Abstract LB-213: ecTMB: A robust method to estimate and classify tumor mutational burden

Abstract

Abstract Tumor Mutational Burden (TMB) is a measure of the abundance of somatic mutations in a tumor, which has been shown to be an emerging biomarker for anti-PD-(L)1 treatment. Nevertheless, multiple challenges still hinder the adoption of TMB for clinical decision-making. The current standard for TMB measurement requires counting the non-synonymous somatic mutations in a paired tumor-normal sample with whole-exome sequencing (WES); however, clinical diagnostics based on sequencing technologies still rely heavily on targeted panel sequencing. Thus, the key challenge is the inconsistency of the panel-based TMB measurement when compared to that of a more comprehensive WES. Targeted panel sequencing might overestimate TMB due to its enrichment of driver mutations and mutation hot spots. Another non-negligible challenge is the relatively arbitrary selection of the TMB-high cutoff. In order to improve the robustness of TMB measurement, we propose a novel TMB prediction method, ecTMB, based on a statistical model with a Bayesian framework. The model takes into account the heterogeneous mutation context in cancer, and other influencing factors, to estimate the sample- and gene-specific background mutation rates, which can systematically reduce driver-mutation impacts and include synonymous mutations in the estimation. The in-silico assessment of TMB estimation for the FoundationOne CDxTM panel by ecTMB showed an improved agreement between the WES-based TMB and the panel-based TMB with a slope close to 1 in Deming regression, whereas the standard counting method had a slope of 1.3. Further analysis of colorectal (n = 520), stomach (n = 435), and endometrial (n = 538) cancers on WES-based TMB using log transformation revealed three hidden cancer subtypes: TMB-low, TMB-high, and a novel subtype - TMB-extreme. Not only did these three cancer subtypes have distinct mutation profiles and tumor-infiltrating immune cell populations, but also they associated with patient survival outcomes significantly. We observed that TMB-high and TMB-extreme are associated with improved patient survival at different levels after considering age and cancer stage (hazard ratio (HR) for TMB-high = 0.8 with p-value = 0.1; hazard ratio (HR) for TMB-extreme = 0.32 with p-value = 0.006). We then extended ecTMB with a Gaussian Mixture Model to classify samples by the aforementioned cancer subtypes. Our results demonstrated that ecTMB could estimate panel-based TMB and classify samples based on TMB more robustly. Results also showed that the novel TMB-extreme subtype had a significantly lower hazard ratio than TMB-high, indicating better survival rate. We believe our method and also can help facilitate the adoption of TMB testing in clinical diagnostics. Citation Format: Lijing Yao, Marghoob Mohiyuddin, Hugo Lam. ecTMB: A robust method to estimate and classify tumor mutational burden [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr LB-213.