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: A method of obtaining cut-off expression values should be selected so as to maximise the separation of the respective survival curves of the two groups of patients. Pairs of genes are statistically significant genes are generated by generating a plurality of models, each of which represents a way of partitioning a set of subjects based on the optimal cut-off expression values of the pair of genes. Those gene pairs are identified for which one of the models has a high prognostic significance. Novel survival significant gene sets forming functional modules which could be used to develop specific prognostic and predictive tests are derived.

Abstract: A method of obtaining cut-off expression values should be selected so as to maximise the separation of the respective survival curves of the two groups of patients. Pairs of genes are statistically significant genes are generated by generating a plurality of models, each of which represents a way of partitioning a set of subjects based on the optimal cut-off expression values of the pair of genes. Those gene pairs are identified for which one of the models has a high prognostic significance. Novel survival significant gene sets forming functional modules which could be used to develop specific prognostic and predictive tests are derived.

Abstract: A survival model, for each of one or more pairs of genes, includes a function of a corresponding measure of the ratio of expression levels of the pairs of genes. For each pair of genes, there is a corresponding a cut-off value, such that patients are classified according to whether the corresponding measure is above or below the cut-off value. It is proposed (in an algorithm called “DDgR”) that the cut-off value should be selected so as to maximise the separation of the respective survival curves of the two groups of patients. It is further proposed that, for each of a number of genes or gene pairs, a selection is made from multiple survival models. The selection is according to whether a proportionality assumption is obeyed and/or according to a measure of data fit, such as the Baysian Information Criterion (BIC). Specific gene pairs identified by the methods are named.