Abstract 201: Multiregion joint somatic genotyping in ovarian cancer proves optimal tumor sampling for clinical sequencing

Abstract

Abstract Introduction. Genetic heterogeneity of ovarian tumors is caused by the accumulation of novel mutations through time. A result of this process is a spatially heterogeneous tumor with a fractal-like architecture, comprising of a mixture of spatially separated clones, sub-clones, and single cells. Spatial heterogeneity renders clinical sequencing a challenging task because clinically relevant mutations can go undetected. Here, we compare the genetic composition of single tumors with multiple DNA samples extracted from different regions of the tumor in diverse combinations to examine the effects of spatial separation on tumor heterogeneity. Methods. We derived tumor samples from five patients with ovarian cancer. Three tumor tissue samples were collected from each patient, in each of these, DNA was extracted from a total of eight segments of different sizes (5x3x8 total segments). Exome sequencing was performed and we examined DNA mutations identified from a biopsy sample, from three merged tissue segments with immediate vicinity to the biopsy, and from segments from spatially distant tissue samples merged into one. We compared identified mutations and copy-number variations called from the exome-seq data. Results. Three patients had heterozygous germline alterations affecting the BRCA pathway, paired with somatic TP53 mutations, one patient had multiple somatic mutations affecting the PI3K pathway, the last patient had the most common activating mutations in the PIK3CA and KRAS genes. When comparing the different sequencing runs, increasing the size of the tumor sample did not affect the overall quantity of mutations identified. Clonal mutations were identifiable in all samples, while sub-clonal mutations shifted. In case of a hypermutating phenotype, increasing the sequenced sample size strongly decreased the number of identified somatic mutations, most probably due to dilution of sub-clones. These observations are confirmed by an in silico model, in which we shuffled regions from publicly available multi-region sequencing data into known compositions. Conclusion. In hypermutating tumors the number of detected mutations increases as the size of the sequenced sample decreases. Generally, increasing the tumor sample size did not affect the number of identifiable mutations. Our findings suggest that clinical sequencing using small biopsy samples can generate adequate mutation calling results. Citation Format: Lorinc Pongor, Gyongyi Munkacsy, Ildiko Vereczkey, Imre Pete, Balazs Gyorffy. Multiregion joint somatic genotyping in ovarian cancer proves optimal tumor sampling for clinical sequencing [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 201.