Abstract 126: Extreme selection constrains ability of EGFR-driven lung adenocarcinoma to diversify in response to erlotinib therapy

Abstract

Abstract The acquisition of resistance by tumors to targeted molecular therapies remains one of the greatest challenges in precision medicine. Evolutionary techniques, such as phylogenetic analysis and the thoughtful calculation of selection intensities, are well suited to offer guiding insights in describing and, ultimately, overcoming therapeutic resistance. Here, we quantify the relative importance of mutations occurring during EGFR targeted therapy on a cohort of Lung Adenocarcinoma (LUAD) patients using clinical records to calibrate phylogenetic chronograms to identify variants occurring on branches of the tumor phylogeny coinciding with targeted EGFR therapy and calculate the evolutionarily-derived cancer effect size for those variants. We then compare these results to other cancer contexts to reveal the strikingly high effect size of the EGFR T790M mutation. To probe and quantify the constraints therapeutically driven selection pressure places on the cohort, we predict the neoantigen composition for each patient for both extant and ancestral tumor states, using a general allele frequency selection model to calculate selection on cancer variants constituting putative neoantigens. We then compare the proportion of negatively and positively selected neoantigens to non-neoantigen variants within the tumor to deduce if neoantigens are subject to additional immune scrutiny in a background of therapeutics. Lastly, we perform ancestral state reconstruction and mutational signature analysis to provide further insight on mutational processes on both individual patient and cohort levels, detailing how these endogenous and exogenous processes shift between etiologically pertinent time-points. Taken together, our results detail the degree of selection on cancer undergoing targeted therapy and highlights unique shifts in mutational processes in response to such therapies, ultimately offering quantitative insights that can inform clinical decision making regarding therapeutic strategies in Lung Adenocarcinoma and provide a framework for analyzing similar cancer systems. Citation Format: J Nicholas Fisk, Stephen Gaffney, Katerina Politi, Scott Gettinger, Fernando de Miguel, Jeffrey Townsend. Extreme selection constrains ability of EGFR-driven lung adenocarcinoma to diversify in response to erlotinib therapy [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 126.