Prioritizing mutations associated with smoking as a variable in lung cancer precision medicine with immunotherapies

Abstract Background: In 2022, 230,000 new lung cancer cases will be diagnosed in the United States. The treatment regimen for non-small cell lung cancer (NSCLC) has drastically changed owing to the superior anti-cancer effects of immunotherapies. Immune checkpoint inhibitors (ICI) and chemo-immunotherapy (chemo-ICI) are the first-line treatments for NSCLC. Tumor Mutation Burden (TMB) and PD-L1 expression in tumor cells are potential biomarkers in predicting a patient’s survival and response to ICI. However, emerging data have shown that TMB and PD-L1 may no longer be an adequate biomarkers in predicting a patient’s response to ICI or chemo-ICI. We hypothesize that by using tumor-sequencing data and taking into effect a patient’s smoking status, we can identify biomarkers that predict survival to either ICI or chemo-ICI. Methods: To identify biomarkers, we collected genomic sequencing data and comprehensive clinical characteristics on 424 NSCLC patients who received ICI or chemo-ICI treatment at Atrium Health Wake Forest Baptist. Cox-proportional hazard regression models were fit to identify mutations that were “beneficial” (HR < 1) or “detrimental” (HR > 1) for patients on different treatment regimens, followed by the generation of mutation composite scores (MCS) for each treatment. Co-occurrence analysis was performed to identify novel co-occurring and mutually exclusive mutations in each treatment and smoking group by mutation interaction analysis. Results: We identify beneficial and harmful mutations in patients that received ICI or chemo-ICI treatment. We also identified unique biomarkers based on smoking statues. We then created an MCS for each smoking statues group and treatment type to assist personalize treatment. Future directions: We will validate these results in other institute cohorts and add other clinical characteristics to personalize treatment based on MCS for an individual patient. Citation Format: Margaret Rose Smith, Yuezhu Wang, Ralph D’Agostino, Yin Lin, Jimmy Ruiz, Thomas Lycan, Umit Topaloglu, Mohammed Abdulhaleem, Michael Chan, Fei Xing. Prioritizing mutations associated with smoking as a variable in lung cancer precision medicine with immunotherapies [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 952.