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OVARIAN CANCER BIOMARKERS AND USES THEREOF
FIELD OF THE INVENTION
[0001] The present application relates generally to the detection of
biomarkers and
the diagnosis of cancer in an individual and, more specifically, to one or
more biomarkers,
methods, devices, reagents, systems, and kits for diagnosing cancer, more
particularly ovarian
cancer, in an individual.
BACKGROUND
[0002] The following description provides a summary of information relevant to
the
present application and is not an admission that any of the information
provided or
publications referenced herein is prior art to the present application.
[0003] Ovarian cancer is the eighth most common cancer in women and the fifth
leading cause of cancer-related deaths in women in the United States. Of all
females born in
the United States, one of every 70 will develop ovarian cancer and one of
every 100 will die
from this disease. The American Cancer Society estimates that approximately
21,550 women
will be diagnosed with ovarian cancer in 2009 (American Cancer Society. Cancer
Facts &
Figures 2009. Atlanta: American Cancer Society; 2009). It is estimated that
14,600 women
will die from this disease in 2009.
[0004] The survival rate and quality of patient life are improved the earlier
ovarian
cancer is detected. There is currently no sufficiently accurate screening test
proven to be
effective in the early detection of ovarian cancer. Thus, a pressing need
exists for sensitive
and specific methods for detecting ovarian cancer, particularly early-stage
ovarian cancer.
[0005] Approximately 7% of the female population is at increased risk for
ovarian
cancer, based on genetic or family history. The risk for ovarian cancer
increases with age.
Women who have had breast cancer or who have a family history of breast or
ovarian cancer
are at increased risk. Inherited mutations in BRCA1 or BRCA2 genes increase
risk. Ovarian
cancer incidence rates are highest in Western industrialized countries.
[0006] Between 75% and 85% of ovarian cancers are diagnosed at an advanced
stage.
There is no consistent, reliable, non-invasive test to signal the presence of
ovarian cancer.
Pelvic examination only occasionally detects ovarian cancer, generally when
the disease is
advanced. Symptoms are often vague or nonexistent until late stages of the
disease.
Symptomatic women report frequent (>12 times/month) abdominal pain, bloating,
increased
girth, difficulty eating or feeling full quickly (Goff et al. Cancer 2007;
109:221). Trans-
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vaginal ultrasound and serum CA 125 levels have been tested as a screen for
ovarian cancer
and have not been found satisfactory. A laparotomy is required when ovarian
cancer is
suspected. The outcome of ovarian cancer patients operated on by a gynecology
oncology
surgical specialist is improved compared to a general gynecological surgeon,
demonstrating
that need for differential diagnosis of ovarian cancer from a suspicious
pelvic mass prior to
surgery. Goff reported on over 10,000 women in nine states undergoing surgery
for a
suspicious pelvic mass. Among the most important factors for receiving
appropriate
surgical management were surgeon specialty of gynecologic oncologist and the
volume of
cases performed by the surgeon annually. There are only 1000 board certified
gynecologic
oncologists in the United States, mostly in the larger medical centers across
the country.
Appropriately directing the women who are most likely to benefit from the care
of such
specialists can be critical for achieving good patient outcomes.
[0007] Currently, cancer antigen 125 (CA-125) is the most widely used serum
biomarker for ovarian cancer. Serum concentrations of CA-125 are elevated (>35
U/ml) in
75-80% of patients with advanced-stage disease and this marker is routinely
used to follow
response to treatment and disease progression in patients from whom CA- 125-
secreting
tumors have been resected. However, because the levels of CA- 125 are
correlated with
tumor volume, only 50% of patients with early-stage disease have elevated
levels, indicating
that the sensitivity of CA-125 as a screening tool for early stage disease is
limited. The utility
of CA- 125 screening is further limited by the high frequency of false-
positive results
associated with a variety of benign conditions, including endometriosis,
pregnancy,
menstruation, pelvic inflammatory disease, peritonitis, pancreatitis, and
liver disease.
[0008] Classification of cancers determines appropriate treatment and helps
determine
the prognosis of the patient. Ovarian cancers are classified according to
histology (i.e.,
"grading") and extent of the disease (i.e., "staging") using recognized grade
and stage
systems. In grade I, the tumor tissue is well differentiated. In grade II,
tumor tissue is
moderately well differentiated. In grade III, the tumor tissue is poorly
differentiated. Grade
III correlates with a less favorable prognosis than either grade I or II.
Stage I is generally
confined within the capsule surrounding one (stage IA) or both (stage IB)
ovaries, although in
some stage I (i.e. stage IC) cancers, malignant cells may be detected in
ascites, in peritoneal
rinse fluid, or on the surface of the ovaries. Stage II involves extension or
metastasis of the
tumor from one or both ovaries to other pelvic structures. In stage IIA, the
tumor extends or
has metastasized to the uterus, the fallopian tubes, or both. Stage IIB
involves metastasis of
the tumor to the pelvis. Stage IIC is stage IIA or IIB with the added
requirement that
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malignant cells may be detected in ascites, in peritoneal rinse fluid, or on
the surface of the
ovaries. In stage III, the tumor comprises at least one malignant extension to
the small bowel
or the omentum, has formed extra-pelvic peritoneal implants of microscopic
(stage IIIA) or
macroscopic (<2 centimeter diameter, stage IIIB; >2 centimeter diameter, stage
IIIC) size, or
has metastasized to a retroperitoneal or inguinal lymph node (an alternate
indicator of stage
IIIC). In stage IV, distant (i.e. non-peritoneal) metastases of the tumor can
be detected.
[0009] Treatment options include surgery, chemotherapy, and occasionally
radiation
therapy. Surgery usually involves removal of one or both ovaries, fallopian
tubes
(salpingoophorectomy), and the uterus (hysterectomy). In younger women with
very early
stage tumors who wish to have children, only the involved ovary and fallopian
tube may be
removed. In more advanced disease, surgically removing all abdominal
metastases enhances
the effect of chemotherapy and helps improve survival. For women with stage
III ovarian
cancer that has been optimally debulked (removal of as much of the cancerous
tissue as
possible), studies have shown that chemotherapy administered both
intravenously and
directly into the peritoneal cavity improves survival. Studies have found that
women who are
treated by a gynecologic oncologist have more successful outcomes.
[0010] Relative survival varies by age; women younger than 65 are about twice
as
likely to survive 5 years (57%) following diagnosis as women 65 and older
(29%). Overall,
the 1- and 5-year relative survival of ovarian cancer patients is 75% and 46%,
respectively. If
diagnosed at the localized stage, the 5-year survival rate is 93%; however,
only 19% of all
cases are detected at this stage, usually fortuitously during another medical
procedure. The
majority of cases (67%) are diagnosed at distant stage. For women with
regional and distant
disease, 5-year survival rates are 71% and 31%, respectively; the chance of
recurrence in
these women is 20-85%. The 10-year relative survival rate for all stages
combined is 39%.
Therefore, ovarian cancer tends to be diagnosed too late to save women's
lives. Detecting
recurrence and predicting and monitoring response to therapy is important for
making
informed decisions on appropriate treatment throughout the care of these
patients.
[0011] A blood screening test that can reliably detect early stage ovarian
cancer will
save thousands of lives each year. Where methods of early diagnosis in cancer
exist, the
benefits are generally accepted by the medical community. Cancers for which
widely utilized
screening protocols exist have the highest 5-year survival rates, such as
breast cancer (88%)
and colon cancer (65%) versus 46% for ovarian cancer. However, fortuitous
detection of
early stage ovarian cancer is associated with a substantial increase in 5-year
survival (>95%).
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Therefore, early detection could significantly impact long-term survival. This
demonstrates
the clear need for diagnostic methods that can reliably detect early-stage
ovarian cancer.
[0012] Biomarker selection for a specific disease state involves first the
identification
of markers that have a measurable and statistically significant difference in
a disease
population compared to a control population for a specific medical
application. Biomarkers
can include secreted or shed molecules that parallel disease development or
progression and
readily diffuse into the blood stream from ovarian tissue or from surrounding
tissues and
circulating cells in response to a tumor. The biomarker or set of biomarkers
identified are
generally clinically validated or shown to be a reliable indicator for the
original intended use
for which it was selected. Biomarkers can include small molecules, peptides,
proteins, and
nucleic acids. Some of the key issues that affect the identification of
biomarkers include
over-fitting of the available data and bias in the data.
[0013] A variety of methods have been utilized in an attempt to identify
biomarkers
and diagnose disease. For protein-based markers, these include two-dimensional
electrophoresis, mass spectrometry, and immunoassay methods. For nucleic acid
markers,
these include mRNA expression profiles, microRNA profiles, FISH, serial
analysis of gene
expression (SAGE), methylation profiles, and large scale gene expression
arrays.
[0014] The utility of two-dimensional electrophoresis is limited by low
detection
sensitivity; issues with protein solubility, charge, and hydrophobicity; gel
reproducibility; and
the possibility of a single spot representing multiple proteins. For mass
spectrometry,
depending on the format used, limitations revolve around the sample processing
and
separation, sensitivity to low abundance proteins, signal to noise
considerations, and inability
to immediately identify the detected protein. Limitations in immunoassay
approaches to
biomarker discovery are centered on the inability of antibody-based multiplex
assays to
measure a large number of analytes. One might simply print an array of high-
quality
antibodies and, without sandwiches, measure the analytes bound to those
antibodies. (This
would be the formal equivalent of using a whole genome of nucleic acid
sequences to
measure by hybridization all DNA or RNA sequences in an organism or a cell.
The
hybridization experiment works because hybridization can be a stringent test
for identity.
Even very good antibodies are not stringent enough in selecting their binding
partners to
work in the context of blood or even cell extracts because the protein
ensemble in those
matrices have extremely different abundances.) Thus, one must use a different
approach with
immunoassay-based approaches to biomarker discovery - one would need to use
multiplexed
ELISA assays (that is, sandwiches) to get sufficient stringency to measure
many analytes
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simultaneously to decide which analytes are indeed biomarkers. Sandwich
immunoassays do
not scale to high content, and thus biomarker discovery using stringent
sandwich
immunoassays is not possible using standard array formats. Lastly, antibody
reagents are
subject to substantial lot variability and reagent instability. The instant
platform for protein
biomarker discovery overcomes this problem.
[0015] Many of these methods rely on or require some type of sample
fractionation
prior to the analysis. Thus the sample preparation required to run a
sufficiently powered
study designed to identify and discover statistically relevant biomarkers in a
series of well-
defined sample populations is extremely difficult, costly, and time consuming.
During
fractionation, a wide range of variability can be introduced into the various
samples. For
example, a potential marker could be unstable to the process, the
concentration of the marker
could be changed, inappropriate aggregation or disaggregation could occur, and
inadvertent
sample contamination could occur and thus obscure the subtle changes
anticipated in early
disease.
[0016] It is widely accepted that biomarker discovery and detection methods
using
these technologies have serious limitations for the identification of
diagnostic biomarkers.
These limitations include an inability to detect low-abundance biomarkers, an
inability to
consistently cover the entire dynamic range of the proteome, irreproducibility
in sample
processing and fractionation, and overall irreproducibility and lack of
robustness of the
method. Further, these studies have introduced biases into the data and not
adequately
addressed the complexity of the sample populations, including appropriate
controls, in terms
of the distribution and randomization required to identify and validate
biomarkers within a
target disease population.
[0017] Although efforts aimed at the discovery of new and effective biomarkers
have
gone on for several decades, the efforts have been largely unsuccessful.
Biomarkers for
various diseases typically have been identified in academic laboratories,
usually through an
accidental discovery while doing basic research on some disease process. Based
on the
discovery and with small amounts of clinical data, papers were published that
suggested the
identification of a new biomarker. Most of these proposed biomarkers, however,
have not
been confirmed as real or useful biomarkers; primarily because the small
number of clinical
samples tested provide only weak statistical proof that an effective biomarker
has in fact been
found. That is, the initial identification was not rigorous with respect to
the basic elements of
statistics. In each of the years 1994 through 2003, a search of the scientific
literature shows
that thousands of references directed to biomarkers were published. During
that same time
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frame, however, the FDA approved for diagnostic use, at most, three new
protein biomarkers
a year, and in several years no new protein biomarkers were approved.
[0018] Based on the history of failed biomarker discovery efforts,
mathematical
theories have been proposed that further promote the general understanding
that biomarkers
for disease are rare and difficult to find. Biomarker research based on 2D
gels or mass
spectrometry supports these notions. Very few useful biomarkers have been
identified
through these approaches. However, it is usually overlooked that 2D gel and
mass
spectrometry measure proteins that are present in blood at approximately 1 nM
concentrations and higher, and that this ensemble of proteins may well be the
least likely to
change with disease. Other than the instant biomarker discovery platform,
proteomic
biomarker discovery platforms that are able to accurately measure protein
expression levels at
much lower concentrations do not exist.
[0019] Much is known about biochemical pathways for complex human biology.
Many biochemical pathways culminate in or are started by secreted proteins
that work locally
within the pathology, for example growth factors are secreted to stimulate the
replication of
other cells in the pathology, and other factors are secreted to ward off the
immune system,
and so on. While many of these secreted proteins work in a paracrine fashion,
some operate
distally in the body. One skilled in the art with a basic understanding of
biochemical
pathways would understand that many pathology-specific proteins ought to exist
in blood at
concentrations below (even far below) the detection limits of 2D gels and mass
spectrometry.
What must precede the identification of this relatively abundant number of
disease
biomarkers is a proteomic platform that can analyze proteins at concentrations
below those
detectable by 2D gels or mass spectrometry.
[0020] Accordingly, a need exists for biomarkers, methods, devices, reagents,
systems, and kits that enable (a) the differentiation of benign pelvic masses
from ovarian
cancer; (b) referral to a gynecologic oncology surgeon rather than a general
gynecologic
surgeon to surgically treat cases of ovarian cancer; (c) the detection of
ovarian cancer
biomarkers; and (d) the diagnosis of ovarian cancer.
SUMMARY
[0021] The present application includes biomarkers, methods, reagents,
devices,
systems, and kits for the detection and diagnosis of cancer and more
particularly, ovarian
cancer. The biomarkers of the present application were identified using a
multiplex aptamer-
based assay, which is described in detail in Example 1. By using the aptamer-
based
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biomarker identification method described herein, this application describes a
surprisingly
large number of ovarian cancer biomarkers that are useful for the detection
and diagnosis of
ovarian cancer. In identifying these biomarkers, over 800 proteins from
hundreds of
individual samples were measured, some of which were at concentrations in the
low
femtomolar range. This is about four orders of magnitude lower than biomarker
discovery
experiments done with 2D gels or mass spectrometry.
[0022] While certain of the described ovarian cancer biomarkers are useful
alone for
detecting and diagnosing ovarian cancer, methods are described herein for the
grouping of
multiple subsets of the ovarian cancer biomarkers that are useful as a panel
of biomarkers.
Once an individual biomarker or subset of biomarkers has been identified, the
detection or
diagnosis of ovarian cancer in an individual can be accomplished using any
assay platform or
format that is capable of measuring differences in the levels of the selected
biomarker or
biomarkers in a biological sample.
[0023] However, it was only by using the aptamer-based biomarker
identification
method described herein, wherein over 800 separate potential biomarker values
were
individually screened from a large number of individuals who were
postoperatively
diagnosed as either having or not having ovarian cancer that it was possible
to identify the
ovarian cancer biomarkers disclosed herein. This discovery approach is in
stark contrast to
biomarker discovery using conditioned media or lysed cells as it queries a
more patient-
relevant system that requires no translation to human pathology.
[0024] Thus, in one aspect of the instant application, one or more biomarkers
are
provided for use either alone or in various combinations to diagnose ovarian
cancer or permit
the differential diagnosis of pelvic masses as benign or malignant. Exemplary
embodiments
include the biomarkers provided in Table 1, which as noted above, were
identified using a
multiplex aptamer-based assay, as described in Examples 1 and 2. The markers
provided in
Table 1 are useful in distinguishing benign pelvic masses from ovarian cancer.
[0025] While certain of the described ovarian cancer biomarkers are useful
alone for
detecting and diagnosing ovarian cancer, methods are also described herein for
the grouping
of multiple subsets of the ovarian cancer biomarkers that are each useful as a
panel of three or
more biomarkers. Thus, various embodiments of the instant application provide
combinations comprising N biomarkers, wherein N is at least two biomarkers. In
other
embodiments, N is selected to be any number from 2-42 biomarkers.
[0026] In yet other embodiments, N is selected to be any number from 2-7, 2-
10, 2-
15, 2-20, 2-25, 2-30, 2-35, 2-40, or 2-42. In other embodiments, N is selected
to be any
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number from 3-7, 3-10, 3-15, 3-20, 3-25, 3-30, 3-35, 3-40, or 3-42. In other
embodiments, N
is selected to be any number from 4-7, 4-10, 4-15, 4-20, 4-25, 4-30, 4-35, 4-
40, or 4-42. In
other embodiments, N is selected to be any number from 5-7, 5-10, 5-15, 5-20,
5-25, 5-30, 5-
35, 5-40, or 5-42. In other embodiments, N is selected to be any number from 6-
10, 6-15, 6-
20, 6-25, 6-30, 6-35, 6-40, or 6-42. In other embodiments, N is selected to be
any number
from 7-10, 7-15, 7-20, 7-25, 7-30, 7-35, 7-40, or 7-42. In other embodiments,
N is selected to
be any number from 8-10, 8-15, 8-20, 8-25, 8-30, 8-35, 8-40, or 8-42. In other
embodiments,
N is selected to be any number from 9-15, 9-20, 9-25, 9-30, 9-35, 9-40, or 9-
42. In other
embodiments, N is selected to be any number from 10-15, 10-20, 10-25, 10-30,
10-35, 10-40,
or 10-42. It will be appreciated that N can be selected to encompass similar,
but higher order,
ranges.
[0027] In another aspect, a method is provided for diagnosing ovarian cancer
in an
individual, the method including detecting, in a biological sample from an
individual, at least
one biomarker value corresponding to at least one biomarker selected from the
group of
biomarkers provided in Table 1, wherein the individual is classified as having
ovarian cancer
based on the at least one biomarker value.
[0028] In another aspect, a method is provided for diagnosing ovarian cancer
in an
individual, the method including detecting, in a biological sample from an
individual,
biomarker values that each correspond to one of at least N biomarkers selected
from the
group of biomarkers set forth in Table 1, wherein the likelihood of the
individual having
ovarian cancer is determined based on the biomarker values.
[0029] In another aspect, a method is provided for diagnosing ovarian cancer
in an
individual, the method including detecting, in a biological sample from an
individual,
biomarker values that each correspond to one of at least N biomarkers selected
from the
group of biomarkers set forth in Table 1, wherein the individual is classified
as having
ovarian cancer based on the biomarker values, and wherein N = 2-10.
[0030] In another aspect, a method is provided for diagnosing ovarian cancer
in an
individual, the method including detecting, in a biological sample from an
individual,
biomarker values that each correspond to one of at least N biomarkers selected
from the
group of biomarkers set forth in Table 1, wherein the likelihood of the
individual having
ovarian cancer is determined based on the biomarker values, and wherein N = 2-
10.
[0031] In another aspect, a method is provided for differentiating an
individual having
a benign pelvic mass from an individual having ovarian cancer, the method
including
detecting, in a biological sample from an individual, at least one biomarker
value
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corresponding to at least one biomarker selected from the group of biomarkers
set forth in
Table 1, wherein the individual is classified as having ovarian cancer, or the
likelihood of the
individual having ovarian cancer is determined, based on the at least one
biomarker value.
[0032] In another aspect, a method is provided for differentiating an
individual having
a benign pelvic mass from an individual having ovarian cancer, the method
including
detecting, in a biological sample from an individual, biomarker values that
each correspond
to one of at least N biomarkers selected from the group of biomarkers set
forth in Table 1,
wherein the individual is classified as having ovarian cancer, or the
likelihood of the
individual having ovarian cancer is determined, based on the biomarker values,
wherein N =
2-10.
[0033] In another aspect, a method is provided for diagnosing that an
individual does
not have ovarian cancer, the method including detecting, in a biological
sample from an
individual, at least one biomarker value corresponding to at least one
biomarker selected from
the group of biomarkers set forth in Table 1, wherein the individual is
classified as not having
ovarian cancer based on the at least one biomarker value.
[0034] In another aspect, a method is provided for diagnosing that an
individual does
not have ovarian cancer, the method including detecting, in a biological
sample from an
individual, biomarker values that each corresponding to one of at least N
biomarkers selected
from the group of biomarkers set forth in Table 1, wherein the individual is
classified as not
having ovarian cancer based on the biomarker values, and wherein N = 2-10.
[0035] In another aspect, a method is provided for diagnosing ovarian cancer,
the
method including detecting, in a biological sample from an individual,
biomarker values that
each correspond to a biomarker on a panel of N biomarkers, wherein the
biomarkers are
selected from the group of biomarkers set forth in Table 1, wherein a
classification of the
biomarker values indicates that the individual has ovarian cancer, and wherein
N = 3-10.
[0036] In another aspect, a method is provided for diagnosing ovarian cancer,
the
method including detecting, in a biological sample from an individual,
biomarker values that
each correspond to a biomarker on a panel of N biomarkers, wherein the
biomarkers are
selected from the group of biomarkers set forth in Table 1, wherein a
classification of the
biomarker values indicates that the individual has ovarian cancer, and wherein
N = 3-15.
[0037] In another aspect, a method is provided for diagnosing ovarian cancer,
the
method including detecting, in a biological sample from an individual,
biomarker values that
each correspond to a biomarker on a panel of biomarkers selected from the
group of panels
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set forth in Tables 2-14, wherein a classification of the biomarker values
indicates that the
individual has ovarian cancer.
[0038] In another aspect, a method is provided for differentiating an
individual having
a benign pelvic mass from an individual having ovarian cancer, the method
including
detecting, in a biological sample from an individual, biomarker values that
each correspond
to a biomarker on a panel of N biomarkers, wherein the biomarkers are selected
from the
group of biomarkers set forth in Table 1, wherein the individual is classified
as having
ovarian cancer, or the likelihood of the individual having ovarian cancer is
determined, based
on the biomarker values, and wherein N = 3-10.
[0039] In another aspect, a method is provided for differentiating an
individual having
a benign pelvic mass from an individual having ovarian cancer, the method
including
detecting, in a biological sample from an individual, biomarker values that
each correspond
to a biomarker on a panel of N biomarkers, wherein the biomarkers are selected
from the
group of biomarkers set forth in Table 1, wherein the individual is classified
as having
ovarian cancer, or the likelihood of the individual having ovarian cancer is
determined, based
on the biomarker values, and wherein N = 3-15.
[0040] In another aspect, a method is provided for diagnosing an absence of
ovarian
cancer, the method including detecting, in a biological sample from an
individual, biomarker
values that each correspond to a biomarker on a panel of N biomarkers, wherein
the
biomarkers are selected from the group of biomarkers set forth in Table 1,
wherein a
classification of the biomarker values indicates an absence of ovarian cancer
in the
individual, and wherein N = 3-10.
[0041] In another aspect, a method is provided for diagnosing an absence of
ovarian
cancer, the method including detecting, in a biological sample from an
individual, biomarker
values that each correspond to a biomarker on a panel of N biomarkers, wherein
the
biomarkers are selected from the group of biomarkers set forth in Table 1,
wherein a
classification of the biomarker values indicates an absence of ovarian cancer
in the
individual, and wherein N = 3-15.
[0042] In another aspect, a method is provided for diagnosing an absence of
ovarian
cancer, the method including detecting, in a biological sample from an
individual, biomarker
values that each correspond to a biomarker on a panel of biomarkers selected
from the group
of panels provided in Tables 2-14, wherein a classification of the biomarker
values indicates
an absence of ovarian cancer in the individual.
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[0043] In another aspect, a method is provided for diagnosing ovarian cancer
in an
individual, the method including detecting, in a biological sample from an
individual,
biomarker values that correspond to one of at least N biomarkers selected from
the group of
biomarkers set forth in Table 1, wherein the individual is classified as
having ovarian cancer
based on a classification score that deviates from a predetermined threshold,
and wherein
N=2-10.
[0044] In another aspect, a method is provided for differentiating an
individual having
a benign pelvic mass from an individual having ovarian cancer, the method
including
detecting, in a biological sample from an individual, biomarker values that
each correspond
to a biomarker on a panel of N biomarkers, wherein the biomarkers are selected
from the
group of biomarkers set forth in Table 1, wherein the individual is classified
as having
ovarian cancer, or the likelihood of the individual having ovarian cancer is
determined, based
on a classification score that deviates from a predetermined threshold, and
wherein N = 3-10.
[0045] In another aspect, a method is provided for differentiating an
individual having
a benign pelvic mass from an individual having ovarian cancer, the method
including
detecting, in a biological sample from an individual, biomarker values that
each correspond
to a biomarker on a panel of N biomarkers, wherein the biomarkers are selected
from the
group of biomarkers set forth in Table 1, wherein the individual is classified
as having
ovarian cancer, or the likelihood of the individual having ovarian cancer is
determined, based
on a classification score that deviates from a predetermined threshold,
wherein N = 3-15.
[0046] In another aspect, a method is provided for diagnosing an absence of
ovarian
cancer in an individual, the method including detecting, in a biological
sample from an
individual, biomarker values that correspond to one of at least N biomarkers
selected from
the group of biomarkers set forth in Table 1, wherein said individual is
classified as not
having ovarian cancer based on a classification score that deviates from a
predetermined
threshold, and wherein N=2-10.
[0047] In another aspect, a computer-implemented method is provided for
indicating
a likelihood of ovarian cancer. The method comprises: retrieving on a computer
biomarker
information for an individual, wherein the biomarker information comprises
biomarker
values that each correspond to one of at least N biomarkers, wherein N is as
defined above,
selected from the group of biomarkers set forth in Table 1; performing with
the computer a
classification of each of the biomarker values; and indicating a likelihood
that the individual
has ovarian cancer based upon a plurality of classifications.
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[0048] In another aspect, a computer-implemented method is provided for
classifying
an individual as either having or not having ovarian cancer. The method
comprises:
retrieving on a computer biomarker information for an individual, wherein the
biomarker
information comprises biomarker values that each correspond to one of at least
N biomarkers
selected from the group of biomarkers provided in Table 1; performing with the
computer a
classification of each of the biomarker values; and indicating whether the
individual has
ovarian cancer based upon a plurality of classifications.
[0049] In another aspect, a computer program product is provided for
indicating a
likelihood of ovarian cancer. The computer program product includes a computer
readable
medium embodying program code executable by a processor of a computing device
or
system, the program code comprising: code that retrieves data attributed to a
biological
sample from an individual, wherein the data comprises biomarker values that
each
correspond to one of at least N biomarkers, wherein N is as defined above, in
the biological
sample selected from the group of biomarkers set forth in Table 1; and code
that executes a
classification method that indicates a likelihood that the individual has
ovarian cancer as a
function of the biomarker values.
[0050] In another aspect, a computer program product is provided for
indicating an
ovarian cancer status of an individual. The computer program product includes
a computer
readable medium embodying program code executable by a processor of a
computing device
or system, the program code comprising: code that retrieves data attributed to
a biological
sample from an individual, wherein the data comprises biomarker values that
each
correspond to one of at least N biomarkers in the biological sample selected
from the group
of biomarkers provided in Table 1; and code that executes a classification
method that
indicates an ovarian cancer status of the individual as a function of the
biomarker values.
[0051] In another aspect, a computer-implemented method is provided for
indicating
a likelihood of ovarian cancer. The method comprises retrieving on a computer
biomarker
information for an individual, wherein the biomarker information comprises a
biomarker
value corresponding to a biomarker selected from the group of biomarkers set
forth in Table
1; performing with the computer a classification of the biomarker value; and
indicating a
likelihood that the individual has ovarian cancer based upon the
classification.
[0052] In another aspect, a computer-implemented method is provided for
classifying
an individual as either having or not having ovarian cancer. The method
comprises
retrieving, from a computer, biomarker information for an individual, wherein
the biomarker
information comprises a biomarker value corresponding to a biomarker selected
from the
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group of biomarkers provided in Table 1; performing with the computer a
classification of the
biomarker value; and indicating whether the individual has ovarian cancer
based upon the
classification.
[0053] In still another aspect, a computer program product is provided for
indicating a
likelihood of ovarian cancer. The computer program product includes a computer
readable
medium embodying program code executable by a processor of a computing device
or
system, the program code comprising: code that retrieves data attributed to a
biological
sample from an individual, wherein the data comprises a biomarker value
corresponding to a
biomarker in the biological sample selected from the group of biomarkers set
forth in Table
1; and code that executes a classification method that indicates a likelihood
that the individual
has ovarian cancer as a function of the biomarker value.
[0054] In still another aspect, a computer program product is provided for
indicating
an ovarian cancer status of an individual. The computer program product
includes a
computer readable medium embodying program code executable by a processor of a
computing device or system, the program code comprising: code that retrieves
data attributed
to a biological sample from an individual, wherein the data comprises a
biomarker value
corresponding to a biomarker in the biological sample selected from the group
of biomarkers
provided in Table 1; and code that executes a classification method that
indicates an ovarian
cancer status of the individual as a function of the biomarker value.
BRIEF DESCRIPTION OF THE DRAWINGS
[0055] Figure IA is a flowchart for an exemplary method for detecting ovarian
cancer
in a biological sample.
[0056] Figure lB is a flowchart for an exemplary method for detecting ovarian
cancer
in a biological sample using a naive Bayes classification method.
[0057] Figure 2 shows a ROC curve for a single biomarker, BAFF Receptor, using
a
naive Bayes classifier for a test that detects ovarian cancer in women with
pelvis masses.
[0058] Figure 3 shows ROC curves for biomarker panels of from one to ten
biomarkers using naive Bayes classifiers for a test that detects ovarian
cancer in women with
pelvis masses.
[0059] Figure 4 illustrates the increase in the classification score
(specificity +
sensitivity) as the number of biomarkers is increased from one to ten using
naive Bayes
classification for an ovarian cancer panel.
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[0060] Figure 5 shows the measured biomarker distributions for BAFF Receptor
as a
cumulative distribution function (cdf) in RFU for the benign control group
(solid line) and the
ovarian cancer disease group (dotted line) along with their curve fits to a
normal cdf (dashed
lines) used to train the naive Bayes classifiers.
[0061] Figure 6 illustrates an exemplary computer system for use with various
computer-implemented methods described herein.
[0062] Figure 7 is a flowchart for a method of indicating the likelihood that
an
individual has ovarian cancer in accordance with one embodiment.
[0063] Figure 8 is a flowchart for a method of indicating the likelihood that
an
individual has ovarian cancer in accordance with one embodiment.
[0064] Figure 9 illustrates an exemplary aptamer assay that can be used to
detect one
or more ovarian cancer biomarkers in a biological sample.
[0065] Figure 10 shows a histogram of frequencies for which biomarkers were
used
in building classifiers to distinguish between ovarian cancer and benign
pelvic masses from
an aggregated set of potential biomarkers.
[0066] Figure 11 shows a histogram of frequencies for which biomarkers were
used
in building classifiers to distinguish between ovarian cancer and benign
pelvic masses from a
site-consistent set of potential biomarkers.
[0067] Figure 12 shows a histogram of frequencies for which biomarkers were
used
in building classifiers to distinguish between ovarian cancer and benign
pelvic masses from a
set of potential biomarkers resulting from a combination of aggregated and
site-consistent
markers.
[0068] Figure 13 shows gel images resulting from pull-down experiments that
illustrate the specificity of aptamers as capture reagents for the proteins
LBP, C9 and IgM.
For each gel, lane 1 is the eluate from the Streptavidin-agarose beads, lane 2
is the final
eluate, and lane is a MW marker lane (major bands are at 110, 50, 30, 15, and
3.5 kDa from
top to bottom).
[0069] Figure 14A shows a pair of histograms summarizing all possible single
protein
naive Bayes classifier scores (sensitivity + specificity) using the biomarkers
set forth in Table
1 (solid) and a set of random non-markers (dotted).
[0070] Figure 14B shows a pair of histograms summarizing all possible two-
protein
protein naive Bayes classifier scores (sensitivity + specificity) using the
biomarkers set forth
in Table 1 (solid) and a set of random non-markers (dotted).
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[0071] Figure 14C shows a pair of histograms summarizing all possible three-
protein
naive Bayes classifier scores (sensitivity + specificity) using the biomarkers
set forth in Table
1 (solid) and a set of non-random markers (dotted).
[0072] Figure 15 shows the sensitivity + specificity score for naive Bayes
classifiers
using from 2-10 markers selected from the full panel (9) and the scores
obtained by dropping
the best 5 (^), 10 (A) and 15 (1) markers during classifier generation.
[0073] Figure 16A shows a set of ROC curves modeled from the data in Table 18
for
panels of from one to five markers.
[0074] Figure 16B shows a set of ROC curves computed from the training data
for
panels of from one to five markers as in Figure 16A.
DETAILED DESCRIPTION
[0075] Reference will now be made in detail to representative embodiments of
the
invention. While the invention will be described in conjunction with the
enumerated
embodiments, it will be understood that the invention is not intended to be
limited to those
embodiments. On the contrary, the invention is intended to cover all
alternatives,
modifications, and equivalents that may be included within the scope of the
present invention
as defined by the claims.
[0076] One skilled in the art will recognize many methods and materials
similar or
equivalent to those described herein, which could be used in and are within
the scope of the
practice of the present invention. The present invention is in no way limited
to the methods
and materials described.
[0077] Unless defined otherwise, technical and scientific terms used herein
have the
same meaning as commonly understood by one of ordinary skill in the art to
which this
invention belongs. Although any methods, devices, and materials similar or
equivalent to
those described herein can be used in the practice or testing of the
invention, the preferred
methods, devices and materials are now described.
[0078] All publications, published patent documents, and patent applications
cited in
this application are indicative of the level of skill in the art(s) to which
the application
pertains. All publications, published patent documents, and patent
applications cited herein
are hereby incorporated by reference to the same extent as though each
individual
publication, published patent document, or patent application was specifically
and
individually indicated as being incorporated by reference.
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[0079] As used in this application, including the appended claims, the
singular forms
"a," "an," and "the" include plural references, unless the content clearly
dictates otherwise,
and are used interchangeably with "at least one" and "one or more." Thus,
reference to "an
aptamer" includes mixtures of aptamers, reference to "a probe" includes
mixtures of probes,
and the like.
[0080] As used herein, the term "about" represents an insignificant
modification or
variation of the numerical value such that the basic function of the item to
which the
numerical value relates is unchanged.
[0081] As used herein, the terms "comprises," "comprising," "includes,"
"including,"
"contains," "containing," and any variations thereof, are intended to cover a
non-exclusive
inclusion, such that a process, method, product-by-process, or composition of
matter that
comprises, includes, or contains an element or list of elements does not
include only those
elements but may include other elements not expressly listed or inherent to
such process,
method, product-by-process, or composition of matter.
[0082] The present application includes biomarkers, methods, devices,
reagents,
systems, and kits for the detection and diagnosis of ovarian cancer.
[0083] In one aspect, one or more biomarkers are provided for use either alone
or in
various combinations to diagnose ovarian cancer, permit the differential
diagnosis of pelvic
masses as benign or malignant, monitor ovarian cancer recurrence, or address
other clinical
indications. As described in detail below, exemplary embodiments include the
biomarkers
provided in Table 1, which were identified using a multiplex aptamer-based
assay, as
described generally in Example 1 and more specifically in Example 2.
[0084] Table 1 sets forth the findings obtained from analyzing blood samples
from
142 individuals diagnosed with ovarian cancer and blood samples from 195
individuals
diagnosed with a benign pelvic mass. The benign pelvic mass group was designed
to match
the population with which an ovarian cancer diagnostic test can have
significant benefit.
(These cases and controls were obtained from two clinical sites). The
potential biomarkers
were measured in individual samples rather than pooling the disease and
control blood; this
allowed a better understanding of the individual and group variations in the
phenotypes
associated with the presence and absence of disease (in this case ovarian
cancer). Since over
800 protein measurements were made on each sample, and 337 samples from both
the disease
and the control populations were individually measured, Table 1 resulted from
an analysis of
an uncommonly large set of data. The measurements were analyzed using the
methods
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described in the section, "Classification of Biomarkers and Calculation of
Disease Scores"
herein.
[0085] Table 1 lists the biomarkers found to be useful in distinguishing
samples
obtained from individuals with ovarian cancer from "control" samples obtained
from
individuals with benign pelvic masses. Using a multiplex aptamer assay, forty-
two
biomarkers were discovered that distinguished samples obtained from
individuals with
ovarian cancer from samples obtained from people who had benign pelvic masses
(see Table
1).
[0086] While certain of the described ovarian cancer biomarkers are useful
alone for
detecting and diagnosing ovarian cancer, methods are also described herein for
the grouping
of multiple subsets of the ovarian cancer biomarkers, where each grouping or
subset selection
is useful as a panel of three or more biomarkers, interchangeably referred to
herein as a
"biomarker panel" and a panel. Thus, various embodiments of the instant
application provide
combinations comprising N biomarkers, wherein N is at least two biomarkers. In
other
embodiments, N is selected from 2-42 biomarkers.
[0087] In yet other embodiments, N is selected to be any number from 2-7, 2-
10, 2-
15, 2-20, 2-25, 2-30, 2-35, 2-40, or 2-42. In other embodiments, N is selected
to be any
number from 3-7, 3-10, 3-15, 3-20, 3-25, 3-30, 3-35, 3-40, or 3-42. In other
embodiments, N
is selected to be any number from 4-7, 4-10, 4-15, 4-20, 4-25, 4-30, 4-35, 4-
40, or 4-42. In
other embodiments, N is selected to be any number from 5-7, 5-10, 5-15, 5-20,
5-25, 5-30, 5-
35, 5-40, or 5-42. In other embodiments, N is selected to be any number from 6-
10, 6-15, 6-
20, 6-25, 6-30, 6-35, 6-40, or 6-42. In other embodiments, N is selected to be
any number
from 7-10, 7-15, 7-20, 7-25, 7-30, 7-35, 7-40, or 7-42. In other embodiments,
N is selected to
be any number from 8-10, 8-15, 8-20, 8-25, 8-30, 8-35, 8-40, or 8-42. In other
embodiments,
N is selected to be any number from 9-15, 9-20, 9-25, 9-30, 9-35, 9-40, or 9-
42. In other
embodiments, N is selected to be any number from 10-15, 10-20, 10-25, 10-30,
10-35, 10-40,
or 10-42. It will be appreciated that N can be selected to encompass similar,
but higher order,
ranges.
[0088] In one embodiment, the number of biomarkers useful for a biomarker
subset
or panel is based on the sensitivity and specificity value for the particular
combination of
biomarker values. The terms "sensitivity" and "specificity" are used herein
with respect to
the ability to correctly classify an individual, based on one or more
biomarker values detected
in their biological sample, as having ovarian cancer or not having ovarian
cancer.
"Sensitivity" indicates the performance of the biomarker(s) with respect to
correctly
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classifying individuals that have ovarian cancer. "Specificity" indicates the
performance of
the biomarker(s) with respect to correctly classifying individuals who do not
have ovarian
cancer. For example, 85% specificity and 90% sensitivity for a panel of
markers used to test
a set of control samples and ovarian cancer samples indicates that 85% of the
control samples
were correctly classified as control samples by the panel, and 90% of the
ovarian cancer
samples were correctly classified as ovarian cancer samples by the panel. The
desired or
preferred minimum value can be determined as described in Example 3.
Representative
panels are set forth in Tables 2-14, which set forth a series of 100 different
panels of 3-15
biomarkers, which have the indicated levels of specificity and sensitivity for
each panel. The
total number of occurrences of each marker in each of these panels is
indicated at the bottom
of each Table.
[0089] In one aspect, ovarian cancer is detected or diagnosed in an individual
by
conducting an assay on a biological sample from the individual and detecting
biomarker
values that each correspond to at least one of the biomarkers SLPI, C9, HGF
and RGM-C and
at least N additional biomarkers selected from the list of biomarkers in Table
1, wherein N
equals 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14 or 15. In a further aspect,
ovarian cancer is
detected or diagnosed in an individual by conducting an assay on a biological
sample from
the individual and detecting biomarker values that each correspond to the
biomarkers SLPI,
C9, HGF and RGM-C and one of at least N additional biomarkers selected from
the list of
biomarkers in Table 1, wherein N equals 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12
or 13. In a further
aspect, ovarian cancer is detected or diagnosed in an individual by conducting
an assay on a
biological sample from the individual and detecting biomarker values that each
correspond to
the biomarker SLPI and one of at least N additional biomarkers selected from
the list of
biomarkers in Table 1, wherein N equals 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,
13, 14 or 15. In a
further aspect, ovarian cancer is detected or diagnosed in an individual by
conducting an
assay on a biological sample from the individual and detecting biomarker
values that each
correspond to the biomarker C9and one of at least N additional biomarkers
selected from the
list of biomarkers in Table 1, wherein N equals 2, 3, 4, 5, 6, 7, 8, 9, 10,
11, 12, 13, 14 or 15.
In a further aspect, ovarian cancer is detected or diagnosed in an individual
by conducting an
assay on a biological sample from the individual and detecting biomarker
values that each
correspond to the biomarker HGF and one of at least N additional biomarkers
selected from
the list of biomarkers in Table 1, wherein N equals 2, 3, 4, 5, 6, 7, 8, 9,
10, 11, 12, 13, 14 or
15. In a further aspect, ovarian cancer is detected or diagnosed in an
individual by
conducting an assay on a biological sample from the individual and detecting
biomarker
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values that each correspond to the biomarker RGM-C and one of at least N
additional
biomarkers selected from the list of biomarkers in Table 1, wherein N equals
2, 3, 4, 5, 6, 7,
8, 9, 10, 11, 12, 13, 14 or 15.
[0090] The ovarian cancer biomarkers identified herein represent a relatively
large
number of choices for subsets or panels of biomarkers that can be used to
effectively detect or
diagnose ovarian cancer. Selection of the desired number of such biomarkers
depends on the
specific combination of biomarkers chosen. It is important to remember that
panels of
biomarkers for detecting or diagnosing ovarian cancer may also include
biomarkers not found
in Table 1, and that the inclusion of additional biomarkers not found in Table
1 may reduce
the number of biomarkers in the particular subset or panel that is selected
from Table 1. The
number of biomarkers from Table 1 used in a subset or panel may also be
reduced if
additional biomedical information is used in conjunction with the biomarker
values to
establish acceptable sensitivity and specificity values for a given assay.
[0091] Another factor that can affect the number of biomarkers to be used in a
subset
or panel of biomarkers is the procedures used to obtain biological samples
from individuals
who are being evaluated for ovarian cancer. In a carefully controlled sample
procurement
environment, the number of biomarkers necessary to meet desired sensitivity
and specificity
values will be lower than in a situation where there can be more variation in
sample
collection, handling and storage. In developing the list of biomarkers set
forth in Table 1,
two sample collection sites were utilized to collect data for classifier
training.
[0092] One aspect of the instant application can be described generally with
reference
to Figures IA and B. A biological sample is obtained from an individual or
individuals of
interest. The biological sample is then assayed to detect the presence of one
or more (N)
biomarkers of interest and to determine a biomarker value for each of said N
biomarkers
(referred to in Figure lB as marker RFU (relative fluorescence units)). Once a
biomarker has
been detected and a biomarker value assigned each marker is scored or
classified as described
in detail herein. The marker scores are then combined to provide a total
diagnostic score,
which indicates the likelihood that the individual from whom the sample was
obtained has
ovarian cancer.
[0093] "Biological sample", "sample", and "test sample" are used
interchangeably
herein to refer to any material, biological fluid, tissue, or cell obtained or
otherwise derived
from an individual. This includes blood (including whole blood, leukocytes,
peripheral blood
mononuclear cells, buffy coat, plasma, and serum), sputum, tears, mucus, nasal
washes, nasal
aspirate, breath, urine, semen, saliva, meningeal fluid, amniotic fluid,
glandular fluid, lymph
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fluid, nipple aspirate, bronchial aspirate, synovial fluid, joint aspirate,
ascites, cells, a cellular
extract, and cerebrospinal fluid. This also includes experimentally separated
fractions of all
of the preceding. For example, a blood sample can be fractionated into serum
or into
fractions containing particular types of blood cells, such as red blood cells
or white blood
cells (leukocytes). If desired, a sample can be a combination of samples from
an individual,
such as a combination of a tissue and fluid sample. The term "biological
sample" also
includes materials containing homogenized solid material, such as from a stool
sample, a
tissue sample, or a tissue biopsy, for example. The term "biological sample"
also includes
materials derived from a tissue culture or a cell culture. Any suitable
methods for obtaining a
biological sample can be employed; exemplary methods include, e.g.,
phlebotomy, swab
(e.g., buccal swab), and a fine needle aspirate biopsy procedure. Samples can
also be
collected, e.g., by micro dissection (e.g., laser capture micro dissection
(LCM) or laser micro
dissection (LMD)), bladder wash, smear (e.g., a PAP smear), or ductal lavage.
A "biological
sample" obtained or derived from an individual includes any such sample that
has been
processed in any suitable manner after being obtained from the individual.
[0094] Further, it should be realized that a biological sample can be derived
by taking
biological samples from a number of individuals and pooling them or pooling an
aliquot of
each individual's biological sample. The pooled sample can be treated as a
sample from a
single individual and if the presence of cancer is established in the pooled
sample, then each
individual biological sample can be re-tested to determine which individuals
have ovarian
cancer.
[0095] For purposes of this specification, the phrase "data attributed to a
biological
sample from an individual" is intended to mean that the data in some form
derived from, or
were generated using, the biological sample of the individual. The data may
have been
reformatted, revised, or mathematically altered to some degree after having
been generated,
such as by conversion from units in one measurement system to units in another
measurement
system; but, the data are understood to have been derived from, or were
generated using, the
biological sample.
[0096] "Target", "target molecule", and "analyte" are used interchangeably
herein to
refer to any molecule of interest that may be present in a biological sample.
A "molecule of
interest" includes any minor variation of a particular molecule, such as, in
the case of a
protein, for example, minor variations in amino acid sequence, disulfide bond
formation,
glycosylation, lipidation, acetylation, phosphorylation, or any other
manipulation or
modification, such as conjugation with a labeling component, which does not
substantially
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alter the identity of the molecule. A "target molecule", "target", or
"analyte" is a set of copies
of one type or species of molecule or multi-molecular structure. "Target
molecules",
"targets", and "analytes" refer to more than one such set of molecules.
Exemplary target
molecules include proteins, polypeptides, nucleic acids, carbohydrates,
lipids,
polysaccharides, glycoproteins, hormones, receptors, antigens, antibodies,
affybodies,
antibody mimics, viruses, pathogens, toxic substances, substrates,
metabolites, transition state
analogs, cofactors, inhibitors, drugs, dyes, nutrients, growth factors, cells,
tissues, and any
fragment or portion of any of the foregoing.
[0097] As used herein, "polypeptide," "peptide," and "protein" are used
interchangeably herein to refer to polymers of amino acids of any length. The
polymer may
be linear or branched, it may comprise modified amino acids, and it may be
interrupted by
non-amino acids. The terms also encompass an amino acid polymer that has been
modified
naturally or by intervention; for example, disulfide bond formation,
glycosylation, lipidation,
acetylation, phosphorylation, or any other manipulation or modification, such
as conjugation
with a labeling component. Also included within the definition are, for
example,
polypeptides containing one or more analogs of an amino acid (including, for
example,
unnatural amino acids, etc.), as well as other modifications known in the art.
Polypeptides
can be single chains or associated chains. Also included within the definition
are preproteins
and intact mature proteins; peptides or polypeptides derived from a mature
protein; fragments
of a protein; splice variants; recombinant forms of a protein; protein
variants with amino acid
modifications, deletions, or substitutions; digests; and post-translational
modifications, such
as glycosylation, acetylation, phosphorylation, and the like.
[0098] As used herein, "thrombin" refers to thrombin, prothrombin, or both
thrombin
and prothrombin.
[0099] As used herein, "marker" and "biomarker" are used interchangeably to
refer to
a target molecule that indicates or is a sign of a normal or abnormal process
in an individual
or of a disease or other condition in an individual. More specifically, a
"marker" or
"biomarker" is an anatomic, physiologic, biochemical, or molecular parameter
associated
with the presence of a specific physiological state or process, whether normal
or abnormal,
and, if abnormal, whether chronic or acute. Biomarkers are detectable and
measurable by a
variety of methods including laboratory assays and medical imaging. When a
biomarker is a
protein, it is also possible to use the expression of the corresponding gene
as a surrogate
measure of the amount or presence or absence of the corresponding protein
biomarker in a
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biological sample or methylation state of the gene encoding the biomarker or
proteins that
control expression of the biomarker.
[00100] As used herein, "biomarker value", "value", "biomarker level", and
"level" are
used interchangeably to refer to a measurement that is made using any
analytical method for
detecting the biomarker in a biological sample and that indicates the
presence, absence,
absolute amount or concentration, relative amount or concentration, titer, a
level, an
expression level, a ratio of measured levels, or the like, of, for, or
corresponding to the
biomarker in the biological sample. The exact nature of the "value" or "level"
depends on the
specific design and components of the particular analytical method employed to
detect the
biomarker.
[00101] When a biomarker indicates or is a sign of an abnormal process or a
disease or
other condition in an individual, that biomarker is generally described as
being either over-
expressed or under-expressed as compared to an expression level or value of
the biomarker
that indicates or is a sign of a normal process or an absence of a disease or
other condition in
an individual. "Up-regulation", "up-regulated", "over-expression", "over-
expressed", and any
variations thereof are used interchangeably to refer to a value or level of a
biomarker in a
biological sample that is greater than a value or level (or range of values or
levels) of the
biomarker that is typically detected in similar biological samples from
healthy or normal
individuals. The terms may also refer to a value or level of a biomarker in a
biological
sample that is greater than a value or level (or range of values or levels) of
the biomarker that
may be detected at a different stage of a particular disease.
[00102] "Down-regulation", "down-regulated", "under-expression", "under-
expressed",
and any variations thereof are used interchangeably to refer to a value or
level of a biomarker
in a biological sample that is less than a value or level (or range of values
or levels) of the
biomarker that is typically detected in similar biological samples from
healthy or normal
individuals. The terms may also refer to a value or level of a biomarker in a
biological
sample that is less than a value or level (or range of values or levels) of
the biomarker that
may be detected at a different stage of a particular disease.
[00103] Further, a biomarker that is either over-expressed or under-expressed
can also
be referred to as being "differentially expressed" or as having a
"differential level" or
"differential value" as compared to a "normal" expression level or value of
the biomarker that
indicates or is a sign of a normal process or an absence of a disease or other
condition in an
individual. Thus, "differential expression" of a biomarker can also be
referred to as a
variation from a "normal" expression level of the biomarker.
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[00104] The term "differential gene expression" and "differential expression"
are used
interchangeably to refer to a gene (or its corresponding protein expression
product) whose
expression is activated to a higher or lower level in a subject suffering from
a specific
disease, relative to its expression in a normal or control subject. The terms
also include genes
(or the corresponding protein expression products) whose expression is
activated to a higher
or lower level at different stages of the same disease. It is also understood
that a
differentially expressed gene may be either activated or inhibited at the
nucleic acid level or
protein level, or may be subject to alternative splicing to result in a
different polypeptide
product. Such differences may be evidenced by a variety of changes including
mRNA levels,
surface expression, secretion or other partitioning of a polypeptide.
Differential gene
expression may include a comparison of expression between two or more genes or
their gene
products; or a comparison of the ratios of the expression between two or more
genes or their
gene products; or even a comparison of two differently processed products of
the same gene,
which differ between normal subjects and subjects suffering from a disease; or
between
various stages of the same disease. Differential expression includes both
quantitative, as well
as qualitative, differences in the temporal or cellular expression pattern in
a gene or its
expression products among, for example, normal and diseased cells, or among
cells which
have undergone different disease events or disease stages.
[00105] As used herein, "individual" refers to a test subject or patient. The
individual
can be a mammal or a non-mammal. In various embodiments, the individual is a
mammal.
A mammalian individual can be a human or non-human. In various embodiments,
the
individual is a human. A healthy or normal individual is an individual in
which the disease or
condition of interest (including, for example, ovarian diseases, ovarian -
associated diseases,
or other ovarian conditions) is not detectable by conventional diagnostic
methods.
[00106] "Diagnose", "diagnosing", "diagnosis", and variations thereof refer to
the
detection, determination, or recognition of a health status or condition of an
individual on the
basis of one or more signs, symptoms, data, or other information pertaining to
that individual.
The health status of an individual can be diagnosed as healthy / normal (i.e.,
a diagnosis of
the absence of a disease or condition) or diagnosed as ill / abnormal (i.e., a
diagnosis of the
presence, or an assessment of the characteristics, of a disease or condition).
The terms
"diagnose", "diagnosing", "diagnosis", etc., encompass, with respect to a
particular disease or
condition, the initial detection of the disease; the characterization or
classification of the
disease; the detection of the progression, remission, or recurrence of the
disease; and the
detection of disease response after the administration of a treatment or
therapy to the
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individual. The diagnosis of ovarian cancer includes distinguishing
individuals who have
cancer from individuals who do not. It further includes distinguishing benign
pelvic masses
from ovarian cancer.
[00107] "Prognose", "prognosing", "prognosis", and variations thereof refer to
the
prediction of a future course of a disease or condition in an individual who
has the disease or
condition (e.g., predicting patient survival), and such terms encompass the
evaluation of
disease response after the administration of a treatment or therapy to the
individual.
[00108] "Evaluate", "evaluating", "evaluation", and variations thereof
encompass both
"diagnose" and "prognose" and also encompass determinations or predictions
about the future
course of a disease or condition in an individual who does not have the
disease as well as
determinations or predictions regarding the likelihood that a disease or
condition will recur in
an individual who apparently has been cured of the disease. The term
"evaluate" also
encompasses assessing an individual's response to a therapy, such as, for
example, predicting
whether an individual is likely to respond favorably to a therapeutic agent or
is unlikely to
respond to a therapeutic agent (or will experience toxic or other undesirable
side effects, for
example), selecting a therapeutic agent for administration to an individual,
or monitoring or
determining an individual's response to a therapy that has been administered
to the individual.
Thus, "evaluating" ovarian cancer can include, for example, any of the
following:
prognosing the future course of ovarian cancer in an individual; predicting
the recurrence of
ovarian cancer in an individual who apparently has been cured of ovarian
cancer; or
determining or predicting an individual's response to an ovarian cancer
treatment or selecting
an ovarian cancer treatment to administer to an individual based upon a
determination of the
biomarker values derived from the individual's biological sample.
[00109] Any of the following examples may be referred to as either
"diagnosing" or
"evaluating" ovarian cancer: initially detecting the presence or absence of
ovarian cancer;
determining a specific stage, type or sub-type, or other classification or
characteristic of
ovarian cancer; determining whether a pelvic mass is benign or malignant; or
detecting or
monitoring ovarian cancer progression (e.g., monitoring ovarian tumor growth
or metastatic
spread), remission, or recurrence.
[00110] As used herein, "additional biomedical information" refers to one or
more
evaluations of an individual, other than using any of the biomarkers described
herein, that are
associated with ovarian cancer risk. "Additional biomedical information"
includes any of the
following: physical descriptors of an individual; physical descriptors of a
pelvic mass
observed by MRI, abdominal ultrasound, or CT imaging; the height and/or weight
of an
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individual; change in weight; the ethnicity of an individual; occupational
history; family
history of ovarian cancer (or other cancer); the presence of a genetic
marker(s) correlating
with a higher risk of ovarian cancer in the individual or a family member; the
presence of a
pelvic mass; size of mass; location of mass; morphology of mass and associated
pelvic region
(e.g., as observed through imaging); clinical symptoms such as bloating,
abdominal pain, or
sudden weight gain or loss; and the like. Additional biomedical information
can be obtained
from an individual using routine techniques known in the art, such as from the
individual
themselves by use of a routine patient questionnaire or health history
questionnaire, etc., or
from a medical practitioner, etc. Alternately, additional biomedical
information can be
obtained from routine imaging techniques, including abdominal or transvaginal
ultrasound,
MRI, CT imaging, and PET-CT. Testing of biomarker levels in combination with
an
evaluation of any additional biomedical information, including other
laboratory tests (e.g.,
CA- 125 testing), may, for example, improve sensitivity, specificity, and/or
AUC for detecting
ovarian cancer (or other ovarian cancer-related uses) as compared to biomarker
testing alone
or evaluating any particular item of additional biomedical information alone
(e.g., ultrasound
imaging alone).
[00111] The term "area under the curve" or "AUC" refers to the area under the
curve of
a receiver operating characteristic (ROC) curve, both of which are well known
in the art.
AUC measures are useful for comparing the accuracy of a classifier across the
complete data
range. Classifiers with a greater AUC have a greater capacity to classify
unknowns correctly
between two groups of interest (e.g., ovarian cancer samples and normal or
control samples).
ROC curves are useful for plotting the performance of a particular feature
(e.g., any of the
biomarkers described herein and/or any item of additional biomedical
information) in
distinguishing between two populations (e.g., cases having ovarian cancer and
controls
without ovarian cancer). Typically, the feature data across the entire
population (e.g., the
cases and controls) are sorted in ascending order based on the value of a
single feature. Then,
for each value for that feature, the true positive and false positive rates
for the data are
calculated. The true positive rate is determined by counting the number of
cases above the
value for that feature and then dividing by the total number of cases. The
false positive rate
is determined by counting the number of controls above the value for that
feature and then
dividing by the total number of controls. Although this definition refers to
scenarios in which
a feature is elevated in cases compared to controls, this definition also
applies to scenarios in
which a feature is lower in cases compared to the controls (in such a
scenario, samples below
the value for that feature would be counted). ROC curves can be generated for
a single
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feature as well as for other single outputs, for example, a combination of two
or more features
can be mathematically combined (e.g., added, subtracted, multiplied, etc.) to
provide a single
sum value, and this single sum value can be plotted in a ROC curve.
Additionally, any
combination of multiple features, in which the combination derives a single
output value, can
be plotted in a ROC curve. These combinations of features may comprise a test.
The ROC
curve is the plot of the true positive rate (sensitivity) of a test against
the false positive rate
(1-specificity) of the test.
[00112] As used herein, "detecting" or "determining" with respect to a
biomarker value
includes the use of both the instrument required to observe and record a
signal corresponding
to a biomarker value and the material/s required to generate that signal. In
various
embodiments, the biomarker value is detected using any suitable method,
including
fluorescence, chemiluminescence, surface plasmon resonance, surface acoustic
waves, mass
spectrometry, infrared spectroscopy, Raman spectroscopy, atomic force
microscopy,
scanning tunneling microscopy, electrochemical detection methods, nuclear
magnetic
resonance, quantum dots, and the like.
[00113] "Solid support" refers herein to any substrate having a surface to
which
molecules may be attached, directly or indirectly, through either covalent or
non-covalent
bonds. A "solid support" can have a variety of physical formats, which can
include, for
example, a membrane; a chip (e.g., a protein chip); a slide (e.g., a glass
slide or coverslip); a
column; a hollow, solid, semi-solid, pore- or cavity- containing particle,
such as, for example,
a bead; a gel; a fiber, including a fiber optic material; a matrix; and a
sample receptacle.
Exemplary sample receptacles include sample wells, tubes, capillaries, vials,
and any other
vessel, groove or indentation capable of holding a sample. A sample receptacle
can be
contained on a multi-sample platform, such as a microtiter plate, slide,
microfluidics device,
and the like. A support can be composed of a natural or synthetic material, an
organic or
inorganic material. The composition of the solid support on which capture
reagents are
attached generally depends on the method of attachment (e.g., covalent
attachment). Other
exemplary receptacles include microdroplets and microfluidic controlled or
bulk oil/aqueous
emulsions within which assays and related manipulations can occur. Suitable
solid supports
include, for example, plastics, resins, polysaccharides, silica or silica-
based materials,
functionalized glass, modified silicon, carbon, metals, inorganic glasses,
membranes, nylon,
natural fibers (such as, for example, silk, wool and cotton), polymers, and
the like. The
material composing the solid support can include reactive groups such as, for
example,
carboxy, amino, or hydroxyl groups, which are used for attachment of the
capture reagents.
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Polymeric solid supports can include, e.g., polystyrene, polyethylene glycol
tetraphthalate,
polyvinyl acetate, polyvinyl chloride, polyvinyl pyrrolidone,
polyacrylonitrile, polymethyl
methacrylate, polytetrafluoroethylene, butyl rubber, styrenebutadiene rubber,
natural rubber,
polyethylene, polypropylene, (poly)tetrafluoroethylene,
(poly)vinylidenefluoride,
polycarbonate, and polymethylpentene. Suitable solid support particles that
can be used
include, e.g., encoded particles, such as Luminex -type encoded particles,
magnetic particles,
and glass particles.
Exemplary Uses of Biomarkers
[00114] In various exemplary embodiments, methods are provided for diagnosing
ovarian cancer in an individual by detecting one or more biomarker values
corresponding to
one or more biomarkers that are present in the circulation of an individual,
such as in serum
or plasma, by any number of analytical methods, including any of the
analytical methods
described herein. These biomarkers are, for example, differentially expressed
in individuals
with ovarian cancer as compared to individuals without ovarian cancer.
Detection of the
differential expression of a biomarker in an individual can be used, for
example, to permit the
early diagnosis of ovarian cancer, to distinguish between a benign pelvic mass
and ovarian
cancer (such as, for example, a mass observed on an abdominal ultrasound or
computed
tomography (CT) scan), to monitor ovarian cancer recurrence, or for other
clinical
indications.
[00115] Any of the biomarkers described herein may be used in a variety of
clinical
indications for ovarian cancer, including any of the following: detection of
ovarian cancer
(such as in a high-risk individual or population); characterizing ovarian
cancer (e.g.,
determining ovarian cancer type, sub-type, or stage), such as by determining
whether a pelvic
mass is benign or malignant; determining ovarian cancer prognosis; monitoring
ovarian
cancer progression or remission; monitoring for ovarian cancer recurrence;
monitoring
metastasis; treatment selection (e.g., pre- or post-operative chemotherapy
selection);
monitoring response to a therapeutic agent or other treatment; combining
biomarker testing
with additional biomedical information, such as CA- 125 level, the presence of
a genetic
marker(s) indicating a higher risk for ovarian cancer, etc., or with mass
size, morphology,
presence of ascites, etc. (such as to provide an assay with increased
diagnostic performance
compared to CA-125 testing or other biomarker testing alone); facilitating the
diagnosis of a
pelvic mass as malignant or benign; facilitating clinical decision making once
a pelvic mass
is observed through imaging; and facilitating decisions regarding clinical
follow-up (e.g.,
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whether to refer an individual to a surgical specialist, such as a gynecologic
oncology
surgeon). Biomarker testing may improve positive predictive value (PPV) over
CA- 125
testing and imaging alone. Furthermore, the described biomarkers may also be
useful in
permitting certain of these uses before indications of ovarian cancer are
detected by imaging
modalities or other clinical correlates, or before symptoms appear.
[00116] As an example of the manner in which any of the biomarkers described
herein
can be used to diagnose ovarian cancer, differential expression of one or more
of the
described biomarkers in an individual who is not known to have ovarian cancer
may indicate
that the individual has ovarian cancer, thereby enabling detection of ovarian
cancer at an
early stage of the disease when treatment is most effective, perhaps before
the ovarian cancer
is detected by other means or before symptoms appear. Increased differential
expression
from "normal" (since some biomarkers may be down-regulated with disease) of
one or more
of the biomarkers during the course of ovarian cancer may be indicative of
ovarian cancer
progression, e.g., an ovarian tumor is growing and/or metastasizing (and thus
indicate a poor
prognosis), whereas a decrease in the degree to which one or more of the
biomarkers is
differentially expressed (i.e., in subsequent biomarker tests, the expression
level in the
individual is moving toward or approaching a "normal" expression level) may be
indicative
of ovarian cancer remission, e.g., an ovarian tumor is shrinking (and thus
indicate a good or
better prognosis). Similarly, an increase in the degree to which one or more
of the
biomarkers is differentially expressed (i.e., in subsequent biomarker tests,
the expression
level in the individual is moving further away from a "normal" expression
level) during the
course of ovarian cancer treatment may indicate that the ovarian cancer is
progressing and
therefore indicate that the treatment is ineffective, whereas a decrease in
differential
expression of one or more of the biomarkers during the course of ovarian
cancer treatment
may be indicative of ovarian cancer remission and therefore indicate that the
treatment is
working successfully. Additionally, an increase or decrease in the
differential expression of
one or more of the biomarkers after an individual has apparently been cured of
ovarian cancer
may be indicative of ovarian cancer recurrence. In a situation such as this,
for example, the
individual can be re-started on therapy (or the therapeutic regimen modified
such as to
increase dosage amount and/or frequency, if the individual has maintained
therapy) at an
earlier stage than if the recurrence of ovarian cancer was not detected until
later.
Furthermore, a differential expression level of one or more of the biomarkers
in an individual
may be predictive of the individual's response to a particular therapeutic
agent. In monitoring
for ovarian cancer recurrence or progression, changes in the biomarker
expression levels may
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indicate the need for repeat imaging, such as to determine ovarian cancer
activity or to
determine the need for changes in treatment.
[00117] Detection of any of the biomarkers described herein may be
particularly useful
following, or in conjunction with, ovarian cancer treatment, such as to
evaluate the success of
the treatment or to monitor ovarian cancer remission, recurrence, and/or
progression
(including metastasis) following treatment. Ovarian cancer treatment may
include, for
example, administration of a therapeutic agent to the individual, performance
of surgery (e.g.,
surgical resection of at least a portion of a pelvic mass), administration of
radiation therapy,
or any other type of ovarian cancer treatment used in the art, and any
combination of these
treatments. For example, any of the biomarkers may be detected at least once
after treatment
or may be detected multiple times after treatment (such as at periodic
intervals), or may be
detected both before and after treatment. Differential expression levels of
any of the
biomarkers in an individual over time may be indicative of ovarian cancer
progression,
remission, or recurrence, examples of which include any of the following: an
increase or
decrease in the expression level of the biomarkers after treatment compared
with the
expression level of the biomarker before treatment; an increase or decrease in
the expression
level of the biomarker at a later time point after treatment compared with the
expression level
of the biomarker at an earlier time point after treatment; and a differential
expression level of
the biomarker at a single time point after treatment compared with normal
levels of the
biomarker.
[00118] As a specific example, the biomarker levels for any of the biomarkers
described herein can be determined in pre-surgery and post-surgery (e.g., 2-8
weeks after
surgery) serum or plasma samples. An increase in the biomarker expression
level(s) in the
post-surgery sample compared with the pre-surgery sample can indicate
progression of
ovarian cancer (e.g., unsuccessful surgery), whereas a decrease in the
biomarker expression
level(s) in the post-surgery sample compared with the pre-surgery sample can
indicate
regression of ovarian cancer (e.g., the surgery successfully removed the
ovarian tumor).
Similar analyses of the biomarker levels can be carried out before and after
other forms of
treatment, such as before and after radiation therapy or administration of a
therapeutic agent
or cancer vaccine.
[00119] In addition to testing biomarker levels as a stand-alone diagnostic
test,
biomarker levels can also be done in conjunction with determination of SNPs or
other genetic
lesions or variability that are indicative of increased risk of susceptibility
of disease. (See,
e.g., Amos et al., Nature Genetics 40, 616-622 (2009)).
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[00120] In addition to testing biomarker levels as a stand-alone diagnostic
test,
biomarker levels can also be done in conjunction with relevant symptoms or
abdominal
ultrasound and CT imaging.
[00121] Detection of any of the biomarkers described herein may be useful
after a
pelvic mass has been observed through imaging to aid in the diagnosis of
ovarian cancer and
guide appropriate clinical care of the individual, including care by an
appropriate surgical
specialist.
[00122] In addition to testing biomarker levels in conjunction with relevant
symptoms
or abdominal ultrasound or CT imaging, information regarding the biomarkers
can also be
evaluated in conjunction with other types of data, particularly data that
indicates an
individual's risk for ovarian cancer (e.g., patient clinical history,
symptoms, family history of
cancer, risk factors such as the presence of a genetic marker(s), and/or
status of other
biomarkers, etc.). These various data can be assessed by automated methods,
such as a
computer program/software, which can be embodied in a computer or other
apparatus/device.
[00123] Any of the described biomarkers may also be used in imaging tests. For
example, an imaging agent can be coupled to any of the described biomarkers,
which can be
used to aid in ovarian cancer diagnosis, to monitor disease
progression/remission or
metastasis, to monitor for disease recurrence, or to monitor response to
therapy, among other
uses.
Detection and Determination of Biomarkers and Biomarker Values
[00124] A biomarker value for the biomarkers described herein can be detected
using
any of a variety of known analytical methods. In one embodiment, a biomarker
value is
detected using a capture reagent. As used herein, a "capture agent" or
"capture reagent"
refers to a molecule that is capable of binding specifically to a biomarker.
In various
embodiments, the capture reagent can be exposed to the biomarker in solution
or can be
exposed to the biomarker while the capture reagent is immobilized on a solid
support. In
other embodiments, the capture reagent contains a feature that is reactive
with a secondary
feature on a solid support. In these embodiments, the capture reagent can be
exposed to the
biomarker in solution, and then the feature on the capture reagent can be used
in conjunction
with the secondary feature on the solid support to immobilize the biomarker on
the solid
support. The capture reagent is selected based on the type of analysis to be
conducted.
Capture reagents include but are not limited to aptamers, antibodies,
adnectins, ankyrins,
other antibody mimetics and other protein scaffolds, autoantibodies, chimeras,
small
CA 02737004 2011-03-09
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molecules, an F(ab')2 fragment, a single chain antibody fragment, an Fv
fragment, a single
chain Fv fragment, a nucleic acid, a lectin, a ligand-binding receptor,
affybodies, nanobodies,
imprinted polymers, avimers, peptidomimetics, a hormone receptor, a cytokine
receptor, and
synthetic receptors, and modifications and fragments of these.
[00125] In some embodiments, a biomarker value is detected using a
biomarker/capture reagent complex.
[00126] In other embodiments, the biomarker value is derived from the
biomarker/capture reagent complex and is detected indirectly, such as, for
example, as a
result of a reaction that is subsequent to the biomarker/capture reagent
interaction, but is
dependent on the formation of the biomarker/capture reagent complex.
[00127] In some embodiments, the biomarker value is detected directly from the
biomarker in a biological sample.
[00128] In one embodiment, the biomarkers are detected using a multiplexed
format
that allows for the simultaneous detection of two or more biomarkers in a
biological sample.
In one embodiment of the multiplexed format, capture reagents are immobilized,
directly or
indirectly, covalently or non-covalently, in discrete locations on a solid
support. In another
embodiment, a multiplexed format uses discrete solid supports where each solid
support has a
unique capture reagent associated with that solid support, such as, for
example quantum dots.
In another embodiment, an individual device is used for the detection of each
one of multiple
biomarkers to be detected in a biological sample. Individual devices can be
configured to
permit each biomarker in the biological sample to be processed simultaneously.
For
example, a microtiter plate can be used such that each well in the plate is
used to uniquely
analyze one of multiple biomarkers to be detected in a biological sample.
[00129] In one or more of the foregoing embodiments, a fluorescent tag can be
used to
label a component of the biomarker/capture complex to enable the detection of
the biomarker
value. In various embodiments, the fluorescent label can be conjugated to a
capture reagent
specific to any of the biomarkers described herein using known techniques, and
the
fluorescent label can then be used to detect the corresponding biomarker
value. Suitable
fluorescent labels include rare earth chelates, fluorescein and its
derivatives, rhodamine and
its derivatives, dansyl, allophycocyanin, PBXL-3, Qdot 605, Lissamine,
phycoerythrin, Texas
Red, and other such compounds.
[00130] In one embodiment, the fluorescent label is a fluorescent dye
molecule. In
some embodiments, the fluorescent dye molecule includes at least one
substituted indolium
ring system in which the substituent on the 3-carbon of the indolium ring
contains a
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chemically reactive group or a conjugated substance. In some embodiments, the
dye
molecule includes an AlexFluor molecule, such as, for example, AlexaFluor 488,
AlexaFluor
532, AlexaFluor 647, AlexaFluor 680, or AlexaFluor 700. In other embodiments,
the dye
molecule includes a first type and a second type of dye molecule, such as,
e.g., two different
AlexaFluor molecules. In other embodiments, the dye molecule includes a first
type and a
second type of dye molecule, and the two dye molecules have different emission
spectra.
[00131] Fluorescence can be measured with a variety of instrumentation
compatible
with a wide range of assay formats. For example, spectrofluorimeters have been
designed to
analyze microtiter plates, microscope slides, printed arrays, cuvettes, etc.
See Principles of
Fluorescence Spectroscopy, by J.R. Lakowicz, Springer Science + Business
Media, Inc.,
2004. See Bioluminescence & Chemiluminescence: Progress & Current
Applications; Philip
E. Stanley and Larry J. Kricka editors, World Scientific Publishing Company,
January 2002.
[00132] In one or more of the foregoing embodiments, a chemiluminescence tag
can
optionally be used to label a component of the biomarker/capture complex to
enable the
detection of a biomarker value. Suitable chemiluminescent materials include
any of oxalyl
chloride, Rodamin 6G, Ru(bipy)321, TMAE (tetrakis(dimethylamino)ethylene),
Pyrogallol
(1,2,3-trihydroxibenzene), Lucigenin, peroxyoxalates, Aryl oxalates,
Acridinium esters,
dioxetanes, and others.
[00133] In yet other embodiments, the detection method includes an
enzyme/substrate
combination that generates a detectable signal that corresponds to the
biomarker value.
Generally, the enzyme catalyzes a chemical alteration of the chromogenic
substrate which
can be measured using various techniques, including spectrophotometry,
fluorescence, and
chemiluminescence. Suitable enzymes include, for example, luciferases,
luciferin, malate
dehydrogenase, urease, horseradish peroxidase (HRPO), alkaline phosphatase,
beta-
galactosidase, glucoamylase, lysozyme, glucose oxidase, galactose oxidase, and
glucose-6-
phosphate dehydrogenase, uricase, xanthine oxidase, lactoperoxidase,
microperoxidase, and
the like.
[00134] In yet other embodiments, the detection method can be a combination of
fluorescence, chemiluminescence, radionuclide or enzyme/substrate combinations
that
generate a measurable signal. Multimodal signaling could have unique and
advantageous
characteristics in biomarker assay formats.
[00135] More specifically, the biomarker values for the biomarkers described
herein
can be detected using known analytical methods including, singleplex aptamer
assays,
multiplexed aptamer assays, singleplex or multiplexed immunoassays, mRNA
expression
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profiling, miRNA expression profiling, mass spectrometric analysis,
histological/cytological
methods, etc. as detailed below.
Determination of Biomarker Values using Aptamer-Based Assays
[00136] Assays directed to the detection and quantification of physiologically
significant molecules in biological samples and other samples are important
tools in scientific
research and in the health care field. One class of such assays involves the
use of a
microarray that includes one or more aptamers immobilized on a solid support.
The aptamers
are each capable of binding to a target molecule in a highly specific manner
and with very
high affinity. See, e.g., U.S. Patent No. 5,475,096 entitled "Nucleic Acid
Ligands"; see also,
e.g., U.S. Patent No. 6,242,246, U.S. Patent No. 6,458,543, and U.S. Patent
No. 6,503,715,
each of which is entitled "Nucleic Acid Ligand Diagnostic Biochip". Once the
microarray is
contacted with a sample, the aptamers bind to their respective target
molecules present in the
sample and thereby enable a determination of a biomarker value corresponding
to a
biomarker.
[00137] As used herein, an "aptamer" refers to a nucleic acid that has a
specific
binding affinity for a target molecule. It is recognized that affinity
interactions are a matter
of degree; however, in this context, the "specific binding affinity" of an
aptamer for its target
means that the aptamer binds to its target generally with a much higher degree
of affinity than
it binds to other components in a test sample. An "aptamer" is a set of copies
of one type or
species of nucleic acid molecule that has a particular nucleotide sequence. An
aptamer can
include any suitable number of nucleotides, including any number of chemically
modified
nucleotides. "Aptamers" refers to more than one such set of molecules.
Different aptamers
can have either the same or different numbers of nucleotides. Aptamers can be
DNA or RNA
or chemically modified nucleic acids and can be single stranded, double
stranded, or contain
double stranded regions, and can include higher ordered structures. An aptamer
can also be a
photoaptamer, where a photoreactive or chemically reactive functional group is
included in
the aptamer to allow it to be covalently linked to its corresponding target.
Any of the aptamer
methods disclosed herein can include the use of two or more aptamers that
specifically bind
the same target molecule. As further described below, an aptamer may include a
tag. If an
aptamer includes a tag, all copies of the aptamer need not have the same tag.
Moreover, if
different aptamers each include a tag, these different aptamers can have
either the same tag or
a different tag.
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[00138] An aptamer can be identified using any known method, including the
SELEX
process. Once identified, an aptamer can be prepared or synthesized in
accordance with any
known method, including chemical synthetic methods and enzymatic synthetic
methods.
[00139] The terms "SELEX" and "SELEX process" are used interchangeably herein
to
refer generally to a combination of (1) the selection of aptamers that
interact with a target
molecule in a desirable manner, for example binding with high affinity to a
protein, with (2)
the amplification of those selected nucleic acids. The SELEX process can be
used to identify
aptamers with high affinity to a specific target or biomarker.
[00140] SELEX generally includes preparing a candidate mixture of nucleic
acids,
binding of the candidate mixture to the desired target molecule to form an
affinity complex,
separating the affinity complexes from the unbound candidate nucleic acids,
separating and
isolating the nucleic acid from the affinity complex, purifying the nucleic
acid, and
identifying a specific aptamer sequence. The process may include multiple
rounds to further
refine the affinity of the selected aptamer. The process can include
amplification steps at one
or more points in the process. See, e.g., U.S. Patent No. 5,475,096, entitled
"Nucleic Acid
Ligands". The SELEX process can be used to generate an aptamer that covalently
binds its
target as well as an aptamer that non-covalently binds its target. See, e.g.,
U.S. Patent No.
5,705,337 entitled "Systematic Evolution of Nucleic Acid Ligands by
Exponential
Enrichment: Chemi-SELEX."
[00141] The SELEX process can be used to identify high-affinity aptamers
containing
modified nucleotides that confer improved characteristics on the aptamer, such
as, for
example, improved in vivo stability or improved delivery characteristics.
Examples of such
modifications include chemical substitutions at the ribose and/or phosphate
and/or base
positions. SELEX process-identified aptamers containing modified nucleotides
are described
in U.S. Patent No. 5,660,985, entitled "High Affinity Nucleic Acid Ligands
Containing
Modified Nucleotides", which describes oligonucleotides containing nucleotide
derivatives
chemically modified at the 5'- and 2'-positions of pyrimidines. U.S. Patent
No. 5,580,737,
see supra, describes highly specific aptamers containing one or more
nucleotides modified
with 2'-amino (2'-NH2), 2'-fluoro (2'-F), and/or 2'-O-methyl (2'-OMe). See
also, U.S. Patent
Application Publication 20090098549, entitled "SELEX and PHOTOSELEX", which
describes nucleic acid libraries having expanded physical and chemical
properties and their
use in SELEX and photoSELEX.
[00142] SELEX can also be used to identify aptamers that have desirable off-
rate
characteristics. See U.S. Patent Application Publication 20090004667, entitled
"Method for
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Generating Aptamers with Improved Off-Rates", which describes improved SELEX
methods
for generating aptamers that can bind to target molecules. Methods for
producing aptamers
and photoaptamers having slower rates of dissociation from their respective
target molecules
are described. The methods involve contacting the candidate mixture with the
target
molecule, allowing the formation of nucleic acid-target complexes to occur,
and performing a
slow off-rate enrichment process wherein nucleic acid-target complexes with
fast dissociation
rates will dissociate and not reform, while complexes with slow dissociation
rates will remain
intact. Additionally, the methods include the use of modified nucleotides in
the production of
candidate nucleic acid mixtures to generate aptamers with improved off-rate
performance.
[00143] A variation of this assay employs aptamers that include photoreactive
functional groups that enable the aptamers to covalently bind or
"photocrosslink" their target
molecules. See, e.g., U.S. Patent No. 6,544,776 entitled "Nucleic Acid Ligand
Diagnostic
Biochip". These photoreactive aptamers are also referred to as photoaptamers.
See, e.g.,
U.S. Patent No. 5,763,177, U.S. Patent No. 6,001,577, and U.S. Patent No.
6,291,184, each of
which is entitled "Systematic Evolution of Nucleic Acid Ligands by Exponential
Enrichment:
Photoselection of Nucleic Acid Ligands and Solution SELEX"; see also, e.g.,
U.S. Patent No.
6,458,539, entitled "Photo selection of Nucleic Acid Ligands". After the
microarray is
contacted with the sample and the photoaptamers have had an opportunity to
bind to their
target molecules, the photoaptamers are photoactivated, and the solid support
is washed to
remove any non-specifically bound molecules. Harsh wash conditions may be
used, since
target molecules that are bound to the photoaptamers are generally not
removed, due to the
covalent bonds created by the photoactivated functional group(s) on the
photoaptamers. In
this manner, the assay enables the detection of a biomarker value
corresponding to a
biomarker in the test sample.
[00144] In both of these assay formats, the aptamers are immobilized on the
solid
support prior to being contacted with the sample. Under certain circumstances,
however,
immobilization of the aptamers prior to contact with the sample may not
provide an optimal
assay. For example, pre-immobilization of the aptamers may result in
inefficient mixing of
the aptamers with the target molecules on the surface of the solid support,
perhaps leading to
lengthy reaction times and, therefore, extended incubation periods to permit
efficient binding
of the aptamers to their target molecules. Further, when photoaptamers are
employed in the
assay and depending upon the material utilized as a solid support, the solid
support may tend
to scatter or absorb the light used to effect the formation of covalent bonds
between the
photoaptamers and their target molecules. Moreover, depending upon the method
employed,
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detection of target molecules bound to their aptamers can be subject to
imprecision, since the
surface of the solid support may also be exposed to and affected by any
labeling agents that
are used. Finally, immobilization of the aptamers on the solid support
generally involves an
aptamer-preparation step (i.e., the immobilization) prior to exposure of the
aptamers to the
sample, and this preparation step may affect the activity or functionality of
the aptamers.
[00145] Aptamer assays that permit an aptamer to capture its target in
solution and
then employ separation steps that are designed to remove specific components
of the
aptamer-target mixture prior to detection have also been described (see U.S.
Patent
Application Publication 20090042206, entitled "Multiplexed Analyses of Test
Samples").
The described aptamer assay methods enable the detection and quantification of
a non-
nucleic acid target (e.g., a protein target) in a test sample by detecting and
quantifying a
nucleic acid (i.e., an aptamer). The described methods create a nucleic acid
surrogate (i.e, the
aptamer) for detecting and quantifying a non-nucleic acid target, thus
allowing the wide
variety of nucleic acid technologies, including amplification, to be applied
to a broader range
of desired targets, including protein targets.
[00146] Aptamers can be constructed to facilitate the separation of the assay
components from an aptamer biomarker complex (or photoaptamer biomarker
covalent
complex) and permit isolation of the aptamer for detection and/or
quantification. In one
embodiment, these constructs can include a cleavable or releasable element
within the
aptamer sequence. In other embodiments, additional functionality can be
introduced into the
aptamer, for example, a labeled or detectable component, a spacer component,
or a specific
binding tag or immobilization element. For example, the aptamer can include a
tag
connected to the aptamer via a cleavable moiety, a label, a spacer component
separating the
label, and the cleavable moiety. In one embodiment, a cleavable element is a
photocleavable
linker. The photocleavable linker can be attached to a biotin moiety and a
spacer section, can
include an NHS group for derivatization of amines, and can be used to
introduce a biotin
group to an aptamer, thereby allowing for the release of the aptamer later in
an assay method.
[00147] Homogenous assays, done with all assay components in solution, do not
require separation of sample and reagents prior to the detection of signal.
These methods are
rapid and easy to use. These methods generate signal based on a molecular
capture or
binding reagent that reacts with its specific target. For ovarian cancer, the
molecular capture
reagents would be an aptamer or an antibody or the like and the specific
target would be an
ovarian cancer biomarker of Table 1.
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[00148] In one embodiment, a method for signal generation takes advantage of
anisotropy signal change due to the interaction of a fluorophore-labeled
capture reagent with
its specific biomarker target. When the labeled capture reacts with its
target, the increased
molecular weight causes the rotational motion of the fluorophore attached to
the complex to
become much slower changing the anisotropy value. By monitoring the anisotropy
change,
binding events may be used to quantitatively measure the biomarkers in
solutions. Other
methods include fluorescence polarization assays, molecular beacon methods,
time resolved
fluorescence quenching, chemiluminescence, fluorescence resonance energy
transfer, and the
like.
[00149] An exemplary solution-based aptamer assay that can be used to detect a
biomarker value corresponding to a biomarker in a biological sample includes
the following:
(a) preparing a mixture by contacting the biological sample with an aptamer
that includes a
first tag and has a specific affinity for the biomarker, wherein an aptamer
affinity complex is
formed when the biomarker is present in the sample; (b) exposing the mixture
to a first solid
support including a first capture element, and allowing the first tag to
associate with the first
capture element; (c) removing any components of the mixture not associated
with the first
solid support; (d) attaching a second tag to the biomarker component of the
aptamer affinity
complex; (e) releasing the aptamer affinity complex from the first solid
support; (f) exposing
the released aptamer affinity complex to a second solid support that includes
a second capture
element and allowing the second tag to associate with the second capture
element; (g)
removing any non-complexed aptamer from the mixture by partitioning the non-
complexed
aptamer from the aptamer affinity complex; (h) eluting the aptamer from the
solid support;
and (i) detecting the biomarker by detecting the aptamer component of the
aptamer affinity
complex.
Determination of Biomarker Values using Immunoassays
[00150] Immunoassay methods are based on the reaction of an antibody to its
corresponding target or analyte and can detect the analyte in a sample
depending on the
specific assay format. To improve specificity and sensitivity of an assay
method based on
immuno-reactivity, monoclonal antibodies are often used because of their
specific epitope
recognition. Polyclonal antibodies have also been successfully used in various
immunoassays because of their increased affinity for the target as compared to
monoclonal
antibodies. Immunoassays have been designed for use with a wide range of
biological
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sample matrices. Immunoassay formats have been designed to provide
qualitative, semi-
quantitative, and quantitative results.
[00151] Quantitative results are generated through the use of a standard curve
created
with known concentrations of the specific analyte to be detected. The response
or signal
from an unknown sample is plotted onto the standard curve, and a quantity or
value
corresponding to the target in the unknown sample is established.
[00152] Numerous immunoassay formats have been designed. ELISA or EIA can be
quantitative for the detection of an analyte. This method relies on attachment
of a label to
either the analyte or the antibody and the label component includes, either
directly or
indirectly, an enzyme. ELISA tests may be formatted for direct, indirect,
competitive, or
sandwich detection of the analyte. Other methods rely on labels such as, for
example,
radioisotopes (I125) or fluorescence. Additional techniques include, for
example,
agglutination, nephelometry, turbidimetry, Western blot, immunoprecipitation,
immunocytochemistry, immunohistochemistry, flow cytometry, Luminex assay, and
others
(see ImmunoAssay: A Practical Guide, edited by Brian Law, published by Taylor
& Francis,
Ltd., 2005 edition).
[00153] Exemplary assay formats include enzyme-linked immunosorbent assay
(ELISA), radioimmunoassay, fluorescent, chemiluminescence, and fluorescence
resonance
energy transfer (FRET) or time resolved-FRET (TR-FRET) immunoassays. Examples
of
procedures for detecting biomarkers include biomarker immunoprecipitation
followed by
quantitative methods that allow size and peptide level discrimination, such as
gel
electrophoresis, capillary electrophoresis, planar electrochromatography, and
the like.
[00154] Methods of detecting and/or quantifying a detectable label or signal
generating
material depend on the nature of the label. The products of reactions
catalyzed by
appropriate enzymes (where the detectable label is an enzyme; see above) can
be, without
limitation, fluorescent, luminescent, or radioactive or they may absorb
visible or ultraviolet
light. Examples of detectors suitable for detecting such detectable labels
include, without
limitation, x-ray film, radioactivity counters, scintillation counters,
spectrophotometers,
colorimeters, fluorometers, luminometers, and densitometers.
[00155] Any of the methods for detection can be performed in any format that
allows
for any suitable preparation, processing, and analysis of the reactions. This
can be, for
example, in multi-well assay plates (e.g., 96 wells or 384 wells) or using any
suitable array or
microarray. Stock solutions for various agents can be made manually or
robotically, and all
subsequent pipetting, diluting, mixing, distribution, washing, incubating,
sample readout, data
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collection and analysis can be done robotically using commercially available
analysis
software, robotics, and detection instrumentation capable of detecting a
detectable label.
Determination of Biomarker Values using Gene Expression Profiling
[00156] Measuring mRNA in a biological sample may be used as a surrogate for
detection of the level of the corresponding protein in the biological sample.
Thus, any of the
biomarkers or biomarker panels described herein can also be detected by
detecting the
appropriate RNA.
[00157] mRNA expression levels are measured by reverse transcription
quantitative
polymerase chain reaction (RT-PCR followed with qPCR). RT-PCR is used to
create a
cDNA from the mRNA. The cDNA may be used in a qPCR assay to produce
fluorescence as
the DNA amplification process progresses. By comparison to a standard curve,
qPCR can
produce an absolute measurement such as number of copies of mRNA per cell.
Northern
blots, microarrays, Invader assays, and RT-PCR combined with capillary
electrophoresis
have all been used to measure expression levels of mRNA in a sample. See Gene
Expression
Profiling: Methods and Protocols, Richard A. Shimkets, editor, Humana Press,
2004.
[00158] miRNA molecules are small RNAs that are non-coding but may regulate
gene
expression. Any of the methods suited to the measurement of mRNA expression
levels can
also be used for the corresponding miRNA. Recently many laboratories have
investigated the
use of miRNAs as biomarkers for disease. Many diseases involve wide-spread
transcriptional regulation, and it is not surprising that miRNAs might find a
role as
biomarkers. The connection between miRNA concentrations and disease is often
even less
clear than the connections between protein levels and disease, yet the value
of miRNA
biomarkers might be substantial. Of course, as with any RNA expressed
differentially during
disease, the problems facing the development of an in vitro diagnostic product
will include
the requirement that the miRNAs survive in the diseased cell and are easily
extracted for
analysis, or that the miRNAs are released into blood or other matrices where
they must
survive long enough to be measured. Protein biomarkers have similar
requirements, although
many potential protein biomarkers are secreted intentionally at the site of
pathology and
function, during disease, in a paracrine fashion. Many potential protein
biomarkers are
designed to function outside the cells within which those proteins are
synthesized.
Detection of Biomarkers Using In Vivo Molecular Imaging Technologies
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[00159] Any of the described biomarkers (see Table 1) may also be used in
molecular
imaging tests. For example, an imaging agent can be coupled to any of the
described
biomarkers, which can be used to aid in ovarian cancer diagnosis, to monitor
disease
progression/remission or metastasis, to monitor for disease recurrence, or to
monitor response
to therapy, among other uses.
[00160] In vivo imaging technologies provide non-invasive methods for
determining
the state of a particular disease in the body of an individual. For example,
entire portions of
the body, or even the entire body, may be viewed as a three dimensional image,
thereby
providing valuable information concerning morphology and structures in the
body. Such
technologies may be combined with the detection of the biomarkers described
herein to
provide information concerning the cancer status, in particular the ovarian
cancer status, of an
individual.
[00161] The use of in vivo molecular imaging technologies is expanding due to
various
advances in technology. These advances include the development of new contrast
agents or
labels, such as radiolabels and/or fluorescent labels, which can provide
strong signals within
the body; and the development of powerful new imaging technology, which can
detect and
analyze these signals from outside the body, with sufficient sensitivity and
accuracy to
provide useful information. The contrast agent can be visualized in an
appropriate imaging
system, thereby providing an image of the portion or portions of the body in
which the
contrast agent is located. The contrast agent may be bound to or associated
with a capture
reagent, such as an aptamer or an antibody, for example, and/or with a peptide
or protein, or
an oligonucleotide (for example, for the detection of gene expression), or a
complex
containing any of these with one or more macromolecules and/or other
particulate forms.
[00162] The contrast agent may also feature a radioactive atom that is useful
in
imaging. Suitable radioactive atoms include technetium-99m or iodine- 123 for
scintigraphic
studies. Other readily detectable moieties include, for example, spin labels
for magnetic
resonance imaging (MRI) such as, for example, iodine-123 again, iodine-131,
indium-111,
fluorine-19, carbon-13, nitrogen-15, oxygen-17, gadolinium, manganese or iron.
Such labels
are well known in the art and could easily be selected by one of ordinary
skill in the art.
[00163] Standard imaging techniques include but are not limited to magnetic
resonance
imaging, contrast-enhanced abdominal or transvaginal ultrasound, computed
tomography
(CT) scanning, positron emission tomography (PET), single photon emission
computed
tomography (SPECT), and the like. For diagnostic in vivo imaging, the type of
detection
instrument available is a major factor in selecting a given contrast agent,
such as a given
CA 02737004 2011-03-09
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radionuclide and the particular biomarker that it is used to target (protein,
mRNA, and the
like). The radionuclide chosen typically has a type of decay that is
detectable by a given type
of instrument. Also, when selecting a radionuclide for in vivo diagnosis, its
half-life should
be long enough to enable detection at the time of maximum uptake by the target
tissue but
short enough that deleterious radiation of the host is minimized.
[00164] Exemplary imaging techniques include but are not limited to PET and
SPECT,
which are imaging techniques in which a radionuclide is synthetically or
locally administered
to an individual. The subsequent uptake of the radiotracer is measured over
time and used to
obtain information about the targeted tissue and the biomarker. Because of the
high-energy
(gamma-ray) emissions of the specific isotopes employed and the sensitivity
and
sophistication of the instruments used to detect them, the two-dimensional
distribution of
radioactivity may be inferred from outside of the body.
[00165] Commonly used positron-emitting nuclides in PET include, for example,
carbon-11, nitrogen-13, oxygen-15, and fluorine-18. Isotopes that decay by
electron capture
and/or gamma-emission are used in SPECT and include, for example iodine-123
and
technetium-99m. An exemplary method for labeling amino acids with technetium-
99m is the
reduction of pertechnetate ion in the presence of a chelating precursor to
form the labile
technetium-99m-precursor complex, which, in turn, reacts with the metal
binding group of a
bifunctionally modified chemotactic peptide to form a technetium-99m-
chemotactic peptide
conjugate.
[00166] Antibodies are frequently used for such in vivo imaging diagnostic
methods.
The preparation and use of antibodies for in vivo diagnosis is well known in
the art. Labeled
antibodies which specifically bind any of the biomarkers in Table 1 can be
injected into an
individual suspected of having a certain type of cancer (e.g., ovarian
cancer), detectable
according to the particular biomarker used, for the purpose of diagnosing or
evaluating the
disease status of the individual. The label used will be selected in
accordance with the
imaging modality to be used, as previously described. Localization of the
label permits
determination of the spread of the cancer. The amount of label within an organ
or tissue also
allows determination of the presence or absence of cancer in that organ or
tissue.
[00167] Similarly, aptamers may be used for such in vivo imaging diagnostic
methods.
For example, an aptamer that was used to identify a particular biomarker
described in Table 1
(and therefore binds specifically to that particular biomarker) may be
appropriately labeled
and injected into an individual suspected of having ovarian cancer, detectable
according to
the particular biomarker, for the purpose of diagnosing or evaluating the
ovarian cancer status
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of the individual. The label used will be selected in accordance with the
imaging modality to
be used, as previously described. Localization of the label permits
determination of the
spread of the cancer. The amount of label within an organ or tissue also
allows determination
of the presence or absence of cancer in that organ or tissue. Aptamer-directed
imaging agents
could have unique and advantageous characteristics relating to tissue
penetration, tissue
distribution, kinetics, elimination, potency, and selectivity as compared to
other imaging
agents.
[00168] Such techniques may also optionally be performed with labeled
oligonucleotides, for example, for detection of gene expression through
imaging with
antisense oligonucleotides. These methods are used for in situ hybridization,
for example,
with fluorescent molecules or radionuclides as the label. Other methods for
detection of gene
expression include, for example, detection of the activity of a reporter gene.
[00169] Another general type of imaging technology is optical imaging, in
which
fluorescent signals within the subject are detected by an optical device that
is external to the
subject. These signals may be due to actual fluorescence and/or to
bioluminescence.
Improvements in the sensitivity of optical detection devices have increased
the usefulness of
optical imaging for in vivo diagnostic assays.
[00170] The use of in vivo molecular biomarker imaging is increasing,
including for
clinical trials, for example, to more rapidly measure clinical efficacy in
trials for new cancer
therapies and/or to avoid prolonged treatment with a placebo for those
diseases, such as
multiple sclerosis, in which such prolonged treatment may be considered to be
ethically
questionable.
[00171] For a review of other techniques, see N. Blow, Nature Methods, 6, 465-
469,
2009.
Determination of Biomarker Values using Histology or Cytology Methods
[00172] For evaluation of ovarian cancer, a variety of tissue samples may be
used in
histological or cytological methods. Sample selection depends on the primary
tumor location
and sites of metastases. For example, fine needle aspirates, cutting needles,
and core biopsies
can be used for histology. Ascites can be used for cyotology. While
cytological analysis is
still used in the diagnosis of ovarian cancer, histological methods are known
to provide better
sensitivity for the detection of cancer. Any of the biomarkers identified
herein that were
shown to be up-regulated (see Table 15) in the individuals with ovarian cancer
can be used to
stain a histological specimen as an indication of disease.
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[00173] In one embodiment, one or more capture reagents specific to the
corresponding biomarker is used in a cytological evaluation of an ovarian cell
sample and
may include one or more of the following: collecting a cell sample, fixing the
cell sample,
dehydrating, clearing, immobilizing the cell sample on a microscope slide,
permeabilizing the
cell sample, treating for analyte retrieval, staining, destaining, washing,
blocking, and
reacting with one or more capture reagent/s in a buffered solution. In another
embodiment,
the cell sample is produced from a cell block.
[00174] In another embodiment, one or more capture reagents specific to the
corresponding biomarker is used in a histological evaluation of an ovarian
tissue sample and
may include one or more of the following: collecting a tissue specimen, fixing
the tissue
sample, dehydrating, clearing, immobilizing the tissue sample on a microscope
slide,
permeabilizing the tissue sample, treating for analyte retrieval, staining,
destaining, washing,
blocking, rehydrating, and reacting with capture reagent/s in a buffered
solution. In another
embodiment, fixing and dehydrating are replaced with freezing.
[00175] In another embodiment, the one or more aptamers specific to the
corresponding biomarker is reacted with the histological or cytological sample
and can serve
as the nucleic acid target in a nucleic acid amplification method. Suitable
nucleic acid
amplification methods include, for example, PCR, q-beta replicase, rolling
circle
amplification, strand displacement, helicase dependent amplification, loop
mediated
isothermal amplification, ligase chain reaction, and restriction and
circularization aided
rolling circle amplification.
[00176] In one embodiment, the one or more capture reagent/s specific to the
corresponding biomarkers for use in the histological or cytological evaluation
are mixed in a
buffered solution that can include any of the following: blocking materials,
competitors,
detergents, stabilizers, carrier nucleic acid, polyanionic materials, etc.
[00177] A "cytology protocol" generally includes sample collection, sample
fixation,
sample immobilization, and staining. "Cell preparation" can include several
processing steps
after sample collection, including the use of one or more slow off-rate
aptamers for the
staining of the prepared cells.
[00178] Sample collection can include directly placing the sample in an
untreated
transport container, placing the sample in a transport container containing
some type of
media, or placing the sample directly onto a slide (immobilization) without
any treatment or
fixation.
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[00179] Sample immobilization can be improved by applying a portion of the
collected
specimen to a glass slide that is treated with polylysine, gelatin, or a
silane. Slides can be
prepared by smearing a thin and even layer of cells across the slide. Care is
generally taken
to minimize mechanical distortion and drying artifacts. Liquid specimens can
be processed in
a cell block method. Or, alternatively, liquid specimens can be mixed 1:1 with
the fixative
solution for about 10 minutes at room temperature.
[00180] Cell blocks can be prepared from residual effusions, sputum, urine
sediments,
gastrointestinal fluids, cell scraping, ascites, or fine needle aspirates.
Cells are concentrated
or packed by centrifugation or membrane filtration. A number of methods for
cell block
preparation have been developed. Representative procedures include the fixed
sediment,
bacterial agar, or membrane filtration methods. In the fixed sediment method,
the cell
sediment is mixed with a fixative like Bouins, picric acid, or buffered
formalin and then the
mixture is centrifuged to pellet the fixed cells. The supernatant is removed,
drying the cell
pellet as completely as possible. The pellet is collected and wrapped in lens
paper and then
placed in a tissue cassette. The tissue cassette is placed in ajar with
additional fixative and
processed as a tissue sample. Agar method is very similar but the pellet is
removed and dried
on paper towel and then cut in half. The cut side is placed in a drop of
melted agar on a glass
slide and then the pellet is covered with agar making sure that no bubbles
form in the agar.
The agar is allowed to harden and then any excess agar is trimmed away. This
is placed in a
tissue cassette and the tissue process completed. Alternatively, the pellet
may be directly
suspended in 2% liquid agar at 65 C and the sample centrifuged. The agar cell
pellet is
allowed to solidify for an hour at 4 C. The solid agar may be removed from the
centrifuge
tube and sliced in half. The agar is wrapped in filter paper and then the
tissue cassette.
Processing from this point forward is as described above. Centrifugation can
be replaced in
any these procedures with membrane filtration. Any of these processes may be
used to
generate a "cell block sample".
[00181] Cell blocks can be prepared using specialized resin including Lowicryl
resins,
LR White, LR Gold, Unicryl, and MonoStep. These resins have low viscosity and
can be
polymerized at low temperatures and with ultra violet (UV) light. The
embedding process
relies on progressively cooling the sample during dehydration, transferring
the sample to the
resin, and polymerizing a block at the final low temperature at the
appropriate UV
wavelength.
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[00182] Cell block sections can be stained with hematoxylin-eosin for
cytomorphological examination while additional sections are used for
examination for
specific markers.
[00183] Whether the process is cytologoical or histological, the sample may be
fixed
prior to additional processing to prevent sample degradation. This process is
called "fixation"
and describes a wide range of materials and procedures that may be used
interchangeably.
The sample fixation protocol and reagents are best selected empirically based
on the targets
to be detected and the specific cell/tissue type to be analyzed. Sample
fixation relies on
reagents such as ethanol, polyethylene glycol, methanol, formalin, or
isopropanol. The
samples should be fixed as soon after collection and affixation to the slide
as possible.
However, the fixative selected can introduce structural changes into various
molecular targets
making their subsequent detection more difficult. The fixation and
immobilization processes
and their sequence can modify the appearance of the cell and these changes
must be
anticipated and recognized by the cytotechnologist. Fixatives can cause
shrinkage of certain
cell types and cause the cytoplasm to appear granular or reticular. Many
fixatives function by
crosslinking cellular components. This can damage or modify specific epitopes,
generate
new epitopes, cause molecular associations, and reduce membrane permeability.
Formalin
fixation is one of the most common cytological and histological approaches.
Formalin forms
methyl bridges between neighboring proteins or within proteins. Precipitation
or coagulation
is also used for fixation and ethanol is frequently used in this type of
fixation. A combination
of crosslinking and precipitation can also be used for fixation. A strong
fixation process is
best at preserving morphological information while a weaker fixation process
is best for the
preservation of molecular targets.
[00184] A representative fixative is 50% absolute ethanol, 2 mM polyethylene
glycol
(PEG), 1.85% formaldehyde. Variations on this formulation include ethanol (50%
to 95%),
methanol (20% - 50%), and formalin (formaldehyde) only. Another common
fixative is 2%
PEG 1500, 50% ethanol, and 3% methanol. Slides are place in the fixative for
about 10 to 15
minutes at room temperature and then removed and allowed to dry. Once slides
are fixed
they can be rinsed with a buffered solution like PBS.
[00185] A wide range of dyes can be used to differentially highlight and
contrast or
"stain" cellular, sub-cellular, and tissue features or morphological
structures. Hematoylin is
used to stain nuclei a blue or black color. Orange G-6 and Eosin Azure both
stain the cell's
cytoplasm. Orange G stains keratin and glycogen containing cells yellow. Eosin
Y is used to
stain nucleoli, cilia, red blood cells, and superficial epithelial squamous
cells. Romanowsky
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stains are used for air dried slides and are useful in enhancing pleomorphism
and
distinguishing extracellular from intracytoplasmic material.
[00186] The staining process can include a treatment to increase the
permeability of
the cells to the stain. Treatment of the cells with a detergent can be used to
increase
permeability. To increase cell and tissue permeability, fixed samples can be
further treated
with solvents, saponins, or non-ionic detergents. Enzymatic digestion can also
improve the
accessibility of specific targets in a tissue sample.
[00187] After staining, the sample is dehydrated using a succession of alcohol
rinses
with increasing alcohol concentration. The final wash is done with xylene or a
xylene
substitute, such as a citrus terpene, that has a refractive index close to
that of the coverslip to
be applied to the slide. This final step is referred to as clearing. Once the
sample is
dehydrated and cleared, a mounting medium is applied. The mounting medium is
selected to
have a refractive index close to the glass and is capable of bonding the
coverslip to the slide.
It will also inhibit the additional drying, shrinking, or fading of the cell
sample.
[00188] Regardless of the stains or processing used, the final evaluation of
the ovarian
cytological specimen is made by some type of microscopy to permit a visual
inspection of the
morphology and a determination of the marker's presence or absence. Exemplary
microscopic methods include brightfield, phase contrast, fluorescence, and
differential
interference contrast.
[00189] If secondary tests are required on the sample after examination, the
coverslip
may be removed and the slide destained. Destaining involves using the original
solvent
systems used in staining the slide originally without the added dye and in a
reverse order to
the original staining procedure. Destaining may also be completed by soaking
the slide in an
acid alcohol until the cells are colorless. Once colorless the slides are
rinsed well in a water
bath and the second staining procedure applied.
[00190] In addition, specific molecular differentiation may be possible in
conjunction
with the cellular morphological analysis through the use of specific molecular
reagents such
as antibodies or nucleic acid probes or aptamers. This improves the accuracy
of diagnostic
cytology. Micro-dissection can be used to isolate a subset of cells for
additional evaluation,
in particular, for genetic evaluation of abnormal chromosomes, gene
expression, or
mutations.
[00191] Preparation of a tissue sample for histological evaluation involves
fixation,
dehydration, infiltration, embedding, and sectioning. The fixation reagents
used in histology
are very similar or identical to those used in cytology and have the same
issues of preserving
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morphological features at the expense of molecular ones such as individual
proteins. Time
can be saved if the tissue sample is not fixed and dehydrated but instead is
frozen and then
sectioned while frozen. This is a more gentle processing procedure and can
preserve more
individual markers. However, freezing is not acceptable for long term storage
of a tissue
sample as subcellular information is lost due to the introduction of ice
crystals. Ice in the
frozen tissue sample also prevents the sectioning process from producing a
very thin slice and
thus some microscopic resolution and imaging of subcellular structures can be
lost. In
addition to formalin fixation, osmium tetroxide is used to fix and stain
phospholipids
(membranes).
[00192] Dehydration of tissues is accomplished with successive washes of
increasing
alcohol concentration. Clearing employs a material that is miscible with
alcohol and the
embedding material and involves a stepwise process starting at 50:50 alcohol:
clearing reagent
and then 100% clearing agent (xylene or xylene substitute). Infiltration
involves incubating
the tissue with a liquid form of the embedding agent (warm wax, nitrocellulose
solution) first
at 50:50 embedding agent: clearing agent and the 100% embedding agent.
Embedding is
completed by placing the tissue in a mold or cassette and filling with melted
embedding agent
such as wax, agar, or gelatin. The embedding agent is allowed to harden. The
hardened
tissue sample may then be sliced into thin section for staining and subsequent
examination.
[00193] Prior to staining, the tissue section is dewaxed and rehydrated.
Xylene is used
to dewax the section, one or more changes of xylene may be used, and the
tissue is
rehydrated by successive washes in alcohol of decreasing concentration. Prior
to dewax, the
tissue section may be heat immobilized to a glass slide at about 80 C for
about 20 minutes.
[00194] Laser capture micro-dissection allows the isolation of a subset of
cells for
further analysis from a tissue section.
[00195] As in cytology, to enhance the visualization of the microscopic
features, the
tissue section or slice can be stained with a variety of stains. A large menu
of commercially
available stains can be used to enhance or identify specific features.
[00196] To further increase the interaction of molecular reagents with
cytological or
histological samples, a number of techniques for "analyte retrieval" have been
developed.
The first such technique uses high temperature heating of a fixed sample. This
method is also
referred to as heat-induced epitope retrieval or HIER. A variety of heating
techniques have
been used, including steam heating, microwaving, autoclaving, water baths, and
pressure
cooking or a combination of these methods of heating. Analyte retrieval
solutions include,
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for example, water, citrate, and normal saline buffers. The key to analyte
retrieval is the time
at high temperature but lower temperatures for longer times have also been
successfully used.
Another key to analyte retrieval is the pH of the heating solution. Low pH has
been found to
provide the best immunostaining but also gives rise to backgrounds that
frequently require
the use of a second tissue section as a negative control. The most consistent
benefit
(increased immunostaining without increase in background) is generally
obtained with a high
pH solution regardless of the buffer composition. The analyte retrieval
process for a specific
target is empirically optimized for the target using heat, time, pH, and
buffer composition as
variables for process optimization. Using the microwave analyte retrieval
method allows for
sequential staining of different targets with antibody reagents. But the time
required to
achieve antibody and enzyme complexes between staining steps has also been
shown to
degrade cell membrane analytes. Microwave heating methods have improved in
situ
hybridization methods as well.
[00197] To initiate the analyte retrieval process, the section is first
dewaxed and
hydrated. The slide is then placed in 10mM sodium citrate buffer pH 6.0 in a
dish or jar. A
representative procedure uses an 1100W microwave and microwaves the slide at
100% power
for 2 minutes followed by microwaving the slides using 20% power for 18
minutes after
checking to be sure the slide remains covered in liquid. The slide is then
allowed to cool in
the uncovered container and then rinsed with distilled water. HIER may be used
in
combination with an enzymatic digestion to improve the reactivity of the
target to
immunochemical reagents.
[00198] One such enzymatic digestion protocol uses proteinase K. A 20 g/ml
concentration of proteinase K is prepared in 50 mM Tris Base, 1mM EDTA, 0.5%
Triton X-
100, pH 8.0 buffer. The process first involves dewaxing sections in 2 changes
of xylene, 5
minutes each. Then the sample is hydrated in 2 changes of 100% ethanol for 3
minutes each,
95% and 80% ethanol for 1 minute each, and then rinsed in distilled water.
Sections are
covered with Proteinase K working solution and incubated 10-20 minutes at 37 C
in
humidified chamber (optimal incubation time may vary depending on tissue type
and degree
of fixation). The sections are cooled at room temperature for 10 minutes and
then rinsed in
PBS Tween 20 for 2x2 min. If desired, sections can be blocked to eliminate
potential
interference from endogenous compounds and enzymes. The section is then
incubated with
primary antibody at appropriate dilution in primary antibody dilution buffer
for 1 hour at
room temperature or overnight at 4 C. The section is then rinsed with PBS
Tween 20 for 2x2
min. Additional blocking can be performed, if required for the specific
application, followed
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by additional rinsing with PBS Tween 20 for 3x2 min and then finally the
immunostaining
protocol completed.
[00199] A simple treatment with 1% SDS at room temperature has also been
demonstrated to improve immunohistochemical staining. Analyte retrieval
methods have
been applied to slide mounted sections as well as free floating sections.
Another treatment
option is to place the slide in a jar containing citric acid and 0.1 Nonident
P40 at pH 6.0 and
heating to 95 C. The slide is then washed with a buffer solution like PBS.
[00200] For immunological staining of tissues it may be useful to block non -
specific
association of the antibody with tissue proteins by soaking the section in a
protein solution
like serum or non-fat dry milk.
[00201] Blocking reactions may include the need to do any of the following,
either
alone or in combination: reduce the level of endogenous biotin; eliminate
endogenous charge
effects; inactivate endogenous nucleases; and inactivate endogenous enzymes
like peroxidase
and alkaline phosphatase. Endogenous nucleases may be inactivated by
degradation with
proteinase K, by heat treatment, use of a chelating agent such as EDTA or
EGTA, the
introduction of carrier DNA or RNA, treatment with a chaotrope such as urea,
thiourea,
guanidine hydrochloride, guanidine thiocyanate, lithium perchlorate, etc, or
diethyl
pyrocarbonate. Alkaline phosphatase may be inactivated by treated with 0.1N
HCl for 5
minutes at room temperature or treatment with 1 mM levamisole. Peroxidase
activity may be
eliminated by treatment with 0.03% hydrogen peroxide. Endogenous biotin may be
blocked
by soaking the slide or section in an avidin (streptavidin, neutravidin may be
substituted)
solution for at least 15 minutes at room temperature. The slide or section is
then washed for
at least 10 minutes in buffer. This may be repeated at least three times. Then
the slide or
section is soaked in a biotin solution for 10 minutes. This may be repeated at
least three
times with a fresh biotin solution each time. The buffer wash procedure is
repeated.
Blocking protocols should be minimized to prevent damaging either the cell or
tissue
structure or the target or targets of interest but one or more of these
protocols could be
combined to "block" a slide or section prior to reaction with one or more slow
off-rate
aptamers. See Basic Medical Histology: the Biology of Cells, Tissues and
Organs, authored
by Richard G. Kessel, Oxford University Press, 1998.
Determination of Biomarker Values using Mass Spectrometry Methods
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[00202] A variety of configurations of mass spectrometers can be used to
detect
biomarker values. Several types of mass spectrometers are available or can be
produced with
various configurations. In general, a mass spectrometer has the following
major components:
a sample inlet, an ion source, a mass analyzer, a detector, a vacuum system,
and instrument-
control system, and a data system. Difference in the sample inlet, ion source,
and mass
analyzer generally define the type of instrument and its capabilities. For
example, an inlet
can be a capillary-column liquid chromatography source or can be a direct
probe or stage
such as used in matrix-assisted laser desorption. Common ion sources are, for
example,
electrospray, including nanospray and microspray or matrix-assisted laser
desorption.
Common mass analyzers include a quadrupole mass filter, ion trap mass analyzer
and time-
of-flight mass analyzer. Additional mass spectrometry methods are well known
in the art
(see Burlingame et al. Anal. Chem. 70:647 R-716R (1998); Kinter and Sherman,
New York
(2000)).
[00203] Protein biomarkers and biomarker values can be detected and measured
by
any of the following: electrospray ionization mass spectrometry (ESI-MS), ESI-
MS/MS, ESI-
MS/(MS)n, matrix-assisted laser desorption ionization time-of-flight mass
spectrometry
(MALDI-TOF-MS), surface-enhanced laser desorption/ionization time-of-flight
mass
spectrometry (SELDI-TOF-MS), desorption/ionization on silicon (DIOS),
secondary ion
mass spectrometry (SIMS), quadrupole time-of-flight (Q-TOF), tandem time-of-
flight
(TOF/TOF) technology, called ultraflex III TOF/TOF, atmospheric pressure
chemical
ionization mass spectrometry (APCI-MS), APCI-MS/MS, APCI-(MS)N, atmospheric
pressure photoionization mass spectrometry (APPI-MS), APPI-MS/MS, and APPI-
(MS) N,
quadrupole mass spectrometry, Fourier transform mass spectrometry (FTMS),
quantitative
mass spectrometry, and ion trap mass spectrometry.
[00204] Sample preparation strategies are used to label and enrich samples
before mass
spectroscopic characterization of protein biomarkers and determination
biomarker values.
Labeling methods include but are not limited to isobaric tag for relative and
absolute
quantitation (iTRAQ) and stable isotope labeling with amino acids in cell
culture (SILAC).
Capture reagents used to selectively enrich samples for candidate biomarker
proteins prior to
mass spectroscopic analysis include but are not limited to aptamers,
antibodies, nucleic acid
probes, chimeras, small molecules, an F(ab')2 fragment, a single chain
antibody fragment, an
Fv fragment, a single chain Fv fragment, a nucleic acid, a lectin, a ligand-
binding receptor,
affybodies, nanobodies, ankyrins, domain antibodies, alternative antibody
scaffolds (e.g.
diabodies etc) imprinted polymers, avimers, peptidomimetics, peptoids, peptide
nucleic acids,
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threose nucleic acid, a hormone receptor, a cytokine receptor, and synthetic
receptors, and
modifications and fragments of these.
[00205] The foregoing assays enable the detection of biomarker values that are
useful
in methods for diagnosing ovarian cancer, where the methods comprise
detecting, in a
biological sample from an individual, at least N biomarker values that each
correspond to a
biomarker selected from the group consisting of the biomarkers provided in
Table 1, wherein
a classification, as described in detail below, using the biomarker values
indicates whether
the individual has ovarian cancer. While certain of the described ovarian
cancer biomarkers
are useful alone for detecting and diagnosing ovarian cancer, methods are also
described
herein for the grouping of multiple subsets of the ovarian cancer biomarkers
that are each
useful as a panel of three or more biomarkers. Thus, various embodiments of
the instant
application provide combinations comprising N biomarkers, wherein N is at
least three
biomarkers. In other embodiments, N is selected to be any number from 2-42
biomarkers. It
will be appreciated that N can be selected to be any number from any of the
above described
ranges, as well as similar, but higher order, ranges. In accordance with any
of the methods
described herein, biomarker values can be detected and classified individually
or they can be
detected and classified collectively, as for example in a multiplex assay
format.
[00206] In another aspect, methods are provided for detecting an absence of
ovarian
cancer, the methods comprising detecting, in a biological sample from an
individual, at least
N biomarker values that each correspond to a biomarker selected from the group
consisting of
the biomarkers provided in Table 1, wherein a classification, as described in
detail below, of
the biomarker values indicates an absence of ovarian cancer in the individual.
While certain
of the described ovarian cancer biomarkers are useful alone for detecting and
diagnosing the
absence of ovarian cancer, methods are also described herein for the grouping
of multiple
subsets of the ovarian cancer biomarkers that are each useful as a panel of
three or more
biomarkers. Thus, various embodiments of the instant application provide
combinations
comprising N biomarkers, wherein N is at least three biomarkers. In other
embodiments, N is
selected to be any number from 2-42 biomarkers. It will be appreciated that N
can be
selected to be any number from any of the above described ranges, as well as
similar, but
higher order, ranges. In accordance with any of the methods described herein,
biomarker
values can be detected and classified individually or they can be detected and
classified
collectively, as for example in a multiplex assay format.
Classification of Biomarkers and Calculation of Disease Scores
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[00207] A biomarker "signature" for a given diagnostic test contains a set of
markers,
each marker having different levels in the populations of interest. Different
levels, in this
context, may refer to different means of the marker levels for the individuals
in two or more
groups, or different variances in the two or more groups, or a combination of
both. For the
simplest form of a diagnostic test, these markers can be used to assign an
unknown sample
from an individual into one of two groups, either diseased or not diseased.
The assignment of
a sample into one of two or more groups is known as classification, and the
procedure used to
accomplish this assignment is known as a classifier or a classification
method. Classification
methods may also be referred to as scoring methods. There are many
classification methods
that can be used to construct a diagnostic classifier from a set of biomarker
values. In
general, classification methods are most easily performed using supervised
learning
techniques where a data set is collected using samples obtained from
individuals within two
(or more, for multiple classification states) distinct groups one wishes to
distinguish. Since
the class (group or population) to which each sample belongs is known in
advance for each
sample, the classification method can be trained to give the desired
classification response. It
is also possible to use unsupervised learning techniques to produce a
diagnostic classifier.
[00208] Common approaches for developing diagnostic classifiers include
decision
trees; bagging + boosting + forests; rule inference based learning; Parzen
Windows; linear
models; logistic; neural network methods; unsupervised clustering; K-means;
hierarchical
ascending/ descending; semi-supervised learning; prototype methods; nearest
neighbor;
kernel density estimation; support vector machines; hidden Markov models;
Boltzmann
Learning; and classifiers may be combined either simply or in ways which
minimize
particular objective functions. For a review, see, e.g., Pattern
Classification, R.O. Duda, et
al., editors, John Wiley & Sons, 2nd edition, 2001; see also, The Elements of
Statistical
Learning - Data Mining, Inference, and Prediction, T. Hastie, et al., editors,
Springer
Science+Business Media, LLC, 2nd edition, 2009; each of which is incorporated
by reference
in its entirety.
[00209] To produce a classifier using supervised learning techniques, a set of
samples
called training data are obtained. In the context of diagnostic tests,
training data includes
samples from the distinct groups (classes) to which unknown samples will later
be assigned.
For example, samples collected from individuals in a control population and
individuals in a
particular disease population can constitute training data to develop a
classifier that can
classify unknown samples (or, more particularly, the individuals from whom the
samples
were obtained) as either having the disease or being free from the disease.
The development
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of the classifier from the training data is known as training the classifier.
Specific details on
classifier training depend on the nature of the supervised learning technique.
For purposes of
illustration, an example of training a naive Bayesian classifier will be
described below (see,
e.g., Pattern Classification, R.O. Duda, et al., editors, John Wiley & Sons,
2nd edition, 2001;
see also, The Elements of Statistical Learning - Data Mining, Inference, and
Prediction, T.
Hastie, et al., editors, Springer Science+Business Media, LLC, 2nd edition,
2009).
[00210] Since typically there are many more potential biomarker values than
samples
in a training set, care must be used to avoid over-fitting. Over-fitting
occurs when a
statistical model describes random error or noise instead of the underlying
relationship.
Over-fitting can be avoided in a variety of way, including, for example, by
limiting the
number of markers used in developing the classifier, by assuming that the
marker responses
are independent of one another, by limiting the complexity of the underlying
statistical model
employed, and by ensuring that the underlying statistical model conforms to
the data.
[00211] An illustrative example of the development of a diagnostic test using
a set of
biomarkers includes the application of a naive Bayes classifier, a simple
probabilistic
classifier based on Bayes theorem with strict independent treatment of the
biomarkers. Each
biomarker is described by a class-dependent probability density function (pdf)
for the
measured RFU values or log RFU (relative fluorescence units) values in each
class. The joint
pdfs for the set of markers in one class is assumed to be the product of the
individual class-
dependent pdfs for each biomarker. Training a naive Bayes classifier in this
context amounts
to assigning parameters ("parameterization") to characterize the class
dependent pdfs. Any
underlying model for the class-dependent pdfs may be used, but the model
should generally
conform to the data observed in the training set.
[00212] Specifically, the class-dependent probability of measuring a value xi
for
biomarker i in the disease class is written as p(xi I d) and the overall naive
Bayes probability
of observing n markers with values x = (xl, x2.... xn) is written as
n
p(x I d) = fJ p(xi I d)
i=1
where the individual xi s are the measured biomarker levels in RFU or log RFU.
The
classification assignment for an unknown is facilitated by calculating the
probability of being
diseased p(d I x) having measured x compared to the probability of being
disease free
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(control) p(c I x) for the same measured values. The ratio of these
probabilities is computed
from the class-dependent pdfs by application of Bayes theorem, i.e.,
p(c I x) p(x I c)(1 - P(d))
p(d I x) p(x I d)P(d)
where P(d) is the prevalence of the disease in the population appropriate to
the test. Taking
the logarithm of both sides of this ratio and substituting the naive Bayes
class-dependent
probabilities from above gives
p(c I x) n
p(xi I C) (1-P(d))
In p(d l x) - j]n p(xi I d) + In
P(d)
This form is known as the log likelihood ratio and simply states that the log
likelihood of
being free of the particular disease versus having the disease and is
primarily composed of
the sum of individual log likelihood ratios of the n individual biomarkers. In
its simplest
form, an unknown sample (or, more particularly, the individual from whom the
sample was
obtained) is classified as being free of the disease if the above ratio is
greater than zero and
having the disease if the ratio is less than zero.
[00213] In one exemplary embodiment, the class-dependent biomarker pdfs p(xi I
c)
and p(xi I d) are assumed to be normal or log-normal distributions in the
measured RFU
values xi, i.e.
(xi -flc,i )2
1 2
p(xi I c) = e 26c'i
2zac, i
with a similar expression for p(xi I d) with ,ud i and u2 i . Parameterization
of the model
requires estimation of two parameters for each class-dependent pdf, a mean u
and a variance
2, from the training data. This may be accomplished in a number of ways,
including, for
example, by maximum likelihood estimates, by least-squares, and by any other
methods
known to one skilled in the art. Substituting the normal distributions for
p(xi I c) and
p(xi I d) into the log-likelihood ratio defined above gives the following
expression:
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p(c I x) n n 2 2
In - _ j7ln 6d'i - I xi uc,i - xi ,ud'i +ln (1-P(d))
p(d I x) i=1 6c i 2 i=1 ac,a 6d,i P(d)
Once a set of us and d2s have been defined for each pdf in each class from the
training data
and the disease prevalence in the population is specified, the Bayes
classifier is fully
determined and may be used to classify unknown samples with measured values x.
[00214] The performance of the naive Bayes classifier is dependent upon the
number
and quality of the biomarkers used to construct and train the classifier. A
single biomarker
will perform in accordance with its KS-distance (Kolmogorov-Smirnov), as
defined in
Example 3, below. If a classifier performance metric is defined as the sum of
the sensitivity
(fraction of true positives, fTp) and specificity (one minus the fraction of
false positives,
1- fFp ), a perfect classifier will have a score of two and a random
classifier, on average,
will have a score of one. Using the definition of the KS-distance, that value
x* which
maximizes the difference in the cdf functions can be found by solving
aKS a (cdfc (x) -cdfd (x)) _ 0
ax ax
for x which leads to p(x* I c) = p(x* I d), i.e, the KS distance occurs where
the class-
dependent pdfs cross. Substituting this value of x* into the expression for
the KS-distance
yields the following definition for KS
KS = cdfc (x ) - cdfd (x
X x
= f p(x I c)dx - f p(x I d)dx
-00 -00
00 x
=1- f p(xl c)dx- f p(xl d)dx
x -
=1-fFP-fFN,
the KS distance is one minus the total fraction of errors using a test with a
cut-off at x*,
essentially a single analyte Bayesian classifier. Since we define a score of
sensitivity + specificity = 2 - fFP - fFN, combining the above definition of
the KS-distance
we see that sensitivity + specificity =1 + KS . We select biomarkers with a
statistic that is
inherently suited for building naive Bayes classifiers.
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[00215] The addition of subsequent markers with good KS distances (>0.3, for
example) will, in general, improve the classification performance if the
subsequently added
markers are independent of the first marker. Using the sensitivity plus
specificity as a
classifier score, it is straightforward to generate many high scoring
classifiers with a variation
of a greedy algorithm. (A greedy algorithm is any algorithm that follows the
problem solving
metaheuristic of making the locally optimal choice at each stage with the hope
of finding the
global optimum.)
[00216] The algorithm approach used here is described in detail in Example 4.
Briefly,
all single analyte classifiers are generated from a table of potential
biomarkers and added to a
list. Next, all possible additions of a second analyte to each of the stored
single analyte
classifiers is then performed, saving a predetermined number of the best
scoring pairs, say,
for example, a thousand, on a new list. All possible three-marker classifiers
are explored
using this new list of the best two-marker classifiers, again saving the best
thousand of these.
This process continues until the score either plateaus or begins to
deteriorate as additional
markers are added. Those high scoring classifiers that remain after
convergence can be
evaluated for the desired performance for an intended use. For example, in one
diagnostic
application, classifiers with a high sensitivity and modest specificity may be
more desirable
than modest sensitivity and high specificity. In another diagnostic
application, classifiers
with a high specificity and a modest sensitivity may be more desirable. The
desired level of
performance is generally selected based upon a trade-off that must be made
between the
number of false positives and false negatives that can each be tolerated for
the particular
diagnostic application. Such trade-offs generally depend on the medical
consequences of an
error, either false positive or false negative.
[00217] Various other techniques are known in the art and may be employed to
generate many potential classifiers from a list of biomarkers using a naive
Bayes classifier.
In one embodiment, what is referred to as a genetic algorithm can be used to
combine
different markers using the fitness score as defined above. Genetic algorithms
are
particularly well suited to exploring a large diverse population of potential
classifiers. In
another embodiment, so-called ant colony optimization can be used to generate
sets of
classifiers. Other strategies that are known in the art can also be employed,
including, for
example, other evolutionary strategies as well as simulated annealing and
other stochastic
search methods. Metaheuristic methods, such as, for example, harmony search
may also be
employed.
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[00218] Exemplary embodiments use any number of the ovarian cancer biomarkers
listed in Table 1 in various combinations to produce diagnostic tests for
detecting ovarian
cancer (see Example 2 for a detailed description of how these biomarkers were
identified). In
one embodiment, a method for diagnosing ovarian cancer uses a naive Bayes
classification
method in conjunction with any number of the ovarian cancer biomarkers listed
in Table 1.
In an illustrative example (see Example 3), the simplest test for detecting
ovarian cancer from
a population of women with pelvic masses can be constructed using a single
biomarker, for
example, BAFF Receptor which is down-regulated in ovarian cancer with a KS-
distance of
0.39 (1 + KS = 1.39). Using the parameters 1c i , 6c i , ,ud,i and 'd i for
BAFF Receptor
from Table 16 and the equation for the log-likelihood described above, a
diagnostic test with
a sensitivity of 0.74 and specificity of 0.56 (sensitivity + specificity =
1.31) can be produced,
see Table 17. The ROC curve for this test is displayed in Figure 2 and has an
AUC of 0.70.
[00219] Addition of biomarker RGM-C, for example, with a KS-distance of 0.43,
significantly improves the classifier performance to a sensitivity of 82% and
specificity of
0.73% (sensitivity + specificity = 1.51) and an AUC = 0.81. Note that the
score for a
classifier constructed of two biomarkers is not a simple sum of the KS-
distances; KS-
distances are not additive when combining biomarkers, and it takes many more
weak markers
to achieve the same level of performance as a strong marker. Adding a third
marker, HGF,
for example, boosts the classifier performance to 83% sensitivity and 74%
specificity and
AUC = 0.84. Adding additional biomarkers, such as, for example, SLPI, C9, a2-
Antiplasmin, SAP, MMP-7, MCP-3, and HSP90a, produces a series of ovarian
cancer tests
summarized in Table 17 and displayed as a series of ROC curves in Figure 3.
The score of
the classifiers as a function of the number of analytes used in classifier
construction is shown
in Figure 4. This exemplary ten-marker classifier has a sensitivity of 97%and
a specificity of
88% with an AUC of 0.94.
[00220] The markers listed in Table 1 can be combined in many ways to produce
classifiers for diagnosing ovarian cancer. In some embodiments, panels of
biomarkers are
comprised of different numbers of analytes depending on a specific diagnostic
performance
criterion that is selected. For example, certain combinations of biomarkers
will produce tests
that are more sensitive (or more specific) than other combinations.
[00221] Once a panel is defined to include a particular set of biomarkers from
Table 1
and a classifier is constructed from a set of training data, the definition of
the diagnostic test
is complete. In one embodiment, the procedure used to classify an unknown
sample is
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outlined in Figure IA. In another embodiment the procedure used to classify an
unknown
sample is outlined in Figure 1B. The biological sample is appropriately
diluted and then run
in one or more assays to produce the relevant quantitative biomarker levels
used for
classification. The measured biomarker levels are used as input for the
classification method
that outputs a classification and an optional score for the sample that
reflects the confidence
of the class assignment.
[00222] Table 1 identifies 42 biomarkers that are useful for diagnosing
ovarian cancer.
This is a surprisingly larger number than expected when compared to what is
typically found
during biomarker discovery efforts and may be attributable to the scale of the
described
study, which encompassed over 800 proteins measured in hundreds of individual
samples, in
some cases at concentrations in the low femtomolar range. Presumably, the
large number of
discovered biomarkers reflects the diverse biochemical pathways implicated in
both tumor
biology and the body's response to the tumor's presence; each pathway and
process involves
many proteins. The results show that no single protein of a small group of
proteins is
uniquely informative about such complex processes; rather, that multiple
proteins are
involved in relevant processes, such as apoptosis or extracellular matrix
repair, for example.
[00223] Given the numerous biomarkers identified during the described study,
one
would expect to be able to derive large numbers of high-performing classifiers
that can be
used in various diagnostic methods. To test this notion, tens of thousands of
classifiers were
evaluated using the biomarkers in Table 1. As described in Example 4, many
subsets of
the biomarkers presented in Table 1 can be combined to generate useful
classifiers. By way
of example, descriptions are provided for classifiers containing 1, 2, and 3
biomarkers for the
diagnosis of ovarian cancer, particularly, the diagnosis of ovarian cancer in
individuals who
have a pelvic mass that is detectable by CT. As described in Example 4, all
classifiers that
were built using the biomarkers in Table 1 perform distinctly better than
classifiers that were
built using "non-markers".
[00224] The performance of ten-marker classifiers obtained by excluding the
"best"
individual markers from the ten-marker aggregation was tested. As described in
Example 4,
Part 3, classifiers constructed without the "best" markers in Table 1
performed well. Many
subsets of the biomarkers listed in Table 1 performed close to optimally, even
after removing
the top 15 of the markers listed in the Table. This implies that the
performance
characteristics of any particular classifier are likely not due to some small
core group of
biomarkers and that the disease process likely impacts numerous biochemical
pathways,
which alters the expression level of many proteins.
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[00225] The results from Example 4 suggest certain possible conclusions:
First, the
identification of a large number of biomarkers enables their aggregation into
a vast number of
classifiers that offer similarly high performance. Second, classifiers can be
constructed such
that particular biomarkers may be substituted for other biomarkers in a manner
that reflects
the redundancies that undoubtedly pervade the complexities of the underlying
disease
processes. That is to say, the information about the disease contributed by
any individual
biomarker identified in Table 1 overlaps with the information contributed by
other
biomarkers, such that it may be that no particular biomarker or small group of
biomarkers in
Table 1 must be included in any classifier.
[00226] Exemplary embodiments use naive Bayes classifiers constructed from the
data
in Table 18 to classify an unknown sample. The procedure is outlined in
Figures 1A and B.
In one embodiment, the biological sample is optionally diluted and run in a
multiplexed
aptamer assay. The data from the assay are normalized and calibrated as
outlined in Example
3, and the resulting biomarker levels are used as input to a Bayes
classification scheme. The
log-likelihood ratio is computed for each measured biomarker individually and
then summed
to produce a final classification score, which is also referred to as a
diagnostic score. The
resulting assignment as well as the overall classification score can be
reported. Optionally,
the individual log-likelihood risk factors computed for each biomarker level
can be reported
as well. The details of the classification score calculation are presented in
Example 3.
Kits
[00227] Any combination of the biomarkers of Table 1 (as well as additional
biomedical information) can be detected using a suitable kit, such as for use
in performing the
methods disclosed herein. Furthermore, any kit can contain one or more
detectable labels as
described herein, such as a fluorescent moiety, etc.
[00228] In one embodiment, a kit includes (a) one or more capture reagents
(such as,
for example, at least one aptamer or antibody) for detecting one or more
biomarkers in a
biological sample, wherein the biomarkers include any of the biomarkers set
forth in Table 1,
and optionally (b) one or more software or computer program products for
classifying the
individual from whom the biological sample was obtained as either having or
not having
ovarian cancer or for determining the likelihood that the individual has
ovarian cancer, as
further described herein. Alternatively, rather than one or more computer
program products,
one or more instructions for manually performing the above steps by a human
can be
provided.
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[00229] The combination of a solid support with a corresponding capture
reagent and a
signal generating material is referred to herein as a "detection device" or
"kit". The kit can
also include instructions for using the devices and reagents, handling the
sample, and
analyzing the data. Further the kit may be used with a computer system or
software to
analyze and report the result of the analysis of the biological sample.
[00230] The kits can also contain one or more reagents (e.g., solubilization
buffers,
detergents, washes, or buffers) for processing a biological sample. Any of the
kits described
herein can also include, e.g., buffers, blocking agents, mass spectrometry
matrix materials,
antibody capture agents, positive control samples, negative control samples,
software and
information such as protocols, guidance and reference data.
[00231] In one aspect, the invention provides kits for the analysis of ovarian
cancer
status. The kits include PCR primers for one or more biomarkers selected from
Table 1. The
kit may further include instructions for use and correlation of the biomarkers
with ovarian
cancer. The kit may also include any of the following, either alone or in
combination: a DNA
array containing the complement of one or more of the biomarkers selected from
Table 1,
reagents, and enzymes for amplifying or isolating sample DNA. The kits may
include
reagents for real-time PCR, such as, for example, TaqMan probes and/or
primers, and
enzymes.
[00232] For example, a kit can comprise (a) reagents comprising at least
capture
reagent for quantifying one or more biomarkers in a test sample, wherein said
biomarkers
comprise the biomarkers set forth in Table 1, or any other biomarkers or
biomarkers panels
described herein, and optionally (b) one or more algorithms or computer
programs for
performing the steps of comparing the amount of each biomarker quantified in
the test sample
to one or more predetermined cutoffs and assigning a score for each biomarker
quantified
based on said comparison, combining the assigned scores for each biomarker
quantified to
obtain a total score, comparing the total score with a predetermined score,
and using said
comparison to determine whether an individual has ovarian cancer.
Alternatively, rather than
one or more algorithms or computer programs, one or more instructions for
manually
performing the above steps by a human can be provided.
Computer Methods and Software
[00233] Once a biomarker or biomarker panel is selected, a method for
diagnosing an
individual can comprise the following: 1) collect or otherwise obtain a
biological sample; 2)
perform an analytical method to detect and measure the biomarker or biomarkers
in the panel
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in the biological sample; 3) perform any data normalization or standardization
required for
the method used to collect biomarker values; 4) calculate the marker score; 5)
combine the
marker scores to obtain a total diagnostic score; and 6) report the
individual's diagnostic
score. In this approach, the diagnostic score may be a single number
determined from the
sum of all the marker calculations that is compared to a preset threshold
value that is an
indication of the presence or absence of disease. Or the diagnostic score may
be a series of
bars that each represent a biomarker value and the pattern of the responses
may be compared
to a pre-set pattern for determination of the presence or absence of disease.
[00234] At least some embodiments of the methods described herein can be
implemented with the use of a computer. An example of a computer system 100 is
shown in
Figure 6. With reference to Figure 6, system 100 is shown comprised of
hardware elements
that are electrically coupled via bus 108, including a processor 101, input
device 102, output
device 103, storage device 104, computer-readable storage media reader 105a,
communications system 106 processing acceleration (e.g., DSP or special-
purpose
processors) 107 and memory 109. Computer-readable storage media reader 105a is
further
coupled to computer-readable storage media 105b, the combination
comprehensively
representing remote, local, fixed and/or removable storage devices plus
storage media,
memory, etc. for temporarily and/or more permanently containing computer-
readable
information, which can include storage device 104, memory 109 and/or any other
such
accessible system 100 resource. System 100 also comprises software elements
(shown as
being currently located within working memory 191) including an operating
system 192 and
other code 193, such as programs, data and the like.
[00235] With respect to Figure 6, system 100 has extensive flexibility and
configurability. Thus, for example, a single architecture might be utilized to
implement one
or more servers that can be further configured in accordance with currently
desirable
protocols, protocol variations, extensions, etc. However, it will be apparent
to those skilled
in the art that embodiments may well be utilized in accordance with more
specific application
requirements. For example, one or more system elements might be implemented as
sub-
elements within a system 100 component (e.g., within communications system
106).
Customized hardware might also be utilized and/or particular elements might be
implemented
in hardware, software or both. Further, while connection to other computing
devices such as
network input/output devices (not shown) may be employed, it is to be
understood that wired,
wireless, modem, and/or other connection or connections to other computing
devices might
also be utilized.
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[00236] In one aspect, the system can comprise a database containing features
of
biomarkers characteristic of ovarian cancer. The biomarker data (or biomarker
information)
can be utilized as an input to the computer for use as part of a computer
implemented method.
The biomarker data can include the data as described herein.
[00237] In one aspect, the system further comprises one or more devices for
providing
input data to the one or more processors.
[00238] The system further comprises a memory for storing a data set of ranked
data
elements.
[00239] In another aspect, the device for providing input data comprises a
detector for
detecting the characteristic of the data element, e.g., such as a mass
spectrometer or gene chip
reader.
[00240] The system additionally may comprise a database management system.
User
requests or queries can be formatted in an appropriate language understood by
the database
management system that processes the query to extract the relevant information
from the
database of training sets.
[00241] The system may be connectable to a network to which a network server
and
one or more clients are connected. The network may be a local area network
(LAN) or a
wide area network (WAN), as is known in the art. Preferably, the server
includes the
hardware necessary for running computer program products (e.g., software) to
access
database data for processing user requests.
[00242] The system may include an operating system (e.g., UNIX or Linux) for
executing instructions from a database management system. In one aspect, the
operating
system can operate on a global communications network, such as the internet,
and utilize a
global communications network server to connect to such a network.
[00243] The system may include one or more devices that comprise a graphical
display
interface comprising interface elements such as buttons, pull down menus,
scroll bars, fields
for entering text, and the like as are routinely found in graphical user
interfaces known in the
art. Requests entered on a user interface can be transmitted to an application
program in the
system for formatting to search for relevant information in one or more of the
system
databases. Requests or queries entered by a user may be constructed in any
suitable database
language.
[00244] The graphical user interface may be generated by a graphical user
interface
code as part of the operating system and can be used to input data and/or to
display inputted
data. The result of processed data can be displayed in the interface, printed
on a printer in
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communication with the system, saved in a memory device, and/or transmitted
over the
network or can be provided in the form of the computer readable medium.
[00245] The system can be in communication with an input device for providing
data
regarding data elements to the system (e.g., expression values). In one
aspect, the input
device can include a gene expression profiling system including, e.g., a mass
spectrometer,
gene chip or array reader, and the like.
[00246] The methods and apparatus for analyzing ovarian cancer biomarker
information according to various embodiments may be implemented in any
suitable manner,
for example, using a computer program operating on a computer system. A
conventional
computer system comprising a processor and a random access memory, such as a
remotely-
accessible application server, network server, personal computer or
workstation may be used.
Additional computer system components may include memory devices or
information storage
systems, such as a mass storage system and a user interface, for example a
conventional
monitor, keyboard and tracking device. The computer system may be a stand-
alone system
or part of a network of computers including a server and one or more
databases.
[00247] The ovarian cancer biomarker analysis system can provide functions and
operations to complete data analysis, such as data gathering, processing,
analysis, reporting
and/or diagnosis. For example, in one embodiment, the computer system can
execute the
computer program that may receive, store, search, analyze, and report
information relating to
the ovarian cancer biomarkers. The computer program may comprise multiple
modules
performing various functions or operations, such as a processing module for
processing raw
data and generating supplemental data and an analysis module for analyzing raw
data and
supplemental data to generate an ovarian cancer status and/or diagnosis.
Diagnosing ovarian
cancer status may comprise generating or collecting any other information,
including
additional biomedical information, regarding the condition of the individual
relative to the
disease, identifying whether further tests may be desirable, or otherwise
evaluating the health
status of the individual.
[00248] Referring now to Figure 7, an example of a method of utilizing a
computer in
accordance with principles of a disclosed embodiment can be seen. In Figure 7,
a flowchart
3000 is shown. In block 3004, biomarker information can be retrieved for an
individual. The
biomarker information can be retrieved from a computer database, for example,
after testing
of the individual's biological sample is performed. The biomarker information
can comprise
biomarker values that each correspond to one of at least N biomarkers selected
from a group
consisting of the biomarkers provided in Table 1, wherein N = 2-42. In block
3008, a
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computer can be utilized to classify each of the biomarker values. And, in
block 3012, a
determination can be made as to the likelihood that an individual has ovarian
cancer based
upon a plurality of classifications. The indication can be output to a display
or other
indicating device so that it is viewable by a person. Thus, for example, it
can be displayed on
a display screen of a computer or other output device.
[00249] Referring now to Figure 8, an alternative method of utilizing a
computer in
accordance with another embodiment can be illustrated via flowchart 3200. In
block 3204, a
computer can be utilized to retrieve biomarker information for an individual.
The biomarker
information comprises a biomarker value corresponding to a biomarker selected
from the
group of biomarkers provided in Table 1. In block 3208, a classification of
the biomarker
value can be performed with the computer. And, in block 3212, an indication
can be made as
to the likelihood that the individual has ovarian cancer based upon the
classification. The
indication can be output to a display or other indicating device so that it is
viewable by a
person. Thus, for example, it can be displayed on a display screen of a
computer or other
output device.
[00250] Some embodiments described herein can be implemented so as to include
a
computer program product. A computer program product may include a computer
readable
medium having computer readable program code embodied in the medium for
causing an
application program to execute on a computer with a database.
[00251] As used herein, a "computer program product" refers to an organized
set of
instructions in the form of natural or programming language statements that
are contained on
a physical media of any nature (e.g., written, electronic, magnetic, optical
or otherwise) and
that may be used with a computer or other automated data processing system.
Such
programming language statements, when executed by a computer or data
processing system,
cause the computer or data processing system to act in accordance with the
particular content
of the statements. Computer program products include without limitation:
programs in
source and object code and/or test or data libraries embedded in a computer
readable
medium. Furthermore, the computer program product that enables a computer
system or data
processing equipment device to act in pre-selected ways may be provided in a
number of
forms, including, but not limited to, original source code, assembly code,
object code,
machine language, encrypted or compressed versions of the foregoing and any
and all
equivalents.
[00252] In one aspect, a computer program product is provided for indicating a
likelihood of ovarian cancer. The computer program product includes a computer
readable
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medium embodying program code executable by a processor of a computing device
or
system, the program code comprising: code that retrieves data attributed to a
biological
sample from an individual, wherein the data comprises biomarker values that
each
correspond to one of at least N biomarkers in the biological sample selected
from the group
of biomarkers provided in Table 1, wherein N = 2-42; and code that executes a
classification
method that indicates an ovarian disease status of the individual as a
function of the
biomarker values.
[00253] In still another aspect, a computer program product is provided for
indicating a
likelihood of ovarian cancer. The computer program product includes a computer
readable
medium embodying program code executable by a processor of a computing device
or
system, the program code comprising: code that retrieves data attributed to a
biological
sample from an individual, wherein the data comprises a biomarker value
corresponding to a
biomarker in the biological sample selected from the group of biomarkers
provided in Table
1; and code that executes a classification method that indicates an ovarian
disease status of
the individual as a function of the biomarker value.
[00254] While various embodiments have been described as methods or
apparatuses, it
should be understood that embodiments can be implemented through code coupled
with a
computer, e.g., code resident on a computer or accessible by the computer. For
example,
software and databases could be utilized to implement many of the methods
discussed above.
Thus, in addition to embodiments accomplished by hardware, it is also noted
that these
embodiments can be accomplished through the use of an article of manufacture
comprised of
a computer usable medium having a computer readable program code embodied
therein,
which causes the enablement of the functions disclosed in this description.
Therefore, it is
desired that embodiments also be considered protected by this patent in their
program code
means as well. Furthermore, the embodiments may be embodied as code stored in
a
computer-readable memory of virtually any kind including, without limitation,
RAM, ROM,
magnetic media, optical media, or magneto-optical media. Even more generally,
the
embodiments could be implemented in software, or in hardware, or any
combination thereof
including, but not limited to, software running on a general purpose
processor, microcode,
PLAs, or ASICs.
[00255] It is also envisioned that embodiments could be accomplished as
computer
signals embodied in a carrier wave, as well as signals (e.g., electrical and
optical) propagated
through a transmission medium. Thus, the various types of information
discussed above
CA 02737004 2011-03-09
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could be formatted in a structure, such as a data structure, and transmitted
as an electrical
signal through a transmission medium or stored on a computer readable medium.
[00256] It is also noted that many of the structures, materials, and acts
recited herein
can be recited as means for performing a function or step for performing a
function.
Therefore, it should be understood that such language is entitled to cover all
such structures,
materials, or acts disclosed within this specification and their equivalents,
including the
matter incorporated by reference.
EXAMPLES
[00257] The following examples are provided for illustrative purposes only and
are not
intended to limit the scope of the application as defined by the appended
claims. All
examples described herein were carried out using standard techniques, which
are well known
and routine to those of skill in the art. Routine molecular biology techniques
described in the
following examples can be carried out as described in standard laboratory
manuals, such as
Sambrook et al., Molecular Cloning: A Laboratory Manual, 3rd. ed., Cold Spring
Harbor
Laboratory Press, Cold Spring Harbor, N.Y., (2001).
Example 1. Multiplexed Aptamer Analysis of Samples For Ovarian Cancer
Biomarker
Selection
[00258] This example describes the multiplex aptamer assay used to analyze the
samples and controls for the identification of the biomarkers set forth in
Table 1 (see Figure
9). In this case, the multiplexed analysis utilized 811 aptamers, each unique
to a specific
target.
[00259] In this method, pipette tips were changed for each solution addition.
[00260] Also, unless otherwise indicated, most solution transfers and wash
additions
used the 96-well head of a Beckman Biomek Fx' . Method steps manually pipetted
used a
twelve channel P200 Pipetteman (Rainin Instruments, LLC, Oakland, CA), unless
otherwise
indicated. A custom buffer referred to as SB 17 was prepared in-house,
comprising 40mM
HEPES, 100mM NaCl, 5mM KC1, 5mM MgC12, 1mM EDTA at pH7.5. All steps were
performed at room temperature unless otherwise indicated.
[00261] 1. Preparation of Aptamer Stock Solution
[00262] For aptamers without a photo-cleavable biotin linker, custom stock
aptamer
solutions for 10%, 1% and 0.03% plasma were prepared at 8x concentration in lx
SB17,
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0.05% Tween-20 with appropriate photo-cleavable, biotinylated primers, where
the resultant
primer concentration was 3 times the relevant aptamer concentration. The
primers hybridized
to all or part of the corresponding aptamer.
[00263] Each of the 3, 8x aptamer solutions were diluted separately 1:4 into
1xSB17,
0.05% Tween-20 (1500 L of 8x stock into 4500 L of 1xSB17, 0.05% Tween-20) to
achieve a 2x concentration. Each diluted aptamer master mix was then split,
1500 L each,
into 4, 2 mL screw cap tubes and brought to 95 C for 5 minutes, followed by a
37 C
incubation for 15 minutes. After incubation, the 4, 2 mL tubes corresponding
to a particular
aptamer master mix were combined into a reagent trough, and 55 L of a 2x
aptamer mix (for
all three mixes) was manually pipetted into a 96-well Hybaid plate and the
plate foil sealed.
The final result was 3, 96-well, foil-sealed Hybaid plates. The individual
aptamer
concentration was 0.5 nM.
[00264] 2. Assay Sample Preparation
[00265] Frozen aliquots of 100% plasma, stored at -80 C, were placed in 25 C
water
bath for 10 minutes. Thawed samples were placed on ice, gently vortexed (set
on 4) for 8
seconds and then replaced on ice.
[00266] A 20% sample solution was prepared by transferring 16 L of sample
using a
50 L 8-channel spanning pipettor into 96-well Hybaid plates, each well
containing 64 L of
the appropriate sample diluent at 4 C (0.8x SB 17, 0.05% Tween-20, 2 M Z-
block_2, 0.6
mM MgCl2 for plasma). This plate was stored on ice until the next sample
dilution steps
were initiated.
[00267] To commence sample and aptamer equilibration, the 20% sample plate was
briefly centrifuged and placed on the Beckman FX where it was mixed by
pipetting up and
down with the 96-well pipettor. A 2% sample was then prepared by diluting 10
L of the
20% sample into 90 L of lxSB17, 0.05% Tween-20. Next, dilution of 6 L of the
resultant
2% sample into 194 L of lxSB17, 0.05% Tween-20 made a 0.06% sample plate.
Dilutions
were done on the Beckman Biomek Fx'. After each transfer, the solutions were
mixed by
pipetting up and down. The 3 sample dilution plates were then transferred to
their respective
aptamer solutions by adding 55 L of the sample to 55 L of the appropriate 2x
aptamer mix.
The sample and aptamer solutions were mixed on the robot by pipetting up and
down.
[00268] 3. Sample Equilibration binding
[00269] The sample/aptamer plates were foil sealed and placed into a 37 C
incubator
for 3.5 hours before proceeding to the Catch 1 step.
[00270] 4. Preparation of Catch 2 bead plate
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[00271] An 11 mL aliquot of MyOne (Invitrogen Corp., Carlsbad, CA)
Streptavidin Cl
beads was washed 2 times with equal volumes of 20 mM NaOH (5 minute incubation
for
each wash), 3 times with equal volumes of lx SB 17, 0.05% Tween-20 and
resuspended in 11
mL lx SB17, 0.05% Tween-20. Using a 12-span multichannel pipettor, 50 L of
this
solution was manually pipetted into each well of a 96-well Hybaid plate. The
plate was then
covered with foil and stored at 4 C for use in the assay.
[00272] 5. Preparation of Catch 1 bead plates
[00273] Three 0.45 m Millipore HV plates (Durapore membrane, Cat#
MAHVN4550) were equilibrated with 100 L of lx SB17, 0.05% Tween-20 for at
least 10
minutes. The equilibration buffer was then filtered through the plate and
133.3 L of a 7.5%
Streptavidin-agarose bead slurry (in lx SB17, 0.05% Tween-20) was added into
each well.
To keep the streptavidin-agarose beads suspended while transferring them into
the filter plate,
the bead solution was manually mixed with a 200 L, 12-channel pipettor, 15
times. After the
beads were distributed across the 3 filter plates, a vacuum was applied to
remove the bead
supernatant. Finally, the beads were washed in the filter plates with 200 L
lx SB17, 0.05%
Tween-20 and then resuspended in 200 L lx SB 17, 0.05% Tween-20. The bottoms
of the
filter plates were blotted and the plates stored for use in the assay.
[00274] 6. Loading the Cytomat
[00275] The cytomat was loaded with all tips, plates, all reagents in troughs
(except
NHS-biotin reagent which was prepared fresh right before addition to the
plates), 3 prepared
catch 1 filter plates and 1 prepared MyOne plate.
[00276] 7. Catch 1
[00277] After a 3.5 hour equilibration time, the sample/aptamer plates were
removed
from the incubator, centrifuged for about 1 minute, foil removed, and placed
on the deck of
the Beckman Biomek FxP. The Beckman Biomek FxP program was initiated. All
subsequent
steps in Catch 1 were performed by the Beckman Biomek FxP robot unless
otherwise noted.
Within the program, the vacuum was applied to the Catch 1 filter plates to
remove the bead
supernatant. One hundred microlitres of each of the 10%, 1% and 0.03%
equilibration
binding reactions were added to their respective Catch 1 filtration plates,
and each plate was
mixed using an on-deck orbital shaker at 800 rpm for 10 minutes.
[00278] Unbound solution was removed via vacuum filtration. The catch 1 beads
were
washed with 190 L of 100 M biotin in lx SB17, 0.05% Tween-20 followed by 190
L of
lx SB17, 0.05% Tween-20 by dispensing the solution and immediately drawing a
vacuum to
filter the solution through the plate.
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[00279] Next, 190 L lx SB17, 0.05% Tween-20 was added to the Catch 1 plates.
Plates were blotted to remove droplets using an on-deck blot station and then
incubated with
orbital shakers at 800 rpm for 10 minutes at 25 C.
[00280] The robot removed this wash via vacuum filtration and blotted the
bottom of
the filter plate to remove droplets using the on-deck blot station.
[00281] 8. Tagging
[00282] A NHS-PEO4-biotin aliquot was thawed at 37 C for 6 minutes and then
diluted 1:100 with tagging buffer (SB 17 at pH=7.25 0.05% Tween-20). The NHS-
PEO4-
biotin reagent was dissolved at 100 mM concentration in anhydrous DMSO and had
been
stored frozen at -20 C. Upon a robot prompt, the diluted NHS-PEO4-biotin
reagent was
manually added to an on-deck trough and the robot program was manually re-
initiated to
dispense 100 L of the NHS-PEO4-biotin into each well of each Catch 1 filter
plate. This
solution was allowed to incubate with Catch 1 beads shaking at 800 rpm for 5
minutes on the
obital shakers.
[00283] 9. Kinetic Challenge and Photo-cleavage
[00284] The tagging reaction was quenched by the addition of 150 L of 20 mM
glycine in lx SB17, 0.05% Tween-20 to the Catch 1 plates while still
containing the NHS
tag. The plates were then incubated for 1 minute on orbital shakers at 800
rpm. The NHS-
tag/glycine solution was removed via vacuum filtration. Next, 190 L 20 mM
glycine (lx
SB17, 0.05% Tween-20) was added to each plate and incubated for 1 minute on
orbital
shakers at 800 rpm before removal by vacuum filtration.
[00285] 190 L of lx SB 17, 0.05% Tween-20 was added to each plate and removed
by
vacuum filtration.
[00286] The wells of the Catch 1 plates were subsequently washed three times
by
adding 190 L lx SB17, 0.05% Tween-20, placing the plates on orbital shakers
for 1 minute
at 800 rpm followed by vacuum filtration. After the last wash the plates were
placed on top
of a 1 mL deep-well plate and removed from the deck. The Catch 1 plates were
centrifuged
at 1000 rpm for 1 minute to remove as much extraneous volume from the agarose
beads
before elution as possible.
[00287] The plates were placed back onto the Beckman Biomek FxP and 85 L of
10
mM DxS04 in lx SB17, 0.05% Tween-20 was added to each well of the filter
plates.
[00288] The filter plates were removed from the deck, placed onto a Variomag
Thermoshaker (Thermo Fisher Scientific, Inc., Waltham, MA ) under the BlackRay
(Ted
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Pella, Inc., Redding, CA) light sources, and irradiated for 10 minutes while
shaking at 800
rpm.
[00289] The photocleaved solutions were sequentially eluted from each Catch 1
plate
into a common deep well plate by first placing the 10% Catch 1 filter plate on
top of a 1 mL
deep-well plate and centrifuging at 1000 rpm for 1 minute. The 1% and 0.03%
catch 1 plates
were then sequentially centrifuged into the same deep well plate.
[00290] 10. Catch 2 bead capture
[00291] The 1 mL deep well block containing the combined eluates of catch 1
was
placed on the deck of the Beckman Biomek FxP for catch 2.
[00292] The robot transferred all of the photo-cleaved eluate from the 1 mL
deep-well
plate onto the Hybaid plate containing the previously prepared catch 2 MyOne
magnetic
beads (after removal of the MyOne buffer via magnetic separation).
[00293] The solution was incubated while shaking at 1350 rpm for 5 minutes at
25 C
on a Variomag Thermoshaker (Thermo Fisher Scientific, Inc., Waltham, MA).
[00294] The robot transferred the plate to the on deck magnetic separator
station. The
plate was incubated on the magnet for 90 seconds before removal and discarding
of the
supernatant.
[00295] 11. 37 C 30% glycerol washes
[00296] The catch 2 plate was moved to the on-deck thermal shaker and 75 L of
lx
SB17, 0.05% Tween-20 was transferred to each well. The plate was mixed for 1
minute at
1350 rpm and 37 C to resuspend and warm the beads. To each well of the catch 2
plate, 75
L of 60% glycerol at 37 C was transferred and the plate continued to mix for
another
minute at 1350 rpm and 37 C. The robot transferred the plate to the 37 C
magnetic separator
where it was incubated on the magnet for 2 minutes and then the robot removed
and
discarded the supernatant. These washes were repeated two more times.
[00297] After removal of the third 30% glycerol wash from the catch 2 beads,
150 L
of lx SB17, 0.05% Tween-20 was added to each well and incubated at 37 C,
shaking at 1350
rpm for 1 minute, before removal by magnetic separation on the 37 C magnet.
[00298] The catch 2 beads were washed a final time using 150 L lx SB19, 0.05%
Tween-20 with incubation for 1 minute while shaking at 1350 rpm, prior to
magnetic
separation.
[00299] 12. Catch 2 Bead Elution and Neutralization
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[00300] The aptamers were eluted from catch 2 beads by adding 105 L of 100 mM
CAPSO with 1 M NaCl, 0.05% Tween-20 to each well. The beads were incubated
with this
solution with shaking at 1300 rpm for 5 minutes.
[00301] The catch 2 plate was then placed onto the magnetic separator for 90
seconds
prior to transferring 90 L of the eluate to a new 96-well plate containing 10
L of 500 mM
HC1, 500 mM HEPES, 0.05% Tween-20 in each well. After transfer, the solution
was mixed
robotically by pipetting 90 L up and down five times.
[00302] 13. Hybridization
[00303] The Beckman Biomek FxP transferred 20 L of the neutralized catch 2
eluate
to a fresh Hybaid plate, and 5 L of lOx Agilent Block, containing a lOx spike
of
hybridization controls, was added to each well. Next, 25 L of 2x Agilent
Hybridization
buffer was manually pipetted to the each well of the plate containing the
neutralized samples
and blocking buffer and the solution was mixed by manually pipetting 25 L up
and down 15
times slowly to avoid extensive bubble formation. The plate was spun at 1000
rpm for 1
minute.
[00304] A gasket slide was placed into an Agilent hybridization chamber and 40
L of
each of the samples containing hybridization and blocking solution was
manually pipetted
into each gasket. An 8-channel variable spanning pipettor was used in a manner
intended to
minimize bubble formation. Custom Agilent microarray slides (Agilent
Technologies, Inc.,
Santa Clara, CA), with their Number Barcode facing up, were then slowly
lowered onto the
gasket slides (see Agilent manual for detailed description).
[00305] The top of the hybridization chambers were placed onto the
slide/backing
sandwich and clamping brackets slid over the whole assembly. These assemblies
were
tightly clamped by turning the screws securely.
[00306] Each slide/backing slide sandwich was visually inspected to assure the
solution bubble could move freely within the sample. If the bubble did not
move freely the
hybridization chamber assembly was gently tapped to disengage bubbles lodged
near the
gasket.
[00307] The assembled hybridization chambers were incubated in an Agilent
hybridization oven for 19 hours at 60 C rotating at 20 rpm.
[00308] 14. Post Hybridization Washing
[00309] Approximately 400 mL Agilent Wash Buffer 1 was placed into each of two
separate glass staining dishes. One of the staining dishes was placed on a
magnetic stir plate
and a slide rack and stir bar were placed into the buffer.
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[00310] A staining dish for Agilent Wash 2 was prepared by placing a stir bar
into an
empty glass staining dish.
[00311] A fourth glass staining dish was set aside for the final acetonitrile
wash.
[00312] Each of six hybridization chambers was disassembled. One-by-one, the
slide/backing sandwich was removed from its hybridization chamber and
submerged into the
staining dish containing Wash 1. The slide/backing sandwich was pried apart
using a pair of
tweezers, while still submerging the microarray slide. The slide was quickly
transferred into
the slide rack in the Wash 1 staining dish on the magnetic stir plate.
[00313] The slide rack was gently raised and lowered 5 times. The magnetic
stirrer was
turned on at a low setting and the slides incubated for 5 minutes.
[00314] When one minute was remaining for Wash 1, Wash Buffer 2 pre-warmed to
37 C in an incubator was added to the second prepared staining dish. The slide
rack was
quickly transferred to Wash Buffer 2 and any excess buffer on the bottom of
the rack was
removed by scraping it on the top of the stain dish. The slide rack was gently
raised and
lowered 5 times. The magnetic stirrer was turned on at a low setting and the
slides incubated
for 5 minutes.
[00315] The slide rack was slowly pulled out of Wash 2, taking approximately
15
seconds to remove the slides from the solution.
[00316] With one minute remaining in Wash 2 acetonitrile (ACN) was added to
the
fourth staining dish. The slide rack was transferred to the acetonitrile stain
dish. The slide
rack was gently raised and lowered 5 times. The magnetic stirrer was turned on
at a low
setting and the slides incubated for 5 minutes.
[00317] The slide rack was slowly pulled out of the ACN stain dish and placed
on an
absorbent towel. The bottom edges of the slides were quickly dried and the
slide was placed
into a clean slide box.
[00318] 15. Microarray Imaging
[00319] The microarray slides were placed into Agilent scanner slide holders
and
loaded into the Agilent Microarray scanner according to the manufacturer's
instructions.
[00320] The slides were imaged in the Cy3-channel at 5 m resolution at the
100%
PMT setting and the XRD option enabled at 0.05. The resulting tiff images were
processed
using Agilent feature extraction software version 10.5.
Example 2. Biomarker Identification
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[00321] The identification of potential ovarian cancer biomarkers was
performed for
diagnosis of ovarian cancer in women with pelvic masses. Enrollment criteria
for this study
were women scheduled for laparotomy or pelvic surgery for suspicion of ovarian
cancer. The
primary criteria for exclusion were women suffering from chronic infectious
(e.g. hepatitis B,
Hepatitis C or HIV), autoimmune, or inflammatory conditions or women being
treated for
malignancy (other than basal or squamous cell carcinomas of the skin) within
the last two
years. Plasma samples were collected from two different clinical sites and
included 142
cases and 195 benign controls. Table 19 summarizes the site sample
information. The
multiplexed aptamer affinity assay was used to measure and report the RFU
value for 811
analytes in each of these 337 samples. Since the plasma samples were obtained
from two
independent sites under similar protocols, an examination of site differences
prior to the
analysis for biomarkers discovery was performed. Each of the two populations,
benign
pelvic mass and ovarian cancer, was separately compared between sites by
generating within-
site, class-dependent cumulative distribution functions (cdfs) for each of the
811 analytes.
The KS-test was then applied to each analyte between both site pairs within a
common class
to identify those analytes that differed not by class but rather by site. In
both site
comparisons among the two classes, statistically significant site-dependent
differences were
observed.
[00322] Such site-dependent effects tend to obscure the ability to identify
specific
control-disease differences. In order to minimize such effects and identify
key disease
dependent biomarkers, three distinct strategies were employed for biomarker
discovery,
namely (1) aggregated class-dependent cdfs across sites, (2) comparison of
within-site class-
dependent cdfs, and (3) blending methods (1) with (2). Details of these three
methodologies
and their results follow.
[00323] These three sets of potential biomarkers can be used to build
classifiers that
assign samples to either a control or disease group. In fact, many such
classifiers were
produced from these sets of biomarkers and the frequency with which any
biomarker was
used in good scoring classifiers determined. Those biomarkers that occurred
most frequently
among the top scoring classifiers were the most useful for creating a
diagnostic test. In this
example, Bayesian classifiers were used to explore the classification space
but many other
supervised learning techniques may be employed for this purpose. The scoring
fitness of any
individual classifier was gauged by summing the sensitivity and specificity of
the classifier at
the Bayesian surface assuming a disease prevalence of 0.5. This scoring metric
varies from
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zero to two, with two being an error-free classifier. The details of
constructing a Bayesian
classifier from biomarker population measurements are described in Example 3.
[00324] By aggregating the class-dependent samples across all sites in method
(1),
those analyte measurements that showed large site-to-site variation, on
average, failed to
exhibit class-dependent differences due to the large site-to-site differences.
Such analytes
were automatically removed from further analysis. However, those analytes that
did show
class-dependent differences across the sites are robust biomarkers that were
relatively
insensitive to sample collection and sample handling variability. KS-distances
were
computed for all analytes using the class-dependent cdfs aggregated across all
sites. Using a
KS-distance threshold of 0.4, fifty-nine potential biomarkers for diagnosing
malignant
ovarian cancer from benign pelvic masses were identified.
[00325] Using the fifty-nine potential biomarkers identified above, a total of
1966 10-
analyte classifiers were found with a score of 1.75 or better (>87.5%
sensitivity and >87.5%
specificity, on average) for diagnosing ovarian cancer from a control group
with benign
pelvic masses using measurements from both sites. From this set of
classifiers, a total of
twenty-five biomarkers were found to be present in 5.0% or more of the high
scoring
classifiers. Table 20 provides a list of these potential biomarkers and Figure
10 is a frequency
plot for the identified biomarkers. This completed the biomarker
identification using method
(1).
[00326] Method (2) focused on consistency of potential biomarker changes
between
the control and case groups among the individual sites. The class-dependent
cdfs were
constructed for all analytes within each site separately and from these cdfs
the KS-distances
were computed to identify potential biomarkers. Sixty-three analytes were
found to have a
KS-distance greater than 0.4 in all the sites. Using these Sixty-three
analytes to build
potential 10-analyte Bayesian classifiers, there were 2031 classifiers that
had a score of 1.75
or better. Twenty-four analytes occurred with a frequency greater than 5%
among these
classifiers and are presented in Table 21 and shown in Figure 11.
[00327] Finally, by combining the criteria for potential biomarker selection
described
for method (1) and (2) above, a set of potential biomarkers were produced by
requiring an
analyte to have a KS distance of 0.4 or better in the aggregated set as well
as the two site
comparisons. Forty-five analytes satisfy these requirements and are referred
to as a blended
set of potential biomarkers. For a classification score of 1.75 or better, a
total of 1563
Bayesian classifiers were built from this set of potential biomarkers and
twenty-seven
biomarkers were identified from this set of classifiers using a frequency cut-
off of 5%. These
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analytes are displayed in Table 22 and Figure 12 is a frequency plot for the
identified
biomarkers.
[00328] A final list of biomarkers is obtained by combining the three sets of
biomarkers identified above with frequencies greater than 5% in high scoring
classifiers,
Tables 20-22. From these sets of twenty-five, twenty-four, and twenty-seven
biomarkers,
forty-two unique biomarkers were identified and are shown in Table 1. Table 15
includes a
dissociation constant for the aptamer used to identify the biomarker, the
limit of
quantification for the marker in the multiplex aptamer assay, and whether the
marker was up-
regulated or down-regulated in the disease population relative to the control
population.
Example 3. Naive Bayesian Classification for Ovarian Cancer
[00329] From the list of biomarkers identified as useful for discriminating
between
benign pelvic masses and ovarian malignancies, a panel of ten biomarkers was
selected and a
naive Bayes classifier was constructed, see Table 18. The class-dependent
probability
density functions (pdfs), p(xi I c) and p(xi I d), where xi is the measured
RFU value for
biomarker i, and c and d refer to the control and disease populations, were
modeled as
normal distribution functions characterized by a mean p and variance u . The
parameters for
pdfs of the ten biomarkers are listed in Table 18 and an example of the raw
data along with
the model fit to a normal cdf is shown in Figure 5 for biomarker BAFF
Receptor. The
underlying assumption appears to fit the data quite well as evidenced by
Figure 5.
[00330] The naive Bayes classification for such a model is given by the
following
equation, where P(d) is the prevalence of the disease in the population -11
p(c l x) n 2 2
in 6d,i 1 xi -PC,i - xi -ftd,i + (1-P(d))
In X
I In
p(d I x) =
ac,i 2 6ci 6d,i P(d)
appropriate to the test and n = 10 here. Each of the terms in the summation is
a log-likelihood
ratio for an individual marker and the total log-likelihood ratio of a sample
x being free from
the disease of interest versus having the disease (i.e. in this case, ovarian
cancer) is simply
the sum of these individual terms plus a term that accounts for the prevalence
of the disease.
For simplicity, we assume P(d) = 0.5 so that In (1 P(d) )) = 0.
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[00331] Given an unknown sample measurement in RFU for each of the ten
biomarkers of x = (701, 34158,182792,19531,170310, 896, 3207, 22545,
733,12535) , the
calculation of the classification is detailed in Table 23. The individual
components
comprising the log likelihood ratio for control versus disease class are
tabulated and can be
computed from the parameters in Table 18 and the values of x. The sum of the
individual log
likelihood ratios is 1.965, or a likelihood of being free from the disease
versus having the
disease of 7:1, where likelihood = e1.965 = 7.14. Four of the ten biomarker
values have
likelihoods more consistent with the disease group (log likelihood < 0) while
the remaining
six biomarkers favor the control group, the largest by a factor of 3.5:1.
Multiplying the
likelihoods together gives the same result as that shown above; an aggregate
likelihood of 7:1
that the unknown sample is free from the disease. In fact, this sample came
from the control
population in the training set.
Example 4. Greedy Algorithm for Selecting Biomarker Panels for Classifiers.
Part 1
[00332] This example describes the selection of biomarkers from Table 1 to
form
panels that can be used as classifiers in any of the methods described herein.
Subsets of the
biomarkers in Table 1 were selected to construct classifiers with good
performance. This
method was also used to determine which potential markers were included as
biomarkers in
Example 2.
[00333] The measure of classifier performance used here is the sum of the
sensitivity
and specificity; a performance of 1.0 is the baseline expectation for a random
(coin toss)
classifier, a classifier worse than random would score between 0.0 and 1.0, a
classifier with
better than random performance would score between 1.0 and 2Ø A perfect
classifier with
no errors would have a sensitivity of 1.0 and a specificity of 1.0, therefore
a performance of
2.0 (1.0+1.0). One can apply other common measures of performance such as area
under the
ROC curve, the F-measure, or the product of sensitivity and specificity.
Specifically one
might want to treat sensitivity and specificity with differing weight, in
order to select those
classifiers that perform with higher specificity at the expense of some
sensitivity, or to select
those classifiers which perform with higher sensitivity at the expense of some
specificity.
Since the method described here only involves a measure of "performance", any
weighting
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scheme which results in a single performance measure can be used. Different
applications
will have different benefits for true positive and true negative findings, and
will have
different costs associated with false positive findings from false negative
findings. For
example, screening and the differential diagnosis of benign pelvic masses will
not in general
have the same optimal trade-off between specificity and sensitivity. The
different demands
of the two tests will in general require setting different weighting to
positive and negative
misclassifications, which will be reflected in the performance measure.
Changing the
performance measure will in general change the exact subset of markers
selected from Table
1 for a given set of data.
[00334] For the Bayesian approach to the discrimination of ovarian cancer
samples
from control samples described in Example 3, the classifier was completely
parameterized by
the distributions of biomarkers in the disease and non-disease training
samples, and the list of
biomarkers was chosen from Table 1; that is to say, the subset of markers
chosen
for inclusion determined a classifier in a one-to-one manner given a set of
training data.
[00335] The greedy method employed here was used to search for the optimal
subset
of markers from Table 1. For small numbers of markers or classifiers with
relatively few
markers, every possible subset of markers was enumerated and evaluated in
terms of the
performance of the classifier constructed with that particular set of markers
(see Example 4,
Part 2). (This approach is well known in the field of statistics as "best
subset selection"; see,
e.g., Hastie et al, supra). However, for the classifiers described herein, the
number of
combinations of multiple markers can be very large, and it was not feasible to
evaluate every
possible set of 10 markers, for example, from the list of 42 markers (Table 1)
(i.e.,
1,471,442,973 combinations). Because of the impracticality of searching
through every
subset of markers, the single optimal subset may not be found; however, by
using this
approach, many excellent subsets were found, and, in many cases, any of these
subsets may
represent an optimal one.
[00336] Instead of evaluating every possible set of markers, a "greedy"
forward
stepwise approach may be followed (see, e.g., Dabney AR, Storey JD (2007)
Optimality
Driven Nearest Centroid Classification from Genomic Data. PLoS ONE 2(10):
e1002.
doi:10.1371/journal.pone.0001002). Using this method, a classifier is started
with the best
single marker (based on KS-distance for the individual markers) and is grown
at each step by
trying, in turn, each member of a marker list that is not currently a member
of the set of
markers in the classifier. The one marker that scores the best in combination
with the
existing classifier is added to the classifier. This is repeated until no
further improvement in
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performance is achieved. Unfortunately, this approach may miss valuable
combinations of
markers for which some of the individual markers are not all chosen before the
process stops.
[00337] The greedy procedure used here was an elaboration of the preceding
forward
stepwise approach, in that, to broaden the search, rather than keeping just a
single candidate
classifier (marker subset) at each step, a list of candidate classifiers was
kept. The list was
seeded with every single marker subset (using every marker in the table on its
own). The list
was expanded in steps by deriving new classifiers (marker subsets) from the
ones currently
on the list and adding them to the list. Each marker subset currently on the
list was extended
by adding any marker from Table 1 not already part of that classifier, and
which would not,
on its addition to the subset, duplicate an existing subset (these are termed
"permissible
markers"). Every existing marker subset was extended by every permissible
marker from the
list. Clearly, such a process would eventually generate every possible subset,
and the list
would run out of space. Therefore, all the generated classifiers were kept
only while the list
was less than some predetermined size (often enough to hold all three marker
subsets). Once
the list reached the predetermined size limit, it became elitist; that is,
only those classifiers
which showed a certain level of performance were kept on the list, and the
others fell off the
end of the list and were lost. This was achieved by keeping the list sorted in
order of classifier
performance; new classifiers which were at least as good as the worst
classifier currently on
the list were inserted, forcing the expulsion of the current bottom
underachiever. One further
implementation detail is that the list was completely replaced on each
generational step;
therefore, every classifier on the list had the same number of markers, and at
each step the
number of markers per classifier grew by one.
[00338] Since this method produced a list of candidate classifiers using
different
combinations of markers, one may ask if the classifiers can be combined in
order to avoid
errors that might be made by the best single classifier, or by minority groups
of the best
classifiers. Such "ensemble" and "committee of experts" methods are well known
in the
fields of statistical and machine learning and include, for example,
"Averaging", "Voting",
"Stacking", "Bagging" and "Boosting" (see, e.g., Hastie et al., supra). These
combinations of
simple classifiers provide a method for reducing the variance in the
classifications due to
noise in any particular set of markers by including several different
classifiers and therefore
information from a larger set of the markers from the biomarker table,
effectively averaging
between the classifiers. An example of the usefulness of this approach is that
it can prevent
outliers in a single marker from adversely affecting the classification of a
single sample. The
requirement to measure a larger number of signals may be impractical in
conventional "one
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marker at a time" antibody assays but has no downside for a fully multiplexed
aptamer assay.
Techniques such as these benefit from a more extensive table of biomarkers and
use the
multiple sources of information concerning the disease processes to provide a
more robust
classification.
Part 2
[00339] The biomarkers selected in Table 1 gave rise to classifiers that
perform better
than classifiers built with "non-markers" (i.e., proteins having signals that
did not meet the
criteria for inclusion in Table 1 (as described in Example 2)).
[00340] For classifiers containing only one, two, and three markers, all
possible
classifiers obtained using the biomarkers in Table 1 were enumerated and
examined for the
distribution of performance compared to classifiers built from a similar table
of randomly
selected non-markers signals.
[00341] In Figure 14, the sum of the sensitivity and specificity was used as
the
measure of performance; a performance of 1.0 is the baseline expectation for a
random (coin
toss) classifier. The histogram of classifier performance was compared with
the histogram of
performance from a similar exhaustive enumeration of classifiers built from a
"non-marker"
table of 42 non-marker analytes; the 42 analytes were randomly chosen from 387
aptamer
measurements that did not demonstrate differential signaling between control
and disease
populations (KS-distance < 0.2).
[00342] Figure 14 shows histograms of the performance of all possible one,
two, and
three-marker classifiers built from the biomarker parameters in Table 18 for
biomarkers that
can discriminate between benign pelvic masses and ovarian cancer and compares
these
classifiers with all possible one, two, and three-marker classifiers built
using the 42 "non-
marker" aptamer RFU signals. Figure 14A shows the histograms of single marker
classifier
performance, Figure 14B shows the histogram of two-marker classifier
performance, and
Figure 14C shows the histogram of three-marker classifier performance.
[00343] In Figure 14, the solid lines represent the histograms of the
classifier
performance of all one, two, and three-marker classifiers using the biomarker
data for benign
pelvic masses and ovarian cancer in Table 18. The dotted lines are the
histograms of the
classifier performance of all one, two, and three-marker classifiers using the
data for benign
pelvic masses and ovarian cancer but using the set of random non-marker
signals.
[00344] The classifiers built from the markers listed in Table 1 form a
distinct
histogram, well separated from the classifiers built with signals from the
"non-markers" for
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all one-marker, two-marker, and three-marker comparisons. The performance and
AUC
score of the classifiers built from the biomarkers in Table 1 also increase at
a higher rate as
markers are added than do the classifiers built from the non-markers. The
separation of
performance increases between the marker and non-marker classifiers as the
number of
markers per classifier increases. All classifiers built using the biomarkers
listed in Table 1
perform distinctly better than classifiers built using the "non-markers".
Part 3
[00345] The distributions of classifier performance show that there are many
possible
multiple-marker classifiers that can be derived from the set of analytes in
Table 1. Although
some biomarkers are better than others on their own, as evidenced by the
distribution of
classifier scores and AUCs for single analytes, it was desirable to determine
whether such
biomarkers are required to construct high performing classifiers. To make this
determination,
the behavior of classifier performance was examined by leaving out some number
of the best
biomarkers. Figure 15 compares the performance of classifiers built with the
full list of
biomarkers in Table 1 with the performance of classifiers built with subsets
of biomarkers
from Table 1 that excluded top-ranked markers.
[00346] Figure 15 demonstrates that classifiers constructed without the best
markers
perform well, implying that the performance of the classifiers was not due to
some small core
group of markers and that the changes in the underlying processes associated
with disease are
reflected in the activities of many proteins. Many subsets of the biomarkers
in Table 1
performed close to optimally, even after removing the top 15 of the 42 markers
from Table 1.
After dropping the 15 top-ranked markers (ranked by KS-distance) from Table 1,
the
classifier performance increased with the number of markers selected from the
table to reach
almost 1.80 (sensitivity + specificity), close to the performance of the
optimal classifier score
of 1.87 selected from the full list of biomarkers.
[00347] Finally, Figure 16 shows how the ROC performance of typical
classifiers
constructed from the list of parameters in Table 18 according to Example 3. A
five analyte
classifier was constructed with TIMP-2, MCP-3, Cadherin-5, SLPI, and C9.
Figure 16A
shows the performance of the model, assuming independence of these markers, as
in
Example 3, and Figure 16B shows the empirical ROC curves generated from the
study data
set used to define the parameters in Table 18. It can be seen that the
performance for a given
number of selected markers was qualitatively in agreement, and that
quantitative agreement
was generally quite good, as evidenced by the AUCs, although the model
calculation tends to
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overestimate classifier performance. This is consistent with the notion that
the information
contributed by any particular biomarker concerning the disease processes is
redundant with
the information contributed by other biomarkers provided in Table 1 while the
model
calculation assumes complete independence. Figure 16 thus demonstrates that
Table 1 in
combination with the methods described in Example 3 enable the construction
and evaluation
of a great many classifiers useful for the discrimination of ovarian cancer
from benign pelvic
masses.
Example 5. Aptamer Specificity Demonstration in a Pull-down Assay
[00348] The final readout on the multiplex assay is based on the amount of
aptamer
recovered after the successive capture steps in the assay. The multiplex assay
is based on the
premise that the amount of aptamer recovered at the end of the assay is
proportional to the
amount of protein in the original complex mixture (e.g., plasma). In order to
demonstrate
that this signal is indeed derived from the intended analyte rather than from
non-specifically
bound proteins in plasma, we developed a gel-based pull-down assay in plasma.
This assay
can be used to visually demonstrate that a desired protein is in fact pulled
out from plasma
after equilibration with an aptamer as well as to demonstrate that aptamers
bound to their
intended protein targets can survive as a complex through the kinetic
challenge steps in the
assay. In the experiments described in this example, recovery of protein at
the end of this
pull-down assay requires that the protein remain non-covalently bound to the
aptamer for
nearly two hours after equilibration. Importantly, in this example we also
provide evidence
that non-specifically bound proteins dissociate during these steps and do not
contribute
significantly to the final signal. It should be noted that the pull-down
procedure described in
this example includes all of the key steps in the multiplex assay described
above.
[00349] A. Plasma Pull-down Assay
[00350] Plasma samples were prepared by diluting 50 L EDTA-plasma to 100 L
in
SB18 with 0.05% Tween-20 (SB18T) and 2 M Z-Block. The plasma solution was
equilibrated with 10 pmoles of a PBDC-aptamer in a final volume of 150 L for
2 hours at 37
C. After equilibration, complexes and unbound aptamer were captured with 133
L of a
7.5% Streptavidin-agarose bead slurry by incubating with shaking for 5 minutes
at RT in a
Durapore filter plate. The samples bound to beads were washed with biotin and
with buffer
under vacuum as described in Example 1. After washing, bound proteins were
labeled with
0.5 mM NHS-S-S-biotin, 0.25 mM NHS-A1exa647 in the biotin diluent for 5
minutes with
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shaking at RT. This staining step allows biotinylation for capture of protein
on streptavidin
beads as well as highly sensitive staining for detection on a gel. The samples
were washed
with glycine and with buffer as described in Example 1. Aptamers were released
from the
beads by photocleavage using a Black Ray light source for 10 minutes with
shaking at RT.
At this point, the biotinylated proteins were captured on 0.5 mg MyOne
Streptavidin beads by
shaking for 5 minutes at RT. This step will capture proteins bound to aptamers
as well as
proteins that may have dissociated from aptamers since the initial
equilibration. The beads
were washed as described in Example 1. Proteins were eluted from the MyOne
Streptavidin
beads by incubating with 50 mM DTT in SB17T for 25 minutes at 37 C with
shaking. The
eluate was then transferred to MyOne beads coated with a sequence
complimentary to the 3'
fixed region of the aptamer and incubated for 25 minutes at 37 C with
shaking. This step
captures all of the remaining aptamer. The beads were washed 2x with 100 L SB
17T for 1
minute and lx with 100 L SB19T for 1 minute. Aptamer was eluted from these
final beads
by incubating with 45 L 20 mM NaOH for 2 minutes with shaking to disrupt the
hybridized
strands. 40 L of this eluate was neutralized with 10 L 80 mM HC1 containing
0.05%
Tween-20. Aliquots representing 5% of the eluate from the first set of beads
(representing all
plasma proteins bound to the aptamer) and 20% of the eluate from the final set
of beads
(representing all plasma proteins remaining bound at the end of our clinical
assay) were run
on a NuPAGE 4-12% Bis-Tris gel (Invitrogen) under reducing and denaturing
conditions.
Gels were imaged on an Alpha Innotech FluorChem Q scanner in the Cy5 channel
to image
the proteins.
[00351] B. Pull-down gels for aptamers were selected against LBP (1x10-7 M in
plasma, polypeptide MW -60 kDa), C9 (-1x10-6 M in plasma, polypeptide MW -60
kDa),
and IgM (9x10-6 M in plasma, MW -70 kDa and 23 kDa), respectively. (See Figure
13).
[00352] For each gel, lane 1 is the eluate from the Streptavidin-agarose
beads, lane 2 is
the final eluate, and lane 3 is a MW marker lane (major bands are at 110, 50,
30, 15, and 3.5
kDa from top to bottom). It is evident from these gels that there is a small
amount non-
specific binding of plasma proteins in the initial equilibration, but only the
target remains
after performing the capture steps of the assay. It is clear that the single
aptamer reagent is
sufficient to capture its intended analyte with no up-front depletion or
fractionation of the
plasma. The amount of remaining aptamer after these steps is then proportional
to the
amount of the analyte in the initial sample.
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[00353] The foregoing embodiments and examples are intended only as examples.
No
particular embodiment, example, or element of a particular embodiment or
example is to be
construed as a critical, required, or essential element or feature of any of
the claims. Further,
no element described herein is required for the practice of the appended
claims unless
expressly described as "essential" or "critical." Various alterations,
modifications,
substitutions, and other variations can be made to the disclosed embodiments
without
departing from the scope of the present application, which is defined by the
appended claims.
The specification, including the figures and examples, is to be regarded in an
illustrative
manner, rather than a restrictive one, and all such modifications and
substitutions are
intended to be included within the scope of the application. Accordingly, the
scope of the
application should be determined by the appended claims and their legal
equivalents, rather
than by the examples given above. For example, steps recited in any of the
method or
process claims may be executed in any feasible order and are not limited to an
order
presented in any of the embodiments, the examples, or the claims. Further, in
any of the
aforementioned methods, one or more biomarkers of Table 1 can be specifically
excluded
either as an individual biomarker or as a biomarker from any panel.
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Table 1. Biomarkers for Ovarian Cancer
Biomarker Alternate Protein Names Gene Designation
Designation
al -Antitrypsin Alpha- l -antitrypsin SERPINA1
API
Alpha-1 protease inhibitor
alpha 1 antitrypsin
alphal-protease inhibitor
Serpin Al
AAT
a2-Antiplasmin alpha-2-plasmin inhibitor SERPINF2
a2-HS- fetuin AHSG
Glycoprotein fetuin A
alpha-2-HS glycoprotein
AHSG
Alpha2-Heremans Schmid glycoprotein
Ba-alpha-2-glycoprotein
Alpha-2-Z-globulin
ADAM 9 Disintegrin and metalloproteinase domain- ADAM9
containing protein 9
Metal loprotease/disintegrin/cysteine-rich
protein 9
Myeloma cell metalloproteinase
Meltrin-gamma
Cellular disintegrin-related protein
ARSB Arylsulfatase B ARSB
G4S
N-acetylgalactosam ine-4-sulfatase
ASB
G4S
BAFF Receptor B cell-activating factor receptor TNFRSF13C
BLyS receptor 3
Tumor necrosis factor receptor superfamily
member 13C
TNFRSF13C
CD268 antigen
C2 Complement C2 C2
C3/C5 convertase
C5 Complement Factor C5 C5
Complement C5
C3 and PZP-like alpha-2-macroglobulin
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Table 1 cont.
domain-containing protein 4
C6 Complement component C6 C6
C9 Complement Factor C9 C9
Complement component C9
Cadherin-5 VE-cadherin CDH5
7B4 antigen
Vascular endothelial-cadherin
CD144 antigen
Coagulation Factor Activated factor Xa heavy chain F10
Xa
Contactin-1 Neural cell surface protein F3 CNTN1
Glycoprotein gp135
Contactin-4 BIG-2 CNTN4
Brain-derived immunoglobulin superfamily
protein 2
CNTN4
ERBB1 Epidermal growth factor receptor EGFR
Receptor tyrosine-protein kinase ErbB-1
ErbB-1
EGFR
HER1
Human EGF Receptor
Growth hormone GH receptor GHR
receptor Somatotropin receptor
GHR
Hatt Histone acetyltransferase type B catalytic HAT1
subunit
HGF Hepatocyte growth factor HGF
Scatter factor
Hepatopoeitin-A
HSP 90a Heat shock protein HSP 90-alpha HSP90AA1
HSP 86
Renal carcinoma antigen NY-REN-38
IL-12 R132 Interleukin-12 receptor beta-2 chain IL12RB2
IL-12R-beta-2
IL-12 receptor beta-2
11 2R2
IL-13 Rat Interleukin-13 receptor alpha-1 IL13RA1
IL-13 receptor alpha-1
IL-13RA-1
IL-13R-alpha-1
Cancer/testis antigen 19
CT19
CD213a1 antigen
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Table 1 cont.
IL13R
IL-18 RR Interleukin-18 receptor accessory protein ILI8RAP
IL-18 receptor accessory protein
IL-18RacP
Interleukin-18 receptor accessory protein-like
IL-18Rbeta
IL-1 R accessory protein-like
IL-1 RAcPL
IL-1 R7
CD218 antigen-like family member B
CDw218b antigen
Kallikrein 6 Protease M KLK6
Neurosin
h K6
Zyme
KLK6
SP59
Serine protease 9
Serine protease 18
Kallistatin Serpin A4 SERPINA4
Kallikrein inhibitor
Protease inhibitor 4
LY9 T-lymphocyte surface antigen Ly-9 LY9
CD229 antigen
Cell-surface molecule Ly-9
Lymphocyte antigen 9
MCP-3 Monocyte chemotactic protein 3 CCL7
Small-inducible cytokine A7
Monocyte chemoattractant protein 3
NC28
CCL7
MIP-5 C-C motif chemokine 15 CCL15
Small-inducible cytokine A15
Macrophage inflammatory protein 5
Chemokine CC-2
HCC-2
NCC-3
MIP-1 delta
Leukotactin-1
LKN-1
Mrp-2b
MMP-7 Matrilysin MMP7
Pump-1 protease
Uterine metalloproteinase
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Table 1 cont.
Matrix metalloproteinase-7
Matrin
MRC2 Macrophage mannose receptor 2 MRC2
CD280 antigen
Endocytic receptor 180
Urokinase receptor-associated protein
ENDO180
NRP1 Neuropilin-1 NRP1
CD304 antigen
Vascular endothelial cell growth factor 165
receptor
PCI Protein C inhibitor SERPINA5
Plasminogen activator inhibitor -3
PAI-3
Plasma serine protease inhibitor
Serpin A5
Acrosomal serine protease inhibitor
Prekallikrein Plasma kallikrein KLKB1
Plasma prekallikrein
Kininogenin
Fletcher factor
Properdin Complement factor P CFP
Factor P
RBP Retinol Binding Protein RBP4
Retinol-binding protein 4
RBP4
Plasma retinol-binding protein
RGM-C Hemojuvelin HFE2
RGM domain family member C
Hemochromatosis type 2 protein
RGMC
SAP Serum Amyloid P Component APCS
9.5S alpha-1-glycoprotein
SCF sR Mast/stem cell growth factor receptor KIT
stem cell growth factor soluble receptor
Proto-oncogene tyrosine-protein kinase Kit
c-kit
CD117
SLPI Secretory leukocyte protease inhibitor SLPI
Antileukoproteinase 1
HUSI-1
Seminal proteinase inhibitor
BLPI
Mucus proteinase inhibitor
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Table 1 cont.
MPI
WAP four-disulfide core domain protein 4
Protease inhibitor WAP4
sL-Selectin sL-Selectin SELL
Leukocyte adhesion molecule-1
Lymph node homing receptor
LAM-1
L-Selectin
L-Selectin, soluble
Leukocyte surface antigen Leu-8
TQ1
gp90-MEL
Leukocyte-endothelial cell adhesion
molecule 1
LECAMI
CD62 antigen-like family m
Thrombin/ Alpha Thrombin/Prothrombin F2
Prothrombin Coagulation factor II
TIMP-2 Tissue inhibitor of metalloproteinases-2 TIMP2
CSC-21 K
Troponin T troponin T cardiac muscle TNNT2
TnTc
cTnT
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Table 2
100 Panels of 3 Biomarkers for Diagnosing Ovarian Cancer from Benign Pelvic
Masses
Sensitivity +
Biomarkers Specificity Sensitivity Specificity AUC
1 ADAM 9 al-Antitrypsin a2-Antiplasmin 0.846 0.851 1.697 0.866
2 ARSB SLPI C9 0.846 0.856 1.703 0.913
3 BAFF Receptor SLPI C9 0.833 0.862 1.695 0.916
4 C2 LY9 SLPI 0.808 0.923 1.731 0.916
C5 Tro onin T C9 0.897 0.800 1.697 0.885
6 C6 ERBB1 SLPI 0.808 0.887 1.695 0.902
7 Cadherin-5 C9 SLPI 0.859 0.887 1.746 0.929
Coagulation Factor
8 Xa LY9 SLPI 0.821 0.882 1.703 0.911
9 Contactin-4 LY9 SLPI 0.833 0.872 1.705 0.906
Growth hormone
Receptor SLPI C9 0.859 0.856 1.715 0.916
11 HGF Troponin T C9 0.897 0.795 1.692 0.886
12 HSP 90a LY9 SLPI 0.846 0.882 1.728 0.896
13 Hatl SLPI C9 0.846 0.867 1.713 0.914
14 IL, 12 R P2 C9 SLPI 0.833 0.872 1.705 0.916
IL, 13 Ral SLPI C9 0.846 0.856 1.703 0.920
16 IL, 18 R13 SLPI C9 0.846 0.856 1.703 0.925
17 Kallikrein 6 SLPI C9 0.821 0.851 1.672 0.921
18 LY9 Kallistatin SLPI 0.795 0.897 1.692 0.912
19 MCP-3 SLPI C9 0.833 0.882 1.715 0.924
MIP-5 C9 SLPI 0.821 0.846 1.667 0.919
21 MRC2 MMP-7 C9 0.859 0.846 1.705 0.898
22 SAP NRP1 SLPI 0.821 0.887 1.708 0.917
23 LY9 PCI SLPI 0.833 0.867 1.700 0.902
24 C2 Prekallikrein SLPI 0.808 0.892 1.700 0.911
Properdin LY9 SLPI 0.846 0.877 1.723 0.905
26 LY9 RBP SLPI 0.782 0.903 1.685 0.897
27 SAP RGM-C SLPI 0.872 0.877 1.749 0.923
28 SCFsR C9 SLPI 0.846 0.856 1.703 0.915
29 TIMP-2 C9 SLPI 0.885 0.856 1.741 0.926
Thrombin/
MCP-3 Prothrombin C9 0.833 0.826 1.659 0.875
a2-HS-
31 Gl co rotein a2-Anti lasmin SLPI 0.808 0.872 1.679 0.887
32 Contactin-1 LY9 SLPI 0.808 0.882 1.690 0.909
33 sL-Selectin C9 SLPI 0.821 0.872 1.692 0.929
34 C2 ADAM 9 SLPI 0.795 0.897 1.692 0.879
Cadherin-5 ARSB al-Antitrypsin 0.769 0.897 1.667 0.867
36 BAFF Receptor C6 SLPI 0.782 0.897 1.679 0.876
37 C5 RGM-C SLPI 0.833 0.862 1.695 0.906
Coagulation Factor
38 Xa SLPI C9 0.846 0.846 1.692 0.923
39 SAP Contactin-4 SLPI 0.821 0.867 1.687 0.891
ERBB1 C9 SLPI 0.846 0.846 1.692 0.920
Growth hormone
41 SAP receptor SLPI 0.808 0.892 1.700 0.917
42 HGF MCP-3 C9 0.872 0.815 1.687 0.872
43 HSP 90a SLPI C9 0.859 0.862 1.721 0.927
44 SAP Hatl SLPI 0.808 0.903 1.710 0.902
IL, 12 R P2 Prekallikrein SLPI 0.821 0.856 1.677 0.889
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Table 2 (cont.)
46 IL-13 Ral RGM-C C9 0.872 0.805 1.677 0.886
47 IL-18 R LY9 C9 0.859 0.826 1.685 0.870
48 Kallikrein 6 LY9 SLPI 0.795 0.872 1.667 0.896
49 Cadherin-5 Kallistatin SLPI 0.769 0.903 1.672 0.910
50 MIP-5 RGM-C C9 0.885 0.774 1.659 0.893
51 RGM-C MMP-7 C9 0.885 0.815 1.700 0.908
52 MRC2 C9 SLPI 0.859 0.862 1.721 0.911
53 NRP1 LY9 SLPI 0.821 0.877 1.697 0.908
54 PCI C9 SLPI 0.821 0.856 1.677 0.917
55 Cadherin-5 Properdin SLPI 0.782 0.908 1.690 0.907
56 RBP SLPI C9 0.833 0.851 1.685 0.910
57 SCFsR al-Antitrypsin SLPI 0.808 0.872 1.679 0.885
58 TIMP-2 a2-Anti lasmin SLPI 0.821 0.882 1.703 0.900
Thrombin/
59 NRP1 Prothrombin C9 0.846 0.805 1.651 0.873
a2-HS-
60 SCF sR Glycoprotein SLPI 0.795 0.872 1.667 0.879
61 Contactin-1 NRP1 SLPI 0.782 0.897 1.679 0.906
62 RGM-C sL-Selectin C9 0.872 0.805 1.677 0.901
63 Cadherin-5 ADAM 9 al-Antitrypsin 0.795 0.892 1.687 0.862
64 Properdin ARSB SLPI 0.769 0.892 1.662 0.889
65 BAFF Receptor a2-Antiplasmin SLPI 0.782 0.887 1.669 0.880
66 C5 Properdin SLPI 0.808 0.882 1.690 0.898
67 C6 RGM-C SLPI 0.821 0.872 1.692 0.908
Coagulation Factor
68 SAP Xa SLPI 0.808 0.872 1.679 0.907
Coagulation Factor
69 Contactin-4 Xa MMP-7 0.808 0.867 1.674 0.868
70 C2 ERBB1 SLPI 0.795 0.892 1.687 0.904
Growth hormone
71 Cadherin-5 receptor al-Antitr sin 0.821 0.872 1.692 0.876
72 HGF SLPI C9 0.872 0.815 1.687 0.916
73 HSP 90a C2 SLPI 0.808 0.872 1.679 0.900
74 Hatl LY9 SLPI 0.808 0.877 1.685 0.903
75 IL, 12 R P2 a2-Anti lasmin SLPI 0.808 0.867 1.674 0.883
76 IL-13 Ral LY9 SLPI 0.795 0.877 1.672 0.900
77 IL-18 R Prekallikrein C9 0.859 0.826 1.685 0.890
78 Kallikrein 6 SCF sR C9 0.846 0.821 1.667 0.882
79 C2 Kallistatin SLPI 0.782 0.887 1.669 0.903
80 MIP-5 Cadherin-5 SLPI 0.782 0.867 1.649 0.885
81 MRC2 Hatl SLPI 0.782 0.897 1.679 0.889
82 PCI a2-Anti lasmin SLPI 0.795 0.867 1.662 0.891
83 SAP RBP SLPI 0.782 0.892 1.674 0.895
84 Cadherin-5 TIMP-2 SLPI 0.808 0.877 1.685 0.907
Thrombin/
85 SCF sR Prothrombin C9 0.859 0.790 1.649 0.865
86 Tro onin T SLPI C9 0.833 0.851 1.685 0.923
a2-HS-
87 Glycoprotein C9 SLPI 0.808 0.851 1.659 0.915
88 Cadherin-5 Contactin-1 SLPI 0.808 0.867 1.674 0.897
89 Cadherin-5 sL-Selectin SLPI 0.795 0.882 1.677 0.901
90 ADAM 9 SLPI a2-Antiplasmin 0.782 0.892 1.674 0.883
91 ARSB ADAM 9 a2-Antiplasmin 0.808 0.851 1.659 0.836
92 BAFF Receptor al-Antitr sin SLPI 0.769 0.897 1.667 0.889
93 CS C9 SLPI 0.833 0.856 1.690 0.920
94 C6 LY9 SLPI 0.782 0.908 1.690 0.908
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Table 2 (cont.)
95 C5 Contactin-4 SLPI 0.808 0.862 1.669 0.883
96 ERBB1 al-Antitrypsin SLPI 0.808 0.877 1.685 0.893
Growth hormone
97 C5 receptor C9 0.872 0.810 1.682 0.881
98 HGF Hatl C9 0.872 0.810 1.682 0.871
99 HSP 90a 11-18 R13 C9 0.859 0.815 1.674 0.885
100 11-12 R P2 al-Antitr sin SLPI 0.795 0.877 1.672 0.887
Marker Count Marker Count
SLPI 77 Contactin-4 4
Coagulation
C9 41 Factor Xa 4
LY9 15 C6 4
BAFF
Cadherin-5 10 Receptor 4
a2-Antiplasmin 8 ARSB 4
al-Antitrypsin 8 sL-Selectin 3
SAP 7 Contactin-1 3
a2-HS-
RGM-C 7 Gl co rotein 3
C5 6 Troponin T 3
Thrombin/
C2 6 Prothrombin 3
SCF sR 5 TIMP-2 3
Hatl 5 RBP 3
ADAM 9 5 Prekallikrein 3
Properdin 4 PCI 3
NRP1 4 MRC2 3
IL, 18 R 4 MMP-7 3
IL, 12 R132 4 MIP-5 3
HSP 90a 4 MCP-3 3
HGF 4 Kallistatin 3
Growth hormone
receptor 4 Kallikrein 6 3
ERBB1 4 IL-13 Ral 3
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Table 3
100 Panels of 4 Biomarkers for Diagnosing Ovarian Cancer from Benign Pelvic
Masses
Sensitivity +
Biomarkers Specificity Sensitivity Specificity AUC
1 LY9 ADAM 9 C9 SLPI 0.872 0.867 1.738 0.910
2 ARSE LY9 C9 SLPI 0.872 0.877 1.749 0.920
3 BAFF Receptor MCP-3 SLPI C9 0.885 0.862 1.746 0.923
4 Cadherin-5 C2 SLPI LY9 0.859 0.918 1.777 0.923
C5 C2 SLPI LY9 0.846 0.897 1.744 0.907
6 C6 LY9 C9 SLPI 0.885 0.867 1.751 0.923
Coagulation
7 Factor Xa LY9 C9 SLPI 0.897 0.862 1.759 0.930
8 Hatt LY9 Contactin-4 SLPI 0.872 0.897 1.769 0.910
9 IL-13 Rat LY9 ERBB1 SLPI 0.872 0.877 1.749 0.906
Growth
hormone
Cadherin-5 SAP receptor SLPI 0.885 0.892 1.777 0.924
11 HGF MRC2 C9 SLPI 0.910 0.856 1.767 0.911
12 HSP 90a LY9 C9 SLPI 0.897 0.897 1.795 0.924
13 Cadherin-5 IL-12 R02 C9 SLPI 0.846 0.892 1.738 0.923
14 IL-18 R SLPI RGM-C C9 0.897 0.862 1.759 0.930
Cadherin-5 LY9 Kallikrein 6 SLPI 0.885 0.887 1.772 0.915
16 MMP-7 a2-Anti lasmin Kallistatin SLPI 0.859 0.882 1.741 0.921
17 MIP-5 LY9 C9 SLPI 0.872 0.877 1.749 0.925
18 NRP1 LY9 Cadherin-5 SLPI 0.859 0.908 1.767 0.924
19 LY9 PCI C9 SLPI 0.872 0.867 1.738 0.917
LY9 Prekallikrein C9 SLPI 0.897 0.856 1.754 0.925
21 SAP Properdin RGM-C SLPI 0.859 0.903 1.762 0.931
22 LY9 RBP C9 SLPI 0.897 0.862 1.759 0.917
23 SCF sR LY9 C9 SLPI 0.885 0.867 1.751 0.923
24 MCP-3 TIMP-2 C9 SLPI 0.897 0.862 1.759 0.920
Thrombin/
MMP-7 Prothrombin SLPI C9 0.885 0.841 1.726 0.925
26 LY9 Troponin T C9 SLPI 0.872 0.872 1.744 0.924
27 al-Antit sin C9 LY9 SLPI 0.885 0.862 1.746 0.919
a2-HS-
28 Cadherin-5 Gl co rotein SLPI sL-Selectin 0.821 0.897 1.718 0.900
29 Contactin-1 LY9 C9 SLPI 0.885 0.882 1.767 0.927
Properdin ADAM 9 C9 SLPI 0.872 0.862 1.733 0.907
31 Cadherin-5 ARSE C9 SLPI 0.872 0.862 1.733 0.922
32 BAFF Receptor LY9 C9 SLPI 0.885 0.856 1.741 0.915
33 Properdin MCP-3 C5 SLPI 0.833 0.908 1.741 0.909
34 C6 C2 SLPI LY9 0.833 0.918 1.751 0.922
Coagulation
SAP C9 Factor Xa SLPI 0.885 0.867 1.751 0.929
36 Contactin-4 LY9 MCP-3 SLPI 0.859 0.892 1.751 0.914
37 LY9 ERBB1 C9 SLPI 0.872 0.872 1.744 0.923
Growth hormone
38 Cadherin-5 receptor C9 SLPI 0.872 0.877 1.749 0.926
39 HGF RGM-C a2-Antiplasmin C9 0.936 0.821 1.756 0.909
HSP 90a Cadherin-5 C9 SLPI 0.859 0.892 1.751 0.928
41 Hatt LY9 C9 SLPI 0.885 0.877 1.762 0.926
42 IL-12 R02 C2 SLPI LY9 0.833 0.903 1.736 0.907
43 IL-13 Rat SLPI Cadherin-5 C9 0.885 0.882 1.767 0.928
44 MRC2 LY9 IL-18 R SLPI 0.833 0.908 1.741 0.913
E4746 Kallikrein 6 LY9 C9 SLPI 0.897 0.867 1.764 0.92
BAFF Receptor LY9 Kallistatin SLPI 0.833 0.903 1.736 0.900
MIP-5 SCF sR SLPI C9 0.872 0.862 1.733 0.914
92
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Table 3 (cont.)
48 NRP1 LY9 C9 SLPI 0.885 0.877 1.762 0.927
49 SAP PCI RGM-C SLPI 0.872 0.862 1.733 0.916
50 BAFF Receptor HGF SLPI Prekallikrein 0.897 0.841 1.738 0.893
51 RGM-C RBP MMP-7 C9 0.897 0.841 1.738 0.905
52 Cadherin-5 TIMP-2 C9 SLPI 0.872 0.882 1.754 0.931
Growth
Thrombin/ hormone
53 C2 Prothrombin receptor SLPI 0.859 0.862 1.721 0.904
al-
54 RGM-C Troponin T C9 Antitrypsin 0.872 0.867 1.738 0.908
a2-HS-
55 sL-Selectin Gl co rotein C9 SLPI 0.833 0.882 1.715 0.920
56 Contactin-1 C2 SLPI Cadherin-5 0.846 0.903 1.749 0.908
57 Cadherin-5 ADAM 9 C9 SLPI 0.833 0.897 1.731 0.916
58 Cadherin-5 Properdin ARSB SLPI 0.821 0.908 1.728 0.909
59 C5 LY9 al-Antit sin SLPI 0.859 0.882 1.741 0.909
60 RGM-C LY9 C6 SLPI 0.859 0.887 1.746 0.920
Coagulation
61 NRP1 LY9 Factor Xa SLPI 0.872 0.872 1.744 0.915
62 RGM-C Contactin-4 MCP-3 SLPI 0.846 0.897 1.744 0.919
63 MCP-3 LY9 ERBB1 SLPI 0.859 0.877 1.736 0.906
64 HSP 90a MCP-3 C9 SLPI 0.897 0.851 1.749 0.922
65 Hatt LY9 C2 SLPI 0.859 0.897 1.756 0.917
66 MRC2 IL-12 R02 Properdin SLPI 0.833 0.897 1.731 0.885
67 Cadherin-5 LY9 IL-13 Rat SLPI 0.872 0.887 1.759 0.917
68 IL-18 R SLPI Cadherin-5 C9 0.859 0.882 1.741 0.933
69 Kallikrein 6 LY9 SCF sR SLPI 0.859 0.887 1.746 0.898
70 Cadherin-5 LY9 Kallistatin SLPI 0.833 0.903 1.736 0.921
71 MIP-5 Hatt SLPI C9 0.859 0.872 1.731 0.907
72 Cadherin-5 LY9 PCI SLPI 0.846 0.887 1.733 0.909
73 Prekallikrein al-Antit sin LY9 SLPI 0.846 0.887 1.733 0.911
74 SCF sR RBP SLPI C9 0.872 0.856 1.728 0.908
75 RGM-C TIMP-2 C9 SLPI 0.885 0.867 1.751 0.931
Thrombin/
76 C2 LY9 Prothrombin SLPI 0.846 0.867 1.713 0.922
77 SAP al-Antitrypsin Troponin T SLPI 0.833 0.903 1.736 0.917
78 HGF a2-Anti lasmin C9 SLPI 0.910 0.841 1.751 0.922
a2-HS-
79 Cadherin-5 Glycoprotein SLPI LY9 0.833 0.882 1.715 0.908
Growth
hormone
80 Contactin-1 LY9 receptor SLPI 0.859 0.887 1.746 0.914
81 sL-Selectin LY9 C9 SLPI 0.885 0.867 1.751 0.926
82 Cadherin-5 Prekallikrein ADAM 9 SLPI 0.846 0.882 1.728 0.897
83 Cadherin-5 ARSB SLPI LY9 0.846 0.882 1.728 0.907
84 Hatt LY9 C5 SLPI 0.859 0.877 1.736 0.909
85 C6 MRC2 Hatt SLPI 0.833 0.908 1.741 0.893
93
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Table 3 (cont.)
Coagulation
86 Cadherin-5 Factor Xa C9 SLPI 0.872 0.872 1.744 0.929
87 HSP 90a Contactin-4 SLPI LY9 0.872 0.872 1.744 0.902
88 Cadherin-5 ERBB1 C9 SLPI 0.846 0.887 1.733 0.926
89 Properdin IL-12 R02 MCP-3 SLPI 0.821 0.908 1.728 0.898
90 IL -13 Rat LY9 C9 SLPI 0.872 0.867 1.738 0.921
91 Cadherin-5 LY9 IL-18 R(3 SLPI 0.846 0.882 1.728 0.918
92 RGM-C Kallikrein 6 SLPI C9 0.872 0.862 1.733 0.926
93 HSP 90a LY9 Kallistatin SLPI 0.833 0.903 1.736 0.911
94 MIP-5 RGM-C SLPI C9 0.872 0.856 1.728 0.930
95 MMP-7 SLPI C9 LY9 0.897 0.877 1.774 0.935
96 Cadherin-5 NRP1 C9 SLPI 0.885 0.877 1.762 0.931
Coagulation
97 Factor Xa LY9 PCI SLPI 0.833 0.892 1.726 0.909
Growth hormone
98 receptor RBP C9 SLPI 0.859 0.867 1.726 0.907
99 Pro erdin TIMP-2 C9 SLPI 0.872 0.872 1.744 0.927
Thrombin/
100 Cadherin-5 Prothrombin Kallistatin SLPI 0.821 0.892 1.713 0.908
Marker Count Marker Count
SLPI 97 NRP1 4
C9 53 MRC2 4
LY9 51 MMP-7 4
Cadherin-5 26 MIP-5 4
RGM-C 11 Kallikrein 6 4
MCP-3 8 IL-18 R(3 4
C2 8 IL-l3 Rat 4
Properdin 7 IL-12 R02 4
Hatt 6 HGF 4
al-Antit sin 5 ERBB1 4
SAP 5 Contactin-4 4
Kallistatin 5 C6 4
HSP 90a 5 C5 4
Growth hormone BAFF
receptor 5 Receptor 4
Coagulation
Factor Xa 5 ARSB 4
Thrombin/
Prothrombin 4 ADAM 9 4
TIMP-2 4 sL-Selectin 3
SCF sR 4 Contactin-1 3
a2-HS-
RBP 4 Glycoprotein 3
a2-
Prekallikrein 4 Anti lasmin 3
PCI 4 Troponin T 3
94
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Table 4
100 Panels of 5 Biomarkers for Diagnosing Ovarian Cancer from Benign Pelvic
Masses
Sensitivity +
Biomarkers Specificity Sensitivity Specificity AUC
1 SCF sR C9 SLPI MCP-3 ADAM 9 0.897 0.882 1.779 0.916
2 IL-18 R(3 C9 SLPI Cadherin-5 ARSB 0.885 0.882 1.767 0.924
3 BAFF Receptor SLPI C9 LY9 MMP-7 0.885 0.877 1.762 0.924
4 C6 SLPI LY9 RGM-C C2 0.885 0.913 1.797 0.931
C5 SLPI LY9 al-Antitrypsin RGM-C 0.885 0.892 1.777 0.919
Coagulation
6 SAP Factor Xa SLPI LY9 NRP1 0.897 0.892 1.790 0.932
7 Cadherin-5 SLPI LY9 IL-13 Rut Contactin-4 0.910 0.887 1.797 0.919
8 Cadherin-5 C9 MCP-3 SLPI ERBB1 0.859 0.908 1.767 0.928
Growth hormone
9 receptor SLPI C9 LY9 Contactin-4 0.910 0.882 1.792 0.923
HGF SLPI C9 MMP-7 Cadherin-5 0.949 0.862 1.810 0.938
11 SLPI NRP1 LY9 SAP HSP 90a 0.923 0.887 1.810 0.923
12 Hatt SLPI C9 RGM-C C2 0.910 0.877 1.787 0.925
13 SLPI C9 Properdin TIMP-2 IL-12 R P2 0.885 0.872 1.756 0.922
14 SLPI NRP1 LY9 SAP Kallikrein 6 0.910 0.887 1.797 0.918
Growth hormone
LY9 al-Antitrypsin SLPI receptor Kallistatin 0.885 0.887 1.772 0.909
16 SLPI NRP1 LY9 SAP MIP-5 0.885 0.908 1.792 0.923
17 HGF SLPI C9 MMP-7 MRC2 0.923 0.862 1.785 0.932
18 RGM-C SLPI Cadherin-5 C9 PCI 0.897 0.877 1.774 0.926
19 LY9 C9 SLPI Prekallikrein MMP-7 0.923 0.862 1.785 0.933
RBP C9 SLPI LY9 RGM-C 0.897 0.877 1.774 0.923
Thrombin/
21 RGM-C SLPI LY9 C9 Prothrombin 0.910 0.862 1.772 0.930
22 Troponin T C9 SLPI LY9 NRP1 0.910 0.867 1.777 0.924
23 HGF SLPI C9 a2-Antiplasmin HSP 90a 0.949 0.851 1.800 0.924
a2-HS-
24 HSP 90a C9 SLPI LY9 Gl co rotein 0.885 0.882 1.767 0.920
SLPI NRP1 Cadherin-5 LY9 Contactin-1 0.885 0.913 1.797 0.928
26 Cadherin-5 C9 SLPI MMP-7 sL-Selectin 0.885 0.892 1.777 0.939
27 RGM-C C9 MCP-3 SLPI ADAM 9 0.897 0.872 1.769 0.923
28 ARSB SLPI C9 LY9 C2 0.885 0.882 1.767 0.923
29 SCF sR C9 SLPI MCP-3 BAFF Receptor 0.885 0.877 1.762 0.924
HGF SLPI C9 a2-Antiplasmin C5 0.923 0.851 1.774 0.921
31 C6 SLPI LY9 C9 Cadherin-5 0.897 0.882 1.779 0.928
Coagulation
32 LY9 SLPI MMP-7 C2 Factor Xa 0.885 0.897 1.782 0.942
33 ERBB1 SLPI LY9 C9 IL-13 Rut 0.897 0.867 1.764 0.919
34 Hatl SLPI LY9 C9 Contactin-4 0.885 0.897 1.782 0.922
Growth hormone
receptor SLPI SAP al-Anti sin IL-12 R P2 0.872 0.882 1.754 0.904
36 IL-18 R C9 SLPI Cadherin-5 RGM-C 0.885 0.882 1.767 0.936
37 Cadherin-5 C9 SLPI MMP-7 Kallikrein 6 0.897 0.887 1.785 0.940
Growth hormone
38 receptor SLPI C9 LY9 Kallistatin 0.897 0.872 1.769 0.922
39 LY9 C9 SLPI MIP-5 HSP 90a 0.897 0.877 1.774 0.923
MRC2 C9 SLPI LY9 NRP1 0.897 0.887 1.785 0.926
41 LY9 C9 SLPI PCI Cadherin-5 0.885 0.887 1.772 0.923
42 SLPI Contactin-4 LY9 MCP-3 Prekallikrein 0.872 0.903 1.774 0.916
Growth hormone
43 SAP SLPI RGM-C Properdin receptor 0.897 0.882 1.779 0.926
44 RBP C9 SLPI LY9 MMP-7 0.897 0.872 1.769 0.927
LY9 SLPI TIMP-2 C9 Kallikrein 6 0.910 0.872 1.782 0.919
46 Troponin T C9 SLPI LY9 RGM-C 0.897 0.872 1.769 0.931
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Table 4 (cont.)
Growth hormone
47 receptor SLPI C9 LY9 Contactin-1 0.897 0.892 1.790 0.925
48 RGM-C C9 MMP-7 SLPI sL-Selectin 0.897 0.877 1.774 0.940
Growth hormone
49 receptor SLPI SAP al-Anti sin ADAM 9 0.872 0.892 1.764 0.899
50 C2 SLPI LY9 C9 ARSB 0.885 0.882 1.767 0.923
51 SAP SLPI RGM-C MCP-3 BAFF Receptor 0.885 0.877 1.762 0.924
52 SLPI NRP1 LY9 C9 C5 0.897 0.877 1.774 0.924
53 IL-13 Ra1 C9 SLPI Cadherin-5 C6 0.885 0.892 1.777 0.925
Coagulation
54 Factor Xa SLPI C9 Cadherin-5 MMP-7 0.885 0.892 1.777 0.945
55 Cadherin-5 C9 SLPI MMP-7 ERBB1 0.872 0.892 1.764 0.933
56 Hatl SLPI LY9 C2 SAP 0.872 0.908 1.779 0.922
57 SLPI NRP1 LY9 C9 IL-12 R132 0.872 0.882 1.754 0.919
58 IL-18 R(3 C9 SLPI RGM-C Cadherin-5 0.885 0.882 1.767 0.936
Growth hormone
59 receptor SLPI C9 Cadherin-5 Kallistatin 0.885 0.882 1.767 0.927
60 RGM-C C9 MMP-7 MRC2 MIP-5 0.923 0.846 1.769 0.926
61 Cadherin-5 SLPI LY9 C9 PCI 0.885 0.887 1.772 0.923
62 C2 SLPI LY9 C9 Prekallikrein 0.897 0.877 1.774 0.931
63 SAP SLPI RGM-C Properdin MCP-3 0.859 0.918 1.777 0.932
64 LY9 SLPI MMP-7 C9 RBP 0.897 0.872 1.769 0.927
65 SCF sR C9 SLPI MCP-3 Cadherin-5 0.885 0.897 1.782 0.930
66 LY9 SLPI TIMP-2 C9 C2 0.897 0.877 1.774 0.928
67 RGM-C SLPI LY9 C9 Tro onin T 0.897 0.872 1.769 0.931
68 a2-Anti lasmin C9 SLPI LY9 HGF 0.936 0.856 1.792 0.925
69 MCP-3 SLPI C9 Contactin-1 Cadherin-5 0.872 0.908 1.779 0.930
70 sL-Selectin C9 SLPI LY9 HSP 90a 0.885 0.882 1.767 0.923
71 Cadherin-5 SLPI LY9 C9 ADAM 9 0.872 0.892 1.764 0.917
72 LY9 al-Antitrypsin SLPI Cadherin-5 ARSB 0.846 0.913 1.759 0.913
73 BAFF Receptor SLPI C9 LY9 MIP-5 0.897 0.862 1.759 0.915
74 RGM-C C9 MCP-3 SLPI C5 0.897 0.877 1.774 0.928
75 C6 SLPI LY9 RGM-C Cadherin-5 0.897 0.877 1.774 0.925
Coagulation
76 Factor Xa SLPI C9 LY9 MMP-7 0.897 0.877 1.774 0.938
77 IL-13 Ra1 C9 SLPI Cadherin-5 ERBB1 0.872 0.892 1.764 0.926
78 MCP-3 SLPI C9 Contactin-1 Hatl 0.885 0.892 1.777 0.917
Coagulation
79 SAP Factor Xa SLPI LY9 IL-12 R132 0.859 0.892 1.751 0.918
80 IL-18 R C9 SLPI RGM-C LY9 0.910 0.856 1.767 0.928
81 LY9 C9 SLPI Kallikrein 6 Cadherin-5 0.897 0.877 1.774 0.928
82 Cadherin-5 SLPI LY9 C9 Kallistatin 0.885 0.882 1.767 0.930
Growth hormone
83 receptor SLPI C9 LY9 MRC2 0.885 0.897 1.782 0.925
84 LY9 C9 SLPI PCI Contactin-1 0.885 0.882 1.767 0.918
85 LY9 C9 SLPI Prekallikrein RGM-C 0.923 0.851 1.774 0.929
86 HSP 90a C9 SLPI LY9 Properdin 0.897 0.877 1.774 0.926
87 RBP C9 SLPI LY9 NRP1 0.885 0.877 1.762 0.916
88 SCF sR C9 SLPI LY9 C2 0.897 0.882 1.779 0.926
89 TIMP-2 SLPI Cadherin-5 C9 MCP-3 0.885 0.887 1.772 0.927
90 SAP SLPI RGM-C Properdin Tro onin T 0.859 0.908 1.767 0.933
91 a2-Anti lasmin C9 SLPI Cadherin-5 HGF 0.936 0.851 1.787 0.926
92 HSP 90a C9 SLPI LY9 sL-Selectin 0.885 0.882 1.767 0.923
93 SAP SLPI RGM-C Properdin ADAM 9 0.859 0.903 1.762 0.920
94 SCF sR C9 SLPI MCP-3 ARSB 0.872 0.887 1.759 0.918
95 LY9 C9 SLPI MIP-5 BAFF Receptor 0.897 0.862 1.759 0.915
96 SCF sR C9 SLPI MCP-3 C5 0.897 0.867 1.764 0.922
97 SAP SLPI RGM-C MCP-3 C6 0.872 0.903 1.774 0.926
98 SLPI Contactin-4 LY9 HSP 90a NRP1 0.885 0.892 1.777 0.916
99 ERBB1 SLPI LY9 C9 Cadherin-5 0.885 0.877 1.762 0.927
100 Hatl SLPI Cadherin-5 al-Antitrypsin MCP-3 0.872 0.903 1.774 0.902
96
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Table 4 (cont.)
Marker Count Marker Count
Coagulation
SLPI 99 Factor Xa 5
C9 75 C6 5
LY9 60 C5 5
Cadherin-5 29 BAFF Receptor 5
RGM-C 23 ARSB 5
MCP-3 16 ADAM 9 5
SAP 14 sL-Selectin 4
MMP-7 14 a2-Antiplasmin 4
NRP1 11 Troponin T 4
Growth hormone
receptor 9 TIMP-2 4
C2 9 RBP 4
HSP 90a 8 Prekallikrein 4
al-Antitrypsin 6 PCI 4
SCFsR 6 MRC2 4
Properdin 6 Kallistatin 4
HGF 6 Kallikrein 6 4
Contactin-1 5 IL-18 R 4
MIP-5 5 IL-13 Rat 4
Hatt 5 IL-12 R P2 4
a2-HS-
ERBB1 5 Glycoprotein 1
Thrombin/
Contactin-4 5 Prothrombin 1
97
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SUBSTITUTE SHEET (RULE 26)
CA 02737004 2011-03-09
WO 2010/042525 PCT/US2009/059706
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SUBSTITUTE SHEET (RULE 26)
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WO 2010/042525 PCT/US2009/059706
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a w a a ppC-0 o o N o o o w
L 04 UD C7 C7 C7 C7
M V7 ~O l~ 00 01 O ti N M V7 ~O l~ 00 01 O
101
SUBSTITUTE SHEET (RULE 26)
CA 02737004 2011-03-09
WO 2010/042525 PCT/US2009/059706
l- 01 N 01 N V7 00 00 M 01 M V7 'C _ N N_
N M N M M M N M N M M N N
. . . . . . . .
O O O O O O O O O O O O O O O O O O
N O 01 N f N f N O N a1 a1 N N r
a1 a1 a1 00 c l~ 00 l~ O c 00 l~ 'O 00 l~ a1
'O l- N N l- N r l~ l~ N r N N r r l~ M
V7 a1 c 00 c l~ 00 00 00 r 00 00 00 l~ c 00 00 O
c
. . . . . . .
O O O O O O O O O O O O O O O O O O
'O l- l- l- V7 M V'l l~ V7 a1 O_ r r V'1 V'l O_ N
M C1 C1 C1 00 N 00 a1 00 C1 C1 C1 00 00 l~
41 c 41 41 c' c 41
. . . . .
O O O O O O O O O O O O O O O O O O
CL4
CLO
U U a a U U
0
n N n n H
C2 C2 'y z U
v c~ " 0
C)C)~Wax~~a~~xU U aCCLOu a a aH~ b a~¾
0
a
CL4 q
G, a N p" a s a a a a i C, i, C 4 o a C-0
z z a z a a H a a L L)
0 0
0. 0.
v v
~ ~ U U U ~ U ~ ~ C7 ~l ~4 U U ~4 ~ ~ ~4 C7
ti N M ~ V7 ~O l~ 00 a1 O ti N M ~ V7 ~O l~ 00
102
SUBSTITUTE SHEET (RULE 26)
CA 02737004 2011-03-09
WO 2010/042525 PCT/US2009/059706
l- 01 r M_ r O c r r N ~I- 'C
O N N M N N N M N N
O O O O O O O O O O O O
N O O N t t C1 ~r C1 r a1 O
l~ O a1 00 ' 00 ' 00 O
cl~
l- M N l- l- l- N l~ N o0 'O M
00 O C1 C1 l- l- r- 00 l- N ~/'~ O
O O O O O O O O O O O O
V`1 V`1 C1 M l~
00 C1 C1 00 C1 C1 C1 C1 C1 r N C1
O O O O O O O O O O O O
Ra.
CL4
a1 a ti a1 p a1
CL4
0
N ~O N ,-y
I '
w w a a a w a
N U
w U Z Z U Z ~l U a U r~ U U
0 0 0
o v v
~-. 0 0 0
C o o '~ o
o o a a a a w
c~1
G~ C7 C7 ~4 ~ ~ ~ U C7 ~4 ~ ~4 ~
103
SUBSTITUTE SHEET (RULE 26)
CA 02737004 2011-03-09
WO 2010/042525 PCT/US2009/059706
C
Y ""V
~7y Y 0
X e2 e2 Y > a o U. A.
L q) 33x~wUDCU~d2
~H Ha
p O O M M M
0
N
C
0 C
~ y =~ ~ U ,~ N m '~ ~ ~ v v ~
104
SUBSTITUTE SHEET (RULE 26)
CA 02737004 2011-03-09
WO 2010/042525 PCT/US2009/059706
U a o 00 N n N n a N a n O
N m m N m m m N m
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
in m co m co o m in m in in co co
.~~. ~ N O N M O N M N ~ N ~ O ~ O ~ O
V W c0 c0 c0 c0 c0 c0 c0 c0 c0 c0 c0 c0 c0 c0 c0
. N N - - - - - - - - - - - - - - - -
.~ M N N N N N c0 N N N N N N N N
N o0 00 00 N 00 O ~O ~O ~O ~O \O C~ V'1 00 N
'y C1 W G0 G0 G0 G0 C1 G0 G0 G0 G0 G0 G0 G0 G0 G0
O O O O O O O O O O O O O O O O
V N M ~D C1 ~D ~D M N C1 N C1 ~D M C1 ~D ~D
Vii +_ C1 N M M M N ~D ~D M N M M
CE '~j W 01 01 01 01 01 01 01 01 01 01 01 01 01 01 01
GC.~ C O O O O O O O O O O O O O O O O
U V
N
a o
C.
=~ V
ca Ga Ga~Ga ~a~ s" U ~C,~~ 3aa,a~ ~0..icoGa ,~.~
=~ O o
A 'n o ~ ~n r" o
y N U a U' O a N a V] a 'J-i O a1 a N a1 a1 id a1 N O a1 O a C/] a
~C4UC4Ua1~a]Ua]~'a]~",~C.)C.)~"a]C.)C.)~"C.)~'C.) C.)C.)C.)C.)
N
w O
O .N
O Ga ~GaU LL' 0.r LL'0.'0.rGap.., ~'Gap..,arUarUar~a+U LL'
o J a U a a U C4 ¾¾ a a U
ti U v U U U z v v U v v v v z v u u t, I
~p w a a w a w w w a w
C~ - N m n v t co C~
N r,
105
CA 02737004 2011-03-09
WO 2010/042525 PCT/US2009/059706
01 ~n 'n N ~O N _ - O 01 M N V
V V N M V V V V N M N V M M V V V V
01 01 01 01 01 01 01 01 01 01 01 01 01 01 01 01 01 01 01
. . . . . . . . .
O O O O O O O O O O O O O O O O O O O
O N --i O --~ N ~n N 00 O O o0 O N M ~n N
. . . . . . . . .
N N N N N --i N N N N N N N N N N N
00 l0 01 N ~O ~n N 00 01 00 ~n N 00 01 ~O l0 N ~f l0
. . . . . . . . .
O O O O O O O O O O O O O O O O O O O
M ~O M 01 01 01 01 O M O N M M O 01 ~O ~O 01 ~O
N M N V V V V --i N --~ ~O N N --i V M M V M
01 01 01 01 01 01 01 01 01 01 01 01 01 01 01 01 01 01 01
. . . . . . . . .
O O O O O O O O O O O O O O O O O O O
O
L7 Q.,
L7 V
Fes". O F^"= O
O Q
15-
a N N 0 i(1
N O U
a a a a a a a x o w x a a a N a M a ~~
a a ~ U ~ ~ ~ U '~ '~ '~ ,~ '~ N ~ N ~ Q /~ C~ N ~ ~ v, a, ~ ~ ~ b '-' ,~'
r~ ,~' P, ,~
v aar~ v H H c c~ac P, C7 viU~
o
~" a a U o a a a a U a C~ a a a C7 oU,
cl)
a x U U U U H x U x U U w U x x a
v a a s a a a a~" P r% a P, a a
w a a
U Z ES
- a a a C7 a N a a
a a a a a a a a N a a o a
0
0
V U U
C'~ P4 `~ U w w w w a w a w ¾ w
x x x x U a C7 CJ U u x P4
N 00 01 O --i N M V ~n l0 N 00 01 O --i N M V ~n
--~ --i --~ N N N N N N N N N N M M M M M M
106
SUBSTITUTE SHEET (RULE 26)
CA 02737004 2011-03-09
WO 2010/042525 PCT/US2009/059706
~O 'n N O N N M V --~ 01 ~n N N ~O N ~O N ~O o0
V V M V M V M V N V V V N --i M --~ V V M
01 01 01 01 01 01 01 01 01 01 01 01 01 01 01 01 01 01 01
. . . . . . . . .
O O O O O O O O O O O O O O O O O O O
'!1 N - M M N 00 00 M 00 00 N f N O 00 f
O 01 M --i N O 01 --i O 01 N N O 01 --i 01 --i --~ O
. . . . . . . . .
N N N N N N N ~O N N N N N 00 N N M ~O N
00 00 00 01 00 ~O 00 ~f N N ~O ~O 01 O ~O 00 - 00
. . . . . . . . .
O O O O O O O O O O O O O O O O O O O
M O 01 M ~O ~O O N ~O M N N O vl 01 O N N M
01 01 01 01 01 01 01 01 01 01 01 01 01 00 01 01 00 01 01
. . . . . . . . .
O O O O O O O O O O O O O O O O O O O
O
p N
~" L7 V
O .rte O E '~ R.~
N M N N M N p N U N C7 .? y C4 o
E
N N U 'C U CL, N N N U a i N N _
c
~ ~ a, C7 a, a ~ ~ U ~ ~ ~, C4 a, ~ /~ a C7 a C7 a, a a ;~' ~ /~ a ~ /~ ~
~ C7 a ~
x a x a H a x x 5 a 7 H v
0
o
a a a a ~" a s C4 C7 a a a a a a ~" a s C7 a C7 a o a a N
0 0
~ a. a.
0
o o
c U 0 U a"i a"i a"i U 0
C7 o Cw7 Cw7 C7 a Cw7 C7 c
M M V V N V V V V N V V O - N
107
SUBSTITUTE SHEET (RULE 26)
CA 02737004 2011-03-09
WO 2010/042525 PCT/US2009/059706
V 00 N N 01 O M N --~ V M 0o N O 01
M M M M M M M V V V V N M V V N M M --i
. . . . . . . . .
O O O O O O O O O O O O O O O O O O O
'!1 N M M ~f N M ~!1 ~O ~f ~f ~f N ~f M ~!1 O M ~!1
O 01 --i O --~ 01 O oo N --i --~ 01 01 01 N O 01 --i
- - - - - - - - - - - - - - - - - -
N M_ --i N N n N N N N N N N N 0o N N N
00 ~f l0 l0 0o V ~O N 01 ~O 01 00 N oo O 01 N 01
. . . . . . . . .
O O O O O O O O O O O O O O O O O O O
M f N ~O 01 O N M 01 M 01 N O M ~O N N ~O N
N oo ~O M V --~ N V N V 01 --i N M 01 01 M 01
OC
. . . . . . . . .
O O O O O O O O O O O O O O O O O O O
O
V E
~ Q =N
N 0
0
N 01 ~ '~ 01 li li b li li U M ;~ M
N
-~ Pa ,~' Pa 'o ~ Pa Pa Pa w Pa o, a. o, Pa P.a w Pa o, Pa i, ~ i, w Pa ~ Pa
.~ Pa o, a.
0
0
0
`~ w a w w w w 1 1 I w
U C7 U U U U U N U o U U n
~!1 ~O N 00 01 O N M V N 00 01 O --~ N M
108
SUBSTITUTE SHEET (RULE 26)
CA 02737004 2011-03-09
WO 2010/042525 PCT/US2009/059706
r vl V M_ N N O 01 --i M M M N 00 V O ~O M_
M M M M M V M V M M V V V V M V M
01 01 01 01 01 01 01 01 01 01 01 01 01 01 01 01 01 01 01
. . . . . . . . .
O O O O O O O O O O O O O O O O O O O
~n ~n ~n ~n O M t~ O ~f ~n M ~n N O oo O O O ~n
O O 01 --i O 01 O o0 --~ --~ 01 01 01 --~ O 01 --~ 01
- - - - - - - - - - - - - - - - - -
00 N N r N r r r N N r N ~O l~ ~O r N N r
00 oc 01 ~O l0 oc r ~O 01 r r ~n l0 ~n l~ 01 ~O 01
. . . . . . . . .
O O O O O O O O O O O O O O O O O O O
l~ M M l~ 01 ~O O M M M ~O M ~O M N M l~ 01 l~
01 N N 01 V M --~ N N N M N M N ~O N 01 V 01
00 01 01 oc 01 01 01 01 01 01 01 01 01 01 01 01 oc 01 00
. . . . . . . . .
O O O O O O O O O O O O O O O O O O O
L7
O
Fes". - 0. O Fes".
=~ ~ ~ 1 ~ 1 ~ 1 ~ ~ =,~ i; M N =,~ p ~~ =~ ~ ~ M
~aA?a ~a~aoo ~a`~' ~' a a ~a~ ~ ~aA? w
0
0
=~ o
~n ~n 'O O E~
x Pa Pa Pa Pa w O1 Pa Pa Pa w Pa w , Pa Pa Pa Pa w , .
~ U U a U~~ a U a U a U~~ a~~~ C7 ~ x a~~~ x a
0
0
a~ U U U
a o _ - a c _ a C4 ~" C7 a a C7 C7 a a C7 a C7
U U z a U a U x U U U x U x U U x U U x
0
0
0
0
o a a
N in
iy+' N U N N g cl~
V ~n l0 l~ 00 01 O --i N M V ~n l0 l~ 00 01 O ~--i N
l~ l~ l~ l~ l~ l~ 00 00 00 00 00 00 00 00 00 00 , 01
109
SUBSTITUTE SHEET (RULE 26)
CA 02737004 2011-03-09
WO 2010/042525 PCT/US2009/059706
N M O O O V N
M V V M N M M M
O O O O O O O O
00 O ~!1 M f N N
O O O
N N ~O N N N N N
O, ~O ~n O, O, ~O N 00
O O O O O O O O
O O, O, O N ~O ~O O
O~ O~ pip O~ ~n ~n ~n ~n ~n ~n ~n ~n ~n ~n ~n ~n V V V V V V V V V
O O O O O O O O V
L7
Q
LS ~ CNl ~ = "~~" Cl LS CNl ~ N O, Fes". ~ ~,
'~ p N b
m a a a y a a .~ o'o m N d w N d a a"i n
a v~v x C10v~ v a x xwUvv ¾ Hxaaa
~C
0
w
waA? _" D, ~aaa
o x~
~ O O 00 00 V V V M M N ~ M --i O, 00 00 l~ l~ l~ ~O ~!1 ~!1
7 C10
O O
V V
Q
w w ,, w U a pp~ on Q
\p o -d -d -d a a, ~ C7 y+ ~ C7 ~' ~ N N U c4 0 A vi U a ~
x x LU xaUc4 v cl ~axv Hx
M V vl ~O I~ 00 O, 00
110
SUBSTITUTE SHEET (RULE 26)
CA 02737004 2011-03-09
WO 2010/042525 PCT/US2009/059706
vl~
m
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
m v a m m a 00 a m 00
E~ m ~ v'~ N m v'~ m m ~ m N N N m ~ m N
=~ 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00
n - - - - - - - - - - - - - - - - -
'~ N N h N N h 00 h 00 00 00 h N ~D N 00 N
h o1 N C 00 N 00 00 00 N V'~ C 00
=~ 0~ 0~ 0~ 0~ 0~ 0~ 0~ 0~ 0~ 0~ 0~ 0~ 0~ 0~ 0~ 0~ 0~
. . . . . . .
v N m N N N N a1 N N a1 N o1 N 't a1 N
=~ a, a, a, a, a, a, a, a, a, a, a, a, a, a, a, a, a,
. . . . . . .
o
CG
E
w o
U 0. 0. 0. 0. 0. 3 0. 0. 0. N m 0. 00 .~ w V
=~ ~~¾~~¾~ ~UUUUUU~w~u~~ ~~x~~U~~~~~UxU~Ua
o 2
m U ... ,~ ,~ '~ cn U =~, =~, t 0 N
a~ wa ~a au a s q U ~aau ~a ao r~~r~
ool 0, 0, 0~ ct T
t5 cl ~UCYv z3UGGv Uv Uv ~v 1 v UUU yUUv ~UU~~~~~
t5
c a U r N r 0 a N a ro N N
O - - Qr Qr Qr Qr - - - Qr - ,--i U Qr Qr
oaU ~~ ~ wax
of a a o o C7o C7 ~v ~N ~~ o C7 C4 a a
~wUx~ U U U UxUx~x~ t5 ul) wU c4l) a,u U U
a a a a a a
c j N=a aU N N N U NU U a U
w w w w w w w
~ Cw7~
~cvCw7~NC7NC7NC~7 ~~ ~~C7~C7~C7~NC~7C7~C~7~~C~7NC7NC~7
Z3 t3 `~. t3 z3
z3
P4 v) v) P4
,~ N m V) 10 h o0 of 0 N M V) ~D h
111
CA 02737004 2011-03-09
WO 2010/042525 PCT/US2009/059706
O N M Vl 00 00 c c r- c c \O N
V1 M M M M - N
. . . . . . . . .
O O O O O O O O O O O O O O O O O O O
M --i M \p M M 00 --i \O M M M \O \O 00 M
N M Vl M M M N Vl N M N M N M N N
. . . . . . . . .
N N N N N N l- l- l- l- l- N N N N l- N N l~
. . . . . . . . .
O O O O O O O O O O O O O O O O O O O
N C N N N N C \O N N C N C \O \O
\O \O \O l~ \O M \O r- M \O \O \O M M
. . . . . . . . .
O O O O O O O O O O O O O O O O O O O
~ N
O
sO O s."
C
d dL~~~~aL~L~L~L~w ~c7CZxL~d
C
N U M N U U L a~ L~ L~ L~ M N
~ ~" a, C7 a, ~ a, a, C7 a, C7 ~ C7 a, C7 a, a ~ o a, N a, C7 ~ o a, C7 a, C7
~ o a, O L~ a
L) L) L) H L) L) &D L) L) L.> L.> L) &D L) L) L) &D L) L.> x ~
H
w a D a w n ~ n a s o a a a ~ w n a ~ ~ a o a a w n ~ ~
~~ N ~ N N N N ~ N N N
C-) ~
a w l w'~ w w w~ w U w ~, a w~ w~ w w U w U 211 w a w
x~x~x~
00 01 O N M \O 00 C O --~ N M Vl \O
--i --i N N N N N N N N N N M M M M M M M
112
SUBSTITUTE SHEET (RULE 26)
CA 02737004 2011-03-09
WO 2010/042525 PCT/US2009/059706
l~ \O \O N N l~ d1 00 V1 V1 V1 V1 00 M V1
M M N M
. . . . . . . . .
O O O O O O O O O O O O O O O O O O O
00 M o0 00 M o0 00 \O \O M M O \O M o0 M \O
N N M N O N N N N N N O N
. . . . . . . . .
l- l- l- r- M N l- N l- l- N N l- l- N N N ,-y r-
VO l~ AO AO O AO OA OA OA AO o0 00
. . . . . . . . .
O O O O O O O O O O O O O O O O O O O
N \O N \O N O \O c c N N M d1 N N N d1
\O M I~ \O M \O - M \O \O N M \O \O
. . . . . . . . .
O O O O O O O O O O O O O O O O O O O
C
O 0
C O
u CIO
U U N U N U N M L~
a, L~7 a, L~7 ~ ~ a, L~7 a ~ a, L~7 a ~ a ~ a, ~ ~ ~ ~ ~ a, L~7 a, ~ a o ~ Cw7
a, L~7 a ~ ~ o a o
x x x x a x a x x L~ c L)
0 0
C
o F~ o t3
t3 t3
ctl .Q'' O
L~ O L~ M M ,~
wacr a wawa c~7a cw7a oat ~a
CdO
x x x x x H x
N N N N ~ N L N N N N
L~ w L> a w L~ o w L a v w L~ L~ o w L~ w L~ w ~, w L> w a w
G~ x x x U x U U x x x x r4 x x r4 CX d
113
SUBSTITUTE SHEET (RULE 26)
CA 02737004 2011-03-09
WO 2010/042525 PCT/US2009/059706
M p\ N M N I~ \p o0 00 O I-- O o0
M M V1 M M V1 - M
. . . . . . . . .
O O O O O O O O O O O O O O O O O O O
V1 \p \p M M O_ \p \p ~/1 \p o0 M \p \p M O_
M N N M N O N N N N N N
. . . . . . . . .
l- N l- l- l- N N l- N N l- l- l- N l- N N N l~
. . . . . . . . .
O O O O O O O O O O O O O O O O O O O
p\ c \p \p p\ c M O \p c c c N C C M
- M M N r- - M \p N
. . . . . . . . .
O O O O O O O O O O O O O O O O O O O
s. p p ~ N
~ w w o ~' o
.~ b C7
C
C; b C N
x L~ a L~ x L~ L~ x L~ H L~ x z
0 0
w w
Ste" or.
n n n p n n
N N N N N
vi t r4 r4 cr C..) C..) 'n 'n cr cr
\p I~ 00 p\ O ,--i N M Vl \p 00 p\ O N M
114
SUBSTITUTE SHEET (RULE 26)
CA 02737004 2011-03-09
WO 2010/042525 PCT/US2009/059706
Vl Vl G1 W) W) \o O r- c N o0 N d1 M M N O
M M M N M M M N
. . . . . . . . .
O O O O O O O O O O O O O O O O O O O
00 --i \O M M Vl M M \O 00
O M N M N N O N O N N M N N O
. . . . . . . . .
N N ,-y r- r- N r- r- r- r- N r- r- r- N N
. . . . . . . . .
O O O O O O O O O O O O O O O O O O O
\O d1 N C C N M \O M \O c c c \O \O c c c O
M \O \O N M N M M M - --i
. . . . . . . . .
O O O O O O O O O O O O O O O O O O O
C
o a
b7 7 7 a r b a ~x bap wa oU
0 0
l~ ,~ U ~, N N M C..)
a Ck7 a a a C~7 a ~ a o a Ck7 a a a a a a Q a ~" Cw7 a C7 a
0 0
w w C
0 0 n
M l~ O l~ cd cd Q'' l~ ,,
a a a 7 a s n a a C7 a o 0 a o N a a a C7 ~4 a o a C7
a Q Q Q Q
Vl \O I~ 00 d1 O --~ N M Vl \O I~ 00 d1 O --~ N M
115
SUBSTITUTE SHEET (RULE 26)
CA 02737004 2011-03-09
WO 2010/042525 PCT/US2009/059706
O O t~ O n M
M C M V C M C
O O O O O O O
V1 --i O M
N O M N M
O O O O O O O
O O O O O O O O
O O
U U
O C C O
O S[. SO. P. O v~ ~ ~'
0. 0. pa ,~N,' ,~N,' N Gp7a ~3 p7~ ~~ O A v
N ~' Vl O W ~l O W VO in ~i Q N sue. c~C
a s a a a
'O ~ 00 I~ O\ ~ M O o0 00 N N O ~ ~ ~ ~ ~ ~ ~
O --i O\ O\ 00 Vl Vl Vl M N --i
O O O O
t3 r4. t3 vi. vi
116
SUBSTITUTE SHEET (RULE 26)
CA 02737004 2011-03-09
WO 2010/042525 PCT/US2009/059706
0, N N - ' N N N N N C N
a\ a\ a\ a\ a\ a\ a\ a\ a\ a\ a\ a\ a\ a\ a\ a\ a\
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
.~ v av co av ~ av ~ ~ ~ ~ co co M ~ ~ ~ ~
w =V OO OO OO OO OO OO OO OO OO OO OO OO OO OO OO OO OO
- - - - - - - - - - - - - - - - -
C"r CL
'y N N N M N N N N N N N N N N N N N
'w^. C\ N C\ O OO C\ OO N N C\ N N N OO OO C\ OO
=~ OO OO OO C\ OO OO OO OO OO OO OO OO OO OO OO OO OO
. . . . . .
O O O O O O O O O O O O O O O O O
U N N N N N N N N N N N N N N N
O O O O O O O O O O O O O O O O O
cC
U
=~ w
N M U U U - N C) ~. ~. N ~. N U U
O ddhhIdLHliHi
U C7 o ~ o
~+ p p ~a o ~a o ~a U N C7 ~a o a N
w ~~Z~UUUUU~~~ z3~C7xU~UxC4C4UU~UU~
E
o E
as o
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117
CA 02737004 2011-03-09
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Q o N O Q Q Q O N N o0 o Q o 0o v
O O O O O O O O O O O O O O O O O O O
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118
SUBSTITUTE SHEET (RULE 26)
CA 02737004 2011-03-09
WO 2010/042525 PCT/US2009/059706
00 M Vl l~ O 01 N V'1 M O O
V'1 't ~t M V'1 N V'1 V'1 M V'1 V'1
O O O O O O O O O O O O O O O O O O O
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119
SUBSTITUTE SHEET (RULE 26)
CA 02737004 2011-03-09
WO 2010/042525 PCT/US2009/059706
00 oc Q1 ' 00 Q, r yr 0 00 00 r t-
r r N M r r r N r M N M M r r M r r r
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120
SUBSTITUTE SHEET (RULE 26)
CA 02737004 2011-03-09
WO 2010/042525 PCT/US2009/059706
M Q1 N O 00 N M Q1 M 10 r-- N Q1 N O 00
M M r r N M N N M r r r r r M M r r M
O O O O O O O O O O O O O O O O O O O
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121
SUBSTITUTE SHEET (RULE 26)
CA 02737004 2011-03-09
WO 2010/042525 PCT/US2009/059706
r r M r r M
O O O O O O O
M 00 00 M
M M N N N M
oc N oc N
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122
SUBSTITUTE SHEET (RULE 26)
CA 02737004 2011-03-09
WO 2010/042525 PCT/US2009/059706
U m o ~o ~n ,~ ~n cal r- cal cal co o N N
w a\ a\ a\ a\ a\ a\ a\ a\ a\ a\ a\ a\ a\ a\ a\ a\ a\ a\ a\ a\ a\ a\
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C C C N N N N N N C C N N N N N N C N
=~ C C C C C C C C C C C C C C C C C C C C C C
h o 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
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123
CA 02737004 2011-03-09
WO 2010/042525 PCT/US2009/059706
l~ to m O to N 00 CA CA o to r- m CA N
a\ a\ a\ a\ a\ a\ a\ a\ a\ a\ a\ a\ a\ a\ a\ a\ a\ a\
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124
CA 02737004 2011-03-09
WO 2010/042525 PCT/US2009/059706
o0 N ti
N N N N ~n o0 ~n ~O N t N D1 N O ~O
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- N M N 00 D1 O ti N M T F D1 O ti
125
SUBSTITUTE SHEET (RULE 26)
CA 02737004 2011-03-09
WO 2010/042525 PCT/US2009/059706
N N V1 O> 00 00 O> N N C, 00 ti O> M M M M N O
V1 M M M V1 M V1 V1 V1 V1 V1
O> O> O> O> O> O> O> O> O> O> O> O> O> O> O> O> O> O> O> O> O>
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126
SUBSTITUTE SHEET (RULE 26)
CA 02737004 2011-03-09
WO 2010/042525 PCT/US2009/059706
l~ ti N 00 M M 00 N t N V1 O N
M V1 V1 V1 M V1 V1 t V1
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SUBSTITUTE SHEET (RULE 26)
CA 02737004 2011-03-09
WO 2010/042525 PCT/US2009/059706
o
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128
SUBSTITUTE SHEET (RULE 26)
CA 02737004 2011-03-09
WO 2010/042525 PCT/US2009/059706
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129
CA 02737004 2011-03-09
WO 2010/042525 PCT/US2009/059706
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a U a 6 0, y' o, o
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M 'l~yiy U M Py 'l~yiy U U M N O M N~ M M~ M U M ~a]y ~~r N M N O ~~]yy U M CG
~ M
U "" U D, U a Q "" U a O D, U CL D, U CL U U U D, U U U D, W D, U D, C~ /= CL
U CL V] CL QD U U N U
x x U x U U x U U U x x U x U U z U x z x U
a w a a a a a a a a a a U a w w
d d d d d O d d d d d d
y cG U ~ ~, x ~, ~ cG U U
N M 'n W O --~ N M M W O N M M
N N ~ N N N N N M M M M M M M M M M ~ ~ ~ ~ ~
130
SUBSTITUTE SHEET (RULE 26)
CA 02737004 2011-03-09
WO 2010/042525 PCT/US2009/059706
N N V1 N ~O M V1 M S oo O M S N N M
O O O O O O O O O O O O O O O O O O O O O O O O
o 'c N N N
M M ~ M M M M ~ ~ M M ~ M ~ ~ ~ M ~ ~ M M M ~ ~
00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00
S S S M N N N N S S oo S oo S M S oo W M N M N S M_
00 00 0o O\ o0 00 00 00 00 0o O\ oo O\ oo O\ oo O\ O\ O\ oo O\ o0 0o O\
O O O O O O O O O O O O O O O O O O O O O O O O
O O O O O O O O O O O O O O O O O O O O O O O O
O O
V V
O O
9 a
p p M li CG U ~+ O y M 'O N N ~ S7 Chi b U S7 M ?7 p M 'b 0 CG CG U 'c~ y
c~ ~ v~ ~ o o C7 6 ~ d' ~ ~ C7 Q o o, c~ o ~ ~ C7 Q ~ ~+
U U U U U U x m x x U U x H H m U U H U U a x a U a x¾ x x a
o
w o
w a w a s w w a w w a w w¾ a w a a' a w w a x
x¾ x x x a x x z a x z x x x¾ x z x x x u
s s
21
C7 y' ~+ ,~ o o, QI o, o o, o, o c~ C7 ,~ C7 cG o, o o, N ~ o, U o, C7 0, ~ ,~
y' ,~ 0 0 0, C7 C7 Q
x~~ a~ U U m U U~ U U U U U U x~ a x w U U~ x U~ U z U~ U x U U~~~ U~ U U x x
m "' w m w ~ "' a q w M M M
aza~aC~7rn~a~aC~7a oadadazadrnNa~aC~7aNaC~7a oaNadO~UO~Uavada~
~ W ~ x ~ CG U ~ ~ x ~ CG ~ U ~ ~ CO ~ W ~ CO U z3 ~ ~, ~ CG ~ z3 ~ CG ~ U ~
z3 ~ CO U ~ U ~ ~ ~, ~ x
I
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a ¾~ p~ o a w w a s p~p~ w w U w w a v w w w a w a U v
o, U o o, o, c~ U ¾ C7 c4 0, U C7 C7 C7 0, U 6 c4 C7 U C7 0 C7 C7 C7 d' C7 U 0
U QI o, U U cG
o
x x a U U U U U U x x x U
131
SUBSTITUTE SHEET (RULE 26)
CA 02737004 2011-03-09
WO 2010/042525 PCT/US2009/059706
M M N W O O M V1 N S N O W M N N N
O O O O O O O O O O O O O O O O O O O O O O O O
M M M M M M M M M M M M
cl~
- - - - - - - - - - - - - - - - - - - - - - - -
S N oo S M S S S o0 00 S N S N S M S N N S S N N S
O O O O O O O O O O O O O O O O O O O O O O O O
O O O O O O O O O O O O O O O O O O O O O O O O
C
O O
00 ~ m ~ ~ N N h
M .~ CG M N .,. U C7 M M M CG Gam., Gam., pp,, T c~ p r,., U
P U wU~ Qj a¾ ~x w v oU U ~~ d6Cw76dc4 UCw7N~ a OA ~v r5~
o
a ~y
a s a s a a a a a a w a
a x x x a 0 x¾ x x d Q 0 x x x x x m x x
0
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C7 0, o, C7 0, C7 C7 U C7 '~ o, QI o U o, o, o, o
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o 0
q q r~ al
M 'A ... U 'A M '~ ... U 0 CG CG U m ~ ~r U ~ U ~' N
a D, V a O q U O a V a O N a U a a a a U D, cC a L a U O, U O, Q a U a O a U'
a a CL
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N N o, U a 6 P. a ,_j a d o, U c4 U C7 o, c4
o
O --~ N M V1 ~O S oo O --~ N M V1 ~O S oo O --~ N M
132
SUBSTITUTE SHEET (RULE 26)
PCTIUS09/59706 19-11-2009
CA 02737004 2011-03-09
WO 2010/042525 PCT/US2009/059706
0 0 0 0 0 0 0
0 0 0 0 0 0 0
S
0 0 0 0 0 0 0
N ~ q x x N x
0
y
S
w mp., _ ~ avw~
~ a ~ =mod w
x x w C7 '7 x U 6 C/i
rn C7 rn rn U ca o o " " Chi Chi N A? ~?, U ~/'
UxUUUHx~~x ~[ ~'U H xC5
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M M L M N M U~
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U~U~x z3~cG~~~UU~
r-~
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C ~ ~ x U ~ oal~C7 oC7~UC4 "`~c~~ a i oN o ~va1UQN~~o
U P4 U as x U U a x u U
133
SUBSTITUTE SHEET (RULE 26)
CA 02737004 2011-03-09
WO 2010/042525 PCT/US2009/059706
U 't Nl Nl 0 - - mm 't 0 C 0 't 00 0 G\ 0 N mm m Nl
.'~ rn rn rn rn rn rn rn rn rn rn rn rn rn rn rn rn rn rn rn rn rn
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
' rn ~ rn ~ N rn ~ N rn ~ rn rn a rn a a N rn a N ~
~' `~ m oV'o o`OO o`OO m oV'o o`OO m - - - - - - m m - m o`OO o`OO o`Oo
S N N N N ~ N N m N m m m m m
p o 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
N rn N rn a N a a N rn N N N N N N rn N N rn rn
d o 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
0
ca ~ C
c o~cCc~cC cC ~ oCw7 ~ o~Cw7~~ ~ o~c~Cw7cCU~j~ ~a'~~ o~~ o~~~~
C
w
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ca >C >C p
q o o ~
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y ~ ~ O 3 3 O '~ a> a> ~ ~ a> ~ u
i cG N cG N U ~; U U ',n m i; U .pro .G N U N cG cG N o cG N ' U ~?
wawa a a a aw wa a war.
0
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o F' F' ~ m F' q ~ rn .p F' ~ ,~ r~W, rn
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C U m m m m C C U ~ C Ei C Ei
m m m U U m U m U m U
E-~
134
CA 02737004 2011-03-09
WO 2010/042525 PCT/US2009/059706
m o O ca n N m 't ca m .~ a, O ca 't o C
m c c c c m c c c c c c c c c c m m c c c m c c
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
7 t N C 7 - - t 7 t N i0 C C C 7 - t N 7 a,
m m m m m m m m m m m m m m m m m m m m m m m m
ca m N m m m N ca m m ca N N ca m m m ca N m m
o~ o O 0 0 0 O O o o O o o O O O o o a, 0 0
o o 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
ca ca C ca ca C C ca ca ca ca 't C ca ca ca ca C ca C ca ca C C
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
0
m R+ m
0 o
U 0 U
w o o w ~
L7 L7 v~ L7 o q ~j m o ry L7 w o 0 0 ~j L7 ~+ ~n L7 i0 c~ ~j q L7 m
F4 P`: 'x U Z ~ fx ~ U ¾' Z U Z U ~ '~ ~ P`: ~ x fx L7 U U ~ U v~ P`; ~ F Z F
v~ ~ F4 a ~ U 'S.' U v~ U Z d 'S.' ~ U
0 0
P. P.
w w
o a q o
w m ~ a w
t w pp~~ pp., t w t w w w w U w m a w a w
L7 W. E U L7 W. ~l m ~ L7 L7 L7 L7 ~~Y, q U o U
m m
0 0
0 0 0 0 0 0 w 0
0 0 s7 0
0~ 0 0 0 0 a i a i a i
~ ' C ~ F4 ~ 0 ~ F4 ~ F4 ~ U ~ ' C A ~ m~ F4 ~ F4 ~ ~ m ~ ~ U ~ F4
ro w qq ~ qq ~ ro ~ N ro ro p., "' ro ~
P. ~' P. `~ P. 6 P. P. P. 6 P. o P, Cw7 a C7 a Cw7 a, o a, a U pad' a C a C
o C.7 a o a 6 0, o, U a a o a c~5
mUmpa~ ~~FU~~`~~~~smfxmlT000Ummfx~m ~m ~UF4mUmmU~U~mamUm ~
0 0 0 0 0.~ ~
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21
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ca m ~ ~n ~ ~ a, o ,-. ca m ~ ~n ~ ~ m a, o ca m ~ ~n
~ ca ca ca ca ca ca ca ca m m m m m m m m m m ~ ~ ~ ~ ~ ~
CC
135
SUBSTITUTE SHEET (RULE 26)
CA 02737004 2011-03-09
WO 2010/042525 PCT/US2009/059706
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
7 7 ~O O1 n 7 7 - t t O1 - - O1 7 7 t
m m m m m m m m m m m m m m m m m m m m m m m m
m m N ca m m m m ca m m N m N ca m ca N m
o o 0 0 o O, O, O O O ca O O O, O O, O, o O O, O, O
O, O, O, O, c a, O, O, O, a, C a, a, O, a, O,
o o 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
ca ca O, ca O, O, ca ca ca O, ca C ca C O, ca O, ca ca ca ca ca ca O,
01 01 01 01 01 01 01 01 01 01 01 01 01 01 01 01 01 01 01 01 01 01 01 01
O O O O O O O O O O O O O O O O O O O O O O O O
O q
~O ~ 7 N N .~ .~ H
ca ca 0 c~ N N N w t~ N ~, ,~ Cl Cl F~+ F~+ Cl 0 a ~ a~i ca `~ ca o i
U~ U~ a~ w a U U U m U w U w U~ ~ U ~ w a a, U U z,, ~;
o ~ o 0
r U 'n '~ -~ fx U N N m- m m N U N- N F4 F4
w w pppp,, ' w w w ww w w w p~~ w a w u¾ a w a_ w pad w w w w
L7 U L7 z U L7 L7 m L7 L7 6 L7 L7 L7 U L7 L7 U L7 `" U L7 U '" c4 x x U a
x m x z m m rx zz a x U x F x Pa x c4 x m m x U zz w x~ x a~~ x k~ x c4 a
>C >C >C
o 0 0
U O U U
kro, 0, km, km, 0 0 0
P P g _ P P P
L7 0m a, a, 6 6 ca a, om L7 0 m o a, Q' ~~" 6 a, Q' a, Q a, d' F4 U
z z x U U~~ U~ U w w ~ ~ U~ U U x U x zz U U~ w~~ U w U¾ U w~ z ~ a~
m O P, w ¾' qq a, O ~ U a, q O ~ m 0 p~
a o a a, C~7 U C7 a q a d a N a~ a~ a q~q a, C~7 a a, ~J a~ a~ a d a N a, a~
a a U a o a~ a, 6
m U m ~, U F4 ~ 'x m d m m m~ m 'x, m U m d U F4 m ~, U m a m 'x, m m m~ U m U
m p., m ~, m U m U U m
0
0 0 0 y~
U ~+~ ' o m 'ti U U o m U ~+ U U U ~+ U- m U U m
~ ~`" ~ ~~ p, ~' Q+ P. ~ ~ Q+ Q' ~ ~ o a, d a C~7 a, C~7 a, C~7 o d a, C~7 o d
U Cw7 Cw7 U a, C~7 a, C~7 ~ Cw7
U U m U x U` U` U` x m U U w m x U x U x U w U x U m x x U x U x U x
C U m m
~ m m m~ U GY, c~ U m m m~ ~y m m m m U m~ U t~ U ~+ t~
`~ 6 U 6 U L7 U m a, L7 U L7 6 U 6 U d' U L7 U d' U L7 U d' c~ d' U L7 L7 Q'
U m L7 o L7 d' m L7 Q' U Q' U U
CC
136
SUBSTITUTE SHEET (RULE 26)
CA 02737004 2011-03-09
WO 2010/042525 PCT/US2009/059706
c c c c h c m c c c m m c c c m c m c m c m c c
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
m m m m m m m m m m m m m m m m m m m m m m m m
m m N N ca m N m m N ca m ca N m m N N ca m N m N
0 0 O O o O o o O O o a, O o O a, O o O
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
ca O 't ca ca ca O O O ca ca ca ca O ca O O ca ca ca O O O ca
~O 7 r C C C 7 7 7 ~O ~O ~O ~O 7 ~O 7 7 ~O ~O ~O 7 7 7 ~O
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
0
m m N W t\ h =n U N m F4 'ti ~ N .n c~ t~ m _o =n U ~~ m ~y ~ ,~' m ~y ~ FL,
pp.~,
0 o U F4 vi ~' Cw7 C.7 ~ Uj m U a o a Cw.7 0 0.j U Uj 0.j F x U P: x U Z o U
~ Uj m 6
U U~ Q v~ U~~ x P. F4 d Z~~~ P. x U Z P. Z P. x F Z v~ U C7 ~ Z x Z x v~
0 0~ 0 a a
t~ W. _P+ m _ N fX N rfy,, m U N m -~ t~ t~ m t~ U F~, t~ - - ~,
~' x Lw7 w ~ w ~ ~ ~' x U o Lw7 ro ~ `" ~ 6 U L~7 Lw7 ~ Lw7 ~'' U Lw7 ro ~ U
x w ~ w w x ¾ w ~ w
0
w 0 ~ 0 0 q 0 0
~ a ~ a a a a
p., ww, ww, ww, ~ P:
6 o a, 6 L7 cqa a 6 a, m L7 U ~ a, Q' q a, m a, a, q a, a, 6 Q ~ Q ~+ a, ~
m ~ ~ UA '~, U 0. F4 ~ m ~ ~ pa U ~U fx m x U 0. d U ~U U ~ U d U ~ U 0. ~ ¾'
m U ~ ¾' ~ ~ ~ a U ~ x
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a s oa Ulu oa a oa ~a,¾a~a a¾a a a¾a a o a~a~a a s
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0
ro a
0
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m ~+ U ~~ t~ F[, ,~ U N W. N ~ m U O N m U O N N m ~, ~ U m U U
U w ww U U a a^ ~' '12 U a w U
U a L7 a o o~ o U w a L7 6 L7 U L7 r~ ~ U L7 L7 ~ L7 a U ¾' a o L7 a L7 U L7 a
L7
~ w U x~ U U U U~~ U x~ w~ x `~ x~ x~~~ x x~~~ x~ U~~ w U~ U x U x~ x~ x
E
U m m U~ m U CI o ca m U N~ U U +~ U m U m ~y
o ca m ~ ~n ~ t~ a, m m m ~ ~ ~ ~ m o ca m
CC
137
SUBSTITUTE SHEET (RULE 26)
CA 02737004 2011-03-09
WO 2010/042525 PCT/US2009/059706
c c c c c c m
o o 0 0 0 0 0
o oo 0 0 0 0 0
o o 0 0 0 0 0
0 0 0 0 0 0 0
0
~ U N U a i
0
o m A 's7 m
Cw7 w a vf'i Cw7 a' Cw7 ro Cw7 Cw7 ~ v
x~~xx~xxxHxH~~
0 0 ~'
o. o. Q `~ F P-. = ~ ~p
Vl Vl tj tj U
C C a J a
a a a a a a w
l~ l~ M N O~ M O l~ M M
0 0
~U m Psi O ~Ur N ~Ur N
115 '2 t P4
N c! aa~,
ce
138
SUBSTITUTE SHEET (RULE 26)
CA 02737004 2011-03-09
WO 2010/042525 PCT/US2009/059706
d o 0 0 0 0 0 0 0 0 0 0 0
co co c~ m r- co co c~ co r- co
0 o C a o C o C o
= c C C cc c cc c C cc C cc C
0 0 0 0 0 0 0 0 0 0 0 0
V N N N N N N N r- N
=~ a\ a\ a\ a\ a\ a\ a\ a\ a\ a\ a\ a\
h o 0 0 0 0 0 0 0 0 0 0 0
0.CC
wxw~ as aaa~w~C vaw~ a ccaawaww<
U FG C7 C7 N Ca Ca U '4 C7 4 C7 N
xUx ~UUv v U xwx <CCxUcl 4
C
O
~UU ~r'o m U'm~CG~U' CG ~U'
A A
s ~a as aba~ab wbaww a
"4 UV~caUUVxv~x~v
o mss. 5C
P1 Cd o
M O s. U s.
C O w O
m U O
C m m m C m CG ~~'' m CG
a4 a4 < a4
aU~aU~aQ no Cw7 0C 114aaU ~a ~aU aaa4 Caa ~aU
o v P~v ~v UUUUv u4Q.) ~v v v U~v CCv k~
0 0
o a
0 0 0 0 0 0 0 0
CG ~ m '~ CG CG ~ ~ CG ~ CG m CG '~ ~ ~ m p U ~+
P4 p4 u P4 r14 U U a4
a Na C4U ~ 4a~ 4 o ~C7o) 4
U~UU~ca~x~~UCaU u cau caU~xxUca
U U ~ U U ~
a4 a4 a4
C7 C7 Cw7 C7 C7 0
,~ v~ v~ CG CG .'c v~ ~ v~ CG CG U v~
CC - N m 'C r- co C
139
CA 02737004 2011-03-09
WO 2010/042525 PCT/US2009/059706
oo ~n o o, o
O> O> O> O> O> O> O> O> O> O> O> O> O> O>
O O O O O O O O O O O O O O
D1 D1 D1 D1 N
. . . .
N M oc m M M oc m N N M_
D1 - O O O O - O O O D1 D1
D1 D1 D1 D1 D1 D1 D1 D1 D1 D1 ~ ~ D1
. . . .
O O O O O O O O O O O O O O
N N N N N N D1 N N N N N N D1
D1 D1 D1 D1 D1 D1 D1 D1 D1 D1 D1 D1 D1 D1
. . . .
O O O O O O O O O O O O O O
pa U~~a pwo 'ww w~rw xd~w p ww¾~~Ud
x w Z u x a U x U x a U U x a U u x w x i U
0
0
0 0 0 0 ~ o ro
o w
p. p. p. p. ~ p.
o o ~ o o '
CG M U CG U o CGx~- CG b u U U o M
U u H U U U _ 0 u U U _
0 0 0
F
M O U M ~ U M S7 M M U U M ~i N M N
a U V] D1 C.7 U a U cd a C.7 O a U O a U 4 U C.7 D1 O C,
U W cG v~ V _4 v~ U F c4 U U U I 5A cn cn u U
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N U~ N M N w N M N N~ N U N N U U M N CG
U u P. U w U a U ww U w U u u w o w¾ A w rl
N U U U
a~ x x c4 x U
ti ti ti N 00 D1 O ti N M
L~ N N N N N N N
140
SUBSTITUTE SHEET (RULE 26)
CA 02737004 2011-03-09
WO 2010/042525 PCT/US2009/059706
~O ~O ~n O O M ~n D1 N D1 D1 - D1
~ ~ ~ ~ ~ ~ M M ~ M ~ V1 M
O> O> O> O> O> O> O> O> O> O> O> O> O> O>
O O O O O O O O O O O O O O
D1 D1 D1 D1 D1 N N D1 N D1
. . . .
M 00 00 N 00 N 00 M M 00 00 00 N M
O O O D1 O D1 ti ti O O O ti D1 O
D1 D1 D1 D1 ~ D1 D1 D1 D1 D1 D1 ~ D1
O O O O O O O O O O O O O O
D1 N N N N N D1 D1 D1 N D1 D1 N D1
D1 D1 D1 D1 D1 D1 D1 D1 D1 D1 D1 D1 D1 D1
O O O O O O O O O O O O O O
~ . C~J ~p in in U in F in ~ Z3
D D
r- u
C7 N ro C7 C7 a Cw7 a Cw7 C7 C7 C7 pp C7 0 U o a N Cw7 n
x x U Z x x x u u x x U x c4 z x U a U U Z x` U
to
0
w o ~ w
U~ b e U b CG U o U '~ M b u b U
U U U ao U U x x u ~x F x u c, cl
U u x u x U
~ o 0 0 0
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N N N N
M CG U+~ N M N M_ M M M U M
, `FyQ~ wU w a u w~a as as w a wa w~a a coa d
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N m N U m c~ " rl cI c~ N U N
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o
I
l- 00 D1 O ti N M N 00 D1 O
L~ N N N M M M M M M M M M M
141
SUBSTITUTE SHEET (RULE 26)
CA 02737004 2011-03-09
WO 2010/042525 PCT/US2009/059706
N 10 o0 N ~O - O M N N M
O> O> O> O> O> O> O> O> O> O> O> O> O> O>
O O O O O O O O O O O O O O
D1 D1 N D1 D1 D1
. . . .
N M oc N M M oc N oc m oc r-
oc
~ D1 D1 D1 ~ D1 D1 D1 D1 ~ D1 D1 D1
. . . .
O O O O O O O O O O O O O O
N D1 N D1 N N D1 D1 D1 N N D1 D1 N
D1 D1 D1 D1 D1 D1 D1 D1 D1 D1 D1 D1 D1 D1
. . . .
O O O O O O O O O O O O O O
CJ Q.
- U
R+ CND yO
U ~ ~n ~ ~n Z3 `~ ~n F ~ 0.r ~ ~ p Z3 cJ Z3
ti oo u a w ti w w x~ p w x w w o a 'o w ~i w w w
U x' x' U x x U u F x a z3 x U F U U 0 x_ _
~ 0 0
p p
o o e~ e~
o U b U N b CG U U U U b CG
o a a a ro a a w W a a N IT-
pi o0
o
0 0 0
o
a cl
M U CG CG M N Z ~i M U M U
w w ~i w N N N
a x4 a CG S ~~~ a Uz C7~aU P.~aC7N
v~ v~ CG v~ v~ v~ r CO v~ F r~ z F r~ CO v~ CG v~ v~ v~ Z z3 C4 Z C7 U U
o
o ¾ o ro
g p. w
N U O
N~ U M M U M U M U M M b N N ~+ M N
U w a o w a a u IT, a s NU a a U a u w a w a U a o~n
C7 o U C7 U o, C7 U CG o C7 U o, C7 U CG U o CG CG N o C7 U CG U
U U S U CG U CO U CG U CG U u_ W U U u
U E
U t U
U r¾ '~" r¾ U
N M V1 to N oc D1 O ti N M
142
SUBSTITUTE SHEET (RULE 26)
CA 02737004 2011-03-09
WO 2010/042525 PCT/US2009/059706
N V1 M N N O~ N - N M
O O O O O O O O O O O O O O
. . . .
N oc oc oc oc N M oc m N M oc oc N
oc O O O O O~ O O O O~ O O O o0
O O O O O O O O O O O O O O
N O~ N O~ N O~ O~ N N O~ O~ N N
O O O O O O O O O O O O O O
O O '=
R
U+ C R
`~ 7J F raj V1 .~ V1
n n U o r U N CG o o n n CG CG CG
n 0. a o w a 0. 0 0.w o o ~q a~ ` ~,d o0
U U o U a Cw7 C7 C7 C7 U o a N C4 U (~ C7 N
U CG a W a F CG F W x U U U __ U z3
0 0
u p
1~ c,
' U '~ c U b CG U '~ U U a~ U U i~ CG o
x4 U x4 U U a u U
x
cl~ Z~ g,4 U 04 g,4 g,4
u, Z U u u U
0 0 0 ~
o C7 o C7 o u N a u a u a Q U u (~ U a a o a a N
U c4 U x W U U U U cl~ U U c cn U cl~ U _ __
C,
R+ Z3 Z3 U` ' ~y '" R+ R+ Z3
~~ M N o , N r%] N .~ N M U U N
w a a a a a w a U U U U w a a o w a a ar U ar
N C7 a s C7 U o o N C7 ro o U o C7 N> C7 N U C7 o C7 U CG U
. x x U U U_ U__ u U U x x a x x x U x U
N ro
o
U CG U U CG
V1 ~O IN oc O~ O ti N M V1 '0 N 00 3 lo
143
SUBSTITUTE SHEET (RULE 26)
CA 02737004 2011-03-09
WO 2010/042525 PCT/US2009/059706
N V1 M M r O - V1 M r V1 N r M
O> O> O> O> O> O> O> O> O> O> O> O> O> O>
O O O O O O O O O O O O O O
D1 r ~O D1 D1 N D1
. . . .
r o0 00 oc m r r o0 oc o0 M o0
D1 O ti O O D1 D1 O O ti O O ti O
~ D1 D1 D1 D1 ~ D1 D1 D1 D1 D1 D1 D1
O O O O O O O O O O O O O O
N D1 D1 D1 N N D1 N D1 D1 D1 N ~ D1
O> O> O> O> O> O> O> O> O> O> O> O> O> O>
O O O O O O O O O O O O O O
CJ ~
O ~ O O
w o ~ o w
~' ,-, H a cl
c
o p y
~^^ r U r U r N .~ r CG ~ r C7 U r b r m
U bA a s ~~- a a O 'd c, py w O py O W p:., y 0.r N
a a w w z u~ u w Z w w w O W sz u u
o C7 0 o U U U U U CG o
U x u x v H x U x U H w H x x o U x u U a ~ x ~ U
U
O o
o o w o o
a a c r,
M M M CG U U CG U U
-ar ~a E w ~a w ti
U (~ a u j a u 0 C7 o o o C7 a o o , C7 o o 0 o U
x En 'n x u a x H x H U U U w x u U U U x u 0 U U U
o w o ~ o
U O U ~ U
U M M M M M-+ U U
uwuy o~n v ¾ d uwuyy d¾ ¾ d aa~ d ~ d U ' aa~ pp.~ w"
0 6 C7 0 o o a Cw7 (~ a U 6 a U Ca a Ca U a N 6 a u N U a a C7 6 a C7 C4 0
U CO cG U v] U U v z3 UJ z3 U cl~ U v~ C4 c/J v~ CG z u U
~ 0 0
E
a C7 o o o u ro o CG o CG CG CG U o U CG C7 U ro C7 U CG a C7
U U a U a x u u z u U u z x x x_ x a
U E
r U U U r
D1 O ti N M ~n ~O r o0 D1 O ti N
~O r r r r r r r r r r o0 00 oc
144
SUBSTITUTE SHEET (RULE 26)
CA 02737004 2011-03-09
WO 2010/042525 PCT/US2009/059706
N D1 N N D1 O - O M O D1 N
O> O> O> O> O> O> O> O> O> O> O> O> O> O>
O O O O O O O O O O O O O O
. . . .
oc N N oc m oc N M oc M N N oc N
o0 D1 O O O D1 O O ti D1 O1 o0
D1 ~ ~ D1 D1 D1 D1 D1 D1 ~ D1 ~
. . . .
O O O O O O O O O O O O O O
N N N D1 N D1 D1 N D1 D1 N D1 D1 N
D1 D1 D1 D1 D1 D1 D1 D1 D1 D1 D1 D1 D1 D1
. . . .
O O O O O O O O O O O O O O
c3 c3 '-' Z3 7J N
C~J R+ C]- R+ C~J R+ Z3 '~~' Q. c1 c1
's~ CG 's~ U U N O 's~ U CG U CG
U w 00 w w w w a s w a a w" U W ti o0
x4 U x x um x a x U U i CA Z~ x x z 'n U w x x x a x
0
o o o o o z3 v
U U
u u u u u z
0 0
0 0
Ur Ur U C, Ur U U Ur Ur Ur U
:-i D1 cd I-~ A I-~ I-~ cd O~ N `Q
U x// a u U z F v / v / U U U
O O O O '~
U U --i U 7J U Z3
U w U U4, U F. U F. P-. rte, U U k., a+ U uy u U
U u C4 CG CG C4 CG o CG U o o U o (~ C7 o C7 U o 6 0 N
~; a x U w U U u z u z u U'n u x a U x u u
m n o N o0 0 o N m n o
00 oc oc oc oc oc oc
o 0 0 0 0 0 0
145
SUBSTITUTE SHEET (RULE 26)
CA 02737004 2011-03-09
WO 2010/042525 PCT/US2009/059706
O O O O
N O ON O
O O O O
O O O O
R+'y r Z3
O y.
,y ~n N O~ 7J
w Q, ~ a+ ~ ~ ~ C7 ~ ~
x4 x x U c4
~ p.
7
C,
0 0 0 o pl
p C,
l a N F
r- U
a ro ro w er p w xi o ~3 x w p
M U U U
u o a s w i u
yy OI O O O O~ 00 N N O o0 lO M o0 N ~n M
O ¾+ Q 0 0 0 c, oc to to N N N
0 U
M yj
N N ro N
U w u w u
r
cG C U cG cG
`/ .sy y l~ U M N y0sy U O U 'd 7~J
a a a a 0. U w ~' a F. cn
i U C7 o 0 U ro (~ N o CG N o
Ux wxU z~¾UU zUa Uw
146
SUBSTITUTE SHEET (RULE 26)
CA 02737004 2011-03-09
WO 2010/042525 PCT/US2009/059706
U m o 7 m N m m o n n 7
7 7 7 7 7 7 m 7 7 7 7 7 7 7
d' 0 0 0 0 0 0 0 0 0 0 0 0 0 0
=S' m m m m N m m 00 N N 00 m N m
N o, 0 0 C, C, o C,
d o 0 0 0 0 0 0 0 0 0 0 0 0 0
N of N of C1 N N N N of N N
=~ a, a, a, a, a, a, a, a, a, a, a, a, a, a,
~ 0 0 0 0 0 0 0 0 0 0 0 0 0 0
r
0 0 0
N
c= C=
N m ~ L m m m U CG U N CG
Pa U CG CG U a' 0.i U 'ti U CG 'ti CG
P U n~UC7a oN N -~C7Q aC7~aC7 aaC7~ U~ cUC7C7
c0 xcaxxwdx~xd~axx~ca
ro >C >C a
'~ w .~ o o w o
o o U m U Gq .G .ai .~ ~'n 'n'om U~
a awp a w aa~~A a as c a s
r C3
u
A
0
w
o 0
FQ C C, a, C ~' C Cl C C '''o Cl
-It
~~~~ m U U CG ECG
Cl
p m m
o ~C7 c4o Cl c; o UN~C7~ ~C7 a
rnxZUx~U~xUUp.rncGxU~UrncG.arncG~Ux~Ux~rnUCGUU ~rnrnUrnU~
ro
-cl
~'^m ~'^'p U =p m m
vas' a v w : a~ a~y~y w¾~p-a a~y_wa
m aU~ a aNC7~~N~U~ C o NN~C7C7N UNUE C7 C7cG
GGUv~ v~v~GGU ~cGv~ C,5 C, U U GG C C v~xCR :3 U x'xCGz
cn k11I.IIUUIIdU
147
CA 02737004 2011-03-09
WO 2010/042525 PCT/US2009/059706
M 7 V'1 M t 7 V'1 N O M V'1 M M
7 N 7 7 7 7 M 7 7 7 V'1 7 7 7 M 7
O O O O O O O O O O O O O O O O
. . . . . .
O Q, Q, - Q, O O O O O C O O O C C
O O O O O O O O O O O O O O O O
N Q, N Q, N N N N Q, N N Q, N N c 7
.
O O O O O O O O O O O O O O O O
O O O ~
N N ti N
o 07 W W W rn~ ' CG c~ 0~ p M U
sa. x~ o w m w w F, w w w~ w F, A ~ a a ~, w F' w p a W ~ o a
w ~ C7 cG C7 C7 cG a C7 ~ C7 m N ww U o a C7 c4 m C7 ~ o m U C7 0
x H V a x CG 'x V x W 07 'x W 'x x k~ U k~ V P, U r~ W H 'x Z x x P, U U CC U
o 0 0 0
O O O
u u
auj
~ m o o. ~ m o. m o ~ m ~ m C7 ~ ~ N ~j
UUUwxUUU U xx~ C5 a,xU Q-) Q-) ~4 UH~ UHUwx74
P P
'T u u 'T
P C7
U U U u W W o U U rn
a~~ a~ a~ v~ ,~ m C7 0. U ~^ o. o m a C7 o a~ N a~ m a~ U a N~ rn o~ a C7 ~ a
C7 Q ~"'a o. N
m 'x CG m m m m U m 'x CG U a U U U m CG U m W U m p7 U m 'x m m x U U U m CG
m m CG m m U x
0 0
U U
o. U CG q U U ~ o. U s '. a N ~ U C7 o. U ~^' o. U ~ N ~ o o. Q C7 ~ CG U o
U CG m o. U ~ CG Q U ~
U U a U x U a U U U U x w x U U w
0
0
vim
U '~ o U U M U y 'q U U
v w a~ U U a U a U a o a U w v w
r-o
V'1 ~ 0 O - N M 7 V'1 ~ l~ 00 C O
N N N N N N N N N N M
148
SUBSTITUTE SHEET (RULE 26)
CA 02737004 2011-03-09
WO 2010/042525 PCT/US2009/059706
c~ cI v~ O o c~ o. v~ o.
M 7 7 M 7 7 7 V'1 7 M 7 M 7 M 7 M
O O O O O O O O O O O O O O O O
c 7 ~ 7 7 ~ c 7 ~O 7 t cI ~O 7
V'1 ~O ~O ~O ~O ~O V'1 V'1 l~ V7 ~O 7 l~ V7 ~O V'1
. . . . . .
l- M N M M N M l- M 00 M 00 r- 00 M M
Q, O Q, O O Q, O Q, - O O O Q, O O O
O O O O O O O O O O O O O O O O
N cI 7 N cI 7 c N N c N 7 Q, N Q,
~O ~O ~O ~O ~ 7 7 ~O M l~ 7 7
O O O O O O O O O O O O O O O O
N 0
0 N
~ O
O O .~. p .~. p, O O
w w w w w w w w w a U a s a a
x x ~Z x x x w x ~x x ~x a~ a C~ x ~x x ~Z x d x x
U~ ~UUUU ~aUxUUmw"~U ~UmCGmUHUx[~m~UUU~UaC~
0
0 0 0 0 0
w o w S S
U O U O U
A C -
M U ~ ~ U A Cl+ U U Cl+ ~. Cl+ '~ M U~ U~ U'
0 0 0
o U U
f'-. M U M U M M M M a+ U ~i U N CG M M M y M'
~24 C4 7 I
o. U o. U C7 U C7 0. U a o. U Q U o. U CG C7 C7 X uw o c~ U U '~' o. U Cwj o.
U o U o
v
U N ~ N N N ~ '~ M U U M ~ v~ c~ c~ c~
~ C7 o C7 U C7 C7 C7 U Cw7 o
N M 7 V'1 ~O l~ 00 c O - N M 7 V'1 ~O
M M M M M M M M M 7 7 7 7 7 7 7
149
SUBSTITUTE SHEET (RULE 26)
CA 02737004 2011-03-09
WO 2010/042525 PCT/US2009/059706
M ~ M M V'1 M N ~ 7 7 7 l~
7 7 7 7 7 7 7 M M 7 7 7 M 7 7 M
.
O O O O O O O O O O O O O O O O
0 7 0 V'1 0 V'1 0 7 0 0 0 V7 7 0 7 0
. . . . . .
M l- M 00 00 00 M_ 00 00 M 00 t- M_ M M
O O O O O O O O O 0 O
.
O O O O O O O O O O O O O O O O
N N N Q, N Q, Q, ~ Q, N Q, N ~ N N N
.
O O O O O O O O O O O O O O O O
U N U N N '7a' v~ \' N
C= C,
w ,~aa, a, a, wx wF,, wx~wp., wp., wpb o,~U '~¾~U,~Ua,
C7 m U m ,n U C7 Q C7 m C7 C7 C4 C7 C4 c m o v cG m CG N CG CG U o
xwk~ Uk~xwmma xa ~xx[~xa xZ ~xZU UU ~UU ~ww [~xa cG ~_~ ~U
asa U~ ~~~ax~~ sm~d~m n w xna F, -a a-
0 0 0 0
U U U
M v~ a'"i W U v w U U w U c a'"i "i U aoi o ~ U r a~i
o. U o. o. m o. cL) ,~ C7 ,~ C7 ,~ C7 < ,~ H a C7 < ,~ m ,~ N m ,~ C7 0. C7 m
o. Q m o. C7 '-a Ca m
Ufa,UUUUwU~x~wx~x~~wH~x~~~x~UU~xUxUU~xUx~~~U
o o 0 0 0
~+q U M M M M M U M N M U M b N U
a c~ ~ a C7 ~ N ~ C~7 U C7 ~ a, U ~ a, U a, U ~ a, U C~7 a, U U ~ Ca U o U
Cw7 ~ '~ ~^ ~ ~ a ~ ~ a o ~ ~^ ~
~U~~xw~ xxxUwUUUwUxU~~Uax~Ua~~wU~U~a~w
0 0
M a U a U w U U a U a U a U w q w w w
`~ ~ cG m cG C7 C7 0 ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ C7 C7 C7 o m o
a> xmx xU m m m m m xx xU UU z~xxx
o. o - c~ M v~ 00 c o - c~
150
SUBSTITUTE SHEET (RULE 26)
CA 02737004 2011-03-09
WO 2010/042525 PCT/US2009/059706
M O l~ N N O N Q, M 7 C
7 7 7 7 M 7 7 7 7 M M 7 7 M 7 M
.
O O O O O O O O O O O O O O O O
7 7 ~O N ~O 7 t c c t c N 7 ~O N
. . . . . .
00 M 00 M_ N 00 M M M_ M M_ N 00 M_
O O Q, O O O Q, O 0 Q, O
.
O O O O O O O O O O O O O O O O
~O N Q, Q, Q, Q, N N N N N Q, N Q, Q,
M ~O 7 7 7 7 ~O ~O ~O M ~O ~O 7 ~O 7 7
.
O O O O O O O O O O O O O O O O
O O O
CG c C M CG CG CG CG
a~ x U x a" H U U U U a1 x U !w x d a~1 L U H
0
s. ~
0 0
a~ U a~ a s a a a a~ a P s a 3 a a
rn ~ rn Q rn Q 07 m o rn m rn~ m rn~ m ~' ~ Q m rn~ rn W rn~ rn U U rn
rn~ ~ rn ~
x w x x Z a a x x H a x
0 0
o 0 0
0 0 o o o 0
U U O U U ~ U U U u
O U ~ U
Q C7 W C7 H a. o s. C7 Ll o C7 m N O v C7 m
m p7 ~ m CG CG m p7 m m CG H m m~ m m C7 m p7 P., U U C7 m CG ~ m U 07 m 'x x~
U m m m m p7 U m W P., m 'x x
w o 0 0
0 yr
rn rn rn U U rn rn c U
91
C7C~7'~U~~~~~~U~~gU~q o~C7Q~C7 oo,~~C7C~7c4~~~~C~7c4C7C~7Q~C7Q~C7Q~~¾~
x x a U s U z a s s U U 07 s~ cG Z 07 U cG Z s cG s s 07 U
`"~ '~ M~ U M N U U U M U~ N '~ M U '~ U M U M U
M 7 V'1 l0 l~ 00 c O - N M 7 V'1 l0 l~ 00
uuu151
SUBSTITUTE SHEET (RULE 26)
CA 02737004 2011-03-09
WO 2010/042525 PCT/US2009/059706
O M V'1 V'1 7 N M Q\ Q\ O
.
O O O O O O O O O O O O O O O O
+cl M 7 7 7 7 M 7 7 7 M 7 M M M 7 7
~O 7 t ~O ~O N 7 ~O N 7 7 Q, 7 7
7 o o o V7 7 o o 7 o V'1 V'1 V'1 7 o o
. . . . . .
N M M M M N N N N M
.
O O O O O O O O O O O O O O O O
Q, Q, N N Q, Q, Q, N c t N N N c N N
7 7 ~O ~O 7 7 7 ~O 7 l~ 7 ~O ~O
.
O O O O O O O O O O O O O O O O
V
U N
ow S
v~ v~ p v ai v 1 .~. =~= Q ~''
a,~ a,~~a,~ wx tea, 5
UU~wUx ~UC7wxa~ixxw"~dx~FUxUHx ~Ux~
0 0 ~ 0 0
N ,ti N 7 U 7 U
w Q m w a w a w a a w w
0 0
o 0 0 0 0 0 0 0
U N U U U O
rn rn w u w w w 3 U o w
0 0
N m v N m U m c~ cn U c~ ~,' `^ U CG U o m
~ Q U o Q. Q ~ Q v~ Q. U C7 U ~ Q. U ~ Q. ~ ~ ~ U C7 C7 c4 ~ C7 Q U ~" a C7 C7
~ a C7 Q. ~ U o Q. U ~
0
0
~+ O
v .~
`~ U U .~ U N N U m U M '~ M U U N
a~xx x~ xx x~ ~x ~x x ~x ~x ~x xx
Q, O - N M 7 V'1 ~O l~ Q, O - N M 7
152
SUBSTITUTE SHEET (RULE 26)
CA 02737004 2011-03-09
WO 2010/042525 PCT/US2009/059706
7 M M 7 t 7
O O O O O O
M N - M_ l~ M_
O O O O O O
N N Q Q Q
~O ~O 7 7 7 7
O O O O O O
O rrC` ,M~ ,M~ Ol Ol Ol o0 o0 o0 00 00 00 o0 oc t- t- t- t
0
o w o
w, o ~ ~ ~ ~ N o " H
oc C~ ,! o N
M U M r C CC U o y O C~ O C~
N
U~d~wx~Ux~xx___
0 0 0 0 0
O O O O O O Q1 Q t- M t- 1 rO CO M M N O O -
~ 0 ~ D\ D\ ~ ~ ~ V'1 ~ M N N N N N N --~
N Q F
M CC
ma~i~x ~x~a~UUw" ma,
C v .
U ~ ~+ U
E
L4
x xx Ux xx v v cox c>c>ca¾xc>c> wUzw
153
SUBSTITUTE SHEET (RULE 26)
CA 02737004 2011-03-09
WO 2010/042525 PCT/US2009/059706
U 7 7 7 7 7 m N 7 7
d' 0 0 0 0 0 0 0 0 0 0 0 0 0 0
N N N al N al N \O \O N al
0 w 0 w 0 0 0 w 0 0 0 0 w w
=S' m m 0 N m m 0 N 0 m 0 0
0 0 0 0 0 0 0 0 0
. . . .
0 0 0 0 0 0 0 0 0 0 0 0 0 0
N C, N N N N N C, C, N N C,
w ~O 7 ~O ~C N N 7 7 ~O ~O 7
=~ a, a, a, a, a, a, a, a, a, a, a, a, a, a,
0 0 0 0 0 0 0 0 0 0 0 0 0 0
r
>C >C
ro
~' 0 0
~ o 0 0
w w
d .~~ m o~~.~ ~ ~o cG o~=~ ~ ~ cG cG o
~ xw~x ~ x x ~ ~ ~Cv~~wxcaUxU~C UUc xuuc, ~¾axca xcaUI
c. o
U ~^
ro o ~ ~
v~ v~ p C N
U r,~.' CG U m CG N o L~ U .a N
mC4 cl~
a, ~
C7~ C7 ~n C7U a,~ C7 ~
C UZ Uf f UUxxUrn ~CG C7U ~x ;Ulm 4 UC~U x CGU ~ f E-~
ro
0
a a
~al
o v 2 xv x'~ v v N v x Ux xv
cl, oa w w w
C~l
o ~C7~~ ~~~C7~~~ o C,N~ ~C7 C7 C',
o .~
Cl
m m N '~ U y N y m m m U m m '~ m
a a v¾~w v a a w aw a a~ a
U o Cl U C7 a, U C7 C7 C, U U C, U N
Cl U a,'-" Cl U Cl
0 0 0 0 0 0
Nw UN N Um mw N Cl NwNw mw N N N
a u uaa u~ ua u u
cl~~ca~x~~~Cx~~C~caCxC~~a~~cax~ca~~caC~ca~~x~~d~~C
154
CA 02737004 2011-03-09
WO 2010/042525 PCT/US2009/059706
7 M N M M V'1 O V'1 M 7 V'1 V'1
7 7 M 7 M 7 M 7 7 7 7 7 7 N 7 7
.
O O O O O O O O O O O O O O O O
- 7 7 7 c N c 7 t ~O N 7 Q, c t
~O l- 7 ~ V7 ~O V'1 V'1 ~O V'1 ~O ~O ~O 7 V'1 V'1
. . . . . .
00 M_ 00 M r- M N M 00 M M 00 N t- N
O - Q, - Q, Q, O O - O O C C C
.
O O O O O O O O O O O O O O O O
N N N - N N N - - N N - N N
7 ~O M ~O ~O 7 ~O ~O ~O 7 7 ~O ~O 7 ~O ~O
O O O O O O O O O O O O O O O O
0
sUs~ N N i0i
O .F. O. O. O O .~.
U M C7 U C7 ~, M U~ o~ M U .P..
~ .'~ C7 C~7 v~ U ~ Cw7 cx5 Cw7 C~7 cx5 ~ ~ ~ ~ ~ ~ U ~ ~ o ~ U ~U ~ Cw7 v~
C7 N cC o ~ C7 C~7 C7 N ~
HxCGU ~x ~Uxx ~c7xwxUUxa,a,x ZUx UHF' U ~xxwx ~w
v; `~' ` 0 '" rn W 07 ~' w `~ U N rn asi CG M asi W rn U
U a U U U a x U U x w U w x U x U H x 'n U U x x U
o ~ o o ~ o 0
U o U o
a o ~ a ~ o, a o a ~ o, U o a C~7 ~ o, o o, Ll ,wj N ~o a o a ~ a C~7 ~ o,
Q ~ a ~ o, U o
~Ua~wHU~ ~wU~~wUU~xaUxUU~U~~UEU~wU~xaU~x~~wUU
0 0
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U U Q, Q, C Sy C3 U. a N R C~ N
C7 C7 rn U U Q rn U Q U v wU N rn U a s Cw7 Cw7 C7 rn U rn U Q ~- C4 U r a N
xxwU U~~~U U~~UU U~ ~~w~x~UxxwU HU ~mwz c)
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u~~~xxUu~~xUx xU~~~~~xu~~xxUUUU~z~~x~ w xxUx~~~~
V'1 ~O 00 c O - N M 7 V'1 ~O l~ 00 C O
N N N N N N N N N N M
155
SUBSTITUTE SHEET (RULE 26)
CA 02737004 2011-03-09
WO 2010/042525 PCT/US2009/059706
~O M N N Q, M 7 M N V'1 7 Q, O
M 7 7 7 7 M 7 7 M M 7 7 M 7 M 7
O O O O O O O O O O O O O O O O
N ~O Q, c t c 00 c c 7 N ~O
V'1 V'1 lp V'1 lp V'1 O O M V'1 lp V'1 V'1 V7 V'1
M M M 00 00 t- M 00 M t 00 N M M 00
O O O O Q, O O O Q, O Q, O Q, O O
O O O O O O O O O O O O O O O O
t t t t N N N N N N N Q, 7 Q, t
7 7 7 7 ~O ~O ~O ~O M ~O ~O ~O 7 ~ 7 7
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7 U ~CG U~ o
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~x~xa~x~x~UdU~~~wx~x~U~~~xUUZ~xU~U~~a~U
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N M 7 V'1 ~O Q, O - N M 7 V'1 ~O
M M M M M M M M M 7 7 7 7 7 7 7
156
SUBSTITUTE SHEET (RULE 26)
CA 02737004 2011-03-09
WO 2010/042525 PCT/US2009/059706
O c t V'1 O V'1 r M 00 C O O V'1 N
7 M 7 t N 7 7 7 M t t M 7 7 t 7
O O O O O O O O O O O O O O O O
Q, 7 - Q, N Q, N t 7
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l~ 00 c O - N M 7 V'1 ~C 00 C O - N
157
SUBSTITUTE SHEET (RULE 26)
CA 02737004 2011-03-09
WO 2010/042525 PCT/US2009/059706
N 7 l- ~O V'1 N c t N O O M
M 7 7 7 M 7 7 7 M 7 M M 7 7 M 7
O O O O O O O O O O O O O O O O
r Q, c 7 ~O 7 ~O ~O 7 Q, Q,
M V'1 lp V'1 V'1 7 N N N lO M N l0 N 7 ~O
. . . . . .
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.
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.
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158
SUBSTITUTE SHEET (RULE 26)
CA 02737004 2011-03-09
WO 2010/042525 PCT/US2009/059706
O O O O O O O O O O O O O O O o
a a a a
7 N N N M 0 N l0 0 N 7 0 0 7 N N
. . . . . .
l- l- M 00 M 00 00 M 00 M M 00 M r- r- M
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.
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Q, O - N M 7 V'1 ~O l~ 00 C O - N M 7
159
SUBSTITUTE SHEET (RULE 26)
CA 02737004 2011-03-09
WO 2010/042525 PCT/US2009/059706
N 7 M
t t M 7 7 t
O O O O O O
O 7 O O 7
V'1 lp M V'1 lp V'1
00 M M 00 M M
O O - O O O
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0 0 O O O
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rn ~ C4
7)
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160
SUBSTITUTE SHEET (RULE 26)
CA 02737004 2011-03-09
WO 2010/042525 PCT/US2009/059706
Table 15
Biomarker Solution Kd (M) Assay LLOQ (M) Up or Down
Designation Regulated
a1-Antitr sin 2 x 1 0 2 x 10-11 Up
a2-Antiplasmin 8 x 1 0 6 x 10 Down
a2-HS-GI co rotein 1 x 10-11 4-x 10- Down
ADAM 9 4 x 10 (pool) NM Down
ARSB 3 x 10- NM Down
BAFF Receptor 5 x 10-9 (pool) NM Down
C2 1 O-lu 10-14 Up
C5 1 x10 4x10 Up
C6 7x 10-12 (pool) 1 x 10-12 Up
C9 1 x 10-11 1 10-14 Up
Cadherin-5 2 x 10 4x 10-12 Down
Coagulation Factor 2 x 10-10 47x 10 Down
Xa
Contactin-1 5 x 10
11 8 x 10 Down
Contactin-4 3 x 10-10 8 x 10 Down
ERBB1 1 x 1 0 1 x 0-IT Down
Growth hormone 3 x 10 5-x 10- Down
receptor
Hatt 1 x 10 NM Down
HGF 4x10 NM Up
HSP 90a 1 x 101 x10 Up
IL-12 R P2 2 x 10 (pool) NM Down
IL-13 Ra1 3x10 NM Up
IL-18R(3 10-11 NM Up
Kallikrein 6 4 x 10 (pool) NM Up
Kallistatin 2 x 10-11 (pool) 7x 10-14 Down
LY9 1 x 10 NM Down
MCP-3 6 x 10-9 2x 10-12 Down
MIP-5 9x10 (pool) 2 x 10Up
MMP-7 7x10 3x10 Up
MRC2 2 x 1 0 1 x 1071 Down
NRP1 9 x 10-11 1 0-IT Up
PCI 1 x 1 0 1 x 10 Down
Prekallikrein 2 x 10-11 (pool) 3 x 10- Down
Pro erdin 2 x 10 2x 10-12 Down
RBP 1 x 10 (pool) 9 x 10- Down
RGM-C 3 x 10- NM Down
SAP 7x10 10-13 Up
SCFsR 5x10 3x10- Down
161
SUBSTITUTE SHEET (RULE 26)
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WO 2010/042525 PCT/US2009/059706
Table 15 cont.
SLPI 2x10 10-13 Up
sL-Selectin 2 x 10 (pool) 2 x 10 Down
Thrombin/Prothrombin 5 x 10- 7 x 10-13 Down
TIMP-2 1 x 10-10 6 x 10-" Down
Tro oninT 2 x 105x10- Down
162
SUBSTITUTE SHEET (RULE 26)
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Table 16
rPC c lid 6d
Designation KS p-value AUC a1-Antitr sin 3386 7.20E+05 5948 5.92E+06 0.62
2.03E-19 0.86
a2-Antiplasmin 19115 3.68E+06 16103 5.43E+06 0.54 3.02E-15 0.80
a2-HS-GI co rotein 1747 6.19E+04 1474 8.61E+04 0.44 3.51 E-10 0.75
ADAM 9 1844 2.17E+04 1685 1.71 E+04 0.47 2.39E-11 0.78
ARSB 6297 2.92E+05 5808 2.21E+05 0.42 3.47E-09 0.76
BAFF Receptor 3265 6.02E+04 3079 3.34E+04 0.38 7.61 E-08 0.71
C2 107229 9.91E+07 117783 1.89E+08 0.43 1.64E-09 0.73
C5 14468 4.15E+06 16477 5.22E+06 0.40 1.89E-08 0.74
C6 92660 1.73E+08 107328 2.82E+08 0.41 9.22E-09 0.76
C9 161177 9.17E+08 208251 9.01E+08 0.61 6.01 E-19 0.86
Cadherin-5 9561 2.58E+06 8221 1.89E+06 0.35 1.96E-06 0.74
Coagulation Factor Xa 18670 1.12E+07 15407 9.80E+06 0.43 7.64E-10 0.76
contactin-1 37472 4.81E+07 29895 7.16E+07 0.41 7.23E-09 0.75
Contactin-4 14963 9.29E+06 12268 8.16E+06 0.41 9.22E-09 0.73
ERBB1 52741 6.94E+07 41543 6.56E+07 0.53 1.08E-14 0.81
Growth hormone
receptor 1057 1.90E+04 942 7.06E+03 0.39 3.02E-08 0.76
Hatt 1019 1.07E+04 928 6.33E+03 0.42 2.11 E-09 0.75
HGF 668 4.07E+03 735 4.67E+03 0.41 5.67E-09 0.75
HSP 90a 40733 3.01 E+08 55087 3.31 E+08 0.38 7.61 E-08 0.71
IL-12 R132 1217 1.42E+04 1099 1.56E+04 0.41 9.22E-09 0.75
IL-13 Rat 614 6.40E+03 697 8.92E+03 0.42 3.47E-09 0.74
IL-18 R13 449 1.30E+03 488 1.48E+03 0.44 3.51 E-10 0.76
Kallikrein 6 256 1.67E+03 298 2.15E+03 0.42 2.11 E-09 0.75
Kallistatin 111611 3.01E+08 85665 5.64E+08 0.48 5.89E-12 0.82
LY9 983 2.19E+04 845 1.46E+04 0.43 9.86E-10 0.75
MCP-3 703 4.88E+03 642 2.71E+03 0.43 9.86E-10 0.75
MIP-5 1531 4.55E+05 2123 7.95E+05 0.33 5.35E-06 0.72
MMP-7 3057 2.61E+06 5936 1.74E+07 0.44 2.70E-10 0.74
MRC2 16105 1.78E+07 12716 1.09E+07 0.39 3.82E-08 0.72
NRP1 5314 1.41E+06 6450 9.96E+05 0.43 9.86E-10 0.74
PCI 31852 4.29E+07 22140 8.05E+07 0.53 1.48E-14 0.80
Prekallikrein 122660 3.23E+08 100877 2.99E+08 0.52 7.01 E-14 0.80
Properdin 65527 1.10E+08 55599 1.25E+08 0.41 1.17E-08 0.74
RBP 5193 1.21E+06 4088 1.36E+06 0.45 1.22E-10 0.73
RGM-C 21625 2.11E+07 17527 9.18E+06 0.43 1.64E-09 0.78
SAP 142805 7.07E+08 167146 7.28E+08 0.38 7.61 E-08 0.75
SCF sR 12432 1.09E+07 9472 5.69E+06 0.44 2.70E-10 0.76
SLPI 25007 2.07E+07 35986 1.22E+08 0.59 1.02E-17 0.85
sL-Selectin 30048 3.31E+07 24163 2.50E+07 0.43 9.86E-10 0.79
Thrombin/Prothrombin 62302 1.67E+07 58099 1.80E+07 0.45 1.59E-10 0.75
TIMP-2 15793 3.16E+06 13796 2.64E+06 0.49 1.04E-12 0.79
Tro onin T 1972 3.68E+04 1767 2.58E+04 0.47 1.81 E-11 0.78
163
SUBSTITUTE SHEET (RULE 26)
CA 02737004 2011-03-09
WO 2010/042525 PCT/US2009/059706
f~ O) N N C))
O CO CO CO O C~ C~ C~ G~ G~
U O O O O O O O O O
CO - N- CO LO CO 0) CY) CO
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Q Q Q Q Q Q < ) < ) < ) < )
mm mmmmmmma=ma= mm mm mm mm
N N C'7 LO Co N- CO 0) O
164
SUBSTITUTE SHEET (RULE 26)
CA 02737004 2011-03-09
WO 2010/042525 PCT/US2009/059706
Table 18. Parameters derived from training set for naive Bayes classifier.
Biomarker 2
PC C I'd 07d
HGF 668 4.07E+03 735 4.67E+03
SLPI 25007 2.07E+07 35986 1.22E+08
C9 161177 9.17E+08 208251 9.01 E+08
a2-Antiplasmin 19115 3.68E+06 16103 5.43E+06
SAP 142805 7.07E+08 167146 7.28E+08
MMP-7 3057 2.61E+06 5936 1.74E+07
BAFF Receptor 3265 6.02E+04 3079 3.34E+04
RGM-C 21625 2.11E+07 17527 9.18E+06
MCP-3 703 4.88E+03 642 2.71 E+03
MRC2 16105 1.78E+07 12716 1.09E+07
165
SUBSTITUTE SHEET (RULE 26)
CA 02737004 2011-03-09
WO 2010/042525 PCT/US2009/059706
Table 19
Number of Samples by Site
Benign Cancer
Site 1 114 87
Site 2 81 55
TOTAL 195 142
166
SUBSTITUTE SHEET (RULE 26)
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Table 20. Biomarkers of Ovarian Cancer from All Site Analysis (Aggregated
Data)
a2-Antiplasmin Contactin-4 NRP1
a2-HS-GI co rotein ERBB1 Pro erdin
ADAM 9 HGF RGM-C
C2 IL-12 R(32 SCF sR
C5 Kallistatin SLPI
C6 LY9 sL-Selectin
C9 MCP-3 Thrombin/Prothrombin
Coagulation Factor Xa MMP-7 Troponin T
Contactin-1
167
SUBSTITUTE SHEET (RULE 26)
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WO 2010/042525 PCT/US2009/059706
Table 21. Biomarkers of Ovarian Cancer Within Sites
a1-Antitr sin Contactin-4 MRC2
a2-Antiplasmin Growth hormone receptor NRP1
BAFF Receptor HGF Prekallikrein
C2 HSP 90a RGM-C
C6 IL-13 Rat SAP
C9 LY9 SCF sR
Cadherin-5 MCP-3 SLPI
Contactin-1 MI P-5 sL-Selectin
168
SUBSTITUTE SHEET (RULE 26)
CA 02737004 2011-03-09
WO 2010/042525 PCT/US2009/059706
Table 22. Biomarkers of Ovarian Cancer from Blended Data Analysis
a2-Antiplasmin HGF PCI
ARSB IL-12 8132 Prekallikrein
C2 IL-13 Rat RBP
C6 IL-18 R(3 RGM-C
C9 Kallikrein 6 SCF sR
Contactin-1 Kallistatin SLPI
Contactin-4 LY9 sL-Selectin
ERBB1 MCP-3 Thrombin/Prothrombin
Hatt NRP1 TIMP-2
169
SUBSTITUTE SHEET (RULE 26)
CA 02737004 2011-03-09
WO 2010/042525 PCT/US2009/059706
0 C ) 0 (D NOCD O)0)NLO LO ~t
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(~ s= O
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0-1mNQQCD EC
= c/ 0 c/) m Ir 75
170
SUBSTITUTE SHEET (RULE 26)
