Abstract 3404: Landscape and genomic correlates of ctDNA-based tumor mutational burden across six solid tumor types
Authors: Katie Quinn, Elena Helman, Tracy Nance, Jennifer Yen, John Latham, Kristin Gleitsman, Ravi Vijaya-Satya, Carlo Artieri, Alex Artyomenko, Marcin Sikora, Darya Chudova, Richard B. Lanman, AmirAli Talasaz
Journal: Cancer Research (2019)

Abstract Background: Tumor mutational burden (TMB) has emerged as a predictive biomarker of response to immune checkpoint inhibitor (ICI) therapy. Current panel-based TMB algorithms aggregate signal from certain types of somatic variants (e.g. non-synonymous coding SNVs); however, delineating the contributions of these and other types of mutations may refine TMB calculation from gene panels. Moreover, early studies suggest other possible genomic correlates of patient outcome to ICI which may be complementary to TMB. Here, we explore the landscape of mutations comprising TMB and other genomic features correlating with TMB on a subset of several thousand late-stage plasma samples run on GuardantOMNITM (OMNI), a highly sensitive 500-gene cfDNA sequencing platform. Methods: We developed a cfDNA-based TMB algorithm which is robust to variable tumor shedding levels and presence of clonal hematopoiesis. We assessed cfDNA-based TMB in over 1,000 plasma samples across six tumor types, including lung and prostate. We examine the contribution of nonsynonymous, synonymous, intronic SNVs, and indels to TMB score. We investigate correlations between TMB and additional genomic features, including chromosomal instability, loss of HLA-bearing chromosome 6p, microsatellite instability (MSI), and common oncogenic and resistance mutations. Results: We found that the distribution of WES-calibrated TMB scores across this cohort of samples is consistent with TCGA, with median 10 mutations/Mb and upper-tertile of 14 mutations/Mb across tumor types. The number of non-synonymous coding SNVs per sample correlated highly with synonymous coding SNV and intronic SNV counts (Pearson’s r &amp;gt; 0.7 for each). Including this additional signal in TMB calculation improves clinical sensitivity by up to 5%. In MSS samples, indels were highly correlated with SNVs, indicating that both likely arise from a similar underlying mechanism. We found no clear correlation between high TMB and chromosomal instability, with high TMB samples exemplifying a range of tumor ploidies. TMB association with oncogenic drivers is consistent with existing literature, with lower median TMB in EGFR-driven lung tumors (p &amp;lt; 0.01), but little to no correlation between TMB and KRAS or PIK3CA driver status, or STK11 loss of function (p &amp;gt; 0.05), suggesting these latter events could be independent clinical biomarkers to TMB. Conclusions: Panel-based TMB scores can leverage synonymous and non-coding mutations to strengthen the signal of exome-wide mutation load. As more patient outcome data becomes available, TMB algorithms and orthogonal biomarkers of tumor genome immunogenicity will evolve further for improved guidance of patient response to immunotherapy. Sequencing panels with high sensitivity for TMB, via large panel space, and the ability to detect copy-number variations and MSI-status, will be important for biomarker development and clinical applications. Citation Format: Katie Quinn, Elena Helman, Tracy Nance, Jennifer Yen, John Latham, Kristin Gleitsman, Ravi Vijaya-Satya, Carlo Artieri, Alex Artyomenko, Marcin Sikora, Darya Chudova, Richard B. Lanman, AmirAli Talasaz. Landscape and genomic correlates of ctDNA-based tumor mutational burden across six solid tumor types [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 3404.