Extracting survival-relevant subnetworks from multi-scale omics data with KeyPathwayMiner

Biological interaction databases can be exploited by pathway-level enrichment methods for downstream analyses in biological and biomedical settings. Classical enrichment methods rely on predefined lists of pathways, biasing the search towards known pathways and risking to overlook unknown, yet important functional modules. To overcome this limitation, so-called de novo network enrichment approaches extract novel pathways from large, molecular interaction networks given molecular profiles of patients, e.g. gene expression, promoter methylation, etc. Network enrichment of molecular profiling data is challenging due to noise and incompleteness of both the data them- selves and the networks. KeyPathwayMiner (KPM) jointly considers multi-scale molecular profiles to extract subnet- works enriched for de-regulated genes, e.g. differentially expressed genes. KPM is available as a feature-rich, user-friend- ly Cytoscape app, standalone software, or web service for de novo network enrichment (http://www.keypathwayminer. compbio.sdu.dk/). Clinical cancer research often focuses on patient survival times. Thus, we developed a new strategy to identify sub- networks most significantly associated with differences in survival. Our approach is based on the Network of Muta- tions Associated with Survival (NoMAS) algorithm that extracts subnetworks enriched in mutations. NoMAS exploits colour-coding to identify candidate subnetworks that are then evaluated with a log-rank test. We adapted NoMAS for multi-scale omics data by introducing a k-means clustering step to split patients into two groups using the candidate subnetworks molecular profile. Next, we apply a log-rank test to assess the significance of the difference in survival times between the two groups. Our overall goal is to find subnetworks significantly associated with survival time, thus creating multi-scale models that connect molecular changes, e.g. on the level of gene expression, to changes in the time-scale of patient survival. The identified subnetworks can be expected to represent important disease mechanisms, making them interesting candidates for further investigation. We thus expect that extending KPM to survival data will make de novo network enrichment considerably more attractive as a systems medicine approach.