Estimating tumor mutation burden using next-generation sequencing assay

• Published in: Journal of clinical oncology Vol. 35; no. 15_suppl; p. e14529
• Main Authors: Chaudhary, Ruchi, Bishop, John, Broomer, Adam, Cyanam, Dinesh, Mandelman, David, Nistala, Goutam, Hyland, Fiona, Sadis, Seth
• Format: Journal Article
• Language: English
• Published: 20.05.2017
• ISSN: 0732-183X; 1527-7755
• DOI: 10.1200/JCO.2017.35.15_suppl.e14529

Abstract only e14529 Background: High tumor mutation burden is a promising biomarker shown in some cancer types to predict positive response to immune checkpoint inhibitors. We show the ability of a targeted cancer research panel to estimate tumor mutation burden per megabase. Methods: We present a single sample analysis workflow for estimating tumor mutation burden from FFPE research samples. Our assay utilizes a PCR-based target enrichment panel that interrogates 409 known key cancer genes covering ~1.7 megabase of genomic space. Our customized workflow requires only 20 ng of input DNA, and enables a 2 day turn-around time from sample to the result. The ease of our workflow enables less than 60 minutes of hands-on time for automated library preparation and templating on a batch of 8 samples. Next-generation Sequencing is performed using high throughput semiconductor sequencing platform to achieve sufficient depth (~500x coverage) and accuracy. Our custom analysis pipeline calls variants with optimized parameters on the tumor sample only, with no matched normal sample required, and applies filters to remove germ-line variants and background noise. Results: Through in silico analysis performed on The Cancer Genome Atlas (TCGA) data we demonstrate that the panel achieves high sensitivity ( > 90%) and specificity ( > 95%) necessary to separate high and low mutation burden samples. Our workflow provides clear separation between allele ratio of somatic and germ-line variants. Our filters consistently eliminate ~98% of germ-line variants from the set of all variants called in single sample analysis workflow. Evidence from tumor-normal analyses on matched tumor and normal samples suggests that our single sample analysis, on the tumor sample only, detects somatic mutations with high sensitivity and specificity with residual of < 3% germ-line variants. Our pipeline identifies mutational signatures consistent with specific mechanisms such as spontaneous deamination of 5-methyl-cytosine, as well as base-damage from FFPE processing. Conclusions: A simple workflow has been developed on the Ion Torrent sequencing platform to estimate per megabase somatic mutational burden from a single tumor FFPE sample. This solution can help advance research in immuno-oncology.