Abstract SY02-03: Stochastic modeling of clonal evolution in carcinogenesis

Abstract In order to develop strategies that can detect cancer earlier, we need to better understand the dynamics of cancer evolution during the human lifetime. Starting from gestation, this includes measuring both the timing of early somatic mutations that lead to clonal mosaicism as well as the growth rates of mutated clones that carry higher potential for malignant transformation. Continuous observation of this evolving clonal composition across a lifetime is not feasible, therefore mathematical models are needed to integrate data from longitudinal and cross-sectional samples and infer these dynamics. Stochastic models of clonal evolution offer such a framework and provide a means for predicting individual clone trajectories into the future. Furthermore, if measurements of evolution are to be utilized clinically for early detection and patient risk stratification, faster methods to apply these models to data are required. To this end, we derived methods using coalescent theory for single-cell DNA sequencing data that enable instantaneous estimation of the growth rate of a clone along with its confidence intervals. Our tool is available in an open source R package, cloneRate, and provides the first method for constructing valid confidence intervals of growth rates analytically without having to rely on Bayesian inference techniques and complex simulations. When applying our methods to recent datasets derived from normal and neoplastic human blood cells, we quantified increased fitness effects of multi-hit driver mutations and found that higher initial clone growth rates led to decreased time to cancer diagnosis in patients. We can also use these models to estimate other important biological parameters that are difficult to measure in vivo such as bounds for total number of hematopoietic stem cells and expansion rates in early development. Ultimately, the ability to easily and quickly parameterize clonal dynamics in individual patients will benefit future early detection efforts and screening strategies for many cancer types. Citation Format: Kit Curtius. Stochastic modeling of clonal evolution in carcinogenesis abstract. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 2 (Late-Breaking, Clinical Trial, and Invited Abstracts) ; 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84 (7Suppl): Abstract nr SY02-03.