Predictive gene regulatory models for precision medicine
The process of matching pathway-targeted drugs to tumor mutational profile regardless of cancer type is critical in the development of targeted therapies. However, actionable mutations interact with distinct gene regulatory programs and signaling networks and can occur against different tumor-specific genetic backgrounds. To better model the context-dependent role of somatic alterations, we developed a novel computational strategy to integrate parallel phosphoproteomic and mRNA sequencing data across the TCGA, linking dysregulation of upstream signaling pathways with altered transcriptional response. We then developed a statistical approach to interpret the impact of somatic alterations in terms of functional outcomes, such as altered signaling and transcription factor activity. Our analysis predicted distinct dysregulated transcriptional regulators downstream of similar somatic alterations in different cancers. These results have implications for the use of targeted drugs and potentially for the design of combination therapies across cancer types.
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