected by any
methodology that can detect and distinguish the modified from the biomarker. A
preferred
method for initial detection involves first capturing the biomarker and
modified forms of it,
e.g., with biospecific capture reagents, and then detecting the captured
proteins by mass
spectrometry. More specifically, the proteins are captured using biospecific
capture reagents,
such as antibodies, interacting fusion proteins, aptamers or Affibodies that
recognize the
biomarker and modified forms of it. This method may also result in the capture
of protein
interactors that are bound=to the proteins or that are otherwise recognized by
antibodies and
that, themselves, can be biomarkers. Preferably, the biospecific capture
reagents are bound to
a solid phase. Then, the captured proteins can be detected by SELDI mass
spectrometry or
by eluting the proteins from the capture reagent and detecting the eluted
proteins by
traditional MALDI or by SELDI. The use of mass spectrometry is especially
attractive
13
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because it can distinguish and quantify modified forms of a protein based on
mass and
without the need for labeling.
[0062] Preferably, the biospecific capture reagent is bound to a solid phase,
such as a bead, a plate, a membrane or a chip. Methods of coupling
biomolecules, such as
antibodies, to a solid phase are well known in the art. They can employ, for
example,
bifunctional linking agents, or the solid phase can be derivatized with a
reactive group, such
as an epoxide or an imidizole, that will bind the molecule on contact.
Biospecific capture
reagents against different target proteins can be mixed in the same place, or
they can be
attached to solid phases in different physical or addressable locations. For
example, one can
load multiple columns with derivatized beads, each column able to capture a
single protein
cluster. Alternatively, one can pack a single column with different beads
derivatized with
capture reagents against a variety of protein clusters, thereby capturing all
the analytes in a
single place. Accordingly, antibody-derivatized bead-based technologies can be
used to
detect the protein clusters. However, the biospecific capture reagents must be
specifically
directed toward the members of a cluster in order to differentiate them.
[0063] In yet another embodiment, the surfaces of biochips can be derivatized
with the capture reagents directed against protein clusters either in the same
location or in
physically different addressable locations. One advantage of capturing
different clusters in
different addressable locations is that the analysis becomes simpler.
[0064] After identification of modified forms of a protein and correlation
with
the clinical parameter of interest, the modified form can be used as a
biomarker in any of the
methods of this invention. At this point, detection of the modified from can
be accomplished
by any specific detection methodology including affinity capture followed by
mass
spectrometry, or traditional immunoassay directed specifically to the modified
form.
Immunoassay requires biospecific capture reagents, such as antibodies, to
capture the
analytes. Furthermore, the assay must be designed to specifically distinguish
a protein and
modified forms of the protein. This can be done, for example, by employing a
sandwich
assay in which one antibody captures more than one form and second, distinctly
labeled
antibodies, specifically bind the various forms, thereby providing distinct
detection of them.
Antibodies can be produced by immunizing animals with the biomolecules. This
invention
contemplates traditional immunoassays including, for example, sandwich
immunoassays
including ELISA or fluorescence-based immunoassays, as well as other enzyme
immunoassays.
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M. DETECTION OF BIOMARKERS FOR BLADDER CANCER
[0065] The biomarkers of this invention can be detected by any suitable
method. Detection paradigms that can be employed to this end include optical
methods,
electrochemical methods (voltametry and amperometry techniques), atomic force
microscopy, and radio frequency methods, e.g., multipolar resonance
spectroscopy.
Illustrative of optical methods, in addition to microscopy, both confocal and
non-confocal,
are detection of fluorescence, luminescence, chemiluminescence, absorbance,
reflectance,
transmittance, and birefringence or refractive index (e.g., surface plasmon
resonance,
ellipsometry, a resonant mirror method, a grating coupler wavegaide method or
interferometry).
[0066] In one embodiment, a sample is analyzed by means of a biochip.
Biochips geneirally comprise solid substrates and have a generally planar
surface, to which a
capture reagent (also called an adsorbent or affinity reagent) is attached.
Frequently, the
surface of a biochip comprises a plurality of addressable locations, each of
which has the
capture reagent bound there.
[0067] Protein biochips are biochips adapted for the capture of polypeptides.
Many protein biochips are described in the art. These include, for example,
protein biochips
produced by Ciphergen Biosystems, Inc. (Fremont, CA), Packard BioScience
Company
(Meriden CT), Zyomyx (Hayward, CA), Phylos (Lexington, MA) and Biacore
(Uppsala,
Sweden). Examples of such protein biochips are described in the following
patents or
published patent applications: U.S. Patent No. 6,225,047; PCT International
Publication No.
WO 99/51773; U.S. Patent No. 6,329,209, PCT International Publication No. WO
00/56934
and U.S. Patent No. 5,242,828.
A. Detection by Mass Spectrometry
[0068] In a preferred embodiment, the biomarkers of this invention are
detected by mass spectrometry, a niethod that employs a mass spectrometer to
detect gas
phase ions. Examples of mass spectrometers are time-of-flight, magnetic
sector, quadrupole
filter, ion trap, ion cyclotron resonance, electrostatic sector analyzer and
hybrids of these.
[0069] In a further preferred method, the mass spectrometer is a laser
desorption/ionization mass spectrometer. In laser desorption/ionization mass
spectrometry,
the analytes are placed on the surface of a mass spectrometry probe, a device
adapted to
CA 02574831 2007-01-23
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engage a probe interface of the mass spectrometer and to present an analyte to
ionizing
energy for ionization and introduction into a mass spectrometer. A laser
desorption mass
spectrometer employs laser energy, typically from an ultraviolet laser, but
also from an
infrared laser, to desorb analytes from a surface, to volatilize and ionize
them and make them
available to the ion optics of the mass spectrometer.
1. SELDI
[0070] A preferred mass spectrometric technique for use in the invention is
"Surface Enhanced Laser Desorption and Ionization" or "SELDI," as described,
for example,
in U.S. Patents No. 5,719,060 and No. 6,225,047, both to Hutchens and Yip.
This refers to a
method of desorption/ionization gas phase ion spectrometry (e.g., mass
spectrometry) in
which an analyte (here, one or more of the biomarkers) is captured on the
surface of a SELDI
mass spectrometry probe. There are several versions of SELDI.
[0071] One version of SELDI is called "affinity capture mass spectrometry."
It also is called "Surface-Enhanced Affinity Capture" or "SEAC". This version
involves the
use of probes that have a material on the probe surface that captures analytes
through a non-
covalent affinity interaction (adsorption) between the material and the
analyte. The material
is variously called an "adsorbent," a "capture reagent," an "affinity reagent"
or a 'binding
moiety." Such probes can be referred to as "affinity capture probes" and as
having an
"adsorbent surface." The capture reagent can be any material capable of
binding an analyte.
The capture reagent may be attached directly to the substrate of the selective
surface,. or the
substrate may have a reactive surface that carries a reactive moiety that is
capable of binding
the capture reagent, e.g., through a reaction forming a covalent or coordinate
covalent bond.
Epoxide and carbodiirnidizole are useful reactive moieties to covalently bind
polypeptide
capture reagents such as antibodies or cellular receptors. Nitriloacetic acid
and iminodiacetic
acid are useful reactive moieties that function as chelating agents to bind
metal ions that
interact non-covalently with histidine containing peptides. Adsorbents are
generally.
classified as chromatographic adsorbents and biospecific adsorbents.
[0072] "Chromatographic adsorbent" refers to an adsorbent material typically
used in chromatography. Chromatographic adsorbents include, for example, ion
exchange
materials, metal chelators (e.g., nitriloacetic acid or iminodiacetic acid),
immobilized metal
chelates, hydrophobic interaction adsorbents, hydrophilic interaction
adsorbents, dyes, simple
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biomolecules (e.g., nucleotides, amino acids, simple sugars and fatty acids)
and mixed mode
adsorbents (e.g., hydrophobic attraction/electrostatic repulsion adsorbents).
[0073] "Biospecific adsorbent" refers to an adsorbent comprising a
biomolecule, e.g., a nucleic acid molecule (e.g., an aptamer), a polypeptide,
a polysaccharide,
a lipid, a steroid or a conjugate of these (e.g., a glycoprotein, a
lipoprotein, a glycolipid, a
nucleic acid (e.g., DNA-protein conjugate). In certain instances, the
biospecific adsorbent
can be a macromolecular structure such as a multiprotein complex, a biological
membrane or
a virus. Examples of biospecific adsorbents are antibodies, receptor proteins
and nucleic
acids. Biospecific adsorbents typically have higher specificity for a target
analyte than
chromatographic adsorbents. Further examples of adsorbents for use in SELDI
can be found
in U.S. Patent No. 6,225,047. A "bioselective adsorbent" refers to an
adsorbent that binds to
an analyte with an affinity of at least 10"8 M.
[0074] Protein biochips produced by Ciphergen Biosystems, Inc. comprise
surfaces having chromatographic or biospecific adsorbents attached thereto at
addressable
locations. Ciphergen ProteinChip arrays include NP20 (hydrophilic); H4 and
H50
(hydrophobic); SAX-2, Q-10 and LSAX-30 (anion exchange); WCX-2, CM-10 and LWCX-
30 (cation exchange); IMAC-3, IMAC-30 and IMAC 40 (metal chelate); and PS-10,
PS-20
(reactive surface with carboimidizole, expoxide) and PG-20 (protein G coupled
through
carboimidizole). Hydrophobic ProteinChip arrays have isopropyl or nonylphenoxy-
poly(ethylene glycol)methacrylate functionalities. Anion exchange ProteinChip
arrays have
quatemary ammonium functionalities. Cation exchange ProteinChip arrays have
carboxylate
functionalities. Immobilized metal chelate ProteinChip arrays have
nitriloacetic acid
functionalities that adsorb transition metal ions, such as copper, nickel,
zinc, and gallium, by
chelation. Preactivated ProteinChip arrays have carboimidizole or epoxide
functional groups
that can react with groups on proteins for covalent binding.
[0075] Such biochips are further described in: U.S. Patent No. 6,579,719
(Hutchens and Yip, "Retentate Chromatography," June 17, 2003); PCT
International
Publication No. WO 00/66265 (Rich et al., "Probes for a Gas Phase Ion
Spectrometer,"
November 9, 2000); U.S. Patent No. 6,555,813 (Beecher et al., "Sample Holder
with
Hydrophobic Coating for Gas Phase Mass Spectrometer," Apri129, 2003); U.S.
Patent
Application No. U.S. 2003 0032043 Al (Pohl and Papanu, "Latex Based Adsorbent
Chip,"
July 16, 2002); and PCT International Publication No. WO 03/040700 (Um et al.,
"Hydrophobic Surface Chip," May 15, 2003); U.S. Patent Application No. US
2003/0218130
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Al (Boschetti et al., "Biochips With Surfaces Coated With Polysaccharide-Based
Hydrogels," April 14, 2003) and U.S. Patent Application No. 60/448,467,
entitled
"Photocrosslinked Hydrogel Surface Coatings" (Huang et al., filed February 21,
2003).
[0076] In general, a probe with an adsorbent surface is contacted with the
sample for a period of time sufficient to allow biomarker or biomarkers that
may be present
in the sample to bind to the adsorbent. After an incubation period, the
substrate is washed to
remove unbound material. Any suitable washing solutions can be used;
preferably, aqueous
solutions are employed. The extent to which molecules remain bound can be
manipulated by
adjusting the stringency of the wash. The elution characteristics of a wash
solution can
depend, for example, on pH, ionic strength, hydrophobicity, degree of
chaotropism, detergent
strength, and temperature. Unless the probe has both SEAC and SEND properties
(as
described herein), an energy absorbing molecule then is applied to the
substrate with the
bound biomarkers.
[0077] The biomarkers bound to the substrates are detected in a gas phase ion.
spectrometer such as a time-of-flight mass spectrometer. The biomarkers are
ionized by an
ionization source such as a laser, the generated ions are collected by an ion
optic assembly,
and then a mass analyzer disperses and analyzes the passing ions. The detector
then
translates information of the detected ions into mass-to-charge ratios.
Detection of a
biomarker typically will involve detection of signal intensity. Thus, both the
quantity and
mass of the biomarker can be determined.
[0078] Another version of SELDI is Surface-Enhanced Neat Desorption
(SEND), which involves the use of probes comprising energy absorbing molecules
that are
chemically bound to the probe surface ("SEND probe"). The phrase "energy
absorbing
molecules" (EAM) denotes molecules that are capable of absorbing energy from a
laser
desorption/ionization source and, thereafter, contribute to desorption and
ionization of analyte
molecules in contact therewith. The EAM category includes molecules used in
MALDI,
frequently referred to as "matrix," and is exemplified by cinnamic acid
derivatives, sinapinic
acid (SPA), cyano-hydroxy-cinnarnic acid (CHCA) and dihydroxybenzoic acid,
ferulic acid,
and hydroxyaceto-phenone derivatives. In certain embodiments, the energy
absorbing
molecule is incorporated into a linear or cross-linked polymer, e.g., a
polymethacrylate. For
example, the composition can be a co-polymer of a-cyano-4-
methacryloyloxycinnamic acid
and acrylate. In another embodiment, the composition is a co-polymer of a-
cyano-4-
methacryloyloxycinnamic acid, acrylate and 3-(tri-ethoxy)silyl propyl
methacrylate. In
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another embodiment, the composition is a co-polymer of a-cyano-4-
methacryloyloxycinnamic acid and octadecylmethacrylate ("C18 SEND"). SEND is
further
described in U.S. Patent No. 6;124,137 and PCT Interrrnational Publication No.
WO 03/64594
(Kitagawa, "Monomers And Polymers Having Energy Absorbing Moieties Of Use In
Desorption/Ionization Of Analytes," August 7, 2003).
[0079] SEAC/SEND is a version of SELDI in which both a capture reagent
and an energy absorbing molecule are attached to the sample presenting
surface.
SEAC/SEND probes therefore allow the capture of analytes through affinity
capture and
ionization/desorption without the need to apply external matrix. The C18 SEND
biochip is a
version of SEAC/SEND, comprising a C18 moiety which functions as a capture
reagent, and
a CHCA moiety which functions as an energy absorbing moiety.
[0080] Another version of SELDI, called Surface-Enhanced Photolabile
Attachment and Release (SEPAR), involves the use of probes having moieties
attached to the
surface that can covalently bind an analyte, and then release the analyte
through breaking a
photolabile bond in the moiety after exposure to light, e.g., to laser light
(see, U.S. Patent No.
5,719,060). SEPAR and other forms of SELDI are readily adapted to detecting a
biomarker
or biomarker profile, pursuant to the present invention.
2. Other mass spectrometry methods
[0081] In another mass spectrometry method, the biomarkers can be first
captured on a chromatographic resin having chromatographic properties that
bind the
biomarkers. In the present example, this could include a variety of methods.
For example,
one could capture the biomarkers on a cation exchange resin, such as CM
Ceramic HyperD F
resin, wash the resin, elute the biomarkers and detect by MALDI.
Alternatively, this method
could be preceded by fractionating the sample on an anion exchange resin
before application
to the cation exchange resin. In another alternative, one could fractionate on
an anion
exchange resin and detect by Iv1ALDI directly. In yet another method, one
could capture the
biomarkers on, an immuno-chromatographic resin that comprises antibodies that
bind the
biomarkers, wash the resin to remove unbound material, elute the biomarkers
from the resin
and detect the eluted biomarkers by MALDI or by SELDI.
3. Data Analysis
[0082] Analysis of analytes by time-of-flight mass spectrometry generates a
time-of-flight spectrum. The time-of-flight spectrum ultimately analyzed
typically does not
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represent the signal from a single pulse of ionizing energy against a sample,
but rather the
sum of signals from a number of pulses. This reduces noise and increases
dynamic range.
This time-of-flight data is then subject to data processing. In Ciphergen's
ProteinChip
software, data processing typically includes TOF-to-M/Z transformation to
generate a mass
spectrum, baseline subtraction to eliminate instrument offsets and high
frequency noise
filtering to reduce high frequency noise.
[0083] Data generated by desorption and detection of biomarkers can be
analyzed with the use of a programmable digital computer. The computer program
analyzes
the data to indicate the number of biomarkers detected, and optionally the
strength of the
signal and the determined molecular mass for each biomarker detected. Data
analysis can
include steps of determining signal strength of a biomarker and removing data
deviating from
a predetermined statistical distribution. For example, the observed peaks can
be normalized,
by calculating the height of each peak relative to some reference. The
reference can be
background noise generated by the instrument and chemicals such as the energy
absorbing
molecule which is set at zero in the scale.
[0084] The computer can transform the resulting data into various formats for
display. The standard spectrum can be displayed, but in one useful format only
the peak
height and mass information are retained from the spectrum view, yielding a
cleaner image
and enabling biomarkers with nearly identical molecular weights to be more
easily seen. In
another useful format, two or more spectra are compared, conveniently
highlighting unique
biomarkers and biomarkers that are up- or down-regulated between samples.
Using any of
these formats, one can readily determine whether a particular biomarker is
present in a
sample.
[0085] Analysis generally involves the identification of peaks in the spectrum
that represent signal from an analyte. Peak selection can be done visually,
but software is
available, as part of Ciphergen's 'ProteinChip software package, that can
automate the
detection of peaks. In general, this software functions by identifying signals
having a signal-
to-noise ratio above a selected threshold and labeling the mass of the peak at
the centrbid of
the peak signal. In one useful application, many spectra are compared to
identify identical
peaks present in some selected percentage of the mass spectra. One version of
this software
clusters all peaks appearing in the various spectra within a defined mass
range, and assigns a
mass (M/Z) to all the peaks that are near the mid-point of the mass (M/Z)
cluster.
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[0086] Software used to analyze the data can include code that applies an
algorithm to the analysis of the signal to determine whether the signal
represents a peak in a
signal that co'rresponds to a biomarker according to the present invention.
The software also
can subject the data regarding observed biomarker peaks to classification tree
or ANN
analysis, to deterrnine whether a biomarker peak or combination of biomarker
peaks is
present that indicates the status of the particular clinical parameter under
examination.
Analysis of the data may be "keyed" to a variety of parameters that are
obtained, either
directly or indirectly, from the mass spectrometric analysis of the sample.
These parameters
include, but are not limited to, the presence or absence of one or more peaks,
the shape of a
peak or group of peaks, the height of one or more peaks, the log of the height
of one or more
peaks, and other arithmetic manipulations of peak height data.
4. General protocol for SELDI detection of biomarkers for bladder
cancer
[0087] A preferred protocol for the detection of the biomarkers of this
invention is as follows. The biological sample to be tested, e.g., urine,
preferably is subject to
pre-fractionation before SELDI analysis. This simplifies the sample and
improves
sensitivity. A preferred method of pre-fractionation involves contacting the
sample with an
anion exchange chromatographic material, such as Q HyperD (BioSepra, SA). The
bound
materials are then subject to stepwise pH elution using buffers at pH 9, pH 7,
pH 5 and pH 4.
Various fractions containing the biomarker are collected.
[0088] The sample to be tested (preferably pre-fractionated) is then contacted
with an affinity capture probe comprising a cation exchange adsorbent
(preferably a WCX
ProteinChip array (Ciphergen Biosystems, Inc.)) or an IMAC adsorbent
(preferably an
IMAC3 ProteinChip array (Ciphergen Biosystems, Inc.)), again as indicated in
Table 2. The
probe is washed with a buffer that will retain the biomarker while washing
away unbound
molecules. A suitable wash for each biomarker is the buffer identified'in
Table 2. The
biomarkers are detected by laser desorption/ionization mass spectrometry.
[0089] Alternatively, if antibodies that recognize the biomarker are
available,
for example in the case of (32-microglobulin, cystatin, transferrin,
transthyretin or albumin,
these can be attached to the surface of a probe, such as a pre-activated PS 10
or PS20
ProteinChip array (Ciphergen Biosystems, Inc.). These antibodies can capture
the
biomarkers from a sample onto the probe surface. Then the biomarkers can be
detected by,
e.g., laser desorption/ionization mass spectrometry.
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B. Detection by Immunoassay
[0090) In another embodiment, the biomarkers of this invention can be
measured by immunoassay. Immunoassay requires biospecific capture reagents,
such as
antibodies, to capture the biomarkers. Antibodies can be produced by methods
well known in
the art, e.g., by immunizing animals with the biomarkers. Biomarkers can be
isolated from
samples based on their binding characteristics. Alternatively, if the amino
acid sequence of a
polypeptide biomarker is known, the polypeptide can be synthesized and used to
generate
antibodies by methods well known in the art.
[0091] This invention contemplates traditional immunoassays including, for
example, sandwich immunoassays including ELISA or fluorescence-based
immunoassays, as
well as other enzyme immunoassays. In the SELDI-based immunoassay, a
biospecific
capture reagent for the biomarker is attached to the surface of an MS probe,
such as a pre-
activated ProteinChip array. The biomarker is then specifically captured on
the biochip
through this reagent, and the captured biomarker is detected by.mass
spectrometry.
IV. DETERMINATION OF SUBJECT BLADDER CANCER STATUS
A. Single Markers
[0092] The biomarkers of the invention can be used in diagnostic tests to
assess bladder cancer status in a subject, e.g., to diagnose bladder cancer.
The phrase
"bladder cancer status" includes distinguishing, inter alia, bladder cancer v.
non-bladder
cancer and, in particular, bladder cancer v. non-bladder cancer normal or
bladder cancer v.
non-bladder cancer. Based on this status, further procedures may be indicated,
including
additional diagnostic tests or therapeutic procedures or regimens.
[0093] The power of a diagnostic test to correctly predict status is commonly
measured as the sensitivity of the assay, the specificity of the assay or the
area under a
receiver operated characteristic ( 'ROC") curve. Sensitivity is the percentage
of tiue positives
that are predicted by a test to be positive, while specificity is the
percentage of true negatives
that are predicted by a test to be negative. An ROC curve provides the
sensitivity of a test as
a function of 1-specificity. The greater the area under the ROC curve, the
more powerful the
predictive value of the test. Other useful measures of the utility of a test
are positive
predictive value and negative predictive value. Positive predictive value is
the percentage of
actual positives that test as positive. Negative predictive value is the
percentage of actual
negatives that test as negative.
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[0094] The biomarkers of this invention show a statistical difference in
different bladder cancer statuses of at least p<_ 0.5, p<_ 0.05, p 5 10'2, p 5
10-3, p 5 10-4 or p_
10. Diagnostic tests that use these biomarkers alone or in combination show a
sensitivity
and specificity of at least 75%, at least 80%, at least 85%, at least 90%, at
least 95%, at least
98% and about 100%.
[0095] Each biomarker listed in Table 1 is individually useful in aiding in
the
determination of bladder cancer status. The method involves, first, measuring
the selected
biomarker in a subject sample using the methods described herein, e.g.,
capture on a SELDI
biochip followed by detection by mass spectrometry and, second, comparing the
measurement with a diagnostic amount or cut-off that distinguishes a positive
bladder cancer
status from a negative bladder cancer status. The diagnostic amount represents
a measured
amount of a biomarker above which or below which a subject is classified as
having a
particular bladder cancer status. For example, if the biomarker is up-
regulated compared to
normal during bladder cancer, then a measured amount above the diagnostic
cutoff provides a
diagnosis of bladder cancer. Alternatively, if the biomarker is down-regulated
during bladder
cancer, then a measured amount below the diagnostic cutoff provides a
diagnosis of bladder
cancer. As is well understood in the airt, by adjusting the particular
diagnostic cut-off used in
an assay, one can increase sensitivity or specificity of the diagnostic assay
depending on the
preference of the diagnostician. The particular diagnostic cut-off can be
determined, for
example, by measuring the amount of the biomarker in a statistically
significant number of
samples from subjects with the different bladder cancer statuses, as was done
here, and
drawing the cut-off to suit the diagnostician's desired levels of specificity
and sensitivity.
B. Combinations of Markers
[0096] While individual biomarkers are useful diagnostic biomarkers, it has
been found that a combination of biomarkers can provide, greater predictive
value of a
particular status than single biomarkers alone. Specifically, the detection of
a plurality of
biomarkers in a sample can increase the sensitivity and/or specificity of the
test.
[0097] The protocols described in the Example below were used to generate
mass spectra from 230 patient samples, 197 of which were diagnosed with
bladder or other
urogenital cancer and 33 of which did not exhibit any form of cancer. The peak
masses and
heights were abstracted into a discovery data set. This data set was used to
train a learning
algorithm employing classification and regression tree analysis (CAR'I)
(Ciphergen
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Biomarker Patterns SoftwareTM). In particular, CART chose many subsets of the
peaks at
random. For each subset, CART generated a best or near best decision tree to
classify a
sample as bladder cancer or non-bladder cancer. Among the many decision trees
generated
by CART, several had excellent sensitivity and specificity in distinguishing
bladder cancer
from non-bladder cancer.
[0098] An exemplary decision tree is presented in FIG. 1. The tree uses
biomarkers of mass to charge ratio M2670.00, M4210.00, M55 10.00, and M9100.00
Da.
Accordingly, these biomarkers are recognized as powerful classifiers for
bladder cancer when
used in combination with each other and, optionally, with other biomarkers. In
particular,
when used together or in further combination with M3540.00, M4960.00, and
M7070.00 Da,
these markers can distinguish bladder cancer from non-bladder cancer with
sensitivities and
specificities of at least 85%. Table 3 presents the performance of the
decision tree presented
in FIG. 1 in predicting bladder cancer. The number in parentheses denotes the
number of
correctly classified out of the total number of samples in the group.
Sensitivity is defined as
the ratio of detected cancers out of the total number of cancers included in
the study.
Specificity is defined as the percent of correctly identified control samples
out of the total
number of controls.
TABLE 3
Sensitivity (%) Specificity (%)
Learning set 87 (76/87) 84 (87/104)
Cross-validation 84 (73/87) 74 (77/104)
Test set (SELDI) 83 (15/18) 67 (14/Z1)
Test set (BTAstat) 78 (14/18) 67 (14/21)
Test set (UBC) 78 (14/18) 67 (14/21)
[0099] It is also noted that the specifics of the decision trees, in
particular the
cut-off values used in making branching decisions, depends on the details of
the assay used to
generate the discovery data set. The data acquisition parameters of the assay
that produced
the data used in the present analysis are provided in the Example. In
developing a
classification algorithm from, for example, a new sample set or a different
assay protocol, the
24
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WO 2006/020302 PCT/US2005/025632
operator uses a protocol that detects these biomarkers and keys the learning
algorithm to
include them.
1. Decision Tree of FIG. 1
[00100] In one embodiment, biomarkers M2670.00, M4210.00,
M55 10.00, M9100.00, M3540.00, M4960.00, and M7070.00 are particularly useful
in
combination to classify bladder cancer v. non-bladder cancer. This combination
is
particularly useful in a recursive partitioning process as shown in FIG. 1.
"C" represents
bladder cancer patients and "B" represents "benign" patients (those that are
non-bladder
cancer normal or those that have benign or other non-bladder cancers). In one
group, the
presence of M5510.00 at a peak intensity threshold value of less than or equal
to 1.260, and
the presence of M2670.00 at a peak intensity of less than or equal to 0.844
may be correlated
to a diagnosis of bladder cancer. In another group, the presence of M55 10.00
at a peak
intensity threshold value of less than or equal to 1.260, and the presence of
M2670.00 at a
peak intensity of greater than 0.844, and the presence of M9100.00 at a peak
intensity of
greater than 0.397, and the presence of M4210.00 at a peak intensity of
greater than 0.454
may be correlated to a probable diagnosis of bladder cancer. In another group,
the presence
of M55 10.00 at a peak intensity threshold value of greater than 1.260, and
the presence of
M4210.00 at a peak intensity of greater than 0.728, and the presence of
M4960.00 at a
threshold of less than or equal to 1.462, and the presence of M3540.00 at a
peak intensity of
less than or equal to 0.602 may be correlated to a probable diagnosis of
bladder cancer. In
another group, the presence of M55 10.00 at a peak intensity threshold value
of greater than
1.260, and the presence of M4210.00 at a peak intensity of greater than 0.728,
and the
presence of M4960.00 at a threshold of less than or equal to 1.462, and the
presence of
M3540.00 at a peak intensity of greater than 0.602, and the presence of
M7070.00 at a
threshold of greater than 0.223 may be correlated to a probable diagnosis of
bladder cancer.
In another group, the presence of M5510.00 at a peak intensity threshold value
of less than or
equal tol.260, and the presence of M2670.00 at a peak intensity of greater
than 0.844, and the
presence of M9100.00 at a peak intensity of less than or equal to 0.397 may be
correlated to a
probable benign diagnosis. In another group, the presence of M55 10.00 at a
peak intensity
threshold value of less than or equal to 1.260, and the presence of M2670.00
at a peak
intensity of greater than 0.844, and the presence of M9 100.00 at a peak
intensity of greater
than 0.397, and the presence of M4210.00 at a peak intensity of less than or
equal to 0.454
may be correlated to a probable benign diagnosis. In another group, the
presence of
CA 02574831 2007-01-23
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M55 10.00 at a peak intensity threshold value of greater than 1.260, and the
presence of
M4210.00 at a peak intensity of less than or equal to 0.728 may be correlated
to a probable
benign diagnosis. In another group, the presence of M55 10.00 at a peak
intensity threshold
value of greater than 1.260, and the presence of M4210.00 at a peak intensity
of greater than
0.728, and the presence of M4960.00 at a threshold of greater than 1.462 may
be correlated to
a probable benign diagnosis. Finally, the presence of M5510.00 at a peak
intensity threshold
value of greater than 1.260, and the presence of M4210.00 at a peak intensity
of greater than
0.728, and the presence of M4960.00 at a threshold of less than or equal to
1.462, and the
presence of M3540.00 at a peak intensity of greater than 0.602, and the
presence of
M7070.00 at a threshold of less than or equal to 0.223 may be correlated to a
probable benign
diagnosis. Preferably, the combination of these groupings makes up a single
classification
tree for a diagnosis of bladder cancer. However, the present invention
contemplates the use
of these individual groupings alone or in combination with other groupings to
aid in the
diagnosis or identification of bladder cancer-positive and bladder cancer-
negative patients.
Thus, one or more of such groupings, preferably two or more, or more
preferably, all of these
groupings aid in the diagnosis.
2. SDS algorithm
[00101] The same data set employed in the previously described CART
analysis was used with the multi-stage Statistical Classification Strategy
(SCS) (Institute for
Biodiagnostics, National Research Council Canada, Winnipeg, MB Canada). SCS
involves
feature (mass peak) selection with a two-stage exhaustive search, using a
wrapper approach.
The classifier used in the wrapper was the simple linear discriminant with
leave-one-out
(LOO) crossvalidation. Once the optimally discruninatory peaks were found, the
final
classifier was obtained with a bootstrap-inspired approach.
[00102] The 7 best mass peaks identified by the SCS detected TCC in
the test set with a sensitivity of 89% and a specificity of 81%. These seven
peaks are at mass
to charge ratios of M5400.00, M5830.00, M8490.00, M9420.00, M10800.00,
M56500.00 and
M67000.OO'-Da. When taking into account pairwise interactions among these
seven peaks,
the seven best of 35 possible linear and quadratic features consist of peaks
M56500.00 and
M67000.00 Da, the quadratic term of the M5830.00 Da peak and interaction
between the
M5400.00 Da & M8490.00 Da, the M5400.00 Da & M56500.00 Da, the M8490.00 Da &
M67000.00 Da and the M9420.00 Da & M10800.00 Da peak pairs. These detected TCC
in
the test set with the same sensitivity of 89% as the seven best single peaks,
but with an
26
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WO 2006/020302 PCT/US2005/025632
improved specificity of 91%. The SCS also identified a second set of 7 peaks
at M33 80.00,
M5580.00, M5700.00, M5830.00, M9420.00, M17000.00 and M67000.00 Da. With this
set
of markers, the overall accuracy was higher on the test set, (87.2% vs.
84.6%), sensitivity
reached 100.0%, and specificity was 76.2%. Accordingly, these biomarkers are
recognized
as powerful classifiers for bladder cancer when used in combination with each
other and,
optionally, with other biomarkers.
C. Subject Management
[00103] In certain embodiments of the methods of qualifying bladder
cancer status, the methods further comprise managing subject treatment based
on the status.
Such management includes the actions of the physician or clinician subsequent
to
determining bladder cancer status. For example, if a physician makes a
diagnosis of bladder
cancer, then a certain regime of treatment, such as prescription or
administration of
chemotherapy or immunotherapy might follow. Alternatively, a diagnosis of non-
bladder
cancer or non-bladder cancer might be followed with further testing to
determine a specific
disease that might the patient might be suffering from. Also, if the
diagnostic test gives an
inconclusive result on bladder cancer status, further tests may be called for.
[0,0104] Additional embodiments of the invention relate to the
communication of assay results or diagnoses or both to technicians, physicians
or patients, for
example. In certain embodiments, computers will be used to communicate assay
results or
diagnoses or both to interested parties, e.g., physicians and their patients.
In some
embodiments, the assays will be performed or the assay results analyzed in a
country or
jurisdiction which differs from the country or jurisdiction to which the
results or diagnoses
are communicated.
[00105] In a preferred embodiment of the invention, a diagnosis based
on the presence or absence in a test subject of any the biomarkers of Table 1
is communicated
to the subject as soon as possible after the diagnosis is obtained. The
diagnosis may be
communicated to the subject by the subject's treating physician.
Alternatively, the diagnosis
may be sent to a test subject by email or communicated to the subject by
phone. A computer
may be used to communicate the diagnosis by email or phone. In certain
embodiments, the
message containing results of a diagnostic test may be generated and delivered
automatically
to the subject using a combination of computer hardware and software which
will be familiar
to artisans skilled in telecommunications. One example of a healthcare-
oriented
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WO 2006/020302 PCT/US2005/025632
communications system is described in U.S. Patent Number 6,283,761; however,
the present
invention is not limited to methods which utilize this particular
communications system. In
certain embodiments of the methods of the invention, all or some of the method
steps,
including the assaying of samples, diagnosing of diseases, and communicating
of assay
results or diagnoses, may be carried out in diverse (e.g., foreign)
jurisdictions.
V. GENERATION OF CLASSIFICATION ALGORITHMS FOR QUALIFYING
BLADDER CANCER STATUS
[00106] In some embodiments, data derived from the spectra (e.g., mass
spectra or time-of-flight spectra) that are generated using samples such as
"known samples"
can then be used to "train" a classification model. A "known sample" is a
sample that has
been pre-classified. The data that are derived from the spectra and are used
to form the
classification model can be referred to as a "training data set." Once
trained, the
classification model can recognize patterns in data derived from spectra
generated using
unknown samples. The classification model can then be used to classify the
unknown
samples into classes. This can be useful, for example, in predicting whether
or not a
particular biological sample is associated with a certain biological condition
(e.g., diseased.
versus non-diseased).
[00107] The training data set that is used to form the classification
model may comprise raw data or pre-processed data. In some embodiments, raw
data can be
obtained directly from time-of-flight spectra or mass spectra, and then may be
optionally
"pre-processed" as described above.
[00108] Classification models can be formed using any suitable
statistical classification (or "learning") method that attempts to segregate
bodies of data into
classes based on objective parameters present in t he data. Classification
methods may be
either supervised or unsupervised. Examples of supervised and unsupervised
classification
processes are described in Jain, "Statistical Pattern Recognition: A Review",
IEEE
Transactions on Pattern Analysis and Machine Intelligence, Vol. 22, No. 1,
January 2000,
the teachings of which are incorporated by reference.
[00109] In supervised classification, training data containing examples
of known categories are presented to a learning mechanism, which learns one or
more sets of
relationships that define each of the known classes. New data may then be
applied to the
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WO 2006/020302 PCT/US2005/025632
learning mechanism, which then classifies the new data using the learned
relationships.
Examples of supervised classification processes include linear regression
processes (e.g.,
multiple linear regression (MLR), partial least squares (PLS) regression and
principal
components regression (PCR)), binary decision trees (e.g., recursive
partitioning processes
such as CART - classification and regression trees), artificial neural
networks such as back
propagation networks, discriminant analyses (e.g., Bayesian classifier or
Fischer analysis),
logistic classifiers, and support vector classifiers (support vector
machines).
[00110] A preferred supervised classification method is a recursive
partitioning process. Recursive partitioning processes use recursive
partitioning trees to
classify spectra derived from unknown samples. Further details about recursive
partitioning
processes are provided in U.S. Patent Application No. 2002 0138208 Al to
Paulse et al.,
"Method for analyzing. mass spectra."
[00111] In other embodiments, the classification models that are created
can be formed using unsupervised learning methods. Unsupervised classification
attempts to
learn classifications based on similarities in the training data set, without
pre-classifying the
spectra from which the training data set was derived. Unsupervised learning
methods include
cluster analyses. A cluster analysis attempts to divide the data into
"clusters" or groups that
ideally should have members that are very similar to each other, and very
dissimilar to
members of other clusters. Similarity is then measured using some distance
metric, which
measures the distance between data items, and clusters together data items
that are closer to
each other. Clustering techniques include the MacQueen's K-means algorithm and
the
Kohonen's Self-Organizing Map algorithm.
[00112] Learning algorithms asserted for use in classifying biological
information are described, for example, in PCT International Publication No.
WO 01/31580
(Barnhill et al., "Methods and devices for identifying patterns in biological
systems and
methods'of use thereof'), U.S. Patent Application No. 2002 0193950 Al (Gavin
et al.,
"Method or analyzing mass spectra"), U.S. Patent Application No. 2003 0004402
Al (Hitt et
al., "Process for discriminating between biological states based on hidden
patterns from
biological data"), and U.S. Patent Application No. 2003 0055615 Al (Zhang and
Zhang,
"Systems and methods for processing biological expression data").
[00113] The classification models can be formed on and used on any
suitable digital computer. Suitable digital computers include micro, mini, or
large computers
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WO 2006/020302 PCT/US2005/025632
using any standard or specialized operating system, such as a Unix, WindowsTM
or LinuxTM
based operating system. The digital computer that is used may be physically
separate from
the mass spectrometer that is used to create the spectra of interest, or it
may be coupled to the
mass spectrometer.
[00114] The training data set and the classification models according to
embodiments of the invention can be embodied by computer code that is executed
or used by
a digital computer. The computer code can be stored on any suitable computer
readable
media including optical or magnetic disks, sticks, tapes, etc., and can be
written in any
suitable computer programming language including C, C++, visual basic, etc.
[00115] The learning algorithms described above are useful both for
developing classification algorithms for the biomarkers already discovered, or
for fmding
new biomarkers for bladder cancer. The classification algorithms, in turn,
form the base for
diagnostic tests by providing diagnostic values (e.g., cut-off points) for
biomarkers used
singly or in combination.
VI. KITS FOR DETECTION OF BIOMARKERS FOR BLADDER CANCER
[00116] In another aspect, the present invention provides kits for
qualifying bladder cancer status, which kits are used to detect biomarkers
according to the
invention. In one embodiment, the kit comprises a solid support, such as a
chip, a microtiter
plate or a bead or resin having a capture reagent attached thereon, wherein
the capture reagent
binds a biomarker of the invention. Thus, for example, the kits of the present
invention can
comprise mass spectrometry probes for SELDI, such as ProteinChip arrays. In
the case of
biospecfic capture reagents, the kit can comprise a solid support with a
reactive surface, and a
container comprising the biospecific capture reagent.
[00117] The kit can also comprise a washing solution or instructions for
making a washing solution, in which the combination of the'capture reagent and
the washing
solution allows capture of the biomarker or biomarkers on the solid support
for subsequent
detection by, e.g., mass spectrometry. The kit may include more than type of
adsorbent, each
present on a different solid support.
[00118] In a further embodiment, such a kit can comprise instructions
for suitable operational parameters in the form of a label or separate insert.
For example, the
instructions may inform a consumer about how to collect the sample, how to
wash the probe
or the particular biomarkers to be detected.
CA 02574831 2007-01-23
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[00119] In yet another embodiment, the kit can comprise one or more
containers with biomarker samples, to be used as standard(s) for calibration.
VII. USE OF BIOMARKERS FOR BLADDER CANCER IN SCREENING ASSAYS
[00120] The methods of the present invention have other applications as
well. For example, the biomarkers can be used to screen for compounds that
modulate the
expression of the biomarkers in vitro or in vivo, which compounds in turn may
be useful in
treating or preventing bladder cancer.in patients. In another example, the
biomarkers can be
used to monitor the response to treatments for bladder cancer. In yet another
example, the
biomarkers can be used in heredity studies to determine if the subject is at
risk for developing
bladder cancer.
[00121] Thus, for example, the kits of this invention could include a
solid substrate having a hydrophobic function, such as a protein biochip
(e.g., a Ciphergen
H50 ProteinChip array, e.g., ProteinChip array) and a sodium acetate buffer
for washing the
substrate, as well as instructions providing a protocol to measure the
biomarkers of this
invention on the chip and to use these measurements to diagnose bladder
cancer.
[00122] Compounds suitable for therapeutic testing may be screened
initially by identifying compounds which interact with one or more biomarkers
listed in
Table 1. By way of example, screening might include recombinantly expressing a
biomarker
listed in Table 1, purifying the biomarker, and affixing the biomarker to a
substrate. Test
compounds would then be contacted with the substrate, typically in aqueous
conditions, and
interactions between the test compound and the biomarker are measured, for
example, by
measuring elution rates as a function of salt concentration. Certain proteins
may recognize
and cleave one or more biomarkers of Table 1, in which case the proteins may
be detected by
monitoring the digestion of one or more biomarkers in a standard assay, e.g.,
by gel
electrophoresis of the proteins.
[00123] In a related embodiment, the ability of a test compound to
inhibit the activity of one or more of the biomarkers of Table 1 may be
measured. One of
skill in the art will recognize that the techniques used to measure the
activity of a particular
biomarker will vary depending on the function and properties of the biomarker.
For example,
an enzymatic activity of a biomarker may be assayed provided that an
appropriate substrate is
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WO 2006/020302 PCT/US2005/025632
available and provided that the coricentration of the substrate or the
appearance of the
reaction product is readily measurable. The ability of potentially therapeutic
test compounds
to inhibit or enhance the activity of a given biomarker may be determined by
measuring the
rates of catalysis in the presence or absence of the test compounds. The
ability of a test
compound to interfere with a non-enzymatic (e.g., structural) function or
activity of one of
the biomarkers of Table 1 may also be measured. For example, the self-assembly
of a multi-
protein complex which includes one of the biomarkers of Table 1 may be
monitored by
spectroscopy in the presence or absence of a test compound. Alternatively, if
the biomarker
is a non-enzymatic enhancer of transcription, test compounds which interfere
with the ability
of the biomarker to enhance transcription may be identified by measuring the
levels of
biomarker-dependent transcription in vivo or in vitro in the presence and
absence of the test
compound.
[00124] Test compounds capable of modulating the activity of any of
the biomarkers of Table 1 may be administered to patients who are suffering
from or are at
risk of developing bladder cancer or other cancer. For example, the
administration of a test
compound which increases the activity of a particular biomarker may decrease
the risk of
bladder cancer in a patient if the activity of the particular biomarker in
vivo prevents the
accumulation of proteins for bladder cancer. Conversely, the administration of
a test
compound which decreases the activity of a particular biomarker may decrease
the risk of
bladder cancer in a patient if the increased activity of the biomarker is
responsible, at least in
part, for the onset of bladder cancer.
[00125] In an additional aspect, the invention provides a method for
identifying compounds useful for the treatment of disorders such as bladder
cancer which are
associated with increased levels of modified forms of any of the biomarkers of
Table 1. For
example, in one embodiment, cell extracts or expression libraries may be
screened for
compounds which catalyze the cleavage of a full-length biomarker to form a
truncated form
of the biomarker. In one embodiment of such a screening assay, cleavage of a
biomarker
may be detected-by attaching a fluorophore to the biomarker whi&remains
quenched when
the biomarker is uncleaved but which fluoresces when the protein is cleaved.
Alternatively,
a version of full-length biomarker modified so as to render the amide bond
between amino
acids x and y uncleavable may be used to selectively bind or "trap" the
cellular protease
which cleaves a full-length biomarker at that site in vivo. Methods for
screening and
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identifying proteases and their targets are well-documented in the scientific
literature, e.g., in
Lopez-Ottin et al. (Nature Reviews, 3:509-519 (2002)).
[00126] In yet another embodiment, the invention provides a method for
treating or reducing the progression or likelihood of a disease, e.g., bladder
cancer, which is
associated with the increased levels of truncated forms of any of the
biomarkers of Table 1.
For example, after one or more proteins have been identified which cleave a
full-length
biomarker, combinatorial libraries may be screened for compounds which inhibit
the
cleavage activity of the identified proteins. Methods of screening chemical
libraries for such
compounds are well-known in art. See, e.g., Lopez-Otin et al. (2002).
Alternatively,
inhibitory compounds may be intelligently designed based on the structure of
any of the
biomarkers of Table 1.
[00127] Compounds which impart a truncated biomarker with the
functionality of a full-length biomarker are likely to be useful in treating
conditions, such as
bladder cancer, which are associated with the truncated form of the biomarker.
Therefore, in
a further embodiment, the invention provides methods for identifying compounds
which
increase the affinity of a truncated form of any of the biomarkers of Table 1
for its target
proteases. For example, compounds may be screened for their ability to impart
a truncated
biomarker with the protease inhibitory activity of the full-length biomarker.
Test compounds
capable of modulating the inhibitory activity of a biomarker or the activity
of molecules
which interact with a biomarker may then be tested in vivo for their ability
to slow or stop the
progression of bladder cancer in a subject.
[00125] At the clinical level, screening a test compound includes
obtaining samples from test subjects before and after the subjects have been
exposed to a test
compound. The levels in the samples of one or more of the biomarkers listed in
Table 1 may
be measured and analyzed to determine whether the levels of the biomarkers
change after
exposure to a test compound. The samples may be analyzed by mass spectrometry,
as
described herein, or the samples may be analyzed by any appropriate means
known to one of
skill in the art. For example, the levels of one or more of the biomarkers
listed in Table 1
may be measured directly by Western blot using radio- or fluorescently-labeled
antibodies
which specifically bind to the biomarkers. Alternatively, changes in the
levels of mRNA
encoding the one or more biomarkers may be measured and correlated with the
administration of a given test compound to a subject. In a further embodiment,
the changes
in the level of expression of one or more of the biomarkers may be measured
using in vitro
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methods and materials. For example, human tissue cultured cells which express,
or are
capable of expressing, one or more of the biomarkers of Table 1 may be
contacted with test
compounds. Subjects who have been treated with test compounds will be
routinely examined
for any physiological effects which may result from the treatment. In
particular, the test
compounds will be evaluated for their ability to decrease disease likelihood
in a subject.
Alternatively, if the test compounds are administered to subjects who have
previously been
diagnosed with bladder cancer, test compounds will be screened for their
ability to slow or
stop the progression of the disease.
[00129] The invention will be described in greater detail by way of
specific examples. The following examples are offered for illustrative
purposes, and are not
intended to limit the invention in any manner. Those of skill in the art will
readily recognize
a variety of non-critical parameters that can be changed or modified to yield
essentially the
same results.
VIL EXAMPLES
EXAMPLE 1. DISCOVERY OF BIOMARKERS FOR BLADDER CANCER USING
CART ANALYSIS
Urine Samples
[00130] Urine samples were obtained patients seen in the Departments
of Urology at the Eastern Virginia Medical School in Norfolk, Virginia and
Laikon.Hospital
in Athens, Greece. Bladder cancer samples (n=105) were obtained from patients
aged 27-91
years, with a mean age of 71.3. Non-bladder cancer samples (n=125) were
obtained from
patients aged 34-86 years, with a mean age of 62.6. In all cases, patients
were consented
according to the regulations for human subject protection of each institution.
The urine
samples were aliquoted and frozen at -80 C until thawed specifically for
SELDI analysis.
[00131] Healthy controls (n=33) included volunteers with no evidence
of disease, and healthy individuals (i.e., no history or evidence of urologic
cancer)
participating in the prostate and screening program at EVMS. Bladder cancer
(n=105
patients) was histologically or cytologically confumed at the time of specimen
collection and
the vast majority of cases involved newly diagnosed cancers (n=83). In the
case of
recurrences (n=22) none of the patients had received chemo-or immunotherapy
within 3
months prior to specimen collection. Other urogenital diseases (n=92) included
clinical or
pathologically confirmed benign prostatic hyperplasia (BPH) (n=47), urinary
tract infections
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(n=13), urolethiasis (13), amyloidosis (n=1), prostate cancer (n=11), renal
cell carcinoma (5),
and seminoma (1).
[00132] Grading was assessed using the World Health Organization
(WHO) system. Tumor stage and grade of patients with TCC are shown in Table 4
below.
TABLE 4
Stage No. of No. of Grade No. of No. of
samples (L) samples (T) samples (L) samples (T)
Ta 36 9 I 5 3
Tl 25 2 II 31 3
T2 18 4 III 51 12
T3-T4 2 0
Ta CIS 3 1
T1CIS 2 2
T2 CIS 1 0
SELDI processing of urine samples
[00133] Prior to their application on protein chips, urine samples were
briefly centrifuged (1 m'vn, 10,000 rpm) for the removal of exfoliated cells.
The supernatants
were then applied to the chips using a Coulter Beckman Biomek 2000 Laboratory
Automation Workstation as follows: 63 l of urine were mixed with 21 p:I of 9M
urea-2%
CHAPS-50 mM Tris pH 9 buffer for 30 minutes at 4 C, followed by the addition
of 84 1 of
binding buffer (100 mM sodium acetate, pH 4.0). One hundred- microliters of
the diluted
samples were then applied onto the weak cation exchange (WCX) chips using a
bioprocessor
(Ciphergen Biosystems Inc.). Following a 45-minute incubation, non-
specificaliy bound
molecules were removed by three brief washes in 200 l binding buffer followed
by three
washes with 200 l HPLC-gradient water. Sinapinic acid (2X1 l of 50% SPA in
50% ACN-
0.1 % TFA) was applied to the chip array surface and mass spectrometry
performed using a
PBS2 SELDI-TOF mass spectrometer (Ciphergen Biosystems Inc.). Mass spectral
data were
CA 02574831 2007-01-23
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collected by averaging the output from a total of 192 laser shots at a laser
intensity of 220.
Mass calibration was performed using the all-in-one peptide standard
(Ciphergen Biosystems
Inc.) and specifically Vasopressin (1084.25), Somatostatin (1637.9), Insulin B-
chain (bovine;
3495.94), Insulin (human recombinant; 5807.65), and Hirudin (7033.61). All
samples were
processed in duplicate.
[00134] One urine sample designated as quality control (QC) was
included in every chip array to estimate reproducibility of the profiling
assay. Three
randomly selected peaks with masses at 2.8, 4.8, and 11.8 kDa were utilized to
estimate the
mass location and peak intensity coefficients of variations (CV). From the
analysis of a total
of 89 QC spectra, the mass CV was found to be 0.05-0.3% and the intensity CV
40-70%.
[00135] Before analysis, the data was divided into two sets: a training
set consisting of 191 samples (87 bladder cancer, 73 other urogenital
diseases, and 31
norrimal), and a test set of 39 samples (18 bladder cancer, 19 other
urogenital diseases, and 2
norrimal).
Processing of SELDI data
[00136] Spectral peaks were labeled and their intensities normalized to
the total ion current (mass range 2.5-30 kDa) to account for variation in
ionization
efficiencies, using the SELDI software (Version 3.1). Peak masses were aligned
and
clustering was performed using the Biomarker Wizard software (Ciphergen
Biosystems).
Specifically the settings for peak labeling and alignment were the following:
in the 2.5 to 30
kDa mass range: signal/noise (first pass) = 3; minimum peak threshold = 10%;
cluster mass
window = 0.3%; and signal/noise (second pass) = 1.5. In the 20 to 100 kDa
range:
signal/noise (first pass) = 5; minimum peak threshold = 10%; mass error =1%
and
signal/noise (second pass) = 2.5. With these settings a total of 101 peaks per
spectrum were
detected (90 in the 2.5-30 kDa and 11 in the 30-150K mass range). Intensity
values for each
of these peaks were exported to an Excel file, and averaged for each duplicate
spectra.
[00137] Pattem recognition and sample classification were performed
using the Biomarker Pattern Software (Ciphergen Biosystems Inc.). The decision
tree was
generated using the Gini method non-linear combinations. Construction of the
decision tree
classification algorithm was performed as described by Breiman, L., et al.,
Classification and
Regression Trees, (1984). Details regarding the Classification and Regression
Tree (CART)
and the artificial intelligence bioinformatics algorithm incorporated within
the BioMarker
36
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WO 2006/020302 PCT/US2005/025632
Pattems software program have also been described in Bertone, P., et al.,
Nucleic Acids Res.
29: 2884-2898 (2001); Kosuda, S., et al., Ann. Nucl. Med. 16: 263-271 (2002).
[00138] Briefly, classification trees split the data into two bins based on
decision rules (squares, Figure 1). The rules are formed by the peak
intensities being either
greater or lesser than a specific value for each selected mass. Samples that
follow the rule
(i.e. peak intensity is equal to or less than the cut-off intensity value) go
to the left daughter
node; others go to the right daughter node. When splitting can no longer be
performed,
terminal nodes are generated and classified according to the samples in the
majority; in this
case the terminal node is either classified as cancer or benign/normal
(circles, Figure 1).
[00139] A 10-fold cross validation analysis was performed as an initial
evaluation of the test error of the algorithm. Briefly, this process involves
splitting up the
data set iteratively into 10 random segments and using nine of them for
training and the tenth
as a.test set for the algorithm. Multiple trees were initially generated, by
varying the splitting
factor by increments of 0.1. The peaks that formed the main splitters of the
tree with the
highest prediction rates in the cross-validation analysis were then selected
and the tree was
rebuilt based on these peaks alone. This tree was then challenged to classify
the samples
included in the test set. The classification provided by the algorithm was
compared to the
specimen pathologic diagnosis.
UBC and BTAstat test analysis of urine samples
[00140] The UBC (II9L Biotech, Sollentona, Sweden) and BTAstat
(Bion Diagnostic Sciences, Redmond, Washington) tests were performed according
to the
manufacturer's instructions. For UBC, a cut-off value of 12 g/1 was selected
based on
receiver operating characteristics curve analysis (Giannopoulos et al., J.
Urol. 166(2): 488-9
(2001)).
CART Analysis
[00141] The benign, other cancers and normal samples were pooled to
form the control group. Seven protein peaks at M2670.00, M4210.00, M5510.00,
M9100.00,
M3540.00, M4960.00, and M7070.00 Da generated a decision tree that provided
optimal
discrimination between the bladder cancer and control group during the
algorithm evaluation
(Figure 1). The sample segregation in the decision nodes, as well as the
samples' intensity
levels for the main splitter, are shown in Figure 3. Representative mass
spectra of the
splitters are shown on Figure 2. Peak intensities between different groups
were compared
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WO 2006/020302 PCT/US2005/025632
with student's t-test. With the exception of the M7070.00 and M9100.00 Da
peaks, the rest
of the main splitters had significantly different intensity levels between the
cancer and control
groups (Table 5).
TABLE 5
Splitter (Da) p
M2670.00 0.006
M3540.00 0.007
M4210.00 0.042
M4960.00 <0.001
M5510.00 0.044
M7070.00 0.41
M9100.00 0.25
[00142] In the cross-validation analysis, the decision tree predicted
bladder cancer with 78.5% (150/191) accuracy (Table 3). In the blinded test
set, this tree
classified accurately 74% (29/39) of the samples. By comparison, in the same
set of samples,
the BTAstat and UBC tests predicted bladder cancer with 72% (28/39) accuracy
(Table 3).
Interestingly, the SELDI decision tree detected 5 out of 6 of the low grade (I
and II) tumors
while the BTAstat detected 2 out of 6 and the UBC test found 4 out of 6.
Nevertheless, the
responses of the three tests were found to be independent of each other
(P>0.05) and
therefore their combination did not improve the overall diagnostic rates.
[00143] A summation of the classification results from the decision tree
is presented for the training and test sets in Table 6 below.
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Table 6. Decision Tree Classification of the Bladder Cancer Training and Test
Sets
A. Training Set
Sample Normal and other Bladder cancer Misclassified Rate
urogenital diseases
4ormal and other 87 83.7% 17 16.3% 17 16.3%
rogenital diseases
N=104)
3Iadder cancer 11 12.6% 76 87.4% 11 12.6%
N=87)
otal 28 14.7%
amples
N=191)
B. Test Set
Sample Normal and other Bladder cancer Misclassified Rate
urogenital diseases
ormal and other 14 66.7% 7 33.3% 7 33.3%
rogenital diseases
N=21)
ladder cancer 3 16.7% 15 83.3% 3 16.7%
N-18)
otal 10 25.6%
amples
N=39)
EXAMPLE 2. DISCOVERY OF BIOMARKERS FOR BLADDER CANCER USING
SCS ANALYSIS
j00144] The 89-peak SELDI dataset as described in Example 1 was
analyzed with SCS. The SCS strategy was developed to deal specifically with
the analysis of
biomedical data, characterized by typically large (0(1000 - 10000)) number of
features (e.g.
m/z values) and few (0(10 - 100)) samples. The SCS is a multi-stage approach.
Before the
first, feature reduction stage, data transformations are usually applied (for
spectra, these can
39
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WO 2006/020302 PCT/US2005/025632
be scaling to unit area, "whitening"; smoothing, peak alignment, replacing the
spectra by
their first or second derivatives, etc). Both the original data and its rank-
ordered version were
used. (Rank ordering is a nonlinear transformation that replaces in each
spectrum the actual
peak intensity values by their ranks.) This helps reduce the influence of
accidentally large or
small feature values. Feature (peak) selection was then applied to both
original and rank-
ordered data.
[00145] Exhaustive search (ES) for the best 7 out of 89.features is
computationally not feasible. However, finding the best 5 out of 89 is. Using
a frequency
count of how many times one of the 89 peaks appeared in the best solutions, 30
peaks were
selected. Best 7 of 30 is quite feasible, even if a wrapper approach is used,
i.e. if classification
accuracy is used as the criterion for selecting the features. For the
classifier of this wrapper
approach, LDA was used with leave-one-out crossvalidation. Of the 2,035,800
possible 7-
peak feature sets, the best 6 sets that maintained an acceptable balance
between sensitivity
(false positive, FP rate) and specificity (false negative, FN rate) were
selected as candidates.
Candidate selection was performed by minimizing the difference between (FP -
FN)2 at the
feature selection stage and also imposing a larger penalty for misclassifying
the TCC samples
[00146] Each of the 6 feature sets was used to develop the best
corresponding classifier. Inspired by the "resampling with replacement '
philosophy of
Efron's bootstrap approach (Efron, B. and Gong, G., American Statistician
37(1): 36-48
(1983)), a robust classifier was created by randomly selecting about half of
the samples (in a
stratified manner) as a training set, developing a crossvalidated classifier,
and using the other
half to test classifier efficacy. The training samples are then returned to
the original pool and
the process repeated, usually B = 5,000 - 10,000 times. The optimized
classifier coefficients
for all B random splits are saved. The improvement over conventional
approaches is that the
fmal classifier is produced as the weighted average of these B sets of
coefficients. The
weight for classifierj is Q1= xiCji2, with 0:5 Cj< 1 the fraction of crisp (p
_ 0.75) class
assignment probabilities, and xj is Cohen's chance-corrected measure of
agreement [16], -0 <_
icJ S 1; n = 1 signifies perfect classification. The B. Q. values found for
the less optimistic test
sets were used (Somorjai, et al., A Data-Driverc, Flexible Machine Learning
Strategy for the
Classification of Biomedical Data, Chapter in "Artificial Intelligence Methods
for Systems
Biology", Dubetzky, W. and Azuaje, F. (eds.), Kluwer Academic Publ. (in
press)). For these
studies, using the top test Qj gave the best classifier.
CA 02574831 2007-01-23
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Discussion
[00147]. Using SELDUTOF-MS techniques coupled with application of
bioinformatic tools, the decision tree achieved 83-87% specificity/67%
sensitivity and SCS
achieved 89% specificity/81% sensitivity for detection of bladder cancer in a
rapid and
reproducible manner and in a large number of samples. While not intending to
be bound by a
particular theory, it appears that the protein pattern, rather than individual
protein alteration,
may be more important for differentiating normal healthy individuals from
those who have,
or are likely to develop, bladder cancer. The high sensitivities and
specificities achieved in
these studies using SELDUTOF-MS techniques, coupled with robust artificial
intelligence
classification algorithms, identified protein patterns in urine samples that
distinguished non-
bladder cancer controls from bladder cancer patients. These techniques provide
data that are
easy to accumulate and should lend itself readily to clinical use:
[00148] While the invention has been illustrated and described in detail
in the drawings and foregoing description, the same is to be considered as
illustrative and not
restrictive in character, it being understood that only the preferred
embodiments have been
shown and described and that all changes and modifications that come within
the spirit of the
invention are desired to be protected. In addition, all references and patents
cited herein are
indicative of the level of skill in the art and hereby incorporated by
reference in their entirety.
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