Abstract: The present invention provides method(s) for measuring gene copy number (CN) of a given locus of interest, comprising 1) obtaining the CN value of the locus of interest, 2) obtaining the CN value or values of one or more CN-invariant locus reference(s) (CNILR) in the biological sample, where the CNILR is a locus which is locally CN-invariant or a locus with a minimal coefficient of variation, 3) obtaining the CN value or values of one or more CN-invariant and survival insignificant locus reference reference(s) (CNISILR) determined based on survival prediction analysis for a specific subgroup; and 4) normalizing the CN value of the locus of interest by the CN values of one or more CNISILRs if defined, otherwise normalizing the CN value of the locus of interest by the CN values of said one or more CNILRs. In one embodiment, the CNILRs or CNISILRs is one or more loci from the group consisting of XRCC5, AUTS2, EIF5, PARN, YEATS2 and FHL2.

Abstract: There are no reliable clinical bio-markers of survival prognosis, patient's risk stratification and treatment prediction for epithelial ovarian cancers(EOC). The most common type of the human EOC is a high grade serous EOC. This cancer is characterized with one of the lowest survival rates compared to other cancers. The present invention relates to an method for a prognosis of survival of a subject diagnosed with EOC, the method comprising determining in a sample of the subject gene expression level of at least one gene in the list of Evi1 pathway genes; and/or copy number of at least one gene in the MECOM locus; wherein the level against at least one expression threshold value will define the risk group of the subject and/or a risk of the disease progression after surgery treatment, and/or an effectiveness of post-surgery chemotherapy.

Abstract: The present invention relates to a method of identification of clinically and genetically distinct sub-groups of patients subject to a medical condition, particularly breast, lung, and colon cancer patients using a composition of respective gene expression values for certain gene pairs. Sense-antisense gene pairs (SAGPs) which are relevant for a medical condition and the disease prognosis are used by the method to generate statistical models based on the expression values of the SAGPs. SAGPs for which the statistical models are found to have high value in prognosis of the variation of medical condition and the diseases are selected and integrated in the prognostic signature including specified parameters (e.g. cut-off values) of the prognostic model.

Abstract: We describe a method of assigning a grade to a breast tumour, which grade is indicative of the aggressiveness of the tumour, the method comprising detecting the expression of a gene selected from the genes set out in Table D0 (6g-TAGs) or Table D1 (SWS Classifier 0). We also describe methods of treating patients having a high aggressiveness tumour or a low aggressiveness tumour, by identifying the aggressiveness tumour by obtaining, from a sample of a histological Grade 2 tumour isolated from the patient, gene expression data of BRRN1, AURKA, MELK, PRR11, CENPW and E2F1; assigning a grade to the tumour by applying a class prediction algorithm to the gene expression data, wherein a Grade 3 tumour is classified as a high aggressiveness tumour and a Grade 1 tumour is classified as a low aggressiveness tumour; and specifically treating the patient accordingly.