Abstract PR004: Computational discovery of paralog dependencies drives target identification in lung cancer

Abstract Lung cancer is the leading cause of cancer-related death in the United States, and worldwide, with the 5-year survival rate of non-small cell lung cancer at 24%. Treatment options are limited, and even in cancers that harbor druggable biomarkers, resistance is near-universal. Identifying novel treatment strategies is essential to improve the outcomes of patients with lung cancer and provide secondary strategies for those who acquire resistance to first-line therapeutics. Identifying targeted therapies based on the genotype or expression pattern of cancers is an ongoing endeavor, and has been fruitful in the discovery of identifying cancer vulnerabilities. In lung cancer, treatment of patients that harbor activating mutations in EGFR with specific EGFR inhibitors has improved progression-free survival rates and extended the lifespan of patients diagnosed with this aggressive cancer, demonstrating the utility of targeted therapies based genetic-based vulnerabilities, or synthetic lethal interactions. A rich source of synthetic lethal genetic interactions are paralogous genes. Paralogs result from gene duplication events through evolutionary history, and many paralogs share both sequence homology and functional redundancy. Co-targeting of some paralogous genes induces a fitness defect that is synergistic compared to individual paralog loss alone. Nearly 70% of genes in the human genome are considered paralogs, and until recent efforts from the Berger lab and others, this buffering effect has caused them to be under-represented in lethality studies. Identifying frequently-inactivated paralog genes in lung cancer will provide a platform to identify new vulnerabilities in lung cancer and other cancer types that share mutational backgrounds. To identify patterns of mutation and expression of paralog genes in lung cancer, we have accessed publicly available data sets for both cell line and patient tumor samples. Using TCGA and CCLE data sources, and a set of 1030 paralog pairs identified previously by the Berger lab as potential drug targets, we have identified paralog genes that harbor high mutational frequencies in lung cancer. Using these early candidates, we next asked whether genetic inactivation or transcriptional repression of one paralog influences the essentiality of its paired gene using CERES scores generated by the Cancer Dependency Map. Preliminary results from this work has identified several paralog pairs with distinct dependency profiles in cell lines for which expression of one paralog is lost, including paralog pairs MAGOH/MAGOHB and TLK1/TLK2. Further work to characterize these dependencies in relevant cell line models is ongoing. Additionally, the druggability of these targets will be tested using targeted degradation using the dTAG system. This work will uncover a new set of paralog synthetic lethality targets, and further validate the utility of targeting known synthetic lethal paralogs for the treatment of highly lethal lung cancer. Citation Format: Alice H. Berger, Siobhan O'Brien. Computational discovery of paralog dependencies drives target identification in lung cancer abstract. In: Proceedings of the AACR Special Conference in Cancer Research: Expanding and Translating Cancer Synthetic Vulnerabilities; 2024 Jun 10-13; Montreal, Quebec, Canada. Philadelphia (PA): AACR; Mol Cancer Ther 2024;23(6 Suppl):Abstract nr PR004.