Combinatorial Therapeutic Discovery Using Perturbational Gene-Expression Signatures

Abstract

Cancer is a complex disease that relies on both oncogenic mutations and non-mutated genes for survival, thereafter coined as oncogene and non-oncogene addictions. The need for more effective combination therapies to overcome drug resistance in oncology has been increasingly recognized, but the identification of potentially synergistic drugs at scale remains challenging. Here we propose a geneexpression- based approach, which uses the recurrent perturbation–transcript regulatory relationships inferred from a large compendium of chemical and genetic perturbation experiments across multiple cell lines, to engender a testable hypothesis for combination therapies. These transcript-level recurrences were distinct from known compound–protein target counterparts, reproducible in external datasets, and correlated with small-molecule sensitivity. Using this new approach, we predicted synergistic drug pairs for cancer, including most frequently the combinations of a topoisomerase inhibitor and an mTOR or PI3K inhibitor. We also experimentally confirmed the synergistic effects of one combination of CD-437 (a retinoid) and sirolimus (an mTOR inhibitor) and the other combination of narciclasine (a protein synthesis inhibitor) and purvalanol A (a CDK inhibitor) in two breast and two lung cancer cell lines. Our results corroborate a gene-expression-based strategy for combinatorial drug screening as a way to target non-mutated genes in complex diseases.