U.S. patent application number 15/909829 was filed with the patent office on 2018-10-25 for diagnosis and prognosis of breast cancer patients.
This patent application is currently assigned to The Netherlands Cancer Institute. The applicant listed for this patent is Merck Sharp & Dohme Corp., The Netherlands Cancer Institute. Invention is credited to Rene Bernards, HongYue Dai, A.A. M. Hart, Yudong He, Peter S. Linsley, Mao Mao, Christopher J. Roberts, Marc J. Van de Vijver, Laura Johanna Van't Veer.
Application Number | 20180305768 15/909829 |
Document ID | / |
Family ID | 26970946 |
Filed Date | 2018-10-25 |
United States Patent
Application |
20180305768 |
Kind Code |
A1 |
Dai; HongYue ; et
al. |
October 25, 2018 |
DIAGNOSIS AND PROGNOSIS OF BREAST CANCER PATIENTS
Abstract
The present invention relates to genetic markers whose
expression is correlated with breast cancer. Specifically, the
invention provides sets of markers whose expression patterns can be
used to differentiate clinical conditions associated with breast
cancer, such as the presence or absence of the estrogen receptor
ESR1, and BRCA1 and sporadic tumors, and to provide information on
the likelihood of tumor distant metastases within five years of
initial diagnosis. The invention relates to methods of using these
markers to distinguish these conditions. The invention also relates
to kits containing ready-to-use microarrays and computer software
for data analysis using the statistical methods disclosed
herein.
Inventors: |
Dai; HongYue; (Bothell,
WA) ; He; Yudong; (Kirkland, WA) ; Linsley;
Peter S.; (Seattle, WA) ; Mao; Mao; (Redmond,
WA) ; Roberts; Christopher J.; (Seattle, WA) ;
Van't Veer; Laura Johanna; (Amsterdam, NL) ; Van de
Vijver; Marc J.; (Amsterdam, NL) ; Bernards;
Rene; (Abcoude, NL) ; Hart; A.A. M.;
(Castricum, NL) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
The Netherlands Cancer Institute
Merck Sharp & Dohme Corp. |
Amsterdam
Rahway |
NJ |
NL
US |
|
|
Assignee: |
The Netherlands Cancer
Institute
Amsterdam
NJ
Merck Sharp & Dohme Corp.
Rahway
|
Family ID: |
26970946 |
Appl. No.: |
15/909829 |
Filed: |
March 1, 2018 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
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13656568 |
Oct 19, 2012 |
9909185 |
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15909829 |
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12953314 |
Nov 23, 2010 |
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13656568 |
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12214878 |
Jun 23, 2008 |
7863001 |
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12953314 |
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10172118 |
Jun 14, 2002 |
7514209 |
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12214878 |
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60298918 |
Jun 18, 2001 |
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60380710 |
May 14, 2002 |
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Current U.S.
Class: |
1/1 |
Current CPC
Class: |
Y02A 90/22 20180101;
G01N 33/57415 20130101; G16B 40/00 20190201; Y02A 90/26 20180101;
C12Q 2600/158 20130101; Y02A 90/10 20180101; G16B 25/00 20190201;
C12Q 1/6886 20130101; G06F 19/00 20130101 |
International
Class: |
C12Q 1/6886 20060101
C12Q001/6886; G06F 19/00 20060101 G06F019/00 |
Claims
1-60. (canceled)
61. A method of classifying and treating an individual having
breast cancer, comprising: obtaining a sample comprising tumor
cells from the individual; extracting polynucleotide molecules from
said sample; synthesizing complementary RNA (cRNA) or cDNA nucleic
acid molecules from the extracted polynucleotide molecules;
exposing the cRNA or cDNA nucleic acid molecules from a breast
cancer tumor of the individual to probes in the range of 20-200
nucleotides that specifically hybridize to a nucleic acid molecule
having SEQ ID NO: 1477; comparing the hybridization of the probe to
the nucleic acid molecule having SEQ ID NO:1477 with the
hybridization of the probe to the nucleic acid molecule having SEQ
ID NO:1477 in a standard or control, wherein the standard or
control comprises complementary RNA (cRNA) or cDNA nucleic acid
molecules from a breast sample from an individual afflicted with a
good prognosis breast cancer, wherein an increase in probe
hybridization, when compared with the standard or control, is
indicative of a poor prognosis; classifying the individual as
having a poor prognosis; and administering chemotherapy to an
individual who is classified as having a poor prognosis.
62. The method according to claim 61, wherein the cRNA or cDNA
nucleic acid molecules are further exposed to probes in the range
of 20-200 nucleotides that specifically hybridize to a nucleic acid
molecule selected from the group consisting of SEQ ID NO:1, SEQ ID
NO:37, SEQ ID NO:55, SEQ ID NO:58, SEQ ID NO:62, SEQ ID NO:75, SEQ
ID NO:88, SEQ ID NO:1205, SEQ ID NO:121, SEQ ID NO:124, SEQ ID
NO:137, SEQ ID NO:169, SEQ ID NO:173, SEQ ID NO:177, SEQ ID NO:183,
SEQ ID NO:189, SEQ ID NO:196, SEQ ID NO:219, SEQ ID NO:257, SEQ ID
NO:259, SEQ ID NO:270, SEQ ID NO:271, SEQ ID NO:272, SEQ ID NO:306,
SEQ ID NO:307, SEQ ID NO:315, SEQ ID NO:326, SEQ ID NO:327, SEQ ID
NO:336, SEQ ID NO:353, SEQ ID NO:357, SEQ ID NO:390, SEQ ID NO:394,
SEQ ID NO:416, SEQ ID NO:430, SEQ ID NO:436, SEQ ID NO:442, SEQ ID
NO:453, SEQ ID NO:462, SEQ ID NO:463, SEQ ID NO:469, SEQ ID NO:487,
SEQ ID NO:491, SEQ ID NO:503, SEQ ID NO:527, SEQ ID NO:530, SEQ ID
NO:550, SEQ ID NO:562, SEQ ID NO:566, SEQ ID NO:574, SEQ ID NO:588,
SEQ ID NO:589, SEQ ID NO:597, SEQ ID NO:645, SEQ ID NO:673, SEQ ID
NO:676, SEQ ID NO:691, SEQ ID NO:711, SEQ ID NO:721, SEQ ID NO:764,
SEQ ID NO:787, SEQ ID NO:822, SEQ ID NO:823, SEQ ID NO:835, SEQ ID
NO:838, SEQ ID NO:881, SEQ ID NO:891, SEQ ID NO:893, SEQ ID NO:896,
SEQ ID NO:906, SEQ ID NO:929, SEQ ID NO:930, SEQ ID NO:938, SEQ ID
NO:941, SEQ ID NO:951, SEQ ID NO:960, SEQ ID NO:962, SEQ ID NO:963,
SEQ ID NO:964, SEQ ID NO:977, SEQ ID NO:985, SEQ ID NO:995, SEQ ID
NO:1022, SEQ ID NO:1026, SEQ ID NO:1043, SEQ ID NO:1046, SEQ ID
NO:1051, SEQ ID NO:1064, SEQ ID NO:1075, SEQ ID NO:1076, SEQ ID
NO:1087, SEQ ID NO:1102, SEQ ID NO:1108, SEQ ID NO:1127, SEQ ID
NO:1143, SEQ ID NO:1157, SEQ ID NO:1173, SEQ ID NO:1215, SEQ ID
NO:1240, SEQ ID NO:1241, SEQ ID NO:1245, SEQ ID NO:1246, SEQ ID
NO:1254, SEQ ID NO 1260, SEQ ID NO:1263, SEQ ID NO:1273, SEQ ID
NO:1306, SEQ ID NO:1315, SEQ ID NO:1341, SEQ ID NO:1349, SEQ ID
NO:1362, SEQ ID NO:1390, SEQ ID NO:1392, SEQ ID NO:1397, SEQ ID
NO:1413, SEQ ID NO:1425, SEQ ID NO:1427, SEQ ID NO:1439, SEQ ID
NO:1449, SEQ ID NO:1451, SEQ ID NO:1480, SEQ ID NO:1527, SEQ ID
NO:1528, SEQ ID NO:1535, SEQ ID NO:1545, SEQ ID NO:1548, SEQ ID
NO:1554, SEQ ID NO:1559, SEQ ID NO:1560, SEQ ID NO:1562, SEQ ID
NO:1587, SEQ ID NO:1636, SEQ ID NO:1638, SEQ ID NO:1645, SEQ ID
NO:1655, SEQ ID NO:1656, SEQ ID NO:1708, SEQ ID NO:1725, SEQ ID
NO:1739, SEQ ID NO:1743, SEQ ID NO:1745, SEQ ID NO:1748, SEQ ID
NO:1766, SEQ ID NO:1774, SEQ ID NO:1782, SEQ ID NO:1783, SEQ ID
NO:1786, SEQ ID NO:1787, SEQ ID NO:1809, SEQ ID NO:1825, SEQ ID
NO:1830, SEQ ID NO:1835, SEQ ID NO:1838, SEQ ID NO:1842, SEQ ID
NO:1844, SEQ ID NO:1868, SEQ ID NO:1878, SEQ ID NO:1881, SEQ ID
NO:1891, SEQ ID NO:1896, SEQ ID NO:1903, SEQ ID NO:1927, SEQ ID
NO:1954, SEQ ID NO:1979, SEQ ID NO:1989, SEQ ID NO:1991, SEQ ID
NO:2017, SEQ ID NO:2022, SEQ ID NO:2037, SEQ ID NO:2052, SEQ ID
NO:2068, SEQ ID NO:2072, SEQ ID NO:2082, SEQ ID NO:2087, SEQ ID
NO:2090, SEQ ID NO:2092, SEQ ID NO:2098, SEQ ID NO:2108, SEQ ID
NO:2120, SEQ ID NO:2155, SEQ ID NO:2156, SEQ ID NO:2169, SEQ ID
NO:2180, SEQ ID NO:2185, SEQ ID NO:2206, SEQ ID NO:2216, SEQ ID
NO:2223, SEQ ID NO:2248, SEQ ID NO:2260, SEQ ID NO:2266, SEQ ID
NO:2276, SEQ ID NO:2291, SEQ ID NO:2311, SEQ ID NO:2315, SEQ ID
NO:2320, SEQ ID NO:2330, SEQ ID NO:2344, SEQ ID NO:2351, SEQ ID
NO:2358, SEQ ID NO:2359, SEQ ID NO:2369, SEQ ID NO:2372, SEQ ID
NO:2384, SEQ ID NO:2400, SEQ ID NO:2434, SEQ ID NO:2445, SEQ ID
NO:2453, SEQ ID NO:2463, SEQ ID NO:2481, SEQ ID NO:2482, SEQ ID
NO:2486, SEQ ID NO:2494, SEQ ID NO:2525, SEQ ID NO:2529, SEQ ID
NO:2538, SEQ ID NO:2542, SEQ ID NO:2586, SEQ ID NO:2590, SEQ ID
NO:2591, SEQ ID NO:2600, SEQ ID NO:2607, SEQ ID NO:2608, SEQ ID
NO:2630, SEQ ID NO:2655, SEQ ID NO:2663, SEQ ID NO:2668, SEQ ID
NO:2676, SEQ ID NO:2684, SEQ ID NO:2686, and SEQ ID NO:2690.
63. The method according to claim 62, comprising exposing nucleic
acids obtained from the individual to at least five different
nucleic acid molecules selected from said group.
64. The method according to claim 62, comprising exposing nucleic
acids obtained from the individual to at least fifty (50) different
nucleic acid molecules selected from said group.
65. The method according to claim 62, comprising exposing nucleic
acids obtained from the individual to at least seventy (70)
different nucleic acid molecules selected from said group.
66. The method according to claim 62, comprising exposing nucleic
acids obtained from the individual to at least one hundred (100)
different nucleic acid molecules selected from said group.
67. The method according to claim 62, comprising exposing nucleic
acids obtained from the individual to at least two hundred (200)
different nucleic acid molecules selected from said group.
68. The method according to claim 61, wherein the probe is about 60
nucleotides.
69. The method according to claim 62, wherein the probes are
positionally-addressable probes that are immobilized to a solid
support or surface.
70. A method of treatment of an individual having breast cancer,
comprising: obtaining a sample comprising tumor cells from the
individual; extracting polynucleotide molecules from said sample;
synthesizing complementary RNA (cRNA) or cDNA nucleic acid
molecules from the extracted polynucleotide molecules; exposing the
cRNA or cDNA nucleic acid molecules to probes in the range of
20-200 nucleotides that specifically hybridize to a nucleic acid
molecule having SEQ ID NO:1477; calculating a first measure of
similarity between an expression profile and both a good prognosis
template and a poor prognosis template; wherein the expression
profile comprises the mRNA expression level of at least one gene in
breast cancer tumor from the individual; wherein the at least one
gene encodes a nucleotide sequence having SEQ ID NO:1477; wherein
the good prognosis template comprises the average mRNA expression
level of the at least one gene in breast cancer tumor from a
plurality of individuals having breast cancer and having a good
prognosis; wherein the poor prognosis template comprises the
average mRNA expression level of the at least one gene in breast
cancer tumor from a plurality of individuals having breast cancer
and having a poor prognosis; wherein the expression profile is
determined with probes in the range of 20-200 nucleotides that
specifically hybridize to a cRNA or cDNA nucleic acid molecule
having SEQ ID NO:1477, classifying the individual as having a poor
prognosis if the expression profile has a higher similarity to the
poor prognosis template than to the good prognosis template; and
administering chemotherapy to an individual who is classified as
having a poor prognosis.
71. The method according to claim 70, wherein the expression
profile further comprises at least one gene encoding a nucleotide
sequence selected from the group consisting of SEQ ID NO:1, SEQ ID
NO:37, SEQ ID NO:55, SEQ ID NO:58, SEQ ID NO:62, SEQ ID NO:75, SEQ
ID NO:88, SEQ ID NO:1205, SEQ ID NO:121, SEQ ID NO:124, SEQ ID
NO:137, SEQ ID NO:169, SEQ ID NO:173, SEQ ID NO:177, SEQ ID NO:183,
SEQ ID NO:189, SEQ ID NO:196, SEQ ID NO:219, SEQ ID NO:257, SEQ ID
NO:259, SEQ ID NO:270, SEQ ID NO:271, SEQ ID NO:272, SEQ ID NO:306,
SEQ ID NO:307, SEQ ID NO:315, SEQ ID NO:326, SEQ ID NO:327, SEQ ID
NO:336, SEQ ID NO:353, SEQ ID NO:357, SEQ ID NO:390, SEQ ID NO:394,
SEQ ID NO:416, SEQ ID NO:430, SEQ ID NO:436, SEQ ID NO:442, SEQ ID
NO:453, SEQ ID NO:462, SEQ ID NO:463, SEQ ID NO:469, SEQ ID NO:487,
SEQ ID NO:491, SEQ ID NO:503, SEQ ID NO:527, SEQ ID NO:530, SEQ ID
NO:550, SEQ ID NO:562, SEQ ID NO:566, SEQ ID NO:574, SEQ ID NO:588,
SEQ ID NO:589, SEQ ID NO:597, SEQ ID NO:645, SEQ ID NO:673, SEQ ID
NO:676, SEQ ID NO:691, SEQ ID NO:711, SEQ ID NO:721, SEQ ID NO:764,
SEQ ID NO:787, SEQ ID NO:822, SEQ ID NO:823, SEQ ID NO:835, SEQ ID
NO:838, SEQ ID NO:881, SEQ ID NO:891, SEQ ID NO:893, SEQ ID NO:896,
SEQ ID NO:906, SEQ ID NO:929, SEQ ID NO:930, SEQ ID NO:938, SEQ ID
NO:941, SEQ ID NO:951, SEQ ID NO:960, SEQ ID NO:962, SEQ ID NO:963,
SEQ ID NO:964, SEQ ID NO:977, SEQ ID NO:985, SEQ ID NO:995, SEQ ID
NO:1022, SEQ ID NO:1026, SEQ ID NO:1043, SEQ ID NO:1046, SEQ ID
NO:1051, SEQ ID NO:1064, SEQ ID NO:1075, SEQ ID NO:1076, SEQ ID
NO:1087, SEQ ID NO:1102, SEQ ID NO:1108, SEQ ID NO:1127, SEQ ID
NO:1143, SEQ ID NO:1157, SEQ ID NO:1173, SEQ ID NO:1215, SEQ ID
NO:1240, SEQ ID NO:1241, SEQ ID NO:1245, SEQ ID NO:1246, SEQ ID
NO:1254, SEQ ID NO:1260, SEQ ID NO:1263, SEQ ID NO:1273, SEQ ID
NO:1306, SEQ ID NO:1315, SEQ ID NO:1341, SEQ ID NO:1349, SEQ ID
NO:1362, SEQ ID NO:1390, SEQ ID NO:1392, SEQ ID NO:1397, SEQ ID
NO:1413, SEQ ID NO:1425, SEQ ID NO:1427, SEQ ID NO:1439, SEQ ID
NO:1449, SEQ ID NO:1451, SEQ ID NO:1480, SEQ ID NO:1527, SEQ ID
NO:1528, SEQ ID NO:1535, SEQ ID NO:1545, SEQ ID NO:1548, SEQ ID
NO:1554, SEQ ID NO:1559, SEQ ID NO:1560, SEQ ID NO:1562, SEQ ID
NO:1587, SEQ ID NO:1636, SEQ ID NO:1638, SEQ ID NO:1645, SEQ ID
NO:1655, SEQ ID NO:1656, SEQ ID NO:1708, SEQ ID NO:1725, SEQ ID
NO:1739, SEQ ID NO:1743, SEQ ID NO:1745, SEQ ID NO:1748, SEQ ID
NO:1766, SEQ ID NO:1774, SEQ ID NO:1782, SEQ ID NO:1783, SEQ ID
NO:1786, SEQ ID NO:1787, SEQ ID NO:1809, SEQ ID NO:1825, SEQ ID
NO:1830, SEQ ID NO:1835, SEQ ID NO:1838, SEQ ID NO:1842, SEQ ID
NO:1844, SEQ ID NO:1868, SEQ ID NO:1878, SEQ ID NO:1881, SEQ ID
NO:1891, SEQ ID NO:1896, SEQ ID NO:1903, SEQ ID NO:1927, SEQ ID
NO:1954, SEQ ID NO:1979, SEQ ID NO:1989, SEQ ID NO:1991, SEQ ID
NO:2017, SEQ ID NO:2022, SEQ ID NO:2037, SEQ ID NO:2052, SEQ ID
NO:2068, SEQ ID NO:2072, SEQ ID NO:2082, SEQ ID NO:2087, SEQ ID
NO:2090, SEQ ID NO:2092, SEQ ID NO:2098, SEQ ID NO:2108, SEQ ID
NO:2120, SEQ ID NO:2155, SEQ ID NO:2156, SEQ ID NO:2169, SEQ ID
NO:2180, SEQ ID NO:2185, SEQ ID NO:2206, SEQ ID NO:2216, SEQ ID
NO:2223, SEQ ID NO:2248, SEQ ID NO:2260, SEQ ID NO:2266, SEQ ID
NO:2276, SEQ ID NO:2291, SEQ ID NO:2311, SEQ ID NO:2315, SEQ ID
NO:2320, SEQ ID NO:2330, SEQ ID NO:2344, SEQ ID NO:2351, SEQ ID
NO:2358, SEQ ID NO:2359, SEQ ID NO:2369, SEQ ID NO:2372, SEQ ID
NO:2384, SEQ ID NO:2400, SEQ ID NO:2434, SEQ ID NO:2445, SEQ ID
NO:2453, SEQ ID NO:2463, SEQ ID NO:2481, SEQ ID NO:2482, SEQ ID
NO:2486, SEQ ID NO:2494, SEQ ID NO:2525, SEQ ID NO:2529, SEQ ID
NO:2538, SEQ ID NO:2542, SEQ ID NO:2586, SEQ ID NO:2590, SEQ ID
NO:2591, SEQ ID NO:2600, SEQ ID NO:2607, SEQ ID NO:2608, SEQ ID
NO:2630, SEQ ID NO:2655, SEQ ID NO:2663, SEQ ID NO:2668, SEQ ID
NO:2676, SEQ ID NO:2684, SEQ ID NO:2686, and SEQ ID NO:2690.
72. The method according to claim 71, wherein the at least one gene
comprises at least five genes encoding a nucleotide sequence
selected from the group.
73. The method according to claim 71, wherein the at least one gene
comprises at least seventy (70) genes encoding a nucleotide
sequence selected from the group.
74. The method according to claim 71, wherein the at least one gene
comprises at least one hundred (100) genes encoding a nucleotide
sequence selected from the group.
75. The method according to claim 71, wherein the at least one gene
comprises at least two hundred (200) genes encoding a nucleotide
sequence selected from the group.
76. The method according to claim 70, wherein the probe is about 60
nucleotides.
77. The method according to claim 71, wherein the probes are
positionally-addressable probes that are immobilized to a solid
support or surface.
Description
[0001] This application claims benefit of U.S. Provisional
Application No. 60/298,918, filed Jun. 18, 2001, and U.S.
Provisional Application No. 60/380,710, filed on May 14, 2002, each
of which is incorporated by reference herein in its entirety.
[0002] This application includes a Sequence Listing submitted on
compact disc, recorded on two compact discs, including one
duplicate, containing Filename 9301175999.txt, of size 6,766,592
bytes, created Jun. 13, 2002. The sequence listing on the compact
discs is incorporated by reference herein in its entirety.
1. FIELD OF THE INVENTION
[0003] The present invention relates to the identification of
marker genes useful in the diagnosis and prognosis of breast
cancer. More particularly, the invention relates to the
identification of a set of marker genes associated with breast
cancer, a set of marker genes differentially expressed in estrogen
receptor (+) versus estrogen receptor (-) tumors, a set of marker
genes differentially expressed in BRCA1 versus sporadic tumors, and
a set of marker genes differentially expressed in sporadic tumors
from patients with good clinical prognosis (i.e., metastasis- or
disease-free >5 years) versus patients with poor clinical
prognosis (i.e., metastasis- or disease-free <5 years). For each
of the marker sets above, the invention further relates to methods
of distinguishing the breast cancer-related conditions. The
invention further provides methods for determining the course of
treatment of a patient with breast cancer.
2. BACKGROUND OF THE INVENTION
[0004] The increased number of cancer cases reported in the United
States, and, indeed, around the world, is a major concern.
Currently there are only a handful of treatments available for
specific types of cancer, and these provide no guarantee of
success. In order to be most effective, these treatments require
not only an early detection of the malignancy, but a reliable
assessment of the severity of the malignancy.
[0005] The incidence of breast cancer, a leading cause of death in
women, has been gradually increasing in the United States over the
last thirty years. Its cumulative risk is relatively high; 1 in 8
women are expected to develop some type of breast cancer by age 85
in the United States. In fact, breast cancer is the most common
cancer in women and the second most common cause of cancer death in
the United States. In 1997, it was estimated that 181,000 new cases
were reported in the U.S., and that 44,000 people would die of
breast cancer (Parker et al., CA Cancer J. Clin. 47:5-27 (1997);
Chu et al., J. Nat. Cancer Inst. 88:1571-1579 (1996)). While
mechanism of tumorigenesis for most breast carcinomas is largely
unknown, there are genetic factors that can predispose some women
to developing breast cancer (Miki et al., Science,
266:66-71(1994)). The discovery and characterization of BRCA1 and
BRCA2 has recently expanded our knowledge of genetic factors which
can contribute to familial breast cancer. Germ-line mutations
within these two loci are associated with a 50 to 85% lifetime risk
of breast and/or ovarian cancer (Casey, Curr. Opin. Oncol 9:88-93
(1997); Marcus et al., Cancer 77:697-709 (1996)). Only about 5% to
10% of breast cancers are associated with breast cancer
susceptibility genes, BRCA1 and BRCA2. The cumulative lifetime risk
of breast cancer for women who carry the mutant BRCA1 is predicted
to be approximately 92%, while the cumulative lifetime risk for the
non-carrier majority is estimated to be approximately 10%. BRCA1 is
a tumor suppressor gene that is involved in DNA repair and cell
cycle control, which are both important for the maintenance of
genomic stability. More than 90% of all mutations reported so far
result in a premature truncation of the protein product with
abnormal or abolished function. The histology of breast cancer in
BRCA1 mutation carriers differs from that in sporadic cases, but
mutation analysis is the only way to find the carrier. Like BRCA1,
BRCA2 is involved in the development of breast cancer, and like
BRCA1 plays a role in DNA repair. However, unlike BRCA1, it is not
involved in ovarian cancer.
[0006] Other genes have been linked to breast cancer, for example
c-erb-2 (HER2) and p53 (Beenken et al., Ann. Surg. 233(5):630-638
(2001). Overexpression of c-erb-2 (HER2) and p53 have been
correlated with poor prognosis (Rudolph et al., Hum. Pathol.
32(3):311-319 (2001), as has been aberrant expression products of
mdm2 (Lukas et al, Cancer Res. 61(7):3212-3219 (2001) and cyclin1
and p27 (Porter & Roberts, International Publication
WO98/33450, published Aug. 6, 1998). However, no other clinically
useful markers consistently associated with breast cancer have been
identified.
[0007] Sporadic tumors, those not currently associated with a known
germline mutation, constitute the majority of breast cancers. It is
also likely that other, non-genetic factors also have a significant
effect on the etiology of the disease. Regardless of the cancer's
origin, breast cancer morbidity and mortality increases
significantly if it is not detected early in its progression. Thus,
considerable effort has focused on the early detection of cellular
transformation and tumor formation in breast tissue.
[0008] A marker-based approach to tumor identification and
characterization promises improved diagnostic and prognostic
reliability. Typically, the diagnosis of breast cancer requires
histopathological proof of the presence of the tumor. In addition
to diagnosis, histopathological examinations also provide
information about prognosis and selection of treatment regimens.
Prognosis may also be established based upon clinical parameters
such as tumor size, tumor grade, the age of the patient, and lymph
node metastasis.
[0009] Diagnosis and/or prognosis may be determined to varying
degrees of effectiveness by direct examination of the outside of
the breast, or through mammography or other X-ray imaging methods
(Jatoi, Am. J. Surg. 177:518-524 (1999)). The latter approach is
not without considerable cost, however. Every time a mammogram is
taken, the patient incurs a small risk of having a breast tumor
induced by the ionizing properties of the radiation used during the
test. In addition, the process is expensive and the subjective
interpretations of a technician can lead to imprecision. For
example, one study showed major clinical disagreements for about
one-third of a set of mammograms that were interpreted individually
by a surveyed group of radiologists. Moreover, many women find that
undergoing a mammogram is a painful experience. Accordingly, the
National Cancer Institute has not recommended mammograms for women
under fifty years of age, since this group is not as likely to
develop breast cancers as are older women. It is compelling to
note, however, that while only about 22% of breast cancers occur in
women under fifty, data suggests that breast cancer is more
aggressive in pre-menopausal women.
[0010] In clinical practice, accurate diagnosis of various subtypes
of breast cancer is important because treatment options, prognosis,
and the likelihood of therapeutic response all vary broadly
depending on the diagnosis. Accurate prognosis, or determination of
distant metastasis-free survival could allow the oncologist to
tailor the administration of adjuvant chemotherapy, with women
having poorer prognoses being given the most aggressive treatment.
Furthermore, accurate prediction of poor prognosis would greatly
impact clinical trials for new breast cancer therapies, because
potential study patients could then be stratified according to
prognosis. Trials could then be limited to patients having poor
prognosis, in turn making it easier to discern if an experimental
therapy is efficacious.
[0011] To date, no set of satisfactory predictors for prognosis
based on the clinical information alone has been identified. The
detection of BRCA1 or BRCA2 mutations represents a step towards the
design of therapies to better control and prevent the appearance of
these tumors. However, there is no equivalent means for the
diagnosis of patients with sporadic tumors, the most common type of
breast cancer tumor, nor is there a means of differentiating
subtypes of breast cancer.
3. SUMMARY OF THE INVENTION
[0012] The invention provides gene marker sets that distinguish
various types and subtypes of breast cancer, and methods of use
therefor. In one embodiment, the invention provides a method for
classifying a cell sample as ER(+) or ER(-) comprising detecting a
difference in the expression of a first plurality of genes relative
to a control, said first plurality of genes consisting of at least
5 of the genes corresponding to the markers listed in Table 1. In
specific embodiments, said plurality of genes consists of at least
50, 100, 200, 500, 1000, up to 2,460 of the gene markers listed in
Table 1. In another specific embodiment, said plurality of genes
consists of each of the genes corresponding to the 2,460 markers
listed in Table 2. In another specific embodiment, said plurality
consists of the 550 markers listed in Table 2. In another specific
embodiment, said control comprises nucleic acids derived from a
pool of tumors from individual sporadic patients. In another
specific embodiment, said detecting comprises the steps of (a)
generating an ER(+) template by hybridization of nucleic acids
derived from a plurality of ER(+) patients within a plurality of
sporadic patients against nucleic acids derived from a pool of
tumors from individual sporadic patients; (b) generating an ER(-)
template by hybridization of nucleic acids derived from a plurality
of ER(-) patients within said plurality of sporadic patients
against nucleic acids derived from said pool of tumors from
individual sporadic patients within said plurality; (c) hybridizing
nucleic acids derived from an individual sample against said pool;
and (d) determining the similarity of marker gene expression in the
individual sample to the ER(+) template and the ER(-) template,
wherein if said expression is more similar to the ER(+) template,
the sample is classified as ER(+), and if said expression is more
similar to the ER(-) template, the sample is classified as
ER(-).
[0013] The invention further provides the above methods, applied to
the classification of samples as BRCA1 or sporadic, and classifying
patients as having good prognosis or poor prognosis. For the
BRCA1/sporadic gene markers, the invention provides that the method
may be used wherein the plurality of genes is at least 5, 20, 50,
100, 200 or 300 of the BRCA1/sporadic markers listed in Table 3. In
a specific embodiment, the optimum 100 markers listed in Table 4
are used. For the prognostic markers, the invention provides that
at least 5, 20, 50, 100, or 200 gene markers listed in Table 5 may
be used. In a specific embodiment, the optimum 70 markers listed in
Table 6 are used.
[0014] The invention further provides that markers may be combined.
Thus, in one embodiment, at least 5 markers from Table 1 are used
in conjunction with at least 5 markers from Table 3. In another
embodiment, at least 5 markers from Table 5 are used in conjunction
with at least 5 markers from Table 3. In another embodiment, at
least 5 markers from Table a used in conjunction with at least 5
markers from Table 5. In another embodiment, at least 5 markers
from each of Tables 1, 3, and 5 are used simultaneously.
[0015] The invention further provides a method for classifying a
sample as ER(+) or ER(-) by calculating the similarity between the
expression of at least 5 of the markers listed in Table 1 in the
sample to the expression of the same markers in an ER(-) nucleic
acid pool and an ER(+) nucleic acid pool, comprising the steps of:
(a) labeling nucleic acids derived from a sample, with a first
fluorophore to obtain a first pool of fluorophore-labeled nucleic
acids; (b) labeling with a second fluorophore a first pool of
nucleic acids derived from two or more ER(+) samples, and a second
pool of nucleic acids derived from two or more ER(-) samples; (c)
contacting said first fluorophore-labeled nucleic acid and said
first pool of second fluorophore-labeled nucleic acid with said
first microarray under conditions such that hybridization can
occur, and contacting said first fluorophore-labeled nucleic acid
and said second pool of second fluorophore-labeled nucleic acid
with said second microarray under conditions such that
hybridization can occur, detecting at each of a plurality of
discrete loci on the first microarray a first fluorescent emission
signal from said first fluorophore-labeled nucleic acid and a
second fluorescent emission signal from said first pool of second
fluorophore-labeled genetic matter that is bound to said first
microarray under said conditions, and detecting at each of the
marker loci on said second microarray said first fluorescent
emission signal from said first fluorophore-labeled nucleic acid
and a third fluorescent emission signal from said second pool of
second fluorophore-labeled nucleic acid; (d) determining the
similarity of the sample to the ER(-) and ER(+) pools by comparing
said first fluorescence emission signals and said second
fluorescence emission signals, and said first emission signals and
said third fluorescence emission signals; and (e) classifying the
sample as ER(+) where the first fluorescence emission signals are
more similar to said second fluorescence emission signals than to
said third fluorescent emission signals, and classifying the sample
as ER(-) where the first fluorescence emission signals are more
similar to said third fluorescence emission signals than to said
second fluorescent emission signals, wherein said similarity is
defined by a statistical method. The invention further provides
that the other disclosed marker sets may be used in the above
method to distinguish BRCA1 from sporadic tumors, and patients with
poor prognosis from patients with good prognosis.
[0016] In a specific embodiment, said similarity is calculated by
determining a first sum of the differences of expression levels for
each marker between said first fluorophore labeled nucleic acid and
said first pool of second fluorophore-labeled nucleic acid, and a
second sum of the differences of expression levels for each marker
between said first fluorophore-labeled nucleic acid and said second
pool of second fluorophore-labeled nucleic acid, wherein if said
first sum is greater, than said second sum, the sample is
classified as ER(-), and if said second sum is greater than said
first sum, the sample is classified as ER(+). In another specific
embodiment, said similarity is calculated by computing a first
classifier parameter P.sub.1 between an ER(+) template and the
expression of said markers in said sample, and a second classifier
parameter P.sub.2 between an ER(-) template and the expression of
said markers in said sample, wherein said P.sub.1 and P.sub.2 are
calculated according to the formula:
P.sub.i=({right arrow over (z)}.sub.i{right arrow over
(y)})/(.parallel.{right arrow over
(z)}.sub.i.parallel..parallel.{right arrow over (y)}.parallel.),
Equation (1)
wherein {right arrow over (Z)}.sub.1 and {right arrow over
(Z)}.sub.2 are ER(-) and ER(+) templates, respectively, and are
calculated by averaging said second fluorescence emission signal
for each of said markers in said first pool of second
fluorophore-labeled nucleic acid and said third fluorescence
emission signal for each of said markers in said second pool of
second fluorophore-labeled nucleic acid, respectively, and wherein
{right arrow over (y)} is said first fluorescence emission signal
of each of said markers in the sample to be classified as ER(+) or
ER(-), wherein the expression of the markers in the sample is
similar to ER(+) if P.sub.1<P.sub.2, and similar to ER(-) if
P.sub.1>P.sub.2.
[0017] The invention further provides a method for identifying
marker genes the expression of which is associated with a
particular phenotype. In one embodiment, the invention provides a
method for determining a set of marker genes whose expression is
associated with a particular phenotype, comprising the steps of:
(a) selecting the phenotype having two or more phenotype
categories; (b) identifying a plurality of genes wherein the
expression of said genes is correlated or anticorrelated with one
of the phenotype categories, and wherein the correlation
coefficient for each gene is calculated according to the
equation
.rho.=({right arrow over (c)}{right arrow over
(r)})/(.parallel.{right arrow over (c)}.parallel..parallel.{right
arrow over (r)}.parallel.) Equation (2)
wherein {right arrow over (c)} is a number representing said
phenotype category and {right arrow over (r)} is the logarithmic
expression ratio across all the samples for each individual gene,
wherein if the correlation coefficient has an absolute value of a
threshold value or greater, said expression of said gene is
associated with the phenotype category, and wherein said plurality
of genes is a set of marker genes whose expression is associated
with a particular phenotype. The threshold depends upon the number
of samples used; the threshold can be calculated as 3.times.1/
{square root over (n-3)}, where 1/ {square root over (n-3)} is the
distribution width and n=the number of samples. In a specific
embodiment where n=98, said threshold value is 0.3. In a specific
embodiment, said set of marker genes is validated by: (a) using a
statistical method to randomize the association between said marker
genes and said phenotype category, thereby creating a control
correlation coefficient for each marker gene; (b) repeating step
(a) one hundred or more times to develop a frequency distribution
of said control correlation coefficients for each marker gene; (c)
determining the number of marker genes having a control correlation
coefficient of a threshold value or above, thereby creating a
control marker gene set; and (d) comparing the number of control
marker genes so identified to the number of marker genes, wherein
if the p value of the difference between the number of marker genes
and the number of control genes is less than 0.01, said set of
marker genes is validated. In another specific embodiment, said set
of marker genes is optimized by the method comprising: (a)
rank-ordering the genes by amplitude of correlation or by
significance of the correlation coefficients, and (b) selecting an
arbitrary number of marker genes from the top of the rank ordered
list. The threshold value depends upon the number of samples
tested.
[0018] The invention further provides a method for assigning a
person to one of a plurality of categories in a clinical trial,
comprising determining for each said person the level of expression
of at least five of the prognosis markers listed in Table 6,
determining therefrom whether the person has an expression pattern
that correlates with a good prognosis or a poor prognosis, and
assigning said person to one category in a clinical trial if said
person is determined to have a good prognosis, and a different
category if that person is determined to have a poor prognosis. The
invention further provides a method for assigning a person to one
of a plurality of categories in a clinical trial, where each of
said categories is associated with a different phenotype,
comprising determining for each said person the level of expression
of at least five markers from a set of markers, wherein said set of
markers includes markers associated with each of said clinical
categories, determining therefrom whether the person has an
expression pattern that correlates with one of the clinical
categories, an assigning said person to one of said categories if
said person is determined to have a phenotype associated with that
category.
[0019] The invention further provides a method of classifying a
first cell or organism as having one of at least two different
phenotypes, said at least two different phenotypes comprising a
first phenotype and a second phenotype, said method comprising: (a)
comparing the level of expression of each of a plurality of genes
in a first sample from the first cell or organism to the level of
expression of each of said genes, respectively, in a pooled sample
from a plurality of cells or organisms, said plurality of cells or
organisms comprising different cells or organisms exhibiting said
at least two different phenotypes, respectively, to produce a first
compared value; (b) comparing said first compared value to a second
compared value, wherein said second compared value is the product
of a method comprising comparing the level of expression of each of
said genes in a sample from a cell or organism characterized as
having said first phenotype to the level of expression of each of
said genes, respectively, in said pooled sample; (c) comparing said
first compared value to a third compared value, wherein said third
compared value is the product of a method comprising comparing the
level of expression of each of said genes in a sample from a cell
or organism characterized as having said second phenotype to the
level of expression of each of said genes, respectively, in said
pooled sample, (d) optionally carrying out one or more times a step
of comparing said first compared value to one or more additional
compared values, respectively, each additional compared value being
the product of a method comprising comparing the level of
expression of each of said genes in a sample from a cell or
organism characterized as having a phenotype different from said
first and second phenotypes but included among said at least two
different phenotypes, to the level of expression of each of said
genes, respectively, in said pooled sample; and (e) determining to
which of said second, third and, if present, one or more additional
compared values, said first compared value is most similar, wherein
said first cell or organism is determined to have the phenotype of
the cell or organism used to produce said compared value most
similar to said first compared value.
[0020] In a specific embodiment of the above method, said compared
values are each ratios of the levels of expression of each of said
genes. In another specific embodiment, each of said levels of
expression of each of said genes in said pooled sample are
normalized prior to any of said comparing steps. In another
specific embodiment, normalizing said levels of expression is
carried out by dividing each of said levels of expression by the
median or mean level of expression of each of said genes or
dividing by the mean or median level of expression of one or more
housekeeping genes in said pooled sample. In a more specific
embodiment, said normalized levels of expression are subjected to a
log transform and said comparing steps comprise subtracting said
log transform from the log of said levels of expression of each of
said genes in said sample from said cell or organism. In another
specific embodiment, said at least two different phenotypes are
different stages of a disease or disorder. In another specific
embodiment, said at least two different phenotypes are different
prognoses of a disease or disorder. In yet another specific
embodiment, said levels of expression of each of said genes,
respectively, in said pooled sample or said levels of expression of
each of said genes in a sample from said cell or organism
characterized as having said first phenotype, said second
phenotype, or said phenotype different from said first and second
phenotypes, respectively, are stored on a computer.
[0021] The invention further provides microarrays comprising the
disclosed marker sets. In one embodiment, the invention provides a
microarray comprising at least 5 markers derived from any one of
Tables 1-6, wherein at least 50% of the probes on the microarray
are present in any one of Tables 1-6. In more specific embodiments,
at least 60%, 70%, 80%, 90%, 95% or 98% of the probes on said
microarray are present in any one of Tables 1-6.
[0022] In another embodiment, the invention provides a microarray
for distinguishing ER(+) and ER(-) cell samples comprising a
positionally-addressable array of polynucleotide probes bound to a
support, said polynucleotide probes comprising a plurality of
polynucleotide probes of different nucleotide sequences, each of
said different nucleotide sequences comprising a sequence
complementary and hybridizable to a plurality of genes, said
plurality consisting of at least 5 of the genes corresponding to
the markers listed in Table 1 or Table 2, wherein at least 50% of
the probes on the microarray are present in any one of Table 1 or
Table 2. In yet another embodiment, the invention provides a
microarray for distinguishing BRCA1-type and sporadic tumor-type
cell samples comprising a positionally-addressable array of
polynucleotide probes bound to a support, said polynucleotide
probes comprising a plurality of polynucleotide probes of different
nucleotide sequences, each of said different nucleotide sequences
comprising a sequence complementary and hybridizable to a plurality
of genes, said plurality consisting of at least 5 of the genes
corresponding to the markers listed in Table 3 or Table 4, wherein
at least 50% of the probes on the microarray are present in any one
of Table 3 or Table 4. In still another embodiment, the invention
provides a microarray for distinguishing cell samples from patients
having a good prognosis and cell samples from patients having a
poor prognosis comprising a positionally-addressable array of
polynucleotide probes bound to a support, said polynucleotide
probes comprising a plurality of polynucleotide probes of different
nucleotide sequences, each of said different nucleotide sequences
comprising a sequence complementary and hybridizable to a plurality
of genes, said plurality consisting of at least 5 of the genes
corresponding to the markers listed in Table 5 or Table 6, wherein
at least 50% of the probes on the microarray are present in any one
of Table 5 or Table 6. The invention further provides for
microarrays comprising at least 5, 20, 50, 100, 200, 500, 100,
1,250, 1,500, 1,750, or 2,000 of the ER-status marker genes listed
in Table 1, at least 5, 20, 50, 100, 200, or 300 of the BRCA1
sporadic marker genes listed in Table 3, or at least 5, 20, 50, 100
or 200 of the prognostic marker genes listed in Table 5, in any
combination, wherein at least 50%, 60%, 70%, 80%, 90%, 95% or 98%
of the probes on said microarrays are present in Table 1, Table 3
and/or Table 5.
[0023] The invention further provides a kit for determining the
ER-status of a sample, comprising at least two microarrays each
comprising at least 5 of the markers listed in Table 1, and a
computer system for determining the similarity of the level of
nucleic acid derived from the markers listed in Table 1 in a sample
to that in an ER(-) pool and an ER(+) pool, the computer system
comprising a processor, and a memory encoding one or more programs
coupled to the processor, wherein the one or more programs cause
the processor to perform a method comprising computing the
aggregate differences in expression of each marker between the
sample and ER(-) pool and the aggregate differences in expression
of each marker between the sample and ER(+) pool, or a method
comprising determining the correlation of expression of the markers
in the sample to the expression in the ER(-) and ER(+) pools, said
correlation calculated according to Equation (4). The invention
provides for kits able to distinguish BRCA1 and sporadic tumors,
and samples from patients with good prognosis from samples from
patients with poor prognosis, by inclusion of the appropriate
marker gene sets. The invention further provides a kit for
determining whether a sample is derived from a patient having a
good prognosis or a poor prognosis, comprising at least one
microarray comprising probes to at least 5 of the genes
corresponding to the markers listed in Table 5, and a computer
readable medium having recorded thereon one or more programs for
determining the similarity of the level of nucleic acid derived
from the markers listed in Table 5 in a sample to that in a pool of
samples derived from individuals having a good prognosis and a pool
of samples derived from individuals having a good prognosis,
wherein the one or more programs cause a computer to perform a
method comprising computing the aggregate differences in expression
of each marker between the sample and the good prognosis pool and
the aggregate differences in expression of each marker between the
sample and the poor prognosis pool, or a method comprising
determining the correlation of expression of the markers in the
sample to the expression in the good prognosis and poor prognosis
pools, said correlation calculated according to Equation (3).
4. BRIEF DESCRIPTION OF THE FIGURES
[0024] FIG. 1 is a Venn-type diagram showing the overlap between
the marker sets disclosed herein, including the 2,460 ER markers,
the 430 BRCA1/sporadic markers, and the 231 prognosis
reporters.
[0025] FIG. 2 shows the experimental procedures for measuring
differential changes in mRNA transcript abundance in breast cancer
tumors used in this study. In each experiment, Cy5-labeled cRNA
from one tumor X is hybridized on a 25 k human microarray together
with a Cy3-labeled cRNA pool made of cRNA samples from tumors 1, 2,
. . . N. The digital expression data were obtained by scanning and
image processing. The error modeling allowed us to assign a p-value
to each transcript ratio measurement.
[0026] FIG. 3 Two-dimensional clustering reveals two distinctive
types of tumors. The clustering was based on the gene expression
data of 98 breast cancer tumors over 4986 significant genes. Dark
gray (red) presents up-regulation, light gray (green) represents
down-regulation, black indicates no change in expression, and gray
indicates that data is not available. 4986 genes were selected that
showed a more than two fold change in expression ratios in more
than five experiments. Selected clinical data for test results of
BR CA1 mutations, estrogen receptor (ER), and proestrogen receptor
(PR), tumor grade, lymphocytic infiltrate, and angioinvasion are
shown at right. Black denotes negative and white denotes positive.
The dominant pattern in the lower part consists of 36 patients, out
of which 34 are ER-negative (total 39), and 16 are BR CA1-mutation
carriers (total 18).
[0027] FIG. 4 A portion of unsupervised clustered results as shown
in FIG. 3. ESR1 (the estrogen receptor gene) is coregulated with a
set of genes that are strongly co regulated to form a dominant
pattern.
[0028] FIG. 5A Histogram of correlation coefficients of significant
genes between their expression ratios and estrogen-receptor (ER)
status (i.e., ER level). The histogram for experimental data is
shown as a gray line. The results of one Monte-Carlo trial is shown
in solid black. There are 2,460 genes whose expression data
correlate with ER status at a level higher than 0.3 or
anti-correlated with ER status at a level lower than -0.3.
[0029] FIG. 5B The distribution of the number of genes that
satisfied the same selection criteria (amplitude of correlation
above 0.3) from 10,000 Monte-Carlo runs. It is estimated that this
set of 2,460 genes reports ER status at a confidence level of
p>99.99%.
[0030] FIG. 6 Classification Type 1 and Type 2 error rates as a
function of the number (out of 2,460) marker genes used in the
classifier. The combined error rate is lowest when approximately
550 marker genes are used.
[0031] FIG. 7 Classification of 98 tumor samples as ER(+) or ER(-)
based on expression levels of the 550 optimal marker genes. ER(+)
samples (above white line) exhibit a clearly different expression
pattern that ER(-) samples (below white line).
[0032] FIG. 8 Correlation between expression levels in samples from
each patient and the average profile of the ER(-) group vs.
correlation with the ER(+) group. Squares represent samples from
clinically ER(-) patients; dots represent samples from clinically
ER(+) patients.
[0033] FIG. 9A Histogram of correlation coefficients of gene
expression ratio of each significant gene with the BRCA1 mutation
status is shown as a solid line. The dashed line indicates a
frequency distribution obtained from one Monte-Carlo run. 430 genes
exhibited an amplitude of correlation or anti-correlation greater
than 0.35.
[0034] FIG. 9B Frequency distribution of the number of genes that
exhibit an amplitude of correlation or anti-correlation greater
than 0.35 for the 10,000 Monte-Carlo run control. Mean=115.
p(n>430)=0.48% and p(>430/2)=9.0%.
[0035] FIG. 10 Classification type 1 and type 2 error rates as a
function of the number of discriminating genes used in the
classifier (template). The combined error rate is lowest when
approximately 100 discriminating marker genes are used.
[0036] FIG. 11A The classification of 38 tumors in the ER(-) group
into two subgroups, BRCA1 and sporadic, by using the optimal set of
100 discriminating marker genes. Patients above the white line are
characterized by BRCA1-related patterns.
[0037] FIG. 11B Correlation between expression levels in samples
from each ER(-) patient and the average profile of the BRCA1 group
vs. correlation with the sporadic group. Squares represent samples
from patients with sporadic-type tumors; dots represent samples
from patients carrying the BRCA1 mutation.
[0038] FIG. 12A Histogram of correlation coefficients of gene
expression ratio of each significant gene with the prognostic
category (distant metastases group and no distant metastases group)
is shown as a solid line. The distribution obtained from one
Monte-Carlo run is shown as a dashed line. The amplitude of
correlation or anti-correlation of 231 marker genes is greater than
0.3.
[0039] FIG. 12B Frequency distribution of the number of genes whose
amplitude of correlation or anti-correlation was greater than 0.3
for 10,000 Monte-Carlo runs.
[0040] FIG. 13 The distant metastases group classification error
rate for type 1 and type 2 as a function of the number of
discriminating genes used in the classifier. The combined error
rate is lowest when approximately 70 discriminating marker genes
are used.
[0041] FIG. 14 Classification of 78 sporadic tumors into two
prognostic groups, distant metastases (poor prognosis) and no
distant metastases (good prognosis) using the optimal set of 70
discriminating marker genes. Patients above the white line are
characterized by good prognosis. Patients below the white line are
characterized by poor prognosis.
[0042] FIG. 15 Correlation between expression levels in samples
from each patient and the average profile of the good prognosis
group vs. correlation with the poor prognosis group. Squares
represent samples from patients having a poor prognosis; dots
represent samples from patients having a good prognosis. Red
squares represent the `reoccurred` patients and the blue dots
represent the `non-reoccurred`. A total of 13 out of 78 were
mis-classified.
[0043] FIG. 16 The reoccurrence probability as a function of time
since diagnosis. Group A and group B were predicted by using a
leave-one-out method based on the optimal set of 70 discriminating
marker genes. The 43 patients in group A consists of 37 patients
from the no distant metastases group and 6 patients from the
distant metastases group. The patients in group B consists of 28
patients from the distant metastases group and 7 patients from the
no distant metastases group.
[0044] FIG. 17 The distant metastases probability as a function of
time since diagnosis for ER(+) (yes) or ER(-) (no) individuals.
[0045] FIG. 18 The distant metastases probability as a function of
time since diagnosis for progesterone receptor (PR)(+) (yes) or
PR(-) (no) individuals.
[0046] FIG. 19A, B The distant metastases probability as a function
of time since diagnosis. Groups were defined by the tumor
grades.
[0047] FIG. 20A Classification of 19 independent sporadic tumors
into two prognostic groups, distant metastases and no distant
metastases, using the 70 optimal marker genes. Patients above the
white line have a good prognosis. Patients below the white line
have a poor prognosis.
[0048] FIG. 20B Correlation between expression ratios of each
patient and the average expression ratio of the good prognosis
group is defined by the training set versus the correlation between
expression ratios of each patient and the average expression ratio
of the poor prognosis training set. Of nine patients in the good
prognosis group, three are from the "distant metastases group"; of
ten patients in the good prognosis group, one patient is from the
"no distant metastases group". This error rate of 4 out of 19 is
consistent with 13 out of 78 for the initial 78 patients.
[0049] FIG. 20C The reoccurrence probability as a function of time
since diagnosis for two groups predicted based on expression of the
optimal 70 marker genes.
[0050] FIG. 21A Sensitivity vs. 1-specificity for good prognosis
classification.
[0051] FIG. 21B Sensitivity vs. 1-specificity for poor prognosis
classification.
[0052] FIG. 21C Total error rate as a function of threshold on the
modeled likelihood. Six clinical parameters (ER status, PR status,
tumor grade, tumor size, patient age, and presence or absence of
angioinvasion) were used to perform the clinical modeling.
[0053] FIG. 22 Comparison of the log(ratio) of individual samples
using the "material sample pool" vs. mean subtracted log(intensity)
using the "mathematical sample pool" for 70 reporter genes in the
78 sporadic tumor samples. The "material sample pool" was
constructed from the 78 sporadic tumor samples.
[0054] FIG. 23A Results of the "leave one out" cross validation
based on single channel data. Samples are grouped according to each
sample's coefficient of correlation to the average "good prognosis"
profile and "poor prognosis" profile for the 70 genes examined. The
white line separates samples from patients classified as having
poor prognoses (below) and good prognoses (above).
[0055] FIG. 23B Scatter plot of coefficients of correlation to the
average expression in "good prognosis" samples and "poor prognosis"
samples. The false positive rate (i.e., rate of incorrectly
classifying a sample as being from a patient having a good
prognosis as being one from a patient having a poor prognosis) was
10 out of 44, and the false negative rate is 6 out of 34.
[0056] FIG. 24A Single-channel hybridization data for samples
ranked according to the coefficients of correlation with the good
prognosis classifier. Samples classified as "good prognosis" lie
above the white line, and those classified as "poor prognosis" lie
below.
[0057] FIG. 24B Scatterplot of sample correlation coefficients,
with three incorrectly classified samples lying to the right of the
threshold correlation coefficient value. The threshold correlation
value was set at 0.2727 to limit the false negatives to
approximately 10% of the samples.
5. DETAILED DESCRIPTION OF THE INVENTION
5.1 Introduction
[0058] The invention relates to sets of genetic markers whose
expression patterns correlate with important characteristics of
breast cancer tumors. i.e., estrogen receptor (ER) status, BRCA1
status, and the likelihood of relapse (i.e., distant metastasis or
poor prognosis). More specifically, the invention provides for sets
of genetic markers that can distinguish the following three
clinical conditions. First, the invention relates to sets of
markers whose expression correlates with the ER status of a
patient, and which can be used to distinguish ER(+) from ER(-)
patients. ER status is a useful prognostic indicator, and an
indicator of the likelihood that a patient will respond to certain
therapies, such as tamoxifen. Also, among women who are ER positive
the response rate (over 50%) to hormonal therapy is much higher
than the response rate (less 10%) in patients whose ER status is
negative. In patients with ER positive tumors the possibility of
achieving a hormonal response is directly proportional to the level
ER (P. Clabresi and P. S. Schein, MEDICAL ONCOLOGY (2ND ED.),
McGraw-Hill, Inc., New York (1993)). Second, the invention further
relates to sets of markers whose expression correlates with the
presence of BRCA1 mutations, and which can be used to distinguish
BRCA1-type tumors from sporadic tumors. Third, the invention
relates to genetic markers whose expression correlates with
clinical prognosis, and which can be used to distinguish patients
having good prognoses (i.e., no distant metastases of a tumor
within five years) from poor prognoses (i.e., distant metastases of
a tumor within five years). Methods are provided for use of these
markers to distinguish between these patient groups, and to
determine general courses of treatment. Microarrays comprising
these markers are also provided, as well as methods of constructing
such microarrays. Each markers correspond to a gene in the human
genome, i.e., such marker is identifiable as all or a portion of a
gene. Finally, because each of the above markers correlates with a
certain breast cancer-related conditions, the markers, or the
proteins they encode, are likely to be targets for drugs against
breast cancer.
5.2 Definitions
[0059] As used herein, "BRCA1 tumor" means a tumor having cells
containing a mutation of the BRCA1 locus.
[0060] The "absolute amplitude" of correlation expressions means
the distance, either positive or negative, from a zero value; i.e.,
both correlation coefficients -0.35 and 0.35 have an absolute
amplitude of 0.35.
[0061] "Status" means a state of gene expression of a set of
genetic markers whose expression is strongly correlated with a
particular phenotype. For example, "ER status" means a state of
gene expression of a set of genetic markers whose expression is
strongly correlated with that of ESR1 (estrogen receptor gene),
wherein the pattern of these genes' expression differs detectably
between tumors expressing the receptor and tumors not expressing
the receptor.
[0062] "Good prognosis" means that a patient is expected to have no
distant metastases of a breast tumor within five years of initial
diagnosis of breast cancer.
[0063] "Poor prognosis" means that a patient is expected to have
distant metastases of a breast tumor within five years of initial
diagnosis of breast cancer.
[0064] "Marker" means an entire gene, or an EST derived from that
gene, the expression or level of which changes between certain
conditions. Where the expression of the gene correlates with a
certain condition, the gene is a marker for that condition.
[0065] "Marker-derived polynucleotides" means the RNA transcribed
from a marker gene, any cDNA or cRNA produced therefrom, and any
nucleic acid derived therefrom, such as synthetic nucleic acid
having a sequence derived from the gene corresponding to the marker
gene.
5.3 Markers Useful in Diagnosis and Prognosis of Breast Cancer
5.3.1 Marker Sets
[0066] The invention provides a set of 4,986 genetic markers whose
expression is correlated with the existence of breast cancer by
clustering analysis. A subset of these markers identified as useful
for diagnosis or prognosis is listed as SEQ ID NOS: 1-2,699. The
invention also provides a method of using these markers to
distinguish tumor types in diagnosis or prognosis.
[0067] In one embodiment, the invention provides a set of 2,460
genetic markers that can classify breast cancer patients by
estrogen receptor (ER) status; i.e., distinguish between ER(+) and
ER(-) patients or tumors derived from these patients. ER status is
an important indicator of the likelihood of a patient's response to
some chemotherapies (i.e., tamoxifen). These markers are listed in
Table 1. The invention also provides subsets of at least 5, 10, 25,
50, 100, 200, 300, 400, 500, 750, 1,000, 1,250, 1,500, 1,750 or
2,000 genetic markers, drawn from the set of 2,460 markers, which
also distinguish ER(+) and ER(-) patients or tumors. Preferably,
the number of markers is 550. The invention further provides a set
of 550 of the 2,460 markers that are optimal for distinguishing ER
status (Table 2). The invention also provides a method of using
these markers to distinguish between ER(+) and ER(-) patients or
tumors derived therefrom.
[0068] In another embodiment, the invention provides a set of 430
genetic markers that can classify ER(-) breast cancer patients by
BRCA1 status; i.e., distinguish between tumors containing a BRCA1
mutation and sporadic tumors. These markers are listed in Table 3.
The invention further provides subsets of at least 5, 10 20, 30,
40, 50, 75, 100, 150, 200, 250, 300 or 350 markers, drawn from the
set of 430 markers, which also distinguish between tumors
containing a BRCA1 mutation and sporadic tumors. Preferably, the
number of markers is 100. A preferred set of 100 markers is
provided in Table 4. The invention also provides a method of using
these markers to distinguish between BRCA1 and sporadic patients or
tumors derived therefrom.
[0069] In another embodiment, the invention provides a set of 231
genetic markers that can distinguish between patients with a good
breast cancer prognosis (no breast cancer tumor distant metastases
within five years) and patients with a poor breast cancer prognosis
(tumor distant metastases within five years). These markers are
listed in Table 5. The invention also provides subsets of at least
5, 10, 20, 30, 40, 50, 75, 100, 50 or 200 markers, drawn from the
set of 231, which also distinguish between patients with good and
poor prognosis. A preferred set of 70 markers is provided in Table
6. In a specific embodiment, the set of markers consists of the
twelve kinase-related markers and the seven cell division- or
mitosis-related markers listed. The invention also provides a
method of using the above markers to distinguish between patients
with good or poor prognosis.
TABLE-US-00001 TABLE 1 2,460 gene markers that distinguish ER(+)
and ER(-) cell samples. GenBank Accession Number SEQ ID NO
AA555029_RC SEQ ID NO 1 AB000509 SEQ ID NO 2 AB001451 SEQ ID NO 3
AB002301 SEQ ID NO 4 AB002308 SEQ ID NO 5 AB002351 SEQ ID NO 6
AB002448 SEQ ID NO 7 AB006628 SEQ ID NO 9 AB006630 SEQ ID NO 10
AB006746 SEQ ID NO 11 AB007458 SEQ ID NO 12 AB007855 SEQ ID NO 13
AB007857 SEQ ID NO 14 AB007863 SEQ ID NO 15 AB007883 SEQ ID NO 16
AB007896 SEQ ID NO 17 AB007899 SEQ ID NO 18 AB007916 SEQ ID NO 19
AB007950 SEQ ID NO 20 AB011087 SEQ ID NO 21 AB011089 SEQ ID NO 22
AB011104 SEQ ID NO 23 AB011105 SEQ ID NO 24 AB011121 SEQ ID NO 25
AB011132 SEQ ID NO 26 AB011152 SEQ ID NO 27 AB011179 SEQ ID NO 28
AB014534 SEQ ID NO 29 AB014568 SEQ ID NO 30 AB018260 SEQ ID NO 31
AB018268 SEQ ID NO 32 AB018289 SEQ ID NO 33 AB018345 SEQ ID NO 35
AB020677 SEQ ID NO 36 AB020689 SEQ ID NO 37 AB020695 SEQ ID NO 38
AB020710 SEQ ID NO 39 AB023139 SEQ ID NO 40 AB023151 SEQ ID NO 41
AB023152 SEQ ID NO 42 AB023163 SEQ ID NO 43 AB023173 SEQ ID NO 44
AB023211 SEQ ID NO 45 AB024704 SEQ ID NO 46 AB028985 SEQ ID NO 47
AB028986 SEQ ID NO 48 AB028998 SEQ ID NO 49 AB029031 SEQ ID NO 51
AB032951 SEQ ID NO 52 AB032966 SEQ ID NO 53 AB032969 SEQ ID NO 54
AB032977 SEQ ID NO 56 AB033007 SEQ ID NO 58 AB033034 SEQ ID NO 59
AB033035 SEQ ID NO 60 AB033040 SEQ ID NO 61 AB033049 SEQ ID NO 63
AB033050 SEQ ID NO 64 AB033053 SEQ ID NO 65 AB033055 SEQ ID NO 66
AB033058 SEQ ID NO 67 AB033073 SEQ ID NO 68 AB033092 SEQ ID NO 69
AB033111 SEQ ID NO 70 AB036063 SEQ ID NO 71 AB037720 SEQ ID NO 72
AB037743 SEQ ID NO 74 AB037745 SEQ ID NO 75 AB037756 SEQ ID NO 76
AB037765 SEQ ID NO 77 AB037778 SEQ ID NO 78 AB037791 SEQ ID NO 79
AB037793 SEQ ID NO 80 AB037802 SEQ ID NO 81 AB037806 SEQ ID NO 82
AB037809 SEQ ID NO 83 AB037836 SEQ ID NO 84 AB037844 SEQ ID NO 85
AB037845 SEQ ID NO 86 AB037848 SEQ ID NO 87 AB037863 SEQ ID NO 88
AB037864 SEQ ID NO 89 AB040881 SEQ ID NO 90 AB040900 SEQ ID NO 91
AB040914 SEQ ID NO 92 AB040926 SEQ ID NO 93 AB040955 SEQ ID NO 94
AB040961 SEQ ID NO 95 AF000974 SEQ ID NO 97 AF005487 SEQ ID NO 98
AF007153 SEQ ID NO 99 AF007155 SEQ ID NO 100 AF015041 SEQ ID NO 101
AF016004 SEQ ID NO 102 AF016495 SEQ ID NO 103 AF020919 SEQ ID NO
104 AF026941 SEQ ID NO 105 AF035191 SEQ ID NO 106 AF035284 SEQ ID
NO 107 AF035318 SEQ ID NO 108 AF038182 SEQ ID NO 109 AF038193 SEQ
ID NO 110 AF042838 SEQ ID NO 111 AF044127 SEQ ID NO 112 AF045229
SEQ ID NO 113 AF047002 SEQ ID NO 114 AF047826 SEQ ID NO 115
AF049460 SEQ ID NO 116 AF052101 SEQ ID NO 117 AF052117 SEQ ID NO
118 AF052155 SEQ ID NO 119 AF052159 SEQ ID NO 120 AF052176 SEQ ID
NO 122 AF052185 SEQ ID NO 123 AF055270 SEQ ID NO 126 AF058075 SEQ
ID NO 127 AF061034 SEQ ID NO 128 AF063725 SEQ ID NO 129 AF063936
SEQ ID NO 130 AF065241 SEQ ID NO 131 AF067972 SEQ ID NO 132
AF070536 SEQ ID NO 133 AF070552 SEQ ID NO 134 AF070617 SEQ ID NO
135 AF073770 SEQ ID NO 138 AF076612 SEQ ID NO 139 AF079529 SEQ ID
NO 140 AF090913 SEQ ID NO 142 AF095719 SEQ ID NO 143 AF098641 SEQ
ID NO 144 AF099032 SEQ ID NO 145 AF100756 SEQ ID NO 146 AF101051
SEQ ID NO 147 AF103375 SEQ ID NO 148 AF103458 SEQ ID NO 149
AF103530 SEQ ID NO 150 AF103804 SEQ ID NO 151 AF111849 SEQ ID NO
152 AF112213 SEQ ID NO 153 AF113132 SEQ ID NO 154 AF116682 SEQ ID
NO 156 AF118224 SEQ ID NO 157 AF118274 SEQ ID NO 158 AF119256 SEQ
ID NO 159 AF119665 SEQ ID NO 160 AF121255 SEQ ID NO 161 AF131748
SEQ ID NO 162 AF131753 SEQ ID NO 163 AF131760 SEQ ID NO 164
AF131784 SEQ ID NO 165 AF131828 SEQ ID NO 166 AF135168 SEQ ID NO
167 AF141882 SEQ ID NO 168 AF148505 SEQ ID NO 169 AF149785 SEQ ID
NO 170 AF151810 SEQ ID NO 171 AF152502 SEQ ID NO 172 AF155120 SEQ
ID NO 174 AF159092 SEQ ID NO 175 AF161407 SEQ ID NO 176 AF161553
SEQ ID NO 177 AF164104 SEQ ID NO 178 AF167706 SEQ ID NO 179
AF175387 SEQ ID NO 180 AF176012 SEQ ID NO 181 AF186780 SEQ ID NO
182 AF217508 SEQ ID NO 184 AF220492 SEQ ID NO 185 AF224266 SEQ ID
NO 186 AF230904 SEQ ID NO 187 AF234532 SEQ ID NO 188 AF257175 SEQ
ID NO 189 AF257659 SEQ ID NO 190 AF272357 SEQ ID NO 191 AF279865
SEQ ID NO 192 AI497657_RC SEQ ID NO 193 AJ012755 SEQ ID NO 194
AJ223353 SEQ ID NO 195 AJ224741 SEQ ID NO 196 AJ224864 SEQ ID NO
197 AJ225092 SEQ ID NO 198 AJ225093 SEQ ID NO 199 AJ249377 SEQ ID
NO 200 AJ270996 SEQ ID NO 202 AJ272057 SEQ ID NO 203 AJ275978 SEQ
ID NO 204 AJ276429 SEQ ID NO 205 AK000004 SEQ ID NO 206 AK000005
SEQ ID NO 207 AK000106 SEQ ID NO 208 AK000142 SEQ ID NO 209
AK000168 SEQ ID NO 210 AK000345 SEQ ID NO 212 AK000543 SEQ ID NO
213 AK000552 SEQ ID NO 214 AK000643 SEQ ID NO 216 AK000660 SEQ ID
NO 217 AK000689 SEQ ID NO 218 AK000770 SEQ ID NO 220 AK000933 SEQ
ID NO 221 AK001100 SEQ ID NO 223 AK001164 SEQ ID NO 224 AK001166
SEQ ID NO 225 AK001295 SEQ ID NO 226 AK001380 SEQ ID NO 227
AK001423 SEQ ID NO 228 AK001438 SEQ ID NO 229 AK001492 SEQ ID NO
230 AK001499 SEQ ID NO 231 AK001630 SEQ ID NO 232 AK001872 SEQ ID
NO 234 AK001890 SEQ ID NO 235 AK002016 SEQ ID NO 236 AK002088 SEQ
ID NO 237 AK002206 SEQ ID NO 240 AL035297 SEQ ID NO 241 AL049265
SEQ ID NO 242 AL049365 SEQ ID NO 244 AL049370 SEQ ID NO 245
AL049381 SEQ ID NO 246 AL049397 SEQ ID NO 247 AL049415 SEQ ID NO
248 AL049667 SEQ ID NO 249 AL049801 SEQ ID NO 250 AL049932 SEQ ID
NO 251 AL049935 SEQ ID NO 252 AL049943 SEQ ID NO 253 AL049949 SEQ
ID NO 254 AL049963 SEQ ID NO 255 AL049987 SEQ ID NO 256 AL050021
SEQ ID NO 257 AL050024 SEQ ID NO 258 AL050090 SEQ ID NO 259
AL050148 SEQ ID NO 260 AL050151 SEQ ID NO 261 AL050227 SEQ ID NO
262 AL050367 SEQ ID NO 263 AL050370 SEQ ID NO 264 AL050371 SEQ ID
NO 265 AL050372 SEQ ID NO 266 AL050388 SEQ ID NO 267 AL079276 SEQ
ID NO 268 AL079298 SEQ ID NO 269
AL080079 SEQ ID NO 271 AL080192 SEQ ID NO 273 AL080199 SEQ ID NO
274 AL080209 SEQ ID NO 275 AL080234 SEQ ID NO 277 AL080235 SEQ ID
NO 278 AL096737 SEQ ID NO 279 AL110126 SEQ ID NO 280 AL110139 SEQ
ID NO 281 AL110202 SEQ ID NO 283 AL110212 SEQ ID NO 284 AL110260
SEQ ID NO 285 AL117441 SEQ ID NO 286 AL117452 SEQ ID NO 287
AL117477 SEQ ID NO 288 AL117502 SEQ ID NO 289 AL117523 SEQ ID NO
290 AL117595 SEQ ID NO 291 AL117599 SEQ ID NO 292 AL117600 SEQ ID
NO 293 AL117609 SEQ ID NO 294 AL117617 SEQ ID NO 295 AL117666 SEQ
ID NO 296 AL122055 SEQ ID NO 297 AL133033 SEQ ID NO 298 AL133035
SEQ ID NO 299 AL133074 SEQ ID NO 301 AL133096 SEQ ID NO 302
AL133105 SEQ ID NO 303 AL133108 SEQ ID NO 304 AL133572 SEQ ID NO
305 AL133619 SEQ ID NO 307 AL133622 SEQ ID NO 308 AL133623 SEQ ID
NO 309 AL133624 SEQ ID NO 310 AL133632 SEQ ID NO 311 AL133644 SEQ
ID NO 312 AL133645 SEQ ID NO 313 AL133651 SEQ ID NO 314 AL137310
SEQ ID NO 316 AL137316 SEQ ID NO 317 AL137332 SEQ ID NO 318
AL137342 SEQ ID NO 319 AL137362 SEQ ID NO 321 AL137381 SEQ ID NO
322 AL137407 SEQ ID NO 323 AL137448 SEQ ID NO 324 AL137502 SEQ ID
NO 326 AL137514 SEQ ID NO 327 AL137540 SEQ ID NO 328 AL137566 SEQ
ID NO 330 AL137615 SEQ ID NO 331 AL137673 SEQ ID NO 335 AL137718
SEQ ID NO 336 AL137736 SEQ ID NO 337 AL137751 SEQ ID NO 338
AL137761 SEQ ID NO 339 AL157431 SEQ ID NO 340 AL157432 SEQ ID NO
341 AL157454 SEQ ID NO 342 AL157476 SEQ ID NO 343 AL157480 SEQ ID
NO 344 AL157482 SEQ ID NO 345 AL157484 SEQ ID NO 346 AL157492 SEQ
ID NO 347 AL157505 SEQ ID NO 348 AL157851 SEQ ID NO 349 AL160131
SEQ ID NO 350 AL161960 SEQ ID NO 351 AL162049 SEQ ID NO 352
AL355708 SEQ ID NO 353 D13643 SEQ ID NO 355 D14678 SEQ ID NO 356
D25328 SEQ ID NO 357 D26070 SEQ ID NO 358 D26488 SEQ ID NO 359
D31887 SEQ ID NO 360 D38521 SEQ ID NO 361 D38553 SEQ ID NO 362
D42043 SEQ ID NO 363 D42047 SEQ ID NO 364 D43950 SEQ ID NO 365
D50402 SEQ ID NO 366 D50914 SEQ ID NO 367 D55716 SEQ ID NO 368
D80001 SEQ ID NO 369 D80010 SEQ ID NO 370 D82345 SEQ ID NO 371
D83781 SEQ ID NO 372 D86964 SEQ ID NO 373 D86978 SEQ ID NO 374
D86985 SEQ ID NO 375 D87076 SEQ ID NO 376 D87453 SEQ ID NO 377
D87469 SEQ ID NO 378 D87682 SEQ ID NO 379 G26403 SEQ ID NO 380
J02639 SEQ ID NO 381 J04162 SEQ ID NO 382 K02403 SEQ ID NO 384
L05096 SEQ ID NO 385 L10333 SEQ ID NO 386 L11645 SEQ ID NO 387
L21934 SEQ ID NO 388 L22005 SEQ ID NO 389 L48692 SEQ ID NO 391
M12758 SEQ ID NO 392 M15178 SEQ ID NO 393 M21551 SEQ ID NO 394
M24895 SEQ ID NO 395 M26383 SEQ ID NO 396 M27749 SEQ ID NO 397
M28170 SEQ ID NO 398 M29873 SEQ ID NO 399 M29874 SEQ ID NO 400
M30448 SEQ ID NO 401 M30818 SEQ ID NO 402 M31932 SEQ ID NO 403
M37033 SEQ ID NO 404 M55914 SEQ ID NO 405 M63438 SEQ ID NO 406
M65254 SEQ ID NO 407 M68874 SEQ ID NO 408 M73547 SEQ ID NO 409
M77142 SEQ ID NO 410 M80899 SEQ ID NO 411 M83822 SEQ ID NO 412
M90657 SEQ ID NO 413 M93718 SEQ ID NO 414 M96577 SEQ ID NO 415
NM_000022 SEQ ID NO 417 NM_000044 SEQ ID NO 418 NM_000050 SEQ ID NO
419 NM_000057 SEQ ID NO 420 NM_000060 SEQ ID NO 421 NM_000064 SEQ
ID NO 422 NM_000073 SEQ ID NO 424 NM_000077 SEQ ID NO 425 NM_000086
SEQ ID NO 426 NM_000087 SEQ ID NO 427 NM_000095 SEQ ID NO 429
NM_000096 SEQ ID NO 430 NM_000100 SEQ ID NO 431 NM_000101 SEQ ID NO
432 NM_000104 SEQ ID NO 433 NM_000109 SEQ ID NO 434 NM_000125 SEQ
ID NO 435 NM_000127 SEQ ID NO 436 NM_000135 SEQ ID NO 437 NM_000137
SEQ ID NO 438 NM_000146 SEQ ID NO 439 NM_000149 SEQ ID NO 440
NM_000154 SEQ ID NO 441 NM_000161 SEQ ID NO 443 NM_000165 SEQ ID NO
444 NM_000168 SEQ ID NO 445 NM_000169 SEQ ID NO 446 NM_000175 SEQ
ID NO 447 NM_000191 SEQ ID NO 448 NM_000201 SEQ ID NO 450 NM_000211
SEQ ID NO 451 NM_000213 SEQ ID NO 452 NM_000224 SEQ ID NO 453
NM_000239 SEQ ID NO 454 NM_000251 SEQ ID NO 455 NM_000268 SEQ ID NO
456 NM_000270 SEQ ID NO 458 NM_000271 SEQ ID NO 459 NM_000283 SEQ
ID NO 460 NM_000284 SEQ ID NO 461 NM_000286 SEQ ID NO 462 NM_000291
SEQ ID NO 463 NM_000299 SEQ ID NO 464 NM_000300 SEQ ID NO 465
NM_000310 SEQ ID NO 466 NM_000311 SEQ ID NO 467 NM_000317 SEQ ID NO
468 NM_000320 SEQ ID NO 469 NM_000342 SEQ ID NO 470 NM_000346 SEQ
ID NO 471 NM_000352 SEQ ID NO 472 NM_000355 SEQ ID NO 473 NM_000358
SEQ ID NO 474 NM_000359 SEQ ID NO 475 NM_000362 SEQ ID NO 476
NM_000365 SEQ ID NO 477 NM_000381 SEQ ID NO 478 NM_000397 SEQ ID NO
480 NM_000399 SEQ ID NO 481 NM_000414 SEQ ID NO 482 NM_000416 SEQ
ID NO 483 NM_000422 SEQ ID NO 484 NM_000424 SEQ ID NO 485 NM_000433
SEQ ID NO 486 NM_000436 SEQ ID NO 487 NM_000450 SEQ ID NO 488
NM_000462 SEQ ID NO 489 NM_000495 SEQ ID NO 490 NM_000507 SEQ ID NO
491 NM_000526 SEQ ID NO 492 NM_000557 SEQ ID NO 493 NM_000560 SEQ
ID NO 494 NM_000576 SEQ ID NO 495 NM_000579 SEQ ID NO 496 NM_000584
SEQ ID NO 497 NM_000591 SEQ ID NO 498 NM_000592 SEQ ID NO 499
NM_000593 SEQ ID NO 500 NM_000594 SEQ ID NO 501 NM_000597 SEQ ID NO
502 NM_000600 SEQ ID NO 504 NM_000607 SEQ ID NO 505 NM_000612 SEQ
ID NO 506 NM_000627 SEQ ID NO 507 NM_000633 SEQ ID NO 508 NM_000636
SEQ ID NO 509 NM_000639 SEQ ID NO 510 NM_000647 SEQ ID NO 511
NM_000655 SEQ ID NO 512 NM_000662 SEQ ID NO 513 NM_000663 SEQ ID NO
514 NM_000666 SEQ ID NO 515 NM_000676 SEQ ID NO 516 NM_000685 SEQ
ID NO 517 NM_000693 SEQ ID NO 518 NM_000699 SEQ ID NO 519 NM_000700
SEQ ID NO 520 NM_000712 SEQ ID NO 521 NM_000727 SEQ ID NO 522
NM_000732 SEQ ID NO 523 NM_000734 SEQ ID NO 524 NM_000767 SEQ ID NO
525 NM_000784 SEQ ID NO 526 NM_000802 SEQ ID NO 528 NM_000824 SEQ
ID NO 529 NM_000849 SEQ ID NO 530 NM_000852 SEQ ID NO 531 NM_000874
SEQ ID NO 532 NM_000878 SEQ ID NO 533 NM_000884 SEQ ID NO 534
NM_000908 SEQ ID NO 537 NM_000909 SEQ ID NO 538 NM_000926 SEQ ID NO
539 NM_000930 SEQ ID NO 540 NM_000931 SEQ ID NO 541 NM_000947 SEQ
ID NO 542 NM_000949 SEQ ID NO 543 NM_000950 SEQ ID NO 544 NM_000954
SEQ ID NO 545 NM_000964 SEQ ID NO 546 NM_001003 SEQ ID NO 549
NM_001016 SEQ ID NO 551 NM_001047 SEQ ID NO 553 NM_001066 SEQ ID NO
555 NM_001071 SEQ ID NO 556 NM_001078 SEQ ID NO 557 NM_001085 SEQ
ID NO 558 NM_001089 SEQ ID NO 559 NM_001109 SEQ ID NO 560 NM_001122
SEQ ID NO 561 NM_001124 SEQ ID NO 562 NM_001161 SEQ ID NO 563
NM_001165 SEQ ID NO 564 NM_001166 SEQ ID NO 565 NM_001168 SEQ ID NO
566 NM_001179 SEQ ID NO 567 NM_001185 SEQ ID NO 569 NM_001203 SEQ
ID NO 570 NM_001207 SEQ ID NO 573 NM_001216 SEQ ID NO 574 NM_001218
SEQ ID NO 575 NM_001223 SEQ ID NO 576 NM_001225 SEQ ID NO 577
NM_001233 SEQ ID NO 578 NM_001236 SEQ ID NO 579 NM_001237 SEQ ID NO
580 NM_001251 SEQ ID NO 581 NM_001255 SEQ ID NO 582 NM_001262 SEQ
ID NO 583 NM_001263 SEQ ID NO 584 NM_001267 SEQ ID NO 585 NM_001276
SEQ ID NO 587 NM_001280 SEQ ID NO 588 NM_001282 SEQ ID NO 589
NM_001295 SEQ ID NO 590 NM_001305 SEQ ID NO 591 NM_001310 SEQ ID NO
592 NM_001312 SEQ ID NO 593 NM_001321 SEQ ID NO 594 NM_001327 SEQ
ID NO 595 NM_001329 SEQ ID NO 596 NM_001333 SEQ ID NO 597 NM_001338
SEQ ID NO 598 NM_001360 SEQ ID NO 599 NM_001363 SEQ ID NO 600
NM_001381 SEQ ID NO 601 NM_001394 SEQ ID NO 602 NM_001395 SEQ ID NO
603 NM_001419 SEQ ID NO 604 NM_001424 SEQ ID NO 605 NM_001428 SEQ
ID NO 606 NM_001436 SEQ ID NO 607 NM_001444 SEQ ID NO 608 NM_001446
SEQ ID NO 609 NM_001453 SEQ ID NO 611 NM_001456 SEQ ID NO 612
NM_001457 SEQ ID NO 613 NM_001463 SEQ ID NO 614 NM_001465 SEQ ID NO
615 NM_001481 SEQ ID NO 616 NM_001493 SEQ ID NO 617 NM_001494 SEQ
ID NO 618 NM_001500 SEQ ID NO 619 NM_001504 SEQ ID NO 620 NM_001511
SEQ ID NO 621 NM_001513 SEQ ID NO 622 NM_001527 SEQ ID NO 623
NM_001529 SEQ ID NO 624 NM_001530 SEQ ID NO 625 NM_001540 SEQ ID NO
626 NM_001550 SEQ ID NO 627 NM_001551 SEQ ID NO 628 NM_001552 SEQ
ID NO 629 NM_001554 SEQ ID NO 631 NM_001558 SEQ ID NO 632 NM_001560
SEQ ID NO 633 NM_001565 SEQ ID NO 634 NM_001569 SEQ ID NO 635
NM_001605 SEQ ID NO 636 NM_001609 SEQ ID NO 637 NM_001615 SEQ ID NO
638 NM_001623 SEQ ID NO 639 NM_001627 SEQ ID NO 640 NM_001628 SEQ
ID NO 641 NM_001630 SEQ ID NO 642 NM_001634 SEQ ID NO 643 NM_001656
SEQ ID NO 644 NM_001673 SEQ ID NO 645 NM_001675 SEQ ID NO 647
NM_001679 SEQ ID NO 648 NM_001689 SEQ ID NO 649 NM_001703 SEQ ID NO
650 NM_001710 SEQ ID NO 651 NM_001725 SEQ ID NO 652 NM_001730 SEQ
ID NO 653 NM_001733 SEQ ID NO 654 NM_001734 SEQ ID NO 655 NM_001740
SEQ ID NO 656 NM_001745 SEQ ID NO 657 NM_001747 SEQ ID NO 658
NM_001756 SEQ ID NO 659 NM_001757 SEQ ID NO 660 NM_001758 SEQ ID NO
661 NM_001762 SEQ ID NO 662 NM_001767 SEQ ID NO 663 NM_001770 SEQ
ID NO 664 NM_001777 SEQ ID NO 665 NM_001778 SEQ ID NO 666 NM_001781
SEQ ID NO 667 NM_001786 SEQ ID NO 668 NM_001793 SEQ ID NO 669
NM_001803 SEQ ID NO 671 NM_001806 SEQ ID NO 672 NM_001809 SEQ ID NO
673 NM_001814 SEQ ID NO 674 NM_001826 SEQ ID NO 675 NM_001830 SEQ
ID NO 677 NM_001838 SEQ ID NO 678 NM_001839 SEQ ID NO 679 NM_001853
SEQ ID NO 681 NM_001859 SEQ ID NO 682 NM_001861 SEQ ID NO 683
NM_001874 SEQ ID NO 685 NM_001885 SEQ ID NO 686 NM_001892 SEQ ID NO
688 NM_001897 SEQ ID NO 689 NM_001899 SEQ ID NO 690 NM_001905 SEQ
ID NO 691 NM_001912 SEQ ID NO 692 NM_001914 SEQ ID NO 693 NM_001919
SEQ ID NO 694 NM_001941 SEQ ID NO 695 NM_001943 SEQ ID NO 696
NM_001944 SEQ ID NO 697 NM_001953 SEQ ID NO 699 NM_001954 SEQ ID NO
700 NM_001955 SEQ ID NO 701 NM_001956 SEQ ID NO 702 NM_001958 SEQ
ID NO 703 NM_001961 SEQ ID NO 705 NM_001970 SEQ ID NO 706 NM_001979
SEQ ID NO 707 NM_001982 SEQ ID NO 708 NM_002017 SEQ ID NO 710
NM_002033 SEQ ID NO 713 NM_002046 SEQ ID NO 714 NM_002047 SEQ ID NO
715 NM_002051 SEQ ID NO 716 NM_002053 SEQ ID NO 717 NM_002061 SEQ
ID NO 718 NM_002065 SEQ ID NO 719 NM_002068 SEQ ID NO 720 NM_002077
SEQ ID NO 722 NM_002091 SEQ ID NO 723 NM_002101 SEQ ID NO 724
NM_002106 SEQ ID NO 725 NM_002110 SEQ ID NO 726 NM_002111 SEQ ID NO
727 NM_002115 SEQ ID NO 728 NM_002118 SEQ ID NO 729 NM_002123 SEQ
ID NO 730 NM_002131 SEQ ID NO 731 NM_002136 SEQ ID NO 732 NM_002145
SEQ ID NO 733 NM_002164 SEQ ID NO 734 NM_002168 SEQ ID NO 735
NM_002184 SEQ ID NO 736 NM_002185 SEQ ID NO 737 NM_002189 SEQ ID NO
738 NM_002200 SEQ ID NO 739 NM_002201 SEQ ID NO 740 NM_002213 SEQ
ID NO 741 NM_002219 SEQ ID NO 742 NM_002222 SEQ ID NO 743 NM_002239
SEQ ID NO 744 NM_002243 SEQ ID NO 745 NM_002245 SEQ ID NO 746
NM_002250 SEQ ID NO 747 NM_002254 SEQ ID NO 748 NM_002266 SEQ ID NO
749 NM_002273 SEQ ID NO 750 NM_002281 SEQ ID NO 751 NM_002292 SEQ
ID NO 752 NM_002298 SEQ ID NO 753 NM_002300 SEQ ID NO 754 NM_002308
SEQ ID NO 755 NM_002314 SEQ ID NO 756 NM_002337 SEQ ID NO 757
NM_002341 SEQ ID NO 758 NM_002342 SEQ ID NO 759 NM_002346 SEQ ID NO
760 NM_002349 SEQ ID NO 761 NM_002350 SEQ ID NO 762 NM_002356 SEQ
ID NO 763 NM_002358 SEQ ID NO 764 NM_002370 SEQ ID NO 765 NM_002395
SEQ ID NO 766 NM_002416 SEQ ID NO 767 NM_002421 SEQ ID NO 768
NM_002426 SEQ ID NO 769 NM_002435 SEQ ID NO 770 NM_002438 SEQ ID NO
771 NM_002444 SEQ ID NO 772 NM_002449 SEQ ID NO 773 NM_002450 SEQ
ID NO 774 NM_002456 SEQ ID NO 775 NM_002466 SEQ ID NO 776 NM_002482
SEQ ID NO 777 NM_002497 SEQ ID NO 778 NM_002510 SEQ ID NO 779
NM_002515 SEQ ID NO 781 NM_002524 SEQ ID NO 782 NM_002539 SEQ ID NO
783 NM_002555 SEQ ID NO 785 NM_002570 SEQ ID NO 787 NM_002579 SEQ
ID NO 788 NM_002587 SEQ ID NO 789 NM_002590 SEQ ID NO 790 NM_002600
SEQ ID NO 791 NM_002614 SEQ ID NO 792 NM_002618 SEQ ID NO 794
NM_002626 SEQ ID NO 795 NM_002633 SEQ ID NO 796 NM_002639 SEQ ID NO
797 NM_002648 SEQ ID NO 798 NM_002659 SEQ ID NO 799 NM_002661 SEQ
ID NO 800 NM_002662 SEQ ID NO 801 NM_002664 SEQ ID NO 802 NM_002689
SEQ ID NO 804 NM_002690 SEQ ID NO 805 NM_002709 SEQ ID NO 806
NM_002727 SEQ ID NO 807 NM_002729 SEQ ID NO 808 NM_002734 SEQ ID NO
809 NM_002736 SEQ ID NO 810 NM_002740 SEQ ID NO 811 NM_002748 SEQ
ID NO 813 NM_002774 SEQ ID NO 814 NM_002775 SEQ ID NO 815 NM_002776
SEQ ID NO 816 NM_002789 SEQ ID NO 817 NM_002794 SEQ ID NO 818
NM_002796 SEQ ID NO 819 NM_002800 SEQ ID NO 820 NM_002801 SEQ ID NO
821 NM_002808 SEQ ID NO 822 NM_002821 SEQ ID NO 824 NM_002826 SEQ
ID NO 825 NM_002827 SEQ ID NO 826 NM_002838 SEQ ID NO 827 NM_002852
SEQ ID NO 828
NM_002854 SEQ ID NO 829 NM_002856 SEQ ID NO 830 NM_002857 SEQ ID NO
831 NM_002858 SEQ ID NO 832 NM_002888 SEQ ID NO 833 NM_002890 SEQ
ID NO 834 NM_002901 SEQ ID NO 836 NM_002906 SEQ ID NO 837 NM_002916
SEQ ID NO 838 NM_002923 SEQ ID NO 839 NM_002933 SEQ ID NO 840
NM_002936 SEQ ID NO 841 NM_002937 SEQ ID NO 842 NM_002950 SEQ ID NO
843 NM_002961 SEQ ID NO 844 NM_002964 SEQ ID NO 845 NM_002965 SEQ
ID NO 846 NM_002966 SEQ ID NO 847 NM_002982 SEQ ID NO 849 NM_002983
SEQ ID NO 850 NM_002984 SEQ ID NO 851 NM_002985 SEQ ID NO 852
NM_002988 SEQ ID NO 853 NM_002996 SEQ ID NO 854 NM_002997 SEQ ID NO
855 NM_002999 SEQ ID NO 856 NM_003012 SEQ ID NO 857 NM_003022 SEQ
ID NO 858 NM_003034 SEQ ID NO 859 NM_003035 SEQ ID NO 860 NM_003039
SEQ ID NO 861 NM_003051 SEQ ID NO 862 NM_003064 SEQ ID NO 863
NM_003066 SEQ ID NO 864 NM_003088 SEQ ID NO 865 NM_003090 SEQ ID NO
866 NM_003096 SEQ ID NO 867 NM_003099 SEQ ID NO 868 NM_003102 SEQ
ID NO 869 NM_003104 SEQ ID NO 870 NM_003108 SEQ ID NO 871 NM_003121
SEQ ID NO 873 NM_003134 SEQ ID NO 874 NM_003137 SEQ ID NO 875
NM_003144 SEQ ID NO 876 NM_003146 SEQ ID NO 877 NM_003149 SEQ ID NO
878 NM_003151 SEQ ID NO 879 NM_003157 SEQ ID NO 880 NM_003158 SEQ
ID NO 881 NM_003165 SEQ ID NO 882 NM_003172 SEQ ID NO 883 NM_003177
SEQ ID NO 884 NM_003197 SEQ ID NO 885 NM_003202 SEQ ID NO 886
NM_003213 SEQ ID NO 887 NM_003217 SEQ ID NO 888 NM_003225 SEQ ID NO
889 NM_003226 SEQ ID NO 890 NM_003236 SEQ ID NO 892 NM_003239 SEQ
ID NO 893 NM_003248 SEQ ID NO 894 NM_003255 SEQ ID NO 895 NM_003258
SEQ ID NO 896 NM_003264 SEQ ID NO 897 NM_003283 SEQ ID NO 898
NM_003318 SEQ ID NO 899 NM_003329 SEQ ID NO 900 NM_003332 SEQ ID NO
901 NM_003358 SEQ ID NO 902 NM_003359 SEQ ID NO 903 NM_003360 SEQ
ID NO 904 NM_003368 SEQ ID NO 905 NM_003376 SEQ ID NO 906 NM_003380
SEQ ID NO 907 NM_003392 SEQ ID NO 908 NM_003412 SEQ ID NO 909
NM_003430 SEQ ID NO 910 NM_003462 SEQ ID NO 911 NM_003467 SEQ ID NO
912 NM_003472 SEQ ID NO 913 NM_003479 SEQ ID NO 914 NM_003489 SEQ
ID NO 915 NM_003494 SEQ ID NO 916 NM_003498 SEQ ID NO 917 NM_003504
SEQ ID NO 919 NM_003508 SEQ ID NO 920 NM_003510 SEQ ID NO 921
NM_003512 SEQ ID NO 922 NM_003528 SEQ ID NO 923 NM_003544 SEQ ID NO
924 NM_003561 SEQ ID NO 925 NM_003563 SEQ ID NO 926 NM_003568 SEQ
ID NO 927 NM_003579 SEQ ID NO 928 NM_003600 SEQ ID NO 929 NM_003615
SEQ ID NO 931 NM_003627 SEQ ID NO 932 NM_003645 SEQ ID NO 935
NM_003651 SEQ ID NO 936 NM_003657 SEQ ID NO 937 NM_003662 SEQ ID NO
938 NM_003670 SEQ ID NO 939 NM_003675 SEQ ID NO 940 NM_003676 SEQ
ID NO 941 NM_003681 SEQ ID NO 942 NM_003683 SEQ ID NO 943 NM_003686
SEQ ID NO 944 NM_003689 SEQ ID NO 945 NM_003714 SEQ ID NO 946
NM_003720 SEQ ID NO 947 NM_003726 SEQ ID NO 948 NM_003729 SEQ ID NO
949 NM_003740 SEQ ID NO 950 NM_003772 SEQ ID NO 952 NM_003791 SEQ
ID NO 953 NM_003793 SEQ ID NO 954 NM_003795 SEQ ID NO 955 NM_003806
SEQ ID NO 956 NM_003821 SEQ ID NO 957 NM_003829 SEQ ID NO 958
NM_003831 SEQ ID NO 959 NM_003862 SEQ ID NO 960 NM_003866 SEQ ID NO
961 NM_003875 SEQ ID NO 962 NM_003878 SEQ ID NO 963 NM_003894 SEQ
ID NO 965 NM_003897 SEQ ID NO 966 NM_003904 SEQ ID NO 967 NM_003929
SEQ ID NO 968 NM_003933 SEQ ID NO 969 NM_003937 SEQ ID NO 970
NM_003940 SEQ ID NO 971 NM_003942 SEQ ID NO 972 NM_003944 SEQ ID NO
973 NM_003953 SEQ ID NO 974 NM_003954 SEQ ID NO 975 NM_003975 SEQ
ID NO 976 NM_003981 SEQ ID NO 977 NM_003982 SEQ ID NO 978 NM_003986
SEQ ID NO 979 NM_004003 SEQ ID NO 980 NM_004010 SEQ ID NO 981
NM_004024 SEQ ID NO 982 NM_004038 SEQ ID NO 983 NM_004049 SEQ ID NO
984 NM_004052 SEQ ID NO 985 NM_004053 SEQ ID NO 986 NM_004079 SEQ
ID NO 987 NM_004104 SEQ ID NO 988 NM_004109 SEQ ID NO 989 NM_004110
SEQ ID NO 990 NM_004120 SEQ ID NO 991 NM_004131 SEQ ID NO 992
NM_004143 SEQ ID NO 993 NM_004154 SEQ ID NO 994 NM_004170 SEQ ID NO
996 NM_004172 SEQ ID NO 997 NM_004176 SEQ ID NO 998 NM_004180 SEQ
ID NO 999 NM_004181 SEQ ID NO 1000 NM_004184 SEQ ID NO 1001
NM_004203 SEQ ID NO 1002 NM_004207 SEQ ID NO 1003 NM_004217 SEQ ID
NO 1004 NM_004219 SEQ ID NO 1005 NM_004221 SEQ ID NO 1006 NM_004233
SEQ ID NO 1007 NM_004244 SEQ ID NO 1008 NM_004252 SEQ ID NO 1009
NM_004265 SEQ ID NO 1010 NM_004267 SEQ ID NO 1011 NM_004281 SEQ ID
NO 1012 NM_004289 SEQ ID NO 1013 NM_004298 SEQ ID NO 1015 NM_004301
SEQ ID NO 1016 NM_004305 SEQ ID NO 1017 NM_004311 SEQ ID NO 1018
NM_004315 SEQ ID NO 1019 NM_004323 SEQ ID NO 1020 NM_004330 SEQ ID
NO 1021 NM_004336 SEQ ID NO 1022 NM_004338 SEQ ID NO 1023 NM_004350
SEQ ID NO 1024 NM_004354 SEQ ID NO 1025 NM_004358 SEQ ID NO 1026
NM_004360 SEQ ID NO 1027 NM_004362 SEQ ID NO 1028 NM_004374 SEQ ID
NO 1029 NM_004378 SEQ ID NO 1030 NM_004392 SEQ ID NO 1031 NM_004395
SEQ ID NO 1032 NM_004414 SEQ ID NO 1033 NM_004418 SEQ ID NO 1034
NM_004425 SEQ ID NO 1035 NM_004431 SEQ ID NO 1036 NM_004436 SEQ ID
NO 1037 NM_004438 SEQ ID NO 1038 NM_004443 SEQ ID NO 1039 NM_004446
SEQ ID NO 1040 NM_004451 SEQ ID NO 1041 NM_004454 SEQ ID NO 1042
NM_004456 SEQ ID NO 1043 NM_004458 SEQ ID NO 1044 NM_004472 SEQ ID
NO 1045 NM_004480 SEQ ID NO 1046 NM_004482 SEQ ID NO 1047 NM_004494
SEQ ID NO 1048 NM_004496 SEQ ID NO 1049 NM_004503 SEQ ID NO 1050
NM_004504 SEQ ID NO 1051 NM_004515 SEQ ID NO 1052 NM_004522 SEQ ID
NO 1053 NM_004523 SEQ ID NO 1054 NM_004525 SEQ ID NO 1055 NM_004556
SEQ ID NO 1056 NM_004559 SEQ ID NO 1057 NM_004569 SEQ ID NO 1058
NM_004577 SEQ ID NO 1059 NM_004585 SEQ ID NO 1060 NM_004587 SEQ ID
NO 1061 NM_004594 SEQ ID NO 1062 NM_004599 SEQ ID NO 1063 NM_004633
SEQ ID NO 1066 NM_004642 SEQ ID NO 1067 NM_004648 SEQ ID NO 1068
NM_004663 SEQ ID NO 1069 NM_004664 SEQ ID NO 1070 NM_004684 SEQ ID
NO 1071 NM_004688 SEQ ID NO 1072 NM_004694 SEQ ID NO 1073 NM_004695
SEQ ID NO 1074 NM_004701 SEQ ID NO 1075 NM_004708 SEQ ID NO 1077
NM_004711 SEQ ID NO 1078 NM_004726 SEQ ID NO 1079 NM_004750 SEQ ID
NO 1081 NM_004761 SEQ ID NO 1082 NM_004762 SEQ ID NO 1083 NM_004780
SEQ ID NO 1085 NM_004791 SEQ ID NO 1086 NM_004798 SEQ ID NO 1087
NM_004808 SEQ ID NO 1088 NM_004811 SEQ ID NO 1089 NM_004833 SEQ ID
NO 1090 NM_004835 SEQ ID NO 1091 NM_004843 SEQ ID NO 1092 NM_004847
SEQ ID NO 1093 NM_004848 SEQ ID NO 1094 NM_004864 SEQ ID NO 1095
NM_004865 SEQ ID NO 1096
NM_004866 SEQ ID NO 1097 NM_004877 SEQ ID NO 1098 NM_004900 SEQ ID
NO 1099 NM_004906 SEQ ID NO 1100 NM_004910 SEQ ID NO 1101 NM_004918
SEQ ID NO 1103 NM_004923 SEQ ID NO 1104 NM_004938 SEQ ID NO 1105
NM_004951 SEQ ID NO 1106 NM_004968 SEQ ID NO 1107 NM_004994 SEQ ID
NO 1108 NM_004999 SEQ ID NO 1109 NM_005001 SEQ ID NO 1110 NM_005002
SEQ ID NO 1111 NM_005012 SEQ ID NO 1112 NM_005032 SEQ ID NO 1113
NM_005044 SEQ ID NO 1114 NM_005046 SEQ ID NO 1115 NM_005049 SEQ ID
NO 1116 NM_005067 SEQ ID NO 1117 NM_005077 SEQ ID NO 1118 NM_005080
SEQ ID NO 1119 NM_005084 SEQ ID NO 1120 NM_005130 SEQ ID NO 1122
NM_005139 SEQ ID NO 1123 NM_005168 SEQ ID NO 1125 NM_005190 SEQ ID
NO 1126 NM_005196 SEQ ID NO 1127 NM_005213 SEQ ID NO 1128 NM_005218
SEQ ID NO 1129 NM_005235 SEQ ID NO 1130 NM_005245 SEQ ID NO 1131
NM_005249 SEQ ID NO 1132 NM_005257 SEQ ID NO 1133 NM_005264 SEQ ID
NO 1134 NM_005271 SEQ ID NO 1135 NM_005314 SEQ ID NO 1136 NM_005321
SEQ ID NO 1137 NM_005322 SEQ ID NO 1138 NM_005325 SEQ ID NO 1139
NM_005326 SEQ ID NO 1140 NM_005335 SEQ ID NO 1141 NM_005337 SEQ ID
NO 1142 NM_005342 SEQ ID NO 1143 NM_005345 SEQ ID NO 1144 NM_005357
SEQ ID NO 1145 NM_005375 SEQ ID NO 1146 NM_005391 SEQ ID NO 1147
NM_005408 SEQ ID NO 1148 NM_005409 SEQ ID NO 1149 NM_005410 SEQ ID
NO 1150 NM_005426 SEQ ID NO 1151 NM_005433 SEQ ID NO 1152 NM_005441
SEQ ID NO 1153 NM_005443 SEQ ID NO 1154 NM_005483 SEQ ID NO 1155
NM_005486 SEQ ID NO 1156 NM_005496 SEQ ID NO 1157 NM_005498 SEQ ID
NO 1158 NM_005499 SEQ ID NO 1159 NM_005514 SEQ ID NO 1160 NM_005531
SEQ ID NO 1162 NM_005538 SEQ ID NO 1163 NM_005541 SEQ ID NO 1164
NM_005544 SEQ ID NO 1165 NM_005548 SEQ ID NO 1166 NM_005554 SEQ ID
NO 1167 NM_005555 SEQ ID NO 1168 NM_005556 SEQ ID NO 1169 NM_005557
SEQ ID NO 1170 NM_005558 SEQ ID NO 1171 NM_005562 SEQ ID NO 1172
NM_005563 SEQ ID NO 1173 NM_005565 SEQ ID NO 1174 NM_005566 SEQ ID
NO 1175 NM_005572 SEQ ID NO 1176 NM_005582 SEQ ID NO 1177 NM_005608
SEQ ID NO 1178 NM_005614 SEQ ID NO 1179 NM_005617 SEQ ID NO 1180
NM_005620 SEQ ID NO 1181 NM_005625 SEQ ID NO 1182 NM_005651 SEQ ID
NO 1183 NM_005658 SEQ ID NO 1184 NM_005659 SEQ ID NO 1185 NM_005667
SEQ ID NO 1186 NM_005686 SEQ ID NO 1187 NM_005690 SEQ ID NO 1188
NM_005720 SEQ ID NO 1190 NM_005727 SEQ ID NO 1191 NM_005733 SEQ ID
NO 1192 NM_005737 SEQ ID NO 1193 NM_005742 SEQ ID NO 1194 NM_005746
SEQ ID NO 1195 NM_005749 SEQ ID NO 1196 NM_005760 SEQ ID NO 1197
NM_005764 SEQ ID NO 1198 NM_005794 SEQ ID NO 1199 NM_005796 SEQ ID
NO 1200 NM_005804 SEQ ID NO 1201 NM_005813 SEQ ID NO 1202 NM_005824
SEQ ID NO 1203 NM_005825 SEQ ID NO 1204 NM_005849 SEQ ID NO 1205
NM_005853 SEQ ID NO 1206 NM_005855 SEQ ID NO 1207 NM_005864 SEQ ID
NO 1208 NM_005874 SEQ ID NO 1209 NM_005876 SEQ ID NO 1210 NM_005880
SEQ ID NO 1211 NM_005891 SEQ ID NO 1212 NM_005892 SEQ ID NO 1213
NM_005899 SEQ ID NO 1214 NM_005915 SEQ ID NO 1215 NM_005919 SEQ ID
NO 1216 NM_005923 SEQ ID NO 1217 NM_005928 SEQ ID NO 1218 NM_005932
SEQ ID NO 1219 NM_005935 SEQ ID NO 1220 NM_005945 SEQ ID NO 1221
NM_005953 SEQ ID NO 1222 NM_005978 SEQ ID NO 1223 NM_005990 SEQ ID
NO 1224 NM_006002 SEQ ID NO 1225 NM_006004 SEQ ID NO 1226 NM_006005
SEQ ID NO 1227 NM_006006 SEQ ID NO 1228 NM_006017 SEQ ID NO 1229
NM_006018 SEQ ID NO 1230 NM_006023 SEQ ID NO 1231 NM_006027 SEQ ID
NO 1232 NM_006029 SEQ ID NO 1233 NM_006033 SEQ ID NO 1234 NM_006051
SEQ ID NO 1235 NM_006055 SEQ ID NO 1236 NM_006074 SEQ ID NO 1237
NM_006086 SEQ ID NO 1238 NM_006087 SEQ ID NO 1239 NM_006096 SEQ ID
NO 1240 NM_006101 SEQ ID NO 1241 NM_006103 SEQ ID NO 1242 NM_006111
SEQ ID NO 1243 NM_006113 SEQ ID NO 1244 NM_006115 SEQ ID NO 1245
NM_006117 SEQ ID NO 1246 NM_006142 SEQ ID NO 1247 NM_006144 SEQ ID
NO 1248 NM_006148 SEQ ID NO 1249 NM_006153 SEQ ID NO 1250 NM_006159
SEQ ID NO 1251 NM_006170 SEQ ID NO 1252 NM_006197 SEQ ID NO 1253
NM_006224 SEQ ID NO 1255 NM_006227 SEQ ID NO 1256 NM_006235 SEQ ID
NO 1257 NM_006243 SEQ ID NO 1258 NM_006264 SEQ ID NO 1259 NM_006271
SEQ ID NO 1261 NM_006274 SEQ ID NO 1262 NM_006290 SEQ ID NO 1265
NM_006291 SEQ ID NO 1266 NM_006296 SEQ ID NO 1267 NM_006304 SEQ ID
NO 1268 NM_006314 SEQ ID NO 1269 NM_006332 SEQ ID NO 1270 NM_006357
SEQ ID NO 1271 NM_006366 SEQ ID NO 1272 NM_006372 SEQ ID NO 1273
NM_006377 SEQ ID NO 1274 NM_006378 SEQ ID NO 1275 NM_006383 SEQ ID
NO 1276 NM_006389 SEQ ID NO 1277 NM_006393 SEQ ID NO 1278 NM_006398
SEQ ID NO 1279 NM_006406 SEQ ID NO 1280 NM_006408 SEQ ID NO 1281
NM_006410 SEQ ID NO 1282 NM_006414 SEQ ID NO 1283 NM_006417 SEQ ID
NO 1284 NM_006430 SEQ ID NO 1285 NM_006460 SEQ ID NO 1286 NM_006461
SEQ ID NO 1287 NM_006469 SEQ ID NO 1288 NM_006470 SEQ ID NO 1289
NM_006491 SEQ ID NO 1290 NM_006495 SEQ ID NO 1291 NM_006500 SEQ ID
NO 1292 NM_006509 SEQ ID NO 1293 NM_006516 SEQ ID NO 1294 NM_006533
SEQ ID NO 1295 NM_006551 SEQ ID NO 1296 NM_006556 SEQ ID NO 1297
NM_006558 SEQ ID NO 1298 NM_006564 SEQ ID NO 1299 NM_006573 SEQ ID
NO 1300 NM_006607 SEQ ID NO 1301 NM_006622 SEQ ID NO 1302 NM_006623
SEQ ID NO 1303 NM_006636 SEQ ID NO 1304 NM_006670 SEQ ID NO 1305
NM_006681 SEQ ID NO 1306 NM_006682 SEQ ID NO 1307 NM_006696 SEQ ID
NO 1308 NM_006698 SEQ ID NO 1309 NM_006705 SEQ ID NO 1310 NM_006739
SEQ ID NO 1311 NM_006748 SEQ ID NO 1312 NM_006759 SEQ ID NO 1313
NM_006762 SEQ ID NO 1314 NM_006763 SEQ ID NO 1315 NM_006769 SEQ ID
NO 1316 NM_006770 SEQ ID NO 1317 NM_006780 SEQ ID NO 1318 NM_006787
SEQ ID NO 1319 NM_006806 SEQ ID NO 1320 NM_006813 SEQ ID NO 1321
NM_006825 SEQ ID NO 1322 NM_006826 SEQ ID NO 1323 NM_006829 SEQ ID
NO 1324 NM_006834 SEQ ID NO 1325 NM_006835 SEQ ID NO 1326 NM_006840
SEQ ID NO 1327 NM_006845 SEQ ID NO 1328 NM_006847 SEQ ID NO 1329
NM_006851 SEQ ID NO 1330 NM_006855 SEQ ID NO 1331 NM_006864 SEQ ID
NO 1332 NM_006868 SEQ ID NO 1333 NM_006875 SEQ ID NO 1334 NM_006889
SEQ ID NO 1336 NM_006892 SEQ ID NO 1337 NM_006912 SEQ ID NO 1338
NM_006931 SEQ ID NO 1341 NM_006941 SEQ ID NO 1342 NM_006943 SEQ ID
NO 1343 NM_006984 SEQ ID NO 1344 NM_007005 SEQ ID NO 1345 NM_007006
SEQ ID NO 1346 NM_007019 SEQ ID NO 1347 NM_007027 SEQ ID NO 1348
NM_007044 SEQ ID NO 1350 NM_007050 SEQ ID NO 1351 NM_007057 SEQ ID
NO 1352 NM_007069 SEQ ID NO 1353 NM_007074 SEQ ID NO 1355 NM_007088
SEQ ID NO 1356 NM_007111 SEQ ID NO 1357 NM_007146 SEQ ID NO 1358
NM_007173 SEQ ID NO 1359 NM_007177 SEQ ID NO 1360 NM_007196 SEQ ID
NO 1361
NM_007203 SEQ ID NO 1362 NM_007214 SEQ ID NO 1363 NM_007217 SEQ ID
NO 1364 NM_007231 SEQ ID NO 1365 NM_007268 SEQ ID NO 1367 NM_007274
SEQ ID NO 1368 NM_007275 SEQ ID NO 1369 NM_007281 SEQ ID NO 1370
NM_007309 SEQ ID NO 1371 NM_007315 SEQ ID NO 1372 NM_007334 SEQ ID
NO 1373 NM_007358 SEQ ID NO 1374 NM_009585 SEQ ID NO 1375 NM_009587
SEQ ID NO 1376 NM_009588 SEQ ID NO 1377 NM_012062 SEQ ID NO 1378
NM_012067 SEQ ID NO 1379 NM_012101 SEQ ID NO 1380 NM_012105 SEQ ID
NO 1381 NM_012108 SEQ ID NO 1382 NM_012110 SEQ ID NO 1383 NM_012124
SEQ ID NO 1384 NM_012142 SEQ ID NO 1386 NM_012155 SEQ ID NO 1388
NM_012175 SEQ ID NO 1389 NM_012177 SEQ ID NO 1390 NM_012205 SEQ ID
NO 1391 NM_012219 SEQ ID NO 1393 NM_012242 SEQ ID NO 1394 NM_012250
SEQ ID NO 1395 NM_012261 SEQ ID NO 1397 NM_012286 SEQ ID NO 1398
NM_012319 SEQ ID NO 1400 NM_012332 SEQ ID NO 1401 NM_012336 SEQ ID
NO 1402 NM_012339 SEQ ID NO 1404 NM_012341 SEQ ID NO 1405 NM_012391
SEQ ID NO 1406 NM_012394 SEQ ID NO 1407 NM_012413 SEQ ID NO 1408
NM_012421 SEQ ID NO 1409 NM_012425 SEQ ID NO 1410 NM_012427 SEQ ID
NO 1411 NM_012429 SEQ ID NO 1413 NM_012446 SEQ ID NO 1414 NM_012463
SEQ ID NO 1415 NM_012474 SEQ ID NO 1416 NM_013230 SEQ ID NO 1417
NM_013233 SEQ ID NO 1418 NM_013238 SEQ ID NO 1419 NM_013239 SEQ ID
NO 1420 NM_013242 SEQ ID NO 1421 NM_013257 SEQ ID NO 1423 NM_013261
SEQ ID NO 1424 NM_013262 SEQ ID NO 1425 NM_013277 SEQ ID NO 1426
NM_013296 SEQ ID NO 1427 NM_013301 SEQ ID NO 1428 NM_013324 SEQ ID
NO 1429 NM_013327 SEQ ID NO 1430 NM_013336 SEQ ID NO 1431 NM_013339
SEQ ID NO 1432 NM_013363 SEQ ID NO 1433 NM_013378 SEQ ID NO 1435
NM_013384 SEQ ID NO 1436 NM_013385 SEQ ID NO 1437 NM_013406 SEQ ID
NO 1438 NM_013437 SEQ ID NO 1439 NM_013451 SEQ ID NO 1440 NM_013943
SEQ ID NO 1441 NM_013994 SEQ ID NO 1442 NM_013995 SEQ ID NO 1443
NM_014026 SEQ ID NO 1444 NM_014029 SEQ ID NO 1445 NM_014036 SEQ ID
NO 1446 NM_014062 SEQ ID NO 1447 NM_014074 SEQ ID NO 1448 NM_014096
SEQ ID NO 1450 NM_014109 SEQ ID NO 1451 NM_014112 SEQ ID NO 1452
NM_014147 SEQ ID NO 1453 NM_014149 SEQ ID NO 1454 NM_014164 SEQ ID
NO 1455 NM_014172 SEQ ID NO 1456 NM_014175 SEQ ID NO 1457 NM_014181
SEQ ID NO 1458 NM_014184 SEQ ID NO 1459 NM_014211 SEQ ID NO 1460
NM_014214 SEQ ID NO 1461 NM_014216 SEQ ID NO 1462 NM_014241 SEQ ID
NO 1463 NM_014246 SEQ ID NO 1465 NM_014268 SEQ ID NO 1466 NM_014272
SEQ ID NO 1467 NM_014274 SEQ ID NO 1468 NM_014289 SEQ ID NO 1469
NM_014298 SEQ ID NO 1470 NM_014302 SEQ ID NO 1471 NM_014315 SEQ ID
NO 1473 NM_014316 SEQ ID NO 1474 NM_014317 SEQ ID NO 1475 NM_014320
SEQ ID NO 1476 NM_014321 SEQ ID NO 1477 NM_014325 SEQ ID NO 1478
NM_014335 SEQ ID NO 1479 NM_014363 SEQ ID NO 1480 NM_014364 SEQ ID
NO 1481 NM_014365 SEQ ID NO 1482 NM_014373 SEQ ID NO 1483 NM_014382
SEQ ID NO 1484 NM_014395 SEQ ID NO 1485 NM_014398 SEQ ID NO 1486
NM_014399 SEQ ID NO 1487 NM_014402 SEQ ID NO 1488 NM_014428 SEQ ID
NO 1489 NM_014448 SEQ ID NO 1490 NM_014449 SEQ ID NO 1491 NM_014450
SEQ ID NO 1492 NM_014452 SEQ ID NO 1493 NM_014453 SEQ ID NO 1494
NM_014456 SEQ ID NO 1495 NM_014479 SEQ ID NO 1497 NM_014501 SEQ ID
NO 1498 NM_014552 SEQ ID NO 1500 NM_014553 SEQ ID NO 1501 NM_014570
SEQ ID NO 1502 NM_014575 SEQ ID NO 1503 NM_014585 SEQ ID NO 1504
NM_014595 SEQ ID NO 1505 NM_014624 SEQ ID NO 1507 NM_014633 SEQ ID
NO 1508 NM_014640 SEQ ID NO 1509 NM_014642 SEQ ID NO 1510 NM_014643
SEQ ID NO 1511 NM_014656 SEQ ID NO 1512 NM_014668 SEQ ID NO 1513
NM_014669 SEQ ID NO 1514 NM_014673 SEQ ID NO 1515 NM_014675 SEQ ID
NO 1516 NM_014679 SEQ ID NO 1517 NM_014680 SEQ ID NO 1518 NM_014696
SEQ ID NO 1519 NM_014700 SEQ ID NO 1520 NM_014715 SEQ ID NO 1521
NM_014721 SEQ ID NO 1522 NM_014737 SEQ ID NO 1524 NM_014738 SEQ ID
NO 1525 NM_014747 SEQ ID NO 1526 NM_014750 SEQ ID NO 1527 NM_014754
SEQ ID NO 1528 NM_014767 SEQ ID NO 1529 NM_014770 SEQ ID NO 1530
NM_014773 SEQ ID NO 1531 NM_014776 SEQ ID NO 1532 NM_014782 SEQ ID
NO 1533 NM_014785 SEQ ID NO 1534 NM_014791 SEQ ID NO 1535 NM_014808
SEQ ID NO 1536 NM_014811 SEQ ID NO 1537 NM_014812 SEQ ID NO 1538
NM_014838 SEQ ID NO 1540 NM_014862 SEQ ID NO 1542 NM_014865 SEQ ID
NO 1543 NM_014870 SEQ ID NO 1544 NM_014875 SEQ ID NO 1545 NM_014886
SEQ ID NO 1547 NM_014889 SEQ ID NO 1548 NM_014905 SEQ ID NO 1549
NM_014935 SEQ ID NO 1550 NM_014945 SEQ ID NO 1551 NM_014965 SEQ ID
NO 1552 NM_014967 SEQ ID NO 1553 NM_014968 SEQ ID NO 1554 NM_015032
SEQ ID NO 1555 NM_015239 SEQ ID NO 1556 NM_015383 SEQ ID NO 1557
NM_015392 SEQ ID NO 1558 NM_015416 SEQ ID NO 1559 NM_015417 SEQ ID
NO 1560 NM_015420 SEQ ID NO 1561 NM_015434 SEQ ID NO 1562 NM_015474
SEQ ID NO 1563 NM_015507 SEQ ID NO 1565 NM_015513 SEQ ID NO 1566
NM_015515 SEQ ID NO 1567 NM_015523 SEQ ID NO 1568 NM_015524 SEQ ID
NO 1569 NM_015599 SEQ ID NO 1571 NM_015623 SEQ ID NO 1572 NM_015640
SEQ ID NO 1573 NM_015641 SEQ ID NO 1574 NM_015678 SEQ ID NO 1575
NM_015721 SEQ ID NO 1576 NM_015892 SEQ ID NO 1578 NM_015895 SEQ ID
NO 1579 NM_015907 SEQ ID NO 1580 NM_015925 SEQ ID NO 1581 NM_015937
SEQ ID NO 1582 NM_015954 SEQ ID NO 1583 NM_015955 SEQ ID NO 1584
NM_015961 SEQ ID NO 1585 NM_015984 SEQ ID NO 1587 NM_015986 SEQ ID
NO 1588 NM_015987 SEQ ID NO 1589 NM_015991 SEQ ID NO 1590 NM_016002
SEQ ID NO 1592 NM_016028 SEQ ID NO 1594 NM_016029 SEQ ID NO 1595
NM_016047 SEQ ID NO 1596 NM_016048 SEQ ID NO 1597 NM_016050 SEQ ID
NO 1598 NM_016056 SEQ ID NO 1599 NM_016058 SEQ ID NO 1600 NM_016066
SEQ ID NO 1601 NM_016072 SEQ ID NO 1602 NM_016073 SEQ ID NO 1603
NM_016108 SEQ ID NO 1605 NM_016109 SEQ ID NO 1606 NM_016121 SEQ ID
NO 1607 NM_016126 SEQ ID NO 1608 NM_016127 SEQ ID NO 1609 NM_016135
SEQ ID NO 1610 NM_016142 SEQ ID NO 1612 NM_016153 SEQ ID NO 1613
NM_016171 SEQ ID NO 1614 NM_016175 SEQ ID NO 1615 NM_016184 SEQ ID
NO 1616 NM_016185 SEQ ID NO 1617 NM_016187 SEQ ID NO 1618 NM_016199
SEQ ID NO 1619 NM_016210 SEQ ID NO 1620 NM_016217 SEQ ID NO 1621
NM_016228 SEQ ID NO 1623 NM_016229 SEQ ID NO 1624 NM_016235 SEQ ID
NO 1625 NM_016240 SEQ ID NO 1626 NM_016243 SEQ ID NO 1627 NM_016250
SEQ ID NO 1628 NM_016267 SEQ ID NO 1629 NM_016271 SEQ ID NO 1630
NM_016299 SEQ ID NO 1631 NM_016306 SEQ ID NO 1632 NM_016308 SEQ ID
NO 1634 NM_016321 SEQ ID NO 1635 NM_016337 SEQ ID NO 1636 NM_016352
SEQ ID NO 1637 NM_016359 SEQ ID NO 1638 NM_016401 SEQ ID NO 1641
NM_016403 SEQ ID NO 1642 NM_016411 SEQ ID NO 1643 NM_016423 SEQ ID
NO 1644
NM_016463 SEQ ID NO 1647 NM_016475 SEQ ID NO 1649 NM_016477 SEQ ID
NO 1650 NM_016491 SEQ ID NO 1651 NM_016495 SEQ ID NO 1652 NM_016542
SEQ ID NO 1653 NM_016548 SEQ ID NO 1654 NM_016569 SEQ ID NO 1655
NM_016577 SEQ ID NO 1656 NM_016582 SEQ ID NO 1657 NM_016593 SEQ ID
NO 1658 NM_016603 SEQ ID NO 1659 NM_016612 SEQ ID NO 1660 NM_016619
SEQ ID NO 1661 NM_016623 SEQ ID NO 1663 NM_016625 SEQ ID NO 1664
NM_016629 SEQ ID NO 1665 NM_016640 SEQ ID NO 1666 NM_016645 SEQ ID
NO 1667 NM_016650 SEQ ID NO 1668 NM_016657 SEQ ID NO 1669 NM_016733
SEQ ID NO 1670 NM_016815 SEQ ID NO 1671 NM_016817 SEQ ID NO 1672
NM_016818 SEQ ID NO 1673 NM_016839 SEQ ID NO 1675 NM_017414 SEQ ID
NO 1676 NM_017422 SEQ ID NO 1677 NM_017423 SEQ ID NO 1678 NM_017447
SEQ ID NO 1679 NM_017518 SEQ ID NO 1680 NM_017522 SEQ ID NO 1681
NM_017540 SEQ ID NO 1682 NM_017555 SEQ ID NO 1683 NM_017572 SEQ ID
NO 1684 NM_017585 SEQ ID NO 1685 NM_017586 SEQ ID NO 1686 NM_017596
SEQ ID NO 1687 NM_017606 SEQ ID NO 1688 NM_017617 SEQ ID NO 1689
NM_017633 SEQ ID NO 1690 NM_017634 SEQ ID NO 1691 NM_017646 SEQ ID
NO 1692 NM_017660 SEQ ID NO 1693 NM_017680 SEQ ID NO 1694 NM_017691
SEQ ID NO 1695 NM_017698 SEQ ID NO 1696 NM_017702 SEQ ID NO 1697
NM_017731 SEQ ID NO 1699 NM_017732 SEQ ID NO 1700 NM_017733 SEQ ID
NO 1701 NM_017734 SEQ ID NO 1702 NM_017746 SEQ ID NO 1703 NM_017750
SEQ ID NO 1704 NM_017761 SEQ ID NO 1705 NM_017763 SEQ ID NO 1706
NM_017770 SEQ ID NO 1707 NM_017779 SEQ ID NO 1708 NM_017780 SEQ ID
NO 1709 NM_017782 SEQ ID NO 1710 NM_017786 SEQ ID NO 1711 NM_017791
SEQ ID NO 1712 NM_017805 SEQ ID NO 1713 NM_017816 SEQ ID NO 1714
NM_017821 SEQ ID NO 1715 NM_017835 SEQ ID NO 1716 NM_017843 SEQ ID
NO 1717 NM_017857 SEQ ID NO 1718 NM_017901 SEQ ID NO 1719 NM_017906
SEQ ID NO 1720 NM_017918 SEQ ID NO 1721 NM_017961 SEQ ID NO 1722
NM_017996 SEQ ID NO 1723 NM_018000 SEQ ID NO 1724 NM_018004 SEQ ID
NO 1725 NM_018011 SEQ ID NO 1726 NM_018014 SEQ ID NO 1727 NM_018022
SEQ ID NO 1728 NM_018031 SEQ ID NO 1729 NM_018043 SEQ ID NO 1730
NM_018048 SEQ ID NO 1731 NM_018062 SEQ ID NO 1732 NM_018069 SEQ ID
NO 1733 NM_018072 SEQ ID NO 1734 NM_018077 SEQ ID NO 1735 NM_018086
SEQ ID NO 1736 NM_018087 SEQ ID NO 1737 NM_018093 SEQ ID NO 1738
NM_018098 SEQ ID NO 1739 NM_018099 SEQ ID NO 1740 NM_018101 SEQ ID
NO 1741 NM_018103 SEQ ID NO 1742 NM_018109 SEQ ID NO 1744 NM_018123
SEQ ID NO 1746 NM_018131 SEQ ID NO 1747 NM_018136 SEQ ID NO 1748
NM_018138 SEQ ID NO 1749 NM_018166 SEQ ID NO 1750 NM_018171 SEQ ID
NO 1751 NM_018178 SEQ ID NO 1752 NM_018181 SEQ ID NO 1753 NM_018186
SEQ ID NO 1754 NM_018188 SEQ ID NO 1756 NM_018194 SEQ ID NO 1757
NM_018204 SEQ ID NO 1758 NM_018208 SEQ ID NO 1759 NM_018212 SEQ ID
NO 1760 NM_018234 SEQ ID NO 1763 NM_018255 SEQ ID NO 1764 NM_018257
SEQ ID NO 1765 NM_018265 SEQ ID NO 1766 NM_018271 SEQ ID NO 1767
NM_018290 SEQ ID NO 1768 NM_018295 SEQ ID NO 1769 NM_018304 SEQ ID
NO 1770 NM_018306 SEQ ID NO 1771 NM_018326 SEQ ID NO 1772 NM_018346
SEQ ID NO 1773 NM_018366 SEQ ID NO 1775 NM_018370 SEQ ID NO 1776
NM_018373 SEQ ID NO 1777 NM_018379 SEQ ID NO 1778 NM_018384 SEQ ID
NO 1779 NM_018389 SEQ ID NO 1780 NM_018410 SEQ ID NO 1783 NM_018439
SEQ ID NO 1785 NM_018454 SEQ ID NO 1786 NM_018455 SEQ ID NO 1787
NM_018465 SEQ ID NO 1788 NM_018471 SEQ ID NO 1789 NM_018478 SEQ ID
NO 1790 NM_018479 SEQ ID NO 1791 NM_018529 SEQ ID NO 1793 NM_018556
SEQ ID NO 1794 NM_018569 SEQ ID NO 1795 NM_018584 SEQ ID NO 1796
NM_018653 SEQ ID NO 1797 NM_018660 SEQ ID NO 1798 NM_018683 SEQ ID
NO 1799 NM_018685 SEQ ID NO 1800 NM_018686 SEQ ID NO 1801 NM_018695
SEQ ID NO 1802 NM_018728 SEQ ID NO 1803 NM_018840 SEQ ID NO 1804
NM_018842 SEQ ID NO 1805 NM_018950 SEQ ID NO 1806 NM_018988 SEQ ID
NO 1807 NM_019000 SEQ ID NO 1808 NM_019013 SEQ ID NO 1809 NM_019025
SEQ ID NO 1810 NM_019027 SEQ ID NO 1811 NM_019041 SEQ ID NO 1812
NM_019044 SEQ ID NO 1813 NM_019063 SEQ ID NO 1815 NM_019084 SEQ ID
NO 1816 NM_019554 SEQ ID NO 1817 NM_019845 SEQ ID NO 1818 NM_019858
SEQ ID NO 1819 NM_020130 SEQ ID NO 1820 NM_020133 SEQ ID NO 1821
NM_020143 SEQ ID NO 1822 NM_020150 SEQ ID NO 1823 NM_020163 SEQ ID
NO 1824 NM_020166 SEQ ID NO 1825 NM_020169 SEQ ID NO 1826 NM_020179
SEQ ID NO 1827 NM_020184 SEQ ID NO 1828 NM_020186 SEQ ID NO 1829
NM_020188 SEQ ID NO 1830 NM_020189 SEQ ID NO 1831 NM_020197 SEQ ID
NO 1832 NM_020199 SEQ ID NO 1833 NM_020215 SEQ ID NO 1834 NM_020347
SEQ ID NO 1836 NM_020365 SEQ ID NO 1837 NM_020386 SEQ ID NO 1838
NM_020445 SEQ ID NO 1839 NM_020639 SEQ ID NO 1840 NM_020659 SEQ ID
NO 1841 NM_020675 SEQ ID NO 1842 NM_020686 SEQ ID NO 1843 NM_020974
SEQ ID NO 1844 NM_020978 SEQ ID NO 1845 NM_020979 SEQ ID NO 1846
NM_020980 SEQ ID NO 1847 NM_021000 SEQ ID NO 1849 NM_021004 SEQ ID
NO 1850 NM_021025 SEQ ID NO 1851 NM_021063 SEQ ID NO 1852 NM_021065
SEQ ID NO 1853 NM_021077 SEQ ID NO 1854 NM_021095 SEQ ID NO 1855
NM_021101 SEQ ID NO 1856 NM_021103 SEQ ID NO 1857 NM_021128 SEQ ID
NO 1858 NM_021147 SEQ ID NO 1859 NM_021151 SEQ ID NO 1860 NM_021181
SEQ ID NO 1861 NM_021190 SEQ ID NO 1862 NM_021198 SEQ ID NO 1863
NM_021200 SEQ ID NO 1864 NM_021203 SEQ ID NO 1865 NM_021238 SEQ ID
NO 1866 NM_021242 SEQ ID NO 1867 S40706 SEQ ID NO 1869 S53354 SEQ
ID NO 1870 S59184 SEQ ID NO 1871 S62138 SEQ ID NO 1872 U09848 SEQ
ID NO 1873 U10991 SEQ ID NO 1874 U17077 SEQ ID NO 1875 U18919 SEQ
ID NO 1876 U41387 SEQ ID NO 1877 U45975 SEQ ID NO 1878 U49835 SEQ
ID NO 1879 U56725 SEQ ID NO 1880 U58033 SEQ ID NO 1881 U61167 SEQ
ID NO 1882 U66042 SEQ ID NO 1883 U68385 SEQ ID NO 1885 U68494 SEQ
ID NO 1886 U74612 SEQ ID NO 1887 U75968 SEQ ID NO 1888 U79293 SEQ
ID NO 1889 U80736 SEQ ID NO 1890 U82987 SEQ ID NO 1891 U83115 SEQ
ID NO 1892 U89715 SEQ ID NO 1893 U90916 SEQ ID NO 1894 U92544 SEQ
ID NO 1895 U96131 SEQ ID NO 1896 U96394 SEQ ID NO 1897 W61000_RC
SEQ ID NO 1898 X00437 SEQ ID NO 1899 X00497 SEQ ID NO 1900 X01394
SEQ ID NO 1901 X03084 SEQ ID NO 1902 X07834 SEQ ID NO 1905 X14356
SEQ ID NO 1906 X16302 SEQ ID NO 1907 X52486 SEQ ID NO 1909 X52882
SEQ ID NO 1910 X56807 SEQ ID NO 1911 X57809 SEQ ID NO 1912 X57819
SEQ ID NO 1913 X58529 SEQ ID NO 1914 X59405 SEQ ID NO 1915 X72475
SEQ ID NO 1918 X73617 SEQ ID NO 1919 X74794 SEQ ID NO 1920 X75315
SEQ ID NO 1921
X79782 SEQ ID NO 1922 X82693 SEQ ID NO 1923 X83301 SEQ ID NO 1924
X93006 SEQ ID NO 1926 X94232 SEQ ID NO 1927 X98834 SEQ ID NO 1929
X99142 SEQ ID NO 1930 Y14737 SEQ ID NO 1932 Z11887 SEQ ID NO 1933
Z48633 SEQ ID NO 1935 NM_004222 SEQ ID NO 1936 NM_016405 SEQ ID NO
1937 NM_017690 SEQ ID NO 1938 Contig29_RC SEQ ID NO 1939
Contig237_RC SEQ ID NO 1940 Contig263_RC SEQ ID NO 1941
Contig292_RC SEQ ID NO 1942 Contig382_RC SEQ ID NO 1944
Contig399_RC SEQ ID NO 1945 Contig448_RC SEQ ID NO 1946
Contig569_RC SEQ ID NO 1947 Contig580_RC SEQ ID NO 1948
Contig678_RC SEQ ID NO 1949 Contig706_RC SEQ ID NO 1950
Contig718_RC SEQ ID NO 1951 Contig719_RC SEQ ID NO 1952
Contig742_RC SEQ ID NO 1953 Contig753_RC SEQ ID NO 1954
Contig758_RC SEQ ID NO 1956 Contig760_RC SEQ ID NO 1957
Contig842_RC SEQ ID NO 1958 Contig848_RC SEQ ID NO 1959
Contig924_RC SEQ ID NO 1960 Contig974_RC SEQ ID NO 1961
Contig1018_RC SEQ ID NO 1962 Contig1056_RC SEQ ID NO 1963
Contig1061_RC SEQ ID NO 1964 Contig1129_RC SEQ ID NO 1965
Contig1148 SEQ ID NO 1966 Contig1239_RC SEQ ID NO 1967 Contig1277
SEQ ID NO 1968 Contig1333_RC SEQ ID NO 1969 Contig1386_RC SEQ ID NO
1970 Contig1389_RC SEQ ID NO 1971 Contig1418_RC SEQ ID NO 1972
Contig1462_RC SEQ ID NO 1973 Contig1505_RC SEQ ID NO 1974
Contig1540_RC SEQ ID NO 1975 Contig1584_RC SEQ ID NO 1976
Contig1632_RC SEQ ID NO 1977 Contig1682_RC SEQ ID NO 1978
Contig1778_RC SEQ ID NO 1979 Contig1829 SEQ ID NO 1981
Contig1838_RC SEQ ID NO 1982 Contig1938_RC SEQ ID NO 1983
Contig1970_RC SEQ ID NO 1984 Contig1998_RC SEQ ID NO 1985
Contig2099_RC SEQ ID NO 1986 Contig2143_RC SEQ ID NO 1987
Contig2237_RC SEQ ID NO 1988 Contig2429_RC SEQ ID NO 1990
Contig2504_RC SEQ ID NO 1991 Contig2512_RC SEQ ID NO 1992
Contig2575_RC SEQ ID NO 1993 Contig2578_RC SEQ ID NO 1994
Contig2639_RC SEQ ID NO 1995 Contig2647_RC SEQ ID NO 1996
Contig2657_RC SEQ ID NO 1997 Contig2728_RC SEQ ID NO 1998
Contig2745_RC SEQ ID NO 1999 Contig2811_RC SEQ ID NO 2000
Contig2873_RC SEQ ID NO 2001 Contig2883_RC SEQ ID NO 2002
Contig2915_RC SEQ ID NO 2003 Contig2928_RC SEQ ID NO 2004
Contig3024_RC SEQ ID NO 2005 Contig3094_RC SEQ ID NO 2006
Contig3164_RC SEQ ID NO 2007 Contig3495_RC SEQ ID NO 2009
Contig3607_RC SEQ ID NO 2010 Contig3659_RC SEQ ID NO 2011
Contig3677_RC SEQ ID NO 2012 Contig3682_RC SEQ ID NO 2013
Contig3734_RC SEQ ID NO 2014 Contig3834_RC SEQ ID NO 2015
Contig3876_RC SEQ ID NO 2016 Contig3902_RC SEQ ID NO 2017
Contig3940_RC SEQ ID NO 2018 Contig4380_RC SEQ ID NO 2019
Contig4388_RC SEQ ID NO 2020 Contig4467_RC SEQ ID NO 2021
Contig4949_RC SEQ ID NO 2023 Contig5348_RC SEQ ID NO 2024
Contig5403_RC SEQ ID NO 2025 Contig5716_RC SEQ ID NO 2026
Contig6118_RC SEQ ID NO 2027 Contig6164_RC SEQ ID NO 2028
Contig6181_RC SEQ ID NO 2029 Contig6514_RC SEQ ID NO 2030
Contig6612_RC SEQ ID NO 2031 Contig6881_RC SEQ ID NO 2032
Contig8165_RC SEQ ID NO 2033 Contig8221_RC SEQ ID NO 2034
Contig8347_RC SEQ ID NO 2035 Contig8364_RC SEQ ID NO 2036
Contig8888_RC SEQ ID NO 2038 Contig9259_RC SEQ ID NO 2039
Contig9541_RC SEQ ID NO 2040 Contig10268_RC SEQ ID NO 2041
Contig10363_RC SEQ ID NO 2042 Contig10437_RC SEQ ID NO 2043
Contig11086_RC SEQ ID NO 2045 Contig11275_RC SEQ ID NO 2046
Contig11648_RC SEQ ID NO 2047 Contig12216_RC SEQ ID NO 2048
Contig12369_RC SEQ ID NO 2049 Contig12814_RC SEQ ID NO 2050
Contig12951_RC SEQ ID NO 2051 Contig13480_RC SEQ ID NO 2052
Contig14284_RC SEQ ID NO 2053 Contig14390_RC SEQ ID NO 2054
Contig14780_RC SEQ ID NO 2055 Contig14954_RC SEQ ID NO 2056
Contig14981_RC SEQ ID NO 2057 Contig15692_RC SEQ ID NO 2058
Contig16192_RC SEQ ID NO 2059 Contig16759_RC SEQ ID NO 2061
Contig16786_RC SEQ ID NO 2062 Contig16905_RC SEQ ID NO 2063
Contig17103_RC SEQ ID NO 2064 Contig17105_RC SEQ ID NO 2065
Contig17248_RC SEQ ID NO 2066 Contig17345_RC SEQ ID NO 2067
Contig18502_RC SEQ ID NO 2069 Contig20156_RC SEQ ID NO 2071
Contig20302_RC SEQ ID NO 2073 Contig20600_RC SEQ ID NO 2074
Contig20617_RC SEQ ID NO 2075 Contig20629_RC SEQ ID NO 2076
Contig20651_RC SEQ ID NO 2077 Contig21130_RC SEQ ID NO 2078
Contig21185_RC SEQ ID NO 2079 Contig21421_RC SEQ ID NO 2080
Contig21787_RC SEQ ID NO 2081 Contig21812_RC SEQ ID NO 2082
Contig22418_RC SEQ ID NO 2083 Contig23085_RC SEQ ID NO 2084
Contig23454_RC SEQ ID NO 2085 Contig24138_RC SEQ ID NO 2086
Contig24252_RC SEQ ID NO 2087 Contig24655_RC SEQ ID NO 2089
Contig25055_RC SEQ ID NO 2090 Contig25290_RC SEQ ID NO 2091
Contig25343_RC SEQ ID NO 2092 Contig25362_RC SEQ ID NO 2093
Contig25617_RC SEQ ID NO 2094 Contig25659_RC SEQ ID NO 2095
Contig25722_RC SEQ ID NO 2096 Contig25809_RC SEQ ID NO 2097
Contig25991 SEQ ID NO 2098 Contig26022_RC SEQ ID NO 2099
Contig26077_RC SEQ ID NO 2100 Contig26310_RC SEQ ID NO 2101
Contig26371_RC SEQ ID NO 2102 Contig26438_RC SEQ ID NO 2103
Contig26706_RC SEQ ID NO 2104 Contig27088_RC SEQ ID NO 2105
Contig27186_RC SEQ ID NO 2106 Contig27228_RC SEQ ID NO 2107
Contig27344_RC SEQ ID NO 2109 Contig27386_RC SEQ ID NO 2110
Contig27624_RC SEQ ID NO 2111 Contig27749_RC SEQ ID NO 2112
Contig27882_RC SEQ ID NO 2113 Contig27915_RC SEQ ID NO 2114
Contig28030_RC SEQ ID NO 2115 Contig28081_RC SEQ ID NO 2116
Contig28152_RC SEQ ID NO 2117 Contig28550_RC SEQ ID NO 2119
Contig28552_RC SEQ ID NO 2120 Contig28712_RC SEQ ID NO 2121
Contig28888_RC SEQ ID NO 2122 Contig28947_RC SEQ ID NO 2123
Contig29126_RC SEQ ID NO 2124 Contig29193_RC SEQ ID NO 2125
Contig29369_RC SEQ ID NO 2126 Contig29639_RC SEQ ID NO 2127
Contig30047_RC SEQ ID NO 2129 Contig30154_RC SEQ ID NO 2131
Contig30209_RC SEQ ID NO 2132 Contig30213_RC SEQ ID NO 2133
Contig30230_RC SEQ ID NO 2134 Contig30267_RC SEQ ID NO 2135
Contig30390_RC SEQ ID NO 2136 Contig30480_RC SEQ ID NO 2137
Contig30609_RC SEQ ID NO 2138 Contig30934_RC SEQ ID NO 2139
Contig31150_RC SEQ ID NO 2140 Contig31186_RC SEQ ID NO 2141
Contig31251_RC SEQ ID NO 2142 Contig31288_RC SEQ ID NO 2143
Contig31291_RC SEQ ID NO 2144 Contig31295_RC SEQ ID NO 2145
Contig31424_RC SEQ ID NO 2146 Contig31449_RC SEQ ID NO 2147
Contig31596_RC SEQ ID NO 2148 Contig31864_RC SEQ ID NO 2149
Contig31928_RC SEQ ID NO 2150 Contig31966_RC SEQ ID NO 2151
Contig31986_RC SEQ ID NO 2152 Contig32084_RC SEQ ID NO 2153
Contig32105_RC SEQ ID NO 2154 Contig32185_RC SEQ ID NO 2156
Contig32242_RC SEQ ID NO 2157 Contig32322_RC SEQ ID NO 2158
Contig32336_RC SEQ ID NO 2159 Contig32558_RC SEQ ID NO 2160
Contig32798_RC SEQ ID NO 2161 Contig33005_RC SEQ ID NO 2162
Contig33230_RC SEQ ID NO 2163 Contig33260_RC SEQ ID NO 2164
Contig33654_RC SEQ ID NO 2166 Contig33741_RC SEQ ID NO 2167
Contig33771_RC SEQ ID NO 2168 Contig33814_RC SEQ ID NO 2169
Contig33815_RC SEQ ID NO 2170 Contig33833 SEQ ID NO 2171
Contig33998_RC SEQ ID NO 2172 Contig34079 SEQ ID NO 2173
Contig34080_RC SEQ ID NO 2174 Contig34222_RC SEQ ID NO 2175
Contig34233_RC SEQ ID NO 2176 Contig34303_RC SEQ ID NO 2177
Contig34393_RC SEQ ID NO 2178 Contig34477_RC SEQ ID NO 2179
Contig34766_RC SEQ ID NO 2181 Contig34952 SEQ ID NO 2182
Contig34989_RC SEQ ID NO 2183 Contig35030_RC SEQ ID NO 2184
Contig35251_RC SEQ ID NO 2185 Contig35629_RC SEQ ID NO 2186
Contig35635_RC SEQ ID NO 2187 Contig35763_RC SEQ ID NO 2188
Contig35814_RC SEQ ID NO 2189 Contig35896_RC SEQ ID NO 2190
Contig35976_RC SEQ ID NO 2191 Contig36042_RC SEQ ID NO 2192
Contig36081_RC SEQ ID NO 2193 Contig36152_RC SEQ ID NO 2194
Contig36193_RC SEQ ID NO 2195 Contig36312_RC SEQ ID NO 2196
Contig36323_RC SEQ ID NO 2197 Contig36339_RC SEQ ID NO 2198
Contig36647_RC SEQ ID NO 2199 Contig36744_RC SEQ ID NO 2200
Contig36761_RC SEQ ID NO 2201 Contig36879_RC SEQ ID NO 2202
Contig36900_RC SEQ ID NO 2203 Contig37015_RC SEQ ID NO 2204
Contig37024_RC SEQ ID NO 2205 Contig37072_RC SEQ ID NO 2207
Contig37140_RC SEQ ID NO 2208 Contig37141_RC SEQ ID NO 2209
Contig37204_RC SEQ ID NO 2210 Contig37281_RC SEQ ID NO 2211
Contig37287_RC SEQ ID NO 2212 Contig37439_RC SEQ ID NO 2213
Contig37562_RC SEQ ID NO 2214 Contig37571_RC SEQ ID NO 2215
Contig37598 SEQ ID NO 2216 Contig37758_RC SEQ ID NO 2217
Contig37778_RC SEQ ID NO 2218 Contig37884_RC SEQ ID NO 2219
Contig37946_RC SEQ ID NO 2220 Contig38170_RC SEQ ID NO 2221
Contig38288_RC SEQ ID NO 2223 Contig38398_RC SEQ ID NO 2224
Contig38580_RC SEQ ID NO 2226 Contig38630_RC SEQ ID NO 2227
Contig38652_RC SEQ ID NO 2228 Contig38683_RC SEQ ID NO 2229
Contig38726_RC SEQ ID NO 2230 Contig38791_RC SEQ ID NO 2231
Contig38901_RC SEQ ID NO 2232 Contig38983_RC SEQ ID NO 2233
Contig39090_RC SEQ ID NO 2234 Contig39132_RC SEQ ID NO 2235
Contig39157_RC SEQ ID NO 2236 Contig39226_RC SEQ ID NO 2237
Contig39285_RC SEQ ID NO 2238 Contig39556_RC SEQ ID NO 2239
Contig39591_RC SEQ ID NO 2240 Contig39826_RC SEQ ID NO 2241
Contig39845_RC SEQ ID NO 2242 Contiq39891_RC SEQ ID NO 2243
Contig39922_RC SEQ ID NO 2244 Contig39960_RC SEQ ID NO 2245
Contig40026_RC SEQ ID NO 2246 Contig40121_RC SEQ ID NO 2247
Contig40128_RC SEQ ID NO 2248 Contig40146 SEQ ID NO 2249
Contig40208_RC SEQ ID NO 2250 Contig40212_RC SEQ ID NO 2251
Contig40238_RC SEQ ID NO 2252 Contig40434_RC SEQ ID NO 2253
Contig40446_RC SEQ ID NO 2254 Contig40500_RC SEQ ID NO 2255
Contig40573_RC SEQ ID NO 2256 Contig40813_RC SEQ ID NO 2258
Contig40816_RC SEQ ID NO 2259 Contig40845_RC SEQ ID NO 2261
Contig40889_RC SEQ ID NO 2262 Contig41035 SEQ ID NO 2263
Contig41234_RC SEQ ID NO 2264 Contig41413_RC SEQ ID NO 2266
Contig41521_RC SEQ ID NO 2267 Contig41530_RC SEQ ID NO 2268
Contig41590 SEQ ID NO 2269 Contig41618_RC SEQ ID NO 2270
Contig41624_RC SEQ ID NO 2271 Contig41635_RC SEQ ID NO 2272
Contig41676_RC SEQ ID NO 2273 Contig41689_RC SEQ ID NO 2274
Contig41804_RC SEQ ID NO 2275 Contig41887_RC SEQ ID NO 2276
Contig41905_RC SEQ ID NO 2277 Contig41954_RC SEQ ID NO 2278
Contig41983_RC SEQ ID NO 2279 Contig42006_RC SEQ ID NO 2280
Contig42014_RC SEQ ID NO 2281 Contig42036_RC SEQ ID NO 2282
Contig42041_RC SEQ ID NO 2283 Contig42139 SEQ ID NO 2284
Contig42161_RC SEQ ID NO 2285 Contig42220_RC SEQ ID NO 2286
Contig42306_RC SEQ ID NO 2287 Contig42311_RC SEQ ID NO 2288
Contig42313_RC SEQ ID NO 2289 Contig42402_RC SEQ ID NO 2290
Contig42421_RC SEQ ID NO 2291 Contig42430_RC SEQ ID NO 2292
Contig42431_RC SEQ ID NO 2293 Contig42542_RC SEQ ID NO 2294
Contig42582 SEQ ID NO 2295 Contig42631_RC SEQ ID NO 2296
Contig42751_RC SEQ ID NO 2297 Contig42759_RC SEQ ID NO 2298
Contig43054 SEQ ID NO 2299 Contig43079_RC SEQ ID NO 2300
Contig43195_RC SEQ ID NO 2301 Contig43368_RC SEQ ID NO 2302
Contig43410_RC SEQ ID NO 2303 Contig43476_RC SEQ ID NO 2304
Contig43549_RC SEQ ID NO 2305 Contig43645_RC SEQ ID NO 2306
Contig43648_RC SEQ ID NO 2307 Contig43673_RC SEQ ID NO 2308
Contig43679_RC SEQ ID NO 2309 Contig43694_RC SEQ ID NO 2310
Contig43747_RC SEQ ID NO 2311 Contig43918_RC SEQ ID NO 2312
Contig43983_RC SEQ ID NO 2313 Contig44040_RC SEQ ID NO 2314
Contig44064_RC SEQ ID NO 2315 Contig44195_RC SEQ ID NO 2316
Contig44226_RC SEQ ID NO 2317 Contig44289_RC SEQ ID NO 2320
Contig44310_RC SEQ ID NO 2321 Contig44409 SEQ ID NO 2322
Contig44413_RC SEQ ID NO 2323 Contig44451_RC SEQ ID NO 2324
Contig44585_RC SEQ ID NO 2325 Contig44656_RC SEQ ID NO 2326
Contig44703_RC SEQ ID NO 2327 Contig44708_RC SEQ ID NO 2328
Contig44757_RC SEQ ID NO 2329 Contig44829_RC SEQ ID NO 2331
Contig44870 SEQ ID NO 2332 Contig44893_RC SEQ ID NO 2333
Contig44909_RC SEQ ID NO 2334 Contig44939_RC SEQ ID NO 2335
Contig45022_RC SEQ ID NO 2336 Contig45032_RC SEQ ID NO 2337
Contig45041_RC SEQ ID NO 2338 Contig45049_RC SEQ ID NO 2339
Contig45090_RC SEQ ID NO 2340 Contig45156_RC SEQ ID NO 2341
Contig45316_RC SEQ ID NO 2342 Contig45321 SEQ ID NO 2343
Contig45375_RC SEQ ID NO 2345 Contig45443_RC SEQ ID NO 2346
Contig45454_RC SEQ ID NO 2347 Contig45537_RC SEQ ID NO 2348
Contig45588_RC SEQ ID NO 2349 Contig45708_RC SEQ ID NO 2350
Contig45816_RC SEQ ID NO 2351 Contig45847_RC SEQ ID NO 2352
Contig45891_RC SEQ ID NO 2353 Contig46056_RC SEQ ID NO 2354
Contig46062_RC SEQ ID NO 2355 Contig46075_RC SEQ ID NO 2356
Contig46164_RC SEQ ID NO 2357 Contig46218_RC SEQ ID NO 2358
Contig46223_RC SEQ ID NO 2359 Contig46244_RC SEQ ID NO 2360
Contig46262_RC SEQ ID NO 2361 Contig46362_RC SEQ ID NO 2364
Contig46443_RC SEQ ID NO 2365 Contig46553_RC SEQ ID NO 2367
Contig46597_RC SEQ ID NO 2368 Contig46653_RC SEQ ID NO 2369
Contig46709_RC SEQ ID NO 2370 Contig46777_RC SEQ ID NO 2371
Contig46802_RC SEQ ID NO 2372 Contig46890_RC SEQ ID NO 2374
Contig46922_RC SEQ ID NO 2375 Contig46934_RC SEQ ID NO 2376
Contig46937_RC SEQ ID NO 2377 Contig46991_RC SEQ ID NO 2378
Contig47016_RC SEQ ID NO 2379 Contig47045_RC SEQ ID NO 2380
Contig47106_RC SEQ ID NO 2381 Contig47146_RC SEQ ID NO 2382
Contig47230_RC SEQ ID NO 2383 Contig47405_RC SEQ ID NO 2384
Contig47456_RC SEQ ID NO 2385 Contig47465_RC SEQ ID NO 2386
Contig47498_RC SEQ ID NO 2387 Contig47578_RC SEQ ID NO 2388
Contig47645_RC SEQ ID NO 2389 Contig47680_RC SEQ ID NO 2390
Contig47781_RC SEQ ID NO 2391 Contig47814_RC SEQ ID NO 2392
Contig48004_RC SEQ ID NO 2393 Contig48043_RC SEQ ID NO 2394
Contig48057_RC SEQ ID NO 2395 Contig48076_RC SEQ ID NO 2396
Contig48249_RC SEQ ID NO 2397 Contig48263_RC SEQ ID NO 2398
Contig48270_RC SEQ ID NO 2399 Contig48328_RC SEQ ID NO 2400
Contig48518_RC SEQ ID NO 2401 Contig48572_RC SEQ ID NO 2402
Contig48659_RC SEQ ID NO 2403 Contig48722_RC SEQ ID NO 2404
Contig48774_RC SEQ ID NO 2405 Contig48776_RC SEQ ID NO 2406
Contig48800_RC SEQ ID NO 2407 Contig48806_RC SEQ ID NO 2408
Contig48852_RC SEQ ID NO 2409 Contig48900_RC SEQ ID NO 2410
Contig48913_RC SEQ ID NO 2411 Contig48970_RC SEQ ID NO 2413
Contig49058_RC SEQ ID NO 2414 Contig49063_RC SEQ ID NO 2415
Contig49093 SEQ ID NO 2416 Contig49098_RC SEQ ID NO 2417
Contig49169_RC SEQ ID NO 2418 Contig49233_RC SEQ ID NO 2419
Contig49270_RC SEQ ID NO 2420 Contig49282_RC SEQ ID NO 2421
Contig49289_RC SEQ ID NO 2422 Contig49342_RC SEQ ID NO 2423
Contig49344 SEQ ID NO 2424 Contig49388_RC SEQ ID NO 2425
Contig49405_RC SEQ ID NO 2426 Contig49445_RC SEQ ID NO 2427
Contig49468_RC SEQ ID NO 2428 Contig49509_RC SEQ ID NO 2429
Contig49578_RC SEQ ID NO 2431 Contig49581_RC SEQ ID NO 2432
Contig49631_RC SEQ ID NO 2433 Contig49673_RC SEQ ID NO 2435
Contig49743_RC SEQ ID NO 2436 Contig49790_RC SEQ ID NO 2437
Contig49818_RC SEQ ID NO 2438 Contig49849_RC SEQ ID NO 2439
Contig49855 SEQ ID NO 2440 Contig49910_RC SEQ ID NO 2441
Contig49948_RC SEQ ID NO 2442 Contig50004_RC SEQ ID NO 2443
Contig50094 SEQ ID NO 2444 Contig50120_RC SEQ ID NO 2446
Contig50153_RC SEQ ID NO 2447 Contig50189_RC SEQ ID NO 2448
Contig50276_RC SEQ ID NO 2449 Contig50288_RC SEQ ID NO 2450
Contig50297_RC SEQ ID NO 2451 Contig50391_RC SEQ ID NO 2452
Contig50410 SEQ ID NO 2453 Contig50523_RC SEQ ID NO 2454
Contig50529 SEQ ID NO 2455 Contig50588_RC SEQ ID NO 2456
Contig50592 SEQ ID NO 2457 Contig50669_RC SEQ ID NO 2458
Contig50719_RC SEQ ID NO 2460 Contig50728_RC SEQ ID NO 2461
Contig50731_RC SEQ ID NO 2462 Contig50802_RC SEQ ID NO 2463
Contig50822_RC SEQ ID NO 2464 Contig50850_RC SEQ ID NO 2466
Contig50860_RC SEQ ID NO 2467
Contig50913_RC SEQ ID NO 2468 Contig50950_RC SEQ ID NO 2469
Contig51066_RC SEQ ID NO 2470 Contig51105_RC SEQ ID NO 2472
Contig51117_RC SEQ ID NO 2473 Contig51196_RC SEQ ID NO 2474
Contig51235_RC SEQ ID NO 2475 Contig51254_RC SEQ ID NO 2476
Contig51352_RC SEQ ID NO 2477 Contig51369_RC SEQ ID NO 2478
Contig51392_RC SEQ ID NO 2479 Contig51403_RC SEQ ID NO 2480
Contig51685_RC SEQ ID NO 2483 Contig51726_RC SEQ ID NO 2484
Contig51742_RC SEQ ID NO 2485 Contig51749_RC SEQ ID NO 2486
Contig51775_RC SEQ ID NO 2487 Contig51800 SEQ ID NO 2488
Contig51809_RC SEQ ID NO 2489 Contig51821_RC SEQ ID NO 2490
Contig51888_RC SEQ ID NO 2491 Contig51953_RC SEQ ID NO 2493
Contig51967_RC SEQ ID NO 2495 Contig51981_RC SEQ ID NO 2496
Contig51994_RC SEQ ID NO 2497 Contig52082_RC SEQ ID NO 2498
Contig52094_RC SEQ ID NO 2499 Contig52320 SEQ ID NO 2500
Contig52398_RC SEQ ID NO 2501 Contig52425_RC SEQ ID NO 2503
Contig52482_RC SEQ ID NO 2504 Contig52543_RC SEQ ID NO 2505
Contig52553_RC SEQ ID NO 2506 Contig52579_RC SEQ ID NO 2507
Contig52603_RC SEQ ID NO 2508 Contig52639_RC SEQ ID NO 2509
Contig52641_RC SEQ ID NO 2510 Contig52684 SEQ ID NO 2511
Contig52705_RC SEQ ID NO 2512 Contig52720_RC SEQ ID NO 2513
Contig52722_RC SEQ ID NO 2514 Contig52723_RC SEQ ID NO 2515
Contig52740_RC SEQ ID NO 2516 Contig52779_RC SEQ ID NO 2517
Contig52957_RC SEQ ID NO 2518 Contig52994_RC SEQ ID NO 2519
Contig53022_RC SEQ ID NO 2520 Contig53038_RC SEQ ID NO 2521
Contig53047_RC SEQ ID NO 2522 Contig53130 SEQ ID NO 2523
Contig53183_RC SEQ ID NO 2524 Contig53242_RC SEQ ID NO 2526
Contig53248_RC SEQ ID NO 2527 Contig53260_RC SEQ ID NO 2528
Contig53296_RC SEQ ID NO 2531 Contig53307_RC SEQ ID NO 2532
Contig53314_RC SEQ ID NO 2533 Contig53401_RC SEQ ID NO 2534
Contig53550_RC SEQ ID NO 2535 Contig53551_RC SEQ ID NO 2536
Contig53598_RC SEQ ID NO 2537 Contig53646_RC SEQ ID NO 2538
Contig53658_RC SEQ ID NO 2539 Contig53698_RC SEQ ID NO 2540
Contig53719_RC SEQ ID NO 2541 Contig53742_RC SEQ ID NO 2542
Contig53757_RC SEQ ID NO 2543 Contig53870_RC SEQ ID NO 2544
Contig53952_RC SEQ ID NO 2546 Contig53962_RC SEQ ID NO 2547
Contig53968_RC SEQ ID NO 2548 Contig54113_RC SEQ ID NO 2549
Contig54142_RC SEQ ID NO 2550 Contig54232_RC SEQ ID NO 2551
Contig54242_RC SEQ ID NO 2552 Contig54260_RC SEQ ID NO 2553
Contig54263_RC SEQ ID NO 2554 Contig54295_RC SEQ ID NO 2555
Contig54318_RC SEQ ID NO 2556 Contig54325_RC SEQ ID NO 2557
Contig54389_RC SEQ ID NO 2558 Contig54394_RC SEQ ID NO 2559
Contig54414_RC SEQ ID NO 2560 Contig54425 SEQ ID NO 2561
Contig54477_RC SEQ ID NO 2562 Contig54503_RC SEQ ID NO 2563
Contig54534_RC SEQ ID NO 2564 Contig54560_RC SEQ ID NO 2566
Contig54581_RC SEQ ID NO 2567 Contig54609_RC SEQ ID NO 2568
Contig54666_RC SEQ ID NO 2569 Contig54667_RC SEQ ID NO 2570
Contig54726_RC SEQ ID NO 2571 Contig54742_RC SEQ ID NO 2572
Contig54745_RC SEQ ID NO 2573 Contig54757_RC SEQ ID NO 2574
Contig54761_RC SEQ ID NO 2575 Contig54813_RC SEQ ID NO 2576
Contig54867_RC SEQ ID NO 2577 Contig54895_RC SEQ ID NO 2578
Contig54898_RC SEQ ID NO 2579 Contig54913_RC SEQ ID NO 2580
Contig54965_RC SEQ ID NO 2582 Contig54968_RC SEQ ID NO 2583
Contig55069_RC SEQ ID NO 2584 Contig55181_RC SEQ ID NO 2585
Contig55188_RC SEQ ID NO 2586 Contig55221_RC SEQ ID NO 2587
Contig55254_RC SEQ ID NO 2588 Contig55265_RC SEQ ID NO 2589
Contig55377_RC SEQ ID NO 2591 Contig55397_RC SEQ ID NO 2592
Contig55448_RC SEQ ID NO 2593 Contig55468_RC SEQ ID NO 2594
Contig55500_RC SEQ ID NO 2595 Contig55538_RC SEQ ID NO 2596
Contig55558_RC SEQ ID NO 2597 Contig55606_RC SEQ ID NO 2598
Contig55674_RC SEQ ID NO 2599 Contig55725_RC SEQ ID NO 2600
Contig55728_RC SEQ ID NO 2601 Contig55756_RC SEQ ID NO 2602
Contig55769_RC SEQ ID NO 2603 Contig55771_RC SEQ ID NO 2605
Contig55813_RC SEQ ID NO 2607 Contig55829_RC SEQ ID NO 2608
Contig55852_RC SEQ ID NO 2609 Contig55883_RC SEQ ID NO 2610
Contig55920_RC SEQ ID NO 2611 Contig55940_RC SEQ ID NO 2612
Contig55950_RC SEQ ID NO 2613 Contig55991_RC SEQ ID NO 2614
Contig55997_RC SEQ ID NO 2615 Contig56023_RC SEQ ID NO 2616
Contig56030_RC SEQ ID NO 2617 Contig56093_RC SEQ ID NO 2618
Contig56205_RC SEQ ID NO 2621 Contig56270_RC SEQ ID NO 2622
Contig56276_RC SEQ ID NO 2623 Contig56291_RC SEQ ID NO 2624
Contig56298_RC SEQ ID NO 2625 Contig56307 SEQ ID NO 2627
Contig56390_RC SEQ ID NO 2628 Contig56434_RC SEQ ID NO 2629
Contig56457_RC SEQ ID NO 2630 Contig56534_RC SEQ ID NO 2631
Contig56670_RC SEQ ID NO 2632 Contig56678_RC SEQ ID NO 2633
Contig56742_RC SEQ ID NO 2634 Contig56759_RC SEQ ID NO 2635
Contig56765_RC SEQ ID NO 2636 Contig56843_RC SEQ ID NO 2637
Contig57011_RC SEQ ID NO 2638 Contig57023_RC SEQ ID NO 2639
Contig57057_RC SEQ ID NO 2640 Contig57076_RC SEQ ID NO 2641
Contig57081_RC SEQ ID NO 2642 Contig57091_RC SEQ ID NO 2643
Contig57138_RC SEQ ID NO 2644 Contig57173_RC SEQ ID NO 2645
Contig57230_RC SEQ ID NO 2646 Contig57258_RC SEQ ID NO 2647
Contiq57270_RC SEQ ID NO 2648 Contig57272_RC SEQ ID NO 2649
Contig57344_RC SEQ ID NO 2650 Contig57430_RC SEQ ID NO 2651
Contig57458_RC SEQ ID NO 2652 Contig57493_RC SEQ ID NO 2653
Contig57584_RC SEQ ID NO 2654 Contig57595 SEQ ID NO 2655
Contig57602_RC SEQ ID NO 2656 Contig57609_RC SEQ ID NO 2657
Contig57610_RC SEQ ID NO 2658 Contig57644_RC SEQ ID NO 2659
Contig57725_RC SEQ ID NO 2660 Contig57739_RC SEQ ID NO 2661
Contig57825_RC SEQ ID NO 2662 Contig57864_RC SEQ ID NO 2663
Contig57940_RC SEQ ID NO 2664 Contig58260_RC SEQ ID NO 2665
Contig58272_RC SEQ ID NO 2666 Contig58301_RC SEQ ID NO 2667
Contig58368_RC SEQ ID NO 2668 Contig58471_RC SEQ ID NO 2669
Contig58755_RC SEQ ID NO 2671 Contig59120_RC SEQ ID NO 2672
Contig60157_RC SEQ ID NO 2673 Contig60864_RC SEQ ID NO 2676
Contig61254_RC SEQ ID NO 2677 Contig61815 SEQ ID NO 2678
Contig61975 SEQ ID NO 2679 Contig62306 SEQ ID NO 2680
Contig62568_RC SEQ ID NO 2681 Contig62922_RC SEQ ID NO 2682
Contig62964_RC SEQ ID NO 2683 Contig63520_RC SEQ ID NO 2685
Contig63649_RC SEQ ID NO 2686 Contig63683_RC SEQ ID NO 2687
Contig63748_RC SEQ ID NO 2688 Contig64502 SEQ ID NO 2689
Contig64688 SEQ ID NO 2690 Contig64775_RC SEQ ID NO 2691
Contig65227 SEQ ID NO 2692 Contig65663 SEQ ID NO 2693
Contig65785_RC SEQ ID NO 2694 Contig65900 SEQ ID NO 2695
Contig66219_RC SEQ ID NO 2696 Contig66705_RC SEQ ID NO 2697
Contig66759_RC SEQ ID NO 2698 Contig67182_RC SEQ ID NO 2699
TABLE-US-00002 TABLE 2 550 preferred ER status markers drawn from
Table 1. Identifier Correlation Name Description NM_002051 0.763977
GATA3 GATA-binding protein 3 AB020689 0.753592 KIAA0882 KIAA0882
protein NM_001218 0.753225 CA12 carbonic anhydrase XII NM_000125
0.748421 ESR1 estrogen receptor 1 Contig56678_RC 0.747816 ESTs
NM_004496 0.729116 HNF3A hepatocyte nuclear factor 3, alpha
NM_017732 0.713398 FLJ20262 hypothetical protein FLJ20262 NM_006806
-0.712678 BTG3 BTG family, member 3 Contig56390_RC 0.705940 ESTs
Contig37571_RC 0.704468 ESTs NM_004559 -0.701617 NSEP1 nuclease
sensitive element binding protein 1 Contig50153_RC -0.696652 ESTs,
Weakly similar to LKHU proteoglycan link protein precursor [H.
sapiens] NM_012155 0.694332 EMAP-2 microtubule-associated protein
like echinoderm EMAP Contig237_RC 0.687485 FLJ21127 hypothetical
protein FLJ21127 NM_019063 -0.686064 C2ORF2 chromosome 2 open
reading frame 2 NM_012219 -0.680900 MRAS muscle RAS oncogene
homolog NM_001982 0.676114 ERBB3 v-erb-b2 avian erythroblastic
leukemia viral oncogene homolog 3 NM_006623 -0.675090 PHGDH
phosphoglycerate dehydrogenase NM_000636 -0.674282 SOD2 superoxide
dismutase 2, mitochondrial NM_006017 -0.670353 PROML1 prominin
(mouse)-like 1 Contig57940_RC 0.667915 MAP-1 MAP-1 protein
Contig46934_RC 0.666908 ESTs, Weakly similar to JE0350 Anterior
gradient-2 [H. sapiens] NM_005080 0.665772 XBP1 X-box binding
protein 1 NM_014246 0.665725 CELSR1 cadherin, EGF LAG seven-pass G-
type receptor 1, flamingo (Drosophila) homolog Contig54667_RC
-0.663727 Human DNA sequence from clone RP1-187J11 on chromosome
6q11.1-22.33. Contains the gene for a novel protein similar to S.
pombe and S. cerevisiae predicted proteins, the gene for a novel
protein similar to protein kinase C inhibitors, the 3' end of the
gene for a novel protein similar to Drosophila L82 and predicted
worm proteins, ESTs, STSs, GSSs and two putative CpG islands
Contig51994_RC 0.663715 ESTs, Weakly similar to B0416.1 [C.
elegans] NM_016337 0.663006 RNB6 RNB6 NM_015640 -0.660165 PAI-RBP1
PAI-1 mRNA-binding protein X07834 - 0.657798 SOD2 superoxide
dismutase 2, mitochondrial NM_012319 0.657666 LIV-1 LIV-1 protein,
estrogen regulated Contig41887_RC 0.656042 ESTs, Weakly similar to
Homolog of rat Zymogen granule membrane protein [H. sapiens]
NM_003462 0.655349 P28 dynein, axonemal, light intermediate
polypeptide Contig58301_RC 0.654268 Homo sapiens mRNA; cDNA
DKFZp667D095 (from clone DKFZp667D095) NM_005375 0.653783 MYB v-myb
avian myeloblastosis viral oncogene homolog NM_017447 -0.652445
YG81 hypothetical protein LOC54149 Contig924_RC -0.650658 ESTs
M55914 -0.650181 MPB1 MYC promoter-binding protein 1 NM_006004
-0.649819 UQCRH ubiquinol-cytochrome c reductase hinge protein
NM_000964 0.649072 RARA retinoic acid receptor, alpha NM_013301
0.647583 HSU79303 protein predicted by clone 23882 AB023211
-0.647403 PDI2 peptidyl arginine deiminase, type II NM_016629
-0.646412 LOC51323 hypothetical protein K02403 0.645532 C4A
complement component 4A NM_016405 -0.642201 HSU93243 Ubc6p homolog
Contig46597_RC 0.641733 ESTs Contig55377_RC 0.640310 ESTs NM_001207
0.637800 BTF3 basic transcription factor 3 NM_018166 0.636422
FLJ10647 hypothetical protein FLJ10647 AL110202 -0.635398 Homo
sapiens mRNA; cDNA DKFZp586I2022 (from clone DKFZp586I2022)
AL133105 -0.635201 DKFZp434F hypothetical protein DKFZp434F2322
2322 NM_016839 -0.635169 RBMS1 RNA binding motif, single stranded
interacting protein 1 Contig53130 -0.634812 ESTs, Weakly similar to
hyperpolarization-activated cyclic nucleotide-gated channel hHCN2
[H. sapiens] NM_018014 -0.634460 BCL11A B-cell CLL/lymphoma 11A
(zinc finger protein) NM_006769 -0.632197 LMO4 LIM domain only 4
U92544 0.631170 JCL-1 hepatocellular carcinoma associated protein;
breast cancer associated gene 1 Contig49233_RC -0.631047 Homo
sapiens, Similar to nuclear receptor binding factor 2, clone IMAGE:
3463191, mRNA, partial cds AL133033 0.629690 KIAA1025 KIAA1025
protein AL049265 0.629414 Homo sapiens mRNA; cDNA DKFZp564F053
(from clone DKFZp564F053) NM_018728 0.627989 MYO5C myosin 5C
NM_004780 0.627856 TCE AL1 transcription elongation factor A
(SII)-like 1 Contig760_RC 0.627132 ESTs Contig399_RC 0.626543
FLJ12538 hypothetical protein FLJ12538 similar to ras-related
protein R AB17 M83822 0.625092 CDC4L cell division cycle 4-like
NM_001255 -0.625089 CDC20 CDC20 (cell division cycle 20, S.
cerevisiae, homolog) NM_006739 -0.624903 MCM5 minichromosome
maintenance deficient (S. cerevisiae) 5 (cell division cycle 46)
NM_002888 -0.624664 RARRES1 retinoic acid receptor responder
(tazarotene induced) 1 NM_003197 0.623850 TCEB1L transcription
elongation factor B (SIII), polypeptide 1-like NM_006787 0.623625
JCL-1 hepatocellular carcinoma associated protein; breast cancer
associated gene 1 Contig49342_RC 0.622179 ESTs AL133619 0.621719
Homo sapiens mRNA; cDNA DKFZp434E2321 (from clone DKFZp434E2321);
partial cds AL133622 0.621577 KIAA0876 KIAA0876 protein NM_004648
-0.621532 PTPNS1 protein tyrosine phosphatase, non- receptor type
substrate 1 NM_001793 -0.621530 CDH3 cadherin 3, type 1, P-cadherin
(placental) NM_003217 0.620915 TEGT testis enhanced gene transcript
(BAX inhibitor 1) NM_001551 0.620832 IGBP1 immunoglobulin (CD79A)
binding protein 1 NM_002539 -0.620683 ODC1 ornithine decarboxylase
1 Contig55997_RC -0.619932 ESTs NM_000633 0.619547 BCL2 B-cell
CLL/lymphoma 2 NM_016267 -0.619096 TONDU TONDU Contig3659_RC
0.618048 FLJ21174 hypothetical protein FLJ21174 NM_000191 0.617250
HMGCL 3-hydroxymethyl-3-methylglutaryl- Coenzyme A lyase
(hydroxymethylglutaricaciduria) NM_001267 0.616890 CHAD
chondroadherin Contig39090_RC 0.616385 ESTs AF055270 -0.616268
HSSG1 heat-shock suppressed protein 1 Contig43054 0.616015 FLJ21603
hypothetical protein FLJ21603 NM_001428 -0.615855 ENO1 enolase 1,
(alpha) Contig51369_RC 0.615466 ESTs Contig36647_RC 0.615310 GFRA1
GDNF family receptor alpha 1 NM_014096 -0.614832 PRO1659 PRO1659
protein NM_015937 0.614735 LOC51604 CGI-06 protein Contig49790_RC
-0.614463 ESTs NM_006759 -0.614279 UGP2 UDP-glucose
pyrophosphorylase 2 Contig53598_RC -0.613787 FLJ11413 hypothetical
protein FLJ11413 AF113132 -0.613561 PSA phosphoserine
aminotransferase AK000004 0.613001 Homo sapiens mRNA for FLJ00004
protein, partial cds Contig52543_RC 0.612960 Homo sapiens cDNA
FLJ13945 fis, clone Y79AA1000969 AB032966 -0.611917 KIAA1140
KIAA1140 protein AL080192 0.611544 Homo sapiens cDNA: FLJ21238 fis,
clone COL01115 X56807 -0.610654 DSC2 desmocollin 2 Contig30390_RC
0.609614 ESTs AL137362 0.609121 FLJ22237 hypothetical protein
FLJ22237 NM_014211 -0.608585 GABRP gamma-aminobutyric acid (GABA) A
receptor, pi NM_006696 0.608474 SMAP thyroid hormone receptor
coactivating protein Contig45588_RC -0.608273 Homo sapiens cDNA:
FLJ22610 fis, clone HSI04930 NM_003358 0.608244 UGCG UDP-glucose
ceramide glucosyltransferase NM_006153 -0.608129 NCK1 NCK adaptor
protein 1 NM_001453 -0.606939 FOXC1 forkhead box C1 Contig54666_RC
0.606475 oy65e02.x1 NCI_CGAP_CLL1 Homo sapiens cDNA clone IMAGE:
1670714 3' similar to TR: Q29168 Q29168 UNKNOWN PROTEIN;, mRNA
sequence. NM_005945 -0.605945 MPB1 MYC promoter-binding protein 1
Contig55725_RC -0.605841 ESTs, Moderately similar to T50635
hypothetical protein DKFZp762L0311.1 [H. sapiens] Contig37015_RC
-0.605780 ESTs, Weakly similar to UAS3_HUMAN UBASH3A PROTEIN [H.
sapiens] AL157480 -0.604362 SH3BP1 SH3-domain binding protein 1
NM_005325 -0.604310 H1F1 H1 histone family, member 1 NM_001446
-0.604061 FABP7 fatty acid binding protein 7, brain Contig263_RC
0.603318 Homo sapiens cDNA: FLJ23000 fis, clone LNG00194
Contig8347_RC -0.603311 ESTs NM_002988 -0.603279 SCYA18 small
inducible cytokine subfamily A (Cys-Cys), member 18, pulmonary and
activation-regulated AF111849 0.603157 HELO1 homolog of yeast long
chain polyunsaturated fatty acid elongation enzyme 2 NM_014700
0.603042 KIAA0665 KIAA0665 gene product NM_001814 -0.602988 CTSC
cathepsin C AF116682 -0.602350 PRO2013 hypothetical protein PRO2013
AB037836 0.602024 KIAA1415 KIAA1415 protein AB002301 0.602005
KIAA0303 KIAA0303 protein NM_002996 -0.601841 SCYD1 small inducible
cytokine subfamily D (Cys-X3-Cys), member 1 (fractalkine,
neurotactin) NM_018410 -0.601765 DKFZp762 hypothetical protein
E1312 DKFZp762E1312 Contig49581_RC -0.601571 KIAA1350 KIAA1350
protein NM_003088 -0.601458 SNL singed (Drosophila)-like (sea
urchin fascin homolog like) Contig47045_RC 0.601088 ESTs, Weakly
similar to DP1_HUMAN POLYPOSIS LOCUS PROTEIN 1 [H. sapiens]
NM_001806 -0.600954 CEBPG CCAAT/enhancer binding protein (C/EBP),
gamma NM_004374 0.600766 COX6C cytochrome c oxidase subunit Vlc
Contig52641_RC 0.600132 ESTs, Weakly similar to CENB MOUSE MAJOR
CENTROMERE AUTOANTIGEN B [M. musculus] NM_000100 -0.600127 CSTB
cystatin B (stefin B) NM_002250 -0.600004 KCNN4 potassium
intermediate/small conductance calcium-activated channel, subfamily
N, member 4 AB033035 -0.599423 KIAA1209 KIAA1209 protein
Contig53968_RC 0.599077 ESTs NM_002300 -0.598246 LDHB lactate
dehydrogenase B NM_000507 0.598110 FBP1 fructose-1,6-bisphosphatase
1 NM_002053 -0.597756 GBP1 guanylate binding protein 1,
interferon-inducible, 67 kD AB007883 0.597043 KIAA0423 KIAA0423
protein NM_004900 -0.597010 DJ742C19.2 phorbolin (similar to
apolipoprotein B mRNA editing protein) NM_004480 0.596321 FUT8
fucosyltransferase 8 (alpha (1,6) fucosyltransferase)
Contig35896_RC 0.596281 ESTs NM_020974 0.595173 CEGP1 CEGP1 protein
NM_000662 0.595114 NAT1 N-acetyltransferase 1 (arylamine N-
acetyltransferase) NM_006113 0.595017 VAV3 vav 3 oncogene NM_014865
-0.594928 KIAA0159 chromosome condensation-related SMC-associated
protein 1 Contig55538_RC -0.594573 BA395L14.2 hypothetical protein
bA395L14.2 NM_016056 0.594084 LOC51643 CGI-119 protein NM_003579
-0.594063 RAD54L RAD54 (S. cerevisiae)-like
NM_014214 -0.593860 IMPA2 inositol(myo)-1(or 4)- monophosphatase 2
U79293 0.593793 Human clone 23948 mRNA sequence NM_005557 -0.593746
KRT16 keratin 16 (focal non-epidermolytic palmoplantar keratoderma)
NM_002444 -0.592405 MSN moesin NM_003681 -0.592155 PDXK pyridoxal
(pyridoxine, vitamin B6) kinase NM_006372 -0.591711 NSAP1
NS1-associated protein 1 NM_005218 -0.591192 DEFB1 defensin, beta 1
NM_004642 -0.591081 DOC1 deleted in oral cancer (mouse, homolog) 1
AL133074 0.590359 Homo sapiens cDNA: FLJ22139 fis, clone HEP20959
M73547 0.590317 D5S346 DNA segment, single copy probe
LNS-CAI/LNS-CAII (deleted in polyposis Contig65663 0.590312 ESTs
AL035297 -0.589728 H. sapiens gene from PAC 747L4 Contig35629_RC
0.589383 ESTs NM_019027 0.588862 FLJ20273 hypothetical protein
NM_012425 -0.588804 Homo sapiens Ras suppressor protein 1 (RSU1),
mRNA NM_020179 -0.588326 FN5 FN5 protein AF090913 -0.587275 TMSB10
thymosin, beta 10 NM_004176 0.587190 SREBF1 sterol regulatory
element binding transcription factor 1 NM_016121 0.586941 LOC51133
NY-REN-45 antigen NM_014773 0.586871 KIAA0141 KIAA0141 gene product
NM_019000 0.586677 FLJ20152 hypothetical protein NM_016243 0.585942
LOC51706 cytochrome b5 reductase 1 (B5R.1) NM_014274 -0.585815
ABP/ZF Alu-binding protein with zinc finger domain NM_018379
0.585497 FLJ11280 hypothetical protein FLJ11280 AL157431 -0.585077
DKFZp762 hypothetical protein DKFZp762A227 A227 D38521 -0.584684
KIAA0077 KIAA0077 protein NM_002570 0.584272 PACE4 paired basic
amino acid cleaving system 4 NM_001809 -0.584252 CENPA centromere
protein A (17 kD) NM_003318 -0.583556 TTK TTK protein kinase
NM_014325 -0.583555 CORO1C coronin, actin-binding protein, 1C
NM_005667 0.583376 ZFP103 zinc finger protein homologous to Zfp103
in mouse NM_004354 0.582420 CCNG2 cyclin G2 NM_003670 0.582235
BHLHB2 basic helix-loop-helix domain containing, class B, 2
NM_001673 -0.581902 ASNS asparagine synthetase NM_001333 -0.581402
CTSL2 cathepsin L2 Contig54295_RC 0.581256 ESTs Contig33998_RC
0.581018 ESTs NM_006002 -0.580592 UCHL3 ubiquitin carboxyl-terminal
esterase L3 (ubiquitin thiolesterase) NM_015392 0.580568 NPDC1
neural proliferation, differentiation and control, 1 NM_004866
0.580138 SCAMP1 secretory carrier membrane protein 1 Contig50391_RC
0.580071 ESTs NM_000592 0.579965 C4B complement component 4B
Contig50802_RC 0.579881 ESTs Contig41635_RC -0.579468 ESTs
NM_006845 -0.579339 KNSL6 kinesin-like 6 (mitotic centromere-
associated kinesin) NM_003720 -0.579296 DSCR2 Down syndrome
critical region gene 2 NM_000060 0.578967 BTD biotinidase AL050388
-0.578736 Homo sapiens mRNA; cDNA DKFZp564M2422 (from clone
DKFZp564M2422); partial cds NM_003772 -0.578395 JRKL jerky (mouse)
homolog-like NM_014398 -0.578388 TSC403 similar to
lysosome-associated membrane glycoprotein NM_001280 0.578213 CIRBP
cold inducible RNA-binding protein NM_001395 -0.577369 DUSP9 dual
specificity phosphatase 9 NM_016229 -0.576290 LOC51700 cytochrome
b5 reductase b5R.2 NM_006096 -0.575615 NDRG1 N-myc downstream
regulated NM_001552 0.575438 IGFBP4 insulin-like growth
factor-binding protein 4 NM_005558 -0.574818 LAD1 ladinin 1
Contig54534_RC 0.574784 Human glucose transporter pseudogene
Contig1239_RC 0.573822 Human Chromosome 16 BAC clone
CIT987SK-A-362G6 Contig57173_RC 0.573807 Homo sapiens mRNA for
KIAA1737 protein, partial cds NM_004414 -0.573538 DSCR1 Down
syndrome critical region gene 1 NM_021103 -0.572722 TMSB10
thymosin, beta 10 NM_002350 -0.571917 LYN v-yes-1 Yamaguchi sarcoma
viral related oncogene homolog Contig51235_RC 0.571049 Homo sapiens
cDNA: FLJ23388 fis, clone HEP17008 NM_013384 0.570987 TMSG1 tumor
metastasis-suppressor NM_014399 0.570936 NET-6 tetraspan NET-6
protein Contig26022_RC -0.570851 ESTs AB023152 0.570561 KIAA0935
KIAA0935 protein NM_021077 -0.569944 NMB neuromedin B NM_003498
-0.569129 SNN stannin U17077 -0.568979 BENE BENE protein D86985
0.567698 KIAA0232 KIAA0232 gene product NM_006357 -0.567513 UBE2E3
ubiquitin-conjugating enzyme E2E 3 (homologous to yeast UBC4/5)
AL049397 -0.567434 Homo sapiens mRNA; cDNA DKFZp586C1019 (from
clone DKFZp586C1019) Contig64502 0.567433 ESTs, Weakly similar to
unknown [M. musculus] Contig56298_RC -0.566892 FLJ13154
hypothetical protein FLJ13154 Contig46056_RC 0.566634 ESTs, Weakly
similar to YZ28_HUMAN HYPOTHETICAL PROTEIN ZAP128 [H. sapiens]
AF007153 0.566044 Homo sapiens clone 23736 mRNA sequence
Contig1778_RC -0.565789 ESTs NM_017702 -0.565789 FLJ20186
hypothetical protein FLJ20186 Contig39226_RC 0.565761 Homo sapiens
cDNA FLJ12187 fis, clone MAMMA1000831 NM_000168 0.564879 GLI3
GLI-Kruppel family member GLI3 (Greig cephalopolysyndactyly
syndrome) Contig57609_RC 0.564751 ESTs, Weakly similar to
T2D3_HUMAN TRANSCRIPTION INITIATION FACTOR TFIID 135 KDA SUBUNIT
[H. sapiens] U45975 0.564602 PIB5PA phosphatidylinositol (4,5)
bisphosphate 5-phosphatase, A AF038182 0.564596 Homo sapiens clone
23860 mRNA sequence Contig5348_RC 0.564480 ESTs, Weakly similar to
1607338A transcription factor BTF3a [H. sapiens] NM_001321
-0.564459 CSRP2 cysteine and glycine-rich protein 2 Contig25362_RC
-0.563801 ESTs NM_001609 0.563782 ACADSB acyl-Coenzyme A
dehydrogenase, short/branched chain Contig40146 0.563731 wi84e12.x1
NCI_CGAP_Kid12 Homo sapiens cDNA clone IMAGE: 2400046 3' similar to
SW: RASD_DICDI P03967 RAS- LIKE PROTEIN RASD;, mRNA sequence.
NM_016002 0.563403 LOC51097 CGI-49 protein Contig34303_RC 0.563157
Homo sapiens cDNA: FLJ21517 fis, clone COL05829 Contig55883_RC
0.563141 ESTs NM_017961 0.562479 FLJ20813 hypothetical protein
FLJ20813 M21551 -0.562340 NMB neuromedin B Contig3940_RC -0.561956
YWHAH tyrosine 3- monooxygenase/tryptophan 5- monooxygenase
activation protein, eta polypeptide AB033111 -0.561746 KIAA1285
KIAA1285 protein Contig43410_RC 0.561678 ESTs Contig42006_RC
-0.561677 ESTs Contig57272_RC 0.561228 ESTs G26403 -0.561068 YWHAH
tyrosine 3- monooxygenase/tryptophan 5- monooxygenase activation
protein, eta polypeptide NM_005915 -0.560813 MCM6 minichromosome
maintenance deficient (mis5, S. pombe) 6 NM_003875 -0.560668 GMPS
guanine monphosphate synthetase AK000142 0.559651 AK000142 Homo
sapiens cDNA FLJ20135 fis, clone COL06818. NM_002709 -0.559621
PPP1CB protein phosphatase 1, catalytic subunit, beta isoform
NM_001276 -0.558868 CHI3L1 chitinase 3-like 1 (cartilage
glycoprotein-39) NM_002857 0.558862 PXF peroxisomal farnesylated
protein Contig33815_RC -0.558741 FLJ22833 hypothetical protein
FLJ22833 NM_003740 -0.558491 KCNK5 potassium channel, subfamily K,
member 5 (TASK-2) Contig53646_RC 0.558455 ESTs NM_005538 -0.558350
INHBC inhibin, beta C NM_002111 0.557860 HD huntingtin (Huntington
disease) NM_003683 -0.557807 D21S2056 DNA segment on chromosome 21
E (unique) 2056 expressed sequence NM_003035 -0.557380 SIL TAL1
(SCL) interrupting locus Contig4388_RC -0.557216 Homo sapiens,
Similar to integral membrane protein 3, clone MGC: 3011, mRNA,
complete cds Contig38288_RC -0.556426 ESTs, Weakly similar to
ISHUSS protein disulfide-isomerase [H. sapiens] NM_015417 0.556184
DKFZP434 DKFZP434I114 protein I114 NM_015507 -0.556138 EGFL6
EGF-like-domain, multiple 6 AF279865 0.555951 KIF13B kinesin family
member 13B Contig31288_RC -0.555754 ESTs NM_002966 -0.555620
S100A10 S100 calcium-binding protein A10 (annexin II ligand,
calpactin I, light polypeptide (p11)) NM_017585 -0.555476 SLC2A6
solute carrier family 2 (facilitated glucose transporter), member 6
NM_013296 -0.555367 HSU54999 LGN protein NM_000224 0.554838 KRT18
keratin 18 Contig49270_RC -0.554593 KIAA1553 KIAA1553 protein
NM_004848 -0.554538 ICB-1 basement membrane-induced gene NM_007275
0.554278 FUS1 lung cancer candidate NM_007044 -0.553550 KATNA1
katanin p60 (ATPase-containing) subunit A 1 Contig1829 0.553317
ESTs AF272357 0.553286 NPDC1 neural proliferation, differentiation
and control, 1 Contig57584_RC -0.553080 Homo sapiens, Similar to
gene rich cluster, C8 gene, clone MGC: 2577, mRNA, complete cds
NM_003039 -0.552747 SLC2A5 solute carrier family 2 (facilitated
glucose transporter), member 5 NM_014216 0.552321 ITPK1 inositol
1,3,4-triphosphate 5/6 kinase NM_007027 -0.552064 TOPBP1
topoisomerase (DNA) II binding protein AF118224 -0.551916 ST14
suppression of tumorigenicity 14 (colon carcinoma, matriptase,
epithin) X75315 -0.551853 HSRNASE seb4D B NM_012101 -0.551824 ATDC
ataxia-telangiectasia group D- associated protein AL157482
-0.551329 FLJ23399 hypothetical protein FLJ23399 NM_012474
-0.551150 UMPK uridine monophosphate kinase Contig57081_RC 0.551103
ESTs NM_006941 -0.551069 SOX10 SRY (sex determining region Y)-box
10 NM_004694 0.550932 SLC16A6 solute carrier family 16
(monocarboxylic acid transporters), member 6 Contig9541_RC 0.550680
ESTs Contig20617_RC 0.550546 ESTs NM_004252 0.550365 SLC9A3R solute
carrier family 9 1 (sodium/hydrogen exchanger), isoform 3
regulatory factor 1 NM_015641 -0.550200 DKFZP586 testin B2022
NM_004336 -0.550164 BUB1 budding uninhibited by benzimidazoles 1
(yeast homolog) Contig39960_RC -0.549951 FLJ21079 hypothetical
protein FLJ21079 NM_020686 0.549659 NPD009 NPD009 protein NM_002633
-0.549647 PGM1 phosphoglucomutase 1 Contig30480_RC 0.548932 ESTs
NM_003479 0.548896 PTP4A2 protein tyrosine phosphatase type IVA,
member 2 NM_001679 -0.548768 ATP1B3 ATPase, Na+/K+ transporting,
beta 3 polypeptide NM_001124 -0.548601 ADM adrenomedullin NM_001216
-0.548375 CA9 carbonic anhydrase IX U58033 -0.548354 MTMR2
myotubularin related protein 2 NM_018389 -0.547875 FLJ11320
hypothetical protein FLJ11320 AF176012 0.547867 JDP1 J domain
containing protein 1 Contig66705_RC -0.546926 ST5 suppression of
tumorigenicity 5 NM_018194 0.546878 FLJ10724 hypothetical protein
FLJ10724 NM_006851 -0.546823 RTVP1 glioma pathogenesis-related
protein Contig53870_RC 0.546756 ESTs
NM_002482 -0.546012 NASP nuclear autoantigenic sperm protein
(histone-binding) NM_002292 0.545949 LAMB2 laminin, beta 2 (laminin
S) NM_014696 -0.545758 KIAA0514 KIAA0514 gene product Contig49855
0.545517 ESTs AL117666 0.545203 DKFZP586 DKFZP586O1624 protein
O1624 NM_004701 -0.545185 CCNB2 cyclin B2 NM_007050 0.544890 PTPRT
protein tyrosine phosphatase, receptor type, T NM_000414 0.544778
HSD17B4 hydroxysteroid (17-beta) dehydrogenase 4 Contig52398_RC
-0.544775 Homo sapiens cDNA: FLJ21950 fis, clone HEP04949 AB007916
0.544496 KIAA0447 KIAA0447 gene product Contig66219_RC 0.544467
FLJ22402 hypothetical protein FLJ22402 D87453 0.544145 KIAA0264
KIAA0264 protein NM_015515 -0.543929 DKFZP434 DKFZP434G032 protein
G032 NM_001530 -0.543898 HIF1A hypoxia-inducible factor 1, alpha
subunit (basic helix-loop-helix transcription factor) NM_004109
-0.543893 FDX1 ferredoxin 1 NM_000381 -0.543871 MID1 midline 1
(Opitz/BBB syndrome) Contig43983_RC 0.543523 CS2 calsyntenin-2
AL137761 0.543371 Homo sapiens mRNA; cDNA DKFZp586L2424 (from clone
DKFZp586L2424) NM_005764 -0.543175 DD96 epithelial protein
up-regulated in carcinoma, membrane associated protein 17
Contig1838_RC 0.542996 Homo sapiens cDNA: FLJ22722 fis, clone
HSI14444 NM_006670 0.542932 5T4 5T4 oncofetal trophoblast
glycoprotein Contig28552_RC -0.542617 Homo sapiens mRNA; cDNA
DKFZp434C0931 (from clone DKFZp434C0931); partial cds
Contig14284_RC 0.542224 ESTs NM_006290 -0.542115 TNFAIP3 tumor
necrosis factor, alpha-induced protein 3 AL050372 0.541463 Homo
sapiens mRNA; cDNA DKFZp434A091 (from clone DKFZp434A091); partial
cds NM_014181 -0.541095 HSPC159 HSPC159 protein Contig37141_RC
0.540990 Homo sapiens cDNA: FLJ23582 fis, clone LNG13759 NM_000947
-0.540621 PRIM2A primase, polypeptide 2A (58 kD) NM_002136 0.540572
HNRPA1 heterogeneous nuclear ribonucleoprotein A1 NM_004494
-0.540543 HDGF hepatoma-derived growth factor (high-mobility group
protein 1-like) Contig38983_RC 0.540526 ESTs Contig27882_RC
-0.540506 ESTs Z11887 -0.540020 MMP7 matrix metalloproteinase 7
(matrilysin, uterine) NM_014575 -0.539725 SCHIP-1 schwannomin
interacting protein 1 Contig38170_RC 0.539708 ESTs Contig44064_RC
0.539403 ESTs U68385 0.539395 MEIS3 Meis (mouse) homolog 3
Contig51967_RC 0.538952 ESTs Contig37562_RC 0.538657 ESTs, Weakly
similar to transformation-related protein [H. sapiens]
Contig40500_RC 0.538582 ESTs, Weakly similar to unnamed protein
product [H. sapiens] Contig1129_RC 0.538339 ESTs NM_002184 0.538185
IL6ST interleukin 6 signal transducer (gp130, oncostatin M
receptor) AL049381 0.538041 Homo sapiens cDNA FLJ12900 fis, clone
NT2RP2004321 NM_002189 -0.537867 IL15RA interleukin 15 receptor,
alpha NM_012110 -0.537562 CHIC2 cystein-rich hydrophobic domain 2
AB040881 -0.537473 KIAA1448 KIAA1448 protein NM_016577 -0.537430
RAB6B RAB6B, member RAS oncogene family NM_001745 0.536940 CAMLG
calcium modulating ligand NM_005742 -0.536738 P5 protein disulfide
isomerase-related protein AB011132 0.536345 KIAA0560 KIAA0560 gene
product Contig54898_RC 0.536094 PNN pinin, desmosome associated
protein Contig45049_RC -0.536043 FUT4 fucosyltransferase 4 (alpha
(1,3) fucosyltransferase, myeloid-specific) NM_006864 -0.535924
LILRB3 leukocyte immunoglobulin-like receptor, subfamily B (with TM
and ITIM domains), member 3 Contig53242_RC -0.535909 Homo sapiens
cDNA FLJ11436 fis, clone HEMBA1001213 NM_005544 0.535712 IRS1
insulin receptor substrate 1 Contig47456_RC 0.535493 CACNA1D
calcium channel, voltage- dependent, L type, alpha 1D subunit
Contig42751_RC -0.535469 ESTs Contig29126_RC -0.535186 ESTs
NM_012391 0.535067 PDEF prostate epithelium-specific Ets
transcription factor NM_012429 0.534974 SEC14L2 SEC14 (S.
cerevisiae)-like 2 NM_018171 0.534898 FLJ10659 hypothetical protein
FLJ10659 Contig53047_RC -0.534773 TTYH1 tweety (Drosophila) homolog
1 Contig54968_RC 0.534754 Homo sapiens cDNA FLJ13558 fis, clone
PLACE1007743 Contig2099_RC -0.534694 KIAA1691 KIAA1691 protein
NM_005264 0.534057 GFRA1 GDNF family receptor alpha 1 NM_014036
-0.533638 SBBI42 BCM-like membrane protein precursor NM_018101
-0.533473 FLJ10468 hypothetical protein FLJ10468 Contig56765_RC
0.533442 ESTs, Moderately similar to K02E10.2 [C. elegans] AB006746
-0.533400 PLSCR1 phospholipid scramblase 1 NM_001089 0.533350 ABCA3
ATP-binding cassette, sub-family A (ABC1), member 3 NM_018188
-0.533132 FLJ10709 hypothetical protein FLJ10709 X94232 -0.532925
MAPRE2 microtubule-associated protein, RP/EB family, member 2
AF234532 -0.532910 MYO10 myosin X Contig292_RC 0.532853 FLJ22386
hypothetical protein FLJ22386 NM_000101 -0.532767 CYBA cytochrome
b-245, alpha polypeptide Contig47814_RC -0.532656 HHGP HHGP protein
NM_014320 -0.532430 SOUL putative heme-binding protein NM_020347
0.531976 LZTFL1 leucine zipper transcription factor- like 1
NM_004323 0.531936 BAG1 BCL2-associated athanogene Contig50850_RC
-0.531914 ESTs Contig11648_RC 0.531704 ESTs NM_018131 -0.531559
FLJ10540 hypothetical protein FLJ10540 NM_004688 -0.531329 NMI
N-myc (and STAT) interactor NM_014870 0.531101 KIAA0478 KIAA0478
gene product Contig31424_RC 0.530720 ESTs NM_000874 -0.530545
IFNAR2 interferon (alpha, beta and omega) receptor 2 Contig50588_RC
0.530145 ESTs NM_016463 0.529998 HSPC195 hypothetical protein
NM_013324 0.529966 CISH cytokine inducible SH2-containing protein
NM_006705 0.529840 GADD45G growth arrest and DNA-damage- inducible,
gamma Contig38901_RC -0.529747 ESTs NM_004184 -0.529635 WARS
tryptophanyl-tRNA synthetase NM_015955 -0.529538 LOC51072 CGI-27
protein AF151810 0.529416 CGI-52 similar to phosphatidylcholine
transfer protein 2 NM_002164 -0.529117 INDO indoleamine-pyrrole 2,3
dioxygenase NM_004267 -0.528679 CHST2 carbohydrate (chondroitin
6/keratan) sulfotransferase 2 Contig32185_RC -0.528529 Homo sapiens
cDNA FLJ13997 fis, clone Y79AA1002220 NM_004154 -0.528343 P2RY6
pyrimidinergic receptor P2Y, G- protein coupled, 6 NM_005235
0.528294 ERBB4 v-erb-a avian erythroblastic leukemia viral oncogene
homolog- like 4 Contig40208_RC -0.528062 LOC56938 transcription
factor BMAL2 NM_013262 0.527297 MIR myosin regulatory light chain
interacting protein NM_003034 -0.527148 SIAT8A sialyltransferase 8
(alpha-N- acetylneuraminate: alpha-2,8- sialytransferase, GD3
synthase) A NM_004556 -0.527146 NFKBIE nuclear factor of kappa
light polypeptide gene enhancer in B- cells inhibitor, epsilon
NM_002046 -0.527051 GAPD glyceraldehyde-3-phosphate dehydrogenase
NM_001905 -0.526986 CTPS CTP synthase Contig42402_RC 0.526852 ESTs
NM_014272 -0.526283 ADAMTS7 a disintegrin-like and metalloprotease
(reprolysin type) with thrombospondin type 1 motif, 7 AF076612
0.526205 CHRD chordin Contig57725_RC -0.526122 Homo sapiens mRNA
for HMG-box transcription factor TCF-3, complete cds Contig42041_RC
-0.525877 ESTs Contig44656_RC -0.525868 ESTs, Highly similar to
S02392 alpha-2-macroglobulin receptor precursor [H. sapiens]
NM_018004 -0.525610 FLJ10134 hypothetical protein FLJ10134
Contig56434_RC 0.525510 Homo sapiens cDNA FLJ13603 fis, clone
PLACE1010270 D25328 -0.525504 PFKP phosphofructokinase, platelet
Contig55950_RC -0.525358 FLJ22329 hypothetical protein FLJ22329
NM_002648 -0.525211 PIM1 pim-1 oncogene AL157505 0.525186 Homo
sapiens mRNA; cDNA DKFZp586P1124 (from clone DKFZp586P1124)
AF061034 -0.525185 FIP2 Homo sapiens FIP2 alternatively translated
mRNA, complete cds. NM_014721 -0.525102 KIAA0680 KIAA0680 gene
product NM_001634 -0.525030 AMD1 S-adenosylmethionine decarboxylase
1 NM_006304 -0.524911 DSS1 Deleted in split-hand/split-foot 1
region Contig37778_RC 0.524667 ESTs, Highly similar to HLHUSB MHC
class II histocompatibility antigen HLA-DP alpha-1 chain precursor
[H. sapiens] NM_003099 0.524339 SNX1 sorting nexin 1 AL079298
0.523774 MCCC2 methylcrotonoyl-Coenzyme A carboxylase 2 (beta)
NM_019013 -0.523663 FLJ10156 hypothetical protein NM_000397
-0.523293 CYBB cytochrome b-245, beta polypeptide (chronic
granulomatous disease) NM_014811 0.523132 KIAA0649 KIAA0649 gene
product Contig20600_RC 0.523072 ESTs NM_005190 -0.522710 CCNC
cyclin C AL161960 -0.522574 FLJ21324 hypothetical protein FLJ21324
AL117502 0.522280 Homo sapiens mRNA; cDNA DKFZp434D0935 (from clone
DKFZp434D0935) AF131753 -0.522245 Homo sapiens clone 24859 mRNA
sequence NM_000320 0.521974 QDPR quinoid dihydropteridine reductase
NM_002115 -0.521870 HK3 hexokinase 3 (white cell) NM_006460
0.521696 HIS1 HMBA-inducible NM_018683 -0.521679 ZNF313 zinc finger
protein 313 NM_004305 -0.521539 BIN1 bridging integrator 1
NM_006770 -0.521538 MARCO macrophage receptor with collagenous
structure NM_001166 -0.521530 BIRC2 baculoviral IAP
repeat-containing 2 D42047 0.521522 KIAA0089 KIAA0089 protein
NM_016235 -0.521298 GPRC5B G protein-coupled receptor, family C,
group 5, member B NM_004504 -0.521189 HRB HIV-1 Rev binding protein
NM_002727 -0.521146 PRG1 proteoglycan 1, secretory granule AB029031
-0.520761 KIAA1108 KIAA1108 protein NM_005556 -0.520692 KRT7
keratin 7 NM_018031 0.520600 WDR6 WD repeat domain 6 AL117523
-0.520579 KIAA1053 KIAA1053 protein NM_004515 -0.520363 ILF2
interleukin enhancer binding factor 2, 45 kD NM_004708 -0.519935
PDCD5 programmed cell death 5 NM_005935 0.519765 MLLT2
myeloid/lymphoid or mixed-lineage leukemia (trithorax (Drosophila)
homolog); translocated to, 2 Contig49289_RC -0.519546 Homo sapiens
mRNA; cDNA DKFZp586J1119 (from clone DKFZp586J1119); complete cds
NM_000211 -0.519342 ITGB2 integrin, beta 2 (antigen CD18 (p95),
lymphocyte function-associated antigen 1; macrophage antigen 1
(mac-1) beta subunit) AL079276 0.519207 LOC58495 putative zinc
finger protein from EUROIMAGE 566589 Contig57825_RC 0.519041 ESTs
NM_002466 -0.518911 MYBL2 v-myb avian myeloblastosis viral oncogene
homolog-like 2 NM_016072 -0.518802 LOC51026 CGI-141 protein
AB007950 -0.518699 KIAA0481 KIAA0481 gene product NM_001550
-0.518549 IFRD1 interferon-related developmental regulator 1
AF155120 -0.518221 UBE2V1 ubiquitin-conjugating enzyme E2 variant
1
Contig49849_RC 0.517983 ESTs, Weakly similar to AF188706 1 g20
protein [H. sapiens] NM_016625 -0.517936 LOC51319 hypothetical
protein NM_004049 -0.517862 BCL2A1 BCL2-related protein A1
Contig50719_RC 0.517740 ESTs D80010 -0.517620 LPIN1 lipin 1
NM_000299 -0.517405 PKP1 plakophilin 1 (ectodermal dysplasia/skin
fragility syndrome) AL049365 0.517080 FTL ferritin, light
polypeptide Contig65227 0.517003 ESTs NM_004865 -0.516808 TBPL1
TBP-like 1 Contig54813_RC 0.516246 FLJ13962 hypothetical protein
FLJ13962 NM_003494 -0.516221 DYSF dysferlin, limb girdle muscular
dystrophy 2B (autosomal recessive) NM_004431 -0.516212 EPHA2 EphA2
AL117600 -0.516067 DKFZP564 DKFZP564J0863 protein J0863 AL080209
-0.516037 DKFZP586 hypothetical protein F2423 DKFZp586F2423
NM_000135 -0.515613 FANCA Fanconi anemia, complementation group A
NM_000050 -0.515494 ASS argininosuccinate synthetase NM_001830
-0.515439 CLCN4 chloride channel 4 NM_018234 -0.515365 FLJ10829
hypothetical protein FLJ10829 Contig53307_RC 0.515328 ESTs, Highly
similar to KIAA1437 protein [H. sapiens] AL117617 -0.515141 Homo
sapiens mRNA; cDNA DKFZp564H0764 (from clone DKFZp564H0764)
NM_002906 -0.515098 RDX radixin NM_003360 -0.514427 UGT8 UDP
glycosyltransferase 8 (UDP- galactose ceramide
galactosyltransferase) NM_018478 0.514332 HSMNP1 uncharacterized
hypothalamus protein HSMNP1 M90657 -0.513908 TM4SF1 transmembrane 4
superfamily member 1 NM_014967 0.513793 KIAA1018 KIAA1018 protein
Contig1462_RC 0.513604 C11ORF15 chromosome 11 open reading frame 15
Contig37287_RC -0.513324 ESTs NM_000355 -0.513225 TCN2
transcobalamin II; macrocytic anemia AB037756 0.512914 KIAA1335
hypothetical protein KIAA1335 Contig842_RC -0.512880 ESTs NM_018186
-0.512878 FLJ10706 hypothetical protein FLJ10706 NM_014668 0.512746
KIAA0575 KIAA0575 gene product NM_003226 0.512611 TFF3 trefoil
factor 3 (intestinal) Contig56457_RC -0.512548 TMEFF1 transmembrane
protein with EGF- like and two follistatin-like domains 1 AL050367
-0.511999 Homo sapiens mRNA; cDNA DKFZp564A026 (from clone
DKFZp564A026) NM_014791 -0.511963 KIAA0175 KIAA0175 gene product
Contig36312_RC 0.511794 ESTs NM_004811 -0.511447 LPXN leupaxin
Contig67182_RC -0.511416 ESTs, Highly similar to epithelial V- like
antigen precursor [H. sapiens] Contig52723_RC -0.511134 ESTs
Contig17105_RC -0.511072 Homo sapiens mRNA for putative
cytoplasmatic protein (ORF1-FL21) NM_014449 0.511023 A protein "A"
Contig52957_RC 0.510815 ESTs Contig49388_RC 0.510582 FLJ13322
hypothetical protein FLJ13322 NM_017786 0.510557 FLJ20366
hypothetical protein FLJ20366 AL157476 0.510478 Homo sapiens mRNA;
cDNA DKFZp761C082 (from clone DKFZp761C082) NM_001919 0.510242 DCI
dodecenoyl-Coenzyme A delta isomerase (3,2 trans-enoyl- Coenzyme A
isomerase) NM_000268 -0.510165 NF2 neurofibromin 2 (bilateral
acoustic neuroma) NM_016210 0.510018 LOC51161 g20 protein
Contig45816_RC -0.509977 ESTs NM_003953 -0.509969 MPZL1 myelin
protein zero-like 1 NM_000057 -0.509669 BLM Bloom syndrome
NM_014452 -0.509473 DR6 death receptor 6 Contig45156_RC 0.509284
ESTs, Moderately similar to motor domain of KIF12 [M. musculus]
NM_006943 0.509149 SOX22 SRY (sex determining region Y)-box 22
NM_000594 -0.509012 TNF tumor necrosis factor (TNF superfamily,
member 2) AL137316 -0.508353 KIAA1609 KIAA1609 protein NM_000557
-0.508325 GDF5 growth differentiation factor 5 (cartilage-derived
morphogenetic protein-1) NM_018685 -0.508307 ANLN anillin
(Drosophila Scraps homolog), actin binding protein Contig53401_RC
0.508189 ESTs NM_014364 -0.508170 GAPDS glyceraldehyde-3-phosphate
dehydrogenase, testis-specific Contig50297_RC 0.508137 ESTs,
Moderately similar to ALU8_HUMAN ALU SUBFAMILY SX SEQUENCE
CONTAMINATION WARNING ENTRY [H. sapiens] Contig51800 0.507891 ESTs,
Weakly similar to ALU6_HUMAN ALU SUBFAMILY SP SEQUENCE
CONTAMINATION WARNING ENTRY [H. sapiens] Contig49098_RC -0.507716
MGC4090 hypothetical protein MGC4090 NM_002985 -0.507554 SCYA5
small inducible cytokine A5 (RANTES) AB007899 0.507439 KIAA0439
KIAA0439 protein; homolog of yeast ubiquitin-protein ligase Rsp5
AL110139 0.507145 Homo sapiens mRNA; cDNA DKFZp564O1763 (from clone
DKFZp564O1763) Contig51117_RC 0.507001 ESTs NM_017660 -0.506768
FLJ20085 hypothetical protein FLJ20085 NM_018000 0.506686 FLJ10116
hypothetical protein FLJ10116 NM_005555 -0.506516 KRT6B keratin 6B
NM_005582 -0.506462 LY64 lymphocyte antigen 64 (mouse) homolog,
radioprotective, 105 kD Contig47405_RC 0.506202 ESTs NM_014808
0.506173 KIAA0793 KIAA0793 gene product NM_004938 -0.506121 DAPK1
death-associated protein kinase 1 NM_020659 -0.505793 TTYH1 tweety
(Drosophila) homolog 1 NM_006227 -0.505604 PLTP phospholipid
transfer protein NM_014268 -0.505412 MAPRE2 microtubule-associated
protein, RP/EB family, member 2 NM_004711 0.504849 SYNGR1
synaptogyrin 1 NM_004418 -0.504497 DUSP2 dual specificity
phosphatase 2 NM_003508 -0.504475 FZD9 frizzled (Drosophila)
homolog 9
TABLE-US-00003 TABLE 3 430 gene markers that distinguish
BRCA1-related tumor samples from sporadic tumor samples GenBank
Accession Number SEQ ID NO AB002301 SEQ ID NO 4 AB004857 SEQ ID NO
8 AB007458 SEQ ID NO 12 AB014534 SEQ ID NO 29 AB018305 SEQ ID NO 34
AB020677 SEQ ID NO 36 AB020689 SEQ ID NO 37 AB023151 SEQ ID NO 41
AB023163 SEQ ID NO 43 AB028986 SEQ ID NO 48 AB029025 SEQ ID NO 50
AB032966 SEQ ID NO 53 AB032988 SEQ ID NO 57 AB033049 SEQ ID NO 63
AB033055 SEQ ID NO 66 AB037742 SEQ ID NO 73 AB041269 SEQ ID NO 96
AF000974 SEQ ID NO 97 AF042838 SEQ ID NO 111 AF052155 SEQ ID NO 119
AF055084 SEQ ID NO 125 AF063725 SEQ ID NO 129 AF070536 SEQ ID NO
133 AF070617 SEQ ID NO 135 AF073299 SEQ ID NO 136 AF079529 SEQ ID
NO 140 AF090353 SEQ ID NO 141 AF116238 SEQ ID NO 155 AF151810 SEQ
ID NO 171 AF220492 SEQ ID NO 185 AJ224741 SEQ ID NO 196 AJ250475
SEQ ID NO 201 AJ270996 SEQ ID NO 202 AJ272057 SEQ ID NO 203
AK000174 SEQ ID NO 211 AK000617 SEQ ID NO 215 AK000959 SEQ ID NO
222 AK001438 SEQ ID NO 229 AK001838 SEQ ID NO 233 AK002107 SEQ ID
NO 238 AK002197 SEQ ID NO 239 AL035297 SEQ ID NO 241 AL049346 SEQ
ID NO 243 AL049370 SEQ ID NO 245 AL049667 SEQ ID NO 249 AL080222
SEQ ID NO 276 AL096737 SEQ ID NO 279 AL110163 SEQ ID NO 282
AL133057 SEQ ID NO 300 AL133096 SEQ ID NO 302 AL133572 SEQ ID NO
305 AL133619 SEQ ID NO 307 AL133623 SEQ ID NO 309 AL137347 SEQ ID
NO 320 AL137381 SEQ ID NO 322 AL137461 SEQ ID NO 325 AL137540 SEQ
ID NO 328 AL137555 SEQ ID NO 329 AL137638 SEQ ID NO 332 AL137639
SEQ ID NO 333 AL137663 SEQ ID NO 334 AL137761 SEQ ID NO 339
AL157431 SEQ ID NO 340 AL161960 SEQ ID NO 351 AL355708 SEQ ID NO
353 AL359053 SEQ ID NO 354 D26488 SEQ ID NO 359 D38521 SEQ ID NO
361 D50914 SEQ ID NO 367 D80001 SEQ ID NO 369 G26403 SEQ ID NO 380
K02276 SEQ ID NO 383 M21551 SEQ ID NO 394 M27749 SEQ ID NO 397
M28170 SEQ ID NO 398 M73547 SEQ ID NO 409 M80899 SEQ ID NO 411
NM_000067 SEQ ID NO 423 NM_000087 SEQ ID NO 427 NM_000090 SEQ ID NO
428 NM_000165 SEQ ID NO 444 NM_000168 SEQ ID NO 445 NM_000196 SEQ
ID NO 449 NM_000269 SEQ ID NO 457 NM_000310 SEQ ID NO 466 NM_000396
SEQ ID NO 479 NM_000397 SEQ ID NO 480 NM_000597 SEQ ID NO 502
NM_000636 SEQ ID NO 509 NM_000888 SEQ ID NO 535 NM_000903 SEQ ID NO
536 NM_000930 SEQ ID NO 540 NM_000931 SEQ ID NO 541 NM_000969 SEQ
ID NO 547 NM_000984 SEQ ID NO 548 NM_001026 SEQ ID NO 552 NM_001054
SEQ ID NO 554 NM_001179 SEQ ID NO 567 NM_001184 SEQ ID NO 568
NM_001204 SEQ ID NO 571 NM_001206 SEQ ID NO 572 NM_001218 SEQ ID NO
575 NM_001275 SEQ ID NO 586 NM_001394 SEQ ID NO 602 NM_001424 SEQ
ID NO 605 NM_001448 SEQ ID NO 610 NM_001504 SEQ ID NO 620 NM_001553
SEQ ID NO 630 NM_001674 SEQ ID NO 646 NM_001675 SEQ ID NO 647
NM_001725 SEQ ID NO 652 NM_001740 SEQ ID NO 656 NM_001756 SEQ ID NO
659 NM_001770 SEQ ID NO 664 NM_001797 SEQ ID NO 670 NM_001845 SEQ
ID NO 680 NM_001873 SEQ ID NO 684 NM_001888 SEQ ID NO 687 NM_001892
SEQ ID NO 688 NM_001919 SEQ ID NO 694 NM_001946 SEQ ID NO 698
NM_001953 SEQ ID NO 699 NM_001960 SEQ ID NO 704 NM_001985 SEQ ID NO
709 NM_002023 SEQ ID NO 712 NM_002051 SEQ ID NO 716 NM_002053 SEQ
ID NO 717 NM_002164 SEQ ID NO 734 NM_002200 SEQ ID NO 739 NM_002201
SEQ ID NO 740 NM_002213 SEQ ID NO 741 NM_002250 SEQ ID NO 747
NM_002512 SEQ ID NO 780 NM_002542 SEQ ID NO 784 NM_002561 SEQ ID NO
786 NM_002615 SEQ ID NO 793 NM_002686 SEQ ID NO 803 NM_002709 SEQ
ID NO 806 NM_002742 SEQ ID NO 812 NM_002775 SEQ ID NO 815 NM_002975
SEQ ID NO 848 NM_002982 SEQ ID NO 849 NM_003104 SEQ ID NO 870
NM_003118 SEQ ID NO 872 NM_003144 SEQ ID NO 876 NM_003165 SEQ ID NO
882 NM_003197 SEQ ID NO 885 NM_003202 SEQ ID NO 886 NM_003217 SEQ
ID NO 888 NM_003283 SEQ ID NO 898 NM_003462 SEQ ID NO 911 NM_003500
SEQ ID NO 918 NM_003561 SEQ ID NO 925 NM_003607 SEQ ID NO 930
NM_003633 SEQ ID NO 933 NM_003641 SEQ ID NO 934 NM_003683 SEQ ID NO
943 NM_003729 SEQ ID NO 949 NM_003793 SEQ ID NO 954 NM_003829 SEQ
ID NO 958 NM_003866 SEQ ID NO 961 NM_003904 SEQ ID NO 967 NM_003953
SEQ ID NO 974 NM_004024 SEQ ID NO 982 NM_004053 SEQ ID NO 986
NM_004295 SEQ ID NO 1014 NM_004438 SEQ ID NO 1038 NM_004559 SEQ ID
NO 1057 NM_004616 SEQ ID NO 1065 NM_004741 SEQ ID NO 1080 NM_004772
SEQ ID NO 1084 NM_004791 SEQ ID NO 1086 NM_004848 SEQ ID NO 1094
NM_004866 SEQ ID NO 1097 NM_005128 SEQ ID NO 1121 NM_005148 SEQ ID
NO 1124 NM_005196 SEQ ID NO 1127 NM_005326 SEQ ID NO 1140 NM_005518
SEQ ID NO 1161 NM_005538 SEQ ID NO 1163 NM_005557 SEQ ID NO 1170
NM_005718 SEQ ID NO 1189 NM_005804 SEQ ID NO 1201 NM_005824 SEQ ID
NO 1203 NM_005935 SEQ ID NO 1220 NM_006002 SEQ ID NO 1225 NM_006148
SEQ ID NO 1249 NM_006235 SEQ ID NO 1257 NM_006271 SEQ ID NO 1261
NM_006287 SEQ ID NO 1264 NM_006296 SEQ ID NO 1267 NM_006378 SEQ ID
NO 1275 NM_006461 SEQ ID NO 1287 NM_006573 SEQ ID NO 1300 NM_006622
SEQ ID NO 1302 NM_006696 SEQ ID NO 1308 NM_006769 SEQ ID NO 1316
NM_006787 SEQ ID NO 1319 NM_006875 SEQ ID NO 1334 NM_006885 SEQ ID
NO 1335 NM_006918 SEQ ID NO 1339 NM_006923 SEQ ID NO 1340 NM_006941
SEQ ID NO 1342 NM_007070 SEQ ID NO 1354 NM_007088 SEQ ID NO 1356
NM_007146 SEQ ID NO 1358 NM_007173 SEQ ID NO 1359 NM_007246 SEQ ID
NO 1366 NM_007358 SEQ ID NO 1374 NM_012135 SEQ ID NO 1385 NM_012151
SEQ ID NO 1387 NM_012258 SEQ ID NO 1396 NM_012317 SEQ ID NO 1399
NM_012337 SEQ ID NO 1403 NM_012339 SEQ ID NO 1404 NM_012391 SEQ ID
NO 1406 NM_012428 SEQ ID NO 1412 NM_013233 SEQ ID NO 1418 NM_013253
SEQ ID NO 1422 NM_013262 SEQ ID NO 1425 NM_013372 SEQ ID NO 1434
NM_013378 SEQ ID NO 1435 NM_014096 SEQ ID NO 1450 NM_014242 SEQ ID
NO 1464 NM_014314 SEQ ID NO 1472 NM_014398 SEQ ID NO 1486 NM_014402
SEQ ID NO 1488 NM_014476 SEQ ID NO 1496 NM_014521 SEQ ID NO 1499
NM_014585 SEQ ID NO 1504 NM_014597 SEQ ID NO 1506 NM_014642 SEQ ID
NO 1510 NM_014679 SEQ ID NO 1517 NM_014680 SEQ ID NO 1518 NM_014700
SEQ ID NO 1520 NM_014723 SEQ ID NO 1523 NM_014770 SEQ ID NO 1530
NM_014785 SEQ ID NO 1534 NM_014817 SEQ ID NO 1539 NM_014840 SEQ ID
NO 1541 NM_014878 SEQ ID NO 1546 NM_015493 SEQ ID NO 1564 NM_015523
SEQ ID NO 1568
NM_015544 SEQ ID NO 1570 NM_015623 SEQ ID NO 1572 NM_015640 SEQ ID
NO 1573 NM_015721 SEQ ID NO 1576 NM_015881 SEQ ID NO 1577 NM_015937
SEQ ID NO 1582 NM_015964 SEQ ID NO 1586 NM_015984 SEQ ID NO 1587
NM_016000 SEQ ID NO 1591 NM_016018 SEQ ID NO 1593 NM_016066 SEQ ID
NO 1601 NM_016073 SEQ ID NO 1603 NM_016081 SEQ ID NO 1604 NM_016140
SEQ ID NO 1611 NM_016223 SEQ ID NO 1622 NM_016267 SEQ ID NO 1629
NM_016307 SEQ ID NO 1633 NM_016364 SEQ ID NO 1639 NM_016373 SEQ ID
NO 1640 NM_016459 SEQ ID NO 1646 NM_016471 SEQ ID NO 1648 NM_016548
SEQ ID NO 1654 NM_016620 SEQ ID NO 1662 NM_016820 SEQ ID NO 1674
NM_017423 SEQ ID NO 1678 NM_017709 SEQ ID NO 1698 NM_017732 SEQ ID
NO 1700 NM_017734 SEQ ID NO 1702 NM_017750 SEQ ID NO 1704 NM_017763
SEQ ID NO 1706 NM_017782 SEQ ID NO 1710 NM_017816 SEQ ID NO 1714
NM_018043 SEQ ID NO 1730 NM_018072 SEQ ID NO 1734 NM_018093 SEQ ID
NO 1738 NM_018103 SEQ ID NO 1742 NM_018171 SEQ ID NO 1751 NM_018187
SEQ ID NO 1755 NM_018188 SEQ ID NO 1756 NM_018222 SEQ ID NO 1761
NM_018228 SEQ ID NO 1762 NM_018373 SEQ ID NO 1777 NM_018390 SEQ ID
NO 1781 NM_018422 SEQ ID NO 1784 NM_018509 SEQ ID NO 1792 NM_018584
SEQ ID NO 1796 NM_018653 SEQ ID NO 1797 NM_018660 SEQ ID NO 1798
NM_018683 SEQ ID NO 1799 NM_019049 SEQ ID NO 1814 NM_019063 SEQ ID
NO 1815 NM_020150 SEQ ID NO 1823 NM_020987 SEQ ID NO 1848 NM_021095
SEQ ID NO 1855 NM_021242 SEQ ID NO 1867 U41387 SEQ ID NO 1877
U45975 SEQ ID NO 1878 U58033 SEQ ID NO 1881 U67784 SEQ ID NO 1884
U68385 SEQ ID NO 1885 U80736 SEQ ID NO 1890 X00437 SEQ ID NO 1899
X07203 SEQ ID NO 1904 X16302 SEQ ID NO 1907 X51630 SEQ ID NO 1908
X57809 SEQ ID NO 1912 X57819 SEQ ID NO 1913 X58529 SEQ ID NO 1914
X66087 SEQ ID NO 1916 X69150 SEQ ID NO 1917 X72475 SEQ ID NO 1918
X74794 SEQ ID NO 1920 X75315 SEQ ID NO 1921 X84340 SEQ ID NO 1925
X98260 SEQ ID NO 1928 Y07512 SEQ ID NO 1931 Y14737 SEQ ID NO 1932
Z34893 SEQ ID NO 1934 Contig237_RC SEQ ID NO 1940 Contig292_RC SEQ
ID NO 1942 Contig372_RC SEQ ID NO 1943 Contig756_RC SEQ ID NO 1955
Contig842_RC SEQ ID NO 1958 Contig1632_RC SEQ ID NO 1977
Contig1826_RC SEQ ID NO 1980 Contig2237_RC SEQ ID NO 1988
Contig2915_RC SEQ ID NO 2003 Contig3164_RC SEQ ID NO 2007
Contig3252_RC SEQ ID NO 2008 Contig3940_RC SEQ ID NO 2018
Contig9259_RC SEQ ID NO 2039 Contig10268_RC SEQ ID NO 2041
Contig10437_RC SEQ ID NO 2043 Contig10973_RC SEQ ID NO 2044
Contig14390_RC SEQ ID NO 2054 Contig16453_RC SEQ ID NO 2060
Contig16759_RC SEQ ID NO 2061 Contig19551 SEQ ID NO 2070
Contig24541_RC SEQ ID NO 2088 Contig25362_RC SEQ ID NO 2093
Contig25617_RC SEQ ID NO 2094 Contig25722_RC SEQ ID NO 2096
Contig26022_RC SEQ ID NO 2099 Contig27915_RC SEQ ID NO 2114
Contig28081_RC SEQ ID NO 2116 Contig28179_RC SEQ ID NO 2118
Contig28550_RC SEQ ID NO 2119 Contig29639_RC SEQ ID NO 2127
Contig29647_RC SEQ ID NO 2128 Contig30092_RC SEQ ID NO 2130
Contig30209_RC SEQ ID NO 2132 Contig32185_RC SEQ ID NO 2156
Contig32798_RC SEQ ID NO 2161 Contig33230_RC SEQ ID NO 2163
Contig33394_RC SEQ ID NO 2165 Contig36323_RC SEQ ID NO 2197
Contig36761_RC SEQ ID NO 2201 Contig37141_RC SEQ ID NO 2209
Contig37778_RC SEQ ID NO 2218 Contig38285_RC SEQ ID NO 2222
Contig38520_RC SEQ ID NO 2225 Contig38901_RC SEQ ID NO 2232
Contig39826_RC SEQ ID NO 2241 Contig40212_RC SEQ ID NO 2251
Contig40712_RC SEQ ID NO 2257 Contig41402_RC SEQ ID NO 2265
Contig41635_RC SEQ ID NO 2272 Contig42006_RC SEQ ID NO 2280
Contig42220_RC SEQ ID NO 2286 Contig42306_RC SEQ ID NO 2287
Contig43918_RC SEQ ID NO 2312 Contig44195_RC SEQ ID NO 2316
Contig44265_RC SEQ ID NO 2318 Contig44278_RC SEQ ID NO 2319
Contig44757_RC SEQ ID NO 2329 Contig45588_RC SEQ ID NO 2349
Contig46262_RC SEQ ID NO 2361 Contig46288_RC SEQ ID NO 2362
Contig46343_RC SEQ ID NO 2363 Contig46452_RC SEQ ID NO 2366
Contig46868_RC SEQ ID NO 2373 Contig46937_RC SEQ ID NO 2377
Contig48004_RC SEQ ID NO 2393 Contig48249_RC SEQ ID NO 2397
Contig48774_RC SEQ ID NO 2405 Contig48913_RC SEQ ID NO 2411
Contig48945_RC SEQ ID NO 2412 Contig48970_RC SEQ ID NO 2413
Contig49233_RC SEQ ID NO 2419 Contig49289_RC SEQ ID NO 2422
Contig49342_RC SEQ ID NO 2423 Contig49510_RC SEQ ID NO 2430
Contig49855 SEQ ID NO 2440 Contig49948_RC SEQ ID NO 2442
Contig50297_RC SEQ ID NO 2451 Contig50669_RC SEQ ID NO 2458
Contig50673_RC SEQ ID NO 2459 Contig50838_RC SEQ ID NO 2465
Contig51068_RC SEQ ID NO 2471 Contig51929 SEQ ID NO 2492
Contig51953_RC SEQ ID NO 2493 Contig52405_RC SEQ ID NO 2502
Contig52543_RC SEQ ID NO 2505 Contig52720_RC SEQ ID NO 2513
Contig53281_RC SEQ ID NO 2530 Contig53598_RC SEQ ID NO 2537
Contig53757_RC SEQ ID NO 2543 Contig53944_RC SEQ ID NO 2545
Contig54425 SEQ ID NO 2561 Contig54547_RC SEQ ID NO 2565
Contig54757_RC SEQ ID NO 2574 Contig54916_RC SEQ ID NO 2581
Contig55770_RC SEQ ID NO 2604 Contig55801_RC SEQ ID NO 2606
Contig56143_RC SEQ ID NO 2619 Contig56160_RC SEQ ID NO 2620
Contig56303_RC SEQ ID NO 2626 Contig57023_RC SEQ ID NO 2639
Contig57138_RC SEQ ID NO 2644 Contig57609_RC SEQ ID NO 2657
Contig58301_RC SEQ ID NO 2667 Contig58512_RC SEQ ID NO 2670
Contig60393 SEQ ID NO 2674 Contig60509_RC SEQ ID NO 2675
Contig61254_RC SEQ ID NO 2677 Contig62306 SEQ ID NO 2680
Contig64502 SEQ ID NO 2689
TABLE-US-00004 TABLE 4 100 preferred markers from Table 3
distinguishing BRCA1-related tumors from sporadic tumors. Sequence
Identifier Correlation Name Description NM_001892 -0.651689 CSNK1A1
casein kinase 1, alpha 1 NM_018171 -0.637696 FLJ10659 hypothetical
protein FLJ10659 Contig40712_RC -0.612509 ESTs NM_001204 -0.608470
BMPR2 bone morphogenetic protein receptor, type II
(serine/threonine kinase) NM_005148 -0.598612 UNC119 unc119 (C.
elegans) homolog G26403 0.585054 YWHAH tyrosine
3-monooxygenase/tryptophan 5- monooxygenase activation protein, eta
polypeptide NM_015640 0.583397 PAI-RBP1 PAI-1 mRNA-binding protein
Contig9259_RC 0.581362 ESTs AB033049 -0.578750 KIAA1223 KIAA1223
protein NM_015523 0.576029 DKFZP566E144 small fragment nuclease
Contig41402_RC -0.571650 Human DNA sequence from clone RP11- 16L21
on chromosome 9. Contains the gene for NADP-dependent leukotriene
B4 12- hydroxydehydrogenase, the gene for a novel DnaJ domain
protein similar to Drosophila, C. elegans and Arabidopsis predicted
proteins, the GNG10 gene for guanine nucleotide binding protein 10,
a novel gene, ESTs, STSs, GSSs and six CpG islands NM_004791
-0.564819 ITGBL1 integrin, beta-like 1 (with EGF-like repeat
domains) NM_007070 0.561173 FAP48 FKBP-associated protein NM_014597
0.555907 HSU15552 acidic 82 kDa protein mRNA AF000974 0.547194
TRIP6 thyroid hormone receptor interactor 6 NM_016073 -0.547072
CGI-142 CGI-142 Contig3940_RC 0.544073 YWHAH tyrosine
3-monooxygenase/tryptophan 5- monooxygenase activation protein, eta
polypeptide NM_003683 0.542219 D21S2056E DNA segment on chromosome
21 (unique) 2056 expressed sequence Contig58512_RC -0.528458 Homo
sapiens pancreas tumor-related protein (FKSG12) mRNA, complete cds
NM_003904 0.521223 ZNF259 zinc finger protein 259 Contig26022_RC
0.517351 ESTs Contig48970_RC -0.516953 KIAA0892 KIAA0892 protein
NM_016307 -0.515398 PRX2 paired related homeobox protein AL137761
-0.514891 Homo sapiens mRNA; cDNA DKFZp586L2424 (from clone
DKFZp586L2424) NM_001919 -0.514799 DCI dodecenoyl-Coenzyme A delta
isomerase (3,2 trans-enoyl-Coenzyme A isomerase) NM_000196
-0.514004 HSD11B2 hydroxysteroid (11-beta) dehydrogenase 2
NM_002200 0.513149 IRF5 interferon regulatory factor 5 AL133572
0.511340 Homo sapiens mRNA; cDNA DKFZp434I0535 (from clone
DKFZp434I0535); partial cds NM_019063 0.511127 C2ORF2 chromosome 2
open reading frame 2 Contig25617_RC 0.509506 ESTs NM_007358
0.508145 M96 putative DNA binding protein NM_014785 -0.507114
KIAA0258 KIAA0258 gene product NM_006235 0.506585 POU2AF1 POU
domain, class 2, associating factor 1 NM_014680 -0.505779 KIAA0100
KIAA0100 gene product X66087 0.500842 MYBL1 v-myb avian
myeloblastosis viral oncogene homolog-like 1 Y07512 -0.500686 PRKG1
protein kinase, cGMP-dependent, type I NM_006296 0.500344 VRK2
vaccinia related kinase 2 Contig44278_RC 0.498260 DKFZP434K
DKFZP434K114 protein 114 Contig56160_RC -0.497695 ESTs NM_002023
-0.497570 FMOD fibromodulin M28170 0.497095 CD19 CD19 antigen
D26488 0.496511 KIAA0007 KIAA0007 protein X72475 0.496125 H.
sapiens mRNA for rearranged Ig kappa light chain variable region
(I.114) K02276 0.496068 MYC v-myc avian myelocytomatosis viral
oncogene homolog NM_013378 0.495648 VPREB3 pre-B lymphocyte gene 3
X58529 0.495608 IGHM immunoglobulin heavy constant mu NM_000168
-0.494260 GLI3 GLI-Kruppel family member GLI3 (Greig
cephalopolysyndactyly syndrome) NM_004866 -0.492967 SCAMP1
secretory carrier membrane protein 1 NM_013253 -0.491159 DKK3
dickkopf (Xenopus laevis) homolog 3 NM_003729 0.488971 RPC RNA
3'-terminal phosphate cyclase NM_006875 0.487407 PIM2 pim-2
oncogene NM_018188 0.487126 FLJ10709 hypothetical protein FLJ10709
NM_004848 0.485408 ICB-1 basement membrane-induced gene NM_001179
0.483253 ART3 ADP-ribosyltransferase 3 NM_016548 -0.482329 LOC51280
golgi membrane protein GP73 NM_007146 -0.481994 ZNF161 zinc finger
protein 161 NM_021242 -0.481754 STRAIT11499 hypothetical protein
STRAIT11499 NM_016223 0.481710 PACSIN3 protein kinase C and casein
kinase substrate in neurons 3 NM_003197 -0.481526 TCEB1L
transcription elongation factor B (SIII), polypeptide 1-like
NM_000067 -0.481003 CA2 carbonic anhydrase II NM_006885 -0.479705
ATBF1 AT-binding transcription factor 1 NM_002542 0.478282 OGG1
8-oxoguanine DNA glycosylase AL133619 -0.476596 Homo sapiens mRNA;
cDNA DKFZp434E2321 (from clone DKFZp434E2321); partial cds D80001
0.476130 KIAA0179 KIAA0179 protein NM_018660 -0.475548 LOC55893
papillomavirus regulatory factor PRF-1 AB004857 0.473440 SLC11A2
solute carrier family 11 (proton-coupled divalent metal ion
transporters), member 2 NM_002250 0.472900 KCNN4 potassium
intermediate/small conductance calcium-activated channel, subfamily
N, member 4 Contig56143_RC -0.472611 ESTs, Weakly similar to A54849
collagen alpha 1(VII) chain precursor [H. sapiens] NM_001960
0.471502 EEF1D eukaryotic translation elongation factor 1 delta
(guanine nucleotide exchange protein) Contig52405_RC -0.470705
ESTs, Weakly similar to ALU8_HUMAN ALU SUBFAMILY SX SEQUENCE
CONTAMINATION WARNING ENTRY [H. sapiens] Contig30092_RC -0.469977
Homo sapiens PR-domain zinc finger protein 6 isoform B (PRDM6)
mRNA, partial cds; alternatively spliced NM_003462 -0.468753 P28
dynein, axonemal, light intermediate polypeptide Contig60393
0.468475 ESTs Contig842_RC 0.468158 ESTs NM_002982 0.466362 SCYA2
small inducible cytokine A2 (monocyte chemotactic protein 1,
homologous to mouse Sig-je) Contig14390_RC 0.464150 ESTs NM_001770
0.463847 CD19 CD19 antigen AK000617 -0.463158 Homo sapiens mRNA;
cDNA DKFZp434L235 (from clone DKFZp434L235) AF073299 -0.463007
SLC9A2 solute carrier family 9 (sodium/hydrogen exchanger), isoform
2 NM_019049 0.461990 FLJ20054 hypothetical protein AL137347
-0.460778 DKFZP761M1511 hypothetical protein NM_000396 -0.460263
CTSK cathepsin K (pycnodysostosis) NM_018373 -0.459268 FLJ11271
hypothetical protein FLJ11271 NM_002709 0.458500 PPP1CB protein
phosphatase 1, catalytic subunit, beta isoform NM_016820 0.457516
OGG1 8-oxoguanine DNA glycosylase Contig10268_RC 0.456933 Human DNA
sequence from clone RP11- 196N14 on chromosome 20 Contains ESTs,
STSs, GSSs and CpG islands. Contains three novel genes, part of a
gene for a novel protein similar to protein serine/threonine
phosphatase 4 regulatory subunit 1 (PP4R1) and a gene for a novel
protein with an ankyrin domain NM_014521 -0.456733 SH3BP4
SH3-domain binding protein 4 AJ272057 -0.456548 STRAIT11499
hypothetical protein STRAIT11499 NM_015964 -0.456187 LOC51673 brain
specific protein Contig16759_RC -0.456169 ESTs NM_015937 -0.455954
LOC51604 CGI-06 protein NM_007246 -0.455500 KLHL2 kelch
(Drosophila)-like 2 (Mayven) NM_001985 -0.453024 ETFB
electron-transfer-flavoprotein, beta polypeptide NM_000984
-0.452935 RPL23A ribosomal protein L23a Contig51953_RC -0.451695
ESTs NM_015984 0.450491 UCH37 ubiquitin C-terminal hydrolase UCH37
NM_000903 -0.450371 DIA4 diaphorase (NADH/NADPH) (cytochrome b-5
reductase) NM_001797 -0.449862 CDH11 cadherin 11, type 2,
OB-cadherin (osteoblast) NM_014878 0.449818 KIAA0020 KIAA0020 gene
product NM_002742 -0.449590 PRKCM protein kinase C, mu
TABLE-US-00005 TABLE 5 231 gene markers that distinguish patients
with good prognosis from patients with poor prognosis. GenBank
Accession Number SEQ ID NO AA555029_RC SEQ ID NO 1 AB020689 SEQ ID
NO 37 AB032973 SEQ ID NO 55 AB033007 SEQ ID NO 58 AB033043 SEQ ID
NO 62 AB037745 SEQ ID NO 75 AB037863 SEQ ID NO 88 AF052159 SEQ ID
NO 120 AF052162 SEQ ID NO 121 AF055033 SEQ ID NO 124 AF073519 SEQ
ID NO 137 AF148505 SEQ ID NO 169 AF155117 SEQ ID NO 173 AF161553
SEQ ID NO 177 AF201951 SEQ ID NO 183 AF257175 SEQ ID NO 189
AJ224741 SEQ ID NO 196 AK000745 SEQ ID NO 219 AL050021 SEQ ID NO
257 AL050090 SEQ ID NO 259 AL080059 SEQ ID NO 270 AL080079 SEQ ID
NO 271 AL080110 SEQ ID NO 272 AL133603 SEQ ID NO 306 AL133619 SEQ
ID NO 307 AL137295 SEQ ID NO 315 AL137502 SEQ ID NO 326 AL137514
SEQ ID NO 327 AL137718 SEQ ID NO 336 AL355708 SEQ ID NO 353 D25328
SEQ ID NO 357 L27560 SEQ ID NO 390 M21551 SEQ ID NO 394 NM_000017
SEQ ID NO 416 NM_000096 SEQ ID NO 430 NM_000127 SEQ ID NO 436
NM_000158 SEQ ID NO 442 NM_000224 SEQ ID NO 453 NM_000286 SEQ ID NO
462 NM_000291 SEQ ID NO 463 NM_000320 SEQ ID NO 469 NM_000436 SEQ
ID NO 487 NM_000507 SEQ ID NO 491 NM_000599 SEQ ID NO 503 NM_000788
SEQ ID NO 527 NM_000849 SEQ ID NO 530 NM_001007 SEQ ID NO 550
NM_001124 SEQ ID NO 562 NM_001168 SEQ ID NO 566 NM_001216 SEQ ID NO
574 NM_001280 SEQ ID NO 588 NM_001282 SEQ ID NO 589 NM_001333 SEQ
ID NO 597 NM_001673 SEQ ID NO 645 NM_001809 SEQ ID NO 673 NM_001827
SEQ ID NO 676 NM_001905 SEQ ID NO 691 NM_002019 SEQ ID NO 711
NM_002073 SEQ ID NO 721 NM_002358 SEQ ID NO 764 NM_002570 SEQ ID NO
787 NM_002808 SEQ ID NO 822 NM_002811 SEQ ID NO 823 NM_002900 SEQ
ID NO 835 NM_002916 SEQ ID NO 838 NM_003158 SEQ ID NO 881 NM_003234
SEQ ID NO 891 NM_003239 SEQ ID NO 893 NM_003258 SEQ ID NO 896
NM_003376 SEQ ID NO 906 NM_003600 SEQ ID NO 929 NM_003607 SEQ ID NO
930 NM_003662 SEQ ID NO 938 NM_003676 SEQ ID NO 941 NM_003748 SEQ
ID NO 951 NM_003862 SEQ ID NO 960 NM_003875 SEQ ID NO 962 NM_003878
SEQ ID NO 963 NM_003882 SEQ ID NO 964 NM_003981 SEQ ID NO 977
NM_004052 SEQ ID NO 985 NM_004163 SEQ ID NO 995 NM_004336 SEQ ID NO
1022 NM_004358 SEQ ID NO 1026 NM_004456 SEQ ID NO 1043 NM_004480
SEQ ID NO 1046 NM_004504 SEQ ID NO 1051 NM_004603 SEQ ID NO 1064
NM_004701 SEQ ID NO 1075 NM_004702 SEQ ID NO 1076 NM_004798 SEQ ID
NO 1087 NM_004911 SEQ ID NO 1102 NM_004994 SEQ ID NO 1108 NM_005196
SEQ ID NO 1127 NM_005342 SEQ ID NO 1143 NM_005496 SEQ ID NO 1157
NM_005563 SEQ ID NO 1173 NM_005915 SEQ ID NO 1215 NM_006096 SEQ ID
NO 1240 NM_006101 SEQ ID NO 1241 NM_006115 SEQ ID NO 1245 NM_006117
SEQ ID NO 1246 NM_006201 SEQ ID NO 1254 NM_006265 SEQ ID NO 1260
NM_006281 SEQ ID NO 1263 NM_006372 SEQ ID NO 1273 NM_006681 SEQ ID
NO 1306 NM_006763 SEQ ID NO 1315 NM_006931 SEQ ID NO 1341 NM_007036
SEQ ID NO 1349 NM_007203 SEQ ID NO 1362 NM_012177 SEQ ID NO 1390
NM_012214 SEQ ID NO 1392 NM_012261 SEQ ID NO 1397 NM_012429 SEQ ID
NO 1413 NM_013262 SEQ ID NO 1425 NM_013296 SEQ ID NO 1427 NM_013437
SEQ ID NO 1439 NM_014078 SEQ ID NO 1449 NM_014109 SEQ ID NO 1451
NM_014321 SEQ ID NO 1477 NM_014363 SEQ ID NO 1480 NM_014750 SEQ ID
NO 1527 NM_014754 SEQ ID NO 1528 NM_014791 SEQ ID NO 1535 NM_014875
SEQ ID NO 1545 NM_014889 SEQ ID NO 1548 NM_014968 SEQ ID NO 1554
NM_015416 SEQ ID NO 1559 NM_015417 SEQ ID NO 1560 NM_015434 SEQ ID
NO 1562 NM_015984 SEQ ID NO 1587 NM_016337 SEQ ID NO 1636 NM_016359
SEQ ID NO 1638 NM_016448 SEQ ID NO 1645 NM_016569 SEQ ID NO 1655
NM_016577 SEQ ID NO 1656 NM_017779 SEQ ID NO 1708 NM_018004 SEQ ID
NO 1725 NM_018098 SEQ ID NO 1739 NM_018104 SEQ ID NO 1743 NM_018120
SEQ ID NO 1745 NM_018136 SEQ ID NO 1748 NM_018265 SEQ ID NO 1766
NM_018354 SEQ ID NO 1774 NM_018401 SEQ ID NO 1782 NM_018410 SEQ ID
NO 1783 NM_018454 SEQ ID NO 1786 NM_018455 SEQ ID NO 1787 NM_019013
SEQ ID NO 1809 NM_020166 SEQ ID NO 1825 NM_020188 SEQ ID NO 1830
NM_020244 SEQ ID NO 1835 NM_020386 SEQ ID NO 1838 NM_020675 SEQ ID
NO 1842 NM_020974 SEQ ID NO 1844 R70506_RC SEQ ID NO 1868 U45975
SEQ ID NO 1878 U58033 SEQ ID NO 1881 U82987 SEQ ID NO 1891 U96131
SEQ ID NO 1896 X05610 SEQ ID NO 1903 X94232 SEQ ID NO 1927
Contig753_RC SEQ ID NO 1954 Contig1778_RC SEQ ID NO 1979
Contig2399_RC SEQ ID NO 1989 Contig2504_RC SEQ ID NO 1991
Contig3902_RC SEQ ID NO 2017 Contig4595 SEQ ID NO 2022
Contig8581_RC SEQ ID NO 2037 Contig13480_RC SEQ ID NO 2052
Contig17359_RC SEQ ID NO 2068 Contig20217_RC SEQ ID NO 2072
Contig21812_RC SEQ ID NO 2082 Contig24252_RC SEQ ID NO 2087
Contig25055_RC SEQ ID NO 2090 Contig25343_RC SEQ ID NO 2092
Contig25991 SEQ ID NO 2098 Contig27312_RC SEQ ID NO 2108
Contig28552_RC SEQ ID NO 2120 Contig32125_RC SEQ ID NO 2155
Contig32185_RC SEQ ID NO 2156 Contig33814_RC SEQ ID NO 2169
Contig34634_RC SEQ ID NO 2180 Contig35251_RC SEQ ID NO 2185
Contig37063_RC SEQ ID NO 2206 Contig37598 SEQ ID NO 2216
Contig38288_RC SEQ ID NO 2223 Contig40128_RC SEQ ID NO 2248
Contig40831_RC SEQ ID NO 2260 Contig41413_RC SEQ ID NO 2266
Contig41887_RC SEQ ID NO 2276 Contig42421_RC SEQ ID NO 2291
Contig43747_RC SEQ ID NO 2311 Contig44064_RC SEQ ID NO 2315
Contig44289_RC SEQ ID NO 2320 Contig44799_RC SEQ ID NO 2330
Contig45347_RC SEQ ID NO 2344 Contig45816_RC SEQ ID NO 2351
Contig46218_RC SEQ ID NO 2358 Contig46223_RC SEQ ID NO 2359
Contig46653_RC SEQ ID NO 2369 Contig46802_RC SEQ ID NO 2372
Contig47405_RC SEQ ID NO 2384 Contig48328_RC SEQ ID NO 2400
Contig49670_RC SEQ ID NO 2434 Contig50106_RC SEQ ID NO 2445
Contig50410 SEQ ID NO 2453 Contig50802_RC SEQ ID NO 2463
Contig51464_RC SEQ ID NO 2481 Contig51519_RC SEQ ID NO 2482
Contig51749_RC SEQ ID NO 2486 Contig51963 SEQ ID NO 2494
Contig53226_RC SEQ ID NO 2525 Contig53268_RC SEQ ID NO 2529
Contig53646_RC SEQ ID NO 2538 Contig53742_RC SEQ ID NO 2542
Contig55188_RC SEQ ID NO 2586 Contig55313_RC SEQ ID NO 2590
Contig55377_RC SEQ ID NO 2591 Contig55725_RC SEQ ID NO 2600
Contig55813_RC SEQ ID NO 2607 Contig55829_RC SEQ ID NO 2608
Contig56457_RC SEQ ID NO 2630 Contig57595 SEQ ID NO 2655
Contig57864_RC SEQ ID NO 2663 Contig58368_RC SEQ ID NO 2668
Contig60864_RC SEQ ID NO 2676 Contig63102_RC SEQ ID NO 2684
Contig63649_RC SEQ ID NO 2686 Contig64688 SEQ ID NO 2690
TABLE-US-00006 TABLE 6 70 Preferred prognosis markers drawn from
Table 5. Sequence Identifier Correlation Name Description AL080059
-0.527150 Homo sapiens mRNA for KIAA1750 protein, partial cds
Contig63649_RC -0.468130 ESTs Contig46218_RC -0.432540 ESTs
NM_016359 -0.424930 LOC51203 clone HQ0310 PRO0310p1 AA555029_RC
-0.424120 ESTs NM_003748 0.420671 ALDH4 aldehyde dehydrogenase 4
(glutamate gamma- semialdehyde dehydrogenase; pyrroline-5-
carboxylate dehydrogenase) Contig38288_RC -0.414970 ESTs, Weakly
similar to ISHUSS protein disulfide-isomerase [H. sapiens]
NM_003862 0.410964 FGF18 fibroblast growth factor 18 Contig28552_RC
-0.409260 Homo sapiens mRNA; cDNA DKFZp434C0931 (from clone
DKFZp434C0931); partial CDs Contig32125_RC 0.409054 ESTs U82987
0.407002 BBC3 Bcl-2 binding component 3 AL137718 -0.404980 Homo
sapiens mRNA; cDNA DKFZp434C0931 (from clone DKFZp434C0931);
partial cds AB037863 0.402335 KIAA1442 KIAA1442 protein NM_020188
-0.400070 DC13 DC13 protein NM_020974 0.399987 CEGP1 CEGP1 protein
NM_000127 -0.399520 EXT1 exostoses (multiple) 1 NM_002019 -0.398070
FLT1 fms-related tyrosine kinase 1 (vascular endothelial growth
factor/vascular permeability factor receptor) NM_002073 -0.395460
GNAZ guanine nucleotide binding protein (G protein), alpha z
polypeptide NM_000436 -0.392120 OXCT 3-oxoacid CoA transferase
NM_004994 -0.391690 MMP9 matrix metalloproteinase 9 (gelatinase B,
92 kD gelatinase, 92 kD type IV collagenase) Contig55377_RC
0.390600 ESTs Contig35251_RC -0.390410 Homo sapiens cDNA: FLJ22719
fis, clone HSI14307 Contig25991 -0.390370 ECT2 epithelial cell
transforming sequence 2 oncogene NM_003875 -0.386520 GMPS guanine
monphosphate synthetase NM_006101 -0.385890 HEC highly expressed in
cancer, rich in leucine heptad repeats NM_003882 0.384479 WISP1
WNT1 inducible signaling pathway protein 1 NM_003607 -0.384390
PK428 Ser-Thr protein kinase related to the myotonic dystrophy
protein kinase AF073519 -0.383340 SERF1A small EDRK-rich factor 1A
(telomeric) AF052162 -0.380830 FLJ12443 hypothetical protein
FLJ12443 NM_000849 0.380831 GSTM3 glutathione S-transferase M3
(brain) Contig32185_RC -0.379170 Homo sapiens cDNA FLJ13997 fis,
clone Y79AA1002220 NM_016577 -0.376230 RAB6B RAB6B, member RAS
oncogene family Contig48328_RC 0.375252 ESTs, Weakly similar to
T17248 hypothetical protein DKFZp586G1122.1 [H. sapiens]
Contig46223_RC 0.374289 ESTs NM_015984 -0.373880 UCH37 ubiquitin
C-terminal hydrolase UCH37 NM_006117 0.373290 PECI peroxisomal
D3,D2-enoyl-CoA isomerase AK000745 -0.373060 Homo sapiens cDNA
FLJ20738 fis, clone HEP08257 Contig40831_RC -0.372930 ESTs
NM_003239 0.371524 TGFB3 transforming growth factor, beta 3
NM_014791 -0.370860 KIAA0175 KIAA0175 gene product X05610 -0.370860
COL4A2 collagen, type IV, alpha 2 NM_016448 -0.369420 L2DTL L2DTL
protein NM_018401 0.368349 HSA250839 gene for serine/threonine
protein kinase NM_000788 -0.367700 DCK deoxycytidine kinase
Contig51464_RC -0.367450 FLJ22477 hypothetical protein FLJ22477
AL080079 -0.367390 DKFZP564D0462 hypothetical protein DKFZp564D0462
NM_006931 -0.366490 SLC2A3 solute carrier family 2 (facilitated
glucose transporter), member 3 AF257175 0.365900 Homo sapiens
hepatocellular carcinoma- associated antigen 64 (HCA64) mRNA,
complete cds NM_014321 -0.365810 ORC6L origin recognition complex,
subunit 6 (yeast homolog)-like NM_002916 -0.365590 RFC4 replication
factor C (activator 1) 4 (37 kD) Contig55725_RC -0.365350 ESTs,
Moderately similar to T50635 hypothetical protein DKFZp762L0311.1
[H. sapiens] Contig24252_RC -0.364990 ESTs AF201951 0.363953 CFFM4
high affinity immunoglobulin epsilon receptor beta subunit
NM_005915 -0.363850 MCM6 minichromosome maintenance deficient
(mis5, S. pombe) 6 NM_001282 0.363326 AP2B1 adaptor-related protein
complex 2, beta 1 subunit Contig56457_RC -0.361650 TMEFF1
transmembrane protein with EGF-like and two follistatin-like
domains 1 NM_000599 -0.361290 IGFBP5 insulin-like growth factor
binding protein 5 NM_020386 -0.360780 LOC57110 H-REV107
protein-related protein NM_014889 -0.360040 MP1 metalloprotease 1
(pitrilysin family) AF055033 -0.359940 IGFBP5 insulin-like growth
factor binding protein 5 NM_006681 -0.359700 NMU neuromedin U
NM_007203 -0.359570 AKAP2 A kinase (PRKA) anchor protein 2
Contig63102_RC 0.359255 FLJ11354 hypothetical protein FLJ11354
NM_003981 -0.358260 PRC1 protein regulator of cytokinesis 1
Contig20217_RC -0.357880 ESTs NM_001809 -0.357720 CENPA centromere
protein A (17 kD) Contig2399_RC -0.356600 SM-20 similar to rat
smooth muscle protein SM-20 NM_004702 -0.356600 CCNE2 cyclin E2
NM_007036 -0.356540 ESM1 endothelial cell-specific molecule 1 NM
018354 -0.356000 FLJ11190 hypothetical protein FLJ11190
[0070] The sets of markers listed in Tables 1-6 partially overlap;
in other words, some markers are present in multiple sets, while
other markers are unique to a set (FIG. 1) Thus, in one embodiment,
the invention provides a set of 256 genetic markers that can
distinguish between ER(+) and ER(-), and also between BRCA1 tumors
and sporadic tumors (i.e., classify a tumor as ER(-) or ER(-) and
BRCA1-related or sporadic). In a more specific embodiment, the
invention provides subsets of at least 20, at least 50, at least
100, or at least 150 of the set of 256 markers, that can classify a
tumor as ER(-) or ER(-) and BRCA1-related or sporadic. In another
embodiment, the invention provides 165 markers that can distinguish
between ER(+) and ER(-), and also between patients with good versus
poor prognosis (i.e., classify a tumor as either ER(-) or ER(+) and
as having been removed from a patient with a good prognosis or a
poor prognosis). In a more specific embodiment, the invention
farther provides subsets of at least 20, 50, 100 or 125 of the full
set of 165 markers, which also classify a tumor as either ER(-) or
ER(+) and as having been removed from a patient with a good
prognosis or a poor prognosis The invention further provides a set
of twelve markers that can distinguish between BRCA1 tumors and
sporadic tumors, and between patients with good versus poor
prognosis. Finally, the invention provides eleven markers capable
of differentiating all three statuses. Conversely, the invention
provides 2,050 of the 2,460 ER-status markers that can determine
only ER status, 173 of the 430 BRCA1 v. sporadic markers that can
determine only BRCA1 v. sporadic status, and 65 of the 231
prognosis markers that can only determine prognosis. In more
specific embodiments, the invention also provides for subsets of at
least 20, 50, 100, 200, 500, 1,000, 1,500 or 2,000 of the 2,050
ER-status markers that also determine only ER status. The invention
also provides subsets of at least 20, 50, 100 or 150 of the 173
markers that also determine only BRCA1 v. sporadic status. The
invention further provides subsets of at least 20, 30, 40, or 50 of
the 65 prognostic markers that also determine only prognostic
status.
[0071] Any of the sets of markers provided above may be used alone
specifically or in combination with markers outside the set. For
example, markers that distinguish ER-status may be used in
combination with the BRCA1 vs. sporadic markers, or with the
prognostic markers, or both. Any of the marker sets provided above
may also be used in combination with other markers for breast
cancer, or for any other clinical or physiological condition.
[0072] The relationship between the marker sets is diagramed in
FIG. 1.
5.3.2 Identification of Markers
[0073] The present invention provides sets of markers for the
identification of conditions or indications associated with breast
cancer. Generally, the marker sets were identified by determining
which of .about.25,000 human markers had expression patters that
correlated with the conditions or indications.
[0074] In one embodiment, the method for identifying marker sets is
as follows. After extraction and labeling of target
polynucleotides, the expression of all markers (genes) in a sample
X is compared to the expression of all markers in a standard or
control. In one embodiment, the standard or control comprises
target polynucleotide molecules derived from a sample from a normal
individual (i.e., an individual not afflicted with breast cancer).
In a preferred embodiment, the standard or control is a pool of
target polynucleotide molecules. The pool may derived from
collected samples from a number of normal individuals. In a
preferred embodiment, the pool comprises samples taken from a
number of individuals having sporadic-type tumors. In another
preferred embodiment, the pool comprises an artificially-generated
population of nucleic acids designed to approximate the level of
nucleic acid derived from each marker found in a pool of
marker-derived nucleic acids derived from tumor samples. In yet
another embodiment, the pool is derived from normal or breast
cancer cell lines or cell line samples.
[0075] The comparison may be accomplished by any means known in the
art. For example, expression levels of various markers may be
assessed by separation of target polynucleotide molecules (e.g.,
RNA or cDNA) derived from the markers in agarose or polyacrylamide
gels, followed by hybridization with marker-specific
oligonucleotide probes. Alternatively, the comparison may be
accomplished by the labeling of target polynucleotide molecules
followed by separation on a sequencing gel. Polynucleotide samples
are placed on the gel such that patient and control or standard
polynucleotides are in adjacent lanes. Comparison of expression
levels is accomplished visually or by means of densitometer. In a
preferred embodiment, the expression of all markers is assessed
simultaneously by hybridization to a microarray. In each approach,
markers meeting certain criteria are identified as associated with
breast cancer.
[0076] A marker is selected based upon significant difference of
expression in a sample as compared to a standard or control
condition. Selection may be made based upon either significant up-
or down regulation of the marker in the patient sample. Selection
may also be made by calculation of the statistical significance
(i.e., the p-value) of the correlation between the expression of
the marker and the condition or indication. Preferably, both
selection criteria are used. Thus, in one embodiment of the present
invention, markers associated with breast cancer are selected where
the markers show both more than two-fold change (increase or
decrease) in expression as compared to a standard, and the p-value
for the correlation between the existence of breast cancer and the
change in marker expression is no more than 0.01 (i.e., is
statistically significant).
[0077] The expression of the identified breast cancer-related
markers is then used to identify markers that can differentiate
tumors into clinical types. In a specific embodiment using a number
of tumor samples, markers are identified by calculation of
correlation coefficients between the clinical category or clinical
parameter(s) and the linear, logarithmic or any transform of the
expression ratio across all samples for each individual gene.
Specifically, the correlation coefficient is calculated as
.rho.=({right arrow over (c)}{right arrow over
(r)})/(.parallel.{right arrow over (c)}.parallel..parallel.{right
arrow over (r)}.parallel.) Equation (2)
wherein {right arrow over (c)} represents the clinical parameters
or categories and {right arrow over (r)} represents the linear,
logarithmic or any transform of the ratio of expression between
sample and control. Markers for which the coefficient of
correlation exceeds a cutoff are identified as breast
cancer-related markers specific for a particular clinical type.
Such a cutoff or threshold corresponds to a certain significance of
discriminating genes obtained by Monte Carlo simulations. The
threshold depends upon the number of samples used; the threshold
can be calculated as 3.times.1/ {square root over (n-3)}, where 1/
{square root over (n-3)} is the distribution width and n=the number
of samples. In a specific embodiment, markers are chosen if the
correlation coefficient is greater than about 0.3 or less than
about -0.3.
[0078] Next, the significance of the correlation is calculated.
This significance may be calculated by any statistical means by
which such significance is calculated. In a specific example, a set
of correlation data is generated using a Monte-Carlo technique to
randomize the association between the expression difference of a
particular marker and the clinical category. The frequency
distribution of markers satisfying the criteria through calculation
of correlation coefficients is compared to the number of markers
satisfying the criteria in the data generated through the
Monte-Carlo technique. The frequency distribution of markers
satisfying the criteria in the Monte-Carlo runs is used to
determine whether the number of markers selected by correlation
with clinical data is significant. See Example 4.
[0079] Once a marker set is identified, the markers may be
rank-ordered in order of significance of discrimination. One means
of rank ordering is by the amplitude of correlation between the
change in gene expression of the marker and the specific condition
being discriminated. Another, preferred means is to use a
statistical metric. In a specific embodiment, the metric is a
Fisher-like statistic:
t=(x.sub.1-x.sub.2)/ {square root over
([.sigma..sub.1.sup.2(n.sub.1-1)+.sigma..sub.2.sup.2(n.sub.2-.sup.1))}]/(-
n.sub.1+n.sub.2-1)/(1/n.sub.1+1/n.sub.2) Equation (3)
In this equation, x.sub.1 is the error-weighted average of the log
ratio of transcript expression measurements within a first
diagnostic group (e.g., ER(-), x.sub.2 is the error-weighted
average of log ratio within a second, related diagnostic group
(e.g., ER(+)), .sigma..sub.1 is the variance of the log ratio
within the ER(-) group and n.sub.1 is the number of samples for
which valid measurements of log ratios are available, .sigma..sub.2
is the variance of log ratio within the second diagnostic group
(e.g., ER(+)), and n.sub.2 is the number of samples for which valid
measurements of log ratios are available. The t-value represents
the variance-compensated difference between two means.
[0080] The rank-ordered marker set may be used to optimize the
number of markers in the set used for discrimination. This is
accomplished generally in a "leave one out" method as follows. In a
first run, a subset, for example 5, of the markers from the top of
the ranked list is used to generate a template, where out of X
samples, X-1 are used to generate the template, and the status of
the remaining sample is predicted. This process is repeated for
every sample until every one of the X samples is predicted once. In
a second run, additional markers, for example 5, are added, so that
a template is now generated from 10 markers, and the outcome of the
remaining sample is predicted. This process is repeated until the
entire set of markers is used to generate the template. For each of
the runs, type 1 error (false negative) and type 2 errors (false
positive) are counted; the optimal number of markers is that number
where the type 1 error rate, or type 2 error rate, or preferably
the total of type 1 and type 2 error rate is lowest.
[0081] For prognostic markers, validation of the marker set may be
accomplished by an additional statistic, a survival model. This
statistic generates the probability of tumor distant metastases as
a function of time since initial diagnosis. A number of models may
be used, including Weibull, normal, log-normal, log logistic,
log-exponential, or log-Rayleigh (Chapter 12 "Life Testing", S-PLUS
2000 GUIDE TO STATISTICS, Vol. 2, p. 368 (2000)). For the "normal"
model, the probability of distant metastases at time t is
calculated as
P=.alpha..times.exp(-t.sup.2/.tau..sup.2) Equation (4)
where .alpha. is fixed and equal to 1, and .tau. is a parameter to
be fitted and measures the "expected lifetime".
[0082] It will be apparent to those skilled in the art that the
above methods, in particular the statistical methods, described
above, are not limited to the identification of markers associated
with breast cancer, but may be used to identify set of marker genes
associated with any phenotype. The phenotype can be the presence or
absence of a disease such as cancer, or the presence or absence of
any identifying clinical condition associated with that cancer. In
the disease context, the phenotype may be a prognosis such as a
survival time, probability of distant metastases of a disease
condition, or likelihood of a particular response to a therapeutic
or prophylactic regimen. The phenotype need not be cancer, or a
disease; the phenotype may be a nominal characteristic associated
with a healthy individual.
5.3.3 Sample Collection
[0083] In the present invention, target polynucleotide molecules
are extracted from a sample taken from an individual afflicted with
breast cancer. The sample may be collected in any clinically
acceptable manner, but must be collected such that marker-derived
polynucleotides (i.e., RNA) are preserved. mRNA or nucleic acids
derived therefrom (i.e., cDNA or amplified DNA) are preferably
labeled distinguishably from standard or control polynucleotide
molecules, and both are simultaneously or independently hybridized
to a microarray comprising some or all of the markers or marker
sets or subsets described above. Alternatively, mRNA or nucleic
acids derived therefrom may be labeled with the same label as the
standard or control polynucleotide molecules, wherein the intensity
of hybridization of each at a particular probe is compared. A
sample may comprise any clinically relevant tissue sample, such as
a tumor biopsy or fine needle aspirate, or a sample of bodily
fluid, such as blood, plasma, serum, lymph, ascitic fluid, cystic
fluid, urine or nipple exudate. The sample may be taken from a
human, or, in a veterinary context, from non-human animals such as
ruminants, horses, swine or sheep, or from domestic companion
animals such as felines and canines.
[0084] Methods for preparing total and poly(A)+ RNA are well known
and are described generally in Sambrook et al., MOLECULAR
CLONING--A LABORATORY MANUAL (2ND ED.), Vols. 1-3, Cold Spring
Harbor Laboratory, Cold Spring Harbor, N.Y. (1989)) and Ausubel et
al., CURRENT PROTOCOLS IN MOLECULAR BIOLOGY, vol. 2, Current
Protocols Publishing, New York (1994)).
[0085] RNA may be isolated from eukaryotic cells by procedures that
involve lysis of the cells and denaturation of the proteins
contained therein. Cells of interest include wild-type cells (i.e.,
non-cancerous), drug-exposed wild-type cells, tumor- or
tumor-derived cells, modified cells, normal or tumor cell line
cells, and drug-exposed modified cells.
[0086] Additional steps may be employed to remove DNA. Cell lysis
may be accomplished with a nonionic detergent, followed by
microcentrifugation to remove the nuclei and hence the bulk of the
cellular DNA. In one embodiment, RNA is extracted from cells of the
various types of interest using guanidinium thiocyanate lysis
followed by CsCl centrifugation to separate the RNA from DNA
(Chirgwin et al, Biochemistry 18:5294-5299 (1979)). Poly(A)+RNA is
selected by selection with oligo-dT cellulose (see Sambrook et al.,
MOLECULAR CLONING--A LABORATORY MANUAL (2ND ED.), Vols. 1-3, Cold
Spring Harbor Laboratory, Cold Spring Harbor, N.Y. (1989).
Alternatively, separation of RNA from DNA can be accomplished by
organic extraction, for example, with hot phenol or
phenol/chloroform/isoamyl alcohol.
[0087] If desired, RNase inhibitors may be added to the lysis
buffer. Likewise, for certain cell types, it may be desirable to
add a protein denaturation/digestion step to the protocol.
[0088] For many applications, it is desirable to preferentially
enrich mRNA with respect to other cellular RNAs, such as transfer
RNA (tRNA) and ribosomal RNA (rRNA). Most mRNAs contain a poly(A)
tail at their 3' end. This allows them to be enriched by affinity
chromatography, for example, using oligo(dT) or poly(U) coupled to
a solid support, such as cellulose or Sephadex.TM. (see Ausubel et
al., CURRENT PROTOCOLS IN MOLECULAR BIOLOGY, vol. 2, Current
Protocols Publishing, New York (1994). Once bound, poly(A)+ mRNA is
eluted from the affinity column using 2 mM EDTA/0.1% SDS.
[0089] The sample of RNA can comprise a plurality of different mRNA
molecules, each different mRNA molecule having a different
nucleotide sequence. In a specific embodiment, the mRNA molecules
in the RNA sample comprise at least 100 different nucleotide
sequences. More preferably, the mRNA molecules of the RNA sample
comprise mRNA molecules corresponding to each of the marker genes.
In another specific embodiment, the RNA sample is a mammalian RNA
sample.
[0090] In a specific embodiment, total RNA or mRNA from cells are
used in the methods of the invention. The source of the RNA can be
cells of a plant or animal, human, mammal, primate, non-human
animal, dog, cat, mouse, rat, bird, yeast, eukaryote, prokaryote,
etc. In specific embodiments, the method of the invention is used
with a sample containing total mRNA or total RNA from
1.times.10.sup.6 cells or less. In another embodiment, proteins can
be isolated from the foregoing sources, by methods known in the
art, for use in expression analysis at the protein level.
[0091] Probes to the homologs of the marker sequences disclosed
herein can be employed preferably wherein non-human nucleic acid is
being assayed.
5.4 Methods of Using Breast Cancer Marker Sets
5.4.1 Diagnostic Methods
[0092] The present invention provides for methods of using the
marker sets to analyze a sample from an individual so as to
determine the individual's tumor type or subtype at a molecular
level, whether a tumor is of the ER(+) or ER(-) type, and whether
the tumor is BRCA1-associated or sporadic. The individual need not
actually be afflicted with breast cancer. Essentially, the
expression of specific marker genes in the individual, or a sample
taken therefrom, is compared to a standard or control. For example,
assume two breast cancer-related conditions, X and Y. One can
compare the level of expression of breast cancer prognostic markers
for condition X in an individual to the level of the marker-derived
polynucleotides in a control, wherein the level represents the
level of expression exhibited by samples having condition X. In
this instance, if the expression of the markers in the individual's
sample is substantially (i.e., statistically) different from that
of the control, then the individual does not have condition X.
Where, as here, the choice is bimodal (i.e., a sample is either X
or Y), the individual can additionally be said to have condition Y.
Of course, the comparison to a control representing condition Y can
also be performed. Preferably both are performed simultaneously,
such that each control acts as both a positive and a negative
control. The distinguishing result may thus either be a
demonstrable difference from the expression levels (i.e., the
amount of marker-derived RNA, or polynucleotides derived therefrom)
represented by the control, or no significant difference.
[0093] Thus, in one embodiment, the method of determining a
particular tumor-related status of an individual comprises the
steps of (1) hybridizing labeled target polynucleotides from an
individual to a microarray containing one of the above marker sets;
(2) hybridizing standard or control polynucleotides molecules to
the microarray, wherein the standard or control molecules are
differentially labeled from the target molecules; and (3)
determining the difference in transcript levels, or lack thereof,
between the target and standard or control, wherein the difference,
or lack thereof, determines the individual's tumor-related status.
In a more specific embodiment, the standard or control molecules
comprise marker-derived polynucleotides from a pool of samples from
normal individuals, or a pool of tumor samples from individuals
having sporadic-type tumors. In a preferred embodiment, the
standard or control is an artificially-generated pool of
marker-derived polynucleotides, which pool is designed to mimic the
level of marker expression exhibited by clinical samples of normal
or breast cancer tumor tissue having a particular clinical
indication (i.e., cancerous or non-cancerous; ER(+) or ER(-) tumor;
BRCA1- or sporadic type tumor). In another specific embodiment, the
control molecules comprise a pool derived from normal or breast
cancer cell lines.
[0094] The present invention provides sets of markers useful for
distinguishing ER(+) from ER(-) tumor types. Thus, in one
embodiment of the above method, the level of polynucleotides (i.e.,
mRNA or polynucleotides derived therefrom) in a sample from an
individual, expressed from the markers provided in Table 1 are
compared to the level of expression of the same markers from a
control, wherein the control comprises marker-related
polynucleotides derived from ER(+) samples, ER(-) samples, or both.
Preferably, the comparison is to both ER(+) and ER(-), and
preferably the comparison is to polynucleotide pools from a number
of ER(+) and ER(-) samples, respectively. Where the individual's
marker expression most closely resembles or correlates with the
ER(+) control, and does not resemble or correlate with the ER(-)
control, the individual is classified as ER(+). Where the pool is
not pure ER(+) or ER(-), for example, a sporadic pool is used. A
set of experiments using individuals with known ER status should be
hybridized against the pool, in order to define the expression
templates for the ER(+) and ER(-) group. Each individual with
unknown ER status is hybridized against the same pool and the
expression profile is compared to the templates (s) to determine
the individual's ER status.
[0095] The present invention provides sets of markers useful for
distinguishing BRCA1-related tumors from sporadic tumors. Thus, the
method can be performed substantially as for the ER(+/-)
determination, with the exception that the markers are those listed
in Tables 3 and 4, and the control markers are a pool of
marker-derived polynucleotides BRCA1 tumor samples, and a pool of
marker-derived polynucleotides from sporadic tumors. A patient is
determined to have a BRCA1 germline mutation where the expression
of the individual's marker-derived polynucleotides most closely
resemble, or are most closely correlated with, that of the BRCA1
control. Where the control is not pure BRCA1 or sporadic, two
templates can be defined in a manner similar to that for ER status,
as described above.
[0096] For the above two embodiments of the method, the full set of
markers may be used (i.e., the complete set of markers for Tables 1
or 3). In other embodiments, subsets of the markers may be used. In
a preferred embodiment, the preferred markers listed in Tables 2 or
4 are used.
[0097] The similarity between the marker expression profile of an
individual and that of a control can be assessed a number of ways.
In the simplest case, the profiles can be compared visually in a
printout of expression difference data. Alternatively, the
similarity can be calculated mathematically.
[0098] In one embodiment, the similarity measure between two
patients x and y, or patient x and a template y, can be calculated
using the following equation:
S = 1 - [ i = 1 N y ( x i - x _ ) .sigma. x i ( y i - y _ ) .sigma.
y i i = 1 N y ( x i - x _ .sigma. x i ) 2 i = 1 N y ( ( y i - y _ )
.sigma. y i ) 2 ] [ Equation 5 ] ##EQU00001##
In this equation, x and y are two patients with components of log
ratio x.sub.1 and y.sub.1, i=I, . . . , N=4,986. Associated with
every value x.sub.1 is error .sigma..sub.x.sub.1. The smaller the
value .sigma..sub.x.sub.1, the more reliable the measurement
x.sub.1.
x _ = i = 1 N y x i .sigma. x 1 2 / i = 1 N y 1 .sigma. x 1 2
##EQU00002##
is the error-weighted arithmetic mean.
[0099] In a preferred embodiment, templates are developed for
sample comparison. The template is defined as the error-weighted
log ratio average of the expression difference for the group of
marker genes able to differentiate the particular breast
cancer-related condition. For example, templates are defined for
ER(+) samples and for ER(-) samples. Next, a classifier parameter
is calculated. This parameter may be calculated using either
expression level differences between the sample and template, or by
calculation of a correlation coefficient. Such a coefficient,
P.sub.i, can be calculated using the following equation:
P.sub.i=({right arrow over (z)}.sub.i{right arrow over
(y)})/(.parallel.{right arrow over
(z)}.sub.i.parallel..parallel.{right arrow over (y)}.parallel.)
Equation (1)
where z.sub.1 is the expression template i, and y is the expression
profile of a patient.
[0100] Thus, in a more specific embodiment, the above method of
determining a particular tumor-related status of an individual
comprises the steps of (1) hybridizing labeled target
polynucleotides from an individual to a microarray containing one
of the above marker sets; (2) hybridizing standard or control
polynucleotides molecules to the microarray, wherein the standard
or control molecules are differentially labeled from the target
molecules; and (3) determining the ratio (or difference) of
transcript levels between two channels (individual and control), or
simply the transcript levels of the individual; and (4) comparing
the results from (3) to the predefined templates, wherein said
determining is accomplished by means of the statistic of Equation 1
or Equation 5, and wherein the difference, or lack thereof,
determines the individual's tumor-related status.
5.4.2 Prognostic Methods
[0101] The present invention provides sets of markers useful for
distinguishing samples from those patients with a good prognosis
from samples from patients with a poor prognosis. Thus, the
invention further provides a method for using these markers to
determine whether an individual afflicted with breast cancer will
have a good or poor clinical prognosis. In one embodiment, the
invention provides for method of determining whether an individual
afflicted with breast cancer will likely experience a relapse
within five years of initial diagnosis (i.e., whether an individual
has a poor prognosis) comprising (1) comparing the level of
expression of the markers listed in Table 5 in a sample taken from
the individual to the level of the same markers in a standard or
control, where the standard or control levels represent those found
in an individual with a poor prognosis; and (2) determining whether
the level of the marker-related polynucleotides in the sample from
the individual is significantly different than that of the control,
wherein if no substantial difference is found, the patient has a
poor prognosis, and if a substantial difference is found, the
patient has a good prognosis. Persons of skill in the art will
readily see that the markers associated with good prognosis can
also be used as controls. In a more specific embodiment, both
controls are run. In case the pool is not pure `good prognosis` or
`poor prognosis`, a set of experiments of individuals with known
outcome should be hybridized against the pool to define the
expression templates for the good prognosis and poor prognosis
group. Each individual with unknown outcome is hybridized against
the same pool and the resulting expression profile is compared to
the templates to predict its outcome.
[0102] Poor prognosis of breast cancer may indicate that a tumor is
relatively aggressive, while good prognosis may indicate that a
tumor is relatively nonaggressive.
[0103] Therefore, the invention provides for a method of
determining a course of treatment of a breast cancer patient,
comprising determining whether the level of expression of the 231
markers of Table 5, or a subset thereof, correlates with the level
of these markers in a sample representing a good prognosis
expression pattern or a poor prognosis pattern; and determining a
course of treatment, wherein if the expression correlates with the
poor prognosis pattern, the tumor is treated as an aggressive
tumor.
[0104] As with the diagnostic markers, the method can use the
complete set of markers listed in Table 5. However, subsets of the
markers may also be used. In a preferred embodiment, the subset
listed in Table 6 is used.
[0105] Classification of a sample as "good prognosis" or "poor
prognosis" is accomplished substantially as for the diagnostic
markers described above, wherein a template is generated to which
the marker expression levels in the sample are compared.
[0106] The use of marker sets is not restricted to the prognosis of
breast cancer-related conditions, and may be applied in a variety
of phenotypes or conditions, clinical or experimental, in which
gene expression plays a role. Where a set of markers has been
identified that corresponds to two or more phenotypes, the marker
sets can be used to distinguish these phenotypes. For example, the
phenotypes may be the diagnosis and/or prognosis of clinical states
or phenotypes associated with other cancers, other disease
conditions, or other physiological conditions, wherein the
expression level data is derived from a set of genes correlated
with the particular physiological or disease condition.
5.4.3 Improving Sensitivity to Expression Level Differences
[0107] In using the markers disclosed herein, and, indeed, using
any sets of markers to differentiate an individual having one
phenotype from another individual having a second phenotype, one
can compare the absolute expression of each of the markers in a
sample to a control; for example, the control can be the average
level of expression of each of the markers, respectively, in a pool
of individuals. To increase the sensitivity of the comparison,
however, the expression level values are preferably transformed in
a number of ways.
[0108] For example, the expression level of each of the markers can
be normalized by the average expression level of all markers the
expression level of which is determined, or by the average
expression level of a set of control genes. Thus, in one
embodiment, the markers are represented by probes on a microarray,
and the expression level of each of the markers is normalized by
the mean or median expression level across all of the genes
represented on the microarray, including any non-marker genes. In a
specific embodiment, the normalization is carried out by dividing
the median or mean level of expression of all of the genes on the
microarray. In another embodiment, the expression levels of the
markers is normalized by the mean or median level of expression of
a set of control markers. In a specific embodiment, the control
markers comprise a set of housekeeping genes. In another specific
embodiment, the normalization is accomplished by dividing by the
median or mean expression level of the control genes.
[0109] The sensitivity of a marker-based assay will also be
increased if the expression levels of individual markers are
compared to the expression of the same markers in a pool of
samples. Preferably, the comparison is to the mean or median
expression level of each the marker genes in the pool of samples.
Such a comparison may be accomplished, for example, by dividing by
the mean or median expression level of the pool for each of the
markers from the expression level each of the markers in the
sample. This has the effect of accentuating the relative
differences in expression between markers in the sample and markers
in the pool as a whole, making comparisons more sensitive and more
likely to produce meaningful results that the use of absolute
expression levels alone. The expression level data may be
transformed in any convenient way; preferably, the expression level
data for all is log transformed before means or medians are
taken.
[0110] In performing comparisons to a pool, two approaches may be
used. First, the expression levels of the markers in the sample may
be compared to the expression level of those markers in the pool,
where nucleic acid derived from the sample and nucleic acid derived
from the pool are hybridized during the course of a single
experiment. Such an approach requires that new pool nucleic acid be
generated for each comparison or limited numbers of comparisons,
and is therefore limited by the amount of nucleic acid available.
Alternatively, and preferably, the expression levels in a pool,
whether normalized and/or transformed or not, are stored on a
computer, or on computer-readable media, to be used in comparisons
to the individual expression level data from the sample (i.e.,
single-channel data).
[0111] Thus, the current invention provides the following method of
classifying a first cell or organism as having one of at least two
different phenotypes, where the different phenotypes comprise a
first phenotype and a second phenotype. The level of expression of
each of a plurality of genes in a first sample from the first cell
or organism is compared to the level of expression of each of said
genes, respectively, in a pooled sample from a plurality of cells
or organisms, the plurality of cells or organisms comprising
different cells or organisms exhibiting said at least two different
phenotypes, respectively, to produce a first compared value. The
first compared value is then compared to a second compared value,
wherein said second compared value is the product of a method
comprising comparing the level of expression of each of said genes
in a sample from a cell or organism characterized as having said
first phenotype to the level of expression of each of said genes,
respectively, in the pooled sample. The first compared value is
then compared to a third compared value, wherein said third
compared value is the product of a method comprising comparing the
level of expression of each of the genes in a sample from a cell or
organism characterized as having the second phenotype to the level
of expression of each of the genes, respectively, in the pooled
sample. Optionally, the first compared value can be compared to
additional compared values, respectively, where each additional
compared value is the product of a method comprising comparing the
level of expression of each of said genes in a sample from a cell
or organism characterized as having a phenotype different from said
first and second phenotypes but included among the at least two
different phenotypes, to the level of expression of each of said
genes, respectively, in said pooled sample. Finally, a
determination is made as to which of said second, third, and, if
present, one or more additional compared values, said first
compared value is most similar, wherein the first cell or organism
is determined to have the phenotype of the cell or organism used to
produce said compared value most similar to said first compared
value.
[0112] In a specific embodiment of this method, the compared values
are each ratios of the levels of expression of each of said genes.
In another specific embodiment, each of the levels of expression of
each of the genes in the pooled sample are normalized prior to any
of the comparing steps. In a more specific embodiment, the
normalization of the levels of expression is carried out by
dividing by the median or mean level of the expression of each of
the genes or dividing by the mean or median level of expression of
one or more housekeeping genes in the pooled sample from said cell
or organism. In another specific embodiment, the normalized levels
of expression are subjected to a log transform, and the comparing
steps comprise subtracting the log transform from the log of the
levels of expression of each of the genes in the sample. In another
specific embodiment, the two or more different phenotypes are
different stages of a disease or disorder. In still another
specific embodiment, the two or more different phenotypes are
different prognoses of a disease or disorder. In yet another
specific embodiment, the levels of expression of each of the genes,
respectively, in the pooled sample or said levels of expression of
each of said genes in a sample from the cell or organism
characterized as having the first phenotype, second phenotype, or
said phenotype different from said first and second phenotypes,
respectively, are stored on a computer or on a computer-readable
medium.
[0113] In another specific embodiment, the two phenotypes are ER(+)
or ER(-) status. In another specific embodiment, the two phenotypes
are BRCA1 or sporadic tumor-type status. In yet another specific
embodiment, the two phenotypes are good prognosis and poor
prognosis.
[0114] Of course, single-channel data may also be used without
specific comparison to a mathematical sample pool. For example, a
sample may be classified as having a first or a second phenotype,
wherein the first and second phenotypes are related, by calculating
the similarity between the expression of at least 5 markers in the
sample, where the markers are correlated with the first or second
phenotype, to the expression of the same markers in a first
phenotype template and a second phenotype template, by (a) labeling
nucleic acids derived from a sample with a fluorophore to obtain a
pool of fluorophore-labeled nucleic acids; (b) contacting said
fluorophore-labeled nucleic acid with a microarray under conditions
such that hybridization can occur, detecting at each of a plurality
of discrete loci on the microarray a fluorescent emission signal
from said fluorophore-labeled nucleic acid that is bound to said
microarray under said conditions; and (c) determining the
similarity of marker gene expression in the individual sample to
the first and second templates, wherein if said expression is more
similar to the first template, the sample is classified as having
the first phenotype, and if said expression is more similar to the
second template, the sample is classified as having the second
phenotype.
5.5 Determination of Marker Gene Expression Levels
5.5.1 Methods
[0115] The expression levels of the marker genes in a sample may be
determined by any means known in the art. The expression level may
be determined by isolating and determining the level (i.e., amount)
of nucleic acid transcribed from each marker gene. Alternatively,
or additionally, the level of specific proteins translated from
mRNA transcribed from a marker gene may be determined.
[0116] The level of expression of specific marker genes can be
accomplished by determining the amount of mRNA, or polynucleotides
derived therefrom, present in a sample. Any method for determining
RNA levels can be used. For example, RNA is isolated from a sample
and separated on an agarose gel. The separated RNA is then
transferred to a solid support, such as a filter. Nucleic acid
probes representing one or more markers are then hybridized to the
filter by northern hybridization, and the amount of marker-derived
RNA is determined. Such determination can be visual, or
machine-aided, for example, by use of a densitometer. Another
method of determining RNA levels is by use of a dot-blot or a
slot-blot. In this method, RNA, or nucleic acid derived therefrom,
from a sample is labeled. The RNA or nucleic acid derived therefrom
is then hybridized to a filter containing oligonucleotides derived
from one or more marker genes, wherein the oligonucleotides are
placed upon the filter at discrete, easily-identifiable locations.
Hybridization, or lack thereof, of the labeled RNA to the
filter-bound oligonucleotides is determined visually or by
densitometer. Polynucleotides can be labeled using a radiolabel or
a fluorescent (i.e., visible) label.
[0117] These examples are not intended to be limiting; other
methods of determining RNA abundance are known in the art.
[0118] The level of expression of particular marker genes may also
be assessed by determining the level of the specific protein
expressed from the marker genes. This can be accomplished, for
example, by separation of proteins from a sample on a
polyacrylamide gel, followed by identification of specific
marker-derived proteins using antibodies in a western blot.
Alternatively, proteins can be separated by two-dimensional gel
electrophoresis systems. Two-dimensional gel electrophoresis is
well-known in the art and typically involves isoelectric focusing
along a first dimension followed by SDS-PAGE electrophoresis along
a second dimension. See, e.g., Hames et al, 1990, GEL
ELECTROPHORESIS OF PROTEINS: A PRACTICAL APPROACH, IRL Press, New
York; Shevchenko et al., Proc. Nat'l Acad. Sci. USA 93:1440-1445
(1996); Sagliocco et al., Yeast 12:1519-1533 (1996); Lander,
Science 274:536-539 (1996). The resulting electropherograms can be
analyzed by numerous techniques, including mass spectrometric
techniques, western blotting and immunoblot analysis using
polyclonal and monoclonal antibodies.
[0119] Alternatively, marker-derived protein levels can be
determined by constructing an antibody microarray in which binding
sites comprise immobilized, preferably monoclonal, antibodies
specific to a plurality of protein species encoded by the cell
genome. Preferably, antibodies are present for a substantial
fraction of the marker-derived proteins of interest. Methods for
making monoclonal antibodies are well known (see, e.g., Harlow and
Lane, 1988, ANTIBODIES: A LABORATORY MANUAL, Cold Spring Harbor,
N.Y., which is incorporated in its entirety for all purposes). In
one embodiment, monoclonal antibodies are raised against synthetic
peptide fragments designed based on genomic sequence of the cell.
With such an antibody array, proteins from the cell are contacted
to the array. and their binding is assayed with assays known in the
art. Generally, the expression, and the level of expression, of
proteins of diagnostic or prognostic interest can be detected
through immunohistochemical staining of tissue slices or
sections.
[0120] Finally, expression of marker genes in a number of tissue
specimens may be characterized using a "tissue array" (Kononen et
al., Nat. Med 4(7):844-7 (1998)). In a tissue array, multiple
tissue samples are assessed on the same microarray. The arrays
allow in situ detection of RNA and protein levels; consecutive
sections allow the analysis of multiple samples simultaneously.
5.5.2 Microarrays
[0121] In preferred embodiments, polynucleotide microarrays are
used to measure expression so that the expression status of each of
the markers above is assessed simultaneously. In a specific
embodiment, the invention provides for oligonucleotide or cDNA
arrays comprising probes hybridizable to the genes corresponding to
each of the marker sets described above (i.e., markers to determine
the molecular type or subtype of a tumor; markers to distinguish ER
status; markers to distinguish BRCA1 from sporadic tumors; markers
to distinguish patients with good versus patients with poor
prognosis; markers to distinguish both ER(+) from ER(-), and BRCA1
tumors from sporadic tumors; markers to distinguish ER(+) from
ER(-), and patients with good prognosis from patients with poor
prognosis; markers to distinguish BRCA1 tumors from sporadic
tumors, and patients with good prognosis from patients with poor
prognosis; and markers able to distinguish ER(+) from ER(-), BRCA1
tumors from sporadic tumors, and patients with good prognosis from
patients with poor prognosis; and markers unique to each
status).
[0122] The microarrays provided by the present invention may
comprise probes hybridizable to the genes corresponding to markers
able to distinguish the status of one, two, or all three of the
clinical conditions noted above. In particular, the invention
provides polynucleotide arrays comprising probes to a subset or
subsets of at least 50, 100, 200, 300, 400, 500, 750, 1,000, 1,250,
1,500, 1,750, 2,000 or 2,250 genetic markers, up to the full set of
2,460 markers, which distinguish ER(+) and ER(-) patients or
tumors. The invention also provides probes to subsets of at least
20, 30, 40, 50, 75, 100, 150, 200, 250, 300, 350 or 400 markers, up
to the full set of 430 markers, which distinguish between tumors
containing a BRCA1 mutation and sporadic tumors within an ER(-)
group of tumors. The invention also provides probes to subsets of
at least 20, 30, 40, 50, 75, 100, 150 or 200 markers, up to the
full set of 231 markers, which distinguish between patients with
good and poor prognosis within sporadic tumors. In a specific
embodiment, the array comprises probes to marker sets or subsets
directed to any two of the clinical conditions. In a more specific
embodiment, the array comprises probes to marker sets or subsets
directed to all three clinical conditions.
[0123] In yet another specific embodiment, microarrays that are
used in the methods disclosed herein optionally comprise markers
additional to at least some of the markers listed in Tables 1-6.
For example, in a specific embodiment, the microarray is a
screening or scanning array as described in Altschuler et al.,
International Publication WO 02/18646, published Mar. 7, 2002 and
Scherer et al., international Publication WO 02/16650, published
Feb. 28, 2002. The scanning and screening arrays comprise
regularly-spaced, positionally-addressable probes derived from
genomic nucleic acid sequence, both expressed and unexpressed. Such
arrays may comprise probes corresponding to a subset of, or all of,
the markers listed in Tables 1-6, or a subset thereof as described
above, and can be used to monitor marker expression in the same way
as a microarray containing only markers listed in Tables 1-6.
[0124] In yet another specific embodiment, the microarray is a
commercially-available cDNA microarray that comprises at least five
of the markers listed in Tables 1-6. Preferably, a
commercially-available cDNA microarray comprises all of the markers
listed in Tables 1-6. However, such a microarray may comprise 5,
10, 15, 25, 50, 100, 150, 250, 500, 1000 or more of the markers in
any of Tables 1-6, up to the maximum number of markers in a Table,
and may comprise all of the markers in any one of Tables 1-6 and a
subset of another of Tables 1-6, or subsets of each as described
above. In a specific embodiment of the microarrays used in the
methods disclosed herein, the markers that are all or a portion of
Tables 1-6 make up at least 50%, 60%, 70%, 80%, 90%, 95% or 98% of
the probes on the microarray.
[0125] General methods pertaining to the construction of
microarrays comprising the marker sets and/or subsets above are
described in the following sections.
5.5.2.1 Construction of Microarrays
[0126] Microarrays are prepared by selecting probes which comprise
a polynucleotide sequence, and then immobilizing such probes to a
solid support or surface. For example, the probes may comprise DNA
sequences, RNA sequences, or copolymer sequences of DNA and RNA.
The polynucleotide sequences of the probes may also comprise DNA
and/or RNA analogues, or combinations thereof. For example, the
polynucleotide sequences of the probes may be full or partial
fragments of genomic DNA. The polynucleotide sequences of the
probes may also be synthesized nucleotide sequences, such as
synthetic oligonucleotide sequences. The probe sequences can be
synthesized either enzymatically in vivo, enzymatically in vitro
(e.g., by PCR), or non-enzymatically in vitro.
[0127] The probe or probes used in the methods of the invention are
preferably immobilized to a solid support which may be either
porous or non-porous. For example, the probes of the invention may
be polynucleotide sequences which are attached to a nitrocellulose
or nylon membrane or filter covalently at either the 3' or the 5'
end of the polynucleotide. Such hybridization probes are well known
in the art (see, e.g., Sambrook et al., MOLECULAR CLONING--A
LABORATORY MANUAL (2ND ED.), Vols. 1-3, Cold Spring Harbor
Laboratory, Cold Spring Harbor, N.Y. (1989). Alternatively, the
solid support or surface may be a glass or plastic surface. In a
particularly preferred embodiment, hybridization levels are
measured to microarrays of probes consisting of a solid phase on
the surface of which are immobilized a population of
polynucleotides, such as a population of DNA or DNA mimics, or,
alternatively, a population of RNA or RNA mimics. The solid phase
may be a nonporous or, optionally, a porous material such as a
gel.
[0128] In preferred embodiments, a microarray comprises a support
or surface with an ordered array of binding (e.g., hybridization)
sites or "probes" each representing one of the markers described
herein. Preferably the microarrays are addressable arrays, and more
preferably positionally addressable arrays. More specifically, each
probe of the array is preferably located at a known, predetermined
position on the solid support such that the identity (i.e., the
sequence) of each probe can be determined from its position in the
array (i.e., on the support or surface). In preferred embodiments,
each probe is covalently attached to the solid support at a single
site.
[0129] Microarrays can be made in a number of ways, of which
several are described below. However produced, microarrays share
certain characteristics. The arrays are reproducible, allowing
multiple copies of a given array to be produced and easily compared
with each other. Preferably, microarrays are made from materials
that are stable under binding (e.g., nucleic acid hybridization)
conditions. The microarrays are preferably small, e.g., between 1
cm.sup.2 and 25 cm.sup.2, between 12 cm.sup.2 and 13 cm.sup.2, or 3
cm.sup.2. However, larger arrays are also contemplated and may be
preferable, e.g., for use in screening arrays. Preferably, a given
binding site or unique set of binding sites in the microarray will
specifically bind (e.g., hybridize) to the product of a single gene
in a cell (e.g., to a specific mRNA, or to a specific cDNA derived
therefrom). However, in general, other related or similar sequences
will cross hybridize to a given binding site.
[0130] The microarrays of the present invention include one or more
test probes, each of which has a polynucleotide sequence that is
complementary to a subsequence of RNA or DNA to be detected.
Preferably, the position of each probe on the solid surface is
known. Indeed, the microarrays are preferably positionally
addressable arrays. Specifically, each probe of the array is
preferably located at a known, predetermined position on the solid
support such that the identity (i.e., the sequence) of each probe
can be determined from its position on the array (i.e., on the
support or surface).
[0131] According to the invention, the invention, the microarray is
an array (i.e., a matrix) in which each position represents one of
the markers described herein. For example, each position can
contain a DNA or DNA analogue based on genomic DNA to which a
particular RNA or cDNA transcribed from that genetic marker car,
specifically hybridize. The DNA or DNA analogue can be, e.g., a
synthetic oligomer or a gene fragment. In one embodiment, probes
representing each of the markers is present on the array. In a
preferred embodiment, the array comprises the 550 of the 2,460
RE-status markers, 70 of the BRCA1/sporadic markers, and all 231 of
the prognosis markers.
5.5.2.2 Preparing Probes for Microarrays
[0132] As noted above, the "probe" to which a particular
polynucleotide molecule specifically hybridizes according to the
invention contains a complementary genomic polynucleotide sequence.
The probes of the microarray preferably consist of nucleotide
sequences of no more than 1,000 nucleotides. In some embodiments,
the probes of the array consist of nucleotide sequences of 10 to
1,000 nucleotides. In a preferred embodiment, the nucleotide
sequences of the probes are in the range of 10-200 nucleotides in
length and are genomic sequences of a species of organism, such
that a plurality of different probes is present, with sequences
complementary and thus capable of hybridizing to the genome of such
a species of organism, sequentially tiled across all or a portion
of such genome. In other specific embodiments, the probes are in
the range of 10-30 nucleotides in length, in the range of 10-40
nucleotides in length, in the range of 20-50 nucleotides in length,
in the range of 40-80 nucleotides in length, in the range of 50-150
nucleotides in length, in the range of 80-120 nucleotides in
length, and most preferably are 60 nucleotides in length.
[0133] The probes may comprise DNA or DNA "mimics" (e.g.,
derivatives and analogues) corresponding to a portion of an
organism's genome. In another embodiment, the probes of the
microarray are complementary RNA or RNA mimics. DNA mimics are
polymers composed of subunits capable of specific,
Watson-Crick-like hybridization with DNA, or of specific
hybridization with RNA. The nucleic acids can be modified at the
base moiety, at the sugar moiety, or at the phosphate backbone.
Exemplary DNA mimics include, e.g., phosphorothioates.
[0134] DNA can be obtained, e.g., by polymerase chain reaction
(PCR) amplification of genomic DNA or cloned sequences. PCR primers
are preferably chosen based on a known sequence of the genome that
will result in amplification of specific fragments of genomic DNA.
Computer programs that are well known in the art are useful in the
design of primers with the required specificity and optimal
amplification properties, such as Oligo version 5.0 (National
Biosciences). Typically each probe on the microarray will be
between 10 bases and 50,000 bases, usually between 300 bases and
1,000 bases in length. PCR methods are well known in the art, and
are described, for example, in Innis et al, eds., PCR PROTOCOLS: A
GUIDE TO METHODS AND APPLICATIONS, Academic Press Inc., San Diego,
Calif. (1990). It will be apparent to one skilled in the art that
controlled robotic systems are useful for isolating and amplifying
nucleic acids.
[0135] An alternative, preferred means for generating the
polynucleotide probes of the microarray is by synthesis of
synthetic polynucleotides or oligonucleotides, e.g., using
N-phosphonate or phosphoramidite chemistries (Froehler et al.,
Nucleic Acid Res. 14:5399-5407 (1986); McBride et al, Tetrahedron
Lett. 24:246-248 (1983)). Synthetic sequences are typically between
about 10 and about 500 bases in length, more typically between
about and about 100 bases, and most preferably between about 40 and
about 70 bases in length. In some embodiments, synthetic nucleic
acids include non-natural bases, such as, but by no means limited
to, inosine. As noted above, nucleic acid analogues may be used as
binding sites for hybridization. An example of a suitable nucleic
acid analogue is peptide nucleic acid (see, e.g., Egholm et al.,
Nature 363:566-568 (1993); U.S. Pat. No. 5,539,083). Probes are
preferably selected using an algorithm that takes into account
binding energies, base composition, sequence complexity,
cross-hybridization binding energies, and secondary structure (see
Friend et al., International Patent Publication WO 01/05935,
published Jan. 25, 2001; Hughes et al., Nat. Biotech. 19:342-7
(2001)).
[0136] A skilled artisan will also appreciate that positive control
probes, e.g., probes known to be complementary and hybridizable to
sequences in the target polynucleotide molecules, and negative
control probes, e.g., probes known to not be complementary and
hybridizable to sequences in the target polynucleotide molecules,
should be included on the array. In one embodiment, positive
controls are synthesized along the perimeter of the array. In
another embodiment, positive controls are synthesized in diagonal
stripes across the array. In still another embodiment, the reverse
complement for each probe is synthesized next to the position of
the probe to serve as a negative control. In yet another
embodiment, sequences from other species of organism are used as
negative controls or as "spike-in" controls.
5.5.2.3 Attaching Probes to the Solid Surface
[0137] The probes are attached to a solid support or surface, which
may be made, e.g., from glass, plastic (e.g., polypropylene,
nylon), polyacrylamide, nitrocellulose, gel, or other porous or
nonporous material. A preferred method for attaching the nucleic
acids to a surface is by printing on glass plates, as is described
generally by Schena et al, Science 270:467-470 (1995). This method
is especially useful for preparing microarrays of cDNA (See also,
DeRisi et al, Nature Genetics 14:457-460 (1996); Shalon et al.,
Genome Res. 6:639-645 (1996); and Schena et al, Proc. Natl. Acad.
Sci. U.S.A. 93:10539-11286 (1995)).
[0138] A second preferred method for making microarrays is by
making high-density oligonucleotide arrays. Techniques are known
for producing arrays containing thousands of oligonucleotides
complementary to defined sequences, at defined locations on a
surface using photolithographic techniques for synthesis in situ
(see, Fodor et al, 1991, Science 251:767-773; Pease et al., 1994,
Proc. Natl. Acad. Sci. 91:5022-5026; Lockhart et al., 1996, Nature
Biotechnology 14:1675; U.S. Pat. Nos. 5,578,832; 5,556,752; and
5,510,270) or other methods for rapid synthesis and deposition of
defined oligonucleotides (Blanchard et al., Biosensors &
Bioelectronics 11:687-690). When these methods are used,
oligonucleotides (e.g., 60-mers) of known sequence are synthesized
directly on a surface such as a derivatized glass slide. Usually,
the array produced is redundant, with several oligonucleotide
molecules per RNA.
[0139] Other methods for making microarrays, e.g., by masking
(Maskos and Southern, 1992, Nuc. Acids. Res. 20:1679-1684), may
also be used. In principle, and as noted supra, any type of array,
for example, dot blots on a nylon hybridization membrane (see
Sambrook et al., MOLECULAR CLONING--A LABORATORY MANUAL (2ND ED.),
Vols, 1-3, Cold Spring Harbor Laboratory, Cold Spring Harbor, N.Y.
(1989)) could be used. However, as will be recognized by those
skilled in the art, very small arrays will frequently be preferred
because hybridization volumes will be smaller.
[0140] In one embodiment, the arrays of the present invention are
prepared by synthesizing polynucleotide probes on a support. In
such an embodiment, polynucleotide probes are attached to the
support covalently at either the 3' or the 5' end of the
polynucleotide.
[0141] In a particularly preferred embodiment, microarrays of the
invention are manufactured by means of an ink jet printing device
for oligonucleotide synthesis, e.g., using the methods and systems
described by Blanchard in U.S. Pat. No. 6,028,189; Blanchard et
al., 1996, Biosensors and Bioelectronics 11:687-690; Blanchard,
1998, in SYNTHETIC DNA ARRAYS IN GENETIC ENGINEERING, Vol. 20, J.
K. Setlow, Ed., Plenum Press, New York at pages 111-123.
Specifically, the oligonucleotide probes in such microarrays are
preferably synthesized in arrays, e.g., on a glass slide, by
serially depositing individual nucleotide bases in "microdroplets"
of a high surface tension solvent such as propylene carbonate. The
microdroplets have small volumes (e.g., 100 pL or less, more
preferably 50 pL or less) and are separated from each other on the
microarray (e.g., by hydrophobic domains) to form circular surface
tension wells which define the locations of the array elements
(i.e., the different probes). Microarrays manufactured by this
ink-jet method are typically of high density, preferably having a
density of at least about 2,500 different probes per 1 cm.sup.2.
The polynucleotide probes are attached to the support covalently at
either the 3' or the 5' end of the polynucleotide.
5.5.2.4 Target Polynucleotide Molecules
[0142] The polynucleotide molecules which may be analyzed by the
present invention (the "target polynucleotide molecules") may be
from any clinically relevant source, but are expressed RNA or a
nucleic acid derived therefrom (e.g., cDNA or amplified RNA derived
from cDNA that incorporates an RNA polymerase promoter), including
naturally occurring nucleic acid molecules, as well as synthetic
nucleic acid molecules. In one embodiment, the target
polynucleotide molecules comprise RNA, including, but by no means
limited to, total cellular RNA, poly(A).sup.+ messenger RNA (mRNA)
or fraction thereof, cytoplasmic mRNA, or RNA transcribed from cDNA
(i.e., cRNA; see, e.g., Linsley & Schelter, U.S. patent
application Ser. No. 09/411,074, filed Oct. 4, 1999, or U.S. Pat.
No. 5,545,522, U.S. Pat. No. 5,891,636, or U.S. Pat. No.
5,716,785). Methods for preparing total and poly(A).sup.+ RNA are
well known in the art, and are described generally, e.g., in
Sambrook et al., MOLECULAR CLONING--A LABORATORY MANUAL (2ND ED.),
Vols. 1-3, Cold Spring Harbor Laboratory, Cold Spring Harbor, N.Y.
(1989). In one embodiment, RNA is extracted from cells of the
various types of interest in this invention using guanidinium
thiocyanate lysis followed by CsCl centrifugation (Chirgwin et al.,
1979, Biochemistry 18:5294-5299). In another embodiment, total RNA
is extracted using a silica gel-based column, commercially
available examples of which include RNeasy (Qiagen, Valencia,
Calif.) and StrataPrep (Stratagene, La Jolla, Calif.). In an
alternative embodiment, which is preferred for S. cerevisiae, RNA
is extracted from cells using phenol and chloroform, as described
in Ausubel et al., eds., 1989, CURRENT PROTOCOLS IN MOLECULAR
BIOLOGY, Vol III, Green Publishing Associates, Inc., John Wiley
& Sons, Inc., New York, at pp. 13.12.1-13.12.5). Poly(A).sup.+
RNA can be selected, e.g., by selection with oligo-dT cellulose or,
alternatively, by oligo-dT primed reverse transcription of total
cellular RNA. In one embodiment, RNA can be fragmented by methods
known in the art, e.g., by incubation with ZnCl.sub.2, to generate
fragments of RNA. In another embodiment, the polynucleotide
molecules analyzed by the invention comprise cDNA, or PCR products
of amplified RNA or cDNA.
[0143] In one embodiment, total RNA, mRNA, or nucleic acids derived
therefrom, is isolated from a sample taken from a person afflicted
with breast cancer. Target polynucleotide molecules that are poorly
expressed in particular cells may be enriched using normalization
techniques (Bonaldo et al., 1996, Genome Res. 6:791-806).
[0144] As described above, the target polynucleotides are
detectably labeled at one or more nucleotides. Any method known in
the art may be used to detectably label the target polynucleotides.
Preferably, this labeling incorporates the label uniformly along
the length of the RNA, and more preferably, the labeling is carried
out at a high degree of efficiency. One embodiment for this
labeling uses oligo-dT primed reverse transcription to incorporate
the label; however, conventional methods of this method are biased
toward generating 3' end fragments. Thus, in a preferred
embodiment, random primers (e.g., 9-mers) are used in reverse
transcription to uniformly incorporate labeled nucleotides over the
full length of the target polynucleotides. Alternatively, random
primers may be used in conjunction with PCR methods or T7
promoter-based in vitro transcription methods in order to amplify
the target polynucleotides.
[0145] In a preferred embodiment, the detectable label is a
luminescent label. For example, fluorescent labels, bio-luminescent
labels, chemi-luminescent labels, and colorimetric labels may be
used in the present invention. In a highly preferred embodiment,
the label is a fluorescent label, such as a fluorescein, a
phosphor, a rhodamine, or a polymethine dye derivative. Examples of
commercially available fluorescent labels include, for example,
fluorescent phosphoramidites such as FluorePrime (Amersham
Pharmacia, Piscataway, N.J.), Fluoredite (Millipore, Bedford,
Mass.), FAM (ABI, Foster City, Calif.), and Cy3 or Cy5 (Amersham
Pharmacia, Piscataway, N.J.). In another embodiment, the detectable
label is a radiolabeled nucleotide.
[0146] In a further preferred embodiment, target polynucleotide
molecules from a patient sample are labeled differentially from
target polynucleotide molecules of a standard. The standard can
comprise target polynucleotide molecules from normal individuals
(i.e., those not afflicted with breast cancer). In a highly
preferred embodiment, the standard comprises target polynucleotide
molecules pooled from samples from normal individuals or tumor
samples from individuals having sporadic-type breast tumors. In
another embodiment, the target polynucleotide molecules are derived
from the same individual, but are taken at different time points,
and thus indicate the efficacy of a treatment by a change in
expression of the markers, or lack thereof, during and after the
course of treatment (i.e., chemotherapy, radiation therapy or
cryotherapy), wherein a change in the expression of the markers
from a poor prognosis pattern to a good prognosis pattern indicates
that the treatment is efficacious. In this embodiment, different
timepoints differentially labeled.
5.5.2.5 Hybridization to Microarrays
[0147] Nucleic acid hybridization and wash conditions are chosen so
that the target polynucleotide molecules specifically bind or
specifically hybridize to the complementary polynucleotide
sequences of the array, preferably to a specific array site,
wherein its complementary DNA is located.
[0148] Arrays containing double-stranded probe DNA situated thereon
are preferably subjected to denaturing conditions to render the DNA
single-stranded prior to contacting with the target polynucleotide
molecules. Arrays containing single-stranded probe DNA (e.g.,
synthetic oligodeoxyribonucleic acids) may need to be denatured
prior to contacting with the target polynucleotide molecules, e.g.,
to remove hairpins or dimers which form due to self complementary
sequences.
[0149] Optimal hybridization conditions will depend on the length
(e.g., oligomer versus polynucleotide greater than 200 bases) and
type (e.g., RNA, or DNA) of probe and target nucleic acids. One of
skill in the art will appreciate that as the oligonucleotides
become shorter, it may become necessary to adjust their length to
achieve a relatively uniform melting temperature for satisfactory
hybridization results. General parameters for specific (i.e.,
stringent) hybridization conditions for nucleic acids are described
in Sambrook et al., MOLECULAR CLONING--A LABORATORY MANUAL (2ND
ED.), Vols. 1-3, Cold Spring Harbor Laboratory, Cold Spring Harbor,
N.Y. (1989), and in Ausubel et al., CURRENT PROTOCOLS IN MOLECULAR
BIOLOGY, vol. 2, Current Protocols Publishing, New York (1994).
Typical hybridization conditions for the cDNA microarrays of Schena
et al. are hybridization in 5.times.SSC plus 0.2% SDS at 65.degree.
C. for four hours, followed by washes at 25.degree. C. in low
stringency wash buffer (1.times.SSC plus 0.2% SDS), followed by 10
minutes at 25.degree. C. in higher stringency wash buffer
(0.1.times.SSC plus 0.2% SDS) (Schena et al., Proc. Natl. Acad.
Sci. U.S. 93:10614 (1993)). Useful hybridization conditions are
also provided in, e.g., Tijessen, 1993, HYBRIDIZATION WITH NUCLEIC
ACID PROBES, Elsevier Science Publishers B.V.; and Kricka, 1992,
NONISOTOPIC DNA PROBE TECHNIQUES, Academic Press, San Diego,
Calif.
[0150] Particularly preferred hybridization conditions include
hybridization at a temperature at or near the mean melting
temperature of the probes (e.g., within 5.degree. C., more
preferably within 2.degree. C.) in 1 M NaCl, 50 mM MES buffet (pH
6.5), 0.5% sodium sarcosine and 30% formamide.
5.5.2.6 Signal Detection and Data Analysis
[0151] When fluorescently labeled probes are used, the fluorescence
emissions at each site of a microarray may be, preferably, detected
by scanning confocal laser microscopy. In one embodiment, a
separate scan, using the appropriate excitation line, is carried
out for each of the two fluorophores used. Alternatively, a laser
may be used that allows simultaneous specimen illumination at
wavelengths specific to the two fluorophores and emissions from the
two fluorophores can be analyzed simultaneously (see Shalon et al.,
1996, "A DNA microarray system for analyzing complex DNA samples
using two-color fluorescent probe hybridization," Genome Research
6:639-645, which is incorporated by reference in its entirety for
all purposes). In a preferred embodiment, the arrays are scanned
with a laser fluorescent scanner with a computer controlled X-Y
stage and a microscope objective. Sequential excitation of the two
fluorophores is achieved with a multi-line, mixed gas laser and the
emitted light is split by wavelength and detected with two
photomultiplier tubes. Fluorescence laser scanning devices are
described in Schena et al., Genome Res. 6:639-645 (1996), and in
other references cited herein. Alternatively, the fiber-optic
bundle described by Ferguson et al., Nature Biotech. 14:1681-1684
(1996), may be used to monitor mRNA abundance levels at a large
number of sites simultaneously.
[0152] Signals are recorded and, in a preferred embodiment,
analyzed by computer, e.g., using a 12 or 16 bit analog to digital
board. In one embodiment the scanned image is despeckled using a
graphics program (e.g., Hijaak Graphics Suite) and then analyzed
using an image gridding program that creates a spreadsheet of the
average hybridization at each wavelength at each site. If
necessary, an experimentally determined correction for "cross talk"
(or overlap) between the channels for the two fluors may be made.
For any particular hybridization site on the transcript array, a
ratio of the emission of the two fluorophores can be calculated.
The ratio is independent of the absolute expression level of the
cognate gene, but is useful for genes whose expression is
significantly modulated in association with the different breast
cancer-related condition.
5.6 Computer-Facilitated Analysis
[0153] The present invention further provides for kits comprising
the marker sets above. In a preferred embodiment, the kit contains
a microarray ready for hybridization to target polynucleotide
molecules, plus software for the data analyses described above.
[0154] The analytic methods described in the previous sections can
be implemented by use of the following computer systems and
according to the following programs and methods. A Computer system
comprises internal components linked to external components. The
internal components of a typical computer system include a
processor element interconnected with a main memory. For example,
the computer system can be an Intel 8086-, 80386-, 80486-,
Pentium.TM., or Pentium.TM.-based processor with preferably 32 MB
or more of main memory.
[0155] The external components may include mass storage. This mass
storage can be one or more hard disks (which are typically packaged
together with the processor and memory). Such hard disks are
preferably of 1 GB or greater storage capacity. Other external
components include a user interface device, which can be a monitor,
together with an inputting device, which can be a "mouse", or other
graphic input devices, and/or a keyboard. A printing device can
also be attached to the computer.
[0156] Typically, a computer system is also linked to network link,
which can be part of an Ethernet link to other local computer
systems, remote computer systems, or wide area communication
networks, such as the Internet. This network link allows the
computer system to share data and processing tasks with other
computer systems.
[0157] Loaded into memory during operation of this system are
several software components, which are both standard in the art and
special to the instant invention. These software components
collectively cause the computer system to function according to the
methods of this invention. These software components are typically
stored on the mass storage device. A software component comprises
the operating system, which is responsible for managing computer
system and its network interconnections. This operating system can
be, for example, of the Microsoft Windows.RTM. family, such as
Windows 3.1, Windows 95, Windows 98, Windows 2000, or Windows NT.
The software component represents common languages and functions
conveniently present on this system to assist programs implementing
the methods specific to this invention. Many high or low level
computer languages can be used to program the analytic methods of
this invention. Instructions can be interpreted during run-time or
compiled. Preferred languages include C/C++, FORTRAN and JAVA. Most
preferably, the methods of this invention are programmed in
mathematical software packages that allow symbolic entry of
equations and high-level specification of processing, including
some or all of the algorithms to be used, thereby freeing a user of
the need to procedurally program individual equations or
algorithms. Such packages include Mathlab from Mathworks (Natick,
Mass.), Mathematica.RTM. from Wolfram Research (Champaign, Ill.),
or S-Plus.RTM. from Math Soft (Cambridge, Mass.). Specifically, the
software component includes the analytic methods of the invention
as programmed in a procedural language or symbolic package.
[0158] The software to be included with the kit comprises the data
analysis methods of the invention as disclosed herein. In
particular, the software may include mathematical routines for
marker discovery; including the calculation of correlation
coefficients between clinical categories (i.e., ER status) and
marker expression. The software may also include mathematical
routines for calculating the correlation between sample marker
expression and control marker expression, using array-generated
fluorescence data, to determine the clinical classification of a
sample.
[0159] In an exemplary implementation; to practice the methods of
the present invention, a user first loads experimental data into
the computer system. These data can be directly entered by the user
from a monitor, keyboard, or from other computer systems linked by
a network connection, or on removable storage media such as a
CD-ROM, floppy disk (not illustrated), tape drive (not
illustrated), ZIP.RTM. drive (not illustrated) or through the
network. Next the user causes execution of expression profile
analysis software which performs the methods of the present
invention.
[0160] In another exemplary implementation, a user first loads
experimental data and/or databases into the computer system. This
data is loaded into the memory from the storage media or from a
remote computer, preferably from a dynamic geneset database system,
through the network. Next the user causes execution of software
that performs the steps of the present invention.
[0161] Alternative computer systems and software for implementing
the analytic methods of this invention will be apparent to one of
skill in the art and are intended to be comprehended within the
accompanying claims. In particular, the accompanying claims are
intended to include the alternative program structures for
implementing the methods of this invention that will be readily
apparent to one of skill in the art.
6. EXAMPLES
Materials And Methods
[0162] 117 tumor samples from breast cancer patients were
collected. RNA samples were then prepared, and each RNA sample was
profiled using inkjet-printed microarrays. Marker genes were then
identified based on expression patterns; these genes were then used
to train classifiers, which used these marker genes to classify
tumors into diagnostic and prognostic categories. Finally, these
marker genes were used to predict the diagnostic and prognostic
outcome for a group of individuals.
[0163] 1. Sample Collection
[0164] 117 breast cancer patients treated at The Netherlands Cancer
Institute/Antoni van Leeuwenhoek Hospital, Amsterdam, The
Netherlands, were selected on the basis of the following clinical
criteria (data extracted from the medical records of the NKI/AvL
Tumor Register, Biometrics Department).
[0165] Group 1 (n=97, 78 for training, 19 for independent tests)
was selected on the basis of: (1) primary invasive breast carcinoma
<5 cm (T1 or T2); (2) no axillary metastases (NO); (3) age at
diagnosis <55 years; (4) calendar year of diagnosis 1983-1996;
and (5) no prior malignancies (excluding carcinoma in situ of the
cervix or basal cell carcinoma of the skin). All patients were
treated by modified radical mastectomy (n=34) or breast conserving
treatment (n=64), including axillary lymph node dissection. Breast
conserving treatment consisted of excision of the tumor, followed
by radiation of the whole breast to a dosis of 50 Gy, followed by a
boost varying from 15 to 25 Gy. Five patients received adjuvant
systemic therapy consisting of chemotherapy (n=3) or hormonal
therapy (n=2), all other patients did not receive additional
treatment. All patients were followed at least annually for a
period of at least 5 years. Patient follow-up information was
extracted from the Tumor Registry of the Biometrics Department.
[0166] Group 2 (n=20) was selected as: (1) carriers of a germline
mutation in BRCA1 or BRCA2; and (2) having primary invasive breast
carcinoma. No selection or exclusion was made based on tumor size,
lymph node status, age at diagnosis, calendar year of diagnosis,
other malignancies. Germline mutation status was known prior to
this research protocol.
[0167] Information about individual from which tumor samples were
collected include: year of birth; sex; whether the individual is
pre- or post-menopausal; the year of diagnosis; the number of
positive lymph nodes and the total number of nodes; whether there
was surgery, and if so, whether the surgery was breast-conserving
or radical; whether there was radiotherapy, chemotherapy or
hormonal therapy. The tumor was graded according to the formula
P=TNM, where T is the tumor size (on a scale of 0-5); N is the
number of nodes that are positive (on a scale of 0-4); and M is
metastases (0=absent, 1=present). The tumor was also classified
according to stage, tumor type (in situ or invasive; lobular or
ductal; grade) and the presence or absence of the estrogen and
progesterone receptors. The progression of the cancer was described
by (where applicable): distant metastases; year of distant
metastases, year of death, year of last follow-up; and BRCA1
genotype.
[0168] 2. Tumors:
[0169] Germline mutation testing of BRCA1 and BRCA2 on DNA isolated
from peripheral blood lymphocytes includes mutation screening by a
Protein Truncation Test (PTT) of exon 11 of BRCA1 and exon 10 and
11 of BRCA2, deletion PCR of BRCA1 genomic deletion of exon 13 and
22, as well Denaturing Gradient Gel Electrophoresis (DGGE) of the
remaining exons. Aberrant bands were all confirmed by genomic
sequencing analyzed on a AB13700 automatic sequencer and confirmed
on a independent DNA sample.
[0170] From all, tumor material was snap frozen in liquid nitrogen
within one hour after surgery Of the frozen tumor material an
H&E (hematoxylin-eosin) stained section was prepared prior to
and after cutting slides for RNA isolation. These H&E frozen
sections were assessed for the percentage of tumor cells; only
samples with >50% tumor cells were selected for further
study.
[0171] For all tumors, surgical specimens fixed in formaldehyde and
embedded in paraffin were evaluated according to standard
histopathological procedures. H&E stained paraffin sections
were examined to assess tumor type (e.g., ductal or lobular
according to the WHO classification); to assess histologic grade
according the method described by Elston and Ellis (grade 1-3); and
to assess the presence of lymphangio-invasive growth and the
presence of an extensive lymphocytic infiltrate. All histologic
factors were independently assessed by two pathologists (MV and
JL); consensus on differences was reached by examining the slides
together. A representative slide of each tumor was used for
immunohistochemical staining with antibodies directed against the
estrogen- and progesterone receptor by standard procedures. The
staining result was scored as the percentage of positively staining
nuclei (0%, 10%, 20%, etc., up to 100%).
[0172] 3. Amplification, Labeling, and Hybridization
[0173] The outline for the production of marker-derived nucleic
acids and hybridization of the nucleic acids to a microarray are
outlined in FIG. 2. 30 frozen sections of 30 .mu.M thickness were
used for total RNA isolation of each snap frozen tumor specimen.
Total RNA was isolated with RNAzol.TM. B (Campro Scientific,
Veenendaal, The Netherlands) according to the manufacturers
protocol, including homogenization of the tissue using a Polytron
PT-MR2100 (Merck, Amsterdam, The Netherlands) and finally dissolved
in RNAse-free H.sub.2O. The quality of the total RNA was assessed
by A260/A280 ratio and had to be between 1.7 and 2.1 as well as
visual inspection of the RNA on an agarose gel which should
indicate a stronger 28S ribosomal RNA band compared to the 18S
ribosomal RNA band. subsequently, 25 .mu.g of total RNA was DNase
treated using the Qiagen RNase-free DNase kit and RNeasy spin
columns (Qiagen Inc, GmbH, Germany) according to the manufacturers
protocol. DNase treated total RNA was dissolved in RNase-free
H.sub.2O to a final concentration of 0.2 .mu.g/.mu.l.
[0174] 5 .mu.g total RNA was used as input for cRNA synthesis. An
oligo-dT primer containing a T7 RNA polymerase promoter sequence
was used to prime first strand cDNA synthesis, and random primers
(pdN6) were used to prime second strand cDNA synthesis by MMLV
reverse transcriptase. This reaction yielded a double-stranded cDNA
that contained the T7 RNA polymerase (T7RNAP) promoter. The
double-stranded cDNA was then transcribed into cRNA by T7RNAP.
[0175] cRNA was labeled with Cy3 or Cy5 dyes using a two-step
process. First, allylamine-derivatized nucleotides were
enzymatically incorporated into cRNA products. For cRNA labeling, a
3:1 mixture of 5-(3-Aminoallyl)uridine 5'-triphosphate (Sigma) and
UTP was substituted for UTP in the in vitro transcription (NVT)
reaction. Allylamine-derivatized cRNA products were then reacted
with N-hydroxy succinimide esters of Cy3 or Cy5 (CyDye, Amersham
Pharmacia Biotech). 5 .mu.g Cy5-labeled cRNA from one breast cancer
patient was mixed with the same amount of Cy3-labeled product from
a pool of equal amount of cRNA from each individual sporadic
patient.
[0176] Microarray hybridizations were done in duplicate with fluor
reversals. Before hybridization, labeled cRNAs were fragmented to
an average size of .about.50-100 nt by heating at 60.degree. C. in
the presence of 10 mM ZnCl2. Fragmented cRNAs were added to
hybridization buffer containing 1 M NaCl.sub.1, 0.5% sodium
sarcosine and 50 mM MES, pH 6.5, which stringency was regulated by
the addition of formamide to a final concentration of 30%.
Hybridizations were carried out in a final volume of 3 mls at
40.degree. C. on a rotating platform in a hybridization oven
(Robbins Scientific) for 48 h. After hybridization, slides were
washed and scanned using a confocal laser scanner (Agilent
Technologies). Fluorescence intensities on scanned images were
quantified, normalized and corrected.
[0177] 4. Pooling of Samples
[0178] The reference cRNA pool was formed by pooling equal amount
of cRNAs from each individual sporadic patient, for a total of 78
tumors.
[0179] 5. 25k Human Microarray
[0180] Surface-bound oligonucleotides were synthesized essentially
as proposed by Blanchard et al., Biosens. Bioelectron. 6(7):687-690
(1996); see also Hughes et al, Nature Biotech. 19(4):342-347
(2000). Hydrophobic glass surfaces (3 inches by 3 inches)
containing exposed hydroxyl groups were used as substrates for
nucleotide synthesis. Phosphoramidite monomers were delivered to
computer-defined positions on the glass surfaces using ink-jet
printer heads. Unreacted monomers were then washed away and the
ends of the extended oligonucleotides were deprotected. This cycle
of monomer coupling, washing and deprotection was repeated for each
desired layer of nucleotide synthesis. Oligonucleotide sequences to
be printed were specified by computer files.
[0181] Microarrays containing approximately 25,000 human gene
sequences (Hu25K microarrays) were used for this study. Sequences
for microarrays were selected from RefSeq (a collection of
non-redundant mRNA sequences, located on the Internet at
nlm.nih.gov/LocusLink/refseq.html) and Phil Green EST contigs,
which is a collection of EST contigs assembled by Dr. Phil Green et
al at the University of Washington (Ewing and Green, Nat. Genet.
25(2):232-4 (2000)), available on the Internet at
phrap.org/est_assembly/index.html. Each mRNA or EST contig was
represented on Hu25K microarray by a single 60 mer oligonucleotide
essentially as described in Hughes et al., Nature Biotech.
19(4):342-347 and in International Publication WO 01/06013,
published Jan. 25, 2001, and in International Publication WO
01/05935, published Jan. 25, 2001, except that the rules for oligo
screening were modified to remove oligonucleotides with more than
30% C or with 6 or more contiguous C residues.
Example 1: Differentially Regulated Gene Sets and Overall
Expression Patterns of Breast Cancer Tumors
[0182] Of the approximately 25,000 sequences represented on the
microarray, a group of approximately 5,000 genes that were
significantly regulated across the group of samples was selected. A
gene was determined to be significantly differentially regulated
with cancer of the breast if it showed more than two-fold of
transcript changes as compared to a sporadic tumor pool, and if the
p-value for differential regulation (Hughes et al., Cell
102:109-126 (2000)) was less than 0.01 either upwards or downwards
in at least five out of 98 tumor samples.
[0183] An unsupervised clustering algorithm allowed us to cluster
patients based on their similarities measured over this set of
.about.5,000 significant genes. The similarity measure between two
patients x and y is defined as
S = 1 - [ i = 1 N y ( x i - x _ ) .sigma. x i ( y i - y _ ) .sigma.
y i i = 1 N y ( x i - x _ .sigma. x i ) 2 i = 1 N y ( ( y i - y _ )
.sigma. y i ) 2 ] [ Equation 5 ] ##EQU00003##
In Equation (5), x and y are two patients with components of log
ratio x.sub.1 and y.sub.1, i=I, . . . , N=5,100. Associated with
every value x.sub.1 is error .sigma..sub.x.sub.1. The smaller the
value .sigma..sub.x.sub.1, the more reliable the measurement
x.sub.1.
x _ = i = 1 N y x i .sigma. x 1 2 / i = 1 N y 1 .sigma. x 1 2
##EQU00004##
is the error-weighted arithmetic mean. The use of correlation as
similarity metric emphasizes the importance of co-regulation in
clustering rather than the amplitude of regulations.
[0184] The set of approximately 5,000 genes can be clustered based
on their similarities measured over the group of 98 tumor samples.
The similarity measure between two genes was defined in the same
way as in Equation (1) except that now for each gene, there are 98
components of log ratio measurements.
[0185] The result of such a two-dimensional clustering is displayed
in FIG. 3. Two distinctive patterns emerge from the clustering. The
first pattern consists of a group of patients in the lower part of
the plot whose regulations are very different from the sporadic
pool. The other pattern is made of a group of patients in the upper
part of the plot whose expressions are only moderately regulated in
comparison with the sporadic pool. These dominant patterns suggest
that the tumors can be unambiguously divided into two distinct
types based on this set of .about.5,000 significant genes.
[0186] To help understand these patterns, they were associated with
estrogen-receptor (ER), proestrogen receptor (PR), tumor grade,
presence of lymphocytic infiltrate, and angioinvasion (FIG. 3). The
lower group in FIG. 3, which features the dominant pattern,
consists of 36 patients. Of the 39 ER-negative patients, 34
patients are clustered together in this group. From FIG. 4, it was
observed that the expression of estrogen receptor alpha gene ESR1
and a large group of co-regulated genes are consistent with this
expression pattern.
[0187] From FIG. 3 and FIG. 4, it was concluded that gene
expression patterns can be used to classify tumor samples into
subgroups of diagnostic interest. Thus, genes co-regulated across
98 tumor samples contain information about the molecular basis of
breast cancers. The combination of clinical data and microarray
measured gene abundance of ESR1 demonstrates that the distinct
types are related to, or at least are reported by, the ER
status.
Example 2: Identification of Genetic Markers Distinguishing
Estrogen Receptor (+) from Estrogen Receptor (-) Patients
[0188] The results described in this Example allow the
identification of expression marker genes that differentiate two
major types of tumor cells: "ER-negative" group and "ER-positive"
group. The differentiation of samples by ER(+) status was
accomplished in three steps: (1) identification of a set of
candidate marker genes that correlate with ER level; (2)
rank-ordering these candidate genes by strength of correlation; (3)
optimization of the number of marker genes; and (4) classifying
samples based on these marker genes.
[0189] 1. Selection of Candidate Discriminating Genes
[0190] In the first step, a set of candidate discriminating genes
was identified based on gene expression data of training samples.
Specifically, we calculated the correlation coefficients .rho.
between the category numbers or ER level and logarithmic expression
ratio {right arrow over (r)} across all the samples for each
individual gene:
.rho.=({right arrow over (c)}{right arrow over
(r)})/(.parallel.{right arrow over (c)}.parallel..parallel.{right
arrow over (r)}.parallel.) Equation (2)
The histogram of resultant correlation coefficients is shown in
FIG. 5A as a gray line. While the amplitude of correlation or
anti-correlation is small for the majority of genes, the amplitude
for some genes is as great as 0.5. Genes whose expression ratios
either correlate or anti-correlate well with the diagnostic
category of interest are used as reporter genes for the
category.
[0191] Genes having a correlation coefficient larger than 0.3
("correlated genes") or less than -0.3 ("anti-correlated genes")
were selected as reporter genes. The threshold of 0.3 was selected
based on the correlation distribution for cases where there is no
real correlation (one can use permutations to determine this
distribution). Statistically, this distribution width depends upon
the number of samples used in the correlation calculation. The
distribution width for control cases (no real correlation) is
approximately 1/ {square root over (n-3)}, where n=the number of
samples. In our case, n=98. Therefore, a threshold of 0.3 roughly
corresponds to 3-.sigma. or in the distribution (3.times.1/ {square
root over (n-3))}.
[0192] 2,460 such genes were found to satisfy this criterion. In
order to evaluate the significance of the correlation coefficient
of each gene with the ER level, a bootstrap technique was used to
generate Monte-Carlo data that randomize the association between
gene expression data of the samples and their categories. The
distribution of correlation coefficients obtained from one
Monte-Carlo trial is shown as a dashed line in FIG. 5A. To estimate
the significance of the 2,460 marker genes as a group, 10,000
Monte-Carlo runs were generated. The collection of 10,000 such
Monte-Carlo trials forms the null hypothesis. The number of genes
that satisfy the same criterion for Monte-Carlo data varies from
run to run. The frequency distribution from 10,000 Monte-Carlo runs
of the number of genes having correlation coefficients of >0.3
or <-0.3 is displayed in FIG. 5B. Both the mean and maximum
value are much smaller than 2,460. Therefore, the significance of
this gene group as the discriminating gene set between ER(+) and
ER(-) samples is estimated to be greater than 99.99%.
[0193] 2. Rank-Ordering of Candidate Discriminating Gene
[0194] In the second step, genes on the candidate list were
rank-ordered based on the significance of each gene as a
discriminating gene. The markers were rank-ordered either by
amplitude of correlation, or by using a metric similar to a Fisher
statistic:
t=(x.sub.1-x.sub.2)/ {square root over
([.sigma..sub.1.sup.2(n.sub.1-1)+.sigma..sub.2.sup.2(n.sub.2-.sup.1))}]/(-
n.sub.1+n.sub.2-1)/(1/n.sub.1+1/n.sub.2) Equation (3)
In Equation (3), x.sub.1 is the error-weighted average of log ratio
within the ER(-), and x.sub.2 is the error-weighted average of log
ratio within the ER(+) group. .sigma..sub.1 is the variance of log
ratio within the ER(-) group and n.sub.1 is the number of samples
that had valid measurements of log ratios. .sigma..sub.2 is the
variance of log ratio within the ER(+) group and n.sub.2 is the
number of samples that had valid measurements of log ratios. The
t-value in Equation (3) represents the variance-compensated
difference between two means. The confidence level of each gene in
the candidate list was estimated with respect to a null hypothesis
derived from the actual data set using a bootstrap technique; that
is, many artificial data sets were generated by randomizing the
association between the clinical data and the gene expression
data.
[0195] 3. Optimization of the Number of Marker Genes
[0196] The leave-one-out method was used for cross validation in
order to optimize the discriminating genes. For a set of marker
genes from the rank-ordered candidate list, a classifier was
trained with 97 samples, and was used to predict the status of the
remaining sample. The procedure was repeated for each of the
samples in the pool, and the number of cases where the prediction
for the one left out is wrong or correct was counted.
[0197] The above performance evaluation from leave-one-out cross
validation was repeated by successively adding more marker genes
from the candidate list. The performance as a function of the
number of marker genes is shown in FIG. 6. The error rates for type
1 and type 2 errors varied with the number of marker genes used,
but were both minimal while the number of the marker genes is
around 550. Therefore, we consider this set of 550 genes is
considered the optimal set of marker genes that can be used to
classify breast cancer tumors into "ER-negative" group and
"ER-positive" group. FIG. 7 shows the classification of patients as
ER(+) or ER(-) based on this 550 marker set. FIG. 8 shows the
correlation of each tumor to the ER-negative template verse the
correlation of each tumor to the ER-positive template.
[0198] 4. Classification Based on Marker Genes
[0199] In the third step, a set of classifier parameters was
calculated for each type of training data set based on either of
the above ranking methods. A template for the ER(-) group ({right
arrow over (Z)}.sub.1) was generated using the error-weighted log
ratio average of the selected group of genes. Similarly, a template
for ER(+) group (called {right arrow over (Z)}.sub.2) was generated
using the error-weighted log ratio average of the selected group of
genes. Two classifier parameters (P.sub.1 and P.sub.2) were defined
based on either correlation or distance. P.sub.1 measures the
similarity between one sample {right arrow over (y)} and the ER(-)
template {right arrow over (Z)}.sub.1 over this selected group of
genes. P.sub.2 measures the similarity between one sample {right
arrow over (y)} and the ER(+) template {right arrow over (Z)}.sub.2
over this selected group of genes. The correlation P.sub.1 is
defined as:
P.sub.1=({right arrow over (z)}.sub.i{right arrow over
(y)})/(.parallel.{right arrow over
(z)}.sub.i.parallel..parallel.{right arrow over (y)}.parallel.),
Equation (1)
[0200] A "leave-one-out" method was used to cross-validate the
classifier built based on the marker genes. In this method, one
sample was reserved for cross validation each time the classifier
was trained. For the set of 550 optimal marker genes, the
classifier was trained with 97 of the 98 samples, and the status of
the remaining sample was predicted. This procedure was performed
with each of the 98 patients. The number of cases where the
prediction was wrong or correct was counted. It was further
determined that subsets of as few as .about.50 of the 2,460 genes
are able classify tumors as ER(+) or ER(-) nearly as well as using
the total set.
[0201] In a small number of cases, there was disagreement between
classification by the 550 marker set and a clinical classification.
In comparing the microarray measured log ratio of expression for
ESR1 to the clinical binary decision (negative or positive) of ER
status for each patient, it was seen that the measured expression
is consistent with the qualitative category of clinical
measurements (mixture of two methods) for the majority of tumors.
For example, two patients who were clinically diagnosed as ER(+)
actually exhibited low expression of ESR1 from microarray
measurements and were classified as ER negative by 550 marker
genes. Additionally, 3 patients who were clinically diagnosed as
ER(-) exhibited high expression of ESR1 from microarray
measurements and were classified as ER(+) by the same 550 marker
genes. Statistically, however, microarray measured gene expression
of ESR1 correlates with the dominant patterns better than
clinically determined ER status.
Example 3: Identification of Genetic Markers Distinguishing BRCA1
Tumors from Sporadic Tumors in Estrogen Receptor (-) Patients
[0202] The BRCA1 mutation is one of the major clinical categories
in breast cancer tumors. It was determined that of tumors of 38
patients in the ER(-) group, 17 exhibited the BRCA1 mutation, while
21 were sporadic tumors. A method was therefore developed that
enabled the differentiation of the 17 BRCA1 mutation tumors from
the 21 sporadic tumors in the ER(-) group.
[0203] 1. Selection of Candidate Discriminating Genes
[0204] In the first step, a set of candidate genes was identified
based on the gene expression patterns of these 38 samples. We first
calculated the correlation between the BRCA1-mutation category
number and the expression ratio across all 38 samples for each
individual gene by Equation (2). The distribution of the
correlation coefficients is shown as a histogram defined by the
solid line in FIG. 9A. We observed that, while the majority of
genes do not correlate with BRCA1 mutation status, a small group of
genes correlated at significant levels. It is likely that genes
with larger correlation coefficients would serve as reporters for
discriminating tumors of BRCA1 mutation carriers from sporadic
tumors within the ER(-) group.
[0205] In order to evaluate the significance of each correlation
coefficient with respect to a null hypothesis that such correlation
coefficient could be found by chance, a bootstrap technique was
used to generate Monte-Carlo data that randomizes the association
between gene expression data of the samples and their categories.
10,000 such Monte-Carlo runs were generated as a control in order
to estimate the significance of the marker genes as a group. A
threshold of 0.35 in the absolute amplitude of correlation
coefficients (either correlation or anti-correlation) was applied
both to the real data and the Monte-Carlo data. Following this
method, 430 genes were found to satisfy this criterion for the
experimental data. The p-value of the significance, as measured
against the 10,000 Monte-Carlo trials, is approximately 0.0048
(FIG. 9B). That is, the probability that this set of 430 genes
contained useful information about BRCA1-like tumors vs sporadic
tumors exceeds 99%.
[0206] 2. Rank-Ordering of Candidate Discriminating Genes
[0207] In the second step, genes on the candidate list were
rank-ordered based on the significance of each gene as a
discriminating gene. Here, we used the absolute amplitude of
correlation coefficients to rank order the marker genes.
[0208] 3. Optimization of Discriminating Genes
[0209] In the third step, a subset of genes from the top of this
rank-ordered list was used for classification. We defined a BRCA1
group template (called {right arrow over (Z)}.sub.1) by using the
error-weighted log ratio average of the selected group of genes.
Similarly, we defined a non-BRCA1 group template (called {right
arrow over (Z)}.sub.2) by using the error-weighted log ratio
average of the selected group of genes. Two classifier parameters
(P1 and P2) were defined based on either correlation or distance.
P1 measures the similarity between one sample {right arrow over
(y)} and the BRCA1 template {right arrow over (Z)}.sub.1 over this
selected group of genes. P2 measures the similarity between one
sample {right arrow over (y)} and the non-BRCA1 template {right
arrow over (Z)}.sub.2 over this selected group of genes. For
correlation, P1 and P2 were defined in the same way as in Equation
(4).
[0210] The leave-one-out method was used for cross validation in
order to optimize the discriminating genes as described in Example
2. For a set of marker genes from the rank-ordered candidate list,
the classifier was trained with 37 samples the remaining one was
predicted. The procedure was repeated for all the samples in the
pool, and the number of cases where the prediction for the one left
out is wrong or correct was counted.
[0211] To determine the number of markers constituting a viable
subset, the above performance evaluation from leave-one-out cross
validation was repeated by cumulatively adding more marker genes
from the candidate list. The performance as a function of the
number of marker genes is shown in FIG. 10. The error rates for
type 1 (false negative) and type 2 (false positive) errors (Bendat
& Piersol, RANDOM DATA ANALYSIS AND MEASUREMENT PROCEDURES, 2D
ED., Wiley Interscience, p. 89) reached optimal ranges when the
number of the marker genes is approximately 100. Therefore, a set
of about 100 genes is considered to be the optimal set of marker
genes that can be used to classify tumors in the ER(-) group as
either BRCA1-related tumors or sporadic tumors.
[0212] The classification results using the optimal 100 genes are
shown in FIGS. 11A and 11B. As shown in FIG. 11A, the co-regulation
patterns of the sporadic patients differ from those of the BRCA1
patients primarily in the amplitude of regulation. Only one
sporadic tumor was classified into the BRCA1 group. Patients in the
sporadic group are not necessarily BRCA1 mutation negative;
however, it is estimated that only approximately 5% of sporadic
tumors are indeed BRCA1-mutation carriers.
Example 4: Identification of Genetic Markers Distinguishing
Sporadic Tumor Patients with >5 Year Versus <5 Year Survival
Times
[0213] 78 tumors from sporadic breast cancer patients were used to
explore prognostic predictors from gene expression data. Of the 78
samples in this sporadic breast cancer group, 44 samples were known
clinically to have had no distant metastases within 5 years since
the initial diagnosis ("no distant metastases group") and 34
samples had distant metastases within 5 years since the initial
diagnosis ("distant metastases group"). A group of 231 markers, and
optimally a group of 70 markers, was identified that allowed
differentiation between these two groups.
[0214] 1. Selection of Candidate Discriminating Genes
[0215] In the first step, a set of candidate discriminating genes
was identified based on gene expression data of these 78 samples.
The correlation between the prognostic category number (distant
metastases vs no distant metastases) and the logarithmic expression
ratio across all samples for each individual gene was calculated
using Equation (2). The distribution of the correlation
coefficients is shown as a solid line in FIG. 12A. FIG. 12A also
shows the result of one Monte-Carlo run as a dashed line. We
observe that even though the majority of genes do not correlate
with the prognostic categories, a small group of genes do
correlate. It is likely that genes with larger correlation
coefficients would be more useful as reporters for the prognosis of
interest-distant metastases group and no distant metastases
group.
[0216] In order to evaluate the significance of each correlation
coefficient with respect to a null hypothesis that such correlation
coefficient can be found by chance, we used a bootstrap technique
to generate data from 10,000 Monte-Carlo runs as a control (FIG.
12B). We then selected genes that either have the correlation
coefficient larger than 0.3 ("correlated genes") or less than -0.3
("anti-correlated genes"). The same selection criterion was applied
both to the real data and the Monte-Carlo data. Using this
comparison, 231 markers from the experimental data were identified
that satisfy this criterion. The probability of this gene set for
discriminating patients between the distant metastases group and
the no distant metastases group being chosen by random fluctuation
is approximately 0.003.
[0217] 2. Rank-Ordering of Candidate Discriminating Genes
[0218] In the second step, genes on the candidate list were
rank-ordered based on the significance of each gene as a
discriminating gene. Specifically, a metric similar to a "Fisher"
statistic, defined in Equation (3), was used for the purpose of
rank ordering. The confidence level of each gene in the candidate
list was estimated with respect to a null hypothesis derived from
the actual data set using the bootstrap technique. Genes in the
candidate list can also be ranked by the amplitude of correlation
coefficients.
[0219] 3. Optimization of Discriminating Genes
[0220] In the third step, a subset of 5 genes from the top of this
rank-ordered list was selected to use as discriminating genes to
classify 78 tumors into a "distant metastases group" or a "no
distant metastases group". The leave-one-out method was used for
cross validation. Specifically, 77 samples defined a classifier
based on the set of selected discriminating genes, and these were
used to predict the remaining sample. This procedure was repeated
so that each of the 78 samples was predicted. The number of cases
in which predictions were correct or incorrect were counted. The
performance of the classifier was measured by the error rates of
type 1 and type 2 for this selected gene set.
[0221] We repeated the above performance evaluation procedure,
adding 5 more marker genes each time from the top of the candidate
list, until all 231 genes were used. As shown in FIG. 13, the
number of mis-predictions of type 1 and type 2 errors change
dramatically with the number of marker genes employed. The combined
error rate reached a minimum when 70 marker genes from the top of
our candidate list never used. Therefore, this set of 70 genes is
the optimal, preferred set of marker genes useful for the
classification of sporadic tumor patients into either the distant
metastases or no distant metastases group. Fewer or more markers
also act as predictors, but are less efficient, either because of
higher error rates, or the introduction of statistical noise.
[0222] 4. Reoccurrence Probability Curves
[0223] The prognostic classification of 78 patients with sporadic
breast cancer tumors into two distinct subgroups was predicted
based on their expression of the 70 optimal marker genes (FIGS. 14
and 15).
[0224] To evaluate the prognostic classification of sporadic
patients, we predicted the outcome of each patient by a classifier
trained by the remaining 77 patients based on the 70 optimal marker
genes. FIG. 16 plots the distant metastases probability as a
function of the time since initial diagnosis for the two predicted
groups. The difference between these two reoccurrence curves is
significant. Using the .chi..sup.2 test (S-PLUS 2000 Guide to
Statistics, vol. 2, MathSoft, p. 44), the p-value is estimated to
be .about.10.sup.-9. The distant metastases probability as a
function of the time since initial diagnosis was also compared
between ER(+) and ER(-) individuals (FIG. 17), PR(+) and PR(-)
individuals (FIG. 18), and between individuals with different tumor
grades (FIGS. 19A, 19B). In comparison, the p-values for the
differences between two prognostic groups based on clinical data
are much less significant than that based on gene expression data,
ranging from 10.sup.-3 to 1.
[0225] To parameterize the reoccurrence probability as a function
of time since initial diagnosis, the curve was fitted to one type
of survival model--"normal":
P=.alpha..times.exp(-t.sup.2/.tau..sup.2) (4)
For fixed .alpha.=1, we found that .tau.=125 months for patients in
the no distant metastases group and .tau.=36 months for patients in
the distant metastases group. Using tumor grades, we found
.tau.=100 months for patients with tumor grades 1 and 2 and
.tau.=60 for patients with tumor grade 3. It is accepted clinical
practice that tumor grades are the best available prognostic
predictor. However, the difference between the two prognostic
groups classified based on 70 marker genes is much more significant
than those classified by the best available clinical
information.
[0226] 5. Prognostic Prediction for 19 Independent Sporadic
Tumors
[0227] To confirm the proposed prognostic classification method and
to ensure the reproducibility, robustness, and predicting power of
the 70 optimal prognostic marker genes, we applied the same
classifier to 19 independent tumor samples from sporadic breast
cancer patients, prepared separately at The Netherlands Cancer
Institute (NKI). The same reference pool was used.
[0228] The classification results of 19 independent sporadic tumors
are shown in FIG. 20. FIG. 20A shows the log ratio of expression
regulation of the same 70 optimum marker genes. Based on our
classifier model, we expected the misclassification of
19*(6+7)/78=3.2 tumors. Consistently, (1+3)=4 of 19 tumors were
misclassified.
[0229] 6. Clinical Parameters as a Group Vs. Microarray
Data--Results of Logistic Regression
[0230] In the previous section, the predictive power of each
individual clinical parameter was compared with that of the
expression data. However, it is more meaningful to combine all the
clinical parameters as a group, and then compare them to the
expression data. This requires multi-variant modeling; the method
chosen was logistic regression. Such an approach also demonstrates
how much improvement the microarray approach adds to the results of
the clinical data.
[0231] The clinical parameters used for the multi-variant modeling
were: (1) tumor grade; (2) ER status; (3) presence or absence of
the progestogen receptor (PR); (4) tumor size; (5) patient age; and
(6) presence or absence of angioinvasion. For the microarray data,
two correlation coefficients were used. One is the correlation to
the mean of the good prognosis group (C1) and the other is the
correlation to the mean of the bad prognosis group (C2). When
calculating the correlation coefficients for a given patient, this
patient is excluded from either of the two means.
[0232] The logistic regression optimizes the coefficient of each
input parameter to best predict the outcome of each patient. One
way to judge the predictive power of each input parameter is by how
much deviance (similar to Chi-square in the linear regression, see
for example, Hasomer & Lemeshow, APPLIED LOGISTIC REGRESSION,
John Wiley & Sons, (2000)) the parameter accounts for. The best
predictor should account for most of the deviance. To fairly assess
the predictive power, each parameter was modeled independently. The
microarray parameters explain most of the deviance, and hence are
powerful predictors.
[0233] The clinical parameters, and the two microarray parameters,
were then monitored as a group. The total deviance explained by the
six clinical parameters was 31.5, and total deviance explained by
the microarray parameters was 39.4. However, when the clinical data
was modeled first, and the two microarray parameters added, the
final deviance accounted for is 57.0.
[0234] The logistic regression computes the likelihood that a
patient belongs to the good or poor prognostic group. FIGS. 21A and
21B show the sensitivity vs. (1-specificity). The plots were
generated by varying the threshold on the model predicted
likelihood. The curve which goes through the top left corner is the
best (high sensitivity with high specificity). The microarray
outperformed the clinical data by a large margin. For example, at a
fixed sensitivity of around 80%, the specificity was .about.80%
from the microarray data, and .about.65% from the clinical data for
the good prognosis group. For the poor prognosis group, the
corresponding specificities were .about.80% and .about.70%, again
at a fixed sensitivity of 80%. Combining the microarray data with
the clinical data further improved the results. The result can also
be displayed as the total error rate as the function of the
threshold in FIG. 21C. At all possible thresholds, the error rate
from the microarray was always smaller than that from the clinical
data. By adding the microarray data to the clinical data, the error
rate is further reduced, as one can see in FIG. 21C.
[0235] Odds ratio tables can be created from the prediction of the
logistic regression. The probability of a patient being in the good
prognosis group is calculated by the logistic regression based on
different combinations of input parameters (clinical and/or
microarray). Patients are divided into the following four groups
according to the prediction and the true outcome: (1) predicted
good and truly good, (2) predicted good but truly poor, (3)
predicted poor but truly good, (4) predicted poor and truly poor.
Groups (1) & (4) represent correct predictions, while groups
(2) & (3) represent mis-predictions. The division for the
prediction is set at probability of 50%, although other thresholds
can be used. The results are listed in Table 7. It is clear from
Table 7 that microarray profiling (Table 7.3 & 7.10)
outperforms any single clinical data (Table 7.4-7.9) and the
combination of the clinical data (Table 7.2). Adding the
micro-array profiling in addition to the clinical data give the
best results (Table 7.1).
[0236] For microarray profiling, one can also make a similar table
(Table 7.11) without using logistic regression. In this case, the
prediction was simply based on C1-C2 (greater than 0 means good
prognosis, less than 0 mean bad prognosis).
TABLE-US-00007 TABLE 7.1 Prediction by clinical + microarray
Predicted good Predicted poor true good 39 5 true poor 4 30
TABLE-US-00008 TABLE 7.2 Prediction by clinical alone Predicted
good Predicted poor true good 34 10 true poor 12 22
TABLE-US-00009 TABLE 7.3 Prediction by microarray Predicted good
Predicted poor true good 39 5 true poor 10 24
TABLE-US-00010 TABLE 7.4 Prediction by grade Predicted good
Predicted poor true good 23 21 true poor 5 29
TABLE-US-00011 TABLE 7.5 Prediction by ER Predicted good Predicted
poor true good 35 9 true poor 21 13
TABLE-US-00012 TABLE 7.6 Prediction by PR Predicted good Predicted
poor true good 35 9 true poor 18 16
TABLE-US-00013 TABLE 7.7 Prediction by size Predicted good
Predicted poor true good 35 9 true poor 13 21
TABLE-US-00014 TABLE 7.8 Prediction by age Predicted good Predicted
poor true good 33 11 true poor 15 19
TABLE-US-00015 TABLE 7.9 Prediction by angioinvasion Predicted good
Predicted poor true good 37 7 true poor 19 15
TABLE-US-00016 TABLE 7.10 Prediction by dC (C1-C2) Predicted good
Predicted poor true good 36 8 true poor 6 28
TABLE-US-00017 TABLE 7.11 No logistic regression, simply judged by
C1-C2 Predicted good Predicted poor true good 37 7 true poor 6
28
Example 5: Concept of Mini-Array for Diagnosis Purposes
[0237] All genes on the marker gene list for the purpose of
diagnosis and prognosis can be synthesized on a small-scale
microarray using ink-jet technology. A microarray with genes for
diagnosis and prognosis can respectively or collectively be made.
Each gene on the list is represented by single or multiple
oligonucleotide probes, depending on its sequence uniqueness across
the genome. This custom designed mini-array, in combination with
sample preparation protocol, can be used as a diagnostic/prognostic
kit in clinics.
Example 6: Biological Significance of Diagnostic Marker Genes
[0238] The public domain was searched for the available functional
annotations for the 430 marker genes for BRCA1 diagnosis in Table
3. The 430 diagnostic genes in Table 3 can be divided into two
groups: (1) 196 genes whose expressions are highly expressed in
BRCA1-like group; and (2) 234 genes whose expression are highly
expressed sporadic group. Of the 196 BRCA1 group genes, 94 are
annotated. Of the 234 sporadic group genes, 100 are annotated. The
terms "T-cell", "B-cell" or "immunoglobulin" are involved in 13 of
the 94 annotated genes, and in 1 of the 100 annotated genes,
respectively. Of 24,479 genes represented on the microarrays, there
are 7,586 genes with annotations to date. "T-cell", B-cell" and
"immunoglobulin" are found in 207 of these 7,586 genes. Given this,
the p-value of the 13 "T-cell", "B-cell" or "immunoglobulin" genes
in the BRCA1 group is very significant
(p-value=1.1.times.10.sup.-6). In comparison, the observation of 1
gene relating to "T-cell", "B-cell", or "immunoglobulin" in the
sporadic group is not significant (p-value=0.18).
[0239] The observation that BRCA1 patients have highly expressed
lymphocyte (T-cell and B-cell) genes agrees with what has been seen
from pathology that BRCA1 breast tumor has more frequently
associated with high lymphocytic infiltration than sporadic cases
(Chappuis et al., 2000, Semin Surg Oncol 18:287-295).
Example 7: Biological Significance of Prognosis Marker Genes
[0240] A search was performed for available functional annotations
for the 231 prognosis marker genes (Table 5). The markers fall into
two groups: (1) 156 markers whose expressions are highly expressed
in poor prognostic group; and (2) 75 genes whose expression are
highly expressed in good prognostic group. Of the 156 markers, 72
genes are annotated; of the 75 genes, 28 genes are annotated.
[0241] Twelve of the 72 markers, but none of the 28 markers, are,
or are associated with, kinases. In contrast, of the 7,586 genes on
the microarray having annotations to date, only 471 involve
kinases. On this basis, the p-value that twelve kinase-related
markers in the poor prognostic group is significant
(p-value=0.001). Kinases are important regulators of intracellular
signal transduction pathways mediating cell proliferation,
differentiation and apoptosis. Their activity is normally tightly
controlled and regulated. Overexpression of certain kinases is well
known involving in oncogenesis, such as vascular endothelial growth
factor receptor1 (VEGFR1 or FLT1), a tyrosine kinase in the poor
prognosis group, which plays a very important role in tumor
angiogenesis. Interestingly, vascular endothelial growth factor
(VEGF), VEGFR's ligand, is also found in the prognosis group, which
means both ligand and receptor are upregulated in poor prognostic
individuals by an unknown mechanism.
[0242] Likewise, 16 of the 72 markers, and only two of the 28
markers, are, or are associated with, ATP-binding or GTP-binding
proteins. In contrast, of the 7,586 genes on the microarray having
annotations to date, only 714 and 153 involve ATP-binding and
GTP-binding, respectively. On this basis, the p-value that 16 GTP-
or ATP-binding-related markers in the poor prognosis group is
significant (p-value 0.001 and 0.0038). Thus, the kinase- and ATP-
or GTP-binding-related markers within the 72 markers can be used as
prognostic indicators.
[0243] Cancer is characterized by deregulated cell proliferation.
On the simplest level, this requires division of the cell or
mitosis. By keyword searching, we found "cell division" or
"mitosis" included in the annotations of 7 genes respectively in
the 72 annotated markers from the 156 poor prognosis markers, but
in none for the 28 annotated genes from 75 good prognosis markers.
Of the 7,586 microarray markers with annotations, "cell division"
is found in 62 annotations and "mitosis" is found in 37
annotations. Based on these findings, the p-value that seven cell
division- or mitosis-related markers are found in the poor
prognosis group is estimated to be highly significant
(p-value=3.5.times.10.sup.-5). In comparison, the absence of cell
division- or mitosis-related markers in the good prognosis group is
not significant (p-value=0.69). Thus, the seven cell division- or
mitosis-related markers may be used as markers for poor
prognosis.
Example 8: Construction of an Artificial Reference Pool
[0244] The reference pool for expression profiling in the above
Examples was made by using equal amount of cRNAs from each
individual patient in the sporadic group. In order to have a
reliable, easy-to-made, and large amount of reference pool, a
reference pool for breast cancer diagnosis and prognosis can be
constructed using synthetic nucleic acid representing, or derived
from, each marker gene. Expression of marker genes for individual
patient sample is monitored only against the reference pool, not a
pool derived from other patients.
[0245] To make the reference pool, 60-mer oligonucleotides are
synthesized according to 60-mer ink-jet array probe sequence for
each diagnostic/prognostic reporter genes, then double-stranded and
cloned into pBluescript SK-vector (Stratagene, La Jolla, Calif.),
adjacent to the T7 promoter sequence. Individual clones are
isolated, and the sequences of their inserts are verified by DNA
sequencing. To generate synthetic RNAs, clones are linearized with
EcoRI and a T7 in vitro transcription (IVT) reaction is performed
according to the MegaScript kit (Ambion, Austin, Tex.). IVT is
followed by DNase treatment of the product. Synthetic RNAs are
purified on RNeasy columns (Qiagen, Valencia, Calif.). These
synthetic RNAs are transcribed, amplified, labeled, and mixed
together to make the reference pool. The abundance of those
synthetic RNAs are a adjusted to approximate the abundance of the
corresponding marker-derived transcripts in the real tumor
pool.
Example 9: Use of Single-Channel Data and a Sample Pol Represented
by Stored Values
[0246] 1. Creation of a Reference Pool of Stored Values
("Mathematical Sample Pool")
[0247] The use of ratio-based data used in Examples 1-7, above,
requires a physical reference sample. In the above Examples, a pool
of sporadic tumor sample was used as the reference. Use of such a
reference, while enabling robust prognostic and diagnostic
predictions, can be problematic because the pool is typically a
limited resource. A classifier method was therefore developed that
does not require a physical sample pool, making application of this
predictive and diagnostic technique much simpler in clinical
applications.
[0248] To test whether single-channel data could be used, the
following procedure was developed. First, the single channel
intensity data for the 70 optimal genes, described in Example 4,
from the 78 sporadic training samples, described in the Materials
and Methods, was selected from the sporadic sample vs. tumor pool
hybridization data. The 78 samples consisted of 44 samples from
patients having a good prognosis and 34 samples from patients
having a poor prognosis. Next, the hybridization intensities for
these samples were normalized by dividing by the median intensity
of all the biological spots on the same microarray. Where multiple
microarrays per sample were used, the average was taken across all
of the microarrays. A log transform was performed on the intensity
data for each of the 70 genes, or for the average intensity for
each of the 70 genes where more than one microarray is hybridized,
and a mean log intensity for each gene across the 78 sporadic
samples was calculated. For each sample, the mean log intensities
thus calculated were subtracted from the individual sample log
intensity. This figure, the mean subtracted log(intensity) was then
treated as the two color log(ratio) for the classifier by
substitution into Equation (5). For new samples, the mean log
intensity is subtracted in the same manner as noted above, and a
mean subtracted log(intensity) calculated.
[0249] The creation of a set of mean log intensities for each gene
hybridized creates a "mathematical sample pool" that replaces the
quantity-limited "material sample pool." This mathematical sample
pool can then be applied to any sample, including samples in hand
and ones to be collected in the future. This "mathematical sample
pool" can be updated as more samples become available.
[0250] 2. Results
[0251] To demonstrate that the mathematical sample pool performs a
function equivalent to the sample reference pool, the
mean-subtracted-log(intensity) (single channel data, relative to
the mathematical pool) vs. the log(ratio) (hybridizations, relative
to the sample pool) was plotted for the 70 optimal reporter genes
across the 78 sporadic samples, as shown in FIG. 22. The ratio and
single-channel quantities are highly correlated, indicating both
have the capability to report relative changes in gene expression.
A classifier was then constructed using the
mean-subtracted-log(intensity) following exactly the same procedure
as was followed using the ratio data, as in Example 4.
[0252] As shown in FIGS. 23A and 23B, single-channel data was
successful at classifying samples based on gene expression
patterns. FIG. 23A shows samples grouped according to prognosis
using single-channel hybridization data. The white line separates
samples from patients classified as having poor prognoses (below)
and good prognoses (above). FIG. 23B plots each sample as its
expression data correlates with the good (open circles) or poor
(filled squares) prognosis classifier parameter. Using the
"leave-one-out" cross validation method, the classifier predicted
10 false positives out of 44 samples from patients having a good
prognosis, and 6 false negatives out of 34 samples from patients
having a poor prognosis, where a poor prognosis is considered a
"positive." This outcome is comparable to the use of the
ratio-based classifier, which predicted 7 out of 44, and 6 out of
34, respectively.
[0253] In clinical applications, it is greatly preferable to have
few false positives, which results in fewer under-treated patients.
To conform the results to this preference, a classifier was
constructed by ranking the patient sample according to its
coefficient of correlation to the "good prognosis" template, and
chose a threshold for this correlation coefficient to allow
approximately 10% false negatives, i.e., classification of a sample
from a patient with poor prognosis as one from a patient with a
good prognosis. Out of the 34 poor prognosis samples used herein,
this represents a tolerance of 3 out of 34 poor prognosis patients
classified incorrectly. This tolerance limit corresponds to a
threshold 0.2727 coefficient of correlation to the "good prognosis"
template. Results using this threshold are shown in FIGS. 24A and
24B. FIG. 24A shows single-channel hybridization data for samples
ranked according to the coefficients of correlation with the good
prognosis classifier; samples classified as "good prognosis" lie
above the white line, and those classified as "poor prognosis" lie
below. FIG. 24B shows a scatterplot of sample correlation
coefficients, with three incorrectly classified samples lying to
the right of the threshold correlation coefficient value. Using
this threshold, the classifier had a false positive rate of 15 out
of the 44 good prognosis samples. This result is not very different
compared to the error rate of 12 out of 44 for the ratio based
classifier.
[0254] In summary, the 70 reporter genes carry robust information
about prognosis; the single channel data can predict the tumor
outcome almost as well as the ratio based data, while being more
convenient in a clinical setting.
7. REFERENCES CITED
[0255] All references cited herein are incorporated herein by
reference in their entirety and for all purposes to the same extent
as if each individual publication or patent or patent application
was specifically and individually indicated to be incorporated by
reference in its entirety for all purposes.
[0256] Many modifications and variations of the present invention
can be made without departing from its spirit and scope, as will be
apparent to those skilled in the art. The specific embodiments
described herein are offered by way of example only, and the
invention is to be limited only by the terms of the appended claims
along with the full scope of equivalents to which such claims are
entitled.
Sequence CWU 0 SQTB SEQUENCE LISTING The patent application
contains a lengthy "Sequence Listing" section. A copy of the
"Sequence Listing" is available in electronic form from the USPTO
web site
(http://seqdata.uspto.gov/?pageRequest=docDetail&DocID=US20180305768A1).
An electronic copy of the "Sequence Listing" will also be available
from the USPTO upon request and payment of the fee set forth in 37
CFR 1.19(b)(3).
0 SQTB SEQUENCE LISTING The patent application contains a lengthy
"Sequence Listing" section. A copy of the "Sequence Listing" is
available in electronic form from the USPTO web site
(http://seqdata.uspto.gov/?pageRequest=docDetail&DocID=US20180305768A1).
An electronic copy of the "Sequence Listing" will also be available
from the USPTO upon request and payment of the fee set forth in 37
CFR 1.19(b)(3).
* * * * *
References