A generic framework for the elicitation of stable and reliable gene expression signatures

In the recent years microarray technologies have gained a lot of popularity for their ability to quickly measure the expression of thousands of genes and provide valuable information for linking complex diseases such as cancer to their genetic underpinnings. Nevertheless the large number of parameters to be estimated in relation to the small number of available samples gives rise to an “ill posed” problem where the possible solution is not stable under slight changes either in the dataset or the initial conditions and starting points. In this work we present a generic classification framework that works in an iterative manner and converges to a stable solution that combines good accuracy with biologically meaningful feature selection. The methodology is orthogonal to the specific classification algorithm used. We compare some of the most widely used classifiers based on their average discrimination power and the size of the derived gene signature. According to our proposed model named Stable Bootstrap Validation (SBV), a unified `77 common-gene signature' was selected, which is closely associated with several aspects of breast tumorigenesis and progression, as well as patient-specific molecular and clinical characteristics.