Abstract B043: Quantifying selection intensity and epistatic interactions among gene variants within angiosarcoma

Abstract

Abstract Evolutionary processes underlie cancer development. Mutations occur and accumulate in somatic tissues, and some mutations result in increases in cell division or survival and fix in cell populations more often than expected by chance. Computational techniques that account for differences in mutation rate across the genome and between tissues have helped pinpoint which genes are contributing to tumor growth within a wide array of cancer types. However, rare cancers are often understudied due to lack of funding, availability of data, and applicable tools that are capable of incorporating data from different sources. Recent initiatives, such as the Angiosarcoma Project, have increased data availability for the study of angiosarcoma, a rare cancer with approximately 300 new cases a year. In this study, we incorporate whole-genome, whole-exome, and targeted sequencing data from different sources—accounting for study-specific differences in genome coverage—into an evolutionary analysis of angiosarcoma tumorigenesis. Prior to our study, potential genes with oncogenic activity for angiosarcoma have been primarily evaluated based on genetic variant prevalence among tumor samples, but substitution prevalence is dependent on intrinsic mutation rate and thus relative prevalence of variants may not correspond with relative tumorigenic potential. We calculate the intensity of selection, a measure of the magnitude of increase of cell division and survival, of individual variants driving angiosarcoma, and complement existing studies that have previously determined KDR and TP53 mutations to be a driver of angiosarcoma, and we also implicate novel putative drivers that have high calculated selection intensity, including BRAF V600E, IGDCC4 S1020A, and SETD2 Q1152E. Additionally, we evaluated epistatic interactions among genes and found that selection for TP53 is increased after mutation of other genes such as MUC16, FAT1, and NOTCH2. This study demonstrates the feasibility of applying computational models informed by evolutionary approaches to a rare disease as well as identifies gene variants that likely have a large impact on angiosarcoma tumorigenesis. Citation Format: Caralynn E. Hampson, Vincent L. Cannataro. Quantifying selection intensity and epistatic interactions among gene variants within angiosarcoma [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Translating Cancer Evolution and Data Science: The Next Frontier; 2023 Dec 3-6; Boston, Massachusetts. Philadelphia (PA): AACR; Cancer Res 2024;84(3 Suppl_2):Abstract nr B043.