Somatic mutation burden in cancer samples determined by targeted next generation sequencing.

Abstract

15 Background: High somatic mutation burden in tumor tissues is associated with the presentation of neoantigens that promote immune responses particularly in the context of immune checkpoint therapies. Herein, we characterize the ability of targeted cancer research panels to generate estimates of somatic mutation burden. Methods: Somatic mutation data from > 8000 cancer samples obtained from The Cancer Genome Atlas (TCGA) was curated and standardized, and the number of single nucleotide variants (SNVs) in exonic regions of each sample determined. Next, the number of SNVs associated with target regions of two Ion AmpliSeq cancer panels (Oncomine Comprehensive Assay [OCA, 146 genes, 0.35 MB]; Comprehensive Cancer Panel [CCP, 409 genes, 1.7 MB]) was likewise determined and the frequency of mutation counts in the exome and the panel target regions was compared. Mutation counts of samples containing truncating mutations in mismatch repair (MMR) and other DNA repair genes were characterized. A facile workflow with less than 60 minutes of hands-on time was developed to estimate mutation counts for a batch of 8 samples using the Ion Chef for automated library preparation and templating followed by sequencing on the Ion S5. Results: The sensitivity of targeted panels in estimating somatic mutation burden was positively correlated with panel size. The area under the Receiver Operating Characteristic (ROC) curve showed that CCP had > 90% sensitivity and > 95% specificity to differentiate high and low mutation burden based on informatics analysis of TCGA data. As expected, truncating mutations in MMR genes were associated with higher somatic mutation counts in colorectal tumor tissue. Using data generated from OCA and CCP, we characterized a set of filters that provided a good estimate of somatic mutation counts when applied to a tumor-only workflow. Conclusions: A simple workflow was developed on the Ion Torrent sequencing platform to estimate somatic mutation burden in cancer samples. The methods described herein will help advance research in immuo-oncology.