Modeling the Impact of Somatic Alterations in Human Cancers

Pan-cancer analyses of somatic mutations and copy number aberrations have confirmed that the same genes or pathways are often altered across multiple tumor types.  There is great interest in deploying targeted therapies in a pan-cancer manner, matching pathway-targeted drugs to the mutational profile of the tumor regardless of cancer type.  However, ‘actionable mutations’ in different tumor types interact with distinct cancer-specific gene regulatory programs and signaling networks and occur against different genetic backgrounds of co-occurring alterations.  To better model the context-dependent role of somatic alterations, we applied a novel computational strategy for integrating parallel phosphoproteomic and mRNA sequencing data across 12 TCGA tumor data sets, linking dysregulation of upstream signaling pathways with altered transcriptional response.  We then developed a statistical approach to interpret the impact of mutations and copy number events in terms of functional outcomes such as altered signaling and transcription factor (TF) activity.  Our analysis revealed both known and novel transcriptional regulators downstream of oncogenic pathways and identified potential synergies between co-occurring mutations.  These results have implications for the applying targeted drugs across cancer contexts and potentially for the design of combination therapies.