Pan-Cancer assessment of tumor mutational burden using a comprehensive genomic profiling assay.
157 Background: Checkpoint inhibitors (CPIs) have been approved for frontline or subsequent therapies in several indications over the last few years. While patient response can be remarkably durable, many patients do not benefit. Current clinical biomarkers of response to CPIs include microsatellite instability (MSI) and PD-L1 expression. While a proportion of many solid tumors display microsatellite instability, the prevalence is often very low. Similarly, while clinically informative, PD-L1 expression alone is not sufficient to predict therapeutic outcomes with high accuracy. The lack of predictive biomarkers for response highlights the need for improved biomarkers with greater prevalence across tumor types to predict response to CPIs. Multiple clinical studies have revealed that high tumor mutational burden (TMB) is associated with improved clinical response. Methods: Here, we describe the development of a method that can be used to accurately infer mutational burden from a discrete set of targeted regions of interest across the exome. Initially, we performed an assessment of the accuracy across multiple bioinformatics methods for identification of individual sequence mutations (SBS/indels) using orthogonally validated data together with publicly available TCGA whole-exome sequencing data. The targeted regions were then isolated from these datasets to demonstrate analytical performance across several different solid tumor types. Finally, we evaluated independent non-small cell lung cancer (NSCLC) and colorectal carcinoma (CRC) cohorts to demonstrate the analytical accuracy of the assay and bioinformatics approach for determination of mutational burden when compared to whole exome sequencing. Results: In summary, high concordance was observed across a large dynamic range of mutations per megabase of coding sequence. Conclusions: Our data indicate that the assay can be used to accurately determine mutational burden in a range of tumor types, across a spectra of potential mutational burden cut-offs using automated, complex mutation identification algorithms.