Abstract 5761: Validation of TMB (tumor mutational burden) quantification using putative driver mutations in customized targeted sequencing platform

Abstract

Abstract Next generation sequencing has revolutionised genomic studies of cancer, having facilitated the development of precision oncology treatments based on a tumour’s molecular profile. Tumor mutational burden (TMB) measurements aid in identifying patients who are likely to benefit from immunotherapy. In most cases, there is no result of a paired normal tissues. Therefore, it is difficult to effectively exclude germline mutations, so it does not effectively reflect the TMB of cancer driver mutation. We aimed to modify the TMB analysis pipeline to reflect the germline and multi-nucleotide variants (MNVs), specifically for Korean solid tumors. “CancerMaster” is custom RNA probes for target enrichment sequencing. It consists of all unions (7,811 regions, 2.5Mb) of reported exons of 524 tumor and immune related genes. The panel contains special RNA probes, which enables detection of microsatellite instability, Epstein-Barr virus and Human papillomavirus. This Panel sensitivity, precision, and TMB were assessed using immunohistochemistry and TruSight Oncology 500 for well-characterized samples (cellularity ≤ 20% and sequencing QC passed, n = 415). We achieved a mean coverage of 1,183x, with sensitivity and specificity of >99% and precision of >97% in alteration. We performed MNV analysis for more accurate mutation analysis. We used WhatsHap for haplotype phasing and the bcftools to call the MNV. The MNV-rich genes were HLA-A (2019 sites), HLA-B (1284 sites), and HLA-C (825 sites), and BRCA2, BRCA1, ATM and ATR, a DNA repair-related genes, was also identified at more than 30 sites. In addition, up to 14 MNVs were detected in one sample. In this study, we analyzed putative driver mutations that removed germline mutations, referring to the ACMG guidelines for sequence variant interpretation based on the Korean genome database (KOVA and KRGDB) and gnomAD. The TMB was recalculated by reflecting the MNV and the putative driver mutation. As a result, it was confirmed that this upgraded TMB analysis was similar to TMB calculated through actual somatic mutation through comparison with normal tissues. The TMB-high (20≤) sample was 8.4% (36/415) for pan-cancer, but 6.9% (20/289) for gastric cancer and 25% (12/48) for colorectal cancer showed differences by cancer type. In addition, it was confirmed that there is a correlation between TMB-high and MSI-H (sensitivity : 70.0%, specificity 99.5%, accuracy 95.9%) In conclusion, we validated TMB analysis pipeline for the expansion of immunotherapeutic applications in treatment-refractory Korean solid cancers. Citation Format: Woo Sun Kwon, Jingmin Che, Sun Young Rha, Hyun Cheol Chung. Validation of TMB (tumor mutational burden) quantification using putative driver mutations in customized targeted sequencing platform [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 5761.