Abstract 3584: Statistical considerations in tumor only variant calling
Authors: Little Paul , Hayes D. Neil , Parker Joel , Zevallos Jose 
Published: July 2017

Targeted exome sequencing and variant calling involves comparing a patient’s tumor and normal sequencing to identify mutated loci within clinically actionable (CA) genes. However sequencing matched normal (MN) increases the marginal cost greatly relative to the amount of genomic information provided since most bases are unchanged. MN may not be available or in some cases poor quality of the MN sequencing may hinder the interpretation of results. Without the MN control, with the expected mix of sequencing errors, artifacts, and mis-mapped regions, germline (GM) variants will further contaminate the list of variant calls (VCs) with up to 10,000 alternate loci. Some reports address TO sequencing challenges and these have significant limitations. In general, more controls leads to both higher computational costs and risk of selecting low quality controls. We aim to return several dozen VCs using a reasonable number of controls. We propose using information obtained from pooling high quality normal samples and GM/somatic databases and applying a set of filtering criteria for a cohort of tumor only (TO) samples to exclude the majority of false positive (FP) VCs while retaining those that are CA. Through benchmarking paired tumor normal (PTN) samples, we aim to find the minimum controls needed to reach an adequate sensitivity (SENS), specificity (SPEC), and final number of VCs. We selected a cohort of patients with clinically validated mutations from a clinical trial of NGS in cancer (LCCC1108: NCT01457196). From a cohort we selected a subset of 100 patients with a spectrum of clinical and genomic parameters relevant to a robust method. All patients were PTN samples as well as CLIA confirmation of relevant CA mutations. We developed a set of filtering and annotations processes to allow VC annotation of the cohort which did not include the MN and compared to results obtained from PTN sequencing. Samples were sequenced using hybrid capture technology and Illumina brand paired-end sequencing. VCs were combined using Strelka, UNCseqR, and Cadabra and annotated with Oncotator. SENS and SPEC are used to quantify the pipeline’s performance by treating the filtered PTN VCs and clinically validated as gold standard. Overlaps in the tumor and pooled normal cohort (PNC) samples were avoided. With respect to a larger set of high quality mutations called by the PTN pipeline, the TO method documented on average 95% SENS and &gt;99% SPEC for variant detection. The method showed a tradeoff between SENS and FP rate allowing for small drops in SENS for large decreases in the numbers of FP (4-5% decrease in SENS from 99% to 95%) resulted in 90% decrease in FP VCs with a final average of 70 filtered VCs per sample. Results suggest that analyzing PTN targeted exome NGS samples as TO and obtaining a disjoint PNC selected based on sequencing quality metrics can achieve high SENS and SPEC with a reasonable number of filtered VCs. Also benchmarking aids in excluding controls from the PNC that may miss CA calls.

Citation Format: Paul Little, D. Neil Hayes, Joel Parker, Jose Zevallos. Statistical considerations in tumor only variant calling [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 3584. doi:10.1158/1538-7445.AM2017-3584