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

High throughput biology enables the measurements of relative concentrations of thousands of biomolecules from e.g. tissue samples. The process leaves the investigator with the problem of how to best interpret the potentially large numbers of differences between samples. Many activities in a cell depend on ordered reactions involving multiple biomolecules, often referred to as pathways. It hence makes sense to study differences between samples in terms of altered pathway activity, using so-called pathway analysis. Traditional pathway analysis gives significance to differences in the pathway components’ concentrations between sample groups, however, less frequently used methods for estimating individual samples’ pathway activities have been suggested. Here we demonstrate that such a method can be used for pathway-based survival analysis. 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. Furthermore, we illustrate how one can visualize the often interdependent measures over hierarchical pathway databases using sunburst plots.Author summary: Most of the important cellular functions are carried out by not just individual biomolecules but are rather dependent on the concerted reactions involving large sets of biomolecules, which are referred to as pathways. Yet, measurement techniques naturally have to measure the abundances of each such molecule individually. To assess the difference in functional activity between samples one often uses statistical techniques to integrate abundances into pathway activity. Here we implemented a method for investigating which such pathway activities that are prognostic for patients’ survival when analyzing breast cancers. We showed that the pathway activities are more prognostic of a patient’s survival time than prognoses made directly from the measured concentrations of individual molecules. We also show which such pathway activities that are not just active due to the overall increased proliferation in malign cancers. We also illustrate how pathway activities can be efficiently and interactively visualized using so-called sunburst plots.