GSE107042
OncoSig: a new algorithm for the accurate and systematic de novo prediction of Oncoprotein-centric Signaling networks
We introduce a new algorithm, OncoSig, to address the deficiencies of canonical pathways through the accurate, context-specific prediction of proteins representing both known and novel functional members of Oncoprotein-centric Signaling networks. OncoSig performs systematic, de novo elucidation of Protein-Centric molecular interaction maps (PC-maps), which include upstream modulators, cognate binding partners, and downstream effectors. The reconstructed KRAS PC-Map in lung adenocarcinoma (LUAD) demonstrates the recapitulation of known biology and, critically, the discovery of novel KRAS effectors that elicit synthetic lethality in LUAD organoid cultures. To illustrate the generalizability of OncoSig, the methodology is applied to elucidate the PC-maps of ten recurrently mutated oncoproteins and oncopathways in LUAD as well as KRAS PC-Maps across three tumor contexts. OncoSig thus reveals tumorigenic architecture to an unprecedented degree and provides a unique mechanistic perspective on how the flow of information in cells is dysregulated in support of tumor homeostasis. To validate predicted KRAS effectors in primary tumor organoid cultures, this study includes a pooled RNAi screening approach (shRNA-seq) in primary lung tumors from a mouse model driven by mutant Kras and p53 loss. Overall design: We analyze shRNA abundance at two different timepoints of 27 sample pools using 3-6 biological replicates for genes predicted to play a functional role in KRAS driven tumorigenesis (2 sample pools x 6 replicates) and compare these results to over 500 genes not predicted to be important for KRAS function (25 sample pools x 3 replicates).