Abstract 4306: Identifying genetic dependencies of replication stress using interpretable artificial intelligence

Abstract

Abstract Rapid proliferation is a hallmark of tumor cells, associated with sensitivity to chemicals that cause DNA replication stress (RS). Due to sustained proliferative signaling and/or defective DNA repair, cancer cells undergo persistent RS making them strongly dependent on the replication stress response (RSR). A consequence of this dependency is that RS becomes an exploitable therapeutic vulnerability in cancer treatment. Classical RS drugs work predominantly by interfering with DNA replication in dividing cells. Recently, an increasing number of drugs have been designed to specifically target RSR proteins. However, molecular pathways responsible for drug response are incompletely understood. Here we build an interpretable deep-learning model aimed at understanding mechanisms of susceptibility and resistance to replicative stress. Instead of associating genetic alterations with drug responses directly, our approach is to project individual mutations on a map of protein complexes and larger molecular assemblies with prior evidence for involvement in cancer. This approach is prompted and supported by the concept that cancer is a network-based disease arising from the action of hallmark cancer pathways. Through systematic interpretation, the model identifies 37 complexes that integrate rare alterations in hundreds of genes for accurate response prediction. The complexes, which cover roles in transcription, DNA repair, cell-cycle checkpoint, and immunity, are further investigated by directed genetic perturbations, validating 24 for which RS effects are phenocopied by CRISPR guide RNAs. For complexes with poorly characterized functions, further insights are obtained via their profiles of in-silico activation across RS agents. This study creates a library of system-level genetic vulnerabilities governing replication stress, with implications for drug selection and combination. Citation Format: Xiaoyu Zhao, Akshat Singhal, Trey Ideker. Identifying genetic dependencies of replication stress using interpretable artificial intelligence. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 4306.