Anticancer Target Combinations: A Network-Informed Signaling-Based Approach to Discovery

Authors

• BR Yavuz
• H Jang
• R Nussinov

Abstract

Single drug discovery has seen dramatic innovations and successes; combinatorial strategies have been lagging. However, it is commonly accepted that sequential single therapies are time-limited by resistance. To select drug combinations the oncologist requires knowledge of the optimal combination of proteins to co-target. The number of possible target combinations is vast. Currently, combinations that the oncologist considers are obtained primarily through empirical observations and clinical praxis. In an innovative first, here we offer a concept-based stratified pipeline aimed at selecting proteins to co-target. Our concept considers which signaling pathway and protein companions to select to mitigate the patient's expected drug resistance. Our method is protein-protein interaction network-informed, harnesses advanced network concepts and metrics, and our recently compiled co-existing mutations with tissue-specific prevalence. Identified 'co-targeting candidates' bridge subnetwork nodes. Subsets include receptor tyrosine kinases (RTKs) and transcription factors (TFs). Co-administered drug combinations may inhibit signaling through the same, parallel, or compensatory pathways. Applied to patient-derived breast and colorectal ESR1-PIK3CA and BRAF-PIK3CA gene-pairs’ subnetworks enhanced responsiveness. In breast cancer, targeting the ESR1-PIK3CA specific subnetwork with an alpelisib-LJM716 combination resulted in significant tumor regression. In colorectal cancer, targeting the BRAF-PIK3CA specific subnetwork with alpelisib, cetuximab, and encorafenib effectively suppressed tumor growth. Targeting the gene-pair specific subnetworks showed promising results that are computationally validated by patient-derived xenografts.

Scientific Focus Area: Systems Biology

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