Abstract 1196: Elucidating compound mechanism of action and polypharmacology with a large-scale perturbational profile compendium

Abstract

Abstract Traditional approaches to elucidate small molecule mechanism of action (MoA) are usually based on affinity binding assays. These often fail to detect lower affinity binding targets and secondary effectors that are usually highly tissue context-dependent. As such, these can vary dramatically across different cancer types. To address this challenge and facilitate more quantitative approaches to targeted cancer therapy, a more comprehensive picture of drug MoA, including poly-pharmacology and toxicity effects mediated by the full range of its high-affinity (primary), as well as its context-specific lower-affinity (secondary) and downstream effector (tertiary) targets. For this purpose, we generated transcriptional genome-wide RNA-Seq profiles of cancer cell lines, following compounds perturbation with FDA approved and late-stage experimental oncology drugs using a fully automated and highly efficient PLATE-Seq technology1. Compared to prior approaches, this provides (a) genome-wide readouts, rather than readouts limited to a small set of landmark genes, (b) in cell lines that were specifically selected as the highest-fidelity models for published human cancer cohorts, using the OncoMatch algorithm2, and (c) for a drug repertoire that is clinically relevant. In addition, to avoid confounding effects from drug stress/death response pathway activation—a common issue in previous studies—drugs were titrated at their maximum sub-lethal concentration (48-hr IC20) using 10-point dose response curves in each cell line. The ~20,000 molecular profiles generated by these assays were analyzed using the VIPER algorithm3 to reproducibly assess the effect of each drug on the activity of ~6,500 regulatory and signaling proteins compared to vehicle control. The resulting PanACEA (Pancancer Activity-based Compound Efficacy Analysis), a database comprising drug perturbation profiles for 23 different cancer cell lines and > 700 oncology drugs, representing the largest resources of functionally annotated, genome-wide perturbational profiles for clinically relevant drugs. Systematic analysis identified critical drug mechanism of action and drugs capable of reproducibly targeting undruggable proteins, such as MYC or KRAS. We also leveraged graph theory-based network approach to generate a conserved drug functional network and drug functional networks in different cancer contexts, which pinpoint critical novel poly-pharmacology effects. Citation Format: Lucas Zhongming Hu, Eugene Douglass, Ron Realubit, Charles Karan, Mariano Alvarez, Andrea Califano. Elucidating compound mechanism of action and polypharmacology with a large-scale perturbational profile compendium [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 1196.