Abstract B101: Subclonal drift predicts tumor response to treatment

Abstract Background: The failure of long-term treatment responses in cancer has been demonstrated in some cases to be due to the existence of small, resistant tumor subpopulations (subclones). ​The prediction of the time-scale for treatment failure depends on the rate of growth of these resistant subclones under treatment, referred to here as the “turnover rate” of the tumor.​ Here, using simulations of subclonal dynamics, we demonstrate a method that uses the baseline of neutral genetic drift during the early stages of treatment to estimate the turnover rate, and therefore the long-term treatment response. Methods: We used a set of standard computational models derived from evolutionary theory to address this question through simulations. First, we used a Moran birth-death model to estimate the range of subpopulation sizes that given a certain fitness advantage would have a high probability of sweeping to dominance in the tumor (“fixation”). We then used a Gillespie algorithm to simulate the growth of a small subpopulation with a fitness advantage against a background population of subclones that lack a fitness advantage under the same conditions; these subpopulations vary in their sizes due to neutral drift. From simulations of fit subpopulations of small sizes, we then calculated the dependence of time to fixation of the resistant subclone on the turnover rate.​ From the simulations of neutral drift, we implemented a variety of turnover rates and found their impact on the population variance in fitness across the subclones undergong drift. Then, we used this relation to predict the minimum turnover rate for a resistant subclone to fixate for a given level of population variance in fitness. Results: We noted that undetectably small subclone sizes are still likely to fixate in the population given a slight growth advantage. We quantified the time to fixation of these small, fit subclones, showing a decrease in fixation time with higher turnover rates and growth advantages. In simulations of neutral drift, we found that the variance of the subclonal population sizes increases roughly linearly with both time and turnover rate. ​From this, we were able to accurately predict the turnover rate given measurements of subclonal dynamics. Conclusions: We find that measurements of neutral drift during pre-treatment or early-treatment stages can be used to set a baseline for the fitness advantage required by resistant subclones to dominate. This in turn can be used to predict the time for a small, fit resistant subclone to fixate in the tumor population. This approach has practical implications for the design of genomic algorithms to predict the emergence of resistance in tumors based on changes in their mutational profiles from next-generation sequencing data. Citation Format: Michael Salazar, Andrew Chen, Madison Stoddard, T. Ryan Gregory, Arijit Chakravarty. Subclonal drift predicts tumor response to treatment [abstract]. In: Proceedings of the AACR-NCI-EORTC International Conference on Molecular Targets and Cancer Therapeutics; 2025 Oct 22-26; Boston, MA. Philadelphia (PA): AACR; Mol Cancer Ther 2025;24(10 Suppl):Abstract nr B101.