Abstract 3901: Charting copy number gain histories from mutational divergence among multi-samples

Abstract In cancer, a somatic copy number gain (SCNG) on a tumor chromosomal region is often assumed to result from a series of minimum number of copy number events, but this parsimonious-trajectory assumption may be violated in reality. For example, an SCNG could develop through a complex trajectory involving a combination of gain and loss events, rather than exclusively through gains. While prior efforts have been made to infer the SCNG trajectories from patient data, the presence of complex trajectories remains to be validated. Furthermore, these methods are limited by either only utilizing SCNG profiles, or inferring trajectories from only a single sample. Here, we hypothesize that SCNG trajectories can be reflected by somatic single nucleotide variants (SSNVs) shared between samples from the same patient. By analyzing multi-regional whole genome sequencing data of tumor specimens, we found evidence where the multiplicities of shared SSNVs between samples pinpoint complex SCNG trajectories. Here, the SSNV multiplicity refers to the number of alleles harboring the corresponding SSNV in the dominating clone of a tumor sample. We also define the joint multiplicity distribution as the proportion of shared SSNVs that occur at different allelic states (multiplicities) across distinct tumor samples. We developed a probabilistic computational pipeline to automate the identification of non-parsimonious multi-sample SCNG trajectories utilizing shared SSNV multiplicities. Extending an existing method GRITIC from single sample to multi-sample setting, this pipeline enumerates all trajectories leading to SCNGs states in multiple samples, and characterizes the probability of observing the joint multiplicity distribution in the real patient data given each possible trajectory. We applied the method on prostate cancer and breast cancer datasets, and identified conclusive cases of complex trajectories on genomic regions affected by SCNGs. On regions showing different copy number states in multiple samples, we identified cases experiencing both gains and losses. On regions showing the same copy number states in multiple samples, we also identified a few of them experiencing independent acquisition of SCNGs in different samples. Our findings call for a stochastic modeling of SCNGs dynamics during tumor evolution. Citation Format: Yunong Xia, Ruping Sun. Charting copy number gain histories from mutational divergence among multi-samples [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 3901.