LAUSR

The somatic evolution of cancer depends on the rate at which new mutations are acquired, as well as the selective pressure acting on them. Further, mutual exclusivity or co-occurrence of mutations suggests that variants can exert antagonistic or synergistic epistatic effects on the selection of new mutations, contributing to a complex landscape of evolutionary trajectories. Estimation of pairwise and higher-order epistatic effects—essential to estimation of the trajectory of likely cancer genotoypes—has remained a challenge. We have developed a continuous-time Markov chain model that enables the estimation of mutation origination and fixation (flux), dependent on somatic cancer genotype. Coupling the continuous-time Markov chain model with a deconvolution approach provides estimates of underlying mutation rates and selection across the trajectory of oncogenesis. We demonstrate computation of fluxes and selection coefficients in a somatic evolutionary model of lung adenocarcinoma informed by a compilation of samples from multiple data sets including The Cancer Genome Atlas and the AACR Project GENIE. The approach enables inference of the most likely routes of site-specific variant evolution and estimation of the selection strength operating on each step along the route, a key component of what we need to know to develop and implement personalized cancer therapies. Finally, by comparing smoker and nonsmoker tumor cohorts, we reveal and describe distinct evolutionary trajectories, influenced not only by differential mutation, but also by the differential physiological effects that smoking can have on the selective regime of lung tissue. These distinctions disambiguate the physiological and mutational influences of smoking and can inform personalized treatment of the two groups. Citation Format: Jorge A. Alfaro-Murillo, Krishna Dasari, Jeffrey P. Townsend. Pairwise and higher-order epistatic effects among somatic cancer mutations across oncogenesis of lung adenocarcinoma [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 862.