ES2366178T3 - DIAGNOSIS AND FORECAST OF BREAST CANCER IN PATIENTS. - Google Patents

Abstract

Method for classifying an individual suffering from breast cancer as having a good prognosis or poor prognosis, where said individual is a human, where said prognosis indicates that said individual is not expected to have distant metastases within five years after initial diagnosis of breast cancer and where said poor prognosis indicates that said individual is expected to have distant metastases within five years after the initial diagnosis of breast cancer, which comprises (ia) calculating a first classifying parameter between a first profile of expression and a good forecast template, or (ib) calculate a second classifier parameter between said first expression profile and said good forecast template and a third classifier parameter between said first expression profile and a poor forecast template; said first expression profile comprising the expression levels of a first plurality of genes in a sample of cells taken from the individual, said template comprising a good prognosis, for each gene in said first plurality of genes, the average expression level of said gene in a plurality of patients who do not have distant metastases within five years of the initial diagnosis of breast cancer; and said poor prognosis template comprising, for each gene in said first plurality, the average level of expression of said gene in a plurality of patients having distant metastases within five years following the initial diagnosis of breast cancer; said first plurality of genes consisting of at least 5 of the genes whose markers are listed in Table 5, and (iia) classifying said individual as having a good prognosis if said first classifying parameter is above a chosen threshold or if said first expression profile is more similar to said good forecast template than to said poor forecast template, or (iib) classifying said individual as having said poor forecast if said first classifying parameter is below said chosen threshold or if said The first expression profile is more similar to said poor forecast template than to said good forecast template.

Description

one.

FIELD OF APPLICATION OF THE INVENTION

[0001] The present invention relates to the identification of marker genes that serve for the diagnosis and prognosis of breast cancer. More specifically, the invention relates to the identification of a set of marker genes associated with breast cancer, a set of marker genes expressed differently in estrogen receptor (+) tumors versus estrogen receptor (-), a set of marker genes expressed differently in BRCA1 against sporadic tumors and a set of marker genes expressed differently in sporadic tumors from patients with good clinical prognosis (that is, without metastasis or disease for more than 5 years) versus patients with poor clinical prognosis (that is, without metastasis or medical condition for less than 5 years). For each of the marker sets mentioned above, the invention also relates to methods for distinguishing the conditions that occur in breast cancer. Methods to determine the course of treatment of breast cancer patients are also described.

2.

BACKGROUND OF THE INVENTION

[0002] The increasing number of cancer cases reported in the United States, and indeed worldwide, is a serious problem. Currently, only a few treatments are available for specific types of cancer, but they offer no guarantee of success. To increase their effectiveness, these treatments would have to facilitate not only a more early detection of the malignant nature of the tumor, but also a reliable assessment of the severity of said tumor.

[0003] The incidence of breast cancer, one of the leading causes of death among women, has been gradually increasing in the United States over the past thirty years. Its cumulative risk is relatively high; It is expected that, in the United States, 1 in 8 women will develop breast cancer before they turn 85. In fact, breast cancer is the most common cancer among women and the second most frequent cause of cancer death in the United States. In 1997, the number of new cases declared in the United States was estimated at 181,000, as well as 44,000 people would die of breast cancer (Parker et a /., CA Cancer J. Clin. 47: 5-27 (1997); Chu et al., J. Nat. Cancer Inst. 88: 1571-1579 (1996)). Although the oncogenesis mechanism of most breast carcinomas is largely known, there are genetic factors that may predispose some women to develop breast cancer ((Miki et al., Science, 266: 66-71 (1994) The discovery and characterization of BRCA1 and BRCA2 has recently expanded our knowledge of the genetic factors that can contribute to family breast cancer.The germ mutations in these two loci account for 50 to 85% of the risk of breast cancer or ovaries throughout life (Casey, Curr. Opin. Oncol. 9: 88-93 (1997); Marcus et al., Cancer 77: 697-709 (1996)). Only 5% to one 10% of cancer cases are related to breast cancer susceptibility genes, BRCA1 and BRCA2 The risk of accumulated cancer throughout life for women carrying mutant BRCA1 is estimated at approximately 92%, while the risk accumulated throughout life for the majority of the unported Flush is estimated at approximately 10%. BRCA1 is a tumor suppressor gene that is involved in DNA repair and cell cycle control, both of utmost importance for maintaining genomic stability. More than 90% of all mutations revealed so far lead to premature truncation of the protein product with abnormal or suppressed function. The histology of breast cancer in carriers of the BRCA1 mutation varies in sporadic cases, but analysis of the mutation is the only way to find the carrier.

Like BRCA1, BRCA2 is involved in the development of breast cancer and, like BRC41, plays its role in DNA repair. However, unlike BRCA1, it is not present in ovarian cancer

[0004] Molecular markers for differentiating tumor types are known in the art.

Perou CM, et al. (Nature 2000 406: 747-752) perform molecular portraits of human breast cancer tumors. A subset of genes was identified in which the variation in expression was greater between different tumors than between paired samples of the same tumor. Alizadeh AA, et al. (Nature 2000 403: 503-511) describe two different forms of diffuse large B-cell lymphoma (DLBCL) identified on the basis of the gene expression pattern. A subset of genes was identified by being expressed selectively in one of two forms of DLBCL. Perou CM, et al. (PNAS 1999 96: 9212-9217) describe patterns of differentiated gene expression in human mammary epithelial cells and breast cancers. In response to a series of experimental disturbances, a subset of genes was identified in which a distinct expression was clearly seen in human mammary epithelial cells. Khan J, et al. (Nature Medicine 2001 7: 673-679) describe a criterion of classification and diagnostic prediction of cancers by means of gene expression profiles and artificial neural networks. A subset of genes was used to classify small samples of round and blue cell tumors into diagnostic categories. Hedenfalk I, et al. (New Eng J Med 2001 344: 539,548) describe gene expression profiles in hereditary breast cancer and identified a subset of genes that were divided into BRCA1 mutant tumors, BRCA2 mutant tumors and sporadic breast cancer tissue tumors . Unlike these documents where methods for distinguishing types of tumors are disclosed, the present invention offers methods for determining whether individuals with breast cancer have had a good or poor prognosis.

[0005] Other genes have also been linked to breast cancer, for example c-erb-2 (HER2) and p53 (Beenken et al., Ann. Surg. 233 (5): 630-638 (2001) The overexpression of c-erb-2 (HER2) and p53 has been associated with poor prognosis (Rudolph et al., Hum. Pathol. 32 (3): 311-319 (2001), as it has been an aberrant expression of mdm2 products (Lukas et al., Cancer Res. 61 (7): 3212-3219 (2001) and cyclin 1 and p27 (Porter & Roberts, International Publication WO98 / 33450, published August 6, 1998). , no other clinically useful and closely associated markers to breast cancer have been identified.

[0006] Sporadic tumors, that is, those not commonly associated with a known germ mutation, constitute breast cancer in most cases. It is also likely that other factors, not genetic, significantly influence the etiology of the disease. Regardless of the origin of the cancer, the morbidity and mortality of breast cancer increase significantly if they are not detected in the initial phase of its progress. Consequently, many efforts have been devoted to the early detection of cell transformation and tumor formation in breast tissue.

[0007] The identification and characterization of the tumor based on markers makes the diagnosis and prognosis more reliable. Typically, a histopathological test of the presence of the tumor is required for the diagnosis of breast cancer. In addition to diagnosis, histopathological exams also provide information on the prognosis and selection of treatment regimens. The prognosis can also be based on clinical parameters, such as tumor size, tumor grade, patient age and lymph node metastasis.

[0008] The diagnosis and / or prognosis can be determined to varying degrees of efficacy by direct observation of the outside of the breast, by mammography or by other X-ray imaging methods (Jatoi, Am. J. Surg. 177: 518 -524 (1999)). The latter, however, have a high cost. Each time a mammogram is done, the patient runs a slight 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 inaccuracy. For example, one study showed significant clinical discrepancies in approximately one third of a set of mammograms that were interpreted individually by a group of radiologists who were surveyed. Also, many women think that having a mammogram is a painful experience. Consequently, the National Cancer Institute advises against mammograms for women who have not turned fifty, since this group is not as prone to developing breast cancer as that of older women. It must be taken into account, however, that although only 22% of breast cancer cases occur in women under fifty, the data shows that breast cancer is more aggressive in premenopausal women.

[0009] In clinical practice, an accurate diagnosis of the various types of breast cancer is important, since treatment options, prognosis and the likelihood of the therapeutic response all vary depending on the diagnosis. With a successful prognosis, or with a determination of survival free from distant metastases, the oncologist can customize the administration of adjuvant chemotherapy, while the most aggressive treatments are applied to women with worse prognoses. In addition, the exact prediction of poor prognoses would have a great impact on the clinical trials of new therapies against breast cancer, since then the potential study patients could be stratified according to the prognosis. Thus, the trials would be limited to patients with poor prognosis, which in turn would be easier to discern if an experimental therapy is effective.

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

3. SUMMARY OF THE INVENTION

[0011] The invention provides sets of gene markers that distinguish between various types and subtypes of breast cancer, as well as methods for their use. The invention provides a method for determining whether an individual suffering from breast cancer has a good or poor prognosis, wherein said individual is a human, wherein said good prognosis indicates that the individual in question is expected not to have distant metastases within five years after the initial diagnosis of breast cancer, and where said poor prognosis indicates that the individual in question is expected to have distant metastases within five years after the initial diagnosis of breast cancer, and that it supposes: (ia ) calculating a first classifier parameter between a first expression profile and a good forecast template, or (ib) calculating a second classifier parameter between said first expression profile and said good forecast template and a third classifier parameter between said first profile of poor expression and forecast template; said first expression profile comprises the levels of expression of a first series of genes in a sample of cells taken from the individual, said template of good prognoses comprises for each gene of said first series of genes the average level of expression of said gene in a series of patients who have distant metastases within five years of the initial diagnosis of breast cancer; said first series of genes consists of at least 5 of the genes for which a list of markers is included in Table 5, and (iia) determining whether said individual has a good prognosis if said first classification parameter is above a threshold established or if said first expression profile is more similar to said good forecast template than to said poor forecast template, or (iib) determine if said individual has said poor forecast if said first classification parameter is below said established threshold or if said first expression profile is more similar to said poor forecast template than to said good forecast template. In one of the materializations a method is described for classifying a sample of cells as ER (+) or ER (-) which involves detecting a difference in the expression of a first series of genes with respect to a control, said first series consisting of genes of at least 5 of the genes corresponding to the markers listed in Table 1. In specific embodiments, said series of genes consists of at least 50, 100, 200, 500, 1,000, up to 2,460 of the genes listed in Table 1. In another specific materialization, said series of genes consists of each of the genes corresponding to the 2,460 markers listed in Table 1. In another specific materialization, said series consists of the 550 markers listed in Table 2. In another materialization specifically, said control comprises nucleic acids obtained from a tumor pool of specific sporadic patients. In another specific materialization, said detection comprises the steps of: (a) generating an ER template (+) by hybridizing nucleic acids obtained from a series of patients with E (+) within a series of sporadic patients against nucleic acids obtained from a tumor pool of specific sporadic patients; (b) generate an ER template (-) by hybridizing nucleic acids obtained from a series of patients with ER () within said series of sporadic patients against nucleic acids obtained from said tumor pool of specific sporadic patients within said series Serie; (c) hybridize nucleic acids obtained from a specific sample against said reserve; and (d) determine the similarity of the marker gene expression in the specific sample with the ER template (+) and the ER template (-), where if said expression is more similar to the ER template (+) , the sample is classified as ER (+), while if said expression is more similar to the ER (-) template, the sample is classified as ER (-).

[0012] Methods applied to classification as BRCA1 or sporadic have already been described above. The invention provides the above methods applied to the classification of patients as having a good prognosis or poor prognosis. For BRCAI / sporadic gene markers, a method can be used in which the gene series is at least 5, 20, 50, 100, 200 or 300 of the BRCAI / sporadic markers listed in Table 3. In a Specific materialization, the 100 optimal markers listed in Table 4 are used. For prognostic markers, the invention implies that at least 5, 20, 50, 100 or 200 gene markers listed in Table 5 can be used. specific materialization, the 70 optimal markers listed in Table 6 are used.

[0013] The invention further enables the combination of the markers. In another embodiment, at least 5 markers in Table 5 are used in conjunction with at least 5 markers in Table 3. In another materialization, at least 5 markers in Table 1 are used in conjunction with at least 5 markers in Table 5. In another materialization, at least 5 markers of each of Tables 1, 3 and 5 are used simultaneously.

[0014] A method for classifying a sample as ER (+) or ER (-) is also described by calculating the similarity between the expression of at least 5 of the markers listed in Table 1 in the sample with the expression of the same markers in an ER (-) nucleic acid reserve and an ER (+) nucleic acid reserve, which comprises the steps of (a) labeling nucleic acids obtained from a sample with a first fluorophore to obtain a first nucleic acid reserve labeled with fluorophore; (b) labeling with a second fluorophore a first reserve of nucleic acids obtained from two or more samples of ER (+) and a second reserve of nucleic acids obtained from two or more samples of ER (-); (c) contacting said first fluorophore labeled nucleic acid and said first fluorophore labeled second nucleic acid with said first biochip under conditions where said hybridization can occur, and contacting said first fluorophore labeled nucleic acid and said second pool of second fluorophore-labeled nucleic acid with said second biochip under conditions where said hybridization can occur, detecting in each of a series of discrete loci in the first biochip a first fluorescent emission signal of said first fluorophore-labeled nucleic acid and a second fluorescent emission signal of said first fluorophore-labeled second gene pool whose destination is said first biochip under said conditions, and detecting in each of the loci markers of said second biochip said first fluorescent emission signal of said first nucleic acid labeled c with fluorophore and a third fluorescent emission signal of said second reserve of second fluorophore-labeled nucleic acid; (d) determine the similarity of the sample with the ER (-) and ER (+) reserves by comparing said first fluorescence emission signals and said first fluorescence emission signals on the one hand, and on the other said first signals of fluorescence emission and said third fluorescence emission signals, and classify as ER (+) the samples in which the first fluorescence emission signals are more similar to said second fluorescence emission signals than to said third fluorescence emission signals and classify as ER (-) the samples in which the first emission signals are more similar to said third fluorescence emission signals than to said second fluorescence emission signals, wherein said similarity is defined as statistical criteria. The invention further points out that the other sets of markers it discloses can be used in the above method to distinguish BRCA1 from sporadic tumors, as well as patients with poor diagnosis from patients with good diagnosis.

[0015] In a specific materialization, said similarity is calculated by determining a first sum of differences in expression levels for each marker between said first fluorophore-labeled nucleic acid and said first fluorophore-labeled second nucleic acid reserve and a second sum of the differences in expression levels for each marker between said first fluorophore labeled nucleic acid and said second fluorophore labeled second 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 mixture is classified as ER (+). In another specific materialization, 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 P1 and P2 are calculated according to the formula:

image 1 Equation (1)

wherein ž1 and ž2 are templates of ER (-) and ER (+), respectively, and are calculated by finding the average of said second fluorescence emission signal for each of said markers in said first reserve of second nucleic acid labeled with fluorophore and said third fluorescence emission signal for each of said markers in said second fluorophore-labeled second nucleic acid reserve, respectively, and where ŷ indicates the first fluorescence emission signal of each of said markers in the sample that it will be classified as ER (+) or ER (-), where the expression of the markers in the sample is similar to ER (+) if P1 <P2, and similar to ER (-) if P1> P2.

[0016] A method for identifying marker genes whose expression is associated with a specific phenotype is also described. A method is also described for determining a set of marker genes whose expression is associated with a particular phenotype and comprising the steps of: (a) selecting the phenotype that has two or more phenotype categories; (b) identify a series of genes where the expression of said genes is in correlation or anticorrelation with one of the phenotype categories and where the correlation coefficient for each gene is calculated according to the equation:

image 1 Equation (2)

where č is a number that represents said category of phenotype and ř is the quotient of logarithmic expression in all samples for each particular gene, where if the correlation coefficient has the absolute value of a threshold value or greater, said expression of said gene is associated with the category of phenotype and where said series of genes is a set of marker genes whose expression is associated with a specific phenotype. The threshold depends on the number of samples used; the threshold can be calculated as image 1 image 1 3 X

where is the distribution width and n = the number of samples. In a specific materialization where n = 98, said threshold value is 0.3. In a specific materialization, said set of marker genes is validated by: (a) the use of a statistical criterion to randomize the association between said marker genes and said phenotype category, thereby creating a control correlation coefficient for each gene marker; (b) the repetition of step (a) one hundred times or more to develop a frequency distribution of said control correlation coefficients for each marker gene; (c) determining the number of marker genes that have a control correlation coefficient of a threshold value or greater, thereby creating a set of control marker genes, and (d) comparing the number of control marker genes identified as such with the number of marker genes, where if the p-value is 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 materialization, said set of marker genes is optimized with a method consisting of: (a) hierarchical ordering of genes by amplitude of correlation or relevance of correlation coefficients, and (b) selection of an arbitrary number of genes markers of the highest positions in the hierarchical list. The threshold value depends on the number of samples tested.

[0017] A method to assign a person a series of categories in a clinical trial is also described, which consists in determining for each person the level of expression of at least five of the prognostic markers listed in Table 6, determining from them if the person has an expression pattern that correlates with a good prognosis or poor prognosis, and classify said person into a category of a clinical trial if it is established that said person has a good prognosis, or in a different category if it is established that said person has a poor prognosis. A method is also described for classifying a person into one of a series of categories of a clinical trial, where each of these categories is associated with a different phenotype, and which consists in determining for each of said persons the level of expression of at least five markers of a set of markers, wherein said set of markers comprises markers associated with each of said clinical categories, determining from said markers if the person has an expression pattern that correlates with one of the clinical categories and classifying said person into one of said categories if it is established that said person has a phenotype associated with said category.

[0018] A method is also described to determine if the first cell or organism has one of at least two different phenotypes, said at least two different phenotypes consisting of a first phenotype and a second phenotype, a method consisting of: (a) comparing the level of expression of each of a series of genes in a first sample of the first cell or organism with the level of expression of each of said genes, respectively, in a collective sample of a series of cells or organisms, said said comprising series of cells or organisms different cells or organisms each of which shows the at least two different phenotypes, respectively, to produce a first value compared; (b) comparing said first value compared to a second value compared, wherein said second value compared is the product of a method that consists in comparing the level of expression of each of said genes in a sample of a cell or organism characterized by having said first phenotype at the level of expression of each of said genes, respectively, in said collective sample; (c) comparing said first value compared to a third value compared, wherein said third value compared is the product of a method that consists in comparing the level of expression of each of said genes in a sample of a cell or organism characterized by having said second phenotype at the level of expression of each of said genes, respectively, in said collective sample; (d) optionally, performing one or more times the step of comparing said first value compared to one or more additional compared values, respectively, each of said additional comparative values being the product of a method consisting of comparing the level of expression of each of said genes in a sample of a cell or organism characterized by having a different phenotype of said first and second phenotypes, but included in said at least two different phenotypes, at the level of expression of each of said genes, respectively, in said collective sample, and (e) determine which of said second, third and, if present, at one or more additional compared values, said first additional compared value is more similar, where it is determined that said first cell or organism has the phenotype of the cell or organism used to produce said compared value more similar to the first compared value.

[0019] In a specific materialization of the method above, each of said values compared is the quotient of the expression levels of each of these genes. In another specific materialization, each of said expression levels of each of said genes in said collective sample is normalized before proceeding to any of said comparison steps. In another specific materialization, the normalization of said expression levels is carried out by dividing each of said expression levels by the median or the average expression level of one or more constituent genes in said collective sample. In a more specific materialization, said normalized expression levels are subjected to a logarithmic transformation and said comparison steps consist of subtracting said logarithmic transformation of said logarithm levels from each of said genes in said sample of said cell or organism. In another specific materialization, said at least two different phenotypes are different stages of a disease or disorder. In another specific materialization, said at least two different phenotypes are different prognoses of a disease or disorder. In a further specific embodiment, said levels of expression of each of said genes, respectively, in said collective sample or said levels of expression of each of said genes in a sample of said cell or organism characterized by having said first phenotype, said Second phenotype or said different phenotype of said first and second phenotypes, respectively, are stored in a computer.

[0020] Also described are biochips comprising the sets of markers that are disclosed. A biochip comprising at least 5 markers derived from any of Tables 1-6 is described, wherein at least 50% of the biochip probes are present in any of Tables 1-6. In more specific materializations, at least 60%, 70%, 80%, 90%, 95% or 98% of the probes of said biochip are present in any of Tables 1-6.

[0021] A biochip is also described to distinguish between ER (+) and ER (-) cell samples comprising an addressable position display of polynucleotide probes connected to a support, said polynucleotide probes comprising a series of polynucleotide probes of different sequences polynucleotides comprising a complementary and hybridizable sequence with a series of genes, said series consisting of at least 5 of the genes corresponding to the markers listed in Table 1 or Table 2, where at least 50% of the probes of the biochip are present in Table 1 or in Table 2. A selection is also described to distinguish samples of Brai-related cells and sporadic tumor type comprising an addressable position display of polynucleotide probes connected to a support, said probes comprising polynucleotides a series of polynucleotide probes of different polynucleotide sequences, comprising each of said polynucleotide sequences a complementary and hybridizable sequence with a series of genes, said series consisting of at least 5 of the genes corresponding to the markers listed in Table 3 or Table 4, where at least 50% of the biochip probes are present in Table 3 or Table 4. A biochip is also described to distinguish cell samples from patients who have a good prognosis and cell samples from patients who have a poor prognosis, which comprises a addressable position display of polynucleotide probes connected to a support, said polynucleotide probes comprising a series of polynucleotide probes of different polynucleotide sequences, each of said different polynucleotide sequences consisting of a complementary and hybridizable sequence with a series of genes, said series consisting of at minus 5 of the genes that correspond to the markers listed in Table 5 or Table 6, where at least 50% of the biochip probes are present in Table 5 or Table 6. Biochips comprising at least 5, 20, 50, 100, 200 are also described. , 500, 100, 1,250, 1,500, 1,750, or 2,000 of the ER-category marker genes listed in Table 1, at least 5, 20, 50, 100, 200 or 300 of the sporadic BRCA1 marker genes listed in the Table 3, or at least 5, 20, 50, 100 or 200 of the prognostic marker genes listed in Table 5, in any combination, where at least 50%, 60%, 70%, 80%, 90% , 95% or 98% of the probes of said biochips are present in Table 1, Table 3 and / or Table 5.

[0022] A kit is described for determining the ER category of a sample comprising at least two biochips, of which each comprises 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 with that of an ER (-) pool and an ER (+) pool, a computer system comprising a processor and a memory that has one or more coupled programs encoded to the processor, wherein said one or more programs cause the processor to execute a method consisting of computing the aggregate differences in expression of each marker between the sample and the reserve of ER (-) and the aggregate differences in expression of each marker between the sample and ER reserve (+), or a method consisting of determining the correlation calculated according to Equation (4). Kits capable of distinguishing between BRCA1 and sporadic tumors and patient samples with a good prognosis of patient samples with poor prognosis are also described, including the inclusion of appropriate marker gene sets. A kit is also described to determine if a sample comes from a patient with a good prognosis or one with a poor prognosis, which comprises at least one biochip comprising probes for at least 5 of the genes corresponding to the markers listed in Table 5, and a readable computer medium in which one or more programs have been recorded to determine the similarity of the level of nucleic acid derived from the markers listed in Table 5 in a sample with that of a stock of samples from individuals with good prognosis and a reserve of samples from individuals with poor prognosis, where said one or more programs causes a computer to execute a method consisting of computing the aggregate differences in expression of each marker between the sample and the reserve of good prognosis and the aggregate differences in expression of each marker between the sample and the poor prognosis reserve, or a method consists entity in determining the correlation of expression of the markers of the sample with the expression in the reserves of good prognosis and poor prognosis, correlation that is calculated according to Equation (3).

4. BRIEF DESCRIPTION OF THE FIGURES

[0023]

FIG. 1 is a Venn-type diagram that shows the overlap between the sets of markers disclosed here, including the 2,460 ER markers, the 430 BRCA1 / sporadic markers and the 231 prognostic indicators.

FIG. 2 shows the experimental procedures for measuring different changes in mRNA transcription density in breast cancer tumors used in this study. In each experiment, the cRNA labeled as Cy5 of a tumor X is hybridized in a 25k human biochip together with a pool of cRNA labeled as Cy3 made from cRNA samples of tumors 1, 2, ... N. Data Digital expression were obtained by scanning and image processing. The modeling error allowed us to assign a p-value to each measurement of the transcription ratio.

FIG. 3 Two-dimensional clustering reveals two different types of tumors. Clustering was based on gene expression data from 98 breast cancer tumors on 4,986 relevant genes. Dark gray (red) represents upward regulation, light gray (green) represents downward regulation, black indicates unchanged expression and gray indicates that data is not available. 4,986 genes were selected that showed a more than double ratio change in more than five experiments. The clinical data selected for the results of the BRCA1, estrogen receptor (ER) and proestrogen receptor (PR) mutations, tumor grade, lymphocytic infiltrate and angioinvasion mutations are shown on the right. Black indicates negative and white indicates positive. The predominant pattern in the lower part consists of 36 patients, of which 34 are ER-negative (total 39), and 16 are carriers of BRCA1 mutation (total 18).

FIG. 4 A portion of unmonitored pooled results, such as those in FIG. 3. ESR1 (the estrogen receptor gene) is regulated in conjunction with a group of genes tightly regulated to form a dominant pattern.

FIG. 5A Histogram of correlation coefficients of relevant genes between their expression ratios and the estrogen receptor (ER) category (that is, the ER level). The histogram for experimental data appears in a gray line. The results of a Monte-Carlo trial appear in thick black line. There are 2,460 genes whose expression data are correlated with the ER category at a level greater than 0.3 or in anticorrelation with the ER category at a level less than -0.3.

FIG. 5B The distribution of the number of genes that fit the same selection criteria (amplitude of correlation above 0.3) among 10,000 Monte-Carlo trials. It is estimated that this set of 2,460 genes reports the ER category at a reliability level of p> 99.99%.

FIG. 6 Classification error rates for Type 1 and Type 2 as a function of the number (except 2,460) of marker genes used in the classifier. The lowest combined error rate is given 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. The ER (+) samples (above the white line) show a clearly different expression pattern than the ER (-) samples (below the white line).

FIG. 8 Correlation between the levels of expression in samples of each patient and the average profile of the ER group (-) with respect to. the correlation with the ER group (+). The squares represent samples of patients clinically ER (-); the rounds represent samples of patients clinically ER (-).

FIG. 9A The histogram of gene expression correlation coefficients on the ratio of each relevant gene with the BRCA1 mutation class appears in a continuous line. The dashed line indicates a frequency distribution from a Monte-Carlo test. 430 genes showed an amplitude of correlation or anticorrelation greater than 0.35.

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

FIG. 10 Classification error rates for Type 1 and Type 2 as a function of the number of discriminant genes used in the classifier (template). The lowest combined error rate is given when approximately 100 discriminant marker genes are used.

FIG. 11A The classification of 38 tumors of the ER group (-) into two subgroups, BRCA1 and sporadic, using the optimal set of 100 discriminant marker genes. Patients above the white line are characterized by patterns associated with BRCA1.

FIG. 11B Correlation between expression levels in samples of each ER (-) patient and the average profile of the BRCA1 group versus correlation with the sporadic group. The squares represent samples of patients with sporadic tumors; the rounds represent samples of patients carrying the BRCA1 mutation.

FIG. 12A The histogram of the correlation coefficients of the gene expression ratio of each relevant gene with the prognosis class (group of distant metastases and group of non-distant metastases appears in a continuous line. The distribution from a Monte-Carlo test appears in dashed line The amplitude of correlation or anticorrelation of 231 marker genes is greater than 0.3.

FIG. 12B Frequency distribution of the number of genes whose amplitude of correlation or anticorrelation was greater than 0.3 in 10,000 Monte-Carlo trials.

FIG. 13 Classification error rate of the distant metastasis group for Type 1 and Type 2 as a function of the number of discriminant genes used in the classifier. The lowest combined error rate occurs when approximately 70 discriminant marker genes are used.

FIG. 14 Classification of 78 sporadic tumors in two prognosis groups, distant metastases (poor prognosis) and non-distant metastases (good prognosis) by the optimal set of 70 discriminant marker genes. Patients above the white line are characterized by having a good prognosis. Patients below the white line are characterized by poor prognosis.

FIG. 15 Correlation between the levels of expression in samples of each patient and the average profile of the good prognosis group versus the correlation with the poor prognosis group. The squares represent samples of patients who have poor prognosis, the rounds represent samples of patients who have poor prognosis. The red squares represent the "relapses", while the blue rounds represent the "non-relapses". 13 of a total of 78 were wrongly classified.

FIG. 16 Probability of relapse as a function of time since diagnosis. The prediction of Group A and Group B was made according to the "leave one out" method based on the optimal set of 70 discriminant marker genes. The 43 patients in group A are divided into 37 patients in the non-distant metastasis group and 6 patients in the distant metastasis group. The 35 patients in group B are divided into 28 patients in the distant metastasis group and 7 patients in the non-distant metastasis group.

FIG. 17 Probability of distant metastases as a function of time since diagnosis for individuals with progesterone PR (+) (yes) or PR (-) (no).

FIG. 18 Probability of distant metastases as a function of time since diagnosis for individuals of ER (+) (yes) or ER (-) (no).

FIG. 19A, B Probability of distant metastases as a function of time since diagnosis. Groups were defined by degrees of tumor.

FIG. 20A Classification of 19 independent sporadic tumors into two prognosis groups, distant metastases and non-distant metastases, using the 70 optimal marker genes. Patients above the white line have a good prognosis. Patients below the white line have poor prognosis.

FIG. 20B The correlation between the expression quotients of each patient and the average expression quotient of the good prognosis group is defined by the test set with respect to the correlation between the expression quotients of each patient and the average expression quotient of the set. of poor prognosis test. Of nine patients in the good prognosis group, three are from the "distant metastasis group"; Of ten patients in the good prognosis group, one patient is from the "non-distant metastasis group". This error rate of 4 out of 19 corresponds to that of 13 out of 78 in the initial 78 patients.

FIG. 20C Probability of relapse as a function of time since diagnosis for two groups whose prediction was made based on the expression of the 70 optimal marker genes.

FIG. 21A Sensitivity to 1-specificity for the classification of good prognosis.

FIG. 21B Sensitivity to 1-specificity for poor prognosis classification.

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

FIG. 22 Comparison of the logarithmic quotient of individual samples by means of the "stock of material samples" versus the average logarithmic intensity subtracted by the "reserve of mathematical samples" for 70 indicator genes in the 78 sporadic tumor samples. The "stock of material samples" consisted of the 78 samples of sporadic tumors.

FIG. 23A The results of cross-validation leaving an element out based on data from a single channel. The samples are grouped according to the correlation coefficient of each sample with the average profile of "good prognosis" and "poor prognosis" in the 70 genes examined. The white line separates samples from patients with poor prognosis (below) from those with good prognosis (above).

FIG. 23B Scatter plot of correlation coefficients with the mean expression in “good prognosis” and “poor prognosis” samples. The false positive rate (that is, the rate of incorrect classifications of a sample of a patient with a good prognosis as belonging to a poor prognosis) was 10 out of 44, while the false negative rate was 6 out of 34.

FIG. 24A Hybridization of single-channel data for samples sorted hierarchically by correlation coefficients with the good forecast classifier. Samples classified as "good prognosis" are above the white line, while those classified as "poor prognosis" are below it.

FIG. 24B Scatter plot of correlation coefficients with three incorrectly classified samples that are to the right of the threshold correlation coefficient value. Said correlation threshold value was set at 0.2727 to limit false negative samples to approximately 10% of them.

DETAILED DESCRIPTION OF THE INVENTION

5.1 INTRODUCTION

[0024] The invention relates to sets of genetic markers whose expression patterns correlate with important characteristics of breast cancer, that is, the category of estrogen receptor (ER), the category of BRCA1 and the probability of relapse (this is, distant metastasis or poor prognosis). Genetic marker sets that can distinguish the following three clinical categories are described. First, the invention relates to sets of markers whose expression correlates with the ER category of a patient and which can be used to distinguish ER (+) patients from ER (-) patients. The ER category is a very useful prognostic indicator and an indicator of the probability that a patient responds to certain therapies such as tamoxifen.

[0025] Similarly, among women who are ER positive the response rate (greater than 50%) to hormonal therapy is much higher than the response rate (less than 10%) in patients with a negative ER category. In patients with positive ER tumors the possibility of achieving a hormonal response is directly proportional to the level of ER (P. Calabresi and PS Schein, MEDICAL ONCOLOGY (2ND ED.), McGraw-Hill, Inc., New York (1993)) . Secondly, the invention also relates to sets of markers whose expression correlates with the presence of BRCA1 mutations and that can be used to distinguish BRCA1 type tumors from sporadic tumors. Third, the invention relates to genetic markers whose expression correlates with the clinical prognosis and that can be used to distinguish between patients who have a good prognosis (that is, who do not have distant metastases from a tumor within five years) and those with poor prognosis (that is, having distant metastasis of a tumor for five years). Regarding the use of these markers, methods are provided to distinguish between these groups of patients and to determine general treatment guidelines. Biochips containing these markers are also supplied, as well as methods for making said biochips. Each of the markers corresponds to a gene of the human genome, that is, said marker is identifiable as the whole or a portion of a gene. Finally, since each of the markers above is correlated with certain conditions related to breast cancer, the markers, or the proteins encoded in these, will most likely be the destination of breast cancer medications.

5.2 DEFINITIONS

[0026] As used herein, "BRCAI tumor" means a tumor that has cells that contain a mutation of the BRCA1 locus.

[0027] The "absolute amplitude" of correlation expressions means the distance, whether positive or negative, to a zero value, that is, both coefficients, -0.35 and 0.35, have an absolute amplitude of 0, 35

[0028] "Category" means a state of gene expression of a set of genetic markers whose expression is closely correlated with a particular phenotype. For example, "ER category" means a state of gene expression of a set of genetic markers whose expression is closely correlated with that of ESR1 (estrogen receptor gene), where the expression pattern of these genes varies significantly between tumors that express the receptor and tumors that do not express the receptor.

[0029] "Good prognosis" means that a patient is not expected to have distant metastases from a breast tumor within five years of the initial diagnosis of breast cancer.

[0030] "Poor prognosis" means that a patient is expected to have distant metastases from a breast tumor within five years of the initial diagnosis of breast cancer.

[0031] "Marker" means an entire gene, or an EST derived from said gene, whose level of expression changes under certain conditions. Where the expression of the gene correlates with a specific state, the gene will be a marker for that state.

[0032] "Marker derived polynucleotides" means the RNA transcribed from a marker gene, any cDNA 5 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 USEFUL MARKERS IN THE DIAGNOSIS AND FORECAST OF BREAST CANCER

5.3.1 MARKER SETS

[0033] A set of 4,986 genetic markers is described whose expression correlates with the

10 existence of breast cancer through cluster analysis. SEQ ID Nos: 1-2,699 lists a subset of these markers identified as useful for diagnosis or prognosis. The invention also relates to a method of using said markers to distinguish tumor types in diagnosis or prognosis.

[0034] A set of 2,460 genetic markers that can classify patients is also described.

15 of breast cancer by the category of estrogen receptor (ER), that is, distinguish between patients or tumors with ER (+) and ER (-) obtained from said patients. The category of ER is an important indicator of the probability of a patient's response to some chemotherapies (that is, tamoxifen). Such markers are listed in Table 1. The invention also relates to 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 , taken from the set

20 of 2,460 markers, which can also distinguish patients or tumors with ER (+) and ER (-). Preferably, the number of markers will be 550. A set of 550 of the 2,460 markers that are optimal for distinguishing the ER category are also described (Table 2). A method of using said markers is also provided to distinguish between patients or ER (+) and ER (-) tumors obtained therefrom.

[0035] In another embodiment a set of 430 genetic markers that can classify a

25 breast cancer patients with ER (-) by the BRCA1 category, that is, distinguish between tumors that contain a BRCA1 mutation and sporadic tumors. These markers are listed in Table 3. In addition, subsets of at least 5, 10, 20, 30, 40, 50, 75, 100, 150, 200, 250, 300 or 350 markers are provided, taken from the set of 430 markers , which also distinguish between tumors that contain a BRCA1 mutation and sporadic tumors. Preferably, the number of markers will be 100. In the Table

30 4 a preferred set of 100 markers is supplied. A method of using said markers is also provided to distinguish between BRCA1 and sporadic patients or tumors obtained therefrom.

[0036] The invention relates to a set of 231 genetic markers that can distinguish between patients with a good prognosis of breast cancer (absence of distant metastases from breast cancer tumors within the next five years) and patients with a poor prognosis of breast cancer 35 (presence of distant metastases from breast cancer tumors within the next five years). These markers are listed in Table 5. Subsets of at least 5, 10, 20, 30, 40, 50, 75, 100, 150 or 200 markers are provided, taken from the set of 231, which also distinguish between patients with good and with poor prognosis. A preferred set of 70 markers is provided in Table 6. In a specific materialization, the set of markers consists of the twelve markers related to the

40 kinase and the seven markers related to cell division or mitosis. The invention also provides a method of using the markers above to distinguish between patients with good prognosis and poor prognosis.

Table 1. 2,460 gene markers that distinguish between ER (+) and ER (-) cell samples.

Gene Bank

Side of Genes

Access Number

SEQ ID NO  Access Number SEQ ID NO

 AA555029_RC

 SEQ ID NO 1 NM_006984  SEQ ID NO 1344

AB000509

 SEQ ID NO 2 NM_007005  SEQ ID NO 1345

AB001451

 SEQ ID NO 3 NM_007006  SEQ ID NO 1346

AB002301

 SEQ ID NO 4 NM_007019  SEQ ID NO 1347

AB002308

 SEQ ID NO 5 NM_007027  SEQ ID NO 1348

AB002351

 SEQ ID NO 6 NM_007044  SEQ ID NO 1350

AB002448

 SEQ ID NO 7 NM_007050  SEQ ID NO 1351

 AB006628

 SEQ ID NO 9 NM_007057  SEQ ID NO 1352

AB006630

 SEQ ID NO 10 NM_007069  SEQ ID NO 1353

AB006746

 SEQ ID NO 11 NM_007074  SEQ ID NO 1355

AB007458

 SEQ ID NO 12 NM_007088  SEQ ID NO 1356

AB007855

 SEQ ID NO 13 NMB_007111  SEQ ID NO 1357

AB007857

 SEQ ID NO 14 NMB_007146  SEQ ID NO 1358

AB007863

 SEQ ID NO 15 NM_007173  SEQ ID NO 1359

AB007883

 SEQ ID NO 16 NMB_007177  SEQ ID NO 1360

AB007896

 SEQ ID NO 17 NMB_007196  SEQ ID NO 1361

AB007899

 SEQ ID NO 18 NM_007203  SEQ ID NO 1362

AB007916

 SEQ ID NO 19 NM_007214  SEQ ID NO 1363

AB007950

 SEQ ID NO 20 NMB_007217  SEQ ID NO 1364

AB011087

 SEQ ID NO 21 NM_007231  SEQ ID NO 1365

AB011089

 SEQ ID NO 22 NM_007268  SEQ ID NO 1367

AB011104

 SEQ ID NO 23 NM_007274  SEQ ID NO 1368

AB011105

 SEQ ID NO 24 NM_007275  SEQ ID NO 1369

AB011121

 SEQ ID NO 25 NM_007281  SEQ ID NO 1370

AB011132

 SEQ ID NO 26 NM_007309  SEQ ID NO 1371

AB011152

 SEQ ID NO 27 NMB_007315  SEQ ID NO 1372

AB011179

 SEQ ID NO 28 NM_007334  SEQ ID NO 1373

AB014534

 SEQ ID NO 29 NM_007358  SEQ ID NO 1374

AB014568

 SEQ ID NO 30 NM_009585  SEQ ID NO 1375

AB018260

 SEQ ID NO 31 NM_009587  SEQ ID NO 1376

AB018268

 SEQ ID NO 32 NM_009588  SEQ ID NO 1377

AB018289

 SEQ ID NO 33 NMB_012062  SEQ ID NO 1378

AB018345

 SEQ ID NO 35 NMB_012067  SEQ ID NO 1379

AB020677

 SEQ ID NO 36 NMB_012101  SEQ ID NO 1380

AB020689

 SEQ ID NO 37 NMB_012105  SEQ ID NO 1381

AB020695

 SEQ ID NO 38 NMB_012108  SEQ ID NO 1382

AB020710

 SEQ ID NO 39 NM_012110  SEQ ID NO 1383

AB023139

 SEQ ID NO 40 NMB_012124  SEQ ID NO 1384

AB023151

 SEQ ID NO 41 NM_012142  SEQ ID NO 1386

AB023152

 SEQ ID NO 42 NM_012155  SEQ ID NO 1388

AB023163

 SEQ ID NO 43 NM_012175  SEQ ID NO 1389

AB023173

 SEQ ID NO 44 NMB_012177  SEQ ID NO 1390

AB023211

 SEQ ID NO 45 NM_012205  SEQ ID NO 1391

AB024704

 SEQ ID NO 46 NM_012219  SEQ ID NO 1393

AB028985

 SEQ ID NO 47 NMB_012242  SEQ ID NO 1394

AB028986

 SEQ ID NO 48 NMB_012250  SEQ ID NO 1395

AB028998

 SEQ ID NO 49 NM_012261  SEQ ID NO 1397

AB029031

 SEQ ID NO 51 NM_012286  SEQ ID NO 1398

AB032951

 SEQ ID NO 52 NM_012319  SEQ ID NO 1400

AB032966

 SEQ ID NO 53 NM_012332  SEQ ID NO 1401

AB032969

 SEQ ID NO 54 NM_012336  SEQ ID NO 1402

AB032977

 SEQ ID NO 56 NMB_012339  SEQ ID NO 1404

AB033007

 SEQ ID NO 58 NMB_012341  SEQ ID NO 1405

AB033034

 SEQ ID NO 59 NMB_012391  SEQ ID NO 1406

12

 AB033035

 SEQ ID NO 60 NM_012394  SEQ ID NO 1407

AB033040

 SEQ ID NO 61 NM_012413  SEQ ID NO 1408

AB033049

 SEQ ID NO 63 NM_012421  SEQ ID NO 1409

AB033050

 SEQ ID NO 64 NMB_012425  SEQ ID NO 1410

AB033053

 SEQ ID NO 65 NM_012427  SEQ ID NO 1411

AB033055

 SEQ ID NO 66 NM_012429  SEQ ID NO 1413

AB033058

 SEQ ID NO 67 NM_012446  SEQ ID NO 1414

AB033073

 SEQ ID NO 68 NM_012463  SEQ ID NO 1415

AB033092

 SEQ ID NO 69 NM_012474  SEQ ID NO 1416

AB033111

 SEQ ID NO 70 NM_013230  SEQ ID NO 1417

AB036063

 SEQ ID NO 71 NM_013233  SEQ ID NO 1418

AB037720

 SEQ ID NO 72 NM_013238  SEQ ID NO 1419

AB037743

 SEQ ID NO 74 NM_013239  SEQ ID NO 1420

AB037745

 SEQ ID NO 75 NM_013242  SEQ ID NO 1421

AB037756

 SEQ ID NO 76 NM_01 3257  SEQ ID NO 1423

AB037765

 SEQ ID NO 77 NM_013261  SEQ ID NO 1424

AB037778

 SEQ ID NO 78 NM_013262  SEQ ID NO 1425

AB037791

 SEQ ID NO 79 NM_013277  SEQ ID NO 1426

AB037793

 SEQ ID NO 80 NM_013296  SEQ ID NO 1427

AB037802

 SEQ ID NO 81 NM_013301  SEQ ID NO 1428

AB037806

 SEQ ID NO 82 NM_013324  SEQ ID NO 1429

AB037809

 SEQ ID NO 83 NM_013327  SEQ ID NO 1430

AB037836

 SEQ ID NO 84 NM_013336  SEQ ID NO 1431

AB037844

 SEQ ID NO 85 NM_013339  SEQ ID NO 1432

AB037845

 SEQ ID NO 86 NM_013363  SEQ ID NO 1433

AB037848

 SEQ ID NO 87 NM_013378  SEQ ID NO 1435

AB037863

 SEQ ID NO 88 NM_013384  SEQ ID NO 1436

AB037864

 SEQ ID NO 89 NM_013385  SEQ ID NO 1437

AB040881

 SEQ ID NO 90 NM_013406  SEQ ID NO 1438

AB040900

 SEQ ID NO 91 NM_013437  SEQ ID NO 1439

AB040914

 SEQ ID NO 92 NM_013451  SEQ ID NO 1440

AB040926

 SEQ ID NO 93 NM_013943  SEQ ID NO 1441

AB040955

 SEQ ID NO 94 NMB_013994  SEQ ID NO 1442

AB040961

 SEQ ID NO 95 NMB_013995  SEQ ID NO 1443

AF000974

 SEQ ID NO 97 NMB_014026  SEQ ID NO 1444

AF005487

 SEQ ID NO 98 NM_014029  SEQ ID NO 1445

AF007153

 SEQ ID NO 99 NM_014036  SEQ ID NO 1446

AF007155

 SEQ ID NO 100 NM_014062  SEQ ID NO 1447

AF015041

 SEQ ID NO 101 NMB_014074  SEQ ID NO 1448

AF016004

 SEQ ID NO 102 NM_014096  SEQ ID NO 1450

AF016495

 SEQ ID NO 103 NM_014109  SEQ ID NO 1451

AF020919

 SEQ ID NO 104 NM_014112  SEQ ID NO 1452

AF026941

 SEQ ID NO 105 NM_014147  SEQ ID NO 1453

AF035191

 SEQ ID NO 106 NM_014149  SEQ ID NO 1454

AF035284

 SEQ ID NO 107 NMB_014164  SEQ ID NO 1455

AF035318

 SEQ ID NO 108 NMB_014172  SEQ ID NO 1456

AF038182

 SEQ ID NO 109 NM_014175  SEQ ID NO 1457

13

 AF038193

 SEQ ID NO 110 NM_014181  SEQ ID NO 1458

AF042838

 SEQ ID NO 111 NM_014184  SEQ ID NO 1459

AF044127

 SEQ ID NO 112 NM_014211  SEQ ID NO 1460

AF045229

 SEQ ID NO 113 NM_014214  SEQ ID NO 1461

AF047002

 SEQ ID NO 114 NM_014216  SEQ ID NO 1462

AF047826

 SEQ ID NO 115 NM_014241  SEQ ID NO 1463

AF049460

 SEQ ID NO 116 NM_014246  SEQ ID NO 1465

AF052101

 SEQ ID NO 117 NM_014268  SEQ ID NO 1466

AF052117

 SEQ ID NO 118 NM_014272  SEQ ID NO 1467

AF052155

 SEQ ID NO 119 NM_014274  SEQ ID NO 1468

AF052159

 SEQ ID NO 120 NM_014289  SEQ ID NO 1469

AF052176

 SEQ ID NO 122 NM_014298  SEQ ID NO 1470

AF052185

 SEQ ID NO 123 NM_014302  SEQ ID NO 1471

AF055270

 SEQ ID NO 126 NM_014315  SEQ ID NO 1473

AF058075

 SEQ ID NO 127 NM_014316  SEQ ID NO 1474

AF061034

 SEQ ID NO 128 NM_014317  SEQ ID NO 1475

AF063725

 SEQ ID NO 129 NM_014320  SEQ ID NO 1476

AF063936

 SEQ ID NO 130 NM_014321  SEQ ID NO 1477

AF065241

 SEQ ID NO 131 NM_014325  SEQ ID NO 1478

AF067972

 SEQ ID NO 132 NM_014335  SEQ ID NO 1479

AF070536

 SEQ ID NO 133 NM_014363  SEQ ID NO 1480

AF070552

 SEQ ID NO 134 NM_014364  SEQ ID NO 1481

AF070617

 SEQ ID NO 135 NM_014365  SEQ ID NO 1482

AF073770

 SEQ ID NO 138 NM_014373  SEQ ID NO 1483

AF076612

 SEQ ID NO 139 NM_014382  SEQ ID NO 1484

AF079529

 SEQ ID NO 140 NM_014395  SEQ ID NO 1485

AF090913

 SEQ ID NO 142 NM_014398  SEQ ID NO 1486

AF095719

 SEQ ID NO 143 NM_014399  SEQ ID NO 1487

AF098641

 SEQ ID NO 144 NM_014402  SEQ ID NO 1488

AF099032

 SEQ ID NO 145 NM_014428  SEQ ID NO 1489

AF100756

 SEQ ID NO 146 NM_014448  SEQ ID NO 1490

AF101051

 SEQ ID NO 147 NM_014449  SEQ ID NO 1491

AF103375

 SEQ ID NO 148 NM_014450  SEQ ID NO 1492

AF103458

 SEQ ID NO 149 NM_014452  SEQ ID NO 1493

AF103530

 SEQ ID NO 150 NM_014453  SEQ ID NO 1494

AF103804

 SEQ ID NO 151 NM_014456  SEQ ID NO 1495

AF111849

 SEQ ID NO 152 NM_014479  SEQ ID NO 1497

AF112213

 SEQ ID NO 153 NM_014501  SEQ ID NO 1498

AF113132

 SEQ ID NO 154 NM_014552  SEQ ID NO 1500

AF116682

 SEQ ID NO 156 NM_014553  SEQ ID NO 1501

AF118224

 SEQ ID NO 157 NM_014570  SEQ ID NO 1502

AF118274

 SEQ ID NO 158 NM_014575  SEQ ID NO 1503

AF119256

 SEQ ID NO 159 NM_014585  SEQ ID NO 1504

AF119665

 SEQ ID NO 160 NM_014595  SEQ ID NO 1505

AF121255

 SEQ ID NO 161 NM_014624  SEQ ID NO 1507

AF131748

 SEQ ID NO 162 NM_014633  SEQ ID NO 1508

AF131753

 SEQ ID NO 163 NM_014640  SEQ ID NO 1509

14

 AF131760

 SEQ ID NO 164 NM_014642  SEQ ID NO 1510

AF131784

 SEQ ID NO 165 NM_014643  SEQ ID NO 1511

AF131828

 SEQ ID NO 166 NM_014656  SEQ ID NO 1512

AF135168

 SEQ ID NO 167 NM_014668  SEQ ID NO 1513

AF141882

 SEQ ID NO 168 NM_014669  SEQ ID NO 1514

AF148505

 SEQ ID NO 169 NM_014673  SEQ ID NO 1515

AF149785

 SEQ ID NO 170 NM_014675  SEQ ID NO 1516

AF151810

 SEQ ID NO 171 NM_014679  SEQ ID NO 1517

AF152502

 SEQ ID NO 172 NM_014680  SEQ ID NO 1518

AF155120

 SEQ ID NO 174 NM_014696  SEQ ID NO 1519

AF159092

 SEQ ID NO 175 NM_014700  SEQ ID NO 1520

AF161407

 SEQ ID NO 176 NM_014715  SEQ ID NO 1521

AF161553

 SEQ ID NO 177 NM_014721  SEQ ID NO 1522

AF164104

 SEQ ID NO 178 NM_014737  SEQ ID NO 1524

AF167706

 SEQ ID NO 179 NM_014738  SEQ ID NO 1525

AF175387

 SEQ ID NO 180 NM_014747  SEQ ID NO 1526

AF176012

 SEQ ID NO 181 NM_014750  SEQ ID NO 1527

AF186780

 SEQ ID NO 182 NM_014754  SEQ ID NO 1528

AF217508

 SEQ ID NO 184 NM_014767  SEQ ID NO 1529

AF220492

 SEQ ID NO 185 NM_014770  SEQ ID NO 1530

AF224266

 SEQ ID NO 186 NM_014773  SEQ ID NO 1531

AF230904

 SEQ ID NO 187 NM_014776  SEQ ID NO 1532

AF234532

 SEQ ID NO 188 NM_014782  SEQ ID NO 1533

AF257175

 SEQ ID NO 189 NM_014785  SEQ ID NO 1534

AF257659

 SEQ ID NO 190 NM_014791  SEQ ID NO 1535

AF272357

 SEQ ID NO 191 NM_014808  SEQ ID NO 1536

AF279865

 SEQ ID NO 192 NM_014811  SEQ ID NO 1537

 AI497657_RC

 SEQ ID NO 193 NM_014812  SEQ ID NO 1538

AJ012755

 SEQ ID NO 194 NM_014838  SEQ ID NO 1540

AJ223353

 SEQ ID NO 195 NM_014862  SEQ ID NO 1542

AJ224741

 SEQ ID NO 196 NM_014865  SEQ ID NO 1543

AJ224864

 SEQ ID NO 197 NM_014870  SEQ ID NO 1544

AJ225092

 SEQ ID NO 198 NM_014875  SEQ ID NO 1545

AJ225093

 SEQ ID NO 199 NM_014886  SEQ ID NO 1547

AJ249377

 SEQ ID NO 200 NM_014889  SEQ ID NO 1548

AJ270996

 SEQ ID NO 202 NM_014905  SEQ ID NO 1549

AJ272057

 SEQ ID NO 203 NM_014935  SEQ ID NO 1550

AJ275978

 SEQ ID NO 204 NM_014945  SEQ ID NO 1551

AJ276429

 SEQ ID NO 205 NM_014965  SEQ ID NO 1552

AK000004

 SEQ ID NO 206 NM_014967  SEQ ID NO 1553

AK000005

 SEQ ID NO 207 NM_014968  SEQ ID NO 1554

AK000106

 SEQ ID NO 208 NM_015032  SEQ ID NO 1555

AK000142

 SEQ ID NO 209 NM_015239  SEQ ID NO 1556

AK000168

 SEQ ID NO 210 NM_015383  SEQ ID NO 1557

AK000345

 SEQ ID NO 212 NM_015392  SEQ ID NO 1558

AK000543

 SEQ ID NO 213 NM_015416  SEQ ID NO 1559

AK000552

 SEQ ID NO 214 NM_015417  SEQ ID NO 1560

fifteen

 AK000643

 SEQ ID NO 216 NM_015420  SEQ ID NO 1561

AK000660

 SEQ ID NO 217 NM_015434  SEQ ID NO 1562

AK000689

 SEQ ID NO 218 NM_015474  SEQ ID NO 1563

AK000770

 SEQ ID NO 220 NM_015507  SEQ ID NO 1565

AK000933

 SEQ ID NO 221 NM_015513  SEQ ID NO 1566

AK001100

 SEQ ID NO 223 NM_015515  SEQ ID NO 1567

AK001164

 SEQ ID NO 224 NM_015523  SEQ ID NO 1568

AK001166

 SEQ ID NO 225 NM_015524  SEQ ID NO 1569

AK001295

 SEQ ID NO 226 NM_015599  SEQ ID NO 1571

AK001380

 SEQ ID NO 227 NM_015623  SEQ ID NO 1572

AK001423

 SEQ ID NO 228 NM_015640  SEQ ID NO 1573

AK001438

 SEQ ID NO 229 NM_015641  SEQ ID NO 1574

AK001492

 SEQ ID NO 230 NM_015678  SEQ ID NO 1575

AK001499

 SEQ ID NO 231 NM_015721  SEQ ID NO 1576

AK001630

 SEQ ID NO 232 NM_015892  SEQ ID NO 1578

AK001872

 SEQ ID NO 234 NM_015895  SEQ ID NO 1579

AK001890

 SEQ ID NO 235 NM_015907  SEQ ID NO 1580

AK002016

 SEQ ID NO 236 NM_015925  SEQ ID NO 1581

AK002088

 SEQ ID NO 237 NM_015937  SEQ ID NO 1582

AK002206

 SEQ ID NO 240 NM_015954  SEQ ID NO 1583

AL035297

 SEQ ID NO 241 NM_015955  SEQ ID NO 1584

AL049265

 SEQ ID NO 242 NM_015961  SEQ ID NO 1585

AL049365

 SEQ ID NO 244 NM_015984  SEQ ID NO 1587

AL049370

 SEQ ID NO 245 NM_015986  SEQ ID NO 1588

AL049381

 SEQ ID NO 246 NM_015987  SEQ ID NO 1589

AL049397

 SEQ ID NO 247 NM_015991  SEQ ID NO 1590

AL049415

 SEQ ID NO 248 NM_016002  SEQ ID NO 1592

AL049667

 SEQ ID NO 249 NM_016028  SEQ ID NO 1594

AL049801

 SEQ ID NO 250 NM_016029  SEQ ID NO 1595

AL049932

 SEQ ID NO 251 NM_016047  SEQ ID NO 1596

AL049935

 SEQ ID NO 252 NM_016048  SEQ ID NO 1597

AL049943

 SEQ ID NO 253 NM_016050  SEQ ID NO 1598

AL049949

 SEQ ID NO 254 NM_016056  SEQ ID NO 1599

AL049963

 SEQ ID NO 255 NM_016058  SEQ ID NO 1600

AL049987

 SEQ ID NO 256 NM_016066  SEQ ID NO 1601

AL050021

 SEQ ID NO 257 NM_016072  SEQ ID NO 1602

AL050024

 SEQ ID NO 258 NM_016073  SEQ ID NO 1603

AL050090

 SEQ ID NO 259 NM_016108  SEQ ID NO 1605

AL050148

 SEQ ID NO 260 NM_016109  SEQ ID NO 1606

AL050151

 SEQ ID NO 261 NM_016121  SEQ ID NO 1607

AL050227

 SEQ ID NO 262 NM_016126  SEQ ID NO 1608

AL050367

 SEQ ID NO 263 NM_016127  SEQ ID NO 1609

AL050370

 SEQ ID NO 264 NM_016135  SEQ ID NO 1610

AL050371

 SEQ ID NO 265 NM_016142  SEQ ID NO 1612

AL050372

 SEQ ID NO 266 NM_016153  SEQ ID NO 1613

AL050388

 SEQ ID NO 267 NM_016171  SEQ ID NO 1614

AL079276

 SEQ ID NO 268 NM_016175  SEQ ID NO 1615

16

 AL079298

 SEQ ID NO 269 NM_016184  SEQ ID NO 1616

AL080079

 SEQ ID NO 271 NM_016185  SEQ ID NO 1617

AL080192

 SEQ ID NO 273 NM_016187  SEQ ID NO 1618

AL080199

 SEQ ID NO 274 NM_016199  SEQ ID NO 1619

AL080209

 SEQ ID NO 275 NM_016210  SEQ ID NO 1620

AL080234

 SEQ ID NO 277 NM_016217  SEQ ID NO 1621

AL080235

 SEQ ID NO 278 NM_016228  SEQ ID NO 1623

AL096737

 SEQ ID NO 279 NM_016229  SEQ ID NO 1624

AL110126

 SEQ ID NO 280 NM_016235  SEQ ID NO 1625

AL110139

 SEQ ID NO 281 NM_016240  SEQ ID NO 1626

AL110202

 SEQ ID NO 283 NM_016243  SEQ ID NO 1627

AL110212

 SEQ ID NO 284 NM_016250  SEQ ID NO 1628

AL110260

 SEQ ID NO 285 NM_016267  SEQ ID NO 1629

AL117441

 SEQ ID NO 286 NM_016271  SEQ ID NO 1630

AL117452

 SEQ ID NO 287 NM_016299  SEQ ID NO 1631

AL117477

 SEQ ID NO 288 NM_016306  SEQ ID NO 1632

AL117502

 SEQ ID NO 289 NM_016308  SEQ ID NO 1634

AL117523

 SEQ ID NO 290 NM_016321  SEQ ID NO 1635

AL117595

 SEQ ID NO 291 NM_016337  SEQ ID NO 1636

AL117599

 SEQ ID NO 292 NM_016352  SEQ ID NO 1637

AL117600

 SEQ ID NO 293 NM_016359  SEQ ID NO 1638

AL117609

 SEQ ID NO 294 NM_016401  SEQ ID NO 1641

AL117617

 SEQ ID NO 295 NM_016403  SEQ ID NO 1642

AL117666

 SEQ ID NO 296 NM_016411  SEQ ID NO 1643

AL122055

 SEQ ID NO 297 NM_016423  SEQ ID NO 1644

AL133033

 SEQ ID NO 298 NM_016463  SEQ ID NO 1647

AL133035

 SEQ ID NO 299 NM_016475  SEQ ID NO 1649

AL133074

 SEQ ID NO 301 NM_016477  SEQ ID NO 1650

AL133096

 SEQ ID NO 302 NM_016491  SEQ ID NO 1651

AL133105

 SEQ ID NO 303 NM_016495  SEQ ID NO 1652

AL133108

 SEQ ID NO 304 NM_016542  SEQ ID NO 1653

AL133572

 SEQ ID NO 305 NM_016548  SEQ ID NO 1654

AL133619

 SEQ ID NO 307 NM_016569  SEQ ID NO 1655

AL133622

 SEQ ID NO 308 NM_016577  SEQ ID NO 1656

AL133623

 SEQ ID NO 309 NM_016582  SEQ ID NO 1657

AL133624

 SEQ ID NO 310 NM_016593  SEQ ID NO 1658

AL133632

 SEQ ID NO 311 NM_016603  SEQ ID NO 1659

AL133644

 SEQ ID NO 312 NM_016612  SEQ ID NO 1660

AL133645

 SEQ ID NO 313 NM_016619  SEQ ID NO 1661

AL133651

 SEQ ID NO 314 NM_016623  SEQ ID NO 1663

AL137310

 SEQ ID NO 316 NM_016625  SEQ ID NO 1664

AL137316

 SEQ ID NO 317 NM_016629  SEQ ID NO 1665

AL137332

 SEQ ID NO 318 NM_016640  SEQ ID NO 1666

AL137342

 SEQ ID NO 319 NM_016645  SEQ ID NO 1667

AL137362

 SEQ ID NO 321 NM_016650  SEQ ID NO 1668

AL137381

 SEQ ID NO 322 NM_016657  SEQ ID NO 1669

AL137407

 SEQ ID NO 323 NM_016733  SEQ ID NO 1670

17

 AL137448

 SEQ ID NO 324 NM_016815  SEQ ID NO 1671

AL137502

 SEQ ID NO 326 NM_016817  SEQ ID NO 1672

AL137514

 SEQ ID NO 327 NM_016818  SEQ ID NO 1673

AL137540

 SEQ ID NO 328 NM_016839  SEQ ID NO 1675

AL137566

 SEQ ID NO 330 NM_017414  SEQ ID NO 1676

AL137615

 SEQ ID NO 331 NM_017422  SEQ ID NO 1677

AL137673

 SEQ ID NO 335 NM_017423  SEQ ID NO 1678

AL137718

 SEQ ID NO 336 NM_017447  SEQ ID NO 1679

AL137736

 SEQ ID NO 337 NM_017518  SEQ ID NO 1680

AL137751

 SEQ ID NO 338 NM_017522  SEQ ID NO 1681

AL137761

 SEQ ID NO 339 NM_017540  SEQ ID NO 1682

AL157431

 SEQ ID NO 340 NM_017555  SEQ ID NO 1683

AL157432

 SEQ ID NO 341 NM_017572  SEQ ID NO 1684

AL157454

 SEQ ID NO 342 NM_017585  SEQ ID NO 1685

AL157476

 SEQ ID NO 343 NM_017586  SEQ ID NO 1686

AL157480

 SEQ ID NO 344 NM_017596  SEQ ID NO 1687

AL157482

 SEQ ID NO 345 NM_017606  SEQ ID NO 1688

AL157484

 SEQ ID NO 346 NM_017617  SEQ ID NO 1689

AL157492

 SEQ ID NO 347 NM_017633  SEQ ID NO 1690

AL157505

 SEQ ID NO 348 NM_017634  SEQ ID NO 1691

AL157851

 SEQ ID NO 349 NM_017646  SEQ ID NO 1692

AL160131

 SEQ ID NO 350 NM_017660  SEQ ID NO 1693

AL161960

 SEQ ID NO 351 NM_017680  SEQ ID NO 1694

AL162049

 SEQ ID NO 352 NM_017691  SEQ ID NO 1695

AL355708

 SEQ ID NO 353 NM_017698  SEQ ID NO 1696

D13643

 SEQ ID NO 355 NM_017702  SEQ ID NO 1697

D14678

 SEQ ID NO 356 NM_017731  SEQ ID NO 1699

D25328

 SEQ ID NO 357 NM_017732  SEQ ID NO 1700

D26070

 SEQ ID NO 358 NM_017733  SEQ ID NO 1701

D26488

 SEQ ID NO 359 NM_017734  SEQ ID NO 1702

D31887

 SEQ ID NO 360 NM_017746  SEQ ID NO 1703

D38521

 SEQ ID NO 361 NM_017750  SEQ ID NO 1704

D38553

 SEQ ID NO 362 NM_017761  SEQ ID NO 1705

D42043

 SEQ ID NO 363 NM_017763  SEQ ID NO 1706

D42047

 SEQ ID NO 364 NM_017770  SEQ ID NO 1707

D43950

 SEQ ID NO 365 NM_017779  SEQ ID NO 1708

D50402

 SEQ ID NO 366 NM_017780  SEQ ID NO 1709

D50914

 SEQ ID NO 367 NM_017782  SEQ ID NO 1710

D55716

 SEQ ID NO 368 NM_017786  SEQ ID NO 1711

D80001

 SEQ ID NO 369 NM_017791  SEQ ID NO 1712

D80010

 SEQ ID NO 370 NM_017805  SEQ ID NO 1713

D82345

 SEQ ID NO 371 NM_017816  SEQ ID NO 1714

D83781

 SEQ ID NO 372 NM_017821  SEQ ID NO 1715

D86964

 SEQ ID NO 373 NM_017835  SEQ ID NO 1716

D86978

 SEQ ID NO 374 NM_017843  SEQ ID NO 1717

D86985

 SEQ ID NO 375 NM_017857  SEQ ID NO 1718

D87076

 SEQ ID NO 376 NM_017901  SEQ ID NO 1719

18

 D87453

 SEQ ID NO 377 NM_017906  SEQ ID NO 1720

D87469

 SEQ ID NO 378 NM_017918  SEQ ID NO 1721

D87682

 SEQ ID NO 379 NM_017961  SEQ ID NO 1722

 G26403

 SEQ ID NO 380 NM_017996  SEQ ID NO 1723

J02639

 SEQ ID NO 381 NM_018000  SEQ ID NO 1724

J04162

 SEQ ID NO 382 NM_018004  SEQ ID NO 1725

K02403

 SEQ ID NO 384 NM_018011  SEQ ID NO 1726

L05096

 SEQ ID NO 385 NM_018014  SEQ ID NO 1727

L10333

 SEQ ID NO 386 NM_018022  SEQ ID NO 1728

L11645

 SEQ ID NO 387 NM_018031  SEQ ID NO 1729

L21934

 SEQ ID NO 388 NM_018043  SEQ ID NO 1730

L22005

 SEQ ID NO 389 NM_018048  SEQ ID NO 1731

L48692

 SEQ ID NO 391 NM_018062  SEQ ID NO 1732

M12758

 SEQ ID NO 392 NM_018069  SEQ ID NO 1733

M15178

 SEQ ID NO 393 NM_018072  SEQ ID NO 1734

M21551

 SEQ ID NO 394 NM_018077  SEQ ID NO 1735

M24895

 SEQ ID NO 395 NM_018086  SEQ ID NO 1736

M26383

 SEQ ID NO 396 NM_018087  SEQ ID NO 1737

M27749

 SEQ ID NO 397 NM_018093  SEQ ID NO 1738

M28170

 SEQ ID NO 398 NM_018098  SEQ ID NO 1739

M29873

 SEQ ID NO 399 NM_018099  SEQ ID NO 1740

M29874

 SEQ ID NO 400 NM_018101  SEQ ID NO 1741

M30448

 SEQ ID NO 401 NM_018103  SEQ ID NO 1742

M30818

 SEQ ID NO 402 NM_018109  SEQ ID NO 1744

M31932

 SEQ ID NO 403 NM_018123  SEQ ID NO 1746

M37033

 SEQ ID NO 404 NM_018131  SEQ ID NO 1747

M55914

 SEQ ID NO 405 NM_018136  SEQ ID NO 1748

M63438

 SEQ ID NO 406 NM_018138  SEQ ID NO 1749

M65254

 SEQ ID NO 407 NM_018166  SEQ ID NO 1750

M68874

 SEQ ID NO 408 NM_018171  SEQ ID NO 1751

M73547

 SEQ ID NO 409 NM_018178  SEQ ID NO 1752

M77142

 SEQ ID NO 410 NM_018181  SEQ ID NO 1753

M80899

 SEQ ID NO 411 NM_018186  SEQ ID NO 1754

M90657

 SEQ ID NO 413 NM_018194  SEQ ID NO 1757

M93718

 SEQ ID NO 414 NM_018204  SEQ ID NO 1758

M96577

 SEQ ID NO 415 NM_018208  SEQ ID NO 1759

NM_000022

 SEQ ID NO 417 NM_018212  SEQ ID NO 1760

NM_000044

 SEQ ID NO 418 NM_018234  SEQ ID NO 1763

NM_000050

 SEQ ID NO 419 NM_018255  SEQ ID NO 1764

NM_000057

 SEQ ID NO 420 NM_018257  SEQ ID NO 1765

NM_000060

 SEQ ID NO 421 NM_018265  SEQ ID NO 1766

NM_000064

 SEQ ID NO 422 NM_018271  SEQ ID NO 1767

NM_000073

 SEQ ID NO 424 NM_018290  SEQ ID NO 1768

NM_000077

 SEQ ID NO 425 NM_018295  SEQ ID NO 1769

NM_000086

 SEQ ID NO 426 NM_018304  SEQ ID NO 1770

NM_000087

 SEQ ID NO 427 NM_018306  SEQ ID NO 1771

NM_000095

 SEQ ID NO 429 NM_018326  SEQ ID NO 1772

19

 NM_000096

 SEQ ID NO 430 NM_018346  SEQ ID NO 1773

NM_000100

 SEQ ID NO 431 NM_018366  SEQ ID NO 1775

NM_000101

 SEQ ID NO 432 NM_018370  SEQ ID NO 1776

NM_000104

 SEQ ID NO 433 NM_018373  SEQ ID NO 1777

NM_000109

 SEQ ID NO 434 NM_018379  SEQ ID NO 1778

NM_000125

 SEQ ID NO 435 NM_018384  SEQ ID NO 1779

NM_000127

 SEQ ID NO 436 NM_018389  SEQ ID NO 1780

NM_000135

 SEQ ID NO 437 NM_018410  SEQ ID NO 1783

NM_000137

 SEQ ID NO 438 NM_018439  SEQ ID NO 1785

NM_000146

 SEQ ID NO 439 NM_018454  SEQ ID NO 1786

NM_000149

 SEQ ID NO 440 NM_018455  SEQ ID NO 1787

NM_000154

 SEQ ID NO 441 NM_018465  SEQ ID NO 9788

NM_000161

 SEQ ID NO 443 NM_018471  SEQ ID NO 1789

NM_000165

 SEQ ID NO 444 NM_018478  SEQ ID NO 1790

NM_000168

 SEQ ID NO 445 NM_018479  SEQ ID NO 1791

NM_000169

 SEQ ID NO 446 NM_018529  SEQ ID NO 1793

NM_000175

 SEQ ID NO 447 NM_018556  SEQ ID NO 1794

NM_000191

 SEQ ID NO 448 NM_018569  SEQ ID NO 1795

NM_000201

 SEQ ID NO 450 NM_018584  SEQ ID NO 1796

NM_000211

 SEQ ID NO 451 NM_018653  SEQ ID NO 1797

NM_000213

 SEQ ID NO 452 NM_018660  SEQ ID NO 1798

NM_000224

 SEQ ID NO 453 NM_018683  SEQ ID NO 1799

NM_000239

 SEQ ID NO 454 NM_018685  SEQ ID NO 1800

NM_000251

 SEQ ID NO 455 NM_018686  SEQ ID NO 1801

NM_000268

 SEQ ID NO 456 NM_018695  SEQ ID NO 1802

NM_000270

 SEQ ID NO 458 NM_018728  SEQ ID NO 1803

NM_000271

 SEQ ID NO 459 NM_018840  SEQ ID NO 1804

NM_000283

 SEQ ID NO 460 NM_018842  SEQ ID NO 1805

NM_000284

 SEQ ID NO 461 NM_018950  SEQ ID NO 1806

NM_000286

 SEQ ID NO 462 NM_018988  SEQ ID NO 1807

NM_000291

 SEQ ID NO 463 NM_019000  SEQ ID NO 1808

NM_000299

 SEQ ID NO 464 NM_019013  SEQ ID NO 1809

NM_000300

 SEQ ID NO 465 NM_019025  SEQ ID NO 1810

NM_000310

 SEQ ID NO 466 NM_019027  SEQ ID NO 1811

NM_000311

 SEQ ID NO 467 NM_019041  SEQ ID NO 1812

NM_000317

 SEQ ID NO 468 NM_019044  SEQ ID NO 1813

NM_000320

 SEQ ID NO 469 NM_019063  SEQ ID NO 1815

NM_000342

 SEQ ID NO 470 NM_019084  SEQ ID NO 1816

NM_000346

 SEQ ID NO 471 NM_019554  SEQ ID NO 1817

NM_000352

 SEQ ID NO 472 NM_019845  SEQ ID NO 1818

NM_000355

 SEQ ID NO 473 NM_019858  SEQ ID NO 1819

NM_000358

 SEQ ID NO 474 NM_020130  SEQ ID NO 1820

NM_000359

 SEQ ID NO 475 NM_020133  SEQ ID NO 1821

NM_000362

 SEQ ID NO 476 NM_020143  SEQ ID NO 1822

NM_000365

 SEQ ID NO 477 NM_020150  SEQ ID NO 1823

NM_000381

 SEQ ID NO 478 NM_020163  SEQ ID NO 1824

NM_000397

 SEQ ID NO 480 NM_020166  SEQ ID NO 1825

twenty

 NM_000399

 SEQ ID NO 481 NM_020169  SEQ ID NO 1826

NM_000414

 SEQ ID NO 482 NM_020179  SEQ ID NO 1827

NM_000416

 SEQ ID NO 483 NM_020184  SEQ ID NO 1828

NM_000422

 SEQ ID NO 484 NM_020186  SEQ ID NO 1829

NM_000424

 SEQ ID NO 485 NM_020188  SEQ ID NO 1830

NM_000433

 SEQ ID NO 486 NM_020189  SEQ ID NO 1831

NM_000436

 SEQ ID NO 487 NM_020197  SEQ ID NO 1832

NM_000450

 SEQ ID NO 488 NM_020199  SEQ ID NO 1833

NM_000462

 SEQ ID NO 489 NM_020215  SEQ ID NO 1834

NM_000495

 SEQ ID NO 490 NM_020347  SEQ ID NO 1836

NM_000507

 SEQ ID NO 491 NM_020365  SEQ ID NO 1837

NM_000526

 SEQ ID NO 492 NM_020386  SEQ ID NO 1838

NM_000557

 SEQ ID NO 493 NM_020445  SEQ ID NO 1839

NM_000560

 SEQ ID NO 494 NM_020639  SEQ ID NO 1840

NM_000576

 SEQ ID NO 495 NM_020659  SEQ ID NO 1841

NM_000579

 SEQ ID NO 496 NM_020675  SEQ ID NO 1842

NM_000584

 SEQ ID NO 497 NM_020686  SEQ ID NO 1843

NM_000591

 SEQ ID NO 498 NM_020974  SEQ ID NO 1844

NM_000592

 SEQ ID NO 499 NM_020978  SEQ ID NO 1845

NM_000593

 SEQ ID NO 500 NM_020979  SEQ ID NO 1846

NM_000594

 SEQ ID NO 501 NM_020980  SEQ ID NO 1847

NM_000597

 SEQ ID NO 502 NM_021000  SEQ ID NO 1849

NM_000600

 SEQ ID NO 504 NM_021004  SEQ ID NO 1850

NM_000607

 SEQ ID NO 505 NM_021025  SEQ ID NO 1851

NM_000612

 SEQ ID NO 506 NM_021063  SEQ ID NO 1852

NM_000627

 SEQ ID NO 507 NM_021065  SEQ ID NO 1853

NM_000633

 SEQ ID NO 508 NM_021077  SEQ ID NO 1854

NM_000636

 SEQ ID NO 509 NM_021095  SEQ ID NO 1855

NM_000639

 SEQ ID NO 510 NM_021101  SEQ ID NO 1856

NM_000647

 SEQ ID NO 511 NM_021103  SEQ ID NO 1857

NM_000655

 SEQ ID NO 512 NM_021128  SEQ ID NO 1858

NM_000662

 SEQ ID NO 513 NM_021147  SEQ ID NO 1859

NM_000663

 SEQ ID NO 514 NM_021151  SEQ ID NO 1860

NM_000666

 SEQ ID NO 515 NM_021181  SEQ ID NO 1861

NM_000676

 SEQ ID NO 516 NM_021190  SEQ ID NO 1862

NM_000685

 SEQ ID NO 517 NM_021198  SEQ ID NO 1863

NM_000693

 SEQ ID NO 518 NM_021200  SEQ ID NO 1864

NM_000699

 SEQ ID NO 519 NM_021203  SEQ ID NO 1865

NM_000700

 SEQ ID NO 520 NM_021238  SEQ ID NO 1866

NM_000712

 SEQ ID NO 521 NM_021242  SEQ ID NO 1867

NM_000727

 SEQ ID NO 522 S40706  SEQ ID NO 1869

NM_000732

 SEQ ID NO 523 S53354  SEQ ID NO 1870

NM_000734

 SEQ ID NO 524 S59184  SEQ ID NO 1871

NM_000767

 SEQ ID NO 525 S62138  SEQ ID NO 1872

NM_000784

 SEQ ID NO 526 U09848  SEQ ID NO 1873

NM_000802

 SEQ ID NO 528 U10991  SEQ ID NO 1874

NM_000824

 SEQ ID NO 529 U17077  SEQ ID NO 1875

twenty-one

 NM_000849

 SEQ ID NO 530 U18919  SEQ ID NO 1876

NM 000852

 SEQ ID NO 531 U41387  SEQ ID NO 1877

NM_000874

 SEQ ID NO 532 U45975  SEQ ID NO 1878

NM_000878

 SEQ ID NO 533 U49835  SEQ ID NO 1879

NM_000884

 SEQ ID NO 534 U56725  SEQ ID NO 1880

NM_000908

 SEQ ID NO 537 U58033  SEQ ID NO 1881

NM_000909

 SEQ ID NO 538 U61167  SEQ ID NO 1882

NM_000926

 SEQ ID NO 539 U66042  SEQ ID NO 1883

NM_000930

 SEQ lD NO 540 U68385  SEQ ID NO 1885

NM_000931

 SEQ ID NO 541 U68494  SEQ ID NO 1886

NM_000947

 SEQ ID NO 542 U74612  SEQ ID NO 1887

NM_000949

 SEQ ID NO 543 U75968  SEQ ID NO 1888

NM_000950

 SEQ ID NO 544 U79293  SEQ ID NO 1889

NM_000954

 SEQ ID NO 545 U80736  SEQ ID NO 1890

NM_000964

 SEQ ID NO 546 U82987  SEQ ID NO 1891

3 NM_001003

 SEQ ID NO 549 U83115  SEQ ID NO 1892

NM_001016

 SEQ ID NO 551 U89715  SEQ ID NO 1893

NM_001047

 SEQ ID NO 553 U90916  SEQ ID NO 1894

NM_001066

 SEQ ID NO 555 U92544  SEQ ID NO 1895

NM_001071

 SEQ ID NO 556 U96131  SEQ ID NO 1896

NM_001078

 SEQ ID NO 557 U96394  SEQ ID NO 1897

NM_001085

 SEQ ID NO 558 W61000_RC  SEQ ID NO 1898

NM_001089

 SEQ ID NO 559 X00437  SEQ ID NO 1899

NM_001109

 SEQ ID NO 560 X00497  SEQ ID NO 1900

NM_001122

 SEQ ID NO 561 X01394  SEQ ID NO 1901

NM_001124

 SEQ ID NO 562 X03084  SEQ ID NO 1902

NM_001161

 SEQ ID NO 563 X07834  SEQ ID NO 1905

NM_001165

 SEQ ID NO 564 X14356  SEQ ID NO 1906

NM_001166

 SEQ ID NO 565 X16302  SEQ ID NO 1907

NM_001168

 SEQ ID NO 566 X52486  SEQ ID NO 1909

NM_001179

 SEQ ID NO 567 X52882  SEQ ID NO 1910

NM_001185

 SEQ ID NO 569 X56807  SEQ ID NO 1911

NM_001203

 SEQ ID NO 570 X57809  SEQ ID NO 1912

NM_001207

 SEQ ID NO 573 X57819  SEQ ID NO 1913

NM_001216

 SEQ ID NO 574 X58529  SEQ ID NO 1914

NM_001218

 SEQ ID NO 575 X59405  SEQ ID NO 1915

NM_001223

 SEQ ID NO 576 X72475  SEQ ID NO 1918

NM_001225

 SEQ ID NO 577 X73617  SEQ ID NO 1919

NM_001233

 SEQ ID NO 578 X74794  SEQ ID NO 1920

NM_001236

 SEQ ID NO 579 X75315  SEQ ID NO 1921

NM_001237

 SEQ ID NO 580 X79782  SEQ ID NO 1922

NM_001251

 SEQ ID NO 581 X82693  SEQ ID NO 1923

NM_001255

 SEQ ID NO 582 X83301  SEQ ID NO 1924

NM_001262

 SEQ ID NO 583 X93006  SEQ ID NO 1926

NM_001263

 SEQ ID NO 584 X94232  SEQ ID NO 1927

NM_001267

 SEQ ID NO 585 X98834  SEQ ID NO 1929

NM_001276

 SEQ ID NO 587 X99142  SEQ ID NO 1930

22

 NM_001280

 SEQ ID NO 588 Y14737  SEQ ID NO 1932

NM_001282

 SEQ ID NO 589 Z11887  SEQ ID NO 1933

NM_001295

 SEQ ID NO 590 Z48633  SEQ ID NO 1935

NM_001305

 SEQ ID NO 591 NM_004222  SEQ ID NO 1936

NM_001310

 SEQ ID NO 592 NM_016405  SEQ ID NO 1937

NM_001312

 SEQ ID NO 593 NM_017690  SEQ ID NO 1938

NM_001321

 SEQ ID NO 594 Contig29 RC  SEQ ID NO 1939

NM_001327

 SEQ ID NO 595  Contig237_RC  SEQ ID NO 1940

NM_001329

 SEQ ID NO 596  Contig263_RC  SEQ ID NO 1941

NM_001333

 SEQ ID NO 597  Contig292_RC  SEQ ID NO 1942

NM_001338

 SEQ ID NO 598  Contig382_RC  SEQ ID NO 1944

NM_001360

 SEQ ID NO 599  Contig399_RC  SEQ ID NO 1945

NM_001363

 SEQ ID NO 600  Contig448_RC  SEQ ID NO 1946

NM_001381

 SEQ ID NO 601  Contig569_RC  SEQ ID NO 1947

NM_001394

 SEQ ID NO 602  Contig580_RC  SEQ ID NO 1948

NM_001395

 SEQ ID NO 603  Contig678_RC  SEQ ID NO 1949

NM_001419

 SEQ ID NO 604  Contig706_RC  SEQ ID NO 1950

NM_001424

 SEQ ID NO 605  Contig718_RC  SEQ ID NO 1951

NM_001428

 SEQ ID NO 606  Contig719_RC  SEQ ID NO 1952

NM_001436

 SEQ ID NO 607  Contig742_RC  SEQ ID NO 1953

NM_001444

 SEQ ID NO 608  Contig753_RC  SEQ ID NO 1954

NM_001446

 SEQ ID NO 609  Contig758_RC  SEQ ID NO 1956

NM_001453

 SEQ ID NO 611  Contig760_RC  SEQ ID NO 1957

NM_001456

 SEQ ID NO 612  Contig842_RC  SEQ ID NO 1958

NM_001457

 SEQ ID NO 613  Contig848_RC  SEQ ID NO 1959

NM_001463

 SEQ ID NO 614  Contig924_RC  SEQ ID NO 1960

NM_001465

 SEQ ID NO 615  Contig974_RC  SEQ ID NO 1961

NM_001481

 SEQ ID NO 616  Contig1018_RC  SEQ ID NO 1962

NM_001493

 SEQ ID NO 617  Contig1056_RC  SEQ ID NO 1963

NM_001494

 SEQ ID NO 618  Contig1061_RC  SEQ ID NO 1964

NM_001500

 SEQ ID NO 619  Contig1129_RC  SEQ ID NO 1965

NM_001504

 SEQ ID NO 620 Contig1148  SEQ ID NO 1966

NM_001511

 SEQ ID NO 621  Contig1239_RC  SEQ ID NO 1967

NM_001513

 SEQ ID NO 622 Contig1277  SEQ ID NO 1968

NM_001527

 SEQ ID NO 623  Contig1333_RC  SEQ ID NO 1969

NM_001529

 SEQ ID NO 624  Contig1386_RC  SEQ ID NO 1970

NM_001530

 SEQ ID NO 625  Contig1389_RC  SEQ ID NO 1971

NM_001540

 SEQ ID NO 626  Contig1418_RC  SEQ ID NO 1972

NM_001550

 SEQ ID NO 627  Contig1462_RC  SEQ ID NO 1973

NM_001551

 SEQ ID NO 628  Contig1505_RC  SEQ ID NO 1974

NM_001552

 SEQ ID NO 629  Contig1540_RC  SEQ ID NO 1975

NM_001554

 SEQ ID NO 631  Contig1584_RC  SEQ ID NO 1976

NM_001558

 SEQ ID NO 632  Contig1632_RC  SEQ ID NO 1977

NM_001560

 SEQ ID NO 633  Contig1682_RC  SEQ ID NO 1978

NM_001565

 SEQ ID NO 634  Contig1778_RC  SEQ ID NO 1979

NM_001569

 SEQ ID, NO 635  Contig1829  SEQ ID NO 1981

NM_001605

 SEQ ID NO 636  Contig1838_RC  SEQ ID NO 1982

2. 3

 NM_001609

 SEQ ID NO 637  Contig1938_RC  SEQ ID NO 1983

NM_001615

 SEQ ID NO 638  Contig1970_RC  SEQ ID NO 1984

NM_001623

 SEQ ID NO 639  Contig1998_RC  SEQ ID NO 1985

NM_001627

 SEQ ID NO 640  Contig2099_RC  SEQ ID NO 1986

NM_001628

 SEQ ID NO 641  Contig2143_RC  SEQ ID NO 1987

NM_001630

 SEQ ID NO 642  Contig2237_RC  SEQ ID NO 1988

NM_001634

 SEQ ID NO 643  Contig2429_RC  SEQ ID NO 1990

NM_001656

 SEQ ID NO 644  Contig2504_RC  SEQ ID NO 1991

NM_001673

 SEQ ID NO 645  Contig2512_RC  SEQ ID NO 1992

NM_001675

 SEQ ID NO 647  Contig2575_RC  SEQ ID NO 1993

NM_001679

 SEQ ID NO 648  Contig2578_RC  SEQ ID NO 1994

NM_001689

 SEQ ID NO 649  Contig2639_RC  SEQ ID NO 1995

NM_001703

 SEQ ID NO 650  Contig2647_RC  SEQ ID NO 1996

NM_001710

 SEQ ID NO 651  Contig2657_RC  SEQ ID NO 1997

NM_001725

 SEQ ID NO 652  Contig2728_RC  SEQ ID NO 1998

NM_001730

 SEQ ID NO 653  Contig2745_RC  SEQ ID NO 1999

NM_001733

 SEQ ID NO 654  Contig2811_RC  SEQ ID NO 2000

NM_001734

 SEQ ID NO 655  Contig2873_RC  SEQ ID NO 2001

NM_001740

 SEQ ID NO 656  Contig2883_RC  SEQ ID NO 2002

NM_001745

 SEQ ID NO 657  Contig2915_RC  SEQ ID NO 2003

NM_001747

 SEQ 1D NO 658  Contig2928_RC  SEQ ID NO 2004

NM_001756

 SEQ ID NO 659  Contig3024_RC  SEQ ID NO 2005

NM_001757

 SEQ ID NO 660  Contig3094_RC  SEQ ID NO 2006

NM_001758

 SEQ ID NO 661  Contig3164_RC  SEQ ID NO 2007

NM_001762

 SEQ ID NO 662  Contig3495_RC  SEQ ID NO 2009

NM_001767

 SEQ ID NO 663  Contig3607_RC  SEQ ID NO 2010

NM_001770

 SEQ ID NO 664  Contig3659_RC  SEQ ID NO 2011

NM_001777

 SEQ ID NO 665  Contig3677_RC  SEQ ID NO 2012

NM_001778

 SEQ ID NO 666  Contig3682_RC  SEQ ID NO 2013

NM_001781

 SEQ ID NO 667  Contig3734_RC  SEQ ID NO 2014

NM_001786

 SEQ ID NO 668  Contig3834_ RC  SEQ ID NO 2015

NM_001793

 SEQ ID NO 669  Contig3876_RC  SEQ ID NO 2016

NM_001803

 SEQ ID NO 671  Contig3902_RC  SEQ ID NO 2017

NM_001806

 SEQ ID NO 672  Contig3940_RC  SEQ ID NO 2018

NM_001809

 SEQ ID NO 673  Contig4380_RC  SEQ ID NO 2019

NM_001814

 SEQ ID NO 674  Contig4388_RC  SEQ ID NO 2020

NM_001826

 SEQ ID NO 675  Contig4467_RC  SEQ ID NO 2021

NM_001830

 SEQ ID NO 677  Contig4949_RC  SEQ ID NO 2023

NM_001838

 SEQ ID NO 678  Contig5348_RC  SEQ ID NO 2024

NM_001839

 SEQ ID NO 679  Contig5403_RC  SEQ ID NO 2025

NM_001853

 SEQ ID NO 681  Contig5716_RC  SEQ ID NO 2026

NM_001859

 SEQ ID NO 682  Contig6118_RC  SEQ ID NO 2027

NM_001861

 SEQ ID NO 683  Contig6164_RC  SEQ ID NO 2028

NM_001874

 SEQ ID NO 685  Contig6181_RC  SEQ ID NO 2029

NM_001885

 SEQ ID NO 686  Contig6514_RC  SEQ ID NO 2030

NM_001892

 SEQ ID NO 688  Contig6612_RC  SEQ ID NO 2031

NM_001897

 SEQ ID NO 689  Contig6881_RC  SEQ ID NO 2032

24

 NM_001899

 SEQ ID NO 690  Contig8165_RC  SEQ ID NO 2033

NM_001905

 SEQ ID NO 691  Contig8221_RC  SEQ ID NO 2034

NM_001912

 SEQ ID NO 692  Contig8347_RC  SEQ ID NO 2035

NM_001914

 SEQ ID NO 693  Contig8364_RC  SEQ ID NO 2036

NM_001919

 SEQ ID NO 694  Contig8888_RC  SEQ ID NO 2038

NM_001941

 SEQ ID NO 695  Contig9259_RC  SEQ ID NO 2039

NM_001943

 SEQ ID NO 696  Contig9541_RC  SEQ ID NO 2040

NM_001944

 SEQ ID NO 697  Contig10268_RC  SEQ ID NO 2041

NM_001953

 SEQ ID NO 699  Contig10363_RC  SEQ ID NO 2042

NM_001954

 SEQ ID NO 700  Contig10437_RC  SEQ ID NO 2043

NMB_001955

 SEQ ID NO 701  Contig11086_RC  SEQ ID NO 2045

NMB_001956

 SEQ ID NO 702  Contig11275_RC  SEQ ID NO 2046

NM_001958

 SEQ ID NO 703  Contig11648_RC  SEQ ID NO 2047

NMB_001961

 SEQ ID NO 705  Contig12216_RC  SEQ ID NO 2048

NMB_001970

 SEQ ID NO 706  Contig12369_RC  SEQ ID NO 2049

NMB_001979

 SEQ ID NO 707  Contig12814_RC  SEQ ID NO 2050

NM_001982

 SEQ ID NO 708  Contig12951_RC  SEQ ID NO 2051

NMB_002017

 SEQ ID NO 710  Contig13480_RC  SEQ ID NO 2052

NM_002033

 SEQ ID NO 713  Contig14284_RC  SEQ ID NO 2053

NM_002046

 SEQ ID NO 714  Contig14390_RC  SEQ ID NO 2054

NM_002047

 SEQ ID NO 715  Contig14780_RC  SEQ ID NO 2055

NM_002051

 SEQ ID NO 716  Contig14954_RC  SEQ ID NO 2056

NM_002053

 SEQ ID NO 717  Contig14981_RC  SEQ ID NO 2057

NM_002061

 SEQ ID NO 718  Contig15692_RC  SEQ ID NO 2058

NM_002065

 SEQ ID NO 719  Contig16192_RC  SEQ ID NO 2059

NM_002068

 SEQ ID NO 720  Contig16759_RC  SEQ ID NO 2061

NM_002077

 SEQ ID NO 722  Contig16786_RC  SEQ ID NO 2062

NM_002091

 SEQ ID NO 723  Contig16905_RC  SEQ ID NO 2063

NMB_002101

 SEQ ID NO 724  Contig17103_RC  SEQ ID NO 2064

NM_002106

 SEQ ID NO 725  Contig17105_RC  SEQ ID NO 2065

NMB_002110

 SEQ ID NO 726  Contig17248_RC  SEQ ID NO 2066

NM_002111

 SEQ ID NO 727  Contig17345_RC  SEQ ID NO 2067

NMB_002115

 SEQ ID NO 728  Contig18502_RC  SEQ ID NO 2069

NMB_002118

 SEQ ID NO 729  Contig20156_RC  SEQ ID NO 2071

NMB_002123

 SEQ ID NO 730  Contig20302_RC  SEQ ID NO 2073

NMB_002131

 SEQ ID NO 731  Contig20600_RC  SEQ ID NO 2074

NMB_002136

 SEQ ID NO 732  Contig20617_RC  SEQ ID NO 2075

NM_002145

 SEQ ID NO 733  Contig20629_RC  SEQ ID NO 2076

NMB_002164

 SEQ ID NO 734  Contig20651_RC  SEQ ID NO 2077

NMB_002168

 SEQ ID NO 735  Contig21130_RC  SEQ ID NO 2078

NM_002184

 SEQ ID NO 736  Contig21185_RC  SEQ ID NO 2079

NM_002185

 SEQ ID NO 737  Contig21421_RC  SEQ ID NO 2080

NM_002189

 SEQ ID NO 738  Contig21787_RC  SEQ ID NO 2081

NM_002200

 SEQ ID NO 739  Contig21812_RC  SEQ ID NO 2082

NM_002201

 SEQ ID NO 740  Contig22418_RC  SEQ ID NO 2083

NM_002213

 SEQ ID NO 741  Contig23085_RC  SEQ ID NO 2084

NMB_002219

 SEQ ID NO 742  Contig23454_RC  SEQ ID NO 2085

25

 NM_002222

 SEQ ID NO 743  Contig24138_RC  SEQ ID NO 2086

NM_002239

 SEQ ID NO 744  Contig24252_RC  SEQ ID NO 2087

NM_002243

 SEQ ID NO 745  Contig24655_RC  SEQ ID NO 2089

NM_002245

 SEQ ID NO 746  Contig25055_RC  SEQ ID NO 2090

NM_002250

 SEQ ID NO 747  Contig25290_RC  SEQ ID NO 2091

NM_002254

 SEQ ID NO 748  Contig25343_RC  SEQ ID NO 2092

NM_002266

 SEQ ID NO 749  Contig25362_RC  SEQ ID NO 2093

NM_002273

 SEQ ID NO 750  Contig25617_RC  SEQ ID NO 2094

NM_002281

 SEQ ID NO 751  Contig25659_RC  SEQ ID NO 2095

NM_002292

 SEQ ID NO 752  Contig25722_RC  SEQ ID NO 2096

NM_002298

 SEQ ID NO 753  Contig25809_RC  SEQ ID NO 2097

NM_002300

 SEQ ID NO 754 Contig25991  SEQ ID NO 2098

NM_002308

 SEQ ID NO 755  Contig26022_RC  SEQ ID NO 2099

NMB_002314

 SEQ ID NO 756  Contig26077_RC  SEQ ID NO 2100

NM_002337

 SEQ ID NO 757  Contig26310_RC  SEQ ID NO 2101

NM_002341

 SEQ ID NO 758  Contig26371_RC  SEQ ID NO 2102

NM_002342

 SEQ ID NO 759  Contig26438_RC  SEQ ID NO 2103

NM_002346

 SEQ ID NO 760  Contig26706_RC  SEQ ID NO 2104

NM_002349

 SEQ ID NO 761  Contig27088_RC  SEQ ID NO 2105

NM_002350

 SEQ ID NO 762  Contig27186_RC  SEQ ID NO 2106

NM_002356

 SEQ ID NO 763  Contig27228_RC  SEQ ID NO 2107

NM_002358

 SEQ ID NO 764  Contig27344_RC  SEQ ID NO 2109

NM_002370

 SEQ ID NO 765  Contig27386_RC  SEQ ID NO 2110

NM_002395

 SEQ ID NO 766  Contig27624_RC  SEQ ID NO 2111

NMB_002416

 SEQ ID NO 767  Contig27749_RC  SEQ ID NO 2112

NM_002421

 SEQ ID NO 768  Contig27882_RC  SEQ ID NO 2113

NM_002426

 SEQ ID NO 769  Contig27915_RC  SEQ ID NO 2114

NM_002435

 SEQ ID NO 770  Contig28030_RC  SEQ ID NO 2115

NM_002438

 SEQ ID NO 771  Contig28081_RC  SEQ ID NO 2116

NM_002444

 SEQ ID NO 772  Contig28152_RC  SEQ ID NO 2117

NM_002449

 SEQ ID NO 773  Contig28550_RC  SEQ ID NO 2119

NM_002450

 SEQ ID NO 774  Contig28552_RC  SEQ ID NO 2120

NM_002456

 SEQ ID NO 775  Contig28712_RC  SEQ ID NO 2121

NM_002466

 SEQ ID NO 776  Contig28888_RC  SEQ ID NO 2122

NM_002482

 SEQ ID NO 777  Contig28947_RC  SEQ ID NO 2123

NM_002497

 SEQ ID NO 778  Contig29126_RC  SEQ ID NO 2124

NM_002510

 SEQ ID NO 779  Contig29193_RC  SEQ ID NO 2125

NMB_002515

 SEQ ID NO 781  Contig29369_RC  SEQ ID NO 2126

NM_002524

 SEQ ID NO 782  Contig29639_RC  SEQ ID NO 2127

NM_002539

 SEQ ID NO 783  Contig30047_RC  SEQ ID NO 2129

NM_002555

 SEQ ID NO 785  Contig30154_RC  SEQ ID NO 2131

NM_002570

 SEQ ID NO 787  Contig30209_RC  SEQ ID NO 2132

NM_002579

 SEQ ID NO 788  Contig30213_RC  SEQ ID NO 2133

NM_002587

 SEQ ID NO 789  Contig30230_RC  SEQ ID NO 2134

NM_002590

 SEQ ID NO 790  Contig30267_RC  SEQ ID NO 2135

NM_002600

 SEQ ID NO 791  Contig30390_RC  SEQ ID NO 2136

NMB_002614

 SEQ ID NO 792  Contig30480_RC  SEQ ID NO 2137

26

 NMB_002618

 SEQ ID NO 794  Contig30609_RC  SEQ ID NO 2138

NM_002626

 SEQ ID NO 795  Contig30934_RC  SEQ ID NO 2139

NM_002633

 SEQ ID NO 796  Contig31150_RC  SEQ ID NO 2140

NM_002639

 SEQ ID NO 797  Contig31186_RC  SEQ ID NO 2141

NM_002648

 SEQ ID NO 798  Contig31251_RC  SEQ ID NO 2142

NM_002659

 SEQ ID NO 799  Contig31288_RC  SEQ ID NO 2143

NM_002661

 SEQ ID NO 800  Contig31291_RC  SEQ ID NO 2144

NM_002662

 SEQ ID NO 801  Contig31295_RC  SEQ ID NO 2145

NM_002664

 SEQ ID NO 802  Contig31424_RC  SEQ ID NO 2146

NM_002689

 SEQ ID NO 804  Contig31449_RC  SEQ ID NO 2147

NM_002690

 SEQ ID NO 805  Contig31596_RC  SEQ ID NO 2148

NM_002709

 SEQ ID NO 806  Contig31864_RC  SEQ ID NO 2149

NM_002727

 SEQ ID NO 807  Contig31928_RC  SEQ ID NO 2150

NM_002729

 SEQ ID NO 808  Contig31966_RC  SEQ ID NO 2151

NM_002734

 SEQ ID NO 809  Contig31986_RC  SEQ ID NO 2152

NM_002736

 SEQ ID NO 810  Contig32084_RC  SEQ ID NO 2153

NM_002740

 SEQ ID NO 811  Contig32105_RC  SEQ ID NO 2154

NM_002748

 SEQ ID NO 813  Contig32185_RC  SEQ ID NO 2156

NM_002774

 SEQ ID NO 814  Contig32242_RC  SEQ ID NO 2157

NM_002775

 SEQ ID NO 815  Contig32322_RC  SEQ ID NO 2158

NM_002776

 SEQ ID NO 816  Contig32336_RC  SEQ ID NO 2159

NM_002789

 SEQ ID NO 817  Contig32558_RC  SEQ ID NO 2160

NM_002794

 SEQ ID NO 818  Contig32798_RC  SEQ ID NO 2161

NM_002796

 SEQ ID NO 819  Contig33005_RC  SEQ ID NO 2162

NM_002800

 SEQ ID NO 820  Contig33230_RC  SEQ ID NO 2163

NM_002801

 SEQ ID NO 821  Contig33260_RC  SEQ ID NO 2164

NM_002808

 SEQ ID NO 822  Contig33654_RC  SEQ ID NO 2166

NM_002821

 SEQ ID NO 824  Contig33741_RC  SEQ ID NO 2167

NM_002826

 SEQ ID NO 825  Contig33771_RC  SEQ ID NO 2168

NM_002827

 SEQ ID NO 826  Contig33814_RC  SEQ ID NO 2169

NM_002838

 SEQ ID NO 827  Contig33815_RC  SEQ ID NO 2170

NM_002852

 SEQ ID NO 828 Contig33833  SEQ ID NO 2171

NM_002854

 SEQ ID NO 829  Contig33998_RC  SEQ ID NO 2172

NM_002856

 SEQ ID NO 830 Contig34079  SEQ ID NO 2173

NM_002857

 SEQ ID NO 831  Contig34080_RC  SEQ ID NO 2174

NM_002858

 SEQ ID NO 832  Contig34222_RC  SEQ ID NO 2175

NM_002888

 SEQ ID NO 833  Contig34233_RC  SEQ ID NO 2176

NM_002890

 SEQ ID NO 834  Contig34303_RC  SEQ ID NO 2177

NM_002901

 SEQ ID NO 836  Contig34393_RC  SEQ ID NO 2178

NM_002906

 SEQ ID NO 837  Contig34477_RC  SEQ ID NO 2179

NMB_002916

 SEQ ID NO 838  Contig34766_RC  SEQ ID NO 2181

NM_002923

 SEQ ID NO 839 Contig34952  SEQ ID NO 2182

NM_002933

 SEQ ID NO 840  Contig34989_RC  SEQ ID NO 2183

NM_002936

 SEQ ID NO 841  Contig35030_RC  SEQ ID NO 2184

NM_002937

 SEQ ID NO 842  Contig35251_RC  SEQ ID NO 2185

NM_002950

 SEQ ID NO 843  Contig35629_RC  SEQ ID NO 2186.

NM_002961

 SEQ ID NO 844  Contig35635_RC  SEQ ID NO 2187

27

 NM_002964

 SEQ ID NO 845  Contig35763_RC  SEQ ID NO 2188

NM_002965

 SEQ ID NO 846  Contig35814_RC  SEQ ID NO 2189

NM_002966

 SEQ ID NO 847  Contig35896_RC  SEQ ID NO 2190

NM_002982

 SEQ ID NO 849  Contig35976_RC  SEQ ID NO 2191

NM_002983

 SEQ ID NO 850  Contig36042_RC  SEQ ID NO 2192

NM_002984

 SEQ ID NO 851  Contig36081_RC  SEQ ID NO 2193

NM_002985

 SEQ ID NO 852  Contig36152_RC  SEQ ID NO 2194

NM_002988

 SEQ ID NO 853  Contig36193_RC  SEQ ID NO 2195

NM_002996

 SEQ ID NO 854  Contig36312_RC  SEQ ID NO 2196

NM_002997

 SEQ ID NO 855  Contig36323_RC  SEQ ID NO 2197

NM_002999

 SEQ ID NO 856  Contig36339_RC  SEQ ID NO 2198

NMB_003012

 SEQ ID NO 857  Contig36647_RC  SEQ ID NO 2199

NM_003022

 SEQ ID NO 858  Contig36744_RC  SEQ ID NO 2200

NM_003034

 SEQ ID NO 859  Contig36761_RC  SEQ ID NO 2201

NM_003035

 SEQ ID NO 860  Contig36879_RC  SEQ ID NO 2202

NM_003039

 SEQ ID NO 861  Contig36900_RC  SEQ ID NO 2203

NM_003051

 SEQ ID NO 862  Contig37015_RC  SEQ ID NO 2204

NM_003064

 SEQ ID NO 863  Contig37024_RC  SEQ ID NO 2205

NM_003066

 SEQ ID NO 864  Contig37072_RC  SEQ ID NO 2207

NM_003088

 SEQ ID NO 865  Contig37140_RC  SEQ ID NO 2208

NM_003090

 SEQ ID NO 866  Contig37141_RC  SEQ ID NO 2209

NM_003096

 SEQ ID NO 867  Contig37204_RC  SEQ ID NO 2210

NM_003099

 SEQ ID NO 868  Contig37281_RC  SEQ ID NO 2211

NMB_003102

 SEQ ID NO 869  Contig37287_RC  SEQ ID NO 2212

NMB_003104

 SEQ ID NO 870  Contig37439_RC  SEQ ID NO 2213

NMB_003108

 SEQ ID NO 871  Contig37562_RC  SEQ ID NO 2214

NMB_003121

 SEQ ID NO 873  Contig37571_RC  SEQ ID NO 2215

NMB_003134

 SEQ ID NO 874 Contig37598  SEQ ID NO 2216

NM_003137

 SEQ ID NO 875  Contig37758_RC  SEQ ID NO 2217

NM_003144

 SEQ ID NO 876  Contig37778_RC  SEQ ID NO 2218

NM_003146

 SEQ ID NO 877  Contig37884_RC  SEQ ID NO 2219

NM_003149

 SEQ ID NO 878  Contig37946_RC  SEQ ID NO 2220

NM_003151

 SEQ ID NO 879  Contig38170_RC  SEQ ID NO 2221

NM_003157

 SEQ ID NO 880  Contig38288_RC  SEQ ID NO 2223

NM_003158

 SEQ ID NO 881  Contig38398_RC  SEQ ID NO 2224

NM_003165

 SEQ ID NO 882  Contig38580_RC  SEQ ID NO 2226

NM_003172

 SEQ ID NO 883  Contig38630_RC  SEQ ID NO 2227

NMB_003177

 SEQ ID NO 884  Contig38652_RC  SEQ ID NO 2228

NM_003197

 SEQ ID NO 885  Contig38683_RC  SEQ ID NO 2229

NM_003202

 SEQ ID NO 886  Contig38726_RC  SEQ ID NO 2230

NM_003213

 SEQ ID NO 887  Contig38791_RC  SEQ ID NO 2231

NMB_003217

 SEQ ID NO 888  Contig38901_RC  SEQ ID NO 2232

NM_003225

 SEQ ID NO 889  Contig38983_RC  SEQ ID NO 2233

NM_003226

 SEQ ID NO 890  Contig39090_RC  SEQ ID NO 2234

NM_003236

 SEQ ID NO 892  Contig39132_RC  SEQ ID NO 2235

NM_003239

 SEQ ID NO 893  Contig39157_RC  SEQ ID NO 2236

NM_003248

 SEQ ID NO 894  Contig39226_RC  SEQ ID NO 2237

28

 NM_003255

 SEQ ID NO 895  Contig39285_RC  SEQ ID NO 2238

NM_003258

 SEQ ID NO 896  Contig39556_RC  SEQ ID NO 2239

NM_003264

 SEQ ID NO 897  Contig39591_RC  SEQ ID NO 2240

NM_003283

 SEQ ID NO 898  Contig39826_RC  SEQ ID NO 2241

NM_003318

 SEQ ID NO 899  Contig39845_RC  SEQ ID NO 2242

NM_003329

 SEQ ID NO 900  Contig39891_RC  SEQ ID NO 2243

NM_003332

 SEQ ID NO 901  Contig39922_RC  SEQ ID NO 2244

NM_003358

 SEQ ID NO 902  Contig39960_RC  SEQ ID NO 2245

NM_003359

 SEQ ID NO 903  Contig40026_RC  SEQ ID NO 2246

NM_003360

 SEQ ID NO 904  Contig40121_RC  SEQ ID NO 2247

NM_003368

 SEQ ID NO 905  Contig40128_RC  SEQ ID NO 2248

NM_003376

 SEQ ID NO 906 Contig40146  SEQ ID NO 2249

NM_003380

 SEQ ID NO 907  Contig40208_RC  SEQ ID NO 2250

NM_003392

 SEQ ID NO 908  Contig40212_RC  SEQ ID NO 2251

NM_003412

 SEQ ID NO 909  Contig40238_RC  SEQ ID NO 2252

NM_003430

 SEQ ID NO 910  Contig40434_RC  SEQ ID NO 2253

NM_003462

 SEQ ID NO 911  Contig40446_RC  SEQ ID NO 2254

NM_003467

 SEQ ID NO 912  Contig40500_RC  SEQ ID NO 2255

NM_003472

 SEQ ID NO 913  Contig40573_RC  SEQ ID NO 2256

NM_003479

 SEQ ID NO 914  Contig40813_RC  SEQ ID NO 2258

NM_003489

 SEQ ID NO 915  Contig40816_RC  SEQ ID NO 2259

NM_003494

 SEQ ID NO 918  Contig40845_RC  SEQ ID NO 2261

NM_003498

 SEQ ID NO 917  Contig40889_RC  SEQ ID NO 2262

NM_003504

 SEQ ID NO 919 Contig41035  SEQ ID NO 2263

NM_003508

 SEQ ID NO 920  Contig41234_RC  SEQ ID NO 2264

NMB_003510

 SEQ ID NO 921  Contig41413_RC  SEQ ID NO 2266

NMB_003512

 SEQ ID NO 922  Contig41521_RC  SEQ ID NO 2267

NM_003528

 SEQ ID NO 923  Contig41530 RC  SEQ ID NO 2268

NM_003544

 SEQ ID NO 924 Contig41590  SEQ ID NO 2269

NM_003561

 SEQ ID NO 925  Contig41618_RC  SEQ ID NO 2270

NM_003563

 SEQ ID NO 926  Contig41624_RC  SEQ ID NO 2271

NM_003568

 SEQ ID NO 927  Contig41635_RC  SEQ ID NO 2272

NM_003579

 SEQ ID NO 928  Contig41676_RC  SEQ ID NO 2273

NM_003600

 SEQ ID NO 929  Contig41689_RC  SEQ ID NO 2274

NM_003615

 SEQ ID NO 931  Contig41804_RC  SEQ ID NO 2275

NM_003627

 SEQ ID NO 932  Contig41887_RC  SEQ ID NO 2276

NM_003645

 SEQ ID NO 935  Contig41905_RC  SEQ ID NO 2277

NM_003651

 SEQ ID NO 936  Contig41954_RC  SEQ ID NO 2278

NM_003657

 SEQ ID NO 937  Contig41983_RC  SEQ ID NO 2279

NM_003662

 SEQ ID NO 938  Contig42006_RC  SEQ ID NO 2280

NM_003670

 SEQ ID NO 939  Contig42014_RC  SEQ ID NO 2281

NM_003675

 SEQ ID NO 940  Contig42036_RC  SEQ ID NO 2282

NM_003676

 SEQ ID NO 941  Contig42041_RC  SEQ ID NO 2283

NM_003681

 SEQ ID NO 942 Contig42139  SEQ ID NO 2284

NM_003683

 SEQ ID NO 943  Contig42161_RC  SEQ ID NO 2285

NM_003686

 SEQ ID NO 944  Contig42220_RC  SEQ ID NO 2286

NM_003689

 SEQ ID NO 945  Contig42306_RC  SEQ ID NO 2287

29

 NM_003714

 SEQ ID NO 946  Contig42311_RC  SEQ ID NO 2288

NM_003720

 SEQ ID NO 947  Contig42313_RC  SEQ ID NO 2289

NM_003726

 SEQ ID NO 948  Contig42402_RC  SEQ ID NO 2290

NM_003729

 SEQ ID NO 949  Contig42421_RC  SEQ ID NO 2291

NM_003740

 SEQ ID NO 950  Contig42430_RC  SEQ ID NO 2292

NM_003772

 SEQ ID NO 952  Contig42431_RC  SEQ ID NO 2293

NM_003791

 SEQ ID NO 953  Contig42542_RC  SEQ ID NO 2294

NM_003793

 SEQ ID NO 954 Contig42582  SEQ ID NO 2295

NM_003795

 SEQ ID NO 955  Contig42631_RC  SEQ ID NO 2296

NM_003806

 SEQ ID NO 956  Contig42751_RC  SEQ ID NO 2297

NM_003821

 SEQ ID NO 957  Contig42759_RC  SEQ ID NO 2298

NM_003829

 SEQ ID NO 958 Contig43054  SEQ ID NO 2299

NM_003831

 SEQ ID NO 959  Contig43079_RC  SEQ ID NO 2300

NM_003862

 SEQ ID NO 960  Contig43195_RC  SEQ ID NO 2301

NM_003866

 SEQ ID NO 961  Contig43368_RC  SEQ ID NO 2302

NM_003875

 SEQ ID NO 962  Contig43410_RC  SEQ ID NO 2303

NM_003878

 SEQ ID NO 963  Contig43476_RC  SEQ ID NO 2304

NM_003894

 SEQ ID NO 965  Contig43549_RC  SEQ ID NO 2305

NM_003897

 SEQ ID NO 966  Contig43645_RC  SEQ ID NO 2306

NM_003904

 SEQ ID NO 967  Contig43648_RC  SEQ ID NO 2307

NM_003929

 SEQ ID NO 968  Contig43673_RC  SEQ ID NO 2308

NM_003933

 SEQ ID NO 969  Contig43679_RC  SEQ ID NO 2309

NM_003937

 SEQ ID NO 970  Contig43694_RC  SEQ ID NO 2310

NM_003940

 SEQ ID NO 971  Contig43747_RC  SEQ ID NO 2311

NM_003942

 SEQ ID NO 972  Contig43918_RC  SEQ ID NO 2312

NM_003944

 SEQ ID NO 973  Contig43983_RC  SEQ ID NO 2313

NM_003953

 SEQ ID NO 974  Contig44040_RC  SEQ ID NO 2314

NM_003954

 SEQ ID NO 975  Contig44064_RC  SEQ ID NO 2315

NM_003975

 SEQ ID NO 976  Contig44195_RC  SEQ ID NO 2316

NM_003981

 SEQ ID NO 977  Contig44226_RC  SEQ ID NO 2317

NM_003982

 SEQ ID NO 978  Contig44289_RC  SEQ ID NO 2320

NM_003986

 SEQ ID NO 979  Contig44310_RC  SEQ ID NO 2321

NM_004003

 SEQ ID NO 980 Contig44409  SEQ ID NO 2322

NM_004010

 SEQ ID NO 981  Contig44413_RC  SEQ ID NO 2323

NM_004024

 SEQ ID NO 982  Contig44451_RC  SEQ ID NO 2324

NM_004038

 SEQ ID NO 983  Contig44585_RC  SEQ ID NO 2325

NM_004049

 SEQ ID NO 984  Contig44656_RC  SEQ ID NO 2326

NM_004052

 SEQ ID NO 985  Contig44703_RC  SEQ ID NO 2327

NM_004053

 SEQ ID NO 986  Contig44708_RC  SEQ ID NO 2328

NM_004079

 SEQ ID NO 987  Contig44757_RC  SEQ ID NO 2329

NM_004104

 SEQ ID NO 988  Contig44829_RC  SEQ ID NO 2331

NM_004109

 SEQ ID NO 989 Contig44870  SEQ ID NO 2332

NM_004110

 SEQ ID NO 990  Contig44893_RC  SEQ ID NO 2333

NM_004120

 SEQ ID NO 991  Contig44909_RC  SEQ ID NO 2334

NM_004131

 SEQ ID NO 992  Contig44939_RC  SEQ ID NO 2335

NM_004143

 SEQ ID NO 993  Contig45022_RC  SEQ ID NO 2336

NM_004154

 SEQ ID NO 994  Contig45032_RC  SEQ ID NO 2337

NM_004170

 SEQ ID NO 996  Contig45041_RC  SEQ ID NO 2338

30

 NM_004172

 SEQ ID NO 997  Contig45049_RC  SEQ ID NO 2339

NM_004176

 SEQ ID NO 998  Contig45090_RC  SEQ ID NO 2340

NM_004180

 SEQ ID NO 999  Contig45156_RC  SEQ ID NO 2341

NM_004181

 SEQ ID NO 1000  Contig45316_RC  SEQ ID NO 2342

NM_004184

 SEQ ID NO 1001 Contig45321  SEQ ID NO 2343

NM_004203

 SEQ ID NO 1002  Contig45375_RC  SEQ ID NO 2345

NM_004207

 SEQ ID NO 1003  Contig45443_RC  SEQ ID NO 2346

NM_004217

 SEQ ID NO 1004  Contig45454_RC  SEQ ID NO 2347

NM_004219

 SEQ ID NO 1005  Contig45537_RC  SEQ ID NO 2348

NM_004221

 SEQ ID NO 1006  Contig45588_RC  SEQ ID NO 2349

NM_004233

 SEQ ID NO 1007  Contig45708_RC  SEQ ID NO 2350

NM_004244

 SEQ ID NO 1008  Contig45816_RC  SEQ ID NO 2351

NM_004252

 SEQ ID NO 1009  Contig45847_RC  SEQ ID NO 2352

NM_004265

 SEQ ID NO 1010  Contig45891_RC  SEQ ID NO 2353

NM_004267

 SEQ ID NO 1011  Contig46056_RC  SEQ ID NO 2354

NM_004281

 SEQ ID NO 1012  Contig46062_RC  SEQ ID NO 2355

NM_004289

 SEQ ID NO 1013  Contig46075_RC  SEQ ID NO 2356

NM_004298

 SEQ ID NO 1015  Contig46164_RC  SEQ ID NO 2357

NM_004301

 SEQ ID NO 1016  Contig46218_RC  SEQ ID NO 2358

NM_004305

 SEQ ID NO 1017  Contig46223_RC  SEQ ID NO 2359

NM_004311

 SEQ ID NO 1018  Contig46244_RC  SEQ ID NO 2360

NM_004315

 SEQ ID NO 1019  Contig46262_RC  SEQ ID NO 2361

NM_004323

 SEQ ID NO 1020  Contig46362_RC  SEQ ID NO 2364

NM_004330

 SEQ ID NO 1021  Contig46443_RC  SEQ ID NO 2365

NM_004336

 SEQ ID NO 1022  Contig46553_RC  SEQ ID NO 2367

NM_004338

 SEQ ID NO 1023  Contig46597_RC  SEQ ID NO 2368

NM_004350

 SEQ ID NO 1024  Contig46653_RC  SEQ ID NO 2369

NM_004354

 SEQ ID NO 1025  Contig46709_RC  SEQ ID NO 2370

NM_004358

 SEQ ID NO 1026  Contig46777_RC  SEQ ID NO 2371

NM_004360

 SEQ ID NO 1027  Contig46802_RC  SEQ ID NO 2372

NM_004362

 SEQ ID NO 1028  Contig46890_RC  SEQ ID NO 2374

NM_004374

 SEQ ID NO 1029  Contig46922_RC  SEQ ID NO 2375

NM_004378

 SEQ ID NO 1030  Contig46934_RC  SEQ ID NO 2376

NM_004392

 SEQ ID NO 1031  Contig46937_RC  SEQ ID NO 2377

NM_004395

 SEQ ID NO 1032  Contig46991_RC  SEQ ID NO 2378

NM_004414

 SEQ ID NO 1033  Contig47016_RC  SEQ ID NO 2379

NM_004418

 SEQ ID NO 1034  Contig47045_RC  SEQ ID NO 2380

NM_004425

 SEQ ID NO 1035  Contig47106_RC  SEQ ID NO 2381

NM_004431

 SEQ ID NO 1036  Contig47146_RC  SEQ ID NO 2382

NM_004436

 SEQ ID NO 1037  Contig47230_RC  SEQ ID NO 2383

NM_004438

 SEQ ID NO 1038  Contig47405_RC  SEQ ID NO 2384

NM_004443

 SEQ ID NO 1039  Contig47456_RC  SEQ ID NO 2385

NM_004446

 SEQ ID NO 1040  Contig47465_RC  SEQ ID NO 2386

NM_004451

 SEQ ID NO 1041  Contig47498_RC  SEQ ID NO 2387

NM_004454

 SEQ ID NO 1042  Contig47578_RC  SEQ ID NO 2388

NM_004456

 SEQ ID NO 1043  Contig47645_RC  SEQ ID NO 2389

NM_004458

 SEQ ID NO 1044  Contig47680_RC  SEQ ID NO 2390

31

 NM_004472

 SEQ ID NO 1045  Contig47781_RC  SEQ ID NO 2391

NM_004480

 SEQ ID NO 1046  Contig47814_RC  SEQ ID NO 2392

NM_004482

 SEQ ID NO 1047  Contig48004_RC  SEQ ID NO 2393

NM_004494

 SEQ ID NO 1048  Contig48043_RC  SEQ ID NO 2394

NM_004496

 SEQ ID NO 1049  Contig48057_RC  SEQ ID NO 2395

NM_004503

 SEQ ID NO 1050  Contig48076_RC  SEQ ID NO 2396

NM_004504

 SEQ ID NO 1051  Contig48249_RC  SEQ ID NO 2397

NM_004515

 SEQ ID NO 1052  Contig48263_RC  SEQ ID NO 2398

NM_004522

 SEQ ID NO 1053  Contig48270_RC  SEQ ID NO 2399

NM_004523

 SEQ ID NO 1054  Contig48328_RC  SEQ ID NO 2400

NM_004525

 SEQ ID NO 1055  Contig48518_RC  SEQ ID NO 2401

NM_004556

 SEQ ID NO 1056  Contig48572_RC  SEQ ID NO 2402

NM_004559

 SEQ ID NO 1057  Contig48659_RC  SEQ ID NO 2403

NM_004569

 SEQ ID NO 1058  Contig48722_RC  SEQ ID NO 2404

NM_004577

 SEQ ID NO 1059  Contig48774_RC  SEQ ID NO 2405

NM_004585

 SEQ ID NO 1060  Contig48776_RC  SEQ ID NO 2406

NM_004587

 SEQ ID NO 1061  Contig48800_RC  SEQ ID NO 2407

NM_004594

 SEQ ID NO 1062  Contig48806_RC  SEQ ID NO 2408

NM_004599

 SEQ ID NO 1063  Contig48852_RC  SEQ ID NO 2409

NM_004633

 SEQ ID NO 1066  Contig48900_RC  SEQ ID NO 2410

NM_004642

 SEQ ID NO 1067  Contig48913_RC  SEQ ID NO 2411

NM_004648

 SEQ ID NO 1068  Contig48970_RC  SEQ ID NO 2413

NM_004663

 SEQ ID NO 1069  Contig49058_RC  SEQ ID NO 2414

NM_004664

 SEQ ID NO 1070  Contig49063_RC  SEQ ID NO 2415

NM_004684

 SEQ ID NO 1071 Contig49093  SEQ ID NO 2416

NM_004688

 SEQ ID NO 1072  Contig49098_RC  SEQ ID NO 2417

NM_004694

 SEQ ID NO 1073  Contig49169_RC  SEQ ID NO 2418

NM_004695

 SEQ ID NO 1074  Contig49233_RC  SEQ ID NO 2419

NM_004701

 SEQ ID NO 1075  Contig49270_RC  SEQ ID NO 2420

NM_004708

 SEQ ID NO 1077  Contig49282_RC  SEQ ID NO 2421

NM_004711

 SEQ ID NO 1078  Contig49289_RC  SEQ ID NO 2422

NM_004726

 SEQ ID NO 1079  Contig49342_RC  SEQ ID NO 2423

NM_004750

 SEQ ID NO 1081 Contig49344  SEQ ID NO 2424

NM_004761

 SEQ ID NO 1082  Contig49388_RC  SEQ ID NO 2425

NM_004762

 SEQ ID NO 1083  Contig49405_RC  SEQ ID NO 2426

NM_004780

 SEQ ID NO 1085  Contig49445_RC  SEQ ID NO 2427

NM_004791

 SEQ ID NO 1086  Contig49468_RC  SEQ ID NO 2428

NM_004798

 SEQ ID NO 1087  Contig49509_RC  SEQ ID NO 2429

NM_004808

 SEQ ID NO 1088  Contig49578_RC  SEQ ID NO 2431

NM_004811

 SEQ ID NO 1089  Contig49581_RC  SEQ ID NO 2432

NM_004833

 SEQ ID NO 1090  Contig49631_RC  SEQ ID NO 2433

NM_004835

 SEQ ID NO 1091  Contig49673_RC  SEQ ID NO 2435

NM_004843

 SEQ ID NO 1092  Contig49743_RC  SEQ ID NO 2436

NM_004847

 SEQ ID NO 1093  Contig49790_RC  SEQ ID NO 2437

NM_004848

 SEQ ID NO 1094  Contig49818_RC  SEQ ID NO 2438

NM_004864

 SEQ ID NO 1095  Contig49849_RC  SEQ ID NO 2439

NM_004865

 SEQ ID NO 1096 Contig49855  SEQ ID NO 2440

32

 NM_004866

 SEQ ID NO 1097  Contig49910_RC  SEQ ID NO 2441

NM_004877

 SEQ ID NO 1098  Contig49948_RC  SEQ ID NO 2442

NM_004900

 SEQ ID NO 1099  Contig50004_RC  SEQ ID NO 2443

NM_004906

 SEQ ID NO 1100 Contig50094  SEQ ID NO 2444

NM_004910

 SEQ ID NO 1101  Contig50120_RC  SEQ ID NO 2446

NM_004918

 SEQ ID NO 1103  Contig50153_RC  SEQ ID NO 2447

NM_004923

 SEQ ID NO 1104  Contig50189_RC  SEQ ID NO 2448

NM_004938

 SEQ ID NO 1105  Contig50276 RC  SEQ ID NO 2449

NM_004951

 SEQ ID NO 1106  Contig50288_RC  SEQ ID NO 2450

NM_004968

 SEQ ID NO 1107  Contig50297_RC  SEQ ID NO 2451

NM_004994

 SEQ ID NO 1108  Contig50391_RC  SEQ ID NO 2452

NM_004999

 SEQ ID NO 1109 Contig50410  SEQ ID NO 2453

NM_005001

 SEQ ID NO 1110  Contig50523_RC  SEQ ID NO 2454

NM_005002

 SEQ ID NO 1111 Contig50529  SEQ ID NO 2455

NM_005012

 SEQ ID NO 1112  Contig50588_RC  SEQ ID NO 2456

NM_005032

 SEQ ID NO 1113 Contig50592  SEQ ID NO 2457

NM_005044

 SEQ ID NO 1114  Contig50669_RC  SEQ ID NO 2458

NM_005046

 SEQ ID NO 1115  Contig50719_RC  SEQ ID NO 2460

NM_005049

 SEQ ID NO 1116  Contig50728_RC  SEQ ID NO 2461

NM_005067

 SEQ ID NO 1117  Contig50731_RC  SEQ ID NO 2462

NM_005077

 SEQ ID NO 1118  Contig50802_RC  SEQ ID NO 2463

NM_005080

 SEQ ID NO 1119  Contig50822_RC  SEQ ID NO 2464

NM_005084

 SEQ ID NO 1120  Contig50850_RC  SEQ ID NO 2466

NM_005130

 SEQ ID NO 1122  Contig50860_RC  SEQ ID NO 2467

NM_005139

 SEQ ID NO 1123  Contig50913_RC  SEQ ID NO 2468

NM_005168

 SEQ ID NO 1125  Contig50950_RC  SEQ ID NO 2469

NM_005190

 SEQ ID NO 1126  Contig51066_RC  SEQ ID NO 2470

NM_005196

 SEQ ID NO 1127  Contig51105_RC  SEQ ID NO 2472

NM_005213

 SEQ ID NO 1128  Contig51117_RC  SEQ ID NO 2473

NM_005218

 SEQ ID NO 1129  Contig51196_RC  SEQ ID NO 2474

NM_005235

 SEQ ID NO 1130  Contig51235_RC  SEQ ID NO 2475

NM_005245

 SEQ ID NO 1131  Contig51254_RC  SEQ ID NO 2476

NM_005249

 SEQ ID NO 1132  Contig51352_RC  SEQ ID NO 2477

NM_005257

 SEQ ID NO 1133  Contig51369_RC  SEQ ID NO 2478

NM_005264

 SEQ ID NO 1134  Contig51392_RC  SEQ ID NO 2479

NM_005271

 SEQ ID NO 1135  Contig51403_RC  SEQ ID NO 2480

NM_005314

 SEQ ID NO 1136  Contig51685_RC  SEQ ID NO 2483

NM_005321

 SEQ ID NO 1137  Contig51726_RC  SEQ ID NO 2484

NM_005322

 SEQ ID NO 1138  Contig51742_RC  SEQ ID NO 2485

NM_005325

 SEQ ID NO 1139  Contig51749_RC  SEQ ID NO 2486

NM_005326

 SEQ ID NO 1140  Contig51775_RC  SEQ ID NO 2487

NM_005335

 SEQ ID NO 1141 Contig51800  SEQ ID NO 2488

NM_005337

 SEQ ID NO 1142  Contig51809_RC  SEQ ID NO 2489

NM_005342

 SEQ ID NO 1143  Contig51821_RC  SEQ ID NO 2490

NM_005345

 SEQ ID NO 1144  Contig51888_RC  SEQ ID NO 2491

NM_005357

 SEQ ID NO 1145  Contig51953_RC  SEQ ID NO 2493

NM_005375

 SEQ ID NO 1146  Contig51967_RC  SEQ ID NO 2495

33

 NM_005391

 SEQ ID NO 1147  Contig51981_RC  SEQ ID NO 2496

NM_005408

 SEQ ID NO 1148  Contig51994_RC  SEQ ID NO 2497

NM_005409

 SEQ ID NO 1149  Contig52082_RC  SEQ ID NO 2498

NM_005410

 SEQ ID NO 1150  Contig52094_RC  SEQ ID NO 2499

NM_005426

 SEQ ID NO 1151 Contig52320  SEQ ID NO 2500

NM_005433

 SEQ ID NO 1152  Contig52398_RC  SEQ ID NO 2501

NM_005441

 SEQ ID NO 1153  Contig52425_RC  SEQ ID NO 2503

NM_005443

 SEQ ID NO 1154  Contig52482_RC  SEQ ID NO 2504

NM_005483

 SEQ ID NO 1155  Contig52543_RC  SEQ ID NO 2505

NM_005486

 SEQ ID NO 1156  Contig52553_RC  SEQ ID NO 2506

NM_005496

 SEQ ID NO 1157  Contig52579_RC  SEQ ID NO 2507

NM_005498

 SEQ ID NO 1158  Contig52603_RC  SEQ ID NO 2508

NM_005499

 SEQ ID NO 1159  Contig52639_RC  SEQ ID NO 2509

NM_005514

 SEQ ID NO 1160  Contig52641_RC  SEQ ID NO 2510

NM_005531

 SEQ ID NO 1162 Contig52684  SEQ ID NO 2511

NM_005538

 SEQ ID NO 1163  Contig52705_RC  SEQ ID NO 2512

NM_005541

 SEQ ID NO 1164  Contig52720_RC  SEQ ID NO 2513

NM_005544

 SEQ ID NO 1165  Contig52722_RC  SEQ ID NO 2514

NM_005548

 SEQ ID NO 1166  Contig52723_RC  SEQ ID NO 2515

NM_005554

 SEQ ID NO 1167  Contig52740_RC  SEQ ID NO 2516

NM_005555

 SEQ ID NO 1168  Contig52779_RC  SEQ ID NO 2517

NM_005556

 SEQ ID NO 1169  Contig52957_RC  SEQ ID NO 2518

NM_005557

 SEQ ID NO 1170  Contig52994_RC  SEQ ID NO 2519

NM_005558

 SEQ ID NO 1171  Contig53022_RC  SEQ ID NO 2520

NM_005562

 SEQ ID NO 1172  Contig53038_RC  SEQ ID NO 2521

NM_005563

 SEQ ID NO 1173  Contig53047_RC  SEQ ID NO 2522

NM_005565

 SEQ ID NO 1174 Contig53130  SEQ ID NO 2523

NM_005566

 SEQ ID NO 1175  Contig53183_RC  SEQ ID NO 2524

NM_005572

 SEQ ID NO 1176  Contig53242_RC  SEQ ID NO 2526

NM_005582

 SEQ ID NO 1177  Contig53248_RC  SEQ ID NO 2527

NM_005608

 SEQ ID NO 1178  Contig53260_RC  SEQ ID NO 2528

NM_005614

 SEQ ID NO 1179  Contig53296_RC  SEQ ID NO 2531

NM_005617

 SEQ ID NO 1180  Contig53307_RC  SEQ ID NO 2532

NM_005620

 SEQ ID NO 1181  Contig53314_RC  SEQ ID NO 2533

NM_005625

 SEQ ID NO 1182  Contig53401_RC  SEQ ID NO 2534

NM_005651

 SEQ ID NO 1183  Contig53550_RC  SEQ ID NO 2535

NM_005658

 SEQ ID NO 1184  Contig53551_RC  SEQ ID NO 2536

NM_005659

 SEQ ID NO 1185  Contig53598_RC  SEQ ID NO 2537

NM_005667

 SEQ ID NO 1186  Contig53646_RC  SEQ ID NO 2538

NM_005686

 SEQ ID NO 1187  Contig53658_RC  SEQ ID NO 2539

NM_005690

 SEQ ID NO 1188  Contig53698_RC  SEQ ID NO 2540

NM_005720

 SEQ ID NO 1190  Contig53719_RC  SEQ ID NO 2541

NM_005727

 SEQ ID NO 1191  Contig53742_RC  SEQ ID NO 2542

NM_005733

 SEQ ID NO 1192  Contig53757_RC  SEQ ID NO 2543

NM_005737

 SEQ ID NO 1193  Contig53870_RC  SEQ ID NO 2544

NM_005742

 SEQ ID NO 1194  Contig53952_RC  SEQ ID NO 2546

NM_005746

 SEQ ID NO 1195  Contig53962_RC  SEQ ID NO 2547

3. 4

 NM_005749

 SEQ ID NO 1196  Contig53968_RC  SEQ ID NO 2548

NM_005760

 SEQ ID NO 1197  Contig54113_RC  SEQ ID NO 2549

NM_005764

 SEQ ID NO 1198  Contig54142_RC  SEQ ID NO 2550

NM_005794

 SEQ ID NO 1199  Contig54232_RC  SEQ ID NO 2551

NM_005796

 SEQ ID NO 1200  Contig54242_RC  SEQ ID NO 2552

NM_005804

 SEQ ID NO 1201  Contig54260_RC  SEQ ID NO 2553

NM_005813

 SEQ ID NO 1202  Contig54263_RC  SEQ ID NO 2554

NM_005824

 SEQ ID NO 1203  Contig54295_RC  SEQ ID NO 2555

NM_005825

 SEQ ID NO 1204  Contig54318_RC  SEQ ID NO 2556

NM_005849

 SEQ ID NO 1205  Contig54325_RC  SEQ ID NO 2557

NM_005853

 SEQ ID NO 1206  Contig54389_RC  SEQ ID NO 2558

NM_005855

 SEQ ID NO 1207  Contig54394_RC  SEQ ID NO 2559

NM_005864

 SEQ ID NO 1208  Contig54414_RC  SEQ ID NO 2560

NM_005874

 SEQ ID NO 1209 Contig54425  SEQ ID NO 2561

NM_005876

 SEQ ID NO 1210  Contig54477_RC  SEQ ID NO 2562

NM_005880

 SEQ ID NO 1211  Contig54503_RC  SEQ ID NO 2563

NM_005891

 SEQ ID NO 1212  Contig54534_RC  SEQ ID NO 2564

NM_005892

 SEQ ID NO 1213  Contig54560_RC  SEQ ID NO 2566

NM_005899

 SEQ ID NO 1214  Contig54581_RC  SEQ ID NO 2567

NM_005915

 SEQ ID NO 1215  Contig54609_RC  SEQ ID NO 2568

NM_005919

 SEQ ID NO 1216  Contig54666_RC  SEQ ID NO 2569

NM_005923

 SEQ ID NO 1217  Contig54667_RC  SEQ ID NO 2570

NM_005928

 SEQ ID NO 1218  Contig54726_RC  SEQ ID NO 2571

NM_005932

 SEQ ID NO 1219  Contig54742_RC  SEQ ID NO 2572

NM_005935

 SEQ ID NO 1220  Contig54745_RC  SEQ ID NO 2573

NM_005945

 SEQ ID NO 1221  Contig54757_RC  SEQ ID NO 2574

NM_005953

 SEQ ID NO 1222  Contig54761_RC  SEQ ID NO 2575

NM_005978

 SEQ ID NO 1223  Contig54813_RC  SEQ ID NO 2576

NM_005990

 SEQ ID NO 1224  Contig54867_RC  SEQ ID NO 2577

NM_006002

 SEQ ID NO 1225  Contig54895_RC  SEQ ID NO 2578

NM_006004

 SEQ ID NO 1226  Contig54898_RC  SEQ ID NO 2579

NM_006005

 SEQ ID NO 1227  Contig54913_RC  SEQ ID NO 2580

NM_006006

 SEQ ID NO 1228  Contig54965_RC  SEQ ID NO 2582

NM_006017

 SEQ ID NO 1229  Contig54968_RC  SEQ ID NO 2583

NM_006018

 SEQ ID NO 1230  Contig55069_RC  SEQ ID NO 2584

NM_006023

 SEQ ID NO 1231  Contig55181_RC  SEQ ID NO 2585

NM_006027

 SEQ ID NO 1232  Contig55188_RC  SEQ ID NO 2586

NM_006029

 SEQ ID NO 1233  Contig55221_RC  SEQ ID NO 2587

NM_006033

 SEQ ID NO 1234  Contig55254_RC  SEQ ID NO 2588

NM_006051

 SEQ ID NO 1235  Contig55265_RC  SEQ ID NO 2589

NM_006055

 SEQ ID NO 1236  Contig55377_RC  SEQ ID NO 2591

NM_006074

 SEQ ID NO 1237  Contig55397_RC  SEQ ID NO 2592

NM_006086

 SEQ ID NO 1238  Contig55448_RC  SEQ ID NO 2593

NM_006087

 SEQ ID NO 1239  Contig55468_RC  SEQ ID NO 2594

NM_006096

 SEQ ID NO 1240  Contig55500_RC  SEQ ID NO 2595

NM_006101

 SEQ ID NO 1241  Contig55538_RC  SEQ ID NO 2596

NM_006103

 SEQ ID NO 1242  Contig55558_RC  SEQ ID NO 2597

35

 NM_006111

 SEQ ID NO 1243  Contig55606_RC  SEQ ID NO 2598

NM_006113

 SEQ ID NO 1244  Contig55674_RC  SEQ ID NO 2599

NM_006115

 SEQ ID NO 1245  Contig55725_RC  SEQ ID NO 2600

NM_006117

 SEQ ID NO 1246  Contig55728_RC  SEQ ID NO 2601

NM_006142

 SEQ ID NO 1247  Contig55756_RC  SEQ ID NO 2602

NM_006144

 SEQ ID NO 1248  Contig55769_RC  SEQ ID NO 2603

NM_006148

 SEQ ID NO 1249  Contig55771_RC  SEQ ID NO 2605

NM_006153

 SEQ ID NO 1250  Contig55813_RC  SEQ ID NO 2607

NM_006159

 SEQ ID NO 1251  Contig55829_RC  SEQ ID NO 2608

NM_006170

 SEQ ID NO 1252  Contig55852_RC  SEQ ID NO 2609

NM_006197

 SEQ ID NO 1253  Contig55883_RC  SEQ ID NO 2610

NM_006224

 SEQ ID NO 1255  Contig55920_RC  SEQ ID NO 2611

NM_006227

 SEQ ID NO 1256  Contig55940_RC  SEQ iD NO 2612

NM_006235

 SEQ ID NO 1257  Contig55950_RC  SEQ ID NO 2613

NM_006243

 SEQ ID NO 1258  Contig55991_RC  SEQ ID NO 2614

NM_006264

 SEQ ID NO 1259  Contig55997_RC  SEQ ID NO 2615

NM_006271

 SEQ ID NO 1261  Contig56023_RC  SEQ ID NO 2616

NM_006274

 SEQ ID NO 1262  Contig56030_RC  SEQ ID NO 2617

NM_006290

 SEQ ID NO 1265  Contig56093_RC  SEQ ID NO 2618

NM_006291

 SEQ ID NO 1266  Contig56205_RC  SEQ ID NO 2621

NM_006296

 SEQ ID NO 1267  Contig56270_RC  SEQ ID NO 2622

NM_006304

 SEQ ID NO 1268  Contig56276_RC  SEQ ID NO 2623

NM_006314

 SEQ ID NO 1269  Contig56291-RC  SEQ ID NO 2624

NM_006332

 SEQ ID NO 1270  Contig56298_RC  SEQ ID NO 2625

NM_006357

 SEQ ID NO 1271 Contig56307  SEQ ID NO 2627

NM_006366

 SEQ ID NO 1272  Contig56390_RC  SEQ ID NO 2628

NM_006372

 SEQ ID NO 1273  Contig56434_RC  SEQ ID NO 2629

NM_006377

 SEQ ID NO 1274  Contig56457 RC  SEQ ID NO 2630

NM_006378

 SEQ ID NO 1275  Contig56534_RC  SEQ ID NO 2631

NM_006383

 SEQ ID NO 1276  Contig56670_RC  SEQ ID NO 2632

NM_006389

 SEQ ID NO 1277  Contig56678_RC  SEQ ID NO 2633

NM_006393

 SEQ ID NO 1278  Contig56742_RC  SEQ ID NO 2634

NM_006398

 SEQ ID NO 1279  Contig56759_RC  SEQ ID NO 2635

NM_006406

 SEQ ID NO 1280  Contig56765_RC  SEQ ID NO 2636

NM_006408

 SEQ ID NO 1281  Contig56843_RC  SEQ ID NO 2637

NM_006410

 SEQ ID NO 1282  Contig57011_RC  SEQ ID NO 2638

NM_006414

 SEQ ID NO 1283  Contig57023_RC  SEQ ID NO 2639

NM_006417

 SEQ ID NO 1284  Contig57057_RC  SEQ ID NO 2640

NM_006430

 SEQ ID NO 1285  Contig57076_RC  SEQ ID NO 2641

NM_006460

 SEQ ID NO 1286  Contig57081_RC  SEQ ID NO 2642

NM_006461

 SEQ ID NO 1287  Contig57091_RC  SEQ ID NO 2643

NM_006469

 SEQ ID NO 1288  Contig57138_RC  SEQ ID NO 2644

NM_006470

 SEQ ID NO 1289  Contig57173_RC  SEQ ID NO 2645

NM_006491

 SEQ ID NO 1290  Contig57230_RC  SEQ ID NO 2646

NM_006495

 SEQ ID NO 1291  Contig57258_RC  SEQ ID NO 2647

NM_006500

 SEQ ID NO 1292  Contig57270_RC  SEQ ID NO 2648

NM_006509

 SEQ ID NO 1293  Contig57272_RC  SEQ ID NO 2649

36

 NMB_006516

 SEQ ID NO 1294  Contig57344_RC  SEQ ID NO 2650

NM_006533

 SEQ ID NO 1295  Contig57430_RC  SEQ ID NO 2651

NM_006551

 SEQ ID NO 1296  Contig57458_RC  SEQ ID NO 2652

NM_006556

 SEQ ID NO 1297  Contig57493_RC  SEQ ID NO 2653

NM_006558

 SEQ ID NO 1298  Contig57584_RC  SEQ ID NO 2654

NM_006564

 SEQ ID NO 1299 Contig57595  SEQ ID NO 2655

NM_006573

 SEQ ID NO 1300  Contig57602_RC  SEQ ID NO 2656

NM_006607

 SEQ ID NO 1301  Contig57609_RC  SEQ ID NO 2657

NM_006622

 SEQ ID NO 1302  Contig57610 RC  SEQ ID NO 2658

NM_006623

 SEQ ID NO 1303  Contig57644_RC  SEQ ID NO 2659

NM_006636

 SEQ ID NO 1304  Contig57725_RC  SEQ ID NO 2660

NM_006670

 SEQ ID NO 1305  Contig57739_RC  SEQ ID NO 2661

NM_006681

 SEQ ID NO 1306  Contig57825_RC  SEQ ID NO 2662

NM_006682

 SEQ ID NO 1307  Contig57864_RC  SEQ ID NO 2663

NM_006696

 SEQ ID NO 1308  Contig57940_RC  SEQ ID NO 2664

NM_006698

 SEQ ID NO 1309  Contig58260_RC  SEQ ID NO 2665

NM_006705

 SEQ ID NO 1310  Contig58272_RC  SEQ ID NO 2666

NM_006739

 SEQ ID NO 1311  Contig58301_RC  SEQ ID NO 2667

NM_006748

 SEQ ID NO 1312  Contig58368_RC  SEQ ID NO 2668

NM_006759

 SEQ ID NO 1313  Contig58471_RC  SEQ ID NO 2669

NM_006762

 SEQ ID NO 1314  Contig58755_RC  SEQ ID NO 2671

NM_006763

 SEQ ID NO 1315  Contig59120-RC  SEQ ID NO 2672

NM_006769

 SEQ ID NO 1316  Contig60157 RC  SEQ ID NO 2673

NM_006770

 SEQ ID NO 1317  Contig60864_RC  SEQ ID NO 2676

NM_006780

 SEQ ID NO 1318  Contig61254_RC  SEQ ID NO 2677

NM_006787

 SEQ ID NO 1319 Contig61815  SEQ ID NO 2678

NM_006806

 SEQ ID NO 1320 Contig61975  SEQ ID NO 2679

NMB_006813

 SEQ ID NO 1321 Contig62306  SEQ ID NO 2680

NM_006825

 SEQ ID NO 1322  Contig62568_RC  SEQ ID NO 2681

NM_006826

 SEQ ID NO 1323  Contig62922_RC  SEQ ID NO 2682

NM_006829

 SEQ ID NO 1324  Contig62964_RC  SEQ ID NO 2683

NM_006834

 SEQ ID NO 1325  Contig63520_RC  SEQ ID NO 2685

NM_006835

 SEQ ID NO 1326  Contig63649_RC  SEQ ID NO 2686

NM_006840

 SEQ ID NO 1327  Contig63683_RC  SEQ ID NO 2687

NM_006845

 SEQ ID NO 1328  Contig63748_RC  SEQ ID NO 2688

NM_006847

 SEQ ID NO 1329 Contig64502  SEQ ID NO 2689

NM_006851

 SEQ ID NO 1330 Contig64688  SEQ ID NO 2690

NM_006855

 SEQ ID NO 1331  Contig64775_RC  SEQ ID NO 2691

NM_006864

 SEQ ID NO 1332 Contig65227  SEQ ID NO 2692

NM_006868

 SEQ ID NO 1333 Contig65663  SEQ ID NO 2693

NM_006875

 SEQ ID NO 1334  Contig65785_RC  SEQ ID NO 2694

NM_006889

 SEQ ID NO 1336 Contig65900  SEQ ID NO 2695

NM_006892

 SEQ ID NO 1337  Contig66219 RC  SEQ ID NO 2696

NMB_006912

 SEQ ID NO 1338  Contig66705_RC  SEQ ID NO 2697

NM_006931

 SEQ ID NO 1341  Contig66759_RC  SEQ ID NO 2698

NM_006941

 SEQ ID NO 1342  Contig67182_RC  SEQ ID NO 2699

NM_006943

 SEQ ID NO 1343

37

Table 2. 550 preferred ER category markers taken from Table 1

Identifier

 correlation  Name Description

NM_002051

0.763977 CAT3  Link protein 3 with GATA

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  1 binding protein with nuclease sensitive element

Contig50153_RC

 -0.696652  ESTs, slightly similar to the proteoglycan binding protein precursor [H.sapiens]

NMB_012155

0.694332 EMAP-2  Microtubule associated protein such as EMAP echinoderm

 Contig237_RC

0.687485 FLJ21127  Hypothetical protein FLJ21127

NMB_01 9063

 -0.686064  C2ORF2  Chromosome 2 open reading frame 2

NMB_012219

 -0.680900 MRAS  Muscle RAS oncogene homolog

NM_001982

0.676114 ERBB3  Viral erythroblastic leukemia viral homolog 3 3 homologue v-erb-b 2

NM_006623

 -0.675090  PHGDH  Phosphoglycerate dehydrogenase

NMB_000636

 -0.674282 SOD2  Superoxide dismutase 2, mitochondrial

NMB_006017

 -0.670353 PROML1  Prominin 1 similar to (mouse)

Contig57940_RC

 0.667915 MAP-1  MAP-1 protein

Contig46934_RC

 0.666908  ESTs, slightly similar to gradient 2 Previous JE0350 [H.sapiens]

NM_005080

0.665772 XBP1  Link protein 1 with X-box

NM_014246

0.665725 CELSR1  Cadherin, seven-pass G-type receptor 1, EGF LAG, flamingo counterpart (Drosophila)

Contig54667_RC

 -0.663727 Human DNA sequence of clone RP1-187J11 on chromosome 6q11.1-22.33. It contains the gene for a new protein similar to the expected proteins S. pombe and S. cerevisiae, the gene for a new protein similar to protein kinase C inhibitors, the 3 'end of the gene for a new protein similar to Drosophila L82 and expected worm proteins, ESTs, STSs, GSSs and two putative CpG islands

Contig51994_RC

 0.663715  ESTs, slightly similar to B0416.1 [C.elegans]

NM_016337

0.663006 RNB6 RNB6

NMB_015640

 -0.660165 PAI-RBP1  PAI-1 mRNA binding protein

X07834

 -0.657798 SOD2  Superoxide dismutase 2, mitochondrial

NMB_012319

0.657666 LIV-1  Estrogen-regulated LIV-1 protein

Contig41887_RC

 0.656042  ESTs, slightly similar to the rat zymogen corpuscle membrane protein homolog [H.sapiens]

NM_003462

0.655349 P28  dynein, axonemal, light intermediate polypeptide

38

Contig58301_RC

 0.654268  MRNA from Homo sapiens; DKFZp667D095 cDNA (from clone DKFZp667D095)

 NM_005375

0.653783  MY B  Viral myeloblastosis avian myeloblastosis homolog v-myb

NMB_017447

 -0.652445 YG81  Hypothetical protein LOC54149

 Contig924_RC

 -0.650658 ESTs

M55914

 -0.650181 MPB1  Protein 1 binding with the MYC promoter

NM_006004

 -0.649819  UQCRH  Protein hinge with ubiquinol-cytochrome c reductase

NM_000964

0.649072 WEIRD  Retinoic acid receptor, alpha

NM_013301

0.647583 HSU79303  Protein provided by clone 23882

AB023211

 -0.647403 POI2 Peptidyl arginine deiminase, type II

NM_016629

 -0.646412  LOC51323  Hypothetical protein

K02403

0.645532 C4A  4A complement component

NM_016405

 -0.642201 HSU93243  Ubc6p counterpart

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  MRNA from Homo sapiens; DKFZp586I2022 cDNA (from clone DKFZp586I2022)

 AL133105

 -0.635201  DKFZp434F 2322  Hypothetical protein DKFZp434F2322

NMB_016839

 -0.635169 RBMS1  Reason for binding with RNA, single-chain interacting protein 1

Contig53130

 -0.634812  ESTs, slightly similar to the hHCN2 hyperpolarization-activated cyclic nucleotide controlled channel [H.sapiens]

NM_018014

 -0.634460 BCL11A  CLUlinfoma 11A B cell (zinc finger protein)

NM_006769

 -0.632197 LMO4 LIM domain only 4

U92544

0.631170 JCL-1  Protein associated with hepatocellular carcinoma; gene 1 associated with breast cancer

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  RNA from Homo sapiens; DKFZp564F053 cDNA (from clone DKFZp564F053)

 NM_018728

0.627989  MYO5C Myosin 5C

NM_004780

0.627856 TCEAL1  Type 1 transcription elongation factor A (SII)

 Contig760_RC

0.627132 ESTs

 Contig399_RC

0.626543 FLJ12538  Hypothetical protein FLJ12538 similar to ras-related protein RAB17

M83822

0.625092 CDC4L  Type 4 cell division cycle

NM_001255

 -0.625089 CDC20 CDC20 (cell division cycle 20, S. cerevisiae, homologue)

 NM_006739

 -0.624903 MCM5  Poor maintenance of minichromosomes (S. cerevisiae) 5 (cell division cycle 46)

NM_002888

 -0.624664 RARRES1  Respondent 1 to retinoic acid receptor (induced by tazarotene)

39

 NM_003197

0.623850 TCEB1L  Transcription elongation factor B (SIII), related to polypeptide 1

NM_006787

0.623625 JCL-1  Protein associated with hepatocellular carcinoma; gene 1 associated with breast cancer

Contig49342_RC

 0.622179 ESTs

AL133619

0.621719  Homo sapiens mRNA: DKFZp434E2321 cDNA (from clone DKFZp434E2321); partial cds

AL133622

0.621577 KIAA0876  KIAA0876 protein

NM_004648

 -0.621532 PTPNS1  Tyrosine protein phosphatase, substrate 1 of non-receptor type

NM_001793

 -0.621530 CDH3  Cadherin 3, type 1, P-caderina (placental)

NM_003217

0.620915 TEGT  Enhanced transcription of testis genes (BAX inhibitor 1)

NM_001551

0.620832 IGBP1  Protein 1 associated with immunoglobulin (CD79A)

 NM_002539

 -0.620683 ODC1  Ornithine Decarboxylase 1

 Contig55997_RC

 -0.619932 ESTs

NM_000633

0.619547 BCL2  CLL / B cell lymphoma 2

NMB_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  Condroadherina

 Contig39090_RC

 0.616385 ESTs

AF055270

 -0.616268 HSSG1  Protein 1 suppressed from heat shock

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 Receiver 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  CDNA from Homo sapiens, fis FLJ13945, clone Y79AA1000969

AB032966

 -0.611917 KIAA1140  KIAA1140 protein

AL080192

 0.611544  CDNA from Homo sapiens; FLJ21238 fis, clone COL01115

 X56807

 -0.610654 DSC2 Desmocolin 2

 Contig30390_RC

 0.609614 ESTs

AL137362

0.609121 FLJ22237  Hypothetical protein FLJ22237

NM_014211

 -0.608585 GABRP  Gamma-aminobutyric acid (GABA) receptor A, pi

NM_006696

0.608474 SMAP  Thyroid hormone receptor coactivating protein

Contig45588_RC

 -0.608273  Homo sapiens DNA; FLJ22610 fis, clone HSI04930

NM_003358

0.608244  UGCG  UDP-glucose ceramide glucosyltransferase

NMB_006153

 -0.608129 NCK1  NCK adapter protein 1

40

 NMB_001453

 -0.606939  FOXC1  Forkhead box C1

Contig54666_RC

 0.606475  oy65e02.x1 NCl_CGAP_CLL1 cDNA from Homo sapiens clone IMAGE: 1670714 3 ’similar to TR: Q29168 UNKNOWN PROTEIN Q29168; mRNA sequence

NM_005945

 -0.605945 MPB1  MYC promoter binding protein 1

Contig55725_RC

 -0.605841  ESTs, slightly similar to T50635 hypothetical protein DKFZp762L0311.1 [H.sapiens]

 Contig37015_RC

 -0.605780  ESTs, slightly similar to UAS3_PROTEÍNA HUMANA UBASH3A PROTEIN [H.sapiens]

AL157480

 -0.604362 SH3BP1  Link protein 1 with SH3 domain

NM_005325

 -0.604310 H1F1  Histone H1 family, member 1

NM_001446

 -0.604061 FABP7  Protein 7 binding with fatty acid, brain

 Contig263_RC

0.603318  CDNA from Homo sapiens cDNA: fis FLJ23000, clone LNG00194

 Contig8347_RC

 -0.603311 ESTs

NM_002988

-0.603279 SCYA18  Small subfamily A induced by cytokine (Cys-Cys), member 18, pulmonary and regulated by activation

AF111849

0.603157 HELO1  Enzyme 2 elongation of long chain polyunsaturated fatty acid yeast counterpart

 NM_014700

0.603042 KIAA0665  Product of the KIAA0665 gene

NM_001814

 -0.602988 CTSC Cathepsis C

AF116682

 -0.602350  PRO2013  Hypothetical PRO2013 protein

AB037836

0.602024 KIAA1415  KIAA1415 protein

AB002301

0.602005 KIAA0303  KIAA0303 protein

NM_002996

 -0.601841  SCYD1  Small subfamily D (Cys-X3-Cys), member 1, (fractalin, neurotactin)

NM_018410

 -0.601765  KFZp762E1312  Hypothetical protein DKFZp762E1312

Contig49581_RC

 -0.601571 KIAA1350  KIAA1350 protein

NM_003088

 -0.601458 SNL  Gaseous (similar to Drosophila) (homologue type of sea urchin fascina)

Contig47045_RC

 0.601088  ESTs, slightly similar to DP1 HUMAN PROTEIN 1 OF LOCUS POLYPOSIS [H.sapiens]

NM_001806

 -0.600954 CEBPG  Protein (C / EBP) binding with CCAAT / enhancer, gamma

NM_004374

0.600766 COX6C Vlc cytochrome c oxidase subunit

 Contig52641_RC

 0.600132 MOUSE  ESTs, slightly similar to AUTOANTIGEN B CENTRÓMERO CENB MAIN [M.musculus]

NM_000100

 -0.600127 CSTB Cystatin B (Stephine B)

NM_002250

 -0.600004 KCNN4  Potassium intermediate / small calcium activated conductance channel, subfamily N, member 4

AB033035

-0.599423  KIAA1209  KIAA1209 protein

Contig53968_RC

 0.599077 ESTs

NM_002300

 -0.598246 LDHB Dehydrogenase B lactate

NM_000507

0.598110 FBP1  Fructose-1,6-bisphosphatase 1

NM_002053

 -0.597756 GBP1  Guanylate-binding protein 1, interferon-inducible, 67kD

AB007883

0.597043 KIAA0423  KIAA0423 protein

41

 NM_004900

 -0.597010 DJ742C19.2  Forboline (similar to apolipoprotein B mRNA editor 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)

NMB_006113

0.595017 VAV3 oncogenic vav 3

NM_014865

 -0.594928 KIAA0159  Protein 1 associated with SMC chromosome condensation

Contig55538_RC

 -0.594573 BA395L14.2  Hypothetical protein bA395L14.2

NM_016056

0.594084  LOC51643  CGI-119 protein

NM_003579

 -0.594063 RAD54L  RAD54 (related to S.cerevisiae)

NM_014214

 -0.593860 IMPA2  Inositol (mine) -1 (or 4) - monophosphatase 2

U79293

0.593793  MRNA sequence of human clone 23948

NM_005557

 -0.593746 KRT16  keratin 16 (palmoplantar keratoderma non epidermolytic focla)

NM_002444

 -0.592405 MSN Moesina

NM_003681

 -0.592155 PDXK  pyridoxal (pyridoxine, vitamin B6) kinase

NM_006372

 -0.591711 NSAP1  Protein 1 associated with NS1

NM_005218

 -0.591192 DEFB1  Defensin, beta 1

NM_004642

 -0.591081 DOC1  Deleted in oral cancer (mouse, homologue) 1

AL133074

0.590359  Homo sapiens cDNA; fis FLJ22139, 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  Transcription factor 1 binding with the sterol regulatory element

NM_016121

0.586941  LOC51133  NY-REN-45 antigen

NM_014773

0.586871 KIAA0141  Product of the KIAA0141 gene

NM_019000

0.586677 FLJ20152  Hypothetical protein

NM_016243

0.585942  LOC51706  Cytochrome b5 reductase 1 (B5R.1)

NM_014274

 -0.585815 ABP / ZF  Alu binding protein with zinc finger domain

 NM_018379

0.585497 FLJ11280  Hypothetical protein FLJ11280

AL157431

-0.585077 DKFZp762A227 Hypothetical protein DKFZp762A227

D38521

 -0.584684 KIAA0077  KIAA0077 protein

NM_002570

0.584272 PACE4  Basic Amino Acid Exfoliate System 4

42

 NM_001809

 -0.584252 CENPA Centromere protein A (17kD)

 NM_003318

 -0.583556 TTK  TTK protein kinase

NM_014325

 -0.583555  CHOIR1C  Coronin, actin binding protein, 1C

NM_005667

0.583376 ZFP103  Homologous zinc homologous protein of Zfp103 in mice

NM_004354

0.582420  CCNG2 Cyclin G2

NM_003670

0.582235 BHLHB2  Basic helix content-loop-domain of helix domain, 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 carboiyl-terminal esterase L3 (ubiquitin thiolesterase)

NM_015392

0.580568 NPDC1  Proliferation, differentiation and neural control, 1

NM_004866

0.580138 SCAMP1  Protein 1 of the secretory carrier membrane 1

Contig50391_RC

 0.580071 ESTs

NM_000592

0.579965 C4B  4B complement component

Contig50802_RC

 0.579881 ESTs

Contig41635_RC

 -0.579468 ESTs

NM_006845

 -0.579339 KNSL6  Type 6 kinesin (mitotic centromere-associated kinesin)

NM_003720

 -0.579296 DSCR2  Gen 2 of the critical region of Down syndrome

 NM_000060

0.578967 BTD Biotinidase

AL050388

 -0.578736  MRNA from Homo sapiens; DKFZp564M2422 cDNA (from clone DKFZp564M2422); partial cds

NM_003772

 -0.578395 JRKL  Jerky-related counterpart (mouse)

NM_014398

 -0.578388 TSC403  Lysosome-associated membrane glycoprotein-like

NM_001280

0.578213 CIRBP  Cold-inducible RNA binding protein

NM_001395

 -0.577369 DUSP9  Phosphatase 9 of dual specificity

NM_016229

 -0.576290  LOC51700  Cytochrome b5 reductase b5R.2

NM_006096

 -0.575615  NDRG1  N-myc regulated downstream

NM_001552

0.575438 IGFBP4 Protein 4 binding to insulin-like growth factor

NM_005558

 -0.574818 LAD1 Ladinina 1

Contig54534_RC

 0.574784  Pseudogen human glucose transporter

 Contig1239_RC

0.573822  Human chromosome 16 BAC clone CIT987SK-A-362G6

Contig57173_RC

 0.573807  Homo sapiens mRNA for KIAA1737 protein, partial cds

NM_004414

 -0.573538 DSCR1  Gen1 of the critical region of Down syndrome

 NM_021103

 -0.572722 TMSB10  Thymosin, beta 10

NM_002350

 -0.571917  LYN  Homologous oncogene related to viral sarcoma Yamaguchi v-yes-1

 Contig51235_RC

 0.571049  Homo sapiens cDNA: fis FLJ23388, 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

43

 AB023152

0.570561 KIAA0935  KIAA0935 protein

NM_021077

 -0.569944 NMB  Neuromedin B

NM_003498

 -0.569129 SNN Stannina

U17077

 -0.568979 BENE BENE protein

D86985

0.567698 KIAA0232  Product of the KIAA0232 gene

NM_006357

 -0.567513 UBE2E3  Enzyme E2E 3 conjugate with ubiquitin (yeast homologue UBC4 / 5)

AL049397

 -0.567434  MRNA from Homo sapiens; DKFZp586C1019 cDNA (from clone DKFZp586C1019)

Contig64502

0.567433 ESTs, slightly similar to [M.musculus] unknown

Contig56298_RC

 -0.566892 FLJ13154  Hypothetical protein FLJ13154

Contig46056_RC

 0.566634  ESTs, slightly similar to YZ28 HUMAN HYPOTHETIC PROTEIN ZAP128 [H.sapiens]

AF007153

0.566044  Clone of Homo sapiens 23736 mRNA sequence

 Contig1778_RC

 -0.565789 ESTs

NM_017702

 -0.565789 FLJ20186  Hypothetical protein FLJ20186

 Contig39226_RC

 0.565761  Fis FLJ12187 cDNA from Homo sapiens, clone MAMMA1000831

NM_000168

0.564879  GLI3  GL13 member of family GLI-Kruppel (Greig syndrome)

 Contig57609_RC

 0.564751  ESTs, slightly similar to TFIID SUBUNITY 135 KDA T2D3_DE HUMAN TRANSCRIPTION INITIATION FACTOR [H.sapiens]

U45975

0.564602 GDP5PA  Phosphatidylinositol (4,5) bisphosphate 5-phosphatase, A

AF038182

0.564596  Clone 23860 of Homo sapiens mRNA sequence

 Contig5348_RC

0.564480  ESTs, slightly similar to the transcription factor 1607338A BTF3a [H.sapiens]

NM_001321

 -0.564459 CSRP2  Protein 2 rich in cysteine and glycine

 Contig25362_RC

 -0.563801 ESTs

NM_001609

0.563782 ACADSB  Acyl-Coenzyme A dehydrogenase, short branched chain

Contig40146

0.563731  CDNA of Homo sapiens wi84e12.x1 NCl_CGAP_Kid12 clone IMAGE: 2400046 3 ’similar to RAS PROTEIN RAS SW TYPE: RASD_DICDI P03967; mRNA sequence

NMB_016002

0.563403  LOC51097  CGI-49 protein

Contig34303_RC

 0.563157  CDNA from Homo sapiens; fis FLJ21517, 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- protein activation Monooxigena-sa / tryptophan 5monooxygenase, 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  3-monooxygenase / tryptophan 5monooxygenase tyrosine activation protein, eta polypeptide

44

 NM_005915

 -0.560813 MCM6  Deficient in maintenance of minichromosome (mis5, S. pombe) 6

NM_003875

-0.560668 GMPS Guanine monophosphate synthetase

 AK000142

0.559651 AK000142  CDNA from Homo sapiens fis FLJ20135 fis, clone COL06818.

NM_002709

 -0.559621 PPP1CB  Protein phosphatase 1, catalytic subunit, beta isoform

NM_001276

 -0.558868 CHI3L1  Type 1 chitinase 3 (cartilage glycoprotein-39)

 NM_002857

0.558862 PXF  Farnesylated peroxisomal 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's disease)

NM_003683

 -0.557807 D21S2056E  DNA segment on chromosome 21 (single) sequence expressed 2056

 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, slightly similar to ISHUSS disulfide-isomerase protein [H.sapiens]

NM_015417

0.556184  DKFZP434I114  DKFZP434I114 protein

NM_015507

 -0.556138 EGFL6  Domain related to EGF, multiple 6

AF279865

0.555951 KIF13B  Family member of kinesia 13B

 Contig31288_RC

 -0.555754 ESTs

NM_002966

 -0.555620 S100A10  A10 calcium binding protein S100 (annexin II ligand, calpactin I, light polypeptide (p11))

NM_017585

 -0.555476 SLC2A6  Family 2 of the solute 2 carrier (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  Basal Mebrana Induced Gene

NM_007275

0.554278 FUS1  Lung Cancer Candidate

NM_007044

 -0.553550 KATNA1  Katanina p60 (ATPase container) subunit A 1

Contig1829

0.553317 ESTs

AF272357

0.553286 NPDC1  Proliferation, differentiation and neural control, 1

 Contig57584_RC

 -0.553080  Homo sapiens, similar to cluster rich in, gene C8, clone MGC: 2577, mRNA, complete cds

NM_003039

 -0.552747 SLC2A5  Family 2 of the solute 2 carrier (facilitated glucose transporter), member 5

NM_014216

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

NM_007027

 -0.552064 TOPBP1  Topoisomerase (DNA) II binding protein

AF118224

 -0.551916 ST14  Tumorigenicity suppression 14 (colon carcinoma, matriptase, epitine)

X75315

 -0.551853 HSRNASEB Seb4D

NM_012101

 -0.551824 ATDC  Protein associated with group D of ataxia-telangiectasia

Four. Five

 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 determination region Y) - box 10

NM_004694

0.550932 SLC16A6  Family 2 of the solute carrier 16 (monocarboxylic acid transporters), member 6

 Contig9541_RC

0.550680 ESTs

Contig20617_RC

 0.550546 ESTs

NM_004252

0.550365 SLC9A3R1  Family 9 of the carrier sodium / hydrogen exchanger), isoform 3 regulatory factor 1

NM_015641

 -0.550200  KFZP586B2022 Testine

NM_004336

 -0.550164 BUB1  Graft disinhibited by benzimidazoles 1 (yeast counterpart)

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  tyrosine phosphatase typo IVA protein, member 2

NM_001679

 -0.548768 ATP1 B3  ATPase, Na + / K + transporter, beta 3 polypeptide

NM_001124

 -0.548601 ADM Adrenomedulin

 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  Domain J qe contains protein 1

 Contig66705_RC

 -0.546926 ST5  Tumorigenicity suppression 5

NMB_018194

0.546878 FLJ10724  Hypothetical protein FLJ10724

NM_006851

 -0.546823 RTVP1  Protein related to the pathogenesis of glioma

Contig53870_RC

 0.546756 ESTs

NM_002482

 -0.546012 NASP  Nuclear autogenous sperm protein (histone binding)

NM_002292

0.545949 LAMB2  Laminin, beta 2 (laminin S)

NMB_014696

 -0.545758 KIAA0514  Product of the KlAA0514 gene

Contig49855

0.545517 ESTs

AL117666

0.545203  DKFZP586  DKFZP58601624 Protein O1624

NM_004701

 -0.545185 CCNB2 B2 Cyclin

NM_007050

0.544890 PTPRT  Tyrosine phosphatase protein, receptor type, T

NMB_000414

0.544778 HSD17B4  Hydroxysteroid (17-beta) dehydrogenase 4

 Contig52398_RC

 -0.544775  Homo sapiens cDNA: fis FLJ21950, clone HEP04949

AB007916

0.544496 KlAA0447  Product of the KIAA0447 gene

 Contig66219_RC

 0.544467 FLJ22402  Hypothetical protein FLJ22402

D87453

0.544145 KlAA0264  KIAA0264 protein

NM_015515

 -0.543929  DKFZP434G032  DKFZP434G032 protein

NM_001530

 -0.543898 HIF1A  Hypoxia-inducible factor 1, alpha subunit (basic helix-loop-helix transcription factor)

NM_004109

 -0.543893 FDX1  Ferredoxin 1

NM_000381

 -0.543871 MID1  Midlina 1 (Opitz / BBB syndrome)

 Contig43983_RC

 0.543523 CS2 Calsintenin-2

46

 AL137761

0.543371  MRNA from Homo sapiens; DKFZp586L2424 cDNA (from clone DKFZp586L2424)

NM_005764

 -0.543175 DD96  Increased epithelial protein in carcinoma, membrane-associated protein 17

 Contig1838_RC

0.542996  Homo sapiens cDNA: fis FLJ22722, clone HSI14444

NM_006670

0.542932 5T4  Oncofetal trophoblast glycoprotein

 Contig28552_RC

 -0.542617  RNA from Homo sapiens; DKFZp434C0931 cDNA (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  MRNA from Homo sapiens; DKFZp434A091 cDNA (from clone DKFZp434A091); partial cds

NM_014181

 -0.541095 HSPC159  HSPC159 protein

Contig37141_RC

 0.540990  Homo sapiens cDNA: fis FLJ23582, clone LNG13759

NM_000947

 -0.540621 PRIM2A Primasa, polypeptide 2A (58kD)

NMB_002136

0.540572 HNRPA1  Heterogeneous nuclear A1 ribonucleoprotein

 NM_004494

 -0.540543 HDGF  Growth factor derived from hepatoma (high mobility group type 1 protein)

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  Protein 1 interacting with schwannomina

 Contig38170_RC

 0.539708 ESTs

 Contig44064_RC

 0.539403 ESTs

U68385

0.539395 MEIS3  Meis counterpart 3 (mouse)

Contig51967_RC

 0.538952 ESTs

Contig37562_RC

 0.538657  ESTs, slightly similar to the transformation-related protein [H.sapiens]

Contig40500_RC

 0.538582  ESTs, slightly similar to unnamed protein product [H.sapiens]

 Contig1129_RC

0.538339 ESTs

NM_002184

0.538185 IL6ST  Interleukin 6 signal transducer (gp130, M oncostatin receptor)

AL049381

0.538041  CDNA from Homo sapiens fis FLJ12900, clone NT2RP2004321

 NM_002189

 -0.537867 IL15RA  Interleukin 15 receptor, alpha

 NM_012110

 -0.537562 CHIC2  Hydrophobic domain 2 rich in cysteine

AB040881

 -0.537473 KIAA1448  KIAA1448 protein

NM_016577

 -0.537430 RAB6B RAB6B, member of the RAS oncogene family

NM_001745

0.536940 CAMLG  Ligand calcium modulator

NM_005742

 -0.536738 P5  Protein related to disulphide isomerase protein

AB011132

0.536345 KIAA0560  Product of the KIAA0560 gene

Contig54898_RC

 0.536094 PNN protein  Pinina, associated with desmosoma

Contig45049_RC

 -0.536043 FUT4  Fucosyltransferase 4 (alpha (1,3) fucosyltransferase, myeloid specific

47

 NM_006864

 -0.535924 LILRB3  Leukocyte-related immunoglobulin receptor, subfamily B (with TM and ITIM domains), member 3

Contig53242_RC

 -0.535909  CDNA from Homo sapiens fis FLJ11436, clone HEMBA1001213

NM_005544

0.535712 IRS1  Insulin receptor substrate 1

Contig47456_RC

 0.535493 CACNA1D  Calcium channel, voltage dependent, type L, subunit alpha 1 D

Contig42751_RC

 -0.535469 ESTs

Contig29126_RC

 -0.535186 ESTs

NM_012391

0.535067 PDEF  Ets transcription factor specific for prostate epithelium

NMB_012429

0.534974 SEC14L2  SEC14 type 2 of (S. cerevisiae)

NMB_018171

0.534898 FLJ10659  Hypothetical protein FLJ10659

Contig53047_RC

 -0.534773  TTYH1  Tweety Homologue 1 (Drosophila)

Contig54968_RC

 0.534754  CDNA from Homo sapiens fis FLJ13558, clone PLACE1007743

 Contig2099_RC

 -0.534694 KIAA1691  KIAA9691 protein

NM_005264

0.534057  GFRA1 GDNF Family Receiver Alpha 1

NM_014036

 -0.533638 SBBI42  BCM type membrane protein precursor

 NMB_018101

 -0.533473 FLJ10468  Hypothetical protein FLJ10468

Contig56765_RC

 0.533442 K02E10.2  ESTs, moderately similar to [C.elegans]

AB006746

 -0.533400 PLSCR1 Phospholipid Scramblase 1

NMB_001089

0.533350 ABCA3  ATP link cassette, subfamily A (ABC1), member 3

NMB_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

NMB_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  Type 1 Leucine zipper transcription factor

NM_004323

0.531936 BAG1  Atangen associated with BCL2

Contig50850_RC

 -0.531914 ESTs

Contig11648_RC

 0.531704 ESTs

NMB_018131

 -0.531559 FLJ10540  Hypothetical protein FLJ10540

NM_004688

 -0.531329 NMI  N-myc (and STAT) Interactor

NM_014870

0.531101 KIAA0478  Product of the KIAA0478 gene

 Contig31424_RC

 0.530720 ESTs

NM_000874

 -0.530545 IFNAR2  Interferon receptor 2 (alpha, beta and omega)

Contig50588_RC

 0.530145 ESTs

NMB_016463

0.529998 HSPC195  Hypothetical protein

NMB_013324

0.529966 CISH  Cytokine-inducible SH2 containing protein

NM_006705

0.529840  GADD45G  Growth stop and inducible to DNA damage, gamma

Contig38901_RC

 -0.529747 ESTs

NM_004184

 -0.529635 WARS  Tryptophanil-tRNA synthetase

NM_015955

 -0.529538  LOC51072  CGI-27 protein

 AF151810

0.529416  CGI-52  Similar to protein 2 transfer

48

 phosphatidylcholine

NMB_002164

 -0.529117 INDO  Indoleamine-pyrrole 2,3 dioxygenase

NM_004267

 -0.528679 CHST2  Carbohydrate (chondroitin 6 / queratane) sulfotransferase 2

Contig32185_RC

 -0.528529  CDNA from Homo sapiens fis FJ13997, clone Y79AA1002220

NM_004154

 -0.528343  P2RY6 P2Y pyrimidinergic receptor, coupled to protein G, 6

NM_005235

0.528294 ERBB4  Type 4 of viral oncogene homolog of avian eritoblastic leukemia v-erb-a

Contig40208_RC

 -0.528062  LOC56938  BMAL2 transcription factor

NMB_013262

0.527297 Look  Interactive protein with the myosin regulatory light chain

NM_003034

 -0.527148 SIAT8A  Sialyltransferase 8 (alpha-N-acetylneuraminate: alpha-2,8-sialitransferase, GD3 synthase) A

NM_004556

 -0.527146 NFKBIE  Nuclear enhancer factor of the kappa light polypeptide gene in B cell inhibitor, epsilon

NM_002046

 -0.527051 GAPD  Glyceraldehyde-3-phosphate dehydrogenase

NMB_001905

 -0.526986 CTPS CTP synthase

Contig42402_RC

 0.526852 ESTs

NM_014272

 -0.526283 ADAMTS7  Type A of disintegrin and metalloprotease (related to reprolysin) with type 1 thrombospondin motif, 7

AF076612

0.526205 CHRD Cordina

Contig57725_RC

 -0.526122  Homo sapiens mRNA for TCF-3 transcription factor of HMG box, complete cds

Contig42041_RC

 -0.525877 ESTs

Contig44656_RC

 -0.525868  ESTs, very similar to the precursor of the S02392 alpha-2-macroglobulin receptor [H.sapiens]

NMB_018004

 -0.525610 FLJ10134  Hypothetical protein FLJ10134

Contig56434_RC

 0.525510  CDNA from Homo sapiens fis FLJ13603, 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  MRNA from Homo sapiens; DKFZp586P1124 cDNA (from clone DKFZp586P1124)

AF061034

 -0.525185 FIP2  Homo sapiens mRNA alternately converted to FIP2, complete cds

NMB_014721

 -0.525102 KIAA0680  Product of the KIAA0680 gene

NMB_001634

 -0.525030 AMD1  S-adenosylmethionine decarboxylase 1

NM_006304

 -0.524911 DSS1  Deletion in region 1 of cleft hand-cleft foot

Contig37778_RC

 0.524667 ESTs, very similar to HLHUSB MHC class II histocompatibility antigen chain precursor HLA-DP alpha-1 [H.sapiens]

NM_003099

0.524339 SNX1  Nexin 1 classification

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)

49

 NM_014811

0.523132 KIAA0649  Product of the KIAA0649 gene

Contig20600_RC

 0.523072 ESTs

NMB_005190

 -0.522710 CCNC Cyclin C

AL161960

 -0.522574 FLJ21324  Hypothetical protein FLJ21324

AL117502

0.522280  MRNA from Homo sapiens; DKFZp434D0935 cDNA (from clone DKFZp434D0935)

AF131753

 -0.522245  Homo sapiens clone 24859 mRNA sequence

NM_000320

0.521974 QDPR  Quinoid Dihydropteridine Reductase

NMB_002115

 -0.521870 HK3  Hexokinase 3 (white cell)

NM_006460

0.521696 HIS1  Inducible by HMBA

NMB_018683

 -0.521679  ZNF313  Zinc finger protein 313

NM_004305

 -0.521539 BIN1  Bridge Integrator 1

NM_006770

 -0.521538 FRAMEWORK  Macrophage receptor with collagen structure

NM_001166

 -0.521530 BIRC2  IAP Baculoviral Repetition Container 2

 D42047

0.521522 KIAA0089  KIAA0089 protein

NMB_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

NMB_018031

0.520600 WDR6  WD Repeat Domain 6

AL117523

 -0.520579 KIAA1053  KIAA1053 protein

NMB_004515

 -0.520363 ILF2  Link factor 2 with the interleukin enhancer, 45kD

NM_004708

 -0.519935 PDCD5  Scheduled death of cell 5

NM_005935

0.519765 MLLT2  Myeloid / lymphoid or mixed-pathway leukemia (trithorax homologue (Drosophila); transferred to 2

Contig49289_RC

 -0.519546  MRNA from Homo sapiens; DKFZp586J1119 cDNA (from clone DKFZp586J1119); full cds

NMB_000211

 -0.519342 ITGB2  Integrin, beta 2 (CD18 antigen (p95), lymphocyte-associated antigen 1; macrophage 1 antigen (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  Type 2 of viral oncogene homolog of avian eritoblastic leukemia v-erb-a

NMB_016072

 -0.518802  LOC51026  CGI-141 protein

AB007950

 -0.518699 KIAA0481  Product of the KIAA0481 gene

NMB_001550

 -0.518549 IFRD1  Interferon-related development regulator 1

AF155120

 -0.518221 UBE2V1  Variant 1 of enzyme E that conjugates with ubiquitin

Contig49849_RC

 0.517983  ESTs, slightly similar to protein AF188706 1 g20 [H.sapiens]

NMB_016625

 -0.517936  LOC51319  Hypothetical protein

NM_004049

 -0.517862 BCL2A1  A1 protein related to BCL2

Contig50719_RC

 0.517740 ESTs

D80010

 -0.517620 LPIN1 Lipina 1

fifty

 NM_000299

 -0.517405 PKP1  Placophilin 1 (ectodermal dysplasia syndrome / epidermal fragility)

 AL049365

0.517080 FTL  Ferritin, light polypeptide

Contig65227

0.517003 ESTs

NM_004865

 -0.516808 TBPL1 Type 1 of TBP

 Contig54813_RC

 0.516246 FLJ13962  Hypothetical protein FLJ13962

NM_003494

 -0.516221  DYSF  Dysferlin, muscular myopathy dystrophy of 2B members (autosomal recessive)

NM_004431

 -0.516212 EPHA2 EphA2

AL117600

 -0.516067  DKFZP564 J0863  DKFZP564J0863 protein

 AL080209

 -0.516037  DKFZP586 F2423  Hypothetical protein DKFZp586F2423

NM_000135

 -0.515613 FANCA  Anemia of Fanconi, complementation group A

NM_000050

 -0.515494 ASS  Argininosuccinate synthetase

NMB_001830

 -0.515439 CLCN4  Chloride Channel 4

NMB_018234

 -0.515365 FLJ10829  Hypothetical protein FLJ10829

Contig53307_RC

 0.515328  ESTs, slightly similar to the KIAA1437 protein [H.sapiens]

AL117617

 -0.515141  MRNA from Homo sapiens; DKFZp564H0764 cDNA (from clone DKFZp564H0764)

NM_002906

 -0.515098 RDX Radixin

NMB_003360

 -0.514427  UGT8  UDP glycosyltransferase 8 (UDP-galactose ceramide galactosyltransferase)

NM_018478

0.514332 HSMNP1  HSMNP1 uncharacterized hypothalamus protein

M90657

 -0.513908 TM4SF1  Member 1 of the transmembrane superfamily 4

NM_014967

0.513793 KIAA1018 KIAA1018 protein

 Contig1462_RC

0.513604 C11ORF15  Open reading frame 15 of chromosome 11

 ontig37287_RC

 -0.513324 ESTs

NM_000355

 -0.513225 TCN2  Transcobalamin II; macrocytic anemia anemia

AB037756

0.512914 KIAA1335  hypothetical protein KIAA1335

 Contig842_RC

 -0.512880 ESTs

NMB_018186

 -0.512878 FLJ10706  Hypothetical protein FLJ10706

NM_014668

0.512746 KIAA0575  Product of the KIAA0575 gene

NM_003226

0.512611  TFF3  Clover factor 3 (intestinal)

Contig56457_RC

 -0.512548  TMEFF1  Transmembrane protein related to EGF and two domains 1 related to folistatin

AL050367

 -0.511999  MRNA from Homo sapiens; DKFZp564A026 cDNA (from clone DKFZp564A026)

NM_014791

 -0.511963 KIAA0175 Product of the KIAA0175 gene

Contig36312_RC

 0.511794 ESTs

NM_004811

 -0.511447 LPXN Leupaxin

Contig67182_RC

 -0.511416  ESTs, very similar to the precursor of type V epithelial antigen [H.sapiens]

Contig52723_RC

 -0.511134 ESTs

 Contigl 7105_RC

 -0.511072  Homo sapiens mRNA for putative cytoplasmic protein (ORF1-FL21)

NMB_014449

0.511023 TO  Protein "A"

51

Contig52957_RC

 0.510815 ESTs

Contig49388_RC

 0.510582 FLJ13322  Hypothetical protein FLJ13322

NM_017786

0.510557 FLJ20366  Hypothetical protein FLJ20366

AL157476

0.510478  MRNA from Homo sapiens; DKFZp761 C082 cDNA (from clone DKFZp761 C082)

NMB_001919

0.510242 DCl  Dodecenoyl-Coenzyme A delta isomerase (3,2 transenoyl-Coenzyme A isomerase)

NM_000268

 -0.510165 NF2  Neurofibromin 2 (bilateral acoustic neuroma)

NMB_016210

0.510018  LOC51161  G20 protein

Contig45816_RC

 -0.509977 ESTs

NM_003953

 -0.509969 MPZL1  Myelin 1 protein type zero

NM_000057

 -0.509669 BLM  Bloom syndrome

NM_014452

 -0.509473 DR6  Death Receiver 6

Contig45156_RC

 0.509284  ESTs, moderately similar to the 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 (morphogenic protein 1 derived from cartilage)

NMB_018685

 -0.508307 ANLN  Aniline (Drosophila Detritus homolog), actin binding protein

Contig53401_RC

 0.508189 ESTs

NM_014364

 -0.508170 GAPDS  Gliceraldehyde-3-phosphate dehydrogenase, specific to the testicles

Contig50297_RC

 0.508137  ESTs, moderately similar to ALU8_ALU HUMAN SUBFAMILIA SX SEQUENCE POLLUTION NOTICE ENTRY [H.sapiens]

Contig51800

0.507891  ESTs, moderately similar to ALU6_ALU HUMANO SUBFAMILIA SP SEQUENCE POLLUTION NOTICE 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; yeast ubiquitin-protein ligase homolog Rsp5

AL110139

0.507145  MRNA from Homo sapiens; DKFZp56401763 cDNA (from clone DKFZp56401763)

Contig51117_RC

 0.507001 ESTs

NMB_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 homolog 64 (mouse homolog, radioprotective, 105kD

Contig47405_RC

 0.506202 ESTs

NM_014808

0.506173 KIAA0793  Product of the KIAA0793 gene

NM_004938

 -0.506121 DAPK1  Protein kinase 1 associated with death

 NM_020659

 -0.505793  TTYH1  Tweety Homologue 1 (Drosophila)

NM_006227

 -0.505604 PLTP  Phospholipid Transfer Protein

52

 NMB_014268

 -0.505412 MAPRE2  Microtubule associated protein, RP / EB fammilia, member 2

 NM_004711

0.504849  SYNGR1 Synaptogirine 1

NMB_004418

 -0.504497 DUSP2  Dual specific phosphatase 2

NM_003508

 -0.504475 FZD9 Homologous 9 (Drosophila) curly

Table 3. 430 gene markers that distinguish between tumor samples related to BRCA1-1 and sporadic tumor samples

Band of Genes Access Number SEQ ID NO

Band of Genes Access Number SEQ ID NO

 AB002301 SEQ ID NO 4

NM_012391 SEQ ID NO 1406

AB004857 SEQ ID NO 8

NM_012428 SEQ ID NO 1412

AB007458 SEQ ID NO 12

NM_013233 SEQ ID NO 1418

AB014534 SEQ ID NO 29

NM_013253 SEQ ID NO 1422

AB018305 SEQ ID NO 34

NM_013262 SEQ ID NO 1425

AB020677 SEQ ID NO 36

NM_013372 SEQ ID NO 1434

AB020689 SEQ ID NO 37

NM_013378 SEQ ID NO 1435

AB023151 SEQ ID NO 41

NM_014096 SEQ ID NO 1450

AB023163 SEQ ID NO 43

NM_014242 SEQ ID NO 1464

AB028986 SEQ ID NO 48

NM_014314 SEQ ID NO 1472

AB029025 SEQ ID NO 50

NM_014398 SEQ ID NO 1486

AB032966 SEQ ID NO 53

NM_014402 SEQ ID NO 1488

AB032988 SEQ ID NO 57

NM_014476 SEQ ID NO 1496

AB033049 SEQ ID NO 63

NM_014521 SEQ ID NO 1499

AB033055 SEQ ID NO 66

NM_014585 SEQ ID NO 1504

AB037742 SEQ ID NO 73

NM_014597 SEQ ID NO 1506

AB041269 SEQ ID NO 96

NM_014642 SEQ ID NO 1510

AF000974 SEQ ID NO 97

NM_014679 SEQ ID NO 1517

AF042838 SEQ ID NO 111

 NM_014680 SEQ ID NO 1518

AF052155 SEQ ID NO 119

 NM_014700 SEQ ID NO 1520

AF055084 SEQ ID NO 125

 NM_014723 SEQ ID NO 1523

AF063725 SEQ ID NO 129

 NM_014770 SEQ ID NO 1530

AF070536 SEQ ID NO 133

 NM_014785 SEQ ID NO 1534

AF070617 SEQ ID NO 135

 NM_014817 SEQ ID NO 1539

AF073299 SEQ ID NO 136

 NM_014840 SEQ ID NO 1541

AF079529 SEQ ID NO 140

 NM_014878 SEQ ID NO 1546

AF090353 SEQ ID NO 141

 NM_015493 SEQ ID NO 1564

AF116238 SEQ ID NO 155

 NM_015523 SEQ ID NO 1568

AF151810 SEQ ID NO 171

 NM_015544 SEQ ID NO 1570

AF220492 SEQ ID NO 185

 NM_015623 SEQ ID NO 1572

AJ224741 SEQ ID NO 196

 NM_015640 SEQ ID NO 1573

AJ250475 SEQ ID NO 201

 NM_015721 SEQ ID NO 1576

AJ270996 SEQ ID NO 202

 NM_015881 SEQ ID NO 1577

AJ272057 SEQ ID NO 203

 NM_015937 SEQ ID NO 1582

AK000174 SEQ ID NO 211

 NM_015964 SEQ ID NO 1586

AK000617 SEQ ID NO 215

 NM_015984 SEQ ID NO 1587

53

 AK000959

              SEQ ID NO 222  NM_016000                       SEQ ID NO 1591

AK001438

              SEQ ID NO 229  NM_016018                       SEQ ID NO 1593

AK001838

              SEQ ID NO 233  NM_016066                       SEQ ID NO 1601

AK002107

              SEQ ID NO 238  NM_016073                       SEQ ID NO 1603

AK002197

              SEQ ID NO 239  NM_016081                       SEQ ID NO 1604

AL035297

             SEQ ID NO 241  NM_016140                       SEQ ID NO 1611

AL049346

             SEQ ID NO 243  NM_016223                       SEQ ID NO 1622

AL049370

             SEQ ID NO 245  NM_016267                       SEQ ID NO 1629

AL049667

             SEQ ID NO 249  NM_016307                       SEQ ID NO 1633

AL080222

             SEQ ID NO 276  NM_016364                       SEQ ID NO 1639

AL096737

             SEQ ID NO 279  NM_016373                       SEQ ID NO 1640

AL110163

             SEQ ID NO 282  NM_016459                       SEQ ID NO 1646

AL133057

             SEQ ID NO 300  NM_016471                       SEQ ID NO 1648

AL133096

             SEQ ID NO 302  NM_016548                       SEQ ID NO 1654

AL133572

             SEQ ID NO 305  NM_016620                       SEQ ID NO 1662

AL133619

             SEQ ID NO 307  NM_016820                       SEQ ID NO 1674

AL133623

             SEQ ID NO 309  NM_017423                       SEQ ID NO 1678

AL137347

             SEQ ID NO 320  NM_017709                       SEQ ID NO 1698

AL137381

             SEQ ID NO 322  NM_017732                       SEQ ID NO 1700

AL137461

             SEQ ID NO 325  NM_017734                       SEQ ID NO 1702

AL137540

             SEQ ID NO 328  NM_017750                       SEQ ID NO 1704

AL137555

             SEQ ID NO 329  NM_017763                       SEQ ID NO 1706

AL137638

             SEQ ID NO 332  NM_017782                       SEQ ID NO 1710

AL137639

             SEQ ID NO 333  NM_017816                       SEQ ID NO 1714

AL137663

             SEQ ID NO 334  NM_018043                       SEQ ID NO 1730

AL137761

             SEQ ID NO 339  NM_018072                       SEQ ID NO 1734

AL157431

             SEQ ID NO 340  NM_018093                       SEQ ID NO 1738

AL161960

             SEQ ID NO 351  NM_018103                        SEQ ID NO 1742

AL355708

             SEQ ID NO 353  NM_018171                        SEQ ID NO 1751

AL359053

             SEQ ID NO 354  NM_018187                        SEQ ID NO 1755

D26488

             SEQ ID NO 359  NM_018188                       SEQ ID NO 1756

D38521

             SEQ ID NO 361  NM_018222                       SEQ ID NO 1761

D50914

             SEQ ID NO 367  NM_018228                       SEQ ID NO 1762

D80001

             SEQ ID NO 369  NM_018373                       SEQ ID NO 1777

 G26403

              SEQ ID NO 380  NM_018390                       SEQ ID NO 1781

K02276

             SEQ ID NO 383  NM_018422                       SEQ ID NO 1784

M21551

             SEQ ID NO 394  NM_018509                       SEQ ID NO 1792

M27749

             SEQ ID NO 397  NM_018584                       SEQ ID NO 1796

M28170

             SEQ ID NO 398  NM_018653                       SEQ ID NO 1797

M73547

             SEQ ID NO 409  NM_018660                       SEQ ID NO 1798

M80899

             SEQ ID NO 411  NM_018683                       SEQ ID NO 1799

NM_000067

            SEQ ID NO 423  NM_019049                       SEQ ID NO 1814

NM_000087

            SEQ ID NO 427  NM_019063                       SEQ ID NO 1815

NM_000090

            SEQ ID NO 428  NM_020150                       SEQ ID NO 1823

NMB_000165

             SEQ ID NO 444  NM_020987                       SEQ ID NO 1848

NM_000168

            SEQ ID NO 445  NM_021095                       SEQ ID NO 1855

NM_000196

            SEQ ID NO 449  NM_021242                       SEQ ID NO 1867

54

 NM_000269

            SEQ ID NO 457  U41387                       SEQ ID NO 1877

NM_000310

            SEQ ID NO 466  U45975                       SEQ ID NO 1878

NM_000396

           SEQ ID NO 479 U58033                       SEQ ID NO 1881

NM_000397

            SEQ ID NO 480  U67784                       SEQ ID NO 1884

NM_000597

            SEQ ID NO 502  U68385                       SEQ ID NO 1885

NM_000636

            SEQ ID NO 509  U80736                       SEQ ID NO 1890

NM_000888

            SEQ ID NO 535  X00437                       SEQ ID NO 1899

NM_000903

             SEQ ID NO 536  X07203                       SEQ ID NO 1904

NM_000930

             SEQ ID NO 540  X16302                       SEQ ID NO 1907

NM_000931

             SEQ ID NO 541  X51630                       SEQ ID NO 1908

NM_000969

             SEQ ID NO 547  X57809                       SEQ ID NO 1912

NM_000984

             SEQ ID NO 548  X57819                       SEQ ID NO 1913

NM_001026

             SEQ ID NO 552  X58529                       SEQ ID NO 1914

NM_001054

             SEQ ID NO 554  X66087                       SEQ ID NO 1916

NM_001179

             SEQ ID NO 567  X69150                       SEQ ID NO 1917

NM_001184

            SEQ ID NO 568  X72475                       SEQ ID NO 1918

NM_001204

            SEQ ID NO 571  X74794                       SEQ ID NO 1920

NM_001206

            SEQ ID NO 572  X75315                       SEQ ID NO 1921

NM_001218

            SEQ ID NO 575  X84340                       SEQ ID NO 1925

NM_001275

            SEQ ID NO 586  X98260                       SEQ ID NO 1928

NM_001394

            SEQ ID NO 602 Y07512                       SEQ ID NO 1931

NM_001424

            SEQ ID NO 605 Y14737                       SEQ ID NO 1932

NM_001448

            SEQ ID NO 610  Z34893                        SEQ ID NO 1934

NM_001504

            SEQ ID NO 620  Contig237_RC                       SEQ ID NO 1940

NM_001553

            SEQ ID NO 630  Contig292_RC                       SEQ ID NO 1942

NM_001674

            SEQ ID NO 646  Contig372_RC                       SEQ ID NO 1943

NM_001675

            SEQ ID NO 647  Contig756_RC                       SEQ ID NO 1955

NM_001725

            SEQ ID NO 652  Contig842_RC                       SEQ ID NO 1958

NM_001740

            SEQ ID NO 656  Contig1632_RC                       SEQ ID NO 1977

NM_001756

            SEQ ID NO 659  Contig1826 RC                       SEQ ID NO 1980

NM_001770

            SEQ ID NO 664  Contig2237_RC                       SEQ ID NO 1988

NM_001797

            SEQ ID NO 670  Contig2915 RC                       SEQ ID NO 2003

NM_001845

            SEQ ID NO 680  Contig3164 RC                       SEQ ID NO 2007

NM_001873

            SEQ ID NO 684  Contig3252_RC                       SEQ ID NO 2008

NM_001888

             SEQ ID NO 687  Contig3940_RC                         SEQ ID NO 2018

NM_001892

             SEQ ID NO 688  Contig9259_RC                          SEQ ID NO 2039

NM_001919

             SEQ ID NO 694  Contig10268_RC SEQ ID NO 2041

 NM_001946

             SEQ ID NO 698  Contig10437_RC SEQ ID NO 2043

 NM_001953

             SEQ ID NO 699  Contig10973_RC SEQ ID NO 2044

 NM_001960

             SEQ ID NO 704  Contig14390_RC SEQ ID NO 2054

 NM_001985

             SEQ ID NO 709  Contig16453_RC SEQ ID NO 2060

 NM_002023

             SEQ ID NO 712  Contig16759 RC                          SEQ ID NO 2061

NM_002051

             SEQ ID NO 716  Contig19551 SEQ ID NO 2070

 NM_002053

             SEQ ID NO 717  Contig24541_RC SEQ ID NO 2088

 NM_002164

             SEQ ID NO 734  Contig25362 RC SEQ ID NO 2093

 NM_002200

             SEQ ID NO 739  Contig25617 RC SEQ ID NO 2094

 NM_002201

             SEQ ID NO 740  Contig25722 RC SEQ ID NO 2096

55

 NM_002213

             SEQ ID NO 741  Contig26022 RC SEQ ID NO 2099

 NM_002250

             SEQ ID NO 747  Contig27915 RC SEQ ID NO 2114

 NM_002512

             SEQ ID NO 780  Contig28081_RC SEQ ID NO 2116

 NM_002542

             SEQ ID NO 784  Contig28179 RC SEQ ID NO 2118

 NM_002561

             SEQ ID NO 786 Contig28550_RC SEQ ID NO 2119 D

NM_002615

 SEQ ID NO 793  Contig29639 RC  SEQ ID NO 2127

NM_002686

 SEQ ID NO 803  Contig29647 RC  SEQ ID NO 2128

NM_002709

 SEQ ID NO 806  Contig30092 RC  SEQ ID NO 2130

NM_002742

 SEQ ID NO 812  Contig30209_RC  SEQ ID NO 2132

NM_002775

 SEQ ID NO 815  Contig32185_RC  SEQ ID NO 2156

NM_002975

 SEQ ID NO 848  Contig32798_RC  SEQ ID NO 2161

NM_002982

 SEQ ID NO 849  Contig33230_RC  SEQ ID NO 2163

NM_003104

 SEQ ID NO 870  Contig33394_RC  SEQ ID NO 2165

NM_003118

 SEQ ID NO 872  Contig36323_RC  SEQ ID NO 2197

NM_003144

 SEQ ID NO 876  Contig36761_RC  SEQ ID NO 2201

NM_003165

 SEQ ID NO 882  Contig37141_RC  SEQ ID NO 2209

NM_003197

 SEQ ID NO 885  Contig37778_RC  SEQ ID NO 2218

NM_003202

 SEQ ID NO 886  Contig38285_RC  SEQ ID NO 2222

NM_003462

SEQ ID NO 911  Contig39826_RC  SEQ ID NO 2241

NM_003500

SEQ ID NO 918  Contig40212_RC  SEQ ID NO 2251

NM_003561

SEQ ID NO 925  Contig40712_RC  SEQ ID NO 2257

NM_003607

SEQ ID NO 930  Contig41402_RC SEQ ID NO 2265

 NM_003633

SEQ ID NO 933  Contig41635_RC  SEQ ID NO 2272

NM_003641

 SEQ ID NO 934  Contig42006_RC  SEQ ID NO 2280

NM_003683

 SEQ ID NO 943  Contig42220_RC  SEQ ID NO 2286

NM_003729

 SEQ ID NO 949  Contig42306_RC  SEQ ID NO 2287

NM_003793

 SEQ ID NO 954  Contig43918_RC  SEQ ID NO 2312

NM_003829

 SEQ ID NO 958  Contig44195_RC  SEQ ID NO 2316

NM_003866

 SEQ ID NO 961  Contig44265_RC  SEQ ID NO 2318

NM_003904

 SEQ ID NO 967  Contig44278_RC  SEQ ID NO 2319

NM_003953

 SEQ ID NO 974  Contig44757_RC  SEQ ID NO 2329

NM_004024

 SEQ ID NO 982  Contig45588_RC  SEQ ID NO 2349

NM_004053

 SEQ ID NO 986  Contig46262_RC  SEQ ID NO 2361

NM_004295

 SEQ ID NO 1014  Contig46288_RC  SEQ ID NO 2362

NM_004438

 SEQ ID NO 1038  Contig46343_RC  SEQ ID NO 2363

NM_004559

 SEQ ID NO 1057  Contig46452_RC  SEQ ID NO 2366

NM_004616

 SEQ ID NO 1065  Contig46868_RC  SEQ ID NO 2373

NM_004741

 SEQ ID NO 1080  Contig46937_RC  SEQ ID NO 2377

NM_004772

 SEQ ID NO 1084  Contig48004_RC  SEQ ID NO 2393

NM_004791

 SEQ ID NO 1086  Contig48249_RC  SEQ ID NO 2397

NM_004848

 SEQ ID NO 1094  Contig48774_RC  SEQ ID NO 2405

NM_004866

 SEQ ID NO 1097  Contig48913_RC  SEQ ID NO 2411

NM_005128

 SEQ ID NO 1121  Contig48945_RC  SEQ ID NO 2412

NM_005148

 SEQ ID NO 1124  Contig48970_RC  SEQ ID NO 2413

NM_005196

 SEQ ID NO 1127  Contig49233_RC  SEQ ID NO 2419

NM_005326

 SEQ ID NO 1140  Contig49289_RC  SEQ ID NO 2422

NM_005518

 SEQ ID NO 1161  Contig49342_RC  SEQ ID NO 2423

56

 NM_005538 SEQ ID NO 1163

 Contig49510_RC SEQ ID NO 2430

NM_005557 SEQ ID NO 1170

 Contig49855_RC SEQ ID NO 2440

NM_005718 SEQ ID NO 1189

 Contig49948_RC SEQ ID NO 2442

NM_005804 SEQ ID NO 1201

 Contig50297_RC SEQ ID NO 2451

NM_005824 SEQ ID NO 1203

 Contig50669_RC SEQ ID NO 2458

NM_005935 SEQ ID NO 1220

 Contig50673_RC SEQ ID NO 2459

NM_006002 SEQ ID NO 1225

 Contig50838_RC SEQ ID NO 2465

NM_006148 SEQ ID NO 1249

 Contig51068_RC SEQ ID NO 2471

NM_006235 SEQ ID NO 1257

Contig51929 SEQ ID NO 2492

NM_006271 SEQ ID NO 1261

 Contig51953_RC SEQ ID NO 2493

NM_006287 SEQ ID NO 1264

 Contig52405_RC SEQ ID NO 2502

NM_006296 SEQ ID NO 1267

 Contig52543_RC SEQ ID NO 2505

NM_006378 SEQ ID NO 1275

 Contig52720_RC SEQ ID NO 2513

NM_006461 SEQ ID NO 1287

 Contig53281_RC SEQ ID NO 2530

NM_006573 SEQ ID NO 1300

 Contig53598_RC SEQ ID NO 2537

NM_006622 SEQ ID NO 1302

 Contig53757_RC SEQ ID NO 2543

NM_006696 SEQ ID NO 1308

 Contig53944_RC SEQ ID NO 2545

NM_006769 SEQ ID NO 1316

Contig54425 SEQ ID NO 2561

NM_006787 SEQ ID NO 1319

 Contig54547_RC SEQ ID NO 2565

NM_006875 SEQ ID NO 1334

 Contig54757_RC SEQ ID NO 2574

NM_006885 SEQ ID NO 1335

 Contig54916_RC SEQ ID NO 2581

NM_006918 SEQ ID NO 1339

 Contig55770_RC SEQ ID NO 2604

NM_006923 SEQ ID NO 1340

 Contig55801_RC SEQ ID NO 2606

NM_006941 SEQ ID NO 1342

 Contig56143_RC SEQ ID NO 2619

NM_007070 SEQ ID NO 1354

 Contig56160_RC SEQ ID NO 2620

NM_007088 SEQ ID NO 1356

 Contig56303_RC SEQ ID NO 2626

NM_007146 SEQ ID NO 1358

 Contig57023_RC SEQ ID NO 2639

NM_007173 SEQ ID NO 1359

 Contig57138_RC SEQ ID NO 2644

NM_007246 SEQ ID NO 1366

 Contig57609_RC SEQ ID NO 2657

 NM_007358 SEQ ID NO 1374

 Contig58301_RC SEQ ID NO 2667

NM_012135 SEQ ID NO 1385

 Contig58512_RC SEQ ID NO 2670

NM_012151 SEQ ID NO 1387

Contig60393 SEQ ID NO 2674

NM_012258 SEQ ID NO 1396

 Contig60509_RC SEQ ID NO 2675

NM_012317 SEQ ID NO 1399

 Contig61254_RC SEQ ID NO 2677

NM_012337 SEQ ID NO 1403

Contig62306 SEQ ID NO 2680

NM_012339 SEQ ID NO 1404

Contig64502 SEQ ID NO 2689

Table 4. 100 preferred markers of Table 3 that distinguish between BRCA1-related tumors and sporadic tumors.

Identifier

Correlation  Sequence 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)

57

 NM_005148

 -0.598612 UNC119  Homologue of unc119 (C.elegans)

 G26403

0.585054 YWHAH  Tyrosine 3- monooxygenase / tryptophan 5monooxygenase 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 nuclease fragment

 Contig41402_RC

-0.571650 Human DNA sequence of clone RP11-16L21 on chromosome 9. Contains the N4AP-dependent leukotriene B4 12-hydroxidehydrogenase gene, the gene for a new DnaJ domain protein similar to Drosophila, C. elegans and the proteins provided by Arabidopsis, the GNG10 gene for guanine 10 nucleotide binding gene protein, a new gene, ESTs, STSs, GSSs and six CpG islands

 NM_004791

 -0.564819 ITGBL1  Integrin, type 1 beta (with repeat domains related to EGF)

 NM_007070

0.561173 FAP48  FKBP associated protein

NM_014597

0.555907 HSU15552  82 kDa protein acid mRNA

AF000974

0.547194 TRIP6  Thyroid Hormone Receptor Interactor 6

NM_016073

 -0.547072  CGI-142  CGI-142

 Contig3940_RC

0.544073 YWHAH  3-mono-oxygenase / tryptophan 5monooxygenase tyrosine activation protein, eta polypeptide

NM_003683

0.542219 D2152056E  AND segment on chromosome 21 (single) sequence expressed 2056

 Contig58512_RC

 -0.528458  Protein mRNA related to the pancreatic tumor of Homo sapiens (FKSG12), cds 21completo

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 Homeo Box Protein Paired

AL137761

 -0.514891  MRNA from Homo sapiens; DKFZp586L2424 cDNA (from clone DKFZp586L2424)

NM_001919

 -0.514799 ICD  Dodecenoyl-Coenzyme A delta isomerase (3,2 transenoyl-Coenzyme A isomerase)

NM_000196

 -0.514004 HSD11B2  Hydroxysteroid (11-beta) dehydrogenase 2

NM_002200

0.513149 IRF5  Factor 5 interferon regulator

AL133572

0.511340  MRNA from Homo sapiens; DKFZp43410535 cDNA (from clone DKFZp43410535); 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  Product of the KIAA0258 gene

NM_006235

0.506585  POU2AF1  POU domain, class 2, associated factor 1

NM_014680

 -0.505779 KIAA0100  KIAA0100 gene product

X66087

0.500842  MYBL1  Type 1 of the viral oncogen homolog of avian myeloblastosis v-myb

Y07512

 -0.500686 PRKG1  CGMP-dependent protein kinase type I

58

 NM_006296

0.500344 VRK2 Vaccine-related kinase 2

 Contig44278_RC

 0.498260  DKFZP434K114  DKFZP434K114 protein

 Contig56160_RC

 -0.497695 ESTs

NM_002023

 -0.497570 FMOD Fibromodulin

M28170

0.497095 CD19  CD19 antigen

D26488

0.496511 KIAA0007  KIAA0007 protein

X72475

0.496125  H.sapiens mRNA for reorganized kappa lg light chain variable region (I.114)

K02276

0.496068  MYC  Homologue of the viral oncogene of avian myeloblastosis v-myc

NM_013378

0.495648 VPREB3  Gen 3 of pre-B lymphocyte

X58529

0.495608 IGHM  Immunoglobulin heavy constant mu

NM_000168

 -0.494260 GLI3 GL13 member of the GLI-Kruppel family (Greig syndrome)

NM_004866

 -0.492967 SCAMP1  Protein 1 of the secretory carrier mebrana

NM_003729

0.488971 CPR  Phosphate cyclase of the 3 ’terminal of RNA

NM_006875

0.487407 PIM2  Pim-2 oncogene

 NM_018188

0.487126 FLJ10709  Hypothetical protein FLJ10709

NM_004848

0.485408 ICB-1  Basal membrane inductive gene

NM_001179

0.483253 ART3 ADP-ribosyltransferase 3

NM_016548

 -0.482329  LOC51280  Golgi membrane GP73 protein

 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), related to polypeptide 1

NM_000067

 -0.481003 CA2  Carbonic Anhydrase II

NM_006885

 -0.479705 ATBF1  Link transcription factor 1 with AT

NM_002542

0.478282 OGG1  8-oxoguanin DNA glycosylase

AL133619

 -0.476596  MRNA from Homo sapiens mRNA; DKFZp434E2321 cDNA (from clone DKFZp434E2321); partial cds cds

D80001

0.476130 KIAA0179  KIAA0179 protein

NM_018660

 -0.475548  LOC55893  PRF-1 papillomavirus regulatory factor

AB004857

0.473440 SLC11A2 Family 11 of the solute carrier (proton-coupled divalent metal ion transporters), member 2

NM_002250

0.472900 KCNN4  Calcium-activated channel of intermediate / small potassium conductance, subfamily N, member 4

 Contig56143_RC

 -0.472611  ESTs, slightly similar to the A54849 alpha 1 (VII) collagen chain precursor [H.sapiens]

NM_001960

0.471502 EEF1D  Delta factor elongation of eukaryotic conversion (guanine nucleotide exchange protein)

 Contig52405_RC

 -0.470705 ESTs, moderately similar to ALU8_ALU HUMAN SUBFAMILIA SX SEQUENCE POLLUTION NOTICE ENTRY [H.sapiens]

 Contig30092_RC

 -0.469977  Isoform B mRNA (PRDM6) of zinc finger protein 6 from the PR domain of Homo sapiens, partial cds; alternately spliced

NM_003462

 -0.468753 P28  Dynein, axonemal, light intermediate polypeptide

Contig60393

0.468475 ESTs

59

 Contig842_RC

0.468158 ESTs

NM_002982

0.466362  SCYA2  Small inducible cytokine A2 (monocyte quemototactic protein 1, Sig-je mouse homologue)

 Contig14390_RC

 0.464150 ESTs

NM_001770

0.463847 CD19  CD19 antigen

AK000617

 -0.463158  MRNA from Homo sapiens; DKFZp434L235 cDNA (from clone DKFZp434L235)

AF073299

 -0.463007 SLC9A2  Family 9 of the solute carrier (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-oxoguanin DNA glycosylase

Contig10268_RC

 0.456933 Human DNA sequence of clone RP11-196N14 on chromosome 20. Contains ESTs, STSs, GSSs and CpG islands. It contains three new genes, part of a gene for a new protein similar to serine protein / threonine phosphatase 4 regulatory subunit 1 (PP4R1) and a gene for a new protein with an anchyrine domain.

 NM_014521

 -0.456733 SH3BP4  Protein 4 binding to the SH3 domain

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 2 related to (Drosophila) (Mayven)

NM_001985

 -0.453024 ETFB  Flavoprotein electron transfer, beta polypeptide

NM_000984

 -0.452935 RPL23A  L23a ribosomal protein

 Contig51953_RC

 -0.451695 ESTs

NM_015984

0.450491 UCH37  Ubiquitin C-terminal hydrolase UCH37

NM_000903

 -0.450371 DAY 4  Diaphorase (NADH / NADPH) (cytochrome b-5 reductase)

NM_001797

 -0.449862 CDH11  Cadherin 11, type 2, OB-cadherin (osteoblast)

NM_014878

0.449818 KIAA0020  Product of the KIAA0020 gene

NM_002742

 -0.449590 PRKCM  Protein kinase C, mu

 Table 5. 231 gene markers that distinguish between patients with good prognosis and patients with poor prognosis.

Gene Bank

SEQ ID NO Side of Genes SEQ ID NO

Access Number

Access Number

AA555029_RC

  SEQ ID NO 1 NM_013296 SEQ ID NO 1427

 AB020689

  SEQ ID NO 37 NM_013437 SEQ ID NO 1439

 AB032973

  SEQ ID NO 55 NM_014078 SEQ ID NO 1449

 AB033007

  SEQ ID NO 58 NM_014109 SEQ ID NO 1451

 AB033043

  SEQ ID NO 62 NM_014321  SEQ ID NO 1477

AB037745

  SEQ ID NO 75 NM_014363 SEQ ID NO 1480

 AB037863

  SEQ ID NO 88 NM_014750 SEQ ID NO 1527

60

 AF052159

  SEQ ID NO 120  NM_014754 SEQ ID NO 1528

 AF052162

  SEQ ID NO 121  NM_014791  SEQ ID NO 1535

AF055033

  SEQ ID NO 124  NM_014875 SEQ ID NO 1545

 AF073519

  SEQ ID NO 137  NM_014889 SEQ ID NO 1548

 AF148505

  SEQ ID NO 169  NM_014968 SEQ ID NO 1554

 AF155117

  SEQ ID NO 173  NM_015416 SEQ ID NO 1559

 AF161553

  SEQ ID NO 177  NM_015417 SEQ ID NO 1560

 AF201951

  SEQ ID NO 183  NM_015434 SEQ ID NO 1562

 AF257175

  SEQ ID NO 189  NM_015984 S  SEQ ID NO 1587

AJ224741

  SEQ ID NO 196  NM_016337  SEQ ID NO 1636

AK000745

  SEQ ID NO 219  NM_016359 SEQ ID NO 1638

 AL050021

  SEQ ID NO 257  NM_016448  SEQ ID NO 1645

AL050090

  SEQ ID NO 259  NM_016569  SEQ ID NO 1655

AL080059

  SEQ ID NO 270  NM_016577  SEQ ID NO 1656

AL080079

  SEQ ID NO 271  NM_017779 SEQ ID NO 1708

 AL080110

  SEQ ID NO 272  NM_018004 SEQ ID NO 1725

 AL133603

  SEQ ID NO 306  NM_018098 SEQ ID NO 1739

 AL133619

  SEQ ID NO 307  NM_018104  SEQ ID NO 1743

AL137295

  SEQ ID NO 315  NM_018120  SEQ ID NO 1745

AL137502

  SEQ ID NO 326  NM_018136  SEQ ID NO 1748

AL137514

  SEQ ID NO 327  NM_018265  SEQ ID NO 1766

AL137718

  SEQ ID NO 336  NM_018354 SEQ ID NO 1774

 AL355708

  SEQ ID NO 353  NM_018401  SEQ ID NO 1782

D25328

 SEQ ID NO 357 NM_018410  SEQ ID NO 1783

L27560

 SEQ ID NO 390 NM_018454  SEQ ID NO 1786

M21551

 SEQ 10 NO 394  NM_018455  SEQ ID NO 1787

NM_000017

 SEQ ID NO 416 NM_019013  SEQ ID NO 1809

NM_000096

 SEQ ID NO 430 NM_020166  SEQ ID NO 1825

NM_000127

 SEQ ID NO 436 NM_020188  SEQ ID NO 1830

NM_000158

 SEQ ID NO 442 NM_020244  SEQ ID NO 1835

NM_000224

 SEQ ID NO 453 NM_020386  SEQ ID NO 1838

NM_000286

 SEQ ID NO 462 NM_020675  SEQ ID NO 1842

NM_000291

 SEQ ID NO 463 NM_020974  SEQ ID NO 1844

NM_000320

 SEQ ID NO 469  R70506_RC  SEQ ID NO 1868

NM_000436

 SEQ ID NO 487 U45975  SEQ ID NO 1878

NM_000507

 SEQ ID NO 491 U58033  SEQ ID NO 1881

NM_000599

 SEQ ID NO 503 U82987  SEQ ID NO 1891

NM_000788

 SEQ ID NO 527 U96131  SEQ ID NO 1896

NM_000849

 SEQ ID NO 530 X05610  SEQ ID NO 1903

NM_001007

 SEQ ID NO 550 X94232  SEQ ID NO 1927

NM_001124

 SEQ ID NO 562  Contig753_RC  SEQ ID NO 1954

NM_001168

 SEQ ID NO 566  Contig1778_RC  SEQ ID NO 1979

NM_001216

 SEQ ID NO 574  Contig2399_RC  SEQ ID NO 1989

NM_001280

 SEQ ID NO 588  Contig2504_RC  SEQ ID NO 1991

NM_001282

 SEQ ID NO 589  Contig3902_RC  SEQ ID NO 2017

NM_001333

 SEQ ID NO 597 Contig4595  SEQ ID NO 2022

NM_001673

 SEQ ID NO 645  Contig8581_RC  SEQ ID NO 2037

61

 NM_001809

 SEQ ID NO 673  Contig13480_RC  SEQ ID NO 2052

NM_001827

 SEQ ID NO 676  Contig17359_RC  SEQ ID NO 2068

NM_001905

 SEQ ID NO 691  Contig20217_RC  SEQ ID NO 2072

NM_002019

 SEQ ID NO 711  Contig21812_RC  SEQ ID NO 2082

NM_002073

 SEQ ID NO 721  Contig24252_RC  SEQ ID NO 2087

NM_002358

 SEQ ID NO 764  Contig25055_RC  SEQ ID NO 2090

NM_002570

 SEQ ID NO 787  Contig25343_RC  SEQ ID NO 2092

NM_002808

 SEQ ID NO 822 Contig25991  SEQ ID NO 2098

NM_002811

 SEQ ID NO 823  Contig27312_RC  SEQ ID NO 2108

NM_002900

 SEQ ID NO 835  Contig28552_RC  SEQ ID NO 2120

NM_002916

 SEQ ID NO 838  Contig32125_RC  SEQ ID NO 2155

NM_003158

 SEQ ID NO 881  Contig32185_RC  SEQ ID NO 2156

NM_003234

 SEQ ID NO 891  Contig33814_RC  SEQ ID NO 2169

NM_003239

 SEQ ID NO 893  Contig34634_RC  SEQ ID NO 2180

NM_003258

 SEQ ID NO 896  Contig35251_RC  SEQ ID NO 2185

NM_003376

 SEQ ID NO 906  Contig37063_RC  SEQ ID NO 2206

NM_003600

 SEQ ID NO 929 Contig37598  SEQ ID NO 2216

NM_003607

 SEQ ID NO 930  Contig38288_RC  SEQ ID NO 2223

NM_003662

 SEQ ID NO 938  Contig40128_RC  SEQ ID NO 2248

NM_003676

 SEQ ID NO 941  Contig40831_RC  SEQ ID NO 2260

NM_003748

 SEQ ID NO 951  Contig41413_RC  SEQ ID NO 2266

NM_003862

 SEQ ID NO 960  Contig41887_RC  SEQ ID NO 2276

NM_003875

 SEQ ID NO 962  Contig42421_RC  SEQ ID NO 2291

NM_003878

 SEQ ID NO 963  Contig43747_RC  SEQ ID NO 2311

NM_003882

 SEQ ID NO 964  Contig44064_RC  SEQ ID NO 2315

NM_003981

 SEQ ID NO 977  Contig44289_RC  SEQ ID NO 2320

NM_004052

 SEQ ID NO 985  Contig44799_RC  SEQ ID NO 2330

NM_004163

 SEQ ID NO 995  Contig45347_RC  SEQ ID NO 2344

NM_004336

SEQ ID NO 1022  Contig45816_RC SEQ ID NO 2351

 NM_004358

SEQ ID NO 1026  Contig46218_RC SEQ ID NO 2358

 NM_004456

SEQ ID NO 1043  Contig46223_RC SEQ ID NO 2359

 NM_004480

SEQ ID NO 1046  Contig46653_RC SEQ ID NO 2369

 NM_004504

SEQ ID NO 1051  Contig46802_RC  SEQ ID NO 2372

NM_004603

 SEQ ID NO 1064  Contig47405_RC  SEQ ID NO 2384

NM_004701

 SEQ ID NO 1075  Contig48328_RC  SEQ ID NO 2400

NM_004702

 SEQ ID NO 1076  Contig49670_RC  SEQ ID NO 2434

NM_004798

 SEQ ID NO 1087  Contig50106_RC  SEQ ID NO 2445

NM_004911

 SEQ ID NO 1102 Contig50410  SEQ ID NO 2453

NM_004994

 SEQ ID NO 1108  Contig50802_RC  SEQ ID NO 2463

NM_005196

 SEQ ID NO 1127  Contig51464_RC  SEQ ID NO 2481

NM_005342

 SEQ ID NO 1143  Contig51519_RC  SEQ ID NO 2482

NM_005496

 SEQ ID NO 1157  Contig51749_RC  SEQ ID NO 2486

NM_005563

 SEQ ID NO 1173 Contig51963  SEQ ID NO 2494

NM_005915

 SEQ ID NO 1215  Contig53226_RC  SEQ ID NO 2525

NM_006096

 SEQ ID NO 1240  Contig53268_RC  SEQ ID NO 2529

NM_006101

 SEQ ID NO 1241  Contig53646_RC  SEQ ID NO 2538

NM_006115

 SEQ ID NO 1245  Contig53742_RC  SEQ ID NO 2542

62

 NM_006117

 SEQ ID NO 1246  Contig55188_RC  SEQ ID NO 2586

NM_006201

 SEQ ID NO 1254  Contig55313_RC  SEQ ID NO 2590

NM_006265

 SEQ ID NO 1260  Contig55377_RC  SEQ ID NO 2591

NM_006281

 SEQ ID NO 1263  Contig55725_RC  SEQ ID NO 2600

NM_006372

 SEQ ID NO 1273  Contig55813_RC  SEQ ID NO 2607

NM_006681

 SEQ ID NO 1306  Contig55829_RC  SEQ ID NO 2608

NM_006763

 SEQ ID NO 1315  Contig56457_RC  SEQ ID NO 2630

NM_006931

 SEQ ID NO 1341 Contig57595  SEQ ID NO 2655

NM_007036

 SEQ ID NO 1349  Contig57864_RC  SEQ ID NO 2663

NM_007203

 SEQ ID NO 1362  Contig58368_RC  SEQ ID NO 2668

NM_012177

 SEQ ID NO 1390  Contig60864_RC  SEQ ID NO 2676

NM_012214

 SEQ ID NO 1392  Contig63102_RC  SEQ ID NO 2684

NM_012261

 SEQ ID NO 1397  Contig63649_RC  SEQ ID NO 2686

NM_012429

 SEQ ID NO 1413 Contig64688  SEQ ID NO 2690

NM_013262

 SEQ ID NO 1425

Table 6. 70 preferred prognostic markers taken from Table 5.

AL080059

 -0.527150  Homo sapiens mRNA for KIAA1750 protein, partial cds

 Contig63649_ RC

 -0.468130 ESTs

 Contig46218_ RC

 -0.432540 ESTs

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_ RC

 -0.414970  ESTs, slightly similar to ISHUSS protein disulfuroisomerase [H.sapiens]

NM_003862

0.410964 FGF18  Fibroblast growth factor 18

 Contig28552_ RC

-0.409260  MRNA from Homo sapiens; DKFZp434C0931 cDNA (from clone DKFZp434C0931); partial cds

 Contig32125_RC

0.409054 ESTs

U82987

0.407002 BBC3  Bcl-2 link component 3

AL137718

 -0.404980 RNA from Homo sapiens; DKFZp434C0931 cDNA (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 Exostosis (multiple) 1

NM_002019

-0.398070  FLT1  Tyrosine kinase 1 related to fms (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, 92kD gelatinase, 92kD

63

type IV collagenase)

 Contig55377_RC

0.390600 ESTs

 Contig35251_RC

-0.390410  Homo sapiens cDNA: fis FLJ22719, clone HSI14307

Contig25991

-0.390370  ECT2  Epithelial cell transforming sequence 2 oncogene

NM_003875

-0.386520  GMPS  Guanine monophosphate synthetase

NM_006101

-0.385890  HEC  Highly expressed in cancer, rich in repetitions of leucine heptates

NM_003882

0.384479 WISP1  WNT1 inducible protein 1 signaling procedure

NM_003607

-0.384390  PK428  Ser-Thr protein kinase related to myotonic dystrophy protein kinase

AF073519

-0.383340  SERF1A  Small factor 1A rich in EDRK (telomeric)

AF052162

-0.380830  FLJ12443  Hypothetical protein FLJ12443

NM_000849

0.380831 GSTM3  Glutathione S-transferase M3 (brain)

 Contig32185_ RC

-0.379170  CDNA from Homo sapiens fis FLJ13997, clone Y79AA1002220

NM_016577

-0.376230  RAB6B RAB6B, RAS oncogene family member

 Contig48328_ RC

0.375252  ESTs, slightly similar to T17248 hypothetical protein DKFZp586G1122.1 [H.sapiens]

 Contig46223_ RC

0.374289 ESTs

NM_015984

-0.373880  UCH37  Ubiquitin C-terminal hydrolase UCH37

NM_006117

0.373290 PECI  Peroxisomal D3, D2-enoyl-CoA isomerase

AK000745

-0.373060  CDNA from Homo sapiens fis FLJ20738, clone HEP08257

 Contig40831_ RC

-0.372930  ESTs

NM_003239

0.371524 TGFB3  Growth transforming factor, beta 3

NM_014791

-0.370860  KIAA0175  Product of the KIAA0175 gene

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_ RC

-0.367450  FLJ22477  Hypothetical protein FLJ22477

AL080079

-0.367390 DKFZP564D0462  Hypothetical protein DKFZp564D0462

NM_006931

-0.366490  SLC2A3  Family 2 of the solute carrier (facilitated glucose transporter), member 3

AF257175

-0.365900  Homo sapiens RNA (HCA64) of antigen 64 associated with hepatocellular carcinoma 64, complete cds

NM_014321

-0.365810  ORC6L  Origin recognition complex, subunit 6 related to (yeast counterpart)

NM_002916

-0.365590  RFC4  Replication factor C (activator 1) 4 (37kD)

 Contig55725_ RC

-0.365350  ESTs, moderately similar to T50635 hypothetical protein DKFZp762L0311.1 [H.sapiens]

 Contig24252_ RC

-0.364990  ESTs

AF201951

0.363953 CFFM4  High affinity immunoglobulin epsilon receptor beta subunit

NM_005915

-0.363850  MCM6  Deficient in maintenance of minichromosome (mis5, S. pombe) 6

NM_001282

0.363326 AP2B1  Adapter-related protein complex 2, beta 1 subunit

64

 Contig56457_ RC

-0.361650  TMEFF1  Transmembrane protein related to EGF and two domains 1 related to folistatin

 NM_000599

-0.361290  IGFBP5  Binding protein 5 with insulin-like growth factor

NM_020386

-0.360780  LOC57110  H-REV107 protein related protein

 NM_014889

-0.360040  MP1  Metalloprotease 1 (pitrilisin family)

AF055033

-0.359940  IGFBP5  Binding protein 5 with insulin-like growth factor

NM_006681

-0.359700  NMU  Neuromedin U

NM_007203

-0.359570  AKAP2  A Mooring protein kinase 2 (PRKA)

 Contig63102_ RC

0.359255 FLJ11354  Hypothetical protein FLJ11354

NM_003981

-0.358260  PRC1  Protein regulator of cytokinesis 1

 Contig20217_ RC

-0.357880  ESTs

NM_001809

-0.357720  CENPA  Centromer protein (17kD)

 Contig2399_RC

-0.356600  SM-20  Similar to rat smooth muscle SM-20 protein

NM_004702

-0.356600  CCNE2 Cyclin E2

NM_007036

-0.356540  ESM1  Endothelial cell specific molecule 1

NM_018354

-0.356000 FLJ11190 FLJ11190 hypothelial protein

[0037] The sets of markers listed in Tables 1-6 are partially overlapping; In other words, some markers are present in multiple sets, while others are unique to a particular set (FIG. 1).

[0038] Thus, in a materialization a set of 256 genetic markers that can distinguish between ER (+) and ER (-) and also between BRCA1 tumors and sporadic tumors (ie, classify a tumor as ER () is described. -) or ER (-) and related to BRCA1 or sporadic). In a more specific materialization, 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 related to BRCA1 or are described sporadic. In other

10 materialization describes 165 markers that can distinguish between ER (+) and ER (-) and also among patients with good prognosis versus poor prognosis (that is, classify a tumor as ER (-) or as ER (+) and as that has to be removed from a patient with a good prognosis or with a poor prognosis). Subsets of at least 20, 50, 100 or 125 of the complete set of 165 markers are also described, which can also classify a tumor as ER (-) or as ER (+) and as having to be removed from a

15 patient with good prognosis or poor prognosis. In addition, a set of twelve markers that can distinguish between BRCA1 tumors and sporadic tumors, and between patients with good prognosis versus poor prognosis is described. Finally, eleven markers are described that are able to differentiate the three categories. Conversely, 2050 of the 2,460 markers with ER category are described that can determine only the ER category, 173 of the 430 ARC47 markers versus sporadic markers that

20 can only determine the BRCA1 versus the sporadic category and 65 of the 231 prognostic markers that can only determine the prognosis. In more specific embodiments, subsets of at least 20, 50, 100, 200, 500, 1,000, 1,500 or 2,000 of the 2,050 markers with ER category are described, which can also only determine the ER category. Subsets of at least 20, 50, 100 or 150 of the 173 markers that also determine only BRCA1 against sporadic tumors are also described. It also

25 describe subsets of at least 20, 30, 40, or 50 of the 65 forecast markers that can also only determine the forecast category.

[0039] Any of the marker sets provided above may be used only specifically or in combination with other markers that are not of the set. For example, markers that distinguish the ER category can be used in combination with the BRCA1 markers versus the 30 sporadic ones, 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.

[0040] The relationship between the sets of markers is shown in the diagram of FIG. one.

5.3.2 MARKER IDENTIFICATION

[0041] Sets of markers for the identification of conditions or indications related to breast cancer are described. In general, the sets of markers were identified by determining which of between -25,000 human markers had expression patterns that correlated with the conditions or indications.

[0042] In a materialization, the method of identifying marker sets is as follows. After extraction and labeling of target polynucleotides, the expression of all markers (genes) in an X sample is compared with the expression of all markers (genes) in a pattern or control. In a materialization, the pattern or control comprises target polynucleotide molecules obtained from a sample of a normal individual (that is, from an individual not suffering from breast cancer). In a preferred embodiment, the pattern or control is a reserve of target polynucleotide molecules. The reserve can be obtained from samples collected from a series of normal individuals. In a preferred embodiment, the pool comprises samples taken from a series of individuals who have sporadic tumors. In another preferred embodiment, the pool comprises a population of artificially generated nucleic acids and intended to approximate the level of nucleic acid derived from each marker found in a pool of nucleic acid derived markers obtained from tumor samples. In a further preferred embodiment, the reserve is obtained from cell lines or from samples of normal cell lines or breast cancer.

[0043] The comparison can be made by any means known in the art. For example, expression levels of various markers can be calculated by separating target polynucleotide molecules (e.g., RNA or cDNA), derived from the markers in agarose or polyacrylamide gels, followed by hybridization with specific oligonucleotide probe probes. . Alternatively, the comparison can be made by labeling target polynucleotide molecules followed by separation in a sequencer gel. Polynucleotide samples are placed in the gel so that patient and control or pattern are in adjacent corridors. The comparison of expression levels is performed visually or by means of a densitometer. In a preferred embodiment, the expression of all markers is calculated simultaneously by hybridization to a biochip. In each approach, markers that meet certain requirements are identified as associated with breast cancer.

[0044] The marker is selected based on some significant difference in expression in a sample when compared to a pattern or control condition. The selection can be made based on a significant up or down regulation of the marker in the patient sample. The selection can also be made by calculating the statistical relevance (that is, the p-value) of the correlation between the expression of the marker and the condition or indication. If possible, both selection criteria should be used. Thus, in a embodiment of the present invention, the markers associated with breast cancer are selected if they meet these two conditions: that they show a change of more than double (increase or decrease) in expression compared to a pattern and that the p-value for the correlation between the existence of breast cancer and the change in the expression marker does not exceed 0.01 (that is, it is statistically significant).

[0045] The expression of the markers identified in relation to breast cancer is then used to identify markers that can differentiate tumors into clinical types. In a specific materialization using a series of tumor samples, the markers are identified by calculating the correlation of coefficients between the clinical category or clinical parameter (s) and the ratio of linear, logarithmic expression or any transformation thereof. in all samples of each gene separately. Specifically, the correlation coefficient is calculated as

image 1 Equation (2)

where č represents the clinical parameters or categories and ř represents the quotient of linear, logarithmic expression or any transformation thereof between sample and control. Markers in which the correlation coefficient exceeds a limit are classified as markers in relation to breast cancer specific to a particular clinical type. This limit or threshold coincides with a certain relevance of the differentiating genes obtained by Monte-Carlo simulations. The threshold depends on the image 1 number of

used samples; The threshold can be calculated as 3 X image 1 where

  It is the distribution width and n = the number of samples. In a specific materialization, the markers are chosen if the correlation coefficient is greater than 0.3, approximately, or less than 0.3, approximately.

[0046] Next, the relevance of the correlation is calculated. Said relevance can be calculated by any statistical means with which this relevance is calculated. In a specific example, a set of data is generated using a Monte-Carlo technique to randomize the association between the difference in expression of a particular marker and the clinical category. The frequency distribution of the markers that meet the requirements by calculating correlation coefficients is compared with the number of markers that meet the requirements in the data generated by the Monte-Carlo technique. The frequency distribution of the markers that meet the requirements in the Monte-Carlo trials is used to determine whether the number of markers selected by correlation with the clinical data is significant. See example

Four.

[0047] Once a marker has been identified, the markers can be sorted hierarchically by their relevance of differentiation. One form of hierarchical ordering is by the breadth of correlation between the change in the expression and the specific condition that is being differentiated. Another also widely used means is to use a statistical metric. In a specific materialization, the metric is a Fisher-type statistic:

image 1

    Equation (3)

[0048] In this equation, (x1) is the weighted mean error of the log ratio of the transcription expression measurements within a first diagnostic group (eg, ER (-), x-2) is the mean weighted 10 of the logarithmic quotient within a second diagnostic group related to the first (e.g., ER (+)), σ1 is the variance of the log of the quotient within the ER (-) group and n1 is the number of samples for which have valid measurements of logarithms of the quotient. σ2 is the variance of the logarithmic quotient within a second diagnostic group (e.g., ER (+)), and n2 is the number of samples for which valid measurements of logarithmic ratios are available. The t-value represents the variance compensation difference

15 between two socks.

[0049] The hierarchical set of markers can be used to optimize the number of markers in the set used for differentiation. This is generally carried out in a method of "leaving one out" as follows: in a first trial, a subset, for example 5, of the markers of the top positions of the hierarchical list is used to generate a template, where from X samples X-1 is used to generate the template and the category of the remaining sample is predicted. This process is repeated for each sample until each of the X samples has been planned once. In a second trial, additional markers are added, for example 5, so that a template is now generated from 10 markers, and the result of the remaining sample is predicted. This process is repeated until the entire set of bookmarks has been used to generate a template. Type 1 errors (false) are counted for each of the tests

25 positive) and type 2 errors (false negative): the optimal number of markers is that number where the error rate 1 or the error rate 2, or preferably the total error rate 1 and error 2 is lower .

[0050] For prognostic markers, validation of the set of markers can be carried out by means of an additional statistic, a survival model. This statistic generates the probability of distant tumor metastases as a function of time since the initial diagnosis. You can use a series of

30 models, such as Weibull, normal, logarithmic normal, logarithmic logistic, logarithmic exponent or logarithmic Rayleigh (Chapter 12 of "Life Testing", S-PLUS 2000 GUIDE TO STATISTICS, Vol. 2, p. 368 ( 2000)). For the "normal" model, the probability of distant metastasis P at a time t is calculated as

image 1 Equation (4)

[0051] where α is fixed and equal to 1 and τ is a parameter that must be adjusted and measures the "life forecast".

[0052] For all those skilled in the art it will be apparent that the above methods, especially the statistical methods, described above, are not limited to the identification of markers associated with breast cancer, but can be used to identify sets of marker genes associated with any phenotype. The phenotype may be the presence or absence of a disease such as cancer, or the presence or absence of any clinical category of identification associated with that cancer.

40 In the context of diseases, the phenotype can be a prognosis such as survival time, the probability of distant metastases from a disease state or the probability of a specific response to a therapeutic or prophylactic regimen. The phenotype does not have to be a cancer or a disease; The phenotype can be a nominal characteristic associated with a healthy individual.

5.3.3 SAMPLE COLLECTION

[0053] In the present invention, target polynucleotide molecules are extracted from a sample taken from an individual suffering from breast cancer. The sample may be collected in any clinically acceptable manner, but always in a manner that conserves marker derived polynucleotides (eg, RNA). If possible, the ANRm or nucleic acids derived from it (i.e., the cDNA or the amplified DNA) should be labeled so that they differ from the control or standard polynucleotide molecules and both are

50 will hybridize simultaneously or independently with a biochip comprising some or all of the markers or sets or subsets of markers described above. Alternatively, the ANRm or nucleic acids derived therefrom can be labeled with the same label as the control or standard polynucleotide molecules, in which the intensity of hybridization of each in a particular probe is compared. The sample may consist of any clinically relevant tissue sample, such as a tumor biopsy or a fine needle aspirate, or a sample of body fluid, such as blood, plasma, serum, lymph, ascites fluid, cystic fluid, urine or nipple exudate . The sample can be taken from a human being or, in a veterinary context, from non-human animals such as ruminants, horses, pigs or sheep, or from domestic pets such as felines and canines.

[0054] The methods for preparing total RNA and poly (A) + are well known and are generally described in Sambrook et al., MOLECULAR CLONING - A MANUAL LABORATORY (2ND ED.), Vols. 1-3, Cold Spring Harbor Laboratory, Cold Spring Harbor, New York (1989)) and Ausubel et al., CURRENT PROTOCOLS IN MOLECULAR BIOLOGY, vol. 2, Current Protocols Publishing, New York (1994)).

[0055] RNA can be isolated from eukaryotic cells by processes that involve cell lysis and denaturation of the proteins contained therein. The cells of interest consist of wild-type cells (that is, non-cancerous), wild-type cells exposed to drugs, tumor or tumor-derived cells, modified cells, normal or tumor cell line cells and modified cells exposed to medications.

[0056] Additional steps can be taken to remove the DNA. A cell lysis can be carried out with a non-ionic detergent followed by microcentrifugation to remove the nuclei and hence the mass of the cellular DNA. In one embodiment, the RNA is extracted from the cells of the various types of interest by guanidine thiocyanate lysis followed by CsCl centrifugation to separate the RNA from the 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, New York (1989) Alternatively, the separation of RNA from DNA can be carried out by organic extraction, for example, with hot phenol or phenol / chloroform / isoamyl alcohol.

[0057] If desired, RNase inhibitors can be added to the lysis buffer. Likewise, for certain types of cells, it may be advisable to add a protein denaturation / digestion step to the protocol.

[0058] For many applications, it is desirable first of all to 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 its 3 ’end. With this it is possible to enrich them by chromatographic affinity, for example, by oligo (dT) or poly (U) coupled to a solid support, such as cellulose or Sephadex ™ (see Ausubel et al., CURRENT PROTOCOLS IN MOLECULAR BIOLOGY, vol. 2, Current Protocols Publishing, New York (1994) Once bound the poly (A) + mRNA, it is eluted from the affinity column by 2 mM EDTA / 0.1% SDS.

[0059] The RNA sample comprises a plurality of different mRNA molecules, each of which has 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 in 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.

[0060] In a specific embodiment, total RNA or mRNA of the cells are used in the methods of the invention. The source of RNA can be cells of a plant or animal, human, mammal, primate, non-human animal, dog, cat, mouse, bird, yeast, eukaryotic, prokaryotic, etc. In specific embodiments, the method of the invention is used with a sample containing mRNA or total RNA of 1x106 cells or less. In another embodiment, proteins can be isolated from previous sources with methods known in the art for use in protein level expression analysis.

[0061] Probes to homologs of the marker sequences disclosed herein may preferably be used where non-human nucleic acid is analyzed.

5.4 EMPLOYMENT METHODS OF BREAST CANCER MARKER SETS

5.4.1 DIAGNOSTIC METHODS

[0062] The following describes methods of using the marker sets to analyze a sample of an individual in order to determine the type or subtype of the individual's tumor at the molecular level, whether it is an ER (+) type tumor. or ER (-) and whether it is a tumor associated with BRCA1 or sporadic. It is not really necessary for the individual to suffer from breast cancer. Essentially, it involves comparing the expression of specific marker genes in the individual, or a sample taken from them, with a pattern or control. For example, suppose two conditions in relation to breast cancer, X and Y. We can compare the level of expression of breast cancer prognosis markers for condition X in a

individual with the level of polynucleotides derived from the marker in a control, wherein the level represents the level of expression indicated by the samples having the condition X. In this case, if the expression of the markers in the individual's sample is substantially (that is, statistically) different from that of the control, then the individual has no condition X. Where, as here, the option is bimodal (that is, a sample is either X 5 or Y), it can additionally be said that the individual it has the Y condition. Of course, the comparison can also be made with a control that represents the Y condition. Preferably, the two will be carried out simultaneously, so that each control acts both as a positive and a negative control. Thus, the typical result may well be a demonstrable difference with respect to expression levels (that is, the amount of RNA derived from the marker, or polynucleotides derived therefrom) represented by the control,

10 or no significant difference.

[0063] Thus, in a materialization, the method for determining the specific state of an individual in relation to a tumor comprises the steps of (1) hybridizing the labeled target nucleotides of an individual with a biochip containing one of the sets of markers above; (2) hybridize the standard or control polynucleotide molecules with the biochip, where the standard or control molecules are labeled differently from the target molecules, and (3) determine the difference in transcription levels,

or the absence thereof, between the destination and the pattern or control, where the difference, or the absence thereof, determines the individual's status in relation to the tumor. In a more specific materialization, the pattern or control molecules comprise polynucleotides obtained from markers from a sample pool of normal individuals, or a tumor sample pool from individuals with sporadic type 20 tumors. In a preferred embodiment, the pattern or control is a reserve of artificially generated markers derived polynucleotides, a reserve that is intended to mimic the level of expression of markers appreciated in clinical samples of normal or breast cancer tissue that has a clinical indication. specific (that is, cancerous or non-cancerous); ER (+) or ER (-) tumor; BRCA1- or sporadic tumor. In another specific materialization, the control molecules comprise a reserve obtained from

25 normal or breast cancer cell lines.

[0064] The following describes sets of markers that serve to distinguish types of ER (+) from ER (-) tumors. Thus, in a materialization of the method above, the level of polynucleotides (that is, mRNA

or polynucleotides derived therefrom) in a sample of an individual, expressed from the markers provided in Table 1, is compared with the level of expression of the same markers from a control, wherein the control comprises polynucleotides in relationship with markers obtained from samples of ER (+), ER (-) or both. Preferably, the comparison will be with both ER (+) and ER (-) and preferably the comparison will be with polynucleotide reserves from a series of ER (+) and ER (-) samples, respectively. Where the expression of individual markers is more similar or in closer correspondence with the ER (+) control and does not resemble or correspond to the ER (-) control, the

35 individual will be classified as ER (+). Where the reservation is not pure ER (+) or ER (-), for example, a sporadic reservation will be used. A set of experiments that use individuals with a known ER category should be hybridized against the reserve, in order to define the expression templates for the ER (+) and ER (-) group. Each individual with an unknown ER category will be hybridized against the same reserve and the expression profile will be compared with the templates (s) to determine the individual's ER category.

[0065] What is described below are sets of markers that serve to distinguish BRCA1-related tumors from sporadic tumors. Therefore, the method can be executed substantially the same as for the determination of ER (+/-), with the exception that the markers are those listed in Tables 3 and 4 and that the control markers are a stock of samples of BRCA1 tumors of marker-derived polynucleotides and a pool of marker-derived polynucleotides from

45 sporadic tumors. A patient is considered to have a germline mutation of BRCA1 where the expression of the individual marker-derived polynucleotides is more similar, or where it is in closer correspondence, to that of the BRCA1 control. When the control is not pure BRCA1 or sporadic, two templates can be defined in a manner similar to that used to find the ER category, as explained above.

[0066] For the previous two materializations of the method, the entire set of markers can be used (that is, the complete set of markers for Tables 1 or 3). In other embodiments, subsets of the markers can be used. In a preferred embodiment, the preferred markers listed in Tables 2 or 4 are used.

[0067] The similarity between the marker expression profile of an individual and that of a control can

55 calculated in several ways. In the simplest case, profiles can be visually compared on a printed list of expression difference data. Alternatively, similarity can be calculated mathematically.

[0068] In a materialization, the measure of similarity between two patients x and y, or one patient x and a template y, can be calculated using the following equation:

image 1

   Equation (5)

In that equation, x e y are two patients with components of logarithmic quotient xi and yi, i = 1, ..., N = 4,986. A is associated with the error σxi. The lower the σxi value, the more reliable the measurement xi

image2

is the weighted arithmetic mean of error.

[0069] In a preferred embodiment, templates are developed by comparison of samples. The template is defined as the weighted mean error of the logarithmic quotient of the difference in expression for the group of marker genes capable of differentiating the specific condition from that related to breast cancer. For example, templates are defined for ER (+) samples and for ER (-) samples. Next, a classifier parameter is calculated. This parameter can be calculated by differences in expression level between the sample and the template or by finding a correlation coefficient. This coefficient, Pi, can be calculated using the following equation:

image 1 Equation (1)

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

[0070] Thus, in a more specific materialization, the above method for determining the specific category of an individual in relation to the tumor comprises the steps of (1) hybridizing the labeled target polynucleotides of an individual with a biochip that contain one of the marker sets above; (2) hybridize standard or control polynucleotide molecules with the biochip, wherein the standard or control molecules are labeled differently from the target molecules; (3) determine the ratio (or difference) of transcription levels between two channels (individual and control), or simply the individual's transcription levels, and (4) compare the results of (3) with the predefined templates, in where said determination is carried out through the statistics of Equation 1 or Equation 5 and where the difference, or the absence thereof, determines the category of the individual in relation to the tumor.

5.4.2 FORECAST METHODS

[0071] The present invention relates to sets of markers that serve to distinguish patient samples with good prognosis from patient samples with poor prognosis. Thus, the invention provides a method of using these markers to determine whether an individual suffering from breast cancer will have a good or poor clinical prognosis. In one embodiment, the invention provides a method to determine if an individual suffering from breast cancer is likely to experience a relapse within five years after the initial diagnosis (that is, if an individual has poor prognosis) comprising (1) compare the level of expression of the markers listed in Table 5 in a sample taken from the individual at the level of the same markers in a pattern or control, where the levels of the pattern or control represent those found in an individual with poor prognosis, and ( 2) determine if the level of nucleotides related to markers in the individual's sample differs significantly from the level of control, where if a substantial difference is not appreciated, the patient has poor prognosis, while if there is a substantial difference, the Patient has a good prognosis. Those skilled in the art will immediately notice that the markers associated with the good prognosis can also be used as controls. In a more specific materialization both controls are tested. In case the reserve is not "good prognosis" or "poor prognosis" pure, a set of experiments with individuals with known results should be hybridized against the reserve, to define the expression templates for the groups of good diagnosis and poor diagnosis. Each individual with an unknown result is hybridized against the same reserve and the resulting expression profile is compared with the templates to predict its outcome.

[0072] The poor prognosis of breast cancer may indicate that a tumor is relatively aggressive, while the good prognosis may indicate that a tumor is relatively non-aggressive. There is a method to determine the course of treatment of a breast cancer patient, which consists in determining whether the level of expression of the 231 markers in Table 5, or a subset thereof, corresponds to the level of said markers. in a sample that represents a good prognostic expression pattern or a poor prognosis pattern, and determine a duration of treatment, where if the expression corresponds to the poor prognosis pattern, the tumor is treated as an aggressive tumor.

[0073] As with diagnostic markers, the method can use the complete set of markers listed in Table 5. However, subsets of the markers can also be used. In a preferred embodiment, the subset listed in Table 6 is used.

[0074] The classification of a sample as "good prognosis" or "poor prognosis" is carried out in substantially the same way as for the diagnostic markers described above, where a template is generated with which the levels of expression of markers in the sample.

[0075] The use of marker sets is not limited to the states related to breast cancer prognosis, so it can be applied in a variety of clinical or experimental phenotypes or states, where gene expression plays a role. Where a set of markers corresponding to two or more phenotypes has been identified, the marker sets can be used to distinguish said phenotypes. For example, the phenotypes can be the diagnosis and / or prognosis of clinical categories or phenotypes associated with other cancers, other disease states or other physiological states, where the level of expression data comes from a set of genes in correlation with the specific disease or physiological state.

5.4.3 IMPROVING SENSITIVITY TO EXPRESSION LEVEL DIFFERENCES

[0076] By using the markers disclosed herein and, in fact, by using any set of markers to differentiate an individual with a phenotype of another individual with a second phenotype, we can compare the absolute expression of each of the markers of a sample with a control; for example, the control may be that of the average level of expression of each of the markers, respectively, in a reserve of individuals. To increase the sensitivity of the comparison, however, the expression level values should preferably be transformed in a number of ways.

[0077] For example, the expression level of each of the markers can be normalized by the average level of expression of all markers whose expression level is determined, or by the average level of expression of a set of control genes. Thus, in a materialization, the markers are represented with probes in a biochip and the expression level of each of the markers is normalized by means of the mean or the median level of expression in all the genes represented in the biochip, including any gene. no marker In a specific materialization, normalization is carried out by dividing the median or average level of expression of all biochip genes. In another embodiment, the level of expression of the markers is normalized by means of the mean or the median level of expression of a set of control markers. In a specific materialization, the control markers comprise a set of constitutive genes. In another specific materialization, normalization is carried out by dividing by the median or average level of expression of the control genes.

[0078] The sensitivity of a marker-based assay will also increase if the expression levels of individual markers are compared with the expression of those same markers in a sample pool. Preferably, the comparison will be made with the mean or median level of expression of each of the marker genes in the sample pool. Such comparison can be made, for example, by dividing by the mean or median the level of expression of the reserve for each of the markers of the level of expression each of the markers of the sample. This has the effect of accentuating the relative differences in expression between the markers in the sample and the markers in the pool as a whole, making comparisons more sensitive and more likely to produce significant results than if only absolute expression levels are used. Expression level data can be transformed in the most convenient way; preferably, the expression level data for all is transformed into logarithm before taking the means or the medians.

[0079] Two methods can be used to make comparisons with a reservation. First, the expression levels of the sample markers can be compared with the expression level of those reserve markers where the nucleic acid from the sample and the nucleic acid from the reserve hybridize in the course of a single experiment. . Said method requires that new nucleic acid be generated in the reserve for each comparison or limited numbers of comparisons, so it is limited by the amount of nucleic acid available. Alternatively, and preferably, the expression levels in a reservation, whether normalized and / or transformed or not, will be stored on a computer, or on computer media, for use in comparisons with the individual expression level data of the sample (that is, single channel data).

[0080] Thus, the present invention provides the following method for 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 gene of a plurality of them in a first sample from the first cell or organism is compared with the level of expression of each of said genes, respectively, in a collective sample of a plurality of cells or organisms. , plurality of cells or organisms comprising different cells or organisms that show at least two different phenotypes, respectively, as stated above, to produce a first compared value. The first compared value is then compared with a second compared value, wherein said second compared value is the product of a method that consists in comparing the level of expression of each of said genes in a sample of a cell or organism that is characterized by having the second phenotype at the level of expression of each of said genes, respectively, in the collective sample. Optionally, the first value can be compared compared to additional compared values, respectively, where each additional compared value is the product of a method that consists in comparing the level of expression of each of said genes in a sample of a cell or organism that It is characterized by having a different phenotype of said first and second phenotypes, but included between at least two different phenotypes, at the level of expression of each of said genes, respectively, in said collective sample. Finally, it is determined to which of said second, third and, if present, one or more additional compared values resembles said first compared value, where the first cell or organism is classified as having the phenotype of the cell or organism used to produce said compared value more similar to said first compared value.

[0081] In a specific embodiment of this method, each of the values compared is a quotient of the expression levels of each of said genes. In another specific materialization, each of the expression levels of each of the genes in the collective sample is normalized before proceeding with any step of the comparison. In a more specific materialization, the normalization of expression levels is carried out by dividing by the median or average level of expression of each of the genes or dividing by the average or median level of expression of one or more constitutive genes of the collective sample of said cell or organism. In another specific materialization, the normalized expression levels undergo a logarithmic transformation and the steps of the comparison consist in subtracting the logarithmic transformation of the logarithm from the expression levels of each of the genes in the sample. In another specific materialization, the two or more different phenotypes are different stages of a disease or disorder. In another specific materialization, the two or more different phenotypes are different prognoses of a disease or disorder. In another specific materialization, the expression levels of each of the genes, respectively, of the collective sample or of said expression levels of each of said genes of a sample of the cell or organism that is characterized by having the first phenotype, second phenotype or said different phenotype of said first and second phenotypes, respectively, are stored on a computer or on a computer medium.

[0082] In another specific materialization, the two phenotypes have the category of ER (+) or ER (-). In another specific materialization, the two phenotypes have the category of BRCA1 or sporadic tumor. In another specific materialization, the two phenotypes are of good prognosis and poor prognosis.

[0083] Of course, single channel data can also be used without specific comparison with a mathematical sample pool. For example, a sample can be classified as having a first or second phenotype, where the first and second phenotypes are related, calculating the similarity of the expression of at least 5 markers in the sample, where the markers are correlated with the first

or the second phenotype, with the expression of the same markers in a first phenotype template and a second phenotype template, (a) labeling nucleic acids from a sample with a fluorophore to obtain a pool of fluorophore-labeled nucleic acids; (b) contacting said fluorophore-labeled nucleic acid with a biochip in conditions where hybridization can occur, detecting in each of a plurality of different biochip loci a fluorescent emission signal from said fluorophore-labeled nucleic acid which is linked to said biochip under said conditions, and (c) determining the similarity of expression of marker genes of the individual sample with the first and second templates, where if said expression is more similar to the first template, the sample is classified as the holder of the first phenotype, while if said expression is more similar to the second template, the sample is classified as having the second phenotype.

5.5 DETERMINATION OF EXPRESSION LEVELS OF MARKER GENES

5.5.1 METHODS

[0084] Expression levels of the marker genes of a sample can be determined by any means known in the art. The level of expression can be determined by isolating and determining the level (that is, the amount) of transcribed nucleic acid from each marker gene. Alternatively, or additionally, the level of specific proteins converted from the transcribed mRNA of a marker gene can be determined.

[0085] The level of expression of specific marker genes can be found by determining the amount of mRNA, or polynucleotides derived therefrom, present in a sample. Any method can be used to determine RNA levels. For example, RNA is isolated from a sample and separated into an agarose gel. The separated RNA is then transferred to a solid support, such as a filter. They are then hybridized with the filter by hybridization northern nucleic acid probes representing one or more markers and the amount of RNA from the label is determined. Said determination can be visual or computer-assisted, for example, by a densitometer. Another method of determining RNA levels is the use of a dot-blot (dot blur) or a slot-blot (slot blur). In this method the RNA of a sample, or the nucleic acid derived from it, is labeled. The RNA or nucleic acid derived therefrom is then hybridized with a filter containing oligonucleotides from one or more marker genes, wherein the oligonucleotides are placed on the filter in differentiated and easily identifiable positions. The hybridization, or the absence thereof, of the RNA labeled with the oligonucleotides bound to the filter is determined visually or by densitometer. The polynucleotides can be labeled by radiolabeling or with a fluorescent tag (that is, visible).

[0086] These examples are not intended to be restrictive; Other methods for determining the abundance of RNA are known in the art.

[0087] The level of expression of specific marker genes can also be calculated by determining the level of the specific protein expressed from the marker genes. This can be done, for example, by separating proteins from a sample in a polyacrylamide gel, followed by the identification of specific marker-derived proteins by antibodies in a western blot. Alternatively, the proteins can be separated by two-dimensional gel electrophoresis systems. Two-dimensional gel electrophoresis is well known in the art and usually consists of an isoelectric approach along a first dimension followed by SDS-PAGE electrophoresis (simple dilution system-polyacrylamide gel electrophoresis) along a second dimension. See, eg, 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 with numerous techniques, including mass spectrometry techniques, western blots and immunoblot analysis by polyclonal and monoclonal antibodies.

[0088] Alternatively, protein levels derived from markers can be determined by constructing an antibody biochip in which the binding centers comprise specific, immobilized and preferably monoclonal antibodies, to a plurality of protein species encoded by the cell genome. Preferably, the antibodies will be present in a substantial fraction of the proteins derived from markers of interest. Methods for making monoclonal antibodies are well known (see, eg, Harlow and Lane, 1988, ANTIBODIES: A LABORATORY MANUAL, Cold Spring Harbor, New York). In one embodiment, monoclonal antibodies are raised against synthetic peptide fragments designed on the basis of the genomic sequence of the cell. With said antibody deployment, proteins from the cell are contacted with the deployment and their binding is tested with assays known in the art.

In general, the expression, and level of expression, of diagnostic or prognostic proteins of interest can be detected by immunohistochemical staining of slices or sections of tissue.

[0089] Finally, the expression of marker genes in a series of tissue specimens can be characterized by "tissue deployment" (Kononen et al., Nat. Med 4 (7): 844-7 (1998)). In a tissue deployment, multiple tissue samples are calculated in the same biochip. The deployments allow in situ detection of RNA and protein levels; Consecutive sections allow the analysis of multiple samples simultaneously.

5.5.2 BIOCHIPS

[0090] In preferred embodiments, biochips are used to measure the expression so that the expression category of each of the above markers is calculated simultaneously. Oligonucleotide and cDNA deployments comprising hybridizable probes with the genes corresponding to each of the sets of markers described above are described (ie, markers to distinguish the ER category; markers to distinguish BRCA1 tumors from sporadic tumors; markers for distinguish patients with good prognosis from 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 of patients with poor prognosis; markers to distinguish BRCAI tumors from sporadic tumors and patients with a good prognosis of patients with poor prognosis; markers capable of distinguishing ER (+) from ER (-), BRCA1 tumors from sporadic tumors and patients with good prognosis of patients with poor prognosis, and unique markers for each category).

[0091] Biochips may comprise hybridizable probes with genes with markers capable of distinguishing the category of one, two or even the three clinical categories indicated above. Polynucleotide displays comprising probes to a subset or subset 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 are provided, up to the total set of

2,460 markers, which distinguish between ER (+) and ER (-) patients or tumors. Probes are also supplied to subsets of at least 20,30, 40, 50, 75, 100, 150, 200, 250, 300, 350 or 400 markers, up to the total set of 430 markers, which distinguish between tumors that contain a mutation of BRCA1 and sporadic tumors within a group of ER (-) tumors. In addition, probes are supplied to subsets of at least 20, 30, 40, 50, 75, 100, 150 or 200 markers, up to the total set of 231 markers, which distinguish between patients with good prognosis and poor prognosis within sporadic tumors . In a specific materialization, the deployment comprises probes to sets or subsets of markers directed to the three clinical categories.

[0092] In another specific materialization, the biochips used in the methods shown here possess additional markers to at least some of the markers listed in Tables 1-6. For example, in a specific materialization, the biochip is a screening or scanning display such as that described in Altschuler et al., International Pub. WO 02/18646, of 7 Marrzo of 2002 and Scherer et al., International Pub. WO 02/16650, of February 28, 2002. Screening and scanning deployments comprise addressable position probes located at regular intervals and derived from the genomic nucleic acid sequence, both express and non-expressed. They can also comprise probes in a subset, or all of the markers listed in Tables 1-6, or a subset thereof, as explained above, and can be used to monitor the expression of markers in the same way as a biochip containing only the markers listed in Tables 1-6.

[0093] In yet another specific materialization, the biochip is a commercially available cDNA biochip comprising at least five of the markers listed in Tables 1-6. Preferably, said commercially available cDNA biochip will comprise all the markers listed in Tables 1-6. However, said biochip may comprise 5, 10, 15, 25, 50, 100, 150, 250, 500, 1,000 or more markers in any of Tables 1-6, up to the maximum number of markers in a Table, and may comprise all the markers of any Tables 1 to 6 and a subset of another of Tables 1-6, or subsets of each, as explained above. In a specific materialization of the methods in this document, the markers that are all or part of Tables 1-6 constitute at least 50%, 60%, 70%, 80%, 90%, 95% or 98% of the probes of the biochip.

[0094] In the following sections, general methods concerning the construction of biochips and comprising the assemblies and / or subsets above are described.

5.5.2.1 BIOCHIPS CONSTRUCTION

[0095] Biochips are prepared by selecting probes comprising a polynucleotide sequence, then immobilizing said probes on a solid support or surface. For example, the probes may comprise DNA sequences, RNA sequences or DNA and RNA copolymer sequences. The polynucleotide sequences of the probes may also comprise analogs of DNA and / or RNA, or combinations thereof. For example, the polynucleotide sequences of the probes may be full of partial fragments of genomic DNA. The polynucleotide sequences of the probes may also be synthesized sequences of nucleotides, such as synthetic oligonucleotide sequences. Probe sequences can be synthesized either enzymatically, in vivo, enzymatically in vitro (eg, by PCR) or non-enzymatically in vitro.

[0096] The probe or probes used in the methods of the invention will preferably be immobilized on a solid support that can be both porous and non-porous. For example, the probes of the invention may be polynucleotide sequences that are attached to a nitrocellulose or nylon membrane or filter by covalent bonding at either end, 3 'or 5', of the polynucleotide. Such hybridization probes are well known in the art (see, eg, Sambrook et al., MOLECULAR CLONING - A LABORATORY MANUAL (2ND ED.), Vols. 1-3, Cold Spring Harbor Laboratory, Cold Spring Harbor, New York (1989) Alternatively, the solid support or surface may be a glass or plastic surface.In a particularly preferred embodiment, hybridization levels are measured with respect to probe biochips consisting of a solid phase on whose surface they are a population of polynucleotides, such as a population of DNA or DNA mimics or, alternatively, a population of RNA or RNA mimics, is immobilized.The solid phase can be a non-porous or optionally porous material, such as a gel

[0097] In preferred embodiments, the biochip comprises a support or surface with an orderly deployment of binding centers (eg, by hybridization) or "probes", each of which represents one of the markers described herein. . Preferably, the biochips will be addressable displays and, more preferably, addressable position displays. More specifically, each probe of the deployment will preferably be located in a known and predetermined location on the solid support so that the identity (that is, the sequence) of each probe can be determined from its position in the deployment (that is, in the support or surface). In preferred embodiments, each test is fixed to the solid support by covalent bonding by a single point.

[0098] Biochips can be made in several ways, some of which are described below. Regardless of the way they occur, biochips share certain characteristics. Biochips are reproducible, so you can produce multiple copies of a specific biochip and compare them with each other. Preferably, the biochips will be made of materials that are stable under binding conditions (eg, nucleic acid hybridization). The biochips will preferably be small, e.g., between 1 cm2 and 25 cm2, between 12 cm2 and 13 cm2, or 3 cm2. However, larger biochips are also contemplated, which may even be preferable, eg. for use in screening displays. Preferably, a specific binding center or a single set of binding centers in the biochip 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 mDNA specific derivative thereof). However, in general, other related or similar sequences will be linked to a specific cross-hybridization junction center.

[0099] The biochips of the present invention include one or more test probes, each of which has a polynucleotide sequence that is complementary to a sub-sequence of RNA or DNA to be detected. Preferably, the location of each probe on the solid surface will be known. In fact, the biochips will preferably be addressable position displays. In particular, each probe of the deployment will preferably be located in a known and predetermined location of the solid support, so that the identity (that is, the sequence) of each probe can be determined from its position in the deployment (that is, in the support or surface).

[0100] According to the invention, the biochip is a display (that is, a matrix) in which each position represents one of the markers described in this document. For example, each position may contain a DNA or a DNA analog based on the genomic DNA with which it can specifically hybridize a specific RNA or cDNA transcribed from the genetic marker. The DNA or DNA analog can be, for example, a synthetic oligomer

or a gene fragment. In a materialization, the probes that represent each of the markers are present in the deployment. In a preferred embodiment, the deployment comprises 550 of the 2,460 ER- category markers, 70 of the BRCA1 / sporadic markers and the total of 231 prognostic markers.

5.5.2.2 PREPARATION OF PROBES FOR BIOCHIPS

[0101] As indicated above, the "probe" with which a specific polynucleotide molecule specifically hybridizes contains a complementary genomic polynucleotide sequence according to the invention. The biochip probes will preferably consist of nucleotide sequences of no more than 1,000 nucleotides. In some embodiments, the deployment probes consist of nucleotide sequences of 10 to 1,000 nucleotides. In a preferred embodiment, the nucleotide sequences of the probes are of the order of 10-200 nucleotides in length and are genomic sequences of a kind of organism, so that a plurality of different probes are present, with complementary sequences and therefore capable of hybridizing with the genome of said species or organism, superimposed in sequence on all or part of said genome. In other specific embodiments, the probes are of the order of 10-30 nucleotides in length, of the order of 10-40 nucleotides in length, of the order of 20-50 nucleotides in length, of the order of 40-80 nucleotides in length, of the order 50-150 nucleotides in length, on the order of 80-120 nucleotides in length and, more preferably, 60 nucleotides in length.

[0102] The tests may comprise DNA or "mimics" of DNA (eg, derivatives and analogs) corresponding to a portion of the genome of an organism. In another embodiment, the biochip probes are complementary to RNA or mimic RNA. DNA mimics are polymers composed of subunits capable of specific hybridization of the Watson-Crick type with DNA, or specific hybridization with RNA. The nucleic acids can be modified in the base fraction, in the sugar fraction or in the phosphate skeleton. As an example of DNA mimics, one can cite, for example. Phosphorothioates

[0103] DNA can be obtained, for example, by polymerase chain reaction (PCR), by amplification of genomic DNA or by cloned sequences. PCR primers will preferably be chosen on the basis of a known genome sequence, which will result in the amplification of specific fragments of genomic DNA. There are computer programs well known in the art that serve to design primers with the required specificity and optimal amplification properties, such as Oligo version 5.0 (National Biosciences). Typically, each probe in the deployment 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, CA (1990). It will be apparent to those skilled in the art that robotic control systems are useful for isolating and amplifying nucleic acids.

[0104] An alternative preferred means of generating the polynucleotide probes of the biochip is the synthesis of synthetic polynucleotides or oligonucleotides, eg, by chemical N-phosphonate or phosphoramidite (Froehler et al., Nucleic Acid Res. 14: 5399- 5407 (1986); McBride et al., Tetrahedron Lett. 24: 246-248 (1983)).

Synthetic sequences are typically about 10 to about 500 bases in length, and more preferably about 40 to about 70 bases in length. In some embodiments, synthetic nucleic acids include unnatural bases, such as, but not limited to, inosine. As indicated above, nucleic acid analogs can be used as binding centers for hybridization. An example of a suitable nucleic acid is the peptide nucleic acid (see, eg, Egholm et al., Nature 363: 566-568 (1993); U.S. Patent No. 5,539,083). The probes will preferably be selected by 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 January 25, 2001; Hughes et al., Nat. Biotech. 19: 342-7 (2001)).

[0105] An experienced operator will also note that positive control probes, eg, probes known to be complementary and hybridizable with sequences in the target polynucleotide molecules and negative control probes, e.g. probes known to be non-complementary and hybridizable with sequences in the target polynucleotide molecules must be included in the deployment. In a materialization, the positive controls are synthesized along the perimeter of the deployment. In another materialization, positive controls are synthesized in diagonal stripes through deployment. In yet another embodiment, the reverse complement for each probe is synthesized next to the position of the probe to serve as a negative control. And in yet another materialization, sequences from other organism species are used as negative controls and as "locked" controls.

5.5.2.3 SUBJECTION OF PROBES TO THE SOLID SURFACE

[0106] The probes are attached to a solid support or surface, which can be made for example. glass, plastic (eg polypropylene, nylon), polyacrylamide, nitrocellulose, gel or other porous or non-porous material. A preferred method of attaching nucleic acids to a surface is by printing on glass plates, as generally explained in Schena et al, Science 270: 467-470 (1995). This method is especially useful for preparing cDNA biochips (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. USA 93: 10539-11286 (1995)).

[0107] A second preferred method for making biochips is to make high density deployments. Techniques for producing deployments containing thousands of complementary oligonucleotides are known for sequences defined at defined locations on a surface by photolithographic techniques for in situ synthesis (see Fodor et al., 1991, Science 251: 767-773; Pease et al., 1994 , Proc. Natl. Acad. Sci. USA 91: 5022-5026; Lockhart et al., 1996, Nature Biotechnology 14: 1675; US Patent 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 (eg, 60-mer) of known sequence are synthesized directly on a surface such as a derivatized glass slide. Usually, the display produced is redundant, with several oligonucleotide molecules per RNA.

[0108] Other methods can be used to make biochips, eg. by masking (Maskos and Southern, 1992, Nuc. Acids. Res. 20: 1679-1684). In principle, and as indicated above, any type of deployment can be used, for example, dot blots on a nylon hybridization membrane (see Sambrook et al., MOLECULAR CLONING - A MANUAL LABORATORY (2ND ED.), Vols. 1-3, Cold Spring Harbor Laboratory, Cold Spring Harbor, New York (1989)). However, as those skilled in the art will notice, very small deployments are often preferable, since hybridization volumes will be smaller.

[0109] In one embodiment, the deployments of the present invention are prepared by synthesizing polynucleotide probes on a support. In said materialization, the polynucleotide probes are attached to the support by covalent bonding at either end, 3 'or 5', of the polynucleotide.

[0110] In a particularly preferred embodiment, biochips of the invention are manufactured by an inkjet printing device for oligonucleotide synthesis, eg. with 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 ARRAY IN GENETIC ENGINEERING, Vol. 20, J.K. Setlow, Ed., Plenum Press, New York, pages 111-123. Specifically, polynucleotide probes of said biochips will preferably be synthesized in deployments, e.g. on a glass slide, serially depositing individual nucleotide bases in "microdroplets" of a solvent of high surface tension, such as propylene carbonate. The microdroplets have a very small volume (eg 100 pL or less, more preferably 50 pL

or less) and are separated from each other in the biochip (eg by hydrophobic domains) to form circular surface tension wells that define the positions of the deployment elements (that is, the different probes). Biochips made with this inkjet method are usually of high density, preferably with a density of at least 2,500 different probes per cm2. Poliucleotide probes are attached to the support by covalent bonding at either end, 3 'or 5', of the polynucleotide.

5.5.2.4 DESTINATION POLYNUCLEOTIC MOLECULES

[0111] The polynucleotide molecules that can be analyzed by the present invention (the "target polynucleotide molecules") can be of any clinically relevant origin, but are expressed as RNA or as a nucleic acid derived therefrom (eg cDNA or amplified RNA cDNA derivative that incorporates an RNA polymerase promoter), including naturally-generated nucleic acid molecules, as well as synthetic nucleic acid molecules. In one embodiment, the target polynucleotide molecules comprise RNA, including, but not limited to, total cellular RNA, poly (A) + (mRNA) messenger RNA or fraction thereof, cytoplasmic mRNA or cDNA transcribed RNA (i.e. , CRNA, see, e.g., Linsley & Schelter, US Patent Application No. 09 / 411,074, registered October 4, 1999, or Patents

U.S. Nos. 5,545,522, 5,891,636, or 5,716,785). Methods for preparing total RNA and poly (A) + are well known in the art and are generally described in, eg, Sambrook et al., MOLECULAR CLONING - A MANUAL LABORATORY (2ND ED.), Vols. 1-3, Cold Spring Harbor Laboratory, Cold Spring Harbor, New York (1989). In one embodiment, RNA is extracted from cells of the various types of interest of this invention by guanidine thiocyanate lysis followed by CsCl centrifugation (Chirgwin et al., 1979, Biochemistry 18: 5294-5299). In another embodiment, total RNA is extracted using a gel-based silicon column, marketed by RNeasy (Qiagen, Valencia, California) and StrataPrep (Stratagene, La Jolla, California). In an alternate embodiment, which is preferred for S. cerevisiae, RNA is extracted from cells by phenol and chloroform, as explained in Ausubel et al., Eds., 1989, CURRENT PROTOCOLS IN MOLECULAR BIOLOGY, Vol III, Green Publishing Associates, Inc., John Wiley & Sons, Inc., New York, p. 13.12.1-13.12.5). Poly (A) + RNA can be selected, for example, by selection with oligo-dT cellulose or, alternatively, by reverse transcription of oligo-dT barley from total cellular RNA. In one embodiment, the RNA can be fragmented with methods known in the art, eg, by incubation with ZnCl2, to generate RNA fragments. In another embodiment, the polynucleotide molecules analyzed by the invention comprise cDNA, or amplified RNA or cDNA PCR products.

[0112] In one embodiment, total RNA, mRNA or nucleic acids derived therefrom are isolated from a sample taken from a person suffering from breast cancer. Target polynucleotide molecules that are poorly expressed in specific cells can be enriched by standardization techniques (Bonaldo et al., 1996, Genome Res. 6: 791-806).

[0113] As explained above, the target polynucleotides are detectably labeled in one or more nucleotides. Any method known in the art serves to detectably label the target polynucleotides. Preferably, this labeling will uniformly incorporate the label all along the RNA and, more preferably, the labeling will be carried out as effectively as possible. A materialization uses oligo-dT barley reverse transcription for this label to incorporate the label; however, the conventional methods of this method tend to generate fragments with a 3 ’end. Accordingly, in a preferred embodiment, random primers (eg 9-mer) are used in reverse transcription to uniformly incorporate labeled nucleotides all along the target polynucleotides. Alternatively, random primers can be used in conjunction with PCR methods or in vitro transcription methods based on the T7 promoter to amplify the target polynucleotides.

[0114] In a preferred embodiment, the detectable label is a luminescent label. In the present invention, for example, fluorescent labels, bioluminescent labels, chemiluminescent labels and colorimetric labels can be used. In a particularly preferred embodiment, the tag is a fluorescent tag, such as a fluorescein, a phosphorus, a rhodamine or a polymetin dye derivative. Examples of commercially available fluorescent labels include, among others, fluorescent phosphoramidites such as FluorePrime (Amersham Pharmacia, Piscataway, NJ), Fluoredite (Millipore, Bedford, Mass.), FAM (ABI, Foster City, Calif.), And Cy3 or Cy5 (Amersham Pharmacia, Piscataway, NJ). In another embodiment, the detectable label is a radiolabeled nucleotide.

[0115] In another preferred embodiment, the target polynucleotide molecules of a patient's sample are labeled differently than the target polynucleotide molecules of a standard. The pattern may comprise target polynucleotide molecules of normal individuals (that is, not suffering from breast cancer). In a particularly preferred embodiment, the pattern comprises target polynucleotide molecules collected from samples from normal individuals or tumor samples from individuals with sporadic cancerous tumors. In another embodiment, the target polynucleotide molecules come from the same individual, but are taken at different time points and thus indicate the effectiveness of a treatment by the change in expression in the markers, or the absence thereof, during and after the course. of the treatment (that is, chemotherapy, radiotherapy or cryotherapy), where the step in the expression of the markers of a poor prognostic pattern to a good prognostic pattern indicates that the treatment is effective. In this materialization, different time points are labeled differently.

5.5.2.5 HYBRIDIZATION WITH BIOCHIPS

[0116] Nucleic acid hybridization and washing conditions are chosen such that the target polynucleotide molecules specifically bind or hybridize with the complementary polynucleotide sequences of the deployment, preferably with a particular deployment site where their complementary DNA is located.

[0117] Deploys containing double-stranded DNA probes located therein will preferably be subjected to denaturation conditions to make the single-stranded DNA before it comes into contact with the target polynucleotide molecules, eg, removing structures in hairpin or dimers that form quota with the autocomplementary sequences.

[0118] Optimal hybridization conditions will depend on the length (eg, oligomers against polynucleotides greater than 200 bases) and the type (eg, RNA or DNA) of the target and probe nucleic acids. Those skilled in the art will note that as the oligonucleotides become shorter, it may be necessary to adjust their length to achieve a relatively uniform melting temperature and therefore satisfactory hybridization results. The general parameters for the specific hybridization conditions (ie, rigorous) of nucleic acids are described in Sambrook et al., MOLECULAR CLONING - A MANUAL LABORATORY (2ND ED.), Vols. 1-3, Cold Spring Harbor Laboratory, Cold Spring Harbor, New York (1989), and in Ausubel et al., CURRENT PROTOCOLS IN MOLECULAR BIOLOGY, vol. 2, Current Protocols Publishing, New York (1994). Typical hybridization conditions for cDNA biochips from Schena et al. they are hybridization in 5 X SSC plus 0.2% SDS at 65˚C for four hours, followed by washing at 25˚C in a low hardness wash buffer (1 X SSC plus 0.2% SDS), followed by 10 minutes at 25 ° C in a wash buffer of greater hardness (0.1 X SSC plus 0.2% SDS) (Schena et al., Proc. Natl. Acad. Sci. USA 93: 10614 (1993)). Hybridization conditions provided in, for example, Tijessen, 1993, HYBRIDIZATION WITH NUCLEIC ACID PROBES, Elsevier Science Publishers B.V .; and Kricka, 1992, NONISOTOPIC DNA PROBE TECHNIQUES, Academic Press, San Diego, CA.

[0119] Especially preferred hybridization conditions are the hybridization of the probes at or near the average melting temperature (eg, not more than 5 ˚C, more preferably no more than 2 ˚C) at 1 M NaCl, 50 mM MES buffer (pH 6.5), 0.5% sodium sarcosine and 30% formamide.

5.5.2.6 SIGNAL OF DETECTION AND DATA ANALYSIS

[0120] When fluorescent-labeled probes are used, fluorescence emissions at each location of a biochip can be detected, preferably, by confocal laser scanning microscopy. In a materalization a sweep is carried out separately, by means of the appropriate excitation line, for each of the two fluorophores used. Alternatively, a laser that allows simultaneous specimen illumination at specific wavelengths to the two fluorophores and that the emissions of the two fluorophores can be analyzed simultaneously can be used (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. In a preferred embodiment, the displays are scanned with a fluorescent laser scanner with a computer-controlled XY phase and microscopic objective. Sequence of the two fluorophores is carried out with a multi-line mixed gas laser and the emitted light is fractionated by wavelength and is detected by two photomultiplier tubes.In Schena et al., Genome Res. 6: 639-645 (1996 ), and other references cited herein, fluorescent laser scanning devices are described. Alternatively, the fiber optic beam can be used described by Ferguson et al., Nature Biotech. 14: 1681-1684 (1996), to monitor the abundance of mRNA levels in a large number of places at once.

[0121] The signals are recorded and, in a preferred embodiment, analyzed by computer, eg. by means of a digital analogue board of 12 or 16 bits. In a materialization, the intermittences of the scanned image are eliminated by means of a graphics program (e.g., Hijaak Graphics Suite) and then analyzed by an image plotting program that creates a spreadsheet of the average hybridization for each length Wave in each place. If necessary, a certain experimental correction can be made to "produce crosstalk" (superimpose) between the channels for the two fluorescents. For any specific place of hybridization of the transcription display, a quotient of the emission of the two fluorophores can be calculated. The quotient is independent of the absolute expression of the related gene, but is useful for genes whose expression is significantly modulated in association with the different states related to breast cancer.

5.6 COMPUTER ASSISTED ANALYSIS

[0122] The following describes kits that comprise the marker sets above. In a preferred embodiment, the kit contains a biochip ready to hybridize with target polynucleotide molecules, plus the software for data analysis explained above.

[0123] The analytical methods explained in the previous sections can be implemented using the following computer systems and according to the following programs and methods. The computer system comprises internal components connected to external components. The internal components of a typical computer system include a processor element interconnected to a main memory. For example, the computer system may be an Intel 8086-, 80386-, 80486-, Pentium ™ or Pentium ™ based processor preferably with 32 MB or more of main memory.

[0124] External components may include mass storage. This mass storage can be one or more hard drives (which is usually included in the same package as the processor and memory). Such hard drives will preferably have a capacity of 1 GB or more of memory. The other external components are a user interface device, which can be a monitor, together with an input device, which can be a "mouse", or other graphic input devices, and / or a keyboard. You can also connect a printing device to the computer.

[0125] Typically, the computer system is also connected to a network link, which may be part of an Ethernet link with other local computer systems, remote computer systems or large-scale communication networks, such as the Internet. This network link allows the computer system to share data and processing tasks with other computer systems.

[0126] During the operation of this system, several software components are loaded into memory, which are both standard in the art and special in the present invention. These software components collectively make the computer system work according to the methods of this invention. These software components are typically stored in the mass storage device. A software component comprises the operating system, which is responsible for the management of the computer system and its interconnections. This operating system can be, for example, from 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 in this system to help programs implement the specific methods of this invention. Many high or low level computer languages can be used to program the analytical methods of this invention. The instructions can be interpreted during the period of operation or summarized. Among the preferred languages are C / C ++, FORTRAN and JAVA. Most preferably, the methods of this invention are programmed in mathematical software packages that allow the symbolic entry of equations and high-level process specification, including some or all of the algorithms to be used, thereby releasing the user from the need to programmatically process individual equations or algorithms. These packages include Mathlab from Mathworks (Natick, MA), Mathematica® from Wolfram Research (Champaign, IL), or S-Plus® from Math Soft (Cambridge, MA). Specifically, the software component includes the analytical methods of the invention as programmed in a procedural language or symbolic package.

[0127] The software to be included with the kit comprises the methods of data analysis of the invention as disclosed herein. Specifically, the software may include mathematical routines for marker discovery, including the calculation of correlation coefficients between clinical categories (that is, the ER category) and the expression of markers. The software can also include mathematical routines to calculate the correlation between expression of sample markers and expression of control markers, using data from the fluorescence generated by the display, to determine the clinical classification of a sample.

[0128] In a model materialization, to implement the methods of the present invention, the user first loads experimental data into the computer system. This data can be entered directly by the user from a monitor, a keyboard or any other computer system connected by network connection, or in a removable storage medium such as a CD-ROM, a floppy disk (not shown in the illustrations), a magnetic tape drive (not shown), a ZIP® drive (not shown) or over the network. The user then proceeds to the execution of the expression profile analysis software that performs the steps of the present invention.

[0129] In another model materialization, the user first loads experimental data and / or databases into the computer system. This data is loaded into memory from storage media or from a remote computer, preferably from a dynamic system of gene set databases, over the network. The user then proceeds to the execution of the software that carries out the steps of the present invention.

[0130] Those skilled in the art will find evident alternative computer systems and software for carrying out the analytical methods of this invention, which should be understood as included in the appended claims. Specifically, it is the purpose of the claims to include the alternative program structures for carrying out the methods of this invention, the evidence of which will be readily apparent to those experienced in the art.

6. EXAMPLES

Materials and methods

[0131] 117 tumor samples were collected from breast cancer patients. RNA samples were then prepared and the profile of each of said samples was drawn by inkjet printed biochips. Then marker genes were identified based on expression patterns; These genes were then used for practices with classifiers, which used these marker genes to classify tumors into diagnostic and prognostic categories. Finally, these marker genes were used to predict the outcome of diagnosis and prognosis in a group of individuals.

1. Sample collection

[0132] A total of 117 breast cancer patients under treatment at the Netherlands Cancer Institute / Antoni van Leeuwenhoek Hospital, Amsterdam, The Netherlands were selected, following the following clinical methods (data extracted from the medical records of the Registry of Tumors NKI / AvL, Department of Biometrics).

[0133] Group 1 (n = 97, 78 for practices, 19 for independent trials) was classified according to the method of:

(1) primary invasive breast carcinoma <5 cm (T1 or T2); (2) absence of axillary metastasis (N0); (3) age of diagnosis <55 years; (4) diagnosis period 1983-1996, and (5) without previously malignant tumors (excluding malignant cervical tumor in situ or basal cell carcinoma of the skin). All patients were treated with modified radical mastectomy (n = 34) or with conservative treatment of the breast (n = 64) including axillary lymph node dissection. Conservative treatment of the breast consisted of the removal of the tumor, followed by radiation of the entire breast at a dose of 50 Gy, followed by a reinforcement of 15 to 25 Gy. Five patients received adjuvant systemic therapy consisting of chemotherapy (n = 3) or hormonal therapy (n = 2), none of the other patients received additional treatment. All patients had at least one annual follow-up for a period of at least 5 years. Patient investigation was extracted from the Tumor Registry of the Biometrics Department.

[0134] The members of group 2 (n = 20) were classified as: (1) carriers of a germline mutation in BRCA1 or BRCA2; and (2) suffering from a primary invasive breast carcinoma. No selection or exclusion was made based on tumor size, lymph node category, age of diagnosis, year of diagnosis or other malignant tumors. The germline mutation class was already known before performing this research protocol.

[0135] Information about the individual from whom tumor samples were collected includes: year of birth; sex; if the individual is pre-menopausal or post-menopausal; year of diagnosis; number of positive lymph nodes and total number of nodes; if there was surgery and, if so, if the surgery was conservative of the breast or radical; if there was radiotherapy, chemotherapy or hormonal therapy. The tumor was graduated according to the formula P = TNM, where T is the size of the tumor (on a scale of 0-5); N is the number of nodes that are positive (on a scale of 0-4) and M is metastatic (0 = absent, 1 = present). The tumor was also classified according to the phase, the type of tumor (in situ or invasive; lobular or ductal; grade) and the presence or absence of estrogen and progesterone receptors. Cancer progression was described (where appropriate) by: distant metastasis, year of distant metastasis, year of death, year of last follow-up and BRCA1 genotype.

2. Tumors

[0136] The BRCA1 and BRCA2 germ mutation assay in DNA isolated from peripheral blood lymphocytes comprises a mutation screening using a Protein Truncation Test (PTT) of exon 11 of BRCA1 and exon 10 and 11 of BRCA2, PCR deletion of BRCA1, genomic deletion of exons 13 and 22, as well as Denaturing Gradient Gel Electrophoresis (DGGE) of the remaining exons. All aberrant bands were confirmed by genomic sequencing analyzed in an ABI3700 automatic sequencer and confirmed in a separate sample. Of all this, the material tumor material was subjected to instant freezing in liquid nitrogen in the hour following the surgery. From the frozen tumor material, a section colored with H&E (hematoxylin-eosin) was prepared before and after cutting slices for RNA isolation. These frozen H&E sections were calculated to find the percentage of tumor cells; Only samples with> 50% of tumor cells were selected for further study.

[0137] For all tumors specimens fixed in formaldehyde and received with paraffin were evaluated with standard histopathological procedures. Paraffin sections colored with H&E were examined to calculate the type of tumor (eg, ductal or lobular, according to WHO classification); to calculate the histological grade according to the method described by Elston and Ellis (grade 1-3); and to calculate the presence of lymphangio-invasive growth and the presence of extensive lymphocytic infiltration. All histological factors were independently calculated by two histologists (MV and JL); consensus was reached on the differences by jointly examining the slides. A representative slide of each tumor was used for immunohistochemical staining with antibodies directed against the estrogen receptor and progesterone with standard procedures. The result of coloring was recorded as a percentage of nuclei of positive coloration (0%, 10%, 20%, etc., and thus up to 100%).

3. Amplification, labeling and hybridization

[0138] The profile of the production of nucleic acids derived from markers and the hybridization of nucleic acids with a biochip is shown in FIG. 2. A total of 30 frozen sections 30 µM thick were used for total RNA isolation of each tumor specimen subjected to instant freezing. Total RNA was isolated with RNAzol ™ B (Campro Scientific, Veenendaal, The Netherlands) according to the manufacturer's protocol, which includes tissue homogenization using a Polytron PT-MR2100 (Merck, Amsterdam, The Netherlands) and finally dissolved in H2O without RNase. The quality of the total RNA was calculated using the ratio A260 / A280 that had to be between 1.7 and 2.1, as well as visual inspection of the agarose gel RNA, which should indicate a stronger 28S ribosomal RNA band in Compared to the 18S ribosomal RNA band, 25 μg of total RNA was subsequently treated with DNase using the Qiagen DNase kit without RNase and centrifugal columns (Qiagen Inc, GmbH, Germany) according to the manufacturer's protocol. The total RNA treated with DNase was dissolved in H2O without RNase until reaching a final concentration of 0.2 μg / μl.

[0139] 5 μg of total RNA was used as input for cRNA synthesis. An oligo-dT primer containing a T7 RNA polymerase promoter sequence was used to prime a first strand DNA synthesis and random primers (pdN6) were used to prime a cDNA synthesis by MMLV reverse transcriptase. This reaction produced a double stranded DNA containing the T7 RNA polymerase promoter (T7RNAP). The double stranded cDNA was then transcribed to cRNA by T7RNAP.

[0140] cRNA was labeled with Cy3 or Cy5 dyes by a two phase process. First, allylamine-derived nucleotides were enzymatically incorporated into cRNA products. For the cRNA labeling, the UTP was replaced by a 3: 1 mixture of 5- (3-aminoali) uridine 5'-triphosphate (Sigma) and UTP in the in vitro transcription reaction (IVT). The allylamine-derived cRNA products were then reacted with Cy3 or Cy5 N-hydroxy succinimide esters (CyDye, Amersham Pharmacia Biotech). 5mg of Cy5-labeled cRNA from a breast cancer patient was mixed with the same amount of Cy3-labeled product from a stockpile of the same amount of cRNA from each individual sporadic patient.

[0141] Biochip hybridizations with fluorine inversions were performed in duplicate. Prior to hybridization, the cRNAs were fragmented to an average size of -50-100nt by heating them at 60 ° C in the presence of 10 mM ZnCl2. The fragmented cRNAs were added to a hybridization buffer containing 1 M NaCl, 0.5% sodium sarcosian and 50 mM MES, pH 6.5, whose hardness was regulated with the addition of formamide to a final concentration of 30%. Hybridizations were carried out in a final volume of mls at 40 ° C on the rotating platform of a hybridization oven (Robbins Scientific) for 48 h. After hybridization, the slides were washed and scanned using a confocal laser scan (Agilent Technologies). The fluorescence intensities of the scanned images were quantified, normalized and corrected.

4. Sample combination

[0142] A reference pool of cRNA was formed by combining the same amount of cRNA from each individual sporadic patient.

5.25K Human Biochip

[0143] Surface bound oligonucleotides were synthesized following essentially what was 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 x 3 inches) containing exposed hydroxyl groups were used as nucleotide synthesis substrates. On the glass surfaces, phosphoramidite monomers were introduced at computer-defined positions by inkjet printing. The unreacted monomers were then washed off and the ends of the extended oligonucleotides were removed. For each nucleotide synthesis desired, this cycle of coupling, washing and deprotection of the monomers was repeated. The oligonucleotide sequences to be printed were specified by computer files.

[0144] Biochips containing approximately 25,000 human gene sequences (Hu25K biochips) were used for this study. Sequences were selected for RefSeq biochips (a collection of non-redundant mRNA sequences located on the Internet site nlm.nih.gov/LocusLink/refseq.html) and Phil Green EST contigs, which is a collection of contiguous EST sequences gathered by Dr. Phil Green et al at the University of Washington (Ewing and Green, Nat. Genet. 25 (2): 232-4 (2000)), available at phrap.org/est_assembly/ index.html . Each contiguous sequence of RNA or EST was represented in Hu25K biochip by a single 60-mer oligonucleotide essentially following the explanations of Hughes et al., Nature Biotech. 19 (4): 342-347 and International Publication WO01 / 06013, published on January 25, 2001, and in International Publication WO01 / 05935, published on January 25, 2001, with the difference that the oligo-writing rules to eliminate oligonucleotides with more than 30% C or with 6 or more

5 contiguous residues of C.

Example 1: Differentially regulated gene sets and general patterns of breast cancer tumor expression

[0145] From the approximately 25,000 sequences represented in the biochip, a group of approximately 5,000 genes was selected that were significantly regulated throughout the sample group. Be

10 determined that a gene was regulated significantly differentiated with breast cancer if it showed more than twice as many transcription changes compared to a pool of sporadic tumors and if the p-value for regulation (Hughes et al., Cell 102 : 109-126 (2000)) was less than 0.01, both up and down, in at least five out of 98 tumor samples.

[0146] An unsupervised clustering logarithm allowed us to group patients based on their

15 similarities measured in this set of -5,000 significant genes. The measure of similarity between two patients x and y is defined as

image 1

Equation (5)

In Equation (5), x and y are two patients with logarithmic ratio components xi and i, i = 1, ..., N = 5,100. The error σxy is associated with each value xi. The lower the σx value and the more reliable the measurement will be.

   is the weighted arithmetic mean of error. The use of correlation as a similarity metric highlights the importance of corregulation in clustering, rather than the breadth of

[0147] The set of approximately 5,000 genes can be grouped according to their similarities measured in the entire group of 98 tumor samples. The measure of similarity between two genes was defined in the same

25 so that in Equation (1), with the difference that there are now 98 components of logarithmic quotient measurements for each gene.

[0148] The result of said two-dimensional grouping is represented in FIG. 3. Two characteristic patterns emerge from the grouping. The first pattern consists of a group of patients at the bottom of the diagram whose regulations are very different from the sporadic reserve. The other pattern is a group

30 of patients at the top of the diagram whose expressions are only moderately regulated compared to the sporadic reserve. These dominant patterns suggest that tumors can be unequivocally divided into two distinct type groups based on said set of -5,000 significant genes.

[0149] To facilitate the identification of 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 of FIG. 3, which constitutes the dominant pattern, consists of 36 patients. Of the 30 patients with negative ER, 34 patients are concentrated in this group. From FIG. 4 it was observed that the expression of the ESR1 estrogen receptor alpha gene and a large group of corregulated genes conform to this expression pattern.

40 [0150] From FIG. 3 and FIG. 4 it was concluded that expression patterns can be used to classify tumor samples into subgroups of diagnostic interest. Thus, genes corregulated in a total of 98 tumor samples contain information on the molecular basis of breast cancers. The combination of clinical data and gene abundance, measured in biochip, of ESR1 demonstrates that the different types are related to, or at least indicated by, the ER category.

45 Example 2: Identification of genetic markers that distinguish between estrogen receptor patients (+) and estrogen receptor patients (-)

image3

[0151] The results described in this Example allow the identification of expression marker genes with which two main groups of tumor cells are differentiated: "ER-negative" group and "ERpositive" group. The differentiation of samples by ER category (+) was carried out in three stages: (1) identification of a set of candidate marker genes that correlate with the ER level; (2) hierarchical ordering of these candidate genes by correlation force; (3) optimization of the number of marker genes and (4) classification of samples based on said marker genes.

1. Selection of candidate discriminant genes

[0152] In the first stage a set of candidate discriminant genes was identified based on the gene expression data of samples for the practices. Specifically, the correlation coefficients ρ between the category or relationship level numbers of ER and logarithmic expression ř in all samples for each gene were calculated separately.

image 1 Equation (2)

The histogram of the resulting correlation coefficients is represented in FIG. 5A with gray line. Although for most genes the amplitude of correlation or anticorrelation is small, for some it reaches 0.5. Genes whose expression quotients have good correlation or good anti-correlation with the diagnostic category of interest are used as indicator genes for the category.

[0153] Genes with a correlation coefficient greater than 0.3 ("correlated genes") or less than -0.3 ("anti-correlated genes") were selected as indicator genes. The threshold of 0.3 was selected based on the correlation distribution for cases where there is no real correlation (permutations can be used to determine this distribution). Statistically, this distribution width depends on the number of samples used in the correlation calculation. The distribution width for cases of image 1

control (no real correlation) is approximately

 where n = the number of samples. In our case, n = 98. Therefore, a threshold of 0.3 corresponds roughly to 3 -σ in the distribution

[0154] It was discovered that 2,460 of these genes met those conditions. In order to assess the relevance of the correlation coefficient of each gene with the ER level, a resampling technique was used to generate Monte-Carlo data that randomized the association between the gene expression data of the samples and their categories. The distribution of the correlation coefficients obtained from a Monte-Carlo test appears with a broken line in FIG 5A. To estimate the relevance of 2,460 marker genes as a group, 10,000 Monte-Carlo trials were generated. The collection of these 10,000 Monte-Carlo trials forms the null hypothesis. The number of genes that meet the same conditions for Monte-Carlo data varies from one trial to another. The frequency distribution from 10,000 Monte-Carlo assays of the number of genes with coefficients of> 0.3 or <-0.3 is shown in FIG. 5B. Both the maximum and the minimum value are much less than 2,460. Therefore, it is estimated that the relevance of this group of genes as a set of discriminant genes between ER (+) and ER (-) samples is greater than 99.99%.

2. Hierarchical ordering of candidate discriminant genes

[0155] In the second stage, the candidate list genes were ordered hierarchically based on the relevance of each gene as a discriminant gene. The markers were ordered hierarchically either by amplitude of correlation or by using a metric similar to a Fisher statistic:

image4

image5

In Equation (3), (x1) is the weighted mean error of the logarithmic quotient within the ER group (-) and (x2) is the weighted average error of the logarithmic quotient within the ER group (+). σ1 is the variance of logarithmic quotient within the ER group (-) and n1 is the number of samples that had valid measurements of logarithmic quotients. σ2 is the variance of logarithmic quotient within the ER (+) group and n2 is the number of samples that had valid measurements of logarithmic quotients. The t-value in Equation (3) represents the difference in compensated variance between two means. The level of reliability of each gene in the list of candidates was stipulated with respect to a null hypothesis derived from the set of real data by means of a resampling technique; that is, many artificial data sets were generated by randomizing the association between clinical data and gene expression data.

3. Optimization of the number of marker genes

[0156] For the cross-validation method of "leaving one out" was used in order to optimize the discriminant genes. One classifier practiced 97 examples of a set of marker genes extracted from the list of candidates in hierarchical order and then predicted the category of the remaining sample. That same procedure was repeated for each of the examples in the reservation and the number of cases in which the prediction of the one left out was erroneous was counted.

[0157] The evaluation of the cross-validation performance of leaving one out just mentioned was repeated by successively adding more marker genes from the candidate list. Performance as a function of the number of marker genes is shown in FIG. 6. The error rates for type 1 and type 2 varied with the number of marker genes used, but both were minimal while the number of marker genes remained in the order of 550. Therefore, we estimate that this set out of 550 genes, the optimal set of marker genes that can be used to classify breast cancer tumors into the "ER-negative" group and "ER-positive" group should be considered. FIG. 7 shows the classification of patients in ER (+)

or ER (-) based on this set of 550 markers. FIG. 8 shows the correlation of each tumor with each ER-negative template versus the correlation of each tumor with the ER-positive template.

4. Classification based on marker genes

[0158] In the third stage, a set of classifier parameters for each type of practice data set was calculated based on any of the above sorting methods. A template for the ER group (-) (ž1) was generated using the weighted mean error of the logarithmic quotient of the selected gene group. Likewise, a template for the ER (+) group (called ž2) was generated using the weighted mean error of the logarithmic quotient of the selected gene group. Two classifier parameters (P1 and P12) were defined based on correlation or distance. P1 measures the similarity between a sample and the ER (-) template ž1 in that selected gene group. P2 measures the similarity between a sample and the ER (+) template ž2 in that selected gene group. The Pi correlation is defined as:

image 1 Equation (1)

[0159] A "leave one out" method was used for cross-validation based on marker genes. In this method, a sample was reserved for cross-validation each time a practice classifier acted. From a set of 550 optimal marker genes, the classifier practiced with 97 of the 98 samples and then predicted the category of the remaining sample. This procedure was repeated with each of the 98 patients. The number of cases in which the prediction was wrong or correct was counted. It was also determined that with subsets of only -50 of the total 2,460 genes tumors can be classified into ER (+) or ER (-) almost as well as using the total set.

[0160] In a small number of cases there was disagreement between the classification using the 550 markers and the clinical classification. When comparing the logarithmic expression ratio of ESR1 measured by biochip with the clinical binary decision (negative or positive) of the ER category for each patient, it was seen that the measured expression matched the qualitative category of clinical measurements (mixture of two methods ) for most tumors. For example, two patients who were clinically diagnosed as ER (+) actually showed low ESR1 expression from biochip measurements and were classified as negative ER according to the 550 marker genes. Likewise, 3 patients who were clinically diagnosed as ER (-) showed high ESR1 expression from biochip measurements and were classified as ER (+) according to the same 550 marker genes. Statistically, however, the expression of ESR1 genes measured with biochip conforms more to the dominant patterns than to the clinically determined ER category.

Example 3: Identification of genetic markers that distinguish between BRCA1 tumors and sporadic tumors in estrogen receptor patients (-)

[0161] The BRCA1 mutation is one of the main clinical categories in breast cancer tumors. It was determined that, of the tumors of 38 patients of the ER (-) group, 17 showed the BRCA1 mutation, while 21 were sporadic tumors. Therefore, a method was developed that made possible the differentiation of tumors from the BRCA1 mutation from the 21 sporadic tumors of the ER (-) group.

1. Selection of candidate discriminant genes

[0162] In the first step, a series of candidate genes were identified based on the gene expression patterns of said 38 examples. First we calculate the correlation between the BRCA1 mutation category number and the expression quotient in all 38 examples for each gene separately using Equation (2). The distribution of correlation coefficients is represented as a histogram defined by continuous line in FIG. 9A. It was observed that, although most of the genes were not correlated with the category of the BRCA1 mutation, a small group of them did correspond to significant levels. It is likely that genes with higher correlation coefficients serve as indicators to distinguish tumor carriers from the BRCA1 mutation from those of sporadic tumors within the ER (-) group.

[0163] In order to assess the relevance of each correlation coefficient against a null hypothesis that said correlation coefficient could be found randomly, a resampling technique was used to generate Monte-Carlo data that would randomize the association between the data of gene expression of the samples and their categories. 10,000 of these Monte-Carlo trials were generated as a control in order to estimate the relevance of the marker genes as a group. A threshold of 0.35 was applied in the absolute amplitude of correlation coefficients (or correlation or anticorrelation) to both real data and Monte-Carlo data. Following this method, 430 genes were found that met these conditions for experimental data. The p-value of the relevance, measured against the 10,000 Monte-Carlo trials, is approximately 0.0048 (FIG. 9B). That is, the probability that this set of 430 genes contains useful information about BRCA1 type tumors against sporadic tumors is more than 99%.

2. Hierarchical ordering of discriminant candidate genes

[0164] In the second step, the candidate list genes were ordered hierarchically based on the relevance of each gene as a discriminant gene. Here the absolute amplitude of correlation coefficients was used to hierarchically order the marker genes.

[0165] In the third step, a subset of genes from the highest positions of the hierarchical list was used for classification. A template of the BRCA1 group (called ž1) was defined by the weighted mean error of the logarithmic quotient of the selected gene group. Likewise, a template of group noBRCA1 (called ž2) was defined by the weighted mean error of the logarithmic quotient of the selected gene group. Two classifier parameters (P1 and P2) were defined based on either correlation or distance. P1 measures the similarity between a sample and and the BRCA1 template ž1 in the entire selected gene group. P2 measures the similarity between a sample and the non-BRCA1 ž2 template in the entire selected gene group. For the correlation, P1 and P2 were defined in the same way as in Equation (4).

[0166] The method of leaving one out for cross validation was used to optimize discriminant genes as explained in Example 2. For a set of marker genes from the hierarchical list of candidates, the classifier practiced with 37 examples and predicted the remaining. The procedure was repeated for all the samples in the reserve and the number of cases in which the prediction proved erroneous or accurate was counted.

[0167] To determine the number of markers that constitute a viable subset, the execution of the previous cross-validation was repeated by cumulatively adding more marker genes from the candidate list. Execution as a function of the number of marker genes is shown in FIG. 10. Error rates for type 1 (false negative) and type 2 (false positive) (Bendat & Piersol, RANDOM DATA ANALYSIS AND MEASUREMENT PROCEDURES, 2D ED., Wiley Interscience, p. 89) reached optimal levels when the number of marker genes was 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 (-) in both BRCA1-related tumors and in sporadic tumors

[0168] The classification results using the 100 optimal genes are shown in FIGS. 11A and 11B. As seen in FIG 11A, the corregulation patterns of sporadic patients differ from those of BRCA1 patients, especially in the extent of regulation. Only one sporadic tumor was classified in the BRCA1 group. Sporadic group patients are not necessarily negative for the BRCA1 mutation; however, it is estimated that only approximately 5% of sporadic tumors are real carriers of BRCA1 mutation.

Example 4: Identification of genetic markers that distinguish patients with sporadic tumor with> 5 years survival versus those with <5 years survival

[0169] 78 tumors of breast cancer patients were used to explore predictors of gene expression data prognosis. Of the 78 examples of this group of sporadic breast cancer, there was clinical evidence that 44 samples had not had distant metastases within 5 years after the initial diagnosis (“group without distant metastases”) and that 34 samples had had distant metastasis within 5 years after the initial diagnosis ("group with distant metastasis"). A group of 231 markers was identified, and within it an optimal group of 70 markers, which allowed differentiation between the two groups of patients mentioned.

1. Selection of candidate discriminant genes

[0170] In the first step, a group of candidate discriminant genes was identified based on the gene expression data of said 78 samples. The correlation between the prognostic category number (distant metastasis versus non-distant metastasis) and the log expression ratio in all samples for each gene were calculated separately using Equation (2). The distribution of correlation coefficients is represented by a continuous line in FIG. 12A. FIG. 12A also shows the result of a Monte-Carlo discontinuous line test. It was observed that although the majority of genes are not correlated with the prognosis categories, a small group of genes is. It is likely that genes with higher correlation coefficients are more useful as indicators of the prognosis of interest - distant metastases group and non-distant metastases group.

[0171] In order to assess the relevance of each correlation coefficient against a null hypothesis that such correlation can be found randomly, a resampling technique was used to generate data from

10,000 Monte-Carlo trials as a control (FIG. 12B). Then genes were selected that had a correlation coefficient greater than 0.3 ("correlated genes") or less than -0.3 ("anti-correlated genes"). That same selection criterion was applied to both real data and Monte-Carlo data. Through this comparison, 231 markers were identified that met that requirement. The probability that said set of genes to differentiate patients from the distant metastasis group from the non-distant metastasis group is chosen by random fluctuation is approximately 0.003.

2.

Hierarchical ordering of discriminant candidate genes

[0172] In the second step, the candidate list genes were ordered based on the relevance of each gene as a discriminant gene. Specifically, a metric similar to a “Fisher” statistic, defined in Equation (3), was used for hierarchical ordering. The level of reliability of each gene in the list of candidates was estimated against a null hypothesis derived from the set of real data using the resampling technique. Candidate list genes can also be sorted by the amplitude of correlation coefficients.

3.

Optimization of discriminant genes

[0173] In the third step, a subset of 5 genes from the highest positions of this hierarchical list was selected for use as discriminant genes in the classification of 78 tumors as a "distant metastasis group" or "non-distant metastasis group" . The method of leaving one out was used for cross validation. Specifically, 77 samples defined a classifier based on the set of selected discriminant genes, which were used to predict the remaining sample. This procedure was repeated until each of the 78 samples was predicted. The number of cases in which the predictions were correct or incorrect was counted. The performance of the classifier was measured by the error rates of type 1 and type 2 for this selected gene set.

[0174] The previous performance evaluation procedure was repeated adding 5 more marker genes each time to the highest positions in the hierarchical list, until the total of 231 genes had been used. As seen in FIG. 13, the number of erroneous predictions of type 2 and type 2 errors changes radically according to the number of marker genes used. The combined error rate was reduced to a minimum when the 70 genes of the highest positions on our list of candidates were never used. Therefore, this set of 70 genes is the set of optimal and preferred marker genes, which is used for the classification of patients with sporadic tumors in a group of distant metastases and a group of non-distant metastases. A larger or smaller number of markers also acts as a predictor, but is less efficient, either because of higher error rates or because of the introduction of statistical noise.

4. Relapse probability curves

[0175] The prognostic classification of 78 patients with sporadic breast cancer tumors in two differentiated subgroups was predicted based on their expression of 70 optimal marker genes (FIGS. 14 and 15).

[0176] To assess the prognostic classification of sporadic patients, the outcome of each patient was predicted by means of a classifier that practiced with the remaining 77 patients based on the 70 optimal marker genes. In FIG. 16 the probability of distant metastases as a function of time since the initial diagnosis for the two planned groups is shown in the diagram. There is a significant difference between these two relapse curves. Using the χ2 test (S-PLUS 2000 Guide to Statistics, vol. 2, MathSoft, p. 44), the p-value was estimated at ~ 10-9. The probability of distant metastasis was also compared as a function of time since the initial diagnosis between individuals with ER (+) and ER (-) (FIG. 17), individuals with PR (+) and PR (-) (FIG. 18) and among individuals with different degrees of tumor (FIGS. 19A, 19B). In comparison, p-values for the differences between two prognostic groups based on clinical data are much less significant than those based on gene expression data, ranging from 10-3 to 1.

[0177] To set the relapse probability parameters as a function of time since the initial diagnosis, the curve was adjusted to a type of “normal” survival model:

image 1 (4)

For a fixed α = 1, we found that τ = 125 months for patients in the non-distant metastasis group and τ = 36

5 months for patients in the distant metastasis group. Using tumor grades, we found that τ = 100 months for patients with tumor grades 1 and 2 and τ = 60 for patients with tumor grade 3. In clinical practice it is recognized that tumor grades are the best predictor of Prognosis available. However, the difference between the two prognostic groups classified on the basis of the 70 marker genes is much more significant than that of the groups classified by the best available clinical information.

10 5. Prognosis prediction for 19 independent sporadic tumors

[0178] To confirm the proposed prognostic classification method and to ensure the reproducibility, robustness and prediction power of the 70 prognostic marker genes, the same classifier was applied to 19 independent tumor samples taken from patients with sporadic breast cancer, prepared separately at the Netherlands Cancer Institute (NKI). The same reference reserve was used.

[0179] The results of the classification of 19 independent sporadic tumors are shown in Figure

20. FIG. 20A shows the logarithmic expression regulation ratio of those same 70 optimal marker genes. Based on our classification model, we expected the wrong classification of 19 * (6 + 7) / 78 = 3.2 tumors. Consequently, (1 + 3) = 4 of 19 tumors were misclassified.

6. Clinical parameters as a group versus biochip data - Logistic regression results

[0180] In the previous section, the predictive power of each clinical parameter separately was compared with that of the expression data. However, it makes more sense to combine all clinical parameters as a group and then compare them with the expression data. This requires a multivariate modeling; The method chosen was logistic regression. This approach also demonstrates the improvement that the biochip method brings to the results of the clinical data.

[0181] The clinical parameters used for multivariate modeling were: (1) tumor grade; (2) ER category; (3) presence or absence of the progestative receptor (PR); (4) tumor size; (5) age of the patient, and (6) presence or absence of angionvasion. For the biochip data two correlation coefficients were used. One is the correlation with the mean of the good prognosis group (C1) and the other is the correlation with the mean of the poor prognosis group (C2). When the correlation coefficients for a

30 given patient, said patient is excluded from either of the two means.

[0182] Logistic regression optimizes the coefficient of each input parameter to optimally predict the outcome of each patient. One way to judge the prediction power of each input parameter is how much deviation (similar to chi-square in linear regression, see, for example, Hasomer & Lemeshow, APPLIED LOGISTIC REGRESSION, John Wiley & Sons, (2000)) is causing the parameter. The best predictor

35 should be the cause of most of the deviation. To accurately calculate the prediction power, each parameter was modeled separately. The biochip parameters explain most of the deviation and therefore are powerful predictors.

[0183] Next the clinical parameters, and the two parameters of the biochip, were monitored as a group. The total deviation explained by the six clinical parameters was of

40 31.5 and the total deviation explained by the biochip parameters was 39.4. However, when the clinical data were first modeled and then the two biochip parameters were added, the final deviation was the cause of 57.0.

[0184] Logistic regression computes the probability that a patient belongs to the group with a good prognosis or to the deficient group. FIGS. 21A and 21B show sensitivity to (1-specificity). Diagrams were generated by varying the threshold in the expected probability of the model. The curve that crosses the upper left corner is the best sensitivity (high sensitivity with high specificity). The biochip exceeded clinical data by a wide margin. For example, at an established sensitivity of about 80%, the specificity was ~ 80% of the biochip data and ~ 65% of the clinical data for the good prognosis group. For the poor prognosis group, the corresponding specificities were ~ 80% and ~ 70%, 50 also at an established sensitivity of 80%. The combination of the biochip data with the clinical data improved the results. The result can also be shown as the total error rate as a function of the threshold

in FIG. 21C. At all possible thresholds, the error rate of the biochip was always smaller than that of the clinical data. By adding the biochip data to the clinical data, the error rate is further reduced, as can be seen in Figure 21C.

[0185] Odd quotient tables can be created from the logistic regression prediction. The

The probability that a patient is in the group with a good prognosis is calculated by logistic regression based on different combinations of input parameters (clinical and / or biochip). The patients are divided into the following four groups according to the prediction the actual result: (1) good and actually good prediction; (2) good but really poor prediction; (3) poor but really good prediction and (4) poor and actually poor prediction. Groups (1) & (4) represent correct predictions, while

10 that groups (2) & (3) represent erroneous predictions. The division for prediction is set to a 50% probability, although other thresholds can be used. The results are listed in Table 7. Table 7 clearly shows that the biochip profile (Table 7.3 & 7.10) exceeds any simple clinical data (Table 7.4-7.9) and the combination of clinical data (Table 7.2) . The addition of the biochip profile, together with the clinical data, gives the best results (Table 7.1).

[0186] For the biochip profile, a similar table can also be made (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 means poor

. Example 5. Concept of biochip for diagnostic purposes.

Table 7.1 Clinical prediction + biochip

Good prediction

Poor prediction

really good

39 5

actually deficient

4 30

Table 7.2 Prediction only by clinician

Good prediction

Poor prediction

really good

3. 4 10

actually deficient

12 22

Table 7.3 Biochip Prediction

Good prediction

Poor prediction

really good

39 5

actually deficient

10 24

Table 7.4 Prediction by degree

Good prediction

Poor prediction

really good

2. 3 twenty-one

actually deficient

5 29

Table 7.5 Prediction by ER

Good prediction

Poor prediction

really good

35 9

actually deficient

twenty-one 13

Table 7.6 PR prediction

Good prediction

Poor prediction

really good

35 9

Table 7.1 Clinical prediction + biochip

Good prediction

Good prediction

actually deficient

18 16

Table 7.7 Size prediction

Good prediction

Poor prediction

really good

35 9

actually deficient

13 twenty-one

Table 7.8 Age prediction

Good prediction

Poor prediction

really good

33 eleven

actually deficient

fifteen 19

Table 7.9 Prediction by angioinvasion

Good prediction

Poor prediction

really good

37 7

actually deficient

19 fifteen

Table 7.10 Prediction by dC (C1-C2)

Good prediction

Poor prediction

really good

36 8

actually deficient

6 28

Table 7.11 Without logistic regression, simply judged by dC (C1-C2)

Good prediction

Poor prediction

really good

37 7

actually deficient

6 28

[0187] All genes in the list of marker genes for diagnostic and prognostic purposes can be synthesized in a small-scale biochip using inkjet technology. You can do a biochip

5 with genes for diagnosis and prognosis respectively or collectively. Each gene in the list is represented by single or multiple oligonucleotide probes, depending on the uniqueness of its sequence throughout the genome. This custom designed biochip, in combination with the sample preparation protocol, can be used in clinics as a diagnostic / prognostic kit.

Example 6. Biological relevance of diagnostic marker genes

10 [0188] The domain was searched for functional annotations available for the 430 marker genes for the diagnosis of BRCA1 in Table 3. The 430 genes in Table 3 can be divided into two groups: (1) 196 genes whose expressions are clearly expressed in the BRCA1 type group, and (2) 234 genes whose expressions are highly expressed as sporadic group. Of the 196 genes of the BRCA1 group, 94 are annotated. Of the 234 genes of the sporadic group, 100 are annotated. The terms "T-cell", "B-cell" or

15 "immunoglobulins" are involved in 13 of the 94 genes scored and in 1 of the 100 genes scored, respectively. Of 24,479 genes represented in the biochips, there are 7,586 genes with annotations to date. "T-cell", "B-cell" and "immunoglobulin" are present in 207 of these 7,586 genes. In view of this, the p-value of the 13 genes of "T-cell", "B-cell" or "immunoglobulin" of the BRCA1 group is very relevant (p-value = 1.1x10-6). In comparison, the observation of 1 gene related to "T-cell", "B-cell" or "immunoglobulin" in the sporadic group is not relevant (p-value = 0.18).

[0189] The observation that patients with BRCA1 have highly expressed lymphocyte genes (T-cell and B-cell) is consistent with what has been seen in the pathology, that is, that the BRCA1 breast tumor is associated with high lymphocytic infiltration more often than sporadic cases (Chappuis et al., 2000, Semin Surg Oncol 18: 287-295).

Example 7. Biological relevance of prognostic marker genes

[0190] A search of available functional annotations was performed for the 231 prognostic marker genes (Table 5). The markers are divided into two groups: (1) 156 markers whose expressions are highly expressed in the poor prognosis group, and (2) 75 genes whose expressions are highly expressed in the good prognosis group. Of the 156 markers, 72 genes are annotated; Of the 75 genes, 28 genes are annotated.

[0191] Twelve of the 72 markers, but none of the 28 markers, are, or are associated with, kinases. By contrast, of the 7,586 biochip genes that have annotations to date, only 471 have kinases. Based on this basis, the p-value of twelve kinase-related markers is in the prognosis group (p-value = 0.001) is significant. Kinases are important regulators of intracellular signal transduction procedures that are involved in cell proliferation, differentiation and apoptosis. Normally, their activity is closely monitored and monitored. Overexpression of certain kinases is well known and is implicated in oncogenesis, as a receptor1 of vascular endothelial growth factor (VEGFR1 or FLT1), a tyrosine kinase of the poor prognosis group, which plays a very important role in tumor angiogenesis. Interestingly, vascular endothelial growth factor (VEGF), a VEGFR ligand, is also in the prognosis group, which means that both the ligand and the receptor are over-regulated in individuals with poor prognosis by an unknown mechanism.

[0192] Similarly, 16 of the 72 markers, and only 2 of the 28 markers, are, or are associated with, ATP binding proteins or GTP binding. By contrast, of the 7,586 biochip genes that have annotations to date, only 714 and 153 involve ATP binding and GTP binding, respectively. Based on this basis, the p-value of 16 markers related to the GTP or ATP link is significant in the poor prognosis group (p-value 0.001 and 0.0038). Thus, markers related to the GTP or ATP link within the 72 markers can be used as prognostic indicators.

[0193] Cancer is characterized by deregulated cell proliferation. At the simplest level, this requires cell division or mitosis. Searching by keyword, we find “cell division” or “mitosis” included in the annotations of 7 genes respectively in the 72 markers scored from the total of 156 markers of poor prognosis, but in none of the 28 genes scored from the total of the 75 markers of good prognosis. Of the 7,586 biochip markers with annotations, we found "cell division" in 62 annotations and "mitosis" in 37 annotations. Based on these findings, the p-value of seven markers related to cell division or mitosis is found to be in the good prognosis group (p-value = 3.5x10-5). In comparison, the absence of markers related to cell division or mitosis in the good prognosis group is not significant (p-value = 0.69). Thus, the seven markers related to cell division or mitosis can be used as markers for poor diagnosis.

Example 8: Development of an artificial reference reserve

[0194] The reference reservation for the expression profile in the Examples above was made using the same amount of cRNA from each individual patient in the sporadic group. In order to have a large number of reliable and easy-to-make reference reserve, a reference reserve for the diagnosis and prognosis of breast cancer can be developed by synthetic nucleic acid that represents, or is derived from, each marker gene. The expression of marker genes for individual patient samples is monitored only against the reference pool, not against a pool derived from other patients.

[0195] To make the reference reservation, 60-mer oligonucleotides are synthesized according to the 60th sequence of inkjet biochip probe for each of the diagnostic / prognostic indicator genes, then double-stranded and cloned into the pBluescript SK vector ( Stratagene, La Jolla, CA), adjacent to the T7 promoter sequence. The clones are isolated separately and the sequences of their inserts are verified by DNA sequencing. To generate synthetic RNAs, they are linearized with EcoRI and an in vitro transcription reaction (IVT) of T7 is carried out according to the MegaScript kit (Ambion, Austin, TX). IVT is followed by treatment of the product with DNase. Synthetic RNAs are purified on RNeasy columns (Qiagen, Valencia, CA). These synthetic RNAs are transcribed, amplified, labeled and mixed with each other to develop the reference pool. The abundance of these synthetic RNAs is adjusted to approximately the abundance of the corresponding transcripts derived from markers in the stock of real tumors.

Example 9: Use of single channel data and a sample pool represented by stored values

1. Creation of a reference reserve of stored values ("mathematical sample reserve")

[0196] The use of quotient-based data in Examples 1-7 above requires a physical reference sample. In these Examples, a sample of sporadic tumors was used as a reference. The use of such a reference, although it allows for solid prognosis and diagnosis predictions, can cause problems, since the reserve is typically a limited resource. Therefore, a classification method was developed that does not require a physical stock of samples, thus making the application of this predictive and diagnostic technique much easier for clinical applications.

[0197] To check if data from a single channel could be used, the following procedure was developed. First, the single channel intensity data for the 70 optimal genes, described in Example 4, was selected from the 78 sporadic samples for practices, described in the Materials and Methods, of the sporadic sample data versus hybridization data of tumor reserve. The 78 samples consisted of 44 samples of patients with good prognosis and 34 samples of patients with poor prognosis. The hybridization intensities for these samples were then normalized by dividing by the median intensity of all the biological stains of the same biochip. Where multiple biochips were used per sample, the average was found in all biochips. A logarithmic transformation was performed in the intensity data for each of the 70 genes, or for the average intensity for each of the 70 genes where more than one biochip is hybridized, and a mean logarithmic intensity for each gene was calculated in all The 78 sporadic samples. For each sample, the mean logarithmic intensities calculated in this way were subtracted from the logarithmic intensity of the individual sample. This figure, the average of subtracted logarithmic intensity, was then treated as the two-color logarithmic quotient for the substitution classifier by Equation (5). For new samples, the average logarithmic intensity is subtracted in the same manner as indicated above and an average subtracted logarithmic intensity is calculated.

[0198] The creation of a set of mean logarithmic intensities for each hybridized gene creates a “mathematical sample reserve” that replaces the “material sample reserve”. This mathematical reserve of samples can then be applied to any sample, including currently available samples and those obtained in the future. This “mathematical sample reserve” can be updated as more samples become available.

2. Results

[0199] To demonstrate that the mathematical reserve of samples performs an equivalent function to the reference sample reserve, the average subtracted logarithmic intensity (single-channel data relative to the mathematical reserve) was plotted against the logarithmic quotient (hybridizations relative to sample pool) for the 70 optimal marker genes in all 78 sporadic samples, as seen in FIG. 22. The ratio and quantities of a single channel are closely correlated, indicating that both have the capacity to report relative changes in gene expression. A classifier was then constructed using the average subtracted logarithmic intensity, following exactly the same procedure that was used for the quotient data, as in Example 4.

[0200] As seen in FIGS. 23A and 23B, with the data of a single channel it was possible to classify samples based on gene expression patterns. In FIG. 23A, samples are grouped according to the prognosis using single channel hybridization data. The white line separates samples of patients classified as poor prognosis (below) and good prognosis (above). In FIG. 23B, a diagram of each sample is made according to its expression data correlated with the forecast prognostic parameter good (open circles) or deficient (filled squares). Using the cross-validation method of leaving one out, the classifier predicted 10 false positives from a total of 44 patient samples with a good prognosis and 6 false negatives from a total of 34 patient samples with a poor prognosis, where a poor prognosis is considered a positive". This result is comparable to the use of the quotient based classifier, which predicted 7 of 44 and 6 of 34, respectively.

[0201] In clinical applications, it is very preferable to have few false positives, resulting in fewer patients with under-treatment. To adapt the results to this preference, a classifier was constructed by hierarchically ordering the patient sample according to its correlation coefficient with the “good prognosis” template and a threshold was chosen for this correlation coefficient that allowed approximately 10% of false negatives , that is, the classification of a sample of a patient with poor prognosis as that of a patient with a good prognosis. Of the 34 poor prognosis samples used here, this represents a tolerance of 3 over 34 patients with poor prognosis incorrectly classified. This tolerance limit corresponds to a threshold coefficient threshold of 0.2727 correlation with the "good forecast" template. The results using this threshold are shown in FIGS. 24A and 24B. FIG. 24A shows single channel hybridization data for hierarchically ordered samples according to correlation coefficients with the good forecast classifier; mixtures classified as "good prognosis" are above the white line, while mixtures classified as "poor prognosis" are below it. FIG. 24B shows a scatter plot of correlation coefficients, with three samples classified incorrectly located to the right of the threshold correlation coefficient value. Using this threshold, the classifier had a false positive rate of 15 out of 44 samples with a good prognosis. This

5 result is not very different when compared to the error rate of 12 out of 44 for the quotient based on the quotient.

[0202] In summary, the 70 indicator genes convey consistent prognostic information; Single-channel data can predict tumor outcome almost as well as quotient-based data and at the same time are more convenient in a clinical setting.

10

Claims (40)

1. Method for classifying an individual suffering from breast cancer as having a good prognosis or poor prognosis, where said individual is a human, where said prognosis indicates that said individual is expected not to have distant metastases within five years after the initial diagnosis of breast cancer and where said poor prognosis indicates that it is expected that said individual has distant metastases within five years after the initial diagnosis of breast cancer, which includes (ia) calculate a first classifier parameter between a first expression profile and a good forecast template, or (ib) calculate a second classifier parameter between said first expression profile and said good forecast template and a third classifier parameter between said first expression profile and a poor forecast template; said first expression profile comprising the expression levels of a first plurality of genes in a sample of cells taken from the individual, said template comprising a good prognosis, for each gene in said first plurality of genes, the average level of expression of said gene in a plurality of patients who do not have distant metastases within five years after the initial diagnosis of breast cancer; and said poor prognosis template comprising, for each gene in said first plurality, the average level of expression of said gene in a plurality of patients having distant metastases within five years following the initial diagnosis of breast cancer; said first plurality of genes consisting of at least 5 of the genes whose markers are listed in Table 5, and (iia) classify said individual as having a good prognosis if said first classifying parameter is above a chosen threshold or if said first expression profile is more similar to said good prognosis template than to said poor prognosis template, or (iib) classifying said individual as having said poor forecast if said first classifying parameter is below said chosen threshold or if said first expression profile is more similar to said poor forecasting template than to said good forecasting template.

2.

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

3.

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

Four.

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

5.

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

6.

The method of claim 1, wherein said first plurality consists of at least 70 of the genes whose markers are listed in the list in Table 6.

7.

The method of claim 1, further comprising the steps of:

(to)

 generate said template of good prognosis by hybridization of nucleic acids derived from said plurality of patients who do not have distant metastases within five years after the initial diagnosis of breast cancer against nucleic acids derived from a tumor pool of a plurality of patients who have breast cancer;

(b)

 generate said template of good prognosis by hybridization of nucleic acids derived from said plurality of patients having distant metastases within five years following the initial diagnosis of breast cancer against nucleic acids derived from a tumor pool of said plurality of patients;

(C)

 generating said first expression profile by hybridizing nucleic acids derived from said sample of cells taken said individual against said reserve, and

(d)

 calculate (d1) said second classifier parameter between said first expression profile and the good forecast template and (d2) said third classifier parameter between said first expression profile and the poor forecast template, wherein if said first expression profile is more similar to the good forecast template than to the poor forecast template, the individual is classified as having a good forecast, and if said expression profile is more similar to the poor forecast template than to the good forecast template, classifies the individual as having a poor prognosis.

8.

The method of claim 1, further comprising:

iv) classifying said individual as ER (+) (estrogen positive receptor) or ER (-) (estrogen negative receptor) based on a second expression profile comprising the expression levels of a second plurality of genes in a sample of cells taken from the individual, said second plurality of genes consisting of at least 5 of the genes whose markers are listed in Table 1, wherein said classification of said individual as ER (+) or as ER (-) is carried out with a method that comprises: a) calculate a first measure of similarity between said second expression profile and an ER (+) template and a second measure of similarity between said second expression profile and an ER (-) template;

(b)

 said ER (+) template comprising, for each gene in said second plurality of genes, the average level of expression of said gene in a plurality of ER (+) patients;

said ER (-) template comprising, for each gene in said second plurality of genes, the average level of expression of said gene in a plurality of ER (-) patients, and

(b)

 classify (b1) said individual as ER (+) if said second expression profile has a greater similarity with said ER (+) template than with said ER (-) template,

or (b2) as ER (-) if said second expression profile has a lower similarity with said ER (+) template than with said ER (-) template. 9. The method of claim 1, further comprising: (iv) classifying said individual as BRCA1 or sporadic based on a second expression profile comprising the expression levels of a second plurality of genes in a sample of cells taken from the individual, said second plurality of genes consisting of at least 5 of the genes whose markers are listed in Table 3, wherein said classification of said individual as BRCA1 or sporadic is carried out with a method comprising: (a) calculate a first measure of similarity between said second expression profile and a BRCA1 template and a second measure of similarity between said second expression profile and a non-BRCA1 template; said BRCA1 template comprising, for each gene in said second plurality of genes, the average level of expression of said gene in a plurality of BRCA1 patients; said non-BRCA1 template comprising, for each gene in said second plurality of genes, the average level of expression of said gene in a plurality of non-BRCA1 patients, and (b) classify (b1) said individual as BRCA1 if said second expression profile has a greater similarity with said BRCA1 template than with said non-BRCA1 template, or (b2) as sporadic if said second expression profile has a less similarity with said BRCA1 template than with said non-BRCA1 template.

10.

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

eleven.

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

12.

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

13.

The method of claim 10, wherein said relative level of expression is represented as a logarithmic quotient.

14.

The method of claim 1, wherein said step (i) comprises calculating said first classifying parameter between said first expression profile and said good prognosis template, and said step (ii) comprises classifying said individual as having a good prognosis if said first classifier parameter is above a chosen threshold.

fifteen.

The method of claim 14, wherein said mean is a weighted average error of the logarithmic quotient.

16.

The method of claim 14, wherein said level of expression of each gene in said first expression profile is a relative level of expression of said gene in said cell sample versus the level of expression of said gene in a reference pool, represented as a logarithmic quotient, and wherein the average level of expression of each gene in said first plurality of genes in said template of good prognosis is an average of relative levels of expression, each relative level of expression being the level of expression of said gene in one of said plurality of patients who do not have distant metastases in the five years following the initial diagnosis of breast cancer against the level of expression of said gene in a reference pool, represented as a logarithmic quotient that is an average of the logarithmic quotients for said gene in said plurality of patients who do not have distant metastases in the five years following diagnosis. initial ico of breast cancer.

17.

The method of claim 14, wherein said first classifier parameter is a correlation coefficient between said first expression profile and said good forecast template.

18.

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 of said individual.

19.

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

twenty.

The method of claim 19, wherein said reference pool is derived from a normal breast cell line.

twenty-one.

The method of claim 19, wherein said reference pool is derived from a breast cancer cell line.

22

The method of claim 19, wherein said relative level of expression is represented as a logarithmic quotient.

2. 3.

The method of claim 8, wherein said mean is a weighted average error of the logarithmic quotient.

24.

The method of claim 8, wherein said expression level of each gene in said second expression profile is a relative level of expression of said gene in said cell sample versus the expression level of said gene in a reference pool, represented as logarithmic quotient; wherein the average level of expression of each gene in said second plurality of genes in said ER template (+) is a mean of relative levels of expression, each relative level of expression being the level of expression of said gene in a patient of said plurality of ER (+) patients versus the level of expression of said gene in a reference pool, represented as a logarithmic quotient which is an average of the logarithmic ratios for said gene in said plurality of ER / + patients), and wherein the average level of expression of each gene in said second plurality of genes in ER template template (-) is a mean of the relative levels of expression, each relative level of expression being the level of expression of said gene in one of said plurality of ER (-) patients with respect to the level of expression of said gene in a reference pool, represented as a logarithmic quotient which is an average of the logarithmic ratios for said gene in said plu rality of ER patients (-).

25.

The method of claim 8, wherein said first 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 second 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 says that it has a greater similarity with said ER template (+) than with 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 which is said to have a lower similarity with said ER (+) template than with 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 (-).

26.

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 of said individual.

27.

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

28.

The method of claim 27, wherein said reference pool is derived from a normal breast cell line.

29.

The method of claim 27, wherein said reference pool is derived from a breast cancer cell line.

30

The method of claim 27, wherein said relative level of expression is represented as a logarithmic quotient.

31.

The method of claim 9, wherein each of said means is a weighted average error of the logarithmic quotient.

32. The method of claim 9, wherein said level of expression of each gene in said second expression profile is a relative level of expression of said gene in said cell sample versus the level of expression of said gene in a reservoir of reference, represented as logarithmic quotient; wherein the average level of expression of each gene in said second plurality of genes in said BRCA1 template is a mean of relative levels of expression, each relative level of expression being the level of expression of said gene in a patient of said plurality of BRCA1 patients versus the level of expression of said gene in a reference pool, represented as a logarithmic quotient that is an average of logarithmic ratios of said gene in said plurality of BRCA1 patients and where the average level of expression of each gene in said second plurality of genes in said non-BRCA1 template is an average of relative levels of expression, each relative level of expression being the level of expression of said gene in a patient of said plurality of non-BRCA1 patients versus the level of expression of said gene in a reference reserve, represented as a logarithmic quotient that is an average of logarithmic quotients of said gene in said plurality ad of non-BRCA1 patients.

33.

The method of claim 32, wherein said first measure of similarity between said second expression profile and said BRCA1 template is a correlation coefficient between said second expression profile and said BRCA1 template, wherein said second similarity measure between second expression profile and said non-BRCA1 template is a correlation coefficient between said second expression profile and said non-BRCA1 template and where said second expression profile is said to have a greater similarity with said BRCA1 template that with said non-BRCA1 template if said correlation coefficient between said second expression profile and said BRCA1 template is greater than said correlation coefficient between said second expression profile and said non-BRCA1 template, or of which says that it has a lower similarity with said non-BRCA1 template than with said BRCA1 template if said correlation coefficient between d said second expression profile and said BRCA1 template is less than the correlation coefficient between said second expression profile and said non-BRCA1 template.

3. 4.

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

35

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

36.

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

37.

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

38.

The method of any of claims 1-6, wherein said step (i) comprises calculating said second classifier parameter between said second expression profile and said good forecast template and said third classifier parameter between said first expression profile and said template of poor prognosis, consisting of said step (ii) in classifying said individual as having good

5 prognosis if said first expression profile is more similar to said good prognostic template than to said poor prognostic template, or classify said individual as having poor prognosis if said first expression profile is more similar to said poor prognosis template than to said good forecast template. 39. The method of claim 38, wherein said second classifier parameter is a coefficient of 10 correlation between said first expression profile and said good forecast template and wherein said third classifier parameter is a correlation coefficient between said first expression profile and said poor forecast template. 40. The method of any of claims 1-6 and 14, wherein said breast cancer is sporadic breast cancer. The method of claim 38, wherein said breast cancer is sporadic breast cancer.