Abstract 580: A method for estimating mutation load from tumor research samples using a targeted next-generation sequencing panel (2018)

Abstract Introduction: Tumor mutation load predicts durable benefit from immune checkpoint inhibitors in several cancer types. Existing methods to estimate tumor mutation load have large input DNA and extensive infrastructure requirements and are associated with delays due to shipping precious biopsy samples to central laboratories. Herein, we demonstrate the ability of a targeted panel with fast turn-around time to estimate mutation load from tumor research samples using low input. Methods: We developed a single sample analysis workflow to estimate mutation load (mutation count per megabase) from FFPE and fresh frozen tumor research samples. The assay utilizes a PCR-based target enrichment panel that covers 409 genes and 1.7 Mb of genomic regions. The workflow requires only 10 ng of input DNA, and enables a 2.5-day turn-around time from sample to the final report. The workflow enables &amp;lt; 60 minutes of hands-on time for automated library preparation and templating on a batch of 8 samples. Sequencing is performed on high throughput semiconductor sequencing platform to achieve sufficient depth (~500x coverage) and accuracy. Our analysis pipeline calls variants at ≥5% allelic frequency with parameters optimized for high accuracy. The workflow is tumor sample only, with no matched normal sample required, and germ-line variants, along with background noise, are removed through application of filters based on variants prevalent in population databases. Results: In-silico analysis demonstrated that the predicted mutation counts associated with the covered regions of the targeted panel could effectively stratify responders and non-responders to immune checkpoint inhibitors in 3 separate cohorts with high statistical significance. The assay was applied to colorectal specimens previously typed for microsatellite instability (MSI) and distinguished samples that were MSI-H (median mutation count 94.5) and MSS (median mutation count 17.8). Matched tumor-normal analyses on 7 colorectal tumor samples showed that the single tumor analysis workflow detected somatic mutations with strong correlation (r = 0.97) with tumor-normal analysis. To assess reproducibility, we compared somatic mutation count in 6 library replicates of breast cancer cell line HCC1143 and observed low relative variability (CV=9.57%) with 19.17 average mutation count. The analysis report characterizes substitution type and context of somatic mutations, and highlights mutation signatures consistent with UV damage and spontaneous deamination of 5-methyl-cytosine, as well as FFPE deamination. Conclusions: We developed a simple analysis workflow on the Ion Torrent sequencing platform with an AmpliSeq panel to estimate mutation load from FFPE and fresh frozen tumor research samples. This solution will advance research in immuno-oncology. Citation Format: Ruchi Chaudhary, Dinesh Cyanam, Vinay Mittal, Warren Tom, Janice Au-Young, Seth Sadis, Fiona Hyland. A method for estimating mutation load from tumor research samples using a targeted next-generation sequencing panel [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 580.