Abstract 3300: Network analysis of the human protein-protein interactome: Tumorigenic signaling mechanisms

Abstract

Abstract The dysregulated signaling responsible for tumor cell state implementation and maintenance has been shown to be mediated by the concerted action of master regulator (MR) proteins. Highly connected MR protein modules serve as “tumor checkpoints” by translating upstream genetic alterations into aberrant protein activity that drives pathophysiological cellular phenotypes. Direct pathways for information flow among MR proteins, and between individual MR proteins and their upstream modulators and downstream effectors, are largely unknown. The Prediction of Protein-Protein Interactions (PrePPI) database contains 1.4M predictions for ~85% of the human proteome; of these, ~300K are predicted to be physical, i.e. direct one-to-one, interactions.We apply network analysis concepts, as implemented in the R package igraph, to the PrePPI interactome to predict structural mechanisms underlying tumorigenic signal transduction. Shortest path and random walk with restart algorithms elucidate 1) physical interactions between MR proteins, 2) physical interactions that connect MR proteins that do not directly bind to each other, 3) upstream signaling proteins with recurrent and patient-specific mutations that physically interact with MR proteins, and 4) downstream cofactors and transcription factors that may be involved in effecting a dysregulated phenotype.Our analysis predicts that physical interactions fully connect BACH2, BCL6, IRF8, and SPIB, established germinal center MR proteins. 1) BACH2 and BCL6 are predicted to interact directly through their BTB/POZ domains; BTB domains are known to mediate heterodimeric protein-protein interactions. 2) Additional proteins, including IBTK, ANFY1, PAX7, ZEB1, GABPB1/B2, connect SPIB to BACH2 and to BCL6, in some cases through ankyrin repeats, which are structural protein-protein interaction motifs. Several of the PrePPI-predicted proteins, e.g. IBTK, ZEB1, GABP, have previously been implicated in B-cell biology. The possible implications of our analyses for a wide range of MR proteins modules will be presented.The computational prediction of physical protein-protein interactions within the regulatory architecture of a tumor cell is a powerful approach to discovering novel tumorigenic signaling mechanisms, formulating specific experimentally testable hypotheses of function, including compound mechanisms of action, and prioritizing novel drug targets. Citation Format: Diana Murray, Kamrun N. Begum, Andrea Califano, Barry Honig. Network analysis of the human protein-protein interactome: Tumorigenic signaling mechanisms [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 3300.