The present invention relates to gene expression profiling in biopsied tumor tissues. In particular, the present invention concerns sensitive methods to measure mRNA levels in biopsied tumor tissues, including archived paraffn-embedded biopsy material. In addition, the invention provides a set of genes the expression of which is important in the diagnosis and treatment of breast cancer.

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 a particular cancer, but for which there is evidence of efficacy in that cancer. Best likelihood of good treatment outcome requires that patients be assigned to optimal available cancer treatment, and that this assignment be made as quickly as possible following diagnosis.

Currently, diagnostic tests used in clinical practice are single analyte, and therefore do not capture the potential value of knowing relationships between dozens of different markers. Moreover, diagnostic tests are frequently not quantitative, relying on immunohistochemistry. This method often yields different results in different laboratories, in part because the reagents are not standardized, and in part because the interpretations are subjective and cannot be easily quantified. RNA-based tests have not often been used because of the problem of RNA degradation over time and the fact that it is difficult to obtain fresh tissue samples from patient for analysis. Fixed paraffin-embedded tissue is more readily available and methods have been established to detect RNA in fixed tissue. However, these methods typically do not allow for the study of large numbers of genes (DNA or RNA) from small amounts of material. Thus, traditionally fixed tissue has been rarely used other than for immunohistochemistry detection of proteins.

Recently, several groups have published studies concerning the classification of various cancer types by microarray gene expression analysis (see, e.g. Golub et al., Science 286:531-537 (1999); Bhattacharjae et al., Proc. Natl Acad. Sci. USA 98:13790-13795 (2001); Chen-Hsiang et al., Bioinformatics 17 (Suppl. 1):S316-S322 (2001); Ramaswamy et al., Proc. Natl. Acad Sci. USA 98:15149-15154 (2001)). Certain classifications of human breast cancers based on gene expression patterns have also been reported (Martin et al., Cancer Res. 60:2232-2238 (2000); West et al., Proc. Natl. Acad Sci. USA 98:11462-11467 (2001); Sorlie et al., Proc. Nail. Acad Sci. USA 98:10869-10874 (2001); Yan et al., Cancer Res. 61:8375-8380 (2001)). However, these studies mostly focus on improving and refining the already established classification of various types of cancer, including breast cancer, and generally do not provide new insights into the relationships of the differentially expressed genes, and do not link the findings to treatment strategies in order to improve the clinical outcome of cancer therapy.

Winters et al. (2001) European Journal of Cancer 37, 2405-12 disclose subcellular localization of cyclin B, Cdc2 and p21 ^WAFI/CIPI protein by immunohistochemistry in breast cancer and its association with prognosis.

WO 00/60076 discloses compositions and methods for the therapy and diagnosis of cancer, such as breast cancer. Compositions may comprise one or more breast tumour proteins, immunogenic portions thereof, or polynucleotides that encode such portions. Alternatively, a therapeutic composition may comprise an antigen presenting cell that expresses a breast tumour protein, or a T cell that is specific for cells expressing such a protein. Such compositions may be used, for example, for the prevention and treatment of diseases such as breast cancer. Diagnostic methods based on detecting a breast tumour protein, or mRNA encoding such a protein, in a sample are also disclosed.

Yu et al. (2001) Molecular Immunology 38, 981-7 disclose immune recognition of cyclin B1 as a tumour antigen resulting from its overexpression in human tumours caused by non-functional p53.

Although modem molecular biology and biochemistry have revealed more than 100 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 tamoxifen. 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.

According to an aspect of the present invention there is provided a method as specified in claim 1.

The present specification discloses (1) sensitive methods to measure mRNA levels in biopsied tumor tissue, (2) a set of approximately 190 genes, the expression of which is important in the diagnosis of breast cancer, and (3) the significance of abnormally low or high expression for the genes identified and included in the gene set, through activation or disruption of biochemical regulatory pathways that influence patient response to particular drugs used or potentially useful in the treatment of breast cancer. These results permit assessment of genomic evidence of the efficacy of more than a dozen relevant drugs.

The present invention accommodates the use of archived paraffin-embedded biopsy material for assay of all markers in the set, and therefore is compatible with the most widely available type of biopsy material. The specification discloses an efficient method for extraction of RNA from wax-embedded, fixed tissues, which reduces cost of mass production process for acquisition of this information without sacrificing quality of the analysis. In addition, the specification discloses a novel highly effective method for amplifying mRNA copy number, which permits increased assay sensitivity and the ability to monitor expression of large numbers of different genes given the limited amounts of biopsy material. The specification also captures the predictive significance of relationships between expressions of certain markers in the breast cancer marker set Finally, for each member of the gene set, the specification specifies the oligonucleotide sequences to be used in the test.

In all previous aspects, in a specific embodiment, the expression level is determined using RNA obtained from a fomnalin-fixed, paraflin-embedded tissue sample. While all techniques of gene expression profiling, as well as proteomics techniques, are suitable for use in performing the foregoing aspects of the invention, the gene expression levels are often determined by reverse transcription polymerase chain reaction (RT-PCR).

If the source of the tissue is a formalin-fixed, paraffin embedded tissue sample, the RNA is often fragmented.

The expression data can be further subjected to multivariate analysis, for example using the Cox Proportional Hazards model.

Although all methods of gene expression profiling are contemplated, in a particular embodiment, gene expression profiling is performed by RT-PCR which may be preceded by an amplification step.

Preferably, the invention concerns a method of predicting the likelihood of long-term survival of a breast cancer patient without the récurrence of breast cancer, following surgical removal of the primary tumor, comprising determining the expression level of one or more prognostic RNA transcripts or their product in a breast cancer tissue sample obtained from said patient, normalized against the expression level of all RNA transcripts or their products in said breast cancer tissue sample, or of a reference set of RNA transcripts or their products, wherein the prognostic transcript is the transcript of one or more genes selected from the group consisting of: FOXM1, PRAME, Bcl2, STK15, CEGP1, Ki-67, GSTM1, CA9, PR, BBC3, NME1, SURV, GATA3, TFRC, YB-1, DPYD, GSTM3, RPS6KB1, Src, Chk1, ID1, EstR1, p27,
XIAP, Chk2, CDC25B, IGF1R, AK055699, P13KC2A, TGFB3, BAGI1, CYP3A4, EpCAM, VEGFC, pS2, hENT1, WISP1, HNF3A, NFKBp65, BRCA2, EGFR, TK1, VDR, Contig51037, pENT1, EPHX1, IF1A, DIABLO, CDH1, HIF1α, IGFBP3, CTSB, and Her2, wherein overexpression of one or more of FOXM1, PRAME, STK15, Ki-67, CA9, NME1, SURV, TFRC, YB-1, RPS6KB1, Src, Chk1, CCNB1, Chk2, CDC25B, CYP3A4, EpCAM, VEGFC, hENT1, BRCA2, EGFR, TK1, VDR, EPHX1, IF1A, Contig51037, CDH1, HIF1α, IGFBP3, CTSB, Her2, and pENT1 indicates a decreased likelihood of long-term survival without breast cancer recurrence, and the overexpression of one or more of Bcl2, CEGP1, GSTM1, PR, BBC3, GATA3, DPYD, GSTM3, ID1, EstR1, p27, XIAP, IGF1R, AK055699, P13KC2A, TGFB3, BAGI1, pS2, WISP1, HNF3A, NFKBp65, and DIABLO indicates an increased likelihood of long-term survival without breast cancer recurrence.

In a particular embodiment of this method, the expression level of at least 2, preferably at least 5, more preferably at least 10, most preferably at least 15 prognostic transcipts or their expression products is determined.

When the breast cancer is invasive breast carcinoma, including both estmgen receptor (ER) overexpressing (ER positive) and ER negative tumors, the analysis includes determination of the expression levels of the transcripts of at least two of the following genes, or their expression products: FOXM1, PRAME, Bcl2, STK 15, CEGP1, Ki-67, GSTM1, PR, BBC3, NME1, SURV, GATA3, TFRC, YB-1, DPYD, Src, CA9, Contig51037, RPS6K1 and Her2.

When the breast cancer is ER positive invasive breast carcinoma, the analysis includes dtermination of the expression levels of the transcripts of at least two of the following genes, or their expression products: PRAME, Bcl2, FOXM1, DIABLO, EPHX1, HIF1A, VEGFC, Ki-67, IGF1R, VDR, NME1, GSTM3, Contig51037, CDC25B, CTSB, p27, CDH1, and IGFBP3.

Just as before, it is preferred to determine the expression levels of at least 5, more preferably at least 10, most preferably at least 15 genes, or their respective expression products.

In a particular embodiment, the expression level of one or more prognostic RNA transcripts is determined, where RNA may, for example, be obtained from a fixed, wax-embedded breast cancer tissue specimen of the patient. The isolation of RNA can, for example, be carried out following any of the procedures described above or throughout the application, or by any other method known in the art.

The polynucleotides can be cDNAs ("cDNA arrays") that are typically about 500 to 5000 bases long, although shorter or longer cDNAs can also be used and are within the scope of this invention. Alternatively, the polynucleotides can be oligonucleotides (DNA microarray), which are typically about 20 to 80 bases long, although shorter and longer oligonucleotides are also suitable and are within the scope of the invention. The solid surface can, for example, be glass or nylon, or any other solid surface typically used in preparing arrays, such as microarrays, and is typically glass.

• Figure 1 is a chart illustrating the overall workflow of the process of the invention for measurement of gene expression. In the Figure, FPET stands for "fixed paraffin-embedded tissue," and "RT-PCR" stands for "reverse transcriptase PCR" RNA concentration is determined by using the commercial RiboGreen™ RNA Quantitation Reagent and Protocol.
• Figure 2 is a flow chart showing the steps of an RNA extraction method according to the invention alongside a flow chart of a representative commercial method.
• Figure 3 is a scheme illustrating the steps of an improved method for preparing fragmented mRNA for expression profiling analysis.
• Figure 4 illustrates methods for amplification of RNA prior to RT-PCR.
• Figure 5 illustrates an alternative scheme for repair and amplification of fragmented mRNA.
• Figure 6 shows the measurement of estrogen receptor mRNA levels, in 40 FPE breast cancer specimens via RT-PCR. Three 10 micron sections were used for each measurement. Each data point represents the average of triplicate measurements.
• Figure 7 shows the results of the measurement of progesterone receptor mRNA levels in 40 FPE breast cancer specimens via RT-PCR performed as described in the legend of Figure 6 above.
• Figure 8 shows results from an IVT/RT-PCR experiment.
• Figure 9 is a representation of the expression of 92 genes across 70 FPE breast cancer specimens. The y-axis shows expression as cycle threshold times. These genes are a subset of the genes listed in Table 1.

Table 1 shows a breast cancer gene list.

Table 2 sets forth amplicon and primer sequences used for amplification of fragmented mRNA.

Table 3 shows the Accession Nos. and SEQ ID NOS of the breast cance genes examined.

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.

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 DNAs 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 a comparison of the ratios of the expression between two or more genes, 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 (mRNA) produced, i.e., the level of gene expression, also increases in the proportion of the number of copies made of the particular gene expressed.

The term "prognosis" is used herein to refer to the prediction of the likelihood of cancer-attributable death or progression, including recurrence, metastatic spread, and drug resistance, of a neoplastic disease, such as breast cancer. The term "prediction" is used herein to refer to the likelihood that a patient will respond either favorably or unfavorably to a drug or set of drugs, and also the extent of those responses. 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.

The term "increased resistance" to a particular drug or treatment option, when used in accordance with the present invention, means decreased response to a standard dose of the drug or to a standard treatment protocol.

The term "decreased sensitivity" to a particular drug or treatment option, when used in accordance with the present invention, means decreased response to a standard dose of the drug or to a standard treatment protocol, where decreased response can be compensated for (at least partially) by increasing the dose of drug, or the intensity of treatment.

"Patient response" can be assessed using any endpoint indicating a benefit to the patient, including, without limitation, (1) inhibition, to some extent, of tumor growth, including slowing down and complete growth arrest; (2) reduction in the number of tumor cells; (3) reduction in tumor size; (4) inhibition (i.e., reduction, slowing down or complete stopping) of tumor cell infiltration into adjacent peripheral organs and/or tissues; (5) inhibition (i.e. reduction, slowing down or complete stopping) of metastasis; (6) enhancement of anti-tumor immune response, which may, but does not have to, result in the regression or rejection of the tumor, (7) relief, to some extent, of one or more symptoms associated with the tumor; (8) increase in the length of survival following treatment; and/or (9) decreased mortality at a given point of time following treatment.

The term "treatment" refers to both therapeutic treatment and prophylactic or preventative measures, wherein the object is to prevent or slow down (lessen) the targeted pathologic condition or disorder. Those in need of treatment include those already with the disorder as well as those prone to have the disorder or those in whom the disorder is to be prevented. In tumor (e.g., cancer) treatment, a therapeutic agent may directly decrease the pathology of tumor cells, or render the tumor cells more susceptible to treatment by other therapeutic agents, e.g., radiation and/or chemotherapy.

The term "tumor," as used herein, refers to all neoplastic cell growth and proliferation, whether malignant or benign, and all pre-cancervus 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 albumen/0.1% Ficoll/0.1 % polyvinylpyrrolidonel50mM sodium phosphate buffer at pH 6.5 with 750 mM sodium chloride, 75 mM sodium citrate at 42°C; or (3) employ 50% formamide, 5 x SSC (0.75 M NaCl, 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 µg/ml), 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 NaCl, 15 mM trisodium citrate), 50 mM sodium phosphate (pH 7.6), 5 x Denhardt's solution, 10% dextran sulfate, and 20 mg/ml 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 "splicing" and "RNA splicing" are used interchangeably and refer to RNA processing that removes introns and joins exons to produce mature mRNA with continuous coding sequence that moves into the cytoplasm of an eukaryotic cell.

In theory, the term "exon" refers to any segment of an interrupted gene that is represented in the mature RNA product (B. Lewin. Genes IV Cell Press, Cambridge Mass. 1990). In theory the term "intron" refers to any segment of DNA that is transcribed but removed from within the transcript by splicing together the exons on either side of it. Operationally, exon sequences occur in the mRNA sequence of a gene as defined by Ref. Seq ID numbers. Operationally, intron sequences are the intervening sequences within the genomic DNA of a gene, bracketed by exon sequences and having GT and AG splice consensus sequences at their 5' and 3' boundaries.

The practice of the present invention will employ, unless otherwise indicated, conventional techniques of molecular biology (including recombinannechniques), 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", 2nd 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. Calos, eds., 1987); "Current Protocols in Molecular Biology" (F.M. Ausubel et al., eds., 1987); and "PCR: The Polymerase Chain Reaction", (Mullis et al., eds., 1994).

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 (MPSS).

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 paraffm-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 Andrés et al., BioTechniques 18:42044 (1995). In particular, RNA isolation can be performed using purification kit, buffer set and protease from commercial manufacturers, such as Qiagen, according to the manufacturer's instructions. For example, total RNA from cells in culture can be isolated using Qiagen RNeasy mini-columns. Other commercially available RNA isolation kits include MasterPure™ 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 (MMLV-RT). The reverse transcription step is typically primed using specific primers, random hexamers, or oligo-dT primers, depending on the circumstances and the goal of expression profiling. For example, extracted RNA can be reverse-transcribed using a GeneAmp RNA PCR kit (Perkin Elmer, CA, USA), following the manufacturer's instructions. The derived cDNA can then be used as a template in the subsequent PCR reaction.

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 7700™ 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 7700™ 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 (C₁).

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 β-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).

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 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.

In a specific embodiment of the microarray technique, PCR amplified inserts of cDNA clones are applied to a substrate in a dense array. Preferably at least 10,000 nucleotide sequences are applied to the substrate. The microarrayed genes, immobilized on the microchip at 10,000 elements each, are suitable for hybridization under stringent conditions. Fluorescently labeled cDNA probes may be generated through incorporation of fluorescent nucleotides by reverse transcription of RNA extracted from tissues of interest. Labeled cDNA probes applied to the chip hybridize with specificity to each spot of DNA on the array. After stringent washing to remove non-specifically bound probes, the chip is scanned by confocal laser microscopy or by another detection method, such as a CCD camera. Quantitation of hybridization of each arrayed element allows for assessment of corresponding mRNA abundance. With dual color fluorescence, separately labeled cDNA probes generated from two sources of RNA are hybridized pairwise to the array. The relative abundance of the transcripts from the two sources corresponding to each specified gene is thus determined simultaneously. The miniaturized scale of the hybridization affords a convenient and rapid evaluation of the expression pattern for large numbers of genes. Such methods have been shown to have the sensitivity required to detect rare transcripts, which are expressed at a few copies per cell, and to reproducibly detect at least approximately two-fold differences in the expression levels (Schena et al., Proc. Natl. Acad Sci. USA 93(2):106-149 (1996)). Microarray analysis can be performed by commercially available equipment, following manufacturer's protocols, such as by using the Affymetrix GenChip technology, or Incyte's microarray technology.

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.

Serial analysis of gene expression (SAGE) is a method that allows the simultaneous and quantitative analysis of a large number of gene transcripts, without the need of providing an individual hybridization probe for each transcript. First, a short sequence tag (about 10-14 bp) is generated that contains sufficient information to uniquely identify a transcript, provided that the tag is obtained from a unique position within each transcript. Then, many transcripts are linked together to form long serial molecules, that can be sequenced, revealing the identity of the multiple tags simultaneously. The expression pattern of any population of transcripts can be quantitatively evaluated by determining the abundance of individual tags, and identifying the gene corresponding to each tag. For more details see, e.g. Velculescu et al., Science 270:484-487 (1995); and Velculescu et al., Cell 88:243-51 (1997).

This method, described by Brenner et at., 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 µm 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.

The steps of a representative protocol of the invention, including mRNA isolation, purification, primer extension and amplification are illustrated in Figure 1. As shown in Figure 1, this representative process starts with cutting about 10 µm thick sections of paraffin-embedded tumor tissue samples. The RNA is then extracted, and protein and DNA are removed, following the method of the invention described below. 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. The individual steps of this protocol will be discussed in greater detail below.

As discussed above, in the first step of the method of the invention, total RNA is extracted ' from the source material of interest, including fixed, paraffin-embedded tissue specimens, and purified sufficiently to act as a substrate in an enzyme assay. Despite the availability of commercial products, and the extensive knowledge available concerning the isolation of nucleic acid, such as RNA, from tissues, isolation of nucleic acid (RNA) from fixed, paraffin-embedded tissue specimens (FPET) is not without difficultly.

In one aspect, the present invention concerns an improved method for the isolation of nucleic acid from archived, e.g. FPET tissue specimens. Measured levels of mRNA species are useful for defining the physiological or pathological status of cells and tissues. RT-PCR (which is discussed above) is one of the most sensitive, reproducible and quantitative methods for this "gene expression profiling". Paraffin-embedded, formalin-fixed tissue is the most widely available material for such studies. Several laboratories have demonstrated that it is possible to successfully use fixed-paraffin-embedded tissue (FPET) as a source of RNA for RT-PCR (Stanta et al., Biotechniques 11:304-308 (1991); Stanta et al., Methods Mol. Biol. 86:23-26 (1998); Jackson et al., Lancet 1:139 (1989); Jackson et al., J. Clin Pathol. 43:499-504 (1999); Finke et at., Biotechniques 14:448-453 (1993); Goldsworthy et al., Mol. Carcinog. 25:86-91 (1999); Stanta and Bonin, Biotechniques 24:271-276 (1998); Godfrey et al., J. Mol. Diagnostics 2:84 (2000); Specht et al., J. Mol. Med. 78:B27 (2000); Specht el al., Am. J. Pathol. 158:419-429 (2001)). This allows gene expression profiling to be carried out on the most commonly available source of human biopsy specimens, and therefore potentially to create new valuable diagnostic and therapeutic information.

The most widely used protocols utilize hazardous organic solvents, such as xylene, or octane (Finke et al., supra) to dewax the tissue in the paraffin blocks before nucleic acid (RNA and/or DNA) extraction. Obligatory organic solvent removal (e.g. with ethanol) and rehydration steps follow, which necessitate multiple manipulations, and addition of substantial total time to the protocol, which can take up to several days. Commercial kits and protocols for RNA extraction from FPET [MasterPure™ Complete DNA and RNA Purification Kit (EPICENTRE®, Madison, WI); paraffin Block RNA Isolation Kit (Ambion, Inc.) and RNeasy™ Mini kit (Qiagen, Chatsworth, CA)] use xylene for deparaffinization, in procedures which typically require multiple centrifugations and ethanol buffer changes, and incubations following incubation with xylene.

The present invention provides an improved nucleic acid extraction protocol that produces nucleic acid, in particular RNA, sufficiently intact for gene expression measurements. The key step in the nucleic acid extraction protocol herein is the performance of dewaxing without the use of any organic solvent, thereby eliminating the need for multiple manipulations associated with the removal of the organic solvent, and substantially reducing the total time to the protocol. According to the invention, wax, e.g. paraffin is removed from wax-embedded tissue samples by incubation at 65-75 °C in a lysis buffer that solubilizes the tissue and hydrolyzes the protein, following by cooling to solidify the wax.

Figure 2 shows a flow chart of an RNA extraction protocol of the present invention in comparison with a representative commercial method, using xylene to remove wax. The times required for individual steps in the processes and for the overall processes are shown in the chart. As shown, the commercial process requires approximately 50% more time than the process of the invention.

The lysis buffer can be any buffer known for cell lysis. It is, however, preferred that oligo-dT-based methods of selectively purifying polyadenylated mRNA not be used to isolate RNA for the present invention, since the bulk of the mRNA molecules are expected to be fragmented and therefore will not have an intact polyadenylated tail, and will not be recovered or available for subsequent analytical assays. Otherwise, any number of standard nucleic acid purification schemes can be used. These include chaotrope and organic solvent extractions, extraction using glass beads or filters, salting out and precipitation based methods, or any of the purification methods known in the art to recover total RNA or total nucleic acids from a biological source.

Lysis buffers are commercially available, such as, for example, from Qiagen, Epicentre, or Ambion. A preferred group of lysis buffers typically contains urea, and Proteinase K or other protease. Proteinase K is very useful in the isolation of high quality, undamaged DNA or RNA, since most mammalian DNases and RNases are rapidly inactivated by this enzyme, especially in the presence of 0.5 - 1% sodium dodecyl sulfate (SDS). This is particularly important in the case of RNA, which is more susceptible to degradation than DNA. While DNases require metal ions for activity, and can therefore be easily inactivated by chelating agents, such as EDTA, there is no similar co-factor requirement for RNases.

Cooling and resultant solidification of the wax permits easy separation of the wax from the total nucleic acid, which can be conveniently precipitated, e.g. by isopropanol. Further processing depends on the intended purpose. If the proposed method of RNA analysis is subject to bias by contaminating DNA in an extract, the RNA extract can be further treated, e.g. by DNase, post purification to specifically remove DNA while preserving RNA. For example, if the goal is to isolate high quality RNA for subsequent RT-PCR amplification, nucleic acid precipitation is followed by the removal of DNA, usually by DNase treatment However, DNA can be removed at various stages of nucleic acid isolation, by DNase or other techniques well known in the art.

While the advantages of the nucleic acid extraction protocol of the invention are most apparent for the isolation of RNA from archived, paraffin embedded tissue samples, the wax removal step of the present invention, which does not involve the use of an organic solvent, can also be included in any conventional protocol for the extraction of total nucleic acid (RNA and DNA) or DNA only. All of these aspects are specifically within the scope of the invention.

By using heat followed by cooling to remove paraffin, the process of the present invention saves valuable processing time, and eliminates a series of manipulations, thereby potentially increasing the yield of nucleic acid. Indeed, experimental evidence presented in the examples below, demonstrates that the method of the present invention does not compromise RNA yield.

RT-PCR requires reverse transcription of the test RNA population as a first step. The most commonly used primer for reverse transcription is oligo-dT, which works well when RNA is intact. However, this primer will not be effective when RNA is highly fragmented as is the case in FPE tissues.

The present invention includes the use of gene specific primers, which are roughly 20 bases in length with a Tm optimum between about 58 °C and 60 °C. These primers will also serve as the reverse primers that drive PCR DNA amplification.

Another aspect of the invention is the inclusion of multiple gene-specific primers in the same reaction mixture. The number of such different primers can vary greatly and can be as low as two and as high as 40,000 or more. Table 2 displays examples of reverse primers that can be successfully used in carrying out the methods of the invention. Figure 9 shows expression data obtained using this multiplexed gene-specific priming strategy. Specifically, Figure 9 is a representation of the expression of 92 genes (a subset of genes listed in Table 1) across 70 FPE breast cancer specimens. The y-axis shows expression as cycle threshold times.

An alternative approach is based on the use of random hexamers as primers for cDNA synthesis. However, we have experimentally demonstrated that the method of using a multiplicity of gene-specific primers is superior over the known approach using random hexamers.

It is of interest to analyze the abundance of specific mRNA species in biological samples, since this expression profile provides an index of the physiological state of that sample mRNA is notoriously difficult to extract and maintain in its native state, consequently, mRNA recovered from biological sources is often fragmented or somewhat degraded. This is especially true of human tissue specimen which have been chemically fixed and stored for extended periods of time.

In one aspect, the present invention provides a means of preparing the mRNA extracted from various sources, including archived tissue specimens, for expression profiling in a way that its relative abundance is preserved and the mRNA's of interest can be successfully measured. This method is useful as a means of preparing mRNA for analysis by any of the known expression profiling methods, including RT-PCR coupled with 5' exonuclease of reporter probes (TaqMan® type assays), as discussed above, flap endonuclease assays (Cleavase® and Invader® type assays), oligonucleotide hybridization arrays, cDNA hybridization arrays, oligonucleotide ligation assays, 3' single nucleotide extension assays and other assays designed to assess the abundance of specific mRNA sequences in a biological sample.

According to the method of the invention, total RNA is extracted from the source material and sufficiently purified to act as a substrate in an enzyme assay. The extraction procedure, including a new and improved way of removing the wax (e.g. paraffin) used for embedding the tissue samples, has been discussed above. It has also been noted that it is preferred that oligo-dT based methods of selectively purifying polyadenylated mRNA not be used to isolate RNA for this invention since the bulk of the mRNA is expected to be fragmented, will not be polyadenylated and, therefore, will not be recovered and available for subsequent analytical assays if an oligo-dT based method is used.

A diagram of an improved method for repairing fragmented RNA is shown in Figure 3. The fragmented RNA purified from the tissue sample is mixed with universal or gene-specific, single-stranded, DNA templates for each mRNA species of interest. These templates may be full length DNA copies of the mRNA derived from cloned gene sources, they may be fragments of the gene representing only the segment of the gene to be assayed, they may be a series of long oligonucleotides representing either the full length gene or the specific segment(s) of interest. The template can represent either a single consensus sequence or be a mixture of polymorphic variants of the gene. This DNA template, or scaffold, will preferably include one or more dUTP or rNTP sites in its length. This will provide a means of removing the template prior to carrying out subsequent analytical steps to avoid its acting as a substrate or target in later analysis assays. This removal is accomplished by treating the sample with uracil-DNA glycosylase (UDG) and heating it to cause strand breaks where UDG has generated abasic sites. In the case of rNTP's, the sample can be heated in the presence of a basic buffer (pH ∼10) to induce strand breaks where rNTP's are located in the template.

The single stranded DNA template is mixed with the purified RNA, the mixture is denatured and annealed so that the RNA fragments complementary to the DNA template effectively become primers that can be extended along the single stranded DNA templates. DNA polymerase I requires a primer for extension but will efficiently use either a DNA or an RNA primer. Therefore in the presence of DNA polymerase I and dNTP's, the fragmented RNA can be extended along the complementary DNA templates. In order to increase the efficiency of the extension, this reaction can be thermally cycled, allowing overlapping templates and extension products to hybridize and extend until the overall population of fragmented RNA becomes represented as double stranded DNA extended from RNA fragment primers.

Following the generation of this "repaired" RNA, the sample should be treated with UDG or heat-treated in a mildly based solution to fragment the DNA template (scaffold) and prevent it from participating in subsequent analytical reactions.

The product resulting from this enzyme extension can then be used as a template in a standard enzyme profiling assay that includes amplification and detectable signal generation such as fluorescent, chemiluminescent, colorimetric or other common read outs from enzyme based assays. For example, for TaqMan® type assays, this double stranded DNA product is added as the template in a standard assay; and, for array hybridization, this product acts as the cDNA template for the cRNA labeling reaction typically used to generate single-stranded, labeled RNA for array hybridization.

This method of preparing template has the advantage of recovering information from mRNA fragments too short to effectively act as templates in standard cDNA generation schemes. In addition, this method acts to preserve the specific locations in mRNA sequences targeted by specific analysis assays. For example, TaqMan® assays rely on a single contiguous sequence in a cDNA copy of mRNA to act as a PCR amplification template targeted by a labeled reporter probe. If mRNA strand breaks occur in this sequence, the assay will not detect that template and will underestimate the quantity of that mRNA in the original sample. This target preparation method minimizes that effect from RNA fragmentation.

The extension product formed in the RNA primer extension assay can be controlled by controlling the input quantity of the single stranded DNA template and by doing limited cycling of the extension reaction. This is important in preserving the relative abundance of the mRNA sequences targeted for analysis.

This method has the added advantage of not requiring parallel preparation for each target sequence since it is easily multiplexed. It is also possible to use large pools of random sequence long oligonucleotides or full libraries of cloned sequences to extend the entire population of mRNA sequences in the sample extract for whole expressed genome analysis rather than targeted gene specific analysis.

Due to the limited amount and poor quality of mRNA that can be isolated from FPET, a new procedure that could accurately amplify mRNAs of interest would be very useful, particularly for real time quantitation of gene expression (TaqMan®) and especially for quantitatively large number (>50) of genes >50 to 10,000.

Current protocols (e.g. Eberwine, Biotechniques 20:584-91 (1996)) are optimized for mRNA amplification from small amount of total or poly A⁺ RNA mainly for microarray analysis. The present invention provides a protocol optimized for amplification of small amounts of fragmented total RNA (average size about 60-150 bps), utilizing gene-specific sequences as primers, as illustrated in Figure 4.

The amplification procedure of the invention uses a very large number, typically as many as 100 - 190,000 gene specific primers (GSP's) in one reverse transcription run. Each GSP contains an RNA polymerase promoter, e.g. a T7 DNA-dependent RNA polymerase promoter, at the 5' end for subsequent RNA amplification. GSP's are preferred as primers because of the small size of the RNA. Current protocols utilize dT primers, which would not adequately represent all reverse transcripts of mRNAs due to the small size of the FPET RNA. GSP's can be designed by optimizing usual parameters, such as length, Tm, etc. For example, GSP's can be designed using the Primer Express® (Applied Biosystems), or Primer 3 (MIT) software program. Typically at least 3 sets per gene are designed, and the ones giving the lowest Ct on FPET RNA (best performers) are selected.

Second strand cDNA synthesis is performed by standard procedures (see Figure 4, Method 1), or by GSP_r primers and Taq pol under PCR conditions (e.g., 95 °C, 10 min (Taq activation) then 60 °C, 45 sec). The advantages of the latter method are that the second gene specific primer, SGF_f adds additional specificity (and potentially more efficient second strand synthesis) and the option of performing several cycles of PCR, if more starting DNA is necessary for RNA amplification by T7 RNA polymerase. RNA amplification is then performed under standard conditions to generate multiple copies of cRNA, which is then used in a standard TaqMan® reaction.

Although this process is illustrated by using T7-based RNA amplification, a person skilled in the art will understand that other RNA polymerase promoters that do not require a primer, such as T3 or Sp6 can also be used, and are within the scope of the invention.

This method, which combines and modifies the inventions described in sections 9 and 10 above, is illustrated in Figure 5. The procedure begins with elongation of fragmented mRNA. This occurs as described above except that the scaffold DNAs are tagged with the T7 RNA polymerase promoter sequence at their 5' ends, leading to double-stranded DNA extended from RNA fragments. The template sequences need to be removed after in vitro transcription. These templates can include dUTP or rNTP nucleotides, enabling enzymatic removal of the templates as described in section 9, or the templates can be removed by DNaseI treatment.

The template DNA can be a population representing different mRNAs of any number. A high sequence complexity source of DNA templates (scaffolds) can be generated by pooling RNA from a variety of cells or tissues. In one embodiment, these RNAs are converted into double stranded DNA and cloned into phagemids. Single stranded DNA can then be rescued by phagemid growth and single stranded DNA isolation from purified phagemids.

This invention is useful because it increases gene expression profile signals two different ways: both by increasing test mRNA polynucleotide sequence length and by in vitro transcription amplification. An additional advantage is that it eliminates the need to carry out reverse transcription optimization with gene specific primers tagged with the T7 RNA polymerase promoter sequence, and thus, is comparatively fast and economical.

This invention can be used with a variety of different methods to profile gene expression, e.g., RT-PCR or a variety of DNA array methods. Just as in the previous protocol, this approach is illustrated by using a T7 promoter but the invention is not so limited. A person skilled in the art will appreciate, however, that other RNA polymerase promoters, such as T3 or Sp6 can also be used.

An important aspect of the present invention is to use the measured expression of certain genes by breast cancer tissue to match patients to best drugs or drug combinations, and 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 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) tumors yields a distribution of normalized levels of each RNA 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.

The breast cancer gene set is shown in Table 1. The gene Accession Numbers, and the SEQ ID NOs for the forward primer, reverse primer and amplicon sequences that can be used for gene amplification, are listed in Table 2. The basis for inclusion of markers, as well as the clinical significance of mRNA level variations with respect to the reference set, is indicated below. Genes are grouped into subsets based on the type of clinical significance indicated by their expression levels: A. Prediction of patient response to drugs used in breast cancer treatment, or to drugs that are approved for other indications and could be used off-label in the treatment of breast cancer. B. Prognostic for survival or recurrence of cancer.

Table 1 lists 74 genes (shown in italics) that specifically influence cellular sensitivity to potent drugs, which are also listed. Most of the drugs shown are approved and already used to treat breast cancer (e.g., anthracyclines; cyclophosphamide; methotrexate; 5-FU and analogues). Several of the drugs are used to treat breast cancer off-label or are in clinical development phase (e.g., bisphosphonates and anti-VEGF mAb). Several of the drugs have not been widely used to treat breast cancer but are used in other cancers in which the indicated target is expressed (e.g., Celebrex is used to treat familial colon cancer; cisplatin is used to treat ovarian and other cancers.)

Patient response to 5FU is indicated if normalized thymidylate synthase mRNA amount is at or below the 15^th percentile, or the sum of expression of thymidylate synthase plus dihydropyrimidine phosphorylase is at or below the 25^th percentile, or the sum of expression of these mRNAs plus thymidine phosphorylase is at or below the 20^th percentile. Patients with dihydropyrimidine dehydrogenase below 5^th percentile are at risk of adverse response to 5FU, or analogs such as Xeloda.

When levels of thymidylate synthase, and dihydropyrimidine dehydrogenase, are within the acceptable range as defined in the preceding paragraph, amplification of c-myc mRNA in the upper 15%, against a background of wild-type p53 [as defined below] predicts a beneficial response to 5FU (see D. Arango et al., Cancer Res. 61:4910-4915 (2001)). In the presence of normal levels of thymidylate synthase and dihydropyrimidine dehydrogenase, levels of NFκB and cIAP2 in the upper 10% indicate resistance of breast tumors to the chemotherapeutic drug 5FU.

Patient resistance to anthracyclines is indicated if the normalized mRNA level of topoisomerase Πα is below the 10^th percentile, or if the topoisomerase Πβ normalized mRNA level is below the 10^th percentile or if the combined normalized topoisomerase Πα and, signals are below the 10^th percentile.

Patient sensitivity to methotrexate is compromised if DHFR levels are more than tenfold higher than the average reference set level for this mRNA species, or if reduced folate carrier levels are below 10^th percentile.

Patients whose tumors express CYPIBI in the upper 10%, have reduced likelihood of responding to docetaxol.

The sum of signals for aldehyde dehydrogenase 1A1 and 1A3, when more than tenfold higher than the reference set average, indicates reduced likelihood of response to cyclophosphamide.

Currently, estrogen and progesterone receptor expression as measured by immunohistochemistry is used to select patients for anti-estrogen therapy. We have demonstrated RT-PCR assays for estrogen and progesterone receptor mRNA levels that predict levels of these proteins as determined by a standard clinical diagnostic tests, with high degree of concordance (Figures 6 and 7).

Patients whose tumors express ERα or PR mRNA in the upper 70%, are likely to respond to tamoxifen or other anti-estrogens (thus, operationally, lower levels of ERa than this are to defined ERα-negative). However, when the signal for microsomal epoxide hydrolase is in the upper 10% or when mRNAs for pS2/trefoil factor, GATA3 or human chorionic gonadotropin are at or below average levels found in ERa-negative tumors, anti-estrogen therapy will not be beneficial.

Absence of XIST signal compromises the likelihood of response to taxanes, as does elevation of the GST-π or prolyl endopeptidase [PREP] signal in the upper 10%. Elevation of PLAG1 in the upper 10% decreases sensitivity to taxanes.

Expression of ERCC1 mRNA in the upper 10% indicate significant risk of resistance to cisplatin or analogs.

An RT-PCR assay of Her2 mRNA expression predicts Her2 overexpression as measured by a standard diagnostic test, with high degree of concordance (data not shown). Patients whose tumors express Her2 (normalized to cyp.1) in the upper 10% have increased likelihood of beneficial response to treatment with Herceptin or other ErbB2 antagonists. Measurement of expression of Grb7 mRNA serves as a test for HER2 gene amplification, because the Grb7 gene is closely linked to Her2. When Her2 is expression is high as defined above in this paragraph, similarly elevated Grb7 indicates Her2 gene amplification. Overexpression of IGF1R and or IGF1 or IGF2 decreases likelihood of beneficial response to Herceptin and -also to EGFR antagonists.

Patients whose tumors express mutant Ha-Ras, and also express farnesyl pyrophosphate synthetase or geranyl pyrophosphonate synthetase mRNAs at levels above the tenth percentile comprise a group that is especially likely to exhibit a beneficial response to bis-phosphonate drugs.

Cox2 is a key control enzyme in the synthesis of prostaglandins. It is frequently expressed at elevated levels in subsets of various types of carcinomas including carcinoma of the breast. Expression of this gene is controlled at the transcriptional level, so RT-PCR serves a valid indicator of the cellular enzyme activity. Nonclinical research has shown that cox2 promotes tumor angiogenesis, suggesting that this enzyme is a promising drug target in solid tumors. Several Cox2 antagonists are marketed products for use in anti-inflammatory conditions. Treatment of familial adenomatous polyposis patients with the cox2 inhibitor Celebrex significantly decreased the number and size of neoplastic polyps. No cox2 inhibitor has yet been approved for treatment of breast cancer, but generally this class of drugs is safe and could be prescribed off-label in breast cancers in which cox2 is over-expressed. Tumors expressing COX2 at levels in the upper ten percentile have increased chance of beneficial response to Celebrex or other cyclooxygenase 2 inhibitors.

The tyrosine kinases ErbB1 [EGFR], ErbB3 [Her3] and ErbB4 [Her4]; also the ligands TGFalpha, amphiregulin, heparin-binding EGF-like growth factor, and epiregulin; also BRK, a non-receptor kinase. Several drugs in clinical development block the EGF receptor. ErbB2-4, the indicated ligands, and BRK also increase the activity of the EGFR pathway. Breast cancer patients whose tumors express high levels of EGFR or EGFR and abnormally high levels of the other indicated activators of the EGFR pathway are potential candidates for treatment with an EGFR antagonist.

Patients whose tumors express less than 10% of the average level of EGFR mRNA observed in the reference panel are relatively less likely to respond to EGFR antagonists [such as Iressa, or ImClone 225]. In cases in which the EGFR is above this low range, the additional presence of epiregulin, TGFα, amphiregulin, or ErbB3, or BRK, CD9, MMP9, or Lotl at levels above the 90^th percentile predisposes to response to EGFR antagonists. Epiregulin gene expression, in particular, is a good surrogate marker for EGFR activation, and can be used to not only to predict response to EGFR antagonists, but also to monitor response to EGFR antagonists [taking fine needle biopsies to provide tumor tissue during treatment]. Levels of CD82 above the 90^th percentile suggest poorer efficacy from EGFR antagonists.

The tyrosine kinases abl, o-kit, PDGFRalpha, PDGFbeta, and ARG; also, the signal transmitting ligands c-kit ligand, PDGFA, B, C and D The listed tyrosine kinases are all targets of the drug Gleevec™ (imatinib mesylate, Novartis), and the listed ligands stimulate one or more of the listed tyrosine kinases. In the two indications for which Gleevec™ is approved, tyrosine kinase targets (bcr-abl and ckit) are overexpressed and also contain activating mutations. A finding that one of the Gleevec™ target tyrosine kinase targets is expressed in breast cancer tissue will prompt a second stage of analysis wherein the gene will be sequenced to determine whether it is mutated. That a mutation found is an activating mutation can be proved by methods known in the art, such as, for example, by measuring kinase enzyme activity or by measuring phosphorylation status of the particular kinase, relative to the corresponding wild-type kinase. Breast cancer patients whose tumors express high levels of mRNAs encoding Gleevec™ target tyrosine kinases, specifically, in the upper ten percentile, or mRNAs for Gleevec™ target tyrosine kinases in the average range and mRNAs for their cognate growth stimulating ligands in the upper ten percentile, are particularly good candidates for treatment with Gleevec™.

VEGF is a potent and pathologically important angiogenic factor. (See below under Prognostic Indicators.) When VEGF mRNA levels are in the upper ten percentile, aggressive treatment is warranted. Such levels particularly suggest the value of treatment with anti-angiogenic drugs, including VEGF antagonists, such as anti-VEGF antibodies. Additionally, KDR or CD31 mRNA level in the upper 20 percentile further increases likelihood of benefit from VEGF antagonists.

Farnesyl pyrophosphatase synthetase and geranyl geranyl pyrophosphatase synthetase. These enzymes are targets of commercialized bisphosphonate drugs, which were developed originally for treatment of osteoporosis but recently have begun to prescribe them off-label in breast cancer. Elevated levels of mRNAs encoding these enzymes in breast cancer tissue, above the 90^th percentile, suggest use of bisphosphonates as a treatment option.

These factors include 10 Genes: gamma glutamyl cysteine synthetase [GCS]; GST-α; GST-π; MDR-1; MRP1-4; breast cancer resistance protein [BCRP]; lung resistance protein [MVP]; SXR; YB-1.

GCS and both GST-α and GST-π regulate glutathione levels, which decrease cellular sensitivity to chemotherapeutic drugs and other toxins by reductive derivatization. Glutathione is a necessary cofactor for multi-drug resistant pumps, MDR-1 and the MRPs. MDR1 and MRPs function to actively transport out of cells several important chemotherapeutic drugs used in breast cancer.

GSTs, MDR-1, and MRP-1 have all been studied extensively to determine possible have prognostic or predictive significance in human cancer. However, a great deal of disagreement exists in the literature with respect to these questions. Recently, new members of the MRP family have been identified: MRP-2, MRP-3, MRP-4, BCRP, and lung resistance protein [major vault protein]. These have substrate specificities that overlap with those of MDR-1 and MRP-1 The incorporation of all of these relevant ABC family members as well as glutathione synthetic enzymes into the present invention captures the contribution of this family to drug resistance, in a way that single or double analyte assays cannot.

MRP-1, the gene coding for the multidrug resistance protein.

P-glycoprotein, is not regulated primarily at the transcriptional level. However, p-glycoprotein stimulates the transcription of PTP1b. An embodiment of the present invention is the use of the level of the mRNA for the phosphatase PTP1b as a surrogate measure of MRP-1/p-glycoprotein activity.

The gene SXR is also an activator of multidrug resistance, as it stimulates transcription of certain multidrug resistance factors.

The impact of multidrug resistance factors with respect to chemotherapeutic agents used in breast cancer is as follows. Beneficial response to doxorubicin is compromised when the mRNA levels of either MDR1, GSTα, GST7π, SXR, BCRP YB-1, or LRP/MVP are in the upper four percentile. Beneficial response to methotrexate is inhibited if mRNA levels of any of MRP1, MRP2, MRP3, or MRP4 or gamma-glutamyl cysteine synthetase are in the upper four percentile.

EIF4E mRNA levels provides evidence of protein expression and so expands the capability of RT-PCR to indicate variation in gene expression. Thus, one claim of the present invention is the use of EIF4E as an added indicator of gene expression of certain genes [e.g., cyclinD1, mdm2, VEGF, and others]. For example, in two tissue specimens containing the same amount of normalized VEGF mRNA, it is likely that the tissue containing the higher normalized level of EIF4E exhibits the greater level of VEGF gene expression.

The background is as follows. A key point in the regulation of mRNA translation is selection of mRNAs by the EIF4G complex to bind to the 43S ribosomal subunit. The protein EIF4E [the m7G CAP-binding protein] is often limiting because more mRNAs than EIF4E copies exist in cells. Highly structured 5'UTRs or highly GC-rich ones are inefficiently translated, and these often code for genes that carry out functions relevant to cancer [e.g., cyclinD1, mum2, and VEGF]. EIF4E is itself regulated at the transcriptional/ mRNA level. Thus, expression of EIF4E provides added indication of increased activity of a number of proteins.

It is also noteworthy that overexpression of EIF4E transforms cultured cells, and hence is an oncogene. Overexpression of EIF4E occurs in several different types of carcinomas but is particularly significant in breast cancer. EIF4E is typically expressed at very low levels in normal breast tissue.

Loss of BRCA1 or BRCA2 activity via mutation represents the critical oncogenic step in the most common type[s] of familial breast cancer. The levels of mRNAs of these important enzymes are abnormal in subsets of sporadic breast cancer as well. Loss of signals from either [to within the lower ten percentile] heightens risk of short survival.

Cell cycle regulators include 14 genes: c-MYC; c-Src; Cyclin D1; Ha-Ras; mdm2; p14ARF; p21WAF1/CIP;p16INK4a/p14; p23; p27; p53; PI3K; PKC-epsilon; PKC-delta.

The gene for p53 [TP53] is mutated in a large fraction of breast cancers. Frequently p53 levels are elevated when loss of function mutation occurs. When the mutation is dominant-negative, it creates survival value for the cancer cell because growth is promoted and apoptosis is inhibited. Thousands of different p53 mutations have been found in human cancer, and the functional consequences of many of them are not clear. A large body of academic literature addresses the prognostic and predictive significance of mutated p53 and the results are highly conflicting. The present invention provides a functional genomic measure of p53 activity, as follows. The activated wild type p53 molecule triggers transcription of the cell cycle inhibitor p21. Thus, the ratio of p53 to p21 should be low when p53 is wild-type and activated. When p53 is detectable and the ratio of p53 to p21 is elevated in tumors relative to normal breast, it signifies nonfunctional or dominant negative p53. The cancer literature provides evidence for this as born out by poor prognosis.

Mdm2 is an important p53 regulator. Activated wildtype p53 stimulates transcription of mdm2. The mdm2 protein binds p53 and promotes its proteolytic destruction. Thus, abnormally low levels of mdm2 in the presence of normal or higher levels of p53 indicate that p53 is mutated and inactivated.

One aspect of the present invention is the use of ratios of mRNAs levels p53:p21 and p53:mdm2 to provide a picture of p53 status. Evidence for dominant negative mutation of p53 (as indicated by high p53:p21 and/or high p53:mdm2 mRNA ratios-specifically in the upper ten percentile) presages higher risk of recurrence in breast cancer and therefore weights toward a decision to use chemotherapy in node negative post surgery breast cancer.

Another important cell cycle regulator is p27, which in the activated form blocks cell cycle progression at the level of cdk4. The protein is regulated primarily via phosphorylation/dephosphorylation, rather than at the transcriptional level. However, levels of p27 mRNAs do vary. Therefore a level of p27 mRNA in the upper ten percentile indicates reduced risk of recurrence of breast cancer post surgery.

Cyclin D1 is a principle positive regulator of entry into S phase of the cell cycle. The gene for cyclin D1 is amplified in about 20% of breast cancer patients, and therefore promotes tumor promotes tumor growth in those cases. One aspect of the present invention is use of cyclin D1 mRNA levels for diagnostic purposes in breast cancer. A level of cyclin D1 mRNA in the upper ten percentile suggests high risk of recurrence in breast cancer following surgery and suggests particular benefit of adjuvant chemotherapy.

These include APC and E-cadherin. It has long been known that the tumor suppressor APC is lost in about 50% of colon cancers, with concomitant transcriptional upregulation of E-cadherin, an important cell adhesion molecule and growth suppressor. Recently, it has been found that the APC gene silenced in 15-40 % of breast cancers. Likewise, the E-cadherin gene is silenced [via CpG island methylation] in about 30% of breast cancers. An abnormally low level of APC and/or E-cadherin mRNA in the lower 5 percentile suggests high risk of recurrence in breast cancer following surgery and heightened risk of shortened survival.

These include BCI/BAX family members BCl2, Bcl-x1, Bak, Bax and related factors, NFκ-B and related factors, and also p53BP1/ASPP1 and p53BP2/ASPP2.

Bax and Bak are pro-apoptotic and BCl2 and Bcl-x1 are anti-apoptotic. Therefore, the ratios of these factors influence the resistance or sensitivity of a cell to toxic (pro-apoptotic) drugs. In breast cancer, unlike other cancers, elevated level of BCl2 (in the upper ten percentile) correlates with good outcome. This reflects the fact that BCl2 has growth inhibitory activity as well as anti-apoptotic activity, and in breast cancer the significance of the former activity outweighs the significance of the latter. The impact of BCl2 is in turn dependent on the status of the growth stimulating transcription factor c-MYC. The gene for c-MYC is amplified in about 20% of breast cancers. When c-MYC message levels are abnormally elevated relative to BCl2 (such that this ratio is in the upper ten percentile), then elevated level of BCl2 mRNA is no longer a positive indicator.

NFκ-B is another important anti-apoptotic factor. Originally, recognized as a pro-inflammatory transcription factor, it is now clear that it prevents programmed cell death in response to several extracellular toxic factors [such as tumor necrosis factor]. The activity of this transcription factor is regulated principally via phosphorylation/dephosphorylation events. However, levels of NFκ-B nevertheless do vary from cell to cell, and elevated levels should correlate with increased resistance to apoptosis. Importantly for present purposes, NFκ-B, exerts its anti-apoptotic activity largely through its stimulation of transcription of mRNAs encoding certain members of the LAP [inhibitor of apoptosis] family of proteins, specifically cIAP1, cIAP2, XIAP, and Survivin. Thus, abnormally elevated levels of mRNAs for these IAPs and for NFκ-B any in the upper 5 percentile) signify activation of the NFκ-B anti-apoptotic pathway. This suggests high risk of recurrence in breast cancer following chemotherapy and therefore poor prognosis. One embodiment of the present invention is the inclusion in the gene set of the above apoptotic regulators, and the above-outlined use of combinations and ratios of the levels of their mRNAs for prognosis in breast cancer.

The proteins p53BP1 and 2 bind to p53 and promote transcriptional activation of pro-apoptotic genes. The levels of p53BP1 and 2 are suppressed in a significant fraction of breast cancers, correlating with poor prognosis. When either is expressed in the lower tenth percentile poor prognosis is indicated.

These include uPA, PA11, cathepsinsB, G and L, scatter factor [HOF], c-met, KDR, VEGF, and CD31. The plasminogen activator uPA and its serpin regulator PAI1 promote breakdown of extracellular matrices and tumor cell invasion. Abnormally elevated levels of both mRNAs in malignant breast tumors (in the upper twenty percentile) signify an increased risk of shortened survival, increased recurrence in breast cancer patients post surgery, and increased importance of receiving adjuvant chemotherapy. On the other hand, node negative patients whose tumors do not express elevated levels of these mRNA species are less likely to have recurrence of this cancer and could more seriously consider whether the benefits of standard chemotherapy justifies the associated toxicity.

Cathepsins B or L, when expressed in the upper ten percentile, predict poor disease-free and overall survival. In particular, cathepsin L predicts short survival in node positive patients.

Scatter factor and its cognate receptor c-met promote cell motility and invasion, cell growth, and angiogenesis. In breast cancer elevated levels of mRNAs encoding these factors should prompt aggressive treatment with chemotherapeutic drugs, when expression of either, or the combination, is above the 90^th percentile.

VEGF is a central positive regulator of angiogenesis, and elevated levels in solid tumors predict short survival [note many references showing that elevated level of VEGF predicts short survival]. Inhibitors of VEGF therefore slow the growth of solid tumors in animals and humans. VEGF activity is controlled at the level of transcription. VEGF mRNA levels in the upper ten percentile indicate significantly worse than average prognosis. Other markers of vascularization, CD31 [PECAM], and KDR indicate high vessel density in tumors and that the tumor will be particularly malignant and aggressive, and hence that an aggressive therapeutic strategy is warranted.

These markers include the genes for Immunoglobulin light chain λ, CD18, CD3, CD68, Fas [CD95], and Fas Ligand.

Several lines of evidence suggest that the mechanisms of action of certain drugs used in breast cancer entail activation of the host immune/inflammatory response (For example, Henxptin®). One aspect of the present invention is the inclusion in the gene set of markers for inflammatory and immune cells, and markers that predict tumor resistance to immune surveillance. Immunoglobulin light chain lambda is a marker for immunoglobulin producing cells. CD18 is a marker for all white cells. CD3 is a marker for T-cells. CD68 is a marker for macrophages.

CD95 and Fas ligand are a receptor: ligand pair that mediate one of two major pathways by which cytotoxic T cells and NK cells kill targeted cells. Decreased expression of CD95 and increased expression of Fas Ligand indicates poor prognosis in breast cancer. Both CD95, and Fas Ligand are transmembrane proteins, and need to be membrane anchored to trigger cell death. Certain tumor cells produce a truncated soluble variant of CD95, created as a result of alternative splicing of the CD95 mRNA. This blocks NK cell and cytotoxic T cell Fas Ligand-mediated killing of the tumors cells. Presence of soluble CD95 correlates with poor survival in breast cancer. The gene set includes both soluble and full-length variants of CD95.

The gene set includes the cell proliferation markers Ki67/MiB1, PCNA, Pin1, and thymidine kinase. High levels of expression of proliferation markers associate with high histologic grade, and short survival. High levels of thymidine kinase in the upper ten percentile suggest in creased risk of short survival. Pin1 is a prolyl isomerase that stimulates cell growth, in part through the transcriptional activation of the cyclin D1 gene, and levels in the upper ten percentile contribute to a negative prognostic profile.

This gene set includes IGF1, IGF2, IGFBP3, IGF1R, FGF2, FGFR1, CSF-R/fms, CSF-1, IL6 and IL8. All of these proteins are expressed in breast cancer. Most stimulate tumor growth. However, expression of the growth factor FGF2 correlates with good outcome. Some have anti-apoptotic activity, prominently IGF1. Activation of the IGF1 axis via elevated IGF1, IGF1R, or IGFBP3 (as indicated by the sum of these signals in the upper ten percentile) inhibits tumor cell death and strongly contributes to a poor prognostic profile.

These include: GRO1 oncogene alpha, Grb7, cytokeratins 5 and 17, retinal binding protein 4, hepatocyte nuclear factor 3, integrin alpha 7, and lipoprotein lipase. These markers subset breast cancer into different cell types that are phenotypically different at the level of gene expression. Tumors expressing signals for Bcl2, hepatocyte nuclear factor 3, LIV1 and ER above the mean have the best prognosis for disease free and overall survival following surgical removal of the cancer. Another category of breast cancer tumor type, characterized by elevated expression of lipoprotein lipase, retinol binding protein 4, and integrin α7, carry intermediate prognosis. Tumors expressing either elevated levels of cytokeratins 5, and 17, GRO oncogene at levels four-fold or greater above the mean, or ErbB2 and Grb7 at levels ten-fold or more above the mean, have worst prognosis.

Although throughout the present description, including the Examples below, various aspects of the invention are explained with reference to gene expression studies, the invention can be performed in a similar manner, and similar results can be reached by applying proteomics techniques that are well known in the art. The proteome is 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 and/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 of the present invention, to detect the products of the gene markers of the present invention.

Further details of the invention will be described in the following non-limiting Examples.

1. (1) Cut 1-6 sections (each 10 µm thick) of paraffin-embedded tissue per sample using a clean microtome blade and place into a 1.5 ml eppendorf tube.
2. (2) To extract paraffin, add 1 ml of xylene and invert the tubes for 10 minutes by rocking on a nutator.
3. (3) Pellet the sections by centrifugation for 10 minutes at 14,000 x g in an eppendorf microcentrifuge.
4. (4) Remove the xylene, leaving some in the bottom to avoid dislodging the pellet.
5. (5) Repeat steps 2-4.
6. (6) Add 1 ml of 100% ethanol and invert for 3 minutes by rocking on the nutator.
7. (7) Pellet the debris by centrifugation for 10 minutes at 14,000 x g in an eppendorf microcentrifuge.
8. (8) Remove the ethanol, leaving some at the bottom to avoid the pellet.
9. (9) Repeat steps 6-8 twice.
10. (10) Remove all of the remaining ethanol.
11. (11) For each sample, add 2 µl of 50 µg/µl Proteinase K to 300 µl of Tissue and Cell Lysis Solution.
12. (12) Add 300 µl of Tissue and Cell Lysis Solution containing the Proteinase K to each sample and mix thoroughly.
13. (13) Incubate at 65 °C for 90 minutes (vortex mixing every 5 minutes). Visually monitor the remaining tissue fragment. If still visible after 30 minutes, add an additional 2 µl of 50 µg/µl Proteinase K and continue incubating at 65 °C until fragment dissolves.
14. (14) Place the samples on ice for 3-5 minutes and proceed with protein removal and total nucleic acid precipitation.

1. (1) Add 150 µl of MPC Protein Precipitation Reagent to each lysed sample and vortex vigorously for 10 seconds.
2. (2) Pellet the debris by centrifugation for 10 minutes at 14,000 x g in an eppendorf microcentrifuge.
3. (3) Transfer the supernatant into clean eppendorf tubes and discard the pellet.
4. (4) Add 500 µl of isopropanol to the recovered supernatant and thoroughly mix by rocking on the nutator for 3 minutes.
5. (5) Pellet the RNA/DNA by centrifugation at 4°C for 10 minutes at 14,000 x g in an eppendorf microcentrifuge.
6. (6) Remove all of the isopropanol with a pipet, being careful not to dislodge the pellet.

1. (1) Prepare 200 µl of DNase I solution for each sample by adding 5 µl of RNase-Free DNase I (1 U/µl) to 195 µl of 1X DNase Buffer.
2. (2) Completely resuspend the pelleted RNA in 200 µl of DNase I solution by vortexing.
3. (3) Incubate the samples at 37°C for 60 minutes.
4. (4) Add 200 µl of 2X T and C Lysis Solution to each sample and vortex for 5 seconds.
5. (5) Add 200 µl of MPC Protein Precipitation Reagent, mix by vortexing for 10 seconds and place on ice for 3-5 minutes.
6. (6) Pellet the debris by centrifugation for 10 minutes at 14,000 x g in an eppendorf microcentrifuge.
7. (7) Transfer the supernatant containing the RNA to clean eppendorf tubes and discard the pellet. (Be careful to avoid transferring the pellet.)
8. (8) Add 500 ul of isopropanol to each supernatant and rock samples on the nutator for 3 minutes.
9. (9) Pellet the RNA by centrifugation at 4°C for 10 minutes at 14,000 x g in an eppendorf microcentrifuge.
10. (10) Remove the isopropanol, leaving some at the bottom to avoid dislodging the pellet.
11. (11) Rinse twice with 1 ml of 75% ethanol. Centrifuge briefly if the RNA pellet is dislodged.
12. (12) Remove ethanol carefully.
13. (13) Set under fume hood for about 3 minutes to remove residual ethanol.
14. (14) Resuspend the RNA in 30 µl of TE Buffer and store at -30 °C.

1. (1) Cut 3 sections (each 10 µm thick) of paraffin-embedded tissue using a clean microtome blade and place into a 1.5 ml eppendorf tube.
2. (2) Add 300 µl of lysis buffer (10 mM Tris 7.5, 0.5% sodium lauroyl sarcosine, 0.1 mM EDTA pH 7.5, 4M Urea) containing 330 µg/ml Proteinase K (added freshly from a 50 µg/µl stock solution) and vortex briefly.
3. (3) Incubate at 65 °C for 90 minutes (vortex mixing every 5 minutes). Visually monitor the tissue fragment. If still visible after 30 minutes, add an additional 2 µl of 50 µg/µl Proteinase K and continue incubating at 65 °C until fragment dissolves.
4. (4) Centrifuge for 5 minutes at 14,000 x g and transfer upper aqueous phase to new tube, being careful not to disrupt the paraffin seal.
5. (5) Place the samples on ice for 3-5 minutes and proceed with protein removal and total nucleic acid precipitation.

1. (1) Add 150 µl of 7.5M NH₄OAc to each lysed sample and vortex vigorously for 10 seconds.
2. (2) Pellet the debris by centrifugation for 10 minutes at 14,000 x g in an eppendorf microcentrifuge.
3. (3) Transfer the supernatant into clean eppendorf tubes and discard the pellet.
4. (4) Add 500 µl of isopropanol to the recovered supernatant and thoroughly mix by rocking on the nutator for 3 minutes.
5. (5) Pellet the RNA/DNA by centrifugation at 4°C for 10 minutes at 14,000 x g in an eppendorf microcentrifuge.
6. (6) Remove all of the isopropanol with a pipet, being careful not to dislodge the pellet..

1. (1) Add 45 µl of 1X DNase I buffer (10 mM Tris-Cl, pH 7,5, 2.5 mM MgCl₂, 0.1 mM CaCl₂) and 5 µl of RNase-Free DNase I (2U/µl, Ambion) to each sample.
2. (2) Incubate the samples at 37 °C for 60 minutes.

Inactivate the DNasel by heating at 70°C for 5 minutes.

Experimental evidence demonstrates that the hot RNA extraction protocol of the invention does not compromise RNA yield. Using 19 FPE breast cancer specimens, extracting RNA from three adjacent sections in the same specimens, RNA yields were measured via capillary electrophoresis with fluorescence detection (Agilent Bioanalyzer). Average RNA yields in nanograms and standard deviations with the invented and commercial methods, respectively, were: 139+/-21 versus 141+/-34.

Also, it was found that the urea-containing lysis buffer of the present invention can be substituted for the EPICENTRE® T&C lysis buffer, and the 7.5 M NH₄OAc reagent used for protein precipitation in accordance with the present invention can be substituted for the EPICENTRE® MPC protein precipitation solution with neither significant compromise of RNA yield nor TaqMan® efficiency.

The method described in section 10 above was used with RNA isolated from fixed, paraffin-embedded breast cancer tissue. TaqMan® analyses were performed with first strand cDNA generated with the T7-GSP primer (unamplified (T7-GSPr)), T7 amplified RNA (amplified (T7-GSPr)). RNA was amplified according to step 2 of Figure 4. As a control, TaqMan® was also performed with cDNA generated with an unmodified GSPr (amplified (GSPr)). An equivalent amount of initial template (1 ng/well) was used in each Taqman® reaction.

The results are shown in Figure 8. In vitro transcription increased RT-PCR signal intensity by more than 10 fold, and for certain genes by more than 100 fold relative to controls in which the RT-PCR primers were the same primers used in method 2 for the generation of double-stranded DNA for in vitro transcription (GSP-T7, and GSPr). Also shown in Figure 8 are RT-PCR data generated when standard optimized RT-PCR primers (i.e., lacking T7 tails) were used. As shown, compared to this control, the new method yielded substantial increases in RT-PCR signal (from 4 to 64 fold in this experiment).

The new method requires that each T7-GSP sequence be optimized so that the increase in the RT-PCR signal is the same for each gene, relative to the standard optimized RT-PCR (with non-T7 tailed primers).

A gene expression study was designed and conducted with the primary goal to molecularly characterize gene expression in paraffin-embedded, fixed tissue samples of invasive breast ductal carcinoma, and to explore the correlation between such molecular profiles and disease-free survival. A further objective of the study was to compare the molecular profiles in tissue samples of invasive breast cancer with the molecular profiles obtained in ductal carcinoma in situ. The study was further designed to obtain data on the molecular profiles in lobular carcinoma in situ and in paraffin-embedded, fixed tissue samples of invasive lobular carcinoma.

Molecular assays were performed on paraffin-embedded, formalin-fixed primary breast tumor tissues obtained from 202 individual patients diagnosed with breast cancer. All patients underwent surgery with diagnosis of invasive ductal carcinoma of the breast, pure ductal carcinoma in situ (DCIS), lobular carcinoma of the breast, or pure lobular carcinoma in situ (LCIS). 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.

The individuals participating in the study were divided into the following groups:

• Group 1: Pure ductal carcinoma in situ (DCIS); n=18
• Group 2: Invasive ductal carcinoma n=130
• Group 3: Pure lobular carcinoma in situ (LCIS); n=7
• Group 4: Invasive lobular carcinoma n=16

Each representative tumor block was characterized by standards 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, all tumor blocks were subjected to the same characterization, as described above, and the block most representative of the pathology was used for analysis.

mRNA was extracted and purified from fixed, paraffin-embedded tissue samples, and prepared for gene expression analysis as described in chapters 7-11 above.

Molecular assays of quantitative gene expression were performed by RT-PCR, using the ABI PRISM 7900™ Sequence Detection System™ (Perkin-Elmer-Applied Biosystems, Foster City, CA, USA). ABI PRISM 7900™ 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.

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 all 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;
• 1 alive without breast cancer recurrence or died due to a cause other than breast cancer

Analysis was performed by:

1. 1. Analysis of the relationship between normalized gene expression and the binary outcomes of 0 or 1.
2. 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.

In the first (binary) approach, analysis was performed on all 146 patients with invasive breast carcinoma. A t test was performed on the group of patients classified as 0 or 1 and the p-values for the differences between the groups for each gene were calculated.

The following Table 4 lists the 45 genes for which the p-value for the differences between the groups was <0.05. Gene/ SEQ ID NO: Mean CT Alive Mean CT Deceased t-value Degrees of freedom p FOXM1 33.66 32.52 3.92 144 0.0001 PRAME 35.45 33.84 3.71 144 0.0003 Bcl2 28.52 29.32 -3.53 144 0.0006 STK15 30.82 30.10 3.49 144 0.0006 CEGP1 29.12 30.86 -3.39 144 0.0009 Ki-67 30.57 29.62 3.34 144 0.0011 GSTM1 30.62 31.63 -3.27 144 0.0014 CA9 34.96 33.54 3.18 144 0.0018 PR 29.56 31.22 -3.16 144 0.0019 BBC3 31.54 32.10 -3.10 144 0.0023 NME1 27.31 26.68 3.04 144 0.0028 SURV 31.64 30.68 2.92 144 0.0041 GATA3 26.06 26.99 -2.91 144 0.0042 TFRC 28.96 28.48 2.87 144 0.0047 YB-1 26.72 26.41 2.79 144 0.0060 DPYD 28.51 28.84 -2.67 144 0.0084 GSTM3 28.21 29.03 -2.63 144 0.0095 RPS6KB1 31.18 30.61 2.61 144 0.0099 Src 27.97 27.69 2.59 144 0.0105 Chk1 32.63 31.99 2.57 144 0.0113 ID1 28.73 29.13 -2.48 144 0.0141 EstR1 24.22 25.40 -2.44 144 0.0160 p27 27.15 27.51 -2.41 144 0.0174 CCNB 1 31.63 30.87 2.40 144 0.0176 XIAP 30.27 30.51 -2.40 144 0.0178 Chk2 31.48 31.11 2.39 144 0.0179 CDC25B 29.75 29.39 2.37 144 0.0193 IGFIR 28.85 29.44 -2.34 144 0.0209 AK055699 33.23 34.11 -2.28 144 0.0242 P13KC2A 31.07 31.42 -2.25 144 0.0257 TGFB3 28.42 28.85 -2.25 144 0.0258 BAGI1 28.40 28.75 -2.24 144 0.0269 CYP3A4 35.70 35.32 2.17 144 0.0317 EpCAM 28.73 28.34 2.16 144 0.0321 VEGFC 32.28 31.82 2.16 144 0.0326 pS2 28.96 30.60 -2.14 144 0.0341 hENT1 27.19 26.91 2.12 144 0.0357 WISP1 31.20 31.64 -2.10 144 0.0377 HNF3A 27.89 28.64 -2.09 144 0.0384 NFKBp65 33.22 33.80 -2.08 144 0.0396 BRCA2 33.06 32.62 2.08 144 0.0397 EGFR 30.68 30.13 2.06 144 0.0414 TK1 32.27 31.72 2.02 144 0.0453 VDR 30.08 29.73 1.99 144 0.0488

In the foregoing Table 4, lower (negative) t-values indicate higher expression (or lower CTs), associated with better outcomes, and, inversely, higher (positive) t-values indicate higher expression (lower CTs) associated with worse outcomes. Thus, for example, elevated expression of the FOXM1 gene (t-value = 3.92, CT mean alive> CT mean deceased) indicates a reduced likelihood of disease free survival. Similarly, elevated expression of the CEGP1 gene (t-value = - 3.39; CT mean alive< CT mean deceased) indicates an increased likelihood of disease free survival.

Based on the data set forth in Table 4, the overexpression of any of the following genes in breast cancer indicates a reduced likelihood of survival without cancer recurrence following surgery: FOXM1; PRAM; SKT15, Ki-67; CA9; NME1; SURV; TFRC; YB-1; RPS6KB1; Src; Chk1; CCNB1; Chk2; CDC25B; CYP3A4; EpCAM; VEGFC; hENT1; BRCA2; EGFR; TK1; VDR.

Based on the data set forth in Table 4, the overexpression of any of the following genes in breast cancer indicates a better prognosis for survival without cancer recurrence following surgery: Blcl2; CEGP1; GSTM1; PR; BBC3; GATA3; DPYD; GSTM3; ID1; EstRl; p27; XIAP; IGF1R; AK055699; P13KC2A; TGFB3; BAGI1; pS2; WISP1; HNF3A; NFKBp65.

108 patients with normalized CT for estrogen receptor (ER) < 25.2 (i.e., ER positive patients) were subjected to separate analysis. A t test was performed on the groups of patients classified as 0 or 1 and the p-values for the differences between the groups for each gene were calculated. The following Table 5 lists the 12 genes where the p-value for the differences between the groups was <0.05. Gene/ SEQ ID NO: Mean CT Alive Mean CT Deceased t-value Degrees of freedom p PRAME 35.54 33.88 3.03 106 0.0031 Bcl2 28.24 28.87 -2.70 106 0.0082 FOXM1 33.82 32.85 2.66 106 0.089 DIABLO 30.33 30.71 -2.47 106 0.0153 EPHX1 28.62 28.03 2.44 106 0.0163 HIF1A 29.37 28.88 2.40 106 0.0180 VEGFC 32.39 31.69 2.39 106 0.0187 Ki-67 30.73 29.82 2.38 106 0.0191 IGF1R 28.60 29.18 -2.37 106 0.0194 VDR 30.14 29.60 2.17 106 0.0322 NME1 27.34 26.80 2.03 106 0.0452 GSTM3 28.08 28.92 -2.00 106 0.0485

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 5, overexpression of the following genes in ER-positive cancer is indicative of a reduced likelihood of survival without cancer recurrence following surgery: PRAME; FOXM1; EPHX1; HIF1A; VEGFC; Ki-67; VDR; NME1. Some of these genes (PRAME; FOXM1; VEGFC; Ki-67; VDR; and NME1) were also identified as indicators of poor prognosis in the previous analysis, not limited to ER-positive breast cancer. The overexpression of the remaining genes (EPHX1 and HIF1A) appears to be negative indicator of disease free survival in ER-positive breast cancer only. Based on the data set forth in Table 5, overexpression of the following genes in ER-positive cancer is indicative of a better prognosis for survival without cancer recurrence following surgery: Bcl-2; DIABLO; IGF1R; GSTM3. Of the latter genes, Bcl-2; IGFR1; and GSTM3 have also been identified as indicators of good prognosis in the previous analysis, not limited to ER-positive breast cancer. The overexpression of DIABLO appears to be positive indicator of disease free survival in ER-positive breast cancer-only.

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 < 0.05 in the Cox model were identified. For each gene, the Cox model provides the relative risk (RR) of recurrence or death for a unit change in the expression of the gene. One can choose to partition the patients into subgroups at any threshold value of the measured expression (on the CT scale), where all patients with expression values above the threshold have higher risk, and all patients with expression values below the threshold have lower risk, or vice versa, depending on whether the gene is an indicator of good (RR>1.01) or poor (RR<1.01) prognosis. Thus, any threshold value will define subgroups of patients with respectively increased or decreased risk. The results are summarized in the following Tables 6 and 7. Cox Model Results for 146 Patients with Invasive Breast Cancer Gene Relative Risk (RR) SE Relative Risk p value FOXM1 0.58 0.15 0.0002 STK15 0.51 0.20 0.0006 PRAME 0.78 0.07 0.0007 Bcl2 1.66 0.15 0.0009 CEGP1 1.25 0.07 0.0014 GSTM1 1.40 0.11 0.0014 Ki67 0.62 0.15 0.0016 PR 1.23 0.07 0.0017 Contig51037 0.81 0.07 0.0022 NME1 0.64 0.15 0.0023 YB-1 0.39 0.32 0.0033 TFRC 0.53 0.21 0.0035 BBC3 1.72 0.19 0.0036 GATA3 1.32 0.10 0.0039 CA9 0.81 0.07 0.0049 SURV 0.69 0.13 0.0049 DPYD 2.58 0.34 0.0052 RPS6KB1 0.60 0.18 0.0055 GSTM3 1.36 0.12 0.0078 Src.2 0.39 0.36 0.0094 TGFB3 1.61 0.19 0.0109 CDC25B 0.54 0.25 0.0122 XIAP 3.20 0.47 0.0126 CCNB1 0.68 0.16 0.0151 IGF1R 1.42 0.15 0.0153 Chlc1 0.68 0.16 0.0155 ID1 1.80 0.25 0.0164 p27 1.69 0.22 0.0168 Chk2 0.52 0.27 0.0175 EstR1 1.17 0.07 0.0196 HNF3A 1.21 0.08 0.206 pS2 1.12 0.05 0.0230 BAGI1 1.88 0.29 0.0266 AK055699 1.24 0.10 0.0276 pENT1 0.51 0.31 0.0293 EpCAM 0.62 0.22 0.0310 WISP1 1.39 0.16 0.0338 VEGFC 0.62 0.23 0.0364 TK1 0.73 0.15 0.0382 NFKBp65 1.32 0.14 0.0384 BRCA2 0.66 0.20 0.0404 CYP3A4 0.60 0.25 0.0417 EGFR 0.72 0.16 0.0436 Cox Model Results for 108 Patients wih ER+ Invasive Breast Cancer Gene Relative Risk (RR) SE Relative Risk p-value PRAME 0.75 0.10 0.0045 Contig51037 0.75 0.11 0.0060 Blc2 2.11 0.28 0.0075 HIF1A 0.42 0.34 0.0117 IGF1R 1.92 0.26 0.0117 FOXM1 0.54 0.24 0.0119 EPHX1 0.43 0.33 0.0120 Ki67 0.60 0.21 0.0160 CDC25B 0.41 0.38 0.0200 VEGFC 0.45 0.37 0.0288 CTSB 0.32 0.53 0.0328 DIABLO 2.91 0.50 0.0328 p27 1.83 0.28 0.0341 CDH1 0.57 0.27 0.0352 IGFBP3 0.45 0.40 0.0499

The binary and time-to-event analyses, with few exceptions, identified the same genes as prognostic markers. For example, comparison of Tables 4 and 6 shows that, with the exception of a single gene, the two analyses generated the same list of top 15 markers (as defined by the smallest p values). Furthermore, when both analyses identified the same gene, they were 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 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: $$\mathrm{RR}=\exp \left[\mathrm{coef}\left(\mathrm{geneA}\right)\mathrm{x\; Ct}\left(\mathrm{geneA}\right)+\mathrm{coef}\left(\mathrm{geneB}\right)\mathrm{x\; Ct}\left(\mathrm{geneB}\right)+\mathrm{coef}\left(\mathrm{geneC}\right)\mathrm{x\; Ct}\left(\mathrm{geneC}\right)+\mathrm{...............}\right]\mathrm{.}$$

In this equation, coefficiencts 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 ΔCts, 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 ΔCts 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.

1. (a) A multivariate stepwise analysis, using the Cox Proportional Hazards Model, was performed on the gene expression data obtained for all 147 patients with invasive breast carcinoma. Genes CEGP1, FOXM1, STK15 and PRAME were excluded from this analysis. The following ten-gene sets have been identified by this analysis as having particularly strong predictive value of patient survival without cancer recurrence following surgical removal of primary tumor.
   1. 1. Bcl2, cyclinG1, NFKBp65, NME1, EPHX1, TOP2B, DR5, TERC, Src, DIABLO;
   2. 2. Kit67, XIAP, hENT1, TS, CD9, p27, cyclinG1, pS2, NFKBp65, CYP3A4;
   3. 3. GSTM1, XIAP, Ki67, TS, cyclinG1, p27, CYP3A4, pS2, NFKBp65, ErbB3;
   4. 4. PR, NME1, XIAP, upa, cyclinG1, Contig51037, TERC, EPHX1, ALDH1A3, CTSL;
   5. 5. CA9, NME1, TERC, cyclinG1, EPHX1, DPYD, Src, TOP2B, NFKBp65, VEGFC;
   6. 6. TFRC, XIAP, Ki67, TS, cyclinG1, p27, CYP3A4, pS2, ErbB3, NFKBp65.
2. (b) A multivariate stepwise analysis, using the Cox Proportional Hazards Model, was performed on the gene expression data obtained for all 147 patients with invasive breast carcinoma, using an interrogation set including a reduced number of genes. The following ten-gene sets have been identified by this analysis as having particularly strong predictive value of patient survival without cancer recurrence following surgical removal of primary tumor.
   1. 1. Bcl2, PRAME, cyclinG1; FOXM1, NFKBp65, TS, XIAP, Ki67, CYP3A4, p27;
   2. 2. FOXM1, cyclinG1, XIAP, Contig51037, PRAME, TS, Ki67, PDGFRa, p27, NFKBp65;
   3. 3. FRAME, FOXM1, cyclinG1, XIAP, Contig51037, TS, Ki6, PDGFRa, p27, NFKBp65;
   4. 4. Ki67, XIAP, FRAME, hENT1, contig51037, TS, CD9, p27, ErbB3, cyclinG1;
   5. 5. STK15, XIAP, PRAME, PLAUR, p27, CTSL, CD18, PREP, p53, RPS6KB1;
   6. 6. GSTM1, XIAP, PRAME, p27, Contig51037, ErbB3, GSTp, EREG, ID1, PLAUR;
   7. 7. PR, PRAME, NME1, XIAP, PLAUR, cyclinG1, Contig51037, TERC, EPHX1, DR5;
   8. 8. CA9, FOXM1, cyclinG1, XIAP, TS, Ki67, NFKBp65, CYP3A4, GSTM3, p27;
   9. 9. TFRC, XIAP, PRAME, p27, Contig51037, ErbB3, DPYD, TERC, NME1, VEGFC;
   10. 10. CEGP1, FRAME, hENT1, XIAP, Contig51037, ErbB3, DPYD, NFKBp65, ID1, TS.

A multivariate stepwise analysis, using the Cox Proportional Hazards Model, was performed on the gene expression data obtained for patients with ER positive invasive breast carcinoma. The following ten-gene sets have been identified by this analysis as having particularly strong predictive value of patient survival without cancer recurrence following surgical removal of primary tumor.

1. 1. FRAME, p27, IGFBP2, HIF1A, TIMP2, ILT2, CYP3A4, ID1, EstR1, DIABLO;
2. 2. Contig51037, EPHX1, Ki67, TIMP2, cyclinG1, DPYD, CYP3A4, TP, AIB1, CYP2C8;
3. 3. Bcl2, hENT1, FOXM1, Contig51037, cyclinG1, Contig46653, PTEN, CYP3A4, TIMP2, AREG;
4. 4. HIF1A, PRAME, p27, IGFBP2, TIMP2, ILT2, CYP3A4, ID1, EstR1, DIABLO;
5. 5. IGF1R, PRAME, EPHX1, Contig51037, cyclinG1, Bcl2, NME1, PTEN, TBP, TIMP2;
6. 6. FOXM1, Contig51037, VEGFC, TBP, HIF1A, DPYD, RAD51C, DCR3, cyclinG1, BAG1;
7. 7. EPHX1, Contig51037, Ki67, TIMP2, cyclinG1, DPYD, CYP3A4, TP, AIB1, CYP2C8;
8. 8. Ki67, VEGFC, VDR, GSTM3, p27, upa, ITGA7, rhoC, TERC, Pin1;
9. 9. CDC25B, Contig51037, hENT1, Bcl2, HLAG, TERC, NME1, upa, ID1, CYP;
10. 10. VEGFC, Ki67, VDR, GSTM3, p27, upa, ITGA7, rhoC, TERC, Pin1;
11. 11. CTSB, PRAME, p27, IGFBP2, EPHX1, CTSL, BAD, DR5, DCR3, XIAP;
12. 12. DIABLO, Ki67, hENT1, TIMP2, ID1, p27, KRT19, IGFBP2, TS, PDGFB;
13. 13. p27, PRAME, IGFBP2, HIF1A, TMAP2, ILT2, CYP3A4, ID1, EstR1, DIABLO;
14. 14. CDH1; PRAME, VEGFC; HIF1A; DPYD, T\IMP2, CYP3A4, EstR1, RBP4, p27;
15. 15. IGFBP3, PRAME, p27, Bcl2, XIAP, EstR1, Ki67, TS, Src, VEGF;
16. 16. GSTM3, PRAME, p27, IGFBP3, XIAP, FGF2, hENT1, PTEN, EstR1, APC;
17. 17. hENT1, Bcl2, FOXM1, Contig51037, CyclinG1, Contig46653, PTEN, CYP3A4, TIMP2, AREG;
18. 18. STK15, VEGFC, PRAME, p27, GCLC, hENT1, ID1, TIMP2, EstR1, MCP1;
19. 19. NME1, PRAM, p27, IGFBP3, XIAP, PTEN, hENT1, Bcl2, CYP3A4, HLAG;
20. 20. VDR, Bcl2, p27, hENT1, p53, PI3KC2A, EIF4E, TFRC, MCM3, ID1;
21. 21. EIF4E, Contig51037, EPHX1, cyclinG1, Bcl2, DR5, TBP, PTEN, NME1, HER2;
22. 22. CCNB1, PRAME, VEGFC, HIF1A, hENT1, GCLC, TEMP2, ID1, p27, upa;
23. 23. ID1, PRAME, DIABLO, hENT1, p27, PDGFRa, NME1, BIN1, BRCA1, TP;
24. 24. FBXO5, PRAME, IGFBP3, p27, GSTM3, bENT1, XIAP, FGF2, TS, PTEN;
25. 25. GUS, HIA1A, VEGFC, GSTM3, DPYD, hENT1, FBXO5, CA9, CYP, KRT18;
26. 26. Bclx, Bcl2, hENT1, Contig51037, HLAG, CD9, ID1, BRCA1, BIN1, HBEGF.

It is noteworthy that many of the foregoing gene sets include genes that alone did not have sufficient predictive value to qualify as prognostic markers under the standards discussed above, but in combination with other genes, their presence provides valuable information about the likelihood of long-term patient survival without cancer recurrence

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 included within the scope of the appended claims. 1. ADD3 (addudn3 42. c-myc 77. Gamma-GCS (glutamyl 115 KDR 156. Pln1 gamma)* 43. cN-1   cysteine synthetase) 116. Ki-66/MIB1 157. PKC-e 2. AKR1/Protein 44. cryptochromel* 78. GATA3^ 117. lipoprotein lipase^ 158. Pke-δ Kinase B 45. c-Sre 79. Reranyl·geranyl 118, LIY1 159. PLAG1 (pleiomorphic adenoins 3. AKT 2 46. Cyclin D1   pyrophosphate synthetase 119. Lung Resistance 1)* 4. AKT 3 47. CYPIB1 80. G-CSF   Protein/MYP 160. PREP prolyl endopeptidase*PRP 5. Aldehyde 48. CYP2C9* 81. GPC3 120. Lot1 161. Progesterone receptor dehydrogenase 1A1 49. Cytokeratin 5^ 82. gravin* [AK AP25*] 121. Maspin 162. pS2Krefoil factor 1 6. Aldehyde 50. Cytokeratin 17^ 83. GRO1 oncogene alpha^ 122. MCM2 163. PIEN dehydrogenase 1A3 51. Cytokeratin 18^ 84. Grb7^ 123. MCM3 164, PTP1b 7. amphiregulin 52. DAP-Kinase-1 85. GST-alpha 124. MCM7 165. RAR-alpha 8. APC 53. DHFR 86. GST-pi^ 125. MCP-1 166. RAR-beta2 9. ARG 54. DIABLO 87. Ha-Ras 126. interotubule-assoclated 167. RCP 10. ATM 55. Dihydropyrimidine 88. HB-EGF   protein 4 168. Reduced Folate Carrier 11. Bak dehydrogenase 89. HE4-extracellular 127. MCT 169. Retinol binding protein 4^ 12. Bax 56. EGF   Proteinase Inhibitor 128. mdm2 170. STK15/BTAK 13. Bcl2 57. ECedherln/CDH1^   Homologue* 129. MDR-1 171. Survivin 14. Bcl-xl 58. ELF.3* 90. hepatocyte nuclear factor 130. microsomal epoxide 172. SXR 15. BRK 59. Endothelin   3^   hydrolase 173. Syk 16. BCRP 60. Epiregulin 91. HER-2 131. MMP9 174. TGD (thymine-DNA 17. BRCA-1 61. ER-alpha^ 92. HGF/Scatter factor 132. MRP1 glycosylase)* 18. BRCA-2 62. ErbB-1 93. hIA1 133. MRP2 175 TGFalpha 19. Caspase-3 63. ErbB-2^ 94. hIAP2 134. MRP3 176. Thymidine Kinnase 20. Cathepsin B 64. ErbB-3 95. HIF-1 135. MRP4 177. Thymidine phosphorylase - 21. Cathepsin G 65. ErbB-4 96. human kallikrein 10 136. MSN (Moesin)* 178. Thymidylate Synthase 22. Cathepsin L 66. ER-Beta 97. MLH1 137. mTOR 179. Topoistnerase II-α 23. CD3 67. Eukaryotic 98. hsp 27 138. Mucl/CA 15-3 180. Topoisomerase II-β 24. CD9   Translation 99. human chorionic 139. NF-kB 181. TRAMP 25. CD18   Initiation Factor   gonadotropin/CGA 140. P14ARF 182. UPA 26. CD31   4B*(EIF4B) 100. Human Extracellular 141. P16INK4a/p14 183. VEGF 27. CD44^ 68. E1F4E   Protein S1-5 142. p21wAF1/CIP1 184. Vimentin 28. CD68 69. farnesyl 101. Id-1 143. p23 185. WTH3 29. CD82/KAI-1 69. farnesylpyrololphosphate 102. Id-2 144. p27 186. XAF1 30. Cdc25A 69. farnesylsynthetase 103. Id-3 145. p311* 187. XIAP 31. Cdc25B 70. FAS(CD95) 104. IGF-1 146. p53 188, XIST 32. CGA 71. FnsL 105. IGF2 147. PAI1 189. XPA 33. COX2 72. FGF R1* 106. IGFIR 148. PCNA 190. YB-1 34. CSF-1 73. FGF [bFGF] 107. IGYBP3 149. PDGF-A 35. CSF-1R/fms 74. 59BP1 108. Interstitial integrin alpha 7 150. PDGF-B *NC160 ring Sens/Reslsrt Marker* 36. cIAP1 75. 53BP2 109. IL6 151. PDGF-C ^In Cluster Defining turnor subclate turnor subclass 37. cIAP2 76. GALC 110. IL8 152. PDGF-D 38. c-abl   (galactosylceramidase)* 111. IRF-2* 153. PDGPR-α 1/19/02 39. c-klt 112. IRF9 Protein 154. PDGF-β 40. c-kit L 113. Kalikrein 5 155. PDK 41. c-met 114. Kalikrein 6 Gene Accession No. Forward Primer SEQ ID NO. Reverse Primer SEQ ID NO. Amplicon SEQ ID NO. ABCB1 NM_000927 1 2 3 ABC1 NM_004996 4 5 6 ABCC2 NM_000392 7 8 9 ABCC3 NM_003786 10 11 12 ABCC4 NM_005845 13 14 15 ABL1 NM_005157 16 17 18 ABL2 NM_005158 19 20 - 21 ACT3 NM_001101 22 23 24 AKT1 NM 005163 25 - 26 27 AKT3 NM_005465 28 29 30 ALDH1 NM_000689 31 32 33 ALDH1A3 NM_000693 34 35 36 APC NM_000038 37 38 39 AREG NM_001657 40 41 42 B2M NM_004048 43 44 45 BAK1 NM_001188 46 47 488 BAX NM_004324 49 50 51 BCL2 NM_000633 52 53 54 BCL2L1 NM_001191 55 56 57 BIRC3 NM_001165 58 59 60 BIRC4 NM_001167 61 62 63 BIRCS. NM_001168 64 65 66 BRCA1 NM_007295 67 68 69 BRCA2 NM_000059 70 71 72 CCND1 NM_001758 73 74 75 CD3Z NM_000734 76 77 78 CD68 NM_001251 79 80 81 CDC25A NM_001789 82 83 84 CDH1 NM_004360 85 86 87 CDKN1A NM_000389 88 89 90 COKN1B NM_004064 91 92 93 CDKN2A NM_000077 94 95 96 CYP1B1 NM_000104 97 98 99 DHFR NM_000791 100 101 102 OPYD NM_000110 103 104 105 ECGF1 NM_001953 106 107 108 EGFR NM_005228 109 110 111. EIF4E NM_001968 112 113 114 ERBB2 NM_004448 115 116 117 ERBB3 NM_001982 118 119 120 ESR1 NM_000125 121 122 123 ESR2 NM_001437 124 125 126 GAPD NM_002046 127 128 129 GATA3 . NM_002051 130 131 132 GRB7 NM_005310 133 134 135 GRO1 NM_001511 136 137 138 GSTP1 NM_000852 139 140 141 GUSB NM_000181 142 143 144 hHGF M29145 145 146 147 HNF3A NM_004496 148 149 150 ID2 NM_002166 151 152 153 IGF1 NM_000618 154 155 156 IGFBP3 NM_000598 157 158 159 ITGA7 NM_002206 160 161 162 ITGB2 NM_00021 163 164 165 KDR NM 002253 166 167 168 KIT NM_000222 169 170 171 KFMG NM_000899 172 173 174 KRT177 NM_000422 175 176 177 KRTS NM_000424 178 179 180 LPL NM_000237 181 182 183 MET NM_000245 184 185 186 MK167 NM_002417 187 188 189 MVP NM_017458 190 191 192 MYC NM_002467 193 194 195 PDGFA NM_002607 196 197 198 PDGFB NM_002608 199 200 201 PDGFC NM_016205 202 203 204 PDGFRA NM_006206 205 206 207 PDGFRB NM_002609 208 209 210 PGK1 NM_00029 211 212 213 PGR NM_000926 214 215 216 PIN1 NM_006221 217 218 219 PLAU NM_002658 220 221 222 PPIH NM_006347 223 224 225 PTEN NM_000314 226 227 228 PTGS2 NM_000963 229 230 231 RBP4 NM_006744 232 233 234 RELY NM_021975 235 236 237 RPL19 NM_000981 238 239 240 RPLP0 NM_001002 241 242 243 SCDGF-B NM_025208 244 245 246 SERPINE1 NM_000602 247 248 249 SLC19A1 NM_003056 250 251 252 TBP NM_003194 253 254 255 TFF1 NM_003225 256 257 258 TFRC NM_003234 259 260 261 TK1 NM_003258 262 263 264 TNFRSF6 NM_000043 265 266 267 TNFSF6 NM_000639 268 269 270 TOP2A NM_001067 271 272 273 TOP2B NM_001068 274 275 276 TP53 NM_000546 277 278 279 TYMS NM_001071 280 281 282 VEGF NM_003376 283 284 285 GENE ACCESSION NO. SEQ ID NO: AK055699 AK055699 286 BAG1 NM_004323 287 BBC3 NM_014417 288 Bcl2 NM_000633 289 BRCA2 NM_000059 290 CA9 NM_001216 291 CNB1 NM_031966 292 CDC25B NM_021874 293 CEGP1 NM_020974 294 Chk1 NM_001274 295 Chk2 NM_007194 296 CYP3A4 NM_017460 297 DIABLO NM_019887 298 DPYD NM_000110 299 EGFR NM_005228 300 EpCAM NM_002354 301 EPHX1 NM_000120 302 EstR1 NM_000125 303 FOXM1 NM_021953 304 GATA3 NM_002051 305 GSTM1 NM_000561 306 GSTM3 NM_000849 307 hENT1 NM_004955 308 HIF1A NM_001530 309 HNF3A NM_004496 310 101 NM_002165 311 IGF1R NM_000875 312 Ki-67 NM_002417 313 NFKBp65 NM_021975 314 NAME1 NM_000269 315 p27 NM_004064 316 P13KC2A NM_002645 317 PR NM_000926 318 PRAME NM_006115 319 pS2 NM_003225 320 RPS6KB1 NM_003161 321 Src NM_004383 322 STK15 NM_003600 323 SURV NM_001168 324 TFRC NM_003234 325 TGFB3 NM_003239 326 TK1 NM_003258 327 VDR NM_000376 328 VEGFC NM_005429 329 WISP1 NM_003882 330 XIAP NM_001167 331 YB-1 NM_004559 332 ITGA7 NM_002206 333 PDGFB NM_002608 334 Upa NM_002658 335 TBP NM_003194 336 POGFRa NM_006206 337 Pin1 NM_006221 338 CYP NM_006347 339 RBP4 NM_006744 340 BRCA1 NM_007295 341 ARC NM_000038 342 GUS NM_000181 343 CD18 NM_000211 344 PTEN NM_000314 345 P53 NM_000546 346 ALDH1A3 NM_000693 347 GSTp NM_006852 348 TOP2B NM_001068 349 TS NM_001071 350 Bclx NM_001191 351 AREG NM_001657 352 TP NM_001953 353 EIF4E NM_001968 354 ErbB3 NM_001982 355 EREG NM_001432 356 GCLC NM_001498 357 CD9 NM_001769 358 HB-EGF NM_001945 359 IGFBP2 NM_000597 369 CTSL NM_001912 361 PREP NM_002726 362 CYP3A4 NM_017460 363 ILT-2 NM_006669 364 MCM3 NM_002388 365 KRT19 NM_002276 366 KRT18 NM_000224 367 TIMP2 NM_003255 368 SAD NM_004322 369 CYP2C8 NM_030878 370 PCR3 NM_016434 371 PLAUR NM_002659 372 PI3KC2A NM_002645 373 FGF2 NM_002006 374 HLA-G NM_002127 375 AIB1 NM_006534 376 MCP1 NM_002982 377 Contig46653 Contig4663 378 RhoC NM_005167 379 DR5 NM_003842 380 RAD51C NM_058216 381 BIN1 NM_004305 382 VOR NM_000376 383 TERC U86046 384

• <110> GENOMIC HEALTH
  Baker, Joffre B.
  Cronin, Maureen T.
  Kiefer, Michael c.
  Shak, Steve
  walker, Michael Graham
• <120> GENE EXPRESSION PROFILING IN BIOPSIED TUMOR TISSUES
• <130> H64199PCEPT1
• <140> to be assigned
  <141> 2007-12-17
• <150> US 60/412,049
  <151> 2002-09-18
• <150> US 60/364,890
  <151> 2002-03-13
• <160> 384
• <170> FastSEQ for Windows version 4.0
• <210> 1
  <211> 18
  <212> DNA
  <213> Homo sapiens
• <400> 1
  gtcccaggag cccatcct 18
• <210> 2
  <211> 19
  <212> DNA
  <213> Homo sapiens
• <400> 2
  cccggctgtt gtctccata 19
• <210> 3
  <211> 68
  <212> DNA
  <213> Homo sapiens
• <400> 3
• <210> 4
  <211> 18
  <212> DNA
  <213> Homo sapiens
• <400> 4
  tcatggtgcc cgtcaatg 18
• <210> 5
  <211> 23
  <212> DNA
  <213> Homo sapiens
• <400> 5
  cgattgtctt tgctcttcat gtg 23
• <210> 6
  <211> 79
  <212> DNA
  <213> Homo sapiens
• <400> 6
• <210> 7
  <211> 20
  <212> DNA
  <213> Homo sapiens
• <400> 7
  aggggatgac ttggacacat 20
• <210> 8
  <211> 20
  <212> DNA
  <213> Homo sapiens
• <400> 8
  aaaactgcat ggctttgtca 20
• <210> 9
  <211> 65
  <212> DNA
  <213> Homo sapiens
• <400> 9
• <210> 10
  <211> 22
  <212> DNA
  <213> Homo sapiens
• <400> 10
  tcatcctggc gatctacttc ct 22
• <210> 11
  <211> 20
  <212> DNA
  <213> Homo sapiens
• <400> 11
  ccgttgagtg gaatcagcaa 20
• <210> 12
  <211> 91
  <212> DNA
  <213> Homo sapiens
• <400> 12
• <210> 13
  <211> 20
  <212> DNA
  <213> Homo sapiens
• <400> 13
  agcgcctgga atctacaact 20
• <210> 14
  <211> 20
  <212> DNA
  <213> Homo sapiens
• <400> 14
  agagcccctg gagagaagat 20
• <210> 15
  <211> 66
  <212> DNA
  <213> Homo sapiens
• <400> 15
• <210> 16
  <211> 24
  <212> DNA
  <213> Homo sapiens
• <400> 16
  gcccagagaa ggtctatgaa ctca 24
• <210> 17
  <211> 22
  <212> DNA
  <213> Homo sapiens
• <400> 17
  gtttcaaagg cttggtggat tt 22
• <210> 18
  <211> 94
  <212> DNA
  <213> Homo sapiens
• <400> 18
• <210> 19
  <211> 21
  <212> DNA
  <213> Homo sapiens
• <400> 19
  cgcagtgcag ctgagtatct g 21
• <210> 20
  <211> 21
  <212> DNA
  <213> Homo sapiens
• <400> 20
  tgcccagggc tactctcact t 21
• <210> 21
  <211> 80
  <212> DNA
  <213> Homo sapiens
• <400> 21
• <210> 22
  <211> 21
  <212> DNA
  <213> Homo sapiens
• <400> 22
  cagcagatgt ggatcagcaa g 21
• <210> 23
  <211> 18
  <212> DNA
  <213> Homo sapiens
• <400> 23
  gcatttgcgg.tggacgat 18
• <210> 24
  <211> 66
  <212> DNA
  <213> Homo sapiens
• <400> 24
• <210> 25
  <211> 20
  <212> DNA
  <213> Homo sapiens
• <400> 25
  cgcttctatg gcgctgagat 20
• <210> 26
  <211> 20
  <212> DNA
  <213> Homo sapiens
• <400> 26
  tcccggtaca ccacgttctt 20
• <210> 27
  <211> 71
  <212> DNA
  <213> Homo sapiens
• <400> 27
• <210> 28
  <211> 25
  <212> DNA
  <213> Homo sapiens
• <400> 28
  ttgtctctgc cttggactat ctaca 25
• <210> 29
  <211> 24
  <212> DNA
  <213> Homo sapiens
• <400> 29
  ccagcattag attctccaac ttga 24
• <210> 30
  <211> 75
  <212> DNA
  <213> Homo sapiens
• <400> 30
• <210> 31
  <211> 25
  <212> DNA
  <213> Homo sapiens
• <400> 31
  gaaggagata aggaggatgt tgaca 25
• <210>
  <211> 18
  <212> DNA
  <213> Homo sapiens
• <400> 32
  cgccacggag atccaatc 18
• <210> 33
  <211> 74
  <212> DNA
  <213> Homo sapiens
• <400> 33
• <210> 34
  <211> 21
  <212> DNA
  <213> Homo sapiens
• <400> 34
  tggtgaacat tgtgccagga t 21
• <210> 35
  <211> 22
  <212> DNA
  <213> Homo sapiens
• <400> 35
  gaaggcgatc ttgttgatct ga 22
• <210> 36
  <211> 80
  <212> DNA
  <213> Homo sapiens
• <400> 36
• <210> 37
  <211> 20
  <212> DNA
  <213> Homo sapiens
• <400> 37 20
  ggacagcagg aatgtgtttc 20
• <210> 38
  <211> 20
  <212> DNA
  <213> Homo sapient
• <400> 38
  acccactcga tttgtttctg 20
• <210> 39
  <211> 69
  <212> DNA
  <213> Homo sapiens
• <400> 39
• <210> 40
  <211> 27
  <212> DNA
  <213> Homo sapiens
• <400> 40
  tgtgagtgaa atgccttcta gtagtga 27
• <210> 41
  <211> 27
  <212> DNA
  <213> Homo sapiens
• <400> 41
  ttgtggttcg ttatcatact cttctga 27
• <210> 42
  <211> 82
  <212> DNA
  <213> Homo sapiens
• <400> 42
• <210> 43
  <211> 19
  <212> DNA
  <213> Homo sapiens
• <400> 43
  gtctcgctcc gtggcctta 19
• <210> 44
  <211> 24
  <212> DNA
  <213> Homo sapiens
• <400> 44
  cgtgagtaaa cctgaatctt tgga 24
• <210> 45
  <211> 93
  <212> DNA
  <213> Homo sapiens
• <400> 45
• <210> 46
  <211> 20
  <212> DNA
  <213> Homo sapiens
• <400> 46
  ccattcccac cattctacct 20
• <210> 47
  <211> 20
  <212> DNA
  <213> Homo sapiens
• <400> 47
  gggaacatag acccaccaat 20
• <210> 48
  <211> 66
  <212> DNA
  <213> Homo sapiens
• <400> 48
• <210> 49
  <211> 18
  <212> DNA
  <213> Homo sapiens
• <400> 49
  ccgccgtgga cacagact 18
• <210> 50
  <211> 21
  <212> DNA
  <213> Homo sapiens
• <400> 50
  ttgccgtcag aaaacatgtc a 21
• <210> 51
  <211> 70
  <212> DNA
  <213> Homo sapiens
• <400> 51
• <210> 52
  <211> 25
  <212> DNA
  <213> Homo sapiens
• <400> 52
  cagatggacc tagtacccac tgaga 25
• <210> 53
  <211> 24
  <212> DNA
  <213> Homo sapiens
• <400> 53
  cctatgattt aagggcattt ttcc 24
• <210> 54
  <211> 73
  <212> DNA
  <213> Homo sapiens
• <400> 54
• <210> 55
  <211> 24
  <212> DNA
  <213> Homo sapiens
• <400> 55
  cttttgtgga actctatggg aaca 24
• <210> 56
  <211> 19
  <212> DNA
  <213> Homo sapiens
• <400> 56
  cagcggttga agcgttcct 19
• <210> 57
  <211> 70
  <212> DNA
  <213> Homo sapiens
• <400> 57
• <210> 58
• <211> 24
  <212> DNA
  <213> Homo sapiens
• <400> 5.8
  ggatatttcc gtggctctta ttca 24
• <210> 59
  <211> 25
  <212> DNA
  <213> Homo sapiens
• <400> 59
  cttctcatca aggcagaaaa atctt 25
• <210> 60
  <211> 86
  <212> DNA
  <213> Homo sapiens
• <400> 60
• <210> 61
  <211> 23
  <212> DNA
  <213> Homo sapiens
• <400> 61
  gcagttggaa gacacaggaa agt 23
• <210> 62
  <211> 21
  <212> DNA
  <213> Homo sapiens
• <400> 62
  tgcgtggcac tattttcaag a 21
• <210> 63
  <211> 77
  <212> DNA
  <213> Homo sapiens
• <400> 63
• <210> 64
  <211> 20
  <212> DNA
  <213> Homo sapiens
• <400> 64
  tgttttgatt cccgggctta 20
• <210> 65
  <211> 24
  <212> DNA
  <213> Homo sapiens
• <400> 65
  caaagctgtc agctctagca aaag 24
• <210> 66
  <211> 80
  <212> DNA
  <213> Homo sapiens
• <400> 66
• <210> 67
  <211> 20
  <212> DNA
  <213> Homo sapiens
• <400> 67
  tcagggggct agaaatctgt 20
• <210> 68
  <211> 20
  <212> DNA
  <213> Homo sapiens
• <400> 68
  ccattccagt tgatctgtgg 20
• <210> 69
  <211> 65
  <212> DNA
  <213> Homo sapiens
• <400> 69
• <210> 70
  <211> 20
  <212> DNA
  <213> Homo sapiens
• <400> 70
  agttcgtgct ttgcaagatg 20
• <210> 71
  <211> 20
  <212> DNA
  <213> Homo sapiens
• <400> 71
  aaggtaagct gggtctgctg 20
• <210> 72
  <211> 70
  <212> DNA
  <213> Homo sapiens
• <400> 72
• <210> 73
  <211> 21
  <212> DNA
  <213> Homo sapiens
• <400> 73
  gcatgttcgt ggcctctaag a 21
• <210> 74
  <211> 22
  <212> DNA
  <213> Homo sapiens
• <400> 74
  cggtgtagat gcacagcttc tc 22
• <210> 75
  <211> 69
  <212> DNA
  <213> Homo sapiens
• <400> 75
• <210> 76
  <211> 20
  <212> DNA
  <213> Homo sapiens
• <400> 76 20
  agatgaagtg gaaggcgctt 20
• <210> 77
  <211> 21
  <212> DNA
  <213> Homo sapiens
• <400> 77 21
  tgcctctgta atcggcaact g 21
• <210> 78
  <211> 65
  <212> DNA
  <213> Homo sapiens
• <400> 78
• <210> 79
  <211> 18
  <212> DNA
  <213> Homo sapiens
• <400> 79
  tggttcccag ccctgtgt 18
• <210> 80
  <211> 19
  <212> DNA
  <213> Homo sapiens
• <400> 80
  ctcctccacc ctgggttgt 19
• <210> 81
  <211> 74
  <212> DNA
  <213> Homo sapiens
• <400> 81
• <210> 82
  <211> 20
  <212> DNA
  <213> Homo sapiens
• <400> 82
  tcttgctggc tacgcctctt 20
• <210> 83
  <211> 21
  <212> DNA
  <213> Homo sapiens
• <400> 83
  ctgcattgtg gcacagttct g 21
• <210> 84
  <211> 71
  <212> DNA
  <213> Homo sapiens
• <400> 844
• <210> 85
  <211> 21
  <212> DNA
  <213> Homo sapiens
• <400> 85
  tgagtgtccc ccggtatctt c 21
• <210> 86
  <211> 21
  <212> DNA
  <213> Homo sapiens
• <400> 86
  cagccgcttt cagattttca t 21
• <210> 87
  <211> 81
  <212> DNA
  <213> Homo sapiens

• <400> 87
• <210> 88
  <211> 21
  <212> DNA
  <213> Homo sapiens
• <400> 88
  tggagactct cagggtcgaa a 21
• <210> 89
  <211> 22
  <212> DNA
  <213> Homo sapiens
• <400> 89
  ggcgtttgga gtggtagaaa tc 22
• <210> 90
  <211> 65
  <212> DNA
  <213> Homo sapiens
• <400> 90
• <210> 91
  <211> 21.
  <212> DNA
  <213> Homo sapiens
• <400> 91
  cggtggacca cgaagagtta a 21
• <210> 92
  <211> 19
  <212> DNA
  <213> Homo sapiens
• <400> 92
  ggctcgcctc ttccatgtc 19
• <210> 93
  <211> 66
  <212> DNA
  <213> Homo sapiens
• <400> 93
• <210> 94
  <211> 19
  <212> DNA
  <213> Homo sapiens
• <400> 94
  gcggaaggtc cctcagaca 19
• <210> 95
  <211> 23
  <212> DNA
  <213> Homo sapiens
• <400> 95
  tctaagtttc ccgaggtttc tca 23
• <210> 96
  <211> 70
  <212> DNA
  <213> Homo sapiens
• <400> 96
• <210> 97
  <211> 22
  <212> DNA
  <213> Homo sapiens
• <400> 97
  ccagctttgt gcctgtcact at 22
• <210> 98
  <211> 20
  <212> DNA
  <213> Homo sapiens
• <400> 98
  gggaatgtgg tagcccaaga 20
• <210> 99
• <211> 71
• <212> DNA
  <213> Homo sapiens
• <400> 99
• <210>
  <211> 27
  <212> DNA
  <213> Homo sapiens
• <400> 100
  ttgctataac taagtgcttc tccaaga 27
• <210> 101
  <211> 22
  <212> DNA
  <213> Homo sapiens
• <400> 101
  gtggaatggc agctcactgt ag 22
• <210> 102
  <211> 73
  <212> DNA
  <213> Homo sapiens
• <400> 102
• <210> 103
  <211> 19
  <212> DNA
  <213> Homo sapiens
• <400> 103
  aggacgcaag gagggtttg 19
• <210> 104
  <211> 21
  <212> DNA
  <213> Homo sapiens
• <400> 104
  gatgtccgcc gagtccttac t 21
• <210> 105
  <211> 87
  <212> DNA
  <213> Homo sapiens
• <400> 105
• <210> 106
  <211> 24
  <212> DNA
  <213> Homo sapiens
• <400> 106
  ctatatgcag ccagagatgt gaca 24
• <210> 107
  <211> 24
  <212> DNA
  <213> Homo sapiens
• <400> 107
  ccacgagttt cttactgaga atgg 24
• <210> 108
  <211> 82
  <212> DNA
  <213> Homo sapiens
• <400> 108
• <210> 109
  <211> 20
  <212> DNA
  <213> Homo sapiens
• <400> 109
  tgtcgatgga cttccagaac 20
• <210> 110
  <211> 19
  <212> DNA
  <213> Homo sapiens
• <400> 110
  attgggacag cttggatca 19
• <210> 111
  <211> 62
  <212> DNA
  <213> Homo sapiens
• <400> 111
• <210> 112
  <211> 23
  <212> DNA
  <213> Homo sapiens
• <400> 112
  gatctaagat ggcgactgtc gaa 23
• <210> 113
  <211> 25
  <212> DNA
  <213> Homo sapiens
• <400> 113
  ttagattccg ttttctcctc ttctg 25
• <210> 114
  <211> 82
  <212> DNA
  <213> Homo sapiens
• <400> 114
• <210> 115
  <211> 20
  <212> DNA
  <213> Homo sapiens
• <400> 115
  cggtgtgaga agtgcagcaa 20
• <210> 116
  <211> 19
  <212> DNA
  <213> Homo sapiens
• <400> 116
  cctctcgcaa gtgctccat 19
• <210> 117
  <211> 70
  <212> DNA
  <213> Homo sapiens
• <400> 117
• <210> 118
  <211> 23
  <212> DNA
  <213> Homo sapiens
• <400> 118
  cggttatgtc atgccagata cac 23
• <210> 119
  <211> 24
  <212> DNA
  <213> Homo sapiens
• <400> 119
  gaactgagac ccactgaaga aagg 24
• <210> 120
  <211> 81
  <212> DNA
  <213> Homo sapiens
• <400> 120
• <210> 121
  <211> 19
  <212> DNA
  <213> Homo sapiens
• <400> 121
  cgtggtgccc ctctatgac 19
• <210> 122
  <211> 19
  <212> DNA
  <213> Homo sapiens
• <400> 122
  ggctagtggg cgcatgtag 19
• <210> 123
  <211> 68
  <212> DNA
  <213> Homo sapiens
• <400> 123
• <210> 124
  <211> 20
  <212> DNA
  <213> Homo sapiens
• <400> 124 20
  tggtccatcg ccagttatca 20
• <210> 125
  <211> 23
  <212> DNA
  <213> Homo sapiens
• <400> 125 23
  tgttctagcg atcttgcttc aca 23
• <210> 126
  <211> 76
  <212> DNA
  <213> Homo sapiens
• <400> 126
• <210> 127
  <211> 24
  <212> DNA
  <213> Homo sapiens
• <400> 127
  catccatgac aactttggta tcgt 24
• <210> 128
  <211> 21
  <212> DNA
  <213> Homo sapiens
• <400> 128
  cagtcttctg ggtggcagtg a 21
• <210> 129
  <211> 74
  <212> DNA
  <213> Homo sapiens
• <400> 129
• <210> 130
  <211> 23
  <212> DNA
  <213> Homo sapiens
• <400> 130
  caaaggagct cactgtggtg tct 23
• <210> 131
  <211> 26
  <212> DNA
  <213> Homo sapiens
• <400> 131
  gagtcagaat ggcttattca cagatg 26
• <210> 132
  <211> 75
  <212> DNA
  <213> Homo sapiens
• <400> 132
• <210> 133
  <211> 20
  <212> DNA
  <213> Homo sapiens
• <400> 133
  ccatctgcat ccatcttgtt 20
• <210> 134
  <211> 20
  <212> DNA
  <213> Homo sapiens
• <400> 134
  ggccaccagg gtattatctg 20
• <210> 135
  <211> 67
  <212> DNA
  <213> Homo sapiens
• <400> 135
• <210> 136
  <211> 23
  <212> DNA
  <213> Homo sapiens
• <400> 136
  cgaaaagatg ctgaacagtg aca 23
• <210> 137
  <211> 20
  <212> DNA
  <213> Homo sapiens
• <400> 137
  tcaggaacag ccaccagtga 20
• <210> 138
  <211> 73
  <212> DNA
  <213> Homo sapiens
• <400> 138
• <210> 139
  <211> 20
  <212> DNA
  <213> Homo sapiens
• <400> 139
  gagaccctgc tgtcccagaa 20
• <210> 140
  <211> 23
  <212> DNA
  <213> Homo sapiens
• <400> 140
  ggttgtagtc agcgaaggag atc 23
• <210> 141
  <211> 76
  <212> DNA
  <213> Homo sapiens
• <400> 141
• <210> 142
  <211> 20
  <212> RNA
  <213> Homo sapiens
• <400> 142
  cccactcagt agccaagtca 20
• <210> 143
  <211> 20
  <212> DNA
  <213> Homo sapiens
• <400> 143
  cacgcaggtg gtatcagtct 20
• <210> 144
  <211> 73
  <212> DNA
  <213> Homo sapiens
• <400> 144
• <210> 145
  <211> 24
  <212> DNA
  <213> Homo sapiens
• <400> 145
  catcaaatgt cagccctgga gttc 24
• <210> 146
  <211> 26
  <212> DNA
  <213> Homo sapiens
• <400> 146
  ttcctgtagg tctttacccc gatagc 26
• <210> 147
  <211> 85
  <212> DNA
  <213> Homo sapiens
• <400> 147
• <210> 148
  <211> 24
  <212> DNA
  <213> Homo sapiens
• <400> 148
  tccaggatgt taggaactgt gaag 24
• <210> 149
  <211> 22
  <212> DNA
  <213> Homo sapiens
• <400> 149
  gcgtgtctgc gtagtagctg tt 22
• <210> 150
  <211> 73
  <212> DNA
  <213> Homo sapiens
• <400> 150
• <210> 151
  <211> 23
  <212> DNA
  <213> Homo sapiens
• <400> 151 23
  aacgactgct actccaagct caa 23
• <210> 152
  <211> 22
  <212> DNA
  <213> Homo sapiens
• <400> 152
  ggatttccat cttgctcacc tt 22
• <210> 153
  <211> 76
  <212> DNA
  <213> Homo sapiens
• <400> 153
• <210> 154
  <211> 21
  <212> DNA
  <213> Homo sapiens
• <400> 154
  tccggagctg tgatctaagg a 21
• <210> 155
  <211> 20
  <212> DNA
  <213> Homo sapiens
• <400> 155
  cggacagagc gagctgactt 20
• <210> 156
  <211> 76
  <212> DNA
  <213> Homo sapiens
• <400> 156
• <210> 157
  <211> 17
  <212> DNA
  <213> Homo sapiens
• <400> 157
  acgcaccggg tgtctga 17
• <210> 158
  <211> 24
  <212> DNA
  <213> Homo sapiens
• <400> 158
  tgccctttct tgatgatgat tatc 24
• <210> 159
  <211> 68
  <212> DNA
  <213> Homo sapiens
• <400> 159
• <210> 160
  <211> 22
  <212> DNA
  <213> Homo sapiens
• <400> 160
  ccattcaccc tgtgtaacag ga 22
• <210> 161
  <211> 21
  <212> DNA
  <213> Homo sapiens
• <400> 161
  ccgaccctct aggttaaggc a 21
• <210> 162
  <211> 68
  <212> DNA
  <213> Homo sapiens
• <400> 162
• <210> 163
  <211> 20
  <212> DNA
  <213> Homo sapiens
• <400> 163
  cgtcaggacc caccatgtct 20
• <210> 164
  <211> 24
  <212> DNA
  <213> Homo sapiens
• <400> 164
  ggttaattgg tgacatcctc aaga 24
• <210> 165
  <211> 81
  <212> DNA
  <213> Homo sapiens
• <400> 165
• <210> 166
  <211> 23
  <212> DNA
  <213> Homo sapiens
• <400> 166
  caaacgctga catgtacggt cta 23
• <210> 167
  <211> 18
  <212> DNA
  <213> Homo sapiens
• <400> 167
  gctcgttggc gcactctt 18
• <210> 168
  <211> 88
  <212> DNA
  <213> Homo sapiens
• <400> 168

• <210> 169
  <211> 25
  <212> DNA
  <213> Homo sapiens
• <400> 169
  gaggcaactg cttatggctt aatta 25
• <210> 170
  <211> 18
  <212> DNA
  <213> Homo sapiens
• <400> 170
  ggcactcggc ttgagcat 18
• <210> 171
  <211> 75
  <212> DNA
  <213> Homo sapiens
• <400> 171
• <210> 172
  <211> 18
  <212> DNA
  <213> Homo sapiens
• <400> 172 18
  gtccccggga tggatgtt 18
• <210> 173
  <211> 25
  <212> DNA
  <213> Homo sapiens
• <400> 173
  gatcagtcaa gctgtctgac aattg 25
• <210> 174
  <211> 79
  <212> DNA
  <213> Homo sapiens
• <400> 174
• <210> 175
  <211> 21
  <212> DNA
  <213> Homo sapiens
• <400> 175
  cgaggattgg ttcttcagca a 21
• <210> 176
  <211> 22
  <212> DNA
  <213> Homo sapiens
• <400> 176
  actctgcacc agctcactgt tg 22
• <210> 177
  <211> 73
  <212> DNA
  <213> Homo sapiens
• <400> 177
• <210> 178
  <211> 20
  <212> DNA
  <213> Homo sapiens
• <400> 178
  tcagtggaga aggagttgga 20
• <210> 179
  <211> 20
  <212> DNA
  <213> Homo sapiens
• <400> 179
  tgccatatcc agaggaaaca 20
• <210> 180
  <211> 69
  <212> DNA
  <213> Homo sapiens
• <400> 180
• <210> 181
  <211> 26
  <212> DNA
  <213> Homo sapiens
• <400> 181
  gtacaagaga gaaccagact ccaatg 26
• <210> 182
  <211> 18
  <212> DNA
  <213> Homo sapiens
• <400> 182
  gtgtagcccg cggacact 18
• <210> 183
  <211> 87
  <212> DNA
  <213> Homo sapiens
• <400> 183
• <210> 184
  <211> 22
  <212> DNA
  <213> Homo sapiens
• <400> 184
  gacatttcca gtcctgcagt ca 22
• <210> 185
  <211> 20
  <212> DNA
  <213> Homo sapiens
• <400> 185
  ctccgatcgc acacatttgt 20
• <210> 186
  <211> 86
  <212> DNA
  <213> Homo sapiens
• <400> 186
• <210> 187
  <211> 24
  <212> DNA
  <213> Homo sapiens.
• <400> 187
  gttttggagg aaatgtgttc ttca 24
• <210> 188
  <211> 26
  <212> DNA
  <213> Homo sapiens
• <400> 188
  ttctctaata cactgccgtc ttaagg 26
• <210> 189
  <211> 101
  <212> DNA
  <213> Homo sapiens
• <400> 189
• <210> 190
  <211> 22
  <212> DNA
  <213> Homo sapiens
• <400> 190
  acgagaacga gggcatctat gt 22
• <210> 191
  <211> 22
  <212> DNA
  <213> Homo sapiens
• <400> 191
  gcatgtaggt gcttccaatc ac 22
• <210> 192
  <211> 75
  <212> DNA
  <213> Homo sapiens
• <400> 192
• <210> 193
  <211> 21
  <212> DNA
  <213> Homo sapiens
• <400> 193
  tccctccact cggaaggact a 21
• <210> 194
  <211> 22
  <212> DNA
  <213> Homo sapiens
• <400> 194
  cggttgttgc tgatctgtct ca 22
• <210> 195
  <211> 84
  <212> DNA
  <213> Homo sapiens
• <400> 195
• <210> 196
  <211> 19
  <212> DNA
  <213> Homo sapiens
• <400> 196
  ttgttggtgt gccctggtg 19
• <-210> 197
  <211> 21
  <212> DNA
  <213> Homo sapiens
• <400> 197
  tgggttctgt ccaaacactg g 21
• <210> 198
  <211> 67
  <212> DNA
  <213> Homo sapiens
• <400> 198
• <210> 199
  <211> 20
  <212> DNA
  <213> Homo sapiens
• <400> 199
  actgaaggag acccttggag 20
• <210> 200
  <211> 20
  <212> DNA
  <213> Homo sapiens
• <400> 200
  taaataaccc tgcccacaca 20
• <210> 201
  <211> 62
  <212> DNA
  <213> Homo sapiens
• <400> 201
• <210> 202
  <211> 28
  <212> DNA
  <213> Homo sapiens
• <400> 202
  agttactaaa aaataccacg aggtcctt 28
• <210> 203
  <211> 21
  <212> DNA
  <213> Homo sapiens
• <400> 203
  gtcggtgagt gatttgtgca a 21
• <210> 204
  <211> 79
  <212> DNA
  <213> Homo sapiens
• <400> 204
• <210> 205
  <211> 20
  <212> DNA
  <213> Homo sapiens
• <400> 205 20
  gggagtttcc aagagatgga 20
• <210> 206
  <211> 20
  <212> DNA
  <213> Homo sapiens
• <400> 206
  cttcaaccac cttcccaaac 20
• <210> 207 20
  <211> 72
  <212> DNA
  <213> Homo sapiens
• <400> 207
• <210> 208
  <211> 23
  <212> DNA
  <213> Homo sapiens
• <400> 208
  aggtgtcatc catcaacgtc tct 23
• <210> 209
  <211> 20
  <212> DNA
  <213> Homo sapiens
• <400> 209
  tcccgatcac aatgcacatg 20
• <210> 210
  <211> 90
  <212> DNA
  <213> Homo sapiens
• <400> 210
• <210> 211
  <211> 24
  <212> DNA
  <213> Homo sapiens
• <400> 211 24
  agagccagtt gctgtagaac tcaa 24
• <210> 212
  <211> 21
  <212> DNA
  <213> Homo sapiens
• <400> 212
  ctgggcctac acagtccttc a 21
• <210> 213
  <211> 74
  <212> DNA
  <213> Homo sapiens
• <400> 213
• <210> 214
  <211> 26
  <212> DNA
  <213> Homo sapiens
• <400> 214 26
  gaaatgactg catcgttgat aaaatc 26
• <210> 215
  <211> 19
  <212> DNA
  <213> Homo sapiens
• <400> 215 19
  tgccagcctg acagcactt 19
• <210> 216
  <211> 78
  <212> DNA
  <213> Homo sapiens
• <400> 216
• <210> 217
  <211> 20
  <212> DNA
  <213> Homo sapiens
• <400> 217
  gatcaacggc tacatccaga 20
• <210> 218
  <211> 20
  <212> DNA
  <213> Homo sapiens
• <400> 218
  tgaactgtga ggccagagac 20
• <210> 219
  <211> 68
  <212> DNA
  <213> Homo sapiens
• <400> 219
• <210> 220
  <211> 19
  <212> DNA
  <213> Homo sapiens
• <400> 220
  gtggatgtgc cctgaagga 19
• <210> 221
  <211> 20
  <212> DNA
  <213> Homo sapiens
• <400> 221
  ctgcggatcc agggtaagaa 20
• <210> 222
  <211> 70
  <212> DNA
  <213> Homo sapiens
• <400> 222
• <210> 223
  <211> 27
  <212> DNA
  <213> Homo sapiens
• <400> 223
  tggacttcta gtgatgagaa agattga 27
• <210> 224
  <211> 22
  <212> DNA
  <213> Homo sapiens
• <400> 224
  cactgcgaga tcaccacagg ta 22
• <210> 225
  <211> 84
  <212> DNA
  <213> Homo sapiens
• <400> 225
• <210> 226
  <211> 25
  <212> DNA
  <213> Homo sapiens
• <400> 226
  tggctaagtg aagatgacaa tcatg 25
• <210> 227
  <211> 25
  <212> DNA
  <213> Homo sapiens
• <400> 227
  tgcacatatc attacaccag ttcgt 25
• <210> 228
  <211> 81
  <212> DNA
  <213> Homo sapiens
• <400> 228
• <210> 229
  <211> 23
  <212> DNA
  <213> Homo sapiens
• <400> 229
  tctgcagagt tggaagcact cta 23
• <210> 230
  <211> 21
  <212> DNA
  <213> Homo sapiens
• <400> 230
  gccgaggctt ttctaccaga a 21
• <210> 231
  <211> 79
  <212> DNA
  <213> Homo sapiens
• <400> 231
• <210> 232
  <211> 24
  <212> DNA
  <213> Homo sapiens
• <400> 232
  acgacacgta tgccgtacag tact 24
• <210> 233
  <211> 18
  <212> DNA
  <213> Homo sapiens
• <400> 233
  ccgggaaaac acgaagga 18
• <210> 234
  <211> 86
  <212> DNA
  <213> Homo sapiens
• <400> 234
• <210> 235
  <211> 19
  <212> DNA
  <213> Homo sapiens
• <400> 235
  ctgccgggat ggcttctat 19
• <210> 236
  <211> 22
  <212> DNA
  <213> Homo sapiens
• <400> 236
  ccaggttctg gaaactgtgg at 22
• <210> 237
  <211> 68
  <212> DNA
  <213> Homo sapiens
• <400> 237
• <210> 238
  <211> 20
  <212> DNA
  <213> Homo sapiens
• <400> 238
  ccacaagctg aaggcagaca 20
• <210> 239
  <211> 21
  <212> DNA
  <213> Homo sapiens
• <400> 239
  gcgtgcttcc ttggtcttag a 21
• <210> 240
  <211> 85
  <212> DNA
  <213> Homo sapiens
• <400> 240
• <210> 241
  <211> 24
  <212> DNA
  <213> Homo sapiens
• <400> 241
  ccattctatc atcaacgggt acaa 24
• <210> 242 24
  <211> 23
  <212> DNA
  <213> Homo sapiens
• <400> 242
  tcagcaagtg ggaaggtgta atc 23
• <210> 243
  <211> 75
  <212> DNA
  <213> Homo sapiens
• <400> 243
• <210> 244
  <211> 20
  <212> DNA
  <213> Homo sapiens
• <400> 244
  tatcgaggca ggtcatacca 20
• <210> 245
  <211> 20
  <212> DNA
  <213> Homo sapiens
• <400> 245
  taacgcttgg catcatcatt 20
• <210>
  <211> 74
  <212> DNA
  <213> Homo sapiens
• <400> 246
• <210> 247
  <211> 19
  <212> DNA
  <213> Homo sapiens
• <400> 247
  ccgcaacgtg gttttctca 19
• <210> 248
  <211> 21
  <212> DNA
  <213> Homo sapiens
• <400> 248
  tgctgggttt ctcctcctgt t 21
• <210> 249
  <211> 81
  <212> DNA
  <213> Homo sapiens
• <400> 249
• <210> 250
  <211> 25
  <212> DNA
  <213> Homo sapiens

• <400> 250
  tcaagaccat catcactttc attgt 25
• <210> 251
  <211> 27
  <212> DNA
  <213> Homo sapiens
• <400> 251
  ggatcaggaa gtacacggag tataact 27
• <210> 252
  <211> 96
  <212> DNA
  <213> Homo sapiens
• <400> 252
• <210> 253
  <211> 19
  <212> DNA
  <213> Homo sapiens
• <400> 253
  gcccgaaacg ccgaatata 19
• <210> 254
  <211> 23
  <212> DNA
  <213> Homo sapiens
• <400> 254
  cgtggctctc ttatcctcat gat 23
• <210> 255
  <211> 65
  <212> DNA
  <213> Homo sapiens
• <400> 255
• <210> 256
  <211> 19
  <212> DNA
  <213> Homo sapiens
• <400> 256
  gccctcccag tgtgcaaat 19
• <210> 257
  <211> 25
  <212> DNA
  <213> Homo sapiens
• <400> 257
  cgtcgatggt attaggatag aagca 25
• <210> 258
  <211> 86
  <212> DNA
  <213> Homo sapiens
• <400> 258
• <210> 259
  <211> 27
  <212> DNA
  <213> Homo sapiens
• <400> 259
  caagctagat cagcattctc taacttg 27
• <210> 260
  <211> 25
  <212> DNA
  <213> Homo sapiens
• <400> 260
  cacatgactg ttatcgccat ctact 25
• <210> 261
  <211> 99
  <212> DNA
  <213> Homo sapiens
• <400> 261
• <210> 262
  <211> 22
  <212> DNA
  <213> Homo sapiens
• <400> 262
  cacaggaaca acagcatctt 22
• <210> 263
  <211> 20
  <212> DNA
  <213> Homo sapiens
• <400> 263
  agataagccc ctgggatcca 20
• <210> 264
  <211> 75
  <212> DNA
  <213> Homo sapiens
• <400> 264
• <210> 265
  <211> 21
  <212> DNA
  <213> Homo sapiens
• <400> 265
  ggattgctca acaaccatgc t 21
• <210> 266
  <211> 24
  <212> DNA
  <213> Homo sapiens
• <400> 266
  ggcattaaca cttttggacg ataa 24
• <210> 267
  <211> 91
  <212> DNA
  <213> Homo sapiens
• <400> 267
• <210> 268
  <211> 24
  <212> DNA
  <213> Homo sapiens
• <400> 268
  gcactttggg attctttcca ttat 24
• <210> 269
  <211> 24
  <212> DNA
  <213> Homo sapiens
• <400> 269
  gcatgtaaga agaccctcac tgaa 24
• <210> 270
  <211> 80
  <212> DNA
  <213> Homo sapiens
• <400> 270
• <210> 271
  <211> 20
  <212> DNA
  <213> Homo sapiens
• <400> 271
  aatccaaggg ggagagtgat 20
• <210> 272
  <211> 20
  <212> DNA
  <213> Homo sapiens
• <400> 272
  gtacagattt tgcccgagga. 20
• <210> 273
  <211> 72
  <212> DNA
  <213> Homo sapiens
• <400> 273
• <210> 274
  <211> 21
  <212> DNA
  <213> Homo sapiens
• <400> 274
  tgtggacatc ttcccctcag a 21
• <210> 275
  <211> 18
  <212> DNA
  <213> Homo sapiens
• <400> 275
  ctagcccgac cggttcgt 18
• <210> 276
  <211> 66
  <212> DNA
  <213> Homo sapiens
• <400> 276
• <210> 277
  <211> 20
  <212> DNA
  <213> Homo sapiens
• <400> 277
  ctttgaaccc ttgcttgcaa 20
• <210> 278
  <211> 18
  <212> DNA
  <213> Homo sapiens
• <400> 278
  cccgggacaa agcaaatg 18
• <210> 279
  <211> 68
  <212> DNA
  <213> Homo sapiens
• <400> 279
• <210> 280
  <211> 18
  <212> DNA
  <213> Homo sapiens
• <400> 280
  gcctcggtgt gcctttca 18
• <210> 281
  <211> 19
  <212> DNA
  <213> Homo sapiens
• <400> 281
  cgtgatgtgc gcaatcatg 19
• <210> 282
  <211> 65
  <212> DNA
  <213> Homo sapiens
• <400> 282
• <210> 283
  <211> 20
  <212> DNA
  <213> Homo sapiens
• <400> 283
  ctgctgtctt gggtgcattg 20
• <210> 284
  <211> 18
  <212> DNA
  <213> Homo sapiens
• <400> 284
  gcagcctggg accacttg 18
• <210> 285
  <211> 71
  <212> DNA
  <213> Homo sapiens
• <400> 285
• <210> 286
  <211> 1947
  <212> DNA
  <213> Homo sapiens
• <400> 286
• <210> 287
  <211> 1311
  <212> DNA
  <213> Homo sapiens
• <400> 287
• <210> 288
  <211> 582
  <212> DNA
  <213> Homo sapiens
• <400> 288
• <210> 289
  <211> 6030
  <212> DNA
  <213> Homo sapiens
• <400> 289
• <210> 290
  <211> 10987
  <212> DNA
  <213> Homo sapiens
• <400> 290

• <210> 291
  <211> 1552
  <212> DNA
  <213> Homo sapiens
• <400> 291
• <210> 292
  <211> 1578
  <212> DNA
  <213> Homo sapiens
• <400> 292
• <210> 293
  <211> 3195
  <212> DNA
  <213> Homo sapiens
• <400> 293
• <210> 294
  <211> 3737
  <212> DNA
  <213> Homo sapiens
• <400> 294
• <210> 295
  <211> 2042
  <212> DNA
  <213> Homo sapiens
• <400> 295
• <210> 296
  <211> 2547
  <212> DNA
  <213> Homo sapiens
• <400> 296
• <210> 297
  <211> 2768
  <212> DNA
  <213> Homo sapiens
• <400> 297
• <210> 298
  <211> 1358
  <212> DNA
  <213> Homo sapiens
• <400> 298
• <210> 299
  <211> 4407
  <212> DNA
  <213> Homo sapiens
• <400> 299
• <210> 300
  <211> 5532
  <212> DNA
  <213> Homo sapiens
• <400> 300
• <210> 301
  <211> 1528
  <212> DNA
  <213> Homo sapiens
• <400> 301
• <210> 302
  <211> 1856
  <212> DNA
  <213> Homo sapiens
• <400> 302
• <210> 303
  <211> 6450
  <212> DNA
  <213> Homo sapiens
• <400> 303

• <210> 304
  <211> 3336
  <212> DNA
  <213> Homo sapiens
• <220>
  <221> unsure
  <222> (0)...(0)
  <223> n = A, T, C or G
• <400> 304.
• <210> 305
  <211> 2365
  <212> DNA
  <213> Homo sapiens
• <400> 305
• <210> 306
  <211> 1117
  <212> DNA
  <213> Homo sapiens
• <400> 306
• <210> 307
  <211> 1266
  <212> DNA
  <213> Homo sapiens
• <400> 307
• <210> 308
  <211> 2162
  <212> DNA
  <213> Homo sapiens
• <400> 308
• <210> 309
  <211> 3933
  <212> DNA
  <213> Homo sapiens
• <400> 309
• <210> 310
  <211> 2872
  <212> DNA
  <213> Homo sapiens
• <400> 310
• <210> 311
  <211> 926
  <212> DNA
  <213> Homo sapiens
• <400> 311
• <210> 312
  <211> 4989
  <212> DNA
  <213> Homo sapiens
• <400> 312
• <210> 313
  <211> 12515
  <212> DNA
  <213> Homo sapiens
• <400> 313

• <210> 314
  <211> 2444
  <212> DNA
  <213> Homo sapiens
• <400> 314
• <210> 315
  <211> 732
  <212> DNA
  <213> Homo sapiens
• <400> 315
• <210> 316
  <211> 2422
  <212> DNA
  <213> Homo sapiens
• <400> 316
• <210> 317
  <211> 5061
  <212> DNA
  <213> Homo sapiens
• <400> 317
• <210> 318
  <211> 3014
  <212> DNA
  <213> Homo sapiens
• <400> 318
• <210> 319
  <211> 2148
  <212> DNA
  <213> Homo sapiens
• <400> 319
• <210> 320
  <211> 540
  <212> DNA
  <213> Homo sapiens
• <400> 320
• <210> 321
  <211> 2346
  <212> DNA
  <213> Homo sapiens
• <400> 321
• <210> 322
  <211> 2420
  <212> DNA
  <213> Homo sapiens
• <400> 322
• <210> 323
  <211> 2253
  <212> DNA
  <213> Homo sapiens
• <400> 323
• <210> 324
  <211> 1619
  <212> DNA
  <213> Homo sapiens
• <400> 324
• <210> 325
  <211> 5010
  <212> DNA
  <213> Homo sapiens
• <400> 325
• <210> 326
  <211> 2574
  <212> DNA
  <213> Homo sapiens
• <400> 326

• <210> 327
  <211> 1421
  <212> DNA
  <213> Homo sapiens
• <400> 327
• <210> 328
  <211> 4604
  <212> DNA
  <213> Homo sapiens
• <400> 328
• <210> 329
  <211> 2076
  <212> DNA
  <213> Homo sapiens
• <400> 329
• <210> 330
  <211> 2819
  <212> DNA
  <213> Homo sapiens
• <400> 330
• <210> 331
  <211> 2540
  <212> DNA
  <213> Homo sapiens
• <400> 331
• <210> 332
  <211> 1474
  <212> DNA
  <213> Homo sapiens
• <400> 332
• <210> 333
  <211> 4079
  <212> DNA
  <213> Homo sapiens
• <400> 333
• <210> 334
  <211> 3373
  <212> DNA
  <213> Homo sapiens
• <400> 334
• <210> 335
  <211> 2304
  <212> DNA
  <213> Homo sapiens
• <400> 335
• <210> 336
  <211> 1876
  <212> DNA
  <213> Homo sapiens
• <400> 336
• <210> 337
  <211> 6633
  <212> DNA
  <213> Homo sapiens
• <400> 337
• <210> 338
  <211> 994
  <212> DNA
  <213> Homo sapiens
• <400> 338

• <210> 339
  <211> 772
  <212> DNA
  <213> Homo sapiens
• <400> 339
• <210> 340
  <211> 919
  <212> DNA
  <213> Homo sapiens
• <400> 340
• <210> 341
  <211> 7365
  <212> DNA
  <213> Homo sapiens
• <400> 341
• <210> 342
  <211> 10386
  <212> DNA
  <213> Homo sapiens
• <220>
  <221> unsure
  <222> (0)...(0)
  <223> n = a, t, c or g
• <400> 342
• <210> 343
  <211> 2191
  <212> DNA
  <213> Homo sapiens
• <400> 343
• <210> 344
  <211> 2776
  <212> DNA
  <213> Homo sapiens
• <400> 344
• <210> 345
  <211> 3160
  <212> DNA
  <213> Homo sapiens
• <400> 345
• <210> 346
  <211> 2629
  <212> DNA
  <213> Homo sapiens
• <400> 346
• <210> 347
  <211> 3442
  <212> DNA
  <213> Homo sapiens
• <400> 347
• <210> 348
  <211> 737
  <212> DNA
  <213> Homo sapiens
• <400> 348
• <210> 349
  <211> 5189
  <212> ONA
  <213> Homo sapiens
• <400> 349

• <210> 350
  <211> 1536
  <212> DNA
  <213> Homo sapiens
• <400> 350
• <210> 351
  <211> 2386
  <212> DNA
  <213> Homo sapiens
• <400> 351
• <210> 352
  <211> 1270
  <212> DNA
  <213> Homo sapiens
• <400> 352
• <210> 353
  <211> 1600
  <212> DNA
  <213> Homo sapiens
• <400> 353
• <210> 354
  <211> 1842
  <212> DNA
  <213> Homo sapiens
• <400> 354
• <210> 355
  <211> 4975
  <212> DNA
  <213> Homo sapiens
• <400> 355
• <210> 356
  <211> 4627
  <212> DNA
  <213> Homo sapiens
• <400> 356
• <210> 357
  <211> 2634
  <212> DNA
  <213> Homo sapiens
• <400> 357
• <210> 358
  <211> 1246
  <212> DNA
  <213> Homo sapiens
• <400> 358
• <210> 359
  <211> 2360
  <212> DNA
  <213> Homo sapiens
• <400> 359
• <210> 360
  <211> 1433
  <212> DNA
  <213> Homo sapiens
• <400> 360
• <210> 361
  <211> 1632
  <212> DNA
  <213> Homo sapiens
• <400> 361
• <210> 362
  <211> 2756
  <212> DNA
  <213> Homo sapiens
• <400> 362

• <210> 363
  <211> 2768
  <212> DNA
  <213> Homo sapiens
• <400> 363
• <210> 364
  <211> 2984
  <212> DNA
  <213> Homo sapiens
• <400> 364
• <210> 365
  <211> 3061
  <212> DNA
  <213> Homo sapiens
• <400> 365
• <210> 366
  <211> 1360
  <212> DNA
  <213> Homo sapiens
• <400> 366
• <210> 367
  <211> 1412
  <212> DNA
  <213> Homo sapiens
• <400> 367
• <210> 368
  <211> 1075
  <212> DNA
  <213> Homo sapiens
• <400> 368
• <210> 369
  <211> 1127
  <212> DATA
  <213> Homo sapiens
• <400> 369
• <210> 370
  <211> 1890
  <212> DNA
  <213> Homo sapiens
• <400> 370
• <210> 371
  <211> 4946
  <212> DNA
  <213> Homo sapiens
• <400> 371
• <210> 372
  <211> 1743
  <212> DNA
  <213> Homo sapiens
• <400> 372
• <210> 373
  <211> 5061
  <212> DNA
  <213> Homo sapiens
• <400> 373
• <210> 374
  <211> 6802
  <212> DNA
  <213> Homo sapiens
• <400> 374
• <210> 375
  <211> 1840
  <212> DNA
  <213> Homo sapiens
• <400> 375

• <210> 376
  <211> 6754
  <212> DNA
  <213> Homo sapiens
• <400> 376
• <210> 377
  <211> 757
  <212> DNA
  <213> Homo sapiens
• <400> 377
• <210> 378
  <211> 476
  <212> DNA
  <213> Homo sapiens
• <400> 378
• <210> 379
  <211> 2518
  <212> DNA
  <213> Homo sapiens
• <400> 379
• <210> 380
  <211> 4160
  <212> DNA
  <213> Homo sapiens
• <400> 380
• <210> 381
  <211> 1295
  <212> DNA
  <213> Homo sapiens
• <400> 381
• <210> 382
  <211> 2210
  <212> DNA
  <213> Homo sapiens
• <400> 382
• <210> 383
  <211> 4604
  <212> DNA
  <213> Homo sapiens
• <400> 383
• <210> 384
  <211> 545
  <212> DNA
  <213> Homo sapiens
• <400> 384