Abstract 4918: Cancer mutations converge on a constellation of molecular assemblies to predict resistance to replication stress agents

Abstract Rapid proliferation is a hallmark of tumor cells, associated with sensitivity to therapeutics that cause DNA replication stress (RS). Many tumors exhibit drug resistance, however, via molecular pathways that are incompletely understood. Here, we develop an ensemble of predictive models that elucidate how cancer mutations impact the response to common RS-inducing (RSi) agents. The models implement recent advances in deep learning to facilitate multi-drug prediction and mechanistic interpretation. Instead of associating individual genetic alterations with RSi drug responses directly, the strategy is to project these alterations on a map of protein complexes and larger molecular assemblies associated with cancer. Initial studies in tumor cells identify 41 molecular assemblies that integrate alterations in hundreds of genes for accurate drug response prediction. These cover roles in transcription, repair, cell-cycle checkpoints, and growth signaling, of which 30 are shown by loss-of-function genetic screens to regulate drug sensitivity or replication restart. The model translates to cisplatin-treated cervical cancer patients, highlighting an RTK-JAK-STAT assembly governing resistance. This study defines a compendium of mechanisms by which mutations affect therapeutic responses, with implications for drug selection and combination. Citation Format: Xiaoyu Zhao, Akshat Singhal, SungJoon Park, JungHo Kong, Robin Bachelder, Trey Ideker. Cancer mutations converge on a constellation of molecular assemblies to predict resistance to replication stress agents abstract. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts) ; 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84 (6Suppl): Abstract nr 4918.