Abstract 4300: Large-scale, low-cost, and accurate measurement of cancer evolutionary dynamics from clinical patient samples

Abstract Introduction: Cancer is a dynamic evolutionary process; thus, the evolutionary history of a cancer should strongly influence its future trajectory and so the clinical outcome of patients. However, accurately assessing an individual cancer’s evolutionary history in a scalable and clinically relevant manner is currently an open problem. To address this, we introduce a novel methodology (EVOFLUx) to quantitively infer the history of a cancer from single time-point bulk samples using inexpensive, standard methylation arrays. Description of Methodology: We recently identified fluctuating CpGs (fCpGs), which are CpGs that neutrally and stochastically change methylation state over time in individual cells, uniquely barcoding cells and thus enabling high temporal-resolution lineage tracing approaches. In a bulk population of cancer cells, the distribution of fCpG methylation is determined by the evolution of the population and thus encode a cancer’s past clonal dynamics. Here, we have developed a general computational approach to model how the patterns of fCpGs vary according to the evolutionary history of a growing cancer, and a Bayesian inference method to infer these parameters from data. Results We applied EVOFLUx to quantify the evolutionary dynamics of 1,976 lymphoid cancers, finding that the tumor growth rates, malignancy age and epimutation rates varied by orders of magnitude across disease types. In addition, EVOFLUx allowed us to test for the presence of ongoing subclonal selection and validated our inference with matched whole exome sequencing data. Alongside characterizing the evolutionary dynamics within single bulk samples, fCpGs also allow for the inference of the phylogenetic relationships between longitudinal samples, which we validated with whole genome sequencing. Within cancer types, more aggressive subtypes typically had elevated growth rates. In our clinically annotated cohort of chronic lymphocytic leukemia (CLL) samples, we found that patient-specific evolutionary dynamics are strongly associated with outcome. Conclusions: Hence, we present a powerful new method that uses widely available, low-cost bulk DNA methylation data to precisely measures cancer evolutionary dynamics in patient samples with clinical implications. Citation Format: Calum Gabbutt, Martí Duran-Ferrer, Heather Grant, Diego Mallo, Ferran Nadeu, Jacob Househam, Neus Villamor, Olga Krali, Jessica Nordlund, Thorsten Zenz, Elias Campo, Armando Lopez-Guillermo, Jude Fitzgibbon, Chris P. Barnes, Darryl Shibata, José I. Martin-Subero, Trevor A. Graham. Large-scale, low-cost, and accurate measurement of cancer evolutionary dynamics from clinical patient samples [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 4300.