GSE161010
A Machine-learning approach to define and predict the therapeutic landscape of pan-cancer Hippo pathway dependency (RNA-seq)
One challenge of cancer precision medicine is the heterogeneity of genetic and non-genetic alterations that result in dysfunctional molecular pathways. As an emerging drug discovery effort, dysregulation in Hippo pathway signaling is known to drive oncogenesis across numerous cancer types but lacks recurrent mutation(s) that are often found in other canonical signaling pathways. Here, we use first principles approach to develop a machine-learning framework to identify a robust, lineage-independent gene expression signature to quantify Hippo pathway dependency in cancers. Through integrating data from multi-omics platforms, this data-driven approach has enabled identifying a proposed combination with MAPK inhibition for direct targeting of Hippo pathway dependent cancers for which we then elucidate the underlying molecular mechanism. The results underscore how a multifaceted approach, computational models combined with laboratory efforts, can accelerate precision medicine efforts toward co-targeting Hippo and MAPK pathways, an approach that can be generalized to other key cancer signaling pathways to define therapeutic strategies. Overall design: RNA sequenced from siNTC and siYAP/TAZ treatment of 7 cell lines