Survival analysis of pathway activity as a prognostic determinant in breast cancer

Most activities in a cell are dependent on sets of biomolecules, often referred to as pathways. Pathways are useful to study as their activities are frequently more directly related to the behavior of the cell than their components. Traditional pathway analysis gives significance to differences in the pathway components’ concentrations between sample groups. Here we instead advocate a singular value decomposition-based method for estimating individual samples’ pathway activities that allow us to investigate more advanced statistical models. Specifically, we investigate the pathway activities’ association with patients’ survival time based on the transcription profiles of the METABRIC dataset. Our implementation shows that pathway activities are better prognostic markers for survival time in METABRIC than the individual transcripts. We also demonstrate that we can regress out the effect of individual pathways on other pathways, which allows us to estimate the other pathways’ residual pathway activity on survival.