U.S. patent application number 13/809785 was filed with the patent office on 2013-12-26 for diagnostic for colorectal cancer.
This patent application is currently assigned to COMMONWEALTH SCIENTIFIC AND INDUSTRIAL RESEARCH ORGANISATION. The applicant listed for this patent is Antony Wilks Burgess, Leah Jane Cosgrave, Edouard Collins Nice, Bruce Tabor. Invention is credited to Antony Wilks Burgess, Leah Jane Cosgrave, Edouard Collins Nice, Bruce Tabor.
Application Number | 20130345322 13/809785 |
Document ID | / |
Family ID | 45468816 |
Filed Date | 2013-12-26 |
United States Patent
Application |
20130345322 |
Kind Code |
A1 |
Cosgrave; Leah Jane ; et
al. |
December 26, 2013 |
DIAGNOSTIC FOR COLORECTAL CANCER
Abstract
The present invention provides a method for diagnosing or
detecting colorectal cancer in a subject, the method comprising
determining the presence and/or level of biomarkers selected from
IL-8, IGFBP2, MAC2BP, M2PK, IL-13, DKK-3, EpCam, MIP1.beta.,
TGF.beta.1, and TIMP-1. The invention also relates to diagnostic
kits comprising reagents for determining the presence and/or level
of the biomarkers and methods of detecting or diagnosing colorectal
cancer.
Inventors: |
Cosgrave; Leah Jane; (Unley,
AU) ; Tabor; Bruce; (Stanhope Gardens, AU) ;
Burgess; Antony Wilks; (Camberwell, AU) ; Nice;
Edouard Collins; (Docklands, AU) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
Cosgrave; Leah Jane
Tabor; Bruce
Burgess; Antony Wilks
Nice; Edouard Collins |
Unley
Stanhope Gardens
Camberwell
Docklands |
|
AU
AU
AU
AU |
|
|
Assignee: |
COMMONWEALTH SCIENTIFIC AND
INDUSTRIAL RESEARCH ORGANISATION
Campbell
AU
|
Family ID: |
45468816 |
Appl. No.: |
13/809785 |
Filed: |
July 14, 2011 |
PCT Filed: |
July 14, 2011 |
PCT NO: |
PCT/AU2011/000895 |
371 Date: |
August 29, 2013 |
Current U.S.
Class: |
514/789 ;
435/6.11; 435/6.12; 435/7.92; 506/9 |
Current CPC
Class: |
G01N 33/57488 20130101;
C12Q 2600/118 20130101; G01N 33/57419 20130101; C12Q 2600/158
20130101; G01N 2800/52 20130101; C12Q 1/6886 20130101 |
Class at
Publication: |
514/789 ;
435/7.92; 506/9; 435/6.12; 435/6.11 |
International
Class: |
G01N 33/574 20060101
G01N033/574 |
Foreign Application Data
Date |
Code |
Application Number |
Jul 14, 2010 |
AU |
2010903140 |
Claims
1. A method for diagnosing or detecting colorectal cancer in a
subject, the method comprising: i) determining the presence and/or
level of at least two biomarkers selected from the group consisting
of IL-8, IGFBP2, MAC2BP, M2PK, IL-13, DKK-3, EpCam, MIP1.beta.,
TGF.beta.1, and TIMP-1 in a sample from the subject, wherein the
presence and/or level of the two biomarkers is indicative of
colorectal cancer.
2. The method of claim 1, wherein the two biomarkers selected from
M2PK, EpCam, IL-13, DKK-3, IL-8 and IGFBP2.
3. The method of claim 1, wherein the method comprises determining
the presence and/or level of expression of at least three of the
biomarkers.
4. The method of claim 3, wherein the three biomarkers are selected
from the group consisting of M2PK, EpCam, IL-13, DKK-3, IL-8,
IGFBP2, MIP1.beta., TGF.beta.1 and MAC2BP.
5. The method of claim 4, wherein the three biomarkers are as
follows: i) DKK-3, M2PK, and IGFBP2; ii) M2PK, IGFBP2, and EpCAM;
iii) M2PK, MIP1.beta., and TGF.beta.1; or iv) IL-8, IL-13, and
MAC2BP.
6. The method of claim 1, wherein the method comprises determining
the presence and/or level of expression of at least four of the
biomarkers.
7. The method of claim 6, wherein four biomarkers are as follows:
i) DKK-3, M2PK, MAC2BP, and IGFBP2; ii) IL-8, IL-13, MAC2BP, and
EpCam; iii) DKK3, M2PK, TGF.beta.1, and TIMP-1; iv) M2PK,
MIP1.beta., IL-13, and TIMP-1; or v) IL-8, MAC2BP, IGFBP2, and
EpCam.
8. The method of claim 1, wherein the method comprises determining
the presence and/or level of at least five of the biomarkers.
9. The method of claim 1, wherein the method comprises determining
the presence and/or level of at least six of the biomarkers.
10. The method of claim 1, wherein the method comprises determining
the presence and/or level of at least seven of the biomarkers.
11. The method of claim 1, wherein the method comprises determining
the presence and/or level of at least eight of the biomarkers.
12. The method of claim 1, wherein the method comprises determining
the presence and or level of at least nine of the biomarkers.
13. The method of claim 1, wherein the method comprises determining
the presence and/or level of at least ten of the biomarkers.
14. The method of claim 1, wherein the method comprises detecting
the presence and/or level of least one additional biomarker
selected from the group consisting of ICF-I, IGF-II, IGF-BP2,
Amphiregulin, VEGFA, VEGFD, MMP-1, MMP-2, MMP-3, MMP-7, MMP-9,
TIMP-1, TIMP-2, ENA-78, MCP-1, MIP-1.beta., IFN-.gamma., IL-10,
IL-13, IL-1.beta., IL-4, IL-8, IL-6, MAC2BP, Tumor M2 pyruvate
kinase, M65, OPN, DKK-3, EpCam, TGF.beta.-1, and VEGFpan.
15. The method of claim 1, wherein the method diagnoses or detects
colorectal cancer with a sensitivity of at least 50%.
16. (canceled)
17. (canceled)
18. The method of claim 1, wherein the method diagnoses or detects
Dukes Stage A colorectal cancer with a sensitivity of at least 50%
and a specificity of at least 95%; a sensitivity of at least 60%
and a specificity of at least 80%; or a sensitivity of at least 50%
and a specificity of at least 90%.
19. (canceled)
20. (canceled)
21. (canceled)
22. (canceled)
23. (canceled)
24. (canceled)
25. (canceled)
26. The method of claim 1, wherein the method comprises: i)
determining the presence and/or level of the biomarkers in the
sample from the subject; and ii) comparing the presence and/or
level of the biomarkers relative to a control, wherein a presence
and/or level in the sample that is different to the control is
indicative of colorectal cancer.
27. The method of claim 1, wherein the sample comprises blood,
plasma, serum, urine, platelets, magakaryocytes or faeces.
28. A method of treatment comprising: (i) diagnosing or detecting
colorectal cancer according to the method of claim 1; and (ii)
administering a therapeutic suitable for the treatment of
colorectal cancer.
29. A method for monitoring the efficacy of treatment of colorectal
cancer in a subject, the method comprising treating the subject for
colorectal cancer and then detecting the presence and/or level of
at least two biomarkers selected from IL-8, IGFBP2, MAC2BP, M2PK,
IL-13, DKK-3, EpCam, MIP1.beta., TGF.beta.1, and TIMP-1 in a sample
from the subject, wherein an absence of and/or reduction in the
level of expression of the biomarkers after treatment as compared
with before treatment is indicative of an effective treatment.
30. (canceled)
31. (canceled)
Description
FIELD OF THE INVENTION
[0001] The present invention relates to determining the presence
and/or level of biomarkers for detecting or diagnosing colorectal
cancer. The invention also relates to diagnostic kits comprising
reagents for determining the presence and/or level of the
biomarkers and methods of detecting or diagnosing colorectal
cancer.
BACKGROUND OF THE INVENTION
[0002] Colorectal cancer, also referred to as colon cancer or bowel
cancer, is the second most common cause of cancer worldwide. There
is an annual incidence of almost a million colorectal cancer cases
with an annual mortality around 500,000 (Cancer in Australia: an
overview, 2008). Unfortunately, 30-50% of patients have occult or
overt metastases at presentation and once tumours have metastasized
prognosis is very poor with a five year survival of less than 10%
(Etzioni et al., 2003). By contrast, greater than 90% of patients
who present while the tumour is still localised will still be alive
after 5 years and can be considered cured. The early detection of
colorectal lesions would therefore significantly reduce the impact
of colon cancer (Etzioni et al., 2003).
[0003] The current screening assays in widespread use for the
diagnosis of colorectal cancer are the faecal occult blood test
(FOBT), flexible sigmoidoscopy, and colonoscopy (Lieberman, 2010).
FOBT has relatively low specificity resulting in a high rate of
false positives. All positive FOBT must therefore be followed up
with colonoscopy. Sampling is done by individuals at home and
requires at least two consecutive faecal samples to be analysed to
achieve optimal sensitivity. Some versions of the FOBT also require
dietary restrictions prior to sampling. FOBT also lacks sensitivity
for early stage cancerous lesions that do not bleed into the bowel
and as stated above, these are the lesions for which treatment is
most successful.
[0004] While FOBT screening does result in reduction of mortality
due to colorectal cancer it suffers from a low compliance rate
(30-40%), most likely due to the unpalatable nature of the test,
which limits its usefulness as a screening tool. Colonoscopy is the
current gold standard and has a specificity of greater then 90% but
it is intrusive and costly with a small but finite risk of
complications (2.1 per 1000 procedures) (Levin, 2004). Development
of a rapid, specific, cheap blood based assay would overcome
compliance issues commonly seen with other screening tests (Tonus,
2006; Hundt et al., 2007) and would be more acceptable as part of a
large screening assay.
SUMMARY OF THE INVENTION
[0005] The present inventors investigated over sixty biomarkers
associated with colorectal cancer, but found that none of the
biomarkers alone would be suitable as a diagnostic test.
Surprisingly, it was found that determining the presence and/or
level of at least two biomarkers associated with colorectal cancer
in a sample from a subject allowed for the detection or diagnosis
of colorectal cancer at any of the stages of disease. Determining
the presence and/or level of at least two biomarkers advantageously
provides a diagnostic test that is at least comparable in
sensitivity and specificity to the FOBT.
[0006] Accordingly, in one aspect, the present invention provides a
method for diagnosing or detecting colorectal cancer in a subject,
the method comprising:
[0007] i) determining the presence and/or level of at least two
biomarkers selected from IL-8, IGFBP2, MAC2BP, M2PK, IL-13, DKK-3,
EpCam, MIP1.beta., TGF.beta.1, and TIMP-1 in a sample from the
subject,
[0008] wherein the presence and/or level of the two biomarkers is
indicative of colorectal cancer.
[0009] In one embodiment, the method comprises determining the
presence and/or level of two biomarkers selected from M2PK, EpCam,
IL-13, DKK-3, IL-8 and IGFBP2.
[0010] In another embodiment, the method comprises determining the
presence and/or level of expression of at least three of the
biomarkers.
[0011] In one embodiment, the three biomarkers are selected from
M2PK, EpCam, IL-13, DKK-3, IL-8, IGFBP2, MIP1.beta., TGF.beta.1 and
MAC2BP.
[0012] In one particular embodiment, the method comprises
determining the presence and/or level of three biomarkers, wherein
the three biomarkers are:
[0013] i) DKK-3, M2PK, and IGFBP2;
[0014] ii) M2PK, IGFBP2, and EpCAM;
[0015] iii) M2PK, MIP1.beta., and TGF.beta.1; or
[0016] iv) IL-8, IL-13, and MAC2BP.
[0017] In another embodiment, the method comprises determining the
presence and/or level of expression of at least four of the
biomarkers.
[0018] In one particular embodiment, the method comprises
determining the presence and/or level of four biomarkers, wherein
the four biomarkers are:
[0019] i) DKK-3, M2PK, MAC2BP, and IGFBP2;
[0020] ii) IL-8, IL-13, MAC2BP, and EpCam;
[0021] iii) DKK3, M2PK, TGF.beta.1, and TIMP-1;
[0022] iv) M2PK, MIP1.beta., IL-13, and TIMP-1; or
[0023] v) IL-8, MAC2BP, IGFBP2, and EpCam.
[0024] In yet another embodiment, the method comprises determining
the presence and/or level of at least five of the biomarkers.
[0025] In one particular embodiment, the five biomarkers are IL-8,
IGFBP2, MAC2BP, M2PK, and IL-13.
[0026] In another embodiment, the method comprises determining the
presence and/or level of at least six of the biomarkers.
[0027] In another embodiment, the method comprises determining the
presence and/or level of at least seven of the biomarkers.
[0028] In one particular embodiment, the seven biomarkers are:
[0029] i) IL-8, IGFBP2, MAC2BP, M2PK, IL-13, DKK-3, and TGF
.beta.1; or
[0030] ii) IL-8, IGFBP2, MAC2BP, M2PK, IL-13, EpCam, and
MIP1.beta..
[0031] In yet another embodiment, the method comprises determining
the presence and/or level of at least eight of the biomarkers.
[0032] In one embodiment, the method comprises determining the
presence and/or level of at least nine of the biomarkers.
[0033] In yet another embodiment, the method comprises determining
the presence and/or level of at least ten of the biomarkers.
[0034] In another embodiment, the method comprises determining the
presence and/or level of a combination of biomarkers as provided in
any of Tables 7 to 18.
[0035] In another embodiment, the method comprises detecting the
presence and/or level of least one additional biomarker selected
from IGF-I, IGF-II, IGF-BP2, Amphiregulin, VEGFA, VEGFD, MMP-1,
MMP-2, MMP-3, MMP-7, MMP-9, TIMP-1, TIMP-2, ENA-78, MCP-1,
MIP-1.beta., IFN-.gamma., IL-10, IL-13, IL-1.beta., IL-4, IL-8,
IL-6, MAC2BP, Tumor M2 pyruvate kinase, M65, OPN, DKK-3, EpCam,
TGF.beta.-1, and VEGFpan.
[0036] In one embodiment, the method diagnoses or detects
colorectal cancer with a sensitivity of at least 50%.
[0037] In another embodiment, the method diagnoses or detects
colorectal cancer with a sensitivity of at least 66%.
[0038] In yet another embodiment, the method diagnoses or detects
colorectal cancer with a sensitivity of at least 77%.
[0039] In one embodiment, the method diagnoses or detects
colorectal cancer with a specificity of at least 75%.
[0040] In one embodiment, the method diagnoses or detects
colorectal cancer with a specificity of at least 80%.
[0041] In another embodiment, the method diagnoses or detects
colorectal cancer with a specificity of at least 90%.
[0042] In yet another embodiment, the method diagnoses or detects
colorectal cancer with a specificity of at least 95%.
[0043] In another embodiment, the method diagnoses or detects Dukes
Stage A colorectal cancer with a sensitivity of at least 50% and a
specificity of at least 95%.
[0044] In yet another embodiment, the method diagnoses or detects
Dukes Stage A colorectal cancer with a sensitivity of at least 60%
and a specificity of at least 80%.
[0045] In another embodiment, the method diagnoses or detects Dukes
Stage A colorectal cancer with a sensitivity of at least 50% and a
specificity of at least 90%.
[0046] The skilled person will understand that Dukes Stage A
corresponds to TNM Classifications T1, N0, M0 and T2, N0, M0.
[0047] Thus in one embodiment, the method diagnoses or detects TNM
Classification T1, N0, M0 or T2, N0, M0 colorectal cancer with a
sensitivity of at least 50% and a specificity of at least 95%.
[0048] In yet another embodiment, the method diagnoses or detects
TNM Classification T1, N0, M0 or T2, N0, M0 colorectal cancer with
a sensitivity of at least 60% and a specificity of at least
80%.
[0049] In another embodiment, the method diagnoses or detects TNM
Classification T1, N0, M0 or T2, N0, M0 colorectal cancer with a
sensitivity of at least 50% and a specificity of at least 90%.
[0050] Any suitable technique for the detection of polypeptides may
be used in the methods of the invention. In one embodiment, the
method comprises contacting the sample with at least one compound
that binds a biomarker polypeptide. Alternatively, the method
comprises detecting the polypeptides by mass spectrometry.
[0051] In one particular embodiment, the compound is detectably
labelled.
[0052] In another embodiment, the compound is an antibody.
[0053] In one embodiment, the compound is bound to a solid
support.
[0054] In the methods of the invention, determining the presence
and/or level of the biomarker may comprise determining the presence
and/or level of a polynucleotide encoding the biomarker, such as a
biomarker gene transcript. Thus, in one embodiment, the biomarkers
are polynucleotides.
[0055] In yet another embodiment of the methods of the invention,
the method comprises:
[0056] i) determining the presence and/or level of the biomarkers
in the sample from the subject; and
[0057] ii) comparing the presence and/or level of the biomarkers to
a control, wherein a presence and/or level in the sample that is
different to the control is indicative of colorectal cancer.
[0058] In one embodiment, the sample comprises blood, plasma,
serum, urine, platelets, magakaryocytes or faeces.
[0059] In another aspect, the present invention provides a method
of treatment comprising:
[0060] (i) diagnosing or detecting colorectal cancer according to
the method of the invention; and
[0061] (ii) administering or recommending a therapeutic for the
treatment of colorectal cancer.
[0062] In yet another aspect, the present invention provides a
method for monitoring the efficacy of treatment of colorectal
cancer in a subject, the method comprising treating the subject for
colorectal cancer and then detecting the presence and/or level of
at least two biomarkers selected from IL-8, IGFBP2, MAC2BP, M2PK,
IL-13, DKK-3, EpCam, MIP1.beta., TGF.beta.1, and TIMP-1 in a sample
from the subject, wherein an absence of and/or reduction in the
level of expression of the polypeptides after treatment when
compared to before treatment is indicative of effective
treatment.
[0063] In another aspect, the present invention provides an array
of at least two compounds for the diagnosis or detection of
colorectal cancer, wherein each of the compounds binds a different
biomarker polypeptide selected from IL-8, IGFBP2, MAC2BP, M2PK,
IL-13, DKK-3, EpCam, MIP1.beta., TGF.beta.1, and TIMP-1.
[0064] In yet another aspect, the present invention provides a kit
for diagnosing or detecting colorectal cancer in a subject, the kit
comprising two compounds that each binds a different biomarker
polypeptide selected from IL-8, IGFBP2, MAC2BP, M2PK, IL-13, DKK-3,
EpCam, MIP1.beta., TGF.beta.1, and TIMP-1.
[0065] Throughout this specification the word "comprise", or
variations such as "comprises" or "comprising", will be understood
to imply the inclusion of a stated element, integer or step, or
group of elements, integers or steps, but not the exclusion of any
other element, integer or step, or group of elements, integers or
steps.
[0066] As will be apparent, preferred features and characteristics
of one aspect of the invention are applicable to many other aspects
of the invention.
[0067] The invention is hereinafter described by way of the
following non-limiting Examples and with reference to the
accompanying figures.
BRIEF DESCRIPTION OF THE ACCOMPANYING DRAWINGS
[0068] FIG. 1. In Study 3 an optimum combination of the 46
potential protein biomarkers was found using logistic regression
modelling, resulting in a panel of seven biomarkers and is
illustrated as a ROC curve (black curve). The performance of this
"panel" on independent data was estimated using "leave one out"
cross-validation (grey curve). The vertical lines are drawn at
points of 80% and 90% specificity--operating points of interest in
screening tests. Performance statistics are given in Table 5.
[0069] FIG. 2. Performance of a seven biomarker model identifying
colorectal cancer patients from normals at each Dukes Stage
illustrated by ROC curves for each stage. A (red)--Stage A, B
(green)--Stage B, C (blue)--Stage C, and D (black)-Stage D from
Study 3a. Performance characteristics are given in Table 6.
[0070] FIG. 3. When biomarker results from Study 4 (also referred
to as Study 3 remeasured) were modelled in pairs a total of 5 pairs
(out of a possible 45 combinations selected from the list of 10
biomarkers above) could be shown to produce a sensitivity above 52%
at a specificity of 95. The performance of these pair wise
biomarker combinations is illustrated as ROC curves (n=5 curves).
Performance characteristics are given in Table 7.
[0071] FIG. 4. An example of a 3 biomarker model generated from
Study 4 data which had a sensitivity of at least 50% at 95%
specificity. There were 968 possible 3-biomarker combinations and
approximately half of those combinations showed a performance of at
least 50% sensitivity at 90% specificity.
[0072] FIG. 5. ROC curves are illustrated for all combinations of
3-10 biomarkers generated from Study 4 data which have a
sensitivity of at least 50% at 95% specificity (n=485 cross
validated curves out of a possible 968 models).
[0073] FIG. 6. Frequency of each biomarker in the best 485 models.
These BMs represent all serum models that gave a sensitivity of at
least 50% at 95% The high representation of all 10 biomarkers in
the useful models demonstrates the unity of our selection of these
10 biomarkers.
[0074] FIG. 7. A 5 biomarker model generated from Study 4 data is
illustrated as a ROC curve (black) and cross validated ROC curve
(grey). This model shows a sensitivity of 68% at 95% specificity
when all stages of disease are included and when cross validated
gave a sensitivity of 64%. Biomarkers included are [IL-8, IGFBP2,
Mac2BP, DKK-3 and M2PK].
[0075] FIG. 8. A 6 biomarker model generated from Study 4 data is
illustrated as a ROC curve (black) and cross validated ROC curve
(Grey). This model shows a sensitivity of 77% at a specificity of
95% when all stages of disease are included and when cross
validated gave a sensitivity of 67%. Biomarkers included are [IL-8,
IGFBP2, Mac2BP, DKK-3, TGFbeta1&M2PK].
[0076] FIG. 9. Two alternative seven biomarker models generated
from Study 3a data are shown. One was optimised for high
specificity (black/new) and an alternative or model optimised for
area under the curve is shown (grey/old). At 90% specificity the
sensitivity was 72% for the new model and 77% for the older model.
Biomarkers included were as follows:
[0077] New: IL8, IGFBP2, s90MAC2BP, M2PK, DKK-3, IL-13 &
TGFbeta,
[0078] Old: IL8, IGFBP2, s90MAC2BP, M2PK, EpCAM, IL13 &
MIP-1b.
[0079] FIG. 10. A seven biomarker model generated from Study 4 data
is illustrated as a ROC curve (black) and cross validated ROC curve
(grey). This model shows, a sensitivity of 84% at a specificity of
95%. Biomarkers included are [M2PK serum, IL8.plasma, TGF
beta1.serum, IGFBP2.plasma, Mac2BP.serum, TIMP1.plasma and Dkk3
plasma.
[0080] FIG. 11. Cross validated ROC curves showing the performance
of a 3 biomarker model for each Dukes stage is illustrated. This
data demonstrates the validity of the choice of three biomarkers
(DKK-3, M2PK and IGFBP2) for detecting cancer at different stages
of the disease progression. The data indicates that at Stage A if
the three markers are used, the test still will achieve a
significant sensitivity of 64% at 95% specificity which is
comparable to the sensitivity achieved at late stage disease (79%).
That is the biomarker panel of three will pick up early disease
states allowing early detection. Biomarkers included are Dkk3, M2PK
and IGFBP2.
KEY TO THE SEQUENCE LISTING
[0081] SEQ ID NO:1--amino acid sequence of IL-8 [0082] SEQ ID
NO:2--amino acid sequence of IGFBP2 [0083] SEQ ID NO:3--amino acid
sequence of MAC2BP [0084] SEQ ID NO:4--amino acid sequence of M2PK
variant 1 [0085] SEQ ID NO:5--amino acid sequence of M2PK variant 2
[0086] SEQ ID NO:6--amino acid sequence of M2PK variant 3 [0087]
SEQ ID NO:7--amino acid sequence of IL-13 [0088] SEQ ID NO:8--amino
acid sequence of DKK-3 variant 1 [0089] SEQ ID NO:9--amino acid
sequence of DKK-3 variant 2 [0090] SEQ ID NO:10--amino acid
sequence of DKK-3 variant 3 [0091] SEQ ID NO:11--amino acid
sequence of EpCam [0092] SEQ ID NO:12--amino acid sequence of
MIP1.beta. [0093] SEQ ID NO:13--amino acid sequence of TGF.beta.1
[0094] SEQ ID NO:14--amino acid sequence of TIMP-1
DETAILED DESCRIPTION
General Techniques and Definitions
[0095] Unless specifically defined otherwise, all technical and
scientific terms used herein shall be taken to have the same
meaning as commonly understood by one of ordinary skill in the art
(e.g., in cell culture, molecular genetics, immunology,
immunohistochemistry, protein chemistry, and biochemistry).
[0096] Unless otherwise indicated, the recombinant protein, cell
culture, and immunological techniques utilized in the present
invention are standard procedures, well known to those skilled in
the art. Such techniques are described and explained throughout the
literature in sources such as, J. Perbal, A Practical Guide to
Molecular Cloning, John Wiley and Sons (1984), J. Sambrook et al.,
Molecular Cloning: A Laboratory Manual, 3.sup.rd edn, Cold Spring
Harbour Laboratory Press (2001), R. Scopes, Protein
Purification--Principals and Practice, 3.sup.rd edn, Springer
(1994), T. A. Brown (editor), Essential Molecular Biology: A
Practical Approach, Volumes 1 and 2, IRL Press (1991), D. M. Glover
and B. D. Hames (editors), DNA Cloning: A Practical Approach,
Volumes 1-4, IRL Press (1995 and 1996), and F. M. Ausubel et al.
(editors), Current Protocols in Molecular Biology, Greene Pub.
Associates and Wiley-Interscience (1988, including all updates
until present), Ed Harlow and David Lane (editors) Antibodies: A
Laboratory Manual, Cold Spring Harbour Laboratory, (1988), and J.
E. Coligan et al. (editors) Current Protocols in Immunology, John
Wiley & Sons (including all updates until present).
[0097] As used herein, the term "colorectal cancer", also known as
"colon cancer", "bowel cancer" or "rectal cancer", refers to all
forms of cancer originating from the epithelial cells lining the
large intestine and/or rectum.
[0098] As used herein, "biomarker" refers to any molecule, such as
a gene, gene transcript (for example mRNA), peptide or protein or
fragment thereof produced by a subject which is useful in
differentiating subjects having colorectal cancer from normal or
healthy subjects.
[0099] As used herein, the term "diagnosis", and variants thereof
such as, but not limited to, "diagnose", "diagnosed" or
"diagnosing" shall not be limited to a primary diagnosis of a
clinical state, but should be taken to include diagnosis of
recurrent disease.
[0100] As used herein, the term "subject" refers to any animal that
may develop colorectal cancer and includes animals such as mammals,
e.g. humans, or non-human mammals such as cats and dogs, laboratory
animals such as mice, rats, rabbits or guinea pigs, and livestock
animals. In a preferred embodiment, the subject is a human.
[0101] The "sample" may be of any suitable type and may refer,
e.g., to a material in which the presence or level of biomarkers
can be detected. Preferably, the sample is obtained from the
subject so that the detection of the presence and/or level of
biomarkers may be performed in vitro. Alternatively, the presence
and/or level of biomarkers can be detected in vivo. The sample can
be used as obtained directly from the source or following at least
one step of (partial) purification. The sample can be prepared in
any convenient medium which does not interfere with the method of
the invention. Typically, the sample is an aqueous solution,
biological fluid, cells or tissue. Preferably, the sample is blood,
plasma, serum, urine, platelets, megakaryocytes or faeces.
Pre-treatment may involve, for example, preparing plasma from
blood, diluting viscous fluids, and the like. Methods of treatment
can involve filtration, distillation, separation, concentration,
inactivation of interfering components, and the addition of
reagents. The selection and pre-treatment of biological samples
prior to testing is well known in the art and need not be described
further.
[0102] As used herein the terms "treating", "treat" or "treatment"
include administering a therapeutically effective amount of a
compound sufficient to reduce or delay the onset or progression of
colorectal cancer, or to reduce or eliminate at least one symptom
of colorectal cancer.
Biomarkers
[0103] The present inventors have shown that determining the
presence and/or level of least two biomarkers in a sample from a
subject allows for the detection or diagnosis of colorectal cancer,
either early detection at Dukes Stage A or at some later stage such
as Dukes Stage B or C or D, with specificity and sensitivity
comparable to or greater than that achieved with the FOBT. The at
least two biomarkers that are useful in the methods of the present
invention are selected from IL-8 (interleukin-8), IGFBP2
(insulin-like growth factor binding protein-2), MAC2BP
(MAC2-binding protein; serum protein 90K), M2PK (pyruvate kinase
muscle 2, pyruvate kinase 3), IL-13 (interleukin-13), DKK-3
(dickkopf homolog, 3), EpCAM (epithelial cell adhesion molecule),
MIP1.beta. (macrophage inflammatory protein 1.beta., CCL4,
MIP1beta), TGF.beta.1 (transforming growth factor .beta.1 ,
TGFbeta1) and TIMP-1 (tissue inhibitor of metalloproteinase 1).
Reference to any of these biomarkers includes reference to all
polypeptide and polynucleotide variants such as isoforms and
transcript variants as would be known by the person skilled in the
art. NCBI accession numbers of representative sequences for each of
the biomarkers are provided in Table 1.
TABLE-US-00001 TABLE 1 NCBI accession numbers for representative
biomarker sequences. Biomarker Representative NCBI Accession
Numbers IL-8 NM_000584.2 (SEQ ID NO: 1) IGFBP2 NM_000597.2 (SEQ ID
NO: 2) MAC2BP NM_005567.3 (SEQ ID NO: 3) M2PK NM_002654.3;
NM_182470.1; NM_182471.1 (SEQ ID NOs: 4-6) IL-13 NM_002188.2 (SEQ
ID NO: 7) DKK-3 NM_015881.5; NM_013253; NM_001018057.1 (SEQ ID NOs:
8-10) EpCam NM_002354.2 (SEQ ID NO: 11) MIP1.beta. NM_002984.2 (SEQ
ID NO: 12) TGF.beta.1 NM_000660.4 (SEQ ID NO: 13) TIMP-1
NM_003254.2 (SEQ ID NO: 14)
Detecting or Diagnosing Colorectal Cancer
[0104] It will be apparent from the preceding description that the
diagnostic methods of the present invention may involve a degree of
quantification to determine levels biomarkers in patient samples.
Such quantification is readily provided by the inclusion of
appropriate control samples.
[0105] In one embodiment, internal controls are included in the
methods of the present invention. A preferred internal control is
one or more samples taken from one or more healthy individuals.
[0106] In the present context, the term "healthy individual" shall
be taken to mean an individual who is known not to suffer from
colorectal cancer, such knowledge being derived from clinical data
on the individual, including, but not limited to, a different
diagnostic assay to that described herein.
[0107] As will be known to those skilled in the art, when internal
controls are not included in each assay conducted, the control may
be derived from an established data set.
[0108] Data pertaining to the control subjects are preferably
selected from the group consisting of:
[0109] 1. a data set comprising measurements of the presence or
level of expression of biomarkers for a typical population of
subjects known to have colorectal cancer;
[0110] 2. a data set comprising measurements of the presence or
level of biomarkers for the subject being tested wherein said
measurements have been made previously, such as, for example, when
the subject was known to be healthy or, in the case of a subject
having colorectal cancer, when the subject was diagnosed or at an
earlier stage in disease progression;
[0111] 3. a data set comprising measurements of the presence or
level of biomarkers for a healthy individual or a population of
healthy individuals; and
[0112] 4. a data set comprising measurements of the presence or
level of biomarkers for a normal individual or a population of
normal individuals.
[0113] In the present context, the term "typical population" with
respect to subjects known to have colorectal cancer shall be taken
to refer to a population or sample of subjects diagnosed with
colorectal cancer that is representative of the spectrum of
colorectal cancer patients. This is not to be taken as requiring a
strict normal distribution of morphological or clinicopathological
parameters in the population, since some variation in such a
distribution is permissible. Preferably, a "typical population"
will exhibit a spectrum of colorectal cancer at different stages of
disease progression. It is particularly preferred that a "typical
population" exhibits the expression characteristics of a cohort of
subjects as described herein.
[0114] The term "normal individual" shall be taken to mean an
individual that does not express a biomarker, or expresses a
biomarker at a low level in a sample. As will be known to those
skilled in the art, data obtained from a sufficiently large sample
of the population will normalize, allowing the generation of a data
set for determining the average level of a particular
biomarker.
[0115] Those skilled in the art are readily capable of determining
the baseline for comparison in any diagnostic assay of the present
invention without undue experimentation, based upon the teaching
provided herein.
[0116] Compounds that bind a biomarker when used diagnostically may
be linked to a diagnostic reagent such as a detectable label to
allow easy detection of binding events in vitro or in vivo.
Suitable labels include radioisotopes, dye markers or other imaging
reagents for detection and/or localisation of target molecules.
Compounds linked to a detectable label can be used with suitable in
vivo imaging technologies such as, for example, radiology,
fluoroscopy, nuclear magnetic resonance imaging (MRI),
CAT-scanning, positron emission tomography (PET), computerized
tomography etc.
[0117] The diagnostic methods of the present invention are able to
diagnose or detect colorectal cancer with a sensitivity and
specificity that is at least comparable to FOBT, or greater. As
would be understood by the person skilled in the art, sensitivity
refers to the proportion of actual positives in the diagnostic test
which are correctly identified as having colorectal cancer.
Specificity measures the proportion of negatives which are
correctly identified as not having colorectal cancer. In one
embodiment, the methods of the invention are able to diagnose or
detect colorectal cancer with a sensitivity of at least 50%, 60% or
66%, or at least 77%, 80%, 83%, 85%, 86%, 87%, 88%, 89%, 90%, or at
least 93%. In another embodiment, the methods of the invention are
able to diagnose or detect colorectal cancer with a sensitivity of
at least 80%, or at least 85% or at least 90%, or at least 95%.
[0118] In one embodiment, the methods of the invention are able to
diagnose or detect colorectal cancer with a specificity of at least
75%, 80%, 85%, 90%, 91%, 92%, 93%, 94% or at least 95%.
[0119] Advantageously, the methods of the present invention are
able to detect colorectal cancer at all of the Dukes Stages with
greater sensitivity than the FOBT. In Dukes Stage A, the tumor has
penetrated into, but not through, the bowel wall. In Dukes Stage B,
the tumor has penetrated through the bowel wall but there is not
yet any lymph node involvement. In Dukes Stage C, the cancer
involves regional lymph nodes. In Dukes Stage D, there is distant
metastasis, for example, to the liver or lung. In one embodiment,
the methods of the present invention are able to diagnose or detect
colorectal cancer at any Dukes Stage with a sensitivity of at least
80%.
[0120] As known to the skilled person, there are other systems for
staging cancer that are know in the art. One example is the TMN
Classification of Malignant Tumors (TNM) that is used by the
American Joint Committee on Cancer (AJCC: Colon and rectum. in Edge
et al., eds; AJCC Cancer Staging Manual, 7.sup.th ed. New York,
N.Y.: Springer, 2010, pp: 143-164). Another example is the Modified
Astler-Coller classification (MAC).
[0121] Accordingly, the skilled person will appreciate that the
Dukes Stages correspond to certain TNM Classifications. For
example, Dukes Stage A corresponds to T1, T2, N0 and M0; Dukes
Stage B corresponds to T3, T4a, T4b, N0 and M0; and Dukes Stage C
corresponds to i) T1-T2, N1/N1c, M0; ii) T1, N2a and M0; iii)
T3-T4a, N1/N1c and M0; iv) T2-T3, N2a and M0; v) T1-T2, N2b and M0;
vi) T4a, N2a and M0; vii) T3-T4a, N2b and M0; and viii) T4b, N1-N2
and M0. Thus, the skilled person will understand that reference to
a Dukes Stage as used herein includes reference to the
corresponding TMN classification as known in the art.
Protein Detection Techniques
[0122] In one embodiment, biomarker polypeptide is detected in a
patient sample, wherein the presence and/or level of the
polypeptide in the sample is indicative of colorectal cancer. For
example, the method may comprise contacting a biological sample
derived from the subject with a compound capable of binding to a
biomarker polypeptide, and detecting the formation of complex
between the compound and the biomarker polypeptide. The term
"biomarker polypeptide" as used herein includes fragments of
biomarker polypeptides, including for example, immunogenic
fragments and epitopes of the biomarker polypeptide.
[0123] In one embodiment, the compound that binds the biomarker is
an antibody.
[0124] The term "antibody" as used herein includes intact molecules
as well as molecules comprising or consisting of fragments thereof,
such as, for example Fab, F(ab')2, Fv and scFv, as well as
engineered variants including diabodies, triabodies, mini-bodies
and single-domain antibodies which are capable of binding an
epitopic determinant. Thus, antibodies may exist as intact
immunoglobulins, or as modifications in a variety of forms.
[0125] In another embodiment, an antibody to a biomarker
polypeptide is detected in a patient sample, wherein the presence
and/or level of the antibody in the sample is indicative of
colorectal cancer.
[0126] Preferred detection systems contemplated herein include any
known assay for detecting proteins or antibodies in a biological
sample isolated from a human subject, such as, for example,
SDS/PAGE, isoelectric focussing, 2-dimensional gel electrophoresis
comprising SDS/PAGE and isoelectric focussing, an immunoassay, flow
cytometry e.g. fluorescence-activated cell sorting (FACS), a
detection based system using an antibody or non-antibody compound,
such as, for example, a small molecule (e.g. a chemical compound,
agonist, antagonist, allosteric modulator, competitive inhibitor,
or non-competitive inhibitor, of the protein). In accordance with
these embodiments, the antibody or small molecule may be used in
any standard solid phase or solution phase assay format amenable to
the detection of proteins. Optical or fluorescent detection, such
as, for example, using mass spectrometry, MALDI-TOF, biosensor
technology, evanescent fiber optics, or fluorescence resonance
energy transfer, is clearly encompassed by the present invention.
Assay systems suitable for use in high throughput screening of mass
samples, e.g. a high throughput spectroscopy resonance method (e.g.
MALDI-TOF, electrospray MS or nano-electrospray MS), are also
contemplated. Another suitable protein detection technique involves
the use of Multiple Reaction Monitoring (MRM) in LC-MS (LC/MRM-MS)
(Anderson and Hunter, 2006).
[0127] Immunoassay formats are particularly suitable, e.g.,
selected from the group consisting of, an immunoblot, a Western
blot, a dot blot, an enzyme linked immunosorbent assay (ELISA),
radioimmunoassay (RIA), enzyme immunoassay. Modified immunoassays
utilizing fluorescence resonance energy transfer (FRET),
isotope-coded affinity tags (ICAT), matrix-assisted laser
desorption/ionization time of flight (MALDI-TOF), electrospray
ionization (ESI), biosensor technology, evanescent fiber-optics
technology or protein chip technology are also useful.
Nucleic Acid Detection Techniques
[0128] Any suitable technique that allows for the qualitative
and/or quantitative assessment of the level of a biomarker
polynucleotide in a sample may be used. The terms "nucleic acid
molecule" or "polynucleotide" as used herein refer to an
oligonucleotide, polynucleotide or any fragment thereof.
[0129] Comparison may be made by reference to a standard control,
or to a control level that is found in healthy tissue. For example,
levels of a transcribed gene can be determined by Northern
blotting, and/or RT-PCR. With the advent of quantitative
(real-time) PCR, quantitative analysis of gene expression can be
achieved by using appropriate primers for the gene of interest. The
nucleic acid may be labelled and hybridised on a gene array, in
which case the gene concentration will be directly proportional to
the intensity of the radioactive or fluorescent signal generated in
the array.
[0130] Methods for direct sequencing of nucleotide sequences are
well known to those skilled in the art and can be found for example
in Ausubel et al., eds., Short Protocols in Molecular Biology, 3rd
ed., Wiley, (1995) and Sambrook et al., Molecular Cloning, 3rd ed.,
Cold Spring Harbor Laboratory Press, (2001). Sequencing can be
carried out by any suitable method, for example, dideoxy
sequencing, chemical sequencing or variations thereof. Direct
sequencing has the advantage of determining variation in any base
pair of a particular sequence.
[0131] Other PCR methods that may be used in carrying out the
invention include hybridization based PCR detection systems, TaqMan
assay (U.S. Pat. No. 5,962,233) and the molecular beacon assay
(U.S. Pat. No. 5,925,517).
[0132] The nucleic acid may be separated from the sample for
testing. Suitable methods will be known to those of skill in the
art. For example, RNA may be isolated from a sample to be analysed
using conventional procedures, such as are supplied by QIAGEN
technology. This RNA is then reverse-transcribed into DNA using
reverse transcriptase and the DNA molecule of interest may then be
amplified by PCR techniques using specific primers.
[0133] Diagnostic procedures may also be performed directly upon
patient samples. Hybridisation or amplification assays, such as,
for example, Southern or Northern blot analysis,
immunohistochemistry, single-stranded conformational polymorphism
analysis (SSCP) and PCR analyses are among techniques that are
useful in this respect. If desired, target or probe nucleic acid
may be immobilised to a solid support such as a microtitre plate,
membrane, polystyrene bead, glass slide or other solid phase.
Kits
[0134] The present invention provides kits for the diagnosis or
detection of colorectal cancer. Such kits may be suitable for
detection of nucleic acid species, or alternatively may be for
detection of a polypeptide gene product, as discussed above.
[0135] For detection of polypeptides, antibodies will most
typically be used as components of kits. However, any agent capable
of binding specifically to a biomarker gene product will be useful
in this aspect of the invention. Other components of the kits will
typically include labels, secondary antibodies, substrates (if the
gene is an enzyme), inhibitors, co-factors and control gene product
preparations to allow the user to quantitate expression levels
and/or to assess whether the diagnosis experiment has worked
correctly. Enzyme-linked immunosorbent assay-based (ELISA) tests
and competitive ELISA tests are particularly suitable assays that
can be carried out easily by the skilled person using kit
components.
[0136] Optionally, the kit further comprises means for the
detection of the binding of an antibody to a biomarker polypeptide.
Such means include a reporter molecule such as, for example, an
enzyme (such as horseradish peroxidase or alkaline phosphatase), a
dye, a radionucleotide, a luminescent group, a fluorescent group,
biotin or a colloidal particle, such as colloidal gold or selenium.
Preferably such a reporter molecule is directly linked to the
antibody.
[0137] In yet another embodiment, a kit may additionally comprise a
reference sample. In one embodiment, a reference sample comprises a
polypeptide that is detected by an antibody. Preferably, the
polypeptide is of known concentration. Such a polypeptide is of
particular use as a standard. Accordingly, various known
concentrations of such a polypeptide may be detected using a
diagnostic assay described herein.
[0138] For detection of nucleic acids, such kits may contain a
first container such as a vial or plastic tube or a microtiter
plate that contains an oligonucleotide probe. The kits may
optionally contain a second container that holds primers. The probe
may be hybridisable to DNA whose altered expression is associated
with colorectal cancer and the primers are useful for amplifying
this DNA. Kits that contain an oligonucleotide probe immobilised on
a solid support could also be developed, for example, using arrays
(see supplement of issue 21(1) Nature Genetics, 1999).
[0139] For PCR amplification of nucleic acid, nucleic acid primers
may be included in the kit that are complementary to at least a
portion of a biomarker gene as described herein. The set of primers
typically includes at least two oligonucleotides, preferably four
oligonucleotides, that are capable of specific amplification of
DNA. Fluorescent-labelled oligonucleotides that will allow
quantitative PCR determination may be included (e.g. TaqMan
chemistry, Molecular Beacons). Suitable enzymes for amplification
of the DNA, will also be included.
[0140] Control nucleic acid may be included for purposes of
comparison or validation. Such controls could either be RNA/DNA
isolated from healthy tissue, or from healthy individuals, or
housekeeping genes such as .beta.-actin or GAPDH whose mRNA levels
are not affected by colorectal cancer.
Regression Algorithms and Statistics
[0141] In order to develop a panel of biomarkers suitable for
diagnosing or detecting colorectal cancer, the present inventors
have analysed numerous biomarkers in a statistical model. Such an
improvement in the performance of a test is sometimes referred to
as the "in-sample" performance. A fair evaluation of a test
requires its assessment using out-of-sample subjects, that is,
subjects not included in the construction of the initial predictive
model. This is achieved by assessing the test performance using
cross validation.
[0142] Tests for statistical significance include linear and non
linear regression, including ANOVA, Kruskal-Wallis, Wilcoxon,
Mann-Whitney and odds ratio, Baysian probability algorithms. As the
number of biomarkers measured increases however, it can be
generally more convenient to use a more sophisticated technique
such as Random Forests, simple logistic, Bayes Net to name a
few.
[0143] For example, Bayesian probability may be adopted. In this
circumstance a 10-fold cross-validation can be used to estimate the
"out-of-sample" performance of the models in question. For each
combination of biomarkers under consideration, the data can be
divided randomly into 10 sub-samples, each with similar proportions
of healthy subject and subjects at each stage of disease. In turn,
each subsample can be excluded, and a logistic model built using
the remaining 90% of the subjects. This model can then be used to
estimate the probability of cancer for the excluded sub-sample,
providing an estimate of "out-of-sample" performance. By repeating
this for the remaining 9 subsamples, "out-of-sample" performance
can be estimated from the study data itself. These out-of sample
predicted probabilities can then be compared with the actual
disease status of the subjects to create a Receiver Operating
Characteristic (ROC) Curve, from which the cross-validated
sensitivity at 95% specificity may be estimated.
[0144] Each estimate of "out-of-sample" performance using
cross-validation (or any other method), whilst unbiased, has an
element of variability to it. Hence a ranking of models (based on
biomarker combinations) can be indicative only of the relative
performance of such models. However a set of biomarkers which is
capable of being used in a large number of combinations to generate
a diagnostic test as demonstrated via "out-of-sample" performance
evaluations, almost certainly contains within itself combinations
of biomarkers that will withstand repeated evaluation.
[0145] Many different combinations can qualify as diagnostic tests
which prove useful and cost effective and have acceptable
sensitivity for a given specificity. As an example, consider the
five biomarkers: IL-8, IGFBP2, MAC2BP, M2PK and DKK-3. A model
discriminating subjects with cancer from healthy controls can be as
follows:
log ( p 1 - p ) = .beta. 0 + .beta. IL 8 C IL 8 + .beta. IGFBP 2 C
IGFBP 2 + .beta. MAC 2 BP C MAC 2 BP + .beta. M 2 PK C M 2 PK +
.beta. DKK 3 C DKK 3 ##EQU00001##
[0146] Here p represents the probability that a person has
colorectal cancer. Each C.sub.i is the logarithm of concentration
biomarker i in the plasma (or serum) of a person. Each beta
(.beta.) is a coefficient applying to that biomarker in the
concentration units in which it is measured--.beta..sub.0 is an
"offset" or "intercept". This linear logistic model is common to
all results presented herein, but is far from the only way in which
a combination of biomarker concentrations may be modelled to
predict the probability of cancer.
[0147] Other non linear or linear logistic algorithms that would be
equally applicable include Random Forest, ANOVA, t-Test, Fisher
analysis, Support Vector Machine, Linear Models for MicroArray data
(LIMMA) and/or Significance Analyses of Microarray Data (SAM), Best
First, Greedy Stepwise, Naive Bayes, Linear Forward Selection,
Scatter Search, Linear Discriminant Analysis (LDA), Stepwise
Logistic Regression, Receiver Operating Characteristic and
Classification Trees (CT).
[0148] Thus, in light of the teachings of the present
specification, the person skilled in the art will appreciate that
the sensitivity and specificity of a test for diagnosing colorectal
cancer may be modulated by selecting a different combination of the
biomarkers as described herein
Knowledge-Based Systems
[0149] It will be apparent from the discussion herein that
knowledge-based computer software and hardware for implementing an
algorithm also form part of the present invention. Such computer
software and/or hardware are useful for performing a method of
diagnosing or detecting colorectal cancer according the invention.
Thus, the present invention also provides software or hardware
programmed to implement an algorithm that processes data obtained
by performing the method of the invention via a multivariate
analysis to provide a disease score and provide or permit a
diagnosis or detection of colorectal cancer and/or determine
progression or status of a colorectal cancer or determine whether
or not a colorectal cancer has progressed or determine whether or
not a subject is responding to treatment for colorectal cancer in
accordance with the results of the disease score in comparison with
predetermined values.
[0150] In one example, a method of the invention may be used in
existing knowledge-based architecture or platforms associated with
pathology services. For example, results from a method described
herein are transmitted via a communications network (e.g. the
internet) to a processing system in which an algorithm is stored
and used to generate a predicted posterior probability value which
translates to the score of disease probability or risk of
recurrence or metastasis or responsiveness to treatment which is
then forwarded to an end user in the form of a diagnostic or
predictive report.
[0151] The method of the invention may, therefore, be in the form
of a kit or computer-based system which comprises the reagents
necessary to detect the concentration of the biomarkers and the
computer hardware and/or software to facilitate determination and
transmission of reports to a clinician.
[0152] The assay of the present invention permits integration into
existing or newly developed pathology architecture or platform
systems. For example, the present invention contemplates a method
of allowing a user to determine the status of a subject with
respect to colorectal cancer, the method including:
(a) receiving data in the form of levels at least two biomarkers
selected from IL-8, IGFBP2, MAC2BP, M2PK, IL-13, DKK-3, EpCam,
MIP1.beta., TGF.beta.1, and TIMP-1 in a readily obtained sample,
optionally in combination with another marker of colorectal cancer;
(b) processing the subject data via multivariate analysis (for
example, regression analysis) to provide a disease score; (c)
determining the status of the subject in accordance with the
results of the disease score in comparison with predetermined
values; and (d) transferring an indication of the status of the
subject to the user via the communications network reference to the
multivariate analysis includes an algorithm which performs the
multivariate analysis function.
[0153] In one embodiment, the method for diagnosing or detecting
colorectal cancer of the invention may be performed by taking a
blood sample from a patient and determining the presence and/or
level of any one or more of the biomarkers as described herein. If
desired, the measurements may be made, for example, on a biochip so
that a single analysis can be used to measure the presence and/or
level of multiple biomarkers. The results of this analysis may then
be inputted into into a computer program that subjects them to
linear regression analysis. The computer could also contain
information as to control values or expected ranges, or the
clinician, nurse, medical administrator or general practitioner
could input such data. This analysis wold then provide a score or
likelihood of having colorectal cancer. If a second test for the
patient is performed, the regression analysis may indicate a change
in the score, thus indicating that the patient's disease has
progressed or worsened.
EXAMPLES
Materials and Methods
Patient Samples
[0154] A collection of plasma and serum samples was taken and
processed from a cohort of colorectal cancer patients (Dukes Stages
A-D) that were being treated at several hospitals.
[0155] Blood was also collected and processed from a group of about
50 healthy volunteers over the age of 65 and from a group of 15
over the age of 50.
[0156] Four separate studies were undertaken with slightly
different biomarkers. Study 1 looked at 52 cancer samples and 50
controls, study 2 looked at 55 cancer samples and 53 controls,
study 3 and 4 looked at 96 cancer samples and 50 controls. In study
2, 3 and 4 the patients were age and gender matched across Dukes
Stages, see Table 2 for summary statistics.
TABLE-US-00002 TABLE 2 Characteristics of normal volunteers and
colorectal cancer patients used in studies 2, 3 and 4. Controls
Cancers n = 50 n = 96 Gender male 25 48 female 25 48 Mean Age (yr)
68 68 Dukes stage A 22 B 30 C 30 D 14 Tumour site colon 73 rectum
17 unknown 6 Proximal (includes caecum, ascending, hepatic 43
flexure and transverse colon) Distal (includes splenic flexure,
descending, 47 sigmoid and rectum)
Biomarker Analysis
[0157] Analysis of biomarkers was done with commercial kits and
sourced antibodies (DSL, R&D Duoset, Calbiotech, Millipore,
Abnova, Genway, Peviva, Schebo, Bender) and using ELISA or Luminex
assays.
Statistical Evaluation and Panel Biomarker Modelling
[0158] Results for each assay were analysed using the statistical
software packages Prism and "R". Individual performance of markers
was evaluated using the non-parametric Mann-Whitney t-test and
individual receiver operator characteristic (ROC) curves were
generated.
[0159] Logistic regression and related modelling strategies were
used to find combinations of biomarkers that best separated
controls and colorectal cancer patients. Four separate studies were
performed with the same samples/aliquots. The results of each of
these is given below.
Results of Study 1, 2 and 3
[0160] Biomarkers chosen to be measured in Study 1 and 2 and 3 are
listed in Table 3. Biomarkers in bold were those identified as
promising from each study (i.e. they were significantly different
in samples from colorectal cancer patients versus control and/or
they were identified in panels of combined biomarkers that
distinguish colorectal cancer from controls).
TABLE-US-00003 TABLE 3 Biomarkers analysed in studies. Study 1
Study 2 Study 3 IGF-I IGF-BP2 IGF-BP2 (DSL) IGF-II IGF-II IGF-II
IGF-BP2 IGF-BP3 IFNg IGF-BP3 Her2 TNFa BTC VegFA IL-10 Amphiregulin
VegFC IL-6 VegFA VegFD GM-CSF VegFC TIMP-1 IL-12 VegFD TIMP-2 IL-13
MMP-2 MMP-1 IL-8 MMP-7 MMP-2 IL-4 MMP-9 MMP-3 Il-2 TIMP-2 MMP-7
IL-1b Her2 MMP-8 MMP-1 MMP-12 MMP-2 MMP-13 MMP-3 ENA-78 MMP-7 MCP-1
MMP-8 MIP-1beta MMP-9 GM-CSF ENA-78 IFN-gamma MIP-1alpha IL-10
MIP-1beta IL-12 MCP-1 IL-13 Mac-2BP IL-1beta TIMP-1 IL-2 TIMP-2
IL-4 Gro-alpha IL-6 Tumour M2 pyruvate kinase IL-8 M30-apoptosense
TNF-alpha M65 Cripto Trail-R2 P-cadherin OPN Dkk-3 EpCam TGFbeta1
REG IV CEA DcR3 CA19.9 Amphiregulin CEACAM6 VegFA pan VegFA165b
Spondin-2 survivin
Statistical Evaluation and Panel Biomarker Modelling
[0161] To find combinations of biomarkers that best separated
controls and colorectal cancer patients, forward variable selection
with Bayesian Information Criteria to penalize log-likelihood to
prevent over-fitting was adopted. To estimate the likely
performance of the panel of biomarkers on an independent dataset,
"N-Fold" or "leave-one-out" cross validation was used. In this
procedure one observation at a time was excluded whilst the entire
model fitting algorithm was applied to the remaining
observations.
[0162] The resulting model was then used to estimate the
probability that the excluded observation is a case. This was
repeated for each observation in the dataset. In this way each
observation in turn acted as an independent test of the
model-building algorithm. The resulting dataset consisting of cases
and controls each with an "independently predicted" case
probability can then be compared with the original model. The
ability to choose from numerous biomarkers and weight them
appropriately allows a search strategy which optimises performance
in regions of interest on the ROC curve. The cost of poor
specificity is large numbers of unnecessary colonoscopies.
[0163] In study 3, 48 potential biomarkers were evaluated to select
a candidate panel of colorectal cancer biomarkers, using block
randomization within plates to avoid bias. From this list of 48
only 42 showed measurable levels. Individually 14 biomarkers showed
significant difference between controls and CRC as assessed by
t-tests; (IGFII, IGFBP2, IL-8, IL-6, MMP-1, MMP-7, s90/Mac2BP,
M2PK, EpCam, TIMP-1 (serum and plasma), M65, OPN, TGF.beta.1,
VEGFpan. As expected, none had sufficient sensitivity or
specificity to be useful as a biomarker by itself (not shown).
However, using a variety of modelling strategies, including use of
logarithmic values, several different panels of biomarkers were
found that exceeded the performance of FOBT especially for early to
late stage disease.
[0164] FIG. 1 shows the results from a 7 biomarker panel which
included IL8 (serum), IL-13 (serum), EpCAM (plasma), M2PK (plasma),
IGFBP2 (serum) and Mac2BP (serum) and which was cross validated to
predict its performance on independent samples.
[0165] This 7 biomarker model, which is described at least
conceptually as
log ( p 1 - p ) = .beta. 0 + .beta. IL 8 C IL 8 + .beta. IGFBP 2 C
IGFBP 2 + .beta. MAC 2 BP C MAC 2 BP + .beta. M 2 PK C M 2 PK +
.beta. DKK 3 C DKK 3 ##EQU00002##
provided good performance at high specificity and was robust under
cross validation. The coefficients estimated to give the best model
for this biomarker combination in plasma are listed in Table 4.
Performance statistics are provided in Table 5. This performance
exceeds that quoted for FOBT (sensitivity 65.8%, specificity 95%)
(Morikawa et al., 2005).
TABLE-US-00004 TABLE 4 Coefficients for the biomarker combination.
Measured Concentration Biomarker in Units Coefficient Intercept NA
NA -37.74 IL-8 serum pg/mL 1.07 IL-13 serum pg/mL -0.28 EpCAM
plasma pg/mL -0.33 M2PK plasma units/mL 1.40 IGFBP2 serum ng/mL
1.99 Mac2BP serum ng/mL 2.39 MIP1beta Serum Pg/ml -1.19
TABLE-US-00005 TABLE 5 Performance of the 7 biomarker model and
cross-validation. Model estimate Cross validation Area Under the
ROC Curve (AUC) 0.91 0.86 Sensitivity at 80% specificity 0.84 0.78
Sensitivity at 90% specificity 0.81 0.69
[0166] This model was also applied separately to patients from each
stage of colorectal cancer (Dukes Stage A, B, C, D) and shown to
perform equally well within each stage (FIG. 2). The AUCs were
0.88-0.93 and were almost equally good at discriminating all Stages
of colorectal cancer. The model shows the highest sensitivity of
90% at 90% specificity for Stage C and the lowest sensitivity of
73% at 90% specificity for Stage B (Table 6).
TABLE-US-00006 TABLE 6 Performance of the Model by Dukes Stage
Stage A Stage B Stage C Stage D Area Under the ROC Curve 0.89 0.88
0.93 0.91 (AUC) Sensitivity at 80% specificity 0.82 0.77 0.90 0.93
Sensitivity at 90% specificity 0.77 0.73 0.90 0.86
Study 4 (Also Referred to as "Study 3 Remeasured")
[0167] In study 4, 10 biomarkers were remeasured in the same cohort
as Study 3. Blood was collected from 96 colorectal cancer patients
and 50 normal subjects (the controls). In this study the focus was
on 10 biomarkers, namely IGFBP2, IL8, IL13, Mac2BP, M2PK, Dkk3,
EpCam, TGFbeta1, TIMP-1, MIP1beta. Assays were performed as
described previously. Both serum and plasma levels of each of the
biomarkers were measured and compared with control values.
[0168] When modelled in pairs (two markers), a total of 5 pairs
(out of a possible 45 combinations selected from the list of 10
biomarkers above) could be shown to produce a sensitivity above 52%
at a specificity of 95%. See Table 7 and FIG. 3.
TABLE-US-00007 TABLE 7 Biomarker Pairs Producing Useful Screening
Tests on Cross-Validation. Estimated Estimated In-Sample
Out-of-Sample (Test) (Cross-Validated) Sensitivity at 95%
Sensitivity at 95% Biomarker 1 Biomarker 2 Specificity Specificity
M2PK EpCAM 58.3% 58.3% M2PK IL13 56.3% 57.3% Dkk3 M2PK 55.2% 55.2%
M2PK IL8 60.4% 54.2% M2PK IGFBP2 58.3% 52.1%
[0169] In analysing combinations of three to ten of the nominated
biomarkers, there are 968 possible combinations. The 968
combinations of between 3 and 10 biomarkers consist of the 120
combinations of 3 marker; 210 combinations of 4 markers; 252
combinations of 5 markers; 210 combinations of 6 markers; 120
combinations of 7 markers; 45 combinations of 8 markers; 10
combinations of 9 markers and the single combination that includes
all 10 biomarkers. When they were modelled using a linear logistic
model, and then tested via 10-fold cross validation, about half of
the 968 combinations had a sensitivity of 50% at a specificity of
95%, see FIG. 4 which shows the results for a three biomarker
combination. More than half of these combinations would have a
specificity of 90% and a sensitivity of 50%.
[0170] FIG. 5 shows all 485 of the estimated out-of sample (10-fold
cross-validated) ROC curves for tests out of a total possible 968
models based on all possible combinations of 3 to 10 of the
biomarkers. Note that many individual segments of the 485 ROC
curves are coincident, due to as each horizontal segment represents
one control and each vertical segment one case. In this instance
50.1% of the combinations have exceeded the 50% sensitivity, 95%
specificity, The best estimated "out-of-sample" performance is a
sensitivity of 76% at 95% specificity. Repeating the
cross-validation will select a different set of models--the
sensitivity of any one combination may vary by 10% at 95%
specificity due to random sampling--but result in a similar
proportion of useful "useful screening tests". Precise validation
of individual models requires repeated experiments and larger
sample sizes.
[0171] FIG. 6 shows how many of the 485 combinations with 50%
sensitivity, 95% specificity, include any given biomarker. At the
high end, 432 of the chosen "useful" combinations include M2PK. At
the low end 227 of the chosen "useful" combinations include
MIP1beta. This high representation of all 10 biomarkers in "useful"
models shows the unity and self-complementarity of the selection of
these 10 biomarkers.
[0172] FIG. 7 to FIG. 11 demonstrate some of the results from this
last study (Study 4) for combinations of 5 and 7 biomarkers,
including a model where the samples are either from plasma or serum
cluster. FIG. 11 demonstrates the validity of the choice of three
biomarkers (DKK-3, M2PK and IGFBP2) at different stages of the
disease progression. The data indicates that at Stage A if the
three markers are used, the test still will achieve a significant
sensitivity (64%) at 95% specificity which is comparable to the
sensitivity achieved at late stage disease, 79%). That is the
biomarker panel of three will pick up early disease states allowing
early detection.
[0173] Tables 8 to 16 list results from various combinations of
various biomarker panel sets. Depending on the linear regression
that is used, as well as the cohort control and other factors such
as sample derivation and assay kit technique, there may be a
variation on the actual figures or order of the markers.
Regardless, many of these combinations will achieve good
selectivity at high specificities so as to be useful for diagnosing
or detecting colorectal cancer at any stage of the disease
progression.
[0174] 10
TABLE-US-00008 TABLE 8 Combination of three biomarkers in serum
that equal or exceed 50% sensitivity at 95% specificity. Cross Test
Validated Sensitivity Sensitivity at 95% at 95% BM1 BM2 BM3
Specificity Specificity Dkk3 M2PK IGFBP2 72.9% 70.8% Dkk3 M2PK IL8
62.5% 61.5% M2PK IL13 IGFBP2 65.6% 61.5% M2PK IGFBP2 EpCAM 63.5%
61.5% M2PK IL8 IGFBP2 65.6% 60.4% M2PK IL8 IL13 61.5% 58.3% M2PK
MIP1beta IL13 55.2% 57.3% M2PK IL8 Mac2BP 59.4% 57.3% M2PK MIP1beta
TGFbeta1 57.3% 56.3% M2PK IL8 EpCAM 64.6% 56.3% Dkk3 M2PK IL13
59.4% 55.2% Dkk3 M2PK EpCAM 56.3% 55.2% M2PK MIP1beta EpCAM 59.4%
55.2% M2PK IL8 TGFbeta1 58.3% 55.2% M2PK IL8 TIMP1 57.3% 55.2%
TGFbeta1 Mac2BP TIMP1 54.2% 55.2% M2PK Mac2BP IGFBP2 58.3% 54.2%
Dkk3 IL8 Mac2BP 55.2% 53.1% M2PK MIP1beta Mac2BP 52.1% 53.1% M2PK
MIP1beta IGFBP2 57.3% 53.1% M2PK TIMP1 EpCAM 58.3% 53.1% M2PK
MIP1beta IL8 56.3% 52.1% M2PK IL13 TIMP1 58.3% 52.1% Dkk3 M2PK
Mac2BP 57.3% 51.0% Dkk3 M2PK TIMP1 55.2% 51.0% M2PK IL13 Mac2BP
58.3% 51.0% M2PK TGFbeta1 IGFBP2 52.1% 51.0% M2PK TGFbeta1 TIMP1
57.3% 51.0% M2PK TGFbeta1 Mac2BP 56.3% 50.0% IL8 IL13 Mac2BP 61.5%
50.0% IL8 TGFbeta1 Mac2BP 53.1% 50.0% M2PK MIP1beta TIMP1 49.0%
49.0% M2PK TGFbeta1 EpCAM 49.0% 47.9% IL8 IL13 IGFBP2 49.0% 47.9%
IL8 Mac2BP IGFBP2 57.3% 47.9% Dkk3 M2PK MIP1beta 55.2% 46.9%
TGFbeta1 Mac2BP IGFBP2 46.9% 46.9% Dkk3 Mac2BP IGFBP2 49.0% 45.8%
M2PK IL13 EpCAM 50.0% 45.8% IL8 Mac2BP TIMP1 52.1% 45.8% IL13
Mac2BP IGFBP2 45.8% 44.8% Dkk3 IL8 TGFbeta1 41.7% 43.8% MIP1beta
IL8 Mac2BP 42.7% 43.8% MIP1beta IL8 EpCAM 44.8% 43.8% IL8 TGFbeta1
EpCAM 46.9% 43.8% Dkk3 IL8 EpCAM 51.0% 42.7% IL8 IGFBP2 EpCAM 43.8%
42.7% M2PK Mac2BP TIMP1 51.0% 41.7%
TABLE-US-00009 TABLE 9 Combination of four biomarkers including
DKK-3 in serum that equal or exceed 50% sensitivity at 95%
specificity. Cross Test Validated Sensitivity Sensitivity at 95% at
95% BM1 BM2 BM3 BM4 Specificity Specificity Dkk3 M2PK Mac2BP IGFBP2
68.8% 69.8% Dkk3 M2PK IL8 IL13 71.9% 68.8% Dkk3 M2PK IL8 EpCAM
70.8% 67.7% Dkk3 M2PK TGFbeta1 Mac2BP 67.7% 65.6% Dkk3 M2PK IL8
IGFBP2 69.8% 64.6% Dkk3 M2PK IL8 Mac2BP 69.8% 63.5% Dkk3 M2PK
MIP1beta TGFbeta1 65.6% 61.5% Dkk3 M2PK IL8 TIMP1 68.8% 61.5% Dkk3
M2PK IL13 IGFBP2 63.5% 61.5% Dkk3 M2PK MIP1beta IL8 59.4% 60.4%
Dkk3 M2PK IGFBP2 EpCAM 68.8% 60.4% Dkk3 M2PK MIP1beta IGFBP2 69.8%
59.4% Dkk3 M2PK TGFbeta1 IGFBP2 61.5% 59.4% Dkk3 M2PK IL13 Mac2BP
56.3% 58.3% Dkk3 M2PK TGFbeta1 TIMP1 65.6% 58.3% Dkk3 M2PK MIP1beta
IL13 57.3% 57.3% Dkk3 IL8 Mac2BP IGFBP2 62.5% 56.3% Dkk3 M2PK IL8
TGFbeta1 65.6% 55.2% Dkk3 M2PK IL13 TIMP1 57.3% 55.2% Dkk3 M2PK
Mac2BP TIMP1 57.3% 55.2% Dkk3 M2PK MIP1beta Mac2BP 57.3% 54.2% Dkk3
IL8 IL13 Mac2BP 61.5% 54.2% Dkk3 M2PK TGFbeta1 EpCAM 61.5% 53.1%
Dkk3 M2PK IGFBP2 TIMP1 61.5% 53.1% Dkk3 M2PK IL13 TGFbeta1 63.5%
52.1% Dkk3 M2PK TIMP1 EpCAM 56.3% 52.1% Dkk3 IL8 Mac2BP TIMP1 57.3%
52.1% Dkk3 M2PK MIP1beta EpCAM 60.4% 51.0% Dkk3 TGFbeta1 Mac2BP
TIMP1 59.4% 51.0%
TABLE-US-00010 TABLE 10 Combination of four biomarkers including
M2PK in serum that equal or exceed 50% sensitivity at 95%
specificity. Cross Test Validated Sensitivity Sensitivity at 95% at
95% BM1 BM2 BM3 BM4 Specificity Specificity M2PK IL8 Mac2BP TIMP1
63.5% 65.6% M2PK Mac2BP IGFBP2 EpCAM 70.8% 65.6% M2PK IL8 IL13
Mac2BP 66.7% 64.6% M2PK IL8 TGFbeta1 Mac2BP 65.6% 64.6% M2PK
MIP1beta IL13 IGFBP2 64.6% 62.5% M2PK IL8 IL13 TIMP1 64.6% 62.5%
M2PK IL8 IL13 EpCAM 65.6% 62.5% M2PK IL13 Mac2BP IGFBP2 69.8% 62.5%
M2PK MIP1beta IL8 IL13 63.5% 61.5% M2PK IL8 Mac2BP EpCAM 65.6%
61.5% M2PK IL13 IGFBP2 EpCAM 69.8% 61.5% M2PK MIP1beta IL8 TGFbeta1
58.3% 58.3% M2PK IL8 IL13 IGFBP2 67.7% 58.3% M2PK IL8 Mac2BP IGFBP2
61.5% 58.3% M2PK IL8 IGFBP2 EpCAM 64.6% 58.3% M2PK IL13 TGFbeta1
IGFBP2 64.6% 58.3% M2PK IL13 TGFbeta1 EpCAM 62.5% 58.3% M2PK IL13
IGFBP2 TIMP1 62.5% 58.3% M2PK TGFbeta1 Mac2BP TIMP1 62.5% 58.3%
M2PK TGFbeta1 IGFBP2 EpCAM 61.5% 58.3% M2PK MIP1beta IL13 TIMP1
57.3% 57.3% M2PK MIP1beta TGFbeta1 TIMP1 58.3% 57.3% M2PK MIP1beta
IGFBP2 EpCAM 65.6% 57.3% M2PK MIP1beta IL8 Mac2BP 54.2% 56.3% M2PK
IL8 IL13 TGFbeta1 64.6% 56.3% M2PK IL8 TIMP1 EpCAM 62.5% 56.3% M2PK
IL13 Mac2BP TIMP1 57.3% 56.3% M2PK TGFbeta1 Mac2BP IGFBP2 60.4%
56.3% M2PK IGFBP2 TIMP1 EpCAM 63.5% 56.3% M2PK MIP1beta IL8 TIMP1
57.3% 55.2% M2PK IL8 TGFbeta1 TIMP1 57.3% 55.2% M2PK MIP1beta IL13
Mac2BP 57.3% 54.2% M2PK MIP1beta IL8 EpCAM 62.5% 53.1% M2PK
MIP1beta TIMP1 EpCAM 59.4% 52.1% M2PK IL13 TGFbeta1 TIMP1 64.6%
52.1% M2PK IL13 Mac2BP EpCAM 51.0% 52.1% M2PK MIP1beta IL13
TGFbeta1 57.3% 51.0% M2PK MIP1beta Mac2BP IGFBP2 57.3% 51.0% M2PK
TGFbeta1 Mac2BP EpCAM 52.1% 51.0% M2PK TGFbeta1 TIMP1 EpCAM 59.4%
51.0% M2PK MIP1beta IL8 IGFBP2 52.1% 50.0% M2PK Mac2BP TIMP1 EpCAM
52.1% 50.0% M2PK MIP1beta TGFbeta1 Mac2BP 63.5% 49.0% M2PK MIP1beta
IL13 EpCAM 50.0% 47.9% M2PK MIP1beta TGFbeta1 EpCAM 53.1% 47.9%
M2PK IL13 TGFbeta1 Mac2BP 60.4% 46.9% M2PK MIP1beta TGFbeta1 IGFBP2
49.0% 44.8%
TABLE-US-00011 TABLE 11 Combination of five biomarkers in serum
that equal or exceed 50% sensitivity at 95% specificity. Test Cross
Validated Sensitivity at Sensitivity at BM1 BM2 BM3 BM4 BM5 95%
Specificity 95% Specificity Dkk3 M2PK IL8 IL13 Mac2BP 74.0% 70.8%
Dkk3 M2PK IL8 IL13 TIMP1 71.9% 70.8% M2PK TGFbeta1 Mac2BP IGFBP2
EpCAM 69.8% 70.8% Dkk3 M2PK MIP1beta IL8 IL13 71.9% 69.8% Dkk3 M2PK
MIP1beta IGFBP2 EpCAM 71.9% 69.8% Dkk3 M2PK IL8 IGFBP2 EpCAM 78.1%
69.8% Dkk3 M2PK TGFbeta1 Mac2BP IGFBP2 68.8% 69.8% M2PK MIP1beta
Mac2BP IGFBP2 EpCAM 69.8% 68.8% Dkk3 M2PK IL8 TGFbeta1 Mac2BP 70.8%
67.7% Dkk3 M2PK Mac2BP IGFBP2 EpCAM 70.8% 67.7% M2PK MIP1beta IL8
IL13 Mac2BP 66.7% 67.7% Dkk3 M2PK IL8 IL13 IGFBP2 70.8% 66.7% Dkk3
M2PK IL13 TGFbeta1 IGFBP2 68.8% 66.7% Dkk3 M2PK IL13 IGFBP2 EpCAM
66.7% 66.7% M2PK IL13 Mac2BP IGFBP2 EpCAM 71.9% 66.7% Dkk3 M2PK IL8
IL13 TGFbeta1 69.8% 65.6% Dkk3 M2PK IL8 Mac2BP TIMP1 67.7% 65.6%
Dkk3 M2PK TGFbeta1 Mac2BP EpCAM 66.7% 65.6% M2PK IL8 IL13 TGFbeta1
Mac2BP 71.9% 65.6% M2PK IL8 IL13 TGFbeta1 IGFBP2 66.7% 65.6% M2PK
IL8 TGFbeta1 Mac2BP IGFBP2 64.6% 65.6% M2PK IL8 TGFbeta1 Mac2BP
EpCAM 69.8% 65.6% Dkk3 M2PK MIP1beta IL8 IGFBP2 66.7% 64.6% Dkk3
M2PK MIP1beta Mac2BP IGFBP2 70.8% 64.6% M2PK MIP1beta IL8 IL13
TIMP1 65.6% 64.6% M2PK MIP1beta IL8 TGFbeta1 Mac2BP 65.6% 64.6%
M2PK IL8 IL13 Mac2BP EpCAM 76.0% 64.6% M2PK IL8 IL13 IGFBP2 EpCAM
72.9% 64.6% M2PK IL8 TGFbeta1 Mac2BP TIMP1 64.6% 64.6% M2PK IL8
Mac2BP IGFBP2 EpCAM 71.9% 64.6% Dkk3 M2PK IL13 TGFbeta1 TIMP1 68.8%
63.5% Dkk3 M2PK IGFBP2 TIMP1 EpCAM 67.7% 63.5% M2PK MIP1beta IL13
IGFBP2 EpCAM 67.7% 63.5% M2PK IL13 TGFbeta1 Mac2BP IGFBP2 66.7%
63.5% M2PK IL13 TGFbeta1 IGFBP2 EpCAM 70.8% 63.5% Dkk3 M2PK
MIP1beta IL13 TGFbeta1 69.8% 62.5% Dkk3 M2PK IL8 IL13 EpCAM 65.6%
62.5% Dkk3 M2PK IL8 TGFbeta1 IGFBP2 70.8% 62.5% Dkk3 M2PK IL8
Mac2BP IGFBP2 69.8% 62.5% Dkk3 M2PK IL13 TGFbeta1 Mac2BP 68.8%
62.5% Dkk3 M2PK TGFbeta1 Mac2BP TIMP1 68.8% 62.5% Dkk3 M2PK
TGFbeta1 TIMP1 EpCAM 67.7% 62.5% M2PK IL8 IL13 Mac2BP TIMP1 64.6%
62.5% M2PK IL8 IL13 IGFBP2 TIMP1 66.7% 62.5% M2PK IL13 IGFBP2 TIMP1
EpCAM 68.8% 62.5% Dkk3 M2PK MIP1beta IL13 IGFBP2 63.5% 61.5% Dkk3
M2PK IL8 TGFbeta1 TIMP1 64.6% 61.5%
TABLE-US-00012 TABLE 12 Combination of seven biomarkers in serum
that equal or exceed 50% sensitivity at 95% specificity. Test Cross
Validated Sensitivity at Sensitivity at BM1 BM2 BM3 BM4 BM5 BM6 BM7
95% Specificity 95% Specificity Dkk3 M2PK IL8 Mac2BP IGFBP2 TIMP1
EpCAM 78% 56% Dkk3 M2PK MIP1beta IL8 Mac2BP IGFBP2 EpCAM 76% 64%
Dkk3 M2PK IL8 IL13 Mac2BP IGFBP2 TIMP1 73% 69% Dkk3 M2PK IL8
TGFbeta1 Mac2BP IGFBP2 TIMP1 72% 68% Dkk3 M2PK IL8 TGFbeta1 Mac2BP
IGFBP2 EpCAM 71% 62% Dkk3 M2PK MIP1beta IL8 Mac2BP IGFBP2 TIMP1 70%
64% Dkk3 M2PK IL8 IL13 Mac2BP IGFBP2 EpCAM 70% 67% Dkk3 M2PK IL8
IL13 TGFbeta1 Mac2BP IGFBP2 69% 66% M2PK MIP1beta IL8 Mac2BP IGFBP2
TIMP1 EpCAM 69% 55% Dkk3 M2PK MIP1beta IL8 IL13 Mac2BP IGFBP2 67%
68% Dkk3 M2PK MIP1beta IL8 TGFbeta1 Mac2BP IGFBP2 67% 64% Dkk3 M2PK
MIP1beta TGFbeta1 Mac2BP IGFBP2 TIMP1 65% 59% Dkk3 M2PK IL8 IL13
TGFbeta1 IGFBP2 TIMP1 65% 52% Dkk3 M2PK IL8 TGFbeta1 IGFBP2 TIMP1
EpCAM 64% 56% Dkk3 M2PK IL13 TGFbeta1 Mac2BP IGFBP2 TIMP1 64% 57%
M2PK MIP1beta IL8 IL13 IGFBP2 TIMP1 EpCAM 64% 50% Dkk3 M2PK
MIP1beta IL8 IL13 Mac2BP TIMP1 63% 55% Dkk3 M2PK MIP1beta IL8
TGFbeta1 IGFBP2 EpCAM 63% 58% Dkk3 M2PK MIP1beta IL13 TGFbeta1
Mac2BP IGFBP2 63% 53% Dkk3 M2PK IL8 IL13 TGFbeta1 Mac2BP TIMP1 63%
59% M2PK MIP1beta IL8 TGFbeta1 Mac2BP IGFBP2 EpCAM 63% 59% Dkk3
M2PK MIP1beta IL8 TGFbeta1 Mac2BP TIMP1 62% 51% Dkk3 M2PK MIP1beta
IL8 IGFBP2 TIMP1 EpCAM 62% 56% Dkk3 M2PK MIP1beta IL13 Mac2BP
IGFBP2 TIMP1 62% 39% Dkk3 M2PK IL8 IL13 TGFbeta1 TIMP1 EpCAM 62%
43% Dkk3 M2PK IL8 TGFbeta1 Mac2BP TIMP1 EpCAM 62% 50% Dkk3 MIP1beta
IL8 IL13 Mac2BP IGFBP2 TIMP1 62% 55% Dkk3 MIP1beta IL8 Mac2BP
IGFBP2 TIMP1 EpCAM 62% 47% M2PK MIP1beta IL8 IL13 Mac2BP IGFBP2
TIMP1 62% 56% M2PK IL8 TGFbeta1 Mac2BP IGFBP2 TIMP1 EpCAM 62% 49%
Dkk3 M2PK IL8 IL13 TGFbeta1 IGFBP2 EpCAM 60% 56% Dkk3 M2PK IL13
TGFbeta1 Mac2BP IGFBP2 EpCAM 60% 55% M2PK MIP1beta IL8 IL13 Mac2BP
IGFBP2 EpCAM 60% 53%
TABLE-US-00013 TABLE 13 Seven biomarker combinations with
Sensitivity between 60% and 52%. Test Cross Validated Sensitivity
at Sensitivity at BM1 BM2 BM3 BM4 BM5 BM6 BM7 95% Specificity 95%
Specificity Dkk3 M2PK MIP1beta IL8 IL13 TGFbeta1 EpCAM 59% 54% Dkk3
M2PK MIP1beta IL8 IL13 IGFBP2 EpCAM 59% 52% Dkk3 M2PK MIP1beta IL8
TGFbeta1 IGFBP2 TIMP1 59% 52% Dkk3 M2PK TGFbeta1 Mac2BP IGFBP2
TIMP1 EpCAM 59% 47% Dkk3 MIP1beta IL8 IL13 TGFbeta1 Mac2BP IGFBP2
59% 52% Dkk3 MIP1beta IL8 TGFbeta1 Mac2BP IGFBP2 TIMP1 59% 45% Dkk3
M2PK MIP1beta IL8 IL13 TGFbeta1 Mac2BP 58% 41% Dkk3 M2PK MIP1beta
IL8 TGFbeta1 TIMP1 EpCAM 58% 50% Dkk3 M2PK IL8 IL13 IGFBP2 TIMP1
EpCAM 58% 55% Dkk3 MIP1beta IL8 IL13 Mac2BP IGFBP2 EpCAM 58% 50%
Dkk3 IL8 IL13 TGFbeta1 Mac2BP IGFBP2 TIMP1 58% 52% M2PK IL8 IL13
Mac2BP IGFBP2 TIMP1 EpCAM 58% 52% Dkk3 M2PK MIP1beta IL8 IL13
TGFbeta1 IGFBP2 57% 49% Dkk3 M2PK MIP1beta IL8 IL13 TIMP1 EpCAM 57%
48% Dkk3 M2PK MIP1beta IL13 Mac2BP IGFBP2 EpCAM 57% 51% Dkk3 M2PK
MIP1beta TGFbeta1 Mac2BP IGFBP2 EpCAM 57% 46% Dkk3 M2PK MIP1beta
TGFbeta1 IGFBP2 TIMP1 EpCAM 57% 49% Dkk3 M2PK IL13 Mac2BP IGFBP2
TIMP1 EpCAM 57% 49% Dkk3 MIP1beta IL8 TGFbeta1 Mac2BP IGFBP2 EpCAM
57% 50% M2PK MIP1beta IL8 IL13 TGFbeta1 TIMP1 EpCAM 57% 43% M2PK
IL8 IL13 TGFbeta1 IGFBP2 TIMP1 EpCAM 57% 52% Dkk3 M2PK MIP1beta
Mac2BP IGFBP2 TIMP1 EpCAM 56% 47% Dkk3 IL8 IL13 Mac2BP IGFBP2 TIMP1
EpCAM 56% 48% MIP1beta IL8 IL13 TGFbeta1 Mac2BP IGFBP2 TIMP1 56%
54% Dkk3 M2PK MIP1beta IL8 TGFbeta1 Mac2BP EpCAM 55% 35% Dkk3 M2PK
MIP1beta IL13 IGFBP2 TIMP1 EpCAM 55% 44% Dkk3 M2PK IL8 IL13
TGFbeta1 Mac2BP EpCAM 55% 35% Dkk3 IL8 IL13 TGFbeta1 Mac2BP IGFBP2
EpCAM 55% 50% Dkk3 IL8 IL13 TGFbeta1 IGFBP2 TIMP1 EpCAM 55% 54%
Dkk3 M2PK MIP1beta IL8 IL13 Mac2BP EpCAM 54% 49% Dkk3 M2PK MIP1beta
IL8 Mac2BP TIMP1 EpCAM 54% 39% Dkk3 M2PK IL13 TGFbeta1 IGFBP2 TIMP1
EpCAM 54% 42% Dkk3 IL8 TGFbeta1 Mac2BP IGFBP2 TIMP1 EpCAM 54% 51%
M2PK MIP1beta IL8 IL13 TGFbeta1 IGFBP2 EpCAM 54% 37% M2PK MIP1beta
IL13 Mac2BP IGFBP2 TIMP1 EpCAM 54% 41% M2PK IL8 IL13 TGFbeta1
Mac2BP IGFBP2 TIMP1 54% 43% MIP1beta IL8 IL13 TGFbeta1 Mac2BP
IGFBP2 EpCAM 54% 42% Dkk3 M2PK MIP1beta IL8 IL13 IGFBP2 TIMP1 53%
52% Dkk3 M2PK IL8 IL13 Mac2BP TIMP1 EpCAM 53% 42% M2PK MIP1beta IL8
TGFbeta1 IGFBP2 TIMP1 EpCAM 53% 34% M2PK MIP1beta IL13 TGFbeta1
Mac2BP IGFBP2 EpCAM 53% 45% IL8 IL13 TGFbeta1 Mac2BP IGFBP2 EpCAM
53% 51% MIP1beta IL8 IL13 Mac2BP IGFBP2 TIMP1 EpCAM 53% 39%
MIP1beta IL8 TGFbeta1 Mac2BP IGFBP2 TIMP1 EpCAM 53% 51%
TABLE-US-00014 TABLE 14 Seven biomarker combinations with
sensitivity <53%. Test Cross Validated Sensitivity at
Sensitivity at BM1 BM2 BM3 BM4 BM5 BM6 BM7 95% Specificity 95%
Specificity M2PK MIP1beta IL8 IL13 TGFbeta1 Mac2BP EpCAM 52% 42%
Dkk3 M2PK MIP1beta IL13 TGFbeta1 IGFBP2 EpCAM 51% 41% Dkk3 MIP1beta
IL8 IL13 TGFbeta1 TIMP1 EpCAM 51% 35% M2PK MIP1beta IL8 IL13
TGFbeta1 IGFBP2 TIMP1 51% 38% M2PK MIP1beta IL8 TGFbeta1 Mac2BP
IGFBP2 TIMP1 51% 30% M2PK IL13 TGFbeta1 Mac2BP IGFBP2 TIMP1 EpCAM
51% 37% Dkk3 M2PK MIP1beta IL8 IL13 TGFbeta1 TIMP1 50% 48% Dkk3
MIP1beta IL8 IL13 TGFbeta1 Mac2BP TIMP1 50% 41% Dkk3 MIP1beta IL8
IL13 TGFbeta1 IGFBP2 TIMP1 50% 42% M2PK MIP1beta IL8 IL13 TGFbeta1
Mac2BP IGFBP2 50% 49% M2PK MIP1beta IL8 IL13 TGFbeta1 Mac2BP TIMP1
50% 35% MIP1beta IL8 IL13 TGFbeta1 IGFBP2 TIMP1 EpCAM 50% 39% Dkk3
M2PK MIP1beta IL13 TGFbeta1 IGFBP2 TIMP1 49% 44% Dkk3 MIP1beta IL8
IL13 TGFbeta1 Mac2BP EpCAM 49% 33% M2PK IL8 IL13 TGFbeta1 Mac2BP
TIMP1 EpCAM 49% 43% Dkk3 M2PK MIP1beta IL13 Mac2BP TIMP1 EpCAM 48%
43% Dkk3 MIP1beta IL8 IL13 Mac2BP TIMP1 EpCAM 48% 38% Dkk3 MIP1beta
IL8 TGFbeta1 Mac2BP TIMP1 EpCAM 48% 40% Dkk3 MIP1beta IL13 Mac2BP
IGFBP2 TIMP1 EpCAM 48% 31% Dkk3 IL8 IL13 TGFbeta1 Mac2BP TIMP1
EpCAM 48% 38% M2PK MIP1beta IL8 IL13 Mac2BP TIMP1 EpCAM 48% 33%
M2PK MIP1beta IL13 TGFbeta1 IGFBP2 TIMP1 EpCAM 48% 45% M2PK
MIP1beta TGFbeta1 Mac2BP IGFBP2 TIMP1 EpCAM 48% 37% M2PK MIP1beta
IL13 TGFbeta1 Mac2BP TIMP1 EpCAM 47% 41% IL8 IL13 TGFbeta1 Mac2BP
IGFBP2 TIMP1 EpCAM 47% 40% Dkk3 MIP1beta IL13 TGFbeta1 Mac2BP
IGFBP2 TIMP1 46% 42% M2PK MIP1beta IL8 TGFbeta1 Mac2BP TIMP1 EpCAM
46% 30% Dkk3 M2PK MIP1beta TGFbeta1 Mac2BP TIMP1 EpCAM 45% 37% Dkk3
M2PK IL13 TGFbeta1 Mac2BP TIMP1 EpCAM 45% 41% Dkk3 MIP1beta IL8
IL13 TGFbeta1 IGFBP2 EpCAM 45% 33% Dkk3 MIP1beta IL8 IL13 IGFBP2
TIMP1 EpCAM 44% 40% Dkk3 MIP1beta IL8 TGFbeta1 IGFBP2 TIMP1 EpCAM
44% 43% Dkk3 MIP1beta IL13 TGFbeta1 Mac2BP TIMP1 EpCAM 44% 43%
MIP1beta IL8 IL13 TGFbeta1 Mac2BP TIMP1 EpCAM 44% 28% Dkk3 M2PK
MIP1beta IL13 TGFbeta1 Mac2BP TIMP1 43% 31% Dkk3 MIP1beta IL13
TGFbeta1 IGFBP2 TIMP1 EpCAM 42% 40% MIP1beta IL13 TGFbeta1 Mac2BP
IGFBP2 TIMP1 EpCAM 42% 31% Dkk3 M2PK MIP1beta IL13 TGFbeta1 Mac2BP
EpCAM 41% 23% Dkk3 M2PK MIP1beta IL13 TGFbeta1 TIMP1 EpCAM 41% 33%
Dkk3 MIP1beta IL13 TGFbeta1 Mac2BP IGFBP2 EpCAM 41% 41% Dkk3
MIP1beta TGFbeta1 Mac2BP IGFBP2 TIMP1 EpCAM 41% 39% Dkk3 IL13
TGFbeta1 Mac2BP IGFBP2 TIMP1 EpCAM 41% 37% M2PK MIP1beta IL13
TGFbeta1 Mac2BP IGFBP2 TIMP1 32% 27%
TABLE-US-00015 TABLE 15 Sensitivity of nine biomarker combinations
in plasma and serum samples at 95% specificity. BM1 BM2 BM3 BM4 BM5
BM6 BM7 BM8 BM9 Plasma Serum Dkk3 M2PK MIP1beta IL8 IL13 TGFbeta1
Mac2BP IGFBP2 TIMP1 73% 77% Dkk3 M2PK MIP1beta IL8 IL13 TGFbeta1
Mac2BP IGFBP2 EpCAM 73% 77% Dkk3 M2PK MIP1beta IL8 IL13 TGFbeta1
Mac2BP TIMP1 EpCAM 54% 72% Dkk3 M2PK MIP1beta IL8 IL13 TGFbeta1
IGFBP2 TIMP1 EpCAM 58% 74% Dkk3 M2PK MIP1beta IL8 IL13 Mac2BP
IGFBP2 TIMP1 EpCAM 74% 70% Dkk3 M2PK MIP1beta IL8 TGFbeta1 Mac2BP
IGFBP2 TIMP1 EpCAM 71% 78% Dkk3 M2PK MIP1beta IL13 TGFbeta1 Mac2BP
IGFBP2 TIMP1 EpCAM 57% 72% Dkk3 M2PK IL8 IL13 TGFbeta1 Mac2BP
IGFBP2 TIMP1 EpCAM 67% 76% Dkk3 MIP1beta IL8 IL13 TGFbeta1 Mac2BP
IGFBP2 TIMP1 EpCAM 54% 55% M2PK MIP1beta IL8 IL13 TGFbeta1 Mac2BP
IGFBP2 TIMP1 EpCAM 58% 69%
TABLE-US-00016 TABLE 16 Sensitivity of ten biomarker combinations
in plasma and serum samples at 95% specificity. BM1 BM2 BM3 BM4 BM5
BM6 BM7 BM8 BM9 BM1o Plasma Serum Dkk3 M2PK MIP1beta IL8 IL13
TGFbeta1 Mac2BP IGFBP2 TIMP1 EpCAM 70% 55% Dkk3 M2PK MIP1beta IL8
IL13 TGFbeta1 Mac2BP IGFBP2 TIMP1 EpCAM 73% 68%
[0175] It will be appreciated by persons skilled in the art that
numerous variations and/or modifications may be made to the
invention as shown in the specific embodiments without departing
from the scope of the invention as broadly described.
[0176] The present embodiments are, therefore, to be considered in
all respects as illustrative and not restrictive.
[0177] All publications discussed and/or referenced herein are
incorporated herein in their entirety.
[0178] The present application claims priority from AU 2010903140,
the entire contents of which are incorporated herein by
reference.
[0179] Any discussion of documents, acts, materials, devices,
articles or the like which has been included in the present
specification is solely for the purpose of providing a context for
the present invention. It is not to be taken as an admission that
any or all of these matters form part of the prior art base or were
common general knowledge in the field relevant to the present
invention as it existed before the priority date of each claim of
this application.
REFERENCES
[0180] Anderson and Hunter (2006) Mol Cell Proteomics, 5:573-588.
[0181] Cancer in Australia, an overview (2008) AIHW (Australian
Institute of Health and Welfare) & AACR (Australian Association
of Cancer Registries), Cancer series no. 46, Cat. No: CAN 42, Can
berra: AIHW. [0182] Etzioni et al. (2003) Nat Rev Cancer,
3:243-252. [0183] Hundt et al. (2007) Cancer Epidemiol Biomarkers
Prev, 16:1935-1953. [0184] Kimmel (1987) Methods Enzymol,
152:507-511. [0185] Kwoh et al. (1989) Proc Natl Acad Sci USA.
86:1173-1177. [0186] Levin (2004) Gastroenterology, 127:1841-1844.
[0187] Lieberman (2010) Gastroenterology, 138:2115-2126. [0188]
Morikawa et al. (2005) Gastroenterology, 129:422-428. [0189] Notomi
et al. (2000) Nucleic Acids Res. 28:E63. [0190] Tonus (2006) World
J Gastroenterol, 12:7007-7011. [0191] Wahl and Berger (1987)
Methods Enzymol, 152:399-407. [0192] Walker et al. (1992a) Proc
Natl Acad Sci USA. 89:392-396. [0193] Walker et al. (1992b) Nucleic
Acids Res. 20:1691-1696.
Sequence CWU 1
1
14199PRTHomo sapiens 1Met Thr Ser Lys Leu Ala Val Ala Leu Leu Ala
Ala Phe Leu Ile Ser 1 5 10 15 Ala Ala Leu Cys Glu Gly Ala Val Leu
Pro Arg Ser Ala Lys Glu Leu 20 25 30 Arg Cys Gln Cys Ile Lys Thr
Tyr Ser Lys Pro Phe His Pro Lys Phe 35 40 45 Ile Lys Glu Leu Arg
Val Ile Glu Ser Gly Pro His Cys Ala Asn Thr 50 55 60 Glu Ile Ile
Val Lys Leu Ser Asp Gly Arg Glu Leu Cys Leu Asp Pro 65 70 75 80 Lys
Glu Asn Trp Val Gln Arg Val Val Glu Lys Phe Leu Lys Arg Ala 85 90
95 Glu Asn Ser 2328PRTHomo sapiens 2Met Leu Pro Arg Val Gly Cys Pro
Ala Leu Pro Leu Pro Pro Pro Pro 1 5 10 15 Leu Leu Pro Leu Leu Pro
Leu Leu Leu Leu Leu Leu Gly Ala Ser Gly 20 25 30 Gly Gly Gly Gly
Ala Arg Ala Glu Val Leu Phe Arg Cys Pro Pro Cys 35 40 45 Thr Pro
Glu Arg Leu Ala Ala Cys Gly Pro Pro Pro Val Ala Pro Pro 50 55 60
Ala Ala Val Ala Ala Val Ala Gly Gly Ala Arg Met Pro Cys Ala Glu 65
70 75 80 Leu Val Arg Glu Pro Gly Cys Gly Cys Cys Ser Val Cys Ala
Arg Leu 85 90 95 Glu Gly Glu Ala Cys Gly Val Tyr Thr Pro Arg Cys
Gly Gln Gly Leu 100 105 110 Arg Cys Tyr Pro His Pro Gly Ser Glu Leu
Pro Leu Gln Ala Leu Val 115 120 125 Met Gly Glu Gly Thr Cys Glu Lys
Arg Arg Asp Ala Glu Tyr Gly Ala 130 135 140 Ser Pro Glu Gln Val Ala
Asp Asn Gly Asp Asp His Ser Glu Gly Gly 145 150 155 160 Leu Val Glu
Asn His Val Asp Ser Thr Met Asn Met Leu Gly Gly Gly 165 170 175 Gly
Ser Ala Gly Arg Lys Pro Leu Lys Ser Gly Met Lys Glu Leu Ala 180 185
190 Val Phe Arg Glu Lys Val Thr Glu Gln His Arg Gln Met Gly Lys Gly
195 200 205 Gly Lys His His Leu Gly Leu Glu Glu Pro Lys Lys Leu Arg
Pro Pro 210 215 220 Pro Ala Arg Thr Pro Cys Gln Gln Glu Leu Asp Gln
Val Leu Glu Arg 225 230 235 240 Ile Ser Thr Met Arg Leu Pro Asp Glu
Arg Gly Pro Leu Glu His Leu 245 250 255 Tyr Ser Leu His Ile Pro Asn
Cys Asp Lys His Gly Leu Tyr Asn Leu 260 265 270 Lys Gln Cys Lys Met
Ser Leu Asn Gly Gln Arg Gly Glu Cys Trp Cys 275 280 285 Val Asn Pro
Asn Thr Gly Lys Leu Ile Gln Gly Ala Pro Thr Ile Arg 290 295 300 Gly
Asp Pro Glu Cys His Leu Phe Tyr Asn Glu Gln Gln Glu Ala Arg 305 310
315 320 Gly Val His Thr Gln Arg Met Gln 325 3585PRTHomo sapiens
3Met Thr Pro Pro Arg Leu Phe Trp Val Trp Leu Leu Val Ala Gly Thr 1
5 10 15 Gln Gly Val Asn Asp Gly Asp Met Arg Leu Ala Asp Gly Gly Ala
Thr 20 25 30 Asn Gln Gly Arg Val Glu Ile Phe Tyr Arg Gly Gln Trp
Gly Thr Val 35 40 45 Cys Asp Asn Leu Trp Asp Leu Thr Asp Ala Ser
Val Val Cys Arg Ala 50 55 60 Leu Gly Phe Glu Asn Ala Thr Gln Ala
Leu Gly Arg Ala Ala Phe Gly 65 70 75 80 Gln Gly Ser Gly Pro Ile Met
Leu Asp Glu Val Gln Cys Thr Gly Thr 85 90 95 Glu Ala Ser Leu Ala
Asp Cys Lys Ser Leu Gly Trp Leu Lys Ser Asn 100 105 110 Cys Arg His
Glu Arg Asp Ala Gly Val Val Cys Thr Asn Glu Thr Arg 115 120 125 Ser
Thr His Thr Leu Asp Leu Ser Arg Glu Leu Ser Glu Ala Leu Gly 130 135
140 Gln Ile Phe Asp Ser Gln Arg Gly Cys Asp Leu Ser Ile Ser Val Asn
145 150 155 160 Val Gln Gly Glu Asp Ala Leu Gly Phe Cys Gly His Thr
Val Ile Leu 165 170 175 Thr Ala Asn Leu Glu Ala Gln Ala Leu Trp Lys
Glu Pro Gly Ser Asn 180 185 190 Val Thr Met Ser Val Asp Ala Glu Cys
Val Pro Met Val Arg Asp Leu 195 200 205 Leu Arg Tyr Phe Tyr Ser Arg
Arg Ile Asp Ile Thr Leu Ser Ser Val 210 215 220 Lys Cys Phe His Lys
Leu Ala Ser Ala Tyr Gly Ala Arg Gln Leu Gln 225 230 235 240 Gly Tyr
Cys Ala Ser Leu Phe Ala Ile Leu Leu Pro Gln Asp Pro Ser 245 250 255
Phe Gln Met Pro Leu Asp Leu Tyr Ala Tyr Ala Val Ala Thr Gly Asp 260
265 270 Ala Leu Leu Glu Lys Leu Cys Leu Gln Phe Leu Ala Trp Asn Phe
Glu 275 280 285 Ala Leu Thr Gln Ala Glu Ala Trp Pro Ser Val Pro Thr
Asp Leu Leu 290 295 300 Gln Leu Leu Leu Pro Arg Ser Asp Leu Ala Val
Pro Ser Glu Leu Ala 305 310 315 320 Leu Leu Lys Ala Val Asp Thr Trp
Ser Trp Gly Glu Arg Ala Ser His 325 330 335 Glu Glu Val Glu Gly Leu
Val Glu Lys Ile Arg Phe Pro Met Met Leu 340 345 350 Pro Glu Glu Leu
Phe Glu Leu Gln Phe Asn Leu Ser Leu Tyr Trp Ser 355 360 365 His Glu
Ala Leu Phe Gln Lys Lys Thr Leu Gln Ala Leu Glu Phe His 370 375 380
Thr Val Pro Phe Gln Leu Leu Ala Arg Tyr Lys Gly Leu Asn Leu Thr 385
390 395 400 Glu Asp Thr Tyr Lys Pro Arg Ile Tyr Thr Ser Pro Thr Trp
Ser Ala 405 410 415 Phe Val Thr Asp Ser Ser Trp Ser Ala Arg Lys Ser
Gln Leu Val Tyr 420 425 430 Gln Ser Arg Arg Gly Pro Leu Val Lys Tyr
Ser Ser Asp Tyr Phe Gln 435 440 445 Ala Pro Ser Asp Tyr Arg Tyr Tyr
Pro Tyr Gln Ser Phe Gln Thr Pro 450 455 460 Gln His Pro Ser Phe Leu
Phe Gln Asp Lys Arg Val Ser Trp Ser Leu 465 470 475 480 Val Tyr Leu
Pro Thr Ile Gln Ser Cys Trp Asn Tyr Gly Phe Ser Cys 485 490 495 Ser
Ser Asp Glu Leu Pro Val Leu Gly Leu Thr Lys Ser Gly Gly Ser 500 505
510 Asp Arg Thr Ile Ala Tyr Glu Asn Lys Ala Leu Met Leu Cys Glu Gly
515 520 525 Leu Phe Val Ala Asp Val Thr Asp Phe Glu Gly Trp Lys Ala
Ala Ile 530 535 540 Pro Ser Ala Leu Asp Thr Asn Ser Ser Lys Ser Thr
Ser Ser Phe Pro 545 550 555 560 Cys Pro Ala Gly His Phe Asn Gly Phe
Arg Thr Val Ile Arg Pro Phe 565 570 575 Tyr Leu Thr Asn Ser Ser Gly
Val Asp 580 585 4531PRTHomo sapiens 4Met Ser Lys Pro His Ser Glu
Ala Gly Thr Ala Phe Ile Gln Thr Gln 1 5 10 15 Gln Leu His Ala Ala
Met Ala Asp Thr Phe Leu Glu His Met Cys Arg 20 25 30 Leu Asp Ile
Asp Ser Pro Pro Ile Thr Ala Arg Asn Thr Gly Ile Ile 35 40 45 Cys
Thr Ile Gly Pro Ala Ser Arg Ser Val Glu Thr Leu Lys Glu Met 50 55
60 Ile Lys Ser Gly Met Asn Val Ala Arg Leu Asn Phe Ser His Gly Thr
65 70 75 80 His Glu Tyr His Ala Glu Thr Ile Lys Asn Val Arg Thr Ala
Thr Glu 85 90 95 Ser Phe Ala Ser Asp Pro Ile Leu Tyr Arg Pro Val
Ala Val Ala Leu 100 105 110 Asp Thr Lys Gly Pro Glu Ile Arg Thr Gly
Leu Ile Lys Gly Ser Gly 115 120 125 Thr Ala Glu Val Glu Leu Lys Lys
Gly Ala Thr Leu Lys Ile Thr Leu 130 135 140 Asp Asn Ala Tyr Met Glu
Lys Cys Asp Glu Asn Ile Leu Trp Leu Asp 145 150 155 160 Tyr Lys Asn
Ile Cys Lys Val Val Glu Val Gly Ser Lys Ile Tyr Val 165 170 175 Asp
Asp Gly Leu Ile Ser Leu Gln Val Lys Gln Lys Gly Ala Asp Phe 180 185
190 Leu Val Thr Glu Val Glu Asn Gly Gly Ser Leu Gly Ser Lys Lys Gly
195 200 205 Val Asn Leu Pro Gly Ala Ala Val Asp Leu Pro Ala Val Ser
Glu Lys 210 215 220 Asp Ile Gln Asp Leu Lys Phe Gly Val Glu Gln Asp
Val Asp Met Val 225 230 235 240 Phe Ala Ser Phe Ile Arg Lys Ala Ser
Asp Val His Glu Val Arg Lys 245 250 255 Val Leu Gly Glu Lys Gly Lys
Asn Ile Lys Ile Ile Ser Lys Ile Glu 260 265 270 Asn His Glu Gly Val
Arg Arg Phe Asp Glu Ile Leu Glu Ala Ser Asp 275 280 285 Gly Ile Met
Val Ala Arg Gly Asp Leu Gly Ile Glu Ile Pro Ala Glu 290 295 300 Lys
Val Phe Leu Ala Gln Lys Met Met Ile Gly Arg Cys Asn Arg Ala 305 310
315 320 Gly Lys Pro Val Ile Cys Ala Thr Gln Met Leu Glu Ser Met Ile
Lys 325 330 335 Lys Pro Arg Pro Thr Arg Ala Glu Gly Ser Asp Val Ala
Asn Ala Val 340 345 350 Leu Asp Gly Ala Asp Cys Ile Met Leu Ser Gly
Glu Thr Ala Lys Gly 355 360 365 Asp Tyr Pro Leu Glu Ala Val Arg Met
Gln His Leu Ile Ala Arg Glu 370 375 380 Ala Glu Ala Ala Ile Tyr His
Leu Gln Leu Phe Glu Glu Leu Arg Arg 385 390 395 400 Leu Ala Pro Ile
Thr Ser Asp Pro Thr Glu Ala Thr Ala Val Gly Ala 405 410 415 Val Glu
Ala Ser Phe Lys Cys Cys Ser Gly Ala Ile Ile Val Leu Thr 420 425 430
Lys Ser Gly Arg Ser Ala His Gln Val Ala Arg Tyr Arg Pro Arg Ala 435
440 445 Pro Ile Ile Ala Val Thr Arg Asn Pro Gln Thr Ala Arg Gln Ala
His 450 455 460 Leu Tyr Arg Gly Ile Phe Pro Val Leu Cys Lys Asp Pro
Val Gln Glu 465 470 475 480 Ala Trp Ala Glu Asp Val Asp Leu Arg Val
Asn Phe Ala Met Asn Val 485 490 495 Gly Lys Ala Arg Gly Phe Phe Lys
Lys Gly Asp Val Val Ile Val Leu 500 505 510 Thr Gly Trp Arg Pro Gly
Ser Gly Phe Thr Asn Thr Met Arg Val Val 515 520 525 Pro Val Pro 530
5531PRTHomo sapiens 5Met Ser Lys Pro His Ser Glu Ala Gly Thr Ala
Phe Ile Gln Thr Gln 1 5 10 15 Gln Leu His Ala Ala Met Ala Asp Thr
Phe Leu Glu His Met Cys Arg 20 25 30 Leu Asp Ile Asp Ser Pro Pro
Ile Thr Ala Arg Asn Thr Gly Ile Ile 35 40 45 Cys Thr Ile Gly Pro
Ala Ser Arg Ser Val Glu Thr Leu Lys Glu Met 50 55 60 Ile Lys Ser
Gly Met Asn Val Ala Arg Leu Asn Phe Ser His Gly Thr 65 70 75 80 His
Glu Tyr His Ala Glu Thr Ile Lys Asn Val Arg Thr Ala Thr Glu 85 90
95 Ser Phe Ala Ser Asp Pro Ile Leu Tyr Arg Pro Val Ala Val Ala Leu
100 105 110 Asp Thr Lys Gly Pro Glu Ile Arg Thr Gly Leu Ile Lys Gly
Ser Gly 115 120 125 Thr Ala Glu Val Glu Leu Lys Lys Gly Ala Thr Leu
Lys Ile Thr Leu 130 135 140 Asp Asn Ala Tyr Met Glu Lys Cys Asp Glu
Asn Ile Leu Trp Leu Asp 145 150 155 160 Tyr Lys Asn Ile Cys Lys Val
Val Glu Val Gly Ser Lys Ile Tyr Val 165 170 175 Asp Asp Gly Leu Ile
Ser Leu Gln Val Lys Gln Lys Gly Ala Asp Phe 180 185 190 Leu Val Thr
Glu Val Glu Asn Gly Gly Ser Leu Gly Ser Lys Lys Gly 195 200 205 Val
Asn Leu Pro Gly Ala Ala Val Asp Leu Pro Ala Val Ser Glu Lys 210 215
220 Asp Ile Gln Asp Leu Lys Phe Gly Val Glu Gln Asp Val Asp Met Val
225 230 235 240 Phe Ala Ser Phe Ile Arg Lys Ala Ser Asp Val His Glu
Val Arg Lys 245 250 255 Val Leu Gly Glu Lys Gly Lys Asn Ile Lys Ile
Ile Ser Lys Ile Glu 260 265 270 Asn His Glu Gly Val Arg Arg Phe Asp
Glu Ile Leu Glu Ala Ser Asp 275 280 285 Gly Ile Met Val Ala Arg Gly
Asp Leu Gly Ile Glu Ile Pro Ala Glu 290 295 300 Lys Val Phe Leu Ala
Gln Lys Met Met Ile Gly Arg Cys Asn Arg Ala 305 310 315 320 Gly Lys
Pro Val Ile Cys Ala Thr Gln Met Leu Glu Ser Met Ile Lys 325 330 335
Lys Pro Arg Pro Thr Arg Ala Glu Gly Ser Asp Val Ala Asn Ala Val 340
345 350 Leu Asp Gly Ala Asp Cys Ile Met Leu Ser Gly Glu Thr Ala Lys
Gly 355 360 365 Asp Tyr Pro Leu Glu Ala Val Arg Met Gln His Leu Ile
Ala Arg Glu 370 375 380 Ala Glu Ala Ala Met Phe His Arg Lys Leu Phe
Glu Glu Leu Val Arg 385 390 395 400 Ala Ser Ser His Ser Thr Asp Leu
Met Glu Ala Met Ala Met Gly Ser 405 410 415 Val Glu Ala Ser Tyr Lys
Cys Leu Ala Ala Ala Leu Ile Val Leu Thr 420 425 430 Glu Ser Gly Arg
Ser Ala His Gln Val Ala Arg Tyr Arg Pro Arg Ala 435 440 445 Pro Ile
Ile Ala Val Thr Arg Asn Pro Gln Thr Ala Arg Gln Ala His 450 455 460
Leu Tyr Arg Gly Ile Phe Pro Val Leu Cys Lys Asp Pro Val Gln Glu 465
470 475 480 Ala Trp Ala Glu Asp Val Asp Leu Arg Val Asn Phe Ala Met
Asn Val 485 490 495 Gly Lys Ala Arg Gly Phe Phe Lys Lys Gly Asp Val
Val Ile Val Leu 500 505 510 Thr Gly Trp Arg Pro Gly Ser Gly Phe Thr
Asn Thr Met Arg Val Val 515 520 525 Pro Val Pro 530 6531PRTHomo
sapiens 6Met Ser Lys Pro His Ser Glu Ala Gly Thr Ala Phe Ile Gln
Thr Gln 1 5 10 15 Gln Leu His Ala Ala Met Ala Asp Thr Phe Leu Glu
His Met Cys Arg 20 25 30 Leu Asp Ile Asp Ser Pro Pro Ile Thr Ala
Arg Asn Thr Gly Ile Ile 35 40 45 Cys Thr Ile Gly Pro Ala Ser Arg
Ser Val Glu Thr Leu Lys Glu Met 50 55 60 Ile Lys Ser Gly Met Asn
Val Ala Arg Leu Asn Phe Ser His Gly Thr 65 70 75 80 His Glu Tyr His
Ala Glu Thr Ile Lys Asn Val Arg Thr Ala Thr Glu 85 90 95 Ser Phe
Ala Ser Asp Pro Ile Leu Tyr Arg Pro Val Ala Val Ala Leu 100 105 110
Asp Thr Lys Gly Pro Glu Ile Arg Thr Gly Leu Ile Lys Gly Ser Gly 115
120 125 Thr Ala Glu Val Glu Leu Lys Lys Gly Ala Thr Leu Lys Ile Thr
Leu 130 135 140 Asp Asn Ala Tyr Met Glu Lys Cys Asp Glu Asn Ile Leu
Trp Leu Asp 145 150 155 160 Tyr Lys Asn Ile Cys Lys Val Val Glu Val
Gly Ser Lys Ile Tyr Val 165 170 175 Asp Asp Gly Leu Ile Ser Leu Gln
Val Lys Gln Lys Gly Ala Asp Phe 180 185 190 Leu Val Thr Glu Val Glu
Asn Gly Gly Ser Leu Gly Ser Lys Lys Gly 195 200 205 Val Asn Leu Pro
Gly Ala Ala Val Asp Leu Pro Ala Val Ser Glu Lys 210
215 220 Asp Ile Gln Asp Leu Lys Phe Gly Val Glu Gln Asp Val Asp Met
Val 225 230 235 240 Phe Ala Ser Phe Ile Arg Lys Ala Ser Asp Val His
Glu Val Arg Lys 245 250 255 Val Leu Gly Glu Lys Gly Lys Asn Ile Lys
Ile Ile Ser Lys Ile Glu 260 265 270 Asn His Glu Gly Val Arg Arg Phe
Asp Glu Ile Leu Glu Ala Ser Asp 275 280 285 Gly Ile Met Val Ala Arg
Gly Asp Leu Gly Ile Glu Ile Pro Ala Glu 290 295 300 Lys Val Phe Leu
Ala Gln Lys Met Met Ile Gly Arg Cys Asn Arg Ala 305 310 315 320 Gly
Lys Pro Val Ile Cys Ala Thr Gln Met Leu Glu Ser Met Ile Lys 325 330
335 Lys Pro Arg Pro Thr Arg Ala Glu Gly Ser Asp Val Ala Asn Ala Val
340 345 350 Leu Asp Gly Ala Asp Cys Ile Met Leu Ser Gly Glu Thr Ala
Lys Gly 355 360 365 Asp Tyr Pro Leu Glu Ala Val Arg Met Gln His Leu
Ile Ala Arg Glu 370 375 380 Ala Glu Ala Ala Met Phe His Arg Lys Leu
Phe Glu Glu Leu Val Arg 385 390 395 400 Ala Ser Ser His Ser Thr Asp
Leu Met Glu Ala Met Ala Met Gly Ser 405 410 415 Val Glu Ala Ser Tyr
Lys Cys Leu Ala Ala Ala Leu Ile Val Leu Thr 420 425 430 Glu Ser Gly
Arg Ser Ala His Gln Val Ala Arg Tyr Arg Pro Arg Ala 435 440 445 Pro
Ile Ile Ala Val Thr Arg Asn Pro Gln Thr Ala Arg Gln Ala His 450 455
460 Leu Tyr Arg Gly Ile Phe Pro Val Leu Cys Lys Asp Pro Val Gln Glu
465 470 475 480 Ala Trp Ala Glu Asp Val Asp Leu Arg Val Asn Phe Ala
Met Asn Val 485 490 495 Gly Lys Ala Arg Gly Phe Phe Lys Lys Gly Asp
Val Val Ile Val Leu 500 505 510 Thr Gly Trp Arg Pro Gly Ser Gly Phe
Thr Asn Thr Met Arg Val Val 515 520 525 Pro Val Pro 530 7146PRTHomo
sapiens 7Met His Pro Leu Leu Asn Pro Leu Leu Leu Ala Leu Gly Leu
Met Ala 1 5 10 15 Leu Leu Leu Thr Thr Val Ile Ala Leu Thr Cys Leu
Gly Gly Phe Ala 20 25 30 Ser Pro Gly Pro Val Pro Pro Ser Thr Ala
Leu Arg Glu Leu Ile Glu 35 40 45 Glu Leu Val Asn Ile Thr Gln Asn
Gln Lys Ala Pro Leu Cys Asn Gly 50 55 60 Ser Met Val Trp Ser Ile
Asn Leu Thr Ala Gly Met Tyr Cys Ala Ala 65 70 75 80 Leu Glu Ser Leu
Ile Asn Val Ser Gly Cys Ser Ala Ile Glu Lys Thr 85 90 95 Gln Arg
Met Leu Ser Gly Phe Cys Pro His Lys Val Ser Ala Gly Gln 100 105 110
Phe Ser Ser Leu His Val Arg Asp Thr Lys Ile Glu Val Ala Gln Phe 115
120 125 Val Lys Asp Leu Leu Leu His Leu Lys Lys Leu Phe Arg Glu Gly
Gln 130 135 140 Phe Asn 145 8350PRTHomo sapiens 8Met Gln Arg Leu
Gly Ala Thr Leu Leu Cys Leu Leu Leu Ala Ala Ala 1 5 10 15 Val Pro
Thr Ala Pro Ala Pro Ala Pro Thr Ala Thr Ser Ala Pro Val 20 25 30
Lys Pro Gly Pro Ala Leu Ser Tyr Pro Gln Glu Glu Ala Thr Leu Asn 35
40 45 Glu Met Phe Arg Glu Val Glu Glu Leu Met Glu Asp Thr Gln His
Lys 50 55 60 Leu Arg Ser Ala Val Glu Glu Met Glu Ala Glu Glu Ala
Ala Ala Lys 65 70 75 80 Ala Ser Ser Glu Val Asn Leu Ala Asn Leu Pro
Pro Ser Tyr His Asn 85 90 95 Glu Thr Asn Thr Asp Thr Lys Val Gly
Asn Asn Thr Ile His Val His 100 105 110 Arg Glu Ile His Lys Ile Thr
Asn Asn Gln Thr Gly Gln Met Val Phe 115 120 125 Ser Glu Thr Val Ile
Thr Ser Val Gly Asp Glu Glu Gly Arg Arg Ser 130 135 140 His Glu Cys
Ile Ile Asp Glu Asp Cys Gly Pro Ser Met Tyr Cys Gln 145 150 155 160
Phe Ala Ser Phe Gln Tyr Thr Cys Gln Pro Cys Arg Gly Gln Arg Met 165
170 175 Leu Cys Thr Arg Asp Ser Glu Cys Cys Gly Asp Gln Leu Cys Val
Trp 180 185 190 Gly His Cys Thr Lys Met Ala Thr Arg Gly Ser Asn Gly
Thr Ile Cys 195 200 205 Asp Asn Gln Arg Asp Cys Gln Pro Gly Leu Cys
Cys Ala Phe Gln Arg 210 215 220 Gly Leu Leu Phe Pro Val Cys Thr Pro
Leu Pro Val Glu Gly Glu Leu 225 230 235 240 Cys His Asp Pro Ala Ser
Arg Leu Leu Asp Leu Ile Thr Trp Glu Leu 245 250 255 Glu Pro Asp Gly
Ala Leu Asp Arg Cys Pro Cys Ala Ser Gly Leu Leu 260 265 270 Cys Gln
Pro His Ser His Ser Leu Val Tyr Val Cys Lys Pro Thr Phe 275 280 285
Val Gly Ser Arg Asp Gln Asp Gly Glu Ile Leu Leu Pro Arg Glu Val 290
295 300 Pro Asp Glu Tyr Glu Val Gly Ser Phe Met Glu Glu Val Arg Gln
Glu 305 310 315 320 Leu Glu Asp Leu Glu Arg Ser Leu Thr Glu Glu Met
Ala Leu Arg Glu 325 330 335 Pro Ala Ala Ala Ala Ala Ala Leu Leu Gly
Gly Glu Glu Ile 340 345 350 9350PRTHomo sapiens 9Met Gln Arg Leu
Gly Ala Thr Leu Leu Cys Leu Leu Leu Ala Ala Ala 1 5 10 15 Val Pro
Thr Ala Pro Ala Pro Ala Pro Thr Ala Thr Ser Ala Pro Val 20 25 30
Lys Pro Gly Pro Ala Leu Ser Tyr Pro Gln Glu Glu Ala Thr Leu Asn 35
40 45 Glu Met Phe Arg Glu Val Glu Glu Leu Met Glu Asp Thr Gln His
Lys 50 55 60 Leu Arg Ser Ala Val Glu Glu Met Glu Ala Glu Glu Ala
Ala Ala Lys 65 70 75 80 Ala Ser Ser Glu Val Asn Leu Ala Asn Leu Pro
Pro Ser Tyr His Asn 85 90 95 Glu Thr Asn Thr Asp Thr Lys Val Gly
Asn Asn Thr Ile His Val His 100 105 110 Arg Glu Ile His Lys Ile Thr
Asn Asn Gln Thr Gly Gln Met Val Phe 115 120 125 Ser Glu Thr Val Ile
Thr Ser Val Gly Asp Glu Glu Gly Arg Arg Ser 130 135 140 His Glu Cys
Ile Ile Asp Glu Asp Cys Gly Pro Ser Met Tyr Cys Gln 145 150 155 160
Phe Ala Ser Phe Gln Tyr Thr Cys Gln Pro Cys Arg Gly Gln Arg Met 165
170 175 Leu Cys Thr Arg Asp Ser Glu Cys Cys Gly Asp Gln Leu Cys Val
Trp 180 185 190 Gly His Cys Thr Lys Met Ala Thr Arg Gly Ser Asn Gly
Thr Ile Cys 195 200 205 Asp Asn Gln Arg Asp Cys Gln Pro Gly Leu Cys
Cys Ala Phe Gln Arg 210 215 220 Gly Leu Leu Phe Pro Val Cys Thr Pro
Leu Pro Val Glu Gly Glu Leu 225 230 235 240 Cys His Asp Pro Ala Ser
Arg Leu Leu Asp Leu Ile Thr Trp Glu Leu 245 250 255 Glu Pro Asp Gly
Ala Leu Asp Arg Cys Pro Cys Ala Ser Gly Leu Leu 260 265 270 Cys Gln
Pro His Ser His Ser Leu Val Tyr Val Cys Lys Pro Thr Phe 275 280 285
Val Gly Ser Arg Asp Gln Asp Gly Glu Ile Leu Leu Pro Arg Glu Val 290
295 300 Pro Asp Glu Tyr Glu Val Gly Ser Phe Met Glu Glu Val Arg Gln
Glu 305 310 315 320 Leu Glu Asp Leu Glu Arg Ser Leu Thr Glu Glu Met
Ala Leu Arg Glu 325 330 335 Pro Ala Ala Ala Ala Ala Ala Leu Leu Gly
Gly Glu Glu Ile 340 345 350 10350PRTHomo sapiens 10Met Gln Arg Leu
Gly Ala Thr Leu Leu Cys Leu Leu Leu Ala Ala Ala 1 5 10 15 Val Pro
Thr Ala Pro Ala Pro Ala Pro Thr Ala Thr Ser Ala Pro Val 20 25 30
Lys Pro Gly Pro Ala Leu Ser Tyr Pro Gln Glu Glu Ala Thr Leu Asn 35
40 45 Glu Met Phe Arg Glu Val Glu Glu Leu Met Glu Asp Thr Gln His
Lys 50 55 60 Leu Arg Ser Ala Val Glu Glu Met Glu Ala Glu Glu Ala
Ala Ala Lys 65 70 75 80 Ala Ser Ser Glu Val Asn Leu Ala Asn Leu Pro
Pro Ser Tyr His Asn 85 90 95 Glu Thr Asn Thr Asp Thr Lys Val Gly
Asn Asn Thr Ile His Val His 100 105 110 Arg Glu Ile His Lys Ile Thr
Asn Asn Gln Thr Gly Gln Met Val Phe 115 120 125 Ser Glu Thr Val Ile
Thr Ser Val Gly Asp Glu Glu Gly Arg Arg Ser 130 135 140 His Glu Cys
Ile Ile Asp Glu Asp Cys Gly Pro Ser Met Tyr Cys Gln 145 150 155 160
Phe Ala Ser Phe Gln Tyr Thr Cys Gln Pro Cys Arg Gly Gln Arg Met 165
170 175 Leu Cys Thr Arg Asp Ser Glu Cys Cys Gly Asp Gln Leu Cys Val
Trp 180 185 190 Gly His Cys Thr Lys Met Ala Thr Arg Gly Ser Asn Gly
Thr Ile Cys 195 200 205 Asp Asn Gln Arg Asp Cys Gln Pro Gly Leu Cys
Cys Ala Phe Gln Arg 210 215 220 Gly Leu Leu Phe Pro Val Cys Thr Pro
Leu Pro Val Glu Gly Glu Leu 225 230 235 240 Cys His Asp Pro Ala Ser
Arg Leu Leu Asp Leu Ile Thr Trp Glu Leu 245 250 255 Glu Pro Asp Gly
Ala Leu Asp Arg Cys Pro Cys Ala Ser Gly Leu Leu 260 265 270 Cys Gln
Pro His Ser His Ser Leu Val Tyr Val Cys Lys Pro Thr Phe 275 280 285
Val Gly Ser Arg Asp Gln Asp Gly Glu Ile Leu Leu Pro Arg Glu Val 290
295 300 Pro Asp Glu Tyr Glu Val Gly Ser Phe Met Glu Glu Val Arg Gln
Glu 305 310 315 320 Leu Glu Asp Leu Glu Arg Ser Leu Thr Glu Glu Met
Ala Leu Arg Glu 325 330 335 Pro Ala Ala Ala Ala Ala Ala Leu Leu Gly
Gly Glu Glu Ile 340 345 350 11314PRTHomo sapiens 11Met Ala Pro Pro
Gln Val Leu Ala Phe Gly Leu Leu Leu Ala Ala Ala 1 5 10 15 Thr Ala
Thr Phe Ala Ala Ala Gln Glu Glu Cys Val Cys Glu Asn Tyr 20 25 30
Lys Leu Ala Val Asn Cys Phe Val Asn Asn Asn Arg Gln Cys Gln Cys 35
40 45 Thr Ser Val Gly Ala Gln Asn Thr Val Ile Cys Ser Lys Leu Ala
Ala 50 55 60 Lys Cys Leu Val Met Lys Ala Glu Met Asn Gly Ser Lys
Leu Gly Arg 65 70 75 80 Arg Ala Lys Pro Glu Gly Ala Leu Gln Asn Asn
Asp Gly Leu Tyr Asp 85 90 95 Pro Asp Cys Asp Glu Ser Gly Leu Phe
Lys Ala Lys Gln Cys Asn Gly 100 105 110 Thr Ser Met Cys Trp Cys Val
Asn Thr Ala Gly Val Arg Arg Thr Asp 115 120 125 Lys Asp Thr Glu Ile
Thr Cys Ser Glu Arg Val Arg Thr Tyr Trp Ile 130 135 140 Ile Ile Glu
Leu Lys His Lys Ala Arg Glu Lys Pro Tyr Asp Ser Lys 145 150 155 160
Ser Leu Arg Thr Ala Leu Gln Lys Glu Ile Thr Thr Arg Tyr Gln Leu 165
170 175 Asp Pro Lys Phe Ile Thr Ser Ile Leu Tyr Glu Asn Asn Val Ile
Thr 180 185 190 Ile Asp Leu Val Gln Asn Ser Ser Gln Lys Thr Gln Asn
Asp Val Asp 195 200 205 Ile Ala Asp Val Ala Tyr Tyr Phe Glu Lys Asp
Val Lys Gly Glu Ser 210 215 220 Leu Phe His Ser Lys Lys Met Asp Leu
Thr Val Asn Gly Glu Gln Leu 225 230 235 240 Asp Leu Asp Pro Gly Gln
Thr Leu Ile Tyr Tyr Val Asp Glu Lys Ala 245 250 255 Pro Glu Phe Ser
Met Gln Gly Leu Lys Ala Gly Val Ile Ala Val Ile 260 265 270 Val Val
Val Val Ile Ala Val Val Ala Gly Ile Val Val Leu Val Ile 275 280 285
Ser Arg Lys Lys Arg Met Ala Lys Tyr Glu Lys Ala Glu Ile Lys Glu 290
295 300 Met Gly Glu Met His Arg Glu Leu Asn Ala 305 310 1292PRTHomo
sapiens 12Met Lys Leu Cys Val Thr Val Leu Ser Leu Leu Met Leu Val
Ala Ala 1 5 10 15 Phe Cys Ser Pro Ala Leu Ser Ala Pro Met Gly Ser
Asp Pro Pro Thr 20 25 30 Ala Cys Cys Phe Ser Tyr Thr Ala Arg Lys
Leu Pro Arg Asn Phe Val 35 40 45 Val Asp Tyr Tyr Glu Thr Ser Ser
Leu Cys Ser Gln Pro Ala Val Val 50 55 60 Phe Gln Thr Lys Arg Ser
Lys Gln Val Cys Ala Asp Pro Ser Glu Ser 65 70 75 80 Trp Val Gln Glu
Tyr Val Tyr Asp Leu Glu Leu Asn 85 90 13390PRTHomo sapiens 13Met
Pro Pro Ser Gly Leu Arg Leu Leu Pro Leu Leu Leu Pro Leu Leu 1 5 10
15 Trp Leu Leu Val Leu Thr Pro Gly Arg Pro Ala Ala Gly Leu Ser Thr
20 25 30 Cys Lys Thr Ile Asp Met Glu Leu Val Lys Arg Lys Arg Ile
Glu Ala 35 40 45 Ile Arg Gly Gln Ile Leu Ser Lys Leu Arg Leu Ala
Ser Pro Pro Ser 50 55 60 Gln Gly Glu Val Pro Pro Gly Pro Leu Pro
Glu Ala Val Leu Ala Leu 65 70 75 80 Tyr Asn Ser Thr Arg Asp Arg Val
Ala Gly Glu Ser Ala Glu Pro Glu 85 90 95 Pro Glu Pro Glu Ala Asp
Tyr Tyr Ala Lys Glu Val Thr Arg Val Leu 100 105 110 Met Val Glu Thr
His Asn Glu Ile Tyr Asp Lys Phe Lys Gln Ser Thr 115 120 125 His Ser
Ile Tyr Met Phe Phe Asn Thr Ser Glu Leu Arg Glu Ala Val 130 135 140
Pro Glu Pro Val Leu Leu Ser Arg Ala Glu Leu Arg Leu Leu Arg Leu 145
150 155 160 Lys Leu Lys Val Glu Gln His Val Glu Leu Tyr Gln Lys Tyr
Ser Asn 165 170 175 Asn Ser Trp Arg Tyr Leu Ser Asn Arg Leu Leu Ala
Pro Ser Asp Ser 180 185 190 Pro Glu Trp Leu Ser Phe Asp Val Thr Gly
Val Val Arg Gln Trp Leu 195 200 205 Ser Arg Gly Gly Glu Ile Glu Gly
Phe Arg Leu Ser Ala His Cys Ser 210 215 220 Cys Asp Ser Arg Asp Asn
Thr Leu Gln Val Asp Ile Asn Gly Phe Thr 225 230 235 240 Thr Gly Arg
Arg Gly Asp Leu Ala Thr Ile His Gly Met Asn Arg Pro 245 250 255 Phe
Leu Leu Leu Met Ala Thr Pro Leu Glu Arg Ala Gln His Leu Gln 260 265
270 Ser Ser Arg His Arg Arg Ala Leu Asp Thr Asn Tyr Cys Phe Ser Ser
275 280 285 Thr Glu Lys Asn Cys Cys Val Arg Gln Leu Tyr Ile Asp Phe
Arg Lys 290 295 300 Asp Leu Gly Trp Lys Trp Ile His Glu Pro Lys Gly
Tyr His Ala Asn 305 310 315 320 Phe Cys Leu Gly Pro Cys Pro Tyr Ile
Trp Ser Leu Asp Thr Gln Tyr 325 330 335 Ser Lys Val Leu Ala Leu Tyr
Asn Gln His Asn Pro Gly Ala Ser Ala 340 345 350 Ala Pro Cys Cys Val
Pro Gln Ala Leu Glu Pro Leu Pro Ile Val Tyr 355 360 365 Tyr Val Gly
Arg Lys Pro Lys
Val Glu Gln Leu Ser Asn Met Ile Val 370 375 380 Arg Ser Cys Lys Cys
Ser 385 390 14207PRTHomo sapiens 14Met Ala Pro Phe Glu Pro Leu Ala
Ser Gly Ile Leu Leu Leu Leu Trp 1 5 10 15 Leu Ile Ala Pro Ser Arg
Ala Cys Thr Cys Val Pro Pro His Pro Gln 20 25 30 Thr Ala Phe Cys
Asn Ser Asp Leu Val Ile Arg Ala Lys Phe Val Gly 35 40 45 Thr Pro
Glu Val Asn Gln Thr Thr Leu Tyr Gln Arg Tyr Glu Ile Lys 50 55 60
Met Thr Lys Met Tyr Lys Gly Phe Gln Ala Leu Gly Asp Ala Ala Asp 65
70 75 80 Ile Arg Phe Val Tyr Thr Pro Ala Met Glu Ser Val Cys Gly
Tyr Phe 85 90 95 His Arg Ser His Asn Arg Ser Glu Glu Phe Leu Ile
Ala Gly Lys Leu 100 105 110 Gln Asp Gly Leu Leu His Ile Thr Thr Cys
Ser Phe Val Ala Pro Trp 115 120 125 Asn Ser Leu Ser Leu Ala Gln Arg
Arg Gly Phe Thr Lys Thr Tyr Thr 130 135 140 Val Gly Cys Glu Glu Cys
Thr Val Phe Pro Cys Leu Ser Ile Pro Cys 145 150 155 160 Lys Leu Gln
Ser Gly Thr His Cys Leu Trp Thr Asp Gln Leu Leu Gln 165 170 175 Gly
Ser Glu Lys Gly Phe Gln Ser Arg His Leu Ala Cys Leu Pro Arg 180 185
190 Glu Pro Gly Leu Cys Thr Trp Gln Ser Leu Arg Ser Gln Ile Ala 195
200 205
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