Diagnosis and prognosis of breast cancer patients

Abstract

The present invention relates to genetic markers whose expression is correlated with breast cancer. Specifically, the invention provides sets of markers whose expression patterns can be used to differentiate clinical conditions associated with breast cancer, such as the presence or absence of the estrogen receptor ESR1, and BRCA1 and sporadic tumors, and to provide information on the likelihood of tumor distant metastases within five years of initial diagnosis. The invention relates to methods of using these markers to distinguish these conditions. The invention also provides methods of classifying and treating patients based on prognosis. The invention also relates to kits containing ready-to-use microarrays and computer software for data analysis using the diagnostic, prognostic and statistical methods disclosed herein.

Description

1. FIELD OF THE INVENTION

[0003] The present invention relates to the identification of marker genes useful in the diagnosis and prognosis of breast cancer. More particularly, the invention relates to the identification of a set of marker genes associated with breast cancer, a set of marker genes differentially expressed in estrogen receptor (+) versus estrogen receptor (−) tumors, a set of marker genes differentially expressed in BRCA1 versus sporadic tumors, and a set of marker genes differentially expressed in sporadic tumors from patients with good clinical prognosis (i.e., metastasis- or disease-free in at least 5 years of follow-up time since diagnosis) versus patients with poor clinical prognosis (i.e., metastasis or disease occurred within 5 years since diagnosis). For each of the marker sets above, the invention further relates to methods of distinguishing the breast cancer-related conditions. The invention further provides methods for determining the course of treatment of a patient with breast cancer.

2. BACKGROUND OF THE INVENTION

[0004] The increased number of cancer cases reported in the United States, and, indeed, around the world, is a major concern. Currently there are only a handful of treatments available for specific types of cancer, and these provide no guarantee of success. In order to be most effective, these treatments require not only an early detection of the malignancy, but a reliable assessment of the severity of the malignancy.

[0005] The incidence of breast cancer, a leading cause of death in women, has been gradually increasing in the United States over the last thirty years. Its cumulative risk is relatively high; 1 in 8 women are expected to develop some type of breast cancer by age 85 in the United States. In fact, breast cancer is the most common cancer in women and the second most common cause of cancer death in the United States. In 1997, it was estimated that 181,000 new cases were reported in the U.S., and that 44,000 people would die of breast cancer (Parker et al., CA Cancer J. Clin. 47:5-27 (1997); Chu et al., J Nat. Cancer Inst. 88:1571-1579 (1996)). While mechanism of tumorigenesis for most breast carcinomas is largely unknown, there are genetic factors that can predispose some women to developing breast cancer (Miki et al., Science, 266:66-71(1994)). The discovery and characterization of BRCA1 and BRCA2 has recently expanded our knowledge of genetic factors which can contribute to familial breast cancer. Germ-line mutations within these two loci are associated with a 50 to 85% lifetime risk of breast and/or ovarian cancer (Casey, Curr. Opin. Oncol. 9:88-93 (1997); Marcus et al., Cancer 77:697-709 (1996)). Only about 5% to 10% of breast cancers are associated with breast cancer susceptibility genes, BRCA1 and BRCA2. The cumulative lifetime risk of breast cancer for women who carry the mutant BRCA1 is predicted to be approximately 92%, while the cumulative lifetime risk for the non-carrier majority is estimated to be approximately 10%. BRCA1 is a tumor suppressor gene that is involved in DNA repair anc cell cycle control, which are both important for the maintenance of genomic stability. More than 90% of all mutations reported so far result in a premature truncation of the protein product with abnormal or abolished function. The histology of breast cancer in BRCA1 mutation carriers differs from that in sporadic cases, but mutation analysis is the only way to find the carrier. Like BRCA1, BRCA2 is involved in the development of breast cancer, and like BRCA1 plays a role in DNA repair. However, unlike BRCA1, it is not involved in ovarian cancer.

[0006] Other genes have been linked to breast cancer, for example c-erb-2 (HER2) and p53 (Beenken et al., Ann. Surg. 233(5):630-638 (2001). Overexpression of c-erb-2 (HER2) and p53 have been correlated with poor prognosis (Rudolph et al., Hum. Pathol. 32(3):311-319 (2001), as has been aberrant expression products of mdm2 (Lukas et al., Cancer Res. 61(7):3212-3219 (2001) and cyclin1 and p27 (Porter & Roberts, International Publication WO98/33450, published Aug. 6, 1998). However, no other clinically useful markers consistently associated with breast cancer have been identified.

[0007] Sporadic tumors, those not currently associated with a known germline mutation, constitute the majority of breast cancers. It is also likely that other, non-genetic factors also have a significant effect on the etiology of the disease. Regardless of the cancer's origin, breast cancer morbidity and mortality increases significantly if it is not detected early in its progression. Thus, considerable effort has focused on the early detection of cellular transformation and tumor formation in breast tissue.

[0008] A marker-based approach to tumor identification and characterization promises improved diagnostic and prognostic reliability. Typically, the diagnosis of breast cancer requires histopathological proof of the presence of the tumor. In addition to diagnosis, histopathological examinations also provide information about prognosis and selection of treatment regimens. Prognosis may also be established based upon clinical parameters such as tumor size, tumor grade, the age of the patient, and lymph node metastasis.

[0009] Diagnosis and/or prognosis may be determined to varying degrees of effectiveness by direct examination of the outside of the breast, or through mammography or other X-ray imaging methods (Jatoi, Am. J. Surg. 177:518-524 (1999)). The latter approach is not without considerable cost, however. Every time a mammogram is taken, the patient incurs a small risk of having a breast tumor induced by the ionizing properties of the radiation used during the test. In addition, the process is expensive and the subjective interpretations of a technician can lead to imprecision. For example, one study showed major clinical disagreements for about one-third of a set of mammograms that were interpreted individually by a surveyed group of radiologists. Moreover, many women find that undergoing a mammogram is a painful experience. Accordingly, the National Cancer Institute has not recommended mammograms for women under fifty years of age, since this group is not as likely to develop breast cancers as are older women. It is compelling to note, however, that while only about 22% of breast cancers occur in women under fifty, data suggests that breast cancer is more aggressive in pre-menopausal women. In clinical practice, accurate diagnosis of various subtypes of breast cancer is important because treatment options, prognosis, and the likelihood of therapeutic response all vary broadly depending on the diagnosis. Accurate prognosis, or determination of distant metastasis-free survival could allow the oncologist to tailor the administration of adjuvant chemotherapy, with women having poorer prognoses being given the most aggressive treatment. Furthermore, accurate prediction of poor prognosis would greatly impact clinical trials for new breast cancer therapies, because potential study patients could then be stratified according to prognosis. Trials could then be limited to patients having poor prognosis, in turn making it easier to discern if an experimental therapy is efficacious.

[0010] To date, no set of satisfactory predictors for prognosis based on the clinical information alone has been identified. The detection of BRCA1 or BRCA2 mutations represents a step towards the design of therapies to better control and prevent the appearance of these tumors. However, there is no equivalent means for the diagnosis of patients with sporadic tumors, the most common type of breast cancer tumor, nor is there a means of differentiating subtypes of breast cancer.

[0011] Adjuvant systemic therapy has been shown to substantially improve the disease-free and overall survival in both premenopausal and postmenopausal women up to age 70 with lymph node negative and lymph node positive breast cancer. See Early Breast Cancer Trialists' Collaborative Group, Lancet 352(9132):930-942 (1998); Early Breast Cancer Trialists' Collaborative Group, Lancet 351(9114):1451-1467 (1998). The absolute benefit from adjuvant treatment is larger for patients with poor prognostic features and this has resulted in the policy to select only these so-called ‘high-risk’ patients for adjuvant chemotherapy. Goldhirsch et al., Meeting highlights: International Consensus Panel on the Treatment of Primary Breast Cancer, Seventh International Conference on Adjuvant Therapy of Primary Breast Cancer, J. Clin. Oncol. 19(18):3817-3827 (2001); Eifel et al., National Institutes of Health Consensus Development Conference Statement: Adjuvant Therapy for Breast Cancer, Nov. 1-3, 2000, J. Natl. Cancer Inst. 93(13):979-989 (2001). Accepted prognostic and predictive factors in breast cancer include age, tumor size, axillary lymph node status, histological tumor type, pathological grade and hormone receptor status. A large number of other factors has been investigated for their potential to predict disease outcome, but these have in general only limited predictive power. Isaacs et al., Semin. Oncol. 28(1):53-67 (2001).

[0012] Using gene expression profiling with cDNA microarrays, Perou et al. showed that there are several subgroups of breast cancer patients based on unsupervised cluster analysis: those of “basal type” and those of “luminal type.” Perou et al., Nature 406(6797):747-752 (2000). These subgroups differ with respect to outcome of disease in patients with locally advanced breast cancer. Sorlie et al., Proc. Natl. Acad. Sci. U.S.A. 98(19):10869-10874 (2001). In addition, microarray analysis has been used to identify diagnostic categories, e.g., BRCA1 and 2 (Hedenfalk et al., N. Engl. J. Med. 344(8):539-548 (2001); van't Veer et al., Nature 415(6871):530-536 (2002)); estrogen receptor (Perou, supra; van't Veer, supra; Gruvberger et al., Cancer. Res. 61(16):5979-5984 (2001)) and lymph node status (West et al., Proc. Natl. Acad. Sci. U.S.A. 98(20):11462-11467 (2001); Ahr et al., Lancet 359(9301):131-132 (2002)).

3. SUMMARY OF THE INVENTION

[0013] The invention provides gene marker sets that distinguish various types and subtypes of breast cancer, and methods of use therefor. In one embodiment, the invention provides a method for classifying a cell sample as ER(+) or ER(−) comprising detecting a difference in the expression of a first plurality of genes relative to a control, said first plurality of genes consisting of at least 5 of the genes corresponding to the markers listed in Table 1. In specific embodiments, said plurality of genes consists of at least 50, 100, 200, 500, 1000, up to 2,460 of the gene markers listed in Table 1. In another specific embodiment, said plurality of genes consists of each of the genes corresponding to the 2,460 markers listed in Table 2. In another specific embodiment, said plurality consists of the 550 markers listed in Table 2. In another specific embodiment, said control comprises nucleic acids derived from a pool of tumors from individual sporadic patients. In another specific embodiment, said detecting comprises the steps of: (a) generating an ER(+) template by hybridization of nucleic acids derived from a plurality of ER(+) patients within a plurality of sporadic patients against nucleic acids derived from a pool of tumors from individual sporadic patients; (b) generating an ER(−) template by hybridization of nucleic acids derived from a plurality of ER(−) patients within said plurality of sporadic patients against nucleic acids derived from said pool of tumors from individual sporadic patients within said plurality; (c) hybridizing nucleic acids derived from an individual sample against said pool; and (d) determining the similarity of marker gene expression in the individual sample to the ER(+) template and the ER(−) template, wherein if said expression is more similar to the ER(+) template, the sample is classified as ER(+), and if said expression is more similar to the ER(−) template, the sample is classified as ER(−).

[0014] The invention further provides the above methods, applied to the classification of samples as BRCA1 or sporadic, and classifying patients as having good prognosis or poor prognosis. For the BRCA1/sporadic gene markers, the invention provides that the method may be used wherein the plurality of genes is at least 5, 20, 50, 100, 200 or 300 of the BRCA1/sporadic markers listed in Table 3. In a specific embodiment, the optimum 100 markers listed in Table 4 are used. For the prognostic markers, the invention provides that at least 5, 20, 50, 100, or 200 gene markers listed in Table 5 may be used. In a specific embodiment, the optimum 70 markers listed in Table 6 are used.

[0015] The invention further provides that markers may be combined. Thus, in one embodiment, at least 5 markers from Table 1 are used in conjunction with at least 5 markers from Table 3. In another embodiment, at least 5 markers from Table 5 are used in conjunction with at least 5 markers from Table 3. In another embodiment, at least 5 markers from Table 1 are used in conjunction with at least 5 markers from Table 5. In another embodiment, at least 5 markers from each of Tables 1, 3, and 5 are used simultaneously.

[0016] The invention further provides a method for classifying a sample as ER(+) or ER(−) by calculating the similarity between the expression of at least 5 of the markers listed in Table 1 in the sample to the expression of the same markers in an ER(−) nucleic acid pool and an ER(+) nucleic acid pool, comprising the steps of: (a) labeling nucleic acids derived from a sample, with a first fluorophore to obtain a first pool of fluorophore-labeled nucleic acids; (b) labeling with a second fluorophore a first pool of nucleic acids derived from two or more ER(+) samples, and a second pool of nucleic acids derived from two or more ER(−) samples; (c) contacting said first fluorophore-labeled nucleic acid and said first pool of second fluorophore-labeled nucleic acid with said first microarray under conditions such that hybridization can occur, and contacting said first fluorophore-labeled nucleic acid and said second pool of second fluorophore-labeled nucleic acid with said second microarray under conditions such that hybridization can occur, detecting at each of a plurality of discrete loci on the first microarray a first flourescent emission signal from said first fluorophore-labeled nucleic acid and a second fluorescent emission signal from said first pool of second fluorophore-labeled genetic matter that is bound to said first microarray under said conditions, and detecting at each of the marker loci on said second microarray said first fluorescent emission signal from said first fluorophore-labeled nucleic acid and a third fluorescent emission signal from said second pool of second fluorophore-labeled nucleic acid; (d) determining the similarity of the sample to the ER(−) and ER(+) pools by comparing said first fluorescence emission signals and said second fluorescence emission signals, and said first emission signals and said third fluorescence emission signals; and (e) classifying the sample as ER(+) where the first fluorescence emission signals are more similar to said second fluorescence emission signals than to said third fluorescent emission signals, and classifying the sample as ER(−) where the first fluorescence emission signals are more similar to said third fluorescence emission signals than to said second fluorescent emission signals, wherein said similarity is defined by a statistical method. The invention further provides that the other disclosed marker sets may be used in the above method to distinguish BRCA1 from sporadic tumors, and patients with poor prognosis from patients with good prognosis.

[0017] In a specific embodiment, said similarity is calculated by determining a first sum of the differences of expression levels for each marker between said first fluorophore-labeled nucleic acid and said first pool of second fluorophore-labeled nucleic acid, and a second sum of the differences of expression levels for each marker between said first fluorophore-labeled nucleic acid and said second pool of second fluorophore-labeled nucleic acid, wherein if said first sum is greater than said second sum, the sample is classified as ER(−), and if said second sum is greater than said first sum, the sample is classified as ER(+). In another specific embodiment, said similarity is calculated by computing a first classifier parameter P1 between an ER(+) template and the expression of said markers in said sample, and a second classifier parameter P2 between an ER(−) template and the expression of said markers in said sample, wherein said P1 and P2 are calculated according to the formula:

P i =({right arrow over (z)}i•{right arrow over (y)})/(∥{right arrow over (z)}i∥·∥{right arrow over (y)}∥),  Equation (1)

[0018] wherein {right arrow over (z)}1 and {right arrow over (z)}2 are ER(−) and ER(+) templates, respectively, and are calculated by averaging said second fluorescence emission signal for each of said markers in said first pool of second fluorophore-labeled nucleic acid and said third fluorescence emission signal for each of said markers in said second pool of second fluorophore-labeled nucleic acid, respectively, and wherein {right arrow over (y)} is said first fluorescence emission signal of each of said markers in the sample to be classified as ER(+) or ER(−), wherein the expression of the markers in the sample is similar to ER(+) if P1<P2, and similar to ER(−) if P1>P2.

[0019] The invention further provides a method for identifying marker genes the expression of which is associated with a particular phenotype. In one embodiment, the invention provides a method for determining a set of marker genes whose expression is associated with a particular phenotype, comprising the steps of: (a) selecting the phenotype having two or more phenotype categories; (b) identifying a plurality of genes wherein the expression of said genes is correlated or anticorrelated with one of the phenotype categories, and wherein the correlation coefficient for each gene is calculated according to the equation

ρ=({right arrow over (c)}•{right arrow over (r)})/(∥{right arrow over (c)}∥·∥{right arrow over (r)}∥)  Equation (2)

[0020] wherein {right arrow over (c)} is a number representing said phenotype category and {right arrow over (r)} is the logarithmic expression ratio across all the samples for each individual gene, wherein if the correlation coefficient has an absolute value of a threshold value or greater, said expression of said gene is associated with the phenotype category, and wherein said plurality of genes is a set of marker genes whose expression is associated with a particular phenotype. The threshold depends upon the number of samples used; the threshold can be calculated as 3×1/{square root}{square root over (n−3)}, where 1/{square root}{square root over (n−3)} is the distribution width and n=the number of samples. In a specific embodiment where n=98, said threshold value is 0.3. In a specific embodiment, said set of marker genes is validated by: (a) using a statistical method to randomize the association between said marker genes and said phenotype category, thereby creating a control correlation coefficient for each marker gene; (b) repeating step (a) one hundred or more times to develop a frequency distribution of said control correlation coefficients for each marker gene; (c) determining the number of marker genes having a control correlation coefficient of a threshold value or above, thereby creating a control marker gene set; and (d) comparing the number of control marker genes so identified to the number of marker genes, wherein if the p value of the difference between the number of marker genes and the number of control genes is less than 0.01, said set of marker genes is validated. In another specific embodiment, said set of marker genes is optimized by the method comprising: (a) rank-ordering the genes by amplitude of correlation or by significance of the correlation coefficients, and (b) selecting an arbitrary number of marker genes from the top of the rank-ordered list. The threshold value depends upon the number of samples tested.

[0021] The invention further provides a method for assigning a person to one of a plurality of categories in a clinical trial, comprising determining for each said person the level of expression of at least five of the prognosis markers listed in Table 6, determining therefrom whether the person has an expression pattern that correlates with a good prognosis or a poor prognosis, and assigning said person to one category in a clinical trial if said person is determined to have a good prognosis, and a different category if that person is determined to have a poor prognosis. The invention further provides a method for assigning a person to one of a plurality of categories in a clinical trial, where each of said categories is associated with a different phenotype, comprising determining for each said person the level of expression of at least five markers from a set of markers, wherein said set of markers includes markers associated with each of said clinical categories, determining therefrom whether the person has an expression pattern that correlates with one of the clinical categories, an assigning said person to one of said categories if said person is determined to have a phenotype associated with that category. The invention further provides a method of classifying a first cell or organism as having one of at least two different phenotypes, said at least two different phenotypes comprising a first phenotype and a second phenotype, said method comprising:

[0022] (a) comparing the level of expression of each of a plurality of genes in a first sample from the first cell or organism to the level of expression of each of said genes, respectively, in a pooled sample from a plurality of cells or organisms, said plurality of cells or organisms comprising different cells or organisms exhibiting said at least two different phenotypes, respectively, to produce a first compared value; (b) comparing said first compared value to a second compared value, wherein said second compared value is the product of a method comprising comparing the level of expression of each of said genes in a sample from a cell or organism characterized as having said first phenotype to the level of expression of each of said genes, respectively, in said pooled sample; (c) comparing said first compared value to a third compared value, wherein said third compared value is the product of a method comprising comparing the level of expression of each of said genes in a sample from a cell or organism characterized as having said second phenotype to the level of expression of each of said genes, respectively, in said pooled sample, (d) optionally carrying out one or more times a step of comparing said first compared value to one or more additional compared values, respectively, each additional compared value being the product of a method comprising comparing the level of expression of each of said genes in a sample from a cell or organism characterized as having a phenotype different from said first and second phenotypes but included among said at least two different phenotypes, to the level of expression of each of said genes, respectively, in said pooled sample; and (e) determining to which of said second, third and, if present, one or more additional compared values, said first compared value is most similar, wherein said first cell or organism is determined to have the phenotype of the cell or organism used to produce said compared value most similar to said first compared value.

[0023] In a specific embodiment of the above method, said compared values are each ratios of the levels of expression of each of said genes. In another specific embodiment, each of said levels of expression of each of said genes in said pooled sample are normalized prior to any of said comparing steps. In another specific embodiment, normalizing said levels of expression is carried out by dividing each of said levels of expression by the median or mean level of expression of each of said genes or dividing by the mean or median level of expression of one or more housekeeping genes in said pooled sample. In a more specific embodiment, said normalized levels of expression are subjected to a log transform and said comparing steps comprise subtracting said log transform from the log of said levels of expression of each of said genes in said sample from said cell or organism. In another specific embodiment, said at least two different phenotypes are different stages of a disease or disorder. In another specific embodiment, said at least two different phenotypes are different prognoses of a disease or disorder. In yet another specific embodiment, said levels of expression of each of said genes, respectively, in said pooled sample or said levels of expression of each of said genes in a sample from said cell or organism characterized as having said first phenotype, said second phenotype, or said phenotype different from said first and second phenotypes, respectively, are stored on a computer.

[0024] The invention further provides microarrays comprising the disclosed marker sets. In one embodiment, the invention provides a microarray comprising at least 5 markers derived from any one of Tables 1-6, wherein at least 50% of the probes on the microarray are present in any one of Tables 1-6. In more specific embodiments, at least 60%, 70%, 80%, 90%, 95% or 98% of the probes on said microarray are present in any one of Tables 1-6.

[0025] In another embodiment, the invention provides a microarray for distinguishing ER(+) and ER(−) cell samples comprising a positionally-addressable array of polynucleotide probes bound to a support, said polynucleotide probes comprising a plurality of polynucleotide probes of different nucleotide sequences, each of said different nucleotide sequences comprising a sequence complementary and hybridizable to a plurality of genes, said plurality consisting of at least 5 of the genes corresponding to the markers listed in Table 1 or Table 2, wherein at least 50% of the probes on the microarray are present in any one of Table 1 or Table 2. In yet another embodiment, the invention provides a microarray for distinguishing BRCA1-type and sporadic tumor-type cell samples comprising a positionally-addressable array of polynucleotide probes bound to a support, said polynucleotide probes comprising a plurality of polynucleotide probes of different nucleotide sequences, each of said different nucleotide sequences comprising a sequence complementary and hybridizable to a plurality of genes, said plurality consisting of at least 5 of the genes corresponding to the markers listed in Table 3 or Table 4, wherein at least 50% of the probes on the microarray are present in any one of Table 3 or Table 4. In still another embodiment, the invention provides a microarray for distinguishing cell samples from patients having a good prognosis and cell samples from patients having a poor prognosis comprising a positionally-addressable array of polynucleotide probes bound to a support, said polynucleotide probes comprising a plurality of polynucleotide probes of different nucleotide sequences, each of said different nucleotide sequences comprising a sequence complementary and hybridizable to a plurality of genes, said plurality consisting of at least 5 of the genes corresponding to the markers listed in Table 5 or Table 6, wherein at least 50% of the probes on the microarray are present in any one of Table 5 or Table 6. The invention further provides for microarrays comprising at least 5, 20, 50, 100, 200, 500, 100, 1,250, 1,500, 1,750, or 2,000 of the ER-status marker genes listed in Table 1, at least 5, 20, 50, 100, 200, or 300 of the BRCA1 sporadic marker genes listed in Table 3, or at least 5, 20, 50, 100 or 200 of the prognostic marker genes listed in Table 5, in any combination, wherein at least 50%, 60%, 70%, 80%, 90%, 95% or 98% of the probes on said microarrays are present in Table 1, Table 3 and/or Table 5.

[0026] The invention further provides a kit for determining the ER-status of a sample, comprising at least two microarrays each comprising at least 5 of the markers listed in Table 1, and a computer system for determining the similarity of the level of nucleic acid derived from the markers listed in Table 1 in a sample to that in an ER(−) pool and an ER(+) pool, the computer system comprising a processor, and a memory encoding one or more programs coupled to the processor, wherein the one or more programs cause the processor to perform a method comprising computing the aggregate differences in expression of each marker between the sample and ER(−) pool and the aggregate differences in expression of each marker between the sample and ER(+) pool, or a method comprising determining the correlation of expression of the markers in the sample to the expression in the ER(−) and ER(+) pools, said correlation calculated according to Equation (4). The invention provides for kits able to distinguish BRCA1 and sporadic tumors, and samples from patients with good prognosis from samples from patients with poor prognosis, by inclusion of the appropriate marker gene sets. The invention further provides a kit for determining whether a sample is derived from a patient having a good prognosis or a poor prognosis, comprising at least one microarray comprising probes to at least 5 of the genes corresponding to the markers listed in Table 5, and a computer readable medium having recorded thereon one or more programs for determining the similarity of the level of nucleic acid derived from the markers listed in Table 5 in a sample to that in a pool of samples derived from individuals having a good prognosis and a pool of samples derived from individuals having a good prognosis, wherein the one or more programs cause a computer to perform a method comprising computing the aggregate differences in expression of each marker between the sample and the good prognosis pool and the aggregate differences in expression of each marker between the sample and the poor prognosis pool, or a method comprising determining the correlation of expression of the markers in the sample to the expression in the good prognosis and poor prognosis pools, said correlation calculated according to Equation (3).

[0027] The invention further provides a method for classifying a breast cancer patient according to prognosis, comprising: (a) comparing the respective levels of expression of at least five genes for which markers are listed in Table 5 in a cell sample taken from said breast cancer patient to respective control levels of expression of said at least five genes; and (b) classifying said breast cancer patient according to prognosis of his or her breast cancer based on the similarity between said levels of expression in said cell sample and said control levels. In a specific embodiment of this method, step (b) comprises determining whether said similarity exceeds one or more predetermined threshold values of similarity. In another more specific embodiment of this method, said control levels are the mean levels of expression of each of said at least five genes in a pool of tumor samples obtained from a plurality of breast cancer patients who have no distant metastases within five years of initial diagnosis. In another specific embodiment of this method, said control levels comprise the expression levels of said genes in breast cancer patients who have had no distant metastases within five years of initial diagnosis. In another specific embodiment of this method, said control levels comprise, for each of said at least five genes, mean log intensity values stored on a computer. In another specific embodiment of this method, said control levels comprise, for each of said at least five genes, the mean log intensity values that are listed in Table 7. In another specific embodiment of this method, said comparing step (a) comprises comparing the respective levels of expression of at least ten of said genes for which markers are listed in Table 5 in said cell sample to said respective control levels of said at least ten of said genes, wherein said control levels of expression of said at least ten genes are the average expression levels of each of said at least ten genes in a pool of tumor samples obtained from breast cancer patients who have had no distant metastases within five years of initial diagnosis. In another specific embodiment of this method, said comparing step (a) comprises comparing the respective levels of expression of at least 25 of said genes for which markers are listed in Table 5 in said cell sample to said respective control levels of expression of said at least 25 genes, wherein said control levels of expression of said at least 25 genes are the average expression levels of each of said at least 25 genes in a pool of tumor samples obtained from breast cancer patients who have had no distant metastases within five years of initial diagnosis. In another specific embodiment of this method, said comparing step (a) comprises comparing the respective levels of expression of each of said genes for which markers are listed in Table 6 in said cell sample to said respective control levels of expression of each of said genes for which markers are listed in Table 6, wherein said control levels of expression of each of said genes for which markers are listed in Table 6 are the average expression levels of each of said genes in a pool of tumor samples obtained from breast cancer patients who have had no distant metastases within five years of initial diagnosis.

[0028] The invention further provides for a method for classifying a breast cancer patient according to prognosis, comprising: (a) determining the similarity between the level of expression of each of at least five genes for which markers are listed in Table 5 in a cell sample taken from said breast cancer patient, to control levels of expression for each respective said at least five genes to obtain a patient similarity value; (b) providing selected first and second threshold values of similarity of said level of expression of each of said at least five genes to said control levels of expression to obtain first and second similarity threshold values, respectively, wherein said second similarity threshold indicates greater similarity to said control than does said first similarity threshold; and (c) classifying said breast cancer patient as having a first prognosis if said patient similarity value exceeds said first and said second similarity threshold values, a second prognosis if said level of expression of said genes exceeds said first similarity threshold value but does not exceed said second similarity threshold value, and a third prognosis if said level of expression of said genes does not exceed said first similarity threshold value or said second similarity threshold value. A specific embodiment of this method comprises determining, prior to step (a), said level of expression of said at least five genes. In another specific embodiment of this method, said determining in step (a) is carried out by a method comprising determining the degree of similarity between the level of expression of each of said at least five genes in a sample taken from said breast cancer patient to the level of expression of each of said at least five genes in a plurality of breast cancer patients who have had no relapse of breast cancer within five years of initial diagnosis. In another specific embodiment of this method, said determining in step (a) is carried out by a method comprising determining the difference between the absolute expression level of each of said at least five genes and the average expression level of each of said at least five genes in a pool of tumor samples obtained from a plurality of breast cancer patients who have had no relapse of breast cancer within five years of initial diagnosis. In another specific embodiment of this method, said first threshold value and said second threshold value are coefficients of correlation to the mean expression level of each of said at least five genes in a pool of tumor samples obtained from a plurality of breast cancer patients who have had no relapse of breast cancer within five years of initial diagnosis. In a more specific embodiment of this method, said first threshold similarity value and said second threshold similarity values are selected by a method comprising: (a) rank ordering in descending order said tumor samples that compose said pool of tumor samples by the degree of similarity between the level of expression of each said at least five genes in each of said tumor samples to the mean level of expression of said at least five genes of the remaining tumor samples that compose said pool to obtain a rank-ordered list, said degree of similarity being expressed as a similarity value; (b) determining an acceptable number of false negatives in said classifying step, wherein a false negative is a breast cancer patient for whom the expression levels of said at least five genes in said cell sample predicts that said breast cancer patient will have no distant metastases within the first five years after initial diagnosis, but who has had a distant metastasis within the first five years after initial diagnosis; (c) determining a similarity value above which in said rank ordered list fewer than said acceptable number of tumor samples are false negatives; (d) selecting said similarity value determined in step (c) as said first threshold similarity value; and (e) selecting a second similarity value, greater than said first similarity value, as said second threshold similarity value. In an even more specific embodiment of this method, said second threshold similarity value is selected in step (e) by a method comprising determining which of said tumor samples, taken from said breast cancer patients having a distant metastasis within the first five years after initial diagnosis, in said rank ordered list has the greatest similarity value, and selecting said greatest similarity value as said second threshold similarity value. In another even more specific embodiment of this method, said first and second threshold similarity values are correlation coefficients, and said first threshold similarity value is 0.4 and said second threshold similarity value is greater than 0.4. In another even more specific embodiment of this method, said first and second threshold similarity values are correlation coefficients, and said second threshold similarity value is 0.636.

[0029] The invention further provides a method of classifying a breast cancer patient according to prognosis comprising the steps of: (a) contacting first nucleic acids derived from a tumor sample taken from said breast cancer patient, and second nucleic acids derived from two or more tumor samples from breast cancer patients who have had no distant metastases within five years of initial diagnosis, with an array under conditions such that hybridization can occur, said array comprising a positionally-addressable ordered array of polynucleotide probes bound to a solid support, said polynucleotide probes being complementary and hybridizable to at least five of the genes respectively for which markers are listed in Table 5, or the RNA encoded by said genes, and wherein at least 50% of the probes on said array are hybridizable to genes respectively for which markers are listed in Table 5, or to the RNA encoded by said genes; (b) detecting at each of a plurality of discrete loci on said array a first fluorescent emission signal from said first nucleic acids and a second fluorescent emission signal from said second nucleic acids that are bound to said array under said conditions; (c) calculating the similarity between said first fluorescent emission signals and said second fluorescent emission signals across said at least five genes respectively for which markers are listed in Table 5; and (d)classifying said breast cancer patient according to prognosis of his or her breast cancer based on the similarity between said first fluorescent emission signals and said second fluorescent emission signals across said at least five genes respectively for which markers are listed in Table 5.

[0030] The invention further provides for methods of assigning therapeutic regimen to breast cancer patients. In one embodiment, the invention provides a method of assigning a therapeutic regimen to a breast cancer patient, comprising: (a) classifying said patient as having a “poor prognosis,” “intermediate prognosis,” or “very good prognosis” on the basis of the levels of expression of at least five genes for which markers are listed in Table 5; and (b) assigning said patient a therapeutic regimen, said therapeutic regimen (i) comprising no adjuvant chemotherapy if the patient is lymph node negative and is classified as having a good prognosis or an intermediate prognosis, or (ii) comprising chemotherapy if said patient has any other combination of lymph node status and expression profile.

[0031] The invention also provides a method of assigning a therapeutic regimen to a breast cancer patient, comprising: (a) determining the lymph node status for said patient; (b) determining the level of expression of at least five genes for which markers are listed in Table 5 in a cell sample from said patient, thereby generating an expression profile; (c) classifying said patient as having a “poor prognosis,” “intermediate prognosis,” or “very good prognosis” on the basis of said expression profile; and (d) assigning said patient a therapeutic regimen, said therapeutic regimen comprising no adjuvant chemotherapy if the patient is lymph node negative and is classified as having a good prognosis or an intermediate prognosis, or comprising chemotherapy if said patient has any other combination of lymph node status and classification. In a specific embodiment of this method, said therapeutic regimen assigned to lymph node negative patients classified as having an “intermediate prognosis” additionally comprises adjuvant hormonal therapy. In another specific embodiment of this method, said classifying step (c) is carried out by a method comprising: (a) rank ordering in descending order a plurality of breast cancer tumor samples that compose a pool of breast cancer tumor samples by the degree of similarity between the level of expression of said at least five genes in each of said tumor samples and the level of expression of said at least five genes across all remaining tumor samples that compose said pool, said degree of similarity being expressed as a similarity value; (b) determining an acceptable number of false negatives in said classifying step, wherein a false negative is a breast cancer patient for whom the expression levels of said at least five genes in said cell sample predicts that said breast cancer patient will have no distant metastases within the first five years after initial diagnosis, but who has had a distant metastasis within the first five years after initial diagnosis; (c) determining a similarity value above which in said rank ordered list said acceptable number of tumor samples or fewer are false negatives; (d) selecting said similarity value determined in step (c) as a first threshold similarity value; (e) selecting a second similarity value, greater than said first similarity value, as a second threshold similarity value; and (f) determining the similarity between the level of expression of each of said at least five genes in a breast cancer tumor sample from the breast cancer patient and the level of expression of each of said respective at least five genes in said pool, to obtain a patient similarity value, wherein if said patient similarity value equals or exceeds said second threshold similarity value, said patient is classified as having a “very good prognosis”; if said patient similarity value equals or exceeds said first threshold similarity value, but is less than said second threshold similarity value, said patient is classified as having an “intermediate prognosis”; and if said patient similarity value is less than said first threshold similarity value, said patient is classified as having a “poor prognosis.” Another specific embodiment of this method comprises determining the estrogen receptor (ER) status of said patient, wherein if said patient is ER positive and lymph node negative, said therapeutic regimen assigned to said patient additionally comprises adjuvant hormonal therapy. In another specific embodiment of this method, said patient is 52 years of age or younger. In another specific embodiment of this method, said patient has stage I or stage II breast cancer. In yet another specific embodiment of this method, said patient is premenopausal.

[0032] The above methods may be computer-implemented. Thus, in another embodiment, the invention provides a computer program product for classifying a breast cancer patient according to prognosis, the computer program product for use in conjunction with a computer having a memory and a processor, the computer program product comprising a computer readable storage medium having a computer program encoded thereon, wherein said computer program product can be loaded into the one or more memory units of a computer and causes the one or more processor units of the computer to execute the steps of: (a) receiving a first data structure comprising the respective levels of expression of each of at least five genes for which markers are listed in Table 5 in a cell sample taken from said patient; (b) determining the similarity of the level of expression of each of said at least five genes to respective control levels of expression of said at least five genes to obtain a patient similarity value; (c) comparing said patient similarity value to selected first and second threshold values of similarity of said respective levels of expression of each of said at least five genes to said respective control levels of expression of said at least five genes, wherein said second threshold value of similarity indicates greater similarity to said respective control levels of expression of said at least five genes than does said first threshold value of similarity; and (d) classifying said patient as having a first prognosis if said patient similarity value exceeds said first and said second threshold similarity values; a second prognosis if said patient similarity value exceeds said first threshold similarity value but does not exceed said second threshold similarity value; and a third prognosis if said patient similarity value does not exceed said first threshold similarity value or said second threshold similarity value. In a specific embodiment of the computer program product, said first threshold value of similarity and said second threshold value of similarity are values stored in said computer. In another specific embodiment of the computer program product, said respective control levels of expression of said at least five genes is stored in said computer. In another specific embodiment of the computer program product, said first prognosis is a “very good prognosis”; said second prognosis is an “intermediate prognosis”; and said third prognosis is a “poor prognosis”; wherein said computer program may be loaded into the memory and further cause said one or more processor units of said computer to execute the step of assigning said breast cancer patient a therapeutic regimen comprising no adjuvant chemotherapy if the patient is lymph node negative and is classified as having a good prognosis or an intermediate prognosis, or comprising chemotherapy if said patient has any other combination of lymph node status and expression profile. In a more specific embodiment, said clinical data includes the lymph node and estrogen receptor (ER) status of said breast cancer patient. In yet another specific embodiment, said computer program may be loaded into the memory and further causes said one or more processor units of the computer to execute the steps of receiving a data structure comprising clinical data specific to said breast cancer patient. In another specific embodiment, said respective control levels of expression of said at least five genes comprises a set of single-channel mean hybridization intensity values for each of said at least five genes, stored on said computer readable storage medium. In a more specific embodiment of this computer program product, said single-channel mean hybridization intensity values are log transformed. In another specific embodiment of the computer program product, said computer program product causes said processing unit to perform said comparing step (c) by calculating the difference between the level of expression of each of said at least five genes in said cell sample taken from said breast cancer patient and said respective control levels of expression of said at least five genes. In another specific embodiment of the computer program product, said computer program product causes said processing unit to perform said comparing step (c) by calculating the mean log level of expression of each of said at least five genes in said control to obtain a control mean log expression level for each gene, calculating the log expression level for each of said at least five genes in a breast cancer sample from said patient to obtain a patient log expression level, and calculating the difference between the patient log expression level and the control mean log expression for each of said at least five genes. In another specific embodiment of the computer program product, said computer program product causes said processing unit to perform said comparing step (c) by calculating similarity between the level of expression of each of said at least five genes in said cell sample taken from said patient and said respective control levels of expression of said at least five genes, wherein said similarity is expressed as a similarity value. In a more specific embodiment of this computer program product, said similarity value is a correlation coefficient.

4. BRIEF DESCRIPTION OF THE FIGURES

[0033] FIG. 1 is a Venn-type diagram showing the overlap between the marker sets disclosed herein, including the 2,460 ER markers, the 430 BRCA1/sporadic markers, and the 231 prognosis reporters.

[0034] FIG. 2 shows the experimental procedures for measuring differential changes in mRNA transcript abundance in breast cancer tumors used in this study. In each experiment, Cy5-labeled cRNA from one tumor X is hybridized on a 25 k human microarray together with a Cy3-labeled cRNA pool made of cRNA samples from tumors 1, 2, . . . N. The digital expression data were obtained by scanning and image processing. The error modeling allowed us to assign a p-value to each transcript ratio measurement.

[0035] FIG. 3 Two-dimensional clustering reveals two distinctive types of tumors. The clustering was based on the gene expression data of 98 breast cancer tumors over 4986 significant genes. Dark gray (red) presents up-regulation, light gray (green) represents down-regulation, black indicates no change in expression, and gray indicates that data is not available. 4986 genes were selected that showed a more than two fold change in expression ratios in more than five experiments. Selected clinical data for test results of BR CAI mutations, estrogen receptor (ER), and proestrogen receptor (PR), tumor grade, lymphocytic infiltrate, and angioinvasion are shown at right. Black denotes negative and white denotes positive. The dominant pattern in the lower part consists of 36 patients, out of which 34 are ER-negative (total 39), and 16 are BR CA1-mutation carriers (total 18).

[0036] FIG. 4A portion of unsupervised clustered results as shown in FIG. 3. ESR1 (the estrogen receptor gene) is coregulated with a set of genes that are strongly co-regulated to form a dominant pattern.

[0037] FIG. 5A Histogram of correlation coefficients of significant genes between their expression ratios and estrogen-receptor (ER) status (i.e., ER level). The histogram for experimental data is shown as a gray line. The results of one Monte-Carlo trial is shown in solid black. There are 2,460 genes whose expression data correlate with ER status at a level higher than 0.3 or anti-correlated with ER status at a level lower than −0.3.

[0038] FIG. 5B The distribution of the number of genes that satisfied the same selection criteria (amplitude of correlation above 0.3) from 10,000 Monte-Carlo runs. It is estimated that this set of 2,460 genes reports ER status at a confidence level of p>99.99%.

[0039] FIG. 6 Classification Type 1 and Type 2 error rates as a function of the number (out of 2,460) marker genes used in the classifier. The combined error rate is lowest when approximately 550 marker genes are used.

[0040] FIG. 7 Classification of 98 tumor samples as ER(+) or ER(−) based on expression levels of the 550 optimal marker genes. ER(+) samples (above white line) exhibit a clearly different expression pattern that ER(−) samples (below white line).

[0041] FIG. 8 Correlation between expression levels in samples from each patient and the average profile of the ER(−) group vs. correlation with the ER(+) group. Squares represent samples from clinically ER(−) patients; dots represent samples from clinically ER(+) patients.

[0042] FIG. 9A Histogram of correlation coefficients of gene expression ratio of each significant gene with the BRCA1 mutation status is shown as a solid line. The dashed line indicates a frequency distribution obtained from one Monte-Carlo run. 430 genes exhibited an amplitude of correlation or anti-correlation greater than 0.35.

[0043] FIG. 9B Frequency distribution of the number of genes that exhibit an amplitude of correlation or anti-correlation greater than 0.35 for the 10,000 Monte-Carlo run control. Mean=115 p(n>430)=0.48% and p(>430/2)=9.0%.

[0044] FIG. 10 Classification type 1 and type 2 error rates as a function of the number of discriminating genes used in the classifier (template). The combined error rate is lowest when approximately 100 discriminating marker genes are used.

[0045] FIG. 11A The classification of 38 tumors in the ER(−) group into two subgroups, BR CA1 and sporadic, by using the optimal set of 100 discriminating marker genes. Patients above the white line are characterized by BRCA1-related patterns.

[0046] FIG. 11B Correlation between expression levels in samples from each ER(−) patient and the average profile of the BRCA1 group vs. correlation with the sporadic group.

[0047] Squares represent samples from patients with sporadic-type tumors; dots represent samples from patients carrying the BRCA1 mutation.

[0048] FIG. 12A Histogram of correlation coefficients of gene expression ratio of each significant gene with the prognostic category (distant metastases group and no distant metastases group) is shown as a solid line. The distribution obtained from one Monte-Carlo run is shown as a dashed line. The amplitude of correlation or anti-correlation of 231 marker genes is greater than 0.3.

[0049] FIG. 12B Frequency distribution of the number of genes whose amplitude of correlation or anti-correlation was greater than 0.3 for 10,000 Monte-Carlo runs.

[0050] FIG. 13 The distant metastases group classification error rate for type 1 and type 2 as a function of the number of discriminating genes used in the classifier. The combined error rate is lowest when approximately 70 discriminating marker genes are used.

[0051] FIG. 14 Classification of 78 sporadic tumors into two prognostic groups, distant metastases (poor prognosis) and no distant metastases (good prognosis) using the optimal set of 70 discriminating marker genes. Patients above the white line are characterized by good prognosis. Patients below the white line are characterized by poor prognosis.

[0052] FIG. 15 Correlation between expression levels in samples from each patient and the average profile of the good prognosis group vs. correlation with the poor prognosis group. Squares represent samples from patients having a poor prognosis; dots represent samples from patients having a good prognosis. Red squares represent the ‘reoccurred’ patients and the blue dots represent the ‘non-reoccurred’. A total of 13 out of 78 were mis-classified.

[0053] FIG. 16 The reoccurrence probability as a function of time since diagnosis. Group A and group B were predicted by using a leave-one-out method based on the optimal set of 70 discriminating marker genes. The 43 patients in group A consists of 37 patients from the no distant metastases group and 6 patients from the distant metastases group. The 35 patients in group B consists of 28 patients from the distant metastases group and 7 patients from the no distant metastases group.

[0054] FIG. 17 The distant metastases probability as a function of time since diagnosis for ER(+) (yes) or ER(−) (no) individuals.

[0055] FIG. 18 The distant metastases probability as a function of time since diagnosis for progesterone receptor (PR)(+) (yes) or PR(−) (no) individuals.

[0056] FIGS. 19A, B The distant metastases probability as a function of time since diagnosis. Groups were defined by the tumor grades.

[0057] FIG. 20A Classification of 19 independent sporadic tumors into two prognostic groups, distant metastases and no distant metastases, using the 70 optimal marker genes. Patients above the white line have a good prognosis. Patients below the white line have a poor prognosis.

[0058] FIG. 20B Correlation between expression ratios of each patient and the average expression ratio of the good prognosis group is defined by the training set versus the correlation between expression ratios of each patient and the average expression ratio of the poor prognosis training set. Of nine patients in the good prognosis group, three are from the “distant metastases group”; of ten patients in the good prognosis group, one patient is from the “no distant metastases group”. This error rate of 4 out of 19 is consistent with 13 out of 78 for the initial 78 patients.

[0059] FIG. 20C The reoccurrence probability as a function of time since diagnosis for two groups predicted based on expression of the optimal 70 marker genes.

[0060] FIG. 21A Sensitivity vs. 1-specificity for good prognosis classification.

[0061] FIG. 21B Sensitivity vs. 1-specificity for poor prognosis classification.

[0062] FIG. 21C Total error rate as a function of threshold on the modeled likelihood. Six clinical parameters (ER status, PR status, tumor grade, tumor size, patient age, and presence or absence of angioinvasion) were used to perform the clinical modeling.

[0063] FIG. 22 Comparison of the log(ratio) of individual samples using the “material sample pool” vs. mean subtracted log(intensity) using the “mathematical sample pool” for 70 reporter genes in the 78 sporadic tumor samples. The “material sample pool” was constructed from the 78 sporadic tumor samples.

[0064] FIG. 23A Results of the “leave one out” cross validation based on single channel data. Samples are grouped according to each sample's coefficient of correlation to the average “good prognosis” profile and “poor prognosis” profile for the 70 genes examined. The white line separates samples from patients classified as having poor prognoses (below) and good prognoses (above).

[0065] FIG. 23B Scatter plot of coefficients of correlation to the average expression in “good prognosis” samples and “poor prognosis” samples. The false positive rate (i.e., rate of incorrectly classifying a sample as being from a patient having a good prognosis as being one from a patient having a poor prognosis) was 10 out of 44, and the false negative rate is 6 out of 34.

[0066] FIG. 24A Single-channel hybridization data for samples ranked according to the coefficients of correlation with the good prognosis classifier. Samples classified as “good prognosis” lie above the white line, and those classified as “poor prognosis” lie below.

[0067] FIG. 24B Scatterplot of sample correlation coefficients, with three incorrectly classified samples lying to the right of the threshold correlation coefficient value. The threshold correlation value was set at 0.2727 to limit the false negatives to approximately 10% of the samples.

[0068] FIG. 25A Gene expression pattern of the 70 optimal prognosis marker genes (see Example 4) for a consecutive series of 295 breast carcinomas. Each row represents a prognostic profile of the 70 marker genes for one tumor and each column represents the relative expression abundance of one gene. Red indicates high mRNA expression in the tumor relative to the reference mRNA (pooled mRNA from all tumor samples); green indicates low expression relative to the reference mRNA. The horizontal dotted line is the previously determined separation between good and poor prognosis signature subgroups. Tumors are rank-ordered according to their correlation with the average profile in tumors of good prognosis patients (CI); the most highly correlated tumors lie at the top of the plot.

[0069] FIG. 25B Time in years to distant metastases as a first event (red dots) or the time of follow-up for all other patients (blue dots).

[0070] FIG. 25C Selected clinical characteristics: lymph node status (blue=pN+, white=pN0); metastases as first event (blue=yes, white=no); death (blue=yes, white=no).

[0071] FIGS. 26A-26F Kaplan-Meier plots for the cohort of 295 breast cancer patients. FIG. 26A shows the metastasis-free probability of all 295 patients according to “good prognosis” (n=115, upper line) and “poor prognosis” (n=180, lower line) signature. FIG. 26B shows the overall survival of all 295 patients according to “good prognosis” and “poor prognosis” signature. FIG. 26C shows the metastasis-free probability of lymph node negative patients within the 295 tumor cohort. FIG. 26D shows the overall survival of lymph node negative patients. FIG. 26E shows the metastasis-free probability for lymph node positive patients. FIG. 26F shows the overall survival of lymph node positive patients. For each of the plots, the number of patients who are metastasis-free (FIGS. 26A, C, E) or have survived (FIGS. 26B, D, F), and for whom information is available, at each time point (years) are indicated for “good signature” patients (upper line; upper row of numbers) or “poor signature” patients (lower line; lower row of numbers). For each plot, P indicates the P-value of the log-rank test.

[0072] FIGS. 27A-27G Kaplan-Meier plots of the metastasis-free probabilities for 151 lymph node negative breast cancer patients within the 295 tumor cohort. FIG. 27A shows the metastasis-free probabilities of the “good prognosis” and “poor prognosis” groups as identified by molecular profiling using the 70 optimal marker genes (i.e., “good prognosis” and “poor prognosis” signatures; see Example 4). FIG. 27B shows the metastasis-free probabilities of “low-risk” and “high-risk” groups as identified by “St. Gallen” criteria. FIG. 27C shows the metastasis-free probabilities of “low-risk” and “high-risk” signature groups as identified by “NIH consensus” criteria. FIG. 27D shows the “St. Gallen” “high-risk” group (n=129) divided into “good prognosis” and “poor prognosis” signature groups by profiling. FIG. 27E shows the “NIH” “high-risk” group (n=140) divided into “good prognosis” and “poor prognosis” signature groups by profiling. FIG. 27F shows the “St. Gallen” “low-risk” group (n=22) divided into “good prognosis” and “poor prognosis” signature groups by profiling. FIG. 27G shows the “NIH” “low-risk” group (n=11) divided into “good prognosis” and “poor prognosis” signature groups by profiling. Patients at risk at each time point (years; see description of FIG. 26) are indicated in each plot for “good signature” patients (upper line; upper row of numbers) or “poor signature” patients (lower line; lower row of numbers). P indicates the P-value of the log-rank test.

[0073] FIGS. 28A-28F Kaplan Meier plots for 295 breast cancer patients classified into “very good prognosis,” “intermediate prognosis,” and “poor prognosis” groups. FIG. 28A shows the metastasis-free probability of all 295 patients according to “very good”, “intermediate” and “poor prognosis” signature. FIG. 27B shows the overall survival of all 295 patients according to “very good,” “intermediate,” and “poor prognosis” signature. FIG. 27C shows the metastasis-free probability for lymph node negative patients similarly classified. FIG. 27D shows the overall survival for lymph node negative patients so classified. FIG. 27E shows the metastasis-free probability for lymph node positive patients so classified. FIG. 27F shows the overall survival of lymph node positive patients so classified. Patients at risk at each time point (years; see description of FIG. 26) are indicated in each plot for “very good” signature patients (top line; top row of numbers), “intermediate” signature patients (middle line; middle row of numbers) or “poor prognosis” signature patients (bottom line; bottom row of numbers) patients. P indicates the P-value of the log-rank test.

5. DETAILED DESCRIPTION OF THE INVENTION

5.1 Introduction

[0074] The invention relates to sets of genetic markers whose expression patterns correlate with important characteristics of breast cancer tumors. i.e., estrogen receptor (ER) status, BRCA1 status, and the likelihood of relapse (i.e., distant metastasis or poor prognosis). More specifically, the invention provides for sets of genetic markers that can distinguish the following three clinical conditions. First, the invention relates to sets of markers whose expression correlates with the ER status of a patient, and which can be used to distinguish ER(+) from ER(−) patients. ER status is a useful prognostic indicator, and an indicator of the likelihood that a patient will respond to certain therapies, such as tamoxifen. Also, among women who are ER positive the response rate (over 50%) to hormonal therapy is much higher than the response rate (less 10%) in patients whose ER status is negative. In patients with ER positive tumors the possibility of achieving a hormonal response is directly proportional to the level ER (P. Calabresi and P. S. Schein, MEDICAL ONCOLOGY (2ND ED.), McGraw-Hill, Inc., New York (1993)). Second, the invention further relates to sets of markers whose expression correlates with the presence of BRCA1 mutations, and which can be used to distinguish BRCA1-type tumors from sporadic tumors. Third, the invention relates to genetic markers whose expression correlates with clinical prognosis, and which can be used to distinguish patients having good prognoses (i.e., no distant metastases of a tumor within five years) from poor prognoses (i.e., distant metastases of a tumor within five years). Methods are provided for use of these markers to distinguish between these patient groups, and to determine general courses of treatment. Microarrays comprising these markers are also provided, as well as methods of constructing such microarrays. Each markers correspond to a gene in the human genome, i.e., such marker is identifiable as all or a portion of a gene. Finally, because each of the above markers correlates with a certain breast cancer-related conditions, the markers, or the proteins they encode, are likely to be targets for drugs against breast cancer.

5.2 Definitions

[0075] As used herein, “BRCA1 tumor” means a tumor having cells containing a mutation of the BRCA1 locus.

[0076] The “absolute amplitude” of correlation expressions means the distance, either positive or negative, from a zero value; i.e., both correlation coefficients −0.35 and 0.35 have an absolute amplitude of 0.35.

[0077] “Status” means a state of gene expression of a set of genetic markers whose expression is strongly correlated with a particular phenotype. For example, “ER status” means a state of gene expression of a set of genetic markers whose expression is strongly correlated with that of ESR1 (estrogen receptor gene), wherein the pattern of these genes' expression differs detectably between tumors expressing the receptor and tumors not expressing the receptor.

[0078] “Good prognosis” means that a patient is expected to have no distant metastases of a breast tumor within five years of initial diagnosis of breast cancer.

[0079] “Poor prognosis” means that a patient is expected to have distant metastases of a breast tumor within five years of initial diagnosis of breast cancer.

[0080] “Marker” means an entire gene, or an EST derived from that gene, the expression or level of which changes between certain conditions. Where the expression of the gene correlates with a certain condition, the gene is a marker for that condition.

[0081] “Marker-derived polynucleotides” means the RNA transcribed from a marker gene, any cDNA or cRNA produced therefrom, and any nucleic acid derived therefrom, such as synthetic nucleic acid having a sequence derived from the gene corresponding to the marker gene.

[0082] A “similarity value” is a number that represents the degree of similarity between two things being compared. For example, a similarity value may be a number that indicates the overall similarity between a patient's expression profile using specific phenotype-related markers and a control specific to that phenotype (for instance, the similarity to a “good prognosis” template, where the phenotype is a good prognosis). The similarity value may be expressed as a similarity metric, such as a correlation coefficient, or may simply be expressed as the expression level difference, or the aggregate of the expression level differences, between a patient sample and a template.

5.3 Markers Useful in Diagnosis and Prognosis of Breast Cancer

5.3.1 Marker Sets

[0083] The invention provides a set of 4,986 genetic markers whose expression is correlated with the existence of breast cancer by clustering analysis. A subset of these markers identified as useful for diagnosis or prognosis is listed as SEQ ID NOS: 1-2,699. The invention also provides a method of using these markers to distinguish tumor types in diagnosis or prognosis.

[0084] In one embodiment, the invention provides a set of 2,460 genetic markers that can classify breast cancer patients by estrogen receptor (ER) status; i.e., distinguish between ER(+) and ER(−) patients or tumors derived from these patients. ER status is an important indicator of the likelihood of a patient's response to some chemotherapies (i.e., tamoxifen). These markers are listed in Table 1. The invention also provides subsets of at least 5, 10, 25, 50, 100, 200, 300, 400, 500, 750, 1,000, 1,250, 1,500, 1,750 or 2,000 genetic markers, drawn from the set of 2,460 markers, which also distinguish ER(+) and ER(−) patients or tumors. Preferably, the number of markers is 550. The invention further provides a set of 550 of the 2,460 markers that are optimal for distinguishing ER status (Table 2). The invention also provides a method of using these markers to distinguish between ER(+) and ER(−) patients or tumors derived therefrom.

[0085] In another embodiment, the invention provides a set of 430 genetic markers that can classify ER(−) breast cancer patients by BRCA1 status; i.e., distinguish between tumors containing a BRCA1 mutation and sporadic tumors. These markers are listed in Table 3. The invention further provides subsets of at least 5, 10 20, 30, 40, 50, 75, 100, 150, 200, 250, 300 or 350 markers, drawn from the set of 430 markers, which also distinguish between tumors containing a BRCA1 mutation and sporadic tumors. Preferably, the number of markers is 100. A preferred set of 100 markers is provided in Table 4. The invention also provides a method of using these markers to distinguish between BRCA1 and sporadic patients or tumors derived therefrom.

[0086] In another embodiment, the invention provides a set of 231 genetic markers that can distinguish between patients with a good breast cancer prognosis (no breast cancer tumor distant metastases within five years) and patients with a poor breast cancer prognosis (tumor distant metastases within five years). These markers are listed in Table 5. The invention also provides subsets of at least 5, 10, 20, 30, 40, 50, 75, 100, 150 or 200 markers, drawn from the set of 231, which also distinguish between patients with good and poor prognosis. A preferred set of 70 markers is provided in Table 6. In a specific embodiment, the set of markers consists of the twelve kinase-related markers and the seven cell division- or mitosis-related markers listed. The invention also provides a method of using the above markers to distinguish between patients with good or poor prognosis. In another embodiment, the invention provides a method of using the prognosis-associated markers to distinguish between patients having a very good prognosis, an intermediate prognosis, and a poor prognosis, and thereby determining the appropriate combination of adjuvant or hormonal therapy.

TABLE 1
2,460 gene markers that distinguish ER(+)
and ER(−) cell samples.
GenBank
Accession Number SEQ ID NO
AA555029_RC      SEQ ID NO 1
AB000509         SEQ ID NO 2
AB001451         SEQ ID NO 3
AB002301         SEQ ID NO 4
AB002308         SEQ ID NO 5
AB002351         SEQ ID NO 6
AB002448         SEQ ID NO 7
AB006628         SEQ ID NO 9
AB006630         SEQ ID NO 10
AB006746         SEQ ID NO 11
AB007458         SEQ ID NO 12
AB007855         SEQ ID NO 13
AB007857         SEQ ID NO 14
AB007863         SEQ ID NO 15
AB007883         SEQ ID NO 16
AB007896         SEQ ID NO 17
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AF000974         SEQ ID NO 97
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AI497657_RC      SEQ ID NO 193
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AK000004         SEQ ID NO 206
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D13643           SEQ ID NO 355
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D25328           SEQ ID NO 357
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D38521           SEQ ID NO 361
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D55716           SEQ ID NO 368
D80001           SEQ ID NO 369
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D82345           SEQ ID NO 371
D83781           SEQ ID NO 372
D86964           SEQ ID NO 373
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D86985           SEQ ID NO 375
D87076           SEQ ID NO 376
D87453           SEQ ID NO 377
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D87682           SEQ ID NO 379
G26403           SEQ ID NO 380
J02639           SEQ ID NO 381
J04162           SEQ ID NO 382
K02403           SEQ ID NO 384
L05096           SEQ ID NO 385
L10333           SEQ ID NO 386
L11645           SEQ ID NO 387
L21934           SEQ ID NO 388
L22005           SEQ ID NO 389
L48692           SEQ ID NO 391
M12758           SEQ ID NO 392
M15178           SEQ ID NO 393
M21551           SEQ ID NO 394
M24895           SEQ ID NO 395
M26383           SEQ ID NO 396
M27749           SEQ ID NO 397
M28170           SEQ ID NO 398
M29873           SEQ ID NO 399
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M30448           SEQ ID NO 401
M30818           SEQ ID NO 402
M31932           SEQ ID NO 403
M37033           SEQ ID NO 404
M55914           SEQ ID NO 405
M63438           SEQ ID NO 406
M65254           SEQ ID NO 407
M68874           SEQ ID NO 408
M73547           SEQ ID NO 409
M77142           SEQ ID NO 410
M80899           SEQ ID NO 411
M83822           SEQ ID NO 412
M90657           SEQ ID NO 413
M93718           SEQ ID NO 414
M96577           SEQ ID NO 415
NM_000022        SEQ ID NO 417
NM_000044        SEQ ID NO 418
NM_000050        SEQ ID NO 419
NM_000057        SEQ ID NO 420
NM_000060        SEQ ID NO 421
NM_000064        SEQ ID NO 422
NM_000073        SEQ ID NO 424
NM_000077        SEQ ID NO 425
NM_000086        SEQ ID NO 426
NM_000087        SEQ ID NO 427
NM_000095        SEQ ID NO 429
NM_000096        SEQ ID NO 430
NM_000100        SEQ ID NO 431
NM_000101        SEQ ID NO 432
NM_000104        SEQ ID NO 433
NM_000109        SEQ ID NO 434
NM_000125        SEQ ID NO 435
NM_000127        SEQ ID NO 436
NM_000135        SEQ ID NO 437
NM_000137        SEQ ID NO 438
NM_000146        SEQ ID NO 439
NM_000149        SEQ ID NO 440
NM_000154        SEQ ID NO 441
NM_000161        SEQ ID NO 443
NM_000165        SEQ ID NO 444
NM_000168        SEQ ID NO 445
NM_000169        SEQ ID NO 446
NM_000175        SEQ ID NO 447
NM_000191        SEQ ID NO 448
NM_000201        SEQ ID NO 450
NM_000211        SEQ ID NO 451
NM_000213        SEQ ID NO 452
NM_000224        SEQ ID NO 453
NM_000239        SEQ ID NO 454
NM_000251        SEQ ID NO 455
NM_000268        SEQ ID NO 456
NM_000270        SEQ ID NO 458
NM_000271        SEQ ID NO 459
NM_000283        SEQ ID NO 460
NM_000284        SEQ ID NO 461
NM_000286        SEQ ID NO 462
NM_000291        SEQ ID NO 463
NM_000299        SEQ ID NO 464
NM_000300        SEQ ID NO 465
NM_000310        SEQ ID NO 466
NM_000311        SEQ ID NO 467
NM_000317        SEQ ID NO 468
NM_000320        SEQ ID NO 469
NM_000342        SEQ ID NO 470
NM_000346        SEQ ID NO 471
NM_000352        SEQ ID NO 472
NM_000355        SEQ ID NO 473
NM_000358        SEQ ID NO 474
NM_000359        SEQ ID NO 475
NM_000362        SEQ ID NO 476
NM_000365        SEQ ID NO 477
NM_000381        SEQ ID NO 478
NM_000397        SEQ ID NO 480
NM_000399        SEQ ID NO 481
NM_000414        SEQ ID NO 482
NM_000416        SEQ ID NO 483
NM_000422        SEQ ID NO 484
NM_000424        SEQ ID NO 485
NM_000433        SEQ ID NO 486
NM_000436        SEQ ID NO 487
NM_000450        SEQ ID NO 488
NM_000462        SEQ ID NO 489
NM_000495        SEQ ID NO 490
NM_000507        SEQ ID NO 491
NM_000526        SEQ ID NO 492
NM_000557        SEQ ID NO 493
NM_000560        SEQ ID NO 494
NM_000576        SEQ ID NO 495
NM_000579        SEQ ID NO 496
NM_000584        SEQ ID NO 497
NM_000591        SEQ ID NO 498
NM_000592        SEQ ID NO 499
NM_000593        SEQ ID NO 500
NM_000594        SEQ ID NO 501
NM_000597        SEQ ID NO 502
NM_000600        SEQ ID NO 504
NM_000607        SEQ ID NO 505
NM_000612        SEQ ID NO 506
NM_000627        SEQ ID NO 507
NM_000633        SEQ ID NO 508
NM_000636        SEQ ID NO 509
NM_000639        SEQ ID NO 510
NM_000647        SEQ ID NO 511
NM_000655        SEQ ID NO 512
NM_000662        SEQ ID NO 513
NM_000663        SEQ ID NO 514
NM_000666        SEQ ID NO 515
NM_000676        SEQ ID NO 516
NM_000685        SEQ ID NO 517
NM_000693        SEQ ID NO 518
NM_000699        SEQ ID NO 519
NM_000700        SEQ ID NO 520
NM_000712        SEQ ID NO 521
NM_000727        SEQ ID NO 522
NM_000732        SEQ ID NO 523
NM_000734        SEQ ID NO 524
NM_000767        SEQ ID NO 525
NM_000784        SEQ ID NO 526
NM_000802        SEQ ID NO 528
NM_000824        SEQ ID NO 529
NM_000849        SEQ ID NO 530
NM_000852        SEQ ID NO 531
NM_000874        SEQ ID NO 532
NM_000878        SEQ ID NO 533
NM_000884        SEQ ID NO 534
NM_000908        SEQ ID NO 537
NM_000909        SEQ ID NO 538
NM_000926        SEQ ID NO 539
NM_000930        SEQ ID NO 540
NM_000931        SEQ ID NO 541
NM_000947        SEQ ID NO 542
NM_000949        SEQ ID NO 543
NM_000950        SEQ ID NO 544
NM_000954        SEQ ID NO 545
NM_000964        SEQ ID NO 546
NM_001003        SEQ ID NO 549
NM_001016        SEQ ID NO 551
NM_001047        SEQ ID NO 553
NM_001066        SEQ ID NO 555
NM_001071        SEQ ID NO 556
NM_001078        SEQ ID NO 557
NM_001085        SEQ ID NO 558
NM_001089        SEQ ID NO 559
NM_001109        SEQ ID NO 560
NM_001122        SEQ ID NO 561
NM_001124        SEQ ID NO 562
NM_001161        SEQ ID NO 563
NM_001165        SEQ ID NO 564
NM_001166        SEQ ID NO 565
NM_001168        SEQ ID NO 566
NM_001179        SEQ ID NO 567
NM_001185        SEQ ID NO 569
NM_001203        SEQ ID NO 570
NM_001207        SEQ ID NO 573
NM_001216        SEQ ID NO 574
NM_001218        SEQ ID NO 575
NM_001223        SEQ ID NO 576
NM_001225        SEQ ID NO 577
NM_001233        SEQ ID NO 578
NM_001236        SEQ ID NO 579
NM_001237        SEQ ID NO 580
NM_001251        SEQ ID NO 581
NM_001255        SEQ ID NO 582
NM_001262        SEQ ID NO 583
NM_001263        SEQ ID NO 584
NM_001267        SEQ ID NO 585
NM_001276        SEQ ID NO 587
NM_001280        SEQ ID NO 588
NM_001282        SEQ ID NO 589
NM_001295        SEQ ID NO 590
NM_001305        SEQ ID NO 591
NM_001310        SEQ ID NO 592
NM_001312        SEQ ID NO 593
NM_001321        SEQ ID NO 594
NM_001327        SEQ ID NO 595
NM_001329        SEQ ID NO 596
NM_001333        SEQ ID NO 597
NM_001338        SEQ ID NO 598
NM_001360        SEQ ID NO 599
NM_001363        SEQ ID NO 600
NM_001381        SEQ ID NO 601
NM_001394        SEQ ID NO 602
NM_001395        SEQ ID NO 603
NM_001419        SEQ ID NO 604
NM_001424        SEQ ID NO 605
NM_001428        SEQ ID NO 606
NM_001436        SEQ ID NO 607
NM_001444        SEQ ID NO 608
NM_001446        SEQ ID NO 609
NM_001453        SEQ ID NO 611
NM_001456        SEQ ID NO 612
NM_001457        SEQ ID NO 613
NM_001463        SEQ ID NO 614
NM_001465        SEQ ID NO 615
NM_001481        SEQ ID NO 616
NM_001493        SEQ ID NO 617
NM_001494        SEQ ID NO 618
NM_001500        SEQ ID NO 619
NM_001504        SEQ ID NO 620
NM_001511        SEQ ID NO 621
NM_001513        SEQ ID NO 622
NM_001527        SEQ ID NO 623
NM_001529        SEQ ID NO 624
NM_001530        SEQ ID NO 625
NM_001540        SEQ ID NO 626
NM_001550        SEQ ID NO 627
NM_001551        SEQ ID NO 628
NM_001552        SEQ ID NO 629
NM_001554        SEQ ID NO 631
NM_001558        SEQ ID NO 632
NM_001560        SEQ ID NO 633
NM_001565        SEQ ID NO 634
NM_001569        SEQ ID NO 635
NM_001605        SEQ ID NO 636
NM_001609        SEQ ID NO 637
NM_001615        SEQ ID NO 638
NM_001623        SEQ ID NO 639
NM_001627        SEQ ID NO 640
NM_001628        SEQ ID NO 641
NM_001630        SEQ ID NO 642
NM_001634        SEQ ID NO 643
NM_001656        SEQ ID NO 644
NM_001673        SEQ ID NO 645
NM_001675        SEQ ID NO 647
NM_001679        SEQ ID NO 648
NM_001689        SEQ ID NO 649
NM_001703        SEQ ID NO 650
NM_001710        SEQ ID NO 651
NM_001725        SEQ ID NO 652
NM_001730        SEQ ID NO 653
NM_001733        SEQ ID NO 654
NM_001734        SEQ ID NO 655
NM_001740        SEQ ID NO 656
NM_001745        SEQ ID NO 657
NM_001747        SEQ ID NO 658
NM_001756        SEQ ID NO 659
NM_001757        SEQ ID NO 660
NM_001758        SEQ ID NO 661
NM_001762        SEQ ID NO 662
NM_001767        SEQ ID NO 663
NM_001770        SEQ ID NO 664
NM_001777        SEQ ID NO 665
NM_001778        SEQ ID NO 666
NM_001781        SEQ ID NO 667
NM_001786        SEQ ID NO 668
NM_001793        SEQ ID NO 669
NM_001803        SEQ ID NO 671
NM_001806        SEQ ID NO 672
NM_001809        SEQ ID NO 673
NM_001814        SEQ ID NO 674
NM_001826        SEQ ID NO 675
NM_001830        SEQ ID NO 677
NM_001838        SEQ ID NO 678
NM_001839        SEQ ID NO 679
NM_001853        SEQ ID NO 681
NM_001859        SEQ ID NO 682
NM_001861        SEQ ID NO 683
NM_001874        SEQ ID NO 685
NM_001885        SEQ ID NO 686
NM_001892        SEQ ID NO 688
NM_001897        SEQ ID NO 689
NM_001899        SEQ ID NO 690
NM_001905        SEQ ID NO 691
NM_001912        SEQ ID NO 692
NM_001914        SEQ ID NO 693
NM_001919        SEQ ID NO 694
NM_001941        SEQ ID NO 695
NM_001943        SEQ ID NO 696
NM_001944        SEQ ID NO 697
NM_001953        SEQ ID NO 699
NM_001954        SEQ ID NO 700
NM_001955        SEQ ID NO 701
NM_001956        SEQ ID NO 702
NM_001958        SEQ ID NO 703
NM_001961        SEQ ID NO 705
NM_001970        SEQ ID NO 706
NM_001979        SEQ ID NO 707
NM_001982        SEQ ID NO 708
NM_002017        SEQ ID NO 710
NM_002033        SEQ ID NO 713
NM_002046        SEQ ID NO 714
NM_002047        SEQ ID NO 715
NM_002051        SEQ ID NO 716
NM_002053        SEQ ID NO 717
NM_002061        SEQ ID NO 718
NM_002065        SEQ ID NO 719
NM_002068        SEQ ID NO 720
NM_002077        SEQ ID NO 722
NM_002091        SEQ ID NO 723
NM_002101        SEQ ID NO 724
NM_002106        SEQ ID NO 725
NM_002110        SEQ ID NO 726
NM_002111        SEQ ID NO 727
NM_002115        SEQ ID NO 728
NM_002118        SEQ ID NO 729
NM_002123        SEQ ID NO 730
NM_002131        SEQ ID NO 731
NM_002136        SEQ ID NO 732
NM_002145        SEQ ID NO 733
NM_002164        SEQ ID NO 734
NM_002168        SEQ ID NO 735
NM_002184        SEQ ID NO 736
NM_002185        SEQ ID NO 737
NM_002189        SEQ ID NO 738
NM_002200        SEQ ID NO 739
NM_002201        SEQ ID NO 740
NM_002213        SEQ ID NO 741
NM_002219        SEQ ID NO 742
NM_002222        SEQ ID NO 743
NM_002239        SEQ ID NO 744
NM_002243        SEQ ID NO 745
NM_002245        SEQ ID NO 746
NM_002250        SEQ ID NO 747
NM_002254        SEQ ID NO 748
NM_002266        SEQ ID NO 749
NM_002273        SEQ ID NO 750
NM_002281        SEQ ID NO 751
NM_002292        SEQ ID NO 752
NM_002298        SEQ ID NO 753
NM_002300        SEQ ID NO 754
NM_002308        SEQ ID NO 755
NM_002314        SEQ ID NO 756
NM_002337        SEQ ID NO 757
NM_002341        SEQ ID NO 758
NM_002342        SEQ ID NO 759
NM_002346        SEQ ID NO 760
NM_002349        SEQ ID NO 761
NM_002350        SEQ ID NO 762
NM_002356        SEQ ID NO 763
NM_002358        SEQ ID NO 764
NM_002370        SEQ ID NO 765
NM_002395        SEQ ID NO 766
NM_002416        SEQ ID NO 767
NM_002421        SEQ ID NO 768
NM_002426        SEQ ID NO 769
NM_002435        SEQ ID NO 770
NM_002438        SEQ ID NO 771
NM_002444        SEQ ID NO 772
NM_002449        SEQ ID NO 773
NM_002450        SEQ ID NO 774
NM_002456        SEQ ID NO 775
NM_002466        SEQ ID NO 776
NM_002482        SEQ ID NO 777
NM_002497        SEQ ID NO 778
NM_002510        SEQ ID NO 779
NM_002515        SEQ ID NO 781
NM_002524        SEQ ID NO 782
NM_002539        SEQ ID NO 783
NM_002555        SEQ ID NO 785
NM_002570        SEQ ID NO 787
NM_002579        SEQ ID NO 788
NM_002587        SEQ ID NO 789
NM_002590        SEQ ID NO 790
NM_002600        SEQ ID NO 791
NM_002614        SEQ ID NO 792
NM_002618        SEQ ID NO 794
NM_002626        SEQ ID NO 795
NM_002633        SEQ ID NO 796
NM_002639        SEQ ID NO 797
NM_002648        SEQ ID NO 798
NM_002659        SEQ ID NO 799
NM_002661        SEQ ID NO 800
NM_002662        SEQ ID NO 801
NM_002664        SEQ ID NO 802
NM_002689        SEQ ID NO 804
NM_002690        SEQ ID NO 805
NM_002709        SEQ ID NO 806
NM_002727        SEQ ID NO 807
NM_002729        SEQ ID NO 808
NM_002734        SEQ ID NO 809
NM_002736        SEQ ID NO 810
NM_002740        SEQ ID NO 811
NM_002748        SEQ ID NO 813
NM_002774        SEQ ID NO 814
NM_002775        SEQ ID NO 815
NM_002776        SEQ ID NO 816
NM_002789        SEQ ID NO 817
NM_002794        SEQ ID NO 818
NM_002796        SEQ ID NO 819
NM_002800        SEQ ID NO 820
NM_002801        SEQ ID NO 821
NM_002808        SEQ ID NO 822
NM_002821        SEQ ID NO 824
NM_002826        SEQ ID NO 825
NM_002827        SEQ ID NO 826
NM_002838        SEQ ID NO 827
NM_002852        SEQ ID NO 828
NM_002854        SEQ ID NO 829
NM_002856        SEQ ID NO 830
NM_002857        SEQ ID NO 831
NM_002858        SEQ ID NO 832
NM_002888        SEQ ID NO 833
NM_002890        SEQ ID NO 834
NM_002901        SEQ ID NO 836
NM_002906        SEQ ID NO 837
NM_002916        SEQ ID NO 838
NM_002923        SEQ ID NO 839
NM_002933        SEQ ID NO 840
NM_002936        SEQ ID NO 841
NM_002937        SEQ ID NO 842
NM_002950        SEQ ID NO 843
NM_002961        SEQ ID NO 844
NM_002964        SEQ ID NO 845
NM_002965        SEQ ID NO 846
NM_002966        SEQ ID NO 847
NM_002982        SEQ ID NO 849
NM_002983        SEQ ID NO 850
NM_002984        SEQ ID NO 851
NM_002985        SEQ ID NO 852
NM_002988        SEQ ID NO 853
NM_002996        SEQ ID NO 854
NM_002997        SEQ ID NO 855
NM_002999        SEQ ID NO 856
NM_003012        SEQ ID NO 857
NM_003022        SEQ ID NO 858
NM_003034        SEQ ID NO 859
NM_003035        SEQ ID NO 860
NM_003039        SEQ ID NO 861
NM_003051        SEQ ID NO 862
NM_003064        SEQ ID NO 863
NM_003066        SEQ ID NO 864
NM_003088        SEQ ID NO 865
NM_003090        SEQ ID NO 866
NM_003096        SEQ ID NO 867
NM_003099        SEQ ID NO 868
NM_003102        SEQ ID NO 869
NM_003104        SEQ ID NO 870
NM_003108        SEQ ID NO 871
NM_003121        SEQ ID NO 873
NM_003134        SEQ ID NO 874
NM_003137        SEQ ID NO 875
NM_003144        SEQ ID NO 876
NM_003146        SEQ ID NO 877
NM_003149        SEQ ID NO 878
NM_003151        SEQ ID NO 879
NM_003157        SEQ ID NO 880
NM_003158        SEQ ID NO 881
NM_003165        SEQ ID NO 882
NM_003172        SEQ ID NO 883
NM_003177        SEQ ID NO 884
NM_003197        SEQ ID NO 885
NM_003202        SEQ ID NO 886
NM_003213        SEQ ID NO 887
NM_003217        SEQ ID NO 888
NM_003225        SEQ ID NO 889
NM_003226        SEQ ID NO 890
NM_003236        SEQ ID NO 892
NM_003239        SEQ ID NO 893
NM_003248        SEQ ID NO 894
NM_003255        SEQ ID NO 895
NM_003258        SEQ ID NO 896
NM_003264        SEQ ID NO 897
NM_003283        SEQ ID NO 898
NM_003318        SEQ ID NO 899
NM_003329        SEQ ID NO 900
NM_003332        SEQ ID NO 901
NM_003358        SEQ ID NO 902
NM_003359        SEQ ID NO 903
NM_003360        SEQ ID NO 904
NM_003368        SEQ ID NO 905
NM_003376        SEQ ID NO 906
NM_003380        SEQ ID NO 907
NM_003392        SEQ ID NO 908
NM_003412        SEQ ID NO 909
NM_003430        SEQ ID NO 910
NM_003462        SEQ ID NO 911
NM_003467        SEQ ID NO 912
NM_003472        SEQ ID NO 913
NM_003479        SEQ ID NO 914
NM_003489        SEQ ID NO 915
NM_003494        SEQ ID NO 916
NM_003498        SEQ ID NO 917
NM_003504        SEQ ID NO 919
NM_003508        SEQ ID NO 920
NM_003510        SEQ ID NO 921
NM_003512        SEQ ID NO 922
NM_003528        SEQ ID NO 923
NM_003544        SEQ ID NO 924
NM_003561        SEQ ID NO 925
NM_003563        SEQ ID NO 926
NM_003568        SEQ ID NO 927
NM_003579        SEQ ID NO 928
NM_003600        SEQ ID NO 929
NM_003615        SEQ ID NO 931
NM_003627        SEQ ID NO 932
NM_003645        SEQ ID NO 935
NM_003651        SEQ ID NO 936
NM_003657        SEQ ID NO 937
NM_003662        SEQ ID NO 938
NM_003670        SEQ ID NO 939
NM_003675        SEQ ID NO 940
NM_003676        SEQ ID NO 941
NM_003681        SEQ ID NO 942
NM_003683        SEQ ID NO 943
NM_003686        SEQ ID NO 944
NM_003689        SEQ ID NO 945
NM_003714        SEQ ID NO 946
NM_003720        SEQ ID NO 947
NM_003726        SEQ ID NO 948
NM_003729        SEQ ID NO 949
NM_003740        SEQ ID NO 950
NM_003772        SEQ ID NO 952
NM_003791        SEQ ID NO 953
NM_003793        SEQ ID NO 954
NM_003795        SEQ ID NO 955
NM_003806        SEQ ID NO 956
NM_003821        SEQ ID NO 957
NM_003829        SEQ ID NO 958
NM_003831        SEQ ID NO 959
NM_003862        SEQ ID NO 960
NM_003866        SEQ ID NO 961
NM_003875        SEQ ID NO 962
NM_003878        SEQ ID NO 963
NM_003894        SEQ ID NO 965
NM_003897        SEQ ID NO 966
NM_003904        SEQ ID NO 967
NM_003929        SEQ ID NO 968
NM_003933        SEQ ID NO 969
NM_003937        SEQ ID NO 970
NM_003940        SEQ ID NO 971
NM_003942        SEQ ID NO 972
NM_003944        SEQ ID NO 973
NM_003953        SEQ ID NO 974
NM_003954        SEQ ID NO 975
NM_003975        SEQ ID NO 976
NM_003981        SEQ ID NO 977
NM_003982        SEQ ID NO 978
NM_003986        SEQ ID NO 979
NM_004003        SEQ ID NO 980
NM_004010        SEQ ID NO 981
NM_004024        SEQ ID NO 982
NM_004038        SEQ ID NO 983
NM_004049        SEQ ID NO 984
NM_004052        SEQ ID NO 985
NM_004053        SEQ ID NO 986
NM_004079        SEQ ID NO 987
NM_004104        SEQ ID NO 988
NM_004109        SEQ ID NO 989
NM_004110        SEQ ID NO 990
NM_004120        SEQ ID NO 991
NM_004131        SEQ ID NO 992
NM_004143        SEQ ID NO 993
NM_004154        SEQ ID NO 994
NM_004170        SEQ ID NO 996
NM_004172        SEQ ID NO 997
NM_004176        SEQ ID NO 998
NM_004180        SEQ ID NO 999
NM_004181        SEQ ID NO 1000
NM_004184        SEQ ID NO 1001
NM_004203        SEQ ID NO 1002
NM_004207        SEQ ID NO 1003
NM_004217        SEQ ID NO 1004
NM_004219        SEQ ID NO 1005
NM_004221        SEQ ID NO 1006
NM_004233        SEQ ID NO 1007
NM_004244        SEQ ID NO 1008
NM_004252        SEQ ID NO 1009
NM_004265        SEQ ID NO 1010
NM_004267        SEQ ID NO 1011
NM_004281        SEQ ID NO 1012
NM_004289        SEQ ID NO 1013
NM_004298        SEQ ID NO 1015
NM_004301        SEQ ID NO 1016
NM_004305        SEQ ID NO 1017
NM_004311        SEQ ID NO 1018
NM_004315        SEQ ID NO 1019
NM_004323        SEQ ID NO 1020
NM_004330        SEQ ID NO 1021
NM_004336        SEQ ID NO 1022
NM_004338        SEQ ID NO 1023
NM_004350        SEQ ID NO 1024
NM_004354        SEQ ID NO 1025
NM_004358        SEQ ID NO 1026
NM_004360        SEQ ID NO 1027
NM_004362        SEQ ID NO 1028
NM_004374        SEQ ID NO 1029
NM_004378        SEQ ID NO 1030
NM_004392        SEQ ID NO 1031
NM_004395        SEQ ID NO 1032
NM_004414        SEQ ID NO 1033
NM_004418        SEQ ID NO 1034
NM_004425        SEQ ID NO 1035
NM_004431        SEQ ID NO 1036
NM_004436        SEQ ID NO 1037
NM_004438        SEQ ID NO 1038
NM_004443        SEQ ID NO 1039
NM_004446        SEQ ID NO 1040
NM_004451        SEQ ID NO 1041
NM_004454        SEQ ID NO 1042
NM_004456        SEQ ID NO 1043
NM_004458        SEQ ID NO 1044
NM_004472        SEQ ID NO 1045
NM_004480        SEQ ID NO 1046
NM_004482        SEQ ID NO 1047
NM_004494        SEQ ID NO 1048
NM_004496        SEQ ID NO 1049
NM_004503        SEQ ID NO 1050
NM_004504        SEQ ID NO 1051
NM_004515        SEQ ID NO 1052
NM_004522        SEQ ID NO 1053
NM_004523        SEQ ID NO 1054
NM_004525        SEQ ID NO 1055
NM_004556        SEQ ID NO 1056
NM_004559        SEQ ID NO 1057
NM_004569        SEQ ID NO 1058
NM_004577        SEQ ID NO 1059
NM_004585        SEQ ID NO 1060
NM_004587        SEQ ID NO 1061
NM_004594        SEQ ID NO 1062
NM_004599        SEQ ID NO 1063
NM_004633        SEQ ID NO 1066
NM_004642        SEQ ID NO 1067
NM_004648        SEQ ID NO 1068
NM_004663        SEQ ID NO 1069
NM_004664        SEQ ID NO 1070
NM_004684        SEQ ID NO 1071
NM_004688        SEQ ID NO 1072
NM_004694        SEQ ID NO 1073
NM_004695        SEQ ID NO 1074
NM_004701        SEQ ID NO 1075
NM_004708        SEQ ID NO 1077
NM_004711        SEQ ID NO 1078
NM_004726        SEQ ID NO 1079
NM_004750        SEQ ID NO 1081
NM_004761        SEQ ID NO 1082
NM_004762        SEQ ID NO 1083
NM_004780        SEQ ID NO 1085
NM_004791        SEQ ID NO 1086
NM_004798        SEQ ID NO 1087
NM_004808        SEQ ID NO 1088
NM_004811        SEQ ID NO 1089
NM_004833        SEQ ID NO 1090
NM_004835        SEQ ID NO 1091
NM_004843        SEQ ID NO 1092
NM_004847        SEQ ID NO 1093
NM_004848        SEQ ID NO 1094
NM_004864        SEQ ID NO 1095
NM_004865        SEQ ID NO 1096
NM_004866        SEQ ID NO 1097
NM_004877        SEQ ID NO 1098
NM_004900        SEQ ID NO 1099
NM_004906        SEQ ID NO 1100
NM_004910        SEQ ID NO 1101
NM_004918        SEQ ID NO 1103
NM_004923        SEQ ID NO 1104
NM_004938        SEQ ID NO 1105
NM_004951        SEQ ID NO 1106
NM_004968        SEQ ID NO 1107
NM_004994        SEQ ID NO 1108
NM_004999        SEQ ID NO 1109
NM_005001        SEQ ID NO 1110
NM_005002        SEQ ID NO 1111
NM_005012        SEQ ID NO 1112
NM_005032        SEQ ID NO 1113
NM_005044        SEQ ID NO 1114
NM_005046        SEQ ID NO 1115
NM_005049        SEQ ID NO 1116
NM_005067        SEQ ID NO 1117
NM_005077        SEQ ID NO 1118
NM_005080        SEQ ID NO 1119
NM_005084        SEQ ID NO 1120
NM_005130        SEQ ID NO 1122
NM_005139        SEQ ID NO 1123
NM_005168        SEQ ID NO 1125
NM_005190        SEQ ID NO 1126
NM_005196        SEQ ID NO 1127
NM_005213        SEQ ID NO 1128
NM_005218        SEQ ID NO 1129
NM_005235        SEQ ID NO 1130
NM_005245        SEQ ID NO 1131
NM_005249        SEQ ID NO 1132
NM_005257        SEQ ID NO 1133
NM_005264        SEQ ID NO 1134
NM_005271        SEQ ID NO 1135
NM_005314        SEQ ID NO 1136
NM_005321        SEQ ID NO 1137
NM_005322        SEQ ID NO 1138
NM_005325        SEQ ID NO 1139
NM_005326        SEQ ID NO 1140
NM_005335        SEQ ID NO 1141
NM_005337        SEQ ID NO 1142
NM_005342        SEQ ID NO 1143
NM_005345        SEQ ID NO 1144
NM_005357        SEQ ID NO 1145
NM_005375        SEQ ID NO 1146
NM_005391        SEQ ID NO 1147
NM_005408        SEQ ID NO 1148
NM_005409        SEQ ID NO 1149
NM_005410        SEQ ID NO 1150
NM_005426        SEQ ID NO 1151
NM_005433        SEQ ID NO 1152
NM_005441        SEQ ID NO 1153
NM_005443        SEQ ID NO 1154
NM_005483        SEQ ID NO 1155
NM_005486        SEQ ID NO 1156
NM_005496        SEQ ID NO 1157
NM_005498        SEQ ID NO 1158
NM_005499        SEQ ID NO 1159
NM_005514        SEQ ID NO 1160
NM_005531        SEQ ID NO 1162
NM_005538        SEQ ID NO 1163
NM_005541        SEQ ID NO 1164
NM_005544        SEQ ID NO 1165
NM_005548        SEQ ID NO 1166
NM_005554        SEQ ID NO 1167
NM_005555        SEQ ID NO 1168
NM_005556        SEQ ID NO 1169
NM_005557        SEQ ID NO 1170
NM_005558        SEQ ID NO 1171
NM_005562        SEQ ID NO 1172
NM_005563        SEQ ID NO 1173
NM_005565        SEQ ID NO 1174
NM_005566        SEQ ID NO 1175
NM_005572        SEQ ID NO 1176
NM_005582        SEQ ID NO 1177
NM_005608        SEQ ID NO 1178
NM_005614        SEQ ID NO 1179
NM_005617        SEQ ID NO 1180
NM_005620        SEQ ID NO 1181
NM_005625        SEQ ID NO 1182
NM_005651        SEQ ID NO 1183
NM_005658        SEQ ID NO 1184
NM_005659        SEQ ID NO 1185
NM_005667        SEQ ID NO 1186
NM_005686        SEQ ID NO 1187
NM_005690        SEQ ID NO 1188
NM_005720        SEQ ID NO 1190
NM_005727        SEQ ID NO 1191
NM_005733        SEQ ID NO 1192
NM_005737        SEQ ID NO 1193
NM_005742        SEQ ID NO 1194
NM_005746        SEQ ID NO 1195
NM_005749        SEQ ID NO 1196
NM_005760        SEQ ID NO 1197
NM_005764        SEQ ID NO 1198
NM_005794        SEQ ID NO 1199
NM_005796        SEQ ID NO 1200
NM_005804        SEQ ID NO 1201
NM_005813        SEQ ID NO 1202
NM_005824        SEQ ID NO 1203
NM_005825        SEQ ID NO 1204
NM_005849        SEQ ID NO 1205
NM_005853        SEQ ID NO 1206
NM_005855        SEQ ID NO 1207
NM_005864        SEQ ID NO 1208
NM_005874        SEQ ID NO 1209
NM_005876        SEQ ID NO 1210
NM_005880        SEQ ID NO 1211
NM_005891        SEQ ID NO 1212
NM_005892        SEQ ID NO 1213
NM_005899        SEQ ID NO 1214
NM_005915        SEQ ID NO 1215
NM_005919        SEQ ID NO 1216
NM_005923        SEQ ID NO 1217
NM_005928        SEQ ID NO 1218
NM_005932        SEQ ID NO 1219
NM_005935        SEQ ID NO 1220
NM_005945        SEQ ID NO 1221
NM_005953        SEQ ID NO 1222
NM_005978        SEQ ID NO 1223
NM_005990        SEQ ID NO 1224
NM_006002        SEQ ID NO 1225
NM_006004        SEQ ID NO 1226
NM_006005        SEQ ID NO 1227
NM_006006        SEQ ID NO 1228
NM_006017        SEQ ID NO 1229
NM_006018        SEQ ID NO 1230
NM_006023        SEQ ID NO 1231
NM_006027        SEQ ID NO 1232
NM_006029        SEQ ID NO 1233
NM_006033        SEQ ID NO 1234
NM_006051        SEQ ID NO 1235
NM_006055        SEQ ID NO 1236
NM_006074        SEQ ID NO 1237
NM_006086        SEQ ID NO 1238
NM_006087        SEQ ID NO 1239
NM_006096        SEQ ID NO 1240
NM_006101        SEQ ID NO 1241
NM_006103        SEQ ID NO 1242
NM_006111        SEQ ID NO 1243
NM_006113        SEQ ID NO 1244
NM_006115        SEQ ID NO 1245
NM_006117        SEQ ID NO 1246
NM_006142        SEQ ID NO 1247
NM_006144        SEQ ID NO 1248
NM_006148        SEQ ID NO 1249
NM_006153        SEQ ID NO 1250
NM_006159        SEQ ID NO 1251
NM_006170        SEQ ID NO 1252
NM_006197        SEQ ID NO 1253
NM_006224        SEQ ID NO 1255
NM_006227        SEQ ID NO 1256
NM_006235        SEQ ID NO 1257
NM_006243        SEQ ID NO 1258
NM_006264        SEQ ID NO 1259
NM_006271        SEQ ID NO 1261
NM_006274        SEQ ID NO 1262
NM_006290        SEQ ID NO 1265
NM_006291        SEQ ID NO 1266
NM_006296        SEQ ID NO 1267
NM_006304        SEQ ID NO 1268
NM_006314        SEQ ID NO 1269
NM_006332        SEQ ID NO 1270
NM_006357        SEQ ID NO 1271
NM_006366        SEQ ID NO 1272
NM_006372        SEQ ID NO 1273
NM_006377        SEQ ID NO 1274
NM_006378        SEQ ID NO 1275
NM_006383        SEQ ID NO 1276
NM_006389        SEQ ID NO 1277
NM_006393        SEQ ID NO 1278
NM_006398        SEQ ID NO 1279
NM_006406        SEQ ID NO 1280
NM_006408        SEQ ID NO 1281
NM_006410        SEQ ID NO 1282
NM_006414        SEQ ID NO 1283
NM_006417        SEQ ID NO 1284
NM_006430        SEQ ID NO 1285
NM_006460        SEQ ID NO 1286
NM_006461        SEQ ID NO 1287
NM_006469        SEQ ID NO 1288
NM_006470        SEQ ID NO 1289
NM_006491        SEQ ID NO 1290
NM_006495        SEQ ID NO 1291
NM_006500        SEQ ID NO 1292
NM_006509        SEQ ID NO 1293
NM_006516        SEQ ID NO 1294
NM_006533        SEQ ID NO 1295
NM_006551        SEQ ID NO 1296
NM_006556        SEQ ID NO 1297
NM_006558        SEQ ID NO 1298
NM_006564        SEQ ID NO 1299
NM_006573        SEQ ID NO 1300
NM_006607        SEQ ID NO 1301
NM_006622        SEQ ID NO 1302
NM_006623        SEQ ID NO 1303
NM_006636        SEQ ID NO 1304
NM_006670        SEQ ID NO 1305
NM_006681        SEQ ID NO 1306
NM_006682        SEQ ID NO 1307
NM_006696        SEQ ID NO 1308
NM_006698        SEQ ID NO 1309
NM_006705        SEQ ID NO 1310
NM_006739        SEQ ID NO 1311
NM_006748        SEQ ID NO 1312
NM_006759        SEQ ID NO 1313
NM_006762        SEQ ID NO 1314
NM_006763        SEQ ID NO 1315
NM_006769        SEQ ID NO 1316
NM_006770        SEQ ID NO 1317
NM_006780        SEQ ID NO 1318
NM_006787        SEQ ID NO 1319
NM_006806        SEQ ID NO 1320
NM_006813        SEQ ID NO 1321
NM_006825        SEQ ID NO 1322
NM_006826        SEQ ID NO 1323
NM_006829        SEQ ID NO 1324
NM_006834        SEQ ID NO 1325
NM_006835        SEQ ID NO 1326
NM_006840        SEQ ID NO 1327
NM_006845        SEQ ID NO 1328
NM_006847        SEQ ID NO 1329
NM_006851        SEQ ID NO 1330
NM_006855        SEQ ID NO 1331
NM_006864        SEQ ID NO 1332
NM_006868        SEQ ID NO 1333
NM_006875        SEQ ID NO 1334
NM_006889        SEQ ID NO 1336
NM_006892        SEQ ID NO 1337
NM_006912        SEQ ID NO 1338
NM_006931        SEQ ID NO 1341
NM_006941        SEQ ID NO 1342
NM_006943        SEQ ID NO 1343
NM_006984        SEQ ID NO 1344
NM_007005        SEQ ID NO 1345
NM_007006        SEQ ID NO 1346
NM_007019        SEQ ID NO 1347
NM_007027        SEQ ID NO 1348
NM_007044        SEQ ID NO 1350
NM_007050        SEQ ID NO 1351
NM_007057        SEQ ID NO 1352
NM_007069        SEQ ID NO 1353
NM_007074        SEQ ID NO 1355
NM_007088        SEQ ID NO 1356
NM_007111        SEQ ID NO 1357
NM_007146        SEQ ID NO 1358
NM_007173        SEQ ID NO 1359
NM_007177        SEQ ID NO 1360
NM_007196        SEQ ID NO 1361
NM_007203        SEQ ID NO 1362
NM_007214        SEQ ID NO 1363
NM_007217        SEQ ID NO 1364
NM_007231        SEQ ID NO 1365
NM_007268        SEQ ID NO 1367
NM_007274        SEQ ID NO 1368
NM_007275        SEQ ID NO 1369
NM_007281        SEQ ID NO 1370
NM_007309        SEQ ID NO 1371
NM_007315        SEQ ID NO 1372
NM_007334        SEQ ID NO 1373
NM_007358        SEQ ID NO 1374
NM_009585        SEQ ID NO 1375
NM_009587        SEQ ID NO 1376
NM_009588        SEQ ID NO 1377
NM_012062        SEQ ID NO 1378
NM_012067        SEQ ID NO 1379
NM_012101        SEQ ID NO 1380
NM_012105        SEQ ID NO 1381
NM_012108        SEQ ID NO 1382
NM_012110        SEQ ID NO 1383
NM_012124        SEQ ID NO 1384
NM_012142        SEQ ID NO 1386
NM_012155        SEQ ID NO 1388
NM_012175        SEQ ID NO 1389
NM_012177        SEQ ID NO 1390
NM_012205        SEQ ID NO 1391
NM_012219        SEQ ID NO 1393
NM_012242        SEQ ID NO 1394
NM_012250        SEQ ID NO 1395
NM_012261        SEQ ID NO 1397
NM_012286        SEQ ID NO 1398
NM_012319        SEQ ID NO 1400
NM_012332        SEQ ID NO 1401
NM_012336        SEQ ID NO 1402
NM_012339        SEQ ID NO 1404
NM_012341        SEQ ID NO 1405
NM_012391        SEQ ID NO 1406
NM_012394        SEQ ID NO 1407
NM_012413        SEQ ID NO 1408
NM_012421        SEQ ID NO 1409
NM_012425        SEQ ID NO 1410
NM_012427        SEQ ID NO 1411
NM_012429        SEQ ID NO 1413
NM_012446        SEQ ID NO 1414
NM_012463        SEQ ID NO 1415
NM_012474        SEQ ID NO 1416
NM_013230        SEQ ID NO 1417
NM_013233        SEQ ID NO 1418
NM_013238        SEQ ID NO 1419
NM_013239        SEQ ID NO 1420
NM_013242        SEQ ID NO 1421
NM_013257        SEQ ID NO 1423
NM_013261        SEQ ID NO 1424
NM_013262        SEQ ID NO 1425
NM_013277        SEQ ID NO 1426
NM_013296        SEQ ID NO 1427
NM_013301        SEQ ID NO 1428
NM_013324        SEQ ID NO 1429
NM_013327        SEQ ID NO 1430
NM_013336        SEQ ID NO 1431
NM_013339        SEQ ID NO 1432
NM_013363        SEQ ID NO 1433
NM_013378        SEQ ID NO 1435
NM_013384        SEQ ID NO 1436
NM_013385        SEQ ID NO 1437
NM_013406        SEQ ID NO 1438
NM_013437        SEQ ID NO 1439
NM_013451        SEQ ID NO 1440
NM_013943        SEQ ID NO 1441
NM_013994        SEQ ID NO 1442
NM_013995        SEQ ID NO 1443
NM_014026        SEQ ID NO 1444
NM_014029        SEQ ID NO 1445
NM_014036        SEQ ID NO 1446
NM_014062        SEQ ID NO 1447
NM_014074        SEQ ID NO 1448
NM_014096        SEQ ID NO 1450
NM_014109        SEQ ID NO 1451
NM_014112        SEQ ID NO 1452
NM_014147        SEQ ID NO 1453
NM_014149        SEQ ID NO 1454
NM_014164        SEQ ID NO 1455
NM_014172        SEQ ID NO 1456
NM_014175        SEQ ID NO 1457
NM_014181        SEQ ID NO 1458
NM_014184        SEQ ID NO 1459
NM_014211        SEQ ID NO 1460
NM_014214        SEQ ID NO 1461
NM_014216        SEQ ID NO 1462
NM_014241        SEQ ID NO 1463
NM_014246        SEQ ID NO 1465
NM_014268        SEQ ID NO 1466
NM_014272        SEQ ID NO 1467
NM_014274        SEQ ID NO 1468
NM_014289        SEQ ID NO 1469
NM_014298        SEQ ID NO 1470
NM_014302        SEQ ID NO 1471
NM_014315        SEQ ID NO 1473
NM_014316        SEQ ID NO 1474
NM_014317        SEQ ID NO 1475
NM_014320        SEQ ID NO 1476
NM_014321        SEQ ID NO 1477
NM_014325        SEQ ID NO 1478
NM_014335        SEQ ID NO 1479
NM_014363        SEQ ID NO 1480
NM_014364        SEQ ID NO 1481
NM_014365        SEQ ID NO 1482
NM_014373        SEQ ID NO 1483
NM_014382        SEQ ID NO 1484
NM_014395        SEQ ID NO 1485
NM_014398        SEQ ID NO 1486
NM_014399        SEQ ID NO 1487
NM_014402        SEQ ID NO 1488
NM_014428        SEQ ID NO 1489
NM_014448        SEQ ID NO 1490
NM_014449        SEQ ID NO 1491
NM_014450        SEQ ID NO 1492
NM_014452        SEQ ID NO 1493
NM_014453        SEQ ID NO 1494
NM_014456        SEQ ID NO 1495
NM_014479        SEQ ID NO 1497
NM_014501        SEQ ID NO 1498
NM_014552        SEQ ID NO 1500
NM_014553        SEQ ID NO 1501
NM_014570        SEQ ID NO 1502
NM_014575        SEQ ID NO 1503
NM_014585        SEQ ID NO 1504
NM_014595        SEQ ID NO 1505
NM_014624        SEQ ID NO 1507
NM_014633        SEQ ID NO 1508
NM_014640        SEQ ID NO 1509
NM_014642        SEQ ID NO 1510
NM_014643        SEQ ID NO 1511
NM_014656        SEQ ID NO 1512
NM_014668        SEQ ID NO 1513
NM_014669        SEQ ID NO 1514
NM_014673        SEQ ID NO 1515
NM_014675        SEQ ID NO 1516
NM_014679        SEQ ID NO 1517
NM_014680        SEQ ID NO 1518
NM_014696        SEQ ID NO 1519
NM_014700        SEQ ID NO 1520
NM_014715        SEQ ID NO 1521
NM_014721        SEQ ID NO 1522
NM_014737        SEQ ID NO 1524
NM_014738        SEQ ID NO 1525
NM_014747        SEQ ID NO 1526
NM_014750        SEQ ID NO 1527
NM_014754        SEQ ID NO 1528
NM_014767        SEQ ID NO 1529
NM_014770        SEQ ID NO 1530
NM_014773        SEQ ID NO 1531
NM_014776        SEQ ID NO 1532
NM_014782        SEQ ID NO 1533
NM_014785        SEQ ID NO 1534
NM_014791        SEQ ID NO 1535
NM_014808        SEQ ID NO 1536
NM_014811        SEQ ID NO 1537
NM_014812        SEQ ID NO 1538
NM_014838        SEQ ID NO 1540
NM_014862        SEQ ID NO 1542
NM_014865        SEQ ID NO 1543
NM_014870        SEQ ID NO 1544
NM_014875        SEQ ID NO 1545
NM_014886        SEQ ID NO 1547
NM_014889        SEQ ID NO 1548
NM_014905        SEQ ID NO 1549
NM_014935        SEQ ID NO 1550
NM_014945        SEQ ID NO 1551
NM_014965        SEQ ID NO 1552
NM_014967        SEQ ID NO 1553
NM_014968        SEQ ID NO 1554
NM_015032        SEQ ID NO 1555
NM_015239        SEQ ID NO 1556
NM_015383        SEQ ID NO 1557
NM_015392        SEQ ID NO 1558
NM_015416        SEQ ID NO 1559
NM_015417        SEQ ID NO 1560
NM_015420        SEQ ID NO 1561
NM_015434        SEQ ID NO 1562
NM_015474        SEQ ID NO 1563
NM_015507        SEQ ID NO 1565
NM_015513        SEQ ID NO 1566
NM_015515        SEQ ID NO 1567
NM_015523        SEQ ID NO 1568
NM_015524        SEQ ID NO 1569
NM_015599        SEQ ID NO 1571
NM_015623        SEQ ID NO 1572
NM_015640        SEQ ID NO 1573
NM_015641        SEQ ID NO 1574
NM_015678        SEQ ID NO 1575
NM_015721        SEQ ID NO 1576
NM_015892        SEQ ID NO 1578
NM_015895        SEQ ID NO 1579
NM_015907        SEQ ID NO 1580
NM_015925        SEQ ID NO 1581
NM_015937        SEQ ID NO 1582
NM_015954        SEQ ID NO 1583
NM_015955        SEQ ID NO 1584
NM_015961        SEQ ID NO 1585
NM_015984        SEQ ID NO 1587
NM_015986        SEQ ID NO 1588
NM_015987        SEQ ID NO 1589
NM_015991        SEQ ID NO 1590
NM_016002        SEQ ID NO 1592
NM_016028        SEQ ID NO 1594
NM_016029        SEQ ID NO 1595
NM_016047        SEQ ID NO 1596
NM_016048        SEQ ID NO 1597
NM_016050        SEQ ID NO 1598
NM_016056        SEQ ID NO 1599
NM_016058        SEQ ID NO 1600
NM_016066        SEQ ID NO 1601
NM_016072        SEQ ID NO 1602
NM_016073        SEQ ID NO 1603
NM_016108        SEQ ID NO 1605
NM_016109        SEQ ID NO 1606
NM_016121        SEQ ID NO 1607
NM_016126        SEQ ID NO 1608
NM_016127        SEQ ID NO 1609
NM_016135        SEQ ID NO 1610
NM_016142        SEQ ID NO 1612
NM_016153        SEQ ID NO 1613
NM_016171        SEQ ID NO 1614
NM_016175        SEQ ID NO 1615
NM_016184        SEQ ID NO 1616
NM_016185        SEQ ID NO 1617
NM_016187        SEQ ID NO 1618
NM_016199        SEQ ID NO 1619
NM_016210        SEQ ID NO 1620
NM_016217        SEQ ID NO 1621
NM_016228        SEQ ID NO 1623
NM_016229        SEQ ID NO 1624
NM_016235        SEQ ID NO 1625
NM_016240        SEQ ID NO 1626
NM_016243        SEQ ID NO 1627
NM_016250        SEQ ID NO 1628
NM_016267        SEQ ID NO 1629
NM_016271        SEQ ID NO 1630
NM_016299        SEQ ID NO 1631
NM_016306        SEQ ID NO 1632
NM_016308        SEQ ID NO 1634
NM_016321        SEQ ID NO 1635
NM_016337        SEQ ID NO 1636
NM_016352        SEQ ID NO 1637
NM_016359        SEQ ID NO 1638
NM_016401        SEQ ID NO 1641
NM_016403        SEQ ID NO 1642
NM_016411        SEQ ID NO 1643
NM_016423        SEQ ID NO 1644
NM_016463        SEQ ID NO 1647
NM_016475        SEQ ID NO 1649
NM_016477        SEQ ID NO 1650
NM_016491        SEQ ID NO 1651
NM_016495        SEQ ID NO 1652
NM_016542        SEQ ID NO 1653
NM_016548        SEQ ID NO 1654
NM_016569        SEQ ID NO 1655
NM_016577        SEQ ID NO 1656
NM_016582        SEQ ID NO 1657
NM_016593        SEQ ID NO 1658
NM_016603        SEQ ID NO 1659
NM_016612        SEQ ID NO 1660
NM_016619        SEQ ID NO 1661
NM_016623        SEQ ID NO 1663
NM_016625        SEQ ID NO 1664
NM_016629        SEQ ID NO 1665
NM_016640        SEQ ID NO 1666
NM_016645        SEQ ID NO 1667
NM_016650        SEQ ID NO 1668
NM_016657        SEQ ID NO 1669
NM_016733        SEQ ID NO 1670
NM_016815        SEQ ID NO 1671
NM_016817        SEQ ID NO 1672
NM_016818        SEQ ID NO 1673
NM_016839        SEQ ID NO 1675
NM_017414        SEQ ID NO 1676
NM_017422        SEQ ID NO 1677
NM_017423        SEQ ID NO 1678
NM_017447        SEQ ID NO 1679
NM_017518        SEQ ID NO 1680
NM_017522        SEQ ID NO 1681
NM_017540        SEQ ID NO 1682
NM_017555        SEQ ID NO 1683
NM_017572        SEQ ID NO 1684
NM_017585        SEQ ID NO 1685
NM_017586        SEQ ID NO 1686
NM_017596        SEQ ID NO 1687
NM_017606        SEQ ID NO 1688
NM_017617        SEQ ID NO 1689
NM_017633        SEQ ID NO 1690
NM_017634        SEQ ID NO 1691
NM_017646        SEQ ID NO 1692
NM_017660        SEQ ID NO 1693
NM_017680        SEQ ID NO 1694
NM_017691        SEQ ID NO 1695
NM_017698        SEQ ID NO 1696
NM_017702        SEQ ID NO 1697
NM_017731        SEQ ID NO 1699
NM_017732        SEQ ID NO 1700
NM_017733        SEQ ID NO 1701
NM_017734        SEQ ID NO 1702
NM_017746        SEQ ID NO 1703
NM_017750        SEQ ID NO 1704
NM_017761        SEQ ID NO 1705
NM_017763        SEQ ID NO 1706
NM_017770        SEQ ID NO 1707
NM_017779        SEQ ID NO 1708
NM_017780        SEQ ID NO 1709
NM_017782        SEQ ID NO 1710
NM_017786        SEQ ID NO 1711
NM_017791        SEQ ID NO 1712
NM_017805        SEQ ID NO 1713
NM_017816        SEQ ID NO 1714
NM_017821        SEQ ID NO 1715
NM_017835        SEQ ID NO 1716
NM_017843        SEQ ID NO 1717
NM_017857        SEQ ID NO 1718
NM_017901        SEQ ID NO 1719
NM_017906        SEQ ID NO 1720
NM_017918        SEQ ID NO 1721
NM_017961        SEQ ID NO 1722
NM_017996        SEQ ID NO 1723
NM_018000        SEQ ID NO 1724
NM_018004        SEQ ID NO 1725
NM_018011        SEQ ID NO 1726
NM_018014        SEQ ID NO 1727
NM_018022        SEQ ID NO 1728
NM_018031        SEQ ID NO 1729
NM_018043        SEQ ID NO 1730
NM_018048        SEQ ID NO 1731
NM_018062        SEQ ID NO 1732
NM_018069        SEQ ID NO 1733
NM_018072        SEQ ID NO 1734
NM_018077        SEQ ID NO 1735
NM_018086        SEQ ID NO 1736
NM_018087        SEQ ID NO 1737
NM_018093        SEQ ID NO 1738
NM_018098        SEQ ID NO 1739
NM_018099        SEQ ID NO 1740
NM_018101        SEQ ID NO 1741
NM_018103        SEQ ID NO 1742
NM_018109        SEQ ID NO 1744
NM_018123        SEQ ID NO 1746
NM_018131        SEQ ID NO 1747
NM_018136        SEQ ID NO 1748
NM_018138        SEQ ID NO 1749
NM_018166        SEQ ID NO 1750
NM_018171        SEQ ID NO 1751
NM_018178        SEQ ID NO 1752
NM_018181        SEQ ID NO 1753
NM_018186        SEQ ID NO 1754
NM_018188        SEQ ID NO 1756
NM_018194        SEQ ID NO 1757
NM_018204        SEQ ID NO 1758
NM_018208        SEQ ID NO 1759
NM_018212        SEQ ID NO 1760
NM_018234        SEQ ID NO 1763
NM_018255        SEQ ID NO 1764
NM_018257        SEQ ID NO 1765
NM_018265        SEQ ID NO 1766
NM_018271        SEQ ID NO 1767
NM_018290        SEQ ID NO 1768
NM_018295        SEQ ID NO 1769
NM_018304        SEQ ID NO 1770
NM_018306        SEQ ID NO 1771
NM_018326        SEQ ID NO 1772
NM_018346        SEQ ID NO 1773
NM_018366        SEQ ID NO 1775
NM_018370        SEQ ID NO 1776
NM_018373        SEQ ID NO 1777
NM_018379        SEQ ID NO 1778
NM_018384        SEQ ID NO 1779
NM_018389        SEQ ID NO 1780
NM_018410        SEQ ID NO 1783
NM_018439        SEQ ID NO 1785
NM_018454        SEQ ID NO 1786
NM_018455        SEQ ID NO 1787
NM_018465        SEQ ID NO 1788
NM_018471        SEQ ID NO 1789
NM_018478        SEQ ID NO 1790
NM_018479        SEQ ID NO 1791
NM_018529        SEQ ID NO 1793
NM_018556        SEQ ID NO 1794
NM_018569        SEQ ID NO 1795
NM_018584        SEQ ID NO 1796
NM_018653        SEQ ID NO 1797
NM_018660        SEQ ID NO 1798
NM_018683        SEQ ID NO 1799
NM_018685        SEQ ID NO 1800
NM_018686        SEQ ID NO 1801
NM_018695        SEQ ID NO 1802
NM_018728        SEQ ID NO 1803
NM_018840        SEQ ID NO 1804
NM_018842        SEQ ID NO 1805
NM_018950        SEQ ID NO 1806
NM_018988        SEQ ID NO 1807
NM_019000        SEQ ID NO 1808
NM_019013        SEQ ID NO 1809
NM_019025        SEQ ID NO 1810
NM_019027        SEQ ID NO 1811
NM_019041        SEQ ID NO 1812
NM_019044        SEQ ID NO 1813
NM_019063        SEQ ID NO 1815
NM_019084        SEQ ID NO 1816
NM_019554        SEQ ID NO 1817
NM_019845        SEQ ID NO 1818
NM_019858        SEQ ID NO 1819
NM_020130        SEQ ID NO 1820
NM_020133        SEQ ID NO 1821
NM_020143        SEQ ID NO 1822
NM_020150        SEQ ID NO 1823
NM_020163        SEQ ID NO 1824
NM_020166        SEQ ID NO 1825
NM_020169        SEQ ID NO 1826
NM_020179        SEQ ID NO 1827
NM_020184        SEQ ID NO 1828
NM_020186        SEQ ID NO 1829
NM_020188        SEQ ID NO 1830
NM_020189        SEQ ID NO 1831
NM_020197        SEQ ID NO 1832
NM_020199        SEQ ID NO 1833
NM_020215        SEQ ID NO 1834
NM_020347        SEQ ID NO 1836
NM_020365        SEQ ID NO 1837
NM_020386        SEQ ID NO 1838
NM_020445        SEQ ID NO 1839
NM_020639        SEQ ID NO 1840
NM_020659        SEQ ID NO 1841
NM_020675        SEQ ID NO 1842
NM_020686        SEQ ID NO 1843
NM_020974        SEQ ID NO 1844
NM_020978        SEQ ID NO 1845
NM_020979        SEQ ID NO 1846
NM_020980        SEQ ID NO 1847
NM_021000        SEQ ID NO 1849
NM_021004        SEQ ID NO 1850
NM_021025        SEQ ID NO 1851
NM_021063        SEQ ID NO 1852
NM_021065        SEQ ID NO 1853
NM_021077        SEQ ID NO 1854
NM_021095        SEQ ID NO 1855
NM_021101        SEQ ID NO 1856
NM_021103        SEQ ID NO 1857
NM_021128        SEQ ID NO 1858
NM_021147        SEQ ID NO 1859
NM_021151        SEQ ID NO 1860
NM_021181        SEQ ID NO 1861
NM_021190        SEQ ID NO 1862
NM_021198        SEQ ID NO 1863
NM_021200        SEQ ID NO 1864
NM_021203        SEQ ID NO 1865
NM_021238        SEQ ID NO 1866
NM_021242        SEQ ID NO 1867
S40706           SEQ ID NO 1869
S53354           SEQ ID NO 1870
S59184           SEQ ID NO 1871
S62138           SEQ ID NO 1872
U09848           SEQ ID NO 1873
U10991           SEQ ID NO 1874
U17077           SEQ ID NO 1875
U18919           SEQ ID NO 1876
U41387           SEQ ID NO 1877
U45975           SEQ ID NO 1878
U49835           SEQ ID NO 1879
U56725           SEQ ID NO 1880
U58033           SEQ ID NO 1881
U61167           SEQ ID NO 1882
U66042           SEQ ID NO 1883
U68385           SEQ ID NO 1885
U68494           SEQ ID NO 1886
U74612           SEQ ID NO 1887
U75968           SEQ ID NO 1888
U79293           SEQ ID NO 1889
U80736           SEQ ID NO 1890
U82987           SEQ ID NO 1891
U83115           SEQ ID NO 1892
U89715           SEQ ID NO 1893
U90916           SEQ ID NO 1894
U92544           SEQ ID NO 1895
U96131           SEQ ID NO 1896
U96394           SEQ ID NO 1897
W61000_RC        SEQ ID NO 1898
X00437           SEQ ID NO 1899
X00497           SEQ ID NO 1900
X01394           SEQ ID NO 1901
X03084           SEQ ID NO 1902
X07834           SEQ ID NO 1905
X14356           SEQ ID NO 1906
X16302           SEQ ID NO 1907
X52486           SEQ ID NO 1909
X52882           SEQ ID NO 1910
X56807           SEQ ID NO 1911
X57809           SEQ ID NO 1912
X57819           SEQ ID NO 1913
X58529           SEQ ID NO 1914
X59405           SEQ ID NO 1915
X72475           SEQ ID NO 1918
X73617           SEQ ID NO 1919
X74794           SEQ ID NO 1920
X75315           SEQ ID NO 1921
X79782           SEQ ID NO 1922
X82693           SEQ ID NO 1923
X83301           SEQ ID NO 1924
X93006           SEQ ID NO 1926
X94232           SEQ ID NO 1927
X98834           SEQ ID NO 1929
X99142           SEQ ID NO 1930
Y14737           SEQ ID NO 1932
Z11887           SEQ ID NO 1933
Z48633           SEQ ID NO 1935
NM_004222        SEQ ID NO 1936
NM_016405        SEQ ID NO 1937
NM_017690        SEQ ID NO 1938
Contig29_RC      SEQ ID NO 1939
Contig237_RC     SEQ ID NO 1940
Contig263_RC     SEQ ID NO 1941
Contig292_RC     SEQ ID NO 1942
Contig382_RC     SEQ ID NO 1944
Contig399_RC     SEQ ID NO 1945
Contig448_RC     SEQ ID NO 1946
Contig569_RC     SEQ ID NO 1947
Contig580_RC     SEQ ID NO 1948
Contig678_RC     SEQ ID NO 1949
Contig706_RC     SEQ ID NO 1950
Contig718_RC     SEQ ID NO 1951
Contig719_RC     SEQ ID NO 1952
Contig742_RC     SEQ ID NO 1953
Contig753_RC     SEQ ID NO 1954
Contig758_RC     SEQ ID NO 1956
Contig760_RC     SEQ ID NO 1957
Contig842_RC     SEQ ID NO 1958
Contig848_RC     SEQ ID NO 1959
Contig924_RC     SEQ ID NO 1960
Contig974_RC     SEQ ID NO 1961
Contig1018_RC    SEQ ID NO 1962
Contig1056_RC    SEQ ID NO 1963
Contig1061_RC    SEQ ID NO 1964
Contig1129_RC    SEQ ID NO 1965
Contig1148       SEQ ID NO 1966
Contig1239_RC    SEQ ID NO 1967
Contig1277       SEQ ID NO 1968
Contig1333_RC    SEQ ID NO 1969
Contig1386_RC    SEQ ID NO 1970
Contig1389_RC    SEQ ID NO 1971
Contig1418_RC    SEQ ID NO 1972
Contig1462_RC    SEQ ID NO 1973
Contig1505_RC    SEQ ID NO 1974
Contig1540_RC    SEQ ID NO 1975
Contig1584_RC    SEQ ID NO 1976
Contig1632_RC    SEQ ID NO 1977
Contig1682_RC    SEQ ID NO 1978
Contig1778_RC    SEQ ID NO 1979
Contig1829       SEQ ID NO 1981
Contig1838_RC    SEQ ID NO 1982
Contig1938_RC    SEQ ID NO 1983
Contig1970_RC    SEQ ID NO 1984
Contig1998_RC    SEQ ID NO 1985
Contig2099_RC    SEQ ID NO 1986
Contig2143_RC    SEQ ID NO 1987
Contig2237_RC    SEQ ID NO 1988
Contig2429_RC    SEQ ID NO 1990
Contig2504_RC    SEQ ID NO 1991
Contig2512_RC    SEQ ID NO 1992
Contig2575_RC    SEQ ID NO 1993
Contig2578_RC    SEQ ID NO 1994
Contig2639_RC    SEQ ID NO 1995
Contig2647_RC    SEQ ID NO 1996
Contig2657_RC    SEQ ID NO 1997
Contig2728_RC    SEQ ID NO 1998
Contig2745_RC    SEQ ID NO 1999
Contig2811_RC    SEQ ID NO 2000
Contig2873_RC    SEQ ID NO 2001
Contig2883_RC    SEQ ID NO 2002
Contig2915_RC    SEQ ID NO 2003
Contig2928_RC    SEQ ID NO 2004
Contig3024_RC    SEQ ID NO 2005
Contig3094_RC    SEQ ID NO 2006
Contig3164_RC    SEQ ID NO 2007
Contig3495_RC    SEQ ID NO 2009
Contig3607_RC    SEQ ID NO 2010
Contig3659_RC    SEQ ID NO 2011
Contig3677_RC    SEQ ID NO 2012
Contig3682_RC    SEQ ID NO 2013
Contig3734_RC    SEQ ID NO 2014
Contig3834_RC    SEQ ID NO 2015
Contig3876_RC    SEQ ID NO 2016
Contig3902_RC    SEQ ID NO 2017
Contig3940_RC    SEQ ID NO 2018
Contig4380_RC    SEQ ID NO 2019
Contig4388_RC    SEQ ID NO 2020
Contig4467_RC    SEQ ID NO 2021
Contig4949_RC    SEQ ID NO 2023
Contig5348_RC    SEQ ID NO 2024
Contig5403_RC    SEQ ID NO 2025
Contig5716_RC    SEQ ID NO 2026
Contig6118_RC    SEQ ID NO 2027
Contig6164_RC    SEQ ID NO 2028
Contig6181_RC    SEQ ID NO 2029
Contig6514_RC    SEQ ID NO 2030
Contig6612_RC    SEQ ID NO 2031
Contig6881_RC    SEQ ID NO 2032
Contig8165_RC    SEQ ID NO 2033
Contig8221_RC    SEQ ID NO 2034
Contig8347_RC    SEQ ID NO 2035
Contig8364_RC    SEQ ID NO 2036
Contig8888_RC    SEQ ID NO 2038
Contig9259_RC    SEQ ID NO 2039
Contig9541_RC    SEQ ID NO 2040
Contig10268_RC   SEQ ID NO 2041
Contig10363_RC   SEQ ID NO 2042
Contig10437_RC   SEQ ID NO 2043
Contig11086_RC   SEQ ID NO 2045
Contig11275_RC   SEQ ID NO 2046
Contig11648_RC   SEQ ID NO 2047
Contig12216_RC   SEQ ID NO 2048
Contig12369_RC   SEQ ID NO 2049
Contig12814_RC   SEQ ID NO 2050
Contig12951_RC   SEQ ID NO 2051
Contig13480_RC   SEQ ID NO 2052
Contig14284_RC   SEQ ID NO 2053
Contig14390_RC   SEQ ID NO 2054
Contig14780_RC   SEQ ID NO 2055
Contig14954_RC   SEQ ID NO 2056
Contig14981_RC   SEQ ID NO 2057
Contig15692_RC   SEQ ID NO 2058
Contig16192_RC   SEQ ID NO 2059
Contig16759_RC   SEQ ID NO 2061
Contig16786_RC   SEQ ID NO 2062
Contig16905_RC   SEQ ID NO 2063
Contig17103_RC   SEQ ID NO 2064
Contig17105_RC   SEQ ID NO 2065
Contig17248_RC   SEQ ID NO 2066
Contig17345_RC   SEQ ID NO 2067
Contig18502_RC   SEQ ID NO 2069
Contig20156_RC   SEQ ID NO 2071
Contig20302_RC   SEQ ID NO 2073
Contig20600_RC   SEQ ID NO 2074
Contig20617_RC   SEQ ID NO 2075
Contig20629_RC   SEQ ID NO 2076
Contig20651_RC   SEQ ID NO 2077
Contig21130_RC   SEQ ID NO 2078
Contig21185_RC   SEQ ID NO 2079
Contig21421_RC   SEQ ID NO 2080
Contig21787_RC   SEQ ID NO 2081
Contig21812_RC   SEQ ID NO 2082
Contig22418_RC   SEQ ID NO 2083
Contig23085_RC   SEQ ID NO 2084
Contig23454_RC   SEQ ID NO 2085
Contig24138_RC   SEQ ID NO 2086
Contig24252_RC   SEQ ID NO 2087
Contig24655_RC   SEQ ID NO 2089
Contig25055_RC   SEQ ID NO 2090
Contig25290_RC   SEQ ID NO 2091
Contig25343_RC   SEQ ID NO 2092
Contig25362_RC   SEQ ID NO 2093
Contig25617_RC   SEQ ID NO 2094
Contig25659_RC   SEQ ID NO 2095
Contig25722_RC   SEQ ID NO 2096
Contig25809_RC   SEQ ID NO 2097
Contig25991      SEQ ID NO 2098
Contig26022_RC   SEQ ID NO 2099
Contig26077_RC   SEQ ID NO 2100
Contig26310_RC   SEQ ID NO 2101
Contig26371_RC   SEQ ID NO 2102
Contig26438_RC   SEQ ID NO 2103
Contig26706_RC   SEQ ID NO 2104
Contig27088_RC   SEQ ID NO 2105
Contig27186_RC   SEQ ID NO 2106
Contig27228_RC   SEQ ID NO 2107
Contig27344_RC   SEQ ID NO 2109
Contig27386_RC   SEQ ID NO 2110
Contig27624_RC   SEQ ID NO 2111
Contig27749_RC   SEQ ID NO 2112
Contig27882_RC   SEQ ID NO 2113
Contig27915_RC   SEQ ID NO 2114
Contig28030_RC   SEQ ID NO 2115
Contig28081_RC   SEQ ID NO 2116
Contig28152_RC   SEQ ID NO 2117
Contig28550_RC   SEQ ID NO 2119
Contig28552_RC   SEQ ID NO 2120
Contig28712_RC   SEQ ID NO 2121
Contig28888_RC   SEQ ID NO 2122
Contig28947_RC   SEQ ID NO 2123
Contig29126_RC   SEQ ID NO 2124
Contig29193_RC   SEQ ID NO 2125
Contig29369_RC   SEQ ID NO 2126
Contig29639_RC   SEQ ID NO 2127
Contig30047_RC   SEQ ID NO 2129
Contig30154_RC   SEQ ID NO 2131
Contig30209_RC   SEQ ID NO 2132
Contig30213_RC   SEQ ID NO 2133
Contig30230_RC   SEQ ID NO 2134
Contig30267_RC   SEQ ID NO 2135
Contig30390_RC   SEQ ID NO 2136
Contig30480_RC   SEQ ID NO 2137
Contig30609_RC   SEQ ID NO 2138
Contig30934_RC   SEQ ID NO 2139
Contig31150_RC   SEQ ID NO 2140
Contig31186_RC   SEQ ID NO 2141
Contig31251_RC   SEQ ID NO 2142
Contig31288_RC   SEQ ID NO 2143
Contig31291_RC   SEQ ID NO 2144
Contig31295_RC   SEQ ID NO 2145
Contig31424_RC   SEQ ID NO 2146
Contig31449_RC   SEQ ID NO 2147
Contig31596_RC   SEQ ID NO 2148
Contig31864_RC   SEQ ID NO 2149
Contig31928_RC   SEQ ID NO 2150
Contig31966_RC   SEQ ID NO 2151
Contig31986_RC   SEQ ID NO 2152
Contig32084_RC   SEQ ID NO 2153
Contig32105_RC   SEQ ID NO 2154
Contig32185_RC   SEQ ID NO 2156
Contig32242_RC   SEQ ID NO 2157
Contig32322_RC   SEQ ID NO 2158
Contig32336_RC   SEQ ID NO 2159
Contig32558_RC   SEQ ID NO 2160
Contig32798_RC   SEQ ID NO 2161
Contig33005_RC   SEQ ID NO 2162
Contig33230_RC   SEQ ID NO 2163
Contig33260_RC   SEQ ID NO 2164
Contig33654_RC   SEQ ID NO 2166
Contig33741_RC   SEQ ID NO 2167
Contig33771_RC   SEQ ID NO 2168
Contig33814_RC   SEQ ID NO 2169
Contig33815_RC   SEQ ID NO 2170
Contig33833      SEQ ID NO 2171
Contig33998_RC   SEQ ID NO 2172
Contig34079      SEQ ID NO 2173
Contig34080_RC   SEQ ID NO 2174
Contig34222_RC   SEQ ID NO 2175
Contig34233_RC   SEQ ID NO 2176
Contig34303_RC   SEQ ID NO 2177
Contig34393_RC   SEQ ID NO 2178
Contig34477_RC   SEQ ID NO 2179
Contig34766_RC   SEQ ID NO 2181
Contig34952      SEQ ID NO 2182
Contig34989_RC   SEQ ID NO 2183
Contig35030_RC   SEQ ID NO 2184
Contig35251_RC   SEQ ID NO 2185
Contig35629_RC   SEQ ID NO 2186
Contig35635_RC   SEQ ID NO 2187
Contig35763_RC   SEQ ID NO 2188
Contig35814_RC   SEQ ID NO 2189
Contig35896_RC   SEQ ID NO 2190
Contig35976_RC   SEQ ID NO 2191
Contig36042_RC   SEQ ID NO 2192
Contig36081_RC   SEQ ID NO 2193
Contig36152_RC   SEQ ID NO 2194
Contig36193_RC   SEQ ID NO 2195
Contig36312_RC   SEQ ID NO 2196
Contig36323_RC   SEQ ID NO 2197
Contig36339_RC   SEQ ID NO 2198
Contig36647_RC   SEQ ID NO 2199
Contig36744_RC   SEQ ID NO 2200
Contig36761_RC   SEQ ID NO 2201
Contig36879_RC   SEQ ID NO 2202
Contig36900_RC   SEQ ID NO 2203
Contig37015_RC   SEQ ID NO 2204
Contig37024_RC   SEQ ID NO 2205
Contig37072_RC   SEQ ID NO 2207
Contig37140_RC   SEQ ID NO 2208
Contig37141_RC   SEQ ID NO 2209
Contig37204_RC   SEQ ID NO 2210
Contig37281_RC   SEQ ID NO 2211
Contig37287_RC   SEQ ID NO 2212
Contig37439_RC   SEQ ID NO 2213
Contig37562_RC   SEQ ID NO 2214
Contig37571_RC   SEQ ID NO 2215
Contig37598      SEQ ID NO 2216
Contig37758_RC   SEQ ID NO 2217
Contig37778_RC   SEQ ID NO 2218
Contig37884_RC   SEQ ID NO 2219
Contig37946_RC   SEQ ID NO 2220
Contig38170_RC   SEQ ID NO 2221
Contig38288_RC   SEQ ID NO 2223
Contig38398_RC   SEQ ID NO 2224
Contig38580_RC   SEQ ID NO 2226
Contig38630_RC   SEQ ID NO 2227
Contig38652_RC   SEQ ID NO 2228
Contig38683_RC   SEQ ID NO 2229
Contig38726_RC   SEQ ID NO 2230
Contig38791_RC   SEQ ID NO 2231
Contig38901_RC   SEQ ID NO 2232
Contig38983_RC   SEQ ID NO 2233
Contig39090_RC   SEQ ID NO 2234
Contig39132_RC   SEQ ID NO 2235
Contig39157_RC   SEQ ID NO 2236
Contig39226_RC   SEQ ID NO 2237
Contig39285_RC   SEQ ID NO 2238
Contig39556_RC   SEQ ID NO 2239
Contig39591_RC   SEQ ID NO 2240
Contig39826_RC   SEQ ID NO 2241
Contig39845_RC   SEQ ID NO 2242
Contig39891_RC   SEQ ID NO 2243
Contig39922_RC   SEQ ID NO 2244
Contig39960_RC   SEQ ID NO 2245
Contig40026_RC   SEQ ID NO 2246
Contig40121_RC   SEQ ID NO 2247
Contig40128_RC   SEQ ID NO 2248
Contig40146      SEQ ID NO 2249
Contig40208_RC   SEQ ID NO 2250
Contig40212_RC   SEQ ID NO 2251
Contig40238_RC   SEQ ID NO 2252
Contig40434_RC   SEQ ID NO 2253
Contig40446_RC   SEQ ID NO 2254
Contig40500_RC   SEQ ID NO 2255
Contig40573_RC   SEQ ID NO 2256
Contig40813_RC   SEQ ID NO 2258
Contig40816_RC   SEQ ID NO 2259
Contig40845_RC   SEQ ID NO 2261
Contig40889_RC   SEQ ID NO 2262
Contig41035      SEQ ID NO 2263
Contig41234_RC   SEQ ID NO 2264
Contig41413_RC   SEQ ID NO 2266
Contig41521_RC   SEQ ID NO 2267
Contig41530_RC   SEQ ID NO 2268
Contig41590      SEQ ID NO 2269
Contig41618_RC   SEQ ID NO 2270
Contig41624_RC   SEQ ID NO 2271
Contig41635_RC   SEQ ID NO 2272
Contig41676_RC   SEQ ID NO 2273
Contig41689_RC   SEQ ID NO 2274
Contig41804_RC   SEQ ID NO 2275
Contig41887_RC   SEQ ID NO 2276
Contig41905_RC   SEQ ID NO 2277
Contig41954_RC   SEQ ID NO 2278
Contig41983_RC   SEQ ID NO 2279
Contig42006_RC   SEQ ID NO 2280
Contig42014_RC   SEQ ID NO 2281
Contig42036_RC   SEQ ID NO 2282
Contig42041_RC   SEQ ID NO 2283
Contig42139      SEQ ID NO 2284
Contig42161_RC   SEQ ID NO 2285
Contig42220_RC   SEQ ID NO 2286
Contig42306_RC   SEQ ID NO 2287
Contig42311_RC   SEQ ID NO 2288
Contig42313_RC   SEQ ID NO 2289
Contig42402_RC   SEQ ID NO 2290
Contig42421_RC   SEQ ID NO 2291
Contig42430_RC   SEQ ID NO 2292
Contig42431_RC   SEQ ID NO 2293
Contig42542_RC   SEQ ID NO 2294
Contig42582      SEQ ID NO 2295
Contig42631_RC   SEQ ID NO 2296
Contig42751_RC   SEQ ID NO 2297
Contig42759_RC   SEQ ID NO 2298
Contig43054      SEQ ID NO 2299
Contig43079_RC   SEQ ID NO 2300
Contig43195_RC   SEQ ID NO 2301
Contig43368_RC   SEQ ID NO 2302
Contig43410_RC   SEQ ID NO 2303
Contig43476_RC   SEQ ID NO 2304
Contig43549_RC   SEQ ID NO 2305
Contig43645_RC   SEQ ID NO 2306
Contig43648_RC   SEQ ID NO 2307
Contig43673_RC   SEQ ID NO 2308
Contig43679_RC   SEQ ID NO 2309
Contig43694_RC   SEQ ID NO 2310
Contig43747_RC   SEQ ID NO 2311
Contig43918_RC   SEQ ID NO 2312
Contig43983_RC   SEQ ID NO 2313
Contig44040_RC   SEQ ID NO 2314
Contig44064_RC   SEQ ID NO 2315
Contig44195_RC   SEQ ID NO 2316
Contig44226_RC   SEQ ID NO 2317
Contig44289_RC   SEQ ID NO 2320
Contig44310_RC   SEQ ID NO 2321
Contig44409      SEQ ID NO 2322
Contig44413_RC   SEQ ID NO 2323
Contig44451_RC   SEQ ID NO 2324
Contig44585_RC   SEQ ID NO 2325
Contig44656_RC   SEQ ID NO 2326
Contig44703_RC   SEQ ID NO 2327
Contig44708_RC   SEQ ID NO 2328
Contig44757_RC   SEQ ID NO 2329
Contig44829_RC   SEQ ID NO 2331
Contig44870      SEQ ID NO 2332
Contig44893_RC   SEQ ID NO 2333
Contig44909_RC   SEQ ID NO 2334
Contig44939_RC   SEQ ID NO 2335
Contig45022_RC   SEQ ID NO 2336
Contig45032_RC   SEQ ID NO 2337
Contig45041_RC   SEQ ID NO 2338
Contig45049_RC   SEQ ID NO 2339
Contig45090_RC   SEQ ID NO 2340
Contig45156_RC   SEQ ID NO 2341
Contig45316_RC   SEQ ID NO 2342
Contig45321      SEQ ID NO 2343
Contig45375_RC   SEQ ID NO 2345
Contig45443_RC   SEQ ID NO 2346
Contig45454_RC   SEQ ID NO 2347
Contig45537_RC   SEQ ID NO 2348
Contig45588_RC   SEQ ID NO 2349
Contig45708_RC   SEQ ID NO 2350
Contig45816_RC   SEQ ID NO 2351
Contig45847_RC   SEQ ID NO 2352
Contig45891_RC   SEQ ID NO 2353
Contig46056_RC   SEQ ID NO 2354
Contig46062_RC   SEQ ID NO 2355
Contig46075_RC   SEQ ID NO 2356
Contig46164_RC   SEQ ID NO 2357
Contig46218_RC   SEQ ID NO 2358
Contig46223_RC   SEQ ID NO 2359
Contig46244_RC   SEQ ID NO 2360
Contig46262_RC   SEQ ID NO 2361
Contig46362_RC   SEQ ID NO 2364
Contig46443_RC   SEQ ID NO 2365
Contig46553_RC   SEQ ID NO 2367
Contig46597_RC   SEQ ID NO 2368
Contig46653_RC   SEQ ID NO 2369
Contig46709_RC   SEQ ID NO 2370
Contig46777_RC   SEQ ID NO 2371
Contig46802_RC   SEQ ID NO 2372
Contig46890_RC   SEQ ID NO 2374
Contig46922_RC   SEQ ID NO 2375
Contig46934_RC   SEQ ID NO 2376
Contig46937_RC   SEQ ID NO 2377
Contig46991_RC   SEQ ID NO 2378
Contig47016_RC   SEQ ID NO 2379
Contig47045_RC   SEQ ID NO 2380
Contig47106_RC   SEQ ID NO 2381
Contig47146_RC   SEQ ID NO 2382
Contig47230_RC   SEQ ID NO 2383
Contig47405_RC   SEQ ID NO 2384
Contig47456_RC   SEQ ID NO 2385
Contig47465_RC   SEQ ID NO 2386
Contig47498_RC   SEQ ID NO 2387
Contig47578_RC   SEQ ID NO 2388
Contig47645_RC   SEQ ID NO 2389
Contig47680_RC   SEQ ID NO 2390
Contig47781_RC   SEQ ID NO 2391
Contig47814_RC   SEQ ID NO 2392
Contig48004_RC   SEQ ID NO 2393
Contig48043_RC   SEQ ID NO 2394
Contig48057_RC   SEQ ID NO 2395
Contig48076_RC   SEQ ID NO 2396
Contig48249_RC   SEQ ID NO 2397
Contig48263_RC   SEQ ID NO 2398
Contig48270_RC   SEQ ID NO 2399
Contig48328_RC   SEQ ID NO 2400
Contig48518_RC   SEQ ID NO 2401
Contig48572_RC   SEQ ID NO 2402
Contig48659_RC   SEQ ID NO 2403
Contig48722_RC   SEQ ID NO 2404
Contig48774_RC   SEQ ID NO 2405
Contig48776_RC   SEQ ID NO 2406
Contig48800_RC   SEQ ID NO 2407
Contig48806_RC   SEQ ID NO 2408
Contig48852_RC   SEQ ID NO 2409
Contig48900_RC   SEQ ID NO 2410
Contig48913_RC   SEQ ID NO 2411
Contig48970_RC   SEQ ID NO 2413
Contig49058_RC   SEQ ID NO 2414
Contig49063_RC   SEQ ID NO 2415
Contig49093      SEQ ID NO 2416
Contig49098_RC   SEQ ID NO 2417
Contig49169_RC   SEQ ID NO 2418
Contig49233_RC   SEQ ID NO 2419
Contig49270_RC   SEQ ID NO 2420
Contig49282_RC   SEQ ID NO 2421
Contig49289_RC   SEQ ID NO 2422
Contig49342_RC   SEQ ID NO 2423
Contig49344      SEQ ID NO 2424
Contig49388_RC   SEQ ID NO 2425
Contig49405_RC   SEQ ID NO 2426
Contig49445_RC   SEQ ID NO 2427
Contig49468_RC   SEQ ID NO 2428
Contig49509_RC   SEQ ID NO 2429
Contig49578_RC   SEQ ID NO 2431
Contig49581_RC   SEQ ID NO 2432
Contig49631_RC   SEQ ID NO 2433
Contig49673_RC   SEQ ID NO 2435
Contig49743_RC   SEQ ID NO 2436
Contig49790_RC   SEQ ID NO 2437
Contig49818_RC   SEQ ID NO 2438
Contig49849_RC   SEQ ID NO 2439
Contig49855      SEQ ID NO 2440
Contig49910_RC   SEQ ID NO 2441
Contig49948_RC   SEQ ID NO 2442
Contig50004_RC   SEQ ID NO 2443
Contig50094      SEQ ID NO 2444
Contig50120_RC   SEQ ID NO 2446
Contig50153_RC   SEQ ID NO 2447
Contig50189_RC   SEQ ID NO 2448
Contig50276_RC   SEQ ID NO 2449
Contig50288_RC   SEQ ID NO 2450
Contig50297_RC   SEQ ID NO 2451
Contig50391_RC   SEQ ID NO 2452
Contig50410      SEQ ID NO 2453
Contig50523_RC   SEQ ID NO 2454
Contig50529      SEQ ID NO 2455
Contig50588_RC   SEQ ID NO 2456
Contig50592      SEQ ID NO 2457
Contig50669_RC   SEQ ID NO 2458
Contig50719_RC   SEQ ID NO 2460
Contig50728_RC   SEQ ID NO 2461
Contig50731_RC   SEQ ID NO 2462
Contig50802_RC   SEQ ID NO 2463
Contig50822_RC   SEQ ID NO 2464
Contig50850_RC   SEQ ID NO 2466
Contig50860_RC   SEQ ID NO 2467
Contig50913_RC   SEQ ID NO 2468
Contig50950_RC   SEQ ID NO 2469
Contig51066_RC   SEQ ID NO 2470
Contig51105_RC   SEQ ID NO 2472
Contig51117_RC   SEQ ID NO 2473
Contig51196_RC   SEQ ID NO 2474
Contig51235_RC   SEQ ID NO 2475
Contig51254_RC   SEQ ID NO 2476
Contig51352_RC   SEQ ID NO 2477
Contig51369_RC   SEQ ID NO 2478
Contig51392_RC   SEQ ID NO 2479
Contig51403_RC   SEQ ID NO 2480
Contig51685_RC   SEQ ID NO 2483
Contig51726_RC   SEQ ID NO 2484
Contig51742_RC   SEQ ID NO 2485
Contig51749_RC   SEQ ID NO 2486
Contig51775_RC   SEQ ID NO 2487
Contig51800      SEQ ID NO 2488
Contig51809_RC   SEQ ID NO 2489
Contig51821_RC   SEQ ID NO 2490
Contig51888_RC   SEQ ID NO 2491
Contig51953_RC   SEQ ID NO 2493
Contig51967_RC   SEQ ID NO 2495
Contig51981_RC   SEQ ID NO 2496
Contig51994_RC   SEQ ID NO 2497
Contig52082_RC   SEQ ID NO 2498
Contig52094_RC   SEQ ID NO 2499
Contig52320      SEQ ID NO 2500
Contig52398_RC   SEQ ID NO 2501
Contig52425_RC   SEQ ID NO 2503
Contig52482_RC   SEQ ID NO 2504
Contig52543_RC   SEQ ID NO 2505
Contig52553_RC   SEQ ID NO 2506
Contig52579_RC   SEQ ID NO 2507
Contig52603_RC   SEQ ID NO 2508
Contig52639_RC   SEQ ID NO 2509
Contig52641_RC   SEQ ID NO 2510
Contig52684      SEQ ID NO 2511
Contig52705_RC   SEQ ID NO 2512
Contig52720_RC   SEQ ID NO 2513
Contig52722_RC   SEQ ID NO 2514
Contig52723_RC   SEQ ID NO 2515
Contig52740_RC   SEQ ID NO 2516
Contig52779_RC   SEQ ID NO 2517
Contig52957_RC   SEQ ID NO 2518
Contig52994_RC   SEQ ID NO 2519
Contig53022_RC   SEQ ID NO 2520
Contig53038_RC   SEQ ID NO 2521
Contig53047_RC   SEQ ID NO 2522
Contig53130      SEQ ID NO 2523
Contig53183_RC   SEQ ID NO 2524
Contig53242_RC   SEQ ID NO 2526
Contig53248_RC   SEQ ID NO 2527
Contig53260_RC   SEQ ID NO 2528
Contig53296_RC   SEQ ID NO 2531
Contig53307_RC   SEQ ID NO 2532
Contig53314_RC   SEQ ID NO 2533
Contig53401_RC   SEQ ID NO 2534
Contig53550_RC   SEQ ID NO 2535
Contig53551_RC   SEQ ID NO 2536
Contig53598_RC   SEQ ID NO 2537
Contig53646_RC   SEQ ID NO 2538
Contig53658_RC   SEQ ID NO 2539
Contig53698_RC   SEQ ID NO 2540
Contig53719_RC   SEQ ID NO 2541
Contig53742_RC   SEQ ID NO 2542
Contig53757_RC   SEQ ID NO 2543
Contig53870_RC   SEQ ID NO 2544
Contig53952_RC   SEQ ID NO 2546
Contig53962_RC   SEQ ID NO 2547
Contig53968_RC   SEQ ID NO 2548
Contig54113_RC   SEQ ID NO 2549
Contig54142_RC   SEQ ID NO 2550
Contig54232_RC   SEQ ID NO 2551
Contig54242_RC   SEQ ID NO 2552
Contig54260_RC   SEQ ID NO 2553
Contig54263_RC   SEQ ID NO 2554
Contig54295_RC   SEQ ID NO 2555
Contig54318_RC   SEQ ID NO 2556
Contig54325_RC   SEQ ID NO 2557
Contig54389_RC   SEQ ID NO 2558
Contig54394_RC   SEQ ID NO 2559
Contig54414_RC   SEQ ID NO 2560
Contig54425      SEQ ID NO 2561
Contig54477_RC   SEQ ID NO 2562
Contig54503_RC   SEQ ID NO 2563
Contig54534_RC   SEQ ID NO 2564
Contig54560_RC   SEQ ID NO 2566
Contig54581_RC   SEQ ID NO 2567
Contig54609_RC   SEQ ID NO 2568
Contig54666_RC   SEQ ID NO 2569
Contig54667_RC   SEQ ID NO 2570
Contig54726_RC   SEQ ID NO 2571
Contig54742_RC   SEQ ID NO 2572
Contig54745_RC   SEQ ID NO 2573
Contig54757_RC   SEQ ID NO 2574
Contig54761_RC   SEQ ID NO 2575
Contig54813_RC   SEQ ID NO 2576
Contig54867_RC   SEQ ID NO 2577
Contig54895_RC   SEQ ID NO 2578
Contig54898_RC   SEQ ID NO 2579
Contig54913_RC   SEQ ID NO 2580
Contig54965_RC   SEQ ID NO 2582
Contig54968_RC   SEQ ID NO 2583
Contig55069_RC   SEQ ID NO 2584
Contig55181_RC   SEQ ID NO 2585
Contig55188_RC   SEQ ID NO 2586
Contig55221_RC   SEQ ID NO 2587
Contig55254_RC   SEQ ID NO 2588
Contig55265_RC   SEQ ID NO 2589
Contig55377_RC   SEQ ID NO 2591
Contig55397_RC   SEQ ID NO 2592
Contig55448_RC   SEQ ID NO 2593
Contig55468_RC   SEQ ID NO 2594
Contig55500_RC   SEQ ID NO 2595
Contig55538_RC   SEQ ID NO 2596
Contig55558_RC   SEQ ID NO 2597
Contig55606_RC   SEQ ID NO 2598
Contig55674_RC   SEQ ID NO 2599
Contig55725_RC   SEQ ID NO 2600
Contig55728_RC   SEQ ID NO 2601
Contig55756_RC   SEQ ID NO 2602
Contig55769_RC   SEQ ID NO 2603
Contig55771_RC   SEQ ID NO 2605
Contig55813_RC   SEQ ID NO 2607
Contig55829_RC   SEQ ID NO 2608
Contig55852_RC   SEQ ID NO 2609
Contig55883_RC   SEQ ID NO 2610
Contig55920_RC   SEQ ID NO 2611
Contig55940_RC   SEQ ID NO 2612
Contig55950_RC   SEQ ID NO 2613
Contig55991_RC   SEQ ID NO 2614
Contig55997_RC   SEQ ID NO 2615
Contig56023_RC   SEQ ID NO 2616
Contig56030_RC   SEQ ID NO 2617
Contig56093_RC   SEQ ID NO 2618
Contig56205_RC   SEQ ID NO 2621
Contig56270_RC   SEQ ID NO 2622
Contig56276_RC   SEQ ID NO 2623
Contig56291_RC   SEQ ID NO 2624
Contig56298_RC   SEQ ID NO 2625
Contig56307      SEQ ID NO 2627
Contig56390_RC   SEQ ID NO 2628
Contig56434_RC   SEQ ID NO 2629
Contig56457_RC   SEQ ID NO 2630
Contig56534_RC   SEQ ID NO 2631
Contig56670_RC   SEQ ID NO 2632
Contig56678_RC   SEQ ID NO 2633
Contig56742_RC   SEQ ID NO 2634
Contig56759_RC   SEQ ID NO 2635
Contig56765_RC   SEQ ID NO 2636
Contig56843_RC   SEQ ID NO 2637
Contig57011_RC   SEQ ID NO 2638
Contig57023_RC   SEQ ID NO 2639
Contig57057_RC   SEQ ID NO 2640
Contig57076_RC   SEQ ID NO 2641
Contig57081_RC   SEQ ID NO 2642
Contig57091_RC   SEQ ID NO 2643
Contig57138_RC   SEQ ID NO 2644
Contig57173_RC   SEQ ID NO 2645
Contig57230_RC   SEQ ID NO 2646
Contig57258_RC   SEQ ID NO 2647
Contig57270_RC   SEQ ID NO 2648
Contig57272_RC   SEQ ID NO 2649
Contig57344_RC   SEQ ID NO 2650
Contig57430_RC   SEQ ID NO 2651
Contig57458_RC   SEQ ID NO 2652
Contig57493_RC   SEQ ID NO 2653
Contig57584_RC   SEQ ID NO 2654
Contig57595      SEQ ID NO 2655
Contig57602_RC   SEQ ID NO 2656
Contig57609_RC   SEQ ID NO 2657
Contig57610_RC   SEQ ID NO 2658
Contig57644_RC   SEQ ID NO 2659
Contig57725_RC   SEQ ID NO 2660
Contig57739_RC   SEQ ID NO 2661
Contig57825_RC   SEQ ID NO 2662
Contig57864_RC   SEQ ID NO 2663
Contig57940_RC   SEQ ID NO 2664
Contig58260_RC   SEQ ID NO 2665
Contig58272_RC   SEQ ID NO 2666
Contig58301_RC   SEQ ID NO 2667
Contig58368_RC   SEQ ID NO 2668
Contig58471_RC   SEQ ID NO 2669
Contig58755_RC   SEQ ID NO 2671
Contig59120_RC   SEQ ID NO 2672
Contig60157_RC   SEQ ID NO 2673
Contig60864_RC   SEQ ID NO 2676
Contig61254_RC   SEQ ID NO 2677
Contig61815      SEQ ID NO 2678
Contig61975      SEQ ID NO 2679
Contig62306      SEQ ID NO 2680
Contig62568_RC   SEQ ID NO 2681
Contig62922_RC   SEQ ID NO 2682
Contig62964_RC   SEQ ID NO 2683
Contig63520_RC   SEQ ID NO 2685
Contig63649_RC   SEQ ID NO 2686
Contig63683_RC   SEQ ID NO 2687
Contig63748_RC   SEQ ID NO 2688
Contig64502      SEQ ID NO 2689
Contig64688      SEQ ID NO 2690
Contig64775_RC   SEQ ID NO 2691
Contig65227      SEQ ID NO 2692
Contig65663      SEQ ID NO 2693
Contig65785_RC   SEQ ID NO 2694
Contig65900      SEQ ID NO 2695
Contig66219_RC   SEQ ID NO 2696
Contig66705_RC   SEQ ID NO 2697
Contig66759_RC   SEQ ID NO 2698
Contig67182_RC   SEQ ID NO 2699

[0087]

TABLE 2
550 preferred ER status markers drawn from Table 1.
Identifier	Correlation	Name	Description
NM_002051	0.763977	GATA3	GATA-binding protein 3
AB020689	0.753592	KIAA0882	KIAA0882 protein
NM_001218	0.753225	CA12	carbonic anhydrase XII
NM_000125	0.748421	ESR1	estrogen receptor 1
Contig56678_RC	0.747816		ESTs
NM_004496	0.729116	HNF3A	hepatocyte nuclear factor 3, alpha
NM_017732	0.713398	FLJ20262	hypothetical protein FLJ20262
NM_006806	−0.712678	BTG3	BTG family, member 3
Contig56390_RC	0.705940		ESTs
Contig37571_RC	0.704468		ESTs
NM_004559	−0.701617	NSEP1	nuclease sensitive element binding
			protein 1
Contig50153_RC	−0.696652		ESTs, Weakly similar to LKHU
			proteoglycan link protein precursor
			[H. sapiens]
NM_012155	0.694332	EMAP-2	microtubule-associated protein like
			echinoderm EMAP
Contig237_RC	0.687485	FLJ21127	hypothetical protein FLJ21127
NM_019063	−0.686064	C2ORF2	chromosome 2 open reading frame 2
NM_012219	−0.680900	MRAS	muscle RAS oncogene homolog
NM_001982	0.676114	ERBB3	v-erb-b2 avian erythroblastic
			leukemia viral oncogene homolog 3
NM_006623	−0.675090	PHGDH	phosphoglycerate dehydrogenase
NM_000636	−0.674282	SOD2	superoxide dismutase 2,
			mitochondrial
NM_006017	−0.670353	PROML1	prominin (mouse)-like 1
Contig57940_RC	0.667915	MAP-1	MAP-1 protein
Contig46934_RC	0.666908		ESTs, Weakly similar to JE0350
			Anterior gradient-2 [H. sapiens]
NM_005080	0.665772	XBP1	X-box binding protein 1
NM_014246	0.665725	CELSR1	cadherin, EGF LAG seven-pass G-
			type receptor 1, flamingo
			(Drosophila) homolog
Contig54667_RC	−0.663727		Human DNA sequence from clone
			RP1-187J11 on chromosome
			6q11.1-22.33. Contains the gene for
			a novel protein similar to S. pombe
			and S. cerevisiae predicted proteins,
			the gene for a novel protein similar
			to protein kinase C inhibitors, the 3′
			end of the gene for a novel protein
			similar to Drosophila L82 and
			predicted worm proteins, ESTs,
			STSs, GSSs and two putative CpG
			islands
Contig51994_RC	0.663715		ESTs, Weakly similar to B0416.1
			[C. elegans]
NM_016337	0.663006	RNB6	RNB6
NM_015640	−0.660165	PAI-RBP1	PAI-1 mRNA-binding protein
X07834	−0.657798	SOD2	superoxide dismutase 2,
			mitochondrial
NM_012319	0.657666	LIV-1	LIV-1 protein, estrogen regulated
Contig41887_RC	0.656042		ESTs, Weakly similar to Homolog of
			rat Zymogen granule membrane
			protein [H. sapiens]
NM_003462	0.655349	P28	dynein, axonemal, light intermediate
			polypeptide
Contig58301_RC	0.654268		Homo sapiens mRNA; cDNA
			DKFZp667D095 (from clone
			DKFZp667D095)
NM_005375	0.653783	MYB	v-myb avian myeloblastosis viral
			oncogene homolog
NM_017447	−0.652445	YG81	hypothetical protein LOC54149
Contig924_RC	−0.650658		ESTs
M55914	−0.650181	MPB1	MYC promoter-binding protein 1
NM_006004	−0.649819	UQCRH	ubiquinol-cytochrome c reductase
			hinge protein
NM_000964	0.649072	RARA	retinoic acid receptor, alpha
NM_013301	0.647583	HSU79303	protein predicted by clone 23882
AB023211	−0.647403	PDI2	peptidyl arginine deiminase, type II
NM_016629	−0.646412	LOC51323	hypothetical protein
K02403	0.645532	C4A	complement component 4A
NM_016405	−0.642201	HSU93243	Ubc6p homolog
Contig46597_RC	0.641733		ESTs
Contig55377_RC	0.640310		ESTs
NM_001207	0.637800	BTF3	basic transcription factor 3
NM_018166	0.636422	FLJ10647	hypothetical protein FLJ10647
AL110202	−0.635398		Homo sapiens mRNA; cDNA
			DKFZp586I2022 (from clone
			DKFZp586I2022)
AL133105	−0.635201	DKFZp434F2322	hypothetical protein DKFZp434F2322
NM_016839	−0.635169	RBMS1	RNA binding motif, single stranded
			interacting protein 1
Contig53130	−0.634812		ESTs, Weakly similar to
			hyperpolarization-activated cyclic
			nucleotide-gated channel hHCN2
			[H. sapiens]
NM_018014	−0.634460	BCL11A	B-cell CLL/lymphoma 11A (zinc
			finger protein)
NM_006769	−0.632197	LMO4	LIM domain only 4
U92544	0.631170	JCL-1	hepatocellular carcinoma associated
			protein; breast cancer associated
			gene 1
Contig49233_RC	−0.631047		Homo sapiens , Similar to nuclear
			receptor binding factor 2, clone
			IMAGE: 3463191, mRNA, partial cds
AL133033	0.629690	KIAA1025	KIAA1025 protein
AL049265	0.629414		Homo sapiens mRNA; cDNA
			DKFZp564F053 (from clone
			DKFZp564F053)
NM_018728	0.627989	MYO5C	myosin 5C
NM_004780	0.627856	TCEAL1	transcription elongation factor A
			(SII)-like 1
Contig760_RC	0.627132		ESTs
Contig399_RC	0.626543	FLJ12538	hypothetical protein FLJ12538
			similar to ras-related protein RAB17
M83822	0.625092	CDC4L	cell division cycle 4-like
NM_001255	−0.625089	CDC20	CDC20 (cell division cycle 20, S.
			cerevisiae, homolog)
NM_006739	−0.624903	MCM5	minichromosome maintenance
			deficient (S. cerevisiae) 5 (cell
			division cycle 46)
NM_002888	−0.624664	RARRES1	retinoic acid receptor responder
			(tazarotene induced) 1
NM_003197	0.623850	TCEB1L	transcription elongation factor B
			(SIII), polypeptide 1-like
NM_006787	0.623625	JCL-1	hepatocellular carcinoma associated
			protein; breast cancer associated
			gene 1
Contig49342_RC	0.622179		ESTs
AL133619	0.621719		Homo sapiens mRNA; cDNA
			DKFZp434E2321 (from clone
			DKFZp434E2321); partial cds
AL133622	0.621577	KIAA0876	KIAA0876 protein
NM_004648	−0.621532	PTPNS1	protein tyrosine phosphatase, non-
			receptor type substrate 1
NM_001793	−0.621530	CDH3	cadherin 3, type 1, P-cadherin
			(placental)
NM_003217	0.620915	TEGT	testis enhanced gene transcript
			(BAX inhibitor 1)
NM_001551	0.620832	IGBP1	immunoglobulin (CD79A) binding
			protein 1
NM_002539	−0.620683	ODC1	ornithine decarboxylase 1
Contig55997_RC	−0.619932		ESTs
NM_000633	0.619547	BCL2	B-cell CLL/lymphoma 2
NM_016267	−0.619096	TONDU	TONDU
Contig3659_RC	0.618048	FLJ21174	hypothetical protein FLJ21174
NM_000191	0.617250	HMGCL	3-hydroxymethyl-3-methylglutaryl-
			Coenzyme A lyase
			(hydroxymethylglutaricaciduria)
NM_001267	0.616890	CHAD	chondroadherin
Contig39090_RC	0.616385		ESTs
AF055270	−0.616268	HSSG1	heat-shock suppressed protein 1
Contig43054	0.616015	FLJ21603	hypothetical protein FLJ21603
NM_001428	−0.615855	ENO1	enolase 1, (alpha)
Contig51369_RC	0.615466		ESTs
Contig36647_RC	0.615310	GFRA1	GDNF family receptor alpha 1
NM_014096	−0.614832	PRO1659	PRO1659 protein
NM_015937	0.614735	LOC51604	CGI-06 protein
Contig49790_RC	−0.614463		ESTs
NM_006759	−0.614279	UGP2	UDP-glucose pyrophosphorylase 2
Contig53598_RC	−0.613787	FLJ11413	hypothetical protein FLJ11413
AF113132	−0.613561	PSA	phosphoserine aminotransferase
AK000004	0.613001		Homo sapiens mRNA for FLJ00004
			protein, partial cds
Contig52543_RC	0.612960		Homo sapiens cDNA FLJ13945 fis,
			clone Y79AA1000969
AB032966	−0.611917	KIAA1140	KIAA1140 protein
AL080192	0.611544		Homo sapiens cDNA: FLJ21238 fis,
			clone COL01115
X56807	−0.610654	DSC2	desmocollin 2
Contig30390_RC	0.609614		ESTs
AL137362	0.609121	FLJ22237	hypothetical protein FLJ22237
NM_014211	−0.608585	GABRP	gamma-aminobutyric acid (GABA) A
			receptor, pi
NM_006696	0.608474	SMAP	thyroid hormone receptor
			coactivating protein
Contig45588_RC	−0.608273		Homo sapiens cDNA: FLJ22610 fis,
			clone HSI04930
NM_003358	0.608244	UGCG	UDP-glucose ceramide
			glucosyltransferase
NM_006153	−0.608129	NCK1	NCK adaptor protein 1
NM_001453	−0.606939	FOXC1	forkhead box C1
Contig54666_RC	0.606475		oy65e02.x1 NCI_CGAP_CLL1
			Homo sapiens cDNA clone
			IMAGE: 1670714 3′ similar to
			TR: Q29168 Q29168 UNKNOWN
			PROTEIN;, mRNA sequence.
NM_005945	−0.605945	MPB1	MYC promoter-binding protein 1
Contig55725_RC	−0.605841		ESTs, Moderately similar to T50635
			hypothetical protein
			DKFZp762L0311.1 [H. sapiens]
Contig37015_RC	−0.605780		ESTs, Weakly similar to
			UAS3_HUMAN UBASH3A
			PROTEIN [H. sapiens]
AL157480	−0.604362	SH3BP1	SH3-domain binding protein 1
NM_005325	−0.604310	H1F1	H1 histone family, member 1
NM_001446	−0.604061	FABP7	fatty acid binding protein 7, brain
Contig263_RC	0.603318		Homo sapiens cDNA: FLJ23000 fis,
			clone LNG00194
Contig8347_RC	−0.603311		ESTs
NM_002988	−0.603279	SCYA18	small inducible cytokine subfamily A
			(Cys-Cys), member 18, pulmonary
			and activation-regulated
AF111849	0.603157	HELO1	homolog of yeast long chain
			polyunsaturated fatty acid
			elongation enzyme 2
NM_014700	0.603042	KIAA0665	KIAA0665 gene product
NM_001814	−0.602988	CTSC	cathepsin C
AF116682	−0.602350	PRO2013	hypothetical protein PRO2013
AB037836	0.602024	KIAA1415	KIAA1415 protein
AB002301	0.602005	KIAA0303	KIAA0303 protein
NM_002996	−0.601841	SCYD1	small inducible cytokine subfamily D
			(Cys-X3-Cys), member 1
			(fractalkine, neurotactin)
NM_018410	−0.601765	DKFZp762E1312	hypothetical protein
			DKFZp762E1312
Contig49581_RC	−0.601571	KIAA1350	KIAA1350 protein
NM_003088	−0.601458	SNL	singed (Drosophila)-like (sea urchin
			fascin homolog like)
Contig47045_RC	0.601088		ESTs, Weakly similar to
			DP1_HUMAN POLYPOSIS LOCUS
			PROTEIN 1 [H. sapiens]
NM_001806	−0.600954	CEBPG	CCAAT/enhancer binding protein
			(C/EBP), gamma
NM_004374	0.600766	COX6C	cytochrome c oxidase subunit VIc
Contig52641_RC	0.600132		ESTs, Weakly similar to CENB
			MOUSE MAJOR CENTROMERE
			AUTOANTIGEN B [M. musculus]
NM_000100	−0.600127	CSTB	cystatin B (stefin B)
NM_002250	−0.600004	KCNN4	potassium intermediate/small
			conductance calcium-activated
			channel, subfamily N, member 4
AB033035	−0.599423	KIAA1209	KIAA1209 protein
Contig53968_RC	0.599077		ESTs
NM_002300	−0.598246	LDHB	lactate dehydrogenase B
NM_000507	0.598110	FBP1	fructose-1,6-bisphosphatase 1
NM_002053	−0.597756	GBP1	guanylate binding protein 1,
			interferon-inducible, 67 kD
AB007883	0.597043	KIAA0423	KIAA0423 protein
NM_004900	−0.597010	DJ742C19.2	phorbolin (similar to apolipoprotein B
			mRNA editing protein)
NM_004480	0.596321	FUT8	fucosyltransferase 8 (alpha (1,6)
			fucosyltransferase)
Contig35896_RC	0.596281		ESTs
NM_020974	0.595173	CEGP1	CEGP1 protein
NM_000662	0.595114	NAT1	N-acetyltransferase 1 (arylamine N-
			acetyltransferase)
NM_006113	0.595017	VAV3	vav 3 oncogene
NM_014865	−0.594928	KIAA0159	chromosome condensation-related
			SMC-associated protein 1
Contig55538_RC	−0.594573	BA395L14.2	hypothetical protein bA395L14.2
NM_016056	0.594084	LOC51643	CGI-119 protein
NM_003579	−0.594063	RAD54L	RAD54 (S. cerevisiae)-like
NM_014214	−0.593860	IMPA2	inositol(myo)-1(or 4)-
			monophosphatase 2
U79293	0.593793		Human clone 23948 mRNA
			sequence
NM_005557	−0.593746	KRT16	keratin 16 (focal non-epidermolytic
			palmoplantar keratoderma)
NM_002444	−0.592405	MSN	moesin
NM_003681	−0.592155	PDXK	pyridoxal (pyridoxine, vitamin B6)
			kinase
NM_006372	−0.591711	NSAP1	NS1-associated protein 1
NM_005218	−0.591192	DEFB1	defensin, beta 1
NM_004642	−0.591081	DOC1	deleted in oral cancer (mouse,
			homolog) 1
AL133074	0.590359		Homo sapiens cDNA: FLJ22139 fis,
			clone HEP20959
M73547	0.590317	D5S346	DNA segment, single copy probe
			LNS-CAI/LNS-CAII (deleted in
			polyposis
Contig65663	0.590312		ESTs
AL035297	−0.589728		H. sapiens gene from PAC 747L4
Contig35629_RC	0.589383		ESTs
NM_019027	0.588862	FLJ20273	hypothetical protein
NM_012425	−0.588804		Homo sapiens Ras suppressor
			protein 1 (RSU1), mRNA
NM_020179	−0.588326	FN5	FN5 protein
AF090913	−0.587275	TMSB10	thymosin, beta 10
NM_004176	0.587190	SREBF1	sterol regulatory element binding
			transcription factor 1
NM_016121	0.586941	LOC51133	NY-REN-45 antigen
NM_014773	0.586871	KIAA0141	KIAA0141 gene product
NM_019000	0.586677	FLJ20152	hypothetical protein
NM_016243	0.585942	LOC51706	cytochrome b5 reductase 1 (B5R.1)
NM_014274	−0.585815	ABP/ZF	Alu-binding protein with zinc finger
			domain
NM_018379	0.585497	FLJ11280	hypothetical protein FLJ11280
AL157431	−0.585077	DKFZp762A227	hypothetical protein DKFZp762A227
D38521	−0.584684	KIAA0077	KIAA0077 protein
NM_002570	0.584272	PACE4	paired basic amino acid cleaving
			system 4
NM_001809	−0.584252	CENPA	centromere protein A (17 kD)
NM_003318	−0.583556	TTK	TTK protein kinase
NM_014325	−0.583555	CORO1C	coronin, actin-binding protein, 1C
NM_005667	0.583376	ZFP103	zinc finger protein homologous to
			Zfp103 in mouse
NM_004354	0.582420	CCNG2	cyclin G2
NM_003670	0.582235	BHLHB2	basic helix-loop-helix domain
			containing, class B, 2
NM_001673	−0.581902	ASNS	asparagine synthetase
NM_001333	−0.581402	CTSL2	cathepsin L2
Contig54295_RC	0.581256		ESTs
Contig33998_RC	0.581018		ESTs
NM_006002	−0.580592	UCHL3	ubiquitin carboxyl-terminal esterase
			L3 (ubiquitin thiolesterase)
NM_015392	0.580568	NPDC1	neural proliferation, differentiation
			and control, 1
NM_004866	0.580138	SCAMP1	secretory carrier membrane protein 1
Contig50391_RC	0.580071		ESTs
NM_000592	0.579965	C4B	complement component 4B
Contig50802_RC	0.579881		ESTs
Contig41635_RC	−0.579468		ESTs
NM_006845	−0.579339	KNSL6	kinesin-like 6 (mitotic centromere-
			associated kinesin)
NM_003720	−0.579296	DSCR2	Down syndrome critical region gene 2
NM_000060	0.578967	BTD	biotinidase
AL050388	−0.578736		Homo sapiens mRNA; cDNA
			DKFZp564M2422 (from clone
			DKFZp564M2422); partial cds
NM_003772	−0.578395	JRKL	jerky (mouse) homolog-like
NM_014398	−0.578388	TSC403	similar to lysosome-associated
			membrane glycoprotein
NM_001280	0.578213	CIRBP	cold inducible RNA-binding protein
NM_001395	−0.577369	DUSP9	dual specificity phosphatase 9
NM_016229	−0.576290	LOC51700	cytochrome b5 reductase b5R.2
NM_006096	−0.575615	NDRG1	N-myc downstream regulated
NM_001552	0.575438	IGFBP4	insulin-like growth factor-binding
			protein 4
NM_005558	−0.574818	LAD1	ladinin 1
Contig54534_RC	0.574784		Human glucose transporter
			pseudogene
Contig1239_RC	0.573822		Human Chromosome 16 BAC clone
			CIT987SK-A-362G6
Contig57173_RC	0.573807		Homo sapiens mRNA for KIAA1737
			protein, partial cds
NM_004414	−0.573538	DSCR1	Down syndrome critical region gene 1
NM_021103	−0.572722	TMSB10	thymosin, beta 10
NM_002350	−0.571917	LYN	v-yes-1 Yamaguchi sarcoma viral
			related oncogene homolog
Contig51235_RC	0.571049		Homo sapiens cDNA: FLJ23388 fis,
			clone HEP17008
NM_013384	0.570987	TMSG1	tumor metastasis-suppressor
NM_014399	0.570936	NET-6	tetraspan NET-6 protein
Contig26022_RC	−0.570851		ESTs
AB023152	0.570561	KIAA0935	KIAA0935 protein
NM_021077	−0.569944	NMB	neuromedin B
NM_003498	−0.569129	SNN	stannin
U17077	−0.568979	BENE	BENE protein
D86985	0.567698	KIAA0232	KIAA0232 gene product
NM_006357	−0.567513	UBE2E3	ubiquitin-conjugating enzyme E2E 3
			(homologous to yeast UBC4/5)
AL049397	−0.567434		Homo sapiens mRNA; cDNA
			DKFZp586C1019 (from clone
			DKFZp586C1019)
Contig64502	0.567433		ESTs, Weakly similar to unknown
			[M. musculus]
Contig56298_RC	−0.566892	FLJ13154	hypothetical protein FLJ13154
Contig46056_RC	0.566634		ESTs, Weakly similar to
			YZ28_HUMAN HYPOTHETICAL
			PROTEIN ZAP128 [H. sapiens]
AF007153	0.566044		Homo sapiens clone 23736 mRNA
			sequence
Contig1778_RC	−0.565789		ESTs
NM_017702	−0.565789	FLJ20186	hypothetical protein FLJ20186
Contig39226_RC	0.565761		Homo sapiens cDNA FLJ12187 fis,
			clone MAMMA1000831
NM_000168	0.564879	GLI3	GLI-Kruppel family member GLI3
			(Greig cephalopolysyndactyly
			syndrome)
Contig57609_RC	0.564751		ESTs, Weakly similar to
			T2D3_HUMAN TRANSCRIPTION
			INITIATION FACTOR TFIID 135 KDA
			SUBUNIT [H. sapiens]
U45975	0.564602	PIB5PA	phosphatidylinositol (4,5)
			bisphosphate 5-phosphatase, A
AF038182	0.564596		Homo sapiens clone 23860 mRNA
			sequence
Contig5348_RC	0.564480		ESTs, Weakly similar to 1607338A
			transcription factor BTF3a
			[H. sapiens]
NM_001321	−0.564459	CSRP2	cysteine and glycine-rich protein 2
Contig25362_RC	−0.563801		ESTs
NM_001609	0.563782	ACADSB	acyl-Coenzyme A dehydrogenase,
			short/branched chain
Contig40146	0.563731		wi84e12.x1 NCI_CGAP_Kid12
			Homo sapiens cDNA clone
			IMAGE: 2400046 3′ similar to
			SW: RASD_DICDI P03967 RAS-
			LIKE PROTEIN RASD;, mRNA
			sequence.
NM_016002	0.563403	LOC51097	CGI-49 protein
Contig34303_RC	0.563157		Homo sapiens cDNA: FLJ21517 fis,
			clone COL05829
Contig55883_RC	0.563141		ESTs
NM_017961	0.562479	FLJ20813	hypothetical protein FLJ20813
M21551	−0.562340	NMB	neuromedin B
Contig3940_RC	−0.561956	YWHAH	tyrosine 3-
			monooxygenase/tryptophan 5-
			monooxygenase activation protein,
			eta polypeptide
AB033111	−0.561746	KIAA1285	KIAA1285 protein
Contig43410_RC	0.561678		ESTs
Contig42006_RC	−0.561677		ESTs
Contig57272_RC	0.561228		ESTs
G26403	−0.561068	YWHAH	tyrosine 3-
			monooxygenase/tryptophan 5-
			monooxygenase activation protein,
			eta polypeptide
NM_005915	−0.560813	MCM6	minichromosome maintenance
			deficient (mis5, S. pombe) 6
NM_003875	−0.560668	GMPS	guanine monphosphate synthetase
AK000142	0.559651	AK000142	Homo sapiens cDNA FLJ20135 fis,
			clone COL06818.
NM_002709	−0.559621	PPP1CB	protein phosphatase 1, catalytic
			subunit, beta isoform
NM_001276	−0.558868	CHI3L1	chitinase 3-like 1 (cartilage
			glycoprotein-39)
NM_002857	0.558862	PXF	peroxisomal farnesylated protein
Contig33815_RC	−0.558741	FLJ22833	hypothetical protein FLJ22833
NM_003740	−0.558491	KCNK5	potassium channel, subfamily K,
			member 5 (TASK-2)
Contig53646_RC	0.558455		ESTs
NM_005538	−0.558350	INHBC	inhibin, beta C
NM_002111	0.557860	HD	huntingtin (Huntington disease)
NM_003683	−0.557807	D21S2056E	DNA segment on chromosome 21
			(unique) 2056 expressed sequence
NM_003035	−0.557380	SIL	TAL1 (SCL) interrupting locus
Contig4388_RC	−0.557216		Homo sapiens , Similar to integral
			membrane protein 3, clone
			MGC: 3011, mRNA, complete cds
Contig38288_RC	−0.556426		ESTs, Weakly similar to ISHUSS
			protein disulfide-isomerase
			[H. sapiens]
NM_015417	0.556184	DKFZP434I114	DKFZP434I114 protein
NM_015507	−0.556138	EGFL6	EGF-like-domain, multiple 6
AF279865	0.555951	KIF13B	kinesin family member 13B
Contig31288_RC	−0.555754		ESTs
NM_002966	−0.555620	S100A10	S100 calcium-binding protein A10
			(annexin II ligand, calpactin I, light
			polypeptide (p11))
NM_017585	−0.555476	SLC2A6	solute carrier family 2 (facilitated
			glucose transporter), member 6
NM_013296	−0.555367	HSU54999	LGN protein
NM_000224	0.554838	KRT18	keratin 18
Contig49270_RC	−0.554593	KIAA1553	KIAA1553 protein
NM_004848	−0.554538	ICB-1	basement membrane-induced gene
NM_007275	0.554278	FUS1	lung cancer candidate
NM_007044	−0.553550	KATNA1	katanin p60 (ATPase-containing)
			subunit A 1
Contig1829	0.553317		ESTs
AF272357	0.553286	NPDC1	neural proliferation, differentiation
			and control, 1
Contig57584_RC	−0.553080		Homo sapiens , Similar to gene rich
			cluster, C8 gene, clone MGC: 2577,
			mRNA, complete cds
NM_003039	−0.552747	SLC2A5	solute carrier family 2 (facilitated
			glucose transporter), member 5
NM_014216	0.552321	ITPK1	inositol 1,3,4-triphosphate 5/6
			kinase
NM_007027	−0.552064	TOPBP1	topoisomerase (DNA) II binding
			protein
AF118224	−0.551916	ST14	suppression of tumorigenicity 14
			(colon carcinoma, matriptase,
			epithin)
X75315	−0.551853	HSRNASEB	seb4D
NM_012101	−0.551824	ATDC	ataxia-telangiectasia group D-
			associated protein
AL157482	−0.551329	FLJ23399	hypothetical protein FLJ23399
NM_012474	−0.551150	UMPK	uridine monophosphate kinase
Contig57081_RC	0.551103		ESTs
NM_006941	−0.551069	SOX10	SRY (sex determining region Y)-box
			10
NM_004694	0.550932	SLC16A6	solute carrier family 16
			(monocarboxylic acid transporters),
			member 6
Contig9541_RC	0.550680		ESTs
Contig20617_RC	0.550546		ESTs
NM_004252	0.550365	SLC9A3R1	solute carrier family 9
			(sodium/hydrogen exchanger),
			isoform 3 regulatory factor 1
NM_015641	−0.550200	DKFZP586	testin
		B2022
NM_004336	−0.550164	BUB1	budding uninhibited by
			benzimidazoles 1 (yeast homolog)
Contig39960_RC	−0.549951	FLJ21079	hypothetical protein FLJ21079
NM_020686	0.549659	NPD009	NPD009 protein
NM_002633	−0.549647	PGM1	phosphoglucomutase 1
Contig30480_RC	0.548932		ESTs
NM_003479	0.548896	PTP4A2	protein tyrosine phosphatase type
			IVA, member 2
NM_001679	−0.548768	ATP1B3	ATPase, Na+/K+ transporting, beta
			3 polypeptide
NM_001124	−0.548601	ADM	adrenomedullin
NM_001216	−0.548375	CA9	carbonic anhydrase IX
U58033	−0.548354	MTMR2	myotubularin related protein 2
NM_018389	−0.547875	FLJ11320	hypothetical protein FLJ11320
AF176012	0.547867	JDP1	J domain containing protein 1
Contig66705_RC	−0.546926	ST5	suppression of tumorigenicity 5
NM_018194	0.546878	FLJ10724	hypothetical protein FLJ10724
NM_006851	−0.546823	RTVP1	glioma pathogenesis-related protein
Contig53870_RC	0.546756		ESTs
NM_002482	−0.546012	NASP	nuclear autoantigenic sperm protein
			(histone-binding)
NM_002292	0.545949	LAMB2	laminin, beta 2 (laminin S)
NM_014696	−0.545758	KIAA0514	KIAA0514 gene product
Contig49855	0.545517		ESTs
AL117666	0.545203	DKFZP586O1624	DKFZP586O1624 protein
NM_004701	−0.545185	CCNB2	cyclin B2
NM_007050	0.544890	PTPRT	protein tyrosine phosphatase,
			receptor type, T
NM_000414	0.544778	HSD17B4	hydroxysteroid (17-beta)
			dehydrogenase 4
Contig52398_RC	−0.544775		Homo sapiens cDNA: FLJ21950 fis,
			clone HEP04949
AB007916	0.544496	KIAA0447	KIAA0447 gene product
Contig66219_RC	0.544467	FLJ22402	hypothetical protein FLJ22402
D87453	0.544145	KIAA0264	KIAA0264 protein
NM_015515	−0.543929	DKFZP434G032	DKFZP434G032 protein
NM_001530	−0.543898	HIF1A	hypoxia-inducible factor 1, alpha
			subunit (basic helix-loop-helix
			transcription factor)
NM_004109	−0.543893	FDX1	ferredoxin 1
NM_000381	−0.543871	MID1	midline 1 (Opitz/BBB syndrome)
Contig43983_RC	0.543523	CS2	calsyntenin-2
AL137761	0.543371		Homo sapiens mRNA; cDNA
			DKFZp586L2424 (from clone
			DKFZp586L2424)
NM_005764	−0.543175	DD96	epithelial protein up-regulated in
			carcinoma, membrane associated
			protein 17
Contig1838_RC	0.542996		Homo sapiens cDNA: FLJ22722 fis,
			clone HSI14444
NM_006670	0.542932	5T4	5T4 oncofetal trophoblast
			glycoprotein
Contig28552_RC	−0.542617		Homo sapiens mRNA; cDNA
			DKFZp434C0931 (from clone
			DKFZp434C0931); partial cds
Contig14284_RC	0.542224		ESTs
NM_006290	−0.542115	TNFAIP3	tumor necrosis factor, alpha-induced
			protein 3
AL050372	0.541463		Homo sapiens mRNA; cDNA
			DKFZp434A091 (from clone
			DKFZp434A091); partial cds
NM_014181	−0.541095	HSPC159	HSPC159 protein
Contig37141_RC	0.540990		Homo sapiens cDNA: FLJ23582 fis,
			clone LNG 13759
NM_000947	−0.540621	PRIM2A	primase, polypeptide 2A (58 kD)
NM_002136	0.540572	HNRPA1	heterogeneous nuclear
			ribonucleoprotein A1
NM_004494	−0.540543	HDGF	hepatoma-derived growth factor
			(high-mobility group protein 1-like)
Contig38983_RC	0.540526		ESTs
Contig27882_RC	−0.540506		ESTs
Z11887	−0.540020	MMP7	matrix metalloproteinase 7
			(matrilysin, uterine)
NM_014575	−0.539725	SCHIP-1	schwannomin interacting protein 1
Contig38170_RC	0.539708		ESTs
Contig44064_RC	0.539403		ESTs
U68385	0.539395	MEIS3	Meis (mouse) homolog 3
Contig51967_RC	0.538952		ESTs
Contig37562_RC	0.538657		ESTs, Weakly similar to
			transformation-related protein
			[H. sapiens]
Contig40500_RC	0.538582		ESTs, Weakly similar to unnamed
			protein product [H. sapiens]
Contig1129_RC	0.538339		ESTs
NM_002184	0.538185	IL6ST	interleukin 6 signal transducer
			(gp130, oncostatin M receptor)
AL049381	0.538041		Homo sapiens cDNA FLJ12900 fis,
			clone NT2RP2004321
NM_002189	−0.537867	IL15RA	interleukin 15 receptor, alpha
NM_012110	−0.537562	CHIC2	cystein-rich hydrophobic domain 2
AB040881	−0.537473	KIAA1448	KIAA1448 protein
NM_016577	−0.537430	RAB6B	RAB6B, member RAS oncogene
			family
NM_001745	0.536940	CAMLG	calcium modulating ligand
NM_005742	−0.536738	P5	protein disulfide isomerase-related
			protein
AB011132	0.536345	KIAA0560	KIAA0560 gene product
Contig54898_RC	0.536094	PNN	pinin, desmosome associated
			protein
Contig45049_RC	−0.536043	FUT4	fucosyltransferase 4 (alpha (1,3)
			fucosyltransferase, myeloid-specific)
NM_006864	−0.535924	LILRB3	leukocyte immunoglobulin-like
			receptor, subfamily B (with TM and
			ITIM domains), member 3
Contig53242_RC	−0.535909		Homo sapiens cDNA FLJ11436 fis,
			clone HEMBA1001213
NM_005544	0.535712	IRS1	insulin receptor substrate 1
Contig47456_RC	0.535493	CACNA1D	calcium channel, voltage-
			dependent, L type, alpha 1D subunit
Contig42751_RC	−0.535469		ESTs
Contig29126_RC	−0.535186		ESTs
NM_012391	0.535067	PDEF	prostate epithelium-specific Ets
			transcription factor
NM_012429	0.534974	SEC14L2	SEC14 (S. cerevisiae)-like 2
NM_018171	0.534898	FLJ10659	hypothetical protein FLJ10659
Contig53047_RC	−0.534773	TTYH1	tweety (Drosophila) homolog 1
Contig54968_RC	0.534754		Homo sapiens cDNA FLJ13558 fis,
			clone PLACE1007743
Contig2099_RC	−0.534694	KIAA1691	KIAA1691 protein
NM_005264	0.534057	GFRA1	GDNF family receptor alpha 1
NM_014036	−0.533638	SBBI42	BCM-like membrane protein
			precursor
NM_018101	−0.533473	FLJ10468	hypothetical protein FLJ10468
Contig56765_RC	0.533442		ESTs, Moderately similar to
			K02E10.2 [C. elegans]
AB006746	−0.533400	PLSCR1	phospholipid scramblase 1
NM_001089	0.533350	ABCA3	ATP-binding cassette, sub-family A
			(ABC1), member 3
NM_018188	−0.533132	FJ10709	hypothetical protein FLJ10709
X94232	−0.532925	MAPRE2	microtubule-associated protein,
			RP/EB family, member 2
AF234532	−0.532910	MYO10	myosin X
Contig292_RC	0.532853	FLJ22386	hypothetical protein FLJ22386
NM_000101	−0.532767	CYBA	cytochrome b-245, alpha
			polypeptide
Contig47814_RC	−0.532656	HHGP	HHGP protein
NM_014320	−0.532430	SOUL	putative heme-binding protein
NM_020347	0.531976	LZTFL1	leucine zipper transcription factor-
			like 1
NM_004323	0.531936	BAG1	BCL2-associated athanogene
Contig50850_RC	−0.531914		ESTs
Contig11648_RC	0.531704		ESTs
NM_018131	−0.531559	FLJ10540	hypothetical protein FLJ10540
NM_004688	−0.531329	NMI	N-myc (and STAT) interactor
NM_014870	0.531101	KIAA0478	KIAA0478 gene product
Contig31424_RC	0.530720		ESTs
NM_000874	−0.530545	IFNAR2	interferon (alpha, beta and omega)
			receptor 2
Contig50588_RC	0.530145		ESTs
NM_016463	0.529998	HSPC195	hypothetical protein
NM_013324	0.529966	CISH	cytokine inducible SH2-containing
			protein
NM_006705	0.529840	GADD45G	growth arrest and DNA-damage-
			inducible, gamma
Contig38901_RC	−0.529747		ESTs
NM_004184	−0.529635	WARS	tryptophanyl-tRNA synthetase
NM_015955	−0.529538	LOC51072	CGI-27 protein
AF151810	0.529416	CGI-52	similar to phosphatidylcholine
			transfer protein 2
NM_002164	−0.529117	INDO	indoleamine-pyrrole 2,3
			dioxygenase
NM_004267	−0.528679	CHST2	carbohydrate (chondroitin 6/keratan)
			sulfotransferase 2
Contig32185_RC	−0.528529		Homo sapiens cDNA FLJ13997 fis,
			clone Y79AA1002220
NM_004154	−0.528343	P2RY6	pyrimidinergic receptor P2Y, G-
			protein coupled, 6
NM_005235	0.528294	ERBB4	v-erb-a avian erythroblastic
			leukemia viral oncogene homolog-
			like 4
Contig40208_RC	−0.528062	LOC56938	transcription factor BMAL2
NM_013262	0.527297	MIR	myosin regulatory light chain
			interacting protein
NM_003034	−0.527148	SIAT8A	sialyltransferase 8 (alpha-N-
			acetylneuraminate: alpha-2,8-
			sialytransferase, GD3 synthase) A
NM_004556	−0.527146	NFKBIE	nuclear factor of kappa light
			polypeptide gene enhancer in B-
			cells inhibitor, epsilon
NM_002046	−0.527051	GAPD	glyceraldehyde-3-phosphate
			dehydrogenase
NM_001905	−0.526986	CTPS	CTP synthase
Contig42402_RC	0.526852		ESTs
NM_014272	−0.526283	ADAMTS7	a disintegrin-like and
			metalloprotease (reprolysin type)
			with thrombospondin type 1 motif, 7
AF076612	0.526205	CHRD	chordin
Contig57725_RC	−0.526122		Homo sapiens mRNA for HMG-box
			transcription factor TCF-3, complete
			cds
Contig42041_RC	−0.525877		ESTs
Contig44656_RC	−0.525868		ESTs, Highly similar to S02392
			alpha-2-macroglobulin receptor
			precursor [H. sapiens]
NM_018004	−0.525610	FLJ10134	hypothetical protein FLJ10134
Contig56434_RC	0.525510		Homo sapiens cDNA FLJ13603 fis,
			clone PLACE 1010270
D25328	−0.525504	PFKP	phosphofructokinase, platelet
Contig55950_RC	−0.525358	FLJ22329	hypothetical protein FLJ22329
NM_002648	−0.525211	PIM1	pim-1 oncogene
AL157505	0.525186		Homo sapiens mRNA; cDNA
			DKFZp586P1124 (from clone
			DKFZp586P1124)
AF061034	−0.525185	FIP2	Homo sapiens FIP2 alternatively
			translated mRNA, complete cds.
NM_014721	−0.525102	KIAA0680	KIAA0680 gene product
NM_001634	−0.525030	AMD1	S-adenosylmethionine
			decarboxylase 1
NM_006304	−0.524911	DSS1	Deleted in split-hand/split-foot 1
			region
Contig37778_RC	0.524667		ESTs, Highly similar to HLHUSB
			MHC class II histocompatibility
			antigen HLA-DP alpha-1 chain
			precursor [H. sapiens]
NM_003099	0.524339	SNX1	sorting nexin 1
AL079298	0.523774	MCCC2	methylcrotonoyl-Coenzyme A
			carboxylase 2 (beta)
NM_019013	−0.523663	FLJ10156	hypothetical protein
NM_000397	−0.523293	CYBB	cytochrome b-245, beta polypeptide
			(chronic granulomatous disease)
NM_014811	0.523132	KIAA0649	KIAA0649 gene product
Contig20600_RC	0.523072		ESTs
NM_005190	−0.522710	CCNC	cyclin C
AL161960	−0.522574	FLJ21324	hypothetical protein FLJ21324
AL117502	0.522280		Homo sapiens mRNA; cDNA
			DKFZp434D0935 (from clone
			DKFZp434D0935)
AF131753	−0.522245		Homo sapiens clone 24859 mRNA
			sequence
NM_000320	0.521974	QDPR	quinoid dihydropteridine reductase
NM_002115	−0.521870	HK3	hexokinase 3 (white cell)
NM_006460	0.521696	HIS1	HMBA-inducible
NM_018683	−0.521679	ZNF313	zinc finger protein 313
NM_004305	−0.521539	BIN1	bridging integrator 1
NM_006770	−0.521538	MARCO	macrophage receptor with
			collagenous structure
NM_001166	−0.521530	BIRC2	baculoviral IAP repeat-containing 2
D42047	0.521522	KIAA0089	KIAA0089 protein
NM_016235	−0.521298	GPRC5B	G protein-coupled receptor, family
			C, group 5, member B
NM_004504	−0.521189	HRB	HIV-1 Rev binding protein
NM_002727	−0.521146	PRG1	proteoglycan 1, secretory granule
AB029031	−0.520761	KIAA1108	KIAA1108 protein
NM_005556	−0.520692	KRT7	keratin 7
NM_018031	0.520600	WDR6	WD repeat domain 6
AL117523	−0.520579	KIAA1053	KIAA1053 protein
NM_004515	−0.520363	ILF2	interleukin enhancer binding factor
			2, 45 kD
NM_004708	−0.519935	PDCD5	programmed cell death 5
NM_005935	0.519765	MLLT2	myeloid/lymphoid or mixed-lineage
			leukemia (trithorax (Drosophila)
			homolog); translocated to, 2
Contig49289_RC	−0.519546		Homo sapiens mRNA; cDNA
			DKFZp586J1119 (from clone
			DKFZp586J1119); complete cds
NM_000211	−0.519342	ITGB2	integrin, beta 2 (antigen CD18 (p95),
			lymphocyte function-associated
			antigen 1; macrophage antigen 1
			(mac-1) beta subunit)
AL079276	0.519207	LOC58495	putative zinc finger protein from
			EUROIMAGE 566589
Contig57825_RC	0.519041		ESTs
NM_002466	−0.518911	MYBL2	v-myb avian myeloblastosis viral
			oncogene homolog-like 2
NM_016072	−0.518802	LOC51026	CGI-141 protein
AB007950	−0.518699	KIAA0481	KIAA0481 gene product
NM_001550	−0.518549	IFRD1	interferon-related developmental
			regulator 1
AF155120	−0.518221	UBE2V1	ubiquitin-conjugating enzyme E2
			variant 1
Contig49849_RC	0.517983		ESTs, Weakly similar to AF188706
			1 g20 protein [H. sapiens]
NM_016625	−0.517936	LOC51319	hypothetical protein
NM_004049	−0.517862	BCL2A1	BCL2-related protein A1
Contig50719_RC	0.517740		ESTs
D80010	−0.517620	LPIN1	lipin 1
NM_000299	−0.517405	PKP1	plakophilin 1 (ectodermal
			dysplasia/skin fragility syndrome)
AL049365	0.517080	FTL	ferritin, light polypeptide
Contig65227	0.517003		ESTs
NM_004865	−0.516808	TBPL1	TBP-like 1
Contig54813_RC	0.516246	FLJ13962	hypothetical protein FLJ13962
NM_003494	−0.516221	DYSF	dysferlin, limb girdle muscular
			dystrophy 2B (autosomal recessive)
NM_004431	−0.516212	EPHA2	EphA2
AL117600	−0.516067	DKFZP564J0863	DKFZP564J0863 protein
AL080209	−0.516037	DKFZP586F2423	hypothetical protein
			DKFZp586F2423
NM_000135	−0.515613	FANCA	Fanconi anemia, complementation
			group A
NM_000050	−0.515494	ASS	argininosuccinate synthetase
NM_001830	−0.515439	CLCN4	chloride channel 4
NM_018234	−0.515365	FLJ10829	hypothetical protein FLJ10829
Contig53307_RC	0.515328		ESTs, Highly similar to KIAA1437
			protein [H. sapiens]
AL117617	−0.515141		Homo sapiens mRNA; cDNA
			DKFZp564H0764 (from clone
			DKFZp564H0764)
NM_002906	−0.515098	RDX	radixin
NM_003360	−0.514427	UGT8	UDP glycosyltransferase 8 (UDP-
			galactose ceramide
			galactosyltransferase)
NM_018478	0.514332	HSMNP1	uncharacterized hypothalamus
			protein HSMNP1
M90657	−0.513908	TM4SF1	transmembrane 4 superfamily
			member 1
NM_014967	0.513793	KIAA1018	KIAA1018 protein
Contig1462_RC	0.513604	C11ORF15	chromosome 11 open reading frame
			15
Contig37287_RC	−0.513324		ESTs
NM_000355	−0.513225	TCN2	transcobalamin II; macrocytic
			anemia
AB037756	0.512914	KIAA1335	hypothetical protein KIAA1335
Contig842_RC	−0.512880		ESTs
NM_018186	−0.512878	FLJ10706	hypothetical protein FLJ10706
NM_014668	0.512746	KIAA0575	KIAA0575 gene product
NM_003226	0.512611	TFF3	trefoil factor 3 (intestinal)
Contig56457_RC	−0.512548	TMEFF1	transmembrane protein with EGF-
			like and two follistatin-like domains 1
AL050367	−0.511999		Homo sapiens mRNA; cDNA
			DKFZp564A026 (from clone
			DKFZp564A026)
NM_014791	−0.511963	KIAA0175	KIAA0175 gene product
Contig36312_RC	0.511794		ESTs
NM_004811	−0.511447	LPXN	leupaxin
Contig67182_RC	−0.511416		ESTs, Highly similar to epithelial V-
			like antigen precursor [H. sapiens]
Contig52723_RC	−0.511134		ESTs
Contig17105_RC	−0.511072		Homo sapiens mRNA for putative
			cytoplasmatic protein (ORF1-FL21)
NM_014449	0.511023	A	protein “A”
Contig52957_RC	0.510815		ESTs
Contig49388_RC	0.510582	FLJ13322	hypothetical protein FLJ13322
NM_017786	0.510557	FLJ20366	hypothetical protein FLJ20366
AL157476	0.510478		Homo sapiens mRNA; cDNA
			DKFZp761C082 (from clone
			DKFZp761C082)
NM_001919	0.510242	DCI	dodecenoyl-Coenzyme A delta
			isomerase (3,2 trans-enoyl-
			Coenzyme A isomerase)
NM_000268	−0.510165	NF2	neurofibromin 2 (bilateral acoustic
			neuroma)
NM_016210	0.510018	LOC51161	g20 protein
Contig45816_RC	−0.509977		ESTs
NM_003953	−0.509969	MPZL1	myelin protein zero-like 1
NM_000057	−0.509669	BLM	Bloom syndrome
NM_014452	−0.509473	DR6	death receptor 6
Contig45156_RC	0.509284		ESTs, Moderately similar to motor
			domain of KIF12 [M. musculus]
NM_006943	0.509149	SOX22	SRY (sex determining region Y)-box
			22
NM_000594	−0.509012	TNF	tumor necrosis factor (TNF
			superfamily, member 2)
AL137316	−0.508353	KIAA1609	KIAA1609 protein
NM_000557	−0.508325	GDF5	growth differentiation factor 5
			(cartilage-derived morphogenetic
			protein-1)
NM_018685	−0.508307	ANLN	anillin (Drosophila Scraps homolog),
			actin binding protein
Contig53401_RC	0.508189		ESTs
NM_014364	−0.508170	GAPDS	glyceraldehyde-3-phosphate
			dehydrogenase, testis-specific
Contig50297_RC	0.508137		ESTs, Moderately similar to
			ALU8_HUMAN ALU SUBFAMILY
			SX SEQUENCE CONTAMINATION
			WARNING ENTRY [H. sapiens]
Contig51800	0.507891		ESTs, Weakly similar to
			ALU6_HUMAN ALU SUBFAMILY
			SP SEQUENCE CONTAMINATION
			WARNING ENTRY [H. sapiens]
Contig49098_RC	−0.507716	MGC4090	hypothetical protein MGC4090
NM_002985	−0.507554	SCYA5	small inducible cytokine A5
			(RANTES)
AB007899	0.507439	KIAA0439	KIAA0439 protein; homolog of yeast
			ubiquitin-protein ligase Rsp5
AL110139	0.507145		Homo sapiens mRNA; cDNA
			DKFZp564O1763 (from clone
			DKFZp564O1763)
Contig51117_RC	0.507001		ESTs
NM_017660	−0.506768	FLJ20085	hypothetical protein FLJ20085
NM_018000	0.506686	FLJ10116	hypothetical protein FLJ10116
NM_005555	−0.506516	KRT6B	keratin 6B
NM_005582	−0.506462	LY64	lymphocyte antigen 64 (mouse)
			homolog, radioprotective, 105 kD
Contig47405_RC	0.506202		ESTs
NM_014808	0.506173	KIAA0793	KIAA0793 gene product
NM_004938	−0.506121	DAPK1	death-associated protein kinase 1
NM_020659	−0.505793	TTYH1	tweety (Drosophila) homolog 1
NM_006227	−0.505604	PLTP	phospholipid transfer protein
NM_014268	−0.505412	MAPRE2	microtubule-associated protein,
			RP/EB family, member 2
NM_004711	0.504849	SYNGR1	synaptogyrin 1
NM_004418	−0.504497	DUSP2	dual specificity phosphatase 2
NM_003508	−0.504475	FZD9	frizzled (Drosophila) homolog 9

[0088]

TABLE 3
430 gene markers that distinguish BRCA1-related tumor samples
from sporadic tumor samples
GenBank
Accession Number SEQ ID NO
AB002301         SEQ ID NO 4
AB004857         SEQ ID NO 8
AB007458         SEQ ID NO 12
AB014534         SEQ ID NO 29
AB018305         SEQ ID NO 34
AB020677         SEQ ID NO 36
AB020689         SEQ ID NO 37
AB023151         SEQ ID NO 41
AB023163         SEQ ID NO 43
AB028986         SEQ ID NO 48
AB029025         SEQ ID NO 50
AB032966         SEQ ID NO 53
AB032988         SEQ ID NO 57
AB033049         SEQ ID NO 63
AB033055         SEQ ID NO 66
AB037742         SEQ ID NO 73
AB041269         SEQ ID NO 96
AF000974         SEQ ID NO 97
AF042838         SEQ ID NO 111
AF052155         SEQ ID NO 119
AF055084         SEQ ID NO 125
AF063725         SEQ ID NO 129
AF070536         SEQ ID NO 133
AF070617         SEQ ID NO 135
AF073299         SEQ ID NO 136
AF079529         SEQ ID NO 140
AF090353         SEQ ID NO 141
AF116238         SEQ ID NO 155
AF151810         SEQ ID NO 171
AF220492         SEQ ID NO 185
AJ224741         SEQ ID NO 196
AJ250475         SEQ ID NO 201
AJ270996         SEQ ID NO 202
AJ272057         SEQ ID NO 203
AK000174         SEQ ID NO 211
AK000617         SEQ ID NO 215
AK000959         SEQ ID NO 222
AK001438         SEQ ID NO 229
AK001838         SEQ ID NO 233
AK002107         SEQ ID NO 238
AK002197         SEQ ID NO 239
AL035297         SEQ ID NO 241
AL049346         SEQ ID NO 243
AL049370         SEQ ID NO 245
AL049667         SEQ ID NO 249
AL080222         SEQ ID NO 276
AL096737         SEQ ID NO 279
AL110163         SEQ ID NO 282
AL133057         SEQ ID NO 300
AL133096         SEQ ID NO 302
AL133572         SEQ ID NO 305
AL133619         SEQ ID NO 307
AL133623         SEQ ID NO 309
AL137347         SEQ ID NO 320
AL137381         SEQ ID NO 322
AL137461         SEQ ID NO 325
AL137540         SEQ ID NO 328
AL137555         SEQ ID NO 329
AL137638         SEQ ID NO 332
AL137639         SEQ ID NO 333
AL137663         SEQ ID NO 334
AL137761         SEQ ID NO 339
AL157431         SEQ ID NO 340
AL161960         SEQ ID NO 351
AL355708         SEQ ID NO 353
AL359053         SEQ ID NO 354
D26488           SEQ ID NO 359
D38521           SEQ ID NO 361
D50914           SEQ ID NO 367
D80001           SEQ ID NO 369
G26403           SEQ ID NO 380
K02276           SEQ ID NO 383
M21551           SEQ ID NO 394
M27749           SEQ ID NO 397
M28170           SEQ ID NO 398
M73547           SEQ ID NO 409
M80899           SEQ ID NO 411
NM_000067        SEQ ID NO 423
NM_000087        SEQ ID NO 427
NM_000090        SEQ ID NO 428
NM_000165        SEQ ID NO 444
NM_000168        SEQ ID NO 445
NM_000196        SEQ ID NO 449
NM_000269        SEQ ID NO 457
NM_000310        SEQ ID NO 466
NM_000396        SEQ ID NO 479
NM_000397        SEQ ID NO 480
NM_000597        SEQ ID NO 502
NM_000636        SEQ ID NO 509
NM_000888        SEQ ID NO 535
NM_000903        SEQ ID NO 536
NM_000930        SEQ ID NO 540
NM_000931        SEQ ID NO 541
NM_000969        SEQ ID NO 547
NM_000984        SEQ ID NO 548
NM_001026        SEQ ID NO 552
NM_001054        SEQ ID NO 554
NM_001179        SEQ ID NO 567
NM_001184        SEQ ID NO 568
NM_001204        SEQ ID NO 571
NM_001206        SEQ ID NO 572
NM_001218        SEQ ID NO 575
NM_001275        SEQ ID NO 586
NM_001394        SEQ ID NO 602
NM_001424        SEQ ID NO 605
NM_001448        SEQ ID NO 610
NM_001504        SEQ ID NO 620
NM_001553        SEQ ID NO 630
NM_001674        SEQ ID NO 646
NM_001675        SEQ ID NO 647
NM_001725        SEQ ID NO 652
NM_001740        SEQ ID NO 656
NM_001756        SEQ ID NO 659
NM_001770        SEQ ID NO 664
NM_001797        SEQ ID NO 670
NM_001845        SEQ ID NO 680
NM_001873        SEQ ID NO 684
NM_001888        SEQ ID NO 687
NM_001892        SEQ ID NO 688
NM_001919        SEQ ID NO 694
NM_001946        SEQ ID NO 698
NM_001953        SEQ ID NO 699
NM_001960        SEQ ID NO 704
NM_001985        SEQ ID NO 709
NM_002023        SEQ ID NO 712
NM_002051        SEQ ID NO 716
NM_002053        SEQ ID NO 717
NM_002164        SEQ ID NO 734
NM_002200        SEQ ID NO 739
NM_002201        SEQ ID NO 740
NM_002213        SEQ ID NO 741
NM_002250        SEQ ID NO 747
NM_002512        SEQ ID NO 780
NM_002542        SEQ ID NO 784
NM_002561        SEQ ID NO 786
NM_002615        SEQ ID NO 793
NM_002686        SEQ ID NO 803
NM_002709        SEQ ID NO 806
NM_002742        SEQ ID NO 812
NM_002775        SEQ ID NO 815
NM_002975        SEQ ID NO 848
NM_002982        SEQ ID NO 849
NM_003104        SEQ ID NO 870
NM_003118        SEQ ID NO 872
NM_003144        SEQ ID NO 876
NM_003165        SEQ ID NO 882
NM_003197        SEQ ID NO 885
NM_003202        SEQ ID NO 886
NM_003217        SEQ ID NO 888
NM_003283        SEQ ID NO 898
NM_003462        SEQ ID NO 911
NM_003500        SEQ ID NO 918
NM_003561        SEQ ID NO 925
NM_003607        SEQ ID NO 930
NM_003633        SEQ ID NO 933
NM_003641        SEQ ID NO 934
NM_003683        SEQ ID NO 943
NM_003729        SEQ ID NO 949
NM_003793        SEQ ID NO 954
NM_003829        SEQ ID NO 958
NM_003866        SEQ ID NO 961
NM_003904        SEQ ID NO 967
NM_003953        SEQ ID NO 974
NM_004024        SEQ ID NO 982
NM_004053        SEQ ID NO 986
NM_004295        SEQ ID NO 1014
NM_004438        SEQ ID NO 1038
NM_004559        SEQ ID NO 1057
NM_004616        SEQ ID NO 1065
NM_004741        SEQ ID NO 1080
NM_004772        SEQ ID NO 1084
NM_004791        SEQ ID NO 1086
NM_004848        SEQ ID NO 1094
NM_004866        SEQ ID NO 1097
NM_005128        SEQ ID NO 1121
NM_005148        SEQ ID NO 1124
NM_005196        SEQ ID NO 1127
NM_005326        SEQ ID NO 1140
NM_005518        SEQ ID NO 1161
NM_005538        SEQ ID NO 1163
NM_005557        SEQ ID NO 1170
NM_005718        SEQ ID NO 1189
NM_005804        SEQ ID NO 1201
NM_005824        SEQ ID NO 1203
NM_005935        SEQ ID NO 1220
NM_006002        SEQ ID NO 1225
NM_006148        SEQ ID NO 1249
NM_006235        SEQ ID NO 1257
NM_006271        SEQ ID NO 1261
NM_006287        SEQ ID NO 1264
NM_006296        SEQ ID NO 1267
NM_006378        SEQ ID NO 1275
NM_006461        SEQ ID NO 1287
NM_006573        SEQ ID NO 1300
NM_006622        SEQ ID NO 1302
NM_006696        SEQ ID NO 1308
NM_006769        SEQ ID NO 1316
NM_006787        SEQ ID NO 1319
NM_006875        SEQ ID NO 1334
NM_006885        SEQ ID NO 1335
NM_006918        SEQ ID NO 1339
NM_006923        SEQ ID NO 1340
NM_006941        SEQ ID NO 1342
NM_007070        SEQ ID NO 1354
NM_007088        SEQ ID NO 1356
NM_007146        SEQ ID NO 1358
NM_007173        SEQ ID NO 1359
NM_007246        SEQ ID NO 1366
NM_007358        SEQ ID NO 1374
NM_012135        SEQ ID NO 1385
NM_012151        SEQ ID NO 1387
NM_012258        SEQ ID NO 1396
NM_012317        SEQ ID NO 1399
NM_012337        SEQ ID NO 1403
NM_012339        SEQ ID NO 1404
NM_012391        SEQ ID NO 1406
NM_012428        SEQ ID NO 1412
NM_013233        SEQ ID NO 1418
NM_013253        SEQ ID NO 1422
NM_013262        SEQ ID NO 1425
NM_013372        SEQ ID NO 1434
NM_013378        SEQ ID NO 1435
NM_014096        SEQ ID NO 1450
NM_014242        SEQ ID NO 1464
NM_014314        SEQ ID NO 1472
NM_014398        SEQ ID NO 1486
NM_014402        SEQ ID NO 1488
NM_014476        SEQ ID NO 1496
NM_014521        SEQ ID NO 1499
NM_014585        SEQ ID NO 1504
NM_014597        SEQ ID NO 1506
NM_014642        SEQ ID NO 1510
NM_014679        SEQ ID NO 1517
NM_014680        SEQ ID NO 1518
NM_014700        SEQ ID NO 1520
NM_014723        SEQ ID NO 1523
NM_014770        SEQ ID NO 1530
NM_014785        SEQ ID NO 1534
NM_014817        SEQ ID NO 1539
NM_014840        SEQ ID NO 1541
NM_014878        SEQ ID NO 1546
NM_015493        SEQ ID NO 1564
NM_015523        SEQ ID NO 1568
NM_015544        SEQ ID NO 1570
NM_015623        SEQ ID NO 1572
NM_015640        SEQ ID NO 1573
NM_015721        SEQ ID NO 1576
NM_015881        SEQ ID NO 1577
NM_015937        SEQ ID NO 1582
NM_015964        SEQ ID NO 1586
NM_015984        SEQ ID NO 1587
NM_016000        SEQ ID NO 1591
NM_016018        SEQ ID NO 1593
NM_016066        SEQ ID NO 1601
NM_016073        SEQ ID NO 1603
NM_016081        SEQ ID NO 1604
NM_016140        SEQ ID NO 1611
NM_016223        SEQ ID NO 1622
NM_016267        SEQ ID NO 1629
NM_016307        SEQ ID NO 1633
NM_016364        SEQ ID NO 1639
NM_016373        SEQ ID NO 1640
NM_016459        SEQ ID NO 1646
NM_016471        SEQ ID NO 1648
NM_016548        SEQ ID NO 1654
NM_016620        SEQ ID NO 1662
NM_016820        SEQ ID NO 1674
NM_017423        SEQ ID NO 1678
NM_017709        SEQ ID NO 1698
NM_017732        SEQ ID NO 1700
NM_017734        SEQ ID NO 1702
NM_017750        SEQ ID NO 1704
NM_017763        SEQ ID NO 1706
NM_017782        SEQ ID NO 1710
NM_017816        SEQ ID NO 1714
NM_018043        SEQ ID NO 1730
NM_018072        SEQ ID NO 1734
NM_018093        SEQ ID NO 1738
NM_018103        SEQ ID NO 1742
NM_018171        SEQ ID NO 1751
NM_018187        SEQ ID NO 1755
NM_018188        SEQ ID NO 1756
NM_018222        SEQ ID NO 1761
NM_018228        SEQ ID NO 1762
NM_018373        SEQ ID NO 1777
NM_018390        SEQ ID NO 1781
NM_018422        SEQ ID NO 1784
NM_018509        SEQ ID NO 1792
NM_018584        SEQ ID NO 1796
NM_018653        SEQ ID NO 1797
NM_018660        SEQ ID NO 1798
NM_018683        SEQ ID NO 1799
NM_019049        SEQ ID NO 1814
NM_019063        SEQ ID NO 1815
NM_020150        SEQ ID NO 1823
NM_020987        SEQ ID NO 1848
NM_021095        SEQ ID NO 1855
NM_021242        SEQ ID NO 1867
U41387           SEQ ID NO 1877
U45975           SEQ ID NO 1878
U58033           SEQ ID NO 1881
U67784           SEQ ID NO 1884
U68385           SEQ ID NO 1885
U80736           SEQ ID NO 1890
X00437           SEQ ID NO 1899
X07203           SEQ ID NO 1904
X16302           SEQ ID NO 1907
X51630           SEQ ID NO 1908
X57809           SEQ ID NO 1912
X57819           SEQ ID NO 1913
X58529           SEQ ID NO 1914
X66087           SEQ ID NO 1916
X69150           SEQ ID NO 1917
X72475           SEQ ID NO 1918
X74794           SEQ ID NO 1920
X75315           SEQ ID NO 1921
X84340           SEQ ID NO 1925
X98260           SEQ ID NO 1928
Y07512           SEQ ID NO 1931
Y14737           SEQ ID NO 1932
Z34893           SEQ ID NO 1934
Contig237_RC     SEQ ID NO 1940
Contig292_RC     SEQ ID NO 1942
Contig372_RC     SEQ ID NO 1943
Contig756_RC     SEQ ID NO 1955
Contig842_RC     SEQ ID NO 1958
Contig1632_RC    SEQ ID NO 1977
Contig1826_RC    SEQ ID NO 1980
Contig2237_RC    SEQ ID NO 1988
Contig2915_RC    SEQ ID NO 2003
Contig3164_RC    SEQ ID NO 2007
Contig3252_RC    SEQ ID NO 2008
Contig3940_RC    SEQ ID NO 2018
Contig9259_RC    SEQ ID NO 2039
Contig10268_RC   SEQ ID NO 2041
Contig10437_RC   SEQ ID NO 2043
Contig10973_RC   SEQ ID NO 2044
Contig14390_RC   SEQ ID NO 2054
Contig16453_RC   SEQ ID NO 2060
Contig16759_RC   SEQ ID NO 2061
Contig19551      SEQ ID NO 2070
Contig24541_RC   SEQ ID NO 2088
Contig25362_RC   SEQ ID NO 2093
Contig25617_RC   SEQ ID NO 2094
Contig25722_RC   SEQ ID NO 2096
Contig26022_RC   SEQ ID NO 2099
Contig27915_RC   SEQ ID NO 2114
Contig28081_RC   SEQ ID NO 2116
Contig28179_RC   SEQ ID NO 2118
Contig28550_RC   SEQ ID NO 2119
Contig29639_RC   SEQ ID NO 2127
Contig29647_RC   SEQ ID NO 2128
Contig30092_RC   SEQ ID NO 2130
Contig30209_RC   SEQ ID NO 2132
Contig32185_RC   SEQ ID NO 2156
Contig32798_RC   SEQ ID NO 2161
Contig33230_RC   SEQ ID NO 2163
Contig33394_RC   SEQ ID NO 2165
Contig36323_RC   SEQ ID NO 2197
Contig36761_RC   SEQ ID NO 2201
Contig37141_RC   SEQ ID NO 2209
Contig37778_RC   SEQ ID NO 2218
Contig38285_RC   SEQ ID NO 2222
Contig38520_RC   SEQ ID NO 2225
Contig38901_RC   SEQ ID NO 2232
Contig39826_RC   SEQ ID NO 2241
Contig40212_RC   SEQ ID NO 2251
Contig40712_RC   SEQ ID NO 2257
Contig41402_RC   SEQ ID NO 2265
Contig41635_RC   SEQ ID NO 2272
Contig42006_RC   SEQ ID NO 2280
Contig42220_RC   SEQ ID NO 2286
Contig42306_RC   SEQ ID NO 2287
Contig43918_RC   SEQ ID NO 2312
Contig44195_RC   SEQ ID NO 2316
Contig44265_RC   SEQ ID NO 2318
Contig44278_RC   SEQ ID NO 2319
Contig44757_RC   SEQ ID NO 2329
Contig45588_RC   SEQ ID NO 2349
Contig46262_RC   SEQ ID NO 2361
Contig46288_RC   SEQ ID NO 2362
Contig46343_RC   SEQ ID NO 2363
Contig46452_RC   SEQ ID NO 2366
Contig46868_RC   SEQ ID NO 2373
Contig46937_RC   SEQ ID NO 2377
Contig48004_RC   SEQ ID NO 2393
Contig48249_RC   SEQ ID NO 2397
Contig48774_RC   SEQ ID NO 2405
Contig48913_RC   SEQ ID NO 2411
Contig48945_RC   SEQ ID NO 2412
Contig48970_RC   SEQ ID NO 2413
Contig49233_RC   SEQ ID NO 2419
Contig49289_RC   SEQ ID NO 2422
Contig49342_RC   SEQ ID NO 2423
Contig49510_RC   SEQ ID NO 2430
Contig49855      SEQ ID NO 2440
Contig49948_RC   SEQ ID NO 2442
Contig50297_RC   SEQ ID NO 2451
Contig50669_RC   SEQ ID NO 2458
Contig50673_RC   SEQ ID NO 2459
Contig50838_RC   SEQ ID NO 2465
Contig51068_RC   SEQ ID NO 2471
Contig51929      SEQ ID NO 2492
Contig51953_RC   SEQ ID NO 2493
Contig52405_RC   SEQ ID NO 2502
Contig52543_RC   SEQ ID NO 2505
Contig52720_RC   SEQ ID NO 2513
Contig53281_RC   SEQ ID NO 2530
Contig53598_RC   SEQ ID NO 2537
Contig53757_RC   SEQ ID NO 2543
Contig53944_RC   SEQ ID NO 2545
Contig54425      SEQ ID NO 2561
Contig54547_RC   SEQ ID NO 2565
Contig54757_RC   SEQ ID NO 2574
Contig54916_RC   SEQ ID NO 2581
Contig55770_RC   SEQ ID NO 2604
Contig55801_RC   SEQ ID NO 2606
Contig56143_RC   SEQ ID NO 2619
Contig56160_RC   SEQ ID NO 2620
Contig56303_RC   SEQ ID NO 2626
Contig57023_RC   SEQ ID NO 2639
Contig57138_RC   SEQ ID NO 2644
Contig57609_RC   SEQ ID NO 2657
Contig58301_RC   SEQ ID NO 2667
Contig58512_RC   SEQ ID NO 2670
Contig60393      SEQ ID NO 2674
Contig60509_RC   SEQ ID NO 2675
Contig61254_RC   SEQ ID NO 2677
Contig62306      SEQ ID NO 2680
Contig64502      SEQ ID NO 2689

[0089]

TABLE 4
100 preferred markers from Table 3 distinguishing BRCA1-related tumors from
sporadic tumors.
		Sequence
Identifier	Correlation	Name	Description
NM_001892	−0.651689	CSNK1A1	casein kinase 1, alpha 1
NM_018171	−0.637696	FLJ10659	hypothetical protein FLJ10659
Contig40712_RC	−0.612509		ESTs
NM_001204	−0.608470	BMPR2	bone morphogenetic protein
			receptor, type II (serine/threonine
			kinase)
NM_005148	−0.598612	UNC119	unc119 (C. elegans) homolog
G26403	0.585054	YWHAH	tyrosine 3-
			monooxygenase/tryptophan 5-
			monooxygenase activation protein,
			eta polypeptide
NM_015640	0.583397	PAI-RBP1	PAI-1 mRNA-binding protein
Contig9259_RC	0.581362		ESTs
AB033049	−0.578750	KIAA1223	KIAA1223 protein
NM_015523	0.576029	DKFZP566E144	small fragment nuclease
Contig41402_RC	−0.571650		Human DNA sequence from clone
			RP11-16L21 on chromosome 9.
			Contains the gene for NADP-
			dependent leukotriene B4 12-
			hydroxydehydrogenase, the gene
			for a novel DnaJ domain protein
			similar to Drosophila, C. elegans
			and Arabidopsis predicted proteins,
			the GNG10 gene for guanine
			nucleotide binding protein 10, a
			novel gene, ESTs, STSs, GSSs
			and six CpG islands
NM_004791	−0.564819	ITGBL1	integrin, beta-like 1 (with EGF-like
			repeat domains)
NM_007070	0.561173	FAP48	FKBP-associated protein
NM_014597	0.555907	HSU15552	acidic 82 kDa protein mRNA
AF000974	0.547194	TRIP6	thyroid hormone receptor interactor 6
NM_016073	−0.547072	CGI-142	CGI-142
Contig3940_RC	0.544073	YWHAH	tyrosine 3-
			monooxygenase/tryptophan 5-
			monooxygenase activation protein,
			eta polypeptide
NM_003683	0.542219	D21S2056E	DNA segment on chromosome 21
			(unique) 2056 expressed sequence
Contig58512_RC	−0.528458		Homo sapiens pancreas tumor-
			related protein (FKSG12) mRNA,
			complete cds
NM_003904	0.521223	ZNF259	zinc finger protein 259
Contig26022_RC	0.517351		ESTs
Contig48970_RC	−0.516953	KIAA0892	KIAA0892 protein
NM_016307	−0.515398	PRX2	paired related homeobox protein
AL137761	−0.514891		Homo sapiens mRNA; cDNA
			DKFZp586L2424 (from clone
			DKFZp586L2424)
NM_001919	−0.514799	DCI	dodecenoyl-Coenzyme A delta
			isomerase (3,2 trans-enoyl-
			Coenzyme A isomerase)
NM_000196	−0.514004	HSD11B2	hydroxysteroid (11-beta)
			dehydrogenase 2
NM_002200	0.513149	IRF5	interferon regulatory factor 5
AL133572	0.511340		Homo sapiens mRNA; cDNA
			DKFZp434I0535 (from clone
			DKFZp434I0535); partial cds
NM_019063	0.511127	C2ORF2	chromosome 2 open reading frame 2
Contig25617_RC	0.509506		ESTs
NM_007358	0.508145	M96	putative DNA binding protein
NM_014785	−0.507114	KIAA0258	KIAA0258 gene product
NM_006235	0.506585	POU2AF1	POU domain, class 2, associating
			factor 1
NM_014680	−0.505779	KIAA0100	KIAA0100 gene product
X66087	0.500842	MYBL1	v-myb avian myeloblastosis viral
			oncogene homolog-like 1
Y07512	−0.500686	PRKG1	protein kinase, cGMP-dependent,
			type I
NM_006296	0.500344	VRK2	vaccinia related kinase 2
Contig44278_RC	0.498260	DKFZP434K114	DKFZP434K114 protein
Contig56160_RC	−0.497695		ESTs
NM_002023	−0.497570	FMOD	fibromodulin
M28170	0.497095	CD19	CD19 antigen
D26488	0.496511	KIAA0007	KIAA0007 protein
X72475	0.496125		H. sapiens mRNA for rearranged Ig
			kappa light chain variable region
			(I.114)
K02276	0.496068	MYC	v-myc avian myelocytomatosis viral
			oncogene homolog
NM_013378	0.495648	VPREB3	pre-B lymphocyte gene 3
X58529	0.495608	IGHM	immunoglobulin heavy constant mu
NM_000168	−0.494260	GLI3	GLI-Kruppel family member GLI3
			(Greig cephalopolysyndactyly
			syndrome)
NM_004866	−0.492967	SCAMP1	secretory carrier membrane protein 1
NM_013253	−0.491159	DKK3	dickkopf (Xenopus laevis) homolog 3
NM_003729	0.488971	RPC	RNA 3′-terminal phosphate cyclase
NM_006875	0.487407	PIM2	pim-2 oncogene
NM_018188	0.487126	FLJ10709	hypothetical protein FLJ10709
NM_004848	0.485408	ICB-1	basement membrane-induced gene
NM_001179	0.483253	ART3	ADP-ribosyltransferase 3
NM_016548	−0.482329	LOC51280	golgi membrane protein GP73
NM_007146	−0.481994	ZNF161	zinc finger protein 161
NM_021242	−0.481754	STRAIT11499	hypothetical protein STRAITI1499
NM_016223	0.481710	PACSIN3	protein kinase C and casein kinase
			substrate in neurons 3
NM_003197	−0.481526	TCEB1L	transcription elongation factor B
			(SIII), polypeptide 1-like
NM_000067	−0.481003	CA2	carbonic anhydrase II
NM_006885	−0.479705	ATBF1	AT-binding transcription factor 1
NM_002542	0.478282	OGG1	8-oxoguanine DNA glycosylase
AL133619	−0.476596		Homo sapiens mRNA; cDNA
			DKFZp434E2321 (from clone
			DKFZp434E2321); partial cds
D80001	0.476130	KIAA0179	KIAA0179 protein
NM_018660	−0.475548	LOC55893	papillomavirus regulatory factor
			PRF-1
AB004857	0.473440	SLC11A2	solute carrier family 11 (proton-
			coupled divalent metal ion
			transporters), member 2
NM_002250	0.472900	KCNN4	potassium intermediate/small
			conductance calcium-activated
			channel, subfamily N, member 4
Contig56143_RC	−0.472611		ESTs, Weakly similar to A54849
			collagen alpha 1(VII) chain
			precursor [H. sapiens]
NM_001960	0.471502	EEF1D	eukaryotic translation elongation
			factor 1 delta (guanine nucleotide
			exchange protein)
Contig52405_RC	−0.470705		ESTs, Weakly similar to
			ALU8_HUMAN ALU SUBFAMILY
			SX SEQUENCE CONTAMINATION
			WARNING ENTRY [H. sapiens]
Contig30092_RC	−0.469977		Homo sapiens PR-domain zinc
			finger protein 6 isoform B (PRDM6)
			mRNA, partial cds; alternatively
			spliced
NM_003462	−0.468753	P28	dynein, axonemal, light
			intermediate polypeptide
Contig60393	0.468475		ESTs
Contig842_RC	0.468158		ESTs
NM_002982	0.466362	SCYA2	small inducible cytokine A2
			(monocyte chemotactic protein 1,
			homologous to mouse Sig-je)
Contig14390_RC	0.464150		ESTs
NM_001770	0.463847	CD19	CD19 antigen
AK000617	−0.463158		Homo sapiens mRNA; cDNA
			DKFZp434L235 (from clone
			DKFZp434L235)
AF073299	−0.463007	SLC9A2	solute carrier family 9
			(sodium/hydrogen exchanger),
			isoform 2
NM_019049	0.461990	FLJ20054	hypothetical protein
AL137347	−0.460778	DKFZP761M1511	hypothetical protein
NM_000396	−0.460263	CTSK	cathepsin K (pycnodysostosis)
NM_018373	−0.459268	FLJ11271	hypothetical protein FLJ11271
NM_002709	0.458500	PPP1CB	protein phosphatase 1, catalytic
			subunit, beta isoform
NM_016820	0.457516	OGG1	8-oxoguanine DNA glycosylase
Contig10268_RC	0.456933		Human DNA sequence from clone
			RP11-196N14 on chromosome 20
			Contains ESTs, STSs, GSSs and
			CpG islands. Contains three novel
			genes, part of a gene for a novel
			protein similar to protein
			serine/threonine phosphatase 4
			regulatory subunit 1 (PP4R1) and a
			gene for a novel protein with an
			ankyrin domain
NM_014521	−0.456733	SH3BP4	SH3-domain binding protein 4
AJ272057	−0.456548	STRAIT11499	hypothetical protein STRAIT11499
NM_015964	−0.456187	LOC51673	brain specific protein
Contig16759_RC	−0.456169		ESTs
NM_015937	−0.455954	LOC51604	CGI-06 protein
NM_007246	−0.455500	KLHL2	kelch (Drosophila)-like 2 (Mayven)
NM_001985	−0.453024	ETFB	electron-transfer-flavoprotein, beta
			polypeptide
NM_000984	−0.452935	RPL23A	ribosomal protein L23a
Contig51953_RC	−0.451695		ESTs
NM_015984	0.450491	UCH37	ubiquitin C-terminal hydrolase
			UCH37
NM_000903	−0.450371	DIA4	diaphorase (NADH/NADPH)
			(cytochrome b-5 reductase)
NM_001797	−0.449862	CDH11	cadherin 11, type 2, OB-cadherin
			(osteoblast)
NM_014878	0.449818	KIAA0020	KIAA0020 gene product
NM_002742	−0.449590	PRKCM	protein kinase C, mu

[0090]

TABLE 5
231 gene markers that distinguish patients
with good prognosis from patients
with poor prognosis.
GenBank
Accession Number SEQ ID NO
AA555029_RC      SEQ ID NO 1
AB020689         SEQ ID NO 37
AB032973         SEQ ID NO 55
AB033007         SEQ ID NO 58
AB033043         SEQ ID NO 62
AB037745         SEQ ID NO 75
AB037863         SEQ ID NO 88
AF052159         SEQ ID NO 120
AF052162         SEQ ID NO 121
AF055033         SEQ ID NO 124
AF073519         SEQ ID NO 137
AF148505         SEQ ID NO 169
AF155117         SEQ ID NO 173
AF161553         SEQ ID NO 177
AF201951         SEQ ID NO 183
AF257175         SEQ ID NO 189
AJ224741         SEQ ID NO 196
AK000745         SEQ ID NO 219
AL050021         SEQ ID NO 257
AL050090         SEQ ID NO 259
AL080059         SEQ ID NO 270
AL080079         SEQ ID NO 271
AL080110         SEQ ID NO 272
AL133603         SEQ ID NO 306
AL133619         SEQ ID NO 307
AL137295         SEQ ID NO 315
AL137502         SEQ ID NO 326
AL137514         SEQ ID NO 327
AL137718         SEQ ID NO 336
AL355708         SEQ ID NO 353
D25328           SEQ ID NO 357
L27560           SEQ ID NO 390
M21551           SEQ ID NO 394
NM_000017        SEQ ID NO 416
NM_000096        SEQ ID NO 430
NM_000127        SEQ ID NO 436
NM_000158        SEQ ID NO 442
NM_000224        SEQ ID NO 453
NM_000286        SEQ ID NO 462
NM_000291        SEQ ID NO 463
NM_000320        SEQ ID NO 469
NM_000436        SEQ ID NO 487
NM_000507        SEQ ID NO 491
NM_000599        SEQ ID NO 503
NM_000788        SEQ ID NO 527
NM_000849        SEQ ID NO 530
NM_001007        SEQ ID NO 550
NM_001124        SEQ ID NO 562
NM_001168        SEQ ID NO 566
NM_001216        SEQ ID NO 574
NM_001280        SEQ ID NO 588
NM_001282        SEQ ID NO 589
NM_001333        SEQ ID NO 597
NM_001673        SEQ ID NO 645
NM_001809        SEQ ID NO 673
NM_001827        SEQ ID NO 676
NM_001905        SEQ ID NO 691
NM_002019        SEQ ID NO 711
NM_002073        SEQ ID NO 721
NM_002358        SEQ ID NO 764
NM_002570        SEQ ID NO 787
NM_002808        SEQ ID NO 822
NM_002811        SEQ ID NO 823
NM_002900        SEQ ID NO 835
NM_002916        SEQ ID NO 838
NM_003158        SEQ ID NO 881
NM_003234        SEQ ID NO 891
NM_003239        SEQ ID NO 893
NM_003258        SEQ ID NO 896
NM_003376        SEQ ID NO 906
NM_003600        SEQ ID NO 929
NM_003607        SEQ ID NO 930
NM_003662        SEQ ID NO 938
NM_003676        SEQ ID NO 941
NM_003748        SEQ ID NO 951
NM_003862        SEQ ID NO 960
NM_003875        SEQ ID NO 962
NM_003878        SEQ ID NO 963
NM_003882        SEQ ID NO 964
NM_003981        SEQ ID NO 977
NM_004052        SEQ ID NO 985
NM_004163        SEQ ID NO 995
NM_004336        SEQ ID NO 1022
NM_004358        SEQ ID NO 1026
NM_004456        SEQ ID NO 1043
NM_004480        SEQ ID NO 1046
NM_004504        SEQ ID NO 1051
NM_004603        SEQ ID NO 1064
NM_004701        SEQ ID NO 1075
NM_004702        SEQ ID NO 1076
NM_004798        SEQ ID NO 1087
NM_004911        SEQ ID NO 1102
NM_004994        SEQ ID NO 1108
NM_005196        SEQ ID NO 1127
NM_005342        SEQ ID NO 1143
NM_005496        SEQ ID NO 1157
NM_005563        SEQ ID NO 1173
NM_005915        SEQ ID NO 1215
NM_006096        SEQ ID NO 1240
NM_006101        SEQ ID NO 1241
NM_006115        SEQ ID NO 1245
NM_006117        SEQ ID NO 1246
NM_006201        SEQ ID NO 1254
NM_006265        SEQ ID NO 1260
NM_006281        SEQ ID NO 1263
NM_006372        SEQ ID NO 1273
NM_006681        SEQ ID NO 1306
NM_006763        SEQ ID NO 1315
NM_006931        SEQ ID NO 1341
NM_007036        SEQ ID NO 1349
NM_007203        SEQ ID NO 1362
NM_012177        SEQ ID NO 1390
NM_012214        SEQ ID NO 1392
NM_012261        SEQ ID NO 1397
NM_012429        SEQ ID NO 1413
NM_013262        SEQ ID NO 1425
NM_013296        SEQ ID NO 1427
NM_013437        SEQ ID NO 1439
NM_014078        SEQ ID NO 1449
NM_014109        SEQ ID NO 1451
NM_014321        SEQ ID NO 1477
NM_014363        SEQ ID NO 1480
NM_014750        SEQ ID NO 1527
NM_014754        SEQ ID NO 1528
NM_014791        SEQ ID NO 1535
NM_014875        SEQ ID NO 1545
NM_014889        SEQ ID NO 1548
NM_014968        SEQ ID NO 1554
NM_015416        SEQ ID NO 1559
NM_015417        SEQ ID NO 1560
NM_015434        SEQ ID NO 1562
NM_015984        SEQ ID NO 1587
NM_016337        SEQ ID NO 1636
NM_016359        SEQ ID NO 1638
NM_016448        SEQ ID NO 1645
NM_016569        SEQ ID NO 1655
NM_016577        SEQ ID NO 1656
NM_017779        SEQ ID NO 1708
NM_018004        SEQ ID NO 1725
NM_018098        SEQ ID NO 1739
NM_018104        SEQ ID NO 1743
NM_018120        SEQ ID NO 1745
NM_018136        SEQ ID NO 1748
NM_018265        SEQ ID NO 1766
NM_018354        SEQ ID NO 1774
NM_018401        SEQ ID NO 1782
NM_018410        SEQ ID NO 1783
NM_018454        SEQ ID NO 1786
NM_018455        SEQ ID NO 1787
NM_019013        SEQ ID NO 18O9
NM_020166        SEQ ID NO 1825
NM_020188        SEQ ID NO 1830
NM_020244        SEQ ID NO 1835
NM_020386        SEQ ID NO 1838
NM_020675        SEQ ID NO 1842
NM_020974        SEQ ID NO 1844
R70506_RC        SEQ ID NO 1868
U45975           SEQ ID NO 1878
U58033           SEQ ID NO 1881
U82987           SEQ ID NO 1891
U96131           SEQ ID NO 1896
X05610           SEQ ID NO 1903
X94232           SEQ ID NO 1927
Contig753_RC     SEQ ID NO 1954
Contig1778_RC    SEQ ID NO 1979
Contig2399_RC    SEQ ID NO 1989
Contig2504_RC    SEQ ID NO 1991
Contig3902_RC    SEQ ID NO 2017
Contig4595       SEQ ID NO 2022
Contig8581_RC    SEQ ID NO 2037
Contig13480_RC   SEQ ID NO 2052
Contig17359_RC   SEQ ID NO 2068
Contig20217_RC   SEQ ID NO 2072
Contig21812_RC   SEQ ID NO 2082
Contig24252_RC   SEQ ID NO 2087
Contig25055_RC   SEQ ID NO 2090
Contig25343_RC   SEQ ID NO 2092
Contig25991      SEQ ID NO 2098
Contig27312_RC   SEQ ID NO 2108
Contig28552_RC   SEQ ID NO 2120
Contig32125_RC   SEQ ID NO 2155
Contig32185_RC   SEQ ID NO 2156
Contig33814_RC   SEQ ID NO 2169
Contig34634_RC   SEQ ID NO 2180
Contig35251_RC   SEQ ID NO 2185
Contig37063_RC   SEQ ID NO 2206
Contig37598      SEQ ID NO 2216
Contig38288_RC   SEQ ID NO 2223
Contig40128_RC   SEQ ID NO 2248
Contig40831_RC   SEQ ID NO 2260
Contig41413_RC   SEQ ID NO 2266
Contig41887_RC   SEQ ID NO 2276
Contig42421_RC   SEQ ID NO 2291
Contig43747_RC   SEQ ID NO 2311
Contig44064_RC   SEQ ID NO 2315
Contig44289_RC   SEQ ID NO 2320
Contig44799_RC   SEQ ID NO 2330
Contig45347_RC   SEQ ID NO 2344
Contig45816_RC   SEQ ID NO 2351
Contig46218_RC   SEQ ID NO 2358
Contig46223_RC   SEQ ID NO 2359
Contig46653_RC   SEQ ID NO 2369
Contig46802_RC   SEQ ID NO 2372
Contig47405_RC   SEQ ID NO 2384
Contig48328_RC   SEQ ID NO 2400
Contig49670_RC   SEQ ID NO 2434
Contig50106_RC   SEQ ID NO 2445
Contig50410      SEQ ID NO 2453
Contig50802_RC   SEQ ID NO 2463
Contig51464_RC   SEQ ID NO 2481
Contig51519_RC   SEQ ID NO 2482
Contig51749_RC   SEQ ID NO 2486
Contig51963      SEQ ID NO 2494
Contig53226_RC   SEQ ID NO 2525
Contig53268_RC   SEQ ID NO 2529
Contig53646_RC   SEQ ID NO 2538
Contig53742_RC   SEQ ID NO 2542
Contig55188_RC   SEQ ID NO 2586
Contig55313_RC   SEQ ID NO 2590
Contig55377_RC   SEQ ID NO 2591
Contig55725_RC   SEQ ID NO 2600
Contig55813_RC   SEQ ID NO 2607
Contig55829_RC   SEQ ID NO 2608
Contig56457_RC   SEQ ID NO 2630
Contig57595      SEQ ID NO 2655
Contig57864_RC   SEQ ID NO 2663
Contig58368_RC   SEQ ID NO 2668
Contig60864_RC   SEQ ID NO 2676
Contig63102_RC   SEQ ID NO 2684
Contig63649_RC   SEQ ID NO 2686
Contig64688      SEQ ID NO 2690

[0091]

TABLE 6
70 Preferred prognosis markers drawn from Table 5.
		Sequence
Identifier	Correlation	Name	Description
AL080059	−0.527150		Homo sapiens mRNA for KIAA1750
			protein, partial cds
Contig63649—	−0.468130		ESTs
RC
Contig46218—	−0.432540		ESTs
RC
NM_016359	−0.424930	LOC51203	clone HQ0310 PRO0310p1
AA555029_RC	−0.424120		ESTs
NM_003748	0.420671	ALDH4	aldehyde dehydrogenase 4
			(glutamate gamma-semialdehyde
			dehydrogenase; pyrroline-5-
			carboxylate dehydrogenase)
Contig38288—	−0.414970		ESTs, Weakly similar to ISHUSS
RC			protein disulfide-isomerase
			[H. sapiens ]
NM_003862	0.410964	FGF18	fibroblast growth factor 18
Contig28552—	−0.409260		Homo sapiens mRNA; cDNA
RC			DKFZp434C0931 (from clone
			DKFZp434C0931); partial cds
Contig32125—	0.409054		ESTs
RC
U82987	0.407002	BBC3	Bcl-2 binding component 3
AL137718	−0.404980		Homo sapiens mRNA; cDNA
			DKFZp434C0931 (from clone
			DKFZp434C0931); partial cds
AB037863	0.402335	KIAA1442	KIAA1442 protein
NM_020188	−0.400070	DC13	DC13 protein
NM_020974	0.399987	CEGP1	CEGP1 protein
NM_000127	−0.399520	EXT1	exostoses (multiple) 1
NM_002019	−0.398070	FLT1	fms-related tyrosine kinase 1
			(vascular endothelial growth
			factor/vascular permeability factor
			receptor)
NM_002073	−0.395460	GNAZ	guanine nucleotide binding protein
			(G protein), alpha z polypeptide
NM_000436	−0.392120	OXCT	3-oxoacid CoA transferase
NM_004994	−0.391690	MMP9	matrix metalloproteinase 9
			(gelatinase B, 92 kD gelatinase,
			92 kD type IV collagenase)
Contig55377—	0.390600		ESTs
RC
Contig35251—	−0.390410		Homo sapiens cDNA: FLJ22719 fis,
RC			clone HSI14307
Contig25991	−0.390370	ECT2	epithelial cell transforming sequence
			2 oncogene
NM_003875	−0.386520	GMPS	guanine monphosphate synthetase
NM_006101	−0.385890	HEC	highly expressed in cancer, rich in
			leucine heptad repeats
NM_003882	0.384479	WISP1	WNT1 inducible signaling pathway
			protein 1
NM_003607	−0.384390	PK428	Ser-Thr protein kinase related to the
			myotonic dystrophy protein kinase
AF073519	−0.383340	SERF1A	small EDRK-rich factor 1A
			(telomeric)
AF052162	−0.380830	FLJ12443	hypothetical protein FLJ12443
NM_000849	0.380831	GSTM3	glutathione S-transferase M3 (brain)
Contig32185—	−0.379170		Homo sapiens cDNA FLJ13997 fis,
RC			clone Y79AA1002220
NM_016577	−0.376230	RAB6B	RAB6B, member RAS oncogene
			family
Contig48328—	0.375252		ESTs, Weakly similar to T17248
RC			hypothetical protein
			DKFZp586G1122.1 [H. sapiens ]
Contig46223—	0.374289		ESTs
RC
NM_015984	−0.373880	UCH37	ubiquitin C-terminal hydrolase
			UCH37
NM_006117	0.373290	PECI	peroxisomal D3,D2-enoyl-CoA
			isomerase
AK000745	−0.373060		Homo sapiens cDNA FLJ20738 fis,
			clone HEP08257
Contig40831—	−0.372930		ESTs
RC
NM_003239	0.371524	TGFB3	transforming growth factor, beta 3
NM_014791	−0.370860	KIAA0175	KIAA0175 gene product
X05610	−0.370860	COL4A2	collagen, type IV, alpha 2
NM_016448	−0.369420	L2DTL	L2DTL protein
NM_018401	0.368349	HSA250839	gene for serine/threonine protein
			kinase
NM_000788	−0.367700	DCK	deoxycytidine kinase
Contig51464—	−0.367450	FLJ22477	hypothetical protein FLJ22477
RC
AL080079	−0.367390	DKFZP564D0462	hypothetical protein
			DKFZp564D0462
NM_006931	−0.366490	SLC2A3	solute carrier family 2 (facilitated
			glucose transporter), member 3
AF257175	0.365900		Homo sapiens hepatocellular
			carcinoma-associated antigen 64
			(HCA64) mRNA, complete cds
NM_014321	−0.365810	ORC6L	origin recognition complex, subunit 6
			(yeast homolog)-like
NM_002916	−0.365590	RFC4	replication factor C (activator 1) 4
			(37 kD)
Contig55725—	−0.365350		ESTs, Moderately similar to T50635
RC			hypothetical protein
			DKFZp762L0311.1 [H. sapiens ]
Contig24252—	−0.364990		ESTs
RC
AF201951	0.363953	CFFM4	high affinity immunoglobulin epsilon
			receptor beta subunit
NM_005915	−0.363850	MCM6	minichromosome maintenance
			deficient (mis5, S. pombe) 6
NM_001282	0.363326	AP2B1	adaptor-related protein complex 2,
			beta 1 subunit
Contig56457—	−0.361650	TMEFF1	transmembrane protein with EGF-
RC			like and two follistatin-like domains 1
NM_000599	−0.361290	IGFBP5	insulin-like growth factor binding
			protein 5
NM_020386	−0.360780	LOC57110	H-REV107 protein-related protein
NM_014889	−0.360040	MP1	metalloprotease 1 (pitrilysin family)
AF055033	−0.359940	IGFBP5	insulin-like growth factor binding
			protein 5
NM_006681	−0.359700	NMU	neuromedin U
NM_007203	−0.359570	AKAP2	A kinase (PRKA) anchor protein 2
Contig63102—	0.359255	FLJ11354	hypothetical protein FLJ11354
RC
NM_003981	−0.358260	PRC1	protein regulator of cytokinesis 1
Contig20217—	−0.357880		ESTs
RC
NM_001809	−0.357720	CENPA	centromere protein A (17 kD)
Contig2399_RC	−0.356600	SM-20	similar to rat smooth muscle protein
			SM-20
NM_004702	−0.356600	CCNE2	cyclin E2
NM_007036	−0.356540	ESM1	endothelial cell-specific molecule 1
NM_018354	−0.356000	FLJ11190	hypothetical protein FLJ11190

[0092]

TABLE 7
Good and poor prognosis templates: mean subtracted
log(intensity) values for each of the seventy markers
listed in Table 6 for 44 breast cancer patients having
a good prognosis (C1) or 34 breast cancer patients
having a poor prognosis (C2) (see Examples).
Marker	C1	C2
Accession #	(good prognosis template)	(poor prognosis template)
AL080059	−5.161569	0.043019
Contig63649_RC	−1.440895	0.966702
Contig46218_RC	−0.937662	0.815081
NM_016359	−1.49878	0.872829
AA555029_RC	−1.283504	0.543442
NM_003748	1.355486	−0.254201
Contig38288_RC	−1.237495	1.085461
NM_003862	0.981236	−1.619658
Contig28552_RC	−1.296043	1.067545
Contig32125_RC	0.855155	−0.7338
U82987	1.256206	−1.362807
AL137718	−0.55046	0.68754
AB037863	0.819061	−1.621057
NM_020188	−1.137582	0.673123
NM_020974	−0.463953	−5.623268
NM_000127	−0.618568	0.552726
NM_002019	−1.409168	0.547285
NM_002073	−1.577177	0.417352
NM_000436	−0.722574	0.599239
NM_004994	−5.561089	−2.180659
Contig55377_RC	0.805683	−1.16728
Contig35251_RC	−0.931146	0.607562
Contig25991	−0.720727	1.045949
NM_003875	−1.206839	1.163244
NM_006101	−0.879965	0.628296
NM_003882	0.529121	−0.467098
NM_003607	−0.959094	0.709653
AF073519	−1.451486	0.163988
AF052162	−1.145575	0.192391
NM_000849	0.944742	−1.499473
Contig32185_RC	−0.887643	0.688257
NM_016577	−2.762008	0.081637
Contig48328_RC	0.405401	−2.946904
Contig46223_RC	0.805424	−0.581849
NM_015984	−1.056531	0.522176
NM_006117	1.129928	−1.262974
AK000745	−2.475715	−0.013002
Contig40831_RC	−1.17091	0.435754
NM_003239	0.457773	−2.150499
NM_014791	−1.14862	0.383018
X05610	−0.768514	0.637938
NM_016448	−0.713264	0.632638
NM_018401	0.618921	−0.286778
NM_000788	−0.995116	0.50246
Contig51464_RC	−0.663538	0.765975
AL080079	−1.794821	0.43708
NM_006931	−0.846271	0.915602
AF257175	1.122354	−0.721924
NM_014321	−1.820261	0.482287
NM_002916	−0.966852	0.599925
Contig55725_RC	−2.935162	0.623397
Contig24252_RC	−2.004671	0.263597
AF201951	0.355839	−2.296556
NM_005915	−0.586121	0.827714
NM_001282	0.762645	−0.970418
Contig56457_RC	−0.920808	0.588269
NM_000599	−3.612469	−0.714313
NM_020386	−0.46073	0.699313
NM_014889	−1.678462	0.2362
AF055033	−2.505271	−0.07576
NM_006681	−0.631302	0.584119
NM_007203	−1.426446	0.504624
Contig63102_RC	0.521511	−1.266163
NM_003981	−2.521877	0.552669
Contig20217_RC	−0.363574	0.449022
NM_001809	−2.171301	0.328419
Contig2399_RC	−1.174844	0.602523
NM_004702	−1.560133	0.619078
NM_007036	−0.950633	0.34945
NM_018354	−1.392354	0.347831

[0093] The sets of markers listed in Tables 1-6 partially overlap; in other words, some markers are present in multiple sets, while other markers are unique to a set (FIG. 1). Thus, in one embodiment, the invention provides a set of 256 genetic markers that can distinguish between ER(+) and ER(−), and also between BRCA1 tumors and sporadic tumors (i.e., classify a tumor as ER(−) or ER(−) and BRCA1-related or sporadic). In a more specific embodiment, the invention provides subsets of at least 20, at least 50, at least 100, or at least 150 of the set of 256 markers, that can classify a tumor as ER(−) or ER(−) and BRCA1-related or sporadic. In another embodiment, the invention provides 165 markers that can distinguish between ER(+) and ER(−), and also between patients with good versus poor prognosis (i.e., classify a tumor as either ER(−) or ER(+) and as having been removed from a patient with a good prognosis or a poor prognosis). In a more specific embodiment, the invention further provides subsets of at least 20, 50, 100 or 125 of the full set of 165 markers, which also classify a tumor as either ER(−) or ER(+) and as having been removed from a patient with a good prognosis or a poor prognosis The invention further provides a set of twelve markers that can distinguish between BRCA1 tumors and sporadic tumors, and between patients with good versus poor prognosis. Finally, the invention provides eleven markers capable of differentiating all three statuses. Conversely, the invention provides 2,050 of the 2,460 ER-status markers that can determine only ER status, 173 of the 430 BRCA1 v. sporadic markers that can determine only BRCA1 v. sporadic status, and 65 of the 231 prognosis markers that can only determine prognosis. In more specific embodiments, the invention also provides for subsets of at least 20, 50, 100, 200, 500, 1,000, 1,500 or 2,000 of the 2,050 ER-status markers that also determine only ER status. The invention also provides subsets of at least 20, 50, 100 or 150 of the 173 markers that also determine only BRCA1 v. sporadic status. The invention further provides subsets of at least 20, 30, 40, or 50 of the 65 prognostic markers that also determine only prognostic status.

[0094] Any of the sets of markers provided above may be used alone specifically or in combination with markers outside the set. For example, markers that distinguish ER-status may be used in combination with the BRCA1 vs. sporadic markers, or with the prognostic markers, or both. Any of the marker sets provided above may also be used in combination with other markers for breast cancer, or for any other clinical or physiological condition.

[0095] The relationship between the marker sets is diagramed in FIG. 1.

5.3.2 Identification of Markers

[0096] The present invention provides sets of markers for the identification of conditions or indications associated with breast cancer. Generally, the marker sets were identified by determining which of ˜25,000 human markers had expression patters that correlated with the conditions or indications.

[0097] In one embodiment, the method for identifying marker sets is as follows. After extraction and labeling of target polynucleotides, the expression of all markers (genes) in a sample X is compared to the expression of all markers in a standard or control. In one embodiment, the standard or control comprises target polynucleotide molecules derived from a sample from a normal individual (i.e., an individual not afflicted with breast cancer). In a preferred embodiment, the standard or control is a pool of target polynucleotide molecules. The pool may derived from collected samples from a number of normal individuals. In a preferred embodiment, the pool comprises samples taken from a number of individuals having sporadic-type tumors. In another preferred embodiment, the pool comprises an artificially-generated population of nucleic acids designed to approximate the level of nucleic acid derived from each marker found in a pool of marker-derived nucleic acids derived from tumor samples. In yet another embodiment, the pool is derived from normal or breast cancer cell lines or cell line samples.

[0098] The comparison may be accomplished by any means known in the art. For example, expression levels of various markers may be assessed by separation of target polynucleotide molecules (e.g., RNA or cDNA) derived from the markers in agarose or polyacrylamide gels, followed by hybridization with marker-specific oligonucleotide probes. Alternatively, the comparison may be accomplished by the labeling of target polynucleotide molecules followed by separation on a sequencing gel. Polynucleotide samples are placed on the gel such that patient and control or standard polynucleotides are in adjacent lanes. Comparison of expression levels is accomplished visually or by means of densitometer. In a preferred embodiment, the expression of all markers is assessed simultaneously by hybridization to a microarray. In each approach, markers meeting certain criteria are identified as associated with breast cancer.

[0099] A marker is selected based upon significant difference of expression in a sample as compared to a standard or control condition. Selection may be made based upon either significant up- or down regulation of the marker in the patient sample. Selection may also be made by calculation of the statistical significance (i.e., the p-value) of the correlation between the expression of the marker and the condition or indication. Preferably, both selection criteria are used. Thus, in one embodiment of the present invention, markers associated with breast cancer are selected where the markers show both more than two-fold change (increase or decrease) in expression as compared to a standard, and the p-value for the correlation between the existence of breast cancer and the change in marker expression is no more than 0.01 (i.e., is statistically significant).

[0100] The expression of the identified breast cancer-related markers is then used to identify markers that can differentiate tumors into clinical types. In a specific embodiment using a number of tumor samples, markers are identified by calculation of correlation coefficients between the clinical category or clinical parameter(s) and the linear, logarithmic or any transform of the expression ratio across all samples for each individual gene. Specifically, the correlation coefficient is calculated as

ρ=({right arrow over (c)}•{right arrow over (r)})/(∥{right arrow over (c)}∥·∥{right arrow over (r)}∥)  Equation (2)

[0101] where {right arrow over (c)} represents the clinical parameters or categories and {right arrow over (r)} represents the linear, logarithmic or any transform of the ratio of expression between sample and control. Markers for which the coefficient of correlation exceeds a cutoff are identified as breast cancer-related markers specific for a particular clinical type. Such a cutoff or threshold corresponds to a certain significance of discriminating genes obtained by Monte Carlo simulations. The threshold depends upon the number of samples used; the threshold can be calculated as 3×1/{square root}{square root over (n−3)}, where 1/{square root}{square root over (n−3)} is the distribution width and n=the number of samples. In a specific embodiment, markers are chosen if the correlation coefficient is greater than about 0.3 or less than about −0.3.

[0102] Next, the significance of the correlation is calculated. This significance may be calculated by any statistical means by which such significance is calculated. In a specific example, a set of correlation data is generated using a Monte-Carlo technique to randomize the association between the expression difference of a particular marker and the clinical category. The frequency distribution of markers satisfying the criteria through calculation of correlation coefficients is compared to the number of markers satisfying the criteria in the data generated through the Monte-Carlo technique. The frequency distribution of markers satisfying the criteria in the Monte-Carlo runs is used to determine whether the number of markers selected by correlation with clinical data is significant. See Example 4.

[0103] Once a marker set is identified, the markers may be rank-ordered in order of significance of discrimination. One means of rank ordering is by the amplitude of correlation between the change in gene expression of the marker and the specific condition being discriminated. Another, preferred, means is to use a statistical metric. In a specific embodiment, the metric is a Fisher-like statistic:

t =(x1−x2)/{square root}{square root over ([σ12(n1−1)+σ22(n2−1)]/(n1+n2−1)/(1/n1+1/n2))}  Equation (3)

[0104] In this equation, x1 is the error-weighted average of the log ratio of transcript expression measurements within a first diagnostic group (e.g., ER(−), x2 is the error-weighted average of log ratio within a second, related diagnostic group (e.g., ER(+)), σ1 is the variance of the log ratio within the ER(−) group and n1 is the number of samples for which valid measurements of log ratios are available. σ2 is the variance of log ratio within the second diagnostic group (e.g., ER(+)), and n2 is the number of samples for which valid measurements of log ratios are available. The t-value represents the variance-compensated difference between two means.

[0105] The rank-ordered marker set may be used to optimize the number of markers in the set used for discrimination. This is accomplished generally in a “leave one out” method as follows. In a first run, a subset, for example 5, of the markers from the top of the ranked list is used to generate a template, where out of X samples, X-1 are used to generate the template, and the status of the remaining sample is predicted. This process is repeated for every sample until every one of the X samples is predicted once. In a second run, additional markers, for example 5, are added, so that a template is now generated from 10 markers, and the outcome of the remaining sample is predicted. This process is repeated until the entire set of markers is used to generate the template. For each of the runs, type 1 error (false negative) and type 2 errors (false positive) are counted; the optimal number of markers is that number where the type 1 error rate, or type 2 error rate, or preferably the total of type 1 and type 2 error rate is lowest.

[0106] For prognostic markers, validation of the marker set may be accomplished by an additional statistic, a survival model. This statistic generates the probability of tumor distant metastases as a function of time since initial diagnosis. A number of models may be used, including Weibull, normal, log-normal, log logistic, log-exponential, or log-Rayleigh (Chapter 12 “Life Testing”, S-PLUS 2000 GUIDE TO STATISTICS, Vol. 2, p. 368 (2000)). For the “normal” model, the probability of distant metastases P at time t is calculated as

P=α×exp (−t2/τ2)  Equation (4)

[0107] where α is fixed and equal to 1, and τ is a parameter to be fitted and measures the “expected lifetime”.

[0108] It will be apparent to those skilled in the art that the above methods, in particular the statistical methods, described above, are not limited to the identification of markers associated with breast cancer, but may be used to identify set of marker genes associated with any phenotype. The phenotype can be the presence or absence of a disease such as cancer, or the presence or absence of any identifying clinical condition associated with that cancer. In the disease context, the phenotype may be a prognosis such as a survival time, probability of distant metastases of a disease condition, or likelihood of a particular response to a therapeutic or prophylactic regimen. The phenotype need not be cancer, or a disease; the phenotype may be a nominal characteristic associated with a healthy individual.

5.3.3 Sample Collection

[0109] In the present invention, target polynucleotide molecules are extracted from a sample taken from an individual afflicted with breast cancer. The sample may be collected in any clinically acceptable manner, but must be collected such that marker-derived polynucleotides (i.e., RNA) are preserved. mRNA or nucleic acids derived therefrom (i.e., cDNA or amplified DNA) are preferably labeled distinguishably from standard or control polynucleotide molecules, and both are simultaneously or independently hybridized to a microarray comprising some or all of the markers or marker sets or subsets described above. Alternatively, mRNA or nucleic acids derived therefrom may be labeled with the same label as the standard or control polynucleotide molecules, wherein the intensity of hybridization of each at a particular probe is compared. A sample may comprise any clinically relevant tissue sample, such as a tumor biopsy or fine needle aspirate, or a sample of bodily fluid, such as blood, plasma, serum, lymph, ascitic fluid, cystic fluid, urine or nipple exudate. The sample may be taken from a human, or, in a veterinary context, from non-human animals such as ruminants, horses, swine or sheep, or from domestic companion animals such as felines and canines.

[0110] Methods for preparing total and poly(A)+ RNA are well known and are described generally in Sambrook et al., MOLECULAR CLONING—A LABORATORY MANUAL (2ND ED.), Vols. 1-3, Cold Spring Harbor Laboratory, Cold Spring Harbor, N.Y. (1989)) and Ausubel et al., CURRENT PROTOCOLS IN MOLECULAR BIOLOGY, vol.2, Current Protocols Publishing, New York (1994)).

[0111] RNA may be isolated from eukaryotic cells by procedures that involve lysis of the cells and denaturation of the proteins contained therein. Cells of interest include wild-type cells (i.e., non-cancerous), drug-exposed wild-type cells, tumor- or tumor-derived cells, modified cells, normal or tumor cell line cells, and drug-exposed modified cells.

[0112] Additional steps may be employed to remove DNA. Cell lysis may be accomplished with a nonionic detergent, followed by microcentrifugation to remove the nuclei and hence the bulk of the cellular DNA. In one embodiment, RNA is extracted from cells of the various types of interest using guanidinium thiocyanate lysis followed by CsCl centrifugation to separate the RNA from DNA (Chirgwin et al., Biochemistry 18:5294-5299 (1979)). Poly(A)+ RNA is selected by selection with oligo-dT cellulose (see Sambrook et al., MOLECULAR CLONING—A LABORATORY MANUAL (2ND ED.), Vols. 1-3, Cold Spring Harbor Laboratory, Cold Spring Harbor, N.Y. (1989). Alternatively, separation of RNA from DNA can be accomplished by organic extraction, for example, with hot phenol or phenol/chloroform/isoamyl alcohol.

[0113] If desired, RNAse inhibitors may be added to the lysis buffer. Likewise, for certain cell types, it may be desirable to add a protein denaturation/digestion step to the protocol.

[0114] For many applications, it is desirable to preferentially enrich mRNA with respect to other cellular RNAs, such as transfer RNA (tRNA) and ribosomal RNA (rRNA). Most mRNAs contain a poly(A) tail at their 3′ end. This allows them to be enriched by affinity chromatography, for example, using oligo(dT) or poly(U) coupled to a solid support, such as cellulose or Sephadex™ (see Ausubel et al., CURRENT PROTOCOLS IN MOLECULAR BIOLOGY, vol. 2, Current Protocols Publishing, New York (1994). Once bound, poly(A)+ mRNA is eluted from the affinity column using 2 mM EDTA/0.1% SDS.

[0115] The sample of RNA can comprise a plurality of different mRNA molecules, each different mRNA molecule having a different nucleotide sequence. In a specific embodiment, the mRNA molecules in the RNA sample comprise at least 100 different nucleotide sequences. More preferably, the mRNA molecules of the RNA sample comprise mRNA molecules corresponding to each of the marker genes. In another specific embodiment, the RNA sample is a mammalian RNA sample.

[0116] In a specific embodiment, total RNA or mRNA from cells are used in the methods of the invention. The source of the RNA can be cells of a plant or animal, human, mammal, primate, non-human animal, dog, cat, mouse, rat, bird, yeast, eukaryote, prokaryote, etc. In specific embodiments, the method of the invention is used with a sample containing total mRNA or total RNA from 1×106 cells or less. In another embodiment, proteins can be isolated from the foregoing sources, by methods known in the art, for use in expression analysis at the protein level.

[0117] Probes to the homologs of the marker sequences disclosed herein can be employed preferably wherein non-human nucleic acid is being assayed.

5.4 Methods of Using Breast Cancer Marker Sets

5.4.1 Diagnostic Methods

[0118] The present invention provides for methods of using the marker sets to analyze a sample from an individual so as to determine the individual's tumor type or subtype at a molecular level, whether a tumor is of the ER(+) or ER(−) type, and whether the tumor is BRCA1-associated or sporadic. The individual need not actually be afflicted with breast cancer. Essentially, the expression of specific marker genes in the individual, or a sample taken therefrom, is compared to a standard or control. For example, assume two breast cancer-related conditions, X and Y. One can compare the level of expression of breast cancer prognostic markers for condition X in an individual to the level of the marker-derived polynucleotides in a control, wherein the level represents the level of expression exhibited by samples having condition X. In this instance, if the expression of the markers in the individual's sample is substantially (i.e., statistically) different from that of the control, then the individual does not have condition X. Where, as here, the choice is bimodal (i.e., a sample is either X or Y), the individual can additionally be said to have condition Y. Of course, the comparison to a control representing condition Y can also be performed. Preferably both are performed simultaneously, such that each control acts as both a positive and a negative control. The distinguishing result may thus either be a demonstrable difference from the expression levels (i.e., the amount of marker-derived RNA, or polynucleotides derived therefrom) represented by the control, or no significant difference.

[0119] Thus, in one embodiment, the method of determining a particular tumor-related status of an individual comprises the steps of (1) hybridizing labeled target polynucleotides from an individual to a microarray containing one of the above marker sets; (2) hybridizing standard or control polynucleotides molecules to the microarray, wherein the standard or control molecules are differentially labeled from the target molecules; and (3) determining the difference in transcript levels, or lack thereof, between the target and standard or control, wherein the difference, or lack thereof, determines the individual's tumor-related status. In a more specific embodiment, the standard or control molecules comprise marker-derived polynucleotides from a pool of samples from normal individuals, or a pool of tumor samples from individuals having sporadic-type tumors. In a preferred embodiment, the standard or control is an artificially-generated pool of marker-derived polynucleotides, which pool is designed to mimic the level of marker expression exhibited by clinical samples of normal or breast cancer tumor tissue having a particular clinical indication (i.e., cancerous or non-cancerous; ER(+) or ER(−) tumor; BRCA1- or sporadic type tumor). In another specific embodiment, the control molecules comprise a pool derived from normal or breast cancer cell lines.

[0120] The present invention provides sets of markers useful for distinguishing ER(+) from ER(−) tumor types. Thus, in one embodiment of the above method, the level of polynucleotides (i.e., mRNA or polynucleotides derived therefrom) in a sample from an individual, expressed from the markers provided in Table 1 are compared to the level of expression of the same markers from a control, wherein the control comprises marker-related polynucleotides derived from ER(+) samples, ER(−) samples, or both. Preferably, the comparison is to both ER(+) and ER(−), and preferably the comparison is to polynucleotide pools from a number of ER(+) and ER(−) samples, respectively. Where the individual's marker expression most closely resembles or correlates with the ER(+) control, and does not resemble or correlate with the ER(−) control, the individual is classified as ER(+). Where the pool is not pure ER(+) or ER(−), for example, a sporadic pool is used. A set of experiments should be performed in which nucleic acids from individuals with known ER status are hybridized against the pool, in order to define the expression templates for the ER(+) and ER(−) group. Nucleic acids from each individual with unknown ER status are hybridized against the same pool and the expression profile is compared to the templates (s) to determine the individual's ER status.

[0121] The present invention provides sets of markers useful for distinguishing BRCA1-related tumors from sporadic tumors. Thus, the method can be performed substantially as for the ER(+/−) determination, with the exception that the markers are those listed in Tables 3 and 4, and the control markers are a pool of marker-derived polynucleotides BRCA1 tumor samples, and a pool of marker-derived polynucleotides from sporadic tumors. A patient is determined to have a BRCA1 germline mutation where the expression of the individual's marker-derived polynucleotides most closely resemble, or are most closely correlated with, that of the BRCA1 control. Where the control is not pure BRCA1 or sporadic, two templates can be defined in a manner similar to that for ER status, as described above.

[0122] For the above two embodiments of the method, the full set of markers may be used (i.e., the complete set of markers for Tables 1 or 3). In other embodiments, subsets of the markers may be used. In a preferred embodiment, the preferred markers listed in Tables 2 or 4 are used.

[0123] The similarity between the marker expression profile of an individual and that of a control can be assessed a number of ways. In the simplest case, the profiles can be compared visually in a printout of expression difference data. Alternatively, the similarity can be calculated mathematically.

[0124] In one embodiment, the similarity between two patients x and y, or patient x and a template y, expressed as a similarity value, can be calculated using the following equation: 1$\begin{array}{cc}\begin{array}{c}S=1-[\sum _{i=1}^{N_V}\frac{\left(x_i-\stackrel{\_}{x}\right)}{{\sigma }_{x_i}}\frac{\left(y_i-\stackrel{\_}{y}\right)}{{\sigma }_{y_i}}/\\ \sqrt{\sum _{i=1}^{N_V}{\left(\frac{x_i-\stackrel{\_}{x}}{{\sigma }_{x_i}}\right)}^2\sum _{i=1}^{N_V}{\left(\frac{y_i-\stackrel{\_}{y}}{{\sigma }_{y_i}}\right)}^2}]\end{array}& \mathrm{Equation}\left(5\right)\end{array}$

[0125] In this equation, x and y are two patients with components of log ratio xi and yi, i—1 . . . ,N=4,986. Associated with every value xi is error σxo. The smaller the value σxi, the more reliable the measurement 2$\stackrel{\_}{x}=\sum _{i=1}^{N_V}\frac{x_i}{{\sigma }_{x_i}^2}/\sum _{i=1}^{N_V}\frac{1}{{\sigma }_{x_i}^2}$

[0126] is the error-weighted arithmetic mean.

[0127] In a preferred embodiment, templates are developed for sample comparison.

[0128] The template is defined as the error-weighted log ratio average of the expression difference for the group of marker genes able to differentiate the particular breast cancer-related condition. For example, templates are defined for ER(+) samples and for ER(−) samples. Next, a classifier parameter is calculated. This parameter may be calculated using either expression level differences between the sample and template, or by calculation of a correlation coefficient. Such a coefficient, Pi, can be calculated using the following equation:

P i =({right arrow over (z)}i•{right arrow over (y)})/(∥{right arrow over (z)}i∥·∥{right arrow over (y)}∥)  Equation (1)

[0129] where zi is the expression template i, and y is the expression profile of a patient.

[0130] Thus, in a more specific embodiment, the above method of determining a particular tumor-related status of an individual comprises the steps of (1) hybridizing labeled target polynucleotides from an individual to a microarray containing one of the above marker sets; (2) hybridizing standard or control polynucleotides molecules to the microarray, wherein the standard or control molecules are differentially labeled from the target molecules; and (3) determining the ratio (or difference) of transcript levels between two channels (individual and control), or simply the transcript levels of the individual; and (4) comparing the results from (3) to the predefined templates, wherein said determining is accomplished by means of the statistic of Equation 1 or Equation 5, and wherein the difference, or lack thereof, determines the individual's tumor-related status.

5.4.2 Prognostic Methods

[0131] The present invention provides sets of markers useful for classifying patients with into different prognostic categories. For example, the invention further provides a method for using these markers to determine whether an individual afflicted with breast cancer will have a good or poor clinical prognosis. The present invention further provides a method of further classifying “good prognosis” patients into two groups: those having a “very good prognosis” and those having an “intermediate prognosis.” For each of the above classifications, the invention further provides recommended therapeutic regimens.

[0132] The method can use the complete set of markers listed in Table 5. However, subsets of the markers listed in Table 5 may also be used. In a preferred embodiment, the subset of 70 markers listed in Table 6 is used. At least 5, 10, 15, 20, 25, 30, 40, 50, 60, or all 70 of the markers in Table 6 may be used.

[0133] Classification of a sample as “good prognosis” or “poor prognosis” is accomplished substantially as for the diagnostic markers described above, wherein a template is generated to which the marker expression levels in the sample are compared. Thus, in one embodiment of the above method, the level of polynucleotides (i.e., mRNA or polynucleotides derived therefrom) in a sample from an individual breast cancer patient, expressed from the markers provided in Table 5, is compared to the level of expression of the same markers from a control, wherein the control comprises marker-related polynucleotides derived from breast cancer tumor samples taken from breast cancer patients clinically determined to have a good prognosis (“good prognosis” control), breast cancer patients clinically determined to have a poor prognosis (“poor prognosis” control), or both. The comparison may be to both good prognosis and poor prognosis controls, and preferably the comparison is to polynucleotide pools from a number of good prognosis and poor prognosis samples, respectively. Where the individual's marker expression most closely resembles or correlates with the good prognosis control, and does not resemble or correlate with the poor prognosis control, the individual is classified as having a good prognosis. Where the pool is not pure ‘good prognosis’ or ‘poor prognosis’, a set of experiments should be performed in which nucleic acids from samples from individuals with known outcomes are hybridized against the pool to define the expression templates for the good prognosis and poor prognosis groups. Nucleic acids from each individual with unknown outcome are hybridized against the same pool and the resulting expression profile is compared to the templates to predict its outcome.

[0134] The control or standard may be presented in a number of different formats. For example, the control, or template, to which the expression of marker genes in a breast cancer tumor sample is compared may be the average absolute level of expression of each of the genes in a pool of marker-derived nucleic acids pooled from breast cancer tumor samples obtained from a plurality of breast cancer patients. In this case, the difference between the absolute level of expression of these genes in the control and in a sample from a breast cancer patient provides the degree of similarity or dissimilarity of the level of expression in the patient sample and the control. The absolute level of expression may be measured by the intensity of the hybridization of the nucleic acids to an array. In other embodiments, the values for the expression levels of the markers in both the patient sample and control are transformed (see Section 5.4.3). For example, the expression level value for the patient, and the average expression level value for the pool, for each of the marker genes selected, may be transformed by taking the logarithm of the value. Moreover, the expression level values may be normalized by, for example, dividing by the median hybridization intensity of all of the samples that make up the pool. The control may be derived from hybridization data obtained simultaneously with the patient sample expression data, or may constitute a set of numerical values stores on a computer, or on computer-readable medium.

[0135] In one embodiment, the invention provides for method of determining whether an individual afflicted with breast cancer will likely experience a relapse within five years of initial diagnosis (i.e., whether an individual has a poor prognosis) comprising (1) comparing the level of expression of the markers listed in Table 5 in a sample taken from the individual to the level of the same markers in a standard or control, where the standard or control levels represent those found in an individual with a poor prognosis; and (2) determining whether the level of the marker-related polynucleotides in the sample from the individual is significantly different than that of the control, wherein if no substantial difference is found, the patient has a poor prognosis, and if a substantial difference is found, the patient has a good prognosis. Persons of skill in the art will readily see that the markers associated with good prognosis can also be used as controls. In a more specific embodiment, both controls are run.

[0136] Poor prognosis of breast cancer may indicate that a tumor is relatively aggressive, while good prognosis may indicate that a tumor is relatively nonaggressive. Therefore, the invention provides for a method of determining a course of treatment of a breast cancer patient, comprising determining whether the level of expression of the 231 markers of Table 5, or a subset thereof, correlates with the level of these markers in a sample representing a good prognosis expression pattern or a poor prognosis pattern; and determining a course of treatment, wherein if the expression correlates with the poor prognosis pattern, the tumor is treated as an aggressive tumor.

[0137] Patients having an expression profile correlating with the good prognosis profile may be further divided into “very good prognosis” and “intermediate prognosis” groups. In the original 78 samples used to determine the 70 optimal prognostic marker genes, patients whose expression profile correlated with (i.e., had a correlation coefficient less than 0.40) the average “good prognosis” expression profile were classified as having a “good prognosis.” It was subsequently found that tumors with an expression profile having a coefficient of correlation to the average “good prognosis” expression profile greater than 0.636 developed no distant metastases. These patients may receive a different therapeutic regimen than patients whose tumors have a “good prognosis” expression profile that correlates less strongly to the average “good prognosis” expression profile. Accordingly, patients were classified as having a “very good prognosis” expression profile if the correlation coefficient exceeded 0.636, and an “intermediate prognosis” if their expression profile correlation coefficient was 0.39 or less but less than or equal to 0.636. The data for the 70 genes listed in Table 6 for these 78 patients is listed in Table 7.

[0138] This methodology may be generalized to situations in which data from other groups of patients is used, where a group of patients is to provide clinical and expression data to be used for classification of subsequent breast cancer patients. A group of patients is selected for which clinical and followup data are available for at least five years after initial diagnosis. Preferably the patients in the group are selected as a consecutive series to reduce or eliminate selection bias. Breast cancer tumor samples are taken from each patient, and marker-related polynucleotides are generated. The expression levels of each of the marker genes listed in Table 5 or a subset thereof, preferably at least five of the marker genes listed in Table 6, is determined for each tumor sample (i.e., for each patient) to generate a patient expression profile. Marker-derived polynucleotides from patients within the group clinically determined to have a good prognosis (i.e., no distant metastases within five years of initial diagnosis) are pooled and mean expression levels for each of the prognosis-related marker genes are determined to obtain a control expression profile. Patients are then rank ordered in descending order of similarity of patient expression profiles to the control expression profile to produce a rank-ordered list of patients, where the similarity is a value expressed by a single similarity metric such as a correlation coefficient. A first threshold similarity value is then selected, which divides the group of patients into those predicted to have a good prognosis and those predicted to have a poor prognosis. This first threshold similarity value may be the similarity value that most accurately predicts clinical outcomes (i.e., results in an expression profile classification that results in the fewest misclassifications when compared to actual clinical outcomes), or a similarity value that results in a particular number or percentage of false negatives in the group, where a false negative is an expression-based good prognosis prediction for a breast cancer patient that actually develops a distant metastasis within the five year period after initial diagnosis. A second threshold similarity value is then selected which divides the “good prognosis” group into two groups. This threshold similarity value is determined empirically as the similarity value for the patient highest on the rank-ordered list of patients who actually develops a distant metastasis within the five-year period. This second threshold similarity value divides the “good prognosis” group into a group of patients having a “very good prognosis,” i.e., those having similarity values equal to or higher than the second threshold similarity value, and an “intermediate prognosis” group, i.e., those having a similarity value equal to or greater than the first threshold similarity value, but less than the second threshold similarity value. Patients whose similarity values are less than the first threshold similarity value are classified as having a “poor prognosis.” Subsequent patients may be similarly classified by calculating a similarity value for the patient, where the control is the “good prognosis” template or expression profile, and comparison of this similarity metric to the similarity metrics obtained above.

[0139] Thus, in one embodiment, the invention provides a method for classifying a breast cancer patient according to prognosis, comprising comparing the levels of expression of at least five of the genes for which markers are listed in Table 5 in a cell sample taken from said breast cancer patient to control levels of expression of said at least five genes; and classifying said breast cancer patient according to prognosis of his or her breast cancer based on the similarity between said levels of expression in said cell sample and said control levels. In a more specific embodiment, the second step of this method comprises determining whether said similarity exceeds one or more predetermined threshold values of similarity. In another more specific embodiment of this method, said control levels are the mean levels of expression of each of said at least five genes in a pool of tumor samples obtained from a plurality of breast cancer patients who have no distant metastases within five years of initial diagnosis. In another more specific embodiment of this method, said control levels comprise the expression levels of said genes in breast cancer patients who have had no distant metastases within five years of initial diagnosis. In yet another more specific embodiment of this method, said control levels comprise, for each of said at least five of the genes for which markers are listed in Table 5, mean log intensity values stored on a computer. In yet another more specific embodiment of this method, said control levels comprise, for each of said at least five of the genes for which markers are listed in Table 6, mean log intensity values stored on a computer. In another more specific embodiment of this method, said control levels comprise, for each of said at least five genes listed in Table 6, the mean log intensity values that are listed in Table 7. The set of mean log intensity values listed in this table may be used as a “good prognosis” template for any of the prognostic methods described herein. The above method may also compare the level of expression of at least ten, 20, 30, 40, 50, 75, 100 or more genes for which markers listed in Table 5, or may use the 70 preferred genes for which markers are listed in Table 6.

[0140] The present invention also provides for the classification of a breast cancer patient into one of three prognostic categories comprising (a) determining the similarity between the level of expression of at least five of the genes for which markers are listed in Table 5 to control levels of expression to obtain a patient similarity value; (b) providing a first threshold similarity value that differentiates persons having a good prognosis from those having a poor prognosis, and providing determining a second threshold similarity value, where said second threshold similarity value indicates a higher degree of similarity of the expression of said genes to said control than said first similarity value; and (c) classifying the breast cancer patient into a first prognostic category if the patient similarity value exceeds the first and second threshold similarity values, a second prognostic category if the patient similarity value equals or exceeds the first but not the second threshold similarity value, and a third prognostic category if the patient similarity value is less than the first threshold similarity value. In a more specific embodiment, the levels of expression of each of said at least five genes is determined first. As above, the control comprises marker-related polynucleotides derived from breast cancer tumor samples taken from breast cancer patients clinically determined to have a good prognosis (“good prognosis” control), breast cancer patients clinically determined to have a poor prognosis “poor prognosis” control), or both. In a preferred embodiment, the control is a “good prognosis” control or template, i.e., a control or template comprising the mean levels of expression of said genes in breast cancer patients who have had no distant metastases within five years of initial diagnosis. In another more specific embodiment, said control levels comprise a set of values, for example mean log intensity values, preferably normalized, stored on a computer. In a more specific embodiment, said control or template is the set of mean log intensity values shown in Table 7. In another specific embodiment, said determining in step (a) may be accomplished by a method comprising determining the difference between the absolute expression level of each of said genes and the average expression level of the same genes in a pool of tumor samples obtained from a plurality of breast cancer patients who have had no relapse of breast cancer within five years of initial diagnosis. In another specific embodiment, said determining in step (a) may be accomplished by a method comprising determining the degree of similarity between the level of expression of each of said genes in a breast cancer tumor sample taken from a breast cancer patient and the level of expression of the same genes in a pool of tumor samples obtained from a plurality of breast cancer patients who have had no relapse of breast cancer within five years of initial diagnosis.

[0141] In a specific embodiment of the above method, said first threshold similarity value and said second threshold similarity values are selected by a method comprising (a) rank ordering in descending order said tumor samples that compose said pool of tumor samples by the degree of similarity between the level of expression of said genes in each of said tumor samples to the mean level of expression of the same genes of the remaining tumor samples that compose said pool to obtain a rank-ordered list, said degree of similarity being expressed as a similarity value; (b) determining an acceptable number of false negatives in said classifying, wherein said false negatives are breast cancer patients for whom the expression levels of said at least five of the genes for which markers are listed in Table 5 in said cell sample predicts that said patient will have no distant metastases within the first five years after initial diagnosis, but who has had a distant metastasis within the first five years after initial diagnosis; (c) determining a similarity value above which in said rank ordered list fewer than said acceptable number of tumor samples are false negatives; and (d) selecting said similarity value determined in step (c) as said first threshold similarity value; and (e) selecting a second similarity value, greater than said first similarity value, as said second threshold similarity value. In an even more specific embodiment of this method, said second threshold similarity value is selected in step (e) by a method comprising determining which of said tumor samples, taken from patients having a distant metastasis within five years of initial diagnosis, in said rank ordered list has the greatest similarity value, and selecting said greatest similarity value as said second threshold similarity value. In even more specific embodiments, said first and second threshold similarity values are correlation coefficients, and said first threshold similarity value is 0.4 and said second threshold similarity value is greater than 0.4. In another even more specific embodiment, using the template data provided in Table 7, said first and second threshold similarity values are correlation coefficients, and said second threshold similarity value is 0.636. In another specific embodiment, said first similarity value is a similarity value above which at most 10% false negatives are predicted in a training set of tumors, and said second correlation coefficient is a coefficient above which at most 5% false negatives are predicted in said training set of tumors. In another specific embodiment, said first correlation coefficient is a coefficient above which 10% false negatives are predicted in a training set of tumors, and said second correlation coefficient is a coefficient above which no false negatives are predicted in said training set of tumors. In the above and other embodiments, “false negatives” are patients classified by the expression of the marker genes as having a good prognosis, or who are predicted by such expression to have a good prognosis, but who actually do develop distant metastases within five years.

[0142] In a specific embodiment of the above methods, the first, second and third prognostic categories are “very good prognosis,” “intermediate prognosis,” and “poor prognosis,” respectively. Patients classified into the first prognostic category (“very good prognosis”) are likely not to have a distant metastasis within five years of initial diagnosis. Patients classified as having an “intermediate prognosis” are also unlikely to have a distant metastasis within five years of initial diagnosis, but may be recommended to undergo a different therapeutic regimen than patients having a “very good prognosis” marker gene expression profile (see below). Patients classified into the third prognostic category (“poor prognosis”) are likely to have a distant metastasis within five years of initial diagnosis.

[0143] In a more specific embodiment, the similarity value is the degree of difference between the absolute (i.e., untransformed) level of expression of each of the genes in a tumor sample taken from a breast cancer patient and the mean absolute level of expression of the same genes in a control. In another more specific embodiment, the similarity value is calculated using expression level data that is transformed (see Section 5.4.3). In another more specific embodiment, the similarity value is expressed as a similarity metric, such as a correlation coefficient, representing the similarity between the level of expression of the marker genes in the tumor sample and the mean level of expression of the same genes in a plurality of breast cancer tumor samples taken from breast cancer patients.

[0144] In another specific embodiment, said first and second similarity values are derived from control expression data obtained in the same hybridization experiment as that in which the patient expression level data is obtained. In another specific embodiment, said first and second similarity values are derived from an existing set of expression data. In a more specific embodiment, said first and second correlation coefficients are derived from a mathematical sample pool (see Section 5.4.3; Example 9). For example, comparison of the expression of marker genes in new tumor samples may be compared to the pre-existing template determined for these genes for the 78 patients in the initial study; the template, or average expression levels of each of the seventy genes can be used as a reference or control for any tumor sample. Preferably, the comparison is made to a template comprising the average expression level of at least five of the 70 genes listed in Table 6 for the 44 out of 78 patients clinically determined to have a good prognosis. The coefficient of correlation of the level of expression of these genes in the tumor sample to the 44 “good prognosis” patient template is then determined to produce a tumor correlation coefficient. For this control patient set, two similarity values have been derived: a first correlation coefficient of 0.4 and a second correlation coefficient of 0.636, derived using the 70 marker gene set listed in Table 6. New breast cancer patients whose coefficients of correlation of the expression of these marker genes with the 44-patient “good prognosis” template equal or exceed 0.636 are classified as having a “very good prognosis”; those having a coefficient of correlation of between 0.4 and 0.635 are classified as having an “intermediate prognosis”; and those having a correlation coefficient of 0.39 or less are classified as having a “poor prognosis.”

[0145] Because the above methods may utilize arrays to which fluorescently-labeled marker-derived target nucleic acids are hybridized, the invention also provides a method of classifying a breast cancer patient according to prognosis comprising the steps of (a) contacting first nucleic acids derived from a tumor sample taken from said breast cancer patient, and second nucleic acids derived from two or more tumor samples from breast cancer patients who have had no distant metastases within five years of initial diagnosis, with an array under conditions such that hybridization can occur, detecting at each of a plurality of discrete loci on said array a first fluorescent emission signal from said first nucleic acids and a second fluorescent emission signal from said second nucleic acids that are bound to said array under said conditions, wherein said array comprises at least five of the genes for which markers are listed in Table 5 and wherein at least 50% of the probes on said array are listed in Table 5; (b) calculating the similarity between said first fluorescent emission signals and said second fluorescent emission signals across said at least five genes; and (c) classifying said breast cancer patient according to prognosis of his or her breast cancer based on the similarity between said first fluorescent emission signals and said second fluorescent emission signals across said at least five genes.

[0146] Once patients have been classified as having a “very good prognosis,” “intermediate prognosis” or “poor prognosis,” this information can be combined with the patient's clinical data to determine an appropriate treatment regimen. In one embodiment, the patient's lymph node metastasis status (i.e., whether the patient is pN+ or pN0) is determined. Patients who are pN0 and have a “very good prognosis” or “intermediate” expression profile may be treated without adjuvant chemotherapy. All other patients should be treated with adjuvant chemotherapy. In a more specific embodiment, the patient's estrogen receptor status is also identified (i.e., whether the patient is ER(+) or ER(−)). Here, patients classified as having an “intermediate prognosis” or “poor prognosis” who are ER(+) are assigned a therapeutic regimen that additionally comprises adjuvant hormonal therapy.

[0147] Thus, the invention provides for a method of assigning a therapeutic regimen to a breast cancer patient, comprising (a) classifying said patient as having a “poor prognosis,” “intermediate prognosis,” or “very good prognosis” on the basis of the levels of expression of at least five of the genes for which markers are listed in Table 5; and (b) assigning said patient a therapeutic regimen, said therapeutic regimen comprising no adjuvant chemotherapy if the patient is lymph node negative and is classified as having a good prognosis or an intermediate prognosis, or comprising chemotherapy if said patient has any other combination of lymph node status and expression profile. In another embodiment, the invention provides a method for assigning a therapeutic regimen for a breast cancer patient, comprising determining the lymph node status for said patient; determining the level of expression of at least five of the genes listed in Table 5 in a tumor sample from said patient, thereby generating an expression profile; classifying said patient as having a “poor prognosis”, “intermediate prognosis” or “very good prognosis” on the basis of said expression profile; and assigning the patient a therapeutic regimen, said therapeutic regimen comprising no adjuvant chemotherapy if the patient is lymph node negative and is classified as having a good prognosis or an intermediate prognosis, or a therapeutic regiment comprising chemotherapy if said patient has any other combination of lymph node status and expression profile. In a more specific embodiment of the above methods, the ER status of the patient is additionally determined, and if the breast cancer patient is ER(+) and has an intermediate or poor prognosis, the therapeutic regimen additionally comprises hormonal therapy. Because in the training set of 78 breast cancer patients it was determined that the great majority of intermediate prognosis patients were also ER(+) (see Example 10), another more specific embodiment is to determine the lymph node status and expression profiles, and to assign intermediate prognosis patients adjuvant hormonal therapy (whether or not ER status has been determined). In another specific embodiment, the breast cancer patient is 52 years of age or younger. In another specific embodiment, the breast cancer patient is premenopausal. In another specific embodiment, the breast cancer patient has stage I or stage II breast cancer.

[0148] The use of marker sets is not restricted to the prognosis of breast cancer-related conditions, and may be applied in a variety of phenotypes or conditions, clinical or experimental, in which gene expression plays a role. Where a set of markers has been identified that corresponds to two or more phenotypes, the marker set can be used to distinguish these phenotypes. For example, the phenotypes may be the diagnosis and/or prognosis of clinical states or phenotypes associated with other cancers, other disease conditions, or other physiological conditions, wherein the expression level data is derived from a set of genes correlated with the particular physiological or disease condition. Further, the expression of markers specific to other types of cancer may be used to differentiate patients or patient populations for those cancers for which different therapeutic regimens are indicated.

5.4.3 Improving Sensitivity to Expression Level Differences

[0149] In using the markers disclosed herein, and, indeed, using any sets of markers to differentiate an individual having one phenotype from another individual having a second phenotype, one can compare the absolute expression of each of the markers in a sample to a control; for example, the control can be the average level of expression of each of the markers, respectively, in a pool of individuals. To increase the sensitivity of the comparison, however, the expression level values are preferably transformed in a number of ways.

[0150] For example, the expression level of each of the markers can be normalized by the average expression level of all markers the expression level of which is determined, or by the average expression level of a set of control genes. Thus, in one embodiment, the markers are represented by probes on a microarray, and the expression level of each of the markers is normalized by the mean or median expression level across all of the genes represented on the microarray, including any non-marker genes. In a specific embodiment, the normalization is carried out by dividing the median or mean level of expression of all of the genes on the microarray. In another embodiment, the expression levels of the markers is normalized by the mean or median level of expression of a set of control markers. In a specific embodiment, the control markers comprise a set of housekeeping genes. In another specific embodiment, the normalization is accomplished by dividing by the median or mean expression level of the control genes.

[0151] The sensitivity of a marker-based assay will also be increased if the expression levels of individual markers are compared to the expression of the same markers in a pool of samples. Preferably, the comparison is to the mean or median expression level of each the marker genes in the pool of samples. Such a comparison may be accomplished, for example, by dividing by the mean or median expression level of the pool for each of the markers from the expression level each of the markers in the sample. This has the effect of accentuating the relative differences in expression between markers in the sample and markers in the pool as a whole, making comparisons more sensitive and more likely to produce meaningful results that the use of absolute expression levels alone. The expression level data may be transformed in any convenient way; preferably, the expression level data for all is log transformed before means or medians are taken.

[0152] In performing comparisons to a pool, two approaches may be used. First, the expression levels of the markers in the sample may be compared to the expression level of those markers in the pool, where nucleic acid derived from the sample and nucleic acid derived from the pool are hybridized during the course of a single experiment. Such an approach requires that new pool nucleic acid be generated for each comparison or limited numbers of comparisons, and is therefore limited by the amount of nucleic acid available.

[0153] Alternatively, and preferably, the expression levels in a pool, whether normalized and/or transformed or not, are stored on a computer, or on computer-readable media, to be used in comparisons to the individual expression level data from the sample (i.e., single-channel data).

[0154] Thus, the current invention provides the following method of classifying a first cell or organism as having one of at least two different phenotypes, where the different phenotypes comprise a first phenotype and a second phenotype. The level of expression of each of a plurality of genes in a first sample from the first cell or organism is compared to the level of expression of each of said genes, respectively, in a pooled sample from a plurality of cells or organisms, the plurality of cells or organisms comprising different cells or organisms exhibiting said at least two different phenotypes, respectively, to produce a first compared value. The first compared value is then compared to a second compared value, wherein said second compared value is the product of a method comprising comparing the level of expression of each of said genes in a sample from a cell or organism characterized as having said first phenotype to the level of expression of each of said genes, respectively, in the pooled sample. The first compared value is then compared to a third compared value, wherein said third compared value is the product of a method comprising comparing the level of expression of each of the genes in a sample from a cell or organism characterized as having the second phenotype to the level of expression of each of the genes, respectively, in the pooled sample. Optionally, the first compared value can be compared to additional compared values, respectively, where each additional compared value is the product of a method comprising comparing the level of expression of each of said genes in a sample from a cell or organism characterized as having a phenotype different from said first and second phenotypes but included among the at least two different phenotypes, to the level of expression of each of said genes, respectively, in said pooled sample. Finally, a determination is made as to which of said second, third, and, if present, one or more additional compared values, said first compared value is most similar, wherein the first cell or organism is determined to have the phenotype of the cell or organism used to produce said compared value most similar to said first compared value.

[0155] In a specific embodiment of this method, the compared values are each ratios of the levels of expression of each of said genes. In another specific embodiment, each of the levels of expression of each of the genes in the pooled sample are normalized prior to any of the comparing steps. In a more specific embodiment, the normalization of the levels of expression is carried out by dividing by the median or mean level of the expression of each of the genes or dividing by the mean or median level of expression of one or more housekeeping genes in the pooled sample from said cell or organism. In another specific embodiment, the normalized levels of expression are subjected to a log transform, and the comparing steps comprise subtracting the log transform from the log of the levels of expression of each of the genes in the sample. In another specific embodiment, the two or more different phenotypes are different stages of a disease or disorder. In still another specific embodiment, the two or more different phenotypes are different prognoses of a disease or disorder. In yet another specific embodiment, the levels of expression of each of the genes, respectively, in the pooled sample or said levels of expression of each of said genes in a sample from the cell or organism characterized as having the first phenotype, second phenotype, or said phenotype different from said first and second phenotypes, respectively, are stored on a computer or on a computer-readable medium.

[0156] In another specific embodiment, the two phenotypes are ER(+) or ER(−) status. In another specific embodiment, the two phenotypes are BRCA1 or sporadic tumor-type status. In yet another specific embodiment, the two phenotypes are good prognosis and poor prognosis.

[0157] In another specific embodiment, the comparison is made between the expression of each of the genes in the sample and the expression of the same genes in a pool representing only one of two or more phenotypes. In the context of prognosis-correlated genes, for example, one can compare the expression levels of prognosis-related genes in a sample to the average level of the expression of the same genes in a “good prognosis” pool of samples (as opposed to a pool of samples that include samples from patients having poor prognoses and good prognoses). Thus, in this method, a sample is classified as having a good prognosis if the level of expression of prognosis-correlated genes exceeds a chosen coefficient of correlation to the average “good prognosis” expression profile (i.e., the level of expression of prognosis-correlated genes in a pool of samples from patients having a “good prognosis.” Patients whose expression levels correlate more poorly with the “good prognosis” expression profile (i.e., whose correlation coefficient fails to exceed the chosen coefficient) are classified as having a poor prognosis. The method can be applied to subdivisions of these prognostic classes. For example, in a specific embodiment, the phenotype is good prognosis and said determination comprises (1) determining the coefficient of correlation between the expression of said plurality of genes in the sample and of the same genes in said pooled sample; (2) selecting a first correlation coefficient value between 0.4 and +1 and a second correlation coefficient value between 0.4 and +1, wherein said second value is larger than said first value; and (3) classifying said sample as “very good prognosis” if said coefficient of correlation equals or is greater than said second correlation coefficient value, “intermediate prognosis” if said coefficient of correlation equals or exceeds said first correlation coefficient value, and is less than said second correlation coefficient value, or “poor prognosis” if said coefficient of correlation is less than said first correlation coefficient value.

[0158] Of course, single-channel data may also be used without specific comparison to a mathematical sample pool. For example, a sample may be classified as having a first or a second phenotype, wherein the first and second phenotypes are related, by calculating the similarity between the expression of at least 5 markers in the sample, where the markers are correlated with the first or second phenotype, to the expression of the same markers in a first phenotype template and a second phenotype template, by (a) labeling nucleic acids derived from a sample with a fluorophore to obtain a pool of fluorophore-labeled nucleic acids; (b) contacting said fluorophore-labeled nucleic acid with a microarray under conditions such that hybridization can occur, detecting at each of a plurality of discrete loci on the microarray a flourescent emission signal from said fluorophore-labeled nucleic acid that is bound to said microarray under said conditions; and (c) determining the similarity of marker gene expression in the individual sample to the first and second templates, wherein if said expression is more similar to the first template, the sample is classified as having the first phenotype, and if said expression is more similar to the second template, the sample is classified as having the second phenotype.

5.5 Determination of Marker Gene Expression Levels

5.5.1 Methods

[0159] The expression levels of the marker genes in a sample may be determined by any means known in the art. The expression level may be determined by isolating and determining the level (i.e., amount) of nucleic acid transcribed from each marker gene. Alternatively, or additionally, the level of specific proteins translated from mRNA transcribed from a marker gene may be determined.

[0160] The level of expression of specific marker genes can be accomplished by determining the amount of mRNA, or polynucleotides derived therefrom, present in a sample. Any method for determining RNA levels can be used. For example, RNA is isolated from a sample and separated on an agarose gel. The separated RNA is then transferred to a solid support, such as a filter. Nucleic acid probes representing one or more markers are then hybridized to the filter by northern hybridization, and the amount of marker-derived RNA is determined. Such determination can be visual, or machine-aided, for example, by use of a densitometer. Another method of determining RNA levels is by use of a dot-blot or a slot-blot. In this method, RNA, or nucleic acid derived therefrom, from a sample is labeled. The RNA or nucleic acid derived therefrom is then hybridized to a filter containing oligonucleotides derived from one or more marker genes, wherein the oligonucleotides are placed upon the filter at discrete, easily-identifiable locations. Hybridization, or lack thereof, of the labeled RNA to the filter-bound oligonucleotides is determined visually or by densitometer. Polynucleotides can be labeled using a radiolabel or a fluorescent (i.e., visible) label.

[0161] These examples are not intended to be limiting; other methods of determining RNA abundance are known in the art.

[0162] The level of expression of particular marker genes may also be assessed by determining the level of the specific protein expressed from the marker genes. This can be accomplished, for example, by separation of proteins from a sample on a polyacrylamide gel, followed by identification of specific marker-derived proteins using antibodies in a western blot. Alternatively, proteins can be separated by two-dimensional gel electrophoresis systems. Two-dimensional gel electrophoresis is well-known in the art and typically involves isoelectric focusing along a first dimension followed by SDS-PAGE electrophoresis along a second dimension. See, e.g., Hames et al, 1990, GEL ELECTROPHORESIS OF PROTEINS: A PRACTICAL APPROACH, IRL Press, New York; Shevchenko et al., Proc. Nat'l Acad. Sci. USA 93:1440-1445 (1996); Sagliocco et al., Yeast 12:1519-1533 (1996); Lander, Science 274:536-539 (1996). The resulting electropherograms can be analyzed by numerous techniques, including mass spectrometric techniques, western blotting and immunoblot analysis using polyclonal and monoclonal antibodies.

[0163] Alternatively, marker-derived protein levels can be determined by constructing an antibody microarray in which binding sites comprise immobilized, preferably monoclonal, antibodies specific to a plurality of protein species encoded by the cell genome. Preferably, antibodies are present for a substantial fraction of the marker-derived proteins of interest. Methods for making monoclonal antibodies are well known (see, e.g., Harlow and Lane, 1988, ANTIBODIES: A LABORATORY MANUAL, Cold Spring Harbor, N.Y., which is incorporated in its entirety for all purposes). In one embodiment, monoclonal antibodies are raised against synthetic peptide fragments designed based on genomic sequence of the cell. With such an antibody array, proteins from the cell are contacted to the array, and their binding is assayed with assays known in the art. Generally, the expression, and the level of expression, of proteins of diagnostic or prognostic interest can be detected through immunohistochemical staining of tissue slices or sections.

[0164] Finally, expression of marker genes in a number of tissue specimens may be characterized using a “tissue array” (Kononen et al., Nat. Med 4(7):844-7 (1998)). In a tissue array, multiple tissue samples are assessed on the same microarray. The arrays allow in situ detection of RNA and protein levels; consecutive sections allow the analysis of multiple samples simultaneously.

5.5.2 Microarrays

[0165] In preferred embodiments, polynucleotide microarrays are used to measure expression so that the expression status of each of the markers above is assessed simultaneously. In a specific embodiment, the invention provides for oligonucleotide or cDNA arrays comprising probes hybridizable to the genes corresponding to each of the marker sets described above (i.e., markers to determine the molecular type or subtype of a tumor; markers to distinguish ER status; markers to distinguish BRCA1 from sporadic tumors; markers to distinguish patients with good versus patients with poor prognosis; markers to distinguish both ER(+) from ER(−), and BRCA1 tumors from sporadic tumors; markers to distinguish ER(+) from ER(−), and patients with good prognosis from patients with poor prognosis; markers to distinguish BRCA1 tumors from sporadic tumors, and patients with good prognosis from patients with poor prognosis; and markers able to distinguish ER(+) from ER(−), BRCA1 tumors from sporadic tumors, and patients with good prognosis from patients with poor prognosis; and markers unique to each status).

[0166] The microarrays provided by the present invention may comprise probes hybridizable to the genes corresponding to markers able to distinguish the status of one, two, or all three of the clinical conditions noted above. In particular, the invention provides polynucleotide arrays comprising probes to a subset or subsets of at least 50, 100, 200, 300, 400, 500, 750, 1,000, 1,250, 1,500, 1,750, 2,000 or 2,250 genetic markers, up to the full set of 2,460 markers, which distinguish ER(+) and ER(−) patients or tumors. The invention also provides probes to subsets of at least 20, 30, 40, 50, 75, 100, 150, 200, 250, 300, 350 or 400 markers, up to the full set of 430 markers, which distinguish between tumors containing a BRCA1 mutation and sporadic tumors within an ER(−) group of tumors. The invention also provides probes to subsets of at least 20, 30, 40, 50, 75, 100, 150 or 200 markers, up to the full set of 231 markers, which distinguish between patients with good and poor prognosis within sporadic tumors. In a specific embodiment, the array comprises probes to marker sets or subsets directed to any two of the clinical conditions. In a more specific embodiment, the array comprises probes to marker sets or subsets directed to all three clinical conditions.

[0167] In specific embodiments, the invention provides polynucleotide arrays in which the breast cancer-related markers described herein comprise at least 50%, 60%, 70%, 80%, 85%, 90%, 95% or 98% of the probes on said array. In another specific embodiment, the invention provides polynucleotide arrays in which ER status-related markers selected from Table 1 comprise at least 50%, 60%, 70%, 80%, 85%, 90%, 95% or 98% of the probes on said array. In another specific embodiment, the invention provides polynucleotide arrays in which BRCA1/sporadic markers selected from Table 3 comprise at least 50%, 60%, 70%, 80%, 85%, 90%, 95% or 98% of the probes on said array. In another specific embodiment, the invention provides polynucleotide arrays in which prognostic markers selected from Table 5 comprise at least 50%, 60%, 70%, 80%, 85%, 90%, 95% or 98% of the probes on said array.

[0168] In yet another specific embodiment, microarrays that are used in the methods disclosed herein optionally comprise markers additional to at least some of the markers listed in Tables 1-6. For example, in a specific embodiment, the microarray is a screening or scanning array as described in Altschuler et al., International Publication WO 02/18646, published Mar. 7, 2002 and Scherer et al., International Publication WO 02/16650, published Feb. 28, 2002. The scanning and screening arrays comprise regularly-spaced, positionally-addressable probes derived from genomic nucleic acid sequence, both expressed and unexpressed. Such arrays may comprise probes corresponding to a subset of, or all of, the markers listed in Tables 1-6, or a subset thereof as described above, and can be used to monitor marker expression in the same way as a microarray containing only markers listed in Tables 1-6.

[0169] In yet another specific embodiment, the microarray is a commercially-available cDNA microarray that comprises at least five of the markers listed in Tables 1-6. Preferably, a commercially-available cDNA microarray comprises all of the markers listed in Tables 1-6. However, such a microarray may comprise 5, 10, 15, 25, 50, 100, 150, 250, 500, 1000 or more of the markers in any of Tables 1-6, up to the maximum number of markers in a Table, and may comprise all of the markers in any one of Tables 1-6 and a subset of another of Tables 1-6, or subsets of each as described above. In a specific embodiment of the microarrays used in the methods disclosed herein, the markers that are all or a portion of Tables 1-6 make up at least 50%, 60%, 70%, 80%, 90%, 95% or 98% of the probes on the microarray.

[0170] General methods pertaining to the construction of microarrays comprising the marker sets and/or subsets above are described in the following sections.

5.5.2.1 Construction of Microarrays

[0171] Microarrays are prepared by selecting probes which comprise a polynucleotide sequence, and then immobilizing such probes to a solid support or surface. For example, the probes may comprise DNA sequences, RNA sequences, or copolymer sequences of DNA and RNA. The polynucleotide sequences of the probes may also comprise DNA and/or RNA analogues, or combinations thereof. For example, the polynucleotide sequences of the probes may be full or partial fragments of genomic DNA. The polynucleotide sequences of the probes may also be synthesized nucleotide sequences, such as synthetic oligonucleotide sequences. The probe sequences can be synthesized either enzymatically in vivo, enzymatically in vitro (e.g., by PCR), or non-enzymatically in vitro.

[0172] The probe or probes used in the methods of the invention are preferably immobilized to a solid support which may be either porous or non-porous. For example, the probes of the invention may be polynucleotide sequences which are attached to a nitrocellulose or nylon membrane or filter covalently at either the 3′ or the 5′ end of the polynucleotide. Such hybridization probes are well known in the art (see, e.g., Sambrook et al., MOLECULAR CLONING—A LABORATORY MANUAL (2ND ED.), Vols. 1-3, Cold Spring Harbor Laboratory, Cold Spring Harbor, N.Y. (1989). Alternatively, the solid support or surface may be a glass or plastic surface. In a particularly preferred embodiment, hybridization levels are measured to microarrays of probes consisting of a solid phase on the surface of which are immobilized a population of polynucleotides, such as a population of DNA or DNA mimics, or, alternatively, a population of RNA or RNA mimics. The solid phase may be a nonporous or, optionally, a porous material such as a gel.

[0173] In preferred embodiments, a microarray comprises a support or surface with an ordered array of binding (e.g., hybridization) sites or “probes” each representing one of the markers described herein. Preferably the microarrays are addressable arrays, and more preferably positionally addressable arrays. More specifically, each probe of the array is preferably located at a known, predetermined position on the solid support such that the identity (i.e., the sequence) of each probe can be determined from its position in the array (i.e., on the support or surface). In preferred embodiments, each probe is covalently attached to the solid support at a single site.

[0174] Microarrays can be made in a number of ways, of which several are described below. However produced, microarrays share certain characteristics. The arrays are reproducible, allowing multiple copies of a given array to be produced and easily compared with each other. Preferably, microarrays are made from materials that are stable under binding (e.g., nucleic acid hybridization) conditions. The microarrays are preferably small, e.g., between 1 cm2 and 25 cm2, between 12 cm2 and 13 cm2, or 3 cm2. However, larger arrays are also contemplated and may be preferable, e.g., for use in screening arrays. Preferably, a given binding site or unique set of binding sites in the microarray will specifically bind (e.g., hybridize) to the product of a single gene in a cell (e.g., to a specific mRNA, or to a specific cDNA derived therefrom). However, in general, other related or similar sequences will cross hybridize to a given binding site.

[0175] The microarrays of the present invention include one or more test probes, each of which has a polynucleotide sequence that is complementary to a subsequence of RNA or DNA to be detected. Preferably, the position of each probe on the solid surface is known. Indeed, the microarrays are preferably positionally addressable arrays. Specifically, each probe of the array is preferably located at a known, predetermined position on the solid support such that the identity (i.e., the sequence) of each probe can be determined from its position on the array (i.e., on the support or surface).

[0176] According to the invention, the microarray is an array (i.e., a matrix) in which each position represents one of the markers described herein. For example, each position can contain a DNA or DNA analogue based on genomic DNA to which a particular RNA or cDNA transcribed from that genetic marker can specifically hybridize. The DNA or DNA analogue can be, e.g., a synthetic oligomer or a gene fragment. In one embodiment, probes representing each of the markers is present on the array. In a preferred embodiment, the array comprises the 550 of the 2,460 RE-status markers, 70 of the BRCA1/sporadic markers, and all 231 of the prognosis markers.

5.5.2.2 Preparing Probes for Microarrays

[0177] As noted above, the “probe” to which a particular polynucleotide molecule specifically hybridizes according to the invention contains a complementary genomic polynucleotide sequence. The probes of the microarray preferably consist of nucleotide sequences of no more than 1,000 nucleotides. In some embodiments, the probes of the array consist of nucleotide sequences of 10 to 1,000 nucleotides. In a preferred embodiment, the nucleotide sequences of the probes are in the range of 10-200 nucleotides in length and are genomic sequences of a species of organism, such that a plurality of different probes is present, with sequences complementary and thus capable of hybridizing to the genome of such a species of organism, sequentially tiled across all or a portion of such genome. In other specific embodiments, the probes are in the range of 10-30 nucleotides in length, in the range of 10-40 nucleotides in length, in the range of 20-50 nucleotides in length, in the range of 40-80 nucleotides in length, in the range of 50-150 nucleotides in length, in the range of 80-120 nucleotides in length, and most preferably are 60 nucleotides in length.

[0178] The probes may comprise DNA or DNA “mimics” (e.g., derivatives and analogues) corresponding to a portion of an organism's genome. In another embodiment, the probes of the microarray are complementary RNA or RNA mimics. DNA mimics are polymers composed of subunits capable of specific, Watson-Crick-like hybridization with DNA, or of specific hybridization with RNA. The nucleic acids can be modified at the base moiety, at the sugar moiety, or at the phosphate backbone. Exemplary DNA mimics include, e.g., phosphorothioates.

[0179] DNA can be obtained, e.g., by polymerase chain reaction (PCR) amplification of genomic DNA or cloned sequences. PCR primers are preferably chosen based on a known sequence of the genome that will result in amplification of specific fragments of genomic DNA. Computer programs that are well known in the art are useful in the design of primers with the required specificity and optimal amplification properties, such as Oligo version 5.0 (National Biosciences). Typically each probe on the microarray will be between 10 bases and 50,000 bases, usually between 300 bases and 1,000 bases in length. PCR methods are well known in the art, and are described, for example, in Innis et al., eds., PCR PROTOCOLS: A GUIDE TO METHODS AND APPLICATIONS, Academic Press Inc., San Diego, Calif. (1990). It will be apparent to one skilled in the art that controlled robotic systems are useful for isolating and amplifying nucleic acids.

[0180] An alternative, preferred means for generating the polynucleotide probes of the microarray is by synthesis of synthetic polynucleotides or oligonucleotides, e.g., using N-phosphonate or phosphoramidite chemistries (Froehler et al., Nucleic Acid Res. 14:5399-5407 (1986); McBride et al., Tetrahedron Lett. 24:246-248 (1983)). Synthetic sequences are typically between about 10 and about 500 bases in length, more typically between about 20 and about 100 bases, and most preferably between about 40 and about 70 bases in length. In some embodiments, synthetic nucleic acids include non-natural bases, such as, but by no means limited to, inosine. As noted above, nucleic acid analogues may be used as binding sites for hybridization. An example of a suitable nucleic acid analogue is peptide nucleic acid (see, e.g., Egholm et al., Nature 363:566-568 (1993); U.S. Pat. No. 5,539,083). Probes are preferably selected using an algorithm that takes into account binding energies, base composition, sequence complexity, cross-hybridization binding energies, and secondary structure (see Friend et al., International Patent Publication WO 01/05935, published Jan. 25, 2001; Hughes et al., Nat. Biotech. 19:342-7 (2001)).

[0181] A skilled artisan will also appreciate that positive control probes, e.g., probes known to be complementary and hybridizable to sequences in the target polynucleotide molecules, and negative control probes, e.g., probes known to not be complementary and hybridizable to sequences in the target polynucleotide molecules, should be included on the array. In one embodiment, positive controls are synthesized along the perimeter of the array. In another embodiment, positive controls are synthesized in diagonal stripes across the array. In still another embodiment, the reverse complement for each probe is synthesized next to the position of the probe to serve as a negative control. In yet another embodiment, sequences from other species of organism are used as negative controls or as “spike-in” controls.

5.5.2.3 Attaching Probes to the Solid Surface

[0182] The probes are attached to a solid support or surface, which may be made, e.g., from glass, plastic (e.g., polypropylene, nylon), polyacrylamide, nitrocellulose, gel, or other porous or nonporous material. A preferred method for attaching the nucleic acids to a surface is by printing on glass plates, as is described generally by Schena et al, Science 270:467-470 (1995). This method is especially useful for preparing microarrays of cDNA (See also, DeRisi et al, Nature Genetics 14:457-460 (1996); Shalon et al., Genome Res. 6:639-645 (1996); and Schena et al., Proc. Natl. Acad. Sci. U.S.A. 93:10539-11286 (1995)).

[0183] A second preferred method for making microarrays is by making high-density oligonucleotide arrays. Techniques are known for producing arrays containing thousands of oligonucleotides complementary to defined sequences, at defined locations on a surface using photolithographic techniques for synthesis in situ (see, Fodor et al., 1991, Science 251:767-773; Pease et al., 1994, Proc. Natl. Acad. Sci. U.S.A. 91:5022-5026; Lockhart et al., 1996, Nature Biotechnology 14:1675; U.S. Pat. Nos. 5,578,832; 5,556,752; and 5,510,270) or other methods for rapid synthesis and deposition of defined oligonucleotides (Blanchard et al., Biosensors & Bioelectronics 11:687-690). When these methods are used, oligonucleotides (e.g., 60-mers) of known sequence are synthesized directly on a surface such as a derivatized glass slide. Usually, the array produced is redundant, with several oligonucleotide molecules per RNA.

[0184] Other methods for making microarrays, e.g., by masking (Maskos and Southern, 1992, Nuc. Acids. Res. 20:1679-1684), may also be used. In principle, and as noted supra, any type of array, for example, dot blots on a nylon hybridization membrane (see Sambrook et al., MOLECULAR CLONING—A LABORATORY MANUAL (2ND ED.), Vols. 1-3, Cold Spring Harbor Laboratory, Cold Spring Harbor, N.Y. (1989)) could be used. However, as will be recognized by those skilled in the art, very small arrays will frequently be preferred because hybridization volumes will be smaller.

[0185] In one embodiment, the arrays of the present invention are prepared by synthesizing polynucleotide probes on a support. In such an embodiment, polynucleotide probes are attached to the support covalently at either the 3′ or the 5′ end of the polynucleotide.

[0186] In a particularly preferred embodiment, microarrays of the invention are manufactured by means of an ink jet printing device for oligonucleotide synthesis, e.g., using the methods and systems described by Blanchard in U.S. Pat. No. 6,028,189; Blanchard et al., 1996, Biosensors and Bioelectronics 11:687-690; Blanchard, 1998, in SYNTHETIC DNA ARRAYS IN GENETIC ENGINEERING, Vol. 20, J. K. Setlow, Ed., Plenum Press, New York at pages 111-123. Specifically, the oligonucleotide probes in such microarrays are preferably synthesized in arrays, e.g., on a glass slide, by serially depositing individual nucleotide bases in “microdroplets” of a high surface tension solvent such as propylene carbonate. The microdroplets have small volumes (e.g., 100 pL or less, more preferably 50 pL or less) and are separated from each other on the microarray (e.g., by hydrophobic domains) to form circular surface tension wells which define the locations of the array elements (i.e., the different probes). Microarrays manufactured by this ink-jet method are typically of high density, preferably having a density of at least about 2,500 different probes per 1 cm2. The polynucleotide probes are attached to the support covalently at either the 3′ or the 5′ end of the polynucleotide.

5.5.2.4 Target Polynucleotide Molecules

[0187] The polynucleotide molecules which may be analyzed by the present invention (the “target polynucleotide molecules”) may be from any clinically relevant source, but are expressed RNA or a nucleic acid derived therefrom (e.g., cDNA or amplified RNA derived from cDNA that incorporates an RNA polymerase promoter), including naturally occurring nucleic acid molecules, as well as synthetic nucleic acid molecules. In one embodiment, the target polynucleotide molecules comprise RNA, including, but by no means limited to, total cellular RNA, poly(A)+ messenger RNA (mRNA) or fraction thereof, cytoplasmic mRNA, or RNA transcribed from cDNA (i.e., cRNA; see, e.g., Linsley & Schelter, U.S. patent application Ser. No. 09/411,074, filed Oct. 4, 1999, or U.S. Pat. Nos. 5,545,522, 5,891,636, or 5,716,785). Methods for preparing total and poly(A)+ RNA are well known in the art, and are described generally, e.g., in Sambrook et al., MOLECULAR CLONING—A LABORATORY MANUAL (2ND ED.), Vols. 1-3, Cold Spring Harbor Laboratory, Cold Spring Harbor, N.Y. (1989). In one embodiment, RNA is extracted from cells of the various types of interest in this invention using guanidinium thiocyanate lysis followed by CsCl centrifugation (Chirgwin et al., 1979, Biochemistry 18:5294-5299). In another embodiment, total RNA is extracted using a silica gel-based column, commercially available examples of which include RNeasy (Qiagen, Valencia, Calif.) and StrataPrep (Stratagene, La Jolla, Calif.). In an alternative embodiment, which is preferred for S. cerevisiae, RNA is extracted from cells using phenol and chloroform, as described in Ausubel et al., eds., 1989, CURRENT PROTOCOLS IN MOLECULAR BIOLOGY, Vol III, Green Publishing Associates, Inc., John Wiley & Sons, Inc., New York, at pp. 13.12.1-13.12.5). Poly(A)+ RNA can be selected, e.g., by selection with oligo-dT cellulose or, alternatively, by oligo-dT primed reverse transcription of total cellular RNA. In one embodiment, RNA can be fragmented by methods known in the art, e.g., by incubation with ZnCl2, to generate fragments of RNA. In another embodiment, the polynucleotide molecules analyzed by the invention comprise cDNA, or PCR products of amplified RNA or cDNA.

[0188] In one embodiment, total RNA, mRNA, or nucleic acids derived therefrom, is isolated from a sample taken from a person afflicted with breast cancer. Target polynucleotide molecules that are poorly expressed in particular cells may be enriched using normalization techniques (Bonaldo et al., 1996, Genome Res. 6:791-806).

[0189] As described above, the target polynucleotides are detectably labeled at one or more nucleotides. Any method known in the art may be used to detectably label the target polynucleotides. Preferably, this labeling incorporates the label uniformly along the length of the RNA, and more preferably, the labeling is carried out at a high degree of efficiency. One embodiment for this labeling uses oligo-dT primed reverse transcription to incorporate the label; however, conventional methods of this method are biased toward generating 3′ end fragments. Thus, in a preferred embodiment, random primers (e.g., 9-mers) are used in reverse transcription to uniformly incorporate labeled nucleotides over the full length of the target polynucleotides. Alternatively, random primers may be used in conjunction with PCR methods or T7 promoter-based in vitro transcription methods in order to amplify the target polynucleotides.

[0190] In a preferred embodiment, the detectable label is a luminescent label. For example, fluorescent labels, bioluminescent labels, chemiluminescent labels, and colorimetric labels may be used in the present invention. In a highly preferred embodiment, the label is a fluorescent label, such as a fluorescein, a phosphor, a rhodamine, or a polymethine dye derivative. Examples of commercially available fluorescent labels include, for example, fluorescent phosphoramidites such as FluorePrime (Amersham Pharmacia, Piscataway, N.J.), Fluoredite (Millipore, Bedford, Mass.), FAM (ABI, Foster City, Calif.), and Cy3 or Cy5 (Amersham Pharmacia, Piscataway, N.J.). In another embodiment, the detectable label is a radiolabeled nucleotide.

[0191] In a further preferred embodiment, target polynucleotide molecules from a patient sample are labeled differentially from target polynucleotide molecules of a standard. The standard can comprise target polynucleotide molecules from normal individuals (i.e., those not afflicted with breast cancer). In a highly preferred embodiment, the standard comprises target polynucleotide molecules pooled from samples from normal individuals or tumor samples from individuals having sporadic-type breast tumors. In another embodiment, the target polynucleotide molecules are derived from the same individual, but are taken at different time points, and thus indicate the efficacy of a treatment by a change in expression of the markers, or lack thereof, during and after the course of treatment (i.e., chemotherapy, radiation therapy or cryotherapy), wherein a change in the expression of the markers from a poor prognosis pattern to a good prognosis pattern indicates that the treatment is efficacious. In this embodiment, different timepoints are differentially labeled.

5.5.2.5 Hybridization to Microarrays

[0192] Nucleic acid hybridization and wash conditions are chosen so that the target polynucleotide molecules specifically bind or specifically hybridize to the complementary polynucleotide sequences of the array, preferably to a specific array site, wherein its complementary DNA is located.

[0193] Arrays containing double-stranded probe DNA situated thereon are preferably subjected to denaturing conditions to render the DNA single-stranded prior to contacting with the target polynucleotide molecules. Arrays containing single-stranded probe DNA (e.g., synthetic oligodeoxyribonucleic acids) may need to be denatured prior to contacting with the target polynucleotide molecules, e.g., to remove hairpins or dimers which form due to self complementary sequences.

[0194] Optimal hybridization conditions will depend on the length (e.g., oligomer versus polynucleotide greater than 200 bases) and type (e.g., RNA, or DNA) of probe and target nucleic acids. One of skill in the art will appreciate that as the oligonucleotides become shorter, it may become necessary to adjust their length to achieve a relatively uniform melting temperature for satisfactory hybridization results. General parameters for specific (i.e., stringent) hybridization conditions for nucleic acids are described in Sambrook et al., MOLECULAR CLONING—A LABORATORY MANUAL (2ND ED.), Vols. 1-3, Cold Spring Harbor Laboratory, Cold Spring Harbor, N.Y. (1989), and in Ausubel et al., CURRENT PROTOCOLS IN MOLECULAR BIOLOGY, vol. 2, Current Protocols Publishing, New York (1994). Typical hybridization conditions for the cDNA microarrays of Schena et al. are hybridization in 5×SSC plus 0.2% SDS at 65° C. for four hours, followed by washes at 25° C. in low stringency wash buffer (1×SSC plus 0.2% SDS), followed by 10 minutes at 25° C. in higher stringency wash buffer (0.1×SSC plus 0.2% SDS) (Schena et al., Proc. Natl. Acad. Sci. U.S.A. 93:10614 (1993)). Useful hybridization conditions are also provided in, e.g., Tijessen, 1993, HYBRIDIZATION WITH NUCLEIC ACID PROBES, Elsevier Science Publishers B. V.; and Kricka, 1992, NONISOTOPIC DNA PROBE TECHNIQUES, Academic Press, San Diego, Calif.

[0195] Particularly preferred hybridization conditions include hybridization at a temperature at or near the mean melting temperature of the probes (e.g., within 5° C., more preferably within 2° C.) in 1 M NaCl, 50 mM MES buffer (pH 6.5), 0.5% sodium sarcosine and 30% formamide.

5.5.2.6 Signal Detection and Data Analysis

[0196] When fluorescently labeled probes are used, the fluorescence emissions at each site of a microarray may be, preferably, detected by scanning confocal laser microscopy. In one embodiment, a separate scan, using the appropriate excitation line, is carried out for each of the two fluorophores used. Alternatively, a laser may be used that allows simultaneous specimen illumination at wavelengths specific to the two fluorophores and emissions from the two fluorophores can be analyzed simultaneously (see Shalon et al., 1996, “A DNA microarray system for analyzing complex DNA samples using two-color fluorescent probe hybridization,” Genome Research 6:639-645, which is incorporated by reference in its entirety for all purposes). In a preferred embodiment, the arrays are scanned with a laser fluorescent scanner with a computer controlled X-Y stage and a microscope objective. Sequential excitation of the two fluorophores is achieved with a multi-line, mixed gas laser and the emitted light is split by wavelength and detected with two photomultiplier tubes. Fluorescence laser scanning devices are described in Schena et al., Genome Res. 6:639-645 (1996), and in other references cited herein. Alternatively, the fiber-optic bundle described by Ferguson et al., Nature Biotech. 14:1681-1684 (1996), may be used to monitor mRNA abundance levels at a large number of sites simultaneously.

[0197] Signals are recorded and, in a preferred embodiment, analyzed by computer, e.g., using a 12 or 16 bit analog to digital board. In one embodiment the scanned image is despeckled using a graphics program (e.g., Hijaak Graphics Suite) and then analyzed using an image gridding program that creates a spreadsheet of the average hybridization at each wavelength at each site. If necessary, an experimentally determined correction for “cross talk” (or overlap) between the channels for the two fluors may be made. For any particular hybridization site on the transcript array, a ratio of the emission of the two fluorophores can be calculated. The ratio is independent of the absolute expression level of the cognate gene, but is useful for genes whose expression is significantly modulated in association with the different breast cancer-related condition.

5.6 Computer-Facilitated Analysis

[0198] The present invention further provides for kits comprising the marker sets above. In a preferred embodiment, the kit contains a microarray ready for hybridization to target polynucleotide molecules, plus software for the data analyses described above.

[0199] The analytic methods described in the previous sections can be implemented by use of the following computer systems and according to the following programs and methods. A computer system comprises internal components linked to external components. The internal components of a typical computer system include a processor element interconnected with a main memory. For example, the computer system can be an Intel 8086-, 80386-, 80486-, Pentium™, or Pentium™-based processor with preferably 32 MB or more of main memory. The computer system may also be a Macintosh or a Macintosh-based system, but may also be a minicomputer or mainframe.

[0200] The external components may include mass storage. This mass storage can be one or more hard disks (which are typically packaged together with the processor and memory). Such hard disks are preferably of 1 GB or greater storage capacity. Other external components include a user interface device, which can be a monitor, together with an inputting device, which can be a “mouse”, or other graphic input devices, and/or a keyboard. A printing device can also be attached to the computer.

[0201] Typically, a computer system is also linked to network link, which can be part of an Ethernet link to other local computer systems, remote computer systems, or wide area communication networks, such as the Internet. This network link allows the computer system to share data and processing tasks with other computer systems.

[0202] Loaded into memory during operation of this system are several software components, which are both standard in the art and special to the instant invention. These software components collectively cause the computer system to function according to the methods of this invention. These software components are typically stored on the mass storage device. A software component comprises the operating system, which is responsible for managing computer system and its network interconnections. This operating system can be, for example, of the Microsoft Windows® family, such as Windows 3.1, Windows 95, Windows 98, Windows 2000, or Windows NT, or may be of the Macintosh OS family, or may be UNIX or an operating system specific to a minicomputer or mainframe. The software component represents common languages and functions conveniently present on this system to assist programs implementing the methods specific to this invention. Many high or low level computer languages can be used to program the analytic methods of this invention. Instructions can be interpreted during run-time or compiled. Preferred languages include C/C++, FORTRAN and JAVA. Most preferably, the methods of this invention are programmed in mathematical software packages that allow symbolic entry of equations and high-level specification of processing, including some or all of the algorithms to be used, thereby freeing a user of the need to procedurally program individual equations or algorithms. Such packages include Mathlab from Mathworks (Natick, Mass.), Mathematica® from Wolfram Research (Champaign, Ill.), or S-Plus® from Math Soft (Cambridge, Mass.). Specifically, the software component includes the analytic methods of the invention as programmed in a procedural language or symbolic package.

[0203] The software to be included with the kit comprises the data analysis methods of the invention as disclosed herein. In particular, the software may include mathematical routines for marker discovery, including the calculation of similarity values between clinical categories (e.g., ER status) and marker expression. The software may also include mathematical routines for calculating the similarity between sample marker expression and control marker expression, using array-generated fluorescence data, to determine the clinical classification of a sample.

[0204] Additionally, the software may also include mathematical routines for determining the prognostic outcome, and recommended therapeutic regimen, for a particular breast cancer patient. Such software would include instructions for the computer system's processor to receive data structures that include the level of expression of five or more of the marker genes listed in Table 5 in a breast cancer tumor sample obtained from the breast cancer patient; the mean level of expression of the same genes in a control or template; and the breast cancer patient's clinical information, including lymph node and ER status. The software may additionally include mathematical routines for transforming the hybridization data and for calculating the similarity between the expression levels for the marker genes in the patient's breast cancer tumor sample and the control or template. In a specific embodiment, the software includes mathematical routines for calculating a similarity metric, such as a coefficient of correlation, representing the similarity between the expression levels for the marker genes in the patient's breast cancer tumor sample and the control or template, and expressing the similarity as that similarity metric.

[0205] The software would include decisional routines that integrate the patient's clinical and marker gene expression data, and recommend a course of therapy. In one embodiment, for example, the software causes the processor unit to receive expression data for the patient's tumor sample, calculate a metric of similarity of these expression values to the values for the same genes in a template or control, compare this similarity metric to a pre-selected similarity metric threshold or thresholds that differentiate prognostic groups, assign the patient to the prognostic group, and, on the basis of the prognostic group, assign a recommended therapeutic regimen. In a specific example, the software additionally causes the processor unit to receive data structures comprising clinical information about the breast cancer patient. In a more specific example, such clinical information includes the patient's age, stage of breast cancer, estrogen receptor status, and lymph node status.

[0206] Where the control is an expression template comprising expression values for marker genes within a group of breast cancer patients, the control can comprise either hybridization data obtained at the same time (i.e., in the same hybridization experiment) as the patient's individual hybridization data, or can be a set of hybridization or marker expression values stores on a computer, or on computer-readable media. If the latter is used, new patient hybridization data for the selected marker genes, obtained from initial or follow-up tumor samples, or suspected tumor samples, can be compared to the stored values for the same genes without the need for additional control hybridizations. However, the software may additionally comprise routines for updating the control data set, i.e., to add information from additional breast cancer patients or to remove existing members of the control data set, and, consequently, for recalculating the average expression level values that comprise the template. In another specific embodiment, said control comprises a set of single-channel mean hybridization intensity values for each of said at least five of said genes, stored on a computer-readable medium.

[0207] Clinical data relating to a breast cancer patient, and used by the computer program products of the invention, can be contained in a database of clinical data in which information on each patient is maintained in a separate record, which record may contain any information relevant to the patient, the patient's medical history, treatment, prognosis, or participation in a clinical trial or study, including expression profile data generated as part of an initial diagnosis or for tracking the progress of the breast cancer during treatment.

[0208] Thus, one embodiment of the invention provides a computer program product for classifying a breast cancer patient according to prognosis, the computer program product for use in conjunction with a computer having a memory and a processor, the computer program product comprising a computer readable storage medium having a computer program mechanism encoded thereon, wherein said computer program product can be loaded into the one or more memory units of a computer and causes the one or more processor units of the computer to execute the steps of (a) receiving a first data structure comprising the level of expression of at least five of the genes for which markers are listed in Table 5 in a cell sample taken from said breast cancer patient; (b) determining the similarity of the level of expression of said at least five genes to control levels of expression of said at least five genes to obtain a patient similarity value; (c) comparing said patient similarity value to selected first and second threshold values of similarity of said level of expression of said genes to said control levels of expression to obtain first and second similarity threshold values, respectively, wherein said second similarity threshold indicates greater similarity to said control levels of expression than does said first similarity threshold; and (d) classifying said breast cancer patient as having a first prognosis if said patient similarity value exceeds said first and said second threshold similarity values, a second prognosis if said patient similarity value exceeds said first threshold similarity value but does not exceed said second threshold similarity value, and a third prognosis if said patient similarity value does not exceed said first threshold similarity value or said second threshold similarity value. In a specific embodiment of said computer program product, said first threshold value of similarity and said second threshold value of similarity are values stored in said computer. In another more specific embodiment, said first prognosis is a “very good prognosis,” said second prognosis is an “intermediate prognosis,” and said third prognosis is a “poor prognosis,” and wherein said computer program mechanism may be loaded into the memory and further cause said one or more processor units of said computer to execute the step of assigning said breast cancer patient a therapeutic regimen comprising no adjuvant chemotherapy if the patient is lymph node negative and is classified as having a good prognosis or an intermediate prognosis, or comprising chemotherapy if said patient has any other combination of lymph node status and expression profile. In another specific embodiment, said computer program mechanism may be loaded into the memory and further cause said one or more processor units of the computer to execute the steps of receiving a data structure comprising clinical data specific to said breast cancer patient. In a more specific embodiment, said clinical data includes the lymph node and estrogen receptor (ER) status of said breast cancer patient. In more specific embodiment, said single-channel hybridization intensity values are log transformed. The computer implementation of the method, however, may use any desired transformation method. In another specific embodiment, the computer program product causes said processing unit to perform said comparing step (c) by calculating the difference between the level of expression of each of said genes in said cell sample taken from said breast cancer patient and the level of expression of the same genes in said control. In another specific embodiment, the computer program product causes said processing unit to perform said comparing step (c) by calculating the mean log level of expression of each of said genes in said control to obtain a control mean log expression level for each gene, calculating the log expression level for each of said genes in a breast cancer sample from said breast cancer patient to obtain a patient log expression level, and calculating the difference between the patient log expression level and the control mean log expression for each of said genes. In another specific embodiment, the computer program product causes said processing unit to perform said comparing step (c) by calculating similarity between the level of expression of each of said genes in said cell sample taken from said breast cancer patient and the level of expression of the same genes in said control, wherein said similarity is expressed as a similarity value. In more specific embodiment, said similarity value is a correlation coefficient. The similarity value may, however, be expressed as any art-known similarity metric.

[0209] In an exemplary implementation, to practice the methods of the present invention, a user first loads experimental data into the computer system. These data can be directly entered by the user from a monitor, keyboard, or from other computer systems linked by a network connection, or on removable storage media such as a CD-ROM, floppy disk (not illustrated), tape drive (not illustrated), ZIP® drive (not illustrated) or through the network. Next the user causes execution of expression profile analysis software which performs the methods of the present invention.

[0210] In another exemplary implementation, a user first loads experimental data and/or databases into the computer system. This data is loaded into the memory from the storage media or from a remote computer, preferably from a dynamic geneset database system, through the network. Next the user causes execution of software that performs the steps of the present invention.

[0211] Additionally, because the data obtained and analyzed in the software and computer system products of the invention are confidential, the software and/or computer system comprises access controls or access control routines, such as Alternative computer systems and software for implementing the analytic methods of this invention will be apparent to one of skill in the art and are intended to be comprehended within the accompanying claims. In particular, the accompanying claims are intended to include the alternative program structures for implementing the methods of this invention that will be readily apparent to one of skill in the art.

[0212] 6. EXAMPLES

[0213] Materials And Methods

[0214] 117 tumor samples from breast cancer patients were collected. RNA samples were then prepared, and each RNA sample was profiled using inkjet-printed microarrays. Marker genes were then identified based on expression patterns; these genes were then used to train classifiers, which used these marker genes to classify tumors into diagnostic and prognostic categories. Finally, these marker genes were used to predict the diagnostic and prognostic outcome for a group of individuals.

[0215] 1. Sample Collection

[0216] 117 breast cancer patients treated at The Netherlands Cancer Institute/Antoni van Leeuwenhoek Hospital, Amsterdam, The Netherlands, were selected on the basis of the following clinical criteria (data extracted from the medical records of the NKI/AvL Tumor Register, Biometrics Department).

[0217] Group 1 (n=97, 78 for training, 19 for independent tests) was selected on the basis of: (1) primary invasive breast carcinoma <5 cm (T1 or T2); (2) no axillary metastases (NO); (3) age at diagnosis <55 years; (4) calender year of diagnosis 1983-1996; and (5) no prior malignancies (excluding carcinoma in situ of the cervix or basal cell carcinoma of the skin). All patients were treated by modified radical mastectomy (n=34) or breast conserving treatment (n=64), including axillary lymph node dissection. Breast conserving treatment consisted of excision of the tumor, followed by radiation of the whole breast to a dosis of 50 Gy, followed by a boost varying from 15 to 25 Gy. Five patients received adjuvant systemic therapy consisting of chemotherapy (n=3) or hormonal therapy (n=2), all other patients did not receive additional treatment. All patients were followed at least annually for a period of at least 5 years. Patient follow-up information was extracted from the Tumor Registry of the Biometrics Department.

[0218] Group 2 (n=20) was selected as: (1) carriers of a germline mutation in BRCA1 or BRCA2; and (2) having primary invasive breast carcinoma. No selection or exclusion was made based on tumor size, lymph node status, age at diagnosis, calender year of diagnosis, other malignancies. Germline mutation status was known prior to this research protocol.

[0219] Information about individual from which tumor samples were collected include: year of birth; sex; whether the individual is pre- or post-menopausal; the year of diagnosis; the number of positive lymph nodes and the total number of nodes; whether there was surgery, and if so, whether the surgery was breast-conserving or radical; whether there was radiotherapy, chemotherapy or hormonal therapy. The tumor was graded according to the formula P=TNM, where T is the tumor size (on a scale of 0-5); N is the number of nodes that are positive (on a scale of 0-4); and M is metastases (0=absent, 1=present). The tumor was also classified according to stage, tumor type (in situ or invasive; lobular or ductal; grade) and the presence or absence of the estrogen and progesterone receptors. The progression of the cancer was described by (where applicable): distant metastases; year of distant metastases, year of death, year of last follow-up; and BRCA1 genotype.

[0220] 2. Tumors:

[0221] Germline mutation testing of BRCA1 and BRCA2 on DNA isolated from peripheral blood lymphocytes includes mutation screening by a Protein Truncation Test (PTT) of exon 11 of BRCA1 and exon 10 and 11 of BRCA2, deletion PCR of BRCA1 genomic deletion of exon 13 and 22, as well Denaturing Gradient Gel Electrophoresis (DGGE) of the remaining exons. Aberrant bands were all confirmed by genomic sequencing analyzed on a ABI3700 automatic sequencer and confirmed on a independent DNA sample.

[0222] From all, tumor material was snap frozen in liquid nitrogen within one hour after surgery. Of the frozen tumor material an H&E (hematoxylin-eosin) stained section was repared prior to and after cutting slides for RNA isolation. These H&E frozen sections ere assessed for the percentage of tumor cells; only samples with >50% tumor cells were selected for further study.

[0223] For all tumors, surgical specimens fixed in formaldehyde and embedded in paraffin were evaluated according to standard histopathological procedures. H&E stained paraffin sections were examined to assess tumor type (e.g., ductal or lobular according to the WHO classification); to assess histologic grade according the method described by Elston and Ellis (grade 1-3); and to assess the presence of lymphangio-invasive growth and the presence of an extensive lymphocytic infiltrate. All histologic factors were independently assessed by two pathologists (MV and JL); consensus on differences was reached by examining the slides together. A representative slide of each tumor was used for immunohistochemical staining with antibodies directed against the estrogen- and progesterone receptor by standard procedures. The staining result was scored as the percentage of positively staining nuclei (0%, 10%, 20%, etc., up to 100%).

[0224] 3. Amplification, Labeling, and Hybridization

[0225] The outline for the production of marker-derived nucleic acids and hybridization of the nucleic acids to a microarray are outlined in FIG. 2. 30 frozen sections of 30 μM thickness were used for total RNA isolation of each snap frozen tumor specimen. Total RNA was isolated with RNAzol™ B (Campro Scientific, Veenendaal, The Netherlands) according to the manufacturers protocol, including homogenization of the tissue using a Polytron PT-MR2100 (Merck, Amsterdam, The Netherlands) and finally dissolved in RNAse-free H2O. The quality of the total RNA was assessed by A260/A280 ratio and had to be between 1.7 and 2.1 as well as visual inspection of the RNA on an agarose gel which should indicate a stronger 28S ribosomal RNA band compared to the 18S ribosomal RNA band, subsequently, 25 μg of total RNA was DNase treated using the Qiagen RNASE-free DNase kit and RNeasy spin columns (Qiagen Inc, GmbH, Germany) according to the manufacturers protocol. DNase treated total RNA was dissolved in RNASE-free H2O to a final concentration of 0.2 μg/μl.

[0226] 5 μg total RNA was used as input for cRNA synthesis. An oligo-dT primer containing a T7 RNA polymerase promoter sequence was used to prime first strand cDNA synthesis, and random primers (pdN6) were used to prime second strand cDNA synthesis by MMLV reverse transcriptase. This reaction yielded a double-stranded cDNA that contained the T7 RNA polymerase (T7RNAP) promoter. The double-stranded cDNA was then transcribed into cRNA by T7RNAP.

[0227] cRNA was labeled with Cy3 or Cy5 dyes using a two-step process. First, allylamine-derivatized nucleotides were enzymatically incorporated into cRNA products. For cRNA labeling, a 3:1 mixture of 5-(3-Aminoallyl)uridine 5′-triphosphate (Sigma) and UTP was substituted for UTP in the in vitro transcription (IVT) reaction. Allylamine-derivatized cRNA products were then reacted with N-hydroxy succinimide esters of Cy3 or Cy5 (CyDye, Amersham Pharmacia Biotech). 5 μg Cy5-labeled cRNA from one breast cancer patient was mixed with the same amount of Cy3-labeled product from a pool of equal amount of cRNA from each individual sporadic patient.

[0228] Microarray hybridizations were done in duplicate with fluor reversals. Before hybridization, labeled cRNAs were fragmented to an average size of 50-100nt by heating at 60° C. in the presence of 10 mM ZnCl2. Fragmented cRNAs were added to hybridization buffer containing 1 M NaCl, 0.5% sodium sarcosine and 50 mM MES, pH 6.5, which stringency was regulated by the addition of formamide to a final concentration of 30%. Hybridizations were carried out in a final volume of 3 ml at 40° C. on a rotating platform in a hybridization oven (Robbins Scientific) for 48 h. After hybridization, slides were washed and scanned using a confocal laser scanner (Agilent Technologies). Fluorescence intensities on scanned images were quantified, normalized and corrected.

[0229] 4. Pooling of Samples

[0230] The reference cRNA pool was formed by pooling equal amount of cRNAs from each individual sporadic patient, for a total of 78 tumors.

[0231] 5. 25 k Human Microarray

[0232] Surface-bound oligonucleotides were synthesized essentially as proposed by Blanchard et al., Biosens. Bioelectron. 6(7):687-690 (1996); see also Hughes et al., Nature Biotech. 19(4):342-347 (2000). Hydrophobic glass surfaces (3 inches by 3 inches) containing exposed hydroxyl groups were used as substrates for nucleotide synthesis. Phosphoramidite monomers were delivered to computer-defined positions on the glass surfaces using ink-jet printer heads. Unreacted monomers were then washed away and the ends of the extended oligonucleotides were deprotected. This cycle of monomer coupling, washing and deprotection was repeated for each desired layer of nucleotide synthesis. Oligonucleotide sequences to be printed were specified by computer files.

[0233] Microarrays containing approximately 25,000 human gene sequences (Hu25K microarrays) were used for this study. Sequences for microarrays were selected from RefSeq (a collection of non-redundant mRNA sequences, located on the Internet at nlm.nih.gov/LocusLink/refseq.html) and Phil Green EST contigs, which is a collection of EST contigs assembled by Dr. Phil Green et al at the University of Washington (Ewing and Green, Nat. Genet. 25(2):232-4 (2000)), available on the Internet at phrap.org/est_assembly/index.html. Each mRNA or EST contig was represented on Hu25K microarray by a single 60mer oligonucleotide essentially as described in Hughes et al., Nature Biotech. 19(4):342-347 and in International Publication WO 01/06013, published Jan. 25, 2001, and in International Publication WO 01/05935, published Jan. 25, 2001, except that the rules for oligo screening were modified to remove oligonucleotides with more than 30% C or with 6 or more contiguous C residues.

Example 1

Differentially Regulated Gene Sets and Overall Expression Patterns of Breast Cancer Tumors

[0234] Of the approximately 25,000 sequences represented on the microarray, a group of approximately 5,000 genes that were significantly regulated across the group of samples was selected. A gene was determined to be significantly differentially regulated with cancer of the breast if it showed more than two-fold of transcript changes as compared to a sporadic tumor pool, and if the p-value for differential regulation (Hughes et al., Cell 102:109-126 (2000)) was less than 0.01 either upwards or downwards in at least five out of 98 tumor samples.

[0235] An unsupervised clustering algorithm allowed us to cluster patients based on their similarities measured over this set of −5,000 significant genes. The similarity between two patients x and y is defined as 3$\begin{array}{cc}\begin{array}{c}S=1-[\sum _{i=1}^{N_V}\frac{\left(x_i-\stackrel{\_}{x}\right)}{{\sigma }_{x_i}}\frac{\left(y_i-\stackrel{\_}{y}\right)}{{\sigma }_{y_i}}/\\ \sqrt{\sum _{i=1}^{N_V}{\left(\frac{x_i-\stackrel{\_}{x}}{{\sigma }_{x_i}}\right)}^2\sum _{i=1}^{N_V}{\left(\frac{y_i-\stackrel{\_}{y}}{{\sigma }_{y_i}}\right)}^2}]\end{array}& \mathrm{Equation}\left(5\right)\end{array}$

[0236] In Equation (5), X and Y are two patients with components of log ratio xi and yi, i=1, . . . , N=5,100. Associated with every value xi is error σxi. The smaller the value σxi, the more reliable the measurement 4$\stackrel{\_}{x}=\sum _{i=1}^{N_V}\frac{x_i}{{\sigma }_{x_i}^2}/\sum _{i=1}^{N_V}\frac{1}{{\sigma }_{x_i}^2}$

[0237] is the error-weighted arithmetic mean. The use of correlation as similarity metric emphasizes the importance of co-regulation in clustering rather than the amplitude of regulations.

[0238] The set of approximately 5,000 genes can be clustered based on their similarities measured over the group of 98 tumor samples. The similarity between two genes was defined in the same way as in Equation (1) except that now for each gene, there are 98 components of log ratio measurements.

[0239] The result of such a two-dimensional clustering is displayed in FIG. 3. Two distinctive patterns emerge from the clustering. The first pattern consists of a group of patients in the lower part of the plot whose regulations are very different from the sporadic pool. The other pattern is made of a group of patients in the upper part of the plot whose expressions are only moderately regulated in comparison with the sporadic pool. These dominant patterns suggest that the tumors can be unambiguously divided into two distinct types based on this set of 5,000 significant genes.

[0240] To help understand these patterns, they were associated with estrogen-receptor (ER), proestrogen receptor (PR), tumor grade, presence of lymphocytic infiltrate, 2 and angioinvasion (FIG. 3). The lower group in FIG. 3, which features the dominant pattern, consists of 36 patients. Of the 39 ER-negative patients, 34 patients are clustered together in this group. From FIG. 4, it was observed that the expression of estrogen receptor alpha gene ESR1 and a large group of co-regulated genes are consistent with this expression pattern.

[0241] From FIG. 3 and FIG. 4, it was concluded that gene expression patterns can be used to classify tumor samples into subgroups of diagnostic interest. Thus, genes co-regulated across 98 tumor samples contain information about the molecular basis of breast cancers. The combination of clinical data and microarray measured gene abundance of ESR1 demonstrates that the distinct types are related to, or at least are reported by, the ER status.

Example 2

Identification of Genetic Markers Distinguishing Estrogen Receptor (+) From Estrogen Receptor (−) Patients

[0242] The results described in this Example allow the identification of expression marker genes that differentiate two major types of tumor cells: “ER-negative” group and “ER-positive” group. The differentiation of samples by ER(+) status was accomplished in our steps: (1) identification of a set of candidate marker genes that correlate with ER level; 2) rank-ordering these candidate genes by strength of correlation; (3) optimization of the umber of marker genes; and (4) classifying samples based on these marker genes.

[0243] 1. Selection of Candidate Discriminating Genes

[0244] In the first step, a set of candidate discriminating genes was identified based on gene expression data of training samples. Specifically, we calculated the correlation coefficients ρ between the category numbers or ER level and logarithmic expression ratio {right arrow over (r)} across all the samples for each individual gene:

ρ=({right arrow over (c)}•{right arrow over (r)})/(∥{right arrow over (c)}∥·∥{right arrow over (r)}∥)  Equation (2)

[0245] The histogram of resultant correlation coefficients is shown in FIG. 5A as a gray line. While the amplitude of correlation or anti-correlation is small for the majority of genes, the amplitude for some genes is as great as 0.5. Genes whose expression ratios either correlate or anti-correlate well with the diagnostic category of interest are used as reporter genes for the category.

[0246] Genes having a correlation coefficient larger than 0.3 (“correlated genes”) or less than −0.3 (“anti-correlated genes”) were selected as reporter genes. The threshold of 0.3 was selected based on the correlation distribution for cases where there is no real correlation (one can use permutations to determine this distribution). Statistically, this distribution width depends upon the number of samples used in the correlation calculation. The distribution width for control cases (no real correlation) is approximately 1/{square root}{square root over (n−3)}, where n=the number of samples. In our case, n=98. Therefore, a threshold of 0.3 roughly corresponds to 3−σ in the distribution (3×1/{square root}{square root over (n−3)}).

[0247] 2,460 such genes were found to satisfy this criterion. In order to evaluate the significance of the correlation coefficient of each gene with the ER level, a bootstrap technique was used to generate Monte-Carlo data that randomize the association between gene expression data of the samples and their categories. The distribution of correlation coefficients obtained from one Monte-Carlo trial is shown as a dashed line in FIG. 5A. To estimate the significance of the 2,460 marker genes as a group, 10,000 Monte-Carlo runs were generated. The collection of 10,000 such Monte-Carlo trials forms the null hypothesis. The number of genes that satisfy the same criterion for Monte-Carlo data varies from run to run. The frequency distribution from 10,000 Monte-Carlo runs of the number of genes having correlation coefficients of >0.3 or <-0.3 is displayed in FIG. 5B. Both the mean and maximum value are much smaller than 2,460. Therefore, the significance of this gene group as the discriminating gene set between ER(+) and ER(−) samples is estimated to be greater than 99.99%.

[0248] 2. Rank-Ordering of Candidate Discriminating Genes

[0249] In the second step, genes on the candidate list were rank-ordered based on the significance of each gene as a discriminating gene. The markers were rank-ordered either by amplitude of correlation, or by using a metric similar to a Fisher statistic:

t =(x1−x2)/{square root}{square root over ([σ12(n1−1)+σ22(n2−1)]/(n1+n2−1)/(1/n1+1/n2))}  Equation (3)

[0250] In Equation (3), x1 is the error-weighted average of log ratio within the ER(−), and x2 is the error-weighted average of log ratio within the ER(+) group. σ1 is the variance of log ratio within the ER(−) group and n1 is the number of samples that had valid measurements of log ratios. σ2 is the variance of log ratio within the ER(+) group and n2 is the number of samples that had valid measurements of log ratios. The t-value in Equation (3) represents the variance-compensated difference between two means. The confidence level of each gene in the candidate list was estimated with respect to a null hypothesis derived from the actual data set using a bootstrap technique; that is, many artificial data sets were generated by randomizing the association between the clinical data and the gene expression data.

[0251] 3. Optimization of the Number of Marker Genes

[0252] The leave-one-out method was used for cross validation in order to optimize the discriminating genes. For a set of marker genes from the rank-ordered candidate list, a classifier was trained with 97 samples, and was used to predict the status of the remaining sample. The procedure was repeated for each of the samples in the pool, and the number of cases where the prediction for the one left out is wrong or correct was counted.

[0253] The above performance evaluation from leave-one-out cross validation was repeated by successively adding more marker genes from the candidate list. The performance as a function of the number of marker genes is shown in FIG. 6. The error rates for type 1 and type 2 errors varied with the number of marker genes used, but were both minimal while the number of the marker genes is around 550. Therefore, we consider this set of 550 genes is considered the optimal set of marker genes that can be used to classify breast cancer tumors into “ER-negative” group and “ER-positive” group. FIG. 7 shows the classification of patients as ER(+) or ER(−) based on this 550 marker set. FIG. 8 shows the correlation of each tumor to the ER-negative template versus the correlation of each tumor to the ER-positive template.

[0254] 4. Classification Based on Marker Genes

[0255] In the third step, a set of classifier parameters was calculated for each type of training data set based on either of the above ranking methods. A template for the ER(−) group ({right arrow over (z)}1) was generated using the error-weighted log ratio average of the selected group of genes. Similarly, a template for ER(+) group (called {right arrow over (z)}2) was generated using the error-weighted log ratio average of the selected group of genes. Two classifier parameters (P1 and P2) were defined based on either correlation or distance. P measures the similarity between one sample {right arrow over (y)} and the ER(−) template {right arrow over (z)}1 over this selected group of genes. P2 measures the similarity between one sample {right arrow over (y)} and the ER(+) template {right arrow over (z)}2 over this selected group of genes. The correlation Pi is defined as:

P i =({right arrow over (z)}i•{right arrow over (y)})/(∥{right arrow over (z)}i∥·∥{right arrow over (y)}∥)  Equation (1)

[0256] A “leave-one-out” method was used to cross-validate the classifier built based on the marker genes. In this method, one sample was reserved for cross validation each time the classifier was trained. For the set of 550 optimal marker genes, the classifier was trained with 97 of the 98 samples, and the status of the remaining sample was predicted. This procedure was performed with each of the 98 patients. The number of cases where the prediction was wrong or correct was counted. It was further determined that subsets of as few as 50 of the 2,460 genes are able classify tumors as ER(+) or ER(−) nearly as well as using the total set.

[0257] In a small number of cases, there was disagreement between classification by the 550 marker set and a clinical classification. In comparing the microarray measured log ratio of expression for ESR1 to the clinical binary decision (negative or positive) of ER status for each patient, it was seen that the measured expression is consistent with the qualitative category of clinical measurements (mixture of two methods) for the majority of tumors. For example, two patients who were clinically diagnosed as ER(+) actually exhibited low expression of ESR1 from microarray measurements and were classified as ER negative by 550 marker genes. Additionally, 3 patients who were clinically diagnosed as ER(−) exhibited high expression of ESR1 from microarray measurements and were classified as ER(+) by the same 550 marker genes. Statistically, however, microarray measured gene expression of ESR 1 correlates with the dominant pattens better than clinically determined ER status.

Example 3

Identification of Genetic Markers Distinguishing BRCA1 Tumors From Sporadic Tumors in Estrogen Receptor (−) Patients

[0258] The BRCA1 mutation is one of the major clinical categories in breast cancer tumors. It was determined that of tumors of 38 patients in the ER(−) group, 17 exhibited the BRCA1 mutation, while 21 were sporadic tumors. A method was therefore developed that enabled the differentiation of the 17 BRCA1 mutation tumors from the 21 sporadic tumors in the ER(−) group.

[0259] 1. Selection of candidate discriminating genes In the first step, a set of candidate genes was identified based on the gene expression patterns of these 38 samples. We first calculated the correlation between the BRCA1-mutation category number and the expression ratio across all 38 samples for each individual gene by Equation (2). The distribution of the correlation coefficients is shown as a histogram defined by the solid line in FIG. 9A. We observed that, while the majority of genes do not correlate with BRCA1 mutation status, a small group of genes correlated at significant levels. It is likely that genes with larger correlation coefficients would serve as reporters for discriminating tumors of BRCA1 mutation carriers from sporadic tumors within the ER(−) group.

[0260] In order to evaluate the significance of each correlation coefficient with respect to a null hypothesis that such correlation coefficient could be found by chance, a bootstrap technique was used to generate Monte-Carlo data that randomizes the association between gene expression data of the samples and their categories. 10,000 such Monte-Carlo runs were generated as a control in order to estimate the significance of the marker genes as a group. A threshold of 0.35 in the absolute amplitude of correlation coefficients (either correlation or anti-correlation) was applied both to the real data and the Monte-Carlo data. Following this method, 430 genes were found to satisfy this criterion for the experimental data. The p-value of the significance, as measured against the 10,000 Monte-Carlo trials, is approximately 0.0048 (FIG. 9B). That is, the probability that this set of 430 genes contained useful information about BRCA1-like tumors vs sporadic tumors exceeds 99%.

[0261] 2. Rank-Ordering of Candidate Discriminating Genes

[0262] In the second step, genes on the candidate list were rank-ordered based on the significance of each gene as a discriminating gene. Here, we used the absolute amplitude of correlation coefficients to rank order the marker genes.

[0263] 3 Optimization of Discriminating Genes

[0264] In the third step, a subset of genes from the top of this rank-ordered list was used for classification. We defined a BRCA1 group template (called {right arrow over (z)}1) by using the error-weighted log ratio average of the selected group of genes. Similarly, we defined a non-BRCA1 group template (called {right arrow over (z)}2) by using the error-weighted log ratio average of the selected group of genes. Two classifier parameters (P1 and P2) were defined based on either correlation or distance. P1 measures the similarity between one sample Y and the BRCA1 template {right arrow over (z)}1 over this selected group of genes. P2 measures the similarity between one sample {right arrow over (y)} and the non-BRCA1 template {right arrow over (z)}2 over this selected group of genes. For correlation, P1 and P2 were defined in the same way as in Equation (4).

[0265] The leave-one-out method was used for cross validation in order to optimize the discriminating genes as described in Example 2. For a set of marker genes from the rank-ordered candidate list, the classifier was trained with 37 samples the remaining one was predicted. The procedure was repeated for all the samples in the pool, and the number of cases where the prediction for the one left out is wrong or correct was counted.

[0266] To determine the number of markers constituting a viable subset, the above performance evaluation from leave-one-out cross validation was repeated by cumulatively adding more marker genes from the candidate list. The performance as a function of the umber of marker genes is shown in FIG. 10. The error rates for type 1 (false negative) and type 2 (false positive) errors (Bendat & Piersol, RANDOM DATA ANALYSIS AND MEASUREMENT PROCEDURES, 2D ED., Wiley Interscience, p. 89) reached optimal ranges when the number of the marker genes is approximately 100. Therefore, a set of about 100 genes is considered to be the optimal set of marker genes that can be used to classify tumors in the ER(−) group as either BRCA1-related tumors or sporadic tumors.

[0267] The classification results using the optimal 100 genes are shown in FIGS. 11A and 11B. As shown in FIG. 11A, the co-regulation patterns of the sporadic patients differ from those of the BRCA1 patients primarily in the amplitude of regulation. Only one sporadic tumor was classified into the BRCA1 group. Patients in the sporadic group are not necessarily BRCA1 mutation negative; however, it is estimated that only approximately 5% of sporadic tumors are indeed BRCA1-mutation carriers.

Example 4

Identification of Genetic Markers Distinguishing Sporadic Tumor Patients with >5 Year Versus <5 Year Survival Times

[0268] 78 tumors from sporadic breast cancer patients were used to explore prognostic predictors from gene expression data. Of the 78 samples in this sporadic breast cancer group, 44 samples were known clinically to have had no distant metastases within 5 years since the initial diagnosis (“no distant metastases group”) and 34 samples had distant metastases within 5 years since the initial diagnosis (“distant metastases group”). A group of 231 markers, and optimally a group of 70 markers, was identified that allowed differentiation between these two groups.

[0269] 1. Selection of Candidate Discriminating Genes

[0270] In the first step, a set of candidate discriminating genes was identified based on gene expression data of these 78 samples. The correlation between the prognostic category number (distant metastases vs no distant metastases) and the logarithmic expression ratio across all samples for each individual gene was calculated using Equation (2). The distribution of the correlation coefficients is shown as a solid line in FIG. 12A. FIG. 12A also shows the result of one Monte-Carlo run as a dashed line. We observe that even though the majority of genes do not correlate with the prognostic categories, a small group of genes do correlate. It is likely that genes with larger correlation coefficients would be more useful as reporters for the prognosis of interest—distant metastases group and no distant metastases group.

[0271] In order to evaluate the significance of each correlation coefficient with respect to a null hypothesis that such correlation coefficient can be found by chance, we used a bootstrap technique to generate data from 10,000 Monte-Carlo runs as a control (FIG. 12B). We then selected genes that either have the correlation coefficient larger than 0.3 (“correlated genes”) or less than −0.3 (“anti-correlated genes”). The same selection criterion was applied both to the real data and the Monte-Carlo data. Using this comparison, 231 markers from the experimental data were identified that satisfy this criterion. The probability of this gene set for discriminating patients between the distant metastases group and the no distant metastases group being chosen by random fluctuation is approximately 0.003.

[0272] 2. Rank-Ordering of Candidate Discriminating Genes

[0273] In the second step, genes on the candidate list were rank-ordered based on the significance of each gene as a discriminating gene. Specifically, a metric similar to a “Fisher” statistic, defined in Equation (3), was used for the purpose of rank ordering. The confidence level of each gene in the candidate list was estimated with respect to a null hypothesis derived from the actual data set using the bootstrap technique. Genes in the candidate list can also be ranked by the amplitude of correlation coefficients.

[0274] 3. Optimization of Discriminating Genes

[0275] In the third step, a subset of 5 genes from the top of this rank-ordered list was selected to use as discriminating genes to classify 78 tumors into a “distant metastases group” or a “no distant metastases group”. The leave-one-out method was used for cross validation. Specifically, 77 samples defined a classifier based on the set of selected discriminating genes, and these were used to predict the remaining sample. This procedure was repeated so that each of the 78 samples was predicted. The number of cases in which predictions were correct or incorrect were counted. The performance of the classifier was measured by the error rates of type 1 and type 2 for this selected gene set.

[0276] We repeated the above performance evaluation procedure, adding 5 more marker genes each time from the top of the candidate list, until all 231 genes were used. As shown in FIG. 13, the number of mis-predictions of type 1 and type 2 errors change dramatically with the number of marker genes employed. The combined error rate reached a minimum when 70 marker genes from the top of our candidate list were used. Therefore, this set of 70 genes is the optimal, preferred set of marker genes useful for the classification of sporadic tumor patients into either the distant metastases or no distant metastases group. Fewer or more markers also act as predictors, but are less efficient, either because of higher error rates, or the introduction of statistical noise.

[0277] 4. Reoccurrence Probability Curves

[0278] The prognostic classification of 78 patients with sporadic breast cancer tumors into two distinct subgroups was predicted based on their expression of the 70 optimal marker genes (FIGS. 14 and 15).

[0279] To evaluate the prognostic classification of sporadic patients, we predicted the outcome of each patient by a classifier trained by the remaining 77 patients based on the 70 optimal marker genes. FIG. 16 plots the distant metastases probability as a function of the time since initial diagnosis for the two predicted groups. The difference between these two reoccurrence curves is significant. Using the %2 test (S-PLUS 2000 Guide to Statistics, vol. 2, MathSoft, p. 44), the p-value is estimated to be 10-9. The distant metastases probability as a function of the time since initial diagnosis was also compared between ER(+) and ER(−) individuals (FIG. 17), PR(+) and PR(−) individuals (FIG. 18), and between individuals with different tumor grades (FIGS. 19A, 19B). In comparison, the p-values for the differences between two prognostic groups based on clinical data are much less significant than that based on gene expression data, ranging from 10−3 to 1.

[0280] To parameterize the reoccurrence probability as a function of time since initial diagnosis, the curve was fitted to one type of survival model—“normal”:

P=α×exp (−t2/τ2)  (4)

[0281] For fixed α=1, we found that τ=125 months for patients in the no distant metastases group and τ=36 months for patients in the distant metastases group. Using tumor grades, we found τ=100 months for patients with tumor grades 1 and 2 and τ=60 for patients with tumor grade 3. It is accepted clinical practice that tumor grades are the best available prognostic predictor. However, the difference between the two prognostic groups classified based on 70 marker genes is much more significant than those classified by the best available clinical information.

[0282] 5. Prognostic Prediction for 19 Independent Sporadic Tumors

[0283] To confirm the proposed prognostic classification method and to ensure the reproducibility, robustness, and predicting power of the 70 optimal prognostic marker genes, we applied the same classifier to 19 independent tumor samples from sporadic breast cancer patients, prepared separately at The Netherlands Cancer Institute (NKI). The same reference pool was used.

[0284] The classification results of 19 independent sporadic tumors are shown in FIG. 20. FIG. 20A shows the log ratio of expression regulation of the same 70 optimum marker genes. Based on our classifier model, we expected the misclassification of 19*(6+7)/78=3.2 tumors. Consistently, (1+3)=4 of 19 tumors were misclassified.

[0285] 6. Clinical Parameters as a Group vs. Microarray Data—Results of Logistic Regression

[0286] In the previous section, the predictive power of each individual clinical parameter was compared with that of the expression data. However, it is more meaningful to combine all the clinical parameters as a group, and then compare them to the expression data. This requires multi-variant modeling; the method chosen was logistic regression. Such an approach also demonstrates how much improvement the microarray approach adds to the results of the clinical data.

[0287] The clinical parameters used for the multi-variant modeling were: (1) tumor grade; (2) ER status; (3) presence or absence of the progestogen receptor (PR); (4) tumor size; (5) patient age; and (6) presence or absence of angioinvasion. For the microarray data, two correlation coefficients were used. One is the correlation to the mean of the good prognosis group (C1) and the other is the correlation to the mean of the bad prognosis group (C2). When calculating the correlation coefficients for a given patient, this patient is excluded from either of the two means.

[0288] The logistic regression optimizes the coefficient of each input parameter to best predict the outcome of each patient. One way to judge the predictive power of each input parameter is by how much deviance (similar to Chi-square in the linear regression, see for example, Hasomer & Lemeshow, APPLIED LOGISTIC REGRESSION, John Wiley & Sons, (2000)) the parameter accounts for. The best predictor should account for most of the deviance. To fairly assess the predictive power, each parameter was modeled independently. The microarray parameters explain most of the deviance, and hence are powerful predictors.

[0289] The clinical parameters, and the two microarray parameters, were then monitored as a group. The total deviance explained by the six clinical parameters was 31.5, and total deviance explained by the microarray parameters was 39.4. However, when the clinical data was modeled first, and the two microarray parameters added, the final deviance accounted for is 57.0.

[0290] The logistic regression computes the likelihood that a patient belongs to the good or poor prognostic group. FIGS. 21A and 21B show the sensitivity vs. (1-specificity). The plots were generated by varying the threshold on the model predicted likelihood. The curve which goes through the top left corner is the best (high sensitivity with high specificity). The microarray outperformed the clinical data by a large margin. For example, at a fixed sensitivity of around 80%, the specificity was ˜80% from the microarray data, and ˜65% from the clinical data for the good prognosis group. For the poor prognosis group, the corresponding specificities were ˜80% and ˜70%, again at a fixed sensitivity of 80%. Combining the microarray data with the clinical data further improved the results. The result can also be displayed as the total error rate as the function of the threshold in FIG. 21C. At all possible thresholds, the error rate from the microarray was always smaller than that from the clinical data. By adding the microarray data to the clinical data, the error rate is further reduced, as one can see in FIG. 21C.

[0291] Odds ratio tables can be created from the prediction of the logistic regression. The probability of a patient being in the good prognosis group is calculated by the logistic regression based on different combinations of input parameters (clinical and/or microarray). Patients are divided into the following four groups according to the prediction and the true outcome: (1) predicted good and truly good, (2) predicted good but truly poor, (3) predicted poor but truly good, (4) predicted poor and truly poor. Groups (1) & (4) represent correct predictions, while groups (2) & (3) represent mis-predictions. The division for the prediction is set at probability of 50%, although other thresholds can be used. The results are listed in Table 8. It is clear from Table 8 that microarray profiling (Table 8.3 & 8.10) outperforms any single clinical data (Table 8.4-8.9) and the combination of the clinical data (Table 8.2). Adding the micro-array profiling in addition to the clinical data give the best results (Table 8.1).

[0292] For microarray profiling, one can also make a similar table (Table 8.11) without using logistic regression. In this case, the prediction was simply based on C1-C2 (greater than 0 means good prognosis, less than 0 mean poor prognosis).

TABLE 8.1
Prediction by clinical + microarray
	Predicted good	Predicted poor
true good	39	5
true poor	4	30

[0293]

TABLE 8.2
Prediction by clinical alone
	Predicted good	Predicted poor
	true good	34	10
	true poor	12	22

[0294]

TABLE 8.3
Prediction by microarray
	predicted good	Predicted poor
	true good	39	5
	true poor	10	24

[0295]

TABLE 8.4
Prediction by grade
	Predicted good	Predicted poor
	true good	23	21
	true poor	5	29

[0296]

TABLE 8.5
Prediction by ER
	Predicted good	Predicted poor
	true good	35	9
	true poor	21	13

[0297]

TABLE 8.6
Prediction by PR
	Predicted good	Predicted poor
	true good	35	9
	true poor	18	16

[0298]

TABLE 8.7
Prediction by size
	Predicted good	Predicted poor
	true good	35	9
	true poor	13	21

[0299]

TABLE 8.8
Prediction by age
	Predicted good	Predicted poor
	true good	33	11
	true poor	15	19

[0300]

TABLE 8.9
Prediction by angioinvasion
	Predicted good	Predicted poor
	true good	37	7
	true poor	19	15

[0301]

TABLE 8.10
Prediction by dC (C1-C2)
	Predicted good	Predicted poor
	true good	36	8
	true poor	6	28

[0302]

TABLE 8.11
No logistic regression, simply
judged by C1-C2
	Predicted good	Predicted poor
	true good	37	7
	true poor	6	28

Example 5

Concept of Mini-Array for Diagnosis Purposes

[0303] All genes on the marker gene list for the purpose of diagnosis and prognosis can be synthesized on a small-scale microarray using ink-jet technology. A microarray with genes for diagnosis and prognosis can respectively or collectively be made. Each gene on the list is represented by single or multiple oligonucleotide probes, depending on its sequence uniqueness across the genome. This custom designed mini-array, in combination with sample preparation protocol, can be used as a diagnostic/prognostic kit in clinics.

Example 6

Biological Significance of Diagnostic Marker Genes

[0304] The public domain was searched for the available functional annotations for the 430 marker genes for BRCA1 diagnosis in Table 3. The 430 diagnostic genes in Table 3 can be divided into two groups: (1) 196 genes whose expressions are highly expressed in BRCA1-like group; and (2) 234 genes whose expression are highly expressed sporadic group. Of the 196 BRCA1 group genes, 94 are annotated. Of the 234 sporadic group genes, 100 are annotated. The terms “T-cell”, “B-cell” or “immunoglobulin” are involved in 13 of the 94 annotated genes, and in 1 of the 100 annotated genes, respectively. Of 24,479 genes represented on the microarrays, there are 7,586 genes with annotations to date. “T-cell”, B-cell” and “immunoglobulin” are found in 207 of these 7,586 genes. Given this, the p-value of the 13 “T-cell”, “B-cell” or “immunoglobulin” genes in the BRCA1 group is very significant (p-value =1.1×10−6). In comparison, the observation of 1 gene relating to “T-cell”, “B-cell”, or “immunoglobulin” in the sporadic group is not significant (p-value 0.18).

[0305] The observation that BRCA1 patients have highly expressed lymphocyte (T-cell and B-cell) genes agrees with what has been seen from pathology that BRCA1 breast tumor has more frequently associated with high lymphocytic infiltration than sporadic cases (Chappuis et al., 2000, Semin Surg Oncol 18:287-295).

Example 7

Biological Significance of Prognosis Marker Genes

[0306] A search was performed for available functional annotations for the 231 prognosis marker genes (Table 5). The markers fall into two groups: (1) 156 markers whose expressions are highly expressed in poor prognostic group; and (2) 75 genes whose expression are highly expressed in good prognostic group. Of the 156 markers, 72 genes are annotated; of the 75 genes, 28 genes are annotated.

[0307] Twelve of the 72 markers, but none of the 28 markers, are, or are associated with, kinases. In contrast, of the 7,586 genes on the microarray having annotations to date, only 471 involve kinases. On this basis, the p-value that twelve kinase-related markers in the poor prognostic group is significant (p-value =0.001). Kinases are important regulators of intracellular signal transduction pathways mediating cell proliferation, differentiation and apoptosis. Their activity is normally tightly controlled and regulated. Overexpression of certain kinases is well known involving in oncogenesis, such as vascular endothelial growth factor receptor1 (VEGFR1 or FLT1), a tyrosine kinase in the poor prognosis group, which lays a very important role in tumor angiogenesis. Interestingly, vascular endothelial growth factor (VEGF), VEGFR's ligand, is also found in the prognosis group, which means both ligand and receptor are upregulated in poor prognostic individuals by an unknown mechanism.

[0308] Likewise, 16 of the 72 markers, and only two of the 28 markers, are, or are associated with, ATP-binding or GTP-binding proteins. In contrast, of the 7,586 genes on the microarray having annotations to date, only 714 and 153 involve ATP-binding and GTP-binding, respectively. On this basis, the p-value that 16 GTP- or ATP-binding-related markers in the poor prognosis group is significant (p-value 0.001 and 0.0038). Thus, the kinase- and ATP— or GTP-binding-related markers within the 72 markers can be used as prognostic indicators.

[0309] Cancer is characterized by deregulated cell proliferation. On the simplest level, this requires division of the cell or mitosis. By keyword searching, we found “cell division” or “mitosis” included in the annotations of 7 genes respectively in the 72 annotated markers from the 156 poor prognosis markers, but in none for the 28 annotated genes from 75 good prognosis markers. Of the 7,586 microarray markers with annotations, “cell division” is found in 62 annotations and “mitosis” is found in 37 annotations. Based on these findings, the p-value that seven cell division- or mitosis-related markers are found in the poor prognosis group is estimated to be highly significant (p-value=3.5×10−5). In comparison, the absence of cell division- or mitosis-related markers in the good prognosis group is not significant (p-value =0.69). Thus, the seven cell division- or mitosis-related markers may be used as markers for poor prognosis.

Example 8

Construction of an Artificial Reference Pool

[0310] The reference pool for expression profiling in the above Examples was made by using equal amount of cRNAs from each individual patient in the sporadic group. In order to have a reliable, easy-to-made, and large amount of reference pool, a reference pool for breast cancer diagnosis and prognosis can be constructed using synthetic nucleic acid representing, or derived from, each marker gene. Expression of marker genes for individual patient sample is monitored only against the reference pool, not a pool derived from other patients.

[0311] To make the reference pool, 60-mer oligonucleotides are synthesized according to 60-mer ink-jet array probe sequence for each diagnostic/prognostic reporter genes, then double-stranded and cloned into pBluescript SK-vector (Stratagene, La Jolla, Calif.), adjacent to the T7 promoter sequence. Individual clones are isolated, and the sequences of their inserts are verified by DNA sequencing. To generate synthetic RNAs, clones are linearized with EcoRI and a T7 in vitro transcription (IVT) reaction is performed according to the MegaScript kit (Ambion, Austin, Tex.). IVT is followed by DNase treatment of the product. Synthetic RNAs are purified on RNeasy columns (Qiagen, Valencia, Calif.). These synthetic RNAs are transcribed, amplified, labeled, and mixed together to make the reference pool. The abundance of those synthetic RNAs are adjusted to approximate the abundance of the corresponding marker-derived transcripts in the real tumor pool.

Example 9

Use of Single-Channel Data and a Sample Pool Represented by Stored Values

[0312] 1. Creation of a Reference Pool of Stored Values (“Mathematical Sample Pool”)

[0313] The use of ratio-based data used in Examples 1-7, above, requires a physical reference sample. In the above Examples, a pool of sporadic tumor sample was used as the reference. Use of such a reference, while enabling robust prognostic and diagnostic predictions, can be problematic because the pool is typically a limited resource. A classifier method was therefore developed that does not require a physical sample pool, making application of this predictive and diagnostic technique much simpler in clinical applications.

[0314] To test whether single-channel data could be used, the following procedure was developed. First, the single channel intensity data for the 70 optimal genes, described in Example 4, from the 78 sporadic training samples, described in the Materials and Methods, was selected from the sporadic sample vs. tumor pool hybridization data. The 78 samples consisted of 44 samples from patients having a good prognosis and 34 samples from patients having a poor prognosis. Next, the hybridization intensities for these samples were normalized by dividing by the median intensity of all the biological spots on the same microarray. Where multiple microarrays per sample were used, the average was taken across all of the microarrays. A log transform was performed on the intensity data for each of the 70 genes, or for the average intensity for each of the 70 genes where more than one microarray is hybridized, and a mean log intensity for each gene across the 78 sporadic samples was calculated. For each sample, the mean log intensities thus calculated were subtracted from the individual sample log intensity. This figure, the mean subtracted log(intensity) was then treated as the two color log(ratio) for the classifier by substitution into Equation (5). For new samples, the mean log intensity is subtracted in the same manner as noted above, and a mean subtracted log(intensity) calculated.

[0315] The creation of a set of mean log intensities for each gene hybridized creates a “mathematical sample pool” that replaces the quantity-limited “material sample pool.” This mathematical sample pool can then be applied to any sample, including samples in hand and ones to be collected in the future. This “mathematical sample pool” can be updated as more samples become available.

[0316] 2. Results

[0317] To demonstrate that the mathematical sample pool performs a function equivalent to the sample reference pool, the mean-subtracted-log(intensity) (single channel data, relative to the mathematical pool) vs. the log(ratio) (hybridizations, relative to the sample pool) was plotted for the 70 optimal reporter genes across the 78 sporadic samples, as shown in FIG. 22. The ratio and single-channel quantities are highly correlated, indicating both have the capability to report relative changes in gene expression. A classifier was then constructed using the mean-subtracted-log(intensity) following exactly the same procedure as was followed using the ratio data, as in Example 4.

[0318] As shown in FIGS. 23A and 23B, single-channel data was successful at classifying samples based on gene expression patterns. FIG. 23A shows samples grouped according to prognosis using single-channel hybridization data. The white line separates samples from patients classified as having poor prognoses (below) and good prognoses (above). FIG. 23B plots each sample as its expression data correlates with the good (open circles) or poor (filled squares) prognosis classifier parameter. Using the “leave-one-out” cross validation method, the classifier predicted 10 false positives out of 44 samples from patients having a good prognosis, and 6 false negatives out of 34 samples from patients having a poor prognosis, where a poor prognosis is considered a “positive.” This outcome is comparable to the use of the ratio-based classifier, which predicted 7 out of 44, and 6 out of 34, respectively.

[0319] In clinical applications, it is greatly preferable to have few false negatives, which results in fewer under-treated patients. To conform the results to this preference, a classifier was constructed by ranking the patient sample according to its coefficient of correlation to the “good prognosis” template, and choosing a threshold for this correlation coefficient to allow approximately 10% false negatives, i.e., classification of a sample from a patient with poor prognosis as one from a patient with a good prognosis. Out of the 34 poor prognosis samples used herein, this represents a tolerance of 3 out of 34 poor prognosis patients classified incorrectly. This tolerance limit corresponds to a threshold 0.2727 coefficient of correlation to the “good prognosis” template. Results using this threshold are shown in FIGS. 24A and 24B. FIG. 24A shows single-channel hybridization data for samples ranked according to the coefficients of correlation with the good prognosis classifier; samples classified as “good prognosis” lie above the white line, and those classified as “poor prognosis” lie below. FIG. 24B shows a scatterplot of sample correlation coefficients, with three incorrectly classified samples lying to the right of the threshold correlation coefficient value. Using this threshold, the classifier had a false positive rate of 15 out of the 44 good prognosis samples. This result is not very different compared to the error rate of 12 out of 44 for the ratio based classifier.

[0320] In summary, the 70 reporter genes carry robust information about prognosis; the single channel data can predict the tumor outcome almost as well as the ratio based data, while being more convenient in a clinical setting.

Example 10

Comparison of Predictive Power of 70 Optimal Genes to Clinical Predictors and Development of Three Prognosis Categories

[0321] Using inkjet-synthesized oligonucleotide microarrays, we have defined a gene expression profile associated with prognosis in breast cancer. To identify this gene expression profile, tumors of less than 5 cm from lymph node negative patients younger than 55 years were used. Surprisingly, a 70 gene-based classifier outperformed all clinical parameters in predicting distant metastases within 5 years. The odds ratio for metastases of the “poor prognosis” versus “good prognosis” signature group based on the gene expression pattern was estimated to be approximately 15 by a cross-validation procedure. Even though these results were highly encouraging, a limitation of this first study was that these results were derived from and tested on two groups of patients which were selected for outcome: one group of patients who developed distant metastases within 5 years and one group of patients who remained disease-free for at least 5 years.

[0322] To provide a more accurate estimate of the risk of metastases associated with the prognosis signature and to further substantiate that the gene expression profile is a clinically meaningful tool, a cohort of 295 young breast cancer patients including both lymph node negative and positive patients was studied. The findings confirm that the prognosis profile is a more powerful predictor of disease outcome than currently used criteria.

[0323] 1. Breast Tumor Selection Criteria

[0324] A consecutive series of 295 tumors was selected from The Netherlands Cancer Institute (NKI) fresh-frozen tissue bank according to the following patient selection criteria: primary invasive breast carcinoma less than 5 cm at pathologic examination (pTI or pT2); tumor-negative apical axillary lymph node as determined by a negative infraclavicular lymph node biopsy; age at diagnosis 52 years or younger; calendar year of diagnosis 1984-1995; and no prior malignancies. All patients had been treated by modified radical mastectomy or breast conserving surgery, including axillary lymph node dissection, followed by radiotherapy if indicated. The 295 tumor samples included 151 taken from lymph node negative (pathologic examination pN0) patients and 144 lymph node positive (pN+) patients. Ten of the 151 lymph node negative patients and 120 of the 144 lymph node positive patients had received adjuvant systemic therapy consisting of chemotherapy (n=90), hormonal therapy (n=20), or both (n=20). All patients were followed at least annually for a period of at least 5 years. Patient follow-up information was extracted from the NKI Medical Registry. Median follow-up of the 207 patients without metastases as first event was 7.8 years (range: 0.05-18.3) versus 2.7 years (0.3-14.0) for the 88 patients with metastasis as first event during follow-up. For all 295 patients median follow-up is 6.7 years (0.05-18.3). There were no missing data. This study was approved by the Medical Ethical Committee of the Netherlands Cancer Institute.

[0325] Clinicopathological parameters were determined as described in Materials and Methods, above. Estrogen receptor (ER) expression was estimated by hybridization intensity obtained from microarray experiments. Using this assay, it was determined that the cohort of 295 tumor samples includes 69 ER negative (ERα log10 intensity ratio below −0.65 units, corresponding to less than 10% nuclei with positive staining by immunohistochemistry) and 226 ER positive tumors. Histological grade was assessed using the method described by Elston and Ellis, Histopathol. 19(5):403-410 (1991). Vascular invasion was assessed as none (−); minor (1-3 vessels; +/−); major (>3 vessels).

[0326] 2. RNA Isolation and Microarray Expression Profiling

[0327] RNA isolation, cRNA labeling, the 25K oligonucleotide microarrays, and hybridization experiments were as described in Materials and Methods. The statistical error model that assigns p values to expression ratios was as described in Example 4. After hybridization, slides were washed and scanned using a confocal laser scanner (Agilent Technologies) (see Hughes et al., Nat. Biotechnol. 19(4):342-347 (2001)).

[0328] 3. Correlation of the Microarray Data with the Previously Determined Prognosis Profile

[0329] The prognostic value of the gene expression profile in a consecutive series of breast cancer patients was determined using the 70 marker genes identified in the experiments described in Example 4. To acquire this consecutive series, 61 of the pN0 patients that were also part of the training series used for the construction of the 70-gene prognosis profile were also included. Leaving out these patients would have resulted in selection bias, because the first series contained a disproportionally large number of patients who developed distant metastases within 5 years. For each of the 234 new tumors in this 295 tumor sample cohort we calculated the correlation coefficient of the expression of the 70 genes with the previously determined average profile of these genes in tumors of good prognosis patients (C1) (see Example 4). A tumor with a correlation coefficient >0.4 (a threshold previously determined in the training set of 78 tumors that allowed 10% false negatives) was then assigned to the “good prognosis” signature group and all other tumors were assigned to the “poor prognosis” signature group. To avoid overfitting by the 61 previously used pN0 patients, the performance cross-validated correlation coefficients were used for the prognosis classification with a threshold correlation coefficient value of 0.55 (corresponding to the threshold for 10% false negatives of this cross-validated classifier).

[0330] 4. Statistical Analysis

[0331] In the analysis of distant metastasis-free probabilities, patients whose first event was distant metastases were counted as failures; all other patients were censored at the date of their last follow-up, non-breast cancer death, local-regional recurrence or second primary malignancy, including contralateral breast cancer. Time was measured from the date of surgery. Metastasis-free curves were drawn using the method of Kaplan and Meier and compared using the log-rank test. Standard errors (SEs) of the metastasis-free percentages were calculated using the method of Tsiatis (Klein, Scand. J of Statistics 18:333-340 (1991)).

[0332] Proportional hazard regression analysis (Cox, J. R. Statist. Soc. B 34:187-220 (1972)) was used to adjust the association between the correlation coefficient C1 and metastases for other variables. SE's were calculated using the sandwich estimator (Lin and Wei, J. Amer. Stat. Assoc. 84:1074-1079 (1989)). Histological grade, vascular invasion and the number of axillary lymph node metastases (0 vs. 1-3 vs. >4) were used as variables. Linearity of the relation between In (relative hazard) and tumor diameter, age and expression level of ER was tested using the Wald test for non-linear components of restricted cubic splines (Themeau et al., hBiometrika 77:147-160 (1990)). No evidence for non-linearity was found (age: p=0.83, tumor diameter: p=0.75, number of positive nodes: p=0.65 and ER expression level: p=0.27). Non-proportionality of the hazard was tested using the Grambsch and Themeau method (Grambsch and Themeau, Biometrika 81:515-526 (1994)). In addition, for C1 the difference between the ln(hazard ratio) before and after 5 years of follow-up was tested using the Wald test. All calculations were done using the Splus2000 or Splus6 statistical package.

[0333] 5. Prognosis Signature of 295 Breast Cancers

[0334] From each of the 295 tumors, total RNA was isolated and used to generate cRNA, which was labeled and hybridized to microarrays containing 25,000 human genes (see Materials and Methods). Fluorescence intensities of scanned images were quantified and normalized to yield the transcript abundance of a gene as an intensity ratio as compared to a reference pool of cRNA made up of equal amounts of cRNA of all tumors combined. The gene expression ratios of the previously determined 70 prognosis marker genes for all 295 tumors in this study are shown in FIG. 25A. Tumors above (i.e., having a correlation coefficient greater than) the previously determined threshold (dotted line) were assigned to the “good prognosis” category (n=115); those below the line were assigned to the “poor prognosis” category (n=180). FIG. 25B displays the time to distant metastases as a first event (red dots) or the time of follow-up for all other patients (blue dots, see methods). FIG. 25C shows the lymph node status, distant metastases and survival for all 295 patients. By comparing FIGS. 25A, 25B, and 25C, it can be seen that there is a strong correlation between having the good prognosis signature and absence of (early) distant metastases or death. Lymph node negative and positive patients are evenly distributed, indicating that the prognosis profile is independent of lymph node status.

[0335] Table 9 summarizes the association between the prognosis profile and clinical parameters, which reveals that the prognosis profile is associated with histological grade, ER status and age, but not significantly with tumor diameter, vascular invasion, number of positive lymph nodes, or with treatment.

TABLE 9
Association of clinical parameters with the prognosis signature
groups based on the expression of the preferred 70
prognostic marker genes.
	Poor signature	Good signature
	(N = 180: 100%)	(N = 115: 100%)
Variable	P-value*	Category	Number of patients (%)
Age	0.0003	<40	52 (29%)	11 (10%)
		40-44	41 (23%)	44 (38%)
		45-49	55 (31%)	43 (37%)
		≧50	32 (18%)	17 (15%)
Number pve nodes	0.6	0 (pN0)	91 (51%)	60 (52%)
		37258	63 (35%)	43 (37%)
		≧4	26 (14%)	12 (10%)
Tumor diameter	0.012	≦20 mm	84 (47%)	71 (62%)
		≧20 mm	96 (53%)	44 (38%)
Histologic grade	<0.0001	I - Good	19 (11%)	56 (49%)
		II - Intermediate	56 (31%)	45 (39%)
		III - Poor	105 (58%)	14 (12%)
Vascular invasion	0.38	−	108 (60%)	77 (67%)
		+/−	18 (10%)	12 (10%)
		+	54 (30%)	26 (23%)
ER expression	<0.0001	<−0.65	66 (37%)	3 (3%)
		≧−0.65	114 (63%)	112 (97%)
Surgery	0.63	BCT	97 (54%)	64 (56%)
		Mastectomy	83 (46%)	51 (44%)
Chemotherapy	0.79	No	114 (63%)	71 (62%)
		Yes	66 (37%)	44 (38%)
Hormonal therapy	0.63	No	157 (87%)	98 (85%)
		Yes	23 (13%)	17 (15%)
*Poor versus good profile.

[0336] 6. Prognostic Value of Gene Expression Signature

[0337] Distant metastasis-free probability and overall survival were calculated for all patients having tumors with either a “good” or “poor prognosis” signature (FIGS. 26A and 26B, Table 10). The resulting Kaplan-Meier curves showed a large difference in metastasis rate and overall survival between the “good prognosis” and “poor prognosis” signature patients. For metastasis as a first event, the hazard ratio (HR) for “poor” versus “good” signature over the whole follow-up period is estimated to be 5.1 (95% CI: 2.9-9.0; p<0.0001). The prognosis profile was even more significant for the first 5 years (HR 8.8; 95% CI: 3.8-20; p<0.0001) as compared to a HR of 1.8 (95% CI: 0.69-4.5; p=0.24) after 5 years. The HR for overall survival is 8.6 (95% CI: 4-19; p<0.0001).

[0338] The prognosis profile was first identified within a selected group of lymph node negative patients. Here, we wished to determine the performance of the prognostic signatures in both lymph node negative and positive patients. In the series of 151 lymph node negative patients (of the 295 patient cohort), the prognosis profile performed extremely well in predicting outcome of disease (FIGS. 26C, 26D; Table 10). For this group of patients, the HR for developing distant metastases is 5.5 (95% CT 2.5-12.2; p<0.0001). To validate our estimated odds ratio for metastases development within five years of our previous study (cross-validated odds ratio 15 (95% CI 4-56; p<0.0001), we calculated the odds ratio for 67 new pN0 patients, who were selected the same way as before (patients with either distant metastases within five years (n=12), or who remained disease-free with a follow-up for at least 5 years (n=55)). The odds ratio of the prognosis classifier for metastases within five years in this validation set is 15.3 (95% CI 1.9-125, p=0.011) (2×2 table, data not shown), in good agreement with our previous findings. These consistent performance results on two sets of tumors, highlight the value of the prognosis profile and the robustness of the profiling technology. Significantly, in the remaining group of 144 lymph node positive patients the prognosis profile was also strongly associated with outcome (FIGS. 26E, 26F, Table 10). Here, the hazard ratio for developing distant metastases is 4.5 (95% CI 2.0-10.2; p=0.0003).

TABLE 10
Percentages metastasis-free and overall survival
for the prognosis signature groups
	5 year	10 year
	distant	distant
	metastasis	metastasis		10 year
	free§	free§	5 year survival	survival
	% (SE)	% (SE)	% (SE)	% (SE)
All patients
Good signature	94.7%	85.2%	97.4% (1.5%)	94.5% (2.6%)
(n = 115)	(2.1)	(4.3)
Poor signature	60.5%	50.6%	74.1% (3.3%)	54.6% (4.4%)
(n = 180)	(3.8)	(4.5)
pN0 patients
Good signature	93.4%	86.8%	96.7% (2.3%)	96.7% (2.3%)
(n = 60)	(3.2%)	(4.8%)
Poor signature	56.2%	44.1%	71.5% (4.8%)	49.6% (6.1%)
(n = 91)	(5.5%)	(6.3%)
pN+ patients
Good signature	95.2%	82.7%	98.2% (1.8%)	92.0% (4.8%)
(n = 55)	(2.6)	(7.8)
Poor signature	66.3%	56.7%	76.5% (4.6%)	59.5% (6.3%)
(n = 89)	(5.2)	(6.4)
§No distant metastasis as first failure.

[0339] 7. Multivariable Analysis

[0340] Results from the multivariable analysis of distant metastases as first event including age, diameter, number of positive nodes, grade, vascular invasion, ER expression, treatment and the gene expression profile are shown in Table 11. The only independent predictive factors were the 70 gene expression profile, tumor diameter and adjuvant chemotherapy. During the period in which these patients were treated, the majority of premenopausal lymph node positive patients received adjuvant chemotherapy; lymph node negative patients usually did not receive adjuvant treatment. There was improved survival for patients who received adjuvant chemotherapy in this series of tumors. The 70 gene expression profile is by far the strongest predictor for distant metastases with an overall hazard ratio of 4.6 (95% CI: 2.3-9.2; p<0.0001). This is not unexpected, since the prognosis profile was established based on tumors from patients that all developed distant metastases within five years.

TABLE 11
Multivariable proportional hazard analysis for metastasis
as first event of the prognosis profile in combination with
clinicopathological variables.
Variable	Unit	HR	95% CI	P-value
Profile signature	Poor vs. good	4.6	2.3-9.2	<0.0001
Age	per 10 years	0.73	0.50-1.06	0.1
No pve nodes	per pve node	1.13	1.03-1.24	0.011
Diameter	per cm	1.56	1.22-2.0	0.0004
Grade	Gr. 2 vs. Gr. 1	1.35	0.61-3.0	0.54
	Gr. 3 vs. Gr. 1	1.03	0.44-2.4
Vasc. Invasion	± vs −	0.66	0.30-1.44	0.045
	+ vs −	1.65	0.98-2.8
ER expression	per point	0.86	0.56-1.31	0.48
Surgery	Mast. vs. BCT	1.27	0.79-2.0	0.32
Chemotherapy	Yes vs. No	0.37	0.20-0.66	0.0008
Hormone	Yes vs. No	0.62	0.29-1.34	0.23
Treatment

[0341] The prognosis profile is also a strong predictor of developing distant metastases within the group of lymph node positive patients (see FIGS. 26E, 26F). This is remarkable, since the presence of lymph node metastases by itself is a strong predictor of poor survival. Because most patients with lymph node positive breast cancer in our study received adjuvant chemotherapy or adjuvant hormonal therapy (120 out of 144 patients), it is not possible to give the prognostic value of the profile in untreated lymph node positive patients. There is, however, no indication that there is a difference in the prognostic value of the prognosis profile between patients who received adjuvant chemotherapy compared to those who did not (data not shown).

[0342] A key question is whether the prognosis profile is a more useful clinical tool to determine eligibility for adjuvant systemic treatment than the presently used “St. Gallen” and “NIH-consensus” criteria, which are based on histological and clinical characteristics (see Goldhirsch et al., Meeting Highlights: International Consensus Panel on the Treatment of Primary Breast Cancer, Seventh International Conference on Adjuvant Therapy of Primary Breast Cancer, J. Clin. Oncol. 19(18):3817-3827 (2001); Eifel et al., National Institutes of Health Consensus Development Conference Statement: Adjuvant Therapy for Breast Cancer, Nov. 1-3, 2000, J. Natl. Cancer Inst. 93(13):979-989 (2001)). FIG. 27 shows the Kaplan-Meier metastasis-free curves for the 151 lymph node negative patients, where the patients were classified as “good prognosis/low-risk” or “poor prognosis/high-risk” using the prognosis profile (FIG. 27A), the “St. Gallen” (FIG. 27B) or the “NIH-consensus” criteria (FIG. 27C).

[0343] Two major conclusions can be drawn from this comparison. First, the prognosis profile assigns many more pN0 patients to the low-risk group than the traditional methods (38% for “profile”, versus 15% for “St. Gallen” and 7% for “NIH consensus”). Second, low-risk patients identified by expression profiling have better metastasis-free survival than those classified by “St. Gallen” or “NIH consensus” criteria. Conversely, patients classified as high-risk according to their expression profile tend to develop distant metastases more often than the high risk “St. Gallen” or “NIH consensus” patients. This indicates that both “St. Gallen” and “NIH” criteria misclassify a significant number of patients. Indeed, the high-risk group as defined by “NIH consensus” criteria contains a significant number of patients having a “good prognosis” signature and corresponding outcome (FIG. 27E). Conversely, the low-risk NIH group includes patients with a “poor prognosis” signature and outcome (FIG. 27G). Similar subgroups can be identified within the “St. Gallen” low- and high-risk patients (FIGS. 27D; 27F). Since both “St. Gallen” and “NIH” subgroups contain misclassified patients (who can be better identified through the prognosis signature), these patients are either over- or undertreated in present clinical practice.

[0344] Tumor size is a major parameter used in the “NIH-consensus” criteria for adjuvant therapy selection. However, the data above (see Table 9) show that the ability to develop distant metastases is only partially dependent on tumor size and suggest that metastatic capacity in many tumors is an early and inherent genetic property. The “good prognosis groups” can be subdivided into two groups whose treatment regimens differ. The subgroups were determined by using another threshold in the correlation with the average profile of the good prognosis tumors. In the initial study that identified markers correlated with a good prognosis (see Example 4), we found that tumors having a correlation coefficient of greater than 0.636 (i.e., whose expression profiles correlated most strongly with the average expression profile of the “good prognosis” group) did not give rise to distant metastases. This was determined empirically for the 78 patient tumor samples by determining the correlation coefficient, in the ranked list, above which patients developed no distant metastases (data not shown). Thus, among the tumors previously identified as having a “good prognosis” signature, those that had a correlation coefficient exceeding 0.636 were classified as having a “very good prognosis” signature. These patients with such a “very good prognosis” signature in their tumor (FIGS. 28A-28F, upper line) have an even better outcome of disease than those having an “intermediate prognosis” signature (remaining “good prognosis” signature patients, correlation coefficient between 0.4 and 0.636, FIGS. 28A-28F, middle line). This is true for the entire cohort (FIGS. 28A, 28B) as well as the lymph node negative (FIGS. 28C, 28D) and positive patients separately (FIGS. 28E, 28F).

[0345] Together, our data indicate that the prognosis profile is a more accurate tool to select lymph node negative premenopausal patients for adjuvant systemic therapy than the presently used consensus criteria and may even be useful to guide adjuvant therapy in lymph node positive patients. We propose the following treatment regimens based upon the particular marker expression signature:

[0346] (1) Lymph node negative patients having a tumor with a “very good prognosis” signature can be treated without adjuvant systemic therapy.

[0347] (2) Lymph node negative patients having a tumor with an “intermediate prognosis” signature can be treated with adjuvant hormonal therapy only. As 97% of tumors having the “intermediate prognosis” signature are ER positive, this group of patients should benefit from adjuvant hormonal treatment. Adding chemotherapy to the treatment regimen of this patient group would result in only marginal survival benefit.

[0348] (3) All other patients should receive adjuvant chemotherapy. Where the tumor is ER+, hormonal therapy is also recommended.

[0349] Implementation of the use of the prognostic profile in breast cancer diagnostics should result in improved and patient-tailored adjuvant systemic treatment, reducing both over- and undertreatment.

7. REFERENCES CITED

[0350] All references cited herein are incorporated herein by reference in their entirety and for all purposes to the same extent as if each individual publication or patent or patent application was specifically and individually indicated to be incorporated by reference in its entirety for all purposes.

[0351] Many modifications and variations of the present invention can be made without departing from its spirit and scope, as will be apparent to those skilled in the art. The specific embodiments described herein are offered by way of example only, and the invention is to be limited only by the terms of the appended claims along with the full scope of equivalents to which such claims are entitled.

Claims

1. A method for classifying a cell sample as ER(+) or ER(−) comprising detecting a difference in the expression by said cell sample of a first plurality of genes relative to a control, said first plurality of genes consisting of at least 5 of the genes corresponding to the markers listed in Table 1.

2. The method of claim 1, wherein said plurality consists of at least 50 of the genes corresponding to the markers listed in Table 1.

3. The method of claim 1, wherein said plurality consists of at least 100 of the genes corresponding to the markers listed in Table 1.

4. The method of claim 1, wherein said plurality consists of at least 200 of the genes corresponding to the markers listed in Table 1.

5. The method of claim 1, wherein said plurality consists of at least 500 of the genes corresponding to the markers listed in Table 1.

6. The method of claim 1, wherein said plurality consists of at least 1000 of the genes corresponding to the markers listed in Table 1.

7. The method of claim 1, wherein said plurality consists of each of the genes corresponding to the 2,460 markers listed in Table 2.

8. The method of claim 1, wherein said plurality consists of the 550 gene markers listed in Table 2.

9. The method of claim 1, wherein said control comprises nucleic acids derived from a pool of tumors from individual sporadic patients.

10. The method of claim 1, wherein said detecting comprises the steps of: (a) generating an ER(+) template by hybridization of nucleic acids derived from a plurality of ER(+) patients within a plurality of sporadic patients against nucleic acids derived from a pool of tumors from individual sporadic patients; (b) generating an ER(−) template by hybridization of nucleic acids derived from a plurality of ER(−) patients within said plurality of sporadic patients against nucleic acids derived from said pool of tumors from individual sporadic patients within said plurality; (c) hybridizing an nucleic acids derived from an individual sample against said pool; and (d) determining the similarity of marker gene expression in the individual sample to the ER(+) template and the ER(−) template, wherein if said expression is more similar to the ER(+) template, the sample is classified as ER(+), and if said expression is more similar to the ER(−) template, the sample is classified as ER(−).

11. A method for classifying a cell sample as BRA4CA1-related or sporadic, comprising detecting a difference in the expression of a first plurality of genes relative to a control, said first plurality of genes consisting of at least 5 of the genes corresponding to the markers listed in Table 3.

12. The method of claim 11, wherein said plurality consists of at least 50 of the genes corresponding to the markers listed in Table 3.

13. The method of claim 11, wherein said plurality consists of at least 100 of the genes corresponding to the markers listed in Table 3.

14. The method of claim 11, wherein said plurality consists of at least 200 of the genes corresponding to the markers listed in Table 3.

15. The method of claim 11, wherein said plurality consists of each of the genes corresponding to the 430 markers listed in Table 3.

16. The method of claim 11, wherein said plurality consists of each of the genes corresponding to the 100 markers listed in Table 4.

17. The method of claim 11, wherein said control comprises nucleic acids derived from a pool of tumors from individual sporadic patients.

18. The method of claim 11, wherein said detecting comprises the steps of (a) generating a BRCA1 template by hybridization of nucleic acids derived from a plurality of BRCA1 patients within a plurality of ER(−) patients against nucleic acids derived from a pool of tumors; (b) generating a sporadic template by hybridization of nucleic acids derived from a plurality of sporadic patients within said plurality of ER(−) patients against nucleic acids derived from said pool of tumors; (c) hybridizing nucleic acids derived from an individual sample against said pool; and (d) determining the similarity of marker gene expression in the individual sample to the BRCA1 template and the sporadic template, wherein if said expression is more similar to the BRCA1 template, the sample is classified as BRCA1, and if said expression is more similar to the sporadic template, the sample is classified as sporadic.

19. A method for classifying an individual as having a good prognosis (no distant metastases within five years of initial diagnosis) or a poor prognosis (distant metastases within five years of initial diagnosis), comprising detecting a difference in the expression of a first plurality of genes in a cell sample taken from the individual relative to a control, said first plurality of genes consisting of at least 5 of the genes corresponding to the markers listed in Table 5.

20. The method of claim 19, wherein said plurality consists of at least 20 of the genes corresponding to the markers listed in Table 5.

21. The method of claim 19, wherein said plurality consists of at least 100 of the genes corresponding to the markers listed in Table 5.

22. The method of claim 19, wherein said plurality consists of at least 150 of the genes corresponding to the markers listed in Table 5.

23. The method of claim 19, wherein said plurality consists of each of the genes corresponding to the 231 markers listed in Table 5.

24. The method of claim 19, wherein said plurality consists of the 70 gene markers listed in Table 6.

25. The method of claim 1, wherein said control comprises nucleic acids derived from a pool of tumors from individual sporadic patients.

26. The method of claim 19, wherein said detecting comprises the steps of: (a) generating a good prognosis template by hybridization of nucleic acids derived from a plurality of good prognosis patients against nucleic acids derived from a pool of tumors from individual patients; (b) generating a poor prognosis template by hybridization of nucleic acids derived from a plurality of poor prognosis patients against nucleic acids derived from said pool of tumors from said plurality of individual patients; (c) hybridizing an nucleic acids derived from and individual sample against said pool; and (d) determining the similarity of marker gene expression in the individual sample to the good prognosis template and the poor prognosis template, wherein if said expression is more similar to the good prognosis template, the sample is classified as having a good prognosis, and if said expression is more similar to the poor prognosis template, the sample is classified as having a poor prognosis.

27. The method of claim 1, wherein the cell sample is additionally classified as BRCA1-related or sporadic by detecting a difference in the expression of a second plurality of genes in a cell sample taken from the individual relative to a control, said second plurality of genes consisting of at least 5 of the genes corresponding to the markers listed in Table 3 or Table 4.

28. The method of claim 1, wherein the cell sample is additionally classified as taken from a patient with a good prognosis or a poor prognosis by detecting a difference in the expression of a second plurality of genes in a cell sample taken from the individual relative to a control, said second plurality of genes consisting of at least 5 of the genes corresponding to the markers listed in Table 5.

29. The method of claim 11, wherein the cell sample is additionally classified as taken from a patient with a good prognosis or a poor prognosis by detecting a difference in the expression of a second plurality of genes in a cell sample taken from the individual relative to a control, said second plurality of genes consisting of at least 20 of the genes corresponding to the markers listed in Table 5.

30. The method of claim 11, wherein the cell sample is additionally classified as ER(+) or ER(−) by detecting a difference in the expression of a second plurality of genes in a cell sample taken from the individual relative to a control, said second plurality of genes consisting of at least 5 of the genes corresponding to the markers listed in Table 1.

31. The method of claim 19, wherein the cell sample is additionally classified as ER(+) or ER(−) by detecting a difference in the expression of a second plurality of genes in a cell sample taken from the individual relative to a control, said second plurality of genes consisting of at least 5 of the genes corresponding to the markers listed in Table 1.

32. The method of claim 19, wherein the cell sample is additionally classified as BRCA1 or sporadic by detecting a difference in the expression of a second plurality of genes in a cell sample taken from the individual relative to a control, said second plurality of genes consisting of at least 5 of the genes corresponding to the markers listed in Table 3.

33. A method for classifying a sample as ER(+) or ER(−) by calculating the similarity between the expression of at least 5 of the markers listed in Table 1 in the sample to the expression of the same markers in an ER(−) nucleic acid pool and an ER(+) nucleic acid pool, comprising the steps of: (a) labeling nucleic acids derived from a sample, with a first fluorophore to obtain a first pool of fluorophore-labeled nucleic acids; (b) labeling with a second fluorophore a first pool of nucleic acids derived from two or more ER(+) samples, and a second pool of nucleic acids derived from two or more ER(−) samples: (c) contacting said first fluorophore-labeled nucleic acid and said first pool of second fluorophore-labeled nucleic acid with a first microarray under conditions such that hybridization can occur, and contacting said first fluorophore-labeled nucleic acid and said second pool of second fluorophore-labeled nucleic acid with a second microarray under conditions such that hybridization can occur, wherein said first microarray and said second microarray are similar to each other, exact replicas of each other, or are identical, detecting at each of a plurality of discrete loci on the first microarray a first flourescent emission signal from said first fluorophore-labeled nucleic acid and a second fluorescent emission signal from said first pool of second fluorophore-labeled genetic matter that is bound to said first microarray under said conditions, and detecting at each of the marker loci on said second microarray said first fluorescent emission signal from said first fluorophore-labeled nucleic acid and a third fluorescent emission signal from said second pool of second fluorophore-labeled nucleic acid; (d) determining the similarity of the sample to the ER(−) and ER(+) pools by comparing said first fluorescence emission signals and said second fluorescence emission signals, and said first emission signals and said third fluorescence emission signals; and (e) classifying the sample as ER(+) where the first fluorescence emission signals are more similar to said second fluorescence emission signals than to said third fluorescent emission signals, and classifying the sample as ER(−) where the first fluorescence emission signals are more similar to said third fluorescence emission signals than to said second fluorescent emission signals.

34. The method of claim 33, wherein said similarity is calculated by determining a first sum of the differences of expression levels for each marker between said first fluorophore-labeled nucleic acid and said first pool of second fluorophore-labeled nucleic acid, and a second sum of the differences of expression levels for each marker between said first fluorophore-labeled nucleic acid and said second pool of second fluorophore-labeled nucleic acid, wherein if said first sum is greater than said second sum, the sample is classified as ER(−), and if said second sum is greater than said first sum, the sample is classified as ER(+).

35. The method of claim 33, wherein said similarity is calculated by computing a first classifier parameter P1 between an ER(+) template and the expression of said markers in said sample, and a second classifier parameter P2 between an ER(−) template and the expression of said markers in said sample, wherein said P1 and P2 are calculated according to the formula:

36. A method for determining a set of marker genes whose expression is associated with a particular phenotype, comprising the steps of: (a) selecting phenotype having two or more phenotype categories; (b) identifying a plurality of genes wherein the expression of said genes is correlated or anticorrelated with one of the phenotype categories, and wherein the correlation coefficient for each gene is calculated according to the equation ρ=({right arrow over (c)}•{right arrow over (r)})/(∥{right arrow over (c)}∥·{right arrow over (r)}∥), wherein C is a number representing said phenotype category and {right arrow over (r)} is the logarithmic expression ratio across all the samples for each individual gene, wherein if the correlation coefficient has an absolute value of 0.3 or greater, said expression of said gene is associated with the phenotype category, wherein said plurality of genes is a set of marker genes whose expression is associated with a particular phenotype.

37. The method of claim 36, wherein said set of marker genes is validated by: (a) using a statistical method to randomize the association between said marker genes and said phenotype category, thereby creating a control correlation coefficient for each marker gene; (b) repeating step (a) one hundred or more times to develop a frequency distribution of said control correlation coefficients for each marker gene; (c) determining the number of marker genes having a control correlation coefficient of 0.3 or above, thereby creating a control marker gene set; and (d) comparing the number of control marker genes so identified to the number of marker genes, wherein if the p value of the difference between the number of marker genes and the number of control genes is less than a threshold, said set of marker genes is validated.

38. The method of claim 36, wherein said set of marker genes is optimized by the method comprising: (a) rank-ordering the genes by amplitude of correlation or by significance of the correlation coefficients to create a rank-ordered list, and (b) selecting an arbitrary number n of marker genes from the top of the rank-ordered list.

39. The method of claim 38, wherein said set of marker genes is further optimized by the method comprising: (a) calculating an error rate for said arbitrary number n of marker genes; (b) increasing by 1 the number of genes selected from the top of the rank-ordered list; (c) calculating an error rate for said number of genes selected from the top of the rank-ordered list; (d) repeating steps (b) and (c) until said number of genes selected from the top of the rank-ordered list includes all genes included in said rank ordered list, and (e) identifying said number of genes selected from the top of the rank-ordered list for which the error rate is smallest, wherein said set of marker genes is optimized when the error rate is the smallest.

40. A method for assigning a person to one of a plurality of categories in a clinical trial, comprising determining for each said person the level of expression of at least five of the prognosis markers listed in Table 6, determining therefrom whether the person has an expression pattern that correlates with a good prognosis or a poor prognosis, and assigning said person to one category in a clinical trial if said person is determined to have a good prognosis, and a different category if that person is determined to have a poor prognosis.

41. A method of classifying a first cell or organism as having one of at least two different phenotypes, said at least two different phenotypes comprising a first phenotype and a second phenotype, said method comprising: (a) comparing the level of expression of each of a plurality of genes in a first sample from the first cell or organism to the level of expression of each of said genes, respectively, in a pooled sample from a plurality of cells or organisms, said plurality of cells or organisms comprising different cells or organisms exhibiting said at least two different phenotypes, respectively, to produce a first compared value; (b) comparing said first compared value to a second compared value, wherein said second compared value is the product of a method comprising comparing the level of expression of each of said genes in a sample from a cell or organism characterized as having said first phenotype to the level of expression of each of said genes, respectively, in said pooled sample; (c) comparing said first compared value to a third compared value, wherein said third compared value is the product of a method comprising comparing the level of expression of each of said genes in a sample from a cell or organism characterized as having said second phenotype to the level of expression of each of said genes, respectively, in said pooled sample, (d) optionally carrying out one or more times a step of comparing said first compared value to one or more additional compared values, respectively, each additional compared value being the product of a method comprising comparing the level of expression of each of said genes in a sample from a cell or organism characterized as having a phenotype different from said first and second phenotypes but included among said at least two different phenotypes, to the level of expression of each of said genes, respectively, in said pooled sample; and (e) determining to which of said second, third and, if present, one or more additional compared values, said first compared value is most similar; wherein said first cell or organism is determined to have the phenotype of the cell or organism used to produce said compared value most similar to said first compared value.

42. The method of claim 40, wherein said compared values are each ratios of the levels of expression of each of said genes.

43. The method of claim 40, wherein each of said levels of expression of each of said genes in said pooled sample are normalized prior to any of said comparing steps.

44. The method of claim 42 wherein normalizing said levels of expression is carried out by dividing each of said levels of expression by the median or mean level of expression of each of said genes or dividing by the mean or median level of expression of one or more housekeeping genes in said pooled sample.

45. The method of claim 42 wherein said normalized levels of expression are subjected to a log transform and said comparing steps comprise subtracting said log transform from the log of said levels of expression of each of said genes in said sample from said cell or organism.

46. The method of claim 40, wherein said at least two different phenotypes are different stages of a disease or disorder.

47. The method of claim 40, wherein said at least two different phenotypes are different prognoses of a disease or disorder.

48. The method of claim 40, wherein said levels of expression of each of said genes, respectively, in said pooled sample or said levels of expression of each of said genes in a sample from said cell or organism characterized as having said first phenotype, said second phenotype, or said phenotype different from said first and second phenotypes, respectively, are stored on a computer.

49. A microarray comprising at least 5 markers derived from any one of Tables 1-6, wherein at least 50% of the probes on the microarray are present in any one of Tables 1-6.

50. The microarray of claim 48, wherein at least 70% of the probes on the microarray are present in any one of Tables 1-6.

51. The microarray of claim 48, wherein at least 80% of the probes on the microarray are present in any one of Tables 1-6.

52. The microarray of claim 48, wherein at least 90% of the probes on the microarray are present in any one of Tables 1-6.

53. The microarray of claim 48, wherein at least 95% of the probes on the microarray are present in any one of Tables 1-6.

54. The microarray of claim 48, wherein at least 98% of the probes on the microarray are present in any one of Tables 1-6.

55. A microarray for distinguishing ER(+) and ER(−) cell samples comprising a positionally-addressable array of polynucleotide probes bound to a support, said polynucleotide probes comprising a plurality of polynucleotide probes of different nucleotide sequences, each of said different nucleotide sequences comprising a sequence complementary and hybridizable to a different gene, said plurality consisting of at least 20 of the genes corresponding to the markers listed in Table 1 or Table 2, wherein at least 50% of the probes on the microarray are present in Table 1 or Table 2.

56. A microarray for distinguishing BRCA1-related and sporadic cell samples comprising a positionally-addressable array of polynucleotide probes bound to a support, said polynucleotide probes comprising a plurality of polynucleotide probes of different nucleotide sequences, each of said different nucleotide sequences comprising a sequence complementary and hybridizable to a different gene, said plurality consisting of at least 20 of the genes corresponding to the markers listed in Table 3 or Table 4, wherein at least 50% of the probes on the microarray are present in Table 3 or Table 4.

57. A microarray for distinguishing cell samples from individuals having a good prognosis and cell samples from individuals having a poor prognosis, comprising a positionally-addressable array of polynucleotide probes bound to a support, said polynucleotide probes comprising a plurality of polynucleotide probes of different nucleotide sequences, each of said different nucleotide sequences comprising a sequence complementary and hybridizable to a different, said plurality consisting of at least 20 of the genes corresponding to the markers listed in Table 5 or Table 6, wherein at least 50% of the probes on the microarray are present in Table 5 or Table 6.

58. A kit for determining whether a sample contains a BRCA1 or sporadic mutation, comprising at least one microarray comprising probes to at least 20 of the genes corresponding to the markers listed in Table 3, and a computer readable medium having recorded thereon one or more programs for determining the similarity of the level of nucleic acid derived from the markers listed in Table 3 in a sample to that in a BRCA1 pool and a sporadic tumor pool, wherein the one or more programs cause a computer to perform a method comprising computing the aggregate differences in expression of each marker between the sample and BRCA1 and the aggregate differences in expression of each marker between the sample and sporadic pool, or a method comprising determining the correlation of expression of the markers in the sample to the expression in the BRCA1 and sporadic pools, said correlation calculated according to Equation (3).

59. A kit for determining the ER-status of a sample, comprising at least one microarray comprising probes to at least 20 of the genes corresponding to the markers listed in Table 1, and a computer readable medium having recorded thereon one or more programs for determining the similarity of the level of nucleic acid derived from the markers listed in Table 1 in a sample to that in an ER(−) pool and an ER(+) pool, wherein the one or more programs cause a computer to perform a method comprising computing the aggregate differences in expression of each marker between the sample and ER(−) pool and the aggregate differences in expression of each marker between the sample and ER(+) pool, or a method comprising determining the correlation of expression of the markers in the sample to the expression in the ER(−) and ER(+) pools, said correlation calculated according to Equation (3).

60. A kit for determining whether a sample is derived from a patient having a good prognosis or a poor prognosis, comprising at least one microarray comprising probes to at least 20 of the genes corresponding to the markers listed in Table 5, and a computer readable medium having recorded thereon one or more programs for determining the similarity of the level of nucleic acid derived from the markers listed in Table 5 in a sample to that in a pool of samples derived from individuals having a good prognosis and a pool of samples derived from individuals having a good prognosis, wherein the one or more programs cause a computer to perform a method comprising computing the aggregate differences in expression of each marker between the sample and the good prognosis pool and the aggregate differences in expression of each marker between the sample and the poor prognosis pool, or a method comprising determining the correlation of expression of the markers in the sample to the expression in the good prognosis and poor prognosis pools, said correlation calculated according to Equation (3).

61. A method for classifying a breast cancer patient according to prognosis, comprising: (a) comparing the respective levels of expression of at least five genes for which markers are listed in Table 5 in a cell sample taken from said breast cancer patient to respective control levels of expression of said at least five genes; and (b) classifying said breast cancer patient according to prognosis of his or her breast cancer based on the similarity between said levels of expression in said cell sample and said control levels.

62. The method according to claim 61, wherein step (b) comprises determining whether said similarity exceeds one or more predetermined threshold values of similarity.

63. A method for classifying a breast cancer patient according to prognosis, comprising: (a) determining the similarity between the level of expression of each of at least five genes for which markers are listed in Table 5 in a cell sample taken from said breast cancer patient, to control levels of expression for each respective said at least five genes to obtain a patient similarity value; (b) providing selected first and second threshold values of similarity of said level of expression of each of said at least five genes to said control levels of expression to obtain first and second similarity threshold values, respectively, wherein said second similarity threshold indicates greater similarity to said control than does said first similarity threshold; and (c) classifying said breast cancer patient as having a first prognosis if said patient similarity value exceeds said first and said second similarity threshold values, a second prognosis if said level of expression of said genes exceeds said first similarity threshold value but does not exceed said second similarity threshold value, and a third prognosis if said level of expression of said genes does not exceed said first similarity threshold value or said second similarity threshold value.

64. The method of claim 63, further comprising determining prior to step (a) said level of expression of said at least five genes.

65. The method of claim 61, wherein said control levels are the mean levels of expression of each of said at least five genes in a pool of tumor samples obtained from a plurality of breast cancer patients who have no distant metastases within five years of initial diagnosis.

66. The method of claim 61, wherein said control levels comprise the expression levels of said genes in breast cancer patients who have had no distant metastases within five years of initial diagnosis.

67. The method of claim 61, wherein said control levels comprise, for each of said at least five genes, mean log intensity values stored on a computer.

68. The method of claim 61, wherein said control levels comprise, for each of said at least five genes, the mean log intensity values that are listed in Table 7.

69. The method of claim 63, wherein said determining in step (a) is carried out by a method comprising determining the degree of similarity between the level of expression of each of said at least five genes in a sample taken from said breast cancer patient to the level of expression of each of said at least five genes in a plurality of breast cancer patients who have had no relapse of breast cancer within five years of initial diagnosis.

70. The method of claim 63, wherein said determining in step (a) is carried out by a method comprising determining the difference between the absolute expression level of each of said at least five genes and the average expression level of each of said at least five genes in a pool of tumor samples obtained from a plurality of breast cancer patients who have had no relapse of breast cancer within five years of initial diagnosis.

71. The method of claim 63, wherein said first threshold value and said second threshold value are coefficients of correlation to the mean expression level of each of said at least five genes in a pool of tumor samples obtained from a plurality of breast cancer patients who have had no relapse of breast cancer within five years of initial diagnosis.

72. The method of claim 71, wherein said first threshold similarity value and said second threshold similarity values are selected by a method comprising: (a) rank ordering in descending order said tumor samples that compose said pool of tumor samples by the degree of similarity between the level of expression of each said at least five genes in each of said tumor samples to the mean level of expression of said at least five genes of the remaining tumor samples that compose said pool to obtain a rank-ordered list, said degree of similarity being expressed as a similarity value; (b) determining an acceptable number of false negatives in said classifying step, wherein a false negative is a breast cancer patient for whom the expression levels of said at least five genes in said cell sample predicts that said breast cancer patient will have no distant metastases within the first five years after initial diagnosis, but who has had a distant metastasis within the first five years after initial diagnosis; (c) determining a similarity value above which in said rank ordered list fewer than said acceptable number of tumor samples are false negatives; (d) selecting said similarity value determined in step (c) as said first threshold similarity value; and (e) selecting a second similarity value, greater than said first similarity value, as said second threshold similarity value.

73. The method of claim 72, wherein said second threshold similarity value is selected in step (e) by a method comprising determining which of said tumor samples, taken from said breast cancer patients having a distant metastasis within the first five years after initial diagnosis, in said rank ordered list has the greatest similarity value, and selecting said greatest similarity value as said second threshold similarity value.

74. The method of claim 72, wherein said first and second threshold similarity values are correlation coefficients, and said first threshold similarity value is 0.4 and said second threshold similarity value is greater than 0.4.

75. The method of claim 72, wherein said first and second threshold similarity values are correlation coefficients, and said second threshold similarity value is 0.636.

76. The method of claim 61, wherein said comparing step (a) comprises comparing the respective levels of expression of at least ten of said genes for which markers are listed in Table 5 in said cell sample to said respective control levels of said at least ten of said genes, wherein said control levels of expression of said at least ten genes are the average expression levels of each of said at least ten genes in a pool of tumor samples obtained from breast cancer patients who have had no distant metastases within five years of initial diagnosis.

77. The method of claim 61, wherein said comparing step (a) comprises comparing the respective levels of expression of at least 25 of said genes for which markers are listed in Table 5 in said cell sample to said respective control levels of expression of said at least 25 genes, wherein said control levels of expression of said at least 25 genes are the average expression levels of each of said at least 25 genes in a pool of tumor samples obtained from breast cancer patients who have had no distant metastases within five years of initial diagnosis.

78. The method of claim 61, wherein said comparing step (a) comprises comparing the respective levels of expression of each of said genes for which markers are listed in Table 6 in said cell sample to said respective control levels of expression of each of said genes for which markers are listed in Table 6, wherein said control levels of expression of each of said genes for which markers are listed in Table 6 are the average expression levels of each of said genes in a pool of tumor samples obtained from breast cancer patients who have had no distant metastases within five years of initial diagnosis.

79. A method of assigning a therapeutic regimen to a breast cancer patient, comprising: (a) classifying said patient as having a “poor prognosis,” “intermediate prognosis,” or “very good prognosis” on the basis of the levels of expression of at least five genes for which markers are listed in Table 5; and (b) assigning said patient a therapeutic regimen, said therapeutic regimen (i) comprising no adjuvant chemotherapy if the patient is lymph node negative and is classified as having a good prognosis or an intermediate prognosis, or (ii) comprising chemotherapy if said patient has any other combination of lymph node status and expression profile.

80. A method of assigning a therapeutic regimen to a breast cancer patient, comprising: (a) determining the lymph node status for said patient; (b) determining the level of expression of at least five genes for which markers are listed in Table 5 in a cell sample from said patient, thereby generating an expression profile; (c) classifying said patient as having a “poor prognosis,” “intermediate prognosis,” or “very good prognosis” on the basis of said expression profile; and (d) assigning said patient a therapeutic regimen, said therapeutic regimen comprising no adjuvant chemotherapy if the patient is lymph node negative and is classified as having a good prognosis or an intermediate prognosis, or comprising chemotherapy if said patient has any other combination of lymph node status and classification.

81. The method of claim 80 in which said therapeutic regimen assigned to lymph node negative patients classified as having an “intermediate prognosis” additionally comprises adjuvant hormonal therapy.

82. The method of claim 80, wherein said classifying step (c) is carried out by a method comprising: (a) rank ordering in descending order a plurality of breast cancer tumor samples that compose a pool of breast cancer tumor samples by the degree of similarity between the level of expression of said at least five genes in each of said tumor samples and the level of expression of said at least five genes across all remaining tumor samples that compose said pool, said degree of similarity being expressed as a similarity value; (b) determining an acceptable number of false negatives in said classifying step, wherein a false negative is a breast cancer patient for whom the expression levels of said at least five genes in said cell sample predicts that said breast cancer patient will have no distant metastases within the first five years after initial diagnosis, but who has had a distant metastasis within the first five years after initial diagnosis; (c) determining a similarity value above which in said rank ordered list said acceptable number of tumor samples or fewer are false negatives; (d) selecting said similarity value determined in step (c) as a first threshold similarity value; (e) selecting a second similarity value, greater than said first similarity value, as a second threshold similarity value; and (f) determining the similarity between the level of expression of each of said at least five genes in a breast cancer tumor sample from the breast cancer patient and the level of expression of each of said respective at least five genes in said pool, to obtain a patient similarity value, wherein if said patient similarity value equals or exceeds said second threshold similarity value, said patient is classified as having a “very good prognosis”; if said patient similarity value equals or exceeds said first threshold similarity value, but is less than said second threshold similarity value, said patient is classified as having an “intermediate prognosis”; and if said patient similarity value is less than said first threshold similarity value, said patient is classified as having a “poor prognosis.”

83. The method of claim 80 which further comprises determining the estrogen receptor (ER) status of said patient, wherein if said patient is ER positive and lymph node negative, said therapeutic regimen assigned to said patient additionally comprises adjuvant hormonal therapy.

84. The method of claim 80, wherein said patient is 52 years of age or younger.

85. The method of claim 80 or 84, wherein said patient has stage I or stage II breast cancer.

86. The method of claim 80, wherein said patient is premenopausal.

87. A method of classifying a breast cancer patient according to prognosis comprising the steps of: (a) contacting first nucleic acids derived from a tumor sample taken from said breast cancer patient, and second nucleic acids derived from two or more tumor samples from breast cancer patients who have had no distant metastases within five years of initial diagnosis, with an array under conditions such that hybridization can occur, said array comprising a positionally-addressable ordered array of polynucleotide probes bound to a solid support, said polynucleotide probes being complementary and hybridizable to at least five of the genes respectively for which markers are listed in Table 5, or the RNA encoded by said genes, and wherein at least 50% of the probes on said array are hybridizable to genes respectively for which markers are listed in Table 5, or to the RNA encoded by said genes; (b) detecting at each of a plurality of discrete loci on said array a first fluorescent emission signal from said first nucleic acids and a second fluorescent emission signal from said second nucleic acids that are bound to said array under said conditions; (c) calculating the similarity between said first fluorescent emission signals and said second fluorescent emission signals across said at least five genes respectively for which markers are listed in Table 5; and (d) classifying said breast cancer patient according to prognosis of his or her breast cancer based on the similarity between said first fluorescent emission signals and said second fluorescent emission signals across said at least five genes respectively for which markers are listed in Table 5.

88. A computer program product for classifying a breast cancer patient according to prognosis, the computer program product for use in conjunction with a computer having a memory and a processor, the computer program product comprising a computer readable storage medium having a computer program encoded thereon, wherein said computer program product can be loaded into the one or more memory units of a computer and causes the one or more processor units of the computer to execute the steps of: (a) receiving a first data structure comprising the respective levels of expression of each of at least five genes for which markers are listed in Table 5 in a cell sample taken from said patient; (b) determining the similarity of the level of expression of each of said at least five genes to respective control levels of expression of said at least five genes to obtain a patient similarity value; (c) comparing said patient similarity value to selected first and second threshold values of similarity of said respective levels of expression of each of said at least five genes to said respective control levels of expression of said at least five genes, wherein said second threshold value of similarity indicates greater similarity to said respective control levels of expression of said at least five genes than does said first threshold value of similarity; and (d) classifying said patient as having a first prognosis if said patient similarity value exceeds said first and said second threshold similarity values; a second prognosis if said patient similarity value exceeds said first threshold similarity value but does not exceed said second threshold similarity value; and a third prognosis if said patient similarity value does not exceed said first threshold similarity value or said second threshold similarity value.

89. The computer program product of claim 88, wherein said first threshold value of similarity and said second threshold value of similarity are values stored in said computer.

90. The computer program product of claim 88, wherein said respective control levels of expression of said at least five genes is stored in said computer.

91. The computer program product of claim 88 wherein said first prognosis is a “very good prognosis”; said second prognosis is an “intermediate prognosis”; and said third prognosis is a “poor prognosis”; wherein said computer program may be loaded into the memory and further cause said one or more processor units of said computer to execute the step of assigning said breast cancer patient a therapeutic regimen comprising no adjuvant chemotherapy if the patient is lymph node negative and is classified as having a good prognosis or an intermediate prognosis, or comprising chemotherapy if said patient has any other combination of lymph node status and expression profile.

92. The computer program product of claim 91 wherein said clinical data includes the lymph node and estrogen receptor (ER) status of said breast cancer patient.

93. The computer program product of claim 88 wherein said computer program may be loaded into the memory and further causes said one or more processor units of the computer to execute the steps of receiving a data structure comprising clinical data specific to said breast cancer patient.

94. The computer program product of claim 88 wherein said respective control levels of expression of said at least five genes comprises a set of single-channel mean hybridization intensity values for each of said at least five genes, stored on said computer readable storage medium.

95. The computer program product of claim 93 wherein said single-channel mean hybridization intensity values are log transformed.

96. The computer program product of claim 88 wherein said computer program product causes said processing unit to perform said comparing step (c) by calculating the difference between the level of expression of each of said atleast five genes in said cell sample taken from said breast cancer patient and said respective control levels of expression of said at least five genes.

97. The computer program product of claim 88 wherein said computer program product causes said processing unit to perform said comparing step (c) by calculating the mean log level of expression of each of said at least five genes in said control to obtain a control mean log expression level for each gene, calculating the log expression level for each of said at least five genes in a breast cancer sample from said patient to obtain a patient log expression level, and calculating the difference between the patient log expression level and the control mean log expression for each of said at least five genes.

98. The computer program product of claim 88 wherein said computer program product causes said processing unit to perform said comparing step (c) by calculating similarity between the level of expression of each of said at least five genes in said cell sample taken from said patient and said respective control levels of expression of said at least five genes, wherein said similarity is expressed as a similarity value.

99. The computer program product of claim 98 wherein said similarity value is a correlation coefficient.