Gene expression profiling of primary breast carcinomas using arrays of candidate genes

[0001] This application claims the benefit of priority of provisional application Serial No. 60/254,090 filed Dec. 8, 2000.

[0002] This invention relates to polynucleotide analysis and, in particular, to polynucleotide expression profiling of carcinomas using arrays of candidate polynucleotides.

[0003] 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 factorsamong conventional histological and clinical featurespredicting 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.

[0004] 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.

[0005] 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 overexpressed 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.

[0006] FIG. 1 shows an example of differential gene expression between normal breast tissue (NB) and breast tumor samples.

[0007] FIG. 2 is a representation of expression levels of 176 genes in normal breast tissue (NB) and 34 samples of breast carcinoma.

[0008] FIG. 3 is prognostic classification of breast cancer by gene expression profiling.

[0009] FIG. 4 shows the correlation of GATA3 (SEQ ID NO: 78) expression with ER phenotype.

[0010] In the context of this disclosure, a number of terms shall be utilized.

[0011] 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.

[0012] The term subsequence refers to a sequence of nucleic acids that comprises a part of a longer sequence of nucleic acids.

[0013] 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.

[0014] 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.

[0015] An important aspect of the invention relates to the use of cDNA arrays, which allows quantitative study of mRNA expression levels of 188 candidate genes in 34 consecutive primary breast carcinomas in three areas: 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 (SEQ ID No: 119) expression was strongly correlated with the lymph node status (p0.0001) and that of GATA3 (SEQ ID No: 78) with the presence of estrogen receptors (p0.001). 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.

[0016] 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, a membrane on a glass support, 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 greater 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.

[0017] DNA microarrays 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.

[0018] Typically, a method of monitoring gene expression involves (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.

[0019] 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 overexpressed in tumor cells, flrher 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.

[0020] Obviously, sequences having a great degree of homology with the above sequences could also be used to realize the molecular characterization of the invention, namely when those sequences present one or a few punctual mutations when compared with any one of the sequences represented by SEQ ID Nos: 1-468.

[0021] A particular embodiment of the invention relates to a polynucleotide library of sequences or subsequences corresponding substantially to any combination of at least one polynucleotide sequence selected among those included in each one of predefined polynucleotide sequence sets 1 to 188 as defined in table 4.

[0022] A polynucleotide sequence 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 gene.

[0023] 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 in table 5, useful in differentiating a normal cell from a cancer cell.

[0024] Preferably the polynucleotide library useful to differentiate a normal cell from a cancer cell corresponds substantially to any combination of at least one polynucleotide sequence selected among those included in each one of predefined polynucleotide sequence sets indicated in table 5A, and of at least one polynucleotide sequence selected among those included in each one of predefined polynucleotide sequence sets indicated in table 5B.

[0025] The detection of an overexpression of genes identified with sets of polynucleotide sequences defined in table 5A, together with detection of an underexpression of genes identified with sets of polynucleotide sequences defined in table 5B allows distinction between normal patients and patients suffering from tumor pathology.

[0026] 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 sequence sets defined in table 6.

[0027] 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 sequence sets defined in table 6A together with at least one polynucleotide sequence selected among those included in each one of predefined polynucleotide sequence sets defined in table 6B.

[0028] The detection of an overexpression of genes identified with sets of polynucleotides sequences defined in table 6A, together with detection of an underexpression of genes identified with sets of polynucleotides sequences defined in table 6B allows distinction between patients having a hormone-sensitive tumor and patients having a hormone-resistant tumor.

[0029] The invention also concerns a polynucleotide library useful to differentiate a tumor in which a lymph node has been invaded by a tumor cell from a tumor in which a lymph node has not been invaded by a 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 sequence sets defined in table 7.

[0030] Preferably, the polynucleotide library useful to differentiate a tumor in which a lymph node has been invaded by a tumor cell from a tumor in which a lymph node has not been invaded by a tumor cell correspond substantially to any combination of at least one polynucleotide sequence selected among those included in each one of predefined polynucleotide sequence sets defined in table 7A together with at least one polynucleotide sequence selected among those included in each one of predefined polynucleotide sequence sets defined in table 7B.

[0031] The detection of an overexpression of genes identified with sets of polynucleotide sequences defined in table 7A, together with detection of an underexpression of genes identified with sets of polynucleotide sequences defined in table 7B allows distinction between patients having a tumor in which a lymph node has been invaded by a tumor cell and patients having a tumor in which a lymph node has not been invaded by a tumor cell.

[0032] The invention concerns also a polynucleotide library useful to differentiate anthracycline-sensitive tumors from anthracycline-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 sequence sets defined in table 8.

[0033] Preferably, the polynucleotide library useful to differentiate anthracycline-sensitive tumors from anthracycline-insensitive tumors correspond substantially to any combination of at least one polynucleotide sequence selected among those included in each one of predefined polynucleotide sequence sets defined in table 8A together with at least one polynucleotide sequence selected among those included in each one of predefined polynucleotide sequence sets defined in table 8B.

[0034] The detection of an overexpression of genes identified with sets of polynucleotide sequences defined in table 8A, together with detection of an underexpression of genes identified with sets of polynucleotide sequences defined in table 8B allows distinction between patients having an anthracycline-sensitive tumor from patients having an anthracycline-insensitive tumor.

[0035] The invention also concerns 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 sequence sets defined in table 9.

[0036] 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 sequence sets defined in table 9A together with at least one polynucleotide sequence selected among those included in each one of predefined polynucleotide sequence sets defined in table 9B.

[0037] The detection of an overexpression of genes identified with sets of polynucleotide sequences defined in table 9A, together with detection of an underexpression of genes identified with sets of polynucleotide sequences defined in table 9B allows to classify patients having good or poor prognosis primary breast tumors.

[0038] In a preferred embodiment, the tumor cell presenting underexpressed or overexpressed sequences from the polynucleotide library of the invention are breast tumor cells.

[0039] 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.

[0040] Another object of the present invention concerns a polynucleotide array useful for prognosis or diagnosis of a tumor bearing at least one immobilized polynucleotide library set as previously defined.

[0041] The invention also concerns a polynucleotide array useful to differentiate a normal cell from a cancer cell bearing any combination of at least one polynucleotide sequence selected among those included in each one of predefined polynucleotide sequence sets indicated in table 5, useful in differentiating a normal cell from a cancer cell.

[0042] 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 sequence sets indicated in table 5A, and of at least one polynucleotide sequence selected among those included in each one of predefined polynucleotide sequence sets indicated in table 5B.

[0043] The invention relates also to a polynucleotide array useful to detect a hormone-sensitive tumor cell bearing any combination of at least one polynucleotide sequence selected among those included in each one of predefined polynucleotide sequence sets defined in table 6.

[0044] 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 sequence sets defined in table 6A together with at least one polynucleotide sequence selected among those included in each one of predefined polynucleotide sequence sets defined in table 6B.

[0045] The invention concerns also a polynucleotide array useful to differentiate a tumor in which a lymph node has been invaded by a tumor cell from a tumor in which a lymph node has not been invaded by a tumor cell bearing any combination of at least one polynucleotide sequence selected among those included in each one of predefined polynucleotide sequence sets defined in table 7.

[0046] Preferably, the polynucleotide array useful to differentiate a tumor in which a lymph node has been invaded by a tumor cell from a tumor in which a lymph node has been invaded by a tumor cell bears any combination of at least one polynucleotide sequence selected among those included in each one of predefined polynucleotide sequence sets defined in table 7A together with at least one polynucleotide sequence selected among those included in each one of predefined polynucleotide sequence sets defined in table 7B.

[0047] The invention also concerns a polynucleotide array useful to differentiate anthracycline-sensitive tumors from anthracycline-insensitive tumors bearing any combination of at least one polynucleotide sequence selected among those included in each one of predefined polynucleotide sequence sets defined in table 8.

[0048] Preferably, the polynucleotide array useful to differentiate anthracycline-sensitive tumors from anthracycline-insensitive tumors bears any combination of at least one polynucleotide sequence selected among those included in each one of predefined polynucleotide sequence sets defined in table 8A together with at least one polynucleotide sequence selected among those included in each one of predefined polynucleotide sequence sets defined in table 8B.

[0049] The invention concerns also a polynucleotide array useful to classify good and poor prognosis primary breast tumors bearing any combination of at least one polynucleotide sequence selected among those included in each one of predefined polynucleotide sequence set defined in table 9.

[0050] 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 sequence sets defined in table 9A together with at least one polynucleotide sequence selected among those included in each one of predefined polynucleotide sequence sets defined in table 9B.

[0051] The present invention also concerns a method for detecting differentially expressed polynucleotide sequences that are correlated with a cancer, said method comprising:

[0052] obtaining a polynucleotide sample from a patient;

[0053] reacting the polynucleotide sample 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; and

[0054] detecting the reaction product of step (b).

[0055] Preferably, the polynucleotide sample obtained at step (a) is labeled before its reaction at step (b) with the probe immobilized on a solid support.

[0056] The label of the polynucleotide sample is selected from the group consisting of radioactive, colorimetric, enzymatic, molecular amplification, bioluminescent or fluorescent labels.

[0057] In a particular embodiment the reaction product of step (c) is quantified by further comparison of said reaction product to a control sample.

[0058] In a first embodiment, the polynucleotide sample isolated from the patient and obtained at step (a) is either RNA or mRNA.

[0059] In another embodiment the polynucleotide sample isolated from the patient is cDNA is obtained by reverse transcription of the mRNA.

[0060] 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.

[0061] Preferably the sample RNA is labeled before hybridization with the probe and the label is selected from the group consisting of radioactive, calorimetric, enzymatic, molecular amplification, bioluminescent or fluorescent labels.

[0062] This method for detecting differentially expressed polynucleotide sequences is particularly useful for detecting, diagnosing, staging, monitoring, predicting, preventing or treating conditions associated with cancer, and particularly breast cancer.

[0063] The method for detecting differentially expressed polynucleotide sequences is also particularly useful when the product encoded by any of the polynucleotide sequence or subsequence set is involved in a receptor-ligand reaction on which detection is based.

[0064] The present invention is also related to a method for screening an anti-tumor agent comprising the above-depicted method for detecting differentially expressed polynucleotide sequences wherein the sample has been treated with the anti-tumor agent to be screened.

[0065] 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 sequence set as previously defined or of products encoded by said library in a sample obtained from a patient.

[0066] Tumor Samples and RNA Extraction

[0067] 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 (n18) or superior to 50 mm (n3), 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 anthracycline-based chemotherapy (n16) was given to patients according to local practice.

[0068] Total RNA was extracted from tumor samples by standard methods (43). Total RNA from normal breast tissue was obtained from Clontech (Palo Alto, Calif.): 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.

[0069] cDNA Arrays Preparation

[0070] 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 website: 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 c 554 gene (used for hybridization signal normalization), 3 poly(A) sequences of different sizes and the vector pT 7T 3D (negative controls).

[0071] 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 c 554 gene was spotted 96-fold scattered over the whole membrane.

[0072] cDNA Array Hybridizations

[0073] 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(dT 25) to saturate the poly(A) tails of the messengers, and to insure that the reverse transcribed product did not contain long poly(T) sequences. A precise amount of c 554 mRNA was added to the total RNA before labeling to allow normalization of the data.

[0074] Five ng of total RNA (100 ng 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.

[0075] Detection and Quantification of cDNA Array Hybridization Signals

[0076] 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, Mich.) 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).

[0077] Array Data Analysis

[0078] 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 CDK 4 (SEQ ID No: 288) or ETV 5 (SEQ ID No: 300), displayed similar expression profiles for the two clones in all samples. This reproducibility was sufficient to consider a 2-fold expression difference as significantly differential.

[0079] 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).

[0080] 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.

[0081] 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 (SEQ ID No: 78) 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.

[0082] Northern Blot Analysis

[0083] Seventy-nine breast tumors, including 22 of the 34 tested on the arrays, were analyzed for GATA3 (SEQ ID No: 78) 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 (SEQ ID No: 78), 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 Pael enzymes. Northern blots were stripped and re-hybridized using an -actin probe (46).

[0084] 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 fg 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, stromelysin 3 (SEQ ID No: 346); 7, ERBB2 (SEQ ID No: 119); 8, MYBL2 (SEQ ID No: 310); 9, FOS (SEQ ID No: 318); 10, TGFR 3; 11, desmin (SEQ ID No: 170)), and between ER- breast tumor and ER breast tumor (12, GATA3).

[0085] 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 (stromelysin 3 (SEQ ID No: 346), IGF2 (SEQ ID No: 61), GATA3 (SEQ ID No: 78) 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 {fraction (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 (n35) along the horizontal axis so that those with the most similar expression profiles are placed adjacent to each other. Similarly, clones (n162) 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 (SEQ ID No: 61), GATA3 (SEQ ID No: 78), stromelysin 3 (SEQ ID No: 346) from top to bottom) and four pairs of different clones representing four genes. (c) Zoom representation of group A from FIG. 2b, excluding the two outlyer tumors at the right. The clustering separates two subgroups of tumors, A1 and A2. The dotted branches correspond to tumors associated with metastatic relapse and death. Follow-up was longer in A2 than in A1 (median 81 months for A2 versus 47 months for A1).

[0086] FIG. 3 is prognostic classification of breast cancer by gene expression profiling showing that gene expression-based tumor classification correlates with clinical outcome. The 12 samples of group A (see FIG. 2band 2c) were reclustered using the top 32 differentially expressed genes between A1 and A2 subgroups. Data were displayed as in FIG. 2band 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 A1 and A2 are shown as well as two groups of differentially expressed clones. The dotted branches of tumor cluster A1 correspond to samples associated with metastatic relapse and death. FIG. 3a shows two-dimensional representation of hierarchical clustering results shown in FIGS. 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 (n16), class BC (n34), class D (n5). FIG. 3b illustrates a Kaplan-Meier plot of overall survival of the 3 classes of patients (p<0.005, log-rank test). And FIG. 3c illustrates a Kaplan-Meier plot of metastasis-free survival of the 3 classes of patients (p<0.05, log-rank test).

[0087] FIG. 4 shows the correlation of GATA3 (SEQ ID No: 78) 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 (n23) versus the ER-negative tumors (n11) using the Mann-Witney test (p0.0004). 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 -actin (bottom). ER status is indicated for each tumor sample.

[0088] Data Representation

[0089] FIG. 1 shows examples of hybridizations of cDNA arrays with probes made from RNA extracted from normal breast tissue and breast tumors.

[0090] 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 (SEQ ID No: 346), IGF2 (SEQ ID No: 61) and GATA3 (SEQ ID No: 78), 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.

[0091] Breast Tumor Classification

[0092] As shown in FIG. 2b, the clustering algorithm identified two groups of samples, designated A (n15, including normal breast, NB) and B (n20). 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.

[0093] 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, A1 (n6) and A2 (n6), which could be further separated by clustering as shown in FIG. 2c. The 12 tumors had a uniformly high risk of metastatic relapse according to conventional prognostic features as shown in Table 1. Most of them had received comparable adjuvant anthracycline-based chemotherapy after surgery, with more women treated in the A1 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 A1 (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 A1 tumors. No such subgrouping could be done in B.

TABLE 1
Subgroup	A1	A2
Tumor position in the cluster	1	2	3	4	5	6	7	8	9	10	11	12
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 size, mm	60	20	26	35	20	30	27	25	30	25	20	22
SBR grade
ER status	neg	neg	neg	neg	neg	neg	pos	neg	pos	pos	pos	pos
Adjuvant chemotherapy	yes	yes	no	yes	yes	yes	yes	yes	no	yes	no	no
Metastasis	yes	no	yes	yes	no	yes	no	no	no	no	no	no
Follow-up, months	58	106	35	47	41	31	85	98	95	49	19	141
Patients status	D	A	D	D	A	D	A	A	A	A	A	A
Patient characteristics in subgroups A1 and A2. The 12 tumors are numbered from 1 to 12 according to their position from left to right in the clustering graphic displayed in FIG. 3. Adjuvant chemotherapy was anthracycline-based. In the line concerning the patient status, A means alive and D means death from cancer progression.

[0094] 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 A1 vs A2. Then, the 12 tumors were reclustered using the expression profiles of these genes as shown in FIG. 3. The same subgroups A1 and A2 were evident and separated by 2 groups of genes: as expected, high expression of ERBB2 (SEQ ID No: 119), MYC (SEQ ID No: 75) and EGFR (SEQ ID No: 137) was associated with bad prognosis subgroup A1 (6-8), and that of E-cadherin (SEQ ID No: 328) and the proto-oncogene MYB (SEQ ID No: 355) 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 (SEQ ID No: 78) (11) and CRABP2 (SEQ ID No: 158) (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).

[0095] Differential Gene Expression Between Normal Breast and Breast Tumors

[0096] 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 (ABCC 5; (SEQ ID No: 325) to 17.76 (GATA3; (SEQ ID No: 78) for the overexpressed genes, and from 0.00 (desmin, (SEQ ID No: 170) to 0.29 (APC; (SEQ ID No: 56) for the underexpressed genes.

TABLE 2
Clone ID	Gene/Protein identity	Gene symbol	Chrom. location	N T/NB
Overexpressed genes
154343	Granzyme H	GZMH	14q11.2	32 9.51
235947	Stromelysin 3	STMY3	22q11.2	31 15.92
207378	MYB Related Protein B	MYBL2	20q13.1	31 (a)
153275	Cellular Retinoic Acid Binding Protein 2	CRABP2	1q21.3	29 7.16
129757	GATA-binding protein 3	GATA3	10p15	28 17.76
120649	T-Lymphocyte surface CD2 antigen	CD2	1p13.1	28 7.54
109677	CREB Binding Protein	CREBBP	16p13.3	28 5.08
172152	EGFR-binding protein GRB2	GRB2	17q24-q25	28 5.00
66969	Transcription factor RELB	RELB	19	28 3.61
182007	ETS-Related Transcription Factor ELF1	ELF1	13q13	27 3.58
153446	LIM domain protein RIL	RIL	5q31.1	26 4.03
203394	ETS Variant gene 5 (ETS-related molecule)	ETV5	3q28	25 3.67
160963	Thrombospondin 1	THBS1	15q15	25 3.39
188393	POU domain, class 2, transcription Factor 2	POU2F2	19	24 4.02
187822	Integrin, beta 2	ITGB2	21q22.3	24 3.01
243907	Nuclear Factor of Activating T cell Subunit p45	NF45	1	24 2.84
158347	EST H27202	EST		23 2.91
230933	EST AW184517	EST		22 2.85
212366	ATP-Binding Cassette, sub-family C (CFTR/MRP), 5	ABCC5	3q27	22 2.70
149401	Cathepsin D	CTSD	11p15.5	21 2.97
Underexpressed genes
153854	Desmin	DES	2q35	34 0.00
208717	P55-C-FOS proto-oncogene protein	FOS	14q24.3	33 0.05
159093	Transcription Factor AP4	TFAP4	16p13	33 0.11
124340	Tenascin XA	TNXA	6p21.3	33 0.14
133738	Prolactin	PRL	6p22.2-p21.3	32 0.00
133891	Chorionic Somatomammotropin Hormone 1	CSH1	17q22-q24	32 0.00
151501	Tyrosine Kinase Receptor TEK	TEK	9p21	32 0.00
183030	Activating Transcription Factor 3	ATF3	1	32 0.07
120916	Phosphodiesterase I	PDNP2	8q24.1	32 0.14
155716	EST R72075	EST		31 0.00
208118	Transforming Growth Factor Beta Receptor Type III	TGFBR3	1p33-p32	31 0.14
187547	Diphtheria Toxin Receptor	DTR	5q23	31 0.17
108490	HIV-1 Rev Binding protein	HRB	2q36	31 0.20
147002	B-cell CLL/lymphoma 2	BCL2	18q21.3	31 0.26
182610	Microsomal Glutathione S Transferase 1	MGST1	12p12.3-p12.1	31 0.28
152802	Phospholipase A2 Membrane Associated, group IIA	PLA2G2A	1p35	30 0.03
183087	Interleukin 3 Receptor Alpha chain	IL3RA	Xp22.3; Yp13.3	30 0.24
108571	Retinoblastoma-Like 2 (p130)	RBL2	16q12.2	29 0.28
125294	Adenomatous Polyposis Coli Protein	APC	5q21-q22	29 0.29
151767	FASL Receptor	TNFRSF6	10q24.1	28 0.27
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.

[0097] High expression of mucin I (SEQ ID No: 58), NM 23, ERBB2 (SEQ ID No: 119), FGFRJ (SEQ ID No: 182) and FGFR 2 (SEQ ID No: 15), MYC (SEQ ID No: 75), stromelysin 3 (SEQ ID No: 346), cathepsin D (SEQ ID No: 128) and downregulation of FOS (SEQ ID No: 318), APC (SEQ ID No: 56), RBL2, FAS, BCL2 (SEQ ID No: 117) were found, reflecting what is known about their biology in cancer. GATA3 (SEQ ID No: 78), which codes for a member of the GATA family of zinc finger transcription factors, and CRABP2 (SEQ ID No: 158), 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

[0098] 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.

[0099] To identify genes potentially relevant to the hormone-responsive phenotype, the gene expression profiles in ER-positive breast cancers (n23) versus ER-negative breast cancers (n11) 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 (SEQ ID No: 78) 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
Clone			ER/
ID	Gene/Protein identity	Gene symbol	ER
129757	GATA-binding protein 3	GATA3	28.6
356763	Granzyme A	GZMA	5.7
248613	MYB proto-oncogene	MYB	3.4
211999	KIAA1075 protein	KIAA1075	3.3
235947	Stromelysin 3	STMY3	3.1
229839	Macrophage Stimulating 1	MST1	2.8
153275	Cellular Retinoic Acid Binding Protein 2	CRABP2	2.7
301950	X-box Binding Protein 1	XBP1	2.7
205314	Tumor Protein p53	TP53	2.5
126233	Insulin-like Growth Factor 2	IGF2	2.4
66322	CD3G antigen, Gamma	CD3G	0.0
195022	Interleukin 2 Receptor Gamma chain	IL2RG	0.0
111461	SOX4 Protein	SOX4	0.4
151475	Epidermal Growth Factor Receptor	EGFR	0.5
195022	Interleukin 2 Receptor Beta chain	IL2RB	0.5
130788	Topoisomerase (DNA) II beta (180 kD)	TOP2B	0.6
323948	SOX9 Protein	SOX9	0.6
183641	S100 calcium-binding protein Beta	S100B	0.6
246620	EST N53133	EST	0.6
231424	Glutathione S Transferase Pi	GSTP1	0.6

[0100] 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 (n19) 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, correlation between the expression levels of these genes and the number of tumor-involved nodes (quantitative variables) was determined. Table 3bshows 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 (SEQ ID No: 119) was the most significantly overexpressed gene in node-positive tumors and displayed the highest correlation coefficient (r0.68; p<0.0001).

TABLE 3b
Clone			N/
ID	Gene/Protein identity	Gene symbol	10N
129757	GATA-binding protein 3	GATA3	11.0
160963	Thrombospondin 1	THBS1	6.6
151475	Epidermal Growth Factor Receptor	EGFR	5.4
120916	Phosphodiesterase I	PDNP2	4.9
183030	Activating Transcription Factor 3	ATF3	4.6
211999	KIAA1075 protein	KIAA1075	4.5
110480	Nuclear Factor 1 A-type	NF1A	4.5
182264	P-Selectin	SELP	4.4
356763	Granzyme A	GZMA	4.3
214008	E-cadherin	CDH1	4.0
147016	ERBB2 Receptor Protein-Tyrosine Kinase	ERBB2	0.2
179197	Protein Phosphatase PP2A, 55 kD Subunit	PP2A BR	0.2
		gamma
231424	Glutathione S Transferase Pi	GSTP1	0.4
111461	SOX4 Protein	SOX4	0.4
195022	Interleukin 2 Receptor Beta chain	IL2RB	0.4
220451	Zinc Finger protein 144	ZNF144	0.5
125413	Mucin 1	MUC1	0.6
290007	CD44 antigen, epithelial form	CD44	0.6
108571	Retinoblastoma-Like 2 (p130)	RBL2	0.7
130788	Topoisomerase (DNA) II Beta (180 kD)	TOP2B	0.7
List of genes differentially expressed between ER-positive and ER-negative breast tumors (a) and between axillary lymph node-negative tumors and tumors with 10 or more involved lymph nodes (b).

[0101] 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 0.95not shown). An example of highly correlated gene expression is that of BCL2 (SEQ ID No: 117) 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 (SEQ ID No;184), AKT2 (SEQ ID No: 254), PRKCSH (SEQ ID No: 264) or TNFRSF6/FAS SEQ ID No.143). In particular, a striking correlated expression between BCL2 and FAS could be observed (r0.91; 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.

[0102] 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.

[0103] 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 from this treatment and those who will not benefit.

[0104] 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 anthracycline-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.

[0105] Similarly, despite molecular heterogeneity, significant correlations between the expression level of genes (GATA3 (SEQ ID No: 78), ERBB2 (SEQ ID No: 119)) 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.

[0106] 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 (SEQ ID No: 355) (10), stromelysin 3 (SEQ ID No: 346) (33), and CRABP2 (SEQ ID No: 158) (34), have been previously reported expressed at high levels in ER-positive breast tumors. The higher levels of TP53 MnRNA 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 (SEQ ID No: 99), IL2RG (SEQ ID No: 281) and CD3G (SEQ ID No: 416) 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 (SEQ ID No: 328) and thrombospondin 1 (SEQ ID No: 217) 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 (SEQ ID No: 376), 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 GSTPI (SEQ ID No: 336) (Glutathione-S-Transferase Pi), recently reported associated with increased tumor size (27).

[0107] 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.

[0108] 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 detection of genes of which expression was correlated with classical prognostic factors.

[0109] 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
CORRELATION BETWEEN SEQ ID NO AS FILED WITH US PROVISIONAL APPLICATION
No 60/254,090 and SEQ ID NO FILLED WITH NEW APPLICATION
Gene				Provisional	Provisional	Current,	Current,	Current,
Symbols	No	Name	Image	Seq3	Seq5	Seq3	Seq5	(mRNA)
GATA3	1	GATA-binding pro-	129757	SEQ ID No:1		SEQ ID No:76	SEQ ID No:77	SEQ ID No:78
		tein 3 (GATA3)
MYB	2	v-myb avian myelo-	248613		SEQ ID No:2	0	SEQ ID No:354	SEQ ID No:355
		blastosis viral onco-
		gene homolog
		(MYB)
KIAA 1075	3	KIAA 1075 protein	211999	SEQ ID No:3	SEQ ID No:4	SEQ ID No:322	SEQ ID No:323	0
STMY3	4	matrix metallopro-	235947	SEQ ID No:5		SEQ ID No:345	0	SEQ ID No:346
		teinase 11 (strom-
		elysin 3) (MMP11)
		(ex STMY3)
HGFL	5	macrophage-stim-	229839	SEQ ID No:6	SEQ ID No:7	SEQ ID No:331	SEQ ID No:332	SEQ ID No:333
		ulating protein
		(MST1) (ex HGFL)
CRABP	6	cellular retinoic	153275	SEQ ID No:8	SEQ ID No:9	SEQ ID No:156	SEQ ID No:157	SEQ ID No:158
		acid-binding protein
		2 (CRABP2)
XBP1	7	X-box binding pro-	301950	SEQ ID No:10	SEQ ID No:11	SEQ ID No:385	SEQ ID No:386	SEQ ID No:387
		tein 1 (XBP1)
TP53	8	tumor protein p53	205314		SEQ ID No:12	SEQ ID No:442	0	0
		(Li-Fraumeni syn-
		drome) (TP53)
IGF2	9	insulin-like growth	126233	SEQ ID No:13	SEQ ID No:14	SEQ ID No:59	SEQ ID No:60	SEQ ID No:61
		factor 2 (somato-
		medin A) (IGF2),
CD3G	10	CD3G antigen,	66322	SEQ ID No:15	SEQ ID No:16	SEQ ID No:414	SEQ ID No:415	SEQ ID No:416
		gamma polypeptide
		(TiT3 complex)
		(CD3G)
IL2RG	11	interleukin 2 recep-	195022	SEQ ID No:17	SEQ ID No:18	SEQ ID No:279	SEQ ID No:280	SEQ ID No:281
		tor, gamma (severe
		comnbined immuno-
		deficiency) (IL2RG)
SOX4	12	SRY (sex determin-	111461	SEQ ID No:19	SEQ ID No:20	SEQ ID No:22	SEQ ID No:23	SEQ ID No:24
		ing region Y)-box 4
		(SOX4)
EGFR	13	epidermal growth	151475	SEQ ID No:21	SEQ ID No:22	SEQ ID No:135	SEQ ID No:136	SEQ ID No:137
		factor receptor
		(avian erythroblastic
TOP2B	14	topIIb mRNA for	130788		SEQ ID No:23	0	SEQ ID No:82	SEQ ID No:83
		topoisomerase IIb.
S100B	15	S100 calcium-bind-	183641		SEQ ID No:24	0	SEQ ID No:255	SEQ ID No:256
		ing protein, beta
		(neural) (S100B)
EST N53133	16	EST N53133	246620	SEQ ID No:25		SEQ ID No:352	0	SEQ ID No:353
GSTP1	17	glutathione S-trans-	231424	SEQ ID No:26	SEQ ID No:27	SEQ ID No:334	SEQ ID No:335	SEQ ID No:336
		ferase pi (GSTP1)
THBS1	18	thrombospondin 1	160963	SEQ ID No:28		SEQ ID No:216	0	SEQ ID No:217
		(THBS1)
PDNP2	19	cctonucleotide	120916	SEQ ID No:29	SEQ ID No:30	SEQ ID No:39	SEQ ID No:40	SEQ ID No:41
		pyrophosphatase/
		phosphodiesterase
		2(autotaxin)
		(ENPP2) (ex
		PDNP2)
ATF3	20	activating transcrip-	183030	SEQ ID No:31	SEQ ID No:32	SEQ ID No:250	SEQ ID No:251	SEQ ID No:252
		tion factor 3 (ATF3)
NF1A	21	(ex NF1A)	110480	SEQ ID No:33		SEQ ID No:16	0	0
SELP	22	selectinm P (granule	182264		SEQ ID No:34	SEQ ID No:438	SEQ ID No:439	0
		membrane protein
		140kD, antigen
		CD62) (SELP)
CDH1	23	cadherin 1, E-	214008	SEQ ID No:35	SEQ ID No:36	SEQ ID No:326	SEQ ID No:327	SEQ ID No:328
		cadherin (epi-
		thelial) (CDH1)
ERBB2	24	v-erb-b2 avian	147016	SEQ ID No:37		0	SEQ ID No:118	SEQ ID No:119
		erythroblastic
		leukemia viral
		oncogene homolog
		2 (neuro/
		glioblastoma
		derived oncogene
		homolog) (ERBB2)
PP2A BR	25	(PP2A BR gamma)	179197	SEQ ID No:38	SEQ ID No:39	SEQ ID No:238	SEQ ID No:239	0
gamma
ZNF144	26	zinc finger pro-	220451	SEQ ID No:40	SEQ ID No:41	0	SEQ ID No:329	SEQ ID No:330
		tein 144 (Mel-18)
		(ZNF144)
MUC1	27	mucin 1, transmem-	125413		SEQ ID No:42	0	SEQ ID No:57	SEQ ID No:58
		brane (MUC1)
CD44	28	CD44E (epithelial	290007	SEQ ID No:43	SEQ ID No:44	SEQ ID No:374	SEQ ID No:375	SEQ ID No:376
		form)
PLA2G2A	29	phospholipase A2,	152802	SEQ ID No:45	SEQ ID No:46	SEQ ID No:147	SEQ ID No:148	SEQ ID No:149
		group IIA (plate-
		lets, synovial
		fluid) (PLA2G2A),
		nuclear gene
		encoding mito-
		chondrial protein
ACVRL1	30	activin A receptor	153350	SEQ ID No:47	SEQ ID No:48	SEQ ID No:159	SEQ ID No:160	SEQ ID No:161
		type II-like
		1 (ACVRL1)
AXL	31	AXL receptor tyro-	112500	SEQ ID No:49	SEQ ID No:50	SEQ ID No:27	SEQ ID No:28	SEQ ID No:29
		sine kinase (AXL)
PKU-ALPHA	32	KU-alpha, partial	109569		SEQ ID No:51	0	SEQ ID No:5	SEQ ID No:6
		cds (new gene
		symbol Tlk2)
ABCC5	33	ATP-binding	212366		SEQ ID No:52	0	SEQ ID No:324	SEQ ID No:325
		cassette, sub-
		family C (CFTR/
		MRP), member
		5 (ABCC5)
EDNRB	34	endothelial recep-	154244		SEQ ID No:53	0	SEQ ID No:176	SEQ ID No:177
		tor type B
		(EDNRB), trans-
		cript variant1
DTR	35	diphtheria toxin	187547		SEQ ID No:54	0	SEQ ID No:265	SEQ ID No:266
		receptor (hep-
		arin-binding
		epidermal
IGF1R	36	insulin-like	150361		SEQ ID No:55	0	SEQ ID No:129	SEQ ID No:130
		growth factor 1
		receptor (IGF1R)
KIAA0427	37	KIAA0427	127507	SEQ ID No:56	SEQ ID No:57	SEQ ID No:65	SEQ ID No:66	SEQ ID No:67
CD69	38	CD69 antigen (p60,	276727		SEQ ID No:58	0	SEQ ID No:370	SEQ ID No:371
		early, T-cell
		activation anti-
		gen)
FGFR4	39	fibroblast	116781	SEQ ID No:59	SEQ ID No:60	SEQ ID No:36	SEQ ID No:37	SEQ ID No:38
		growth factor
		receptor 4
		(FGFR4)
EST T85683	40	EST T85683 cathe-	112622		SEQ ID No:61	0	SEQ ID No:30	SEQ ID No:31
		spin B (CTSB)
EST R00569	41	EST R00569 IL2-	123871		SEQ ID No:62	0	SEQ ID No:44	SEQ ID No:45
		inducible T-
		cell kinase (ITK)
TGFBR3	42	transforming growth	208118	SEQ ID No:63	SEQ ID No:64	SEQ ID No:311	SEQ ID No:312	SEQ ID No:313
		factor, beta
		receptor III
		(TGFBR3)
INSR	43	insulin receptor	151149		SEQ ID No:65	0	SEQ ID NO;131	SEQ ID No:132
		(INSR)
MARK3	44	MAP/microtubule	110599	SEQ ID No:66	SEQ ID No:67	N/A	N/A	N/A
		affinity-reg-
		ulating kinase 3
		(MARK3)
TIMP2	45	tissue inhibitor	131504		SEQ ID No:68	0	SEQ ID No:86	SEQ ID No:87
		of metallopro-
		teinase 2 (TIMP2)
EST R85557	46	EST R85557 throm-	180219	SEQ ID No:69		SEQ ID No:240	0	SEQ ID No:241
		bospondin 3
		(THBD3)
GNRH1	47	gonadotropin-releas-	192688		SEQ ID No:70	0	SEQ ID No:277	SEQ ID No:278
		ing hormone 1
		(GNHR1)
FGFR2	48	fibroblast growth	110387	SEQ ID No:71	SEQ ID No:72	SEQ ID No:13	SEQ ID No:14	SEQ ID No:15
		factor receptor
		2 (FGFR2)
NFKB2	49	NFKB2	114879	SEQ ID No:73		SEQ ID No:35	0	0
VIL2	50	villin 2 (ezrin)	124701	SEQ ID No:74	SEQ ID No:75	SEQ ID No:51	SEQ ID No:52	SEQ ID No:53
		(VIL2)
ENG	51	endoglin (ENG)	156979	SEQ ID No:76	SEQ ID No:77	SEQ ID No:196	SEQ ID No:197	SEQ ID No:198
EPHA2	52	EphA2 (EPHA2)	162004	SEQ ID No:78		SEQ ID No:221	0	SEQ ID No:222
CREM	53	cAMP responsive	258584	SEQ ID No:79	SEQ ID No:80	SEQ ID No:358	SEQ ID No:359	SEQ ID No:360
		element modulator
		(CREM)
ETV5-a	54	ets variant	270549	SEQ ID No:81	SEQ ID No:82	SEQ ID No:368	SEQ ID No:369	SEQ ID No:300
		gene 5 (ETV5)
EST N68536	55	EST N68536 MAX-	298242	SEQ ID No:83	SEQ ID No:84	0	SEQ ID No:380	SEQ ID No:381
		interacting pro-
		tein 1 (MX11)
EST R81126	56	EST R81126 lym-	146635	SEQ ID No:85	SEQ ID No:86	SEQ ID No:114	0	0
		photoxin beta re-
		ceptor (LTBR)
POU2F2	57	(POu2F2)	188393	SEQ ID No:87	SEQ ID No:88	SEQ ID No:271	0	SEQ ID No:272
FLI1	58	Friend leukemia vir-	198144	SEQ ID No:89	SEQ ID No:90	SEQ ID No:293	SEQ ID No:294	SEQ ID No:295
		us integration 1
		(FLI1)
TIE	59	tyrosine kinase with	144081		SEQ ID No:91	0	SEQ ID No:109	SEQ ID No:110
		immunoglobulin and
		epidermal growth
		factor homology
		domains
		(TIE)
PRLR	60	prolactin receptor	138788	SEQ ID No:92	SEQ ID No:93	SEQ ID No:94	SEQ ID No:95	SEQ ID No:96
		(PRLR)
PPP3CA	61	protein phosphatase	110481	SEQ ID No:94	SEQ ID No:95	SEQ ID No:17	SEQ ID No:18	SEQ ID No:19
		3 (formerly 2B),
		catalytic subunit,
		gamma isoform
		(calcineurin A
		gamma) (PPP3CC)
		(ex PPP3CA)
PTPN2	62	protein tyrosine	161451	SEQ ID No:96	SEQ ID No:97	SEQ ID No:218	SEQ ID No:219	SEQ ID No:220
		phosphatase, non-re-
		ceptor type 2
		(PTPN2)
PGF	63	placental growth	139326		SEQ ID No:98	0	SEQ ID No:102	SEQ ID No:103
		factor, vascular
		endothelial growth
		factor-related
		protein (PGF)
TNFAIP3	64	tumor necrosis	309943	SEQ ID No:99		SEQ ID No:388	SEQ ID No:389	SEQ ID No:390
		factor, alpha-in-
		duced protein 3
		(TNFAIP3)
PHB	65	PHB (prohibitin)	236008	SEQ ID No:100		SEQ ID No:347	SEQ ID No:348	SEQ ID No:349
RIL	66	LIM domain pro-	153446		SEQ ID No:101	0	SEQ ID No:162	SEQ ID No:163
		tein (RIL)
MYBL2	67	v-myb avian mye-	207378	SEQ ID No:102	SEQ ID No:103	SEQ ID No:308	SEQ ID No:309	SEQ ID No:310
		loblastosis viral
		oncogene homolog-
		like 2 (MYBL2)
RELB	68	v-rel avian retic-	66969	SEQ ID No:104	SEQ ID No:105	SEQ ID No:417	SEQ ID No:418	SEQ ID No:419
		uloendotheliosis
		viral oncogene
		homolog B (nuclear
		factor of kappa light
		polypeptide gene
		enhancer in B-cells
		3) (RELB)
EST R97218	69	Est R97218	200394	SEQ ID No:106		SEQ ID No:296	SEQ ID No:297	0
GZMH	70	granzyme B (gran-	154343	SEQ ID No:107		SEQ ID No:178	0	SEQ ID No:179
		zyme 2, cytotoxic
		T-lymphocyte-ass-
		ociated serine es-
		terase 1) (GZMB)
		(ex GZMH)
MYC	71	c-myc proto-onco-	129438	SEQ ID No:108	SEQ ID No:109	SEQ ID No:73	SEQ ID No:74	SEQ ID No:75
		gene
CASP1	72	caspase 4, apop-	131502		SEQ ID No:110	SEQ ID No:84	0	SEQ ID No:85
		tosis-related cy-
		steine protease
		(CASP4) (ex
		CASP1)
SYK	73	spleen tyrosine	128142	SEQ ID No:111	SEQ ID No:112	SEQ ID No:68	SEQ ID No:69	SEQ ID No:70
		kinase (SYK)
EST H27202	74	EST H27202 trans-	158347	SEQ ID No:113	SEQ ID No:114	SEQ ID No:204	SEQ ID No:205	0
		cription factor
		E1AF gene
HRB	75	syndecan 1)	108490	SEQ ID No:115	SEQ ID No:116	SEQ ID No:1	0	SEQ ID No:2
		(SDC1) (ex HRB)
SHC1	76	p66shc (SHC)	153548		SEQ ID No:117	0	SEQ ID No:164	SEQ ID No:165
CSF1	77	colony stimulating	124554	SEQ ID No:118	SEQ ID No:119	SEQ ID No:48	SEQ ID No:49	SEQ ID No:50
		factor 1 (CSF1)
UBE3A	78	ubiquitin protein	141924		SEQ ID No:120	0	SEQ ID No:104	SEQ ID No:105
		ligase E3A
		(UBE3A)
FKHR	79	forkhead box	151247		SEQ ID No:121	0	SEQ ID No:133	SEQ ID No:134
		O1A (rhabdomyo-
		sarcoma)
		(FOXO1A) (ex
		FKHR)
CSF1R	80	colony stimulating	196282	SEQ ID No:122		SEQ ID No:291	0	SEQ ID No:292
		factor 1 re-
		ceptor (CSF1R)
IFI75	81	interferon-induced	205612	SEQ ID No:123	SEQ ID No:124	SEQ ID No:305	SEQ ID No:306	SEQ ID No:307
		protein 75 (IFI75)
GATA1	82	GATA-binding pro-	109093		SEQ ID No:125	0	SEQ ID No:3	SEQ ID No:4
		tein 1 (globin
		transcription
		factor 1) (GATA1)
STAT1	83	signal transducer	110101		SEQ ID No:126	0	SEQ ID No:11	SEQ ID No:12
		and activator of
		transcription 1
		(STAT1)
CREBBP	84	CREB binding pro-	109677	SEQ ID No:127	SEQ ID No:128	SEQ ID No:7	SEQ ID No:8	0
		tein (Rubinstein-
		Taybi syndrome)
		(CREBBP)
IL7R	85	interleukin 7	129059		SEQ ID No:129	0	SEQ ID No:71	SEQ ID No:72
		receptor (IL7R)
ANXA7	86	annexin A7	160580		SEQ ID No:130	0	SEQ ID No:214	SEQ ID No:215
	(AN-
	XA7)
TNXA	87	tenascin XA	124340		SEQ ID No:131	0	SEQ ID No:46	SEQ ID No:47
	(TN-
	XA)
CNBP1	88	zinc finger pro-	251963	SEQ ID No:132		SEQ ID No:356	0	SEQ ID No:357
		tein 9 (a cellular
		retroviral nucleic
		acid binding pro-
		tein) (ZNF9) (ex
		CNBP1)
CDK4-a	89	cyclin-dependent	204586	SEQ ID No:133	SEQ ID No:134	SEQ ID No:301	SEQ ID No:302	SEQ ID No:288
		kinase 4 (CDK4)
CSNK2B	90	gene for casein	153879		SEQ ID No:135	0	SEQ ID No:171	SEQ ID No:172
		kinase II subunit
		beta (EC 2.7.1.37).
EFNA1	91	ephrin-A1 (EFNA1)	162997		SEQ ID No:136	0	SEQ ID No:226	SEQ ID No:227
SELE	92	selectin E (endo-	186132	SEQ ID No:137	SEQ ID No:138	SEQ ID No:259	SEQ ID No:260	SEQ ID No:261
		thelial adhesion
		molecule 1) (SELE)
APC	93	adenomatosis poly-	125294	SEQ ID NO:139	SEQ ID No:140	SEQ ID No:54	SEQ ID No:55	SEQ ID No:56
		posis coli (APC)
FAK	94	PTK2 protein tyro-	195731		SEQ ID No:141	0	SEQ ID No:284	SEQ ID No:285
		sine kinase 2
		(PTK2) (ex FAK)
FOS-a	95	v-fos FBJ murine	208717		SEQ ID No:142	0	SEQ ID No:317	SEQ ID No:318
		osteosarcoma
		viral oncogene
		homolog (FOS)
FGFR1	96	fibroblast growth	154472	SEQ ID No:143	SEQ ID No:144	SEQ ID No:180	SEQ ID No:181	SEQ ID No:182
		factor receptor
		(FGFr)
MC1R	97	melanocortin 1 re-	155691		SEQ ID No:145	0	SEQ ID No:187	SEQ ID No:188
		ceptor (alpha
		melanocyte stim-
		ulating hormone
		receptor) (MC1R)
PCNA	98	proliferating cell	232941	SEQ ID No:146	SEQ ID No:147	SEQ ID No:339	SEQ ID No:340	SEQ ID No:341
		nuclear antigen
		(PCNA)
DDT	99	D-dopachrome tau-	132109	SEQ ID No:148	SEQ ID No:149	SEQ ID No:88	SEQ ID No:89	SEQ ID No:90
		tomerase (DDT)
GRB2	100	growth factor re-	172152	SEQ ID No:150	SEQ ID No:151	SEQ ID No:230	SEQ ID No:231	SEQ ID No:232
		ceptor-bound
		protein 2 (GRB2)
AMFR	101	autocrine motility	146280	SEQ ID No:152	SEQ ID No:153	SEQ ID No:111	SEQ ID No:112	SEQ ID No:113
		factor receptor
		(AMFR)
ITGB2	102	integrin, beta 2	187822	SEQ ID No:154		0	SEQ ID No:267	SEQ ID No:268
		2 (antigen CD18
		(p95), lymphocyte
		function-ass-
		ociated antigen 1;
		macrophage antigen
		1 (mac-1) beta
		subunit) (ITGB2)
JUND	103	jun D proto-	175421	SEQ ID No:155		SEQ ID No:233	0	SEQ ID No:234
		oncogene (JUND)
NF45	104	interleukin en-	243907		SEQ ID No:156	0	SEQ ID No:350	SEQ ID No:351
		hancer binding
		factor 2 (ILF2) (ex
		NF45)
PPP4C	105	protein phosphatase	114097	SEQ ID No:157	SEQ ID No:158	SEQ ID No:32	SEQ ID No:33	SEQ ID No:34
		4 (formerly X)
		(PPP4C)
EMS1	106	ATX1 (antioxidant	149172	SEQ ID No:159		SEQ ID No:123	SEQ ID No:124	SEQ ID No:125
		protein 1, yeast)
		homolog 1
		(ATOX1) (ex
		EMS1)
BCL2	107	B-cell CLL/lymph-	147002	SEQ ID No:160	SEQ ID No:161	SEQ ID No:115	SEQ ID No:116	SEQ ID No:117
		oma 2 (BCL2), nu-
		clear gene encoding
		mitochondrial pro-
		tein, transcript var-
		iant alpha
MGST1	108	protein phosphatase	182610	SEQ ID No:162	SEQ ID No:163	SEQ ID No:248	0	SEQ ID No:249
		1, catalytic sub-
		unit, alpha iso-
		form (PPP1CA) (ex
		MGST1)
PDGFRB	109	platelet-derived	158976		SEQ ID No:164	0	SEQ ID No:208	SEQ ID No:209
		growth factor re-
		ceptor, beta poly-
		peptide (PDGFRB)
ANXA11	110	annexin A11	158892		SEQ ID No:165	0	SEQ ID No:206	SEQ ID No:207
		(ANXA11)
GPX1	111	histocompatability	159809		SEQ ID No:166	0	SEQ ID No:212	SEQ ID No:213
		class II antigen
		gamma chain
		(CD74) (ex GPX1
		Glutation S trans-
		frase)
CFR-1	112	Golgi apparatus pro-	153974	SEQ ID No:167	SEQ ID No:168	SEQ ID No:173	SEQ ID No:174	SEQ ID No:175
		tein 1 (GLG1) (ex
		CFR-1)
BTF3L3	113	basic transcription	195889	SEQ ID No:169		SEQ ID No:289	0	SEQ ID No:290
		factor 3 (BTF3)
EST R55460	114	EST R55460	154997		SEQ ID No:170	0	SEQ ID No:185	0
AKT2	115	v-akt murine thy-	182552	SEQ ID No:171		SEQ ID No:253	0	SEQ ID No:254
		moma viral onco-
		gene homolog 2
		(ATK2)
CDKN1A	116	cyclin-dependent	152524	SEQ ID No:172	SEQ ID No:173	SEQ ID No:144	SEQ ID No:145	SEQ ID No:146
		kinase inhibitor
		(CDKN1A)
PPP2CA	117	protein phosphatase	54685	SEQ ID No:174	SEQ ID No:175	0	SEQ ID No:183	SEQ ID No:184
		2 (formerly 2A),
		catalytic subunit,
		alpha isoform
		(PPP2CA)
MDM2	118	mouse double min-	148052	SEQ ID No:176		0	SEQ ID No:120	SEQ ID No:121
		ute 2, human homo-
		logy of; p53-binding
		protein (MDM2),
		transcript variant
		MDM2
TNFRSF6	119	tumor necrosis	151767	SEQ ID No:177	SEQ ID No:178	SEQ ID No:141	SEQ ID No:142	SEQ ID No:143
		factor receptor
		superfamily, mem-
		ber 6 (TNFRSF6)
CNTFR	120	ciliary neurotrophic	156431		SEQ ID No:179	0	SEQ ID No:192	SEQ ID No:193
		factor receptor
		(CNTFR)
JUNB	121	jun B proto-onco-	153213	SEQ ID No:180	SEQ ID No:181	SEQ ID No:153	SEQ ID No:154	SEQ ID No:155
		gene (JUNB)
CCND1	122	cyclin D1 (PRAD1:	110022	SEQ ID No:182		SEQ ID No:9	0	SEQ ID No:10
		parathyroid
		adenomatosis 1)
		(CCND1)
TDPX1	123	peroxiredoxin 2	208439	SEQ ID No:183	SEQ ID No:184	SEQ ID No:314	SEQ ID No:315	SEQ ID No:316
		(PRDX2) (ex
		TDPX1)
GRB7	124	growth factor	130323	SEQ ID No:185	SEQ ID No:186	SEQ ID No:79	SEQ ID No:80	SEQ ID No:81
		receptor-bound pro-
		tein 7 (GRB7)
RBBP7	125	retinoblastoma-bind-	210874	SEQ ID No:187	SEQ ID No:188	SEQ ID No:319	SEQ ID No:320	SEQ ID No:321
		ing protein 7
		(RBBP7)
TIMP1	126	tissue inhibitor of	162246	SEQ ID No:190	SEQ ID No:223	SEQ ID No:224	SEQ ID No:225	SEQ ID NO:189
		metalloproteinase 1
		(erythyroid po-
		tentiating act-
		ivity, collagen-
		ase inhibitor)
		(TIMP1)
YES1	127	v-yes-1 Yamaguchi	204634	SEQ ID No:191		SEQ ID No:303	0	SEQ ID No:304
		sarcoma viral onco-
		gene homolog 1
		(YES1)
RNF5	128	ring finger protein	112098		SEQ ID No:192	0	SEQ ID No:25	SEQ ID No:26
		5 (RNF5)
PRKCSH	129	protein kinase C	187232		SEQ ID No:193	0	SEQ ID No:263	SEQ ID No:264
		substrate 80K-H
		(PRKCSH)
CTSD	130	cathepsin D (lyso-	149401	SEQ ID No:194	SEQ ID No:195	SEQ ID No:126	SEQ ID No:127	SEQ ID No:128
		somal aspartyl pro-
		tease) (CTSD)
NEO1	131	neogenin (chicken)	188380		SEQ ID No:196	0	SEQ ID No:269	SEQ ID No:270
		homolog 1 (NEO1)
GAPD-a	132	glyceraldehyde-3-	152847	SEQ ID No:197		SEQ ID No:150	SEQ ID No:151	SEQ ID No:152
		phosphatase dehy-
		drogenase (GAPD)
ACTG1	133	actin, gamma 1	182291	SEQ ID No:198	SEQ ID No:199	SEQ ID No:242	SEQ ID No:243	SEQ ID No:244
		(ACTG1)
ITGA6	134	integrin, alpha 6	182431	SEQ ID No:200	SEQ ID No:201	SEQ ID No:245	SEQ ID No:246	SEQ ID No:247
		(ITGA6)
GAPD-b	135	glyceraldehyde-3-	153607	SEQ ID No:202	SEQ ID No:203	SEQ ID No:166	SEQ ID No:167	SEQ ID No:152
		phosphate dehydro-
		genase (GAPD)
ETV5-b	136	ets variant gene 5	203394	SEQ ID No:204	SEQ ID No:205	SEQ ID No:298	SEQ ID No:299	SEQ ID No:300
		(ets-related mole-
		cule) (ETV5)
CDK4-b	137	cyclin-dependent	195800	SEQ ID No:206	SEQ ID No:207	SEQ ID No:286	SEQ ID No:287	SEQ ID No:288
		kinase 4 (CDK4)
FOS-b	138	v-fos FBJ murine	363796	SEQ ID No:208	SEQ ID No:209	SEQ ID No:404	SEQ ID No:405	SEQ ID No:318
		osteosarcoma viral
		oncogene homo-
		log (FOS)
HOXA5	139	homebox protein	300564	SEQ ID No:210	SEQ ID No:211	SEQ ID No:382	SEQ ID No:383	SEQ ID No:384
		(HOX-1.3) (ex Hox
		A5)
RELA	140	NF-kappa-B trans-	122056	SEQ ID No:212		SEQ ID No:42	0	SEQ ID No:43
		cription factor p65
		DNA binding sub-
		unit (ex RELa)
SUI1	141	S100 calcium-bind-	155345	SEQ ID No:213	SEQ ID No:214	SEQ ID No:186	0	0
		ing protein A11
		(calgizzarin)
		(S100A11)
ANG	142	angiogenin, ribonu-	156720		SEQ ID No:215	0	SEQ ID N:194	SEQ ID No:195
		clease, RNase
		A family, 5
		(ANG)
ITGA6	143	integrin, alpha 6	182431	SEQ ID No:216	SEQ ID No:217	SEQ ID No:245	SEQ ID No:246	SEQ ID No:247
		(ITGA6)
PRMT2	144	HMT1 (hnRNP	158038	SEQ ID No:218	SEQ ID No:219	SEQ ID No:201	SEQ ID No:202	SEQ ID No:203
		methyltransfer-
		ase, S. cerevis-
		iae)-like 1
		(HRMTIL1) (ex
		PRMT2)
EST R55460	145	EST R55460	154997		SEQ ID No:220	0	SEQ ID No:185	0
GZMA	146	granzyme A (gran-	356763	SEQ ID No:221	SEQ ID No:222	SEQ ID No:402	0	SEQ ID No:403
		zyme 1, cytotoxic
		T-lymphocyte-ass-
		ociated serine es-
		terase 3) (GZMA)
SOX9	147	SRY (sex-deter-	323948	SEQ ID No:223		SEQ ID No:394	0	SEQ ID No:395
		mining region Y)-
		box 9 (campomel-
		ic dysplasia, auto-
		somal sex-reversal)
		(SOX9)
SRF	148	serum response	321329		SEQ ID No:224	SEQ ID No:391	SEQ ID No:392	SEQ ID No:393
		factor (c-fos serum
		response element-
		binding transcription
		factor) (SRF)
EDNI	149	endothelial 1	153424	SEQ ID No:225		N/A	N/A	N/A
		(EDN1)
PTPN6	150	protein tyrosine	66778	SEQ ID No:226		N/A	N/A	N/A
		phosphatase, non-
		receptor type 6
		(PTPN6)
TFAP4	151	transcription factor	159093	SEQ ID No:227		0	SEQ ID No:210	SEQ ID No:211
		AP-4 (activating
		enhancer bind-
		ing protein 4)
		(TFAP4)
ELF1	152	Human cis-acting-	182007	SEQ ID No:228		SEQ ID No:417	0	0
		sequence Elf-1
CD2	153	CD2 antigen (p50),	120649	SEQ ID No:229		SEQ ID No:431	0	0
		sheep red blood
		cell receptor
		(CD2)
CCND2	154	cyclin D2 (CCND2)	175256	SEQ ID No:230		N/A	N/A	N/A
IL3RA	155	interleukin 3 recep-	183087	SEQ ID No:231		SEQ ID No:440	SEQ ID No:441	0
		tor (hIL-3Ra)
JUP	156	junction plakoglobin	157958	SEQ ID No:232		N/A	N/A	N/A
		(JUP)
RBL2	157	retinoblastoma-like	108571	SEQ ID No:233		SEQ ID No:430	0	0
		2 (p130) (RBL2)
HOXA4	158	homeo box A4	110731	SEQ ID No:234		SEQ ID No:20	SEQ ID No:21	0
		(HOXA4)
ACY1	159	aminoacylase	160764	SEQ ID No:235		SEQ ID No:435	SEQ ID No:436	0
		(ACY1)
GADD45A	160	growth arrest and	115176	SEQ ID No:236		N/A	N/A	N/a
		DNA-damage-in-
		ducible, alpha
		(GADD45A)
nm23	161	non-metastatic	174388	SEQ ID No:237		N/A	N/A	N/A
		cells 1, protein
		(NM23A) express-
		ed (NME1)
BBC1	162	ribosomal protein	178317	SEQ ID No:238		N/A	N/A	N/A
		L13 (RPL13) (ex
		BBC1)
VEGFB	163	vascular endothe-	162499	SEQ ID No:239		N/A	N/A	N/A
		lial growth factor B
		(VEGFB)
LAMR1	164	laminin receptor 1	199837	SEQ ID No:240		N/A	N/A	N/A
		(67kD, ribosomal
		protein SA)
		(LAMR1)
IL2RB	165	interleukin 2 re-	139073	SEQ ID No:241	SEQ ID No:242	SEQ ID No:97	SEQ ID No:98	SEQ ID No:99
		ceptor, beta
		(IL2RB)
DES	166	desmin	153854	SEQ ID No:243		SEQ ID No:168	SEQ ID No:169	SEQ ID No:170
PRL	167	prolactin	133738	SEQ ID No:244		SEQ ID No:91	SEQ ID No:92	SEQ ID No:93
CSH1	168	Chorionic soma-	133891		SEQ ID No:245	SEQ ID No:432	0	0
		tomammotropin hor-
		mone 1 (placental
		lactogen) LAC-
		TOGEN Precursor
TEK	169	tyrosine proteine	151501	SEQ ID No:246	SEQ ID No:247	SEQ ID No:138	SEQ ID No:139	SEQ ID No:140
		kinase receptor
Nrg1	170	neuregulin 1 (EST	155716	SEQ ID No:248	SEQ ID No:249	SEQ ID No:189	SEQ ID No:190	SEQ ID No:191
		R72075)
PLAT	rien	pas dEST ni	160149			SEQ ID No:433	SEQ ID No:434	0
		mRNA
EST	rien		image
AW184517

[0110] Tables 5 hereunder displays subpopulations of polynucleotide sequences interesting to distinguish a person without cancer from a cancer patient.

TABLE 5
Gene
symbol	No	Name	Seq3	Seq5	Ref
HRB	1	hiv-1 rev binding protein	SEQ ID		SEQ ID
			No:1		No:2
EST T81919	4	ests, weakly similar to alu7_human alu subfamily	SEQ ID	SEQ ID
		sq sequence contamination warning entry h. sapines	No:7	No:8
ENPP2	18	ectonucleotide pyrophosphatase/phosphodiesterase 2	SEQ ID	SEQ ID	SEQ ID
		(autotaxin)	No:39	No:40	No:41
TNXB	21	tenascin xb		SEQ ID	SEQ ID
				No:46	No:47
APC	24	adenomatosis polyposis coli	SEQ ID	SEQ ID	SEQ ID
			No:54	No:55	NO:56
GATA3	32	gata-binding protein 3	SEQ ID	SEQ ID	SEQ ID
			No:76	No:77	No:78
PRL	38	prolactin	SEQ ID	SEQ ID	SEQ ID
			No:91	No:92	No:93
BCL2	48	b-cell cll/lymphoma 2	SEQ ID	SEQ ID	SEQ ID
			No:115	No:116	No:117
CTSD	53	cathepsin d (lysosomal aspartyl protease)	SEQ ID	SEQ ID	SEQ ID
			No:126	No:127	No:128
TEK	58	tek tyrosine kinase, endothelial (venous	SEQ ID	SEQ ID	SEQ ID
		malformations, multiple cutaneous and mucosal)	No:138	No:139	No:140
TNFRSF6	59	tumor necrosis factor receptor superfamily, member	SEQ ID	SEQ ID	SEQ ID
		6	No:141	No:142	No:143
PLA2G2A	61	phospholipase a2, group iia (platelets, synovial	SEQ ID	SEQ ID	SEQ ID
		fluid	No:147	No:148	No:149
CRABP2	64	cellular retinoic acid-binding protein 2	SEQ ID	SEQ ID	SEQ ID
			No:156	No:157	No:158
RIL	66	lim domain protein	SEQ ID	SEQ ID	SEQ ID
				No:162	No:163
DES	69	desmin	SEQ ID	SEQ ID	SEQ ID
			No:168	No:169	No:170
GZMB	73	granzyme b (granzyme 2, cytotoxic t-lymphocyte-	SEQ ID		SEQ ID
		associated serine esterase 1)	No:178		No:179
ETV4	85	ets variant gene 4 (e1a enhancer-binding protein,	SEQ ID	SEQ ID
		e1af)	No:204	No:205
WBSCR14	88	williams-beuren syndrome chromosome region 14		SEQ ID	SEQ ID
				No:210	No:211
THBS1	91	thrombospondin 1	SEQ ID		SEQ ID
			No:216		No:217
GRB2	97	growth factor receptor-bound protein 2	SEQ ID	SEQ ID	SEQ ID
			No:230	No:231	No:232
RAD9	104	rad9 (s. pombe) homolog	SEQ ID		SEQ ID
			No:248		No:249
ATF3	105	activating transcription factor 3	SEQ ID	SEQ ID	SEQ ID
			No:250	No:251	No:252
DTR	112	diphtheria receptor (heparin-binding epidermal		SEQ ID	SEQ ID
		growth factor-like growth factor)		No:265	No:266
ITGB2	113	integrin, beta 2 (antigen cd18 (p95), lymphocyte		SEQ ID	SEQ ID
		function-associated antigen 1, macrophage entigen 1		No:267	No:268
		(mac-1) beta subunit)
POU2F2	115	pou domain, class 2, transcription factor 2	SEQ ID		SEQ ID
			No:271		No:272
MYBL2	131	v-myb avian myeoblastosis viral oncogene	SEQ ID	SEQ ID	SEQ ID
		homolog-like 2	No:308	No:309	No:310
TGFBR3	132	transforming growth factor, beta receptor iii	SEQ ID	SEQ ID	SEQ ID
		(betaglycan, 300kd)	No:311	No:312	No:313
FOS	134	v-fos fbj murine osteosarcoma viral oncogene		SEQ ID	SEQ ID
		homolog		No:317	No:318
ABCC5	137	atp-binding cassette, sub-family c (cftr/mrp),		SEQ ID	SEQ ID
		member 5		No:324	No:325
MMP11	145	matrix metalloproteinase 11 (stromelysin 3)	SEQ ID		SEQ ID
			No:345		No:346
ILF2	147	interleukin enhancer binding factor 2, 45kd		SEQ ID	SEQ ID
				No:350	No:351
ETV5	155	ets variant gene 5 (ets-related molecule)	SEQ ID	SEQ ID	SEQ ID
			No:368	No:369	No:300
RELB	175	v-rel avian reticuloendotheliosis viral oncogene	SEQ ID	SEQ ID	SEQ ID
		homolog b (nuclear factor of kappa light polypeptide	No:417	No:418	No:419
		gene enhancer in b-cells 3)
EST T80406	180	similar to SP:S36648 S36648 RB2/P130 PROTEIN	SEQ ID
			No:430
EST Y95640	181	similar to gb:M16336 T-CELL SURFACE	SEQ ID
		ANTIGEN CD2	No:431
EST R28523	182	similar to placental lactogen (CSH1)	SEQ ID
			No:432
EST H28056	185	Homo sapines E74-like factor 1 (ets domain	SEQ ID
		transcription factor) (ELF1)	No:437
ESTs H42957 &	187	Human interleukin 3 receptor (hIL-3Ra)	SEQ ID	SEQ ID
H42888			No:440	No:441

[0111] 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 a person without cancer from a cancer patient.

TABLE 5A
overexpressed genes:top 5
Gene
symbol	No	Name	Seq3	Seq5	Ref
GATA3	32	gata-binding protein 3	SEQ ID	SEQ ID	SEQ ID
			No:76	No:77	No:78
GZMB	73	granzyme b (granzyme 2, cytotoxic t-	SEQ ID		SEQ ID
		lymphocyte-associated serine esterase 1)	No:178		No:179
MYBL2	131	v-myb avian myeloblastosis viral oncogene	SEQ ID	SEQ ID	SEQ ID
		homolog-like 2	No:308	No:309	No:310
MMP11	145	matrix metallopropteinase 11 (stromelysin 3)	SEQ ID		SEQ ID
			No:345		No:346
EST	181	similar to gb:M16336 T-CELL SURFACE	SEQ ID
T95640		ANTIGEN CD2	No:431

[0112]

TABLE 5B
underexpressed genes:top 5
Gene
symbol	No	Name	Seq3	Seq5	Ref
PRL	38	prolactin	SEQ ID	SEQ ID	SEQ ID
			No:91	No:92	No:93
TEK	58	tek tyrosine kinase, endothelial (venous	SEQ ID	SEQ ID	SEQ ID
		malformations, multiple cutaneous and mucosal)	No:138	No:139	No:140
PLA2GA	612	phospholipase a2, group iia (platelets, synovial fluid)	SEQ ID	SEQ ID	SEQ ID
			No:147	No:148	No:149
DES	69	desmin	SEQ ID	SEQ ID	SEQ ID
			No:168	No:169	No:170
EST R28523	182	similar to placental lactogen (CSH1)	SEQ ID
			No:432

[0113] Table 6 hereunder relates to subpopulations of polynucleotide sequences interesting to detect hormone-sensitive tumors allowing distinction between ER and ER-samples.

TABLE 6
Gene
symbol	No	Name	Seq3	Seq5	Ref
SOX4	11	sry (sex determining region y)-box 4	SEQ ID	SEQ ID	SEQ ID
			No:22	No:23	No:24
IGF2	26	insulin-like growth factor 2 (somatomedian a)	SEQ ID	SEQ ID	SEQ ID
			No:59	No:60	No:61
GATA3	32	gata-binding protein 3	SEQ ID	SEQ ID	SEQ ID
			No:76	No:77	No:78
TOP2B	34	topoisomerase (dna) ii beta (180kd)		SEQ ID	SEQ ID
				No:82	No:83
IL2RB	40	interleukin 2 receptor, beta	SEQ ID	SEQ ID	SEQ ID
			No:97	No:98	No:99
EGFR	57	epidermal growth factor receptor (avian	SEQ ID	SEQ ID	SEQ ID
		erythroblastic leukemia viral (v-erb-b) oncogene	No:135	No:136	No:137
		homolog)
CRABP2	64	cellular retinoic acid-binding protein 2	SEQ ID	SEQ ID	SEQ ID
			No:156	No:157	No:158
S100B	107	s100 calcium-binding protein, beta (neural)		SEQ ID	SEQ ID
				No:255	No:256
IL2RG	119	interleukin 2 receptor, gamma (severe combined	SEQ ID	SEQ ID	SEQ ID
		immunodeficiency)	No:279	No:280	No:281
KIAA1075	136	kiaa 1075 protein	SEQ ID	SEQ ID
			No:322	No:323
MST1	140	macrophage stimulating 1 (hepatocyte growth factor-	SEQ ID	SEQ ID	SEQ ID
		like)	No:331	No:332	No:333
GSTP1	141	glutathione s-transferase pi	SEQ ID	SEQ ID	SEQ ID
			No:334	No:335	No:336
MMP11	145	matrix metalloproteinase 11 (stromelysin 3)	SEQ ID		SEQ ID
			No:345		No:346
FLJ11307	148	hypothetical protein flj11307	SEQ ID		SEQ ID
			No:352		No:353
MYB	149	v-myb avian myeloblastosis viral oncogene homolog		SEQ ID	SEQ ID
				No:354	No:355
XBP1	162	x-box binding protein 1	SEQ ID	SEQ ID	SEQ ID
			No:385	No:386	No:387
SOX9	165	sry (sex dtermining region y)-boc 9 (campomelic	SEQ ID		SEQ ID
		dysplasia, autosomal sex-reversal)	No:394		No:395
GZMA	169	granzyme a (granzyme 1, cytotoxic t-lymphocyte-	SEQ ID		SEQ ID
		associated serine esterase 3)	No:402		No:403
CD3G	174	cd3g antigen, gamma polypeptide (tit3 complex)	SEQ ID	SEQ ID	SEQ ID
			No:414	No:415	No:416
EST	188	Human tumor protein p53 (Li-Fraumeni syndrome)	SEQ ID
H57912		(TP53)	No:442

[0114] Tables 6A and 6B hereunder relate to two subpopulations of polynucleotide sequences particularly interesting to detect hormone-sensitive tumors allowing distinction between ER and ER samples

TABLE 6A
overexpressed genes:top 5
ER /ER
Gene	CL
symbol	No	Name	Seq3	Seq5	Ref
GATA3	32	gata-binding protein 3	SEQ ID	SEQ ID	SEQ ID
			No:76	No:77	No:78
KIAA1075	136	kiaa 1075 protein	SEQ ID	SEQ ID
			No:322	No:323
MMP11	145	matrix metalloproteinase 11	SEQ ID		SEQ ID
		(stromelysin 3)	No:345		No:346
MYB	149	v-myb avian myeloblastosis viral		SEQ ID	SEQ ID
		oncogene homolog		No:354	No:355
GZMA	169	granzyme a (granzyme 1, yutotoxic t-	SEQ ID		SEQ ID
		lymphocyte-associated serine esterase 3)	No:402		No:403

[0115]

TABLE 6B
underexpressed genes:top 5
Gene
symbol	No	Name	Seq3	Seq5	Ref
SOX4	11	sry (sex determining region y)-box 4	SEQ ID	SEQ ID	SEQ ID
			No:22	No:23	No:24
IL2RB	40	interleukin 2 receptor, beta	SEQ ID	SEQ ID	SEQ ID
			No:97	No:98	No:99
EGFR	57	epidermal growth factor receptor (avian	SEQ ID	SEQ ID	SEQ ID
		eryhtroblastic leukemia viral (v-erb-b)	No:135	No:136	No:137
		oncogene homolog)
IL2RG	119	interleukin 2 receptor, gamma (severe	SEQ ID	SEQ ID	SEQ ID
		combined immunodeficiency)	No:279	No:280	No:281
CD3G	174	cd3g antigen, gamma polypeptide (tit3	SEQ ID	SEQ ID	SEQ ID
		complex)	No:414	No:415	No:416

[0116] Tables 7 hereunder relates to subpopulations of polynucleotide sequences interesting to distinguish tumors in which a lymph node has been invaded by a tumor cell from tumors in which a lymph node has not been so invaded.

TABLE 7
Gene	CL
symbol	No	Name	Seq3	Seq5	Ref
EST T89980	8	ests	SEQ ID
			No:16
SOX4	11	sry (sex determining region y)-box 4	SEQ ID	SEQ ID	SEQ ID
			No:22	No:23	No:24
ENPP2	18	ectonucleotide	SEQ ID	SEQ ID	SEQ ID
		pyrophosphatase/phosphodiesterase 2	No:39	No:40	No:41
		(autotoxin)
MUC1	25	mucin 1, transmembrane		SEQ ID	SEQ ID
				No:57	No:58
GATA3	32	gata-binding protein 3	SEQ ID	SEQ ID	SEQ ID
			No:76	No:77	No:78
TOP2B	34	topoisomerase (dna) it beta (180kd)		SEQ ID	SEQ ID
				No:82	No:83
IL2RB	40	interleukin 2 receptor, beta	SEQ ID	SEQ ID	SEQ ID
			No:97	No:98	No:99
ERBB2	49	v-erb-b2 avian erythroblastic		SEQ ID	SEQ ID
		leukemia viral oncogene homolog 2		No:118	No:119
		(neuro/glioblastoma derived oncogene
		homolog)
EGFR	57	epidermal growth factor receptor (avian	SEQ ID	SEQ ID	SEQ ID
		erythroblastic leukemia viral (v-erb-b)	No:135	No:136	No:137
		oncogene homolog)
THBS1	91	thrombospondin 1	SEQ ID		SEQ ID
			No:216		No:217
PPP2R2C	100	protein phosphatase 2 (formerly 2a),	SEQ ID	SEQ ID
		regulatory subunit b (pr 52), gamma	No:238	No:239
		isoform
ATF3	105	activating transcription factor 3	SEQ ID	SEQ ID	SEQ ID
			No:250	No:251	No:252
KIAA1075	136	kiaa 1075 protein	SEQ ID	SEQ ID
			No:322	No:323
CDH1	138	cadherin 1, type 1, e-cadherin (epithelial)	SEQ ID	SEQ ID	SEQ ID
			No:326	No:327	No:328
ZNF144	139	zinc finger protein 144 (mel-18)		SEQ ID	SEQ ID
				No:329	No:330
GSTP1	141	glutathione s-transferase pi	SEQ ID	SEQ ID	SEQ ID
			No:334	No:335	No:336
CD44	158	cd44 antigen (homing function and indian	SEQ ID	SEQ ID	SEQ ID
		blood group system)	No:374	No:375	No:376
GZMA	169	granzyme a (granzyme 1, cytotoxic t-lym-	SEQ ID		SEQ ID
		phocyte-associated serine esterase 3)	No:402		No:403
EST T80406	180	similar to SP;S36648 S36648 RB2/P130	SEQ ID
		PROTEIN	No:430
ESTs H30141 &	186	Homo sapiens selectin P	SEQ ID	SEQ ID
H27466			No:438	No:439

[0117] Tables 7A and 7B hereunder relate to two subpopulations of polynucleotide sequences particularly interesting to distinguish tumors in which a lymph node has been invaded by a tumor cell from tumors in which a lymph node has not been so invaded.

TABLE 7A
Overexpressed genes:top 5
Gene
symbol	No	Name	Seq3	Seq5	Ref
ENPP2	18	ectonucleotide	SEQ ID	SEQ ID	SEQ ID
		pyrophosphatase/phosphodiesterase 2	No:39	No:40	No:41
		(autotaxin)
GATA3	32	gata-binding protein 3	SEQ ID	SEQ ID	SEQ ID
			No:76	No:77	No:78
EGFR	57	epidermal growth factor receptor (avian	SEQ ID	SEQ ID	SEQ ID
		erythroblastic leukemia viral (v-erb-b)	No:135	No:136	No:137
		oncogene homolog)
THBS1	91	thrombospondin 1	SEQ ID		SEQ ID
			No:216		No:217
ATF3	105	activating transcription factor 3	SEQ ID	SEQ ID	SEQ ID
			No:250	No:251	No:252

[0118]

TABLE 7B
Underexpressed genes: top 5
Gene
symbol	No	Name	Seq 3	Seq 5	Ref
SOX4	11	sry (sex determining region y)-box 4	SEQ ID	SEQ ID	SEQ ID
			No: 22	No: 23	No: 24
IL2RB	40	interleukin 2 receptor, beta	SEQ ID	SEQ ID	SEQ ID
			No: 97	No: 98	No: 99
ERBB2	49	v-erb-b2 avian erythroblastic leukemia		SEQ ID	SEQ ID
		viral oncogene homolog 2		No: 118	No: 119
		(neuro/glioblastoma derived oncogene
		homolog)
PPP2R2C	100	protein phosphatase 2 (formerly 2a),	SEQ ID	SEQ ID
		regulatory subunit b (pr 52), gamma	No: 238	No: 239
		isoform
GSTP1	141	glutathione s-transferase pi	SEQ ID	SEQ ID	SEQ ID
			No: 334	No: 335	No:336

[0119] Table 8 hereunder relates to subpopulations of polynucleotide sequences particularly interesting to distinguish tumors sensitive to anthracycline from tumors insensitive to anthracycline.

TABLE 8
A1/A2
Gene
symbol	No	Name	Seq 3	Seq 5	Ref
SOX4	11	sry (sex determining region y)-box	SEQ ID	SEQ ID	SEQ ID
			No: 22	No: 23	No: 24
CSF1	22	colony stimulating factor 1 (macrophage)	SEQ ID	SEQ ID	SEQ ID
			No: 48	No: 49	No: 50
VIL2	23	villin 2 (ezrin)	SEQ ID	SEQ ID	SEQ ID
			No: 51	No: 52	No: 53
IGF2	26	insulin-like growth factor 2 (somatomedin a)	SEQ ID	SEQ ID	SEQ ID
			No: 59	No: 60	No: 61
KIAA0427	28	kiaa0427 gene product	SEQ ID	SEQ ID	SEQ ID
			No: 65	No: 66	No: 67
MYC	31	v-myc avian myelocytomatosis viral oncogene	SEQ ID	SEQ ID	SEQ ID
		homolog	No: 73	No: 74	No: 75
GATA3	32	gata-binding protein 3	SEQ ID	SEQ ID	SEQ ID
			No: 76	No: 77	No: 78
TOP2B	34	topoisomerase (dna) ii beta (180 kd)		SEQ ID	SEQ ID
				No: 82	No: 83
ERBB2	49	v-erb-b2 avian erythroblastic leukemia viral		SEQ ID	SEQ ID
		oncogene homolog 2 (neuro/glioblastoma		No: 118	No: 119
		derived oncogene homolog)
EGFR	57	epidermal growth factor receptor (avian	SEQ ID	SEQ ID	SEQ ID
		erythroblastic leukemia viral (v-erb-b)	No: 135	No: 136	No: 137
		oncogene homolog)
CRABP2	64	cellular retinoic acid-binding protein 2	SEQ ID	SEQ ID	SEQ ID
			No: 156	No: 157	No: 158
GZMB	73	granzyme b (granzyme 2, cytotoxic t-	SEQ ID		SEQ ID
		lymphocyte-associated serine esterase 1)	No: 178		No: 179
IGKC	77	immunoglobulin kappa constant	SEQ ID
			No: 186
ANG	81	angiogenin, ribonuclease, rnase a family, 5	SEQ ID	SEQ ID
			No: 194	No: 195
EFNA1	95	ephrin-al		SEQ ID	SEQ ID
				No: 226	No: 227
MYBL2	131	v-myb avian myeloblastosis viral oncogene	SEQ ID	SEQ ID	SEQ ID
		homolog-like 2	No: 308	No: 309	No: 310
CDH1	138	cadherin 1, type 1, e-cadherin (epithelial)	SEQ ID	SEQ ID	SEQ ID
			No: 326	No: 327	No: 328
MST1	140	macrophage stimulating 1 (hepatocyte growth	SEQ ID	SEQ ID	SEQ ID
		factor-like)	No: 331	No: 332	No: 333
MYB	149	v-myb avian myeloblastosis viral oncogene		SEQ ID	SEQ ID
		homolog		No: 354	No: 355
XBP1	162	x-box binding protein 1	SEQ ID	SEQ ID	SEQ ID
			No: 385	No: 386	No: 387
SRF	164	serum response factor (c-fos serum response	SEQ ID	SEQ ID	SEQ ID
		element-binding transcription factor)	No: 391	No: 392	No: 393
SOX9	165	sry (sex determining region y)-box 9	SEQ ID		SEQ ID
		(campomelic dysplasia, autosomal sex-reversal)	No: 394		No: 395
ESTs H21879	183	Homo sapiens plasminogen activator (PLAT)	SEQ ID	SEQ ID
& H21880			No: 433	No: 434

[0120] Tables 8A and 8B hereunder relate to two subpopulations of polynucleotide sequences particularly interesting to distinguish tumors sensitive to anthracycline from tumors insensitive to anthracycline.

TABLE 8A
Overexpressed genes: top 5
Gene
symbol	No	Name	Seq 3	Seq 5	Ref
GATA3	32	gata-binding protein 3	SEQ ID	SEQ ID	SEQ ID
			No: 76	No: 77	No: 78
KIAA1075	136	kiaa1075 protein	SEQ ID	SEQ ID
			No: 322	No: 323
MMP11	145	matrix metalloproteinase 11	SEQ ID		SEQ ID
		(stromelysin 3)	No: 345		No: 346
MYB	149	v-myb avian myeloblastosis viral		SEQ ID	SEQ ID
		oncogene homolog		No: 354	No: 355
GZMA	169	Granzyme a (granzyme 1, cytotoxic t-	SEQ ID		SEQ ID
		lymphocyte-associated serine esterase 3)	No: 402		No: 403

[0121]

TABLE 8B
underexpressed genes: top 5
Gene
symbol	No	Name	Seq 3	Seq 5	Ref
SOX4	11	sry (sex determining region y)-box 4	SEQ ID	SEQ ID	SEQ ID
			No: 22	No: 23	No: 24
IL2RB	40	interleukin 2 receptor, beta	SEQ ID	SEQ ID	SEQ ID
			No: 97	No: 98	No: 99
EGFR	57	epidermal growth factor receptor (avian	SEQ ID	SEQ ID	SEQ ID
		erythroblastic leukemia viral (v-erb-b)	No: 135	No: 136	No: 137
		oncogene homolog)
IL2RG	119	interleukin 2 receptor, gamma (severe	SEQ ID	SEQ ID	SEQ ID
		combined immunodeficiency)	No: 279	No: 280	No: 281
CD3G	174	cd3g antigen, gamma polypeptide (tit3	SEQ ID	SEQ ID	SEQ ID
		complex)	No: 414	No: 415	No:416

[0122] Tables 9, 9A and 9B hereunder relate to subpopulations of polynucleotide sequences particularly interesting in classifying good and poor prognosis primary breast tumors.

TABLE 9
Gene	SET
symbol	No	Name	Seq 3	Seq 5	Ref
CTSB	14	cathepsin b		SEQ ID	SEQ ID
				No: 30	No: 31
VIL2	23	villin 2 (ezrin)	SEQ ID	SEQ ID	SEQ ID
			No: 51	No: 52	No: 53
MUC1	25	mucin 1, transmembrane		SEQ ID	SEQ ID
				No: 57	No: 58
EMR1	27	egf-like module containing, mucin-like,	SEQ ID	SEQ ID	SEQ ID
		hormone receptor-like sequence 1	No: 62	No: 63	No: 64
KIAA0427	28	kiaa0427 gene product	SEQ ID	SEQ ID	SEQ ID
			No: 65	No: 66	No: 67
GATA3	32	gata-binding protein 3	SEQ ID	SEQ ID	SEQ ID
			No: 76	No: 77	No: 78
PRLR	39	prolactin receptor	SEQ ID	SEQ ID	SEQ ID
			No: 94	No: 95	No: 96
GATA3	41	gata-binding protein 3	SEQ ID	SEQ ID	SEQ ID
			No: 100	No: 101	No: 78
TC21	44	oncogene tc21	SEQ ID	SEQ ID	SEQ ID
			No: 106	No: 107	No: 108
BCL2	48	b-cell cll/lymphoma 2	SEQ ID	SEQ ID	SEQ ID
			No: 115	No: 116	No: 117
GATA3	51	gata-binding protein 3	SEQ ID		SEQ ID
			No: 122		No: 78
CRABP2	64	cellular retinoic acid-binding protein 2	SEQ ID	SEQ ID	SEQ ID
			No: 156	No: 157	No: 158
ANG	81	angiogenin, ribonuclease, mase a		SEQ ID	SEQ ID
		family, 5		No: 194	No: 195
EGF	83	epidermal growth factor (beta-	SEQ ID		SEQ ID
		urogastrone)	No: 199		No: 200
THBS1	91	thrombospondin 1	SEQ ID		SEQ ID
			No: 216		No: 217
EDNRA	96	endothelin receptor type a	SEQ ID		SEQ ID
			No: 228		No: 229
SMARCA2	99	swi/snf related, matrix associated, actin	SEQ ID	SEQ ID	SEQ ID
		dependent regulator of chromatin,	No: 235	No: 236	No: 237
		subfamily a, member 2
ABCB1	108	atp-binding cassette, sub-family b	SEQ ID		SEQ ID
		(mdr/tap), member 1	No: 257		No: 258
EGF	110	epidermal growth factor (beta-	SEQ ID		SEQ ID
		urogastrone)	No: 262		No: 200
BIRC4	116	baculoviral iap repeat-containing 4	SEQ ID		SEQ ID
			No: 273		No: 274
DAP3	117	death associated protein 3	SEQ ID		SEQ ID
			No: 275		No: 276
GNRH1	118	gonadotropin-releasing hormone 1		SEQ ID	SEQ ID
		(leutinizing-releasing hormone)		No: 277	No: 278
DAP3	120	death associated protein 3	SEQ ID	SEQ ID	SEQ ID
			No: 282	No: 283	No: 276
EST R97218	126	ests, highly similar to tvhume	SEQ ID	SEQ ID
		hepatocyte growth factor receptor	No: 296	No: 297
		precursor h. sapiens
BCL2	142	b-cell cll/lymphoma 2	SEQ ID	SEQ ID	SEQ ID
			No: 337	No: 338	No: 117
BS69	144	adenovirus 5 ela binding protein	SEQ ID	SEQ ID	SEQ ID
			No: 342	No: 343	No: 344
MYB	149	v-myb avian myeloblastosis viral		SEQ ID	SEQ ID
		oncogene homolog		No: 354	No: 355
CTSB	152	cathepsin b	SEQ ID		SEQ ID
			No: 361		No: 31
MLANA	153	melan-a	SEQ ID	SEQ ID	SEQ ID
			No: 362	No: 363	No: 364
APR-1	154	apr-1 protein	SEQ ID	SEQ ID	SEQ ID
			No: 365	No: 366	No: 367
TC21	157	oncogene tc21	SEQ ID	SEQ ID	SEQ ID
			No: 372	No: 373	No: 108
CDKN3	159	cyclin-dependent kinase inhibitor 3	SEQ ID	SEQ ID	SEQ ID
		(cdk2-associated dual specificity	No: 377	No: 378	No: 379
		phosphatase)
XBP1	162	x-box binding protein 1	SEQ ID	SEQ ID	SEQ ID
			No: 385	No: 386	No: 387
CDH15	166	cadherin 15, m-cadherin (myotubule)	SEQ ID	SEQ ID	SEQ ID
			No: 396	No: 397	No: 398
BCL2	167	b-cell cll/lymphoma 2	SEQ ID	SEQ ID	SEQ ID
			No: 399	No: 400	No: 117
EST W73386	168	ests	SEQ ID
			No: 401
ILF1	171	interleukin enhancer binding factor 1	SEQ ID	SEQ ID	SEQ ID
			No: 406	No: 407	No: 408
ARHGDIA	172	rho gdp dissociation inhibitor (gdi)	SEQ ID	SEQ ID	SEQ ID
		alpha	No: 409	No: 410	No: 411
C4A	173	complement component 4a	SEQ ID		SEQ ID
			No: 412		No: 413
ESR1	176	estrogen receptor 1	SEQ ID	SEQ ID	SEQ ID
			No: 420	No: 421	No: 422
PBX1	177	pre-b-cell leukemia transcription factor	SEQ ID	SEQ ID	SEQ ID
		1	No: 423	No: 424	No: 425
GLI3	178	gli-kruppel family member gli3 (greig	SEQ ID	SEQ ID	SEQ ID
		cephalopolysyndactyly syndrome)	No: 426	No: 427	No: 428
ILF1	179	interleukin enhancer binding factor 1	SEQ ID		SEQ ID
			No: 429		No: 408
ESTs	184	Homo sapiens aminoacylase 1 (ACY1).	SEQ ID	SEQ ID
H24628 &			No: 435	No: 436
H24592
EST H28056	185	Homo sapiens E74-like factor 1 (ets	SEQ ID
		domain transcription factor) (ELF1)	No: 437

[0123]

TABLE 9A
Gene	SET
symbol	No	Name	Seq 3	Seq 5	Ref
VIL2	23	villin 2 (ezrin)	SEQ ID	SEQ ID	SEQ ID
			No: 51	No: 52	No: 53
MUC1	25	mucin 1, transmembrane		SEQ ID	SEQ ID
				No: 57	No: 58
GATA3	32	gata-binding protein 3	SEQ ID	SEQ ID	SEQ ID
			No: 76	No: 77	No: 78
GATA3	41	gata-binding protein 3	SEQ ID	SEQ ID	SEQ ID
			No: 100	No: 101	No: 78
BCL2	48	b-cell cll/lymphoma 2	SEQ ID	SEQ ID	SEQ ID
			No: 115	No: 116	No: 117
GATA3	51	gata-binding protein 3	SEQ ID		SEQ ID
			No: 122		No: 78
CRABP2	64	cellular retinoic acid-binding protein 2	SEQ ID	SEQ ID	SEQ ID
			No: 156	No: 157	No: 158
ANG	81	angiogenin, ribonuclease, rnase a family, 5		SEQ ID	SEQ ID
				No: 194	No: 195
EGF	83	epidermal growth factor (beta-urogastrone)	SEQ ID		SEQ ID
			No: 199		No: 200
THBS1	91	thrombospondin 1	SEQ ID		SEQ ID
			No: 216		No: 217
SMARCA2	99	swi/snf related, matrix associated, actin	SEQ ID	SEQ ID	SEQ ID
		dependent regulator of chromatin, subfamily	No. 235	No.236	No: 237
		a, member 2
EGF	110	epidermal growth factor (beta-urogastrone)	SEQ ID		SEQ ID
			No: 262		No: 200
BIRC4	116	baculoviral iap repeat-containing 4	SEQ ID		SEQ ID
			No: 273		No: 274
BCL2	142	b-cell cll/lymphoma 2	SEQ ID	SEQ ID	SEQ ID
			No: 337	No: 338	No: 117
BS69	144	adenovirus 5 ela binding protein	SEQ ID	SEQ ID	SEQ ID
			No: 342	No: 343	No: 344
MYB	149	v-myb avian myeloblastosis viral oncogene		SEQ ID	SEQ ID
		homolog		No: 354	No: 355
XBP1	162	x-box binding protein 1	SEQ ID	SEQ ID	SEQ ID
			No: 385	No: 386	No: 387
BCL2	167	b-cell cll/lymphoma 2	SEQ ID	SEQ ID	SEQ ID
			No: 399	No: 400	No: 117
ILF1	171	interleukin enhancer binding factor 1	SEQ ID	SEQ ID	SEQ ID
			No: 406	No: 407	No: 408
ARHGDIA	172	rho gdp dissociation inhibitor (gdi) alpha	SEQ ID	SEQ ID	SEQ ID
			No: 409	No: 410	No: 411
C4A	173	complement component 4a	SEQ ID		SEQ ID
			No: 412		No: 413
ESR1	176	estrogen receptor 1	SEQ ID	SEQ ID	SEQ ID
			No: 420	No: 421	No: 422
PBX1	177	pre-b-cell leukemia transcription factor 1	SEQ ID	SEQ ID	SEQ ID
			No: 423	No: 424	No: 425
GLI3	178	gli-kruppel family member gli3 (greig	SEQ ID	SEQ ID	SEQ ID
		cephalopolysyndactyly syndrome)	No: 426	No: 427	No: 428
ILF1	179	interleukin enhancer binding factor 1	SEQ ID		SEQ ID
			No: 429		No: 408
ESTs	184	Homo sapiens aminoacylase 1 (ACY1).	SEQ ID	SEQ ID
H24628 &			No: 435	No: 436
H24592
EST	185	Homo sapiens E74-like factor 1 (ets domain	SEQ ID
H28056		transcription factor) (ELF1)	No: 437

[0124]

TABLE 9B
Gene
symbol	SET No	Name	Seq 3	Seq 5	Ref
CTSB	14	cathepsin b		SEQ ID	SEQ ID
				No: 30	No: 31
EMR1	27	egf-like module containing, mucin-like,	SEQ ID	SEQ ID	SEQ ID
		hormone receptor-like sequence 1	No: 62	No: 63	No: 64
KIAA0427	28	kiaa0427 gene product	SEQ ID	SEQ ID	SEQ ID
			No: 65	No: 66	No: 67
PRLR	39	prolactin receptor	SEQ ID	SEQ ID	SEQ ID
			No: 94	No: 95	No: 96
TC21	44	oncogene tc21	SEQ ID	SEQ ID	SEQ ID
			No: 106	No: 107	No: 108
EDNRA	96	endothelin receptor type a	SEQ ID		SEQ ID
			No: 228		No: 229
ABCB1	108	atp-binding cassette, sub-family b	SEQ ID		SEQ ID
		(mdr/tap), member 1	No: 257		No: 258
DAP3	117	death associated protein 3	SEQ ID		SEQ ID
			No: 275		No: 276
GNRH1	118	gonadotropin-releasing hormone 1		SEQ ID	SEQ ID
		(leutinizing-releasing hormone)		No: 277	No: 278
DAP3	120	death associated protein 3	SEQ ID	SEQ ID	SEQ ID
			No: 282	No: 283	No: 276
EST R97218	126	ests, highly similar to tvhume hepatocyte	SEQ ID	SEQ ID
		growth factor receptor precursor	No: 296	No: 297
		h. sapiens
CTSB	152	cathepsin b	SEQ ID		SEQ ID
			No: 361		No: 31
MLANA	153	melan-a	SEQ ID	SEQ ID	SEQ ID
			No: 362	No: 363	No: 364
APR-1	154	apr-1 protein	SEQ ID	SEQ ID	SEQ ID
			No: 365	No: 366	No: 367
TC21	157	oncogene tc21	SEQ ID	SEQ ID	SEQ ID
			No: 372	No: 373	No: 108
CDKN3	159	cyclin-dependent kinase inhibitor 3 (cdk2-	SEQ ID	SEQ ID	SEQ ID
		associated dual specificity phosphatase)	No: 377	No: 378	No: 379
CDH15	166	cadherin 15, m-cadherin (myotubule)	SEQ ID	SEQ ID	SEQ ID
			No: 396	No: 397	No: 398
EST	168	ests	SEQ ID
W73386			No: 401