U.S. patent application number 16/807769 was filed with the patent office on 2020-09-24 for prediction of likelihood of cancer recurrence.
This patent application is currently assigned to Genomic Health, Inc.. The applicant listed for this patent is Genomic Health, Inc.. Invention is credited to Joffre B. Baker, John L. Bryant, Soonmyung Paik, Steven Shak.
Application Number | 20200299781 16/807769 |
Document ID | / |
Family ID | 1000004869812 |
Filed Date | 2020-09-24 |
United States Patent
Application |
20200299781 |
Kind Code |
A1 |
Baker; Joffre B. ; et
al. |
September 24, 2020 |
Prediction of Likelihood of Cancer Recurrence
Abstract
The present invention provides gene sets the expression of which
is important in the diagnosis and/or prognosis of cancer, in
particular of breast cancer.
Inventors: |
Baker; Joffre B.; (Montara,
CA) ; Bryant; John L.; (US) ; Paik;
Soonmyung; (Pittsburgh, PA) ; Shak; Steven;
(Hillsborough, CA) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
Genomic Health, Inc. |
Redwood City |
CA |
US |
|
|
Assignee: |
Genomic Health, Inc.
Redwood City
CA
|
Family ID: |
1000004869812 |
Appl. No.: |
16/807769 |
Filed: |
March 3, 2020 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
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15674817 |
Aug 11, 2017 |
10619215 |
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16807769 |
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12707574 |
Feb 17, 2010 |
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15674817 |
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11345611 |
Jan 31, 2006 |
7723033 |
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12707574 |
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10872063 |
Jun 17, 2004 |
7056674 |
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11345611 |
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60482339 |
Jun 24, 2003 |
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Current U.S.
Class: |
1/1 |
Current CPC
Class: |
C12Q 2600/106 20130101;
C12Q 2600/158 20130101; C12Q 1/6886 20130101; C12Q 2600/118
20130101 |
International
Class: |
C12Q 1/6886 20060101
C12Q001/6886 |
Claims
1.-40. (canceled)
41. A method for analyzing the expression of genes in a human
breast cancer patient, comprising: obtaining a fixed, wax-embedded
tissue sample from a breast cancer patient; extracting RNA from the
tissue sample; reverse transcribing RNA transcripts of a set of
genes consisting of 15-25 genes, wherein the set includes the
following genes: (a) each of Ki-67, STK15, SURV, CCNB1, MYBL2, and
STMY3, and (b) at least one reference gene, to produce cDNAs of the
RNA transcripts of the set of genes; and amplifying the cDNAs to
produce amplicons from the cDNAs for determination of amplicon.
42. The method of claim 41, wherein the amplicon levels have been
normalized against an amplicon level of an RNA transcript of at
least one reference gene in the tissue sample.
43. The method of claim 41, wherein the amplicon levels are
threshold cycle (Ct) values.
44. The method of claim 41, wherein the breast cancer patient has
invasive, ductal carcinoma.
45. The method of claim 41, wherein the breast cancer patient has
node negative and ER positive breast cancer.
46. The method of claim 44, wherein the breast cancer patient has
node negative and ER positive breast cancer.
47. The method of claim 41, wherein the breast cancer patient has
been treated with tamoxifen.
48. The method of claim 44, wherein the breast cancer patient has
been treated with tamoxifen.
49. The method of claim 41, wherein the tissue sample comprises
core biopsy or fine needle aspirate cells.
50. The method of claim 41, wherein the set of genes consists of 20
genes.
51. The method of claim 41, wherein the at least one reference gene
comprises GADPH.
Description
[0001] The present application claims the benefit under 35 U.S.C.
119(e) of the filing date of U.S. Application Ser. No. 60/482,339,
filed on Jun. 24, 2003.
BACKGROUND OF THE INVENTION
Field of the Invention
[0002] The present invention provides gene sets the expression of
which is important in the diagnosis and/or prognosis of cancer.
Description of the Related Art
[0003] Oncologists have a number of treatment options available to
them, including different combinations of chemotherapeutic drugs
that are characterized as "standard of care," and a number of drugs
that do not carry a label claim for particular cancer, but for
which there is evidence of efficacy in that cancer. Best likelihood
of good treatment outcome requires that patients be assigned to
optimal available cancer treatment, and that this assignment be
made as quickly as possible following diagnosis.
[0004] Currently, diagnostic tests used in clinical practice are
single analyte, and therefore do not capture the potential value of
knowing relationships between dozens of different markers.
Moreover, diagnostic tests are frequently not quantitative, relying
on immunohistochemistry. This method often yields different results
in different laboratories, in part because the reagents are not
standardized, and in part because the interpretations are
subjective and cannot be easily quantified. RNA-based tests have
not often been used because of the problem of RNA degradation over
time and the fact that it is difficult to obtain fresh tissue
samples from patients for analysis. Fixed paraffin-embedded tissue
is more readily available and methods have been established to
detect RNA in fixed tissue. However, these methods typically do not
allow for the study of large numbers of genes (DNA or RNA) from
small amounts of material. Thus, traditionally fixed tissue has
been rarely used other than for immunohistochemistry detection of
proteins.
[0005] In the past few years, several groups have published studies
concerning the classification of various cancer types by microarray
gene expression analysis (see, e.g. Golub et al., Science
286:531-537 (1999); Bhattacharjae et al., Proc. Natl. Acad. Sci.
USA 98:13790-13795 (2001); Chen-Hsiang et al., Bioinformatics 17
(Suppl. 1):S316-S322 (2001); Ramaswamy et al., Proc. Natl. Acad.
Sci. USA 98:15149-15154 (2001)). Certain classifications of human
breast cancers based on gene expression patterns have also been
reported (Martin et al., Cancer Res. 60:2232-2238 (2000); West et
al., Proc. Natl. Acad. Sci. USA 98:11462-11467 (2001); Sorlic et
al., Proc. Natl. Acad. Sci. USA 98:10869-10874 (2001); Yan et al.,
Cancer Res. 61:8375-8380 (2001)). However, these studies mostly
focus on improving and refining the already established
classification of various types of cancer, including breast cancer,
and generally do not provide new insights into the relationships of
the differentially expressed genes, and do not link the findings to
treatment strategies in order to improve the clinical outcome of
cancer therapy.
[0006] Although modern molecular biology and biochemistry have
revealed hundreds of genes whose activities influence the behavior
of tumor cells, state of their differentiation, and their
sensitivity or resistance to certain therapeutic drugs, with a few
exceptions, the status of these genes has not been exploited for
the purpose of routinely making clinical decisions about drug
treatments. One notaale exception is the use of estrogen receptor
(ER) protein expression in breast carcinomas to select patients to
treatment with anti-estrogen drugs, such as tamoxifen. Another
exceptional example is the use of ErbB2 (Her2) protein expression
in breast carcinomas to select patients with the Her2 antagonist
drug Herceptin.RTM. (Genentech, Inc., South San Francisco,
Calif.).
[0007] Despite recent advances, the challenge of cancer treatment
remains to target specific treatment regimens to pathogenically
distinct tumor types, and ultimately personalize tumor treatment in
order to maximize outcome. Hence, a need exists for tests that
simultaneously provide predictive information about patient
responses to the variety of treatment options. This is particularly
true for breast cancer, the biology of which is poorly understood.
It is clear that the classification of breast cancer into a few
subgroups, such as ErbB2.sup.+ subgroup, and subgroups
characterized by low to absent gene expression of the estrogen
receptor (ER) and a few additional transcriptional factors (Perou
et al., Nature 406:747-752 (2000)) does not reflect the cellular
and molecular heterogeneity of breast cancer, and does not allow
the design of treatment strategies maximizing patient response.
[0008] In particular, once a patient is diagnosed with cancer, such
as breast or ovarian cancer, there is a strong need for methods
that allow the physician to predict the expected course of disease,
including the likelihood of cancer recurrence, long-term survival
of the patient, and the like, and select the most appropriate
treatment option accordingly.
SUMMARY OF THE INVENTION
[0009] The present invention provides a set of genes, the
expression of which has prognostic value, specifically with respect
to disease-free survival.
[0010] The present invention accommodates the use of archived
paraffin-embedded biopsy material for assay of all markers in the
set, and therefore is compatible with the most widely available
type of biopsy material. It is also compatible with several
different methods of tumor tissue harvest, for example, via core
biopsy or fine needle aspiration. Further, for each member of the
gene set, the invention specifies oligonucleotide sequences that
can be used in the test.
[0011] In one aspect, the present invention concerns a method of
predicting the likelihood of long-term survival of a cancer patient
without the recurrence of cancer, comprising determining the
expression level of one or more prognostic RNA transcripts or their
expression products in a cancer cell obtained from the patient,
normalized against the expression level of all RNA transcripts or
their products in said cancer cell, or of a reference set of RNA
transcripts or their expression products, wherein the prognostic
RNA transcript is the transcript of one or more genes selected from
the group consisting of B_Catenin; BAG1; BIN1; BUB1; C20_orf1;
CCNB1; CCNE2; CDC20; CDH1; CEGP1; CIAP1; cMYC; CTSL2; DKFZp586M07;
DR5; EpCAM; EstR1; FOXM1; GRB7; GSTM1; GSTM3; HER2; HNRPAB; ID1;
IGF1R; ITGA7; Ki_67; KNSL2; LMNB1; MCM2; MELK; MMP12; MMP9; MYBL2;
NEK2; NME1; NPD009; PCNA; PR; PREP; PTTG1; RPLPO; Src; STK15;
STMY3; SURV; TFRC; TOP2A; and TS;
[0012] wherein expression of one or more of BUB1; C20_orf1; CCNB1;
CCNE2; CDC20; CDH1; CTSL2; EpCAM; FOXM1; GRB7; HER2; HNRPAB; Ki_67;
KNSL2; LMNB1; MCM2; MELK; MMP12; MMP9; MYBL2; NEK2; NME1; PCNA;
PREP; PTTG1; Src; STK15; STMY3; SURV; TFRC; TOP2A; and TS indicates
a decreased likelihood of long-term survival without cancer
recurrence; and
[0013] the expression of one or more of BAG1; BCatenin; BIN1;
CEGP1; CIAP1; cMYC; DKFZp586M07; DR5; EstR1; GSTM1; GSTM3; ID1;
IGFIR; ITGA7; NPD009; PR; and RPLPO indicates an increased
likelihood of long-term survival without cancer recurrence.
[0014] In various embodiments, the expression level of at least 2,
or at least 5, or at least 10, or at least 15, or at least 20, or a
least 25 prognostic RNA transcripts or their expression products is
determined.
[0015] In another embodiment, the cancer is breast cancer or
ovarian cancer.
[0016] In yet another embodiment, the cancer is node negative, ER
positive breast cancer.
[0017] In a further embodiment, the RNA comprises intronic RNA.
[0018] In a still further embodiment, the expression level of one
or more prognostic RNA transcripts or their expression products of
one or more genes selected from the group consisting of MMP9,
GSTM1, MELK, PR, DKFZp586M07, GSTM3, CDC20, CCNB1, STMY3, GRB7,
MYBL2, CEGP1, SURV, LMNB1, CTSL2, PTTG1, BAG1, KNSL2, CIAP1, PREP,
NEK2, EpCAM, PCNA, C20_orf1, ITGA7, ID1 B_Catenin, EstR1 CDH1, TS
HER2, and cMYC is determined,
[0019] wherein expression of one or more of C20_orf1; CCNB1; CDC20;
CDH1; CTSL2; EpCAM; GRB7; HER2; KNSL2; LMNB1; MCM2; MMP9; MYBL2;
NEK2; PCNA; PREP; PTTG1; STMY3; SURV; TS; and MELK indicates a
decreased likelihood of long-term survival without cancer
recurrence; and
[0020] the expression of one or more of BAG1; BCrtenin; CEGP1;
CIAP1; cMYC; DKFZp586MO7; EstR1; GSTM1; GSTM3; ID1; ITGA7; and PR
indicates an increased likelihood of long-term survival without
cancer recurrence.
[0021] In another embodiment, the expression level of one or more
prognostic RNA transcripts or their expression products of one or
more genes selected from the group consisting of GRB7, SURV, PR,
LMNB1, MYBL2, HER2, GSTM1, MELK, S20_orf1, PTTG1, BUB1, CDC20,
CCNB1, STMY3, KNSL2, CTSL2, MCM2, NEK2, DR5, Ki_67, CCNE2, TOP2A,
PCNA, PREP, FOXM1, NME1, CEGP1, BAG1, STK15, HNRPAB, EstR1, MMP9,
DKFZp586MO7, TS, Src, BIN1, NP009, RPLPO, GSTM3, MMP12, TFRC, and
IGF1R is determined,
[0022] wherein expression of one or more of GRB7; SURV; LMNB1;
MYBL2; HER2; MELK; C20 orf1; PTTG1; BUB1; CDC20; CCNB1; STMY3;
KNSL2; CTSL2; MCM2; NEK2; Ki_67; CCNE2; TOP2A_4; PCNA; PREP; FOXM1;
NME1; STK15; HNRPAB; MMP9; TS; Src; MMPI2; and TFRC indicates a
decreased likelihood of long-term survival without cancer
recurrence; and
[0023] the expression of one or more of PR; GSTM1; DR5; CEGP1;
BAG1; EstR1; DKFZp586MO7; BIN1; NPD009; RPLPO; GSTM3; IGFIR
indicates an increased likelihood of long-term survival without
cancer recurrence.
[0024] In another aspect, the invention concerns a method of
predicting the likelihood of long-term survival of a cancer patient
without the recurrence of cancer, comprising determining the
expression level of one or more prognostic RNA transcripts or their
expression products in a cancer cell obtained from said patient,
normalized against the expression level of all RNA transcripts or
their products in the cancer cell, or of a reference set of RNA
transcripts or their expression products, wherein the prognostic
RNA transcript is the transcript of one or more genes selected from
the group consisting of GRB7; LMNB1; ER; STMY3; KLK10; PR; KRT5;
FGFR1; MCM6; SNRPF,
[0025] wherein expression of one or more of GRB7, LMNB1, STMY3,
KLK10, FGFR1, and SNRPF indicates a decreased likelihood or long
term survival without cancer recurrence; and the expression of one
or more of ER, PR, KRT5 and MCM6 ER, PR, KRT5 and MCM6 indicates an
increased likelihood of long-term survival without cancer
recurrence.
[0026] In an embodiment of this method, the RNA is isolated from a
fixed, wax-embedded breast cancer tissue specimen of the
patient.
[0027] In another embodiment, the RNA is isolated from core biopsy
tissue or fine needle aspirate cells.
[0028] In a different aspect, the invention concerns an array
comprising polynucleotides hybridizing to two or more of the
following genes: B_Catenin; BAG1; BIN1; BUB1; C20_orf1; CCNB1;
CCNE2; CDC20; CDH1; CEGP1; CIAP1; cMYC; CTSL2; DKFZp586MO7; DR5;
EpCAM; EstR1; FOXM1; GRB7; GSTM1; GSTM3; HER2; HNRPAB; ID1; IGFIR;
ITGA7; Ki_67; KNSL2; LMNB1; MCM2; MELK; MMP12; MMP9; MYBL2; NEK2;
NME1; NPD009; PCNA; PR; PREP; PTTG1; RPLPO; Src; STK15; STMY3;
SURV; TFRC; TOP2A; and TS, immobilized on a solid surface.
[0029] In an embodiment, the array comprises polynucleotides
hybridizing to two or more of the following genes: MMP9, GSTM1,
MELK, PR, DKFZp586MO7, GSTM3, CDC20, CCNB1, STMY3, GRB7, MYBL2,
CEGP1, SURV, LMNB1, CTSL2, PTTG1, BAG1, KNSL2, CIAP1, PREP, NEK2,
EpCAM, PCNA, C20_orf1, ITGA7, ID1 B_Catenin, EstR1, CDH1, TS HER2,
and cMYC.
[0030] In another embodiment, the array comprises polynucleotides
hybridizing to two or more of the following genes: GRB7, SURV, PR,
LMNB1, MYBL2, HER2, GSTM1, MELK, S20 orf1, PTTG1, BUB1, CDC20,
CCNB1, STMY3, KNSL2, CTSL2, MCM2, NEK2, DR5, Ki_67, CCNE2, TOP2A,
PCNA, PREP, FOXM1, NME1, CEGP1, BAG1, STK15, HNRPAB, EstR1, MMP9,
DKFZp586M07, TS, Src, BIN1, NP009, RPLPO, GSTM3, MMP12, TFRC, and
IGFIR.
[0031] In a further embodiment, the arrays comprise polynucleotides
hybridizing to at least 3, or at least 5, or at least 10, qr at
least 15, or at least 20, or at least 25 of the listed genes.
[0032] In a still further embodiment, the arrays comprise
polynucleotides hybridizing to all of the listed genes.
[0033] In yet another embodiment, the arrays comprise more than one
polynucleotide hybridizing to the same gene.
[0034] In an additional embodiment, the arrays comprise
intron-based sequences.
[0035] In another embodiment, the polynucleotides are cDNAs, which
can, for example, be about 500 to 5000 bases long.
[0036] In yet another embodiment, the polynucleotides are
oligonucleotides, which can, for example, be about 20 to 80 bases
long.
[0037] The arrays can, for example, be immobilized on glass, and
can contain hundreds of thousand, e.g. 330,000
oligonucleotides.
[0038] In a further aspect, the invention concerns a method of
predicting the likelihood of long-term survival of a patient
diagnosed with invasive breast cancer, without the recurrence of
breast cancer, comprising the steps of
[0039] (a) determining the expression levels of the RNA transcripts
or the expression products of genes of a gene set selected from the
group consisting of B_Catenin; BAG1; BIN1; BUB1; C20_orf1; CCNB1;
CCNE2; CDC20; CDH1; CEGI1; CIAP1; cMYC; CTSL2; DKFZp586M07; DR5;
EpCAM; EstR1; FOXM1; GRB7; GSTM1; GSTM3; HER2; HNRPAB; ID1; IGFlR;
ITGA7; Ki_67; KNSL2; LMNB1; MCM2; MELK; MMP12; MMP9; MYBL2; NEK2;
NME1; NPD009; PCNA; PR; PREP; PTTG1; RPLPO; Src; STK15; STMY3;
SURV; TFRC; TOP2A; and TS in abreast cancer cell obtained from the
patient, normalized against the expression levels of all RNA
transcripts or their expression products in said breast cancer
cell, or of a reference set of RNA transcripts or their
products;
[0040] (b) subjecting the data obtained in step (a) to statistical
analysis; and;
[0041] (c) determining whether the likelihood of said long-term
survival has increased or decreased.
[0042] In a still further aspect, the invention concerns a method
of preparing a personalized genomics profile for a patient,
comprising the steps of
[0043] (a) subjecting RNA extracted from a breast tissue obtained
from the patient to gene expression analysis;
[0044] (b) determining the expression level in the tissue of one or
more genes selected from the breast cancer gene set listed in any
one of Tables 1 and 2, wherein the expression level is normalized
against a control gene or genes and optionally is compared to the
amount found in a breast cancer reference tissue set; and
[0045] (c) creating a report summarizing the data obtained by said
gene expression analysis.
[0046] The breast tissue may comprise breast cancer cells.
[0047] In another embodiment, the breast tissue is obtained from a
fixed, paraffin-embedded biopsy sample, in which the RNA may be
fragmented.
[0048] The report may include prediction of the likelihood of long
term survival of the patient and/or a recommendation for a
treatment modality of said patient.
[0049] In a further aspect, the invention concerns a method for
measuring levels of mRNA products of genes listed in Tables 1 and 2
by real time polymerase chain reaction (RT-PCR), by using an
amplicon listed in Table 3 and a primer-probe set listed in Tables
4A-4D.
[0050] In a still further aspect, the invention concerns a PCR
primer-probe set listed in Tables 4A-4D, and a PCR amplicon listed
in Table 3.
DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENT
A. Definitions
[0051] Unless defined otherwise, technical and scientific terms
used herein have the same meaning as commonly understood by one of
ordinary skill in the art to which this invention belongs.
Singleton et al., Dictionary of Microbiology and Molecular Biology
2nd ed., J. Wiley & Sons (New York, N.Y. 1994), and March,
Advanced Organic Chemistry Reactions, Mechanisms and Structure 4th
ed., John Wiley & Sons (New York, N.Y. 1992), provide one
skilled in the art with a general guide to many of the terms used
in the present application.
[0052] One skilled in the art will recognize many methods and
materials similar or equivalent to those described herein, which
could be used in the practice of the present invention. Indeed, the
present invention is in no way limited to the methods and materials
described. For purposes of the present invention, the following
terms are defined below.
[0053] The term "microarray" refers to an ordered arrangement of
hybridizable array elements, preferably polynucleotide probes, on a
substrate.
[0054] The term "polynucleotide," when used in singular or plural,
generally refers to any polyribonucleotide or
polydeoxribonucleotide, which may be unmodified RNA or DNA or
modified RNA or DNA. Thus, for instance, polynucleotides as defined
herein include, without limitation, single- and double-stranded
DNA, DNA including single- and double-stranded regions, single- and
double-stranded RNA, and RNA including single- and double-stranded
regions, hybrid molecules comprising DNA and RNA that may be
single-stranded or, more typically, double-stranded or include
single- and double-stranded regions. In addition, the term
"polynucleotide" as used herein refers to triple-stranded regions
comprising RNA or DNA or both RNA and DNA. The strands ii such
regions may be from the same molecule or from different molecules.
The regions may include all of one or more of the molecules, but
more typically involve only a region of some of the molecules. One
of the molecules of a triple-helical region often is an
oligonucleotide. The term "polynucleotide" specifically includes
cDNAs. The term includes DNAs (including cDNAs) and RNAs that
contain one or more modified bases. Thus, DNAs or RNAs with
backbones modified for stability or for other reasons are
"polynucleotides" as that term is intended herein. Moreover, DNAs
or RNAs comprising unusual bases, such as inosine, or modified
bases, such as tritiated bases, are included within the term
"polynucleotides" as defined herein. In general, the term
"polynucleotide" embraces all chemically, enzymatically and/or
metabolically modified forms of unmodified polynucleotides, as well
as the chemical forms of DNA and RNA characteristic of viruses and
cells, including simple and complex cells.
[0055] The term "oligonucleotide" refers to a relatively short
polynucleotide, including, without limitation, single-stranded
deoxyribonucleotides, single- or double-stranded ribonucleotides,
RNA:DNA hybrids and double-stranded DNAs. Oligonucleotides, such as
single-stranded DNA probe oligonucleotides, are often synthesized
by chemical methods, for example using automated oligonucleotide
synthesizers that are commercially available. However,
oligonucleotides can be made by a variety of other methods,
including in vitro recombinant DNA-mediated techniques and by
expression of DNAs in cells and organisms.
[0056] The terms "differentially expressed gene," "differential
gene expression" and their synonyms, which are used
interchangeably, refer to a gene whose expression is activated to a
higher or lower level in a subject suffering from a disease,
specifically cancer, such as breast cancer, relative to its
expression in a normal or control subject. The terms also include
genes whose expression is activated to a higher or lower level at
different stages of the same disease. It is also understood that a
differentially expressed gene may be either activated or inhibited
at the nucleic acid level or protein level, or may be subject to
alternative splicing to result in a different polypeptide product.
Such differences may be evidenced by a change in mRNA levels,
surface expression, secretion or other partitioning of a
polypeptide, for example. Differential gene expression may include
a comparison of expression between two or more genes or their gene
products, or a comparison of the ratios of the expression between
two or more genes or their gene products, or even a comparison of
two differently processed products of the same gene, which differ
between normal subjects and, subjects suffering from a disease,
specifically cancer, or between various stages of the same disease.
Differential expression includes both quantitative, as well as
qualitative, differences in the temporal or cellular expression
pattern in a gene or its expression products among, for example,
normal and diseased cells, or among cells which have undergone
different disease events or disease stages. For the purpose of this
invention, "differential gene expression" is considered to be
present when there is at least an about two-fold, preferably at
least about four-fold, more preferably at least about six-fold,
most preferably at least about ten-fold difference between the
expression of a given gene in normal and diseased subjects, or in
various stages of disease development in a diseased subject.
[0057] The term "over-expression" with regard to an RNA transcript
is used to refer to the level of the transcript determined by
normalization to the level of reference mRNAs, which might be all
measured transcripts in the specimen or a particular reference set
of mRNAs.
[0058] The phrase "gene amplification" refers to a process by which
multiple copies of a gene or gene fragment are formed in a
particular cell or cell line. The duplicated region (a stretch of
amplified DNA) is often referred to as "amplicon." Usually, the
amount of the messenger RNA (mRNA) produced, i.e., the level of
gene expression, also increases in the proportion of the number of
copies made of the particular gene expressed.
[0059] The term "prognosis" is used herein to refer to the
prediction of the likelihood of cancer-attributable death or
progression, including recurrence, metastatic spread, and drug
resistance, of a neoplastic disease, such as breast cancer. The
term "prediction" is used herein to refer to the likelihood that a
patient will respond either favorably or unfavorably to a drug or
set of drugs, and also the extent of those responses, or that a
patient will survive, following surgical removal or the primary
tumor and/or chemotherapy for a certain period of time without
cancer recurrence. The predictive methods of the present invention
can be used clinically to make treatment decisions by choosing the
most appropriate treatment modalities for any particular patient.
The predictive methods of the present invention are valuable tools
in predicting if a patient is likely to respond favorably to a
treatment regimen, such as surgical intervention, chemotherapy with
a given drug or drug combination, and/or radiation therapy, or
whether long-term survival of the patient, following surgery and/or
termination of chemotherapy or other treatment modalities is
likely.
[0060] The term "long-term" survival is used herein to refer to
survival for at least 3 years, more preferably for at least 8
years, most preferably: for at least 10 years following surgery or
other treatment.
[0061] The term "tumor," as used herein, refers to all neoplastic
cell growth and proliferation, whether malignant or benign, and all
pre-cancerous and cancerous cells and tissues.
[0062] The terms "cancer" and "cancerous" refer to or describe the
physiological condition in mammals that is typically characterized
by unregulated cell growth. Examples of cancer include, but are not
limited to, breast cancer, ovarian cancer, colon cancer, lung
cancer, prostate cancer, hepatocellular cancer, gastric cancer,
pancreatic cancer, cervical cancer, liver cancer, bladder cancer,
cancer of the urinary tract, thyroid cancer, renal cancer,
carcinoma, melanoma, and brain cancer.
[0063] The "pathology" of cancer includes all phenomena that
compromise the well-being of the patient. This includes, without
limitation, abnormal or uncontrollable cell growth, metastasis,
interference with the normal functioning of neighboring cells,
release of cytokines or other secretory products at abnormal
levels, suppression or aggravation of inflammatory or immunological
response, neoplasia, premalignancy, malignancy, invasion of
surrounding or distant tissues or organs, such as lymph nodes,
etc.
[0064] "Stringency" of hybridization reactions is readily
determinable by one of ordinary skill in the art, and generally is
an empirical calculation dependent upon probe length, washing
temperature, and salt concentration. In general, longer probes
require higher temperatures for proper annealing, while shorter
probes need lower temperatures. Hybridization generally depends on
the ability of denatured DNA to reanneal when complementary strands
are present in an environment below their melting temperature. The
higher the degree of desired homology between the probe and
hybridizable sequence, the higher the relative temperature which
can be used. As a result, it follows that higher relative
temperatures would tend to make the reaction conditions more
stringent, while lower temperatures less so. For additional details
and explanation of stringency of hybridization reactions, see
Ausubel et al., Current Protocols in Molecular Biology. Wiley
Interscience Publishers, (1995).
[0065] "Stringent conditions" or "high stringency conditions", as
defined herein, typically: (1) employ low ionic strength and high
temperature for washing, for example 0.015 M sodium chloride/0.0015
M sodium citrate/0.1% sodium dodecyl sulfate at 50.degree. C.; (2)
employ during hybridization a denaturing agent, such as formamide,
for example, 50% (v/v) formamide with 0.1% bovine serum
albumin/0.1% Ficoll/0.1% polyvinylpyrrolidone/50 mM sodium
phosphate buffer at pH 6.5 with 750 mM sodium chloride, 75 mM
sodium citrate at 42.degree. C.; or (3) employ 50% formamide,
5.times.SSC (0.75 M NaCl, 0.075 M sodium citrate), 50 mM sodium
phosphate (pH 6.8), 0.1% sodium pyrophosphate, 5.times.Denhardt's
solution, sonicated salmon sperm DNA (50 .mu.g/ml), 0.1% SDS, and
10% dextran sulfate at 42.degree. C., with washes at 42.degree. C.
in 0.2.times.SSC (sodium chloride/sodium citrate) and 50% formamide
at 55.degree. C., followed by a high-stringency wash consisting of
0.1.times.SSC containing EDTA at 55.degree. C.
[0066] "Moderately stringent conditions" may be identified as
described by Sambrook et al., Molecular Cloning: A Laboratory
Manual, New York: Cold Spring Harbor Press, 1989, and include the
use of washing solution and hybridization conditions (e.g.,
temperature, ionic strength and % SDS) less stringent that those
described above. An example of moderately stringent conditions is
overnight incubation at 37.degree. C. in a solution comprising: 20%
formamide, 5.times.SSC (150 mM NaCl, 15 mM trisodium citrate), 50
mM sodium phosphate (pH 7.6), 5.times.Denhardt's solution, 10%
dextran sulfate, and 20 mg/ml denatured sheared salmon sperm DNA,
followed by washing the filters in 1.times.SSC at about
37-50.degree. C. The skilled artisan will recognize how to adjust
the temperature, ionic strength, etc. as necessary to accommodate
factors such as probe length and the like.
[0067] In the context of the present invention, reference to "at
least one," "at least two," "at least five," etc. of the genes
listed in any particular gene set means any one or any and all
combinations of the genes listed.
[0068] The term "node negative" cancer, such as "node negative"
breast cancer, is used herein to refer to cancer that has not
spread to the lymph nodes.
[0069] The terms "splicing" and "RNA splicing" are used
interchangeably and refer to RNA processing that removes introns
and joins exons to produce mature mRNA with continuous coding
sequence that moves into the cytoplasm of an eukaryotic cell.
[0070] In theory, the term "exon" refers to any segment of an
interrupted gene that is represented in the mature RNA product (B.
Lewin. Genes IV Cell Press, Cambridge Mass. 1990). In theory the
term "intron" refers to any segment of DNA that is transcribed but
removed from within the transcript by splicing together the exons
on either side of it. Operationally, exon sequences occur in the
mRNA sequence of a gene as defined by Ref. SEQ ID numbers.
Operationally, intron sequences are the intervening sequences
within the genomic DNA of a gene, bracketed by exon sequences and
having GT and AG splice consensus sequences at their 5' and 3'
boundaries.
B. Detailed Description
[0071] The practice of the present invention will employ, unless
otherwise indicated, conventional techniques of molecular biology
(including recombinant techniques), microbiology, cell biology, and
biochemistry, which are within the skill of the art. Such
techniques are explained fully in the literature, such as,
"Molecular Cloning: A Laboratory Manual", 2.sup.nd edition
(Sambrook et al., 1989); "Oligonucleotide Synthesis" (M. J. Gait,
ed., 1984); "Animal Cell Culture" (R. I. Freshney, ed., 1987);
"Methods in Enzymology" (Academic Press, Inc.); "Handbook of
Experimental Immunology", 4.sup.th edition (D. M. Weir & C. C.
Blackwell, eds., Blackwell Science Inc., 1987); "Gene Transfer
Vectors for Mammalian Cells" (J. M. Miller & M. P. Calos, eds.,
1987); "Current Protocols in Molecular Biology" (F. M. Ausubel et
al., eds., 1987); and "PCR: The Polymerase Chain Reaction", (Mullis
et al., eds., 1994).
[0072] 1. Gene Expression Profiling
[0073] Methods of gene expression profiling include methods based
on hybridization analysis of polynucleotides, methods based on
sequencing of polynucleotides, and proteomics-based methods. The
most commonly used methods known in the art for the quantification
of mRNA expression in a sample include northern blotting and in
situ hybridization (Parker & Barnes, Methods in Molecular
Biology 106:247-283 (1999)); RNAse protection assays (Hod,
Biotechniques 13:852-854 (1992)); and PCR-based methods, such as
reverse transcription polymerase chain reaction (RT-PCR) (Weis et
al., Trends in Genetics 8:263-264 (1992)). Alternatively,
antibodies may be employed that can recognize specific duplexes,
including DNA duplexes, RNA duplexes, and DNA-RNA hybrid duplexes:
or DNA-protein duplexes. Representative methods for
sequencing-based gene expression analysis include Serial Analysis
of Gene Expression (SAGE), and gene expression analysis by
massively parallel signature sequencing (MPSS).
[0074] 2. PCR-Based Gene Expression Profiling Methods
[0075] a. Reverse Transcriptase PCR (RT-PCR)
[0076] Of the techniques listed above, the most sensitive and most
flexible quantitative method is RT-PCR, which can be used to
compare mRNA levels in different sample populations, in normal and
tumor tissues, with or without drug treatment, to characterize
patterns of gene expression, to discriminate between closely
related mRNAs, and to analyze RNA structure.
[0077] The first step is the isolation of mRNA from a target
sample. The starting material is typically total RNA isolated from
human tumors or tumor cell lines, and corresponding normal tissues
or cell lines, respectively. Thus RNA can be isolated from a
variety of primary tumors, including breast, lung, colon, prostate,
brain, liver, kidney, pancreas, spleen, thymus, testis, ovary,
uterus, etc., tumor, or tumor cell lines, with pooled DNA from
healthy donors. If the source of mRNA is a primary tumor, mRNA can
be extracted, for example, from frozen or archived
paraffin-embedded and fixed (e.g. formalin-fixed) tissue
samples.
[0078] General methods for mRNA extraction are well known in the
art and are disclosed in standard textbooks of molecular biology,
including Ausubel et al., Current Protocols of Molecular Biolony.
John Wiley and Sons (1997). Methods for RNA extraction from
paraffin embedded tissues are disclosed, for example, in Rupp and
Locker, Lab Invest. 56:A67 (1987), and De Andres et al.,
BioTechniques 18:42044 (1995). In particular, RNA isolation can be
performed using purification kit, buffer set and protease from
commercial manufacturers, such as Qiagen, according to the
manufacturer's instructions. For example, total RNA from cells in
culture can be isolated using Qiagen RNeasy mini-columns. Other
commercially available RNA isolation kits include MasterPure.TM.
Complete DNA and RNA Purification Kit (EPICENTRE.RTM., Madison,
Wis.), and Paraffin Block RNA Isolation Kit (Ambion, Inc.). Total
RNA from tissue samples can be isolated using RNA Stat-60
(Tel-Test). RNA prepared from tumor can be isolated, for example,
by cesium chloride density gradient centrifugation.
[0079] As RNA cannot serve as a template for PCR, the first step in
gene expression profiling by RT-PCR is the reverse transcription of
the RNA template into cDNA, followed by its exponential
amplification in a PCR reaction. The two most commonly used reverse
transcriptases are avilo myeloblastosis virus reverse transcriptase
(AMV-RT) and Moloney murine leukemia virus reverse transcriptase
(MMLV-RT). The reverse transcription step is typically primed using
specific primers, random hexamers, or oligo-dT primers, depending
on the circumstances and the goal of expression profiling. For
example, extracted RNA can be reverse-transcribed using a GeneAmp
RNA PCR kit (Perkin Elmer, CA, USA), following the manufacturer's
instructions. The derived cDNA can then be used as a template in
the subsequent PCR reaction.
[0080] Although the PCR step can use a variety of thermostable
DNA-dependent DNA polymerases, it typically employs the Taq DNA
polymerase, which has a 5'-3' nuclease activity but lacks a 3'-5'
proofreading endonuclease activity. Thus, TaqMan.RTM. PCR typically
utilizes the 5'-nuclease activity of Taq or Tth polymerase to
hydrolyze a hybridization probe bound to its target amplicon, but
any enzyme with equivalent 5' nuclease activity can be used. Two
oligonucleotide primers are used to generate an amplicon typical of
a PCR reaction. A third oligonucleotide, or probe, is designed to
detect nucleotide sequence located between the two PCR primers. The
probe is non-extendible by Taq DNA polymerase enzyme, and is
labeled with a reporter fluorescent dye and a quencher fluorescent
dye. Any laser-induced emission from the reporter dye is quenched
by the quenching dye when the two dyes are located close together
as they are on the probe. During the amplification reaction, the
Taq DNA polymerase enzyme cleaves the probe in a template-dependent
manner. The resultant probe fragments disassociate in solution, and
signal from the released reporter dye is free from the quenching
effect of the second fluorophoret One molecule of reporter dye is
liberated for each new molecule synthesized, and detection of the
unquenched reporter dye provides the basis for quantitative
interpretation of the data.
[0081] TaqMan.RTM. RT-PCR can be performed using commercially
available equipment, such as, for example, ABI PRISM 7700.TM.
Sequence Detection System.TM. (Perkin-Elmer-Applied Biosystems,
Foster City, Calif., USA), or Lightcycler (Roche Molecular
Biochemicals, Mannheim, Germany). In a preferred embodiment, the 5'
nuclease procedure is run on a real-time quantitative PCR device
such as the ABI PRISM 7700.TM. Sequence Detection System.TM.. The
system consists of a thermocycler, laser, charge-coupled device
(CCD), camera and computer. The system amplifies samples in a
96-well format on a thermocycler. During amplification,
laser-induced fluorescent signal is collected in real-time through
fiber optics cables for all 96 wells, and detected at the CCD. The
system includes software for running the instrument and for
analyzing the data.
[0082] 5'-Nuclease assay data are initially expressed as Ct, or the
threshold cycle. As discussed above, fluorescence values are
recorded during every cycle and represent the amount of product
amplified to that point in the amplification reaction. The point
when the fluorescent signal is first recorded as statistically
significant is the threshold cycle (C).
[0083] To minimize errors and the effect of sample-to-sample
variation, RT-PCR is usually performed using an internal standard.
The ideal internal standard is expressed at a constant level among
different tissues, and is unaffected by the experimental treatment.
RNAs most frequently used to normalize patterns of gene expression
are mRNAs for the housekeeping genes
glyceraldehyde-3-phosphate-dehydrogenase (GAPDH) and
.beta.-actin.
[0084] A more recent variation of the RT-PCR technique is the real
time quantitative PCR, which measures PCR product accumulation
through a dual-labeled fluorigenic probe (i.e., TaqMan.RTM. probe).
Real time PCR is compatible both with quantitative competitive PCR,
where internal competitor for each target sequence is used for
normalization, and with quantitative comparative PCR using a
normalization gene contained within the sample, or a housekeeping
gene for RT-PCR. For further details see, e.g. Held et al., Genome
Research 6:986-994 (1996).
[0085] The steps of a representative protocol for profiling gene
expression using fixed, paraffin-embedded tissues as the RNA
source, including mRNA isolation, purification, primer extension
and amplification are given in various published journal articles
{for example: T. E. Godfrey et al. J. Molec. Diagnostics 2: 84-91
[2000]; K. Specht et al., Am. J. Pathol. 158: 419-29 [2001]}.
Briefly, a representative process starts with cutting about 10 upm
thick sections of paraffin-embedded tumor tissue samples. The RNA
is then extracted, and protein and DNA are removed. After analysis
of the RNA concentration, RNA repair and/or amplification steps may
be included, if necessary, and RNA is reverse transcribed using
gene specific promoters followed by RT-PCR.
[0086] b. MassARRAY System
[0087] In the MassARRAY-based gene expression profiling method,
developed by Sequenom, Inc. (San Diego, Calif.) following the
isolation of RNA and reverse transcription, the obtained cDNA is
spiked with a synthetic DNA molecule (competitor), which matches
the targeted cDNA region in all positions, except a single base,
and serves as an internal standard. The cDNA/competitor mixture is
PCR amplified and is subjected to a post-PCR shrimp alkaline
phosphatase (SAP) enzyme treatment, which results in the
dephosphorylation of the remaining nucleotides. After inactivation
of the alkaline phosphatase, the PCR products from the competitor
and cDNA are subjected to primer extension, which generates
distinct mass signals for the competitor- and cDNA-derives PCR
products. After purification, these products are dispensed on a
chip array, which is pre-loaded with components needed for analysis
with matrix-assisted laser desorption ionization time-of-flight
mass spectrometry (MALDI-TOF MS) analysis. The cDNA present in the
reaction is then quantified by analyzing the ratios of the peak
areas in the mass spectrum generated. For further details see, e.g.
Ding and Cantor, Proc. Natl. Acad. Sci. USA 100:3059-3064
(2003).
[0088] c. Other PCR-Based Methods
[0089] Further PCR-based techniques include, for example,
differential display (Liang and Pardee, Science 257:967-971
(1992)); amplified fragment length polymorphism (iAFLP) (Kawamoto
et al., Genome Res. 12:1305-1312 (1999)); BeadArray.TM. technology
(Illumina, San Diego, Calif.; Oliphant et al., Discovery of Markers
for Disease (Supplement to Biotechniques), June 2002; Ferguson et
al., Analytical Chemistry 72:5618 (2000)); It BeadsArray for
Detection of Gene Expression (BADGE), using the commercially
available Luminex100 LabMAP system and multiple color-coded
microspheres (Luminex Corp., Austin, Tex.) in a rapid assay for
gene expression (Yang et al., Genome Res. 11:1888-1898 (2001)); and
high coverage expression profiling (HiCEP) analysis (Fukumura et
al., Nucl. Acids. Res. 31(16) e94 (2003)).
[0090] 3. Microarrays
[0091] Differential gene expression can also be identified, or
confirmed using the microarray technique. Thus, the expression
profile of breast cancer-associated genes can be measured in either
fresh or paraffin-embedded tumor tissue, using microarray
technology. In this method, polynucleotide sequences of interest
(including cDNAs and oligonucleotides) are plated, or arrayed, on a
microchip substrate. The arrayed sequences are then hybridized with
specific DNA probes from cells or tissues of interest. Just as in
the RT-PCR method, the source of mRNA typically is total RNA
isolated from human tumors or tumor cell lines, and corresponding
normal tissues or cell lines. Thus RNA can be isolated from a
variety of primary tumors or tumor cell lines. If the source of
mRNA is a primary tumor, mRNA can be extracted, for example, from
frozen or archived paraffin-embedded and fixed (e.g.
formalin-fixed) tissue samples, which are routinely prepared and
preserved in everyday clinical practice.
[0092] In a specific embodiment of the microarray technique, PCR
amplified inserts of cDNA clones are applied to a substrate in a
dense array. Preferably at least 10,000 nucleotide sequences are
applied to the substrate. The microarrayed genes, immobilized on
the microchip at 10,000 elements each, are suitable for
hybridization under stringent conditions. Fluorescently labeled
cDNA probes may be generated through incorporation of fluorescent
nucleotides by reverse transcription of RNA extracted from tissues
of interest. Labeled cDNA probes applied to the chip hybridize with
specificity to each spot of DNA on the array. After stringent
washing to remove non-specifically bound probes, the chip is
scanned by confocal laser microscopy or by another detection
method, such as a. CCD camera. Quantitation of hybridization of
each arrayed element allows for assessment of corresponding mRNA
abundance. With dual color fluorescence, separately labeled cDNA
probes generated from two sources of RNA are hybridized pairwise to
the array. The relative abundance of the transcripts from the two
sources corresponding to each specified gene is thus determined
simultaneously. The miniaturized scale of the hybridization affords
a convenient and rapid evaluation of the expression pattern for
large numbers of genes. Such methods have been shown to have the
sensitivity required to detect rare transcripts, which are
expressed at a few copies per cell, and to reproducibly detect at
least approximately two-fold differences in the expression levels
(Schena et al., Proc. Natl. Acad. Sci. USA 93(2):106-149 (1996)).
Microarray analysis can be performed by commercially available
equipment, following manufacturer's protocols, such as by using the
Affymetrix GenChip technology, or Incyte's microarray
technology.
[0093] The development of microarray methods for large-scale
analysis of gene expression makes it possible to search
systematically for molecular markers of cancer classification and
outcome prediction in a variety of tumor types.
[0094] 4. Serial Analysis of Gene Expression (SAGE)
[0095] Serial analysis of gene expression (SAGE) is a method that
allows the simultaneous and quantitative analysis of a large number
of gene transcripts, without the need of providing an individual
hybridization probe for each transcript. First, a short sequence
tag (about 10-14 bp) is generated that contains sufficient
information to uniquely identify a transcript, provided that the
tag is obtained from a unique position within each transcript.
Then, many transcripts are linked together to form long serial
molecules, that can be sequenced, revealing the identity of the
multiple tags simultaneously. The expression pattern of any
population of transcripts can be quantitatively evaluated by
determining the abundance of individual tags, and identifying the
gene corresponding to each tag. For more details see, e.g.
Velculescu et al., Science 270:484-487 (1995); and Velculescu et
al., Cell 88:243-51 (1997).
[0096] 5. Gene Expression Analysis by Massively Parallel Signature
Sequencing (MPSS)
[0097] This method, described by Brenner et al., Nature
Biotechnology 18:630-634 (2000), is a sequencing approach that
combines non-gel-based signature sequencing with in vitro cloning
of millions of templates on separate 5 .mu.m diameter microbeads.
First, a microbead library of DNA templates is constructed by in
vitro cloning. This is followed by the assembly of a planar array
of the template-containing microbeads in a flow cell at a high
density (typically greater than 3.times.10.sup.6
microbeads/cm.sup.2). The free ends of the cloned templates on each
microbead are analyzed simultaneously, using a fluorescence-based
signature sequencing method that does not require DNA fragment
separation. This method has been shown to simultaneously and
accurately provide, in a single operation, hundreds of thousands of
gene signature sequences from a yeast cDNA library.
[0098] 6. Immunohistochemistry
[0099] Immunohistochemistry methods are also suitable for detecting
the expression levels of the prognostic markers of the present
invention. Thus, antibodies or antisera, preferably polyclonal
antisera, and most preferably monoclonal antibodies specific for
each marker are used to detect expression. The antibodies can be
detected by direct labeling of the antibodies themselves, for
example, with rajlioactive labels, fluorescent labels, hapten
labels such as, biotin, or an enzyme such as horse radish
peroxidase or alkaline phosphatase. Alternatively, unlabeled
primary antibody is used in conjunction with a labeled secondary
antibody, comprising antisera, polyclonal antisera or a monoclonal
antibody specific for the primary antibody. Immunohistochemistry
protocols and kits are well known in the art and are commercially
available.
[0100] 7. Proteomics
[0101] The term "proteome" is defined as the totality of the
proteins present in a sample (e.g. tissue, organism, or cell
culture) at a certain point of time. Proteomics includes, among
other things, study of the global changes of protein expression in
a sample (also referred to as "expression proteomics"). Proteomics
typically includes the following steps: (1) separation of
individual proteins in a sample by 2-D gel electrophoresis (2-D
PAGE); (2) identification of the individual proteins recovered from
the gel, e.g. my mass spectrometry or N-terminal sequencing, and
(3) analysis of the data using bioinformatics. Proteomics methods
are valuable supplements to other methods of gene expression
profiling, and can be used, alone or in combination with other
methods, to detect the products of the prognostic markers of the
present invention.
[0102] 8. General Description of the mRNA Isolation, Purification
and Amplification
[0103] The steps of a representative protocol for profiling gene
expression using fixed, paraffin-embedded tissues as the RNA
source, including mRNA isolation, purification, primer extension
and amplification are provided in various published journal
articles (for example: T. E. Godfrey et al., J. Molec. Diagnostics
2: 84-91 [2000]; K. Specht et al., Am. J. Pathol. 158: 419-29
[2001]). Briefly, a representative process starts with cutting
about 10 .mu.m thick sections of paraffin-embedded tumor tissue
samples. The RNA is then extracted, and protein and DNA are
removed. After analysis of the RNA concentration, RNA repair and/or
amplification steps may be included, if necessary, and RNA is
reverse transcribed using gene specific promoters followed by
RT-PCR. Finally, the data are analyzed to identify the best
treatment option(s) available to the patient on the basis of the
characteristic gene expression pattern identified in the tumor
sample examined, dependent on the predicted likelihood of cancer
recurrence.
[0104] 9. Breast Cancer Gene Set, Assayed Gene Subsequences. and
Clinical Application of Gene Expression Data
[0105] An important aspect of the present invention is to use the
measured expression of certain genes by breast cancer tissue to
provide prognostic information. For this purpose it is necessary to
correct for (normalize away) both differences in the amount of RNA
assayed and variability in the quality of the RNA used. Therefore,
the assay typically measures and incorporates the expression of
certain normalizing genes, including well known housekeeping genes,
such as GAPDH and Cyp1. Alternatively, normalization can be based
on the mean or median signal (Ct) of all of the assayed genes or a
large subset thereof (global normalization approach). On a
gene-by-gene basis, measured normalized amount of a patient tumor
mRNA is compared to the amount found in a breast cancer tissue
reference set. The number (N) of breast cancer tissues in this
reference set should be sufficiently high to ensure that different
reference sets (as a whole) behave essentially the same way. If
this condition is met, the identity of the individual breast cancer
tissues present in a particular set will have no significant impact
on the relative amounts of the genes assayed. Usually, the breast
cancer tissue reference set consists of at least about 30,
preferably at least about 40 different FPE breast cancer tissue
specimens. Unless noted otherwise, normalized expression levels for
each mRNA/tested tumor/patient will be expressed as a percentage of
the expression level measured in the reference set. More
specifically, the reference set of a sufficiently high number (e.g.
40) of tumors yields a distribution of normalized levels of each
mRNA species. The level measured in a particular tumor sample to be
analyzed falls at some percentile within this range, which can be
determined by methods well known in the art. Below, unless noted
otherwise, reference to expression levels of a gene assume
normalized expression relative to the reference set although this
is not always explicitly stated.
[0106] 10. Design of Intron-Based PCR Primers and Probes
[0107] According to one aspect of the present invention, PCR
primers and probes are designed based upon intron sequences present
in the gene to be amplified. Accordingly, the first step in the
primer/probe design is the delineation of intron, sequences within
the genes. This can be done by publicly available software, such as
the DNA BLAT software developed by Kent, W. J., Genome Res.
12(4):656-64 (2002), or by the BLAST software including its
variations. Subsequent steps follow well established methods of PCR
primer and probe design.
[0108] In order to avoid non-specific signals, it is important to
mask repetitive sequences within the introns when designing the
primers and probes. This can be easily accomplished by using the
Repeat Masker program available on-line through the Baylor College
of Medicine, which screens DNA sequences against a library of
repetitive elements and returns a query sequence in which the
repetitive elements are masked. The masked intron sequences can
then be used to design primer and probe sequences using any
commercially or otherwise publicly available primer/probe design
packages, such as Primer Express (Applied Biosystems), MGB
assay-by-design (Applied Biosystems); Primer3 (Steve Rozen and
Helen J. Skaletsky (2000) Primer3 on the WWW for general users and
for biologist programmers. In: Krawetz S, Misener S (eds)
Bioinformatics Methods and Protocols: Methods in Molecular Biology.
Humana Press, Totowa, N.J., pp 365-386)
[0109] The most important factors considered in PCR primer design
include primer length, melting temperature (Tm), and G/C content,
specificity, complementary primer sequences, and 3'-end sequence.
In general, optimal PCR primers are generally 17-30 bases in
length, and contain about 20-80%, such as, for example, about
50-60% G+C bases. Tm's between 50 and 80.degree. C., e.g. about 50
to 70.degree. C. are typically preferred.
[0110] For further guidelines for PCR primer and probe design see,
e.g. Dieffenbach, C. W. et al., "General Concepts for PCR Primer
Design" in: PCR Primer, A Laboratory Manual, Cold Spring Harbor
Laboratory Press, New York, 1995, pp. 133-155; Innis and Gelfand,
"Optimization of PCRs" in: PCR Protocols, A Guide to Methods and
Applications, CRC Press, London, 1994, pp. 5-11; and Plasterer, T.
N. Primerselect: Primer and probe design. Methods Mol. Biol.
70:520-527 (1997), the entire disclosures of which are hereby
expressly incorporated by reference.
[0111] Further details of the invention will be described in the
following non-limiting Example.
Example
[0112] A Phase II Study of Gene Expression in 242 Malignant Breast
Tumors
[0113] A gene expression study was designed and conducted with the
primary goal to molecularly characterize gene expression in
paraffin-embedded, fixed tissue samples of invasive, breast ductal
carcinoma, and to explore the correlation between such molecular
profiles and disease-free survival.
[0114] Study Design
[0115] Molecular assays were performed on paraffin-embedded,
formalin-fixed primary breast tumor tissues obtained from 252
individual patients diagnosed with invasive breast cancer. All
patients were lymph node-negative, ER-positive, and treated with
Tamoxifen. Mean age was 52 years, and mean clinical tumor size was
2 cm. Median follow-up was 10.9 years. As of Jan. 1, 2003, 41
patients had local or distant disease recurrence or breast cancer
death. Patients were included in the study only if histopathologic
assessment, performed as described in the Materials and Methods
section, indicated adequate amounts of tumor tissue and homogeneous
pathology.
[0116] Materials and Methods
[0117] Each representative tumor block was characterized by
standard histopathology for diagnosis, semi-quantitative assessment
of amount of tumor, and tumor grade. When tumor area was less than
70% of the section, the tumor area was grossly dissected and tissue
was taken from 6 (10 micron) sections. Otherwise, a total of 3
sections (also 10 microns in thickness each) were prepared.
Sections were placed in two Costar Brand Microcentrifuge Tubes
(Polypropylene, 1.7 mL tubes, clear). If more than one tumor block
was obtained as part of the surgical procedure, the block most
representative of the pathology was used for analysis.
[0118] Gene Expression Analysis
[0119] mRNA was extracted and purified from fixed,
paraffin-embedded tissue samples, and prepared for gene expression
analysis as described in chapter 6 above.
[0120] Molecular assays of quantitative gene expression were
performed by RT-PCR, using the ABI PRISM 7900.TM. Sequence
Detection System.TM. (Perkin-Elmer-Applied Biosystems, Foster City,
Calif., USA). ABI PRISM 7900.TM. consists of a thermocycler, laser,
charge-coupled device (CCD), camera and computer. The system
amplifies samples in a 384-well format on a thermocycler. During
amplification, laser-induced fluorescent signal is collected in
real-time through fiber optics cables for all 384 wells, and
detected at the CCD. The system includes software for running the
instrument and for analyzing the data.
[0121] Analysis and Results
[0122] Tumor tissue was analyzed for 187 cancer-related genes and 5
reference genes. Adequate RT-PCR profiles were obtained from 242 of
the 252 patients. The threshold cycle (CT) values for each patient
were normalized based on the median of the 7 reference genes for
that particular patient. Clinical outcome data were available for
all patients from a review of registry data and selected patient
charts. Outcomes were classified as:
[0123] Event: Alive with local, regional or distant breast cancer
recurrence or death due to breast cancer.
[0124] No Event: Alive without local, regional or distant breast
cancer recurrence or alive with contralateral breast cancer
recurrence or alive with non-breast second primary cancer or died
prior to breast cancer recurrence.
[0125] Analysis was performed by:
[0126] A. determination of the relationship between normalized gene
expression and the binary outcomes of 0 or 1;
[0127] B. Analysis of the relationship between normalized gene
expression and the time to outcome (0 or 1 as defined above) where
patients who were alive without breast cancer recurrence or who
died due to a cause other than breast cancer were censored. This
approach was used to evaluate the prognostic impact of individual
genes and also sets of multiple genes.
[0128] Analysis of Patients with Invasive Breast Carcinoma by
Binary Approach
[0129] In the first (binary) approach, analysis was performed on
all 242 patients with invasive breast carcinoma. A t test was
performed on the groups of patients classified as either no
recurrence and no breast cancer related death at 10 years, versus
recurrence, or breast cancer-related death at 10 years, and the
p-values for the differences between the groups for each gene were
calculated.
[0130] Table 1 lists the 33 genes for which the p-value for the
differences between the groups was <0.05. The first column of
mean expression values pertains to patients who had a metastatic
recurrence of nor died from breast cancer. The second column of
mean expression values pertains to patients who neither had a
metastatic recurrence of nor died from breast cancer.
TABLE-US-00001 TABLE 1 Mean Mean group B group A No T Gene Event
event statistic P value MMP9 -3.15 -4.27 3.75 0.00 GSTM1 -5.02
-4.03 -3.56 0.00 MELK -3.89 -4.66 3.34 0.00 PR -4.56 -3.18 -3.27
0.00 DKFZp586M07 -3.83 -2.94 -3.09 0.00 GSTM3 -2.56 -1.69 -3.06
0.00 MCM2 -3.51 -4.08 3.03 0.00 CDC20 -3.01 -3.75 3.01 0.00 CCNB1
-4.48 -5.17 3.02 0.00 STMY3 -0.58 -1.20 2.95 0.00 GRB7 -1.93 -3.01
2.98 0.00 MYBL2 -3.91 -4.78 2.91 0.01 CEGP1 -3.00 -1.85 -2.89 0.01
SURV -4.23 -5.06 2.88 0.01 LMNB1 -2.40 -2.91 2.81 0.01 CTSL2 -5.74
-6.39 2.83 0.01 PTTG1 -3.49 -4.14 2.72 0.01 BAG1 -1.76 -1.30 -2.58
0.01 KNSL2 -3.35 -4.06 2.60 0.01 CIAP1 -4.44 -4.02 -2.58 0.01 PREP
-3.34 -3.74 2.56 0.01 NEK2 -5.25 -5.80 2.53 0.01 EpCAM -1.95 -2.31
2.50 0.01 PCNA -2.79 -3.13 2.42 0.02 C20_orf1 -2.48 -3.09 2.39 0.02
ITGA7 -4.53 -3.87 -2.37 0.02 ID1 -2.58 -2.17 -2.30 0.02 B_Catenin
-1.32 -1.08 -2.28 0.03 EstR1 -0.78 -0.12 -2.28 0.03 CDH1 -2.76
-3.27 2.20 0.03 TS -2.86 -3.29 2.18 0.03 HER2 0.53 -0.22 2.18 0.03
cMYC -3.22 -2.85 -2.16 0.04
[0131] In the foregoing Table 1, negative t-values indicate higher
expression, associated with better outcomes, and, inversely, higher
(positive) t-values indicate higher expression associated with
worse outcomes. Thus, for example, elevated expression of the CCNB1
gene (t-value=3.02; CT mean alive<CT mean deceased) indicates a
reduced likelihood of disease free survival. Similarly, elevated
expression of the GSTM1 gene (t-value=-3.56; CT mean alive>CT
mean deceased) indicates an increased likelihood of disease free
survival.
[0132] Thus, based on the data set forth in Table 1, the expression
of any of the following genes in breast cancer indicates a reduced
likelihood of survival without cancer recurrence: C20_orf1; CCNB1;
CDC20; CDH1; CTSL2; EpCAM; GRB7; HER2; KNSL2; LMNB; MCM2; MMP9;
MYBL2; NEK2; PCNA; PREP; PTTG1; STMY3; SURV; TS; MELK
[0133] Based on the data set forth in Table 1, the expression of
any of the following genes in breast cancer indicates a better
prognosis for survival without cancer recurrence: BAG1; BCatenin;
CEGP1; CIAP1; cMYC; DKFZp586M07; EstR1; GSTM1; GSTM3; ID1; ITGA7;
PR.
[0134] Analysis of Multiple Genes and Indicators of Outcome
[0135] Two approaches were taken in order to determine whether
using multiple genes would provide better discrimination between
outcomes. First, a discrimination analysis was performed using a
forward stepwise approach. Models were generated that classified
outcome with greater discrimination than was obtained with any
single gene alone. According to a second approach (time-to-event
approach), for each gene a Cox Proportional Hazards model (see,
e.g. Cox, D. R., and Oakes, D. (1984), Analysis of Survival Data,
Chapman and Hall, London, New York) was defined with time to
recurrence or death as the dependent variable, and the expression
level of the gene as the independent variable. The genes that hive
a p-value <0.05 in the Cox model were identified. For each gene,
the Cox model provides the relative risk (RR) of recurrence or
death for a unit change in the expression of the gene. One can
choose to partition the patients into subgroups at any threshold
value of the measured expression (on the CT scale), where all
patients with expression values above the threshold have higher
risk, and all patients with expression values below the threshold
have lower risk, or vice versa, depending on whether the gene is an
indicator of bad (RR>1.01) or good (RR<1.01) prognosis. Thus,
any threshold value will define subgroups of patients with
respectively increased or decreased risk. The results are
summarized in Table 2, which lists the 42 genes for which the
p-value for the differences between the groups was <0.05.
TABLE-US-00002 TABLE 2 Gene Relative Risk p-value GRB7 1.52
0.000011 SURV 1.57 0.000090 PR 0.74 0.000129 LMNB1 1.92 0.000227
MYBL2 1.46 0.000264 HER2 1.46 0.000505 GSTM1 0.68 0.000543 MELK
1.59 0.000684 C20_orf1 1.59 0.000735 PTTG1 1.63 0.001135 BUB1 1.58
0.001425 CDC20 1.54 0.001443 CCNB1 1.60 0.001975 STMY3 1.47
0.002337 KNSL2 1.48 0.002910 CTSL2 1.43 0.003877 MCM2 1.59 0.005203
NEKS 1.48 0.006533 DR5 0.62 0.006660 Ki_67 1.46 0.008188 CCNE2 1.38
0.009505 TOP2A 1.38 0.009551 PCNA 1.67 0.010237 PREP 1.69 0.012308
FOXM1 1.52 0.012837 NME1 1.46 0.013622 CEGP1 0.84 0.013754 BAG1
0.68 0.015422 STK15 1.46 0.017013 HNRPAB 1.96 0.017942 EstR1 0.80
0.018877 MMP9 1.19 0.019591 DKFZp586M07 0.79 0.020073 TS 1.44
0.025186 Src 1.70 0.037398 BIN1 0.75 0.038979 NPD009 0.80 0.039020
RPLPO 0.52 0.041575 GSTM3 0.84 0.041848 MMP12 1.27 0.042074 TFRC
1.57 0.046145 IGF1R 0.78 0.046745
[0136] Based on the data set forth in Table 2, the expression of
any of the following genes in breast cancer indicates a reduced
likelihood of survival without cancer recurrence: GRB7; SURV; LMNB;
MYBL2; HER2; MELK; C20_orf1; PTTG1; BUB1; CDC20; CCNB1; STMY3;
KNSL2; CTSL2; MCM2; NEK2; Ki_67; CCNE2; TOP2A-4; PCNA; PREP; FOXM1;
NME1; STKI5; HNRPAB; MMP9; TS; Src; MMPI2; TFRC.
[0137] Based on the data set forth in Table 2, the expression of
any of the following genes in breast cancer indicates a better
prognosis for survival without cancer recurrence: PR; GSTM1; DR5;
CEGP1; BAG1; EstR1; DKFZp586MO7; BIN1; NPD009; RPLPO; GSTM3;
IGFIR.
[0138] The binary and time-to-event analyses, with few exceptions,
identified the same genes as prognostic markers. For example,
comparison of Tables 1 and 2 shows that 10 genes were represented
in the top 15 genes in both lists. Furthermore, when both analyses
identified the same gene at [p<0.10], which happened for 26
genes, they were always 1 concordant with respect to the direction
(positive or negative sign) of the correlation with
survival/recurrence. Overall, these results strengthen the
conclusion that the identified markers have significant prognostic
value.
[0139] Multivariate Gene Analysis of 242 Patients with Invasive
Breast Carcinoma
[0140] For Cox models comprising more than two genes (multivariate
models), stepwise entry of each individual gene into the model is
performed, where the first gene entered is pre-selected from among
those genes having significant univariate p-values, and the gene
selected for entry into the model at each subsequent step is the
gene that best improves the fit of the model to the data. This
analysis can be performed with any total number of genes. In the
analysis the results of which are shown below, stepwise entry was
performed for up to 10 genes.
[0141] Multivariate analysis was performed using the following
equation:
RR=exp[coef(geneA).times.Ct(geneA)+coef(geneB).times.Ct(geneB)+coef(gene-
C).times.Ct(geneC)+ . . . ].
[0142] In this equation, coefficients for genes that are predictors
of beneficial outcome are positive numbers and coefficients for
genes that are predictors of unfavorable outcome are negative
numbers. The "Ct" values in the equation are ACts, i.e. reflect the
difference between the average normalized Ct value for a population
and the normalized Ct measured for the patient in question. The
convention used in the present analysis has been that ACts below
and above the population average have positive signs and negative
signs, respectively (reflecting greater or lesser mRNA abundance).
The relative risk (RR) calculated by solving this equation will
indicate if the patient has an enhanced or reduced chance of
long-term survival without cancer recurrence.
[0143] A multivariate stepwise analysis, using the Cox Proportional
Hazards Model, was performed on the gene expression data obtained
for all 242 patients with invasive breast carcinoma. The following
ten-gene set has been identified by this analysis as having
particularly strong predictive value of patient survival: GRB7;
LMNB1; ER; STMY3; KLK10; PR; KRT5; FGFR1; MCM6; SNRPF. In this gene
set ER, PR, KRT5 and MCM6 contribute to good prognosis, while GRB7,
LMNB1, STMY3, KLK10, FGFR1, and SNRPF contribute to poor
prognosis.
[0144] While the present invention has been described with
reference to what are considered to be the specific embodiments; it
is to be understood that the invention is not limited to Fuch
embodiments. To the contrary, the invention is intended to cover
various modifications and equivalents included within the spirit
and scope of the appended claims. For example, while the disclosure
focuses on the identification of various breast cancer associated
genes and gene sets, and on the personalized prognosis of breast
cancer, similar genes, gene sets and methods concerning other types
of cancer are specifically within the scope herein. In particular,
the present gene sets or variants thereof can be used as prognostic
markers to predict the likelihood of long-term survival or cancer
recurrence in the case of ovarian cancer.
[0145] All references cited throughout the disclosure are hereby
expressly incorporated by reference.
TABLE-US-00003 TABLE 3 Gene Accession Start Stop SEQ ID NO.
Sequence B-Catenin NM_001904 1549 1629 SEQ ID NO: 1
GGCTCTTGTGCGTACTGTCCTTCGGGCTGGTGACAGGGAAGACATCACTGAGCCTGCCATCTGTGCTCTTCGT-
CATCTGA BAG1 NM_004323 673 754 SEQ ID NO: 2
CGTTGTCAGCACTTGGAATACAAGATGGTTGCCGGGTCATGTTAATTGGGAAAAAGAACAGTCCACAGGAAGA-
GGTTGAAC BIN1 NM_004305 866 942 SEQ ID NO: 3
CCTGCAAAAGGGAACAAGAGCCCTTCGCCTCCAGATGGCTCCCCTGCCGCCACCCCCGAGATCAGAGTCAACC-
ACG BUB1 NM_004338 1002 1070 SEQ ID NO: 4
CCGAGGTTAATCCAGCACGTATGGGGCCAAGTGTAGGCTCCCAGCAGGAACTGAGAGCGCCATGTCTT
C20 orf1 NM_012112 2675 2740 SEQ ID NO: 5
TCAGCTGTGAGCTGCGGATACCGCCCGGCAATGGGACCTGCTCTTAACCTCAAACCTAGGACCGT
CCNB1 NM_031966 823 907 SEQ ID NO: 6
TTCAGGTTGTTGCAGGAGACCATGTACATGACTGTCTCCATTATTGATCGGTTCATGCAGAATAATTGTGTGC-
CCAAGAAGATG CCNE2 NM_057749 2026 2108 SEQ ID NO: 7
ATGCTGTGGCTCCTTCCTAACTGGGGCTTTCTTGACATGTAGGTTGCTTGGTAATAACCTTTTTGTATATCAC-
AATTTGGGT CDC20 NM_001255 679 747 SEQ ID NO: 8
TGGATTGGAGTTCTGGGAATGTACTGGCCGTGGCACTGGACAACAGTGTGTACCTGTGGAGTGCAAGC
CDH1 NM_004360 2499 2580 SEQ ID NO: 9
TGAGTGTCCCCCGGTATCTTCCCCGCCCTGCCAATCCCGATGAAATTGGAAATTTTATTGATGAAAATCTGAA-
AGCGGCTG CEGP1 NM_020974 563 640 SEQ ID NO: 10
TGACAATCAGCACACCTGCATTCACCGCTCGGAAGAGGGCCTGAGCTGCATGAATAAGGATCACGGCTGTAGT-
GACA CIAP1 MM 001166 1822 1894 3E0 ID NO: 11
TGCCTGTGGTGGGAAGCTCAGTAACTGGGAACCAAAGGATGATGCTATGTCAGAACACCGGAGGCATTTTCC
dMYC NM_002467 1494 1578 SEQ ID NO: 12
TCCCTCCACTCGGAAGGACTATCCTGCTGCCAAGAGGGTCAAGTTGGACAGTGTCAGAGTCCTGAGACAGATC-
AGCAACAACCG CTSL2 NM_001333 671 738 SEQ ID NO: 13
TGTCTCACTGAGCGAGCAGAATCTGGTGGACTGTTCGCGTCCTCAAGGCAATCAGGGCTGCAATGGT
DKFZp566 AL050227 559 633 SEQ ID NO: 14
TCCATTTTCTACCTGTTAACCTTCATCATTTTGTGCAGGCCCTGGAAGCAAAGAGAGGAAGGGACCGACTGCA-
T DR5 NM_003842 1127 1211 SEQ ID NO: 15
CTCTGAGACAGTGCTTCGATGACTTTGCAGACTTGGTGCCCTTTGACTCCTGGGAGCCGCTCATGAGGAAGTT-
GGGCCTCATGG EpCAM NM_002354 435 510 SEQ ID NO: 16
GGGCCCTCCAGAACAATGATGGGCTTTATGATCCTGACTGCGATGAGAGCGGGCTCTTTAAGGCCAAGCAGTG-
CA EstR1 NM_000125 1956 2024 SEQ ID NO: 17
CGTGGTGCCCCTCTATGACCTGCTGCTGGAGATGCTGGACGCCCACCGCCTACATGCGCCCACTAGCC
FGFR1 MM 023109 2685 2759 SEQ ID NO: 18
CACGGGACATTCACCACATCGACTACTATAAAAAGACAACCAACGGCCGACTGCCTGTGAAGTGGATGGCACC-
C FOXM1 NM_021953 1898 1980 SEQ ID NO: 19
CCACCCCGAGCAAATCTGTCCTCCCCAGAACCCCTGAATCCTGGAGGCTCACGCCCCCAGCCAAAGTAGGGGG-
ACTGGATTT GRB7 NM_005310 1275 1342 SEQ ID NO: 20
CCATCTGCATCCATCTTGTTTGGGCTCCCCACCCTTGAGAAGTGCCTCAGATAATACCCTGGTGGCC
GSTM1 NM_000551 93 179 SEQ ID NO: 21
AAGCTATGAGGAAAAGAAGTACACGATGGGGGACGCTCCTGATTATGACAGAAGCCAGTGGCTGAATGAAAAA-
TTCAAGCTGGGCC GSTM3 NM_000849 248 324 SEQ ID NO: 22
CAATGCCATCTTGCGCTACATCGCTCGCAAGCACAACATGTGTGGTGAGACTGAAGAAGAAAAGATTCGAGTG-
GAC HER2 NM_004443 1138 1208 SEQ ID NO: 23
CGGTGTGAGAAGTGCAGCAAGCCCTGTGCCCGAGTGTGCTATGGTCTGGGCATGGAGCACTTGCGAGAGG
HNRPAB NM_004199 1086 1170 SEQ ID NO: 24
CAAGGGAGCGACCAACTGATCGCACACATGCTTTGTTTGGATATGGAGTGAACACAATTATGTACCAAATTTA-
ACTTGGCAAAC ID1 NM_002165 286 356 SEQ ID NO: 25
AGAACCGCAAGGTGAGCAAGGTGGAGATTCTCCAGCACGTCATCGACTACATCAGGGACCTTCAGTTGGA
IGF1R NM_000875 3467 3550 SEQ ID NO: 26
GCATGGTAGCCGAAGATTTCACAGTCAAAATCGGAGATTTTGGTATGACGCGAGATATCTATGAGACAGACTA-
TTACCGGAAA ITGA7 NM_002206 633 712 SEQ ID NO: 27
GATATGATTGGTCGCTGCTTTGTGCTCAGCCAGGACCTGGCCATCCGGGATGAGTTGGATGGTGGGGAATGGA-
AGTTCT KI-67 NM_002417 42 122 SEQ ID NO: 28
CGGACTTTGGGTGCGACTTGACGAGCGGTGGTTCGACAAGTGGCCTTGCGGGCCGGATCGTCCCAGTGGAAGA-
GTTGTAA KLK10 NM_002776 966 1044 SEQ ID NO: 29
GCCCAGAGGCTCCATCGTCCATCCTCTTCCTCCCCAGTCGGCTGAACTCTCCCCTTGTCTGCACTGTTCAAAC-
CTCTG KNSL2 BC000712 1266 1343 SEQ ID NO: 30
CCACCTCGCCATGATTTTTCCTTTGACCGGGTATTCCCACCAGGAAGTGGACAGGATGAAGTGTTTGAAGAGA-
TTGC KRT5 NM_000424 1605 1674 SEQ ID NO: 31
TCAGTGGAGAAGGAGTTGGACCAGTCAACATCTCTGTTGTCACAAGCAGTGTTTCCTCTGGATATGGCA
LMNB1 NM_005573 1500 1566 SEQ ID NO: 32
TGCAAACGCTGGTGTCACAGCCAGCCCCCCAACTGACCTCATCTGGAAGAACCAGAACTCGTGGGG
MCM2 MM 004526 2442 2517 SEQ ID NO: 33
GACTTTTGCCCGCTACCTTTCATTCCGGCGTGACAACAATGAGCTGTTGCTCTTCATACTGAAGCAGTTAGTG-
GC MCM6 NM_005915 2669 2751 SEQ ID NO: 34
TGATGGTCCTATGTGTCACATTCATCACAGGTTTCATACCAACACAGGCTTCAGCACTTCCTTTGGTGTGTTT-
CCTGTCCCA MELK MM 014791 22 87 SEQ ID NO: 35
AACCCGGCGATCGAAAAGATTCTTAGGAACGCCGTACCAGCCGCGTCTCTCAGGACAGCAGGCCC
MMP12 NM_002426 816 894 5E0 ID NO: 36
CCAACGCTTGCCAAATCCTGACAATTCAGAACCAGCTCTCTGTGACCCCAATTTGAGTTTTGATGCTGTCACT-
ACCGT MMP9 NM_004994 124 191 SEQ ID NO: 37
GAGAACCAATCTCACCGACAGGCAGCTGGCAGAGGAATACCTGTACCGCTATGGTTACACTCGGGTG
MYBL2 NM_002466 599 673 SEQ ID NO: 38
GCCGAGATCGCCAAGATGTTGCCAGGGAGGACAGACAATGCTGTGAAGAATCACTGGAACTCTACCATCAAAA-
G NEK2 NM_032497 102 161 SEQ ID NO: 39
GTGAGGCAGCGCGACTCTGGCGACTGGCCGGCCATGCCTTCCCGGGCTGAGGACTATGAAGTGTTGTACACCA-
TTGGCA NME1 NM_000259 365 439 SEQ ID NO: 40
CCAACCCTGCACACTCCAAGCCTGGGACCATCCGTGGAGACTTCTGCATACAAGTTGGCAGGAACATTATACA-
T NPD009 NM_020686 589 662 SEQ ID NO: 41
GGCTGTGGCTGAGGCTGTAGCATCTCTGCTGGAGGTGAGACACTCTGGGAACTGATTTGACCTCGAATGCTCC
PCNA NM_092592 157 228 SEQ ID NO: 42
GAAGGTGTTGGAGGCACTCAAGGACCTCATCAACGAGGCCTGCTGGGATATTAGCTCCAGCGGTGTAAACC
PR NM_000926 1895 1980 SEQ ID NO: 43
GCATCAGGCTGTCATTATGGTGTCCTTACCTGTGGGAGCTGTAAGGTCTTCTTTAAGAGGGCAATGGAAGGGC-
AGCACAACTACT PREP NM_002726 889 965 SEQ ID NO: 44
GGGACGGTGTTCACATTCAAGACGAATCGCCAGTCTCCCAACTATCGCGTGATCAACATTGACTTCTGGGATC-
CTG PTTG1 NM_004219 48 122 SEQ ID NO: 45
GGCTACTCTGATCTATGTTGATAAGGAAAATGGAGAACCAGGCACCCGTGTGGTTGCTAAGGATGGGCTGAAG-
C RPLPO NM_001002 791 866 SEQ ID NO: 46
CCATTCTATCATCAACGGGTACAAACGAGTCCTGGCCTTGTCTGTGGAGACGGATTACACCTTCCCACTTGCT-
GA SNRPF NM_003095 71 150 SEQ ID NO: 47
GGCTGGTCGGCAGAGAGTAGCCTGCAACATTCGGCCGTGGTTTACATGAGTTTACCCCTCAATCCCAAACCTT-
TCCTCA Src NM_004383 979 1043 SEQ ID NO: 48
CCTGAACATGAAGGAGCTGAAGCTGCTGCAGACCATCGGGAAGGGGGAGTTCGGAGACGTGATG
STK15 NM_003600 1101 1170 SEQ ID NO: 49
CATCTTCCAGGAGGACCACTCTCTGTGGCACCCTGGACTACCTGCCCCCTGAAATGATTGAAGGTCGGA
STMY3 NM_005940 2990 2180 SEQ ID NO: 50
CCTGGAGGCTGCAACATACCTCAATCCTGTCCCAGGGCGGATCCTCCTGAAGCCCTTTTCGCAGCACTGCTAT-
CCTCCAAAGCCATTGTA SURV NM_001188 737 817 SEQ ID NO: 51
TGTTTTGATTCCCGGGCTTACCAGGTGAGAAGTGAGGGAGGAAGAAGGCAGTGTCCCTTTTGCTAGAGCTGAC-
AGCTTG TFRC NM_003234 2110 2178 SEQ ID NO: 52
GCCAACTGCTTTCATTTGTGAGGGATCTGAACCAATACAGAGCAGACATAAAGGAAATGGGCCTGAGT
TOP2A NM_001087 4505 4577 SEQ ID NO: 53
AATCCAAGGGGGACAGTGATGACTTCCATATGGACTTTGACTCAGCTGTGGCTCCTCGGGCAAAATCTGTAC
YS NM_001071 784 629 SEQ ID NO: 54
GCCTCGGTGTGCCTTTCAACATCGCCAGCTACGCCCTGCTCACGTACATGATTGCGCACATCACG
TABLE-US-00004 TABLE 4A Gene Acceesion Name SEQ ID NO Sequence
B-Catenin NM_001904 S2150/B-Cate.f3 SEQ ID NO: 55
GGCTCTTGTGCGTACTGTCCTT 22 B-Catenin NM_001904 S2151/B-Cate.r3 SEQ
ID NO: 56 TCAGATGACGAAGAGCACAGATG 23 B-Catenin NM_001904
S5046/B-Cate.p3 SEQ ID NO: 57 AGGCTCAGTGATGTCTTCCCTGTCACCAG 29 BAG1
NM_004323 S1386/BAG1.f2 SEQ ID NO: 58 CGTTGTCAGCACTTGGAATACAA 23
BAG1 NM_004323 S1387/BAG1.r2 SEQ ID NO: 59 GTTCAACCTCTTCCTGTGGACTGT
24 BAG1 NM_004323 S4731/BAG1.p2 SEQ ID NO: 60
CCCAATTAACATGACCCGGCAACCAT 26 BIN1 NM_004305 S2651/BIN1.f3 SEQ ID
NO: 61 CCTGCAAAAGGGAACAAGAG 20 BIN1 NM_004305 S2652/BIN1.r3 SEQ ID
NO: 62 CGTGGTTGACTCTGATCTCG 20 BIN1 NM_004305 S4954/BIN1.p3 SEQ ID
NO: 63 CTTCGCCTCCAGATGGCTCCC 21 BUB1 NM_004336 S4294/BUB1.f1 SEQ ID
NO: 64 CCGAGGTTAATCCAGCACGTA 21 BUB1 NM_004336 S4295/BUB1.r1 SEQ ID
NO: 65 AAGACATGGCGCTCTCAGTTC 21 BUB1 NM_004336 S4296/BUB1.p1 SEQ ID
NO: 66 TGCTGGGAGCCTACACTTGGCCC 23 C20 orf1 NM_012112 S3560/C20
or.f1 SEQ ID NO: 67 TCAGCTGTGAGCTGCGGATA 20 C20 orf1 NM_012112
S3561/C20 or.r1 SEQ ID NO: 68 ACGGTCCTAGGTCTGAGGTTAAGA 24 C20 orfl
NM_012112 S3562/C20 or.p1 SEQ ID NO: 69 CAGGTCCCATTGCCGGGCG 19
CCNB1 NM_031966 S1720/CCNB1.f2 SEQ ID NO: 70 TTCAGGTTGTTGCAGGAGAC
20 CCNB1 NM_031966 S1721/CCNB1.r2 SEQ ID NO: 71
CATCTTCTTGGGCACACAAT 20 CCNB1 NM_031966 S4733/CCNB1.p2 SEQ ID NO:
72 TGTCTCCATTATTGATCGGTTCATGCA 27 CCNE2 NM_057749 S1458/CCNE2.f2
SEQ ID NO: 73 ATGCTGTGGCTCCTTCCTAACT 22 CCNE2 NM_057749
S1459/CCNE1.r2 SEQ ID NO: 74 ACCCAAATTGTGATATACAAAAAGGTT 27 CCNE2
NM_057749 S4945/CCNE2.p2 SEQ ID NO: 75
TACCAAGCAACCTACATGTCAAGAAAGCCC 30 CDC20 NM_001255 S4447/CDC20.f1
SEQ ID NO: 76 TGGATTGGAGTTCTGGGAATG 21 CDC20 NM_001255
S4448/CDC20.r1 SEQ ID NO: 77 GCTTGCACTCCACAGGTACACA 22 CDC20
NM_001255 S4449/CDC20.p1 SEQ ID NO: 78 ACTGGCCGTGGCACTGGACAACA 23
CDH1 NM_004360 S0073/CDH1.f3 SEQ ID NO: 79 TGAGTGTCCCCCGGTATCTTC 21
CDH1 NM_004360 S0075/CDH1.r3 SEQ ID NO: 80 CAGCCGCTTTCAGATTTTCAT 21
CDH1 NM_004360 S4990/CDH1.p3 SEQ ID NO: 81
TGCCAATCCCGATGAAATTGGAAATTT 27 CEGP1 NM_020974 S1494/CEGP1.f2 SEQ
ID NO: 82 TGACAATCAGCACACCTGCAT 21 CEGP1 NM_020974 S1495/GEGP1.r2
SEQ ID NO: 83 TGTGACTACAGCCGTGATCCTTA 23 CEGP1 NM_020974
S4735/CEGP1.p2 SEQ ID NO: 84 CAGGCCCTCTTCCGAGCGGT 20 CIAP1
NM_001166 S0764/CIAP1.f2 SEQ ID NO: 85 TGCCTGTGGTGGGAAGCT 18 CIAP1
NM_001166 S0765/CIAP1.r2 SEQ ID NO: 86 GGAAAATGCCTCCGGTGTT 19 CIAP1
NM_001166 S4802/CIAP1.p2 SEQ ID NO: 87
TGACATAGCATCATCCTTTGGTTCCCAGTT 30 cMYC NM_002467 S0085/cMYC.f3 SEQ
ID NO: 88 TCCCTCCACTCGGAAGGACTA 21 cMYC NM_002467 S0087/cMYC.r3 SEQ
ID NO: 89 CGGTTGTTGCTGATCTGTCTCA 22 cMYC NM_002467 S4994/cMYC.p3
SEQ ID NO: 90 TCTGACACTGTCCAACTTGACCCTCTT 27 CTSL2 NM_001333
S4354/CTSL2.f1 SEQ ID NO: 91 TGTCTCACTGAGCGAGCAGAA 21 CTSL2
NM_001333 S4355/CTSL2.r1 SEQ ID NO: 92 ACCATTGCAGCCCTGATTG 19 CTSL2
NM_001333 S4356/CTSL2.p1 SEQ ID NO: 93 CTTGAGGACGCGAACAGTCCACCA 24
DKFZp586M0723 AL050227 S4396/DKFZp5.f1 SEQ ID NO: 94
TCCATTTTCTACCTGTTAACCTTCATC 27 DKFZp586M0723 AL050227
S4397/DKF2p5.r1 SEQ ID NO: 95 ATGCAGTCGGTCCCTTCCT 19 DKFZp586M0723
AL050227 S4398/DKFZpS.p1 SEQ ID NO: 96 TTGCTTCCAGGGCCTGCACAAAA 23
DR5 NM_003842 S2551/DR5.f2 SEQ ID NO: 97 CTCTGAGACAGTGCTTCGATGACT
24 DR5 NM_003842 S2552/DR5.r2 SEQ ID NO: 98 CCATGAGGCCCAACTTCCT 19
DR5 NM_003842 S4979/DR5.p2 SEQ ID NO: 99 CAGACTTGGTGCCCTTTGACTCC 23
EpCAM NM_002354 S1807/EpCAM.f1 SEQ ID NO: 100 GGGCCCTCCAGAACAATGAT
20
TABLE-US-00005 TABLE 4B EpCAM NM_002354 S1808/EpCAM.r1 SEQ ID NO
101 TGCACTGCTTGGCCTTAAAGA 21 EpCAM NM_002354 S4984/EpCAM.p1 SEQ ID
NO: 102 CCGCTCTCATCGCAGTCAGGATCAT 25 EstR1 NM_000125 S0115/EstR1.f1
SEQ ID NO: 103 CGTGGTGCCCCTCTATGAC 19 EstR1 NM_000125
S0117/EstR1.r1 SEQ ID NO: 104 GGCTAGTGGGCGCATGTAG 19 EstR1
NM_000125 S4737/EstR1.p1 SEQ ID NO: 105 CTGGAGATGCTGGACGCCC 19
FGFR1 NM_023109 S0818/FGFR1.f3 SEQ ID NO: 106 CACGGGACATTCACCACATC
20 FGFR1 NM_023109 S0819/FGFR1.r3 SEQ ID NO: 107
GGGTGCCATCCACTTCACA 19 FGFR1 NM_023109 S4816/FGFR1.p3 SEQ ID NO:
108 ATAAAAAGACAACCAACGGCCGACTGC 27 FOXM1 NM_021953 S2006/FOXM1.f1
SEQ ID NO: 109 CCACCCCGAGCAAATCTGT 19 FOXM1 NM_021953
S2007/FOXM1.r1 SEQ ID NO: 110 AAATCCAGTCCCCCTACTTTGG 22 FOXM1
NM_021953 S4757/FOXM1.p1 SEQ ID NO: 111 CCTGAATCCTGGAGGCTCACGCC 23
GRB7 NM_005310 S0130/GRB7.f2 SEQ ID NO: 112 ccatctgcatccatcftgft 20
GRB7 NM_005310 S0132/GRB7.r2 SEQ ID NO: 113 ggccaccagggtattatctg 20
GRB7 NM_005310 S4726/GRB7.p2 SEQ ID NO: 114 ctccccacccttgagaagtgcct
23 GSTM1 NM_000561 S2026/GSTM1.r1 SEQ ID NO: 115
GGCCCAGCTTGAATTTTTCA 20 GSTM1 NM_000561 S2027/GSTM1.f1 SEQ ID NO:
116 AAGCTATGAGGAAAAGAAGTACACGAT 27 GSTM1 NM_000561 S4739/GSTMl.p1
SEQ ID NO: 117 TCAGCCACTGGCTTCTGTCATAATCAGGAG 30 GSTM3 NM_000849
S2038/GSTM3.f2 SEQ ID NO: 118 CAATGCCATCTTGCGCTACAT 21 GSTM3
NM_000849 S2039/GSTM3.r2 SEQ ID NO: 119 GTCCACTCGAATCTTTTCTTCTTCA
25 GSTM3 NM_000849 S5064/GSTM3.p2 SEQ ID NO: 120
CTCGCAAGCACAACATGTGTGGTGAGA 27 HER2 NM_004448 S0142/HER2.f3 SEQ ID
NO: 121 CGGTGTGAGAAGTGCAGCAA 20 HER2 NM_004448 S0144/HER2.r3 SEQ ID
NO: 122 CCTCTCGCAAGTGCTCCAT 19 HER2 NM_004448 S4729/HER2.p3 SEQ ID
NO: 123 CCAGACCATAGCACACTCGGGCAC 24 HNRPAB NM_004499
S4510/HNRPAB.f3 SEQ ID NO: 124 CAAGGGAGCGACCAACTGA 19 HNRPAB
NM_004499 S4511/HNRPAB.r3 SEQ ID NO: 125
GTTTGCCAAGTTAAATTTGGTACATAAT 28 HNRPAB NM_004499 S4512/HNRPAB.p3
SEQ ID NO: 126 CTCCATATCCAAACAAAGCATGTGTGCG 28 ID1 NM_002165
S0620/ID1.f1 SEQ ID NO: 127 AGAACCGCAAGGTGAGCAA 19 ID1 NM_002165
S0821/ID1.r1 SEQ ID NO: 128 TCCAACTGAAGGTCCCTGATG 21 ID1 NM_002165
S4832/ID1.p1 SEQ ID NO: 129 TGGAGATTCTCCAGCACGTCATCGAC 26 IGF1R
NM_000875 S1249/IGF1R.f3 SEQ ID NO: 130 GCATGGTAGCCGAAGATTTCA 21
IGF1R NM_000875 S1250/IGF1R.r3 SEQ ID NO: 131
TTTCCGGTAATAGTCTGTCTCATAGATATC 30 IGF1R NM_000875 S4895/IGF1R.p3
SEQ ID NO: 132 CGCGTCATACCAAAATCTCCGATTTTGA 28 ITGA7 NM_002206
S0859/ITGA7.f1 SEQ ID NO: 133 GATATGATTGGTCGCTGCTTTG 22 ITGA7
NM_002206 S0920/17GA7.r1 SEQ ID NO: 134 AGAACTTCCATTCCCCACCAT 21
ITGA7 NM_002206 S4795/ITGA7.p1 SEQ ID NO: 135 CAGCCAGGACCTGGCCATCCG
21 Ki-67 NM_002417 S0436/Ki-67.f2 SEQ ID NO: 136
CGGACTTTGGGTGCGACTT 19 Ki-67 NM_002417 S0437/Ki-67.r2 SEQ ID NO:
137 TTACAACTCTTCCACTGGGACGAT 24 Ki-67 NM_002417 S4741/K1-67.p2 SEQ
ID NO: 138 CCACTTGTCGAACCACCGCTCGT 23 KLK10 NM_002776
S2624/KLK10.f3 SEQ ID NO: 139 GCCCAGAGGCTCCATCGT 18 KLK10 NM_002776
S2625/KLK10.r3 SEQ ID NO: 140 CAGAGGTTTGAACAGTGCAGACA 23 KLK10
NM_002776 S4978/KLK10.p3 SEQ ID NO: 141 CCTCTTCCTCCCCAGTCGGCTGA 23
KNSL2 BC000712 S4432/KNSL2.f2 SEQ ID NO: 142 CCACCTCGCCATGATTTTTC
20 KNSL2 BC000712 S4433/KNSL2.r2 SEQ ID NO: 143
GCAATCTCTTCAAACACTTCATCCT 25 KNSL2 BC000712 S4434/KNSL2.p2 SEQ ID
NO: 144 TTTGACCGGGTATTCCCACCAGGAA 25 KRT5 NM_000424 S0175/KRT5.f3
SEQ ID NO: 145 tcagtggagaaggagttgga 20 KRT5 NM_000424 S0177/KRT5.r3
SEQ ID NO: 146 tgccatatccagaggaaaca 20 KRT5 NM_000424 S5015/KRT5.p3
SEQ ID NO: 147 ccagtcaacatctctgttgtcacaagca 28 LMNB1 NM_005573
S4477/LMNB1.f1 SEQ ID NO: 148 TGCAAACGCTGGTGTCACA 19
TABLE-US-00006 TABLE 4C LMNB1 NM_005573 S4478/LMNB1.r1 SEQ ID NO:
149 CCCCACGAGTTCTGGTTCTTC 21 LMNB1 NM_005573 S4479/LMNB1.p1 SEQ ID
NO: 150 CAGCCCCCCAACTGACCTCATC 22 MCM2 NM_004526 S1602/MCM2.f2 SEQ
ID NO: 151 GACTTTTGCCCGCTACCTTTC 21 MCM2 NM_004526 S1603/MCM2.r2
SEQ ID NO: 152 GCCACTAACTGCTTCAGTATGAAGAG 26 MCM2 NM_004526
S4900/MCM2.p2 SEQ ID NO: 153 ACAGCTCATTGTTGTCACGCCGGA 24 MCM6
NM_005915 S1704/MCM6.f3 SEQ ID NO: 154 TGATGGTCCTATGTGTCACATTCA 24
MCM6 NM_005915 S1705/MCM6.r3 SEQ ID NO: 155 TGGGACAGGAAACACACCAA 20
MCM6 NM_005915 S4919/MCM6.p3 SEQ ID NO: 156
CAGGTTTCATACCAACACAGGCTTCAGCAC 30 MELK NM_014791 S4318/MELK.f1 SEQ
ID NO: 157 AACCCGGCGATCGAAAAG 18 MELK NM_014791 S4319/MELK.r1 SEQ
ID NO: 158 GGGCCTGCTGTCCTGAGA 18 MELK NM_014791 S4320/MELK.p1 SEQ
ID NO: 159 TCTTAGGAACGCCGTACCAGCCGC 24 MMP12 NM_002426
S4381/MMP12.f2 SEQ ID NO: 160 CCAACGCTTGCCAAATCCT 19 MMP12
NM_002426 S4382/MMP12.r2 SEQ ID NO: 161 ACGGTAGTGACAGCATCAAAACTC 24
MMP12 NM_002426 S4383/MMP12.p2 SEQ ID NO: 162
AACCAGCTCTCTGTGACCCCAATT 24 MMP9 NM_004994 S0656/MMP9.f1 SEQ ID NO:
163 GAGAACCAATCTCACCGACA 20 MMP9 NM_004994 S0657/MMP9.r1 SEQ ID NO:
164 CACCCGAGTGTAACCATAGC 20 MMP9 NM_004994 S4760/MMP9.p1 SEQ ID NO:
165 ACAGGTATTCCTCTGCCAGCTGCC 24 MYBL2 NM_002466 S3270/MYBL2.f1 SEQ
ID NO: 166 GCCGAGATCGCCAAGATG 18 MYBL2 NM_002466 S3271/MYBL2.r1 SEQ
ID NO: 167 CTTTTGATGGTAGAGTTCCAGTGATTC 27 MYBL2 NM_002466
S4742/MYSL2.p1 SEQ ID NO: 168 CAGCATTGTCTGTCCTCCCTGGCA 24 NEK2
NM_002497 S4327/NEK2.f1 SEQ ID NO: 169 GTGAGGCAGCGCGACTCT 18 NEK2
NM_002497 S4328/NEK2.r1 SEQ ID NO: 170 TGCCAATGGTGTACAACACTTCA 23
NEK2 NM_002497 S4329/NEK2.p1 SEQ ID NO: 171 TGCCTTCCCGGGCTGAGGACT
21 NME1 NM_000269 S2526/NME1.f3 SEQ ID NO: 172 CCAACCCTGCAGACTCCAA
19 NME1 NM_000269 S2527/NME1.r3 SEQ ID NO: 173
ATGTATAATGTTCCTGCCAACTTGTATG 28 NME1 NM_000269 S4949/NME1.p3 SEQ ID
NO: 174 CCTGGGACCATCCGTGGAGACTTCT 25 NPD009 NM_020686
S4474/NP0009.f3 SEQ ID NO: 175 GGCTGTGGCTGAGGCTGTAG 20 NPD009
NM_020686 S4475/NP0009.r3 SEQ ID NO: 176 GGAGCATTCGAGGTCAAATCA 21
NPD009 NM_020686 S4476/NP0009.p3 SEQ ID NO: 177
TTCCCAGAGTGTCTCACCTCCAGCAGAG 28 PCNA NM_002592 S0447/PCNA.f2 SEQ ID
NO: 178 GAAGGTGTTGGAGGCACTCAAG 22 PCNA NM_002592 S0448/PCNA.r2 SEQ
ID NO: 179 GGTTTACACCGCTGGAGCTAA 21 PCNA NM_002592 S4784/PCNA.p2
SEQ ID NO: 180 ATCCCAGCAGGCCTCGTTGATGAG 24 PR NM_000926 S1336/PR.f6
SEQ ID NO: 181 GCATCAGGCTGTCATTATGG 20 PR NM_000926 S1337/PR.r6 SEQ
ID NO: 182 AGTAGTTGTGCTGCCCTTCC 20 PR NM_000926 S4743/PR.p6 SEQ ID
NO: 183 TGTCCTTACCTGTGGGAGCTGTAAGGTC 28 PREP NM_002726
S1771/PREP.f1 SEQ ID NO: 184 GGGACGGTGTTCACATTCAAG 21 PREP
NM_002726 S1772/PREP.r1 SEQ ID NO: 185 CAGGATCCCAGAAGTCAATGTTG 23
PREP NM_002726 S4929/PREP.p1 SEQ ID NO: 186
TCGCCAGTCTCCCAACTATCGCGT 24 PTTG1 NM_004219 S4525/PTTG1.f2 SEQ ID
NO: 187 GGCTACTCTGATCTATGTTGATAAGGAA 28 PTTG1 NM_004219
S4526/PTTG1.r2 SEQ ID NO: 188 GCTTCAGCCCATCCTTAGCA 20 PTTG1
NM_004219 S4527/PTTG1.p2 SEQ ID NO: 189 CACACGGGTGCCTGGTTCTCCA 22
RPLPO NM_001002 S0256/RPLPO.f2 SEQ ID NO: 190
CCATTCTATCATCAACGGGTACAA 24 RPLPO NM_001002 S0258/RPLPO.r2 SEQ ID
NO: 191 TCAGCAAGTGGGAAGGTGTAATC 23 RPLPO NM_001002 S4744/RPLPO.p2
SEQ ID NO: 192 TCTCCACAGACAAGGCCAGGACTCG 25 SNRPF NM_003095
S4489/SNRPF.f2 SEQ ID NO: 193 GGCTGGTCGGCAGAGAGTAG 20 SNRPF
NM_003095 S4490/SNRPF.r2 SEQ ID NO: 194 TGAGGAAAGGTTTGGGATTGA 21
SNRPF NM_003095 S4491/SNRPF.p2 SEQ ID NO: 195
AAACTCATGTAAACCAGGGCCGAATGTTG 29 Src NM_004383 S1820/Src.f2 SEQ ID
NO: 196 CCTGAACATGAAGGAGCTGA 20
TABLE-US-00007 TABLE 4D Src NM_004383 S1621/Src.r2 SEQ ID NO: 197
CATCACGTCTCCGAACTCC 19 Src NM_004383 S5034/Src.p2 SEQ ID NO: 198
TCCCGATGGTCTGCAGCAGCT 21 STK15 NM_003600 S0794/STK15.f2 SEQ ID NO:
199 CATCTTCCAGGAGGACCACT 20 STK15 NM_003600 S0795/STK15.r2 SEQ ID
NO: 200 TCCGACCTTCAATCATTTCA 20 STK15 NM_003600 S4745/STK15.p2 SEQ
ID NO: 201 CTCTGTGGCACCCTGGACTACCTG 24 STMY3 NM_005940
S2067/STMY3.f3 SEQ ID NO: 202 CCTGGAGGCTGCAACATACC 20 STMY3
NM_005940 S2068/STMY3.r3 SEQ ID NO: 203 TACAATGGCTTTGGAGGATAGCA 23
STMY3 NM_005940 S4746/STMY3.p3 SEQ ID NO: 204
ATCCTCCTGAAGCCCTTTTCGCAGC 25 SURV NM_001168 S02591SURV.f2 SEQ ID
NO: 205 TGTTTTGATTCCCGGGCTTA 20 SURV NM_001168 S0261/SURV.r2 SEQ ID
NO: 206 CAAAGCTGTCAGCTCTAGCAAAAG 24 SURV NM_001168 S4747/SURV.p2
SEQ ID NO: 207 TGCCTTCTTCCTCCCTCACTTCTCACCT 28 TFRC NM_003234
S1352/TFRC.f3 SEQ ID NO: 208 GCCAACTGCTTTCATTTGTG 20 TFRC NM_003234
S1353/TFRC.r3 SEQ ID NO: 209 ACTCAGGCCCATTTCCTTTA 20 TFRC NM_003234
S4747/TFRC.p3 SEQ ID NO: 210 AGGGATCTGAACCAATACAGAGCAGACA 28 TOP2A
NM_001067 S0271/TOP2A.f4 SEQ ID NO: 211 AATCCAAGGGGGAGAGTGAT 20
TOP2A NM_001067 S0273/TOP2A.r4 SEQ ID NO: 212 GTACAGATTTTGCCCGAGGA
20 TOP2A NM_001067 S4777/TOP2A.p4 SEQ ID NO: 213
CATATGGACTTTGACTCAGCTGTGGC 26 TS NM_001071 S0280/TS.f1 SEQ ID NO:
214 GCCTCGGTGTGCCTTTCA 18 TS NM_001071 S0282/TS.r1 SEQ ID NO: 215
CGTGATGTGCGCAATCATG 19 TS NM_001071 S4780/TS.p1 SEQ ID NO: 216
CATCGCCAGCTACGCCCTGCTC 22
Sequence CWU 1
1
216180DNAArtificial SequencePCR Amplicon 1ggctcttgtg cgtactgtcc
ttcgggctgg tgacagggaa gacatcactg agcctgccat 60ctgtgctctt cgtcatctga
80281DNAArtificial SequencePCR Amplicon 2cgttgtcagc acttggaata
caagatggtt gccgggtcat gttaattggg aaaaagaaca 60gtccacagga agaggttgaa
c 81376DNAArtificial SequencePCR Amplicon 3cctgcaaaag ggaacaagag
cccttcgcct ccagatggct cccctgccgc cacccccgag 60atcagagtca accacg
76468DNAArtificial SequencePCR Amplicon 4ccgaggttaa tccagcacgt
atggggccaa gtgtaggctc ccagcaggaa ctgagagcgc 60catgtctt
68565DNAArtificial SequencePCR Amplicon 5tcagctgtga gctgcggata
ccgcccggca atgggacctg ctcttaacct caaacctagg 60accgt
65684DNAArtificial SequencePCR Amplicon 6ttcaggttgt tgcaggagac
catgtacatg actgtctcca ttattgatcg gttcatgcag 60aataattgtg tgcccaagaa
gatg 84782DNAArtificial SequencePCR Amplicon 7atgctgtggc tccttcctaa
ctggggcttt cttgacatgt aggttgcttg gtaataacct 60ttttgtatat cacaatttgg
gt 82868DNAArtificial SequencePCR Amplicon 8tggattggag ttctgggaat
gtactggccg tggcactgga caacagtgtg tacctgtgga 60gtgcaagc
68981DNAArtificial SequencePCR Amplicon 9tgagtgtccc ccggtatctt
ccccgccctg ccaatcccga tgaaattgga aattttattg 60atgaaaatct gaaagcggct
g 811077DNAArtificial SequencePCR Amplicon 10tgacaatcag cacacctgca
ttcaccgctc ggaagagggc ctgagctgca tgaataagga 60tcacggctgt agtcaca
771172DNAArtificial SequencePCR Amplicon 11tgcctgtggt gggaagctca
gtaactggga accaaaggat gatgctatgt cagaacaccg 60gaggcatttt cc
721284DNAArtificial SequencePCR Amplicon 12tccctccact cggaaggact
atcctgctgc caagagggtc aagttggaca gtgtcagagt 60cctgagacag atcagcaaca
accg 841367DNAArtificial SequencePCR Amplicon 13tgtctcactg
agcgagcaga atctggtgga ctgttcgcgt cctcaaggca atcagggctg 60caatggt
671474DNAArtificial SequencePCR Amplicon 14tccattttct acctgttaac
cttcatcatt ttgtgcaggc cctggaagca aagagaggaa 60gggaccgact gcat
741584DNAArtificial SequencePCR Amplicon 15ctctgagaca gtgcttcgat
gactttgcag acttggtgcc ctttgactcc tgggagccgc 60tcatgaggaa gttgggcctc
atgg 841675DNAArtificial SequencePCR Amplicon 16gggccctcca
gaacaatgat gggctttatg atcctgactg cgatgagagc gggctcttta 60aggccaagca
gtgca 751768DNAArtificial SequencePCR Amplicon 17cgtggtgccc
ctctatgacc tgctgctgga gatgctggac gcccaccgcc tacatgcgcc 60cactagcc
681874DNAArtificial SequencePCR Amplicon 18cacgggacat tcaccacatc
gactactata aaaagacaac caacggccga ctgcctgtga 60agtggatggc accc
741982DNAArtificial SequencePCR Amplicon 19ccaccccgag caaatctgtc
ctccccagaa cccctgaatc ctggaggctc acgcccccag 60ccaaagtagg gggactggat
tt 822067DNAArtificial SequencePCR Amplicon 20ccatctgcat ccatcttgtt
tgggctcccc acccttgaga agtgcctcag ataataccct 60ggtggcc
672186DNAArtificial SequencePCR Amplicon 21aagctatgag gaaaagaagt
acacgatggg ggacgctcct gattatgaca gaagccagtg 60gctgaatgaa aaattcaagc
tgggcc 862276DNAArtificial SequencePCR Amplicon 22caatgccatc
ttgcgctaca tcgctcgcaa gcacaacatg tgtggtgaga ctgaagaaga 60aaagattcga
gtggac 762370DNAArtificial SequencePCR Amplicon 23cggtgtgaga
agtgcagcaa gccctgtgcc cgagtgtgct atggtctggg catggagcac 60ttgcgagagg
702484DNAArtificial SequencePCR Amplicon 24caagggagcg accaactgat
cgcacacatg ctttgtttgg atatggagtg aacacaatta 60tgtaccaaat ttaacttggc
aaac 842570DNAArtificial SequencePCR Amplicon 25agaaccgcaa
ggtgagcaag gtggagattc tccagcacgt catcgactac atcagggacc 60ttcagttgga
702683DNAArtificial SequencePCR Amplicon 26gcatggtagc cgaagatttc
acagtcaaaa tcggagattt tggtatgacg cgagatatct 60atgagacaga ctattaccgg
aaa 832779DNAArtificial SequencePCR Amplicon 27gatatgattg
gtcgctgctt tgtgctcagc caggacctgg ccatccggga tgagttggat 60ggtggggaat
ggaagttct 792880DNAArtificial SequencePCR Amplicon 28cggactttgg
gtgcgacttg acgagcggtg gttcgacaag tggccttgcg ggccggatcg 60tcccagtgga
agagttgtaa 802978DNAArtificial SequencePCR Amplicon 29gcccagaggc
tccatcgtcc atcctcttcc tccccagtcg gctgaactct ccccttgtct 60gcactgttca
aacctctg 783077DNAArtificial SequencePCR Amplicon 30ccacctcgcc
atgatttttc ctttgaccgg gtattcccac caggaagtgg acaggatgaa 60gtgtttgaag
agattgc 773169DNAArtificial SequencePCR Amplicon 31tcagtggaga
aggagttgga ccagtcaaca tctctgttgt cacaagcagt gtttcctctg 60gatatggca
693266DNAArtificial SequencePCR Amplicon 32tgcaaacgct ggtgtcacag
ccagcccccc aactgacctc atctggaaga accagaactc 60gtgggg
663375DNAArtificial SequencePCR Amplicon 33gacttttgcc cgctaccttt
cattccggcg tgacaacaat gagctgttgc tcttcatact 60gaagcagtta gtggc
753482DNAArtificial SequencePCR Amplicon 34tgatggtcct atgtgtcaca
ttcatcacag gtttcatacc aacacaggct tcagcacttc 60ctttggtgtg tttcctgtcc
ca 823565DNAArtificial SequencePCR Amplicon 35aacccggcga tcgaaaagat
tcttaggaac gccgtaccag ccgcgtctct caggacagca 60ggccc
653678DNAArtificial SequencePCR Amplicon 36ccaacgcttg ccaaatcctg
acaattcaga accagctctc tgtgacccca atttgagttt 60tgatgctgtc actaccgt
783767DNAArtificial SequencePCR Amplicon 37gagaaccaat ctcaccgaca
ggcagctggc agaggaatac ctgtaccgct atggttacac 60tcgggtg
673874DNAArtificial SequencePCR Amplicon 38gccgagatcg ccaagatgtt
gccagggagg acagacaatg ctgtgaagaa tcactggaac 60tctaccatca aaag
743979DNAArtificial SequencePCR Amplicon 39gtgaggcagc gcgactctgg
cgactggccg gccatgcctt cccgggctga ggactatgaa 60gtgttgtaca ccattggca
794074DNAArtificial SequencePCR Amplicon 40ccaaccctgc agactccaag
cctgggacca tccgtggaga cttctgcata caagttggca 60ggaacattat acat
744173DNAArtificial SequencePCR Amplicon 41ggctgtggct gaggctgtag
catctctgct ggaggtgaga cactctggga actgatttga 60cctcgaatgc tcc
734271DNAArtificial SequencePCR Amplicon 42gaaggtgttg gaggcactca
aggacctcat caacgaggcc tgctgggata ttagctccag 60cggtgtaaac c
714385DNAArtificial SequencePCR Amplicon 43gcatcaggct gtcattatgg
tgtccttacc tgtgggagct gtaaggtctt ctttaagagg 60gcaatggaag ggcagcacaa
ctact 854476DNAArtificial SequencePCR Amplicon 44gggacggtgt
tcacattcaa gacgaatcgc cagtctccca actatcgcgt gatcaacatt 60gacttctggg
atcctg 764574DNAArtificial SequencePCR Amplicon 45ggctactctg
atctatgttg ataaggaaaa tggagaacca ggcacccgtg tggttgctaa 60ggatgggctg
aagc 744675DNAArtificial SequencePCR Amplicon 46ccattctatc
atcaacgggt acaaacgagt cctggccttg tctgtggaga cggattacac 60cttcccactt
gctga 754779DNAArtificial SequencePCR Amplicon 47ggctggtcgg
cagagagtag cctgcaacat tcggccgtgg tttacatgag tttacccctc 60aatcccaaac
ctttcctca 794864DNAArtificial SequencePCR Amplicon 48cctgaacatg
aaggagctga agctgctgca gaccatcggg aagggggagt tcggagacgt 60gatg
644969DNAArtificial SequencePCR Amplicon 49catcttccag gaggaccact
ctctgtggca ccctggacta cctgccccct gaaatgattg 60aaggtcgga
695090DNAArtificial SequencePCR Amplicon 50cctggaggct gcaacatacc
tcaatcctgt cccaggccgg atcctcctga agcccttttc 60gcagcactgc tatcctccaa
agccattgta 905179DNAArtificial SequencePCR Amplicon 51tgttttgatt
cccgggctta ccaggtgaga agtgagggag gaagaaggca gtgtcccttt 60tgctagagct
gacagcttg 795268DNAArtificial SequencePCR Amplicon 52gccaactgct
ttcatttgtg agggatctga accaatacag agcagacata aaggaaatgg 60gcctgagt
685372DNAArtificial SequencePCR Amplicon 53aatccaaggg ggagagtgat
gacttccata tggactttga ctcagctgtg gctcctcggg 60caaaatctgt ac
725465DNAArtificial SequencePCR Amplicon 54gcctcggtgt gcctttcaac
atcgccagct acgccctgct cacgtacatg attgcgcaca 60tcacg
655522DNAArtificial SequencePCR primer-probe 55ggctcttgtg
cgtactgtcc tt 225623DNAArtificial SequencePCR primer-probe
56tcagatgacg aagagcacag atg 235729DNAArtificial SequencePCR
primer-probe 57aggctcagtg atgtcttccc tgtcaccag 295823DNAArtificial
SequencePCR primer-probe 58cgttgtcagc acttggaata caa
235924DNAArtificial SequencePCR primer-probe 59gttcaacctc
ttcctgtgga ctgt 246026DNAArtificial SequencePCR primer-probe
60cccaattaac atgacccggc aaccat 266120DNAArtificial SequencePCR
primer-probe 61cctgcaaaag ggaacaagag 206220DNAArtificial
SequencePCR primer-probe 62cgtggttgac tctgatctcg
206321DNAArtificial SequencePCR primer-probe 63cttcgcctcc
agatggctcc c 216421DNAArtificial SequencePCR primer-probe
64ccgaggttaa tccagcacgt a 216521DNAArtificial SequencePCR
primer-probe 65aagacatggc gctctcagtt c 216623DNAArtificial
SequencePCR primer-probe 66tgctgggagc ctacacttgg ccc
236720DNAArtificial SequencePCR primer-probe 67tcagctgtga
gctgcggata 206824DNAArtificial SequencePCR primer-probe
68acggtcctag gtttgaggtt aaga 246919DNAArtificial SequencePCR
primer-probe 69caggtcccat tgccgggcg 197020DNAArtificial SequencePCR
primer-probe 70ttcaggttgt tgcaggagac 207120DNAArtificial
SequencePCR primer-probe 71catcttcttg ggcacacaat
207227DNAArtificial SequencePCR primer-probe 72tgtctccatt
attgatcggt tcatgca 277322DNAArtificial SequencePCR primer-probe
73atgctgtggc tccttcctaa ct 227427DNAArtificial SequencePCR
primer-probe 74acccaaattg tgatatacaa aaaggtt 277530DNAArtificial
SequencePCR primer-probe 75taccaagcaa cctacatgtc aagaaagccc
307621DNAArtificial SequencePCR primer-probe 76tggattggag
ttctgggaat g 217722DNAArtificial SequencePCR primer-probe
77gcttgcactc cacaggtaca ca 227823DNAArtificial SequencePCR
primer-probe 78actggccgtg gcactggaca aca 237921DNAArtificial
SequencePCR primer-probe 79tgagtgtccc ccggtatctt c
218021DNAArtificial SequencePCR primer-probe 80cagccgcttt
cagattttca t 218127DNAArtificial SequencePCR primer-probe
81tgccaatccc gatgaaattg gaaattt 278221DNAArtificial SequencePCR
primer-probe 82tgacaatcag cacacctgca t 218323DNAArtificial
SequencePCR primer-probe 83tgtgactaca gccgtgatcc tta
238420DNAArtificial SequencePCR primer-probe 84caggccctct
tccgagcggt 208518DNAArtificial SequencePCR primer-probe
85tgcctgtggt gggaagct 188619DNAArtificial SequencePCR primer-probe
86ggaaaatgcc tccggtgtt 198730DNAArtificial SequencePCR primer-probe
87tgacatagca tcatcctttg gttcccagtt 308821DNAArtificial SequencePCR
primer-probe 88tccctccact cggaaggact a 218922DNAArtificial
SequencePCR primer-probe 89cggttgttgc tgatctgtct ca
229027DNAArtificial SequencePCR primer-probe 90tctgacactg
tccaacttga ccctctt 279121DNAArtificial SequencePCR primer-probe
91tgtctcactg agcgagcaga a 219219DNAArtificial SequencePCR
primer-probe 92accattgcag ccctgattg 199324DNAArtificial SequencePCR
primer-probe 93cttgaggacg cgaacagtcc acca 249427DNAArtificial
SequencePCR primer-probe 94tccattttct acctgttaac cttcatc
279519DNAArtificial SequencePCR primer-probe 95atgcagtcgg tcccttcct
199623DNAArtificial SequencePCR primer-probe 96ttgcttccag
ggcctgcaca aaa 239724DNAArtificial SequencePCR primer-probe
97ctctgagaca gtgcttcgat gact 249819DNAArtificial SequencePCR
primer-probe 98ccatgaggcc caacttcct 199923DNAArtificial SequencePCR
primer-probe 99cagacttggt gccctttgac tcc 2310020DNAArtificial
SequencePCR primer-probe 100gggccctcca gaacaatgat
2010121DNAArtificial SequencePCR primer-probe 101tgcactgctt
ggccttaaag a 2110225DNAArtificial SequencePCR primer-probe
102ccgctctcat cgcagtcagg atcat 2510319DNAArtificial SequencePCR
primer-probe 103cgtggtgccc ctctatgac 1910419DNAArtificial
SequencePCR primer-probe 104ggctagtggg cgcatgtag
1910519DNAArtificial SequencePCR primer-probe 105ctggagatgc
tggacgccc 1910620DNAArtificial SequencePCR primer-probe
106cacgggacat tcaccacatc 2010719DNAArtificial SequencePCR
primer-probe 107gggtgccatc cacttcaca 1910827DNAArtificial
SequencePCR primer-probe 108ataaaaagac aaccaacggc cgactgc
2710919DNAArtificial SequencePCR primer-probe 109ccaccccgag
caaatctgt 1911022DNAArtificial SequencePCR primer-probe
110aaatccagtc cccctacttt gg 2211123DNAArtificial SequencePCR
primer-probe 111cctgaatcct ggaggctcac gcc 2311220DNAArtificial
SequencePCR primer-probe 112ccatctgcat ccatcttgtt
2011320DNAArtificial SequencePCR primer-probe 113ggccaccagg
gtattatctg 2011423DNAArtificial SequencePCR primer-probe
114ctccccaccc ttgagaagtg cct 2311520DNAArtificial SequencePCR
primer-probe 115ggcccagctt gaatttttca 2011627DNAArtificial
SequencePCR primer-probe 116aagctatgag gaaaagaagt acacgat
2711730DNAArtificial SequencePCR primer-probe 117tcagccactg
gcttctgtca taatcaggag 3011821DNAArtificial SequencePCR primer-probe
118caatgccatc ttgcgctaca t 2111925DNAArtificial
SequencePCR primer-probe 119gtccactcga atcttttctt cttca
2512027DNAArtificial SequencePCR primer-probe 120ctcgcaagca
caacatgtgt ggtgaga 2712120DNAArtificial SequencePCR primer-probe
121cggtgtgaga agtgcagcaa 2012219DNAArtificial SequencePCR
primer-probe 122cctctcgcaa gtgctccat 1912324DNAArtificial
SequencePCR primer-probe 123ccagaccata gcacactcgg gcac
2412419DNAArtificial SequencePCR primer-probe 124caagggagcg
accaactga 1912528DNAArtificial SequencePCR primer-probe
125gtttgccaag ttaaatttgg tacataat 2812628DNAArtificial SequencePCR
primer-probe 126ctccatatcc aaacaaagca tgtgtgcg 2812719DNAArtificial
SequencePCR primer-probe 127agaaccgcaa ggtgagcaa
1912821DNAArtificial SequencePCR primer-probe 128tccaactgaa
ggtccctgat g 2112926DNAArtificial SequencePCR primer-probe
129tggagattct ccagcacgtc atcgac 2613021DNAArtificial SequencePCR
primer-probe 130gcatggtagc cgaagatttc a 2113130DNAArtificial
SequencePCR primer-probe 131tttccggtaa tagtctgtct catagatatc
3013228DNAArtificial SequencePCR primer-probe 132cgcgtcatac
caaaatctcc gattttga 2813322DNAArtificial SequencePCR primer-probe
133gatatgattg gtcgctgctt tg 2213421DNAArtificial SequencePCR
primer-probe 134agaacttcca ttccccacca t 2113521DNAArtificial
SequencePCR primer-probe 135cagccaggac ctggccatcc g
2113619DNAArtificial SequencePCR primer-probe 136cggactttgg
gtgcgactt 1913724DNAArtificial SequencePCR primer-probe
137ttacaactct tccactggga cgat 2413823DNAArtificial SequencePCR
primer-probe 138ccacttgtcg aaccaccgct cgt 2313918DNAArtificial
SequencePCR primer-probe 139gcccagaggc tccatcgt
1814023DNAArtificial SequencePCR primer-probe 140cagaggtttg
aacagtgcag aca 2314123DNAArtificial SequencePCR primer-probe
141cctcttcctc cccagtcggc tga 2314220DNAArtificial SequencePCR
primer-probe 142ccacctcgcc atgatttttc 2014325DNAArtificial
SequencePCR primer-probe 143gcaatctctt caaacacttc atcct
2514425DNAArtificial SequencePCR primer-probe 144tttgaccggg
tattcccacc aggaa 2514520DNAArtificial SequencePCR primer-probe
145tcagtggaga aggagttgga 2014620DNAArtificial SequencePCR
primer-probe 146tgccatatcc agaggaaaca 2014728DNAArtificial
SequencePCR primer-probe 147ccagtcaaca tctctgttgt cacaagca
2814819DNAArtificial SequencePCR primer-probe 148tgcaaacgct
ggtgtcaca 1914921DNAArtificial SequencePCR primer-probe
149ccccacgagt tctggttctt c 2115022DNAArtificial SequencePCR
primer-probe 150cagcccccca actgacctca tc 2215121DNAArtificial
SequencePCR primer-probe 151gacttttgcc cgctaccttt c
2115226DNAArtificial SequencePCR primer-probe 152gccactaact
gcttcagtat gaagag 2615324DNAArtificial SequencePCR primer-probe
153acagctcatt gttgtcacgc cgga 2415424DNAArtificial SequencePCR
primer-probe 154tgatggtcct atgtgtcaca ttca 2415520DNAArtificial
SequencePCR primer-probe 155tgggacagga aacacaccaa
2015630DNAArtificial SequencePCR primer-probe 156caggtttcat
accaacacag gcttcagcac 3015718DNAArtificial SequencePCR primer-probe
157aacccggcga tcgaaaag 1815818DNAArtificial SequencePCR
primer-probe 158gggcctgctg tcctgaga 1815924DNAArtificial
SequencePCR primer-probe 159tcttaggaac gccgtaccag ccgc
2416019DNAArtificial SequencePCR primer-probe 160ccaacgcttg
ccaaatcct 1916124DNAArtificial SequencePCR primer-probe
161acggtagtga cagcatcaaa actc 2416224DNAArtificial SequencePCR
primer-probe 162aaccagctct ctgtgacccc aatt 2416320DNAArtificial
SequencePCR primer-probe 163gagaaccaat ctcaccgaca
2016420DNAArtificial SequencePCR primer-probe 164cacccgagtg
taaccatagc 2016524DNAArtificial SequencePCR primer-probe
165acaggtattc ctctgccagc tgcc 2416618DNAArtificial SequencePCR
primer-probe 166gccgagatcg ccaagatg 1816727DNAArtificial
SequencePCR primer-probe 167cttttgatgg tagagttcca gtgattc
2716824DNAArtificial SequencePCR primer-probe 168cagcattgtc
tgtcctccct ggca 2416918DNAArtificial SequencePCR primer-probe
169gtgaggcagc gcgactct 1817023DNAArtificial SequencePCR
primer-probe 170tgccaatggt gtacaacact tca 2317121DNAArtificial
SequencePCR primer-probe 171tgccttcccg ggctgaggac t
2117219DNAArtificial SequencePCR primer-probe 172ccaaccctgc
agactccaa 1917328DNAArtificial SequencePCR primer-probe
173atgtataatg ttcctgccaa cttgtatg 2817425DNAArtificial SequencePCR
primer-probe 174cctgggacca tccgtggaga cttct 2517520DNAArtificial
SequencePCR primer-probe 175ggctgtggct gaggctgtag
2017621DNAArtificial SequencePCR primer-probe 176ggagcattcg
aggtcaaatc a 2117728DNAArtificial SequencePCR primer-probe
177ttcccagagt gtctcacctc cagcagag 2817822DNAArtificial SequencePCR
primer-probe 178gaaggtgttg gaggcactca ag 2217921DNAArtificial
SequencePCR primer-probe 179ggtttacacc gctggagcta a
2118024DNAArtificial SequencePCR primer-probe 180atcccagcag
gcctcgttga tgag 2418120DNAArtificial SequencePCR primer-probe
181gcatcaggct gtcattatgg 2018220DNAArtificial SequencePCR
primer-probe 182agtagttgtg ctgcccttcc 2018328DNAArtificial
SequencePCR primer-probe 183tgtccttacc tgtgggagct gtaaggtc
2818421DNAArtificial SequencePCR primer-probe 184gggacggtgt
tcacattcaa g 2118523DNAArtificial SequencePCR primer-probe
185caggatccca gaagtcaatg ttg 2318624DNAArtificial SequencePCR
primer-probe 186tcgccagtct cccaactatc gcgt 2418728DNAArtificial
SequencePCR primer-probe 187ggctactctg atctatgttg ataaggaa
2818820DNAArtificial SequencePCR primer-probe 188gcttcagccc
atccttagca 2018922DNAArtificial SequencePCR primer-probe
189cacacgggtg cctggttctc ca 2219024DNAArtificial SequencePCR
primer-probe 190ccattctatc atcaacgggt acaa 2419123DNAArtificial
SequencePCR primer-probe 191tcagcaagtg ggaaggtgta atc
2319225DNAArtificial SequencePCR primer-probe 192tctccacaga
caaggccagg actcg 2519320DNAArtificial SequencePCR primer-probe
193ggctggtcgg cagagagtag 2019421DNAArtificial SequencePCR
primer-probe 194tgaggaaagg tttgggattg a 2119529DNAArtificial
SequencePCR primer-probe 195aaactcatgt aaaccacggc cgaatgttg
2919620DNAArtificial SequencePCR primer-probe 196cctgaacatg
aaggagctga 2019719DNAArtificial SequencePCR primer-probe
197catcacgtct ccgaactcc 1919821DNAArtificial SequencePCR
primer-probe 198tcccgatggt ctgcagcagc t 2119920DNAArtificial
SequencePCR primer-probe 199catcttccag gaggaccact
2020020DNAArtificial SequencePCR primer-probe 200tccgaccttc
aatcatttca 2020124DNAArtificial SequencePCR primer-probe
201ctctgtggca ccctggacta cctg 2420220DNAArtificial SequencePCR
primer-probe 202cctggaggct gcaacatacc 2020323DNAArtificial
SequencePCR primer-probe 203tacaatggct ttggaggata gca
2320425DNAArtificial SequencePCR primer-probe 204atcctcctga
agcccttttc gcagc 2520520DNAArtificial SequencePCR primer-probe
205tgttttgatt cccgggctta 2020624DNAArtificial SequencePCR
primer-probe 206caaagctgtc agctctagca aaag 2420728DNAArtificial
SequencePCR primer-probe 207tgccttcttc ctccctcact tctcacct
2820820DNAArtificial SequencePCR primer-probe 208gccaactgct
ttcatttgtg 2020920DNAArtificial SequencePCR primer-probe
209actcaggccc atttccttta 2021028DNAArtificial SequencePCR
primer-probe 210agggatctga accaatacag agcagaca 2821120DNAArtificial
SequencePCR primer-probe 211aatccaaggg ggagagtgat
2021220DNAArtificial SequencePCR primer-probe 212gtacagattt
tgcccgagga 2021326DNAArtificial SequencePCR primer-probe
213catatggact ttgactcagc tgtggc 2621418DNAArtificial SequencePCR
primer-probe 214gcctcggtgt gcctttca 1821519DNAArtificial
SequencePCR primer-probe 215cgtgatgtgc gcaatcatg
1921622DNAArtificial SequencePCR primer-probe 216catcgccagc
tacgccctgc tc 22
* * * * *