BackgroundPatients treated with immunotherapy are at risk of considerable adverse events, and the ongoing struggle is to accurately identify the subset of patients who will benefit. Tumor mutational burden (TMB) has emerged as a promising predictive biomarker but requires tumor tissue which is not always available. Blood-based TMB (bTMB) may provide a minimally invasive assessment of mutational load. However, because of the required sequencing depth, bTMB analysis is costly and prone to false negative results. This study attempted to design a minimally sized bTMB panel, examined a counting-based method for bTMB in patients with stage I to IV non-small cell lung cancer (NSCLC) and evaluated both technical factors such as bTMB and tissue-based TMB (tTMB) cut-off, as well as sample-related factors such as cell-free DNA input mass which influence the correlation between bTMB and tTMB.MethodsTissue, plasma, and whole blood samples collected as part of the LEMA trial (NCT02894853) were used in this study. Samples of 185 treatment naïve patients with stage I to IV NSCLC were sequenced at the Roche Sequencing Solutions with a custom panel designed for TMB, using reagents and workflows derived from the AVENIO Tumor Tissue and circulating tumor DNA Analysis Kits.ResultsA TMB panel of 1.1 Mb demonstrated highly accurate TMB high calls with a positive predictive value of 95% when using a tTMB cut-off of 16 mut/Mb, corresponding with 42 mut/Mb for bTMB. The positive per cent agreement (PPA) of bTMB was relatively low at 32%. In stage IV samples with at least 20 ng of cfDNA input, PPA of bTMB improved to 63% and minimizing the panel to a subset of 577 kb was possible while maintaining 63% PPA.ConclusionPlasma samples with high bTMB values are highly correspondent with tTMB, whereas bTMB low results may also be the result of low tumor burden at earlier stages of disease as well as poorly shedding tumors. For advanced stages of disease, PPA (sensitivity) of bTMB is satisfactory in comparison to tTMB, even when using a panel of less than 600 kb, warranting consideration of bTMB as a predictive biomarker for patients with NSCLC eligible for immunotherapy in the future.

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Background Tumor Mutation Burden (TMB) has grown in importance as a predictive biomarker for predicting response to check-point immunotherapy in several cancer disease areas such as non-small cell lung cancer (NSCLC) and melanoma. Although TMB derived from analysis of WES data is the current gold standard measure of the number of neoantigens in the system, WES is often cost-prohibitive as a routine practice for clinical interpretation. Targeted panels offer a more flexible and cost-effective option, but TMB estimates based on these subsets of the exome can be more variable and often disagree with each other. Current best practices for TMB panels advocate for panels with sizes of 1 Mb with high correlation between panel-derived TMB and WES TMB demonstrating effectiveness of a panel at assessing TMB accurately. Methods We present a systematic approach to designing panels for detecting TMB effectively while minimizing cost of sequencing, based on our Surveillance panel design algorithm. Our approach relies on the prioritization of highly recurrent non-driver mutations present in large cohorts (such as TCGA) for disease areas of interest. Such an approach greatly boosts the number of observations of short variants using a targeted panel while still meeting expectations outlined within current best practices such as a high correlation with WES-derived TMB, and high PPA and NPA with WES-derived TMB. Our NGS based workflow combines whole genome library preparation, hybrid capture target enrichment, high-throughput sequencing and a proprietary analysis algorithm that utilizes short exonic variants that are likely to be somatic. Results Using a combination of in silico data and clinical cohorts, we demonstrate the effectiveness of the above approach in designing panels for the assessment of TMB. Results show high correlations between panel-derived TMB and WES-derived TMB at panel sizes as low as 450Kb while highlighting the steady decrease in variability of the TMB score as panel size increases. We apply this panel and WES analysis to clinical NSCLC and CRC samples to demonstrate that recommendations from current best practices are met in terms of PPA and NPA with WES-derived TMB and high correlation with WES-derived TMB. Citation Format: Amrita Pati, Daniel Klass, Zhongyun Huang, Melissa Loyzer, Alex Lovejoy. Assessment of tumor mutation burden using a novel panel design strategy utilizing highly mutated genomic regions [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 3379.