GENE EXPRESSION PROFILING OF PRIMARY BREAST CARCINOMAS USING ARRAYS OF CANDIDATE GENES

Abstract: The invention relates to a polynucleotide library useful in the molecular characterization of a carcinoma, the library including a pool of polynucleotide sequences of subsequences thereof wherein the sequences of subsequences are overpressed in tumor cells, further wherein the sequences of subsequences correspond substantially to any of the polynucleotide sequences set forth in any of SEQ ID NOS: 1- 468 or the complement thereof. The invention relates also to polynucleotides arrays useful to differentiate tumor cells from normal cells comprising combinations of selected immobilized polynucleotide sequences sets.

Description: GENE EXPRESSION PROFILING OF PRIMARY BREAST CARCINOMAS USING ARRAYS OF CANDIDATE GENES This invention relates to polynucleotide analysis and, in particular, to polynucleotide expression profiling of carcinomas using arrays of candidate polynucleotides . Pathologists and clinicians in charge of the management of breast cancer patients are facing two major problems, namely the extensive heterogeneity of the disease and the lack of factors - among conventional histological and clinical features - predicting with reliability the evolution of the disease and its sensitivity to cancer therapies. Breast tumors of the same apparent prognostic type vary widely in their responsiveness to therapy and consequent survival of the patient. New prognostic and predictive factors are needed to allow an individualization of therapy for each patient. Great hope is currently being placed on molecular studies, which address the problem in a global fashion. Methods such as cytogenetics, comparative genomic hybridization, and whole-genome allelotyping have addressed the issue at the genome level. Currently, the modifications that take place in human tumors at the level of transcription can also be studied in a large, unprecedented scale, using new methods such as cDNA arrays that allow quantitative measurement of the mRNA expression levels of many genes simultaneously. Thus, it would be advantageous to provide a means to assess the capacity of cDNA array testing in clinical practice to better classify an heterogeneous cancer into tumor subtypes with more homogeneous clinical outcomes, and to identify new potential prognostic factors and therapeutics targets . The invention relates to a polynucleotide library useful in the molecular characterization of a carcinoma, the library including a pool of polynucleotide sequences or subsequences thereof wherein the sequences or subsequences are either underexpressed or overpressed in tumor cells, further wherein the sequences or subsequences correspond substantially to any of the polynucleotide sequences set forth in any of SEQ ID NOS: 1 - 468 or the complement thereof . Fig. 1 shows an example of differential gene expression between normal breast tissue (NB) and breast tumor samples. Fig. 2 is a representation of expression levels of 176 genes in normal breast tissue (NB) and 34 samples of breast carcinoma. Fig. 3 is prognostic classification of breast cancer by gene expression profiling. Fig. 4 shows the correlation of GATA3 expression with ER phenotype. In the context of this disclosure, a number of terms shall be utilized. The term "polynucleotide" refers to a polymer of RNA or DNA that is single-stranded, optionally containing synthetic, non-natural or altered nucleotide bases. A polynucleotide in the form of a polymer of DNA may be comprised of one or more segments of cDNA, genomic DNA or synthetic DNA. The term "subsequence" refers to a sequence of nucleic acids that comprises a part of a longer sequence of nucleic acids. The term "immobilized on a support" means bound directly or indirectly thereto including attachment by covalent binding, hydrogen bonding, ionic interaction, hydrophobic interaction or otherwise. Breast cancer is characterized by an important histoclinical heterogeneity that currently hampers the selection of the most appropriate treatment for each case. This problem could be solved by the identification of new parameters that better predict the natural history of the disease and its sensitivity to treatment . An important object of the present invention relates to a large-scale molecular characterization of breast cancer that could help in prediction, prognosis and cancer treatment. An important aspect of the invention relates to the use of cDNA arrays, which allows to quantitative study mRNA expression levels of 188 candidate genes in 34 consecutive primary breast carcinomas along three directions: comparison of tumor samples, correlations of molecular data with conventional histoclinical prognostic features and gene correlations. The experimentation evidenced extensive heterogeneity of breast tumors at the transcriptional level. Hierarchical clustering algorithm identified two molecularly distinct subgroups of tumors characterized by a different clinical outcome after chemotherapy. This outcome could not have been predicted by the commonly used histoclinical parameters . No correlation was found with the age of patients, tumor size, histological type and grade. However, expression of genes was differential in tumors with lymph node metastasis and according to the estrogen receptor status; ERBB2 expression was strongly correlated with the lymph node status (p < 0.0001) and that of GATA3 with the presence of estrogen receptors (p ≤ 0.001). Thus, experimental results identified new ways to group tumors according to outcome and new potential targets of carcinogenesis . They show that the systematic use of cDNA array testing holds great promise to improve the classification of breast cancer in terms of prognosis and chemosensitivity and to provide new potential therapeutic targets . DNA arrays consist of large numbers of DNA molecules spotted in a systematic order on a solid support or substrate such as a nylon membrane, glass slide, glass beads or a silicon chip. Depending on the size of each DNA spot on the array, DNA arrays can be categorized as microarrays (each DNA spot has a diameter less than 250 microns) and macroarrays (spot diameter is grater than 300 microns) . When the solid substrate used is small in size, arrays are also referred to as DNA chips. Depending on the spotting technique used, the number of spots on a glass microarray can range from hundreds to thousands . DNA microarrays have serve a variety of purposes, including, gene expression profiling, de novo gene sequencing, gene mutation analysis, gene mapping and genotyping. cDNA microarrays are printed with distinct cDNA clones isolated from cDNA libraries. Therefore, each spot represents an expressed gene, since it is derived from a distinct mRNA. Typically, a method of monitoring gene expression involves providing (1) providing a pool of sample polynucleotides comprising RNA transcript (s) of one or more target gene(s) or nucleic acids derived from the RNA transcript (s) ; (2) reacting, such as hybridizing the sample polynucleotide to an array of probes (for example, polynucleotides obtained from a polynucleotide library) (including control probes) and (3) detecting the reacted/hybridized polynucleotides. Detection can also involve calculating/quantifying a relative expression (transcription) level. The present invention concerns a polynucleotide library useful in the molecular characterization of a carcinoma, said library comprising a pool of polynucleotide sequences or subsequences thereof wherein said sequences or subsequences are either underexpressed or overpressed in tumor cells, further wherein said sequences or subsequences correspond substantially to any of the polynucleotide sequences set forth in any of SEQ ID Nos: 1 - 468 in annex or the complement thereof. Obviously, sequences having a great degree of homology with the above sequences could also been used to realize the molecular caracterization of the invention, namely when those sequences present one or a few punctual mutations when compared with anyone of sequences SEQ ID Nos : 1 - 468. The invention concerns a polynucleotide library useful in the molecular characterization of a carcinoma, said library comprising a pool of polynucleotide sequences or subsequences thereof wherein said sequences or subsequences are overpressed in tumor cells, further wherein said sequences or subsequences correspond substantially to any of the polynucleotide sequences set forth in any of SEQ ID NOS: 1 - 249 (Here, these SEQ ID N° refer to old SEQ ID N° 1-249 in priority document, the correlation table 10 allows to identify these sequences in the sequence listing of the present application in annex ) or the complement thereof Preferably the pool of polynucleotide sequences or subsequences correspond substantially to the polynucleotide sequences set forth in any of SEQ ID NOS: 1 - 247 (Here, these SEQ ID N° refer to old SEQ ID N° 1-247 in priority document, the correlation table 10 allows to identify these sequences in the sequence listing of the present application in annex) ; further wherein said sequences are useful in differentiating a normal cell from a cancer cell. The invention relates also to a polynucleotide library wherein the pool of polynucleotide sequences or subsequences correspond substantially to the polynucleotide sequences set forth in any of SEQ ID NOS: 1 - 242 (Here, these SEQ ID N° refer to old SEQ ID N° 1-242 in priority document, the correlation table 10 allows to identify these sequences in the sequence listing of the present application in annex) ; wherein said sequences are useful in detecting a hormone sensitive tumor cell, or wherein said sequences are useful in differentiating a tumor with lymph nodes from a tumor without lymph nodes . The invention relates also to a polynucleotide library wherein the pool of polynucleotide sequences or subsequences correspond substantially to the polynucleotide sequences set forth in any of SEQ ID NOS: 1 - 224; (Here, these SEQ ID N° refer to old SEQ ID N° 1-224 in priority document, the correlation table 10 allows to identify these sequences in the sequence listing of the present application in annex) wherein said sequences are useful in differentiating tetracycline-sensitive tumors from tetracycline-insensitive tumors . The invention relates also to any polynucleotide library as previously described wherein said polynucleotides are immobilized on a solid support in order to form a polynucleotide array. Preferably the support is selected from the group consisting of a nylon membrane, glass slide, glass beads, or a silicon chip. The invention concerns also a method for detecting differentially expressed polynucleotide sequences which are correlated with a cancer, said method comprising: a) obtaining a polynucleotide sample from a patient; and b) reacting the sample polynucleotide obtained in step (a) with a probe immobilized on a solid support wherein said probe comprises any of the polynucleotide sequences of the libraries previously described or an expression product encoded by any of the polynucleotide sequences of said libraries and c) detecting the reaction product of step (b) . The invention relates also to a such method for detecting differentially expressed polynucleotide sequences of the invention wherein the amount of reaction product of step (c) is compared to a control sample. Preferably the polynucleotide sample isolated for, the sample is RNA or mRNA. Preferably the polynucleotide sample is cDNA obtained by reverse transcription of the mRNA. In a prefered embodiment the method for detecting differentially expressed polynucleotide sequences, the step (b) comprises a hybridization of the sample RNA with the labeled probe. The method for detecting differentially expressed polynucleotide sequences is used for detecting, diagnosing, staging, monitoring, prognosticating, preventing or treating conditions associated with cancer, and namelly breast cancer. The method for detecting differentially expressed polynucleotide sequences is particular useful wherein the product encoded by any of the polynucleotide sequences or subsequences is involved in a receptor-ligand reaction on which detection is based. The invention relates also to a method for screening an anti- umor agent comprising the method for detecting differentially expressed polynucleotide sequences previously described wherein the sample has been treated with the anti- tumor agent to be screened. Le label used to label polynucleotide samples is selected from the group consisting of radioactive, colorimetric, enzymatic, molecular amplification, bioluminescent or fluorescent label. Yhe invention also relates to a library of polynucleotides comprising a population of polynucleotide sequences overexpressed or underexpresses in cells derived from a tumor selected from SEQ ID NO :1 to SEQ ID NO :249 and their respective complements. (Here, these SEQ ID N° refer to old SEQ ID N° 1-249 in priority document, the correlation table 10 allows to identify these sequences in the sequence listing of the present application in annex) . In a particular embodiment the invention relates to polynucleotide sequences: SEQ ID No : 1 ; SEQ ID No : 5 , • SEQ ID No : 8 ; SEQ ID No : 9 ; SEQ ID No : 28 ; SEQ ID No : 29 ; SEQ ID No : 30 ; SEQ ID No : 31 ; SEQ ID No : 32 ; SEQ ID No : 45 ; SEQ ID No : 46 ; SEQ ID No : 52 ; SEQ ID No : 54 ; SEQ ID No : 63 ; SEQ ID No : 64 ; SEQ ID No : 81 ; SEQ ID No : 82 ; SEQ ID No : 87 ; SEQ ID No : 88 ; SEQ ID No : 101 ; SEQ ID No : 102 ; SEQ ID No : 103 ; SEQ ID No : 104 ; SEQ ID No : 105 ; SEQ ID No : 107 ; SEQ ID No : 113 ; SEQ ID No : 114 ; SEQ ID No : 115 ; SEQ ID No : 116 ; SEQ ID No : 127 ; SEQ ID No : 128 ; SEQ ID No : 131 ; SEQ ID No : 139 ; SEQ ID No : 140 ; SEQ ID No : 142 ; SEQ ID No : 150 ; SEQ ID No : 151 ; SEQ ID No : 154 ; SEQ ID No : 156 ; SEQ ID No : 160 ; SEQ ID No : 161 ; SEQ ID No : 162 ; SEQ ID No : 177 ; SEQ ID No : 178 ; SEQ ID No : 194 ; SEQ ID No : 195 ; SEQ ID No : 227 ; SEQ ID No : 228 ; SEQ ID No : 229 ; SEQ ID No : 231 ; SEQ ID No : 233 ; SEQ ID No : 243 ; SEQ ID No : 244 ; SEQ ID No : 245 ; SEQ ID No : 246 ; SEQ ID No : 247, (Here, these SEQ ID N° refer to old SEQ ID N° presented on table 5 in priority document, the correlation table 10 allows to identify these sequences in the sequence listing of the present application in annex) , which distinguish a healthy person from a person with cancer. Preferably the invention relates to polynucleotide sequences: SEQ ID No : 1 ; SEQ ID No : 5 ; SEQ ID No : 102 ; SEQ ID No : 103 ; SEQ ID No : 107 ; SEQ ID No : 229 ; SEQ ID No : 45 ; SEQ ID No : 46; SEQ ID No : 243 ; SEQ ID No : 244; SEQ ID No : 245 ; SEQ ID No : 246 ; SEQ ID No : 247 (Here, these SEQ ID N° refer to old SEQ ID N° presented on table 6 in priority document, the correlation table 10 allows to identify these sequences in the sequence listing of the present application in annex) , which distinguish a healthy person from a person with cancer. In another particular embodiment the invention relates to polynucleotide sequences: SEQ ID No : 2 ; SEQ ID No : 3 SEQ ID No : 4 ; SEQ ID No : 5 ; SEQ ID No : 6 ; SEQ ID No : 7 SEQ ID No : 8 ; SEQ ID No : 9 ; SEQ ID No : 10 ; SEQ ID No : 11 SEQ ID No : 12 ; SEQ ID No : 13 ; SEQ ID No : 14 ; SEQ ID No : 15 ; SEQ ID No : 16 ; SEQ ID No : 17 ; SEQ ID No : 18 ; SEQ ID No : 19 ; SEQ ID No : 20 ; SEQ ID No : 21 ; SEQ ID No : 22 ; SEQ ID No : 23 ; ; SEQ ID No : 24 ; SEQ ID No : 25 ; SEQ ID No : 26 ; SEQ ID No : 27 ; SEQ ID No : 221 ; SEQ ID No : 222 ; SEQ ID No : 223 ; SEQ ID No : 241 ; SEQ ID No : 242 (Here, these SEQ ID N° refer to old SEQ ID N° presented on table 7 in priority document, the correlation table 10 allows to identify these sequences in the sequence listing of the present application in annex) which detect hormone sensitive tumors . Preferably the invention relates to polynucleotide sequences SEQ ID No : 1; SEQ ID No : 2 SEQ ID No : 3; SEQ ID No : 4; SEQ ID No : 5; SEQ ID No : 221; SEQ ID No : 222 ; SEQ ID No : 15; SEQ ID No : 16; SEQ ID No : 17; SEQ ID No : 18 ; SEQ ID No : 19; SEQ ID No : 20 ; SEQ ID No : 21; SEQ ID No : 22 ; SEQ ID No : 241; SEQ ID No : 242 (Here, these SEQ ID N° refer to old SEQ ID N° presented on table 8 in priority document, the correlation table 10 allows to identify these sequences in the sequence listing of the present application in annex) , which detect hormone sensitive tumors. In another particular embodiment the invention relates to polynucleotide sequences: SEQ ID No : 1 ; SEQ ID No : 3 ; SEQ ID No : 4 ; SEQ ID No : 19 ; SEQ ID No : 20 ; SEQ ID No : 2 1; SEQ ID No : 22 ; SEQ ID No : 23 ; SEQ ID No : 26 ; SEQ ID No : 27 ; SEQ ID No : 28 ; SEQ ID No : 29 ; SEQ ID No : 30 ; SEQ ID No : 31 ; SEQ ID No : 32 ; SEQ ID No : 33 ; SEQ ID No : 34 ; SEQ ID No : 35 ; SEQ ID No : 36; SEQ ID No : 37; SEQ ID No : 38; SEQ ID No : 39; SEQ ID No : 40 ; SEQ ID No : 41 ; SEQ ID No : 42 ; SEQ ID No : 43 ; SEQ ID No : 44 ; SEQ ID No : 221 ; SEQ ID No : 222 ; SEQ ID No : 233 ; SEQ ID No : 241 ; SEQ ID No : 242 (Here, these SEQ ID N° refer to old SEQ ID N° presented on table 8 in priority document, the correlation table 10 allows to identify these sequences in the sequence listing of the present application in annex) , which distinguish tumors with lymphe node from tumors with no lymphe node . Preferably the invention relates to polynucleotide sequences : SEQ ID No : 1 ; SEQ ID No : 21 ; SEQ ID No : 22 ; SEQ ID No : 28; ; SEQ ID No : 29 ; SEQ ID No : 29 ; SEQ ID No : 31 ; SEQ ID No : 32 ; SEQ ID No : 19 ; SEQ ID No : 20 ; SEQ ID No : 26 ; SEQ ID No : 27 ; SEQ ID No : 37 ; SEQ ID No : 38 ; SEQ ID No : 39 ; SEQ ID No : 241 ; SEQ ID No : 241, (Here, these SEQ ID N° refer to old SEQ ID N° presented on table 8 in priority document, the correlation table 10 allows to identify these sequences in the sequence listing of the present application in annex) , which distinguish tumors with lymphe node from tumors with no lymphe node . In another particular embodiment the invention relates to polynucleotide sequences : SEQ ID No : 1 ; SEQ ID No : 2 ; SEQ ID No : 6 ; SEQ ID No : 7 ; SEQ ID No : 8 ; SEQ ID No : 9 ; SEQ ID No : 10 ; SEQ ID No : 11 ; SEQ ID No : 13 ; SEQ ID No : 14 ; SEQ ID No : 19 ; SEQ ID No : 20 ; SEQ ID No : 21 ; SEQ ID No : 22 ; SEQ ID No : 23 ; SEQ ID No : 35 ; SEQ ID No : 36 ; ; SEQ ID No : 37 ; SEQ ID No : 56 ; SEQ ID No : 57 ; SEQ ID No : 74 ; SEQ ID NO : 75 ; SEQ ID No : 102 ; SEQ ID No : 104 ; SEQ ID No : 107 ; SEQ ID No : 108 ; SEQ ID No : 109 ; SEQ ID No : 118 ; SEQ ID No : 119 ; ; SEQ ID No : 136 ; SEQ ID No : 213 ; SEQ ID No : 214 ; SEQ ID No : 215 ; SEQ ID No : 223 ; SEQ ID No : 224 (Here, these SEQ ID N° refer to old SEQ ID N° presented on table 11 in priority document, the correlation table 10 allows to identify these sequences in the sequence listing of the present application in annex) which distinguish tumors sensitive to antracycline from tumors unsensitive to antracycline. The invention relates also to a method of detecting differentially expressed genes correlated with a cancer comprising detecting at least one library of polynucleotide sequences as above defined or of products encoded by said library in a sample obtained from a patient. A particular embodiment of the invention relates to a polynucleotide library of corresponding substantially to any combination of at least one polynucleotide sequence selected among those included in each one of predefined polynucleotide sequences sets 1 to set 212 as defined in table 4 The invention relates obviously to polynucleotide libraries comprising at least one polynucleotide selected among those included in at least 50%, preferably 75% and more preferably 100% of said predefined sets, allowing to obtain a discriminating gene pattern, namely to distinguish between normal patients and patients suffering from tumor pathology, between patients having an hormone sensitive tumor and patients having an hormone resistant tumor, between patients having a tumor with lymph nodes from patients having a tumor without lymph nodes, between patients having an antracycline- sensitive tumor from patients having an antracycline- insensitive tumor and between patients having good prognosis primary breast tumors and patients having poor prognosis primary breast tumors. Polynucleotide sequences library useful for the realization of the invention can comprise also any sequence comprised between 3 ' end and 5 'end of each polynucleotide sequence set as defined in table 4, allowing the complete detection of the implicated genes. The invention relates also to a polynucleotide library useful to differentiate a normal cell from a cancer cell wherein the pool of polynucleotide sequences or subsequences correspond substantially to any combination of at least one polynucleotide sequence selected among those included in each one of predefined polynucleotide sequences sets indicated on table 5, useful in differentiating a normal cell from a cancer cell. Preferably the polynucleotide library useful to differentiate a normal cell from a cancer cell correspond substantially to any combination of at least one polynucleotide sequence selected among those included in each one of predefined polynucleotide sequences sets indicated on table 5A, and of at least one polynucleotide sequence selected among those included in each one of predefined polynucleotide sequences sets indicated in table 5B. The detection of an overexpression of genes identified with sets of polynucleotides sequences defined on table 5A, together with detection of an underexpression of genes identified with sets of polynucleotides sequences defined in table 5B allows to distinguish between normal patients, and patients suffering from tumor pathology. The invention relates also to a polynucleotide library useful to detect a hormone sensitive tumor cell wherein the pool of polynucleotide sequences or subsequences correspond substantially to any combination of at least one polynucleotide sequence selected among those included in each one of predefined polynucleotide sequences sets defined in table 6 Preferably the polynucleotide library useful to detect a hormone sensitive tumor cell correspond substantially to any combination of at least one polynucleotide sequence selected among those included in each one of predefined polynucleotide sequences sets defined in table 6A together with at least one polynucleotide sequence selected among those included in each one of predefined polynucleotide sequences sets defined in table 6B. The detection of an overexpression of genes identified with sets of polynucleotides sequences defined on table 6A, together with detection of an underexpression of genes identified with sets of polynucleotides sequences defined in table 6B allows to distinguish between patients having an hormone sensitive tumor and patients having an hormone resistant tumor. The invention concerns also a polynucleotide library useful to differentiate a tumor with lymph nodes from a tumor without lymph nodes wherein the pool of polynucleotide sequences or subsequences correspond substantially to any combination of at least one polynucleotide sequence selected among those included in each one of predefined polynucleotide sequences sets defined in table 7. Preferably, the polynucleotide library useful to differentiate a tumor with lymph nodes from a tumor without lymph nodes correspond substantially to any combination of at least one polynucleotide sequence selected among those included in each one of predefined polynucleotide sequences sets defined in table 7A together with at least one polynucleotide sequence selected among those included in each one of predefined polynucleotide sequences sets defined in table 7B. The detection of an overexpression of genes identified with sets of polynucleotides sequences defined on table 7A, together with detection of an underexpression of genes identified with sets of polynucleotides sequences defined in table 7B allows to distinguish between patients having a tumor with lymph nodes from patients having a tumor without lymph nodes. The invention concerns also a polynucleotide library useful to differentiate antracycline-sensitive tumors from antracycline-insensitive tumors wherein the pool of polynucleotide sequences or subsequences correspond substantially to any combination of at least one polynucleotide sequence selected among those included in each one of predefined polynucleotide sequences sets defined in table 8. Preferably, the polynucleotide library useful to differentiate antracycline-sensitive tumors from antracycline-insensitive tumors correspond substantially to any combination of at least one polynucleotide sequence selected among those included in each one of predefined polynucleotide sequences sets defined in table 8A together with at least one polynucleotide sequence selected among those included in each one of predefined polynucleotide sequences sets defined in table 8B. The detection of an overexpression of genes identified with sets of polynucleotides sequences defined on table 8A, together with detection of an underexpression of genes identified with sets of polynucleotides sequences defined in table 8B allows to distinguish between patients having an antracycline-sensitive tumor from patients having an antracycline-insensitive tumor. The invention concerns also a polynucleotide library useful to classify good and poor prognosis primary breast tumors wherein the pool of polynucleotide sequences or subsequences correspond substantially to any combination of at least one polynucleotide sequence selected among those included in each one of predefined polynucleotide sequences sets defined in table 9. Preferably, the polynucleotide library useful to classify good and poor prognosis primary breast tumors correspond substantially to any combination of at least one polynucleotide sequence selected among those included in each one of predefined polynucleotide sequences sets defined in table 9A together with at least one polynucleotide sequence selected among those included in each one of predefined polynucleotide sequences sets defined in table 9B. The detection of an overexpression of genes identified with sets of polynucleotides sequences defined on table 9A, together with detection of an underexpression of genes identified with sets of polynucleotides sequences defined in table 9B allows to classify patients having good and poor prognosis primary breast tumors . In a preferred embodiment, the tumor cell presenting underexpressed or overpressed sequences from the polynucleotide library of the invention are breast tumor cells . In a particular embodiment the polynucleotides of the polynucleotide library of the present invention are immobilized on a solid support in order to form a polynucleotide array, and said solid support is selected from the group consisting of a nylon membrane, nitrocellulose membrane, glass slide, glass beads, membranes on glass support or a silicon chip. Another object of the present invention concerns a polynucleotide array useful for prognosis or diagnostic of tumor comprising at least one immobilized polynucleotide library set as previously defined. Then the invention concerns a polynucleotide array useful to differentiate a normal cell from a cancer cell comprising any combination of at least one polynucleotide sequence selected among those included in each one of predefined polynucleotide sequences sets indicated on table 5, useful in differentiating a normal cell from a cancer cell. Preferably the polynucleotide array useful to differentiate a normal cell from a cancer cell bears any combination of at least one polynucleotide sequence selected among those included in each one of predefined polynucleotide sequences sets indicated on table 5A, and of at least one' polynucleotide sequence selected among those included in each one of predefined polynucleotide sequences sets indicated in table 5B. The invention relates also to a polynucleotide array useful to detect a hormone sensitive tumor cell comprising any combination of at least one polynucleotide sequence selected among those included in each one of predefined polynucleotide sequences sets defined in table 6 Preferably the polynucleotide array useful to detect a hormone sensitive tumor cell bears any combination of at least one polynucleotide sequence selected among those included in each one of predefined polynucleotide sequences sets defined in table 6A together with at least one polynucleotide sequence selected among those included in each one of predefined polynucleotide sequences sets defined in table 6B. The invention concerns also a polynucleotide array useful to differentiate a tumor with lymph nodes from a tumor without lymph nodes comprising any combination of at least one polynucleotide sequence selected among those included in each one of predefined polynucleotide sequences sets defined in table 7. Preferably, the polynucleotide array useful to differentiate a tumor with lymph nodes from a tumor without lymph nodes bears any combination of at least one polynucleotide sequence selected among those included in each one of predefined polynucleotide sequences sets defined in table 7A together with at least one polynucleotide sequence selected among those included in each one of predefined polynucleotide sequences sets defined in table 7B. The invention concerns also a polynucleotide array useful to differentiate antracycline-sensitive tumors from antracycline-insensitive tumors comprising any combination of at least one polynucleotide sequence selected among those included in each one of predefined polynucleotide sequences sets defined in table 8. Preferably, the polynucleotide array useful to differentiate antracycline-sensitive tumors from antracycline-insensitive tumors bears any combination of at least one polynucleotide sequence selected among those included in each one of predefined polynucleotide sequences sets defined in table 8A together with at least one polynucleotide sequence selected among those included in each one of predefined polynucleotide sequences sets defined in table 8B . The invention concerns also a polynucleotide array useful to classify good and poor prognosis primary breast tumors comprising any combination of at least one polynucleotide sequence selected among those included in each one of predefined polynucleotide sequences sets defined in table 9. Preferably, the polynucleotide array useful to classify good and poor prognosis primary breast tumors bears any combination of at least one polynucleotide sequence selected among those included in each one of predefined polynucleotide sequences sets defined in table 9A together with at least one polynucleotide sequence selected among those included in each one of predefined polynucleotide sequences sets defined in table 9B. The present invention concerns also a method for detecting differentially expressed polynucleotide sequences that are correlated with a cancer, said method comprising: a) obtaining a polynucleotide sample from a patient; and b) reacting the sample polynucleotide obtained in step (a) with a probe immobilized on a solid support wherein said probe comprises any of the polynucleotide sequences of the libraries previously defined or an expression product encoded by any of the polynucleotide sequences of the libraries previously defined c) detecting the reaction product of step (b) . Preferably, the polynucleotide sample obtained at step (a) is labeled before its reaction at step (b) with the probe immobilized on a solid support. The label of the polynucleotide sample is selected from the group consisting of radioactive, colorimetric, enzymatic, molecular amplification, bioluminescent or fluorescent. In a particular embodiment the reaction product of step (c ) is quantified by further comparison of said reaction product to a control sample. In a first embodiment, the polynucleotide sample isolated from the patient and obtained at step (a) is either RNA or mRNA. In another embodiment the polynucleotide sample isolated from the patient is cDNA is obtained by reverse transcription of the mRNA. Preferably the reaction step (b) of the method for detecting differentially expressed polynucleotide sequences comprises a hybridization of the sample RNA issued from patient with the probe. Preferably the sample RNA is labeled before hybridization with the probe and the label is selected from the group consisting of radioactive, colorimetric, enzymatic, molecular amplification, bioluminescent or fluorescent. This method for detecting differentially expressed polynucleotide sequences is particularly useful for detecting, diagnosing, staging, monitoring, prognosticating, preventing or treating conditions associated with cancer, and particularly breast cancer. The method for detecting differentially expressed polynucleotide sequences is also particularly useful when the product encoded by any of the polynucleotide sequences or subsequences set is involved in a receptor-ligand reaction on which detection is based. The present invention is also related with a method for screening an anti-tumor agent comprising the method the above-depicted method for detecting differentially expressed polynucleotide sequences wherein the sample has been treated with the anti-tumor agent to be screened. In a particular embodiment the method for screening an anti-tumor agent comprises detecting polynucleotide sequences reacting with at least one library of polynucleotides or polynucleotide sequences set as previously defined or of products encoded by said library in a sample obtained from a patient. The invention is illustrated by examples detailed below related to particular experimental results obtained with selected libraries of polypeptides useful to identify and distinguish tumor samples from normal ones. Tumor samples and RNA extraction To avoid any bias of selection as to the type and size of the tumors, the RNAs to be tested were prepared from unselected samples . Samples of primary invasive breast carcinomas were collected from 34 patients undergoing surgery at the Institute Paoli-Calmette. After surgical resection, the tumors were macrodissected: a section was taken for the pathologist ' s diagnosis and an adjacent piece was quickly frozen in liquid nitrogen for molecular analyses. The median age of patients at the time of diagnosis was 55 years (range 39, 83) and most of them were post-menopausal . Tumors were classified according to the WHO histological typing of breast tumors in: 29 ductal carcinomas, 2 lobular carcinomas, 1 mixed ductal and lobular carcinoma, and 2 medullar carcinomas. They had various sizes, inferior or equal to 20 mm (n = 13) , between 20 and 50 mm (n = 18) or superior to 50 mm (n = 3), axillary' s lymph node status (negative: 19 tumors, positive: 15 tumors), SBR grading (I: 3 tumors, II: 20 tumors, III: 10 tumors, not evaluable: 1 tumor), and estrogen receptor status (ER) evaluated by immunohistochemical assay (23 ER-positive, 11 ER-negative) . ER positivity cutoff value was 10%. Adjuvant treatment with radiotherapy and when necessary multi-agent anthracyclin- based chemotherapy (n = 16) was given to patients according to local practice. Total RNA was extracted from tumor samples by standard methods (43) . Total RNA from normal breast tissue was obtained from Clontech (Palo Alto, CA) : RNA was isolated from 8 tissue specimens from Caucasian females, age range 23 - 47. RNA integrity was controlled by denaturing formaldehyde agarose gel electrophoresis and Northern blots using a 28S-specific oligonucleotide. cDNA arrays preparation Gene expression was analyzed by hybridization of arrays with radioactive probes . The arrays contained PCR products of 5 control clones, and 180 IMAGE human cDNA clones selected with practical criteria (3' sequence of mRNA, same cloning vector, host bacteria and insert size) . This represented 176 genes (4 genes were represented by 2 different clones) : 121 with proven or putative implication in cancer and 55 implicated in immune reactions (the list is available on the web site: http:/tagc .univ- mrs . fr/pub/Cancer/) . Their identity was verified by 5' tag- sequencing of plasmid DNA and comparison with sequences in the EST (dbEST) and nucleotide (GenBank) databases at the NCBI. Identity was confirmed for all but 14 clones without significant gene similarity, which were referenced by their GenBank accession number. The control clones were: Arabidopsis thaliana cytochrome c554 gene (used for hybridization signal normalization), 3 poly (A) sequences of different sizes and the vector pT7T3D (negative controls) . PCR amplification, purification and robotical spotting of PCR products onto Hybond-N+ membranes (Amersham) were done according to described protocols (4) . All PCR products were spotted in duplicate. For normalization purpose, the c554 gene was spotted 96-fold scattered over the whole membrane . cDNA array hybridizations Hybridizations were done successively with a vector oligonucleotide (to precisely determine the amount of target DNA accessible to hybridization in each spot) , then after stripping of vector probe, with complex probes made from the RNAs (4) . Each complex probe was hybridized to a distinct filter. Probes were prepared from total RNA with an excess of oligo(dT25) to saturate the pol (A) tails of the messengers, and to insure that the reverse transcribed product did not contain long poly(T) sequences. A precise amount of c554 mRNA was added to the total RNA before labeling to allow normalization of the data. Five ng of total RNA (~100ng of mRNA) from tissue samples were used for each labeling. Probe preparation and hybridization of the membranes were done according to known procedures (http: /tagc.univ-mrs . fr/pub/Cancer/) . Hybridization was done in excess of target (-15 ng of DNA in each spot) and binding of cDNAs to the targets was linear and proportional to the quantity of cDNA in the probe . Detection and quantification of cDNA array hybridization signals Quantitative data were obtained using an imaging plate device. Hybridization signal detection with a FUJI BAS 1500 machine and quantification with the HDG Analyzer software (Genomic Solutions, Ann Arbor, MI) were done as previously described (http: /tagc.univ-mrs . fr/pub/Cancer/) . Quantification was done by integrating all spot pixel intensities and substracting a spot background value determined in the neighboring area. Spots were located with a LaPlacian transformation. Spot background level was the median intensity of all the pixels present in a small window centered on the spot and which were not part of any spot (44) . Quantified data were normalized in three steps and expressed as absolute gene expression levels (i.e. in percentage of abundance of individual mRNA with respect to mRNA within the sample) , as described (4) . Array data analysis Before analysis of the results, the reproducibility of the experiments was verified by comparing duplicate spots, or one hybridization with the same probe on two independent arrays, or two independent hybridizations with probes prepared from the same RNA. In every case, the results showed good reproducibility with respective correlation coefficients of 0.95, 0.98 and 0.98 (data not shown) . Moreover, genes represented by two different clones on the array, such as CDK4 or ETV5 , displayed similar expression profiles for the two clones in all samples. This reproducibility was sufficient enough to consider a 2-fold expression difference as significantly differential. For graphical representation, data were displayed as absolute expression levels (Fig. 2a) . For better visualization of clustering, results were log-transformed and displayed as relative values median-centered in each row and in each column (Fig. 2b) . Hierarchical clustering was applied to the tissue samples and the genes using the Cluster program developed by Eisen (45) (average linkage clustering using Pearson correlation as similarity metric) . Results in Figs. 2 and 3 were displayed with the TreeView program (45) . Subsequent analysis was done using Excel software (Microsoft) and statistical analyses with the SPSS software. Metastasis-free survival and overall survival were measured from diagnosis until the first metastatic relapse or death respectively. They were estimated with the Kaplan-Meier method and compared between groups with the Log-Rank test. Correlations of gene pairs based on expression profiles were measured with the correlation coefficient r. The search for genes with expression levels correlated with tumor parameters was done in several successive steps . First, genes were detected by comparing their median expression level in the two subgroups of tumors discordant according to the parameter of interest. The median values rather than the mean values were used because of the high variability of the expression levels for many genes, resulting in a standard deviation of expression level similar or superior to the mean value and making comparisons with means impossible. Second, these detected genes were inspected visually on graphics, and finally, an appropriate statistical analysis was applied to those that were convincing to validate the correlation. Comparison of GATA3 expression between ER-positive tumors and ER-negative tumors was validated using a Mann-Witney test. Correlation coefficients were used to compare the gene expression levels to the number of axillary nodes involved. Northern blot analysis Seventy-nine breast tumors, including 22 of the 34 tested on the arrays, were analyzed for GATA3 expression by Northern blot hybridization. RNA extraction from tumor samples and Northern blots were done as previously described (43) . The GATA3 probe was prepared from the IMAGE cDNA clone 129757, which corresponds to the 3' region (from +843 to +1689) of the GATA3 cDNA sequence (GenBank accession no. X55122) . The insert (846 bp) was obtained by digestion of the clone with EcoRI and Pad enzymes. Northern blots were stripped and re-hybridized using a a-actin probe (46) . Fig. 1 shows an example of differential gene expression between normal breast tissue (NB) and breast tumor samples. Each cDNA array on Nylon filter was hybridized with a complex probe made from 5 μg of total RNA. The top image corresponds to the whole membrane . For the two bottom images, only the right portion of the membranes is shown. Numbers below the spots indicate housekeeping genes (1, GAPDH and 2, actin) , negative control clones (3, 4 and 5) and examples of genes differentially expressed between NB and breast tumor (6, stromelysin3 ; 7, ERBB2 ; 8, MYBL2 ; 9, FOS; 10, TGFaR3 ; 11, desmin) , and between ER- breast tumor and ER+ breast tumor (12, GATA3) . Fig. 2 is a representation of expression levels of 176 genes in normal breast tissue (NB) and 34 samples of breast carcinoma. Each column corresponds to a single tissue, and each row to a single gene. (a) The results are expressed as percentage abundance of individual mRNA within the sample, and are represented using a blue color scale. The color scale (log scale with a 3 -fold interval) indicated at the bottom left ranges from light blue (expression level 0.001%) to dark blue (expression level > 3%). White squares indicate clones with undetectable expression levels and gray squares indicate missing data. The tissue samples are arbitrarily ordered and the clones are ordered from top to bottom according to increasing median expression levels. Horizontal black arrows on the right of the figure mark three clones with highly variable expression levels between the tumors (stromelysin3, IGF2, GATA3 from top to bottom). (b) The results are shown as relative expression levels (relative to the median value of each row and each column) and are represented with a color scale indicated at the bottom left ranging from 1/100 to 100 fold changes (gray squares: missing data) . Eighteen clones with median expression level equal to zero in the 34 tumors are omitted. The clustering program arranges samples (n = 35) along the horizontal axis so that those with the most similar expression profiles are placed adjacent to each other. Similarly, clones (n = 162) are near each other along the vertical axis if they show a strong expression profile correlation across all tissues. The length of the branches of the dendrograms capturing respectively the samples (top) and the clones (left) reflects the similarity of the related elements. Two groups of tumors are separated and color coded: group A (blue) and group B (orange) . Horizontal black and horizontal red arrows on the right of the figure respectively mark three genes with highly variable expression levels between the tumors (IGF2, GATA3 , stromelysin3 from top to bottom) and four pairs of different clones representing four genes. (c) Zoom representation of group A from Figure 2b, excluding the two outIyer tumors at the right. The clustering separates two subgroups of tumors, Al and A2. The dotted branches correspond to tumors associated with metastatic relapse and death. Follow-up was longer in A2 than in Al (median 81 months vs 47 for Al) . Fig. 3 is prognostic classification of breast cancer by gene expression profiling showing that gene expression-based tumour classification correlates with clinical outcome. The 12 samples of group A (see figure 2b and 2c) were reclustered using the top 32 differentially expressed genes between Al and A2 subgroups. Data were displayed as in Fig. 2b and shown with the same color key. The hierarchical clustering was applied to expression data from the 23 clones, out of 32, of which expression levels presented an at least two-fold change in at least two samples (out of 12) . Two subgroups of tumors Al and A2 are shown as well as two groups of differentially expressed clones. The dotted branches of tumor cluster Al correspond to samples associated with metastatic relapse and death. Figure 3a shows Two-dimensional representation of hierarchical clustering results shown in figures 2a and 2b. The analysis delineates 4 groups of tumours A, B, C and D. Black squares indicate patients alive at last follow-up visit and red squares indicate patients who died. Three classes of patients with a statistically different clinical outcome were defined according to gene expression profiles: class A (n = 16), class B+C (n = 34) , class D (n = 5) . Figure 3b illustrates Kaplan-Meier plot of overall survival of the 3 classes of patients (p<0.005, log-rank test). And figure 3c illustrates Kaplan-Meier plot of metastasis-free survival of the 3 classes of patients (p<0.05, log-rank test). Fig. 4 shows the correlation of GATA3 expression with ER phenotype. (a) The expression levels of GATA3 in 34 breast cancer samples (y axis) monitored by cDNA array analysis are reported in percentage of abundance of individual mRNA with respect to mRNA within the sample (log scale) . GATA3 is significantly overexpressed in the ER- positive tumors (n = 23) versus the ER-negative tumors (n = 11) using the Mann-Witney test (p = 0.0004). The expression level of GATA3 in normal breast tissue is reported on the right (NB) . (b) Northern blot analysis of GATA3 in normal breast sample (NB) and 9 breast cancer samples (AT: tumor analyzed with cDNA array and Northern blot; NT: tumor analyzed with Northern blot) . Blots were probed successively with cDNA from GATA3 (top) and a-actin (bottom) . ER status is indicated for each tumor sample. Data representation Fig. 1 shows examples of hybridizations of cDNA arrays with probes made from RNA extracted from normal breast tissue and breast tumors. The crude results of all hybridizations were processed to be presented either as absolute or relative values in schematic figures. The normalization procedure allowed display of absolute values expressed in percent of abundance of mRNA in the probe as shown in Fig. 2a. Each level of the blue color ladder represents a 3 -fold interval of absolute abundance of mRNA. Each column corresponds to a tissue sample and each row to a gene. For graphic purposes, genes were ordered from top to bottom according to increasing median expression levels. Tumor samples were not ordered. The values in each sample displayed a wide range of intensities (3 decades in log scale) corresponding to expression levels ranging from approximately 0.002% to 5% of mRNA abundance. Many genes (see for example stromelysin 3, IGF2 and GATA3 , arrows) displayed highly variable expression levels across all tumor samples, scattered over the whole dynamic range of values. A representation of relative values is shown in Fig. 2b. Absolute values were log-transformed, omitting 18 clones whose median intensity was equal to zero across all tissues. Data for each of the 162 remaining clones were then median-centered, as well as data for each sample, so that the relative variation was shown, rather than the absolute intensity. A color scale was used to display data: red for expression level higher than the median and green for expression level lower than the median. The magnitude of the deviation from the median was represented by the color intensity. A hierarchical clustering program was then applied to group the 35 samples according to their overall gene expression profiles, and to group the 162 clones on the basis of similarity of their expression levels in all tissues. This resulted in a picture highlighting groups of correlated tissues and groups of correlated genes as depicted by dendrograms . Breast tumor classification As shown in Fig. 2b, the clustering algorithm identified two groups of samples, designated A (n = 15, including normal breast, NB) and B (n = 20) . These groups were similar with respect to patient age, menopausal status at diagnosis, SBR grading and tumor pathological size. However, 72% of tumors in group A were node-positive and 75% in group B were node-negative . Moreover, 80% of the tumors in group B were estrogen receptor (ER) positive and 50% in group A were ER-negative. With a median follow-up of 44 months after diagnosis, overall survival was different between A and B groups: 5 women died in A (median follow-up 58 months) and 1 in B (median follow-up 40 months) . But the frequency of metastatic relapse was relatively similar in the two groups, with 5 women who relapsed in A and 6 in B . Because the time between the diagnosis of metastasis and last follow-up is too short in B, a longer follow-up is needed to determine if these two different groups, defined with expression profiles, have really a different outcome with respect to overall survival. In the group A of 15 samples, three samples (normal breast and two tumors) were different from each other and from the other 12 samples. The latter constituted two subgroups of tumors, Al (n = 6) and A2 (n = 6) , which could be further separated by clustering as shown in Fig. 2c. The 12 tumors had an uniformly high risk of metastatic relapse according to conventional prognostic features as shown in Table 1. Most of them had received comparable adjuvant anthracyclin-based chemotherapy after surgery, with more women treated in the Al subgroup. Interestingly, these two subgroups, which could not be distinguished with commonly used histoclinical features, had a very different clinical outcome: there were 4 metastatic relapses and 4 deaths in Al (median follow-up: 44 months) . In contrast and despite a longer median follow-up (90 months) , no metastasis or death occurred in A2. This resulted in a significant better metastasis-free survival (p 0.01) and overall survival (p 0.005) for group A2 than for group Al tumors. No such subgrouping could be done in B . TABLE 1 Subgroup Al A2 Tumor position 1 2 3 4 5 6 7 8 9 10 11 12 in the cluster Age , years 46 58 60 63 51 58 46 47 50 47 46 66 Nodal status 1 0 0 16 13 37 10 4 1 2 0 0 Histological 60 20 26 35 20 30 27 25 30 25 20 22 size, mm SBR grade II I I I I I I I I II I I I II I I I I I I II I I I ER status neg neg neg neg neg neg pos neg pos pos pos pos Adjuvant yes yes no yes yes yes yes yes no yes no no chemotherapy Metastasis yes no yes yes no yes no no no no no no Follow-up, 58 106 35 47 41 31 85 98 95 49 19 141 months Patient status D A D D A D A A A A A A Patient characteristics in subgroups Al and A2. The 12 tumors are numbered from 1 to 12 accord to their position from left to right in the clustering graphic displayed in Fig. 3. Adjuvant chemotherapy anthracyclin-based. In the line concerning the patient status, A means alive and D means death from can progression. Genes responsible for group A substructure were searched. These are potentially relevant to the prognosis and the sensitivity to chemotherapy in these tumors . Thirty- two genes out of 188 were identified by comparing their median expression level in Al vs A2. Then, the 12 tumors were reclustered using the expression profiles of these genes as shown in Fig. 3. The same subgroups Al and A2 were evident and separated by 2 groups of genes: as expected, high expression of ERBB2 , MYC and EGFR was associated with bad prognosis subgroup Al (6-8) , and that of E-cadherin and the proto-oncogene MYB with good prognosis subgroup A2 (9, 10) . For most of the other genes, these results may stimulate new investigations. Differentiation state is a good prognostic factor in breast cancer and, accordingly, genes associated with cell differentiation, such as GATA3 (11) and CRABP2 (12) , had a high level of expression in the better outcome group. The high expression of Ephrin-Al mRNA in the bad prognosis subgroup suggests a role of this growth factor in breast cancer and can be paralleled with its up-regulation during melanoma progression (13) . Differential gene expression between normal breast and breast tumors To identify genes differentially expressed between breast tumors (T) and normal breast (NB) , the NB value for each gene was compared to its expression level in each tumor. When the expression level of a gene in NB was undetectable, only qualitative information could be deduced and the mRNA was considered as differentially expressed if the signal intensity in the tumor was superior to the reproducibility threshold (0.002% of mRNA abundance). In the other cases, differential expression was defined by an at least 2-fold expression difference. Also, the number of tumors where it was over- or underexpressed was measured. Table 2 shows a list of the top 20 over- and underexpressed genes. For these genes, the T/NB ratio is reported, where T represented their median expression value in the 34 tumors. This ratio ranged from 2.70 (ABCC5) to 17.76 (GATA3) for the overexpressed genes, and from 0.00 (desmin) to 0.29 (APC) for the underexpressed genes. TABLE 2 List of the genes that show the most frequent differential expression between normal breast tissue and 34 breast carcinomas as measured by cDNA array analysis. N indicates the number of tumor samples where the gene is dysregulated (fold change > 2) compared to normal breast tissue. T/NB represents the ratio: median expression level in 34 breast tumors / expression level in normal breast. (a) MYBL2 transcript displayed a median expression level of 0.025% in breast tumors and was undetectable in NB. High expression of mucin 1, NM23, ERBB2 , FGFRl and FGFR2 , MYC, stromelysin3 , cathepsin D and downregulation of FOS, APC, RBL2, FAS, BCL2 were found, reflecting what is known about their biology in cancer. GATA3 , which codes for a member of the GATA family of zinc finger transcription factors, and CRABP2 , encoding one of the two cellular retinoic acid-binding proteins, showed high expression of mRNA, extending previous results on cDNA arrays (4) . Differential gene expression among various breast tumors and correlation with histoclinical prognostic parameters To search for potential prognostic markers in breast cancer, genes with expression levels correlated with conventional histoclinical prognostic parameters were looked for: age of patients, axillary node status, tumor size, histological grade and ER status. No significant correlation was found with age, tumor size and histological grade. However, the expression profiles of some genes correlated with ER status and axillary node involvement. To identify genes potentially relevant to the hormone-responsive phenotype, the gene expression profiles in ER-positive breast cancers (n = 23) vs ER-negative breast cancers (n = 11) were compared. Sixteen clones displayed a median intensity of 0 in both groups. Twenty-five presented a fold change superior to 2. Table 3a displays the top 10 over- and underexpressed genes. Among them, the most differentially expressed was GATA3 with a median intensity ratio ER+/ER- of 28.6 and a value for the first quartile of ER-positive tumors superior (5-fold) to the value of the third quartile of the ER-negative tumors as shown in Fig. 4a. The high expression of GATA3 in ER-positive tumors was statistically significant using a Mann-Witney test (p 0.001) . All ER-positive tumors and only 18% of ER-negative tumors displayed a GATA3 expression level greatly superior (fold change > 3) to the normal breast value. Furthermore GATA3.expression was analyzed by Northern blot hybridization (Fig. 4b) in a panel of 79 breast cancers (21 ER-negative tumors and 58 ER-positive tumors) , including 22 of the tumors analyzed with cDNA arrays. It confirmed the array results for those 22 tumors as well as the strong correlation between ER status and GATA3 RNA expression (Mann-Witney test, p 0.0001) . TABLE 3A To search for genes whose expression profile was correlated with axillary lymph node status, a strong prognostic factor in breast cancer, the group of node- negative tumors (n = 19) was compared with the group of tumors with massive axillary extension (10 or more positive nodes) . Furthermore, because survival decreases with the increase of the number of tumor-involved lymph nodes and because the expression measurements were quantitative, it was looked for a correlation between the expression levels of these genes and the number of tumor-involved nodes (quantitative variables) . Table 3b shows a list of the top 10 over- and underexpressed genes between these 2 groups. Most of these genes have not been previously reported as associated with node status, but some of these results are in agreement with literature data. The gene encoding the tyrosine kinase receptor ERBB2 was the most significantly overexpressed gene in node-positive tumors and displayed the highest correlation coefficient (r = 0.68 ; p < 0.0001). TABLE 3B Gene clusters Gene clustering from Fig. 2b showed groups of genes with correlated expression across samples. When different clones represented the same gene, they were clustered next to each other (red arrows) . Correlation coefficients between gene pairs in the 34 tumors were often high (1% of the 13,041 gene pairs showed a correlation coefficient superior to 0.95 - not shown) . An example of highly correlated gene expression is that of BCL2 and RBL2. Such correlated expression, although it has not been described in the literature, probably reflects a common mechanism of regulation for these two genes. Furthermore, these genes also exhibited significant correlated expression with other genes such as PPP2CA, AKT2 , PRKCSH or TNFRSF6/FAS. In particular, a striking correlated expression between BCL2 and FAS could be observed (r = 0.91; data not shown) . The exact meaning of this correlation is unknown, although it may reflect the necessary balance between apoptosis and anti- apoptosis for cell survival. Although in human cancer the proportion of changes that is reflected at the RNA level is not known, monitoring gene expression patterns appears as a very promising way of increasing the knowledge of the disease. Several different types of cancer have been investigated using cDNA arrays: cervical (14), hepatocellular (15), ovarian (16) , colon (17) and renal carcinomas (18) , glioblastomas (19) , melanomas (20) (21) , rhabdomyosarcomas (22) , acute leukemias (23) and lymphomas (24) . In breast cancer, pioneering studies have yielded the first expression patterns (4, 25-31) . They have in particular addressed the important issue of molecular differences in hormone responsive and non-responsive breast tumors. Thus, Yang et al . (28) and Hoch et al . (25) compared expression profiles of breast carcinoma cell lines known to represent these two categories and identified a few genes with differential expression. One of these genes was GATA3. In these studies, cell lines were mostly used and tumor samples were rarely tested and generally in small numbers. The first study analyzing the expression profiles of a large series of breast cancers was published recently (32) , but no correlation with clinical outcome was mentioned. Several interesting points can be made based on the present experimentation. First, the differences in expression patterns among the tumors provided molecular transcriptional evidence of the histoclinical heterogeneity of breast cancer. This diversity was multifactorial, linked to many different genes, highlighting the interest of high throughput analysis in this context. It was possible, with a hierarchical clustering program integrating the expression profiles, to separate normal breast tissue from most tumors and, moreover, to identify two different groups of tumors. Most importantly, two different subgroups of tumors with a very distinct clinical outcome that could not be predicted with classical prognostic factors have been identified by clustering. Indeed, all these tumors had a theoretically bad prognosis as evaluated by current histoclinical tools. All these patients would be at the present time treated with adjuvant chemotherapy, but without the capacity for the physicians to identify patients who will benefit of this treatment and those who will not benefit. Gene expression profiles were able to make this discrimination. Such predictive tools have important therapeutic implications. Patients with features of poor prognosis are candidates for other treatment than standard chemotherapy, avoiding loss of time and toxicities related to first-line chemotherapy. These results suggest that the histoclinical category of poor prognosis breast cancer, currently treated with adjuvant anthracyclin-based chemotherapy, groups together at least two molecularly distinct subgroups of tumors with different outcome which would require distinct chemotherapy regimens. Expression profiles could thus provide a new and more accurate way of classifying breast tumors of poor prognosis and managing patients . Similarly, despite molecular heterogeneity, significant correlations between the expression level of genes (GATA3, ERBB2) and histological tumor parameters were identified. The ER-positivity in breast cancer has been correlated with tumor differentiation, low proliferating rate, favorable prognosis and response to hormonal therapy. The relation between hormone sensitivity of breast cancer and ER status is not perfect, and it is possible that some genes related to ER expression are more important than ER to characterize the hormone sensitive phenotype. These genes could serve as predictive factors to guide the therapy. GATA3 mRNA expression was highly correlated with ER status. GATA3 , which is not estrogen-regulated (25), is a transcription factor that could regulate the expression of genes involved in the ER-positive phenotype. Among the other genes that were found associated with ER status during the experimental work leading to the present invention, some, such as MYB (10) , stromelysin 3 (33) , and CRABP2 (34) , have been previously reported expressed at high levels in ER- positive breast tumors. The higher levels of TP53 mRNA in ER-positive tumors studied were surprising, although in agreement with a recent study (27) . Most studies concerning TP53 expression analyzed the protein level rather than the mRNA level, and TP53 protein levels are classically negatively correlated with the ER status (35) . The high expression of CRABP2 could be related to the better differentiated status of the ER-positive tumors. The low expression of the three immunity-related genes IL2RB, IL2RG and CD3G may be related to the low lymphoid infiltration in these well differentiated tumors. ERBB2 high expression in breast cancer has been associated with a poor prognosis and some resistance to hormonal therapy and chemotherapy (36) . It is involved in the regulation of cellular differentiation, adhesion, and motility. The motility-enhancing activity of ERBB2 (37) could be responsible for the increased metastatic potential and the unfavorable prognosis of the breast tumors that overexpress ERBB2. The low expression of E-cadherin and thrombospondin 1 in node-positive tumors are consistent with their putative role in different steps of metastatic spread: E-cadherin is an epithelial cell adhesion molecule whose disturbance is a prerequisite for the release of invasive cells in carcinomas (38) and thrombospondin 1 inhibits angiogenesis (39) . Similarly, the high expression of the molecule surface antigen Mucin 1 in node-positive tumors (40) can reduce cell-cell interactions facilitating cell detachment and metastasis. CD44, encoding a transmembrane glycoprotein involved in cell adhesion and lymph node homing (41) was expressed at high levels in node-positive tumors as well as GSTP1 (Glutathione-S-Transferase Pi) , recently reported associated with increased tumor size (27) . Second, there were a number of genes with highly correlated expression patterns. Gene correlations have already been reported with larger series of genes, essentially under dynamic experimental conditions (42) and recently in steady states (17) . Here, correlations were based on expression profiles of a relatively small but selected series of genes and in steady states represented by different breast tumors. Gene correlations are potentially useful tools for cancer research in two ways: i) - they can provide information about the general regulation circuitry of a cancerous cell, allowing the identification of regulatory elements controlling expression networks; ii) - they offer the possibility of reducing the complexity of the system analyzed by replacing, for example, the intensities of a large number of genes present in a gene cluster by their respective mean intensities . Finally, these results highlight the great potential of cDNA array in cancer research. The gene expression profiles confirmed the heterogeneity of breast cancer, and most importantly allowed us to identify, among a series of poor prognosis breast tumors, two subtypes of the disease not yet recognized with usual histoclinical parameters but with a different clinical outcome after adjuvant chemotherapy. Furthermore, the present invention allows detecting genes of which expression was correlated with classical prognostic factors. Table 4 displays a library of polynucleotides SEQ ID NO :1 to SEQ ID NO : 468 corresponding to a population of polynucleotide sequences underexpressed or overexpressed in cells derived from tumors, more particularly breast tumors, and their respective complements. TABLE 4 Tables 5A and 5B hereunder displays two subpopulations corresponding to the 5 top overexpressed and to the 5 top underexpressed polynucleotide sequences particularly interesting to distinguish healthy person from cancer patient . TABLE 5A overexpressed genes : top 5 TABLE 5B underexpressed genes : top 5 Table 6 hereunder relate to sub populations of polynucleotide sequences interesting to detect hormone sensitive tumors allowing to distinguish between ER+ and ER- samples . TABLE 6 Tables 6A et 6B hereunder relate to two sub populations of polynucleotide sequences particularly interesting to detect hormone sensitive tumors allowing to distinguish between ER+ and ER- samples Table 6 overexpressed genes : top 5 ER + / ER - Table 6B underexpressed genes : top 5 Tables 7 hereunder relates to subpopulations of polynucleotide sequences interesting to distinguish tumors with lymphe node from tumors with no lymphe node . TABLE 7 Tables 7A and 7B hereunder relate to two sub populations of polynucleotide sequences particularly interesting to distinguish tumors with lymphe node from tumors with no lymphe node . TABLE 7A Overexpressed genes : top 5 TABLE 7B Underexpressed genes : top 5 Tables 8, 8A and 8B hereunder relates to sub populations of polynucleotide sequences particularly interesting to distinguish tumors sensitive to antracycline from tumors unsensitive to antracycline. TABLE 8 Al /A2 Tables 8A and 8B hereunder relate to two sub populations of polynucleotide sequences particularly interesting to distinguish tumors sensitive to antracycline from tumors unsensitive to antracycline. TABLEAU 8A overexpressed genes : top 5 TABLEAU 8B underexpressed genes : top 5 Tables 9, 9A and 9B hereunder relates to sub populations of polynucleotide sequences particularly interesting in classifying good and poor prognosis primary breast tumors . TABLE 9 TABLE 9A TABLE 9B Overexpression of genes detected by using at least one polynucleotide sequence selected among those included in each one of predefined polynucleotide sequences indicated in table 9A combined with underexpression of genes detected with at least one polynucleotide sequence selected among those included in each one of predefined polynucleotide sequence indicated on table 9B present a Good outcome. So, a preferred DNA array according to the invention comprises at least one polynucleotide sequence selected among those included in each one of predefined polynucleotide sequences indicated in table 9A and at least one polynucleotide sequence selected among those included in each one of predefined polynucleotide sequence indicated on table 9B. Such DNA arrays are particularly useful to distinguish patients having a high risk (Bad Outcome) from those having a good pronostic (Good Outcome) . TABLE 10 CORRELATION BETWEEN SEQ ID NO AS FILED WITH US PROVISIONAL APPLICATION N° 60/254,090 and SEQ ID NO FILED WITH PCT APPLICATION References 1. DeRisi, J., Penland, L., Brown, P. 0., Bittner, M. L., Meltzer, P. S., Ray, M. , Chen, Y., Su, Y. A., and Trent, J. M. (1996) Use of a cDNA microarray to analyze gene expression patterns in human cancer. Nat Genet ,14, 457- 460. 2. Jordan, B. R. 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Claims: CLAIMS 1. A polynucleotide library useful in the molecular characterization of a carcinoma, said library comprising a pool of polynucleotide sequences or subsequences thereof wherein said sequences or subsequences are either underexpressed or overpressed in tumor cells, further wherein said sequences or subsequences correspond substantially to any of the polynucleotide sequences set forth in any of SEQ ID Nos: 1 - 468 or the complement thereof. 2. A polynucleotide library according to Claim 1 wherein said polynucleotide sequences or subsequences thereof of said pool correspond to any combination of at least one polynucleotide selected among those included in anyone of the following predefined sets : SET 1: (SEQ ID No : 1 ; SEQ ID No : 2 ) ; SET 2: (SEQ ID No:3; SEQ ID Nθ:4); SET 3: (SEQ ID No : 5 ; SEQ ID Nθ:6); SET 4: (SEQ ID No:7;SEQ ID No:8); SET 5: (SEQ ID No : 9 ; SEQ ID No: 10); SET 6: (SEQ ID No:ll: SEQ ID No: 12); SET 7: (SEQ ID No:13; SEQ ID No:14;SEQ ID No:15); SET 8: (SEQ ID No:16); SET 9: (SEQ ID Nθ:17; SEQ ID Nθ:18; SEQ ID Nθ:19); SET 10: (SEQ ID Nθ:20; SEQ ID No:21); SET 11: (SEQ ID Nθ:22; SEQ ID No:23; SEQ ID Nθ:24); SET 12: (SEQ ID No:25; SEQ ID No:26); SET 13: (SEQ ID Nθ:27; SEQ ID Nθ:28; SEQ ID No:29); SET 14: (SEQ ID Nθ:30; SEQ ID No:31); SET 15: (SEQ ID No:32; SEQ ID Nθ:33; SEQ ID Nθ:34)) ; SET 16 : (SEQ ID Nθ:35) ; SET 17 : (SEQ ID Nθ:36; SEQ ID Nθ:37; SEQ ID No:38) ; SET 18 : (SEQ ID No: 39; SEQ ID No: 40; SEQ ID No: 41) ; SET 19 : (SEQ ID Nθ:42; SEQ ID No:43) ; SET 20 : (SEQ ID Nθ:44; SEQ ID Nθ:45) ; SET 21 : (SEQ ID Nθ:46; SEQ ID No: 47) ; SET 22 : (SEQ ID Nθ:48; SEQ ID No: 49; SEQ ID No: 50) ; SET 23 : (SEQ ID No: 51; SEQ ID No: 52; SEQ ID No: 53) ; SET 24: (SEQ ID No: 54; SEQ ID No: 55; SEQ ID No: 56) ; SET 25: (SEQ ID No: 57; SEQ ID Nθ:58) ; SET 26: (SEQ ID No: 59; SEQ ID No: 60; SEQ ID No: 61) ; SET 27: (SEQ ID No: 62; SEQ ID No: 63; SEQ ID No:64) ; SET 28: (SEQ ID Nθ:65; SEQ ID Nθ:66; SEQ ID Nθ:67) ; SET 29: (SEQ ID No:68; SEQ ID Nθ:69; SEQ ID No:70) ; SET 30: (SEQ ID No: 71; SEQ ID No: 72) ; SET 31 : (SEQ ID No: 73; SEQ ID No: 74; SEQ ID No: 75) ; SET 32 : (SEQ ID No: 76; SEQ ID No: 77; SEQ ID No: 78) ; SET 33 : (SEQ ID No: 79; SEQ ID No: 80; SEQ ID No: 81) ; SET 34: (SEQ ID No: 82; SEQ ID No: 83) ; SET 35: (SEQ ID No: 84; SEQ ID No:85) ; SET 36: (SEQ ID Nθ:86; SEQ ID No:87) ; SET 37: (SEQ ID No: 88; SEQ ID Nθ:89; SEQ ID No: 90) ; SET 38: (SEQ ID No: 91; SEQ ID No: 92; SEQ ID No: 93) ; SET 39: (SEQ ID No : 94 ; SEQ ID Nθ:95; SEQ ID No: 96) ; SET 40: (SEQ ID Nθ:97; SEQ ID No: 98; SEQ ID No: 99) ; SET 41: (SEQ ID No:100; SEQ ID Nθ:101; SEQ ID Nθ:78) ; SET 42: (SEQ ID No: 102; SEQ ID No : 103) ; SET 43: (SEQ ID No: 104; SEQ ID No: 105) ; SET 44: (SEQ ID No: 106; SEQ ID No: 107; SEQ ID No: 108) ; SET 45: (SEQ ID No:109; SEQ ID Nθ:110) ; SET 46: (SEQ ID No: 111; SEQ ID No: 112; SEQ ID No: 113) ; SET 47: (SEQ ID No: 114) ; SET 48: (SEQ ID No: 115; SEQ ID No: 116; SEQ ID No: 117) ; SET 49: (SEQ ID No : 118; SEQ ID No: 119) ; SET 50: (SEQ ID No: 120; SEQ ID No: 121) ; SET 51: (SEQ ID No: 122; SEQ ID Nθ:78) ; SET 52: (SEQ ID No: 123; SEQ ID No: 124; SEQ ID No: 125) ; SET 53: (SEQ ID No: 126; SEQ ID No : 127; SEQ ID No:128) ; SET 54: (SEQ ID Nθ:129; SEQ ID Nθ:130) ; SET 55: (SEQ ID Nθ:131; SEQ ID Nθ:132) ; SET 56: (SEQ ID No:133; SEQ ID No:134) ; SET 57: (SEQ ID Nθ:135; SEQ ID Nθ:136; SEQ ID Nθ:137) ; SET 58: (SEQ ID No: 138; SEQ ID No: 139; SEQ ID No: 140) ; SET 59: (SEQ ID No: 141; SEQ ID No: 142; SEQ ID No : 143) ; SET 60: (SEQ ID No: 144; SEQ ID No: 145; SEQ ID No: 146) ; SET 61: (SEQ ID No : 147; SEQ ID No: 148; SEQ ID No: 149) ; SET 62: (SEQ ID No: 150; SEQ ID No: 151; SEQ ID No: 152) ; SET 63: (SEQ ID No : 153; SEQ ID No : 154; SEQ ID No: 155) ; SET 64: (SEQ ID No: 156; SEQ ID No: 157; SEQ ID Nθ:158) ; SET 65: (SEQ ID Nθ:159; SEQ ID Nθ:160; SEQ ID No:161) ; SET 66: (SEQ ID No: 162; SEQ ID No: 163) ; SET 67: (SEQ ID No : 164; SEQ ID Nθ:165) ; SET 68: (SEQ ID Nθ:166; SEQ ID Nθ:167; SEQ ID No: 152) ; SET 69: (SEQ ID No: 168; SEQ ID No: 169; SEQ ID No: 170) ; SET 70: (SEQ ID Nθ:171; SEQ ID Nθ:172) ; SET 71: (SEQ ID Nθ:173; SEQ ID No: 174; SEQ ID No: 175) ; SET 72: (SEQ ID No: 176; SEQ ID No: 177) ; SET 73: (SEQ ID No: 178; SEQ ID No: 179) ; SET 74: (SEQ ID No:180; SEQ ID Nθ:181; SEQ ID Nθ:182) ; SET 75: (SEQ ID Nθ:183; SEQ ID No:184) ; SET 76: (SEQ ID Nθ:185) ; SET 77: (SEQ ID Nθ:186) ; SET 78: (SEQ ID No:187; SEQ ID Nθ:188) ; SET 79: (SEQ ID Nθ:189; SEQ ID No: 190; SEQ ID No: 191) ; SET 80: (SEQ ID No: 192; SEQ ID No: 193) ; SET 81: (SEQ ID No: 194; SEQ ID No: 195) ; SET 82: (SEQ ID No: 196; SEQ ID No : 197; SEQ ID No: 198) ; SET 83: (SEQ ID No : 199; SEQ ID Nθ:200) ; SET 84: (SEQ ID Nθ:201; SEQ ID No:202; SEQ ID No:203) ; SET 85: (SEQ ID Nθ:204; SEQ ID No:205) ; SET 86: (SEQ ID NO-.206; SEQ ID Nθ:207) ; SET 87: (SEQ ID Nθ:208; SEQ ID Nθ:209) SET 88: (SEQ ID No: 210; SEQ ID No: 211) ; SET 89: (SEQ ID No: 212 SEQ ID No: 213) ; SET 90: (SEQ ID No : 214; SEQ ID No: 215) ; SET 91 (SEQ ID Nθ:216; SEQ ID No:217) ; SET 92: (SEQ ID No:218; SEQ ID No:219; SEQ ID Nθ:220) ; SET 93: (SEQ ID Nθ:221; SEQ ID Nθ:222) ; SET 94: (SEQ ID No: 223; SEQ ID No: 224; SEQ ID No: 225) ; SET 95: (SEQ ID No: 226; SEQ ID No: 227) ; SET 96: (SEQ ID No: 228; SEQ ID Nθ:229) ; SET 97: (SEQ ID Nθ:230; SEQ ID Nθ:231; SEQ ID Nθ:232) ; SET 98: (SEQ ID No.-233; SEQ ID No: 234) ; SET 99 : (SEQ ID No : 235; SEQ ID No:236; SEQ ID Nθ:237) ; SET 100: (SEQ ID No:238; SEQ ID No: 239) ; SET 101: (SEQ ID No: 240; SEQ ID No: 241) ; SET 102: (SEQ ID No: 242; SEQ ID No: 243; SEQ ID No -.244) ; SET 103: (SEQ ID No -.245; SEQ ID No: 246; SEQ ID No: 247) ; SET 104: (SEQ ID No : 248; SEQ ID No: 249) ; SET 105: (SEQ ID No: 250; SEQ ID No: 251; SEQ ID No: 252) ; SET 106: (SEQ ID No: 253; SEQ ID No: 254) ; SET 107: (SEQ ID No:255; SEQ ID Nθ:256) ; SET 108: (SEQ ID No:257; SEQ ID NO.-258) ; SET 109: (SEQ ID Nθ:259; SEQ ID NOJ 260 ; SEQ ID Nθ:261) ; SET 110: (SEQ ID Nθ:262; SEQ ID Nθ:200) ; SET 111: (SEQ ID No:263; SEQ ID No: 264) ; SET 112: (SEQ ID No : 265; SEQ ID No: 266) ; SET 113: (SEQ ID Nθ:267; SEQ ID Nθ:268) ; SET 114: (SEQ ID Nθ:269; SEQ ID No: 270) ; SET 115: (SEQ ID No: 271; SEQ ID No: 272) ; SET 116: (SEQ ID Nθ:273; SEQ ID No:274) ; SET 117: (SEQ ID Nθ:275; SEQ ID Nθ:276) ; SET 118: (SEQ ID Nθ:277; SEQ ID Nθ:278) ; SET 119: (SEQ ID No: 279; SEQ ID No: 280; SEQ ID No: 281) ; SET 120: (SEQ ID Nθ:282; SEQ ID No:283; SEQ ID Nθ:276) ; SET 121: (SEQ ID Nθ:284; SEQ ID Nθ:285) ; SET 122: (SEQ ID Nθ:286; SEQ ID Nθ:287; SEQ ID No: 288) ; SET 123: (SEQ ID No: 289; SEQ ID No: 290) ; SET 124: (SEQ ID No: 291; SEQ ID No: 292) ; SET 125: (SEQ ID No: 293; SEQ ID No: 294; SEQ ID No: 295) ; SET 126: (SEQ ID No: 296; SEQ ID No : 297) ; SET 127: (SEQ ID Nθ:298; SEQ ID Nθ:299; SEQ ID Nθ:300) ; SET 128: (SEQ ID No:301; SEQ ID No:302; SEQ ID Nθ:288) ; SET 129: (SEQ ID Nθ:303; SEQ ID No:304) ; SET 130: (SEQ ID Nθ:305; SEQ ID Nθ:306; SEQ ID No:307) ; SET 131: (SEQ ID No:308; SEQ ID Nθ:309; SEQ ID NO.-310) ; SET 132: (SEQ ID Nθ:311; SEQ ID No:312; SEQ ID Nθ:313) ; SET 133: (SEQ ID Nθ:314; SEQ ID Nθ:315; SEQ ID Nθ:316) ; SET 134: (SEQ ID No: 317; SEQ ID No: 318) ; SET 135: (SEQ ID No: 319; SEQ ID No: 320; SEQ ID No -.321) ; SET 136: (SEQ ID No: 322; SEQ ID No: 323) ; SET 137: (SEQ ID No: 324; SEQ ID No: 325) ; SET 138: (SEQ ID No : 326; SEQ ID No: 327; SEQ ID No: 328) ; SET 139: (SEQ ID No: 329; SEQ ID Nθ:330) ; SET 140: (SEQ ID Nθ:331; SEQ ID Nθ:332; SEQ ID No:333) ; SET 141: (SEQ ID Nθ:334; SEQ ID Nθ:335; SEQ ID Nθ:336) ; SET 142: (SEQ ID No:337; SEQ ID Nθ:338; SEQ ID Nθ:117) ; SET 143: (SEQ ID No: 339; SEQ ID No: 340; SEQ ID No: 341) ; SET 144: (SEQ ID No : 342; SEQ ID No: 343; SEQ ID No: 344) ; SET 145: (SEQ ID No: 345; SEQ ID No: 346) ; SET 146: (SEQ ID No: 347; SEQ ID No: 348; SEQ ID No: 349) ; SET 147: (SEQ ID No: 350; SEQ ID No: 351) ; SET 148: (SEQ ID No : 352; SEQ ID Nθ:353) ; SET 149: (SEQ ID Nθ:354; SEQ ID No:355) ; SET 150: (SEQ ID Nθ:356; SEQ ID Nθ:357) ; SET 151: (SEQ ID Nθ:358; SEQ ID Nθ:359; SEQ ID Nθ:360) ; SET 152: (SEQ ID No:361; SEQ ID Nθ:31) ; SET 153: (SEQ ID No: 362; SEQ ID No: 363; SEQ ID No: 364) ; SET 154: (SEQ ID Nθ:365; SEQ ID Nθ:366; SEQ ID Nθ:367) ; SET 155: (SEQ ID Nθ:368; SEQ ID No:369; SEQ ID Nθ:300) ; SET 156: (SEQ ID No: 370; SEQ ID No: 371) ; SET 157: (SEQ ID No: 372; SEQ ID No : 373; SEQ ID No:108) ; SET 158: (SEQ ID Nθ:374; SEQ ID Nθ:375; SEQ ID No:376) ; SET 159: (SEQ ID Nθ:377; SEQ ID Nθ:378; SEQ ID Nθ:379) ; SET 160: (SEQ ID No:380; SEQ ID Nθ:381) ; SET 161: (SEQ ID Nθ:382; SEQ ID No: 383; SEQ ID No: 384) ; SET 162: (SEQ ID No: 385; SEQ ID No: 386; SEQ ID No: 387) ; SET 163: (SEQ ID No: 388; SEQ ID No : 389; SEQ ID No:390) ; SET 164: (SEQ ID Nθ:391; SEQ ID Nθ:392; SEQ ID No:393) ; SET 165: (SEQ ID Nθ:394; SEQ ID Nθ:395) ; SET 166: (SEQ ID No:396; SEQ ID Nθ:397; SEQ ID Nθ:398) ; SET 167: (SEQ ID No:399; SEQ ID Nθ:400; SEQ ID Nθ:117) ; SET 168: (SEQ ID Nθ:401) ; SET 169: (SEQ ID No: 402; SEQ ID No: 403) ; SET 170: (SEQ ID No : 404; SEQ ID No: 405; SEQ ID No: 318) ; SET 171: (SEQ ID No: 406; SEQ ID No: 407; SEQ ID No: 408) ; SET 172: (SEQ ID No: 409; SEQ ID No : 410; SEQ ID No: 411) ; SET 173: (SEQ ID No: 412; SEQ ID No: 413) ; SET 174: (SEQ ID No: 414; SEQ ID No: 415; SEQ ID No: 416) ; SET 175: (SEQ ID No: 417; SEQ ID No: 418; SEQ ID No: 419) ; SET 176: (SEQ ID No: 420; SEQ ID No : 421; SEQ ID No: 422) ; SET 177: (SEQ ID No: 423; SEQ ID No: 424; SEQ ID No: 425) ; SET 178: (SEQ ID No: 426; SEQ ID No:427; SEQ ID Nθ:428) ; SET 179: (SEQ ID No:429; SEQ ID Nθ:408) ; SET 180: (SEQ ID Nθ:430) ; SET 181: (SEQ ID Nθ:431) ; SET 182: (SEQ ID No: 432) ; SET 183: (SEQ ID No: 433; SEQ ID No: 434) ; SET 184: (SEQ ID No: 435; SEQ ID No: 436) ; SET 185: (SEQ ID No: 437) ; SET 186: (SEQ ID Nθ:438; SEQ ID Nθ:439) ; SET 187: (SEQ ID Nθ:440; SEQ ID No: 441) ; SET 188: (SEQ ID No: 442) ; SET 189: (SEQ ID Nθ:444) ; SET 190: (SEQ ID Nθ:445) ; SET 191 (SEQ ID Nθ:446 ; SEQ ID No:447) ; SET 192: (SEQ ID Nθ:448) ; SET 193: (SEQ ID Nθ:449) ; SET 194: (SEQ ID Nθ:450): SET 195: (SEQ ID No:451) ; SET 196: (SEQ ID No: 452) ; SET 197: (SEQ ID No: 453) ; SET 198: (SEQ ID No: 454) ; SET 199: (SEQ ID Nθ:455) ; SET 200: (SEQ ID Nθ:456) ; SET 201: (SEQ ID No:457) ; SET 202: (SEQ ID No:458) ; SET 203: (SEQ ID No:459) ; SET 204: (SEQ ID Nθ:460) ; SET 205: (SEQ ID No:461) ; SET 206: (SEQ ID Nθ:462) ; SET 207: (SEQ ID Nθ:463) ; SET 208: (SEQ ID No:464) ; SET 209: (SEQ ID Nθ:465) ; SET 210: (SEQ ID Nθ:466) ; SET 211: (SEQ ID Nθ:467) ; SET 212: (SEQ ID Nθ:468) 3. A polynucleotide library according to Claim 2 wherein said polynucleotide sequences or subsequences thereof of said pool correspond to any combination of at least one polynucleotide selected among those included in at least 50%, preferably 75% and more preferably 100% of the predefined sets. 4. A library according to anyone Claim 1 or 2 wherein the pool of polynucleotide sequences or subsequences correspond substantially to any combination of at least one polynucleotide sequence selected among those included in each one of predefined polynucleotide sequences sets comprising: SET 1: (SEQ ID No : 1 ; SEQ ID No: 2) ; SET 4: (SEQ ID No: 7 ; SEQ ID No : 8 ) ; SET 18: (SEQ ID No: 39 ; SEQ ID No: 40 ; SEQ ID No: 41) ; SET 21: (SEQ ID No: 46 ; SEQ ID No: 47) ; SET 24: (SEQ ID No: 54 ; SEQ ID No: 55 ; SEQ ID No: 56) ; SET 32: (SEQ ID No: 76 ; SEQ ID No: 77 ; SEQ ID No: 78) ; SET 38: (SEQ ID No: 91 ; SEQ ID No: 92 ; SEQ ID No : 93 ) ; SET 48: (SEQ ID No: 115 ; SEQ ID No: 116 ; SEQ ID No: 117) ; SET 53: (SEQ ID No: 126 ; SEQ ID No : 127 ; SEQ ID No:128) ; SET 58: (SEQ ID Nθ:138 ; SEQ ID Nθ:139 ; SEQ ID Nθ:140) ; SET 59: (SEQ ID No: 141 ; SEQ ID No : 142 ; SEQ ID No: 143) ; SET 61: (SEQ ID No: 147 ; SEQ ID No: 148 ; SEQ ID No : 149) ; SET 64: (SEQ ID No: 156 ; SEQ ID No: 157 ; SEQ ID No: 158) ; SET 66: (SEQ ID No: 162 ; SEQ ID No: 163) ; SET 69: (SEQ ID No: 168 ; SEQ ID No : 169; SEQ ID No: 170) ; SET 73: (SEQ ID No: 178; SEQ ID No: 179) ; SET 85: (SEQ ID No: 204; SEQ ID No : 205) ; SET 88: (SEQ ID No: 210; SEQ ID No: 211) ; SET 91: (SEQ ID No: 216; SEQ ID No: 217) ; SET 97: (SEQ ID No:230; SEQ ID Nθ:231; SEQ ID Nθ:232) ; SET 104: (SEQ ID Nθ:248; SEQ ID No: 249) ; SET 105: (SEQ ID No: 250 ; SEQ ID No: 251 ; SEQ ID No:252) ; SET 112: (SEQ ID Nθ:265 ; SEQ ID Nθ:266) ; SET 113: (SEQ ID No:267 ; SEQ ID No:268) ; SET 115 ; (SEQ ID Nθ:271 ; SEQ ID No:272) ; SET 131: (SEQ ID Nθ:308 ; SEQ ID No:309 ; SEQ ID Nθ:310) ; SET 132: (SEQ ID No: 311 ; SEQ ID No: 312 ; SEQ ID No: 313) ; SET 134: (SEQ ID No: 317 ; SEQ ID No: 318) ; SET 137: (SEQ ID No: 324 ; SEQ ID No: 325) ; SET 145: (SEQ ID No: 345 ; SEQ ID No: 346) ; SET 147: (SEQ ID No: 350 ; SEQ ID No: 351) ; SET 155: (SEQ ID No: 368 ; SEQ ID Nθ:369 ; SEQ ID No:300) ; SET 175: (SEQ ID Nθ:417 ; SEQ ID No: 418 ; SEQ ID No: 419) ; SET 180: (SEQ ID No: 430) ; SET 181: (SEQ ID NO.-431) ; SET 182: (SEQ ID Nθ:432) ; SET 185: (SEQ ID No:437) ; SET 187: (SEQ ID No: 440 ; SEQ ID No -.441, wherein said sequences are useful in differentiating a normal cell from a cancer cell. 5. A polynucleotide library according to Claim 4 wherein said polynucleotide sequences or subsequences thereof of said pool correspond to any combination of at least one polynucleotide selected among those included in at least 50%, preferably 75% and more preferably 100% of the predefined sets. 6. A polynucleotide library according to Claim 4 wherein the pool of polynucleotide sequences or subsequences correspond substantially to any combination of at least one polynucleotide sequence selected among those included in each one of predefined polynucleotide sequences sets comprising: SET 32: (SEQ ID Nθ:76 ; SEQ ID Nθ:77 ; SEQ ID Nθ:78) ; SET 73: (SEQ ID No: 178 ; SEQ ID No: 179) ; SET 131: (SEQ ID No:308 ; SEQ ID Nθ:309 ; SEQ ID Nθ:310) ; SET 145: (SEQ ID Nθ:345 ; SEQ ID No: 346) and SET 181: (SEQ ID No: 431) and of at least one polynucleotide sequence selected among those included in each one of predefined polynucleotide sequences sets comprising: SET 38: (SEQ ID No: 91 ; SEQ ID No: 92 ; SEQ ID No: 93) ; SET 58: (SEQ ID Nθ:138 ; SEQ ID Nθ:139 ; SEQ ID Nθ:140); SET 61: (SEQ ID NO.-147 ; SEQ ID Nθ:148 ; SEQ ID Nθ:149); SET 69: (SEQ ID No: 168 ; SEQ ID No: 169 ; SEQ ID No: 170) and SET 182: (SEQ ID No:432) . 7 A polynucleotide library according to Claim 6 wherein said polynucleotide sequences or subsequences thereof of said pool correspond to any combination of at least one polynucleotide selected among those included in at least 50%, preferably 75% and more preferably 100% of the predefined sets. 8. A library according to anyone Claim 1 or 2 wherein the pool of polynucleotide sequences or subsequences correspond substantially to any combination of at least one polynucleotide sequence selected among those included in each one of predefined polynucleotide sequences sets comprising: SET 11: (SEQ ID Nθ:22 ; SEQ ID No: 23 ; SEQ ID No: 24) ; SET 26: (SEQ ID Nθ:59; SEQ ID Nθ:60 ; SEQ ID Nθ:61) ; SET 32: (SEQ ID No: 76; SEQ ID No: 77 ; SEQ ID No: 78) ; SET 34: (SEQ ID No: 82 ; SEQ ID No: 83) ; SET 40: (SEQ ID No: 97 ; SEQ ID No: 98 ; SEQ ID No: 99) ; SET 57: (SEQ ID No: 135 ; SEQ ID No: 136 ;SEQ ID No: 137) ; SET 64: (SEQ ID Nθ:156 ; SEQ ID No:157; SEQ ID Nθ:158) ; SET 107: (SEQ ID Nθ:255 ; SEQ ID Nθ:256) ; SET 119: (SEQ ID No:279 ; SEQ ID No: 280 ; SEQ ID Nθ:281) ; SET 136: (SEQ ID Nθ:322 ; SEQ ID No:323) ; SET 140: (SEQ ID Nθ:331 ; SEQ ID Nθ:332 ; SEQ ID Nθ:333) ; SET 141: (SEQ ID No: 334; SEQ ID No: 335 ; SEQ ID No: 336) ; SET 145: (SEQ ID No: 345; SEQ ID No: 346) ; SET 148: (SEQ ID No: 352; SEQ ID No:353) ; SET 149: (SEQ ID Nθ:354 ; SEQ ID Nθ:355) ; SET 162: (SEQ ID Nθ:385; SEQ ID Nθ:386; SEQ ID Nθ:387) ; SET 165: (SEQ ID Nθ:394 ; SEQ ID No:395) ; SET 169: (SEQ ID Nθ:402 ; SEQ ID Nθ:403) ; SET 174: (SEQ ID No : 414 ; SEQ ID No: 415 ; SEQ ID No: 416) and SET 188: (SEQ ID Nθ:442) , wherein said sequences are useful in detecting a hormone sensitive tumor cell 9. A polynucleotide library according to Claim 8 wherein said polynucleotide sequences or subsequences thereof of said pool correspond to any combination of at least one polynucleotide selected among those included in at least 50%, preferably 75% and more preferably 100% of the predefined sets. 10. A library according to Claim 8 wherein the pool of polynucleotide sequences or subsequences correspond substantially to any combination of at least one polynucleotide sequence selected among those included in each one of predefined polynucleotide sequences sets comprising: SET 32: (SEQ ID Nθ:76 ; SEQ ID No: 77 ; SEQ ID No: 78) ; SET 136: (SEQ ID No: 322 ; SEQ ID No: 323) ; SET 145: (SEQ ID No:345 ; SEQ ID Nθ:346); SET 149: (SEQ ID Nθ:354 ; SEQ ID Nθ:355) and SET 169: (SEQ ID No: 402 ; SEQ ID No: 403) and of at least one polynucleotide sequence selected among those included in each one of predefined polynucleotide sequences sets comprising: SET 11: (SEQ ID No: 22 ; SEQ ID No: 23 ; SEQ ID No: 24) ; SET 40: (SEQ ID Nθ:97 ; SEQ ID Nθ:98 ; SEQ ID No:99); SET 57: (SEQ ID NO.-135 ; SEQ ID Nθ:136 ; SEQ ID Nθ:137); SET 119: (SEQ ID No: 279; SEQ ID No: 280 ; SEQ ID No: 281) and SET 174: (SEQ ID No: 414 ; SEQ ID No: 415 ; SEQ ID No: 416) 11. A polynucleotide library according to Claim 10 wherein said polynucleotide sequences or subsequences thereof of said pool correspond to any combination of at least one polynucleotide selected among those included in at least 50%, preferably 75% and more preferably 100% of the predefined sets . 12. A library according to anyone Claim 1 or 2 wherein the pool of polynucleotide sequences or subsequences correspond substantially to any combination of at least one polynucleotide sequence selected among those included in each one of predefined polynucleotide sequences sets comprising: SET 8: (SEQ ID No: 16) ; SET 11: (SEQ ID No: 22 ; SEQ ID No:23 ; SEQ ID No:24) ; SET 18: (SEQ ID Nθ:39 ; SEQ ID Nθ:40 ; SEQ ID No:41) ; SET 25: (SEQ ID Nθ:57 ; SEQ ID Nθ:58) ; SET 32: (SEQ ID No: 76 ; SEQ ID No: 77 ; SEQ ID No: 78) ; SET 34: (SEQ ID No: 82 ; SEQ ID Nθ:83) ; SET 40: (SEQ ID Nθ:97 ; SEQ ID No: 98 ; SEQ ID Nθ:99) ; SET 49: (SEQ ID Nθ:118 ; SEQ ID Nθ:119) ; SET 57: (SEQ ID No: 135 ; SEQ ID No: 136 ; SEQ ID No: 137) ; SET 91: (SEQ ID Nθ:216 ; SEQ ID Nθ:217) ; SET 100: (SEQ ID Nθ:238 ; SEQ ID Nθ:239) ; SET 105: (SEQ ID No : 250 ; SEQ ID No: 251: SEQ ID No: 252) ; SET 136: (SEQ ID No: 322 ; SEQ ID No: 323) ; SET 138: (SEQ ID No: 326 ; SEQ ID No: 327 ; SEQ ID No: 328) ; SET 139: (SEQ ID No: 329 ; SEQ ID Nθ:330) ; SET 141: (SEQ ID Nθ:334 ; SEQ ID Nθ:335 ; SEQ ID Nθ:336) ; SET 158: (SEQ ID No : 374 ; SEQ ID No: 375 ; SEQ ID No: 376) ; SET 169: (SEQ ID Nθ:402 ; SEQ ID Nθ:403) ; SET 180: (SEQ ID Nθ:430) and SET 186: (SEQ ID Nθ:438 ; SEQ ID Nθ:439), wherein said sequences are useful in differentiating a tumor with lymph nodes from a tumor without lymph nodes . 13. A polynucleotide library according to Claim 12 wherein said polynucleotide sequences or subsequences thereof of said pool correspond to any combination of at least one polynucleotide selected among those included in at least 50%, preferably 75% and more preferably 100% of the predefined sets. 14. A library according to Claim 12 wherein the pool of polynucleotide sequences or subsequences correspond substantially to any combination of at least one polynucleotide sequence selected among those included in each one of predefined polynucleotide sequences sets comprising SET 18: (SEQ ID No: 39 ; SEQ ID Nθ:40 ; SEQ ID No: 41) ; SET 32: (SEQ ID No: 76 ; SEQ ID No: 77 ; SEQ ID No: 78) ; SET 57: (SEQ ID Nθ:135 ; SEQ ID Nθ:136; SEQ ID Nθ:137); SET 91: (SEQ ID No: 216 ; SEQ ID No: 217) and SET 105: (SEQ ID No: 250 ; SEQ ID No: 251 ; SEQ ID No : 252) and of at least one polynucleotide sequence selected among those included in each one of predefined polynucleotide sequences sets comprising: SET 11: (SEQ ID No: 22 ; SEQ ID No: 23; SEQ ID No: 24) ; SET 40: (SEQ ID Nθ:97; SEQ ID No: 98 SEQ ID No: 99) ; SET 49: (SEQ ID Nθ:118 ; SEQ ID Nθ:119) ; SET 100: (SEQ ID Nθ:238 ; SEQ ID No: 239) and SET 141: (SEQ ID No: 334; SEQ ID No: 335 ; SEQ ID Nθ:336) . 15. A polynucleotide library according to Claim 14 wherein said polynucleotide sequences or subsequences thereof of said pool correspond to any combination of at least one polynucleotide selected among those included in at least 50%, preferably 75% and more preferably 100% of the predefined sets. 16. A library according to anyone of Claims 1 or 2 wherein the pool of polynucleotide sequences or subsequences correspond substantially to any combination of at least one polynucleotide sequence selected among those included in each one of predefined polynucleotide sequences sets comprising: SET 11: (SEQ ID No: 22 ; SEQ ID No: 23 ; SEQ ID No: 24) ; SET 22: (SEQ ID No: 48 ; SEQ ID No: 49 ; SEQ ID No: 50) ; SET 23: (SEQ ID No: 51 ; SEQ ID No: 52 ; SEQ ID No: 53) ; SET 26: (SEQ ID No: 59 ; SEQ ID No: 60 ; SEQ ID No: 61) ; SET 28: (SEQ ID No: 65 ; SEQ ID No: 66 ; SEQ ID No: 67) ; SET 31: (SEQ ID No: 73 ; SEQ ID Nθ:74 ; SEQ ID Nθ:75) ; SET 32: (SEQ ID No:76 ; SEQ ID Nθ:77 ; SEQ ID No: 78) ; SET 34: (SEQ ID No: 82 ; SEQ ID No: 83) ; SET 49: (SEQ ID No: 118 ; SEQ ID No: 119) ; SET 57: (SEQ ID No: 135 ; SEQ ID No: 136 ; SEQ ID No:137) ; SET 64: (SEQ ID No:156 ; SEQ ID Nθ:157 ; SEQ ID No: 158) ; SET 73: (SEQ ID No: 178; SEQ ID No : 179) ; SET 77: (SEQ ID No: 186) ; SET 81: (SEQ ID No: 194 ; SEQ ID No: 195) ; SET 95: (SEQ ID No: 226 ; SEQ ID No: 227) ; SET 131: (SEQ ID No: 308 ; SEQ ID No:309 ; SEQ ID Nθ:310) ; SET 138: (SEQ ID Nθ:326 ; SEQ ID Nθ:327 ; SEQ ID No: 328) ; SET 140: (SEQ ID No: 331 ; SEQ ID No: 332 ; SEQ ID Nθ:333) ; SET 149: (SEQ ID Nθ:354 ; SEQ ID Nθ:355) ; SET 162: (SEQ ID No:385 ; SEQ ID No:386 ; SEQ ID No:387) ; SET 164: (SEQ ID No: 391 ; SEQ ID No: 392 ; SEQ ID No: 393) ; SET 165: (SEQ ID No: 394 ; SEQ ID No:395) and SET 183: (SEQ ID No:433 ; SEQ ID No:434), wherein said sequences are useful in differentiating antracycline-sensitive tumors from antracycline-insensitive tumors. 17. A polynucleotide library according to Claim 16 wherein said polynucleotide sequences or subsequences thereof of said pool correspond to any combination of at least one polynucleotide selected among those included in at least 50%, preferably 75% and more preferably 100% of the predefined sets. 18. A library according to Claim 16 wherein the pool of polynucleotide sequences or subsequences correspond substantially to any combination of at least one polynucleotide sequence selected among those included in each one of predefined polynucleotide sequences sets comprising SET N° 32: (SEQ ID Nθ:76; SEQ ID No:77; SEQ ID Nθ:78) ; SET N°136: (SEQ ID Nθ:322 ; SEQ ID Nθ:323) ; SET N° 145: (SEQ ID No:345; SEQ ID Nθ:346) ; SET N° 149: SEQ ID Nθ:354; SEQ ID Nθ:355) ; SET N°169: (SEQ ID Nθ:402 ; SEQ ID Nθ:403) and of at least one polynucleotide sequence selected among those included in each one of predefined polynucleotide sequences sets comprising: SET No 11: (SEQ ID No: 22; SEQ ID Nθ:23 ; SEQ ID No:24); SET No 40: (SEQ ID Nθ:97 ; SEQ ID Nθ:98 ; SEQ ID Nθ:99) ; SET No 57: (SEQ ID Nθ:135 ; SEQ ID Nθ:136 ; SEQ ID Nθ:137) ; SET No 119: (SEQ ID No: 279 ; SEQ ID No: 280 ; SEQ ID No: 281) ; SET No 174: (SEQ ID No:414 ; SEQ ID Nθ:415; SEQ ID No:416). 19. A polynucleotide library according to Claim 18 wherein said polynucleotide sequences or subsequences thereof of said pool correspond to any combination of at least one polynucleotide selected among those included in at least 50%, preferably 75% and more preferably 100% of the predefined sets. 20. A library according to anynone of Claims 1 or 2 wherein the pool of polynucleotide sequences or subsequences correspond substantially to any combination of at least one polynucleotide sequence selected among those included in each one of predefined polynucleotide sequences sets comprising SET No 14 (SEQ ID Nθ:30; SEQ ID No: 31) ; SET No 23 (SEQ ID No:51; SEQ ID No:52; SEQ ID Nθ:53) ; SET No 25 (SEQ ID No: 57; SEQ ID Nθ:58) ; SET No 27 (SEQ ID No: 62; SEQ ID No: 63; SEQ ID No: 64) ; SET No 28 (SEQ ID No: 65; SEQ ID Nθ:66; SEQ ID No: 67) ; SET No 32 (SEQ ID No: 76; SEQ ID No: 77; SEQ ID No: 78) ; SET No 39 (SEQ ID No:94; SEQ ID No:95; SEQ ID Nθ:96) ; SET No 41 (SEQ ID No: 100; SEQ ID No : 101; SEQ ID No: 78) ; SET No 44 (SEQ ID No : 106; SEQ ID No: 107; SEQ ID No: 108) ; SET No 48 (SEQ ID No: 115; SEQ ID No: 116; SEQ ID No: 117) ; SET No 51 (SEQ ID No: 122; SEQ ID No: 78) ; SET No 64 (SEQ ID No:156; SEQ ID Nθ:157; SEQ ID Nθ:158) ; SET No 81 (SEQ ID No: 194; SEQ ID No: 195) ; SET No 83 (SEQ ID No: 199; SEQ ID No: 200) ; SET No 91 (SEQ ID No: 216; SEQ ID No: 217) ; SET No 96 (SEQ ID No: 228; SEQ ID No : 229) ; SET No 99 (SEQ ID No: 235; SEQ ID Nθ:236; SEQ ID Nθ:237) ; SET No 108 (SEQ ID Nθ:257; SEQ ID No:258) ; SET No 110 (SEQ ID No: 262; SEQ ID No: 200) ; SET No 116 (SEQ ID No :273; SEQ ID No: 274) ; SET No 117 (SEQ ID No: 275; SEQ ID No: 276) ; SET No 118 (SEQ ID Nθ:277; SEQ ID Nθ:278) ; SET No 120 (SEQ ID No: 282; SEQ ID No : 283; SEQ ID No: 276) ; SET No 126 (SEQ ID No : 296; SEQ ID No:297;) ; SET No 142 (SEQ ID Nθ:337; SEQ ID Nθ:338; SEQ ID No: 117) ; SET No 144 (SEQ ID No: 342; SEQ ID No: 343; SEQ ID No: 344) ; SET No 149 (SEQ ID No: 354; SEQ ID No: 355) ; SET No 152 (SEQ ID No: 361; SEQ ID No: 31) ; SET No 153 (SEQ ID No: 362; SEQ ID No : 363; SEQ ID No: 364) ; SET No 154 (SEQ ID No: 365; SEQ ID No: 366; SEQ ID No:367) ; SET No 157 (SEQ ID No:372; SEQ ID Nθ:373; SEQ ID No:108) ; SET No 159 (SEQ ID No: 377; SEQ ID No: 378; SEQ ID No: 379) ; SET No 162 (SEQ ID Nθ:385; SEQ ID Nθ:386; SEQ ID Nθ:387) ; SET No 166 (SEQ ID No:396; SEQ ID Nθ:397; SEQ ID Nθ:398) ; SET No 167 (SEQ ID No:399; SEQ ID Nθ:400; SEQ ID Nθ:117) ; SET No 168 (SEQ ID No:401) ; SET No 171 (SEQ ID Nθ:406; SEQ ID No:407; SEQ ID Nθ:408) ; SET No 172 (SEQ ID No: 409; SEQ ID No: 410; SEQ ID No: 411) ; SET No 173 (SEQ ID No: 412; SEQ ID No: 413) ; SET No 176 (SEQ ID No -.420; SEQ ID No: 421; SEQ ID No: 422) ; SET No 177 (SEQ ID No: 423; SEQ ID No : 424; SEQ ID No:425) ; SET No 178 (SEQ ID Nθ:426; SEQ ID Nθ:427; SEQ ID Nθ:428) ; SET No 179 (SEQ ID Nθ:429; SEQ ID Nθ:408) ; SET No 184 (SEQ ID Nθ:435; SEQ ID Nθ:436) ; SET No 185 (SEQ ID Nθ:437), wherein said sequences are useful in classifying good and poor prognosis primary breast tumors . 21. A polynucleotide library according to Claim 20 wherein said polynucleotide sequences or subsequences thereof of said pool correspond to any combination of at least one polynucleotide selected among those included in at least 50%, preferably 75% and more preferably 100% of the predefined sets. 22. A library according to Claim 20 wherein the pool of polynucleotide sequences or subsequences correspond substantially to any combination of at least one polynucleotide sequence selected among those included in each one of predefined polynucleotide sequences sets comprising SET N° 23 (SEQ ID No: 51 ; SEQ ID No: 52 ; SEQ ID No: 53) ; SET N° 25 (SEQ ID No: 57 ; SEQ ID No: 58) ; SET N° 32 (SEQ ID No: 76 ; SEQ ID No: 77 ; SEQ ID No: 78) ; SET N° 41 (SEQ ID No: 100 ; SEQ ID No: 101 ; SEQ ID No: 78) ; SET N° 48 (SEQ ID No: 115 ; SEQ ID No: 116 ; SEQ ID No: 117) ; SET N° 51 (SEQ ID No: 122 ; SEQ ID Nθ:78) ; SET N° 64 (SEQ ID Nθ:156 ; SEQ ID No:157 ; SEQ ID No:158) ; SET N° 81 (SEQ ID No: 194 ; SEQ ID No: 195) ; SET N° 83 (SEQ ID No: 199 ; SEQ ID No: 200) ; SET N° 91 (SEQ ID No: 216 ; SEQ ID No: 217) ; SET N° 99 (SEQ ID No: 235 ; SEQ ID No: 236 ; SEQ ID No:237) ; SET N° 110 (SEQ ID Nθ:262 ; SEQ ID Nθ:200) ; SET N° 116 (SEQ ID Nθ:273 ; SEQ ID No:274) ; SET N° 142 (SEQ ID Nθ:337 ; SEQ ID No: 338 ; SEQ ID No: 117) ; SET N° 144 (SEQ ID No: 342 ; SEQ ID No: 343 ; SEQ ID No: 344) ; SET N° 149 (SEQ ID No: 354 SEQ ID No:355) SET N° 162 (SEQ ID No:385 SEQ ID No: 386 SEQ ID Nθ:387) SET N° 167 (SEQ ID No: 399 SEQ ID No: 400 SEQ ID No:117) SET N° 171 (SEQ ID Nθ:406 SEQ ID No: 407 SEQ ID No:408) SET N° 172 (SEQ ID No:409 SEQ ID No:410 SEQ ID Nθ:411) SET N° 173 (SEQ ID No: 412 ; SEQ ID No: 413) ; SET N° 176 (SEQ ID No: 420 ; SEQ ID No: 421 ; SEQ ID No: 422) ; SET N° 177 (SEQ ID No: 423 ; SEQ ID No: 424 ; SEQ ID No: 425) ; SET N° 178 (SEQ ID NO.-426 ; SEQ ID Nθ:427 ; SEQ ID Nθ:428) ; SET N° 179 (SEQ ID No:429 ; SEQ ID No:408) ; SET N° 184 (SEQ ID Nθ:435 ; SEQ ID Nθ:436) ; SET N° 185 (SEQ ID Nθ:437), and at least one polynucleotide sequence selected among those included in each one of predefined polynucleotide sequences sets comprising: SET No 14 (SEQ ID No:30 ; SEQ ID No:31) ; SET No 27 (SEQ ID No: 62 ; SEQ ID No: 63 ; SEQ ID No: 64) ; SET No 28 (SEQ ID No: 65 ; SEQ ID No: 66 ; SEQ ID NO: 67) ; SET No 39 (SEQ ID Nθ:94 ; SEQ ID No: 95 ; SEQ ID No: 96) ; SET No 44 (SEQ ID No: 106 ; SEQ ID No -.107 ; SEQ ID No: 108) ; SET No 96 (SEQ ID No: 228 ; SEQ ID No:229) ; SET No 108 (SEQ ID No:257 ; SEQ ID No:258) ; SET No 117 (SEQ ID No: 275 ; SEQ ID No: 276) ; SET No 118 (SEQ ID No : 277 ; SEQ ID Nθ:278) ; SET No 120 (SEQ ID Nθ:282 ; SEQ ID Nθ:283 ; SEQ ID No: 276) ; SET No 126 (SEQ ID No: 296 ; SEQ ID No : 297) ; SET No 152 (SEQ ID No: 361 ; SEQ ID No: 31) ; SET No 153 (SEQ ID No: 362 ; SEQ ID Nθ:363 ; SEQ ID Nθ:364) ; SET No 154 (SEQ ID Nθ:365 ; SEQ ID No: 366 SEQ ID No:367) SET No 157 (SEQ ID No : 372 SEQ ID No: 373 SEQ ID No: 108) SET No 159 (SEQ ID No: 377 SEQ ID No: 378 SEQ ID No: 379) SET No 166 (SEQ ID No : 396 SEQ ID No: 397 SEQ ID No: 398) ; SET No 168 (SEQ ID Nθ:401), wherein the combination of overexpression of the genes identified by said first group of cluster sequences with the underexpression of the genes identified by said second group of cluster sequences are useful in classifying good and poor prognosis primary breast tumors. 23. A polynucleotide library according to Claim 22 wherein said polynucleotide sequences or subsequences thereof of said pool correspond to any combination of at least one polynucleotide selected among those included in at least 50%, preferably 75% and more preferably 100% of the predefined sets. 24. A polynucleotide library according to anyone of Claims 1 to 23 wherein said tumor cells are breast tumor cells . 25. A polynucleotide library according to any of Claims 1 to 23 wherein said polynucleotides are immobilized on a solid support in order to form a polynucleotide array. 26. A polynucleotide library according to Claim 25 wherein the support is selected from the group comprising a nylon membrane, nitrocellulose membrane, glass slide, glass beads, membranes on glass support or a silicon chip. 27. A polynucleotide array useful for prognosis or diagnostic of tumor comprising an immobilized polynucletide library according to Claims 1 to 3. 28. A polynucleotide array useful to differentiate a normal cell from a cancer cell comprising any combination of immobilized polynucletide sequences sets according to claims 4 to 7. 29. A polynucleotide array useful to detect a hormone sensitive tumor cell comprising any combination of immobilized polynucletide sequences sets according to claims 8 to 11. 30. A polynucleotide array useful to differentiate a tumor with lymph nodes from a tumor without lymph nodes comprising any combination of immobilized polynucletide sequences sets according to claims 12 to 15. 31. A polynucleotide array useful to differentiate antracycline-sensitive tumors from antracycline-insensitive tumors comprising any combination of immobilized polynucletide sequences sets according to claims 16 to 19. 32. A polynucleotide array useful to classify good and poor prognosis primary breast tumors comprising any combination of immobilized polynucletide sequences sets according to claim 20 to 23. 33. A method of detecting differentially expressed polynucleotide sequences which are correlated with a cancer, said method comprising: a) obtaining a polynucleotide sample from a patient and b) reacting said polynucleotide sample obtained in step (a) with a probe immobilized on a solid support wherein said probe comprises any combination of the polynucleotide sequences of the polynucleotide library of Claims 1 to 23 or any combination of expression products encoded by any of the polynucleotide sequences of the libraries of Claims 1 to 23 and c) detecting the reaction product of step (b) . 34. A method for detecting differentially expressed polynucleotide sequences according to Claim 33 wherein said polynucleotide sample is labeled before its reaction step. 35. A method for detecting differentially expressed polynucleotide sequences according to Claim 34 wherein the label of the polynucleotide sample is selected from the group consisting of radioactive, colorimetric, enzymatic, molecular amplification, bioluminescent or fluorescent labels . 36. A method for detecting differentially expressed polynucleotide sequences according to Claims 33 to 35 further comprising obtaining a control polynucleotide sample, reacting said control sample with said probe detecting a control sample reaction product and comparing the amount of said polynucleotide sample reaction product to the amount od said control sample reaction product. 37. A method for detecting differentially expressed polynucleotide sequences according to Claims 33 to 36 wherein the polynucleotide sample is cDNA, RNA or mRNA. 38. A method for detecting differentially expressed polynucleotide sequences according to Claim 37 wherein mRNA is isolated from said polynucleotide sample and cDNA is obtained by reverse transcription of said mRNA. 39. A method for detecting differentially expressed polynucleotide sequences according to Claims 33 to 38 wherein said reaction step is performed by hybridising the polynucleotide sample with the probe. 40. A method for detecting differentially expressed polynucleotide sequences according to Claims 33 to 39 wherein said method is used for detecting, diagnosing, staging, monitoring, predicting, preventing or treating conditions associated with cancer. 41. A method for detecting differentially expressed polynucleotide sequences according to Claims 33 to 40 wherein the cancer is breast cancer. 42. A method for detecting differentially expressed polynucleotide sequences according to Claims 33 to 41 wherein the product encoded by any of the polynucleotide sequences or polynucleotide sequences sets is involved in a receptor-ligand reaction on which detection is based. 43. A method for screening an anti-tumor agent comprising the method of Claim 33 wherein said polynucleotide sample is obtained from a patient treated with the anti-tumor agent to be screened.

Inventors: BERTUCCI FRANCOIS, BIRNBAUM DANIEL, FERT VINCENT, HOULGATTE REMI, NGUYEN CATHERINE, VIENS PATRICE, BERTUCCI, FRANCOIS, BIRNBAUM, DANIEL, FERT, VINCENT, HOULGATTE, REMI, NGUYEN, CATHERINE, VIENS, PATRICE
Applicants: BERTUCCI FRANCOIS, BIRNBAUM DANIEL, FERT VINCENT, HOULGATTE REMI, IPSOGEN, NGUYEN CATHERINE, VIENS PATRICE, BERTUCCI, FRANCOIS, BIRNBAUM, DANIEL, FERT, VINCENT, HOULGATTE, REMI, NGUYEN, CATHERINE, VIENS, PATRICE
Application year: 2001
Main classification: C12Q1
IPC codes: C12M1, C12N15, C12Q1, G01N33