CA2829476A1 - Gene expression markers for breast cancer prognosis - Google Patents

Gene expression markers for breast cancer prognosis Download PDF

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CA2829476A1
CA2829476A1 CA2829476A CA2829476A CA2829476A1 CA 2829476 A1 CA2829476 A1 CA 2829476A1 CA 2829476 A CA2829476 A CA 2829476A CA 2829476 A CA2829476 A CA 2829476A CA 2829476 A1 CA2829476 A1 CA 2829476A1
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Melody A. Cobleigh
Steve Shak
Joffre B. Baker
Maureen T. Cronin
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    • C12Q2600/158—Expression markers

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Abstract

The present invention provides gene sets the expression of which is important in the diagnosis and/or prognosis of breast cancer.

Description

DEMANDES OU BREVETS VOLUMINEUX
LA PRESENTE PARTIE DE CETTE DEMANDE OU CE BREVETS
COMPREND PLUS D'UN TOME.

NOTE: Pour les tomes additionels, veillez contacter le Bureau Canadien des Brevets.
JUMBO APPLICATIONS / PATENTS
THIS SECTION OF THE APPLICATION / PATENT CONTAINS MORE
THAN ONE VOLUME., NOTE: For additional volumes please contact the Canadian Patent Office.

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Gene Expression Markers for Breast Cancer Prognosis Background of the Invention Field of the Invention The present invention provides genes and gene sets the expression of which is important in the diagnosis and/or prognosis of breast cancer.
Description of the Related Art 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 quicldy as possible following diagnosis.
- 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 inununohistochemistry. =
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 immunohisto chemistry detection of proteins.
Recently, several groups have published studies concerning the classification of various cancer types by rnicroarray gene expression analysis (see, e.g. Golub et al., Science 286:531-537 (1999); Bhattacharjae et al., Proc. Natl. Acad. ScL 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); Sorlie et al., Proc. Natl. Acad. Sci. USA 98:10869-10874 (2001); Yam 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.
Although modem 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 notable exception is the use of estrogen receptor (ER) protein expression in breast carcinomas to select patients to treatment with anti-estrogen drugs, such as tamcorifen. Another exceptional example is the use of ErbB2 (Her2) protein expression in breast carcinomas to select patients with the Her2 antagonist drug Herceptin (Genentech, Inc., South San Francisco, CA).
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+ 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.
Summary of the Invention The present invention provides a set of genes, the expression of which has prognostic value, specifically with respect to disease-free survival.
2 Various embodiments of this invention provide a method of predicting a likelihood of long-term survival of a breast cancer patient without recurrence of breast cancer, comprising:
determining an expression level of an RNA transcript of KRT14 or its expression product, in a breast cancer tumor sample from said patient; normalizing said expression level to obtain a normalized expression level of KRT14; and providing information regarding the likelihood of breast cancer recurrence for said patient, wherein increased normalized expression of KRT14 indicates an increased likelihood of long-term survival without breast cancer recurrence. The method may further comprise determining a normalized expression level of an RNA transcript of at least one further gene or its expression product. Such a further gene may be: KRT5, KRT17, KRT18, KRT19 or MYBL2, wherein increased normalized expression level for the at least one further gene indicates a decreased likelihood of long-term survival without breast cancer recurrence.
Various embodiments of this invention provide a method of preparing a personalized genomics profile for a patient, comprising the steps of: (a) subjecting RNA
extracted from breast tissue from the patient to gene expression analysis; (b) determining an expression level of an RNA transcript of KRT14 or its expression product, wherein the expression level is normalized against at least one control gene to obtain normalized data, and optionally is compared to expression levels found in a breast cancer reference tissue set;
and (c) creating a report including a prediction of the likelihood of long-term survival without breast cancer recurrence for said patient, wherein increased normalized expression of KRT14 indicates an increased likelihood of long-term survival without breast cancer recurrence.
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 2a
3 PCT/US2004,00985 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.
In one aspect, the invention concerns a method of predicting the likelihood of long-term survival of a breast cancer patient without the recurrence of breast cancer, comprising determining the expression level of one or more prognostic RNA transcripts or their expression products in a breast cancer tissue sample obtained from the patient, normalized against the expression level of all RNA transcripts or their products in the breast cancer tissue sample, 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: TP53BP2, GRB7, PR, CD68, Bc12, KRT14, 1RS1, CTSL, EstR1, Chkl, IGFBP2, BAG1, CEGP1, STK15, GSTM1, FHIT, R1Z1, AIB1, SURV, BBC3, IGF1R, p27, GATA3, ZNF217, EGFR, CD9, MYBL2, HIF1a, pS2, ErbB3, TOP2B, MDM2, RAD51C, KRT19, TS, Her2, KLK10, 13-Catenin, y-Catenin, MCM2, PI3KC2A, IGF1, TBP, CCNB1, 113X05, and DR5, wherein expression of one or more of GRB7, CD68, CTSL, Chkl, ADM, CCNB1, MCM2, FBX05, Her2, STK15, SURV, EGFR, MYBL2, HIFI a, and TS indicates a decreased likelihood of long-term survival without breast cancer recurrence, and the expression of one or more of TP53BP2, PR, Bc12, KRT14, EstR1, IGFBP2, BAG1, CEGP1, KLK10, 13-Catenin, y-Catenin, DR5, PI3KCA2, RAD51C, GSTM1, FHIT, RIZ1, BBC3, TBP, p27, IRS1, IGF1R, GATA3, ZNF217, CD9, pS2, ErbB3, TOP2B, MDM2, IGF1, and KRT19 indicates an increased likelihood of long-term survival without breast cancer recurrence.
In a particular embodiment, the expression levels of at least two, or at least 5, or at least 10, or at least 15 of the prognostic RNA transcripts or their expression products are determined. In another embodiment, the method comprises the determination of the expression levels of all prognostic RNA transcripts or their expression products.
In another particular embodiment, the breast cancer is invasive breast carcinoma.
In a further embodiment, RNA is isolated from a fixed, wax-embedded breast cancer tissue specimen of the patient. Isolation may be performed by any technique known in the art, for example from core biopsy tissue or fine needle aspirate cells.

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PCT/US2004/00098:s In another aspect, the invention concerns an array comprising polynucleotides hybridizing to two or more of the following genes: a-Catenin, A1B1, AKT1, AKT2, 13-actin, BAG1, BBC3, Bc12, CCNB1, CCND1, CD68, CD9, CDH1, CEGP1, Chkl, CIAP1, cMet.2, Contig 27882, CTSL, DR5, EGFR, ElF4E, EPHX1, ErbB3, EstR1, FBX05, FHIT1 FRP1, GAPDH, GATA3, G-Catenin, GRB7, GRO1, GSTM1, GUS, HER2, HIF1A, HNF3A, IGF1R, IGFBP2, KLK10, KRT14, KRT17, KRT18, KRT19, KRT5, Maspin, MCM2, MCM3, MDM2, MMP9, MTA1, MYBL2, P14ARF, p2'7, P53, PI3KC2A, PR, PRAME, pS2, RAD51C,.3RB1, RIZ1, STK15, STMY3, SURV, TGFA, TOP2B, TP53BP2, TRAIL, TS, upa, VDR, VEGF, and ZNF217.
In particular embodiments, the array comprises polynucleotides hybridizing to at least 3, or at least 5, or at least 10, or at least 15, or at least 20, or all of the genes listed above.
In another specific embodiment, the array comprises polynucleotides hybridizing to the following genes: TP53BP2, GRB7, PR, CD68, Bc12, KRT14, IRS1, CTSL, EstR1, Chkl, IGFBP2, BAG1, CEGP1, STK15, GSTM1, FHIT, RIZ1, A161, SURV, BBC3, IGF1R, p27, GATA3, ZNF217, EGFR, CD9, MYBL2, HiFla, pS2, RIZ1, ErbB3, TOP2B, MDM2, RAD51C, KRT19, TS, Her2, KLK10, 13-Catenin, y-Catenin, MCM2, PI3KC2A, IGF1, TBP, CCNB1, FBX05 and DR5.
The polynucleotides can be cDNAs, or oligonucleotides, and the solid surface on .
which they are displayed may, for example, be glass.
In another 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:
(1) determining the expression levels of the RNA transcripts or the expression products of genes or a gene set selected from the group consisting of (a) TP53BP2, Bc12, BAD, EPHX1, PDGFRI3, DIABLO, XIAP, YB1, CA9, and KRT8;
(b) GRB7, CD68, TOP2A, Bc12, DIABLO, CD3, 11)1, PPM1D, MCM6, and WISP1;
(c) PR, TP53BP2, PRAME, DIABLO, CTSL, IGFBP2, TIMP1, CA9, MMP9, and COX2;
(d) CD68, GRB7, TOP2A, Bc12, DIABLO, CD3, Dl, PPM1D, MCM6, and WISP1;
(e) Bc12, TP53BP2, BAD, EPHX1, PDGFR13, DIABLO, XIAP, YB1, CA9, and ICRT8;
(f) KRT14, KRT5, PRAME, TP5313P2, GUS1, A1131, MCM3, CCNE1, MCM6, and 1D1;
4 (g) PRAME, TP53BP2, EstR1, DIABLO, CTSL, PPM1D, GRB7, DAPK1, BBC3, and VEGFB;
(h) CTSL2, GRB7, TOP2A, CCNB1, Bc12, DIABLO, PRAME, EMS1, CA9, and EpCAM;
(i) EstR1, TP53BP2, PRAME, DIABLO, CTSL, PPM1D, GRB7, DAPK1, BBC3, and VEGFB;
(k) Chkl, PRAME, TP53BP2, GRB7, CA9, CTSL, CCNBI, TOP2A, tumor size, and IGFBP2;
(1) IGFBP2, GRB7, PRAME, DIABLO, CTSL, P-Catenin, PPM1D, Chkl, WISP1, and LOT1;
(m) HER2, TP53BP2, Bc12, DIABLO, TIMPI, EPHX1, TOP2A, TRAM, CA9, and AREG;
(n) BAG1, TP53BP2, PRAME, IL6, CCNB1, PAI1, AREG, tumor size, CA9, and Ki67;
(o) CEGP1, TP53BP2, PRAME, DIABLO, Bc12, COX2, CCNE1, STK15, and AKT2, and FGF18;
(p) STK15, TP53BP2, PRAME, IL6, CCNB1, AKT2, DIABLO, cMet, CCNE2, and COX2;
(q) KLK10, EstR1, TP53BP2, PRAME, DIABLO, CTSL, PPM1D, GRB7, DAPK1, and BBC3;
(r) AEB1, TP53BP2, Bc12, DIABLO, TINP1, CD3, p53, CA9, GRB7, and EPHX1 (s) BBC3, GRB7, CD68, PRAME, TOP2A, CCNB1, EPHX1, CTSL
GSTM1, and APC;
(t) CD9, GRB7, CD68, TOP2A, Bc12, CCNB1, CD3, DIABLO, ID1, and PPM1D;
(w) EGFR, KRT14, GRB7, TOP2A, CCNB1, CTSL, Bc12, TP, KLK10, and CA9;
(x) HIF1a, PR, DIABLO, PRAME, Chkl, AKT2, GRB7, CCNE1, TOP2A, and CCNB1;
(y) MDM2, TP53BP2, DIABLO, Bc12, AlB1, TIMP1, CD3, p53, CA9, and HER2;
(z) MYBL2, TP53BP2, PRAME, IL6, Bc12, DIABLO, CCNE1, EPHX1, TIMP1, and CA9;
(aa) p27, TP53BP2, PRAME, DIABLO, Bc12, COX2, CCNE1, STK15, AKT2, and 1D1;
(ab) RAD51, GRB7, CD68, TOP2A, CIAP2, CCNB1, BAG1, IL6, FGFR1, and TP53BP2;
(ac) SURV, GRB7, TOP2A, PRAME, CTSL, GSTM1, CCNB1, VDR, CA9.,' and CCNE2;
(ad) TOP2B, TP53BP2, DIABLO, Bc12, TIMP1, AlE1, CA9, p53, KRT8, and BAD;
5 PCT/US2004/00098:, . J 204/065583 ZNF217, GRB7, TP53BP2, PRAME, DIABLO, Bc12, COX2, CCNE1, APC4, and p-Catenin, in a breast cancer tissue sample obtained from the patient, normalized against the expression levels of all RNA transcripts or their expression products in said breast cancer tissue sample, or of a reference set of RNA transcripts or their products;
(2) subjecting the data obtained in step (1) to statistical analysis; and (3) determining whether the likelihood of said long-term survival has increased or decreased.
In a further aspect, the invention concerns a method of predicting the likelihood of long-term survival of a patient diagnosed with estrogen receptor (ER)-positive invasive breast cancer, without the recurrence of breast cancer, comprising the steps of:
(1) determining the expression levels of the RNA transcripts or the expression products of genes of a gene set selected from the group consisting of CD68;
CTSL; FBX05;
SURV; CCNB1; MCM2; Chkl; MYBL2; HIF1A; cMET; EGFR; TS; STK15, IGFR1; BC12;
HNF3A; TP53BP2; GATA3; BBC3; RAD51C; BAG1; IGFBP2; PR; CD9; RB1; EPHX1;
CEGP1; TRAIL; DR5; p27; p53; MTA; RIZ1; ErbB3; TOP2B; ElF4E, wherein expression of the following genes in ER-positive cancer is indicative of a reduced likelihood of survival without cancer recurrence following surgery. CD68; ML; FBX05; SURV; CCNB1;
MCM2; Chkl; MYBL2; HIF1A; cMET; EGFR; TS; STK15, and wherein expression of the following genes is indicative of a better prognosis for survival without cancer recurrence following surgery: IGFR1; BCI2; IINF3A; TP53BP2; GATA3; BBC3; RAD51C; BAG1;
IGFBP2; PR; CD9; RB1; EPHX1; CEGP1; TRAIL; DR5; p27; p53; MTA; RTZ1; ErbB3;
TOP2B; ElF4E.
(2) subjecting the data obtained in step (1) to statistical analysis; and (3) determining whether the likelihood of said long-term survival has increased or decreased.
In yet another aspect, the invention concerns a method of predicting the likelihood of long-term survival of a patient diagnosed with estrogen receptor (ER)-negative invasive breast cancer, without the recurrence of breast cancer, comprising determining the expression levels of the RNA transcripts or the expression products of genes of the gene set CCND1; IRA;
IINF3A; CDH1; Her2; GRB7; AKT1; STMY3; a-Catenin; VDR; GR01; KT14; KLK10;
Maspin, TGFa, and FRP1, wherein expression of the following genes is indicative of a
6 reduced likelihood of survival without cancer recurrence: CCND1; UPA; HNF3A;
CDH1;
Her2; GRB7; AKT1; STMY3; a-Catenin; VDR; GRO1, and wherein expression of the following genes is indicative of a better prognosis for survival without cancer recurrence:
KT14; KLK10; Maspin, TGFa, and FRP1.
In a different aspect, the invention concerns a method of preparing a personalized genomics profile for a patient, comprising the steps of:
(a) subjecting RNA extracted from a breast tissue obtained from the patient to gene expression analysis;
(b) determining the expression level of one or more genes selected from the breast cancer gene set listed in any one of Tables 1-5, 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 (c) creating a report summarizing the data obtained by the gene expression analysis.
The report may, for example, include prediction of the likelihood of long term survival of the patient and/or recommendation for a treatment modality of said patient.
In a further aspect, the invention concerns a method for amplification of a gene listed in Tables 5A and B by polymerase chain reaction .(PCR), comprising performing said PCR by using an amplicon listed in Tables 5A and B and a primer-probe set listed in Tables 6A-F.
In a still further aspect, the invention concerns a PCR amplicon listed in Tables 5A and B.
In yet another aspect, the invention concerns a PCR primer-probe set listed in Tables 6A-F.
The invention further concerns a prognostic method comprising:
(a) subjecting a sample comprising breast cancer cells obtained from a patient to quantitative analysis of the expression level of the RNA transcript of at least one gene selected from the group consisting of GR137, CD68, CTSL, Chid, AlB1, CCNB1, MCM2, FBX05, Her2, STK15, SURV, EGFR, MYBL2, lilFia, and TS, or their product, and (b) identifying the patient as likely to have a decreased likelihood of long-term survival without breast cancer recurrence if the normalized expression levels of the gene or genes, or their products, are elevated above a defined expression threshold.
In a different aspect, the invention concerns a prognostic method comprising:
7 2004/065583 PCT/US2004/00098:, (a) subjecting a sample comprising breast cancer cells obtained from a patient to quantitative analysis of the expression level of the RNA transcript of at least one gene selected from the group consisting of TP53BP2, PR, Bc12, KRT14, EstR1, IGFBP2, BAG1, CEGP1, KLK10, p-Catenin, y-Catenin, DR5, PI3KCA2, RAD51C, GSTM1, FHIT, 1UZ1, BBC3, TBP, p27, IRS1, IGF1R, GATA3, ZNF217, CD9, pS2, ErbB3, TOP2B, MDM2, IGF1, and KRT19, and (b) identifying the patient as likely to have an increased likelihood of long-term survival without breast cancer recurrence if the normalized expression levels of the gene or genes, or their products, are elevated above a defined expression threshold.
The invention further concerns a kit comprising one or more of (1) extraction buffer/reagents and protocol; (2) reverse transcription buffer/reagents and protocol; and (3) qPCR buffer/reagents and protocol suitable for performing any of the foregoing methods.
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Description of the Tables Table 1 is a list of genes, expression of which correlate with breast cancer survival.
Results from a retrospective clinical trial. Binary statistical analysis.
Table 2 is a list of genes, expression of which correlates with breast cancer survival in estrogen receptor (ER) positive patients. Results from a retrospective clinical trial. Binary statistical analysis.
Table 3 is a list of genes, expression of which correlates with breast cancer survival in estrogen receptor (ER) negative patients. Results from a retrospective clinical trial. Binary statistical analysis.
Table 4 is a list of genes, expression of which correlates with breast cancer survival.
Results from a retrospective clinical trial. Cox proportional hazards statistical analysis.
Tables 5A and B show a list of genes, expression of which correlate with breast cancer survival. Results from a retrospective clinical trial. The table includes accession numbers for the genes, and amplicon sequences used for PCR amplification.
Tables 6A-6F The table includes sequences for the forward and reverse primers (designated by "f" and "r", respectively) and probes (designated by "p") used for PCR
amplification of the amplicons listed in Tables 5A-B.
Detailed Description of the Preferred Embodiment A. Definitions 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, NY 1994), and March, Advanced Organic Chemistry Reactions, Mechanisms and Structure 4th ed., John Wiley & Sons (New York, NY 1992), provide one skilled in the art with a general guide to many of the terms used in the present application.
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.
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2004/065583 PCT/US2004/00098..
The term "microarray" refers to an ordered arrangement of hybridizable array elements, preferably polynucleotide probes, on a substrate.
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 in 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.
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.
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.
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 (ruRNA) produced, i.e., the level of gene expression, also increases in the proportion of the number of copies made of the particular gene expressed.
The term "diagnosis" is used herein to refer to the identification of a molecular or pathological state, disease or condition, such as the identification of a molecular subtype of head and neck cancer, colon cancer, or other type of cancer.
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 =
r- C.

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 sugery and/or termination of chemotherapy or other treatment modalities is likely.
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.
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.
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, colon cancer, lung cancer, prostate cancer, hepatocellular cancer, gastric cancer,.pancreatic cancer, cervical cancer, ovarian cancer, liver cancer, bladder cancer, cancer of the urinary tract, thyroid cancer, renal cancer, carcinoma, melanoma, and brain cancer.
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.
"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).
"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 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% Fico11/0.1% polyvinylpyrrolidone/50mM sodium phosphate buffer at pH 6.5 with 750 mM sodium chloride, 75 mIVI sodium citrate at 42 C;
or (3) employ 50% formamide, 5 x SSC (0.75 M NaC1, 0.075 M sodium citrate), 50 mM sodium phosphate (pH 6.8), 0.1% sodium pyrophosphate, 5 x Denhardt's solution, sonicated salmon sperm DNA
(50 pg/m1), 0.1% SDS, and 10% dextran sulfate at 42 C, with washes at 42 C in 0.2 x SSC
(sodium chloride/sodium citrate) and 50% formamide at 55 C, followed by a high-stringency wash consisting of 0.1 x SSC containing EDTA at 55 C.
"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 C in a solution comprising:
20%
formamide, 5 x SSC (150 mM NaC1, 15 mM trisodium citrate), 50 mM sodium phosphate (pH 7.6), 5 x Denhardt's solution, 10% dextran sulfate, and 20 mg/nil denatured sheared salmon sperm DNA, followed by washing the filters in 1 x SSC at about 37-50 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.
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.
The terms "expression threshold," and "defined expression threshold" are used interchangeably and refer to the level of a gene or gene product in question above which the gene or gene product serves as a predictive marker for patient survival without cancer s _I 2004/065583 PCTPUS2004/000983 recurrence. The threshold is defined experimentally from clinical studies such as those described in the Example below. The expression threshold can be selected either for maximum sensitivity, or for maximum selectivity, or for minimum error. The determination of the expression threshold for any situation is well within the knowledge of those skilled in the art.
B. Detailed Description 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", ri 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", 4th edition (D.M. Weir &
C.C. Blackwell, eds., Blackwell Science Inc., 1987); "Gene Transfer Vectors for Mammalian Cells" (J.M. Miller & M.P. Cabs, 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).
1. Gene Expression Profiling In general, methods of gene expression profiling can be divided into two large groups:
methods based on hybridization analysis of polynucleotides, and methods based on sequencing of polynucleotides. 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 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 (MYSS).
2. Reverse Transcriptase PCR (RT-PCR) 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.
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.
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 Biology, 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 MasterPureTM Complete DNA and RNA
Purification Kit (EPICENTRE , Madison, WI), 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.
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 (MNILV-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 J 2004/065583 PCT/US2004/00098:, manufacturer's instructions. The derived cDNA can then be used as a template in the subsequent PCR reaction.
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 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 fluorophore. 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.
TaqMan RT-PCR can be performed using commercially available equipment, such as, for example, ABI PRISM 7700TM Sequence Detection System' (Perkin-Elmer-Applied Biosystems, Foster City, CA, 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 7700TM Sequence Detection System.
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.
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 (Ct).

WO 200-1/065583 PCT/US2004,600985 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 P-actin.
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 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).
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 vim 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.
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. In this embodiment, 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.
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, NJ, pp 365-386) 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-PC bases. Tm's between 50 and 80 C, e.g. about 50 to 70 C are typically preferred.
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).
3. Microarrays 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.

WO 2004/065583 PCT/US2004/m/0985 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.
. 5 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. /S'ci. 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.
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.
4. Serial Analysis of Gene Expression ('SAGE) 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 J 2004/065583 PCT/US2004/00098:, 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).
5. MassARRAY Technology The MassARRAY (Sequenom, San Diego, California) technology is an automated, high-throughput method of gene expression analysis using mass spectrometry (MS) for detection. According to this method, following the isolation of RNA, reverse transcription and PCR amplification, the cDNAs are subjected to primer extension. The cDNA-derived primer extension products are purified, and dipensed on a chip array that is pre-loaded with the components needed for MALTI-TOF MS sample preparation. The various cDNAs present in the reaction are quantitated by analyzing the peak areas in the mass spectrum obtained.
6. Gene Expression Analysis by Massively Parallel Signature Sequencing (MPSS) 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 inn 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 x 106 microbeads/cm2). 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.
7. linmunohistochemistry 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 radioactive 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 Pcrius200400085 antibody. Immunohistochemistry protocols and kits are well known in the art and are commercially available.
8. Proteomics 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.
9. General Description of the niRNA Isolation, Purification and Amplification 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. Wee. 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 Inn 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.
10. Breast Cancer Gene Set, Assayed Gene Subsequences, and Clinical Application of Gene Expression Data 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 Cypl. 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.
Further details of the invention will be described in the following non-limiting Example Example A Phase II Study of Gene Expression in 79 Malignant Breast Tumors A gene expression study was designed and conducted with the primary goal to molecularly characterize gene expression in paraffm-embedded, fixed tissue samples of invasive breast ductal carcinoma, and to explore the correlation between such molecular profiles and disease-free survival.
=
Study design Molecular assays were performed on paraffin-embedded, formalin-fixed primary breast tumor tissues obtained from 79 individual patients diagnosed with invasive breast cancer. All patients in the study had 10 or more positive nodes. Mean age was 57 years, and mean clinical tumor size was 4.4 cm. 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.
Materials and Methods Each representative tumor block was characterized by standard histopathology for diagnosis, semi-quantitative assessment of amount of tumor, and tumor grade. A
total of 6 sections (10 microns in thickness each) were prepared and placed in two Costar Brand Microcentrifuge Tubes (Polypropylene, 1.7 mL tubes, clear; 3 sections in each tube). If the tumor constituted less than 30% of the total specimen area, the sample may have been crudely dissected by the pathologist, using gross microdissection, putting the tumor tissue directly into the Costar tube.
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.
Gene Expression Analysis mRNA was extracted and purified from fixed, paraffin-embedded tissue samples, and prepared for gene expression analysis as described in section 9 above.
Molecular assays of quantitative gene expression were performed by RT-PCR, using the ABI PRISM 7900Tm Sequence Detection SystemTm (Perkin-Elmer-Applied Biosystems, Foster City, CA, USA). ABI PRISM 7900114 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.
Analysis and Results Tumor tissue was analyzed for 185 cancer-related genes and 7 reference genes.
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:
0 died due to breast cancer or to unknown cause or alive with breast cancer recurrence;

PCT/US2004/00098, 1 alive without breast cancer recurrence or died due to a cause other than breast cancer Analysis was performed by:
1. Analysis of the relationship between normalized gene expression and the binary outcomes of 0 or 1.
2. 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.
Analysis of _patients with invasive breast carcinoma by binaly approach In the first (binary) approach, analysis was performed on all 79 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 three years, versus recurrence, or breast cancer-related death at three years, and the p-values for the differences between the groups for each gene were calculated.
Table 1 lists the 47 genes for which the p-value for the differences between the groups was <0.10. The first column of mean expression values pertains to patients who neither had a metastatic recurrence of nor died from breast cancer. The second column of mean expression values pertains to patients who either had a metastatic recurrence of or died from breast cancer.
Table 1 Mean Mean t-value. df p Valid N
Valid N
Bc12 -0.15748 -1.22816 4.00034 75 0.000147 PR -2.67225 -5.49747 3.61540 75 0.000541 IGF1R -0.59390 -1.71506 3.49158 75 0.000808 BAG1 0.18844 -0.68509 3.42973 75 0.000985 CD68 -0.52275 0.10983 -3.41186 75 0.001043 EstR1 -0.35581 -3.00699 3.32190 75 0.001384 CTSL -0.64894 -0.09204 -3.26781 75 0.001637 IGFBP2 -0.81181 -1.78398 3.24158 75 0.001774 35 42 GATA3 1.80525 0.57428 3.15608 75 0.002303 TP53BP2 -4.71118 -6.09289 3.02888 75 0.003365 35 42 EstR1 3.67801 1.64693 3.01073 75 0.003550 CEGP1 -2.02566 -4.25537 2.85620 75 0.005544 SURV -3.67493 -2.96982 -2.70544 75 0.008439 p27 0.80789 0.28807 2.55401 75 0.012678 Chk1 -3.37981 -2.80389 -2.46979 75 0.015793 BBC3 -4.71789 -5.62957 2.46019 75 0.016189 , , ,__ k, rõ., ' ZNF217 1.10038 0.62730 2.42282 75 0.017814 EGFR -2.88172 -2.20556 -2.34774 75 0.021527 35 42 CD9 1.29955 0.91025 2.31439 75 0.023386 MYBL2 -3.77489 -3.02193 -2.29042 75 0.024809 35 42 HIF1A -0.44248 0.03740 -2.25950 75 0.026757 35 42 GRB7 -1.96063 -1.05007 -2.25801 75 0.026854 35 42 pS2 -1.00691 -3.13749 2.24070 75 0.028006 35 42 RIZ1 -7.62149 -8.38750 2.20226 75 0.030720 35 42 ErbB3 -6.89508 -7.44326 2.16127 75 0.033866 35 42 TOP2B 0.45122 0.12665 2.14616 75 0.035095 MDM2 1.09049 0.69001 2_10967 75 0.038223 PRAM E -6.40074 -7.70424 2.08126 75 0.040823 35 42 GUS -1.51683 -1.89280 2.05200 75 0.043661 35 42 RAD51C -5.85618 -6.71334 2.04575 75 0.044288 35 42 AlB1 -3.08217 -228784 -2.00600 75 0.048462 35 42 STK15 -3.11307 -2.59454 -2.00321 75 0.048768 35 42 GAPDH -0.35829 -0.02292 -1.94326 75 0.055737 35 42 FHIT -3.00431 -3.67175 1.86927 75 0.065489 35 42 KRT19 2.52397 2.01694 1.85741 75 0.067179 TS -2.83607 -2.29048 -1.83712 75 0.070153 35 42 GSTM1 -3.69140 -4.38623 1.83397 75 0.070625 35 42 G- 0.31875 -0.15524 1.80823 75 0.074580 Catenin AKT2 0.78858 0.46703 1.79276 75 0.077043 CCNB1 -4.26197 -3.51628 -1.78803 75 0.077810 35 42 PI3KC2A -2.27401 -2.70265 1.76748 75 0.081215 35 42 FBX05 -4.72107 -4.24411 -1.75935 75 0.082596 35 42 DR5 -5.80850 -6.55501 1.74345 75 0.085353 35 42 C IAP1 -2.81825 -3.09921 1.72480 75 0.088683 35 42 MCM2 -2.87541 -2.50683 -1.72061 75 0.089445 35 42 .
CCND1 1.30995 0.80905 1.68794 75 0.095578 -El F4E -5.37657 -6.47156 1.68169 75 0.096788 35 42 In the foregoing Table 1, negative t-values indicate higher expression, associated with worse outcomes, and, inversely, higher (positive) t-values indicate higher expression associated with better outcomes. Thus, for example, elevated expression of the CD68 gene (t-value = -3.41, CT mean alive< CT mean deceased) indicates a reduced likelihood of disease free survival. Similarly, elevated expression of the BC12 gene (t-value =
4.00; CT mean alive> CT mean deceased) indicates an increased likelihood of disease free survival.
Based on the data set forth in Table 1, the expression of any of the following genes in . .
breast cancer above a defined expression threshold indicates a reduced likelihood of survival without cancer recurrence following surgery: Grb7, CD68, CTSL, Chkl, Her2, STK15, MB!, SURV, EGFR, MYBL2, HIF la.
Based on the data set forth in Table 1, the expression of any of the following genes in breast cancer above a defined expression threshold indicates a better prognosis for survival , , C l ie--.
.. 0 2004/065583 PCT/U52004/0009&.
without cancer recurrence following surgery: TP53BP2, PR, Bc12, KRT14, EstR1, IGFBP2, BAG1, CEGP1, KLK10, 0 Catenin, GSTM1, FRET, Rizl, IGF1, BBC3, IGFR1, TBP, p27, IRS1, 1GF1R, GATA3, CEGP1, ZNF217, CD9, pS2, ErbB3, TOP2B, MDM2, RAD51, and KRT19.
Analysis of ER positive patients by binaly approach 57 patients with normalized CT for estrogen receptor (ER) >0 (i.e., ER
positive patients) were subjected to separate analysis. A t test was performed on the two groups of patients classified as either no recurrence and no breast cancer related death at three years, or recurrence or breast cancer-related death at three years, and the p-values for the differences between the groups for each gene were calculated. Table 2, below, lists the genes where the p-value for the differences between the groups was <0.105. The first column of mean expression values pertains to patients who neither had a metastatic recurrence nor died from breast cancer. The second column of mean expression values pertains to patients who either had a metastatic recurrence of or died from breast cancer.
Table 2 Mean Mean t-value df a Valid N Valid N
IGF1R -0.13975 -1.00435 3.65063 55 0.000584 30 Bc12 0.15345 -0.70480 3.55488 55 0.000786 30 CD68 -0.54779 0.19427 -3.41818 55 0.001193 30 HNF3A 0.39617 -0.63802 3.20750 55 0.002233 ' 30 CTSL -0.66726 0.00354 -3.20692 55 0.002237 30 TP538P2 -4.81858 -6.44425 3.13698 55 0.002741 30 27 GATA3 2.33386 1.40803 3.02958 55 0.003727 30 BBC3 -4.54979 -5.72333 2.91943 55 0.005074 30 RAD51C -5.63363 -6.94841 2.85475 55 0.006063 30 27 BAG1 0.31087 -0.50669 2.61524 55 0.011485 30 IGFBP2 -0.49300 -1.30983 2.59121 55 0.012222 30 27 FBX05 -4.86333 -4.05564 -2.56325 55 0.013135 30 EstR1 0.68368 -0.66555 2.56090 55 0.013214 30 PR -1.89094 -3.86602 2.52803 55 0.014372 30 SU RV -3.87857 -3.10970 -2.49622 55 0.015579 30 CD9 1.41691 0.91725 2.43043 55 0.018370 30 RBI -2.51662 -2.97419 2.41221 55 0.019219 30 EPHX1 -3.91703 -5.85097 2.29491 55 0.025578 30 CEGP1 -1.18600 -2.95139 2.26608 55 0.027403 30 CCNB1 -4.44522 -3.35763 -2.25148 55 0.028370 30 27 =
TRAIL 0.34893 -0.56574 2.20372 55 0.031749 30 EstR1 4.60346 3.60340 2.20223 55 0.031860 30 =
DR5 -5.71827 -6.79088 2.14548 55 0.036345 30 MCM2 -2.96800 -2.48458 -2.10518 55 0.039857 30 Chk1 -3.46968 -2.85708 -2.08597 55 0.041633 30 p27 0.94714 0.49656 2.04313 55 0.045843 30 MYBL2 -3.97810 -3.14837 -2.02921 55 0.047288 30 GUS -1.42486 -1.82900 1.99758 55 0.050718 30 WO 2004/065583 PCT/US2004,m0985 P53 -1.08810 -1.47193 1.92087 55 0.059938 30 HIFI A -0.40925 0.11688 -1.91278 55 0.060989 30 cMet -6.36835 -5.58479 -1.88318 55 0.064969 EGFR -2.95785 -2.28105 -1.86840 55 0.067036 MTA1 -7.55365 -8.13656 1.81479 55 0.075011 30 RIZ1 -7.52785 -8.25903 1.79518 55 0.078119 30 ErbB3 -6.62488 -7.10826 1.79255 55 0.078545 30 TOP2B 0.54974 0.27531 1.74888 55 0.085891 30 ElF4E -5.06603 -6.31426 1.68030 55 0.098571 30 TS -2.95042 -2.36167 -1.67324 55 0.099959 STK15 -3.25010 -2.72118 -1.64822 55 0.105010 For each gene, a classification algorithm was utilized to identify the best threshold value (CT) for using each gene alone in predicting clinical outcome.
Based on the data set forth in Table 2, expression of the following genes in ER-positive cancer above a defined expression level is indicative of a reduced likelihood of survival without cancer recurrence following surgery: CD68; CTSL; FBX05; SURV;

CCNB1; MCM2; Chkl; MYBL2; HIF1A; cMET; EGFR; TS; STK15. Many of these genes (CD68, CTSL, SURV, CCNB1, MCM2, Chkl, MYBL2, EGFR, and STK15) were also identified as indicators of poor prognosis in the previous analysis, not limited Co ER-positive breast cancer. Based on the data set forth in Table 2, expression of the following genes in ER-positive cancer above a defined expression level is indicative of a better prognosis for survival without cancer recurrence following surgery: IGFR1; BC12; HNF3A;
TP53BP2;
GATA3; BBC3; RAD51C; BAG1; IGFBP2; PR; CD9; RB1; EPHX1; CEGP1; TRAIL; DR5;
p27; p53; MTA; RIZ1; ErbB3; TOP2B; E1f4E. Of the latter genes, IGFR1; BC12;
TP53BP2;
GATA3; BBC3; RAD51C; BAG!; IGEBP2; PR; CD9; CEGP1; DR5; p27; RIZ1; ErbB3;
TOP2B; E1F4E have also been identified as indicators of good prognosis in the previous analysis, not limited to ER-positive breast cancer.
Analysis of ER negative patients by binaly approach Twenty patients with normalized CT for estrogen receptor (ER) <1.6 (i.e., ER
negative patients) were subjected to separate analysis. A t test was performed on the two groups of patients classified as either no recurrence and no breast cancer related death at three years, or recurrence or breast cancer-related death at three years, and the p-values for the differences between the groups for each gene were calculated. Table 3 lists the genes where the p-value for the differences between the groups was <0.118. The first colunm of mean expression values pertains to patients who neither had a metastatic recurrence nor died from breast ' , C
_ C
J 2004/065583 PCT/US2004/00098_.
cancer. The second column of mean expression values pertains to patients who either had a metastatic recurrence of or died from breast cancer.
Table 3 Mean Mean t-value df P Valid N
Valid N
KRT14 -1.95323 -6.69231 4.03303 18 0.000780 5 KLK10 -2.68043 -7.11288 3.10321 18 0.006136 5 CCND1 -1.02285 0.03732 -2.77992 18 0.012357 5 15 Upa -0.91272 -0.04773 -2.49460 18 0.022560 5 15 HNF3A -6.04780 -2.36469 -2.43148 18 0.025707 5 15 Maspin -3.56145 -6.18678 2.40169 18 0.027332 5 CDH1 -3.54450 -2.34984 -2.38755 18 0.028136 5 15 HER2 -1.48973 1.53108 -2.35826 18 0.029873 5 15 .
GRB7 -2.55289 0.00036 -2.32890 18 0.031714 5 AKT1 -0.36849 0.46222 -2.29737 18 0.033807 5 15 TGFA -4.03137 -5.67225 2.28546 18 0.034632 5 FRP1 1.45776 -1.39459 2.27884 18 0.035097 5 STMY3 -1.59610 -0.26305 -2.23191 18 0.038570 5 15 Contig 2 -4.27585 -7.34338 2.18700 18 0.042187 5 A-Catenin -1.19790 -0.39085 -2.15624 18 0.044840 5 15 VDR -4.37823 -2.37167 -2.15620 18 0.044844 5 15 GRO1 -3.65034 -5.97002 2.12286 18 0.047893 5 15 MCM3 -3.86041 -5.55078 2.10030 18 0.050061 5 B-actin 4.69672 5.19190 -2.04951 18 0.055273 5 HIF1A -0.64183 -0.10566 -2.02301 18 0.058183 5 15 MMP9 -8.90613 -7.35163 -1.88747 18 0.075329 5 1.5 VEGF 0.37904 1.10778 -1.87451 18 0.077183 5 PRAME -4.95855 -7.41973 1.86668 18 0.078322 5 15 AIB1 -3.12245 -1.92934 -1.86324 18 0.078829 5 15 KRT5 -1.32418 -3.62027 1.85919 18 0.079428 5 KRT18 1.08383 2.25369 -1.83831 18 0.082577 5 KRT17 -0.69073 -3.56536 1.78449 18 0.091209 5 P14ARF -1.87104 -3.36534 1.63923 18 0.118525 5 15 Based on the data set forth in Table 3, expression of the following genes in ER-negative cancer above a= defined expression level is indicative of a reduced likelihood of survival without cancer recurrence (p<0.05): CCND1; UPA; HNF3A; CDH1; Her2;
GRB7;
AKT1; STMY3; a-Catenin; VDR; GROL Only 2 of these genes (Her2 and Grb7) were also identified as indicators of poor prognosis in the previous analysis, not limited to ER-negative =
breast cancer. Based on the data set forth in Table 3, expression of the following genes in ER-negative cancer above a defined expression level is indicative of a better prognosis for survival without cancer recurrence (KT14; KLK10; Maspin, TGFa, and FRP1. Of the latter genes, only KLK10 has been identified as an indicator of good prognosis in the previous analysis, not limited to ER-negative breast cancer.

W. 2004/065583 Analysis of multiple genes and indicators of outcome 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 have a p-value <0.10 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 4. The third column, with the heading: exp(coef), shows RR
values.
=

PCT/US2004/00098t, Table 4 Gene coef exp(coef) se(coef) z TP53BP2 -0.21892 0.803386 0.068279 -3.20625 0.00134 =
GRB7 0.235697 1.265791 0.073541 3.204992 0.00135 PR -0.10258 0.90251 0.035864 -2.86018 0.00423 CD68 0.465623 1.593006 0.167785 2.775115 0.00552 BcI2 -0.26769 0.765146 0.100785 -2.65603 0.00791 KRT14 -0.11892 0.887877 0.046938 -2.53359 0.0113 MAME -0.13707 0.871912 0.054904 -2.49649 0.0125 CTSL 0.431499 1.539564 0.185237 2.329444 0.0198 EstR1 -0.07686 0.926018 0.034848 -2.20561 0.0274 Chk1 0.284466 1.329053 0.130823 2.174441 0.0297 IGFBP2 -0.2152 0.806376 0.099324 -2.16669 0.0303 HER2 0.155303 1.168011 0.072633 2.13818 0.0325 BAG1 -0.22695 0.796959 0.106377 -2.13346 0.0329 CEGP1 -0.07879 0.924236 0.036959 -2.13177 0.033 STK15 0.27947 1.322428 0.132762 2.105039 0.0353 KLK10 -0.11028 0.895588 0.05245 -2.10248 0.0355 B.Catenin -0.16536 0.847586 0.084796 -1.95013 0.0512 EstR1 -0.0803 0.922842 0.042212 -1.90226 0.0571 GSTM1 -0.13209 0.876266 0.072211 -1.82915 0.0674 TOP2A -0.11148 0.894512 0.061855 -1.80222 0.0715 AIB1 0.152968 1.165288 0.086332 1.771861 0.0764 FHIT -0.15572 0.855802 0.088205 -1.7654 0.0775 RIZ1 -0.17467 0.839736 0.099464 -1.75609 0.0791 SURV 0.185784 1.204162 0.106625 1.742399 0.0814 IGF1 -0.10499 0.900338 0.060482 -1.73581 0.0826 =
BBC3 -0.1344 0.874243 0.077613 -1.73163 0.0833 IGF1R -0.13484 0.873858 0.077889 -1.73115 0.0834 DIABLO 0.284336 1.32888 0.166556 1.707148 0.0878 TBP -0.34404 0.7089 0.20564 -1.67303 0.0943 p27 -0.26002 0.771033 0.1564 -1.66256 0.0964 IRS1 -0.07585 0.926957 0.046096 -1.64542 0.0999 The binary and time-to-event analyses, with few exceptions, identified the same genes as prognostic markers-. For example, comparison of Tables 1 and 4 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 21 genes, they were always 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.
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 r-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.
Multivariate analysis is performed using the following equation:
RR-exp[coef(geneA) x Ct(geneA) + coef(geneB) x Ct(geneB) + coef(geneC) x Ct(geneC) + ........ 1.
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.
Multivariate gene analysis of 79 patients with invasive breast carcinoma A multivariate stepwise analysis, using the Cox Proportional Hazards Model, was performed on the gene expression data obtained for all 79 patients with invasive breast carcinoma. The following ten-gene sets have been identified by this analysis as having particularly strong predictive value of patient survival:
(a) TP53BP2, Bc12, BAD, EPHX1, PDGFRii, DIABLO, XIAP, YB1, CA9, and KRT8.
(b) GRB7, CD68, TOP2A, Bc12, DIABLO, CD3, Dl, PPM1D, MCM6, and WISP1.
(c) PR, TP53BP2, PRAME, DIABLO, CTSL, IGFBP2, TIMP1, CA9, MIVIP9, and COX2.
(d) CD68, GRB7, TOP2A, Bc12, DIABLO, CD3, Dl, PPM1D, MCM6, and WISP1.
(e) Bc12, TP53BP2, BAD, EPHX1, PDGFRO, DIABLO, MAP, YB1, CA9, and KRT8.
(1) KRT14, KRT5, PRAME, TP53BP2, GUS!, AIB1, MCM3, CCNE1, MCM6, and Dl.
(g) PRAME, TP53BP2, EstR1, DIABLO, CTSL, PPM1D, GRB7, DAPK1, BBC3, and VEGFB.
(h) CTSL2, GRB7, TOP2A, CCNB1, Bc12, DIABLO, PRAME, EMS I , CA9, and EpCAIVI.

Ci PCT/US2004/00098:.
(i) EstR1, TP53BP2, PRAME, DIABLO, CTSL, PPM1D, GRB7, DAPK1, BBC3, and VEGFB.
(k) Chid, PRAME, p53BP2, GRB7, CA9, CTSL, CCNB1, TOP2A, tumor size, and IGFBP2.
(1) IGFBP2, GRB7, PRAME, DIABLO, CTSL, I3-Catenin, PPM1D, Chkl, WISP1, and LOT1 .
(m) HER2, TP53BP2, Bc12, DIABLO, TIMP1, EPHX1, TOP2A, TRAIL, CA9, and AREG.
(n) BAG1, TP53BP2, PRAME, IL6, CCNB1, PAIL AREG, tumor size, CA9, and Ki67.
(o) CEGP1, TP53BP2, PRAME, DIABLO, Bc12, COX2, CCNE1, STK15, and AKT2, and FGF18.
(p) STK15, TP53BP2, PRAME, IL6, CCNE1, AKT2, DIABLO, cMet, CCNE2, and COX2.
(q) KLK10, EstR1, TP53BP2, PRAME, DIABLO, CTSL, PPM1D, GRB7, DAPK1, and BBC3.
(r) AIB1, TP53BP2, Bc12, DIABLO, TIMP1, CD3, p53, CA9, GRB7, and EPHX1 (s) BBC3, GRB7, CD68, PRAME, TOP2A, CCNB1, EPHX1, CTSL
GSTM1, and APC.
(t) CD9, GRB7, CD68, TOP2A, Bc12, CCNB1, CD3, DIABLO, Dl, and PPM1D.
(w) EGFR, KRT14, GRB7, TOP2A, CCNB1, CTSL, Bc12, TP, KLK10, and CA9.
(x) HIF1a, PR, DIABLO, PRAME, Chkl, AKT2, GRB7, CCNE1, TOP2A, and CCNB1.
(y) MDM2, TP53BP2, DIABLO, Bc12, AlB1, TIMP1, CD3, p53, CA9, and 1]IER2.
(z) MYBL2, TP53BP2, PRAME, IL6, Bc12, DIABLO, CCNE1, EPHX1, TNT% and CA9.
(aa) p27, TP53BP2, PRAME, DIABLO, Bc12, COX2, CCNE1, STK15, AKT2, and ID1.
(ab) RAD51, GRB7, CD68, TOP2A, CIAP2, CCNB1, BAG1, IL6, FGFR1, and TP53BP2.
(ac) SURV, GRB7, TOP2A, PRAME, CTSL, GSTM1, CCNB1, VDR, CA9, and CCNE2.
(ad) TOP2B, TP53BP2, DIABLO, Bc12, T1MP1, A131, CA9, p53, KRT8, and BAD.
(ae) ZNF217, GRB7, p53BP2, PRAME, DIABLO, Bc12, COX2, CCNE1, APC4, and 3-Catenin.

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 such embodiments. To the contrary, the invention is intended to cover various modifications and equivalents included within the 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.

=
Table 5A
Gene =' Accession Sag = =
Al81 NM_006534GCGGCGAG1TTCCGAMAAAGCTGAGCTGCGAGGAAAATGGCGGCGGGAG3ATCAAAATAC1TGCTGGATG
GTGGACTCA

NM_005163CGCTTCTATGGCGCTGAGATTGTGTCAGCCCTGGACTACCTGCACTCGGAGAAGAACGTGGTGTACCGGG
A

NM_001626TCCTGCCACCCTTCAAACCTCAGGTCACGTCCGAGGTCGACACAAGGTACTTCGAT3ATGAATITACCGC
C =
APC NM_000038 GGACAGGAGGAATGTGTTTCTCCATACAGGTCACGGGGAGCCAATGGTTCAGAAACAAATCGAGTGOGT =
.AREG NM_001667 TGTGAGTGAAATGCCTTCTAGTAGTGAACCGTCCTCGGGAGCCGACTATGACTACTCAGAAGAGTATGATAACGAACCA
CAA
B-actin NM_001101CAGCAGATGIGGATCAGCAAGCAGGAGTATGACGAGTCCGGCCCCTCCATCGTCCACCGCAAATGC.

CatenInNM_001904GGCTCTTGTGCGTACTGTCCTTCOGGCTGGTGACAGGGAAGACATCACTGAGCCTGCCATCTG

BAD NM_032989GGGTCAGGTGCCTCGAGATCGGGCTTGGGCCCAGAGCATGI1-CCAGATCCCAGAG1TTGAGCCGAGTGAGCAG =
BAG1 NM_004323 CGTTGTCAGCACTTGGAATACAAGATGGTTGCCGGGICATGTTAATTGGGAAAAAGAACAGTCCACAGGAAGAGGTTGA
AC
BBC3 NM_014417 CCTGGAGGGTCCTGTACAATCTCATCATGGGACTCCTGCCOTTACCCAGGGGCCACAGAGCCCCCGAGATGGAGCCCAA
TTAG
5c12 NM 000633 CAGATGGACCTAGTACCCACTGAGATTTCCACGCCGAAGGACAGCGATGGGAAAAATGCCCTTAAATCATAGG
'CAS NNL001216ATCCTAGCCCTGGITTTIGGCCTCCTITITGCTGTCACCAGCGTCGCG1-TCCTTGTGCAGATGAGAAGGCAG
CCNBI

TGCCCAAGAAGATG =
CCNO1 NNW:01755 GCATGTTCGTGGCCTCTAAGATGAAGGAGACCATCCCCCTGACGGCCGAGAAGCTOTGCATCTACACCG , CCNEI
NM_001238AAAGAAGATGATGACCGGGITTACCCAAACTCAACGTGCAAGCCTCGGATTATrGCACCATCCAGAGGCT
C.
CCNE2 NUL.057749ATGCTGTGGCTCCTTCCTAACTG3GGC1-CD3z NM_000734AGA1GAAGTGGAAGGCGCTITTCACCGCGGCCATCCTGCAGGCACAGTTGCCGATTACAGAGGCA

GGAG=

NM_001769GGGCGTGGAACAGTTTATCTCAGACATCTGCCCCAAGAAGGACGTACTCGAAACCTICACCGWG
CDHI

GAAAGCGGCTG
CEGPI
NN1_020974TGACAATCAGCACACCTGCATTCACCGCTCGGAAGAGGGCCTGAGCTGCATGAATAAGGATCACGGCTG
TAGTCACA
Clad NN1_001274GATAAATTGGTACAAGGGATCAGC1TTTCCCAG000ACATGTCCTGATCATATGCTTTTGAATAMTCAG
TTACTTGGCACCC
CIAP1 NM...001166 TGCCTGTGOTGGGAAGCTCAGTAACTGGGAACCAAAGGATGATGCTATGTCAGAACACCOGAGGCAllTTCC
dAP2 =
mumissGGATATTTCCGTGGCTCTTATTCAAACTCTCCATCAAATCCTGTAAACTCCAGAGCAAATCAAGATTTTTCTG
CCTTGATGAGAAG
aled NM 000245 GACAmCCAGTCCTGCAGTCMTGCCTCTCTGCCCCACCCTTTGTTCAGTGTGGCTGGTGCCACGACAAATGTGTGCGATC
GGAG
Con6927BAK0006113.13GCATCCTGGCCCAAAGITTCCCAAATCCAGGCGGCTAGAGGCCCACTGCTTCCCAACTA
CCAGCTGAGGGGGTC

NM_000963TCTGCAGAGTTGGAAGCACTCTATGGTGACATCGATGCTGTGGAGCTGTATCCTGCCCTTCTGGTAGAAA
AGCCTCGGC
CTSL
NM_001912GGGAGGCTTATCTCACTGAGTGAGCAGAATCTGGTAGACTGCTCTGGGCCTCAAGGCAATGAAGGCTGCA
ATGG
= OTSL2 ,NM_001333=TGTCTCACTGAGCGAGCAGAATCTGGIGGACTGTTCGCGTCCTCAAGGCAATCAGGGCTGCAATGGT

Nt4..004938CGCTGACATCATGAATGTTCCTCGACCGGCTGGA000GAGITTGGATATGACAAAGACACATCGITGC
TGAAAGAGA
DIABLO-NM...0/9BU
CACAATGGCGGCTCTGAAGAGTTGGCTGTCGCGCAGCGTAACTTCATTOTTCAGGTACAGACAGTGTTTGTGT
ORS
NM_003842CTCTGAGACAGTGOTTCGATGACTTTGCAGACITGGTGCCC1TrGACTCCTGGGAGCCGCTCATGAGGAA
GTTGGGCCTCATGG
EGFR NM_005228TGTCGATGGACTTCCAGAACCACCTGGGCAGCTGCCAAAAGTGTGATCCAAGCTGTCCCAAT
ElF4E NI=4_001966 GATCTAAGATGGCGACTGTCGAACCGGAAACCACCCCTACTCCTAATCCCCCGACTACAGAAGAGGAGAAAACGGAATC
TAA
EMS1 NM_005231 GGCAGTGTCACTGAGTCCTTGAAATCCTCCGCTGCCCCGCGGGTCTCTGGATTGGGACGCACAGTGCA
EpCAM NM
002354=GGGCCCTCCAGAACAATGATGOGCTTTATGATCCTGACTGCGATGAGAGCGGGCTCTTTAAGGCCAAGCAGT
GCA
EPHX1 =
NM_000120ACCGTAGGCTCTGCTCTGAATGACTCTCCTGTGGGTCTGGCTGCCTATATTCTAGAGAAG1TTTCCACCT
GGACCA
-ErbB3 NN1_001962 CGGTTATGICATGCCAGATACACACCTCAAAGGTACTCCCTCCTCCCGGGAAGGCACCCTTTCITCAGTGGGICTCAGT
TC
EiR1 NM _000125 CGTGGTGCCCCTCTATGACCTGCTGCTGGAGATGCTGGACGCCCACCGCCTACATGCGCCCACTAGCC
FBXOS
NM_012177GGCTATTCCTCATTTTCTCTACAAAGTGGCCTCAGTGAACATGAAGAAGGTAGCCTCCTGGAGGAGAA1T
TCGGTGACAGTCTACAATCC

NN1_003662CGGTAGTCAAGTCCGGATCAAGGGCAAGGAGACG000aTCTACCTGTGCATGAA000CAAAGGCAAGC

CACGGGACATTCACCACATCGACTACTATAAAAAGACAACCAACGGCCGACTGCCTGTGAAGTGGATGGCACCC . =
, FHIT
NM_002012CCAGTGGAGCGOITCCATGACCTGCGTCCTGATGAAGI6000GATTTGITTCAGACGACCCAGAGAG
'FRPI
NM_00301277GGTACCTGTGGGTTAGCATCAAGTTCTCCCCAGGGTAGAATTCAATCAGAGCTCCAGTTTGCAT1TGG
ATGTG
G-Calenin NM 002230 TCAGCAGCAAGGGCATCATGGAGGAGGATGAGGCCTGCGGGCGCCAGTACACGCTCAAGAAAACCACC =
.GAPOH
NM_002046ATTCCACCCATGGCAAATTCCATGGCACCGTCAAGGCTGAGAACGGGAAGCTTGTCATCAAT3GAAATCC
CATC

GACTC

CCATCTGCATCCATCITGTTIGGGCTCCCCACCCTTGAGAAGTGCCTCAGATAATACCCTGGTGGCC
GROI
NM_001511CGAAAAGATGCTGAACAGTGACAAATCCAACTGACCAGAAGGGAGGAGGAAGCTCACTGGT600TGTTCC
TGA
GSTMI
NM_000561AAGCTATGAGGAAAAGAAGTACACGAT00000ACGCTCCTGATTATGACAGAAGCCAGTGGCTGAATGAA
AAATTCAAGCTGGGCC
GUS NM_000181 CCCACTCAGTAGCCAAGTCACAATGITTGGAAAACAGCCCGTTTACTTGAGCAAGACTGATACCACCTGCGTG

NM_004414B000IGTGAGAAGTGCAGCAAGCCCTGTGCCCGAGTGTGCTATGOTCT0000ATGGAGCACTTGCGAGAG
G
HIFtA
NA4_001530TGAACATAAAGTCTGCAACATGGAAGGTATTGCACTGCACAGGCCACATTCACGTATATGATACCAACA
GTAACCAACCTCA =

NM_004496TCCAGGATGTTAGGAACTGTGAAGATGGAAGGGCATGAAACCAGCGACTGGAACAGCTACTA000AGACA
CGC

AGAACCGCAAGGTGAGCAAGGTGGAGATTCTCCAGCACGTCATCGACTACATCAGGGACCTTCAGTTGGA

NM_00061BTCCGGAGCTGTGATCTAAGGAGGCTGGA3ATGTATTGCGCACCCCTCAAGCCTGCCAAGTCAGCTCGCTC
TGTCCG

NM_000875GCATGGTAGCCGAAGATTTCACAGTCAAAATCGGAGAT1TTGGTATGACGCGAGATATCTATGAGACAGA
CTATTACCGGAAA.

NM_000697GTGGACAGCACCATGAACATGTTIGGGC00000AGGCAGTGCTGGCOGGAAGCCCCTCAAGT0000TATG
AAGG

CCTGAACCTTCCAAAGATGGCTGAAAAAGATGGATGCTTCCAATCTGGATTCAATGAGGAGACTTGCCTGGT =
5,M1 NM_005544CCACAGCTCACCTTCTGTCAGGTGTCCATCCCAGCTCCAGCCAGCTCCCAGAGAGGAAGAGACT300ACT
GAGG- = =

CGGACTTIGGGTGCGACTTGACGAGCGGTGGTTCGACAAGTGGCCTTGICGGGCCGGATCGTOCCAGTGGAAGAGTTGT
Ak-Woo NM_002776GCCCAGAGGCTCCATCGTCCATCCTCTTCCTCCCCAGTCGGCTGAACTCTCCCCTTGTCTGCACTGTTCA
AACCTCTG

NM_000526GGCCTGCTGAGATCAAAGACTACAGTCCCTACTICAAGACCATTGAGGACCTGAGGAACAAGATTCTCAC
AGCCACAGTGGAC
KRT17 NM_000422 CGAGGATTGGTTCTTCAGCAAGACAGAGGAACTGAACCGCGAGGTGGCCACCAACAGTGAGOTGGTGCAGAGT =
KRTUI
NM_000224AGAGATCGAGGCTCTCAAGGAGGAGCTGCTCTTCATGAAGAAGAACCACGAAGAGGAAGTAAAAG3CC

TGAGCGGCAGAATCAGGAGTACCAGOGGCTCATGGACATCAAGTCGCGGCTGGAGGAGGAGATTOCCACCTACCGCA

TCAGTGGAGAAGGAGTTGGACCAGTCAACATCTOTGTTGTCACAAGCAGTGITTCCTCTGOATATGGCA =
KRIM
NM_002273GGATGAAGCTTACATGAACAAGGTAGAGCTGGAGTCTCGCCTGGAAGGGCTGACCGACGAGATCAACTTC
CTCAGGCAGCTATATG
LOT1varLINM_002655 GGAAAGACCACCTGAAAAACCACCTCCAGACCCACGACCCCAACAAAATGGCCTTTGGGIGTGAGGAGTGTGGGAAGAA
GTAC
Maspin NM

TGCC

NM_004526GAC117TGCC000TACCTTTCATTC000CGTGACAACAATGAGCTGTTGCTCTTCATACTGAAGCAGTTA
GTGGC

NM_002388GGAGAACAATCCCCITGAGACAGAATATG0CCITTCTGICTACAAGGATCACCAGACCATCACCATCCAG
GAGAT

CCA

CTACAGGGACGCCATCGAATCCOGATCTTGATGCTGGTGTAAGTGAACATTCAGGTGATTGGTTGGAT

= , .

NM_004689CCGCCCTCACCTGAAGAGAAACGCGCTCCTTGGCGGACACTGGGGGAGGAGAGGAAGAAGCGCGGCTAAC

GCCGAGATCGCCAAGATGTTGCCAGGGAGGACAGACAATGCTGTGAAGAATCACTGGAACTCTACCATCAAAAG

CCCTCGTGCTGATGCTACTGAGGAGCCAGCGTCTAGGGCAGCAGCCGCTTCCTAGAAGACCAGGTCATGATG
p27 NM_004064CGGTGGACCACGAAGAGTTAACCC000ACTTGGAGAAGCACTGCAGAGACATGGAAGAGGCGAGCC
P53 NM_000546 CTTTGAACCCTTGCTTGCAATAGGTGTGCGTCAGAAGCACCCAGGACTICCATTTGCTITGTCCCGGG
PAM
NN1_000602CCGCAACGTGGTITTCTCACCCTAT000OT000CTCGGIGTI=030CATGCTCCAGCTGACAACAGGAG
GAGAAACCCAGCA
PDGFRb NM_002609CCAGCTCTCCITCCAGCTACAGATCAATGICCCTGTCCGAGTGCTGGAGCTAAGTGAGAGCCACCC
P131<C2A NM_002645 ATACCAATCACCGCACAAACCCAGGCTATTTGTTAAGTCCAGTCACAGCGCAAAGAAACATATGCGGAGAAAATGCTAG
TGTG
PPM1D NM_003620GCCATCCGCAAAGGCTTTCTCGCTTGTCACCTTGCCATGTGGAAGAAACTGGCGGAAT0000 PR NM_000926 GCATCAGGCTGICATTATGGIGTCCTTACCTGIGGGAGCTGTAAGGTCTTCTTTAAGAGGGCAATGGAAGGGCAGCACA
ACTACT

TCTCCATATCTGCCTTGCAGAGTCTCCTGCAGCACCTCATCGGGCTGAGCAATCTGACCCACGTGC
pS2 NM 003225 GCCCTCCCAGTGTGCAAATAAGGGCTOCTGTTTCGACGACACCGTTCGTGGGGTCCCCTGGTGCTTCTATCCTAATACC
ATCGACG
RADS1C NM_ 058216 GAACTTCTTGAGCAGGAGCATACCCAGGGCTTCATAATCACCTICTGITCAGCACTAGATGATATTCTI'GGGGGIGGA
=
REM NM_000321 CCAGACGAGCGATTAGAAGCGGCAGCTTGTGAGGTGAATGATTTGGGGGAAGAGGAGGAGGAGGAAGAGGAGGA
STK15 NM_003600 CATCTTCCAGGAGGACCACTCTCTGTGGCACCCTGGACTACCTGCCCCCTGAAATGATTGAAGGTCGGA

CCTGGAGGCTGCAACATACCTCAATCCTGTCCCAGGCCGGATCCTCCTGAAGCCCTTITCGCAGCACTGCTATCCTCCA
AAGCCATTGTA
=
=
34. = =
.

=
= Table 5B
=
SURV
NM_0011B8TGTTTTGATTCCOGGGOTTACCAGGTGAGAAGTGAGGGAGGAAGAAGGCAGTGTOCCTTTTGOTAGAGCT
GACAGOTTTG
TBP
NM_0031S4GCCCGAAACGCCGAATATAATCCCAAGCGGTTTGCTGCGGTAATCATGAGGATAAGAGAGCCAOG
TGFA NM_003236 GGTGTGCCACAGACOTTOCTACTTGGCCTGTAATOACCTGTGCAGCTITTTGTGGGCCTTCAAAACTOTGTCAAGAACT
OCGT
TUN
NM_003254TCCOTGOGGTOCCAGATAGOCTGAATOCTGCCOGGAGTGGAACTGAAGOCTGOACAGTGTCCACCCTGTT
COCAO

NM_001067AATCCAAGGGGGAGAGTGATGACTICCATATGGACTTTGACTCAGOTGTGGCTOCTOGGGCAPAATCTGT
AC

Nm_oolosaTGTGGACATCTTCCCCTCAGACTTCCCTACTGAGCCACCITCTCTGCCACGAACCGGTCGGGCTAG
TP NNL.001953 CTATATGCAGCCAGAGATGTGACAGCCACCGTGGACAGCCTGCCACTCATCACAGCCTOCATTOTCAGTAAGAAACTCG
TGG
TP538p2 NM_005426 GGGCCAJuq'ATTCAGAAGCTUTAaATCAGAGGACCACCATAGCGGCCATGGAGACCATCTCTGTOCCATCATACCCAT
CC
TRAIL Nm_003810 OTTCACAGTGOTOCTGCAGTOTCTOTGTGTGGCTGTAACTTACGTGTACTTTACCAACGAGOTGAAGCAGATG
TS NM_001071 oCCTOGGTGTGOOTTTCAACATCGCCAGOTACGCCOTGOTCACGTAOATGATTGCGCACATCACG =
upa NM_002658 GTGGATGTGOCCTGAAGGACAAGCCAGGCGTOTACACGAGAGTOTCACACTTOTTAOCCTGGATCCGCAG

GOCCTGGATTICAGAAAGAGCCAAGTOTGGATCTGGGACCCTTTOCTTOCTTCCOTGGOTTGTAAOT
VEGF
NM_003376CTGCTGTCTTGGGTGCATTGGAGCCTTGCCTTGCTGCTCTACCTCCACCATGCCAAGTGGTCCCAGGCTG
C
VEGFB NM_003377 TGACGATGGCCTGGAGTGTGTGOCCACTGGGCAGOACCAAGTCOGGATGCAGATCCTCATGATCCGGTACC
wspi NM_003882 AGAGGCATCCATGAACTTOACACTTGCGGGCTGCATCAGCACACGCTCCTATCAACCCAAGTACTGTGGAGTTTG .
=
XlAp NM_001157GcAGTTGGAAGACACAGGAAAGTATCCCCAAATTGCAGATTTATCAACGGCTTTTATCTTGAAAATAGTG
CCACGCA
Ys-i NN1_004559AGACTGTGGAGITTGAIGTTGTTGAAGGAGAAAAGGGTGCGGAGGCAGCAAATGTTACAGGTCCTG6TG
GTGTTCC

NM_006526ACCCAGTAGCAAGGAGAAGCCCACTCACTGCTCCGAGTGCGGCAAAGCTTTCAGAACCTACCACCAGCTG

'=
=
=
=
=
=
=
=
=
=
=
=
=
= =
=
=
=
=
=

, .
. , , =
Table 6A
.
.
. .
-. .
Gene Accession = Probe Name Seq = Len .
' AIB1 , NM 006534 S1994/AIB1.f3 ' Al B1 'NM:006534 S1995/A1B1.r3 TGAGTCCACCATCCAGCAAGT 21 ' ' AlB1 NM_006534 S5055/A1Bi .p3 ATGGCGGCGGGAGGATCAAAA21 .
. .
AKT1 NM 005183 S0010/AKT1.f3 _ AKT1 NM 005163 S0012/AKT1.r3 TCCCGGTACACCACGTTCTT
_ 20 AKT1. NM_005163 S4776/AKT1.p3 CAGCCCTGGACTACCTGCACTCGG 24 AKT2 NM 001626 S0828/AKT2.f3 = TCCTGCCACCCTTCAAACC
_ 19 AKT2 NM 001626 S0829/AKT2s3 GGCGGTAAATT.CATCATCGAA '.
_ 21 , AKT2 NM_001626 S4727/AKT2.p3 CAGGTCACGTCCGAGGTCGACACA . = . 24 APC NM 000038 S0022/APC.f4 = GGACAGCAGGAATGTGTTTC
_ 20 .
.
APC NM 000038 S0024/APC.r4 _ ACCCACTCGATTTGITICTG 20 APC NM_000038 S4888/APC.p4 CATTGGCTCCCCGTGACCTGTA 22 =
AREG NM 001657 S0025/AREG.f2 TGTGAGTGAAATGCCTTCTAGTAGTGA , 27 .
AREG NM_001657 S0027/AREG.r2 TTGTGGTTCGTTATCATACTCTTCTGA 27 AREG NM_001657 ' S4889/AREG.p2 CCGTCCTCGGGAGCCGACTATGA = 23 B-actin NM 001101 S0034/B-acti.f2-B-actin . NM 001101 S0036/B-acti.r2 B-actin NM:001101 S4730/B-acti.p2 AGGAGTATGACGAGTCCGGCCCC 23 B-Catenin NM_001904 S2150/B-Cate.f3 GGCTCTTGTGCGTACTGTCCTT 22 B-Catenin NM_001904 S2151/B-Cate.r3 TCAGATGACGAAGAGCACAGATG 23 B-Catenin NM 001904 . = S5046/B-Cate.p3 AGGCTCAGTGATGTCTTCCCTGTCACCAG

BAD NM:032989 S2011/BAD.f1 GGGTCAGGTGCCTCGAGAT 19 BAD NM 032989 S2012/BAD.r1 _ CTGCTCACTCGGCTCAAACTC = 21 =
BAD .NM 032989 S5058/BAD.p1 TGGGCCCAGAGCATGTTCCAGATC ' - 24 BAG1 *. NM 004323 S1386/BAG1.f2 = CGTTGTCAGCACTTGGAATACAA = .

BAG1 NM_004323 Si 387/BAG1.r2 GTTCAACCTCTTCCTGTGGACTGT 24 . =
BAG1 . NM_004323 S4731/BAG1.p2 = CCCAATTAACATGACCCGGCAACCAT
26 .
BBC3 NM 014417 S1584/BBC3.f2 CCTGGAGGGTCCTGTACAAT = 20 . = .
BBC3 NM 014417 ' S1585/BBC3.r2 %.' CTAATTGGGCTCCATCTCG =

- BBC3 NM 014417 S4890/BBC3.p2 CATCATGGGACTCCTGCCCTTACC - = 24 - Bct2 NM_000633 S0043/Bd2.12 CAGATGGACCTAGTACCCACTGAGA ' 25 .
BcI2 NM_000633 S0045/Bc12.r2 _____________ CCTATGATITAAGGGCA
II it 1 CC = . = 24 BcI2 NM_000633 S4732/Bc12.p2 TTCCACGCCGAAGGACAGCGAT = 22 CA9 = NM_001216 S1398/CA9.f3 __________ ATCCTAGCCCTGG ii II
1 GG .20 =
=CA9. NM_001216 -S1399/CA9.r3 *
CTGCCTTCTCATCTGCACAA ' 20 CA9 NM 001216 S4938/CA9.p3 CCNB1 NM 031966 S1720/CCNB1.f2 TTCAGGTTGTTGCAGGAGAC . 20 CCNB1 NM:031966 S1721/CCNB1.r2 . CATCTTCTTGGGCACACAAT 20 CCNB1 NM_031966 S4733/CCNB1.p2 TGTCTCCATTATTGATCGGTTCATGCA 27 CCN D1 NM_001758 S0058/CCND1.13 GCATGTTCGTGGCCTCTAAGA 21 CC N D1 NM 001758 S0060/CCN D1, r3 CGGTGTAGATGCACAGCTTCTC 22 CCN D1 NM:001758 S4986/CaND1.p3 AAGGAGACCATCCCCCTGACGGC 23- .
CCNE1 = NM_001238 S1446/CCNE1M = AAAGAAGATGATGACCGGGTTTAC = . =
24 ' CCNE1 NM_001238 = Si 447/CCNE1.r1 CCNE1 NM 001238 S4944/CCN Et pi. CAAACTCAACGTGCAAGCCTCGGA 24 CCNE2 NM:057749 .S1458/CCNE2.f2 = ATGCTGTGGCTCCTTCCTAACT22 CCNE2 NM_057749 S1459/CCNE2.r2 ACCCAAATTGTGATATACAAAAAGG* 27 CCNE2 NM 057749 S4945/CCNE2.p2 TACCAAGCAACCTACATGTCAAGAAAGCCC
30 =
CD3z NM:000734 S0064/CD3z.f1 AGATGAAGTGGAAGGCGCTT . 20 =
CD3z NM_000734 S0066/CD3z.r1 TGCCTCTGTAATCGGCAACTG 21 ' CD3z NM 000734 S4988/CD3z.p1 CD68 NM_001251 S0067/CD68.f2 CD68 NM 001251 S0069/C068.r2 CD68 " NM:001251 S4734/CD68.p2 CTCCAAGCCCAGATTCAGATTCGAGTCA 28 C09 NM_001769 .S0686/CD9J1 .
GGGCGTGGAACAGTTTATCT. 20 CD9 NM_001769 S0687/CD9.r1 CACGGTGAAGGTTTCGAGT 19 ' CD9 NM 001769 S4792/CD9.pl AGACATCTG6cCCAA6AAGGACGT 24 CDH1 NM-004360 S0073/CDH1 .f3 TGAGTGTCCCCCGGTATCTTC 21 . .
=
CDH1 NM:004360- S0075/CDH 1 .r3 CDH1 NM_004360 S4990/CDH1.p3 TGCCAATCCCGATGAAATTGGAAATTT 27 CEGP1 NM 020974 S1494/CEGP1.f2 TGACAATCAGCACACCTGCAT 21 ,..
= .

' CA 02829476 2013-10-03 ' = .
. .
Table 6B
, .
. , .
.
CEGP1 -NM_020974 S1495/CEGP1.r2 TGTGACTACAGCCGTGATCCTTA 23 -CEGP1 = NM_020974 S4735/CEGP1.p2 CAGGCCCTCTTCCGAGCGGT 20 ' Chk1 = NM 001274 S1422/Chk1.f2 _ 26 Chk1 NM_001274 31423/Chk1.12 . ' GGGTGCCAAGTAACTGACTATTCA 24 ' Chk1 NM_001274 . S4941/Chk1.p2 CCAGCCCACATGTCCTGATCATATGC . , 26 ' CIAP1 NM 001166 80764/C IAP 1.f2 TGCCTGTGGTGGGAAGCT
_ 18 CIAP1 NM 001166 S0765/CIAP1.r2 GGAAAATGCCTCCGGTGTT
_ - 19 =

NM_001166 S4802/CIAPl.p2 TGACATAGCATCATCCTTTGGTTCCCAGTT 30 clAP2 NM 001165 S0076/cIAP2.f2 - GGATATTTCCGTGGCTCTTATTCA ' _ 24 clAP2 . NM_001165 S0078/cIAP2.r2 CTTCTCATCAAGGCAGAAAAATCTT . 25 . cIAP2 NM_001165 S4991/cIAP2.p2 " TCTCCATCAAATCCTGTAAACTCCAGAGCA
30 .
cMet .NM_000245 S0082/cMet.f2 _ GACATTTCCAGTCCTGCAGTCA 22 cMet . NM 000245 S0084/cMet.r2 CTC

cMet NM_000245 S4993/cM et.p2 . TGC CTCTCTGC CC CAC 6C1TTGT 23 Contig 27882 AK000618 S2633/Contig.f3 GGCATCCIGGCCCAAAdT = 18 Contig 27882 AK000618 - S2634/Contig.r3 Contig 27882 AK000618 . S4977/Contig.p3 COX2 = NM 000963 S0088/C0X2J1 TCTGCAGAGTTGGAAGCACTCTA = 23 COX2 NM 000963 S0090/C0X2.11 COX2 NM_000963 S4995/C0X2.p1 = CAGGATACAGCTCCACAGCATCGATGTC . . 28 .
CTSL NM_001912 S1303/CTSL.f2 GGGAGGCTTATCTCACTGAGTGA . . 23 CTSL NM_001912 S1304/CTSL.r2 CCATTGCAGCCTTCATTGC 19 CTSL NM_001912 - S4899/CTSL.p2 CTSL2 NM 001333 . S4354/C1SL2J1 CTSL2 =NM:001333 = S4355/CTSL2.r1 AC

CTSL2 " NM_001333 S4356/CTSL2,p1 CTTGAGGACGCGAACAGTCCACCA 24 ' ..
DAPK1 " 1 NM_004938 . 51768/DAPK1J3 CGCTGACATCATGAATGTTCCT . 22 DAPK1 = NM_004938 S1769/DAPK1.r3 TCTCTITCAGCAACGATGTGICTT . 24 . .DAPK1 NM_004938 ' S4927/DAPK1.p3 TCATATCCAAACTCGCCTCCAGCCG 25 =
' DIABLO ' NM_019887 S0808/DIABLO.f1 CACAATGpCGGCTCTGAAG 19 =
DIABLO ' _ NM_019887 S0809/DIABLO.r1 ACACAAACACTGTCTGTACCTGAAGA 26 DIABLO NM_019887 ' S4813/DIABLO.pl . AAGTTAC GCTGC GC GACAGC CAA .

D R5 NM_003842 S2551/DR5.f2 CTCTGAGACAGTGCTTCGATGACT 24 = , DR5 NM_003842 S2552/DR5.r2 " CCATGAGGC CCAACTTC CT 19 D R5 NM_003842 54979/DR5.p2 CAGACTTGGTGCCCTTTGACTCC = 23 EGFR ' . NM 005228. S0103/EGFR.f2 . TGTCGATGGACTTCCAGAAC 20 EGFR NM:005228 S0105/EGFR.r2 ATTGGGACAGCTTGGATCA 19 EGFR NM 006228 S4999/EGFR.p2 ' CACCT.GGGCAGCTGCCAA 18.
=
El F4E . NM_001968 S0106/EIF4E.f1 GATCTAAGATGGCGACTGTCGAA 23 =
ElF4E NM 001968 - S0108/E1F4E.r1 ' TTAGATTCCG ______ I i i i ElF4E NM:001968 S5000/EIF4E.p1 ACCACCCCTACTCCTAATCCCCCGACT 27 .
EMS1 NM_005231 =52663/EMS1.fl GGCAGTGTCACTGAGTCCTTGA 22 .
EMS1 NM_005231 S2664/EMS1.r1 TGCACTGTGCGTCCCAAT 18 .
EMS1 = . NM_005231 . S4956/EMS1.pl AT.CCTCCCCTGCCCCGCG 18 EpCAM . NM_002354 61807/EpCAM.f1 GGGCCCTCCAGAACAATGAT -EpCAM . NM_002354 S1808/EpCAM.r1 TGCACTGCTIGGCCITAAAGA 21 EpCAM . NKL002354 S4984/EpCAM.pl CCGCTCTCATCGCAGTCAGGATCAT 25 EPHX1 NM 000120 S1865/EPHX1.f2 ACCGTAGGCTCTGCTCTGAA -_ 20 EPHX1 NM 000120 S1866/EPHX1:r2 TGGTCCAGGTGGAAAACTTC 20 =
=
EPHX1 " NM:000120 S4754/EPHX1.p2 AGGCAGCCAGACCCACAGGA . 20 ErbB3 NM 001982 S0112/ErbB3.f1 CGGTTATGTCATGCCAGATACAC , 23 ErbB3 NM_001982 S0114/ErbB3.r1 GAACTGAGACCCACTGAAGAAAGG = 24 ErbB3 NM_001982 S5002/ErbB3.p1 = CCTCAAAGGTACTCCCTCCTCCCGG 25 EstR1 NM 000125 S0115/EstR1J1 EstR1 NM:000125 S0117/EstR1.r1 ' GGCTAGTGGGCGCATGTAG . ' 19 .
EstR1 NM_000125 S4737/EstR1.p1 CTGGAGATGCTGGACGCCC 19 FBX05 NM 012177 S2017/FBX05.r1 GGATTGTAGACTGTCACCGAAATTC " " 25 FBX05 NM-012177 S2018/FBX05.f1 GGCTATTCCTCATTTTCTCTACAAAGTG 28 FBX05 , NM:012177 = S5061/FBX05.pl CCTCCAGGAGGCTACCTICTICATGTTCAC .30 =
FGF18 NM_003862 S1665/FGF18.f2 CGGTAGTCAAGTCCGGATCAA 21 ' FGF18 NM_003862 S1666/FGF.18.r2 GCTTGCCITTGCGGTTCA. 18 =
FGF18 NM_003862 S4914/FGF18.p2 CAAGGAGACGGAATTCTACCTGTGC 25=
-.
.
, , CA 02829476 2013-10-03 , =
Table 6C
. .
FGFR1 NM_023109 S0818/FGFR1.f3 FGFRI NM 023109 ' 80819/FGF RI .r3 GGGTGCCATCCACTTCACA 19 FGFR 1 NM:023109 84816/FGFR1.p3 ATAAAAAGACAACCAACGGCCGACTGC = 27 FHIT NM_002012 S2443/FHIT.f1 CCAGTGGAGCGCTTCCAT = ' 16 FHIT NM_002012 S2444/FHIT.r1 CTCTCTGGGTCGTCTGAAACAA .

FHIT NM_002012' . S2445/FHIT.pl . FHIT NM_002012 S4921/FHIT.p1 .TCGGCCACTTCATCAGGACGCAG 23 . FRPI NM_003012 S1804/FRP I .f3 FRPI NM _003012 S1805/FRPl.r3 FRPI NM_003012 54983/FRPI .p3 G-Catenin NM_002230 S2153/G-Cate.f1 TCAGCAGCAAGGGCATCAT
= . 19 G-Catenin NM_002230 S2154/G-Cate.r1 GGTGGITTTCTTGAGCGTGTACT 23 G-Catenin NM_002230 S5044/G-Cate.pl CGCCCGCAGGCCTCATCCT 19 .
GAPDH NM_002046 S0374/GAPDH.fl ATTCCACCCATGGCAAATTC 20 GAPDH NM 002046 S0375/GAPDH.r1 GATGGGATTTCCATTGAIGACA 22 GAP DH NM:002046 S4738/GAPDH.p1 CCGTTCTCAGCCTTGACGGTGC 22 GATA3 NM_002051 S0127/GATA3.f3 CAAAGGAGCTCACTGTGGTGTCT . 23 GATA3 ; NM_002051 S0129/GATA3.r3 GAGTCAGAATGGCTTATTCACAGATG 26 GATA3 NM 002051 , S5005/GATA3.p3 TGTTCCAACCACTGAATCTGGACC 24 GRB7 NM:005310 S0130/GRB7.f2 CCATCTGCATCCATCTTGTT - 20 .
GRB7 NM_005310 S0132/GRB7.r2 GGCCACCAGGGTATTATCTG 20 GRB7 NM_005310 S4726/GRS7.p2 CTCCCCACCCTTGAGAAGTGCCT 23. =
GRO1 NM 001511 S0133/GR01.f2 GRO1 NM-001511 S0135/GRO1 .12 TCAGGAACAGCCACCAGTGA 20 ' GRO1 NM:001511 S5006/GR01.p2 CTTCCTCCTCCCTTCTGGTCAGTTGGAT 28 GSTM1 * - NM_000561 S2026/GSTM1.r1 GGCCCAGCTTGAA i i i I i GSTM1 ' NM 000561 S2027/GSTM1.f1 AAGCTATGAGGAAAAGAAGTACACGAT 27 GSTM1 NM:000561 S4739/GSTM1.p1 TCAGCCACTGGCTTCTGTCATAAT-CAGGAG 30 GUS NM_000181 ' 50139/GUS.f1 CCCACTCAGTAGCCAAGTCA 20 .
GUS ' NM_000181 S0141/GUS sl CAC
GCAGGTGGTATCAGTCT 20 .
GUS NM_000181 S4740/GUS.p1 = TCAAGTAAACGGGCTGTTTTCCAAACA - 27 HER2 NM_004448 S0142./HER2.f3 CGGTGTGAGAAGTGCAGCAA 20 HER2 NM _004448 S0144/HER2.r3 HER2 = NM 004448 S4729/HER2.p3 CCAGACCATAGCACACTCGGGCAC - 24 .
HIFIA NM:001530 S1207/H1F1A.f3 TGAACATAAAGTCTGCAACATGGA 24 HIFIA NM_001530 S1208/H1F1A.r3 TGAGGTTGGTTACTGTTGGTATCATATA 28 HIFI A NM_001530 S4753/HIF1A.p3 , HNF3A NM_004496 S0148/HNF3A.fl TCCAGGATGTTAG,GAACTGTGAAG 24 HNF3A NM 004496 . S0150/HNF3A.r1 GCGTGTCTGCGTAGTAGCTGTT ' 22 HNF3A NM:004496 S5008/HNF3A.p1 AGTCGCTGGTTTCATGCCCTTCCA 24 1D1 NM_002165 S0820/1D1.f1 AGAACCGCAAGGTGAGCAA 19 1D1 . NM_002165 S0821/1D1.r1 TCCAACTGAAGGTCCCTGATG 21 1D1 NM_002165 S4832/1D1.p1 =

IGFI NM 000618 S0154/1GF1.f2 TCCGGAGCTGTGATCTAAGGA . 21 '.
IGFI NM 000618 80156/1GF1.12 IGFI NM_000618 S5010/1GF1.p2 TGTATTGCGCACCCCTCAAGCCTG 24 1GF1 R NM_000875 S1249/IGF1R.f3 IGFI R NM_000875 S1 250/IG F1R.r3 IGF1R NM_000875 84895/1GF I R.p3 CGCGTCATACCAAAATCTCCGATTTTGA 28 IGFBP2 NM_ 000597 S1129/1GFBP2.r1 CCTTCATACCCGACTTGAGG 20 ' _ 1GFBP2 NM_000597 S4837/IGFBP2.p1 CTTCCGGCCAGCACTGCCTC 20 =
1L6 NM_000600 S0760/1L6.f3 CCTGAACCTTCCAAAGATGG 20 1L6 NM 000600 S0761/1L6.r3 ACCAGGCAAGTCTCCTCATT = 20 IL6 NM_000600 S4800/IL6.p3 ______________________ IRS I NM 005544 S1943/IRS1 .f3 CCACAGCTCACCTTCTGTCA 20 ' IRS1 NM 005544 S1944/IRS1.r3 CCTCAGTGCCAGTCTCTTCC = 20=
1RS1 NM:005544 S5050/IRS1.p3 TCCATCCCAGCTCCAGGCAG 20 Ki-67 NM 002417 S0436/K1-67.f2 K1-67 NM:002417 S0437/K1-67.r2 TTACAACTCTTCCACTGGGAC GAT 24 .
1<I-67 NM 002417 S4741/K1-67.p2 CCACTTGTC

KLKI 0 NM 002776 S2624/KLK10.f3 38 =

, ' . , Table 6D
- . - -=
KLK10 NM 002776 S2625/KLK10.r3 ' CAGAGGTrTGAACAGTGCAGACA 23 KLK10 . NM,..002776 54978/KLK10.p3 = CCTCTTCCTCCCCAGTCGGCTGA 23 KRT14 NM_000526 S1853/KRT14.fl GGCCTGCTGAGATCAAAGAC 20 ' KRT14 NM_000526 = S1854/KRT14.r1 GTCCACTGTGGCTGTGAGAA 20 = =
KRT14 NM_000526 S5037/KRT14.p1 TGTTCCTCAGGTCCTCAATGGTCTTG 26 KRT17 NM_000422 S0172/KRT17.f2 CGAGGATTGGTTCTTCAGGAA 21 KRT17 NM_000422 50174/KRT17.r2 ACTCTGCACCAGCTCACTGTTG 22 KRT17 . NM_000422 = 55013/KRT17.p2 CACCTCGCGGTTCAGTTCCTCTGT 24 =
KRT18 NM_000224 S1710/KRT18.f2 AGAGATCGAGGCTCTCAAGG 20 .
KRT18 NM_000224 S1711 /KRT18.r2 =
.
= KRT18 NM_000224 S4762/KRT18.p2 TGGTICTICTICATGAAGAGCAGCTDC 27 KRT19 NM_002276 S1515/KRT19.f3 TGAGCGGCAGAATCAGGAGTA .

KRT19 NM_002276 S1516/KRT19.r3 TGCGGTAGGTGGCAATCTC 19 =
KRT19 NM_002276 S4866/KRT19.p3 CTCATGGACATCAAGTCGCGGCTG 24 KRT5 NM_000424 S0175/KRT5.f3 TCAGTGGAGAAGGAGTTGGA 20 KRT5 NM_000424 50177/KRT5.r3 TGCCATATCCAGAGGAAACA20 KRT5 NM_000424 S5015/KRT5.p3 CCAGTCAACATCTCTGTTGTCACAAGCA 28 KRT8 . NM 002273 62588/KRT8.f3 KRT8 -NM_002273 S2589/KRT8s3 CATATAGCTGCCTGAGGAAGTTGAT 25 ' KRT8 NM 002273 54952/KRT8.p3 LOT1 variant 1 NM:002656 . 30692/LOT1 v.f2 . GGAAAGACCACCTGAAAAACCA 22 LOT1 variant 1 NM 002656 50693/LOT1 v.r2 GTACTTCTTCCCACACTCCTCACA 24 "
LOT1 variant 1 NM_002656 = 54793/LOT1 v.p2 ACCCACGACCCCAACAAAATGGC 23 Maspin NM_002639 S0836/Maspin.f2 CAGATGGCCACTTTGAGAACATT 23 Maspin NM_002639 .S0837/Maspin.r2 GGCAGCATTAACCACAAGGATT 22 Maspin = . NM_002639 . S4835/Maspin.p2 . . AGCTGACAACAGTGTGAACGACCAGACC

MoM2 NM 004526 S1602/MCM2.f2 GACTMGCCCGCTACCITTC '21 =
MC M2 NM,004526 , = S1603/MCM212 MCM2 NM 004526 S4900/MCM2.p2 ACAGCTCATTGTTGTCAC GCCG GA 24 .
=
. MCM3 NM:002388 S1524/MCM3.f3 MCM3 .NM_002388 = S1525/MCM3.r3 . MCM3 NM 002388 S4870/MCM3.p3 TGGCCTTTCTGTCTACAAGGATCAC.CA 27 MCM6 ' . NM:005915 S1704/MCM6.f3 TGATGGTCCTATGTGTCACATTCA 24 MCM6 NM_005915 S1705/MCM6.r3 ' MCM6 N M..005915 S4919/MCM6.p3 CAGGTTTCATACCAACACAGGCTTCAGCAC

MDM2 NM 002392 S0830/MDM2.f1 MDM2 NM:002392 S0831/MDM211 ATCCAACCAATCACCTGAATGTT 23 MDM2 NM 002392 S4834/MDM2.p1 CTTACACCAGCATCAAGATCCGG , ' 23 MMP9 NMT004994 S0656/MMP9.f1 GAGAACCAATCTCACCGACA .

=
=
MMP9 NM 004994 . S0657/MMP9s1 CAC C C GAGTGTAACCATAGC ' 20 MMP9 NM 004994 . S4760/MMP9.pl ACAGGTATTCCTCTGCCAGCTGCC = = 24 MTA1 NM 004689 S2369/MTA1 .fl MTA1 NM_004689 S2370/MTA1.r1 MTA1 ' NM 004689 S4855/MTAl.p1 CoCAGTGTCCGCCAAGGAGCG 21 .. .
MYBL2 NM_002466 S3270/MYBL2J1 GCCGAGATCGCCAAGATG 18 MYBL2 NM 002466 S3271/MYBLZr1 CTTTTGATGGTAGAGTTCCAGTGATTC 27 MYBL2 NM-002466 S4742JMYBL2.p1 CAGCATTGTCTGTCCTCCCTGGCA - 24 P14ARF S78-535 S28421P14ARF.fl CCCTCGTGCTGATGCTACT ' 19 -P14ARF S78535 S2843/P14ARF.r1 CATCATGACCTGGTCTTCTAGG 22 P14ARF S78535 S4971/P14ARF.p1 CTGCCCTAGACGCTGGCTCCTC 22 p27 NM_004064 S0205/p27.f3 CGGTGGACCACGAAGAGTTAA . 21 p27 NM 004064 S0207/p27.r3 GGCTC

p27 NM:004064 54750/p27.p3 .
CCGGGACTTGGAGAAGCACTGCA . 23 P53 NM 000546 S0208/P53.f2 P53 NM 00054530210/P53, r2 CCCGGGACAAAGCAAATG 18 P53 ' NM:000546 85065/P53.p2 AAGTCCTGGGTGCTTCTGACGCACA , 25 PAll NM 000602 S0211/PAH.f3 CCGCAACGTGGilliCTCA 19 PAM NM:000602 50213/PAH .r3 PAH NM_000602 35066/PA11.p3 CTCGGTGTTGGCCATGCTCCAG 22 =
PDGFRb NM_002609 S1346/PDGFRb.f3 CCAGCTCTCCTTCCAGCTAC 20 =
PDGF.Rb NM 002609 S1347/PDGFRb.r3 GOGTGGCTCTCACTTAGCTC 20 PDGFRb NM_002609 S4931/PDGFRb.p3 ATCAATGTCCCTGTCCGAGTGCTG 24 ' CA 02829476 2013-10-03 = =
=
Table 6E
. .
P13KC2A NM_002645 S2020/P13KC2.r1 CACACTAGCATTTTCTCCGCATA . 23 - ' P 13KC2A NM_002645 S2021/P13KC2.f1 P13KC2A , NM_002645 S5062/P13KC2.p1 TGCGCTGTGACTGGACTTAACAAATAGCCT 30 ' PPM1D " NM_003620 S3159/PPM1D.f1 GCCATCCGCAAAGGCTIT - 18 PPM1D NM_003620 S3160/PPM1D.r1 GGCCATTCCGCCAGTTTC 18 PPM1D NM_003620 S4856/PPM1D.pl TCGCTTGTCACCTTGCCATGTGG 23 PR N1\k_000926 S1336/PR.f6 GCATCAGGCTGTCATTATGG 20 PR NM _000926 S1337/PR.r6 AGTAGTTGTGCTGCCCTTCC .
PR NM_000926 S4743/PR.p6 TGTCCTTACCTGTGGGAGCTGTAAGGTC 28 PRAME NM 006115 S1985/PRAME.13 TCTCCATATCTGCCTTGCAGAGT . 23 PRAME NM:006115 S 1986/P RAME.r3 GCACGTGGGTCAGATTGCT . 19 PRAME NM_006115 S4756/PRAME.p3 TC CTGCAGCAC CTCATC GG G CT 22 pS2 - NM_003225 S0241/pS2.f2 GC C CTC C

pS2 NM_003225 S0243/pS2.r2 CGTCGATG GTATTAG GATAGAAG CA 25 pS2 NM_003225 S5026/pS2.p2 TGCTGTITCGACGACACCGTTCG 23 RAD51C NM_058216 = S2606/RAD51C.f3 GAACTTOrfGAGCAGGAGCATACC . 24 .
RAD51C NM 058216 S2607/RAD51C.r3 TCCACCCCCAAGAATATCATCTAGT 25 _ RAD51C . NM_058216 S4764/RAD51C.p3 AGGGCTTCATAATCACCTTCTGTTC 25 RB1 .NM_000321 =S2700/RB1.fl C GAAG CC

RBI NM .000321 S2701/RB1s1 GGACTCTTCAGGGGTGAAAT 20 RBI NM 000321 S4765/RB1.p1 CCCTTACGGATTCCTGGAGGGAAC 24 RIZ1 NM 012231S1320/R1Z1.f2 CCAGACGAGCGATTAGAAGC 20 _ R1Z1 NM_ 012231 S1321/RIZ1.12 R1Z1 = NM_012231 S4761/R1Z1.p2 TGTGAGGTGAATGATTTGGGGGA 23 =
STK15 NM_003600 S0794/STK15.f2 CATCTTCCAGGAGGACCACT 20 STK15 NM 003600 S0795/STKi5.r2 TCCGACCTTCAATCATTTCA = 20 STK15 ' NM 003600 ' S4745/STK15.p2 CTCTGTGGCACCCTGGACTACCTG 24 STMY3 NM:005940 = S2067/STMY3.f3 CCTGGAGGCTGCAACATACC 20 STMY3 NM_005940 S2068/STMY3.r3 TACAATGGCTTTGGAGGATAGCA 23 .
STMY3 = NM_005940 S4746/STMY3.p3 ATCCTCCTGAAGCCCITTTCGCAGC = 25 SURV NM_001168 ' S0259/SURV.f2 = TGTTTTGATTCCCGGGCTTA . 20 .
SURV NM 001168 S0261/SURV.r2 - SURV NM 001168 S4747/SUFN.p2 TGCCTTCTTCCTCCCTCACTTCTCACCT . 28 TBP NM:003194 = S0262/TBP.f1 GC CCGAAACGCCGAATATA . 19 TBP NM_003194 S0264/T3P.r1 CGTGGCTCTCTTATCCTCATGAT.
' 23 .
TBP NM_003194 S4751/TBP.p1 TACCGCAGCAAACCGCTTGGG 21 TGFA NM_003236 S0489/TGFA.f2 G GTGTG C
CACAGACCTTCCT -20 .
TGFA NM 003236 S0490/TGFA.r2 TGFA NM:003236 . S4768/TGFA.p2 TTGGCCTGTAATCACCTGTGCAGCCTT 27 TIMP1 NM 003254 S1695/TIMPl.f3 TCCCTGCGGTCCCAGATAG = 19. .
TIMP1 NM:003254 S1696TT1MP1.r3 GTGGGAACAGGGTGGACACT 20 -TIMP1 NM_003254 34918/TIMP1 .p3 ATCCTGCCCGGAGTGGAACTGAAGC 25 TOP2A NM_001067 S0271/TOP2A.f4 AATCCAAGGGGGAGAGTGAT 20 -TOP2A ' NM_001067 S0273/10P2A.r4 GTACAGATTTTGCCCGAGGA 20 .
TOP2A . NM 001067 S4777/TOP2A.p4 CATATGGACTITGACTCAGCTGTGGC 26 .
TOP2B NM:001068 S0274/10P2B.f2 TGTGGACATCTTCCCCTCAGA 21 .
TOP2B NM 001068 S027.6/T0P2B.r2 CTAGCCCGACCGGTTCGT 18 TOP2B NM 001066, S4778/T0P2B.p2 TTCCCTACTGAGCCACCTTCTCTG 24 =
TP NM 001953 30277/TP.f3 CTATATGCAGCCAGAGATGTGACA 24 TP NM_001953 S0279/TP.r3 CCACGAGTTTOTTACTGAGAATGG = 24 TP NM:_001953 S4779/1P.p3 ACAGCCTGCCACTCATCACAGCC 23 TP536P2 NM_005426 S1931/TP53BP.f2 GGGCCAAATATTCAGAAGC 19 TP538 P2 101_005426 S1932/TP53BP.r2 GGATGGGTATGATGGGACAG 20 TP53BP2 NM 005426 S5049/TP53BP .p2 CCACCATAGCGGCCATGGAG 20 TRAIL NM:003810 32539/TRAIL.f1 TRAIL . NM_003810 = S2540/TRAIL.r1 CATCTGCTTCAGCTCGTTGGT = . 21 TRAIL NM 003810 S4980/TRAIL.pl AAGTACACGTAAGTTACAGCCACACA ' 26 IS NM:001071 S0280/TS.f1 GCCTCGGTGTGCCTTTCA 18 TS NM 001071 S0282/TS.r1 CGTGATGTGCGCAATCATG 19 TS NM_001071 S4780/TS.pl CATC G C CAG CTAC G CC CTGCTC 22 ' upa NM_002658 S0283/upa.f3 GTGGATGTGCCCTGAAGGA 19 upa NM_002658 S0285/upa.r3 CTGCGGATCCAGGGTAAGAA ' 20 .
-40 .

= CA 02829476 2013-10-03 =
Table 6F
=
upa NM_002658 S4769/upa.p3 AAGC CAG

VDR = NM 000376 S2745NDR.f2 VDR NM 000376 S2746NDR.r2 VDR NM_000376 S4962NDR.p2 CAAGTCTGGATCTGGGACCCITTCC 25 VEGF NM 003376 S0286NEGF.fl VEGF NM:003376 S0288NEGF.r1 GCAGCCTGGGACCACTTG 18 VEGF NM_003376 S4782NE0F.p1 TTGCCT1GCTGCTCTACCTCDACCA 25 VEGFB NM_003377 S2724NEGFB.f1 TGAGGATGGCCIGGAGTGT 19 VEGFB NM_003377 S2725NEGF B.r1 GGTACCGGATCATGAGGATCTG 22 VEGFB NM 003377 S4960NEGFB.p1 CTGGGCAGCACCAAGTCCGGA 21 WISP1 NM-003882 S1671/WISPl.fl AGAGGCATCCATGAACTTCACA

WISP1 NM_003882 S1672/WISP1 .r1 WISP1 NM_003882 S4915/WISP1.p1 C GGGCTGCATCAGCACAC GC 20 X1AP NM 001167 60289/X1AP:11 GCAGTTGGAAGACACAGGAAAGT
= . 23 XIAP NM 001167 S0291/MAP.r1 TGCGTGGCACTATTTTCAAGA
. - 21 XIAP NM:001167 S4752/XIAP.p1 TCCCCAAATTGCAGATTTATCAACGGC 27 YB-1 NM_004559 S1194/YB-1.12 AGACTGTGGAGTTTGATGTTGTTGA 25 YB-1 NM_004559 61195/YB-1.r2 GGAACACCACCAGGACCTGTAA 22 YB-1 NM_004559 S4843/YB-1.p2 TTGCTGCCTCCGCACCCTTTTCT 23 ZNF217 NM_006526 ' S2739/ZNF217.f3 ACCCAGTAGCAAGGAGAAGC 20 ZNF217 NM_006526 S2740/ZNF217.r3 CAGCTGGTGGTAGGTTCTGA 20 ZNF217 NM_006526 S49611ZNF217.p3 CACTCACTGCTCCGAGTGCGG 21 =
=
. =
=
=
=
=

DEMANDES OU BREVETS VOLUMINEUX
LA PRESENTE PARTIE DE CETTE DEMANDE OU CE BREVETS
COMPREND PLUS D'UN TOME.

NOTE: Pour les tomes additionels, veillez contacter le Bureau Canadien des Brevets.
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Claims (12)

We claim:
1. A method of predicting a likelihood of long-term survival of a breast cancer patient without recurrence of breast cancer, comprising:
determining an expression level of an RNA transcript of KRT14 or its expression product, in a breast cancer tumor sample from said patient;
normalizing said expression level to obtain a normalized expression level of KRT14;
and providing information regarding the likelihood of breast cancer recurrence for said patient, wherein increased normalized expression of KRT14 indicates an increased likelihood of long-term survival without breast cancer recurrence.
2. The method of claim 1, further comprising:
determining a normalized expression level of an RNA transcript of at least one gene selected from the group consisting of KRT5, KRT17, KRT18, KRT19 and MYBL2, or the transcript's expression product; and identifying said patient as likely to have a decreased likelihood of long-term survival without breast cancer recurrence if normalized expression of the RNA
transcript or expression product is elevated above a defined expression threshold.
3. The method of claim 1 or claim 2, wherein the breast cancer is invasive breast carcinoma.
4. The method of claim 1, 2 or 3, wherein the breast cancer tumor sample is a fixed, wax-embedded tissue specimen.
5. The method of any one of claims 1 to 4, wherein the breast cancer is estrogen-receptor positive breast cancer.
6. The method of any one of claims 1 to 5, wherein the expression level is determined by quantitative reverse-transcription polymerase chain reaction (qRT-PCR).
7. The method of any one of claims 1 to 5, wherein the expression level is determined by immunohistochemistry or proteomics technology.
8. The method of any one of claims 1 to 5, wherein the expression level is determined by microarray analysis.
9. A method of preparing a personalized genomics profile for a patient, comprising the steps of:
(a) subjecting RNA extracted from breast tissue from the patient to gene expression analysis;
(b) determining an expression level of an RNA transcript of KRT14 or its expression product, wherein the expression level is normalized against at least one control gene to obtain normalized data, and optionally is compared to expression levels found in a breast cancer reference tissue set; and (c) creating a report including a prediction of the likelihood of long-term survival without breast cancer recurrence for said patient, wherein increased normalized expression of KRT14 indicates an increased likelihood of long-term survival without breast cancer recurrence.
10. The method of claim 9, wherein said breast tissue comprises breast cancer cells.
11. The method of claim 9 or 10, wherein said breast tissue is obtained from a fixed, wax-embedded sample.
12. The method of claim 9, 10 or 11, wherein said RNA is fragmented.
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