# Patent

## Title
Diagnosis and prognosis of breast cancer patients

## Abstract

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

## Background
This application claims benefit of U.S. Provisional Application No. 60/298,918, filed Jun. 18, 2001, and U.S. Provisional Application No. 60/380,710, filed on May 14, 2002, each of which is incorporated by reference herein in its entirety.


This application includes a Sequence Listing submitted on compact disc, recorded on two compact discs, including one duplicate, containing Filename 9301175999.txt, of size 6,766,592 bytes, created Jun. 13, 2002. The sequence listing on the compact discs is incorporated by reference herein in its entirety.
1. FIELD OF THE INVENTION
The present invention relates to the identification of marker genes useful in the diagnosis and prognosis of breast cancer. More particularly, the invention relates to the identification of a set of marker genes associated with breast cancer, a set of marker genes differentially expressed in estrogen receptor (+) versus estrogen receptor (−) tumors, a set of marker genes differentially expressed in BRCA1 versus sporadic tumors, and a set of marker genes differentially expressed in sporadic tumors from patients with good clinical prognosis (i.e., metastasis- or disease-free >5 years) versus patients with poor clinical prognosis (i.e., metastasis- or disease-free <5 years). For each of the marker sets above, the invention further relates to methods of distinguishing the breast cancer-related conditions. The invention further provides methods for determining the course of treatment of a patient with breast cancer.
2. BACKGROUND OF THE INVENTION
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.
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.
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.
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.
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.
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.
To date, no set of satisfactory predictors for prognosis based on the clinical information alone has been identified. The detection of BRCA1 or BRCA2 mutations represents a step towards the design of therapies to better control and prevent the appearance of these tumors. However, there is no equivalent means for the diagnosis of patients with sporadic tumors, the most common type of breast cancer tumor, nor is there a means of differentiating subtypes of breast cancer.
3. SUMMARY OF THE INVENTION
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(−).
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.
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.
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.
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:

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

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.

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)

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 over (n−3)}, where 1/√{square root over (n−3)} is the distribution width and n=the number of samples. In a specific embodiment where n=98, said threshold value is 0.3. In a specific embodiment, said set of marker genes is validated by: (a) using a statistical method to randomize the association between said marker genes and said phenotype category, thereby creating a control correlation coefficient for each marker gene; (b) repeating step (a) one hundred or more times to develop a frequency distribution of said control correlation coefficients for each marker gene; (c) determining the number of marker genes having a control correlation coefficient of a threshold value or above, thereby creating a control marker gene set; and (d) comparing the number of control marker genes so identified to the number of marker genes, wherein if the p value of the difference between the number of marker genes and the number of control genes is less than 0.01, said set of marker genes is validated. In another specific embodiment, said set of marker genes is optimized by the method comprising: (a) rank-ordering the genes by amplitude of correlation or by significance of the correlation coefficients, and (b) selecting an arbitrary number of marker genes from the top of the rank-ordered list. The threshold value depends upon the number of samples tested.

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: (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.
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.
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.
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.
The invention further provides a kit for determining the ER-status of a sample, comprising at least two microarrays each comprising at least 5 of the markers listed in Table 1, and a computer system for determining the similarity of the level of nucleic acid derived from the markers listed in Table 1 in a sample to that in an ER(−) pool and an ER(+) pool, the computer system comprising a processor, and a memory encoding one or more programs coupled to the processor, wherein the one or more programs cause the processor to perform a method comprising computing the aggregate differences in expression of each marker between the sample and ER(−) pool and the aggregate differences in expression of each marker between the sample and ER(+) pool, or a method comprising determining the correlation of expression of the markers in the sample to the expression in the ER(−) and ER(+) pools, said correlation calculated according to Equation (4). The invention provides for kits able to distinguish BRCA1 and sporadic tumors, and samples from patients with good prognosis from samples from patients with poor prognosis, by inclusion of the appropriate marker gene sets. The invention further provides a kit for determining whether a sample is derived from a patient having a good prognosis or a poor prognosis, comprising at least one microarray comprising probes to at least 5 of the genes corresponding to the markers listed in Table 5, and a computer readable medium having recorded thereon one or more programs for determining the similarity of the level of nucleic acid derived from the markers listed in Table 5 in a sample to that in a pool of samples derived from individuals having a good prognosis and a pool of samples derived from individuals having a good prognosis, wherein the one or more programs cause a computer to perform a method comprising computing the aggregate differences in expression of each marker between the sample and the good prognosis pool and the aggregate differences in expression of each marker between the sample and the poor prognosis pool, or a method comprising determining the correlation of expression of the markers in the sample to the expression in the good prognosis and poor prognosis pools, said correlation calculated according to Equation (3).
4. BRIEF DESCRIPTION OF THE FIGURES
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.
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.
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 BRCA1 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).
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 coregulated to form a dominant pattern.
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.
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%.
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.
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).
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.
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.
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%.
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.
FIG. 11A The classification of 38 tumors in the ER(−) group into two subgroups, BRCA1 and sporadic, by using the optimal set of 100 discriminating marker genes. Patients above the white line are characterized by BRCA1-related patterns.
FIG. 11B Correlation between expression levels in samples from each ER(−) patient and the average profile of the BRCA1 group vs. correlation with the sporadic group. Squares represent samples from patients with sporadic-type tumors; dots represent samples from patients carrying the BRCA1 mutation.
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.
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.
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.
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.
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.
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.
FIG. 17 The distant metastases probability as a function of time since diagnosis for ER(+) (yes) or ER(−) (no) individuals.
FIG. 18 The distant metastases probability as a function of time since diagnosis for progesterone receptor (PR)(+) (yes) or PR(−) (no) individuals.
FIG. 19A, B The distant metastases probability as a function of time since diagnosis. Groups were defined by the tumor grades.
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.
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.
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.
FIG. 21A Sensitivity vs. 1-specificity for good prognosis classification.
FIG. 21B Sensitivity vs. 1-specificity for poor prognosis classification.
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.
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.
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).
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.
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.
FIG. 24B Scatterplot of sample correlation coefficients, with three incorrectly classified samples lying to the right of the threshold correlation coefficient value. The threshold correlation value was set at 0.2727 to limit the false negatives to approximately 10% of the samples.


5. DETAILED DESCRIPTION OF THE INVENTION
5.1 Introduction
The invention relates to sets of genetic markers whose expression patterns correlate with important characteristics of breast cancer tumors, i.e., estrogen receptor (ER) status, BRCA1 status, and the likelihood of relapse (i.e., distant metastasis or poor prognosis). More specifically, the invention provides for sets of genetic markers that can distinguish the following three clinical conditions. First, the invention relates to sets of markers whose expression correlates with the ER status of a patient, and which can be used to distinguish ER(+) from ER(−) patients. ER status is a useful prognostic indicator, and an indicator of the likelihood that a patient will respond to certain therapies, such as tamoxifen. Also, among women who are ER positive the response rate (over 50%) to hormonal therapy is much higher than the response rate (less 10%) in patients whose ER status is negative. In patients with ER positive tumors the possibility of achieving a hormonal response is directly proportional to the level ER (P. Clabresi and P. S. Schein, MEDICAL ONCOLOGY (2ND ED.), McGraw-Hill, Inc., New York (1993)). Second, the invention further relates to sets of markers whose expression correlates with the presence of BRCA1 mutations, and which can be used to distinguish BRCA1-type tumors from sporadic tumors. Third, the invention relates to genetic markers whose expression correlates with clinical prognosis, and which can be used to distinguish patients having good prognoses (i.e., no distant metastases of a tumor within five years) from poor prognoses (i.e., distant metastases of a tumor within five years). Methods are provided for use of these markers to distinguish between these patient groups, and to determine general courses of treatment. Microarrays comprising these markers are also provided, as well as methods of constructing such microarrays. Each marker corresponds 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
As used herein, “BRCA1 tumor” means a tumor having cells containing a mutation of the BRCA1 locus.
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.
“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.
“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.
“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.
“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.
“Marker-derived polynucleotides” means the RNA transcribed from a marker gene, any cDNA or cRNA produced therefrom, and any nucleic acid derived therefrom, such as synthetic nucleic acid having a sequence derived from the gene corresponding to the marker gene.
5.3 Markers Useful in Diagnosis and Prognosis of Breast Cancer
5.3.1 Marker Sets
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.
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.
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.
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.







TABLE 1







2,460 gene markers that distinguish


ER(+) and ER(−) cell samples.










GenBank




Accession Number
SEQ ID NO







AA555029_RC
SEQ ID NO 1



AB000509
SEQ ID NO 2



AB001451
SEQ ID NO 3



AB002301
SEQ ID NO 4



AB002308
SEQ ID NO 5



AB002351
SEQ ID NO 6



AB002448
SEQ ID NO 7



AB006628
SEQ ID NO 9



AB006630
SEQ ID NO 10



AB006746
SEQ ID NO 11



AB007458
SEQ ID NO 12



AB007855
SEQ ID NO 13



AB007857
SEQ ID NO 14



AB007863
SEQ ID NO 15



AB007883
SEQ ID NO 16



AB007896
SEQ ID NO 17



AB007899
SEQ ID NO 18



AB007916
SEQ ID NO 19



AB007950
SEQ ID NO 20



AB011087
SEQ ID NO 21



AB011089
SEQ ID NO 22



AB011104
SEQ ID NO 23



AB011105
SEQ ID NO 24



AB011121
SEQ ID NO 25



AB011132
SEQ ID NO 26



AB011152
SEQ ID NO 27



AB011179
SEQ ID NO 28



AB014534
SEQ ID NO 29



AB014568
SEQ ID NO 30



AB018260
SEQ ID NO 31



AB018268
SEQ ID NO 32



AB018289
SEQ ID NO 33



AB018345
SEQ ID NO 35



AB020677
SEQ ID NO 36



AB020689
SEQ ID NO 37



AB020695
SEQ ID NO 38



AB020710
SEQ ID NO 39



AB023139
SEQ ID NO 40



AB023151
SEQ ID NO 41



AB023152
SEQ ID NO 42



AB023163
SEQ ID NO 43



AB023173
SEQ ID NO 44



AB023211
SEQ ID NO 45



AB024704
SEQ ID NO 46



AB028985
SEQ ID NO 47



AB028986
SEQ ID NO 48



AB028998
SEQ ID NO 49



AB029031
SEQ ID NO 51



AB032951
SEQ ID NO 52



AB032966
SEQ ID NO 53



AB032969
SEQ ID NO 54



AB032977
SEQ ID NO 56



AB033007
SEQ ID NO 58



AB033034
SEQ ID NO 59



AB033035
SEQ ID NO 60



AB033040
SEQ ID NO 61



AB033049
SEQ ID NO 63



AB033050
SEQ ID NO 64



AB033053
SEQ ID NO 65



AB033055
SEQ ID NO 66



AB033058
SEQ ID NO 67



AB033073
SEQ ID NO 68



AB033092
SEQ ID NO 69



AB033111
SEQ ID NO 70



AB036063
SEQ ID NO 71



AB037720
SEQ ID NO 72



AB037743
SEQ ID NO 74



AB037745
SEQ ID NO 75



AB037756
SEQ ID NO 76



AB037765
SEQ ID NO 77



AB037778
SEQ ID NO 78



AB037791
SEQ ID NO 79



AB037793
SEQ ID NO 80



AB037802
SEQ ID NO 81



AB037806
SEQ ID NO 82



AB037809
SEQ ID NO 83



AB037836
SEQ ID NO 84



AB037844
SEQ ID NO 85



AB037845
SEQ ID NO 86



AB037848
SEQ ID NO 87



AB037863
SEQ ID NO 88



AB037864
SEQ ID NO 89



AB040881
SEQ ID NO 90



AB040900
SEQ ID NO 91



AB040914
SEQ ID NO 92



AB040926
SEQ ID NO 93



AB040955
SEQ ID NO 94



AB040961
SEQ ID NO 95



AF000974
SEQ ID NO 97



AF005487
SEQ ID NO 98



AF007153
SEQ ID NO 99



AF007155
SEQ ID NO 100



AF015041
SEQ ID NO 101



AF016004
SEQ ID NO 102



AF016495
SEQ ID NO 103



AF020919
SEQ ID NO 104



AF026941
SEQ ID NO 105



AF035191
SEQ ID NO 106



AF035284
SEQ ID NO 107



AF035318
SEQ ID NO 108



AF038182
SEQ ID NO 109



AF038193
SEQ ID NO 110



AF042838
SEQ ID NO 111



AF044127
SEQ ID NO 112



AF045229
SEQ ID NO 113



AF047002
SEQ ID NO 114



AF047826
SEQ ID NO 115



AF049460
SEQ ID NO 116



AF052101
SEQ ID NO 117



AF052117
SEQ ID NO 118



AF052155
SEQ ID NO 119



AF052159
SEQ ID NO 120



AF052176
SEQ ID NO 122



AF052185
SEQ ID NO 123



AF055270
SEQ ID NO 126



AF058075
SEQ ID NO 127



AF061034
SEQ ID NO 128



AF063725
SEQ ID NO 129



AF063936
SEQ ID NO 130



AF065241
SEQ ID NO 131



AF067972
SEQ ID NO 132



AF070536
SEQ ID NO 133



AF070552
SEQ ID NO 134



AF070617
SEQ ID NO 135



AF073770
SEQ ID NO 138



AF076612
SEQ ID NO 139



AF079529
SEQ ID NO 140



AF090913
SEQ ID NO 142



AF095719
SEQ ID NO 143



AF098641
SEQ ID NO 144



AF099032
SEQ ID NO 145



AF100756
SEQ ID NO 146



AF101051
SEQ ID NO 147



AF103375
SEQ ID NO 148



AF103458
SEQ ID NO 149



AF103530
SEQ ID NO 150



AF103804
SEQ ID NO 151



AF111849
SEQ ID NO 152



AF112213
SEQ ID NO 153



AF113132
SEQ ID NO 154



AF116682
SEQ ID NO 156



AF118224
SEQ ID NO 157



AF118274
SEQ ID NO 158



AF119256
SEQ ID NO 159



AF119665
SEQ ID NO 160



AF121255
SEQ ID NO 161



AF131748
SEQ ID NO 162



AF131753
SEQ ID NO 163



AF131760
SEQ ID NO 164



AF131784
SEQ ID NO 165



AF131828
SEQ ID NO 166



AF135168
SEQ ID NO 167



AF141882
SEQ ID NO 168



AF148505
SEQ ID NO 169



AF149785
SEQ ID NO 170



AF151810
SEQ ID NO 171



AF152502
SEQ ID NO 172



AF155120
SEQ ID NO 174



AF159092
SEQ ID NO 175



AF161407
SEQ ID NO 176



AF161553
SEQ ID NO 177



AF164104
SEQ ID NO 178



AF167706
SEQ ID NO 179



AF175387
SEQ ID NO 180



AF176012
SEQ ID NO 181



AF186780
SEQ ID NO 182



AF217508
SEQ ID NO 184



AF220492
SEQ ID NO 185



AF224266
SEQ ID NO 186



AF230904
SEQ ID NO 187



AF234532
SEQ ID NO 188



AF257175
SEQ ID NO 189



AF257659
SEQ ID NO 190



AF272357
SEQ ID NO 191



AF279865
SEQ ID NO 192



AI497657_RC
SEQ ID NO 193



AJ012755
SEQ ID NO 194



AJ223353
SEQ ID NO 195



AJ224741
SEQ ID NO 196



AJ224864
SEQ ID NO 197



AJ225092
SEQ ID NO 198



AJ225093
SEQ ID NO 199



AJ249377
SEQ ID NO 200



AJ270996
SEQ ID NO 202



AJ272057
SEQ ID NO 203



AJ275978
SEQ ID NO 204



AJ276429
SEQ ID NO 205



AK000004
SEQ ID NO 206



AK000005
SEQ ID NO 207



AK000106
SEQ ID NO 208



AK000142
SEQ ID NO 209



AK000168
SEQ ID NO 210



AK000345
SEQ ID NO 212



AK000543
SEQ ID NO 213



AK000552
SEQ ID NO 214



AK000643
SEQ ID NO 216



AK000660
SEQ ID NO 217



AK000689
SEQ ID NO 218



AK000770
SEQ ID NO 220



AK000933
SEQ ID NO 221



AK001100
SEQ ID NO 223



AK001164
SEQ ID NO 224



AK001166
SEQ ID NO 225



AK001295
SEQ ID NO 226



AK001380
SEQ ID NO 227



AK001423
SEQ ID NO 228



AK001438
SEQ ID NO 229



AK001492
SEQ ID NO 230



AK001499
SEQ ID NO 231



AK001630
SEQ ID NO 232



AK001872
SEQ ID NO 234



AK001890
SEQ ID NO 235



AK002016
SEQ ID NO 236



AK002088
SEQ ID NO 237



AK002206
SEQ ID NO 240



AL035297
SEQ ID NO 241



AL049265
SEQ ID NO 242



AL049365
SEQ ID NO 244



AL049370
SEQ ID NO 245



AL049381
SEQ ID NO 246



AL049397
SEQ ID NO 247



AL049415
SEQ ID NO 248



AL049667
SEQ ID NO 249



AL049801
SEQ ID NO 250



AL049932
SEQ ID NO 251



AL049935
SEQ ID NO 252



AL049943
SEQ ID NO 253



AL049949
SEQ ID NO 254



AL049963
SEQ ID NO 255



AL049987
SEQ ID NO 256



AL050021
SEQ ID NO 257



AL050024
SEQ ID NO 258



AL050090
SEQ ID NO 259



AL050148
SEQ ID NO 260



AL050151
SEQ ID NO 261



AL050227
SEQ ID NO 262



AL050367
SEQ ID NO 263



AL050370
SEQ ID NO 264



AL050371
SEQ ID NO 265



AL050372
SEQ ID NO 266



AL050388
SEQ ID NO 267



AL079276
SEQ ID NO 268



AL079298
SEQ ID NO 269



AL080079
SEQ ID NO 271



AL080192
SEQ ID NO 273



AL080199
SEQ ID NO 274



AL080209
SEQ ID NO 275



AL080234
SEQ ID NO 277



AL080235
SEQ ID NO 278



AL096737
SEQ ID NO 279



AL110126
SEQ ID NO 280



AL110139
SEQ ID NO 281



AL110202
SEQ ID NO 283



AL110212
SEQ ID NO 284



AL110260
SEQ ID NO 285



AL117441
SEQ ID NO 286



AL117452
SEQ ID NO 287



AL117477
SEQ ID NO 288



AL117502
SEQ ID NO 289



AL117523
SEQ ID NO 290



AL117595
SEQ ID NO 291



AL117599
SEQ ID NO 292



AL117600
SEQ ID NO 293



AL117609
SEQ ID NO 294



AL117617
SEQ ID NO 295



AL117666
SEQ ID NO 296



AL122055
SEQ ID NO 297



AL133033
SEQ ID NO 298



AL133035
SEQ ID NO 299



AL133074
SEQ ID NO 301



AL133096
SEQ ID NO 302



AL133105
SEQ ID NO 303



AL133108
SEQ ID NO 304



AL133572
SEQ ID NO 305



AL133619
SEQ ID NO 307



AL133622
SEQ ID NO 308



AL133623
SEQ ID NO 309



AL133624
SEQ ID NO 310



AL133632
SEQ ID NO 311



AL133644
SEQ ID NO 312



AL133645
SEQ ID NO 313



AL133651
SEQ ID NO 314



AL137310
SEQ ID NO 316



AL137316
SEQ ID NO 317



AL137332
SEQ ID NO 318



AL137342
SEQ ID NO 319



AL137362
SEQ ID NO 321



AL137381
SEQ ID NO 322



AL137407
SEQ ID NO 323



AL137448
SEQ ID NO 324



AL137502
SEQ ID NO 326



AL137514
SEQ ID NO 327



AL137540
SEQ ID NO 328



AL137566
SEQ ID NO 330



AL137615
SEQ ID NO 331



AL137673
SEQ ID NO 335



AL137718
SEQ ID NO 336



AL137736
SEQ ID NO 337



AL137751
SEQ ID NO 338



AL137761
SEQ ID NO 339



AL157431
SEQ ID NO 340



AL157432
SEQ ID NO 341



AL157454
SEQ ID NO 342



AL157476
SEQ ID NO 343



AL157480
SEQ ID NO 344



AL157482
SEQ ID NO 345



AL157484
SEQ ID NO 346



AL157492
SEQ ID NO 347



AL157505
SEQ ID NO 348



AL157851
SEQ ID NO 349



AL160131
SEQ ID NO 350



AL161960
SEQ ID NO 351



AL162049
SEQ ID NO 352



AL355708
SEQ ID NO 353



D13643
SEQ ID NO 355



D14678
SEQ ID NO 356



D25328
SEQ ID NO 357



D26070
SEQ ID NO 358



D26488
SEQ ID NO 359



D31887
SEQ ID NO 360



D38521
SEQ ID NO 361



D38553
SEQ ID NO 362



D42043
SEQ ID NO 363



D42047
SEQ ID NO 364



D43950
SEQ ID NO 365



D50402
SEQ ID NO 366



D50914
SEQ ID NO 367



D55716
SEQ ID NO 368



D80001
SEQ ID NO 369



D80010
SEQ ID NO 370



D82345
SEQ ID NO 371



D83781
SEQ ID NO 372



D86964
SEQ ID NO 373



D86978
SEQ ID NO 374



D86985
SEQ ID NO 375



D87076
SEQ ID NO 376



D87453
SEQ ID NO 377



D87469
SEQ ID NO 378



D87682
SEQ ID NO 379



G26403
SEQ ID NO 380



J02639
SEQ ID NO 381



J04162
SEQ ID NO 382



K02403
SEQ ID NO 384



L05096
SEQ ID NO 385



L10333
SEQ ID NO 386



L11645
SEQ ID NO 387



L21934
SEQ ID NO 388



L22005
SEQ ID NO 389



L48692
SEQ ID NO 391



M12758
SEQ ID NO 392



M15178
SEQ ID NO 393



M21551
SEQ ID NO 394



M24895
SEQ ID NO 395



M26383
SEQ ID NO 396



M27749
SEQ ID NO 397



M28170
SEQ ID NO 398



M29873
SEQ ID NO 399



M29874
SEQ ID NO 400



M30448
SEQ ID NO 401



M30818
SEQ ID NO 402



M31932
SEQ ID NO 403



M37033
SEQ ID NO 404



M55914
SEQ ID NO 405



M63438
SEQ ID NO 406



M65254
SEQ ID NO 407



M68874
SEQ ID NO 408



M73547
SEQ ID NO 409



M77142
SEQ ID NO 410



M80899
SEQ ID NO 411



M83822
SEQ ID NO 412



M90657
SEQ ID NO 413



M93718
SEQ ID NO 414



M96577
SEQ ID NO 415



NM_000022
SEQ ID NO 417



NM_000044
SEQ ID NO 418



NM_000050
SEQ ID NO 419



NM_000057
SEQ ID NO 420



NM_000060
SEQ ID NO 421



NM_000064
SEQ ID NO 422



NM_000073
SEQ ID NO 424



NM_000077
SEQ ID NO 425



NM_000086
SEQ ID NO 426



NM_000087
SEQ ID NO 427



NM_000095
SEQ ID NO 429



NM_000096
SEQ ID NO 430



NM_000100
SEQ ID NO 431



NM_000101
SEQ ID NO 432



NM_000104
SEQ ID NO 433



NM_000109
SEQ ID NO 434



NM_000125
SEQ ID NO 435



NM_000127
SEQ ID NO 436



NM_000135
SEQ ID NO 437



NM_000137
SEQ ID NO 438



NM_000146
SEQ ID NO 439



NM_000149
SEQ ID NO 440



NM_000154
SEQ ID NO 441



NM_000161
SEQ ID NO 443



NM_000165
SEQ ID NO 444



NM_000168
SEQ ID NO 445



NM_000169
SEQ ID NO 446



NM_000175
SEQ ID NO 447



NM_000191
SEQ ID NO 448



NM_000201
SEQ ID NO 450



NM_000211
SEQ ID NO 451



NM_000213
SEQ ID NO 452



NM_000224
SEQ ID NO 453



NM_000239
SEQ ID NO 454



NM_000251
SEQ ID NO 455



NM_000268
SEQ ID NO 456



NM_000270
SEQ ID NO 458



NM_000271
SEQ ID NO 459



NM_000283
SEQ ID NO 460



NM_000284
SEQ ID NO 461



NM_000286
SEQ ID NO 462



NM_000291
SEQ ID NO 463



NM_000299
SEQ ID NO 464



NM_000300
SEQ ID NO 465



NM_000310
SEQ ID NO 466



NM_000311
SEQ ID NO 467



NM_000317
SEQ ID NO 468



NM_000320
SEQ ID NO 469



NM_000342
SEQ ID NO 470



NM_000346
SEQ ID NO 471



NM_000352
SEQ ID NO 472



NM_000355
SEQ ID NO 473



NM_000358
SEQ ID NO 474



NM_000359
SEQ ID NO 475



NM_000362
SEQ ID NO 476



NM_000365
SEQ ID NO 477



NM_000381
SEQ ID NO 478



NM_000397
SEQ ID NO 480



NM_000399
SEQ ID NO 481



NM_000414
SEQ ID NO 482



NM_000416
SEQ ID NO 483



NM_000422
SEQ ID NO 484



NM_000424
SEQ ID NO 485



NM_000433
SEQ ID NO 486



NM_000436
SEQ ID NO 487



NM_000450
SEQ ID NO 488



NM_000462
SEQ ID NO 489



NM_000495
SEQ ID NO 490



NM_000507
SEQ ID NO 491



NM_000526
SEQ ID NO 492



NM_000557
SEQ ID NO 493



NM_000560
SEQ ID NO 494



NM_000576
SEQ ID NO 495



NM_000579
SEQ ID NO 496



NM_000584
SEQ ID NO 497



NM_000591
SEQ ID NO 498



NM_000592
SEQ ID NO 499



NM_000593
SEQ ID NO 500



NM_000594
SEQ ID NO 501



NM_000597
SEQ ID NO 502



NM_000600
SEQ ID NO 504



NM_000607
SEQ ID NO 505



NM_000612
SEQ ID NO 506



NM_000627
SEQ ID NO 507



NM_000633
SEQ ID NO 508



NM_000636
SEQ ID NO 509



NM_000639
SEQ ID NO 510



NM_000647
SEQ ID NO 511



NM_000655
SEQ ID NO 512



NM_000662
SEQ ID NO 513



NM_000663
SEQ ID NO 514



NM_000666
SEQ ID NO 515



NM_000676
SEQ ID NO 516



NM_000685
SEQ ID NO 517



NM_000693
SEQ ID NO 518



NM_000699
SEQ ID NO 519



NM_000700
SEQ ID NO 520



NM_000712
SEQ ID NO 521



NM_000727
SEQ ID NO 522



NM_000732
SEQ ID NO 523



NM_000734
SEQ ID NO 524



NM_000767
SEQ ID NO 525



NM_000784
SEQ ID NO 526



NM_000802
SEQ ID NO 528



NM_000824
SEQ ID NO 529



NM_000849
SEQ ID NO 530



NM_000852
SEQ ID NO 531



NM_000874
SEQ ID NO 532



NM_000878
SEQ ID NO 533



NM_000884
SEQ ID NO 534



NM_000908
SEQ ID NO 537



NM_000909
SEQ ID NO 538



NM_000926
SEQ ID NO 539



NM_000930
SEQ ID NO 540



NM_000931
SEQ ID NO 541



NM_000947
SEQ ID NO 542



NM_000949
SEQ ID NO 543



NM_000950
SEQ ID NO 544



NM_000954
SEQ ID NO 545



NM_000964
SEQ ID NO 546



NM_001003
SEQ ID NO 549



NM_001016
SEQ ID NO 551



NM_001047
SEQ ID NO 553



NM_001066
SEQ ID NO 555



NM_001071
SEQ ID NO 556



NM_001078
SEQ ID NO 557



NM_001085
SEQ ID NO 558



NM_001089
SEQ ID NO 559



NM_001109
SEQ ID NO 560



NM_001122
SEQ ID NO 561



NM_001124
SEQ ID NO 562



NM_001161
SEQ ID NO 563



NM_001165
SEQ ID NO 564



NM_001166
SEQ ID NO 565



NM_001168
SEQ ID NO 566



NM_001179
SEQ ID NO 567



NM_001185
SEQ ID NO 569



NM_001203
SEQ ID NO 570



NM_001207
SEQ ID NO 573



NM_001216
SEQ ID NO 574



NM_001218
SEQ ID NO 575



NM_001223
SEQ ID NO 576



NM_001225
SEQ ID NO 577



NM_001233
SEQ ID NO 578



NM_001236
SEQ ID NO 579



NM_001237
SEQ ID NO 580



NM_001251
SEQ ID NO 581



NM_001255
SEQ ID NO 582



NM_001262
SEQ ID NO 583



NM_001263
SEQ ID NO 584



NM_001267
SEQ ID NO 585



NM_001276
SEQ ID NO 587



NM_001280
SEQ ID NO 588



NM_001282
SEQ ID NO 589



NM_001295
SEQ ID NO 590



NM_001305
SEQ ID NO 591



NM_001310
SEQ ID NO 592



NM_001312
SEQ ID NO 593



NM_001321
SEQ ID NO 594



NM_001327
SEQ ID NO 595



NM_001329
SEQ ID NO 596



NM_001333
SEQ ID NO 597



NM_001338
SEQ ID NO 598



NM_001360
SEQ ID NO 599



NM_001363
SEQ ID NO 600



NM_001381
SEQ ID NO 601



NM_001394
SEQ ID NO 602



NM_001395
SEQ ID NO 603



NM_001419
SEQ ID NO 604



NM_001424
SEQ ID NO 605



NM_001428
SEQ ID NO 606



NM_001436
SEQ ID NO 607



NM_001444
SEQ ID NO 608



NM_001446
SEQ ID NO 609



NM_001453
SEQ ID NO 611



NM_001456
SEQ ID NO 612



NM_001457
SEQ ID NO 613



NM_001463
SEQ ID NO 614



NM_001465
SEQ ID NO 615



NM_001481
SEQ ID NO 616



NM_001493
SEQ ID NO 617



NM_001494
SEQ ID NO 618



NM_001500
SEQ ID NO 619



NM_001504
SEQ ID NO 620



NM_001511
SEQ ID NO 621



NM_001513
SEQ ID NO 622



NM_001527
SEQ ID NO 623



NM_001529
SEQ ID NO 624



NM_001530
SEQ ID NO 625



NM_001540
SEQ ID NO 626



NM_001550
SEQ ID NO 627



NM_001551
SEQ ID NO 628



NM_001552
SEQ ID NO 629



NM_001554
SEQ ID NO 631



NM_001558
SEQ ID NO 632



NM_001560
SEQ ID NO 633



NM_001565
SEQ ID NO 634



NM_001569
SEQ ID NO 635



NM_001605
SEQ ID NO 636



NM_001609
SEQ ID NO 637



NM_001615
SEQ ID NO 638



NM_001623
SEQ ID NO 639



NM_001627
SEQ ID NO 640



NM_001628
SEQ ID NO 641



NM_001630
SEQ ID NO 642



NM_001634
SEQ ID NO 643



NM_001656
SEQ ID NO 644



NM_001673
SEQ ID NO 645



NM_001675
SEQ ID NO 647



NM_001679
SEQ ID NO 648



NM_001689
SEQ ID NO 649



NM_001703
SEQ ID NO 650



NM_001710
SEQ ID NO 651



NM_001725
SEQ ID NO 652



NM_001730
SEQ ID NO 653



NM_001733
SEQ ID NO 654



NM_001734
SEQ ID NO 655



NM_001740
SEQ ID NO 656



NM_001745
SEQ ID NO 657



NM_001747
SEQ ID NO 658



NM_001756
SEQ ID NO 659



NM_001757
SEQ ID NO 660



NM_001758
SEQ ID NO 661



NM_001762
SEQ ID NO 662



NM_001767
SEQ ID NO 663



NM_001770
SEQ ID NO 664



NM_001777
SEQ ID NO 665



NM_001778
SEQ ID NO 666



NM_001781
SEQ ID NO 667



NM_001786
SEQ ID NO 668



NM_001793
SEQ ID NO 669



NM_001803
SEQ ID NO 671



NM_001806
SEQ ID NO 672



NM_001809
SEQ ID NO 673



NM_001814
SEQ ID NO 674



NM_001826
SEQ ID NO 675



NM_001830
SEQ ID NO 677



NM_001838
SEQ ID NO 678



NM_001839
SEQ ID NO 679



NM_001853
SEQ ID NO 681



NM_001859
SEQ ID NO 682



NM_001861
SEQ ID NO 683



NM_001874
SEQ ID NO 685



NM_001885
SEQ ID NO 686



NM_001892
SEQ ID NO 688



NM_001897
SEQ ID NO 689



NM_001899
SEQ ID NO 690



NM_001905
SEQ ID NO 691



NM_001912
SEQ ID NO 692



NM_001914
SEQ ID NO 693



NM_001919
SEQ ID NO 694



NM_001941
SEQ ID NO 695



NM_001943
SEQ ID NO 696



NM_001944
SEQ ID NO 697



NM_001953
SEQ ID NO 699



NM_001954
SEQ ID NO 700



NM_001955
SEQ ID NO 701



NM_001956
SEQ ID NO 702



NM_001958
SEQ ID NO 703



NM_001961
SEQ ID NO 705



NM_001970
SEQ ID NO 706



NM_001979
SEQ ID NO 707



NM_001982
SEQ ID NO 708



NM_002017
SEQ ID NO 710



NM_002033
SEQ ID NO 713



NM_002046
SEQ ID NO 714



NM_002047
SEQ ID NO 715



NM_002051
SEQ ID NO 716



NM_002053
SEQ ID NO 717



NM_002061
SEQ ID NO 718



NM_002065
SEQ ID NO 719



NM_002068
SEQ ID NO 720



NM_002077
SEQ ID NO 722



NM_002091
SEQ ID NO 723



NM_002101
SEQ ID NO 724



NM_002106
SEQ ID NO 725



NM_002110
SEQ ID NO 726



NM_002111
SEQ ID NO 727



NM_002115
SEQ ID NO 728



NM_002118
SEQ ID NO 729



NM_002123
SEQ ID NO 730



NM_002131
SEQ ID NO 731



NM_002136
SEQ ID NO 732



NM_002145
SEQ ID NO 733



NM_002164
SEQ ID NO 734



NM_002168
SEQ ID NO 735



NM_002184
SEQ ID NO 736



NM_002185
SEQ ID NO 737



NM_002189
SEQ ID NO 738



NM_002200
SEQ ID NO 739



NM_002201
SEQ ID NO 740



NM_002213
SEQ ID NO 741



NM_002219
SEQ ID NO 742



NM_002222
SEQ ID NO 743



NM_002239
SEQ ID NO 744



NM_002243
SEQ ID NO 745



NM_002245
SEQ ID NO 746



NM_002250
SEQ ID NO 747



NM_002254
SEQ ID NO 748



NM_002266
SEQ ID NO 749



NM_002273
SEQ ID NO 750



NM_002281
SEQ ID NO 751



NM_002292
SEQ ID NO 752



NM_002298
SEQ ID NO 753



NM_002300
SEQ ID NO 754



NM_002308
SEQ ID NO 755



NM_002314
SEQ ID NO 756



NM_002337
SEQ ID NO 757



NM_002341
SEQ ID NO 758



NM_002342
SEQ ID NO 759



NM_002346
SEQ ID NO 760



NM_002349
SEQ ID NO 761



NM_002350
SEQ ID NO 762



NM_002356
SEQ ID NO 763



NM_002358
SEQ ID NO 764



NM_002370
SEQ ID NO 765



NM_002395
SEQ ID NO 766



NM_002416
SEQ ID NO 767



NM_002421
SEQ ID NO 768



NM_002426
SEQ ID NO 769



NM_002435
SEQ ID NO 770



NM_002438
SEQ ID NO 771



NM_002444
SEQ ID NO 772



NM_002449
SEQ ID NO 773



NM_002450
SEQ ID NO 774



NM_002456
SEQ ID NO 775



NM_002466
SEQ ID NO 776



NM_002482
SEQ ID NO 777



NM_002497
SEQ ID NO 778



NM_002510
SEQ ID NO 779



NM_002515
SEQ ID NO 781



NM_002524
SEQ ID NO 782



NM_002539
SEQ ID NO 783



NM_002555
SEQ ID NO 785



NM_002570
SEQ ID NO 787



NM_002579
SEQ ID NO 788



NM_002587
SEQ ID NO 789



NM_002590
SEQ ID NO 790



NM_002600
SEQ ID NO 791



NM_002614
SEQ ID NO 792



NM_002618
SEQ ID NO 794



NM_002626
SEQ ID NO 795



NM_002633
SEQ ID NO 796



NM_002639
SEQ ID NO 797



NM_002648
SEQ ID NO 798



NM_002659
SEQ ID NO 799



NM_002661
SEQ ID NO 800



NM_002662
SEQ ID NO 801



NM_002664
SEQ ID NO 802



NM_002689
SEQ ID NO 804



NM_002690
SEQ ID NO 805



NM_002709
SEQ ID NO 806



NM_002727
SEQ ID NO 807



NM_002729
SEQ ID NO 808



NM_002734
SEQ ID NO 809



NM_002736
SEQ ID NO 810



NM_002740
SEQ ID NO 811



NM_002748
SEQ ID NO 813



NM_002774
SEQ ID NO 814



NM_002775
SEQ ID NO 815



NM_002776
SEQ ID NO 816



NM_002789
SEQ ID NO 817



NM_002794
SEQ ID NO 818



NM_002796
SEQ ID NO 819



NM_002800
SEQ ID NO 820



NM_002801
SEQ ID NO 821



NM_002808
SEQ ID NO 822



NM_002821
SEQ ID NO 824



NM_002826
SEQ ID NO 825



NM_002827
SEQ ID NO 826



NM_002838
SEQ ID NO 827



NM_002852
SEQ ID NO 828



NM_002854
SEQ ID NO 829



NM_002856
SEQ ID NO 830



NM_002857
SEQ ID NO 831



NM_002858
SEQ ID NO 832



NM_002888
SEQ ID NO 833



NM_002890
SEQ ID NO 834



NM_002901
SEQ ID NO 836



NM_002906
SEQ ID NO 837



NM_002916
SEQ ID NO 838



NM_002923
SEQ ID NO 839



NM_002933
SEQ ID NO 840



NM_002936
SEQ ID NO 841



NM_002937
SEQ ID NO 842



NM_002950
SEQ ID NO 843



NM_002961
SEQ ID NO 844



NM_002964
SEQ ID NO 845



NM_002965
SEQ ID NO 846



NM_002966
SEQ ID NO 847



NM_002982
SEQ ID NO 849



NM_002983
SEQ ID NO 850



NM_002984
SEQ ID NO 851



NM_002985
SEQ ID NO 852



NM_002988
SEQ ID NO 853



NM_002996
SEQ ID NO 854



NM_002997
SEQ ID NO 855



NM_002999
SEQ ID NO 856



NM_003012
SEQ ID NO 857



NM_003022
SEQ ID NO 858



NM_003034
SEQ ID NO 859



NM_003035
SEQ ID NO 860



NM_003039
SEQ ID NO 861



NM_003051
SEQ ID NO 862



NM_003064
SEQ ID NO 863



NM_003066
SEQ ID NO 864



NM_003088
SEQ ID NO 865



NM_003090
SEQ ID NO 866



NM_003096
SEQ ID NO 867



NM_003099
SEQ ID NO 868



NM_003102
SEQ ID NO 869



NM_003104
SEQ ID NO 870



NM_003108
SEQ ID NO 871



NM_003121
SEQ ID NO 873



NM_003134
SEQ ID NO 874



NM_003137
SEQ ID NO 875



NM_003144
SEQ ID NO 876



NM_003146
SEQ ID NO 877



NM_003149
SEQ ID NO 878



NM_003151
SEQ ID NO 879



NM_003157
SEQ ID NO 880



NM_003158
SEQ ID NO 881



NM_003165
SEQ ID NO 882



NM_003172
SEQ ID NO 883



NM_003177
SEQ ID NO 884



NM_003197
SEQ ID NO 885



NM_003202
SEQ ID NO 886



NM_003213
SEQ ID NO 887



NM_003217
SEQ ID NO 888



NM_003225
SEQ ID NO 889



NM_003226
SEQ ID NO 890



NM_003236
SEQ ID NO 892



NM_003239
SEQ ID NO 893



NM_003248
SEQ ID NO 894



NM_003255
SEQ ID NO 895



NM_003258
SEQ ID NO 896



NM_003264
SEQ ID NO 897



NM_003283
SEQ ID NO 898



NM_003318
SEQ ID NO 899



NM_003329
SEQ ID NO 900



NM_003332
SEQ ID NO 901



NM_003358
SEQ ID NO 902



NM_003359
SEQ ID NO 903



NM_003360
SEQ ID NO 904



NM_003368
SEQ ID NO 905



NM_003376
SEQ ID NO 906



NM_003380
SEQ ID NO 907



NM_003392
SEQ ID NO 908



NM_003412
SEQ ID NO 909



NM_003430
SEQ ID NO 910



NM_003462
SEQ ID NO 911



NM_003467
SEQ ID NO 912



NM_003472
SEQ ID NO 913



NM_003479
SEQ ID NO 914



NM_003489
SEQ ID NO 915



NM_003494
SEQ ID NO 916



NM_003498
SEQ ID NO 917



NM_003504
SEQ ID NO 919



NM_003508
SEQ ID NO 920



NM_003510
SEQ ID NO 921



NM_003512
SEQ ID NO 922



NM_003528
SEQ ID NO 923



NM_003544
SEQ ID NO 924



NM_003561
SEQ ID NO 925



NM_003563
SEQ ID NO 926



NM_003568
SEQ ID NO 927



NM_003579
SEQ ID NO 928



NM_003600
SEQ ID NO 929



NM_003615
SEQ ID NO 931



NM_003627
SEQ ID NO 932



NM_003645
SEQ ID NO 935



NM_003651
SEQ ID NO 936



NM_003657
SEQ ID NO 937



NM_003662
SEQ ID NO 938



NM_003670
SEQ ID NO 939



NM_003675
SEQ ID NO 940



NM_003676
SEQ ID NO 941



NM_003681
SEQ ID NO 942



NM_003683
SEQ ID NO 943



NM_003686
SEQ ID NO 944



NM_003689
SEQ ID NO 945



NM_003714
SEQ ID NO 946



NM_003720
SEQ ID NO 947



NM_003726
SEQ ID NO 948



NM_003729
SEQ ID NO 949



NM_003740
SEQ ID NO 950



NM_003772
SEQ ID NO 952



NM_003791
SEQ ID NO 953



NM_003793
SEQ ID NO 954



NM_003795
SEQ ID NO 955



NM_003806
SEQ ID NO 956



NM_003821
SEQ ID NO 957



NM_003829
SEQ ID NO 958



NM_003831
SEQ ID NO 959



NM_003862
SEQ ID NO 960



NM_003866
SEQ ID NO 961



NM_003875
SEQ ID NO 962



NM_003878
SEQ ID NO 963



NM_003894
SEQ ID NO 965



NM_003897
SEQ ID NO 966



NM_003904
SEQ ID NO 967



NM_003929
SEQ ID NO 968



NM_003933
SEQ ID NO 969



NM_003937
SEQ ID NO 970



NM_003940
SEQ ID NO 971



NM_003942
SEQ ID NO 972



NM_003944
SEQ ID NO 973



NM_003953
SEQ ID NO 974



NM_003954
SEQ ID NO 975



NM_003975
SEQ ID NO 976



NM_003981
SEQ ID NO 977



NM_003982
SEQ ID NO 978



NM_003986
SEQ ID NO 979



NM_004003
SEQ ID NO 980



NM_004010
SEQ ID NO 981



NM_004024
SEQ ID NO 982



NM_004038
SEQ ID NO 983



NM_004049
SEQ ID NO 984



NM_004052
SEQ ID NO 985



NM_004053
SEQ ID NO 986



NM_004079
SEQ ID NO 987



NM_004104
SEQ ID NO 988



NM_004109
SEQ ID NO 989



NM_004110
SEQ ID NO 990



NM_004120
SEQ ID NO 991



NM_004131
SEQ ID NO 992



NM_004143
SEQ ID NO 993



NM_004154
SEQ ID NO 994



NM_004170
SEQ ID NO 996



NM_004172
SEQ ID NO 997



NM_004176
SEQ ID NO 998



NM_004180
SEQ ID NO 999



NM_004181
SEQ ID NO 1000



NM_004184
SEQ ID NO 1001



NM_004203
SEQ ID NO 1002



NM_004207
SEQ ID NO 1003



NM_004217
SEQ ID NO 1004



NM_004219
SEQ ID NO 1005



NM_004221
SEQ ID NO 1006



NM_004233
SEQ ID NO 1007



NM_004244
SEQ ID NO 1008



NM_004252
SEQ ID NO 1009



NM_004265
SEQ ID NO 1010



NM_004267
SEQ ID NO 1011



NM_004281
SEQ ID NO 1012



NM_004289
SEQ ID NO 1013



NM_004298
SEQ ID NO 1015



NM_004301
SEQ ID NO 1016



NM_004305
SEQ ID NO 1017



NM_004311
SEQ ID NO 1018



NM_004315
SEQ ID NO 1019



NM_004323
SEQ ID NO 1020



NM_004330
SEQ ID NO 1021



NM_004336
SEQ ID NO 1022



NM_004338
SEQ ID NO 1023



NM_004350
SEQ ID NO 1024



NM_004354
SEQ ID NO 1025



NM_004358
SEQ ID NO 1026



NM_004360
SEQ ID NO 1027



NM_004362
SEQ ID NO 1028



NM_004374
SEQ ID NO 1029



NM_004378
SEQ ID NO 1030



NM_004392
SEQ ID NO 1031



NM_004395
SEQ ID NO 1032



NM_004414
SEQ ID NO 1033



NM_004418
SEQ ID NO 1034



NM_004425
SEQ ID NO 1035



NM_004431
SEQ ID NO 1036



NM_004436
SEQ ID NO 1037



NM_004438
SEQ ID NO 1038



NM_004443
SEQ ID NO 1039



NM_004446
SEQ ID NO 1040



NM_004451
SEQ ID NO 1041



NM_004454
SEQ ID NO 1042



NM_004456
SEQ ID NO 1043



NM_004458
SEQ ID NO 1044



NM_004472
SEQ ID NO 1045



NM_004480
SEQ ID NO 1046



NM_004482
SEQ ID NO 1047



NM_004494
SEQ ID NO 1048



NM_004496
SEQ ID NO 1049



NM_004503
SEQ ID NO 1050



NM_004504
SEQ ID NO 1051



NM_004515
SEQ ID NO 1052



NM_004522
SEQ ID NO 1053



NM_004523
SEQ ID NO 1054



NM_004525
SEQ ID NO 1055



NM_004556
SEQ ID NO 1056



NM_004559
SEQ ID NO 1057



NM_004569
SEQ ID NO 1058



NM_004577
SEQ ID NO 1059



NM_004585
SEQ ID NO 1060



NM_004587
SEQ ID NO 1061



NM_004594
SEQ ID NO 1062



NM_004599
SEQ ID NO 1063



NM_004633
SEQ ID NO 1066



NM_004642
SEQ ID NO 1067



NM_004648
SEQ ID NO 1068



NM_004663
SEQ ID NO 1069



NM_004664
SEQ ID NO 1070



NM_004684
SEQ ID NO 1071



NM_004688
SEQ ID NO 1072



NM_004694
SEQ ID NO 1073



NM_004695
SEQ ID NO 1074



NM_004701
SEQ ID NO 1075



NM_004708
SEQ ID NO 1077



NM_004711
SEQ ID NO 1078



NM_004726
SEQ ID NO 1079



NM_004750
SEQ ID NO 1081



NM_004761
SEQ ID NO 1082



NM_004762
SEQ ID NO 1083



NM_004780
SEQ ID NO 1085



NM_004791
SEQ ID NO 1086



NM_004798
SEQ ID NO 1087



NM_004808
SEQ ID NO 1088



NM_004811
SEQ ID NO 1089



NM_004833
SEQ ID NO 1090



NM_004835
SEQ ID NO 1091



NM_004843
SEQ ID NO 1092



NM_004847
SEQ ID NO 1093



NM_004848
SEQ ID NO 1094



NM_004864
SEQ ID NO 1095



NM_004865
SEQ ID NO 1096



NM_004866
SEQ ID NO 1097



NM_004877
SEQ ID NO 1098



NM_004900
SEQ ID NO 1099



NM_004906
SEQ ID NO 1100



NM_004910
SEQ ID NO 1101



NM_004918
SEQ ID NO 1103



NM_004923
SEQ ID NO 1104



NM_004938
SEQ ID NO 1105



NM_004951
SEQ ID NO 1106



NM_004968
SEQ ID NO 1107



NM_004994
SEQ ID NO 1108



NM_004999
SEQ ID NO 1109



NM_005001
SEQ ID NO 1110



NM_005002
SEQ ID NO 1111



NM_005012
SEQ ID NO 1112



NM_005032
SEQ ID NO 1113



NM_005044
SEQ ID NO 1114



NM_005046
SEQ ID NO 1115



NM_005049
SEQ ID NO 1116



NM_005067
SEQ ID NO 1117



NM_005077
SEQ ID NO 1118



NM_005080
SEQ ID NO 1119



NM_005084
SEQ ID NO 1120



NM_005130
SEQ ID NO 1122



NM_005139
SEQ ID NO 1123



NM_005168
SEQ ID NO 1125



NM_005190
SEQ ID NO 1126



NM_005196
SEQ ID NO 1127



NM_005213
SEQ ID NO 1128



NM_005218
SEQ ID NO 1129



NM_005235
SEQ ID NO 1130



NM_005245
SEQ ID NO 1131



NM_005249
SEQ ID NO 1132



NM_005257
SEQ ID NO 1133



NM_005264
SEQ ID NO 1134



NM_005271
SEQ ID NO 1135



NM_005314
SEQ ID NO 1136



NM_005321
SEQ ID NO 1137



NM_005322
SEQ ID NO 1138



NM_005325
SEQ ID NO 1139



NM_005326
SEQ ID NO 1140



NM_005335
SEQ ID NO 1141



NM_005337
SEQ ID NO 1142



NM_005342
SEQ ID NO 1143



NM_005345
SEQ ID NO 1144



NM_005357
SEQ ID NO 1145



NM_005375
SEQ ID NO 1146



NM_005391
SEQ ID NO 1147



NM_005408
SEQ ID NO 1148



NM_005409
SEQ ID NO 1149



NM_005410
SEQ ID NO 1150



NM_005426
SEQ ID NO 1151



NM_005433
SEQ ID NO 1152



NM_005441
SEQ ID NO 1153



NM_005443
SEQ ID NO 1154



NM_005483
SEQ ID NO 1155



NM_005486
SEQ ID NO 1156



NM_005496
SEQ ID NO 1157



NM_005498
SEQ ID NO 1158



NM_005499
SEQ ID NO 1159



NM_005514
SEQ ID NO 1160



NM_005531
SEQ ID NO 1162



NM_005538
SEQ ID NO 1163



NM_005541
SEQ ID NO 1164



NM_005544
SEQ ID NO 1165



NM_005548
SEQ ID NO 1166



NM_005554
SEQ ID NO 1167



NM_005555
SEQ ID NO 1168



NM_005556
SEQ ID NO 1169



NM_005557
SEQ ID NO 1170



NM_005558
SEQ ID NO 1171



NM_005562
SEQ ID NO 1172



NM_005563
SEQ ID NO 1173



NM_005565
SEQ ID NO 1174



NM_005566
SEQ ID NO 1175



NM_005572
SEQ ID NO 1176



NM_005582
SEQ ID NO 1177



NM_005608
SEQ ID NO 1178



NM_005614
SEQ ID NO 1179



NM_005617
SEQ ID NO 1180



NM_005620
SEQ ID NO 1181



NM_005625
SEQ ID NO 1182



NM_005651
SEQ ID NO 1183



NM_005658
SEQ ID NO 1184



NM_005659
SEQ ID NO 1185



NM_005667
SEQ ID NO 1186



NM_005686
SEQ ID NO 1187



NM_005690
SEQ ID NO 1188



NM_005720
SEQ ID NO 1190



NM_005727
SEQ ID NO 1191



NM_005733
SEQ ID NO 1192



NM_005737
SEQ ID NO 1193



NM_005742
SEQ ID NO 1194



NM_005746
SEQ ID NO 1195



NM_005749
SEQ ID NO 1196



NM_005760
SEQ ID NO 1197



NM_005764
SEQ ID NO 1198



NM_005794
SEQ ID NO 1199



NM_005796
SEQ ID NO 1200



NM_005804
SEQ ID NO 1201



NM_005813
SEQ ID NO 1202



NM_005824
SEQ ID NO 1203



NM_005825
SEQ ID NO 1204



NM_005849
SEQ ID NO 1205



NM_005853
SEQ ID NO 1206



NM_005855
SEQ ID NO 1207



NM_005864
SEQ ID NO 1208



NM_005874
SEQ ID NO 1209



NM_005876
SEQ ID NO 1210



NM_005880
SEQ ID NO 1211



NM_005891
SEQ ID NO 1212



NM_005892
SEQ ID NO 1213



NM_005899
SEQ ID NO 1214



NM_005915
SEQ ID NO 1215



NM_005919
SEQ ID NO 1216



NM_005923
SEQ ID NO 1217



NM_005928
SEQ ID NO 1218



NM_005932
SEQ ID NO 1219



NM_005935
SEQ ID NO 1220



NM_005945
SEQ ID NO 1221



NM_005953
SEQ ID NO 1222



NM_005978
SEQ ID NO 1223



NM_005990
SEQ ID NO 1224



NM_006002
SEQ ID NO 1225



NM_006004
SEQ ID NO 1226



NM_006005
SEQ ID NO 1227



NM_006006
SEQ ID NO 1228



NM_006017
SEQ ID NO 1229



NM_006018
SEQ ID NO 1230



NM_006023
SEQ ID NO 1231



NM_006027
SEQ ID NO 1232



NM_006029
SEQ ID NO 1233



NM_006033
SEQ ID NO 1234



NM_006051
SEQ ID NO 1235



NM_006055
SEQ ID NO 1236



NM_006074
SEQ ID NO 1237



NM_006086
SEQ ID NO 1238



NM_006087
SEQ ID NO 1239



NM_006096
SEQ ID NO 1240



NM_006101
SEQ ID NO 1241



NM_006103
SEQ ID NO 1242



NM_006111
SEQ ID NO 1243



NM_006113
SEQ ID NO 1244



NM_006115
SEQ ID NO 1245



NM_006117
SEQ ID NO 1246



NM_006142
SEQ ID NO 1247



NM_006144
SEQ ID NO 1248



NM_006148
SEQ ID NO 1249



NM_006153
SEQ ID NO 1250



NM_006159
SEQ ID NO 1251



NM_006170
SEQ ID NO 1252



NM_006197
SEQ ID NO 1253



NM_006224
SEQ ID NO 1255



NM_006227
SEQ ID NO 1256



NM_006235
SEQ ID NO 1257



NM_006243
SEQ ID NO 1258



NM_006264
SEQ ID NO 1259



NM_006271
SEQ ID NO 1261



NM_006274
SEQ ID NO 1262



NM_006290
SEQ ID NO 1265



NM_006291
SEQ ID NO 1266



NM_006296
SEQ ID NO 1267



NM_006304
SEQ ID NO 1268



NM_006314
SEQ ID NO 1269



NM_006332
SEQ ID NO 1270



NM_006357
SEQ ID NO 1271



NM_006366
SEQ ID NO 1272



NM_006372
SEQ ID NO 1273



NM_006377
SEQ ID NO 1274



NM_006378
SEQ ID NO 1275



NM_006383
SEQ ID NO 1276



NM_006389
SEQ ID NO 1277



NM_006393
SEQ ID NO 1278



NM_006398
SEQ ID NO 1279



NM_006406
SEQ ID NO 1280



NM_006408
SEQ ID NO 1281



NM_006410
SEQ ID NO 1282



NM_006414
SEQ ID NO 1283



NM_006417
SEQ ID NO 1284



NM_006430
SEQ ID NO 1285



NM_006460
SEQ ID NO 1286



NM_006461
SEQ ID NO 1287



NM_006469
SEQ ID NO 1288



NM_006470
SEQ ID NO 1289



NM_006491
SEQ ID NO 1290



NM_006495
SEQ ID NO 1291



NM_006500
SEQ ID NO 1292



NM_006509
SEQ ID NO 1293



NM_006516
SEQ ID NO 1294



NM_006533
SEQ ID NO 1295



NM_006551
SEQ ID NO 1296



NM_006556
SEQ ID NO 1297



NM_006558
SEQ ID NO 1298



NM_006564
SEQ ID NO 1299



NM_006573
SEQ ID NO 1300



NM_006607
SEQ ID NO 1301



NM_006622
SEQ ID NO 1302



NM_006623
SEQ ID NO 1303



NM_006636
SEQ ID NO 1304



NM_006670
SEQ ID NO 1305



NM_006681
SEQ ID NO 1306



NM_006682
SEQ ID NO 1307



NM_006696
SEQ ID NO 1308



NM_006698
SEQ ID NO 1309



NM_006705
SEQ ID NO 1310



NM_006739
SEQ ID NO 1311



NM_006748
SEQ ID NO 1312



NM_006759
SEQ ID NO 1313



NM_006762
SEQ ID NO 1314



NM_006763
SEQ ID NO 1315



NM_006769
SEQ ID NO 1316



NM_006770
SEQ ID NO 1317



NM_006780
SEQ ID NO 1318



NM_006787
SEQ ID NO 1319



NM_006806
SEQ ID NO 1320



NM_006813
SEQ ID NO 1321



NM_006825
SEQ ID NO 1322



NM_006826
SEQ ID NO 1323



NM_006829
SEQ ID NO 1324



NM_006834
SEQ ID NO 1325



NM_006835
SEQ ID NO 1326



NM_006840
SEQ ID NO 1327



NM_006845
SEQ ID NO 1328



NM_006847
SEQ ID NO 1329



NM_006851
SEQ ID NO 1330



NM_006855
SEQ ID NO 1331



NM_006864
SEQ ID NO 1332



NM_006868
SEQ ID NO 1333



NM_006875
SEQ ID NO 1334



NM_006889
SEQ ID NO 1336



NM_006892
SEQ ID NO 1337



NM_006912
SEQ ID NO 1338



NM_006931
SEQ ID NO 1341



NM_006941
SEQ ID NO 1342



NM_006943
SEQ ID NO 1343



NM_006984
SEQ ID NO 1344



NM_007005
SEQ ID NO 1345



NM_007006
SEQ ID NO 1346



NM_007019
SEQ ID NO 1347



NM_007027
SEQ ID NO 1348



NM_007044
SEQ ID NO 1350



NM_007050
SEQ ID NO 1351



NM_007057
SEQ ID NO 1352



NM_007069
SEQ ID NO 1353



NM_007074
SEQ ID NO 1355



NM_007088
SEQ ID NO 1356



NM_007111
SEQ ID NO 1357



NM_007146
SEQ ID NO 1358



NM_007173
SEQ ID NO 1359



NM_007177
SEQ ID NO 1360



NM_007196
SEQ ID NO 1361



NM_007203
SEQ ID NO 1362



NM_007214
SEQ ID NO 1363



NM_007217
SEQ ID NO 1364



NM_007231
SEQ ID NO 1365



NM_007268
SEQ ID NO 1367



NM_007274
SEQ ID NO 1368



NM_007275
SEQ ID NO 1369



NM_007281
SEQ ID NO 1370



NM_007309
SEQ ID NO 1371



NM_007315
SEQ ID NO 1372



NM_007334
SEQ ID NO 1373



NM_007358
SEQ ID NO 1374



NM_009585
SEQ ID NO 1375



NM_009587
SEQ ID NO 1376



NM_009588
SEQ ID NO 1377



NM_012062
SEQ ID NO 1378



NM_012067
SEQ ID NO 1379



NM_012101
SEQ ID NO 1380



NM_012105
SEQ ID NO 1381



NM_012108
SEQ ID NO 1382



NM_012110
SEQ ID NO 1383



NM_012124
SEQ ID NO 1384



NM_012142
SEQ ID NO 1386



NM_012155
SEQ ID NO 1388



NM_012175
SEQ ID NO 1389



NM_012177
SEQ ID NO 1390



NM_012205
SEQ ID NO 1391



NM_012219
SEQ ID NO 1393



NM_012242
SEQ ID NO 1394



NM_012250
SEQ ID NO 1395



NM_012261
SEQ ID NO 1397



NM_012286
SEQ ID NO 1398



NM_012319
SEQ ID NO 1400



NM_012332
SEQ ID NO 1401



NM_012336
SEQ ID NO 1402



NM_012339
SEQ ID NO 1404



NM_012341
SEQ ID NO 1405



NM_012391
SEQ ID NO 1406



NM_012394
SEQ ID NO 1407



NM_012413
SEQ ID NO 1408



NM_012421
SEQ ID NO 1409



NM_012425
SEQ ID NO 1410



NM_012427
SEQ ID NO 1411



NM_012429
SEQ ID NO 1413



NM_012446
SEQ ID NO 1414



NM_012463
SEQ ID NO 1415



NM_012474
SEQ ID NO 1416



NM_013230
SEQ ID NO 1417



NM_013233
SEQ ID NO 1418



NM_013238
SEQ ID NO 1419



NM_013239
SEQ ID NO 1420



NM_013242
SEQ ID NO 1421



NM_013257
SEQ ID NO 1423



NM_013261
SEQ ID NO 1424



NM_013262
SEQ ID NO 1425



NM_013277
SEQ ID NO 1426



NM_013296
SEQ ID NO 1427



NM_013301
SEQ ID NO 1428



NM_013324
SEQ ID NO 1429



NM_013327
SEQ ID NO 1430



NM_013336
SEQ ID NO 1431



NM_013339
SEQ ID NO 1432



NM_013363
SEQ ID NO 1433



NM_013378
SEQ ID NO 1435



NM_013384
SEQ ID NO 1436



NM_013385
SEQ ID NO 1437



NM_013406
SEQ ID NO 1438



NM_013437
SEQ ID NO 1439



NM_013451
SEQ ID NO 1440



NM_013943
SEQ ID NO 1441



NM_013994
SEQ ID NO 1442



NM_013995
SEQ ID NO 1443



NM_014026
SEQ ID NO 1444



NM_014029
SEQ ID NO 1445



NM_014036
SEQ ID NO 1446



NM_014062
SEQ ID NO 1447



NM_014074
SEQ ID NO 1448



NM_014096
SEQ ID NO 1450



NM_014109
SEQ ID NO 1451



NM_014112
SEQ ID NO 1452



NM_014147
SEQ ID NO 1453



NM_014149
SEQ ID NO 1454



NM_014164
SEQ ID NO 1455



NM_014172
SEQ ID NO 1456



NM_014175
SEQ ID NO 1457



NM_014181
SEQ ID NO 1458



NM_014184
SEQ ID NO 1459



NM_014211
SEQ ID NO 1460



NM_014214
SEQ ID NO 1461



NM_014216
SEQ ID NO 1462



NM_014241
SEQ ID NO 1463



NM_014246
SEQ ID NO 1465



NM_014268
SEQ ID NO 1466



NM_014272
SEQ ID NO 1467



NM_014274
SEQ ID NO 1468



NM_014289
SEQ ID NO 1469



NM_014298
SEQ ID NO 1470



NM_014302
SEQ ID NO 1471



NM_014315
SEQ ID NO 1473



NM_014316
SEQ ID NO 1474



NM_014317
SEQ ID NO 1475



NM_014320
SEQ ID NO 1476



NM_014321
SEQ ID NO 1477



NM_014325
SEQ ID NO 1478



NM_014335
SEQ ID NO 1479



NM_014363
SEQ ID NO 1480



NM_014364
SEQ ID NO 1481



NM_014365
SEQ ID NO 1482



NM_014373
SEQ ID NO 1483



NM_014382
SEQ ID NO 1484



NM_014395
SEQ ID NO 1485



NM_014398
SEQ ID NO 1486



NM_014399
SEQ ID NO 1487



NM_014402
SEQ ID NO 1488



NM_014428
SEQ ID NO 1489



NM_014448
SEQ ID NO 1490



NM_014449
SEQ ID NO 1491



NM_014450
SEQ ID NO 1492



NM_014452
SEQ ID NO 1493



NM_014453
SEQ ID NO 1494



NM_014456
SEQ ID NO 1495



NM_014479
SEQ ID NO 1497



NM_014501
SEQ ID NO 1498



NM_014552
SEQ ID NO 1500



NM_014553
SEQ ID NO 1501



NM_014570
SEQ ID NO 1502



NM_014575
SEQ ID NO 1503



NM_014585
SEQ ID NO 1504



NM_014595
SEQ ID NO 1505



NM_014624
SEQ ID NO 1507



NM_014633
SEQ ID NO 1508



NM_014640
SEQ ID NO 1509



NM_014642
SEQ ID NO 1510



NM_014643
SEQ ID NO 1511



NM_014656
SEQ ID NO 1512



NM_014668
SEQ ID NO 1513



NM_014669
SEQ ID NO 1514



NM_014673
SEQ ID NO 1515



NM_014675
SEQ ID NO 1516



NM_014679
SEQ ID NO 1517



NM_014680
SEQ ID NO 1518



NM_014696
SEQ ID NO 1519



NM_014700
SEQ ID NO 1520



NM_014715
SEQ ID NO 1521



NM_014721
SEQ ID NO 1522



NM_014737
SEQ ID NO 1524



NM_014738
SEQ ID NO 1525



NM_014747
SEQ ID NO 1526



NM_014750
SEQ ID NO 1527



NM_014754
SEQ ID NO 1528



NM_014767
SEQ ID NO 1529



NM_014770
SEQ ID NO 1530



NM_014773
SEQ ID NO 1531



NM_014776
SEQ ID NO 1532



NM_014782
SEQ ID NO 1533



NM_014785
SEQ ID NO 1534



NM_014791
SEQ ID NO 1535



NM_014808
SEQ ID NO 1536



NM_014811
SEQ ID NO 1537



NM_014812
SEQ ID NO 1538



NM_014838
SEQ ID NO 1540



NM_014862
SEQ ID NO 1542



NM_014865
SEQ ID NO 1543



NM_014870
SEQ ID NO 1544



NM_014875
SEQ ID NO 1545



NM_014886
SEQ ID NO 1547



NM_014889
SEQ ID NO 1548



NM_014905
SEQ ID NO 1549



NM_014935
SEQ ID NO 1550



NM_014945
SEQ ID NO 1551



NM_014965
SEQ ID NO 1552



NM_014967
SEQ ID NO 1553



NM_014968
SEQ ID NO 1554



NM_015032
SEQ ID NO 1555



NM_015239
SEQ ID NO 1556



NM_015383
SEQ ID NO 1557



NM_015392
SEQ ID NO 1558



NM_015416
SEQ ID NO 1559



NM_015417
SEQ ID NO 1560



NM_015420
SEQ ID NO 1561



NM_015434
SEQ ID NO 1562



NM_015474
SEQ ID NO 1563



NM_015507
SEQ ID NO 1565



NM_015513
SEQ ID NO 1566



NM_015515
SEQ ID NO 1567



NM_015523
SEQ ID NO 1568



NM_015524
SEQ ID NO 1569



NM_015599
SEQ ID NO 1571



NM_015623
SEQ ID NO 1572



NM_015640
SEQ ID NO 1573



NM_015641
SEQ ID NO 1574



NM_015678
SEQ ID NO 1575



NM_015721
SEQ ID NO 1576



NM_015892
SEQ ID NO 1578



NM_015895
SEQ ID NO 1579



NM_015907
SEQ ID NO 1580



NM_015925
SEQ ID NO 1581



NM_015937
SEQ ID NO 1582



NM_015954
SEQ ID NO 1583



NM_015955
SEQ ID NO 1584



NM_015961
SEQ ID NO 1585



NM_015984
SEQ ID NO 1587



NM_015986
SEQ ID NO 1588



NM_015987
SEQ ID NO 1589



NM_015991
SEQ ID NO 1590



NM_016002
SEQ ID NO 1592



NM_016028
SEQ ID NO 1594



NM_016029
SEQ ID NO 1595



NM_016047
SEQ ID NO 1596



NM_016048
SEQ ID NO 1597



NM_016050
SEQ ID NO 1598



NM_016056
SEQ ID NO 1599



NM_016058
SEQ ID NO 1600



NM_016066
SEQ ID NO 1601



NM_016072
SEQ ID NO 1602



NM_016073
SEQ ID NO 1603



NM_016108
SEQ ID NO 1605



NM_016109
SEQ ID NO 1606



NM_016121
SEQ ID NO 1607



NM_016126
SEQ ID NO 1608



NM_016127
SEQ ID NO 1609



NM_016135
SEQ ID NO 1610



NM_016142
SEQ ID NO 1612



NM_016153
SEQ ID NO 1613



NM_016171
SEQ ID NO 1614



NM_016175
SEQ ID NO 1615



NM_016184
SEQ ID NO 1616



NM_016185
SEQ ID NO 1617



NM_016187
SEQ ID NO 1618



NM_016199
SEQ ID NO 1619



NM_016210
SEQ ID NO 1620



NM_016217
SEQ ID NO 1621



NM_016228
SEQ ID NO 1623



NM_016229
SEQ ID NO 1624



NM_016235
SEQ ID NO 1625



NM_016240
SEQ ID NO 1626



NM_016243
SEQ ID NO 1627



NM_016250
SEQ ID NO 1628



NM_016267
SEQ ID NO 1629



NM_016271
SEQ ID NO 1630



NM_016299
SEQ ID NO 1631



NM_016306
SEQ ID NO 1632



NM_016308
SEQ ID NO 1634



NM_016321
SEQ ID NO 1635



NM_016337
SEQ ID NO 1636



NM_016352
SEQ ID NO 1637



NM_016359
SEQ ID NO 1638



NM_016401
SEQ ID NO 1641



NM_016403
SEQ ID NO 1642



NM_016411
SEQ ID NO 1643



NM_016423
SEQ ID NO 1644



NM_016463
SEQ ID NO 1647



NM_016475
SEQ ID NO 1649



NM_016477
SEQ ID NO 1650



NM_016491
SEQ ID NO 1651



NM_016495
SEQ ID NO 1652



NM_016542
SEQ ID NO 1653



NM_016548
SEQ ID NO 1654



NM_016569
SEQ ID NO 1655



NM_016577
SEQ ID NO 1656



NM_016582
SEQ ID NO 1657



NM_016593
SEQ ID NO 1658



NM_016603
SEQ ID NO 1659



NM_016612
SEQ ID NO 1660



NM_016619
SEQ ID NO 1661



NM_016623
SEQ ID NO 1663



NM_016625
SEQ ID NO 1664



NM_016629
SEQ ID NO 1665



NM_016640
SEQ ID NO 1666



NM_016645
SEQ ID NO 1667



NM_016650
SEQ ID NO 1668



NM_016657
SEQ ID NO 1669



NM_016733
SEQ ID NO 1670



NM_016815
SEQ ID NO 1671



NM_016817
SEQ ID NO 1672



NM_016818
SEQ ID NO 1673



NM_016839
SEQ ID NO 1675



NM_017414
SEQ ID NO 1676



NM_017422
SEQ ID NO 1677



NM_017423
SEQ ID NO 1678



NM_017447
SEQ ID NO 1679



NM_017518
SEQ ID NO 1680



NM_017522
SEQ ID NO 1681



NM_017540
SEQ ID NO 1682



NM_017555
SEQ ID NO 1683



NM_017572
SEQ ID NO 1684



NM_017585
SEQ ID NO 1685



NM_017586
SEQ ID NO 1686



NM_017596
SEQ ID NO 1687



NM_017606
SEQ ID NO 1688



NM_017617
SEQ ID NO 1689



NM_017633
SEQ ID NO 1690



NM_017634
SEQ ID NO 1691



NM_017646
SEQ ID NO 1692



NM_017660
SEQ ID NO 1693



NM_017680
SEQ ID NO 1694



NM_017691
SEQ ID NO 1695



NM_017698
SEQ ID NO 1696



NM_017702
SEQ ID NO 1697



NM_017731
SEQ ID NO 1699



NM_017732
SEQ ID NO 1700



NM_017733
SEQ ID NO 1701



NM_017734
SEQ ID NO 1702



NM_017746
SEQ ID NO 1703



NM_017750
SEQ ID NO 1704



NM_017761
SEQ ID NO 1705



NM_017763
SEQ ID NO 1706



NM_017770
SEQ ID NO 1707



NM_017779
SEQ ID NO 1708



NM_017780
SEQ ID NO 1709



NM_017782
SEQ ID NO 1710



NM_017786
SEQ ID NO 1711



NM_017791
SEQ ID NO 1712



NM_017805
SEQ ID NO 1713



NM_017816
SEQ ID NO 1714



NM_017821
SEQ ID NO 1715



NM_017835
SEQ ID NO 1716



NM_017843
SEQ ID NO 1717



NM_017857
SEQ ID NO 1718



NM_017901
SEQ ID NO 1719



NM_017906
SEQ ID NO 1720



NM_017918
SEQ ID NO 1721



NM_017961
SEQ ID NO 1722



NM_017996
SEQ ID NO 1723



NM_018000
SEQ ID NO 1724



NM_018004
SEQ ID NO 1725



NM_018011
SEQ ID NO 1726



NM_018014
SEQ ID NO 1727



NM_018022
SEQ ID NO 1728



NM_018031
SEQ ID NO 1729



NM_018043
SEQ ID NO 1730



NM_018048
SEQ ID NO 1731



NM_018062
SEQ ID NO 1732



NM_018069
SEQ ID NO 1733



NM_018072
SEQ ID NO 1734



NM_018077
SEQ ID NO 1735



NM_018086
SEQ ID NO 1736



NM_018087
SEQ ID NO 1737



NM_018093
SEQ ID NO 1738



NM_018098
SEQ ID NO 1739



NM_018099
SEQ ID NO 1740



NM_018101
SEQ ID NO 1741



NM_018103
SEQ ID NO 1742



NM_018109
SEQ ID NO 1744



NM_018123
SEQ ID NO 1746



NM_018131
SEQ ID NO 1747



NM_018136
SEQ ID NO 1748



NM_018138
SEQ ID NO 1749



NM_018166
SEQ ID NO 1750



NM_018171
SEQ ID NO 1751



NM_018178
SEQ ID NO 1752



NM_018181
SEQ ID NO 1753



NM_018186
SEQ ID NO 1754



NM_018188
SEQ ID NO 1756



NM_018194
SEQ ID NO 1757



NM_018204
SEQ ID NO 1758



NM_018208
SEQ ID NO 1759



NM_018212
SEQ ID NO 1760



NM_018234
SEQ ID NO 1763



NM_018255
SEQ ID NO 1764



NM_018257
SEQ ID NO 1765



NM_018265
SEQ ID NO 1766



NM_018271
SEQ ID NO 1767



NM_018290
SEQ ID NO 1768



NM_018295
SEQ ID NO 1769



NM_018304
SEQ ID NO 1770



NM_018306
SEQ ID NO 1771



NM_018326
SEQ ID NO 1772



NM_018346
SEQ ID NO 1773



NM_018366
SEQ ID NO 1775



NM_018370
SEQ ID NO 1776



NM_018373
SEQ ID NO 1777



NM_018379
SEQ ID NO 1778



NM_018384
SEQ ID NO 1779



NM_018389
SEQ ID NO 1780



NM_018410
SEQ ID NO 1783



NM_018439
SEQ ID NO 1785



NM_018454
SEQ ID NO 1786



NM_018455
SEQ ID NO 1787



NM_018465
SEQ ID NO 1788



NM_018471
SEQ ID NO 1789



NM_018478
SEQ ID NO 1790



NM_018479
SEQ ID NO 1791



NM_018529
SEQ ID NO 1793



NM_018556
SEQ ID NO 1794



NM_018569
SEQ ID NO 1795



NM_018584
SEQ ID NO 1796



NM_018653
SEQ ID NO 1797



NM_018660
SEQ ID NO 1798



NM_018683
SEQ ID NO 1799



NM_018685
SEQ ID NO 1800



NM_018686
SEQ ID NO 1801



NM_018695
SEQ ID NO 1802



NM_018728
SEQ ID NO 1803



NM_018840
SEQ ID NO 1804



NM_018842
SEQ ID NO 1805



NM_018950
SEQ ID NO 1806



NM_018988
SEQ ID NO 1807



NM_019000
SEQ ID NO 1808



NM_019013
SEQ ID NO 1809



NM_019025
SEQ ID NO 1810



NM_019027
SEQ ID NO 1811



NM_019041
SEQ ID NO 1812



NM_019044
SEQ ID NO 1813



NM_019063
SEQ ID NO 1815



NM_019084
SEQ ID NO 1816



NM_019554
SEQ ID NO 1817



NM_019845
SEQ ID NO 1818



NM_019858
SEQ ID NO 1819



NM_020130
SEQ ID NO 1820



NM_020133
SEQ ID NO 1821



NM_020143
SEQ ID NO 1822



NM_020150
SEQ ID NO 1823



NM_020163
SEQ ID NO 1824



NM_020166
SEQ ID NO 1825



NM_020169
SEQ ID NO 1826



NM_020179
SEQ ID NO 1827



NM_020184
SEQ ID NO 1828



NM_020186
SEQ ID NO 1829



NM_020188
SEQ ID NO 1830



NM_020189
SEQ ID NO 1831



NM_020197
SEQ ID NO 1832



NM_020199
SEQ ID NO 1833



NM_020215
SEQ ID NO 1834



NM_020347
SEQ ID NO 1836



NM_020365
SEQ ID NO 1837



NM_020386
SEQ ID NO 1838



NM_020445
SEQ ID NO 1839



NM_020639
SEQ ID NO 1840



NM_020659
SEQ ID NO 1841



NM_020675
SEQ ID NO 1842



NM_020686
SEQ ID NO 1843



NM_020974
SEQ ID NO 1844



NM_020978
SEQ ID NO 1845



NM_020979
SEQ ID NO 1846



NM_020980
SEQ ID NO 1847



NM_021000
SEQ ID NO 1849



NM_021004
SEQ ID NO 1850



NM_021025
SEQ ID NO 1851



NM_021063
SEQ ID NO 1852



NM_021065
SEQ ID NO 1853



NM_021077
SEQ ID NO 1854



NM_021095
SEQ ID NO 1855



NM_021101
SEQ ID NO 1856



NM_021103
SEQ ID NO 1857



NM_021128
SEQ ID NO 1858



NM_021147
SEQ ID NO 1859



NM_021151
SEQ ID NO 1860



NM_021181
SEQ ID NO 1861



NM_021190
SEQ ID NO 1862



NM_021198
SEQ ID NO 1863



NM_021200
SEQ ID NO 1864



NM_021203
SEQ ID NO 1865



NM_021238
SEQ ID NO 1866



NM_021242
SEQ ID NO 1867



S40706
SEQ ID NO 1869



S53354
SEQ ID NO 1870



S59184
SEQ ID NO 1871



S62138
SEQ ID NO 1872



U09848
SEQ ID NO 1873



U10991
SEQ ID NO 1874



U17077
SEQ ID NO 1875



U18919
SEQ ID NO 1876



U41387
SEQ ID NO 1877



U45975
SEQ ID NO 1878



U49835
SEQ ID NO 1879



U56725
SEQ ID NO 1880



U58033
SEQ ID NO 1881



U61167
SEQ ID NO 1882



U66042
SEQ ID NO 1883



U68385
SEQ ID NO 1885



U68494
SEQ ID NO 1886



U74612
SEQ ID NO 1887



U75968
SEQ ID NO 1888



U79293
SEQ ID NO 1889



U80736
SEQ ID NO 1890



U82987
SEQ ID NO 1891



U83115
SEQ ID NO 1892



U89715
SEQ ID NO 1893



U90916
SEQ ID NO 1894



U92544
SEQ ID NO 1895



U96131
SEQ ID NO 1896



U96394
SEQ ID NO 1897



W61000_RC
SEQ ID NO 1898



X00437
SEQ ID NO 1899



X00497
SEQ ID NO 1900



X01394
SEQ ID NO 1901



X03084
SEQ ID NO 1902



X07834
SEQ ID NO 1905



X14356
SEQ ID NO 1906



X16302
SEQ ID NO 1907



X52486
SEQ ID NO 1909



X52882
SEQ ID NO 1910



X56807
SEQ ID NO 1911



X57809
SEQ ID NO 1912



X57819
SEQ ID NO 1913



X58529
SEQ ID NO 1914



X59405
SEQ ID NO 1915



X72475
SEQ ID NO 1918



X73617
SEQ ID NO 1919



X74794
SEQ ID NO 1920



X75315
SEQ ID NO 1921



X79782
SEQ ID NO 1922



X82693
SEQ ID NO 1923



X83301
SEQ ID NO 1924



X93006
SEQ ID NO 1926



X94232
SEQ ID NO 1927



X98834
SEQ ID NO 1929



X99142
SEQ ID NO 1930



Y14737
SEQ ID NO 1932



Z11887
SEQ ID NO 1933



Z48633
SEQ ID NO 1935



NM_004222
SEQ ID NO 1936



NM_016405
SEQ ID NO 1937



NM_017690
SEQ ID NO 1938



Contig29_RC
SEQ ID NO 1939



Contig237_RC
SEQ ID NO 1940



Contig263_RC
SEQ ID NO 1941



Contig292_RC
SEQ ID NO 1942



Contig382_RC
SEQ ID NO 1944



Contig399_RC
SEQ ID NO 1945



Contig448_RC
SEQ ID NO 1946



Contig569_RC
SEQ ID NO 1947



Contig580_RC
SEQ ID NO 1948



Contig678_RC
SEQ ID NO 1949



Contig706_RC
SEQ ID NO 1950



Contig718_RC
SEQ ID NO 1951



Contig719_RC
SEQ ID NO 1952



Contig742_RC
SEQ ID NO 1953



Contig753_RC
SEQ ID NO 1954



Contig758_RC
SEQ ID NO 1956



Contig760_RC
SEQ ID NO 1957



Contig842_RC
SEQ ID NO 1958



Contig848_RC
SEQ ID NO 1959



Contig924_RC
SEQ ID NO 1960



Contig974_RC
SEQ ID NO 1961



Contig1018_RC
SEQ ID NO 1962



Contig1056_RC
SEQ ID NO 1963



Contig1061_RC
SEQ ID NO 1964



Contig1129_RC
SEQ ID NO 1965



Contig1148
SEQ ID NO 1966



Contig1239_RC
SEQ ID NO 1967



Contig1277
SEQ ID NO 1968



Contig1333_RC
SEQ ID NO 1969



Contig1386_RC
SEQ ID NO 1970



Contig1389_RC
SEQ ID NO 1971



Contig1418_RC
SEQ ID NO 1972



Contig1462_RC
SEQ ID NO 1973



Contig1505_RC
SEQ ID NO 1974



Contig1540_RC
SEQ ID NO 1975



Contig1584_RC
SEQ ID NO 1976



Contig1632_RC
SEQ ID NO 1977



Contig1682_RC
SEQ ID NO 1978



Contig1778_RC
SEQ ID NO 1979



Contig1829
SEQ ID NO 1981



Contig1838_RC
SEQ ID NO 1982



Contig1938_RC
SEQ ID NO 1983



Contig1970_RC
SEQ ID NO 1984



Contig1998_RC
SEQ ID NO 1985



Contig2099_RC
SEQ ID NO 1986



Contig2143_RC
SEQ ID NO 1987



Contig2237_RC
SEQ ID NO 1988



Contig2429_RC
SEQ ID NO 1990



Contig2504_RC
SEQ ID NO 1991



Contig2512_RC
SEQ ID NO 1992



Contig2575_RC
SEQ ID NO 1993



Contig2578_RC
SEQ ID NO 1994



Contig2639_RC
SEQ ID NO 1995



Contig2647_RC
SEQ ID NO 1996



Contig2657_RC
SEQ ID NO 1997



Contig2728_RC
SEQ ID NO 1998



Contig2745_RC
SEQ ID NO 1999



Contig2811_RC
SEQ ID NO 2000



Contig2873_RC
SEQ ID NO 2001



Contig2883_RC
SEQ ID NO 2002



Contig2915_RC
SEQ ID NO 2003



Contig2928_RC
SEQ ID NO 2004



Contig3024_RC
SEQ ID NO 2005



Contig3094_RC
SEQ ID NO 2006



Contig3164_RC
SEQ ID NO 2007



Contig3495_RC
SEQ ID NO 2009



Contig3607_RC
SEQ ID NO 2010



Contig3659_RC
SEQ ID NO 2011



Contig3677_RC
SEQ ID NO 2012



Contig3682_RC
SEQ ID NO 2013



Contig3734_RC
SEQ ID NO 2014



Contig3834_RC
SEQ ID NO 2015



Contig3876_RC
SEQ ID NO 2016



Contig3902_RC
SEQ ID NO 2017



Contig3940_RC
SEQ ID NO 2018



Contig4380_RC
SEQ ID NO 2019



Contig4388_RC
SEQ ID NO 2020



Contig4467_RC
SEQ ID NO 2021



Contig4949_RC
SEQ ID NO 2023



Contig5348_RC
SEQ ID NO 2024



Contig5403_RC
SEQ ID NO 2025



Contig5716_RC
SEQ ID NO 2026



Contig6118_RC
SEQ ID NO 2027



Contig6164_RC
SEQ ID NO 2028



Contig6181_RC
SEQ ID NO 2029



Contig6514_RC
SEQ ID NO 2030



Contig6612_RC
SEQ ID NO 2031



Contig6881_RC
SEQ ID NO 2032



Contig8165_RC
SEQ ID NO 2033



Contig8221_RC
SEQ ID NO 2034



Contig8347_RC
SEQ ID NO 2035



Contig8364_RC
SEQ ID NO 2036



Contig8888_RC
SEQ ID NO 2038



Contig9259_RC
SEQ ID NO 2039



Contig9541_RC
SEQ ID NO 2040



Contig10268_RC
SEQ ID NO 2041



Contig10363_RC
SEQ ID NO 2042



Contig10437_RC
SEQ ID NO 2043



Contig11086_RC
SEQ ID NO 2045



Contig11275_RC
SEQ ID NO 2046



Contig11648_RC
SEQ ID NO 2047



Contig12216_RC
SEQ ID NO 2048



Contig12369_RC
SEQ ID NO 2049



Contig12814_RC
SEQ ID NO 2050



Contig12951_RC
SEQ ID NO 2051



Contig13480_RC
SEQ ID NO 2052



Contig14284_RC
SEQ ID NO 2053



Contig14390_RC
SEQ ID NO 2054



Contig14780_RC
SEQ ID NO 2055



Contig14954_RC
SEQ ID NO 2056



Contig14981_RC
SEQ ID NO 2057



Contig15692_RC
SEQ ID NO 2058



Contig16192_RC
SEQ ID NO 2059



Contig16759_RC
SEQ ID NO 2061



Contig16786_RC
SEQ ID NO 2062



Contig16905_RC
SEQ ID NO 2063



Contig17103_RC
SEQ ID NO 2064



Contig17105_RC
SEQ ID NO 2065



Contig17248_RC
SEQ ID NO 2066



Contig17345_RC
SEQ ID NO 2067



Contig18502_RC
SEQ ID NO 2069



Contig20156_RC
SEQ ID NO 2071



Contig20302_RC
SEQ ID NO 2073



Contig20600_RC
SEQ ID NO 2074



Contig20617_RC
SEQ ID NO 2075



Contig20629_RC
SEQ ID NO 2076



Contig20651_RC
SEQ ID NO 2077



Contig21130_RC
SEQ ID NO 2078



Contig21185_RC
SEQ ID NO 2079



Contig21421_RC
SEQ ID NO 2080



Contig21787_RC
SEQ ID NO 2081



Contig21812_RC
SEQ ID NO 2082



Contig22418_RC
SEQ ID NO 2083



Contig23085_RC
SEQ ID NO 2084



Contig23454_RC
SEQ ID NO 2085



Contig24138_RC
SEQ ID NO 2086



Contig24252_RC
SEQ ID NO 2087



Contig24655_RC
SEQ ID NO 2089



Contig25055_RC
SEQ ID NO 2090



Contig25290_RC
SEQ ID NO 2091



Contig25343_RC
SEQ ID NO 2092



Contig25362_RC
SEQ ID NO 2093



Contig25617_RC
SEQ ID NO 2094



Contig25659_RC
SEQ ID NO 2095



Contig25722_RC
SEQ ID NO 2096



Contig25809_RC
SEQ ID NO 2097



Contig25991
SEQ ID NO 2098



Contig26022_RC
SEQ ID NO 2099



Contig26077_RC
SEQ ID NO 2100



Contig26310_RC
SEQ ID NO 2101



Contig26371_RC
SEQ ID NO 2102



Contig26438_RC
SEQ ID NO 2103



Contig26706_RC
SEQ ID NO 2104



Contig27088_RC
SEQ ID NO 2105



Contig27186_RC
SEQ ID NO 2106



Contig27228_RC
SEQ ID NO 2107



Contig27344_RC
SEQ ID NO 2109



Contig27386_RC
SEQ ID NO 2110



Contig27624_RC
SEQ ID NO 2111



Contig27749_RC
SEQ ID NO 2112



Contig27882_RC
SEQ ID NO 2113



Contig27915_RC
SEQ ID NO 2114



Contig28030_RC
SEQ ID NO 2115



Contig28081_RC
SEQ ID NO 2116



Contig28152_RC
SEQ ID NO 2117



Contig28550_RC
SEQ ID NO 2119



Contig28552_RC
SEQ ID NO 2120



Contig28712_RC
SEQ ID NO 2121



Contig28888_RC
SEQ ID NO 2122



Contig28947_RC
SEQ ID NO 2123



Contig29126_RC
SEQ ID NO 2124



Contig29193_RC
SEQ ID NO 2125



Contig29369_RC
SEQ ID NO 2126



Contig29639_RC
SEQ ID NO 2127



Contig30047_RC
SEQ ID NO 2129



Contig30154_RC
SEQ ID NO 2131



Contig30209_RC
SEQ ID NO 2132



Contig30213_RC
SEQ ID NO 2133



Contig30230_RC
SEQ ID NO 2134



Contig30267_RC
SEQ ID NO 2135



Contig30390_RC
SEQ ID NO 2136



Contig30480_RC
SEQ ID NO 2137



Contig30609_RC
SEQ ID NO 2138



Contig30934_RC
SEQ ID NO 2139



Contig31150_RC
SEQ ID NO 2140



Contig31186_RC
SEQ ID NO 2141



Contig31251_RC
SEQ ID NO 2142



Contig31288_RC
SEQ ID NO 2143



Contig31291_RC
SEQ ID NO 2144



Contig31295_RC
SEQ ID NO 2145



Contig31424_RC
SEQ ID NO 2146



Contig31449_RC
SEQ ID NO 2147



Contig31596_RC
SEQ ID NO 2148



Contig31864_RC
SEQ ID NO 2149



Contig31928_RC
SEQ ID NO 2150



Contig31966_RC
SEQ ID NO 2151



Contig31986_RC
SEQ ID NO 2152



Contig32084_RC
SEQ ID NO 2153



Contig32105_RC
SEQ ID NO 2154



Contig32185_RC
SEQ ID NO 2156



Contig32242_RC
SEQ ID NO 2157



Contig32322_RC
SEQ ID NO 2158



Contig32336_RC
SEQ ID NO 2159



Contig32558_RC
SEQ ID NO 2160



Contig32798_RC
SEQ ID NO 2161



Contig33005_RC
SEQ ID NO 2162



Contig33230_RC
SEQ ID NO 2163



Contig33260_RC
SEQ ID NO 2164



Contig33654_RC
SEQ ID NO 2166



Contig33741_RC
SEQ ID NO 2167



Contig33771_RC
SEQ ID NO 2168



Contig33814_RC
SEQ ID NO 2169



Contig33815_RC
SEQ ID NO 2170



Contig33833
SEQ ID NO 2171



Contig33998_RC
SEQ ID NO 2172



Contig34079
SEQ ID NO 2173



Contig34080_RC
SEQ ID NO 2174



Contig34222_RC
SEQ ID NO 2175



Contig34233_RC
SEQ ID NO 2176



Contig34303_RC
SEQ ID NO 2177



Contig34393_RC
SEQ ID NO 2178



Contig34477_RC
SEQ ID NO 2179



Contig34766_RC
SEQ ID NO 2181



Contig34952
SEQ ID NO 2182



Contig34989_RC
SEQ ID NO 2183



Contig35030_RC
SEQ ID NO 2184



Contig35251_RC
SEQ ID NO 2185



Contig35629_RC
SEQ ID NO 2186



Contig35635_RC
SEQ ID NO 2187



Contig35763_RC
SEQ ID NO 2188



Contig35814_RC
SEQ ID NO 2189



Contig35896_RC
SEQ ID NO 2190



Contig35976_RC
SEQ ID NO 2191



Contig36042_RC
SEQ ID NO 2192



Contig36081_RC
SEQ ID NO 2193



Contig36152_RC
SEQ ID NO 2194



Contig36193_RC
SEQ ID NO 2195



Contig36312_RC
SEQ ID NO 2196



Contig36323_RC
SEQ ID NO 2197



Contig36339_RC
SEQ ID NO 2198



Contig36647_RC
SEQ ID NO 2199



Contig36744_RC
SEQ ID NO 2200



Contig36761_RC
SEQ ID NO 2201



Contig36879_RC
SEQ ID NO 2202



Contig36900_RC
SEQ ID NO 2203



Contig37015_RC
SEQ ID NO 2204



Contig37024_RC
SEQ ID NO 2205



Contig37072_RC
SEQ ID NO 2207



Contig37140_RC
SEQ ID NO 2208



Contig37141_RC
SEQ ID NO 2209



Contig37204_RC
SEQ ID NO 2210



Contig37281_RC
SEQ ID NO 2211



Contig37287_RC
SEQ ID NO 2212



Contig37439_RC
SEQ ID NO 2213



Contig37562_RC
SEQ ID NO 2214



Contig37571_RC
SEQ ID NO 2215



Contig37598
SEQ ID NO 2216



Contig37758_RC
SEQ ID NO 2217



Contig37778_RC
SEQ ID NO 2218



Contig37884_RC
SEQ ID NO 2219



Contig37946_RC
SEQ ID NO 2220



Contig38170_RC
SEQ ID NO 2221



Contig38288_RC
SEQ ID NO 2223



Contig38398_RC
SEQ ID NO 2224



Contig38580_RC
SEQ ID NO 2226



Contig38630_RC
SEQ ID NO 2227



Contig38652_RC
SEQ ID NO 2228



Contig38683_RC
SEQ ID NO 2229



Contig38726_RC
SEQ ID NO 2230



Contig38791_RC
SEQ ID NO 2231



Contig38901_RC
SEQ ID NO 2232



Contig38983_RC
SEQ ID NO 2233



Contig39090_RC
SEQ ID NO 2234



Contig39132_RC
SEQ ID NO 2235



Contig39157_RC
SEQ ID NO 2236



Contig39226_RC
SEQ ID NO 2237



Contig39285_RC
SEQ ID NO 2238



Contig39556_RC
SEQ ID NO 2239



Contig39591_RC
SEQ ID NO 2240



Contig39826_RC
SEQ ID NO 2241



Contig39845_RC
SEQ ID NO 2242



Contiq39891_RC
SEQ ID NO 2243



Contig39922_RC
SEQ ID NO 2244



Contig39960_RC
SEQ ID NO 2245



Contig40026_RC
SEQ ID NO 2246



Contig40121_RC
SEQ ID NO 2247



Contig40128_RC
SEQ ID NO 2248



Contig40146
SEQ ID NO 2249



Contig40208_RC
SEQ ID NO 2250



Contig40212_RC
SEQ ID NO 2251



Contig40238_RC
SEQ ID NO 2252



Contig40434_RC
SEQ ID NO 2253



Contig40446_RC
SEQ ID NO 2254



Contig40500_RC
SEQ ID NO 2255



Contig40573_RC
SEQ ID NO 2256



Contig40813_RC
SEQ ID NO 2258



Contig40816_RC
SEQ ID NO 2259



Contig40845_RC
SEQ ID NO 2261



Contig40889_RC
SEQ ID NO 2262



Contig41035
SEQ ID NO 2263



Contig41234_RC
SEQ ID NO 2264



Contig41413_RC
SEQ ID NO 2266



Contig41521_RC
SEQ ID NO 2267



Contig41530_RC
SEQ ID NO 2268



Contig41590
SEQ ID NO 2269



Contig41618_RC
SEQ ID NO 2270



Contig41624_RC
SEQ ID NO 2271



Contig41635_RC
SEQ ID NO 2272



Contig41676_RC
SEQ ID NO 2273



Contig41689_RC
SEQ ID NO 2274



Contig41804_RC
SEQ ID NO 2275



Contig41887_RC
SEQ ID NO 2276



Contig41905_RC
SEQ ID NO 2277



Contig41954_RC
SEQ ID NO 2278



Contig41983_RC
SEQ ID NO 2279



Contig42006_RC
SEQ ID NO 2280



Contig42014_RC
SEQ ID NO 2281



Contig42036_RC
SEQ ID NO 2282



Contig42041_RC
SEQ ID NO 2283



Contig42139
SEQ ID NO 2284



Contig42161_RC
SEQ ID NO 2285



Contig42220_RC
SEQ ID NO 2286



Contig42306_RC
SEQ ID NO 2287



Contig42311_RC
SEQ ID NO 2288



Contig42313_RC
SEQ ID NO 2289



Contig42402_RC
SEQ ID NO 2290



Contig42421_RC
SEQ ID NO 2291



Contig42430_RC
SEQ ID NO 2292



Contig42431_RC
SEQ ID NO 2293



Contig42542_RC
SEQ ID NO 2294



Contig42582
SEQ ID NO 2295



Contig42631_RC
SEQ ID NO 2296



Contig42751_RC
SEQ ID NO 2297



Contig42759_RC
SEQ ID NO 2298



Contig43054
SEQ ID NO 2299



Contig43079_RC
SEQ ID NO 2300



Contig43195_RC
SEQ ID NO 2301



Contig43368_RC
SEQ ID NO 2302



Contig43410_RC
SEQ ID NO 2303



Contig43476_RC
SEQ ID NO 2304



Contig43549_RC
SEQ ID NO 2305



Contig43645_RC
SEQ ID NO 2306



Contig43648_RC
SEQ ID NO 2307



Contig43673_RC
SEQ ID NO 2308



Contig43679_RC
SEQ ID NO 2309



Contig43694_RC
SEQ ID NO 2310



Contig43747_RC
SEQ ID NO 2311



Contig43918_RC
SEQ ID NO 2312



Contig43983_RC
SEQ ID NO 2313



Contig44040_RC
SEQ ID NO 2314



Contig44064_RC
SEQ ID NO 2315



Contig44195_RC
SEQ ID NO 2316



Contig44226_RC
SEQ ID NO 2317



Contig44289_RC
SEQ ID NO 2320



Contig44310_RC
SEQ ID NO 2321



Contig44409
SEQ ID NO 2322



Contig44413_RC
SEQ ID NO 2323



Contig44451_RC
SEQ ID NO 2324



Contig44585_RC
SEQ ID NO 2325



Contig44656_RC
SEQ ID NO 2326



Contig44703_RC
SEQ ID NO 2327



Contig44708_RC
SEQ ID NO 2328



Contig44757_RC
SEQ ID NO 2329



Contig44829_RC
SEQ ID NO 2331



Contig44870
SEQ ID NO 2332



Contig44893_RC
SEQ ID NO 2333



Contig44909_RC
SEQ ID NO 2334



Contig44939_RC
SEQ ID NO 2335



Contig45022_RC
SEQ ID NO 2336



Contig45032_RC
SEQ ID NO 2337



Contig45041_RC
SEQ ID NO 2338



Contig45049_RC
SEQ ID NO 2339



Contig45090_RC
SEQ ID NO 2340



Contig45156_RC
SEQ ID NO 2341



Contig45316_RC
SEQ ID NO 2342



Contig45321
SEQ ID NO 2343



Contig45375_RC
SEQ ID NO 2345



Contig45443_RC
SEQ ID NO 2346



Contig45454_RC
SEQ ID NO 2347



Contig45537_RC
SEQ ID NO 2348



Contig45588_RC
SEQ ID NO 2349



Contig45708_RC
SEQ ID NO 2350



Contig45816_RC
SEQ ID NO 2351



Contig45847_RC
SEQ ID NO 2352



Contig45891_RC
SEQ ID NO 2353



Contig46056_RC
SEQ ID NO 2354



Contig46062_RC
SEQ ID NO 2355



Contig46075_RC
SEQ ID NO 2356



Contig46164_RC
SEQ ID NO 2357



Contig46218_RC
SEQ ID NO 2358



Contig46223_RC
SEQ ID NO 2359



Contig46244_RC
SEQ ID NO 2360



Contig46262_RC
SEQ ID NO 2361



Contig46362_RC
SEQ ID NO 2364



Contig46443_RC
SEQ ID NO 2365



Contig46553_RC
SEQ ID NO 2367



Contig46597_RC
SEQ ID NO 2368



Contig46653_RC
SEQ ID NO 2369



Contig46709_RC
SEQ ID NO 2370



Contig46777_RC
SEQ ID NO 2371



Contig46802_RC
SEQ ID NO 2372



Contig46890_RC
SEQ ID NO 2374



Contig46922_RC
SEQ ID NO 2375



Contig46934_RC
SEQ ID NO 2376



Contig46937_RC
SEQ ID NO 2377



Contig46991_RC
SEQ ID NO 2378



Contig47016_RC
SEQ ID NO 2379



Contig47045_RC
SEQ ID NO 2380



Contig47106_RC
SEQ ID NO 2381



Contig47146_RC
SEQ ID NO 2382



Contig47230_RC
SEQ ID NO 2383



Contig47405_RC
SEQ ID NO 2384



Contig47456_RC
SEQ ID NO 2385



Contig47465_RC
SEQ ID NO 2386



Contig47498_RC
SEQ ID NO 2387



Contig47578_RC
SEQ ID NO 2388



Contig47645_RC
SEQ ID NO 2389



Contig47680_RC
SEQ ID NO 2390



Contig47781_RC
SEQ ID NO 2391



Contig47814_RC
SEQ ID NO 2392



Contig48004_RC
SEQ ID NO 2393



Contig48043_RC
SEQ ID NO 2394



Contig48057_RC
SEQ ID NO 2395



Contig48076_RC
SEQ ID NO 2396



Contig48249_RC
SEQ ID NO 2397



Contig48263_RC
SEQ ID NO 2398



Contig48270_RC
SEQ ID NO 2399



Contig48328_RC
SEQ ID NO 2400



Contig48518_RC
SEQ ID NO 2401



Contig48572_RC
SEQ ID NO 2402



Contig48659_RC
SEQ ID NO 2403



Contig48722_RC
SEQ ID NO 2404



Contig48774_RC
SEQ ID NO 2405



Contig48776_RC
SEQ ID NO 2406



Contig48800_RC
SEQ ID NO 2407



Contig48806_RC
SEQ ID NO 2408



Contig48852_RC
SEQ ID NO 2409



Contig48900_RC
SEQ ID NO 2410



Contig48913_RC
SEQ ID NO 2411



Contig48970_RC
SEQ ID NO 2413



Contig49058_RC
SEQ ID NO 2414



Contig49063_RC
SEQ ID NO 2415



Contig49093
SEQ ID NO 2416



Contig49098_RC
SEQ ID NO 2417



Contig49169_RC
SEQ ID NO 2418



Contig49233_RC
SEQ ID NO 2419



Contig49270_RC
SEQ ID NO 2420



Contig49282_RC
SEQ ID NO 2421



Contig49289_RC
SEQ ID NO 2422



Contig49342_RC
SEQ ID NO 2423



Contig49344
SEQ ID NO 2424



Contig49388_RC
SEQ ID NO 2425



Contig49405_RC
SEQ ID NO 2426



Contig49445_RC
SEQ ID NO 2427



Contig49468_RC
SEQ ID NO 2428



Contig49509_RC
SEQ ID NO 2429



Contig49578_RC
SEQ ID NO 2431



Contig49581_RC
SEQ ID NO 2432



Contig49631_RC
SEQ ID NO 2433



Contig49673_RC
SEQ ID NO 2435



Contig49743_RC
SEQ ID NO 2436



Contig49790_RC
SEQ ID NO 2437



Contig49818_RC
SEQ ID NO 2438



Contig49849_RC
SEQ ID NO 2439



Contig49855
SEQ ID NO 2440



Contig49910_RC
SEQ ID NO 2441



Contig49948_RC
SEQ ID NO 2442



Contig50004_RC
SEQ ID NO 2443



Contig50094
SEQ ID NO 2444



Contig50120_RC
SEQ ID NO 2446



Contig50153_RC
SEQ ID NO 2447



Contig50189_RC
SEQ ID NO 2448



Contig50276_RC
SEQ ID NO 2449



Contig50288_RC
SEQ ID NO 2450



Contig50297_RC
SEQ ID NO 2451



Contig50391_RC
SEQ ID NO 2452



Contig50410
SEQ ID NO 2453



Contig50523_RC
SEQ ID NO 2454



Contig50529
SEQ ID NO 2455



Contig50588_RC
SEQ ID NO 2456



Contig50592
SEQ ID NO 2457



Contig50669_RC
SEQ ID NO 2458



Contig50719_RC
SEQ ID NO 2460



Contig50728_RC
SEQ ID NO 2461



Contig50731_RC
SEQ ID NO 2462



Contig50802_RC
SEQ ID NO 2463



Contig50822_RC
SEQ ID NO 2464



Contig50850_RC
SEQ ID NO 2466



Contig50860_RC
SEQ ID NO 2467



Contig50913_RC
SEQ ID NO 2468



Contig50950_RC
SEQ ID NO 2469



Contig51066_RC
SEQ ID NO 2470



Contig51105_RC
SEQ ID NO 2472



Contig51117_RC
SEQ ID NO 2473



Contig51196_RC
SEQ ID NO 2474



Contig51235_RC
SEQ ID NO 2475



Contig51254_RC
SEQ ID NO 2476



Contig51352_RC
SEQ ID NO 2477



Contig51369_RC
SEQ ID NO 2478



Contig51392_RC
SEQ ID NO 2479



Contig51403_RC
SEQ ID NO 2480



Contig51685_RC
SEQ ID NO 2483



Contig51726_RC
SEQ ID NO 2484



Contig51742_RC
SEQ ID NO 2485



Contig51749_RC
SEQ ID NO 2486



Contig51775_RC
SEQ ID NO 2487



Contig51800
SEQ ID NO 2488



Contig51809_RC
SEQ ID NO 2489



Contig51821_RC
SEQ ID NO 2490



Contig51888_RC
SEQ ID NO 2491



Contig51953_RC
SEQ ID NO 2493



Contig51967_RC
SEQ ID NO 2495



Contig51981_RC
SEQ ID NO 2496



Contig51994_RC
SEQ ID NO 2497



Contig52082_RC
SEQ ID NO 2498



Contig52094_RC
SEQ ID NO 2499



Contig52320
SEQ ID NO 2500



Contig52398_RC
SEQ ID NO 2501



Contig52425_RC
SEQ ID NO 2503



Contig52482_RC
SEQ ID NO 2504



Contig52543_RC
SEQ ID NO 2505



Contig52553_RC
SEQ ID NO 2506



Contig52579_RC
SEQ ID NO 2507



Contig52603_RC
SEQ ID NO 2508



Contig52639_RC
SEQ ID NO 2509



Contig52641_RC
SEQ ID NO 2510



Contig52684
SEQ ID NO 2511



Contig52705_RC
SEQ ID NO 2512



Contig52720_RC
SEQ ID NO 2513



Contig52722_RC
SEQ ID NO 2514



Contig52723_RC
SEQ ID NO 2515



Contig52740_RC
SEQ ID NO 2516



Contig52779_RC
SEQ ID NO 2517



Contig52957_RC
SEQ ID NO 2518



Contig52994_RC
SEQ ID NO 2519



Contig53022_RC
SEQ ID NO 2520



Contig53038_RC
SEQ ID NO 2521



Contig53047_RC
SEQ ID NO 2522



Contig53130
SEQ ID NO 2523



Contig53183_RC
SEQ ID NO 2524



Contig53242_RC
SEQ ID NO 2526



Contig53248_RC
SEQ ID NO 2527



Contig53260_RC
SEQ ID NO 2528



Contig53296_RC
SEQ ID NO 2531



Contig53307_RC
SEQ ID NO 2532



Contig53314_RC
SEQ ID NO 2533



Contig53401_RC
SEQ ID NO 2534



Contig53550_RC
SEQ ID NO 2535



Contig53551_RC
SEQ ID NO 2536



Contig53598_RC
SEQ ID NO 2537



Contig53646_RC
SEQ ID NO 2538



Contig53658_RC
SEQ ID NO 2539



Contig53698_RC
SEQ ID NO 2540



Contig53719_RC
SEQ ID NO 2541



Contig53742_RC
SEQ ID NO 2542



Contig53757_RC
SEQ ID NO 2543



Contig53870_RC
SEQ ID NO 2544



Contig53952_RC
SEQ ID NO 2546



Contig53962_RC
SEQ ID NO 2547



Contig53968_RC
SEQ ID NO 2548



Contig54113_RC
SEQ ID NO 2549



Contig54142_RC
SEQ ID NO 2550



Contig54232_RC
SEQ ID NO 2551



Contig54242_RC
SEQ ID NO 2552



Contig54260_RC
SEQ ID NO 2553



Contig54263_RC
SEQ ID NO 2554



Contig54295_RC
SEQ ID NO 2555



Contig54318_RC
SEQ ID NO 2556



Contig54325_RC
SEQ ID NO 2557



Contig54389_RC
SEQ ID NO 2558



Contig54394_RC
SEQ ID NO 2559



Contig54414_RC
SEQ ID NO 2560



Contig54425
SEQ ID NO 2561



Contig54477_RC
SEQ ID NO 2562



Contig54503_RC
SEQ ID NO 2563



Contig54534_RC
SEQ ID NO 2564



Contig54560_RC
SEQ ID NO 2566



Contig54581_RC
SEQ ID NO 2567



Contig54609_RC
SEQ ID NO 2568



Contig54666_RC
SEQ ID NO 2569



Contig54667_RC
SEQ ID NO 2570



Contig54726_RC
SEQ ID NO 2571



Contig54742_RC
SEQ ID NO 2572



Contig54745_RC
SEQ ID NO 2573



Contig54757_RC
SEQ ID NO 2574



Contig54761_RC
SEQ ID NO 2575



Contig54813_RC
SEQ ID NO 2576



Contig54867_RC
SEQ ID NO 2577



Contig54895_RC
SEQ ID NO 2578



Contig54898_RC
SEQ ID NO 2579



Contig54913_RC
SEQ ID NO 2580



Contig54965_RC
SEQ ID NO 2582



Contig54968_RC
SEQ ID NO 2583



Contig55069_RC
SEQ ID NO 2584



Contig55181_RC
SEQ ID NO 2585



Contig55188_RC
SEQ ID NO 2586



Contig55221_RC
SEQ ID NO 2587



Contig55254_RC
SEQ ID NO 2588



Contig55265_RC
SEQ ID NO 2589



Contig55377_RC
SEQ ID NO 2591



Contig55397_RC
SEQ ID NO 2592



Contig55448_RC
SEQ ID NO 2593



Contig55468_RC
SEQ ID NO 2594



Contig55500_RC
SEQ ID NO 2595



Contig55538_RC
SEQ ID NO 2596



Contig55558_RC
SEQ ID NO 2597



Contig55606_RC
SEQ ID NO 2598



Contig55674_RC
SEQ ID NO 2599



Contig55725_RC
SEQ ID NO 2600



Contig55728_RC
SEQ ID NO 2601



Contig55756_RC
SEQ ID NO 2602



Contig55769_RC
SEQ ID NO 2603



Contig55771_RC
SEQ ID NO 2605



Contig55813_RC
SEQ ID NO 2607



Contig55829_RC
SEQ ID NO 2608



Contig55852_RC
SEQ ID NO 2609



Contig55883_RC
SEQ ID NO 2610



Contig55920_RC
SEQ ID NO 2611



Contig55940_RC
SEQ ID NO 2612



Contig55950_RC
SEQ ID NO 2613



Contig55991_RC
SEQ ID NO 2614



Contig55997_RC
SEQ ID NO 2615



Contig56023_RC
SEQ ID NO 2616



Contig56030_RC
SEQ ID NO 2617



Contig56093_RC
SEQ ID NO 2618



Contig56205_RC
SEQ ID NO 2621



Contig56270_RC
SEQ ID NO 2622



Contig56276_RC
SEQ ID NO 2623



Contig56291_RC
SEQ ID NO 2624



Contig56298_RC
SEQ ID NO 2625



Contig56307
SEQ ID NO 2627



Contig56390_RC
SEQ ID NO 2628



Contig56434_RC
SEQ ID NO 2629



Contig56457_RC
SEQ ID NO 2630



Contig56534_RC
SEQ ID NO 2631



Contig56670_RC
SEQ ID NO 2632



Contig56678_RC
SEQ ID NO 2633



Contig56742_RC
SEQ ID NO 2634



Contig56759_RC
SEQ ID NO 2635



Contig56765_RC
SEQ ID NO 2636



Contig56843_RC
SEQ ID NO 2637



Contig57011_RC
SEQ ID NO 2638



Contig57023_RC
SEQ ID NO 2639



Contig57057_RC
SEQ ID NO 2640



Contig57076_RC
SEQ ID NO 2641



Contig57081_RC
SEQ ID NO 2642



Contig57091_RC
SEQ ID NO 2643



Contig57138_RC
SEQ ID NO 2644



Contig57173_RC
SEQ ID NO 2645



Contig57230_RC
SEQ ID NO 2646



Contig57258_RC
SEQ ID NO 2647



Contiq57270_RC
SEQ ID NO 2648



Contig57272_RC
SEQ ID NO 2649



Contig57344_RC
SEQ ID NO 2650



Contig57430_RC
SEQ ID NO 2651



Contig57458_RC
SEQ ID NO 2652



Contig57493_RC
SEQ ID NO 2653



Contig57584_RC
SEQ ID NO 2654



Contig57595
SEQ ID NO 2655



Contig57602_RC
SEQ ID NO 2656



Contig57609_RC
SEQ ID NO 2657



Contig57610_RC
SEQ ID NO 2658



Contig57644_RC
SEQ ID NO 2659



Contig57725_RC
SEQ ID NO 2660



Contig57739_RC
SEQ ID NO 2661



Contig57825_RC
SEQ ID NO 2662



Contig57864_RC
SEQ ID NO 2663



Contig57940_RC
SEQ ID NO 2664



Contig58260_RC
SEQ ID NO 2665



Contig58272_RC
SEQ ID NO 2666



Contig58301_RC
SEQ ID NO 2667



Contig58368_RC
SEQ ID NO 2668



Contig58471_RC
SEQ ID NO 2669



Contig58755_RC
SEQ ID NO 2671



Contig59120_RC
SEQ ID NO 2672



Contig60157_RC
SEQ ID NO 2673



Contig60864_RC
SEQ ID NO 2676



Contig61254_RC
SEQ ID NO 2677



Contig61815
SEQ ID NO 2678



Contig61975
SEQ ID NO 2679



Contig62306
SEQ ID NO 2680



Contig62568_RC
SEQ ID NO 2681



Contig62922_RC
SEQ ID NO 2682



Contig62964_RC
SEQ ID NO 2683



Contig63520_RC
SEQ ID NO 2685



Contig63649_RC
SEQ ID NO 2686



Contig63683_RC
SEQ ID NO 2687



Contig63748_RC
SEQ ID NO 2688



Contig64502
SEQ ID NO 2689



Contig64688
SEQ ID NO 2690



Contig64775_RC
SEQ ID NO 2691



Contig65227
SEQ ID NO 2692



Contig65663
SEQ ID NO 2693



Contig65785_RC
SEQ ID NO 2694



Contig65900
SEQ ID NO 2695



Contig66219_RC
SEQ ID NO 2696



Contig66705_RC
SEQ ID NO 2697



Contig66759_RC
SEQ ID NO 2698



Contig67182_RC
SEQ ID NO 2699

















TABLE 2







550 preferred ER status markers drawn from Table 1.










Identifier
Correlation
Name
Description













NM_002051
0.763977
GATA3
GATA-binding protein 3


AB020689
0.753592
KIAA0882
KIAA0882 protein


NM_001218
0.753225
CA12
carbonic anhydrase XII


NM_000125
0.748421
ESR1
estrogen receptor 1


Contig56678_RC
0.747816

ESTs


NM_004496
0.729116
HNF3A
hepatocyte nuclear factor 3, alpha


NM_017732
0.713398
FLJ20262
hypothetical protein FLJ20262


NM_006806
−0.712678
BTG3
BTG family, member 3


Contig56390_RC
0.705940

ESTs


Contig37571_RC
0.704468

ESTs


NM_004559
−0.701617
NSEP1
nuclease sensitive element binding





protein 1


Contig50153_RC
−0.696652

ESTs, Weakly similar to LKHU





proteoglycan link protein precursor





[H. sapiens]


NM_012155
0.694332
EMAP-2
microtubule-associated protein like





echinoderm EMAP


Contig237_RC
0.687485
FLJ21127
hypothetical protein FLJ21127


NM_019063
−0.686064
C2ORF2
chromosome 2 open reading frame





2


NM_012219
−0.680900
MRAS
muscle RAS oncogene homolog


NM_001982
0.676114
ERBB3
v-erb-b2 avian erythroblastic





leukemia viral oncogene homolog 3


NM_006623
−0.675090
PHGDH
phosphoglycerate dehydrogenase


NM_000636
−0.674282
SOD2
superoxide dismutase 2,





mitochondrial


NM_006017
−0.670353
PROML1
prominin (mouse)-like 1


Contig57940_RC
0.667915
MAP-1
MAP-1 protein


Contig46934_RC
0.666908

ESTs, Weakly similar to JE0350





Anterior gradient-2 [H. sapiens]


NM_005080
0.665772
XBP1
X-box binding protein 1


NM_014246
0.665725
CELSR1
cadherin, EGF LAG seven-pass G-





type receptor 1, flamingo





(Drosophila) homolog


Contig54667_RC
−0.663727

Human DNA sequence from clone





RP1-187J11 on chromosome





6q11.1-22.33. Contains the gene for





a novel protein similar to S. pombe





and S. cerevisiae predicted proteins,





the gene for a novel protein similar





to protein kinase C inhibitors, the 3′





end of the gene for a novel protein





similar to Drosophila L82 and





predicted worm proteins, ESTs,





STSs, GSSs and two putative CpG





islands


Contig51994_RC
0.663715

ESTs, Weakly similar to B0416.1





[C. elegans]


NM_016337
0.663006
RNB6
RNB6


NM_015640
−0.660165
PAI-RBP1
PAI-1 mRNA-binding protein


X07834
−0.657798
SOD2
superoxide dismutase 2,





mitochondrial


NM_012319
0.657666
LIV-1
LIV-1 protein, estrogen regulated


Contig41887_RC
0.656042

ESTs, Weakly similar to Homolog of





rat Zymogen granule membrane





protein [H. sapiens]


NM_003462
0.655349
P28
dynein, axonemal, light intermediate





polypeptide


Contig58301_RC
0.654268

Homo sapiens mRNA; cDNA





DKFZp667D095 (from clone





DKFZp667D095)


NM_005375
0.653783
MYB
v-myb avian myeloblastosis viral





oncogene homolog


NM_017447
−0.652445
YG81
hypothetical protein LOC54149


Contig924_RC
−0.650658

ESTs


M55914
−0.650181
MPB1
MYC promoter-binding protein 1


NM_006004
−0.649819
UQCRH
ubiquinol-cytochrome c reductase





hinge protein


NM_000964
0.649072
RARA
retinoic acid receptor, alpha


NM_013301
0.647583
HSU79303
protein predicted by clone 23882


AB023211
−0.647403
PDI2
peptidyl arginine deiminase, type II


NM_016629
−0.646412
LOC51323
hypothetical protein


K02403
0.645532
C4A
complement component 4A


NM_016405
−0.642201
HSU93243
Ubc6p homolog


Contig46597_RC
0.641733

ESTs


Contig55377_RC
0.640310

ESTs


NM_001207
0.637800
BTF3
basic transcription factor 3


NM_018166
0.636422
FLJ10647
hypothetical protein FLJ10647


AL110202
−0.635398

Homo sapiens mRNA; cDNA





DKFZp586I2022 (from clone





DKFZp586I2022)


AL133105
−0.635201
DKFZp434F
hypothetical protein DKFZp434F2322




2322


NM_016839
−0.635169
RBMS1
RNA binding motif, single stranded





interacting protein 1


Contig53130
−0.634812

ESTs, Weakly similar to





hyperpolarization-activated cyclic





nucleotide-gated channel hHCN2





[H. sapiens]


NM_018014
−0.634460
BCL11A
B-cell CLL/lymphoma 11A (zinc





finger protein)


NM_006769
−0.632197
LMO4
LIM domain only 4


U92544
0.631170
JCL-1
hepatocellular carcinoma associated





protein; breast cancer associated





gene 1


Contig49233_RC
−0.631047

Homo sapiens, Similar to nuclear





receptor binding factor 2, clone





IMAGE: 3463191, mRNA, partial cds


AL133033
0.629690
KIAA1025
KIAA1025 protein


AL049265
0.629414

Homo sapiens mRNA; cDNA





DKFZp564F053 (from clone





DKFZp564F053)


NM_018728
0.627989
MYO5C
myosin 5C


NM_004780
0.627856
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
DKFZp762
hypothetical protein




E1312
DKFZp762E1312


Contig49581_RC
−0.601571
KIAA1350
KIAA1350 protein


NM_003088
−0.601458
SNL
singed (Drosophila)-like (sea urchin





fascin homolog like)


Contig47045_RC
0.601088

ESTs, Weakly similar to





DP1_HUMAN POLYPOSIS LOCUS





PROTEIN 1 [H. sapiens]


NM_001806
−0.600954
CEBPG
CCAAT/enhancer binding protein





(C/EBP), gamma


NM_004374
0.600766
COX6C
cytochrome c oxidase subunit Vlc


Contig52641_RC
0.600132

ESTs, Weakly similar to CENB





MOUSE MAJOR CENTROMERE





AUTOANTIGEN B [M. musculus]


NM_000100
−0.600127
CSTB
cystatin B (stefin B)


NM_002250
−0.600004
KCNN4
potassium intermediate/small





conductance calcium-activated





channel, subfamily N, member 4


AB033035
−0.599423
KIAA1209
KIAA1209 protein


Contig53968_RC
0.599077

ESTs


NM_002300
−0.598246
LDHB
lactate dehydrogenase B


NM_000507
0.598110
FBP1
fructose-1,6-bisphosphatase 1


NM_002053
−0.597756
GBP1
guanylate binding protein 1,





interferon-inducible, 67 kD


AB007883
0.597043
KIAA0423
KIAA0423 protein


NM_004900
−0.597010
DJ742C19.2
phorbolin (similar to apolipoprotein B





mRNA editing protein)


NM_004480
0.596321
FUT8
fucosyltransferase 8 (alpha (1,6)





fucosyltransferase)


Contig35896_RC
0.596281

ESTs


NM_020974
0.595173
CEGP1
CEGP1 protein


NM_000662
0.595114
NAT1
N-acetyltransferase 1 (arylamine N-





acetyltransferase)


NM_006113
0.595017
VAV3
vav 3 oncogene


NM_014865
−0.594928
KIAA0159
chromosome condensation-related





SMC-associated protein 1


Contig55538_RC
−0.594573
BA395L14.2
hypothetical protein bA395L14.2


NM_016056
0.594084
LOC51643
CGI-119 protein


NM_003579
−0.594063
RAD54L
RAD54 (S. cerevisiae)-like


NM_014214
−0.593860
IMPA2
inositol(myo)-1 (or 4)-





monophosphatase 2


U79293
0.593793

Human clone 23948 mRNA





sequence


NM_005557
−0.593746
KRT16
keratin 16 (focal non-epidermolytic





palmoplantar keratoderma)


NM_002444
−0.592405
MSN
moesin


NM_003681
−0.592155
PDXK
pyridoxal (pyridoxine, vitamin B6)





kinase


NM_006372
−0.591711
NSAP1
NS1-associated protein 1


NM_005218
−0.591192
DEFB1
defensin, beta 1


NM_004642
−0.591081
DOC1
deleted in oral cancer (mouse,





homolog) 1


AL133074
0.590359

Homo sapiens cDNA: FLJ22139 fis,





clone HEP20959


M73547
0.590317
D5S346
DNA segment, single copy probe





LNS-CAI/LNS-CAII (deleted in





polyposis


Contig65663
0.590312

ESTs


AL035297
−0.589728

H. sapiens gene from PAC 747L4


Contig35629_RC
0.589383

ESTs


NM_019027
0.588862
FLJ20273
hypothetical protein


NM_012425
−0.588804

Homo sapiens Ras suppressor





protein 1 (RSU1), mRNA


NM_020179
−0.588326
FN5
FN5 protein


AF090913
−0.587275
TMSB10
thymosin, beta 10


NM_004176
0.587190
SREBF1
sterol regulatory element binding





transcription factor 1


NM_016121
0.586941
LOC51133
NY-REN-45 antigen


NM_014773
0.586871
KIAA0141
KIAA0141 gene product


NM_019000
0.586677
FLJ20152
hypothetical protein


NM_016243
0.585942
LOC51706
cytochrome b5 reductase 1 (B5R.1)


NM_014274
−0.585815
ABP/ZF
Alu-binding protein with zinc finger





domain


NM_018379
0.585497
FLJ11280
hypothetical protein FLJ11280


AL157431
−0.585077
DKFZp762
hypothetical protein DKFZp762A227




A227


D38521
−0.584684
KIAA0077
KIAA0077 protein


NM_002570
0.584272
PACE4
paired basic amino acid cleaving





system 4


NM_001809
−0.584252
CENPA
centromere protein A (17 kD)


NM_003318
−0.583556
TTK
TTK protein kinase


NM_014325
−0.583555
CORO1C
coronin, actin-binding protein, 1C


NM_005667
0.583376
ZFP103
zinc finger protein homologous to





Zfp103 in mouse


NM_004354
0.582420
CCNG2
cyclin G2


NM_003670
0.582235
BHLHB2
basic helix-loop-helix domain





containing, class B, 2


NM_001673
−0.581902
ASNS
asparagine synthetase


NM_001333
−0.581402
CTSL2
cathepsin L2


Contig54295_RC
0.581256

ESTs


Contig33998_RC
0.581018

ESTs


NM_006002
−0.580592
UCHL3
ubiquitin carboxyl-terminal esterase





L3 (ubiquitin thiolesterase)


NM_015392
0.580568
NPDC1
neural proliferation, differentiation





and control, 1


NM_004866
0.580138
SCAMP1
secretory carrier membrane protein





1


Contig50391_RC
0.580071

ESTs


NM_000592
0.579965
C4B
complement component 4B


Contig50802_RC
0.579881

ESTs


Contig41635_RC
−0.579468

ESTs


NM_006845
−0.579339
KNSL6
kinesin-like 6 (mitotic centromere-





associated kinesin)


NM_003720
−0.579296
DSCR2
Down syndrome critical region gene





2


NM_000060
0.578967
BTD
biotinidase


AL050388
−0.578736

Homo sapiens mRNA; cDNA





DKFZp564M2422 (from clone





DKFZp564M2422); partial cds


NM_003772
−0.578395
JRKL
jerky (mouse) homolog-like


NM_014398
−0.578388
TSC403
similar to lysosome-associated





membrane glycoprotein


NM_001280
0.578213
CIRBP
cold inducible RNA-binding protein


NM_001395
−0.577369
DUSP9
dual specificity phosphatase 9


NM_016229
−0.576290
LOC51700
cytochrome b5 reductase b5R.2


NM_006096
−0.575615
NDRG1
N-myc downstream regulated


NM_001552
0.575438
IGFBP4
insulin-like growth factor-binding





protein 4


NM_005558
−0.574818
LAD1
ladinin 1


Contig54534_RC
0.574784

Human glucose transporter





pseudogene


Contig1239_RC
0.573822

Human Chromosome 16 BAG clone





CIT987SK-A-362G6


Contig57173_RC
0.573807

Homo sapiens mRNA for KIAA1737





protein, partial cds


NM_004414
−0.573538
DSCR1
Down syndrome critical region gene





1


NM_021103
−0.572722
TMSB10
thymosin, beta 10


NM_002350
−0.571917
LYN
v-yes-1 Yamaguchi sarcoma viral





related oncogene homolog


Contig51235_RC
0.571049

Homo sapiens cDNA: FLJ23388 fis,





clone HEP17008


NM_013384
0.570987
TMSG1
tumor metastasis-suppressor


NM_014399
0.570936
NET-6
tetraspan NET-6 protein


Contig26022_RC
−0.570851

ESTs


AB023152
0.570561
KIAA0935
KIAA0935 protein


NM_021077
−0.569944
NMB
neuromedin B


NM_003498
−0.569129
SNN
stannin


U17077
−0.568979
BENE
BENE protein


D86985
0.567698
KIAA0232
KIAA0232 gene product


NM_006357
−0.567513
UBE2E3
ubiquitin-conjugating enzyme E2E 3





(homologous to yeast UBC4/5)


AL049397
−0.567434

Homo sapiens mRNA; cDNA





DKFZp586C1019 (from clone





DKFZp586C1019)


Contig64502
0.567433

ESTs, Weakly similar to unknown





[M. musculus]


Contig56298_RC
−0.566892
FLJ13154
hypothetical protein FLJ13154


Contig46056_RC
0.566634

ESTs, Weakly similar to





YZ28_HUMAN HYPOTHETICAL





PROTEIN ZAP128 [H. sapiens]


AF007153
0.566044

Homo sapiens clone 23736 mRNA





sequence


Contig1778_RC
−0.565789

ESTs


NM_017702
−0.565789
FLJ20186
hypothetical protein FLJ20186


Contig39226_RC
0.565761

Homo sapiens cDNA FLJ12187 fis,





clone MAMMA1000831


NM_000168
0.564879
GLI3
GLI-Kruppel family member GLI3





(Greig cephalopolysyndactyly





syndrome)


Contig57609_RC
0.564751

ESTs, Weakly similar to





T2D3_HUMAN TRANSCRIPTION





INITIATION FACTOR TFIID 135





KDA SUBUNIT [H. sapiens]


U45975
0.564602
PIB5PA
phosphatidylinositol (4,5)





bisphosphate 5-phosphatase, A


AF038182
0.564596

Homo sapiens clone 23860 mRNA





sequence


Contig5348_RC
0.564480

ESTs, Weakly similar to 1607338A





transcription factor BTF3a





[H. sapiens]


NM_001321
−0.564459
CSRP2
cysteine and glycine-rich protein 2


Contig25362_RC
−0.563801

ESTs


NM_001609
0.563782
ACADSB
acyl-Coenzyme A dehydrogenase,





short/branched chain


Contig40146
0.563731

wi84e12.x1 NCI_CGAP_Kid12





Homo sapiens cDNA clone





IMAGE: 2400046 3′ similar to





SW: RASD_DICDI P03967 RAS-





LIKE PROTEIN RASD;, mRNA





sequence.


NM_016002
0.563403
LOC51097
CGI-49 protein


Contig34303_RC
0.563157

Homo sapiens cDNA: FLJ21517 fis,





clone COL05829


Contig55883_RC
0.563141

ESTs


NM_017961
0.562479
FLJ20813
hypothetical protein FLJ20813


M21551
−0.562340
NMB
neuromedin B


Contig3940_RC
−0.561956
YWHAH
tyrosine 3-





monooxygenase/tryptophan 5-





monooxygenase activation protein,





eta polypeptide


AB033111
−0.561746
KIAA1285
KIAA1285 protein


Contig43410_RC
0.561678

ESTs


Contig42006_RC
−0.561677

ESTs


Contig57272_RC
0.561228

ESTs


G26403
−0.561068
YWHAH
tyrosine 3-





monooxygenase/tryptophan 5-





monooxygenase activation protein,





eta polypeptide


NM_005915
−0.560813
MCM6
minichromosome maintenance





deficient (mis5, S. pombe) 6


NM_003875
−0.560668
GMPS
guanine monphosphate synthetase


AK000142
0.559651
AK000142
Homo sapiens cDNA FLJ20135 fis,





clone COL06818.


NM_002709
−0.559621
PPP1CB
protein phosphatase 1, catalytic





subunit, beta isoform


NM_001276
−0.558868
CHI3L1
chitinase 3-like 1 (cartilage





glycoprotein-39)


NM_002857
0.558862
PXF
peroxisomal farnesylated protein


Contig33815_RC
−0.558741
FLJ22833
hypothetical protein FLJ22833


NM_003740
−0.558491
KCNK5
potassium channel, subfamily K,





member 5 (TASK-2)


Contig53646_RC
0.558455

ESTs


NM_005538
−0.558350
INHBC
inhibin, beta C


NM_002111
0.557860
HD
huntingtin (Huntington disease)


NM_003683
−0.557807
D21S2056
DNA segment on chromosome 21




E
(unique) 2056 expressed sequence


NM_003035
−0.557380
SIL
TAL1 (SCL) interrupting locus


Contig4388_RC
−0.557216

Homo sapiens, Similar to integral





membrane protein 3, clone





MGC: 3011, mRNA, complete cds


Contig38288_RC
−0.556426

ESTs, Weakly similar to ISHUSS





protein disulfide-isomerase





[H. sapiens]


NM_015417
0.556184
DKFZP434
DKFZP434I114 protein




I114


NM_015507
−0.556138
EGFL6
EGF-like-domain, multiple 6


AF279865
0.555951
KIF13B
kinesin family member 13B


Contig31288_RC
−0.555754

ESTs


NM_002966
−0.555620
S100A10
S100 calcium-binding protein A10





(annexin II ligand, calpactin I, light





polypeptide (p11))


NM_017585
−0.555476
SLC2A6
solute carrier family 2 (facilitated





glucose transporter), member 6


NM_013296
−0.555367
HSU54999
LGN protein


NM_000224
0.554838
KRT18
keratin 18


Contig49270_RC
−0.554593
KIAA1553
KIAA1553 protein


NM_004848
−0.554538
ICB-1
basement membrane-induced gene


NM_007275
0.554278
FUS1
lung cancer candidate


NM_007044
−0.553550
KATNA1
katanin p60 (ATPase-containing)





subunit A 1


Contig1829
0.553317

ESTs


AF272357
0.553286
NPDC1
neural proliferation, differentiation





and control, 1


Contig57584_RC
−0.553080

Homo sapiens, Similar to gene rich





cluster, C8 gene, clone MGC: 2577,





mRNA, complete cds


NM_003039
−0.552747
SLC2A5
solute carrier family 2 (facilitated





glucose transporter), member 5


NM_014216
0.552321
ITPK1
inositol 1,3,4-triphosphate 5/6





kinase


NM_007027
−0.552064
TOPBP1
topoisomerase (DMA) II binding





protein


AF118224
−0.551916
ST14
suppression of tumorigenicity 14





(colon carcinoma, matriptase,





epithin)


X75315
−0.551853
HSRNASE
seb4D




B


NM_012101
−0.551824
ATDC
ataxia-telangiectasia group D-





associated protein


AL157482
−0.551329
FLJ23399
hypothetical protein FLJ23399


NM_012474
−0.551150
UMPK
uridine monophosphate kinase


Contig57081_RC
0.551103

ESTs


NM_006941
−0.551069
SOX10
SRY (sex determining region Y)-box





10


NM_004694
0.550932
SLC16A6
solute carrier family 16





(monocarboxylic acid transporters),





member 6


Contig9541_RC
0.550680

ESTs


Contig20617_RC
0.550546

ESTs


NM_004252
0.550365
SLC9A3R
solute carrier family 9




1
(sodium/hydrogen exchanger),





isoform 3 regulatory factor 1


NM_015641
−0.550200
DKFZP586
testin




B2022


NM_004336
−0.550164
BUB1
budding uninhibited by





benzimidazoles 1 (yeast homolog)


Contig39960_RC
−0.549951
FLJ21079
hypothetical protein FLJ21079


NM_020686
0.549659
NPD009
NPD009 protein


NM_002633
−0.549647
PGM1
phosphoglucomutase 1


Contig30480_RC
0.548932

ESTs


NM_003479
0.548896
PTP4A2
protein tyrosine phosphatase type





IVA, member 2


NM_001679
−0.548768
ATP1B3
ATPase, Na+/K+ transporting, beta





3 polypeptide


NM_001124
−0.548601
ADM
adrenomedullin


NM_001216
−0.548375
CA9
carbonic anhydrase IX


U58033
−0.548354
MTMR2
myotubularin related protein 2


NM_018389
−0.547875
FLJ11320
hypothetical protein FLJ11320


AF176012
0.547867
JDP1
J domain containing protein 1


Contig66705_RC
−0.546926
ST5
suppression of tumorigenicity 5


NM_018194
0.546878
FLJ10724
hypothetical protein FLJ10724


NM_006851
−0.546823
RTVP1
glioma pathogenesis-related protein


Contig53870_RC
0.546756

ESTs


NM_002482
−0.546012
NASP
nuclear autoantigenic sperm protein





(histone-binding)


NM_002292
0.545949
LAMB2
laminin, beta 2 (laminin S)


NM_014696
−0.545758
KIAA0514
KIAA0514 gene product


Contig49855
0.545517

ESTs


AL117666
0.545203
DKFZP586
DKFZP586O1624 protein




O1624


NM_004701
−0.545185
CCNB2
cyclin B2


NM_007050
0.544890
PTPRT
protein tyrosine phosphatase,





receptor type, T


NM_000414
0.544778
HSD17B4
hydroxysteroid (17-beta)





dehydrogenase 4


Contig52398_RC
−0.544775

Homo sapiens cDNA: FLJ21950 fis,





clone HEP04949


AB007916
0.544496
KIAA0447
KIAA0447 gene product


Contig66219_RC
0.544467
FLJ22402
hypothetical protein FLJ22402


D87453
0.544145
KIAA0264
KIAA0264 protein


NM_015515
−0.543929
DKFZP434
DKFZP434G032 protein




G032


NM_001530
−0.543898
HIF1A
hypoxia-inducible factor 1, alpha





subunit (basic helix-loop-helix





transcription factor)


NM_004109
−0.543893
FDX1
ferredoxin 1


NM_000381
−0.543871
MID1
midline 1 (Opitz/BBB syndrome)


Contig43983_RC
0.543523
CS2
calsyntenin-2


AL137761
0.543371

Homo sapiens mRNA; cDNA





DKFZp586L2424 (from clone





DKFZp586L2424)


NM_005764
−0.543175
DD96
epithelial protein up-regulated in





carcinoma, membrane associated





protein 17


Contig1838_RC
0.542996

Homo sapiens cDNA: FLJ22722 fis,





clone HSI14444


NM_006670
0.542932
5T4
5T4 oncofetal trophoblast





glycoprotein


Contig28552_RC
−0.542617

Homo sapiens mRNA; cDNA





DKFZp434C0931 (from clone





DKFZp434C0931); partial cds


Contig14284_RC
0.542224

ESTs


NM_006290
−0.542115
TNFAIP3
tumor necrosis factor, alpha-induced





protein 3


AL050372
0.541463

Homo sapiens mRNA; cDNA





DKFZp434A091 (from clone





DKFZp434A091); partial cds


NM_014181
−0.541095
HSPC159
HSPC159 protein


Contig37141_RC
0.540990

Homo sapiens cDNA: FLJ23582 fis,





clone LNG13759


NM_000947
−0.540621
PRIM2A
primase, polypeptide 2A (58 kD)


NM_002136
0.540572
HNRPA1
heterogeneous nuclear





ribonucleoprotein A1


NM_004494
−0.540543
HDGF
hepatoma-derived growth factor





(high-mobility group protein 1-like)


Contig38983_RC
0.540526

ESTs


Contig27882_RC
−0.540506

ESTs


Z11887
−0.540020
MMP7
matrix metalloproteinase 7





(matrilysin, uterine)


NM_014575
−0.539725
SCHIP-1
schwannomin interacting protein 1


Contig38170_RC
0.539708

ESTs


Contig44064_RC
0.539403

ESTs


U68385
0.539395
MEIS3
Meis (mouse) homolog 3


Contig51967_RC
0.538952

ESTs


Contig37562_RC
0.538657

ESTs, Weakly similar to





transformation-related protein





[H. sapiens]


Contig40500_RC
0.538582

ESTs, Weakly similar to unnamed





protein product [H. sapiens]


Contig1129_RC
0.538339

ESTs


NM_002184
0.538185
IL6ST
interleukin 6 signal transducer





(gp130, oncostatin M receptor)


AL049381
0.538041

Homo sapiens cDNA FLJ12900 fis,





clone NT2RP2004321


NM_002189
−0.537867
IL15RA
interleukin 15 receptor, alpha


NM_012110
−0.537562
CHIC2
cystein-rich hydrophobic domain 2


AB040881
−0.537473
KIAA1448
KIAA1448 protein


NM_016577
−0.537430
RAB6B
RAB6B, member RAS oncogene





family


NM_001745
0.536940
CAMLG
calcium modulating ligand


NM_005742
−0.536738
P5
protein disulfide isomerase-related





protein


AB011132
0.536345
KIAA0560
KIAA0560 gene product


Contig54898_RC
0.536094
PNN
pinin, desmosome associated





protein


Contig45049_RC
−0.536043
FUT4
fucosyltransferase 4 (alpha (1,3)





fucosyltransferase, myeloid-specific)


NM_006864
−0.535924
LILRB3
leukocyte immunoglobulin-like





receptor, subfamily B (with TM and





ITIM domains), member 3


Contig53242_RC
−0.535909

Homo sapiens cDNA FLJ11436 fis,





clone HEMBA1001213


NM_005544
0.535712
IRS1
insulin receptor substrate 1


Contig47456_RC
0.535493
CACNA1D
calcium channel, voltage-





dependent, L type, alpha 1D subunit


Contig42751_RC
−0.535469

ESTs


Contig29126_RC
−0.535186

ESTs


NM_012391
0.535067
PDEF
prostate epithelium-specific Ets





transcription factor


NM_012429
0.534974
SEC14L2
SEC14 (S. cerevisiae)-like 2


NM_018171
0.534898
FLJ10659
hypothetical protein FLJ10659


Contig53047_RC
−0.534773
TTYH1
tweety (Drosophila) homolog 1


Contig54968_RC
0.534754

Homo sapiens cDNA FLJ13558 fis,





clone PLACE1007743


Contig2099_RC
−0.534694
KIAA1691
KIAA1691 protein


NM_005264
0.534057
GFRA1
GDNF family receptor alpha 1


NM_014036
−0.533638
SBBI42
BCM-like membrane protein





precursor


NM_018101
−0.533473
FLJ10468
hypothetical protein FLJ10468


Contig56765_RC
0.533442

ESTs, Moderately similar to





K02E10.2 [C. elegans]


AB006746
−0.533400
PLSCR1
phospholipid scramblase 1


NM_001089
0.533350
ABCA3
ATP-binding cassette, sub-family A





(ABC1), member 3


NM_018188
−0.533132
FLJ10709
hypothetical protein FLJ10709


X94232
−0.532925
MAPRE2
microtubule-associated protein,





RP/EB family, member 2


AF234532
−0.532910
MYO10
myosin X


Contig292_RC
0.532853
FLJ22386
hypothetical protein FLJ22386


NM_000101
−0.532767
CYBA
cytochrome b-245, alpha





polypeptide


Contig47814_RC
−0.532656
HHGP
HHGP protein


NM_014320
−0.532430
SOUL
putative heme-binding protein


NM_020347
0.531976
LZTFL1
leucine zipper transcription factor-





like 1


NM_004323
0.531936
BAG1
BCL2-associated athanogene


Contig50850_RC
−0.531914

ESTs


Contig11648_RC
0.531704

ESTs


NM_018131
−0.531559
FLJ10540
hypothetical protein FLJ10540


NM_004688
−0.531329
NMI
N-myc (and STAT) interactor


NM_014870
0.531101
KIAA0478
KIAA0478 gene product


Contig31424_RC
0.530720

ESTs


NM_000874
−0.530545
IFNAR2
interferon (alpha, beta and omega)





receptor 2


Contig50588_RC
0.530145

ESTs


NM_016463
0.529998
HSPC195
hypothetical protein


NM_013324
0.529966
CISH
cytokine inducible SH2-containing





protein


NM_006705
0.529840
GADD45G
growth arrest and DNA-damage-





inducible, gamma


Contig38901_RC
−0.529747

ESTs


NM_004184
−0.529635
WARS
tryptophanyl-tRNA synthetase


NM_015955
−0.529538
LOC51072
CGI-27 protein


AF151810
0.529416
CGI-52
similar to phosphatidylcholine





transfer protein 2


NM_002164
−0.529117
INDO
indoleamine-pyrrole 2,3





dioxygenase


NM_004267
−0.528679
CHST2
carbohydrate (chondroitin 6/keratan)





sulfotransferase 2


Contig32185_RC
−0.528529

Homo sapiens cDNA FLJ13997 fis,





clone Y79AA1002220


NM_004154
−0.528343
P2RY6
pyrimidinergic receptor P2Y, G-





protein coupled, 6


NM_005235
0.528294
ERBB4
v-erb-a avian erythroblastic





leukemia viral oncogene homolog-





like 4


Contig40208_RC
−0.528062
LOC56938
transcription factor BMAL2


NM_013262
0.527297
MIR
myosin regulatory light chain





interacting protein


NM_003034
−0.527148
SIAT8A
sialyltransferase 8 (alpha-N-





acetylneuraminate: alpha-2,8-





sialytransferase, GD3 synthase) A


NM_004556
−0.527146
NFKBIE
nuclear factor of kappa light





polypeptide gene enhancer in B-





cells inhibitor, epsilon


NM_002046
−0.527051
GAPD
glyceraldehyde-3-phosphate





dehydrogenase


NM_001905
−0.526986
CTPS
CTP synthase


Contig42402_RC
0.526852

ESTs


NM_014272
−0.526283
ADAMTS7
a disintegrin-like and





metalloprotease (reprolysin type)





with thrombospondin type 1 motif, 7


AF076612
0.526205
CHRD
chordin


Contig57725_RC
−0.526122

Homo sapiens mRNA for HMG-box





transcription factor TCF-3, complete





cds


Contig42041_RC
−0.525877

ESTs


Contig44656_RC
−0.525868

ESTs, Highly similar to S02392





alpha-2-macroglobulin receptor





precursor [H. sapiens]


NM_018004
−0.525610
FLJ10134
hypothetical protein FLJ10134


Contig56434_RC
0.525510

Homo sapiens cDNA FLJ13603 fis,





clone PLACE1010270


D25328
−0.525504
PFKP
phosphofructokinase, platelet


Contig55950_RC
−0.525358
FLJ22329
hypothetical protein FLJ22329


NM_002648
−0.525211
PIM1
pim-1 oncogene


AL157505
0.525186

Homo sapiens mRNA; cDNA





DKFZp586P1124 (from clone





DKFZp586P1124)


AF061034
−0.525185
FIP2
Homo sapiens FIP2 alternatively





translated mRNA, complete cds.


NM_014721
−0.525102
KIAA0680
KIAA0680 gene product


NM_001634
−0.525030
AMD1
S-adenosylmethionine





decarboxylase 1


NM_006304
−0.524911
DSS1
Deleted in split-hand/split-foot 1





region


Contig37778_RC
0.524667

ESTs, Highly similar to HLHUSB





MHC class II histocompatibility





antigen HLA-DP alpha-1 chain





precursor [H. sapiens]


NM_003099
0.524339
SNX1
sorting nexin 1


AL079298
0.523774
MCCC2
methylcrotonoyl-Coenzyme A





carboxylase 2 (beta)


NM_019013
−0.523663
FLJ10156
hypothetical protein


NM_000397
−0.523293
CYBB
cytochrome b-245, beta polypeptide





(chronic granulomatous disease)


NM_014811
0.523132
KIAA0649
KIAA0649 gene product


Contig20600_RC
0.523072

ESTs


NM_005190
−0.522710
CCNC
cyclin C


AL161960
−0.522574
FLJ21324
hypothetical protein FLJ21324


AL117502
0.522280

Homo sapiens mRNA; cDNA





DKFZp434D0935 (from clone





DKFZp434D0935)


AF131753
−0.522245

Homo sapiens clone 24859 mRNA





sequence


NM_000320
0.521974
QDPR
quinoid dihydropteridine reductase


NM_002115
−0.521870
HK3
hexokinase 3 (white cell)


NM_006460
0.521696
HIS1
HMBA-inducible


NM_018683
−0.521679
ZNF313
zinc finger protein 313


NM_004305
−0.521539
BIN1
bridging integrator 1


NM_006770
−0.521538
MARCO
macrophage receptor with





collagenous structure


NM_001166
−0.521530
BIRC2
baculoviral IAP repeat-containing 2


D42047
0.521522
KIAA0089
KIAA0089 protein


NM_016235
−0.521298
GPRC5B
G protein-coupled receptor, family





C, group 5, member B


NM_004504
−0.521189
HRB
HIV-1 Rev binding protein


NM_002727
−0.521146
PRG1
proteoglycan 1, secretory granule


AB029031
−0.520761
KIAA1108
KIAA1108 protein


NM_005556
−0.520692
KRT7
keratin 7


NM_018031
0.520600
WDR6
WD repeat domain 6


AL117523
−0.520579
KIAA1053
KIAA1053 protein


NM_004515
−0.520363
ILF2
Interleukin enhancer binding factor





2, 45 kD


NM_004708
−0.519935
PDCD5
programmed cell death 5


NM_005935
0.519765
MLLT2
myeloid/lymphoid or mixed-lineage





leukemia (trithorax (Drosophila)





homolog); translocated to, 2


Contig49289_RC
−0.519546

Homo sapiens mRNA; cDNA





DKFZp586J1119 (from clone





DKFZp586J1119); complete cds


NM_000211
−0.519342
ITGB2
integrin, beta 2 (antigen CD18 (p95),





lymphocyte function-associated





antigen 1; macrophage antigen 1





(mac-1) beta subunit)


AL079276
0.519207
LOC58495
putative zinc finger protein from





EUROIMAGE 566589


Contig57825_RC
0.519041

ESTs


NM_002466
−0.518911
MYBL2
v-myb avian myeloblastosis viral





oncogene homolog-like 2


NM_016072
−0.518802
LOC51026
CGI-141 protein


AB007950
−0.518699
KIAA0481
KIAA0481 gene product


NM_001550
−0.518549
IFRD1
interferon-related developmental





regulator 1


AF155120
−0.518221
UBE2V1
ubiquitin-conjugating enzyme E2





variant 1


Contig49849_RC
0.517983

ESTs, Weakly similar to AF188706





1 g20 protein [H. sapiens]


NM_016625
−0.517936
LOC51319
hypothetical protein


NM_004049
−0.517862
BCL2A1
BCL2-related protein A1


Contig50719_RC
0.517740

ESTs


D80010
−0.517620
LPIN1
lipin 1


NM_000299
−0.517405
PKP1
plakophilin 1 (ectodermal





dysplasia/skin fragility syndrome)


AL049365
0.517080
FTL
ferritin, light polypeptide


Contig65227
0.517003

ESTs


NM_004865
−0.516808
TBPL1
TBP-like 1


Contig54813_RC
0.516246
FLJ13962
hypothetical protein FLJ13962


NM_003494
−0.516221
DYSF
dysferlin, limb girdle muscular





dystrophy 2B (autosomal recessive)


NM_004431
−0.516212
EPHA2
EphA2


AL117600
−0.516067
DKFZP564
DKFZP564J0863 protein




J0863


AL080209
−0.516037
DKFZP586
hypothetical protein




F2423
DKFZp586F2423


NM_000135
−0.515613
FANCA
Fanconi anemia, complementation





group A


NM_000050
−0.515494
ASS
argininosuccinate synthetase


NM_001830
−0.515439
CLCN4
chloride channel 4


NM_018234
−0.515365
FLJ10829
hypothetical protein FLJ10829


Contig53307_RC
0.515328

ESTs, Highly similar to KIAA1437





protein [H. sapiens]


AL117617
−0.515141

Homo sapiens mRNA; cDNA





DKFZp564H0764 (from clone





DKFZp564H0764)


NM_002906
−0.515098
RDX
radixin


NM_003360
−0.514427
UGT8
UDP glycosyltransferase 8 (UDP-





galactose ceramide





galactosyltransferase)


NM_018478
0.514332
HSMNP1
uncharacterized hypothalamus





protein HSMNP1


M90657
−0.513908
TM4SF1
transmembrane 4 superfamily





member 1


NM_014967
0.513793
KIAA1018
KIAA1018 protein


Contig1462_RC
0.513604
C11ORF15
chromosome 11 open reading frame





15


Contig37287_RC
−0.513324

ESTs


NM_000355
−0.513225
TCN2
transcobalamin II; macrocytic





anemia


AB037756
0.512914
KIAA1335
hypothetical protein KIAA1335


Contig842_RC
−0.512880

ESTs


NM_018186
−0.512878
FLJ10706
hypothetical protein FLJ10706


NM_014668
0.512746
KIAA0575
KIAA0575 gene product


NM_003226
0.512611
TFF3
trefoil factor 3 (intestinal)


Contig56457_RC
−0.512548
TMEFF1
transmembrane protein with EGF-





like and two follistatin-like domains 1


AL050367
−0.511999

Homo sapiens mRNA; cDNA





DKFZp564A026 (from clone





DKFZp564A026)


NM_014791
−0.511963
KIAA0175
KIAA0175 gene product


Contig36312_RC
0.511794

ESTs


NM_004811
−0.511447
LPXN
leupaxin


Contig67182_RC
−0.511416

ESTs, Highly similar to epithelial V-





like antigen precursor [H. sapiens]


Contig52723_RC
−0.511134

ESTs


Contig17105_RC
−0.511072

Homo sapiens mRNA for putative





cytoplasmatic protein (ORF1-FL21)


NM_014449
0.511023
A
protein “A”


Contig52957_RC
0.510815

ESTs


Contig49388_RC
0.510582
FLJ13322
hypothetical protein FLJ13322


NM_017786
0.510557
FLJ20366
hypothetical protein FLJ20366


AL157476
0.510478

Homo sapiens mRNA; cDNA





DKFZp761C082 (from clone





DKFZp761C082)


NM_001919
0.510242
DCI
dodecenoyl-Coenzyme A delta





isomerase (3,2 trans-enoyl-





Coenzyme A isomerase)


NM_000268
−0.510165
NF2
neurofibromin 2 (bilateral acoustic





neuroma)


NM_016210
0.510018
LOC51161
g20 protein


Contig45816_RC
−0.509977

ESTs


NM_003953
−0.509969
MPZL1
myelin protein zero-like 1


NM_000057
−0.509669
BLM
Bloom syndrome


NM_014452
−0.509473
DR6
death receptor 6


Contig45156_RC
0.509284

ESTs, Moderately similar to motor





domain of KIF12 [M. musculus]


NM_006943
0.509149
SOX22
SRY (sex determining region Y)-box





22


NM_000594
−0.509012
TNF
tumor necrosis factor (TNF





superfamily, member 2)


AL137316
−0.508353
KIAA1609
KIAA1609 protein


NM_000557
−0.508325
GDF5
growth differentiation factor 5





(cartilage-derived morphogenetic





protein-1)


NM_018685
−0.508307
ANLN
anillin (Drosophila Scraps homolog),





actin binding protein


Contig53401_RC
0.508189

ESTs


NM_014364
−0.508170
GAPDS
glyceraldehyde-3-phosphate





dehydrogenase, testis-specific


Contig50297_RC
0.508137

ESTs, Moderately similar to





ALU8_HUMAN ALU SUBFAMILY





SX SEQUENCE CONTAMINATION





WARNING ENTRY [H. sapiens]


Contig51800
0.507891

ESTs, Weakly similar to





ALU6_HUMAN ALU SUBFAMILY





SP SEQUENCE CONTAMINATION





WARNING ENTRY [H. sapiens]


Contig49098_RC
−0.507716
MGC4090
hypothetical protein MGC4090


NM_002985
−0.507554
SCYA5
small inducible cytokine A5





(RANTES)


AB007899
0.507439
KIAA0439
KIAA0439 protein; homolog of yeast





ubiquitin-protein ligase Rsp5


AL110139
0.507145

Homo sapiens mRNA; cDNA





DKFZp564O1763 (from clone





DKFZp564O1763)


Contig51117_RC
0.507001

ESTs


NM_017660
−0.506768
FLJ20085
hypothetical protein FLJ20085


NM_018000
0.506686
FLJ10116
hypothetical protein FLJ10116


NM_005555
−0.506516
KRT6B
keratin 6B


NM_005582
−0.506462
LY64
lymphocyte antigen 64 (mouse)





homolog, radioprotective, 105 kD


Contig47405_RC
0.506202

ESTs


NM_014808
0.506173
KIAA0793
KIAA0793 gene product


NM_004938
−0.506121
DAPK1
death-associated protein kinase 1


NM_020659
−0.505793
TTYH1
tweety (Drosophila) homolog 1


NM_006227
−0.505604
PLTP
phospholipid transfer protein


NM_014268
−0.505412
MAPRE2
microtubule-associated protein,





RP/EB family, member 2


NM_004711
0.504849
SYNGR1
synaptogyrin 1


NM_004418
−0.504497
DUSP2
dual specificity phosphatase 2


NM_003508
−0.504475
FZD9
frizzled (Drosophila) homolog 9
















TABLE 3







430 gene markers that distinguish BRCA1-related


tumor samples from sporadic tumor samples










GenBank




Accession Number
SEQ ID NO







AB002301
SEQ ID NO 4



AB004857
SEQ ID NO 8



AB007458
SEQ ID NO 12



AB014534
SEQ ID NO 29



AB018305
SEQ ID NO 34



AB020677
SEQ ID NO 36



AB020689
SEQ ID NO 37



AB023151
SEQ ID NO 41



AB023163
SEQ ID NO 43



AB028986
SEQ ID NO 48



AB029025
SEQ ID NO 50



AB032966
SEQ ID NO 53



AB032988
SEQ ID NO 57



AB033049
SEQ ID NO 63



AB033055
SEQ ID NO 66



AB037742
SEQ ID NO 73



AB041269
SEQ ID NO 96



AF000974
SEQ ID NO 97



AF042838
SEQ ID NO 111



AF052155
SEQ ID NO 119



AF055084
SEQ ID NO 125



AF063725
SEQ ID NO 129



AF070536
SEQ ID NO 133



AF070617
SEQ ID NO 135



AF073299
SEQ ID NO 136



AF079529
SEQ ID NO 140



AF090353
SEQ ID NO 141



AF116238
SEQ ID NO 155



AF151810
SEQ ID NO 171



AF220492
SEQ ID NO 185



AJ224741
SEQ ID NO 196



AJ250475
SEQ ID NO 201



AJ270996
SEQ ID NO 202



AJ272057
SEQ ID NO 203



AK000174
SEQ ID NO 211



AK000617
SEQ ID NO 215



AK000959
SEQ ID NO 222



AK001438
SEQ ID NO 229



AK001838
SEQ ID NO 233



AK002107
SEQ ID NO 238



AK002197
SEQ ID NO 239



AL035297
SEQ ID NO 241



AL049346
SEQ ID NO 243



AL049370
SEQ ID NO 245



AL049667
SEQ ID NO 249



AL080222
SEQ ID NO 276



AL096737
SEQ ID NO 279



AL110163
SEQ ID NO 282



AL133057
SEQ ID NO 300



AL133096
SEQ ID NO 302



AL133572
SEQ ID NO 305



AL133619
SEQ ID NO 307



AL133623
SEQ ID NO 309



AL137347
SEQ ID NO 320



AL137381
SEQ ID NO 322



AL137461
SEQ ID NO 325



AL137540
SEQ ID NO 328



AL137555
SEQ ID NO 329



AL137638
SEQ ID NO 332



AL137639
SEQ ID NO 333



AL137663
SEQ ID NO 334



AL137761
SEQ ID NO 339



AL157431
SEQ ID NO 340



AL161960
SEQ ID NO 351



AL355708
SEQ ID NO 353



AL359053
SEQ ID NO 354



D26488
SEQ ID NO 359



D38521
SEQ ID NO 361



D50914
SEQ ID NO 367



D80001
SEQ ID NO 369



G26403
SEQ ID NO 380



K02276
SEQ ID NO 383



M21551
SEQ ID NO 394



M27749
SEQ ID NO 397



M28170
SEQ ID NO 398



M73547
SEQ ID NO 409



M80899
SEQ ID NO 411



NM_000067
SEQ ID NO 423



NM_000087
SEQ ID NO 427



NM_000090
SEQ ID NO 428



NM_000165
SEQ ID NO 444



NM_000168
SEQ ID NO 445



NM_000196
SEQ ID NO 449



NM_000269
SEQ ID NO 457



NM_000310
SEQ ID NO 466



NM_000396
SEQ ID NO 479



NM_000397
SEQ ID NO 480



NM_000597
SEQ ID NO 502



NM_000636
SEQ ID NO 509



NM_000888
SEQ ID NO 535



NM_000903
SEQ ID NO 536



NM_000930
SEQ ID NO 540



NM_000931
SEQ ID NO 541



NM_000969
SEQ ID NO 547



NM_000984
SEQ ID NO 548



NM_001026
SEQ ID NO 552



NM_001054
SEQ ID NO 554



NM_001179
SEQ ID NO 567



NM_001184
SEQ ID NO 568



NM_001204
SEQ ID NO 571



NM_001206
SEQ ID NO 572



NM_001218
SEQ ID NO 575



NM_001275
SEQ ID NO 586



NM_001394
SEQ ID NO 602



NM_001424
SEQ ID NO 605



NM_001448
SEQ ID NO 610



NM_001504
SEQ ID NO 620



NM_001553
SEQ ID NO 630



NM_001674
SEQ ID NO 646



NM_001675
SEQ ID NO 647



NM_001725
SEQ ID NO 652



NM_001740
SEQ ID NO 656



NM_001756
SEQ ID NO 659



NM_001770
SEQ ID NO 664



NM_001797
SEQ ID NO 670



NM_001845
SEQ ID NO 680



NM_001873
SEQ ID NO 684



NM_001888
SEQ ID NO 687



NM_001892
SEQ ID NO 688



NM_001919
SEQ ID NO 694



NM_001946
SEQ ID NO 698



NM_001953
SEQ ID NO 699



NM_001960
SEQ ID NO 704



NM_001985
SEQ ID NO 709



NM_002023
SEQ ID NO 712



NM_002051
SEQ ID NO 716



NM_002053
SEQ ID NO 717



NM_002164
SEQ ID NO 734



NM_002200
SEQ ID NO 739



NM_002201
SEQ ID NO 740



NM_002213
SEQ ID NO 741



NM_002250
SEQ ID NO 747



NM_002512
SEQ ID NO 780



NM_002542
SEQ ID NO 784



NM_002561
SEQ ID NO 786



NM_002615
SEQ ID NO 793



NM_002686
SEQ ID NO 803



NM_002709
SEQ ID NO 806



NM_002742
SEQ ID NO 812



NM_002775
SEQ ID NO 815



NM_002975
SEQ ID NO 848



NM_002982
SEQ ID NO 849



NM_003104
SEQ ID NO 870



NM_003118
SEQ ID NO 872



NM_003144
SEQ ID NO 876



NM_003165
SEQ ID NO 882



NM_003197
SEQ ID NO 885



NM_003202
SEQ ID NO 886



NM_003217
SEQ ID NO 888



NM_003283
SEQ ID NO 898



NM_003462
SEQ ID NO 911



NM_003500
SEQ ID NO 918



NM_003561
SEQ ID NO 925



NM_003607
SEQ ID NO 930



NM_003633
SEQ ID NO 933



NM_003641
SEQ ID NO 934



NM_003683
SEQ ID NO 943



NM_003729
SEQ ID NO 949



NM_003793
SEQ ID NO 954



NM_003829
SEQ ID NO 958



NM_003866
SEQ ID NO 961



NM_003904
SEQ ID NO 967



NM_003953
SEQ ID NO 974



NM_004024
SEQ ID NO 982



NM_004053
SEQ ID NO 986



NM_004295
SEQ ID NO 1014



NM_004438
SEQ ID NO 1038



NM_004559
SEQ ID NO 1057



NM_004616
SEQ ID NO 1065



NM_004741
SEQ ID NO 1080



NM_004772
SEQ ID NO 1084



NM_004791
SEQ ID NO 1086



NM_004848
SEQ ID NO 1094



NM_004866
SEQ ID NO 1097



NM_005128
SEQ ID NO 1121



NM_005148
SEQ ID NO 1124



NM_005196
SEQ ID NO 1127



NM_005326
SEQ ID NO 1140



NM_005518
SEQ ID NO 1161



NM_005538
SEQ ID NO 1163



NM_005557
SEQ ID NO 1170



NM_005718
SEQ ID NO 1189



NM_005804
SEQ ID NO 1201



NM_005824
SEQ ID NO 1203



NM_005935
SEQ ID NO 1220



NM_006002
SEQ ID NO 1225



NM_006148
SEQ ID NO 1249



NM_006235
SEQ ID NO 1257



NM_006271
SEQ ID NO 1261



NM_006287
SEQ ID NO 1264



NM_006296
SEQ ID NO 1267



NM_006378
SEQ ID NO 1275



NM_006461
SEQ ID NO 1287



NM_006573
SEQ ID NO 1300



NM_006622
SEQ ID NO 1302



NM_006696
SEQ ID NO 1308



NM_006769
SEQ ID NO 1316



NM_006787
SEQ ID NO 1319



NM_006875
SEQ ID NO 1334



NM_006885
SEQ ID NO 1335



NM_006918
SEQ ID NO 1339



NM_006923
SEQ ID NO 1340



NM_006941
SEQ ID NO 1342



NM_007070
SEQ ID NO 1354



NM_007088
SEQ ID NO 1356



NM_007146
SEQ ID NO 1358



NM_007173
SEQ ID NO 1359



NM_007246
SEQ ID NO 1366



NM_007358
SEQ ID NO 1374



NM_012135
SEQ ID NO 1385



NM_012151
SEQ ID NO 1387



NM_012258
SEQ ID NO 1396



NM_012317
SEQ ID NO 1399



NM_012337
SEQ ID NO 1403



NM_012339
SEQ ID NO 1404



NM_012391
SEQ ID NO 1406



NM_012428
SEQ ID NO 1412



NM_013233
SEQ ID NO 1418



NM_013253
SEQ ID NO 1422



NM_013262
SEQ ID NO 1425



NM_013372
SEQ ID NO 1434



NM_013378
SEQ ID NO 1435



NM_014096
SEQ ID NO 1450



NM_014242
SEQ ID NO 1464



NM_014314
SEQ ID NO 1472



NM_014398
SEQ ID NO 1486



NM_014402
SEQ ID NO 1488



NM_014476
SEQ ID NO 1496



NM_014521
SEQ ID NO 1499



NM_014585
SEQ ID NO 1504



NM_014597
SEQ ID NO 1506



NM_014642
SEQ ID NO 1510



NM_014679
SEQ ID NO 1517



NM_014680
SEQ ID NO 1518



NM_014700
SEQ ID NO 1520



NM_014723
SEQ ID NO 1523



NM_014770
SEQ ID NO 1530



NM_014785
SEQ ID NO 1534



NM_014817
SEQ ID NO 1539



NM_014840
SEQ ID NO 1541



NM_014878
SEQ ID NO 1546



NM_015493
SEQ ID NO 1564



NM_015523
SEQ ID NO 1568



NM_015544
SEQ ID NO 1570



NM_015623
SEQ ID NO 1572



NM_015640
SEQ ID NO 1573



NM_015721
SEQ ID NO 1576



NM_015881
SEQ ID NO 1577



NM_015937
SEQ ID NO 1582



NM_015964
SEQ ID NO 1586



NM_015984
SEQ ID NO 1587



NM_016000
SEQ ID NO 1591



NM_016018
SEQ ID NO 1593



NM_016066
SEQ ID NO 1601



NM_016073
SEQ ID NO 1603



NM_016081
SEQ ID NO 1604



NM_016140
SEQ ID NO 1611



NM_016223
SEQ ID NO 1622



NM_016267
SEQ ID NO 1629



NM_016307
SEQ ID NO 1633



NM_016364
SEQ ID NO 1639



NM_016373
SEQ ID NO 1640



NM_016459
SEQ ID NO 1646



NM_016471
SEQ ID NO 1648



NM_016548
SEQ ID NO 1654



NM_016620
SEQ ID NO 1662



NM_016820
SEQ ID NO 1674



NM_017423
SEQ ID NO 1678



NM_017709
SEQ ID NO 1698



NM_017732
SEQ ID NO 1700



NM_017734
SEQ ID NO 1702



NM_017750
SEQ ID NO 1704



NM_017763
SEQ ID NO 1706



NM_017782
SEQ ID NO 1710



NM_017816
SEQ ID NO 1714



NM_018043
SEQ ID NO 1730



NM_018072
SEQ ID NO 1734



NM_018093
SEQ ID NO 1738



NM_018103
SEQ ID NO 1742



NM_018171
SEQ ID NO 1751



NM_018187
SEQ ID NO 1755



NM_018188
SEQ ID NO 1756



NM_018222
SEQ ID NO 1761



NM_018228
SEQ ID NO 1762



NM_018373
SEQ ID NO 1777



NM_018390
SEQ ID NO 1781



NM_018422
SEQ ID NO 1784



NM_018509
SEQ ID NO 1792



NM_018584
SEQ ID NO 1796



NM_018653
SEQ ID NO 1797



NM_018660
SEQ ID NO 1798



NM_018683
SEQ ID NO 1799



NM_019049
SEQ ID NO 1814



NM_019063
SEQ ID NO 1815



NM_020150
SEQ ID NO 1823



NM_020987
SEQ ID NO 1848



NM_021095
SEQ ID NO 1855



NM_021242
SEQ ID NO 1867



U41387
SEQ ID NO 1877



U45975
SEQ ID NO 1878



U58033
SEQ ID NO 1881



U67784
SEQ ID NO 1884



U68385
SEQ ID NO 1885



U80736
SEQ ID NO 1890



X00437
SEQ ID NO 1899



X07203
SEQ ID NO 1904



X16302
SEQ ID NO 1907



X51630
SEQ ID NO 1908



X57809
SEQ ID NO 1912



X57819
SEQ ID NO 1913



X58529
SEQ ID NO 1914



X66087
SEQ ID NO 1916



X69150
SEQ ID NO 1917



X72475
SEQ ID NO 1918



X74794
SEQ ID NO 1920



X75315
SEQ ID NO 1921



X84340
SEQ ID NO 1925



X98260
SEQ ID NO 1928



Y07512
SEQ ID NO 1931



Y14737
SEQ ID NO 1932



Z34893
SEQ ID NO 1934



Contig237_RC
SEQ ID NO 1940



Contig292_RC
SEQ ID NO 1942



Contig372_RC
SEQ ID NO 1943



Contig756_RC
SEQ ID NO 1955



Contig842_RC
SEQ ID NO 1958



Contig1632_RC
SEQ ID NO 1977



Contig1826_RC
SEQ ID NO 1980



Contig2237_RC
SEQ ID NO 1988



Contig2915_RC
SEQ ID NO 2003



Contig3164_RC
SEQ ID NO 2007



Contig3252_RC
SEQ ID NO 2008



Contig3940_RC
SEQ ID NO 2018



Contig9259_RC
SEQ ID NO 2039



Contig10268_RC
SEQ ID NO 2041



Contig10437_RC
SEQ ID NO 2043



Contig10973_RC
SEQ ID NO 2044



Contig14390_RC
SEQ ID NO 2054



Contig16453_RC
SEQ ID NO 2060



Contig16759_RC
SEQ ID NO 2061



Contig19551
SEQ ID NO 2070



Contig24541_RC
SEQ ID NO 2088



Contig25362_RC
SEQ ID NO 2093



Contig25617_RC
SEQ ID NO 2094



Contig25722_RC
SEQ ID NO 2096



Contig26022_RC
SEQ ID NO 2099



Contig27915_RC
SEQ ID NO 2114



Contig28081_RC
SEQ ID NO 2116



Contig28179_RC
SEQ ID NO 2118



Contig28550_RC
SEQ ID NO 2119



Contig29639_RC
SEQ ID NO 2127



Contig29647_RC
SEQ ID NO 2128



Contig30092_RC
SEQ ID NO 2130



Contig30209_RC
SEQ ID NO 2132



Contig32185_RC
SEQ ID NO 2156



Contig32798_RC
SEQ ID NO 2161



Contig33230_RC
SEQ ID NO 2163



Contig33394_RC
SEQ ID NO 2165



Contig36323_RC
SEQ ID NO 2197



Contig36761_RC
SEQ ID NO 2201



Contig37141_RC
SEQ ID NO 2209



Contig37778_RC
SEQ ID NO 2218



Contig38285_RC
SEQ ID NO 2222



Contig38520_RC
SEQ ID NO 2225



Contig38901_RC
SEQ ID NO 2232



Contig39826_RC
SEQ ID NO 2241



Contig40212_RC
SEQ ID NO 2251



Contig40712_RC
SEQ ID NO 2257



Contig41402_RC
SEQ ID NO 2265



Contig41635_RC
SEQ ID NO 2272



Contig42006_RC
SEQ ID NO 2280



Contig42220_RC
SEQ ID NO 2286



Contig42306_RC
SEQ ID NO 2287



Contig43918_RC
SEQ ID NO 2312



Contig44195_RC
SEQ ID NO 2316



Contig44265_RC
SEQ ID NO 2318



Contig44278_RC
SEQ ID NO 2319



Contig44757_RC
SEQ ID NO 2329



Contig45588_RC
SEQ ID NO 2349



Contig46262_RC
SEQ ID NO 2361



Contig46288_RC
SEQ ID NO 2362



Contig46343_RC
SEQ ID NO 2363



Contig46452_RC
SEQ ID NO 2366



Contig46868_RC
SEQ ID NO 2373



Contig46937_RC
SEQ ID NO 2377



Contig48004_RC
SEQ ID NO 2393



Contig48249_RC
SEQ ID NO 2397



Contig48774_RC
SEQ ID NO 2405



Contig48913_RC
SEQ ID NO 2411



Contig48945_RC
SEQ ID NO 2412



Contig48970_RC
SEQ ID NO 2413



Contig49233_RC
SEQ ID NO 2419



Contig49289_RC
SEQ ID NO 2422



Contig49342_RC
SEQ ID NO 2423



Contig49510_RC
SEQ ID NO 2430



Contig49855
SEQ ID NO 2440



Contig49948_RC
SEQ ID NO 2442



Contig50297_RC
SEQ ID NO 2451



Contig50669_RC
SEQ ID NO 2458



Contig50673_RC
SEQ ID NO 2459



Contig50838_RC
SEQ ID NO 2465



Contig51068_RC
SEQ ID NO 2471



Contig51929
SEQ ID NO 2492



Contig51953_RC
SEQ ID NO 2493



Contig52405_RC
SEQ ID NO 2502



Contig52543_RC
SEQ ID NO 2505



Contig52720_RC
SEQ ID NO 2513



Contig53281_RC
SEQ ID NO 2530



Contig53598_RC
SEQ ID NO 2537



Contig53757_RC
SEQ ID NO 2543



Contig53944_RC
SEQ ID NO 2545



Contig54425
SEQ ID NO 2561



Contig54547_RC
SEQ ID NO 2565



Contig54757_RC
SEQ ID NO 2574



Contig54916_RC
SEQ ID NO 2581



Contig55770_RC
SEQ ID NO 2604



Contig55801_RC
SEQ ID NO 2606



Contig56143_RC
SEQ ID NO 2619



Contig56160_RC
SEQ ID NO 2620



Contig56303_RC
SEQ ID NO 2626



Contig57023_RC
SEQ ID NO 2639



Contig57138_RC
SEQ ID NO 2644



Contig57609_RC
SEQ ID NO 2657



Contig58301_RC
SEQ ID NO 2667



Contig58512_RC
SEQ ID NO 2670



Contig60393
SEQ ID NO 2674



Contig60509_RC
SEQ ID NO 2675



Contig61254_RC
SEQ ID NO 2677



Contig62306
SEQ ID NO 2680



Contig64502
SEQ ID NO 2689

















TABLE 4







100 preferred markers from Table 3 distinguishing


BRCA1-related tumors from sporadic tumors.












Sequence



Identifier
Correlation
Name
Description













NM_001892
−0.651689
CSNK1A1
casein kinase 1, alpha 1


NM_018171
−0.637696
FLJ10659
hypothetical protein FLJ10659


Contig40712_RC
−0.612509

ESTs


NM_001204
−0.608470
BMPR2
bone morphogenetic protein





receptor, type II (serine/threonine





kinase)


NM_005148
−0.598612
UNC119
unc119 (C. elegans) homolog


G26403
0.585054
YWHAH
tyrosine 3-





monooxygenase/tryptophan 5-





monooxygenase activation protein,





eta polypeptide


NM_015640
0.583397
PAI-RBP1
PAI-1 mRNA-binding protein


Contig9259_RC
0.581362

ESTs


AB033049
−0.578750
KIAA1223
KIAA1223 protein


NM_015523
0.576029
DKFZP566E144
small fragment nuclease


Contig41402_RC
−0.571650

Human DNA sequence from clone





RP11-16L21 on chromosome 9.





Contains the gene for NADP-





dependent leukotriene B4 12-





hydroxydehydrogenase, the gene





for a novel DnaJ domain protein





similar to Drosophila, C. elegans





and Arabidopsis predicted proteins,





the GNG10 gene for guanine





nucleotide binding protein 10, a





novel gene, ESTs, STSs, GSSs





and six CpG islands


NM_004791
−0.564819
ITGBL1
integrin, beta-like 1 (with EGF-like





repeat domains)


NM_007070
0.561173
FAP48
FKBP-associated protein


NM_014597
0.555907
HSU15552
acidic 82 kDa protein mRNA


AF000974
0.547194
TRIP6
thyroid hormone receptor interactor





6


NM_016073
−0.547072
CGI-142
CGI-142


Contig3940_RC
0.544073
YWHAH
tyrosine 3-





monooxygenase/tryptophan 5-





monooxygenase activation protein,





eta polypeptide


NM_003683
0.542219
D21S2056E
DNA segment on chromosome 21





(unique) 2056 expressed sequence


Contig58512_RC
−0.528458

Homo sapiens pancreas tumor-





related protein (FKSG12) mRNA,





complete cds


NM_003904
0.521223
ZNF259
zinc finger protein 259


Contig26022_RC
0.517351

ESTs


Contig48970_RC
−0.516953
KIAA0892
KIAA0892 protein


NM_016307
−0.515398
PRX2
paired related homeobox protein


AL137761
−0.514891

Homo sapiens mRNA; cDNA





DKFZp586L2424 (from clone





DKFZp586L2424)


NM_001919
−0.514799
DCI
dodecenoyl-Coenzyme A delta





isomerase (3,2 trans-enoyl-





Coenzyme A isomerase)


NM_000196
−0.514004
HSD11B2
hydroxysteroid (11-beta)





dehydrogenase 2


NM_002200
0.513149
IRF5
interferon regulatory factor 5


AL133572
0.511340

Homo sapiens mRNA; cDNA





DKFZp434I0535 (from clone





DKFZp434I0535); partial cds


NM_019063
0.511127
C2ORF2
chromosome 2 open reading frame





2


Contig25617_RC
0.509506

ESTs


NM_007358
0.508145
M96
putative DNA binding protein


NM_014785
−0.507114
KIAA0258
KIAA0258 gene product


NM_006235
0.506585
POU2AF1
POU domain, class 2, associating





factor 1


NM_014680
−0.505779
KIAA0100
KIAA0100 gene product


X66087
0.500842
MYBL1
v-myb avian myeloblastosis viral





oncogene homolog-like 1


Y07512
−0.500686
PRKG1
protein kinase, cGMP-dependent,





type I


NM_006296
0.500344
VRK2
vaccinia related kinase 2


Contig44278_RC
0.498260
DKFZP434K
DKFZP434K114 protein




114


Contig56160_RC
−0.497695

ESTs


NM_002023
−0.497570
FMOD
fibromodulin


M28170
0.497095
CD19
CD19 antigen


D26488
0.496511
KIAA0007
KIAA0007 protein


X72475
0.496125

H. sapiens mRNA for rearranged Ig





kappa light chain variable region





(I.114)


K02276
0.496068
MYC
v-myc avian myelocytomatosis viral





oncogene homolog


NM_013378
0.495648
VPREB3
pre-B lymphocyte gene 3


X58529
0.495608
IGHM
immunoglobulin heavy constant mu


NM_000168
−0.494260
GLI3
GLI-Kruppel family member GLI3





(Greig cephalopolysyndactyly





syndrome)


NM_004866
−0.492967
SCAMP1
secretory carrier membrane protein





1


NM_013253
−0.491159
DKK3
dickkopf (Xenopus laevis) homolog





3


NM_003729
0.488971
RPC
RNA 3′-terminal phosphate cyclase


NM_006875
0.487407
PIM2
pim-2 oncogene


NM_018188
0.487126
FLJ10709
hypothetical protein FLJ10709


NM_004848
0.485408
ICB-1
basement membrane-induced gene


NM_001179
0.483253
ART3
ADP-ribosyltransferase 3


NM_016548
−0.482329
LOC51280
golgi membrane protein GP73


NM_007146
−0.481994
ZNF161
zinc finger protein 161


NM_021242
−0.481754
STRAIT11499
hypothetical protein STRAIT11499


NM_016223
0.481710
PACSIN3
protein kinase C and casein kinase





substrate in neurons 3


NM_003197
−0.481526
TCEB1L
transcription elongation factor B





(SIII), polypeptide 1-like


NM_000067
−0.481003
CA2
carbonic anhydrase II


NM_006885
−0.479705
ATBF1
AT-binding transcription factor 1


NM_002542
0.478282
OGG1
8-oxoguanine DNA glycosylase


AL133619
−0.476596

Homo sapiens mRNA; cDNA





DKFZp434E2321 (from clone





DKFZp434E2321); partial cds


D80001
0.476130
KIAA0179
KIAA0179 protein


NM_018660
−0.475548
LOC55893
papillomavirus regulatory factor





PRF-1


AB004857
0.473440
SLC11A2
solute carrier family 11 (proton-





coupled divalent metal ion





transporters), member 2


NM_002250
0.472900
KCNN4
potassium intermediate/small





conductance calcium-activated





channel, subfamily N, member 4


Contig56143_RC
−0.472611

ESTs, Weakly similar to A54849





collagen alpha 1(VII) chain





precursor [H. sapiens]


NM_001960
0.471502
EEF1D
eukaryotic translation elongation





factor 1 delta (guanine nucleotide





exchange protein)


Contig52405_RC
−0.470705

ESTs, Weakly similar to





ALU8_HUMAN ALU SUBFAMILY





SX SEQUENCE CONTAMINATION





WARNING ENTRY [H. sapiens]


Contig30092_RC
−0.469977

Homo sapiens PR-domain zinc





finger protein 6 isoform B (PRDM6)





mRNA, partial cds; alternatively





spliced


NM_003462
−0.468753
P28
dynein, axonemal, light





intermediate polypeptide


Contig60393
0.468475

ESTs


Contig842_RC
0.468158

ESTs


NM_002982
0.466362
SCYA2
small inducible cytokine A2





(monocyte chemotactic protein 1,





homologous to mouse Sig-je)


Contig14390_RC
0.464150

ESTs


NM_001770
0.463847
CD19
CD19 antigen


AK000617
−0.463158

Homo sapiens mRNA; cDNA





DKFZp434L235 (from clone





DKFZp434L235)


AF073299
−0.463007
SLC9A2
solute carrier family 9





(sodium/hydrogen exchanger),





isoform 2


NM_019049
0.461990
FLJ20054
hypothetical protein


AL137347
−0.460778
DKFZP761M1511
hypothetical protein


NM_000396
−0.460263
CTSK
cathepsin K (pycnodysostosis)


NM_018373
−0.459268
FLJ11271
hypothetical protein FLJ11271


NM_002709
0.458500
PPP1CB
protein phosphatase 1, catalytic





subunit, beta isoform


NM_016820
0.457516
OGG1
8-oxoguanine DNA glycosylase


Contig10268_RC
0.456933

Human DNA sequence from clone





RP11-196N14 on chromosome 20





Contains ESTs, STSs, GSSs and





CpG islands. Contains three novel





genes, part of a gene for a novel





protein similar to protein





serine/threonine phosphatase 4





regulatory subunit 1 (PP4R1) and a





gene for a novel protein with an





ankyrin domain


NM_014521
−0.456733
SH3BP4
SH3-domain binding protein 4


AJ272057
−0.456548
STRAIT11499
hypothetical protein STRAIT11499


NM_015964
−0.456187
LOC51673
brain specific protein


Contig16759_RC
−0.456169

ESTs


NM_015937
−0.455954
LOC51604
CGI-06 protein


NM_007246
−0.455500
KLHL2
kelch (Drosophila)-like 2 (Mayven)


NM_001985
−0.453024
ETFB
electron-transfer-flavoprotein, beta





polypeptide


NM_000984
−0.452935
RPL23A
ribosomal protein L23a


Contig51953_RC
−0.451695

ESTs


NM_015984
0.450491
UCH37
ubiquitin C-terminal hydrolase





UCH37


NM_000903
−0.450371
DIA4
diaphorase (NADH/NADPH)





(cytochrome b-5 reductase)


NM_001797
−0.449862
CDH11
cadherin 11, type 2, OB-cadherin





(osteoblast)


NM_014878
0.449818
KIAA0020
KIAA0020 gene product


NM_002742
−0.449590
PRKCM
protein kinase C, mu
















TABLE 5







231 gene markers that distinguish patients with good


prognosis from patients with poor prognosis.










GenBank




Accession Number
SEQ ID NO







AA555029_RC
SEQ ID NO 1



AB020689
SEQ ID NO 37



AB032973
SEQ ID NO 55



AB033007
SEQ ID NO 58



AB033043
SEQ ID NO 62



AB037745
SEQ ID NO 75



AB037863
SEQ ID NO 88



AF052159
SEQ ID NO 120



AF052162
SEQ ID NO 121



AF055033
SEQ ID NO 124



AF073519
SEQ ID NO 137



AF148505
SEQ ID NO 169



AF155117
SEQ ID NO 173



AF161553
SEQ ID NO 177



AF201951
SEQ ID NO 183



AF257175
SEQ ID NO 189



AJ224741
SEQ ID NO 196



AK000745
SEQ ID NO 219



AL050021
SEQ ID NO 257



AL050090
SEQ ID NO 259



AL080059
SEQ ID NO 270



AL080079
SEQ ID NO 271



AL080110
SEQ ID NO 272



AL133603
SEQ ID NO 306



AL133619
SEQ ID NO 307



AL137295
SEQ ID NO 315



AL137502
SEQ ID NO 326



AL137514
SEQ ID NO 327



AL137718
SEQ ID NO 336



AL355708
SEQ ID NO 353



D25328
SEQ ID NO 357



L27560
SEQ ID NO 390



M21551
SEQ ID NO 394



NM_000017
SEQ ID NO 416



NM_000096
SEQ ID NO 430



NM_000127
SEQ ID NO 436



NM_000158
SEQ ID NO 442



NM_000224
SEQ ID NO 453



NM_000286
SEQ ID NO 462



NM_000291
SEQ ID NO 463



NM_000320
SEQ ID NO 469



NM_000436
SEQ ID NO 487



NM_000507
SEQ ID NO 491



NM_000599
SEQ ID NO 503



NM_000788
SEQ ID NO 527



NM_000849
SEQ ID NO 530



NM_001007
SEQ ID NO 550



NM_001124
SEQ ID NO 562



NM_001168
SEQ ID NO 566



NM_001216
SEQ ID NO 574



NM_001280
SEQ ID NO 588



NM_001282
SEQ ID NO 589



NM_001333
SEQ ID NO 597



NM_001673
SEQ ID NO 645



NM_001809
SEQ ID NO 673



NM_001827
SEQ ID NO 676



NM_001905
SEQ ID NO 691



NM_002019
SEQ ID NO 711



NM_002073
SEQ ID NO 721



NM_002358
SEQ ID NO 764



NM_002570
SEQ ID NO 787



NM_002808
SEQ ID NO 822



NM_002811
SEQ ID NO 823



NM_002900
SEQ ID NO 835



NM_002916
SEQ ID NO 838



NM_003158
SEQ ID NO 881



NM_003234
SEQ ID NO 891



NM_003239
SEQ ID NO 893



NM_003258
SEQ ID NO 896



NM_003376
SEQ ID NO 906



NM_003600
SEQ ID NO 929



NM_003607
SEQ ID NO 930



NM_003662
SEQ ID NO 938



NM_003676
SEQ ID NO 941



NM_003748
SEQ ID NO 951



NM_003862
SEQ ID NO 960



NM_003875
SEQ ID NO 962



NM_003878
SEQ ID NO 963



NM_003882
SEQ ID NO 964



NM_003981
SEQ ID NO 977



NM_004052
SEQ ID NO 985



NM_004163
SEQ ID NO 995



NM_004336
SEQ ID NO 1022



NM_004358
SEQ ID NO 1026



NM_004456
SEQ ID NO 1043



NM_004480
SEQ ID NO 1046



NM_004504
SEQ ID NO 1051



NM_004603
SEQ ID NO 1064



NM_004701
SEQ ID NO 1075



NM_004702
SEQ ID NO 1076



NM_004798
SEQ ID NO 1087



NM_004911
SEQ ID NO 1102



NM_004994
SEQ ID NO 1108



NM_005196
SEQ ID NO 1127



NM_005342
SEQ ID NO 1143



NM_005496
SEQ ID NO 1157



NM_005563
SEQ ID NO 1173



NM_005915
SEQ ID NO 1215



NM_006096
SEQ ID NO 1240



NM_006101
SEQ ID NO 1241



NM_006115
SEQ ID NO 1245



NM_006117
SEQ ID NO 1246



NM_006201
SEQ ID NO 1254



NM_006265
SEQ ID NO 1260



NM_006281
SEQ ID NO 1263



NM_006372
SEQ ID NO 1273



NM_006681
SEQ ID NO 1306



NM_006763
SEQ ID NO 1315



NM_006931
SEQ ID NO 1341



NM_007036
SEQ ID NO 1349



NM_007203
SEQ ID NO 1362



NM_012177
SEQ ID NO 1390



NM_012214
SEQ ID NO 1392



NM_012261
SEQ ID NO 1397



NM_012429
SEQ ID NO 1413



NM_013262
SEQ ID NO 1425



NM_013296
SEQ ID NO 1427



NM_013437
SEQ ID NO 1439



NM_014078
SEQ ID NO 1449



NM_014109
SEQ ID NO 1451



NM_014321
SEQ ID NO 1477



NM_014363
SEQ ID NO 1480



NM_014750
SEQ ID NO 1527



NM_014754
SEQ ID NO 1528



NM_014791
SEQ ID NO 1535



NM_014875
SEQ ID NO 1545



NM_014889
SEQ ID NO 1548



NM_014968
SEQ ID NO 1554



NM_015416
SEQ ID NO 1559



NM_015417
SEQ ID NO 1560



NM_015434
SEQ ID NO 1562



NM_015984
SEQ ID NO 1587



NM_016337
SEQ ID NO 1636



NM_016359
SEQ ID NO 1638



NM_016448
SEQ ID NO 1645



NM_016569
SEQ ID NO 1655



NM_016577
SEQ ID NO 1656



NM_017779
SEQ ID NO 1708



NM_018004
SEQ ID NO 1725



NM_018098
SEQ ID NO 1739



NM_018104
SEQ ID NO 1743



NM_018120
SEQ ID NO 1745



NM_018136
SEQ ID NO 1748



NM_018265
SEQ ID NO 1766



NM_018354
SEQ ID NO 1774



NM_018401
SEQ ID NO 1782



NM_018410
SEQ ID NO 1783



NM_018454
SEQ ID NO 1786



NM_018455
SEQ ID NO 1787



NM_019013
SEQ ID NO 1809



NM_020166
SEQ ID NO 1825



NM_020188
SEQ ID NO 1830



NM_020244
SEQ ID NO 1835



NM_020386
SEQ ID NO 1838



NM_020675
SEQ ID NO 1842



NM_020974
SEQ ID NO 1844



R70506_RC
SEQ ID NO 1868



U45975
SEQ ID NO 1878



U58033
SEQ ID NO 1881



U82987
SEQ ID NO 1891



U96131
SEQ ID NO 1896



X05610
SEQ ID NO 1903



X94232
SEQ ID NO 1927



Contig753_RC
SEQ ID NO 1954



Contig1778_RC
SEQ ID NO 1979



Contig2399_RC
SEQ ID NO 1989



Contig2504_RC
SEQ ID NO 1991



Contig3902_RC
SEQ ID NO 2017



Contig4595
SEQ ID NO 2022



Contig8581_RC
SEQ ID NO 2037



Contig13480_RC
SEQ ID NO 2052



Contig17359_RC
SEQ ID NO 2068



Contig20217_RC
SEQ ID NO 2072



Contig21812_RC
SEQ ID NO 2082



Contig24252_RC
SEQ ID NO 2087



Contig25055_RC
SEQ ID NO 2090



Contig25343_RC
SEQ ID NO 2092



Contig25991
SEQ ID NO 2098



Contig27312_RC
SEQ ID NO 2108



Contig28552_RC
SEQ ID NO 2120



Contig32125_RC
SEQ ID NO 2155



Contig32185_RC
SEQ ID NO 2156



Contig33814_RC
SEQ ID NO 2169



Contig34634_RC
SEQ ID NO 2180



Contig35251_RC
SEQ ID NO 2185



Contig37063_RC
SEQ ID NO 2206



Contig37598
SEQ ID NO 2216



Contig38288_RC
SEQ ID NO 2223



Contig40128_RC
SEQ ID NO 2248



Contig40831_RC
SEQ ID NO 2260



Contig41413_RC
SEQ ID NO 2266



Contig41887_RC
SEQ ID NO 2276



Contig42421_RC
SEQ ID NO 2291



Contig43747_RC
SEQ ID NO 2311



Contig44064_RC
SEQ ID NO 2315



Contig44289_RC
SEQ ID NO 2320



Contig44799_RC
SEQ ID NO 2330



Contig45347_RC
SEQ ID NO 2344



Contig45816_RC
SEQ ID NO 2351



Contig46218_RC
SEQ ID NO 2358



Contig46223_RC
SEQ ID NO 2359



Contig46653_RC
SEQ ID NO 2369



Contig46802_RC
SEQ ID NO 2372



Contig47405_RC
SEQ ID NO 2384



Contig48328_RC
SEQ ID NO 2400



Contig49670_RC
SEQ ID NO 2434



Contig50106_RC
SEQ ID NO 2445



Contig50410
SEQ ID NO 2453



Contig50802_RC
SEQ ID NO 2463



Contig51464_RC
SEQ ID NO 2481



Contig51519_RC
SEQ ID NO 2482



Contig51749_RC
SEQ ID NO 2486



Contig51963
SEQ ID NO 2494



Contig53226_RC
SEQ ID NO 2525



Contig53268_RC
SEQ ID NO 2529



Contig53646_RC
SEQ ID NO 2538



Contig53742_RC
SEQ ID NO 2542



Contig55188_RC
SEQ ID NO 2586



Contig55313_RC
SEQ ID NO 2590



Contig55377_RC
SEQ ID NO 2591



Contig55725_RC
SEQ ID NO 2600



Contig55813_RC
SEQ ID NO 2607



Contig55829_RC
SEQ ID NO 2608



Contig56457_RC
SEQ ID NO 2630



Contig57595
SEQ ID NO 2655



Contig57864_RC
SEQ ID NO 2663



Contig58368_RC
SEQ ID NO 2668



Contig60864_RC
SEQ ID NO 2676



Contig63102_RC
SEQ ID NO 2684



Contig63649_RC
SEQ ID NO 2686



Contig64688
SEQ ID NO 2690

















TABLE 6







70 Preferred prognosis markers drawn from Table 5.










Identifier
Correlation
Sequence 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—
−0.356600
SM-20
similar to rat smooth muscle protein


RC


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









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.
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.
The relationship between the marker sets is diagramed in FIG. 1.
5.3.2 Identification of Markers
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.
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.
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.
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).
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 (c)})/(∥{right arrow over (c)}∥·∥{right arrow over (r)}∥)  Equation (2)

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 over (n−3)}, where 1/√{square root over (n−3)} is the distribution width and n=the number of samples. In a specific embodiment, markers are chosen if the correlation coefficient is greater than about 0.3 or less than about −0.3.

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.
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 over ([σ12(n1−1)+σ22(n2−1)]/(n1+n2−1)/(1/n1+1/n2))}{square root over ([σ12(n1−1)+σ22(n2−1)]/(n1+n2−1)/(1/n1+1/n2))}{square root over ([σ12(n1−1)+σ22(n2−1)]/(n1+n2−1)/(1/n1+1/n2))}{square root over ([σ12(n1−1)+σ22(n2−1)]/(n1+n2−1)/(1/n1+1/n2))}  Equation (3)

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.

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.
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)

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

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
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.
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)).
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.
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.
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.
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 SephadexTM (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.
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.
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.
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
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.
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.
The present invention provides sets of markers useful for distinguishing ER(+) from ER(−) tumor types. Thus, in one embodiment of the above method, the level of polynucleotides (i.e., mRNA or polynucleotides derived therefrom) in a sample from an individual, expressed from the markers provided in Table 1 are compared to the level of expression of the same markers from a control, wherein the control comprises marker-related polynucleotides derived from ER(+) samples, ER(−) samples, or both. Preferably, the comparison is to both ER(+) and ER(−), and preferably the comparison is to polynucleotide pools from a number of ER(+) and ER(−) samples, respectively. Where the individual's marker expression most closely resembles or correlates with the ER(+) control, and does not resemble or correlate with the ER(−) control, the individual is classified as ER(+). Where the pool is not pure ER(+) or ER(−), for example, a sporadic pool is used. A set of experiments using individuals with known ER status should be hybridized against the pool, in order to define the expression templates for the ER(+) and ER(−) group. Each individual with unknown ER status is hybridized against the same pool and the expression profile is compared to the templates (s) to determine the individual's ER status.
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.
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.
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.
In one embodiment, the similarity measure between two patients x and y, or patient x and a template y, can be calculated using the following equation:




  
    
      
        
          
            
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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 σxi. The smaller the value σxi, the more reliable the measurement





  
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is the error-weighted arithmetic mean.

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

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

where Zi is the expression template i, and y is the expression profile of a patient.

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
The present invention provides sets of markers useful for distinguishing samples from those patients with a good prognosis from samples from patients with a poor prognosis. Thus, the invention further provides a method for using these markers to determine whether an individual afflicted with breast cancer will have a good or poor clinical prognosis. In one embodiment, the invention provides for method of determining whether an individual afflicted with breast cancer will likely experience a relapse within five years of initial diagnosis (i.e., whether an individual has a poor prognosis) comprising (1) comparing the level of expression of the markers listed in Table 5 in a sample taken from the individual to the level of the same markers in a standard or control, where the standard or control levels represent those found in an individual with a poor prognosis; and (2) determining whether the level of the marker-related polynucleotides in the sample from the individual is significantly different than that of the control, wherein if no substantial difference is found, the patient has a poor prognosis, and if a substantial difference is found, the patient has a good prognosis. Persons of skill in the art will readily see that the markers associated with good prognosis can also be used as controls. In a more specific embodiment, both controls are run. In case the pool is not pure ‘good prognosis’ or ‘poor prognosis’, a set of experiments of individuals with known outcome should be hybridized against the pool to define the expression templates for the good prognosis and poor prognosis group. Each individual with unknown outcome is hybridized against the same pool and the resulting expression profile is compared to the templates to predict its outcome.
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.
As with the diagnostic markers, the method can use the complete set of markers listed in Table 5. However, subsets of the markers may also be used. In a preferred embodiment, the subset listed in Table 6 is used.
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.
The use of marker sets is not restricted to the prognosis of breast cancer-related conditions, and may be applied in a variety of phenotypes or conditions, clinical or experimental, in which gene expression plays a role. Where a set of markers has been identified that corresponds to two or more phenotypes, the marker sets can be used to distinguish these phenotypes. For example, the phenotypes may be the diagnosis and/or prognosis of clinical states or phenotypes associated with other cancers, other disease conditions, or other physiological conditions, wherein the expression level data is derived from a set of genes correlated with the particular physiological or disease condition.
5.4.3 Improving Sensitivity to Expression Level Differences
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.
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.
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.
In performing comparisons to a pool, two approaches may be used. First, the expression levels of the markers in the sample may be compared to the expression level of those markers in the pool, where nucleic acid derived from the sample and nucleic acid derived from the pool are hybridized during the course of a single experiment. Such an approach requires that new pool nucleic acid be generated for each comparison or limited numbers of comparisons, and is therefore limited by the amount of nucleic acid available. Alternatively, and preferably, the expression levels in a pool, whether normalized and/or transformed or not, are stored on a computer, or on computer-readable media, to be used in comparisons to the individual expression level data from the sample (i.e., single-channel data).
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.
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.
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.
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
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.
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.
These examples are not intended to be limiting; other methods of determining RNA abundance are known in the art.
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.
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.
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
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).
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.
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.
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.
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
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.
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.
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.
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, large 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.
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).
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
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.
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.
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.
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)).
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
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)).
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.
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.
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.
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
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.
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).
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 fill 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.
In a preferred embodiment, the detectable label is a luminescent label. For example, fluorescent labels, bio-luminescent labels, chemi-luminescent labels, and colorimetric labels may be used in the present invention. In a highly preferred embodiment, the label is a fluorescent label, such as a fluorescein, a phosphor, a rhodamine, or a polymethine dye derivative. Examples of commercially available fluorescent labels include, for example, fluorescent phosphoramidites such as FluorePrime (Amersham Pharmacia, Piscataway, N.J.), Fluoredite (Millipore, Bedford, Mass.), FAM (ABI, Foster City, Calif.), and Cy3 or Cy5 (Amersham Pharmacia, Piscataway, N.J.). In another embodiment, the detectable label is a radiolabeled nucleotide.
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
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.
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.
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.
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
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.
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
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.
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-, PentiumTM, or PentiumTM-based processor with preferably 32 MB or more of main memory.
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.
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.
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. 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.
The software to be included with the kit comprises the data analysis methods of the invention as disclosed herein. In particular, the software may include mathematical routines for marker discovery, including the calculation of correlation coefficients between clinical categories (i.e., ER status) and marker expression. The software may also include mathematical routines for calculating the correlation between sample marker expression and control marker expression, using array-generated fluorescence data, to determine the clinical classification of a sample.
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.
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.
Alternative computer systems and software for implementing the analytic methods of this invention will be apparent to one of skill in the art and are intended to be comprehended within the accompanying claims. In particular, the accompanying claims are intended to include the alternative program structures for implementing the methods of this invention that will be readily apparent to one of skill in the art.
6. EXAMPLES
Materials and Methods
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.
1. Sample Collection
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).
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 (N0); (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.
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.
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.
2. Tumors:
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.
From all, tumor material was snap frozen in liquid nitrogen within one hour after surgery. Of the frozen tumor material an H&E (hematoxylin-eosin) stained section was prepared prior to and after cutting slides for RNA isolation. These H&E frozen sections were assessed for the percentage of tumor cells; only samples with >50% tumor cells were selected for further study.
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%).
3. Amplification, Labeling, and Hybridization
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 RNAzolTM 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.
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.
cRNA was labeled with Cy3 or Cy5 dyes using a two-step process. First, allylamine-derivitized 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-derivitized 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.
Microarray hybridizations were done in duplicate with fluor reversals. Before hybridization, labeled cRNAs were fragmented to an average size of  ̃50-100 nt 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 mis 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.
4. Pooling of Samples
The reference cRNA pool was formed by pooling equal amount of cRNAs from each individual sporadic patient, for a total of 78 tumors.
5.25 k Human Microarray
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.
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
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.
An unsupervised clustering algorithm allowed us to cluster patients based on their similarities measured over this set of  ̃5,000 significant genes. The similarity measure between two patients x and y is defined as




  
    
      
        
          
            
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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.

The set of approximately 5,000 genes can be clustered based on their similarities measured over the group of 98 tumor samples. The similarity measure between two genes was defined in the same way as in Equation (1) except that now for each gene, there are 98 components of log ratio measurements.
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.
To help understand these patterns, they were associated with estrogen-receptor (ER), proestrogen receptor (PR), tumor grade, presence of lymphocytic infiltrate, and angioinvasion (FIG. 3). The lower group in FIG. 3, which features the dominant pattern, consists of 36 patients. Of the 39 ER-negative patients, 34 patients are clustered together in this group. From FIG. 4, it was observed that the expression of estrogen receptor alpha gene ESR1 and a large group of co-regulated genes are consistent with this expression pattern.
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
The results described in this Example allow the identification of expression marker genes that differentiate two major types of tumor cells: “ER-negative” group and “ER-positive” group. The differentiation of samples by ER(+) status was accomplished in three steps: (1) identification of a set of candidate marker genes that correlate with ER level; (2) rank-ordering these candidate genes by strength of correlation; (3) optimization of the number of marker genes; and (4) classifying samples based on these marker genes.
1. Selection of Candidate Discriminating Genes
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)

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.

Genes having a correlation coefficient larger than 0.3 (“correlated genes”) or less than −0.3 (“anti-correlated genes”) were selected as reporter genes. The threshold of 0.3 was selected based on the correlation distribution for cases where there is no real correlation (one can use permutations to determine this distribution). Statistically, this distribution width depends upon the number of samples used in the correlation calculation. The distribution width for control cases (no real correlation) is approximately 1/√{square root over (n−3)}, where n=the number of samples. In our case, n=98. Therefore, a threshold of 0.3 roughly corresponds to 3−σ in the distribution (3×1/√{square root over (n−3)}).
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%.
2. Rank-Ordering of Candidate Discriminating Genes
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 over ([σ12(n1−1)+σ22(n2−1)]/(n1+n2−1)/(1/n1+1/n2))}{square root over ([σ12(n1−1)+σ22(n2−1)]/(n1+n2−1)/(1/n1+1/n2))}{square root over ([σ12(n1−1)+σ22(n2−1)]/(n1+n2−1)/(1/n1+1/n2))}{square root over ([σ12(n1−1)+σ22(n2−1)]/(n1+n2−1)/(1/n1+1/n2))}  Equation (3)

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.

3. Optimization of the Number of Marker Genes
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.
The above performance evaluation from leave-one-out cross validation was repeated by successively adding more marker genes from the candidate list. The performance as a function of the number of marker genes is shown in FIG. 6. The error rates for type 1 and type 2 errors varied with the number of marker genes used, but were both minimal while the number of the marker genes is around 550. Therefore, we consider this set of 550 genes is considered the optimal set of marker genes that can be used to classify breast cancer tumors into “ER-negative” group and “ER-positive” group. FIG. 7 shows the classification of patients as ER(+) or ER(−) based on this 550 marker set. FIG. 8 shows the correlation of each tumor to the ER-negative template verse the correlation of each tumor to the ER-positive template.
4. Classification Based on Marker Genes
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. P1 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:

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

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.
In a small number of cases, there was disagreement between classification by the 550 marker set and a clinical classification. In comparing the microarray measured log ratio of expression for ESR1 to the clinical binary decision (negative or positive) of ER status for each patient, it was seen that the measured expression is consistent with the qualitative category of clinical measurements (mixture of two methods) for the majority of tumors. For example, two patients who were clinically diagnosed as ER(+) actually exhibited low expression of ESR1 from microarray measurements and were classified as ER negative by 550 marker genes. Additionally, 3 patients who were clinically diagnosed as ER(−) exhibited high expression of ESR1 from microarray measurements and were classified as ER(+) by the same 550 marker genes. Statistically, however, microarray measured gene expression of ESR1 correlates with the dominant pattern better than clinically determined ER status.
Example 3
Identification of Genetic Markers Distinguishing BRCA1 Tumors From Sporadic Tumors in Estrogen Receptor (−) Patients
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.
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.
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%.
2. Rank-ordering of Candidate Discriminating Genes
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.
3 Optimization of Discriminating Genes
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 {right arrow over (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).
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.
To determine the number of markers constituting a viable subset, the above performance evaluation from leave-one-out cross validation was repeated by cumulatively adding more marker genes from the candidate list. The performance as a function of the number of marker genes is shown in FIG. 10. The error rates for type 1 (false negative) and type 2 (false positive) errors (Bendat & Piersol, RANDOM DATA ANALYSIS AND MEASUREMENT PROCEDURES, 2D ED., Wiley Interscience, p. 89) reached optimal ranges when the number of the marker genes is approximately 100. Therefore, a set of about 100 genes is considered to be the optimal set of marker genes that can be used to classify tumors in the ER(−) group as either BRCA1-related tumors or sporadic tumors.
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
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.
1. Selection of Candidate Discriminating Genes
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.
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.
2. Rank-Ordering of Candidate Discriminating Genes
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.
3. Optimization of Discriminating Genes
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.
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 changes 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.
4. Reoccurrence Probability Curves
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).
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 χ2 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.
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=αexp(−t2/τ2)  (4)

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.

5. Prognostic Prediction for 19 Independent Sporadic Tumors
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.
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.
6. Clinical Parameters as a Group vs. Microarray Data—Results of Logistic Regression
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.
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.
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.
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.
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.
Odds ratio tables can be created from the prediction of the logistic regression. The probability of a patient being in the good prognosis group is calculated by the logistic regression based on different combinations of input parameters (clinical and/or microarray). Patients are divided into the following four groups according to the prediction and the true outcome: (1) predicted good and truly good, (2) predicted good but truly poor, (3) predicted poor but truly good, (4) predicted poor and truly poor. Groups (1) & (4) represent correct predictions, while groups (2) & (3) represent mis-predictions. The division for the prediction is set at probability of 50%, although other thresholds can be used. The results are listed in Table 7. It is clear from Table 7 that microarray profiling (Table 7.3 & 7.10) outperforms any single clinical data (Table 7.4-7.9) and the combination of the clinical data (Table 7.2). Adding the micro-array profiling in addition to the clinical data give the best results (Table 7.1).
For microarray profiling, one can also make a similar table (Table 7.11) without using logistic regression. In this case, the prediction was simply based on C1-C2 (greater than 0 means good prognosis, less than 0 mean bad prognosis).







TABLE 7.1







Prediction by clinical + microarray










Predicted good
Predicted poor















true good
39
5



true poor
4
30

















TABLE 7.2







Prediction by clinical alone










Predicted good
Predicted poor













true good
34
10


true poor
12
22
















TABLE 7.3







Prediction by microarray










predicted good
Predicted poor













true good
39
 5


true poor
10
24
















TABLE 7.4







Prediction by grade










Predicted good
Predicted poor













true good
23
21


true poor
 5
29
















TABLE 7.5







Prediction by ER










Predicted good
Predicted poor













true good
35
 9


true poor
21
13
















TABLE 7.6







Prediction by PR










Predicted good
Predicted poor













true good
35
 9


true poor
18
16
















TABLE 7.2







Prediction by size










Predicted good
Predicted poor













true good
35
 9


true poor
13
21
















TABLE 7.8







Prediction by age










Predicted good
Predicted poor













true good
33
11


true poor
15
19
















TABLE 7.9







Prediction by angioinvasion










Predicted good
Predicted poor













true good
37
 7


true poor
19
15
















TABLE 7.10







Prediction by dC (C1-C2)










Predicted good
Predicted poor













true good
36
 8


true poor
 6
28
















TABLE 7.11







No logistic regression, simply


judged by C1-C2










Predicted good
Predicted poor













true good
37
 7


true poor
 6
28









Example 5
Concept of Mini-array for Diagnosis Purposes.
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
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).
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
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.
Twelve of the 72 markers, but none of the 28 markers, are, or are associated with, kinases. In contrast, of the 7,586 genes on the microarray having annotations to date, only 471 involve kinases. On this basis, the p-value that twelve kinase-related markers in the poor prognostic group is significant (p-value=0.001). Kinases are important regulators of intracellular signal transduction pathways mediating cell proliferation, differentiation and apoptosis. Their activity is normally tightly controlled and regulated. Overexpression of certain kinases is well known involving in oncogenesis, such as vascular endothelial growth factor receptor1 (VEGFR1 or FLT1), a tyrosine kinase in the poor prognosis group, which plays a very important role in tumor angiogenesis. Interestingly, vascular endothelial growth factor (VEGF), VEGFR's ligand, is also found in the prognosis group, which means both ligand and receptor are upregulated in poor prognostic individuals by an unknown mechanism.
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.
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
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.
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
1. Creation of a Reference Pool of Stored Values (“Mathematical Sample Pool”)
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.
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.
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.
2. Results
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.
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.
In clinical applications, it is greatly preferable to have few false positives, which results in fewer under-treated patients. To conform the results to this preference, a classifier was constructed by ranking the patient sample according to its coefficient of correlation to the “good prognosis” template, and chose a threshold for this correlation coefficient to allow approximately 10% false negatives, i.e., classification of a sample from a patient with poor prognosis as one from a patient with a good prognosis. Out of the 34 poor prognosis samples used herein, this represents a tolerance of 3 out of 34 poor prognosis patients classified incorrectly. This tolerance limit corresponds to a threshold 0.2727 coefficient of correlation to the “good prognosis” template. Results using this threshold are shown in FIGS. 24A and 24B. FIG. 24A shows single-channel hybridization data for samples ranked according to the coefficients of correlation with the good prognosis classifier; samples classified as “good prognosis” lie above the white line, and those classified as “poor prognosis” lie below. FIG. 24B shows a scatterplot of sample correlation coefficients, with three incorrectly classified samples lying to the right of the threshold correlation coefficient value. Using this threshold, the classifier had a false positive rate of 15 out of the 44 good prognosis samples. This result is not very different compared to the error rate of 12 out of 44 for the ratio based classifier.
In summary, the 70 reporter genes carry robust information about prognosis; the single channel data can predict the tumor outcome almost as well as the ratio based data, while being more convenient in a clinical setting.
7. REFERENCES CITED
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.
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 an individual afflicted with breast cancer as having a good prognosis or a poor prognosis, wherein said individual is a human, wherein said good prognosis indicates that said individual is expected to have no distant metastases within five years of initial diagnosis of breast cancer, and wherein said poor prognosis indicates that said individual is expected to have distant metastases within five years of initial diagnosis of breast cancer, comprising:
(i) calculating a measure of similarity between a first expression profile and a good prognosis template, or calculating a first measure of similarity between said first expression profile and said good prognosis template and a second measure of similarity between said first expression profile and a poor prognosis template; said first expression profile comprising the expression levels of a first plurality of genes in a cell sample taken from the individual; said good prognosis template comprising, for each gene in said first plurality of genes, the average expression level of said gene in a plurality of patients having no distant metastases within five years of initial diagnosis of breast cancer; and said poor prognosis template comprising, for each gene in said first plurality of genes, the average expression level of said gene in a plurality of patients having distant metastases within five years of initial diagnosis of breast cancer; said first plurality of genes consisting of at least 5 of the genes for which markers are listed in Table 5;
(ii) classifying said individual as having said good prognosis if said first expression profile has a high similarity to said good prognosis template or has a higher similarity to said good prognosis template than to said poor prognosis template, or classifying said individual as having said poor prognosis if said first expression profile has a low similarity to said good prognosis template or has a higher similarity to said poor prognosis template than to said good prognosis template; wherein said first expression profile has a high similarity to said good prognosis template if the similarity to said good prognosis template is above a predetermined threshold, or has a low similarity to said good prognosis template if the similarity to said good prognosis template is below said predetermined threshold; and
(iii) displaying; or outputting to a user interface device, a computer readable storage medium, or a local or remote computer system; the classification produced by said classifying step (ii).

2. The method of claim 1, wherein said first plurality consists of at least 20 of the genes for which markers are listed in Table 5.

3. The method of claim 1, wherein said first plurality consists of at least 100 of the genes for which markers are listed in Table 5.

4. The method of claim 1, wherein said first plurality consists of at least 150 of the genes for which markers are listed in Table 5.

5. The method of claim 1, wherein said first plurality consists of each of the genes for which markers are listed in Table 5.

6. The method of claim 1, wherein said first plurality consists of the 70 genes for which markers are listed in Table 6.

7. The method of claim 1, which further comprises the steps of:
(a) generating said good prognosis template by hybridization of nucleic acids derived from said plurality of patients having no distant metastases within five years of initial diagnosis of breast cancer against nucleic acids derived from a pool of tumors from a plurality of patients having breast cancer;
(b) generating said poor prognosis template by hybridization of nucleic acids derived from said plurality of patients having distant metastases within five years of initial diagnosis of breast cancer against nucleic acids derived from said pool of tumors from said plurality of patients;
(c) generating said first expression profile by hybridizing nucleic acids derived from said cell sample taken from said individual against nucleic acids derived from said pool of tumors from said plurality of patients; and
(d) calculating (d1) said first measure of similarity between said first expression profile and the good prognosis template and (d2) said second measure of similarity between said first expression profile and the poor prognosis template, wherein if said first expression profile is more similar to the good prognosis template than to the poor prognosis template, the individual is classified as having a good prognosis, and if said first expression profile is more similar to the poor prognosis template than to the good prognosis template, the individual is classified as having a poor prognosis.

8. The method of claim 1, further comprising (iv) classifying said individual as ER(+) (estrogen receptor positive) or ER(−) (estrogen receptor negative) based on a second expression profile comprising the expression levels of a second plurality of genes in a cell sample taken from the individual, said second plurality of genes consisting of at least 5 of the genes for which markers are listed in Table 1, wherein said classifying said individual as ER(+) or ER(−) is carried out by a method comprising: (a) calculating a third measure of similarity between said second expression profile and an ER(+) template and a fourth measure of similarity between said second expression profile and an ER(−) template; said ER(+) template comprising, for each gene in said second plurality of genes, the average expression level of said gene in a plurality of ER(+) patients; said ER(−) template comprising, for each gene in said second plurality of genes, the average expression level of said gene in a plurality of ER(−) patients; and (b) classifying said individual as ER(+) if said second expression profile has a higher similarity to said ER(+) template than to said ER(−) template, or as ER(−) if said second expression profile has a lower similarity to said ER(+) template than to said ER(−) template; and (v) displaying; or outputting to a user interface device, a computer readable storage medium, or a local or remote computer system; the classification produced by said classifying step (iv).

9. The method of claim 1, further comprising (iv) classifying said individual as BRCA1 or sporadic based on a third expression profile comprising the expression levels of a third plurality of genes in a cell sample taken from the individual, said third plurality of genes consisting of at least 5 of the genes for which markers are listed in Table 3, wherein said classifying said individual as BRCA1 or sporadic is carried out by a method comprising: (a) calculating a third measure of similarity between said third expression profile and a BRCA1 template and a fourth measure of similarity between said third expression profile and a non-BRCA1 template; said BRCA1 template comprising, for each gene in said third plurality of genes, the average expression level of said gene in a plurality of BRCA1 patients; said non-BRCA1 template comprising, for each gene in said third plurality of genes, the average expression level of said gene in a plurality of non-BRCA1 patients; and (b) classifying said individual as BRCA1 if said third expression profile has a higher similarity to said BRCA1 template than to said non-BRCA1 template, or as sporadic if said third expression profile has a lower similarity to said BRCA1 template than to said non-BRCA1 template; and (v) displaying; or outputting to a user interface device, a computer readable storage medium, or a local or remote computer system; the classification produced by said classifying step (iv).

10. The method of claim 1, wherein said expression level of each gene in said first expression profile is a relative expression level of said gene in said cell sample versus expression level of said gene in a reference pool.

11. The method of claim 10, wherein said reference pool is derived from a normal breast cell line.

12. The method of claim 10, wherein said reference pool is derived from a breast cancer cell line.

13. The method of claim 10, wherein said relative expression level is represented as a log ratio.

14. The method of claim 1, wherein said step (i) comprises calculating said measure of similarity between said first expression profile and said good prognosis template; and said step (ii) comprises classifying said individual as having said good prognosis if similarity of said first expression profile to said good prognosis template is above a predetermined threshold.

15. The method of claim 14, wherein said average is an error-weighted average.

16. The method of claim 14, wherein said expression level of each gene in said first expression profile is a relative expression level of said gene in said cell sample versus expression level of said gene in a reference pool, represented as a log ratio; and wherein the average expression level of each gene in said first plurality of genes in said good prognosis template is an average of relative expression levels, each relative expression level being the expression level of said gene in one of said plurality of patients having no distant metastases within five years of initial diagnosis of breast cancer versus the expression level of said gene in a reference pool, represented as a log ratio.

17. The method of claim 16, wherein said average is an error-weighted log ratio average.

18. The method of claim 14, wherein said measure of similarity is a correlation coefficient between said first expression profile and said good prognosis template.

19. The method of claim 1, further comprising determining said first expression profile by measuring the expression levels of said first plurality of genes in said cell sample from said individual.

20. The method of claim 8, wherein said expression level of each gene in said second expression profile is a relative expression level of said gene in said cell sample versus expression level of said gene in a reference pool.

21. The method of claim 20, wherein said reference pool is derived from a normal breast cell line.

22. The method of claim 20, wherein said reference pool is derived from a breast cancer cell line.

23. The method of claim 20, wherein said relative expression level is represented as a log ratio.

24. The method of claim 8, wherein each said average is an error-weighted average.

25. The method of claim 8, wherein said expression level of each gene in said second expression profile is a relative expression level of said gene in said cell sample versus expression level of said gene in a reference pool, represented as a log ratio; wherein the average expression level of each gene in said second plurality of genes in said ER(+) template is an average of relative expression levels, each relative expression level being the expression level of said gene in one of said plurality of ER(+) patients versus the expression level of said gene in a reference pool, represented as a log ratio; and wherein the average expression level of each gene in said second plurality of genes in said ER(−) template is an average of relative expression levels, each relative expression level being the expression level of said gene in one of said plurality of ER(−) patients versus the expression level of said gene in a reference pool, represented as a log ratio.

26. The method of claim 25, wherein each said average is an error-weighted log ratio average.

27. The method of claim 8, wherein said third measure of similarity between said second expression profile and said ER(+) template is a correlation coefficient between said second expression profile and said ER(+) template, wherein said fourth measure of similarity between said second expression profile and said ER(−) template is a correlation coefficient between said second expression profile and said ER(−) template, and wherein said second expression profile is said to have a higher similarity to said ER(+) template than to said ER(−) template if said correlation coefficient between said second expression profile and said ER(+) template is greater than said correlation coefficient between said second expression profile and said ER(−) template, or is said to have a lower similarity to said ER(+) template than to said ER(−) template if said correlation coefficient between said second expression profile and said ER(+) template is less than said correlation coefficient between said second expression profile and said ER(−) template.

28. The method of claim 8, further comprising determining said second expression profile by measuring the expression levels of said second plurality of genes in said cell sample from said individual.

29. The method of claim 9, wherein said expression level of each gene in said third expression profile is a relative expression level of said gene in said cell sample versus expression level of said gene in a reference pool.

30. The method of claim 29, wherein said reference pool is derived from a normal breast cell line.

31. The method of claim 29, wherein said reference pool is derived from a breast cancer cell line.

32. The method of claim 29, wherein said relative expression level is represented as a log ratio.

33. The method of claim 9, wherein each said average is an error-weighted average.

34. The method of claim 9, wherein said expression level of each gene in said third expression profile is a relative expression level of said gene in said cell sample versus expression level of said gene in a reference pool, represented as a log ratio; wherein the average expression level of each gene in said third plurality of genes in said BRCA1 template is an average of relative expression levels, each relative expression level being the expression level of said gene in one of said plurality of BRCA1 patients versus the expression level of said gene in a reference pool, represented as a log ratio; and wherein the average expression level of each gene in said third plurality of genes in said non-BRCA1 template is an average of relative expression levels, each relative expression level being the expression level of said gene in one of said plurality of non-BRCA1 patients versus the expression level of said gene in a reference pool, represented as a log ratio.

35. The method of claim 34, wherein each said average is an error-weighted log ratio average.

36. The method of claim 35, wherein said third measure of similarity between said third expression profile and said BRCA1 template is a correlation coefficient between said third expression profile and said BRCA1 template, wherein said fourth measure of similarity between said third expression profile and said non-BRCA1 template is a correlation coefficient between said third expression profile and said non-BRCA1 template, and wherein said third expression profile is said to have a higher similarity to said BRCA1 template than to said non-BRCA1 template if said correlation coefficient between said third expression profile and said BRCA1 template is greater than said correlation coefficient between said third expression profile and said non-BRCA1 template, or is said to have a lower similarity to said BRCA1 template than to said non-BRCA1 template if said correlation coefficient between said third expression profile and said BRCA1 template is less than said correlation coefficient between said third expression profile and said non-BRCA1 template.

37. The method of claim 9, further comprising determining said third expression profile by measuring the expression levels of said third plurality of genes in said cell sample from said individual.

38. The method of any one of claims 10-14 and 15-16, wherein said first plurality consists of at least 20 of the genes for which markers are listed in Table 5.

39. The method of any one of claims 10-14 and 15-16, wherein said first plurality consists of at least 50 of the genes for which markers are listed in Table 5.

40. The method of any one of claims 10-14 and 15-16, wherein said first plurality consists of at least 75 of the genes for which markers are listed in Table 5.

41. The method of any one of claims 1-6, wherein said step (i) comprises calculating said first measure of similarity between said first expression profile and said good prognosis template and said second measure of similarity between said first expression profile and said poor prognosis template, and said step (ii) comprises classifying said individual as having said good prognosis if said first expression profile has a higher similarity to said good prognosis template than to said poor prognosis template, or classifying said individual as having said poor prognosis if said first expression profile has a lower similarity to said good prognosis template than to said poor prognosis template.

42. The method of claim 41, wherein said first measure of similarity is a correlation coefficient between said first expression profile and said good prognosis template, and wherein said second measure of similarity is a correlation coefficient between said first expression profile and said poor prognosis template.

43. The method of any one of claims 1-6, and 14, wherein said breast cancer is sporadic breast cancer.

44. The method of claim 41, wherein said breast cancer is sporadic breast cancer.