Abstract 236: Genomic loss in cancers enable discovery of metabolic targets for precision cancer therapy via multiobjective flux analysis and machine learning

Abstract

Abstract Large-scale chromosomal alterations, particularly chromosomal deletions in cancer genomes confer functional advantages to cancer cells via the loss of tumor suppressor genes (TSGs). However, due to the nature of these focal and arm-level deletions, essential house-keeping genes in the neighborhood of TSGs are potentially lost. We explore the emergence of metabolic adaptations and vulnerabilities that arise due to the collateral loss of essential metabolic genes. In our previous work, we showed that genomic loss in the locus containing SMAD4 and ME2 in pancreatic ductal adenocarcinomas forces these cells to rely on ME3 to compensate for the collateral loss of ME2; thereby revealing a highly selective metabolic target in these cells. Cancer cells can not only exploit such genetic redundancies but also rely on redundancies built into their complex metabolic network to compensate for the loss of metabolic function. Importantly, there is an unexplored landscape of these genomic loss events beyond well-characterized TSGs. To address these challenges, we have developed a platform to identify patient-specific metabolic vulnerabilities emerging due to distinct patterns of genomic loss events across tumors. Our platform presents opportunities for precision-based therapeutic intervention by targeting metabolic vulnerabilities in cancer patients. It uses genomic and clinical data available in cancer patient databases to obtain candidate metabolic genes that are lost to genomic deletions in an unbiased manner. To delineate metabolic redundances and tackle the complexity of genome-scale metabolic models, we employ an innovative multi-objective metabolic flux analysis approach. The utility of this platform is demonstrated via the discovery of a novel metabolic target in a cohort of ovarian cancer patients. The predicted collateral lethal target is validated in vitro using RNA interference and small-molecule inhibitors. Furthermore, we verify the metabolic mechanism of vulnerability predicted by the algorithm using deuterium tracing experiments. The target is also validated in vivo with mice containing ovarian tumors derived from cancer cells with and without the genomic deletion. Surprisingly, the collateral lethal metabolic target was also found to exist in a subset of aggressive endometrial cancers. Finally, we developed a multi-layer machine learning model to predict occurrence of the particular genomic deletion in ovarian and endometrial cancer patients with minimal molecular information to remove the need for whole-genome sequencing data. The model was trained and tested using the publicly-available molecular data from TCGA and AACR GENIE datasets. Citation Format: Abhinav Achreja, Tao Yu, Anjali Mittal, Srinadh Choppara, Noah Meurs, Olamide Animasahun, Jin Heon Jeon, Aradhana Mohan, Anusha Jayaraman, Reva Kulkarni, Justin Reinhold, Michele Cusato, Analisa Difeo, Xiongbin Lu, Deepak Nagrath. Genomic loss in cancers enable discovery of metabolic targets for precision cancer therapy via multiobjective flux analysis and machine learning [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 236.