Abstract LB-245: Validating models of imputing germline versus somatic status for variants detected by tumor-only genomic profiling using germline clinical testing data
Authors: Gomy Israel , Jalloul Nahed , Stokes Samantha , Gusev Alexander , Ganesan Shridar , Garber Judy , Khiabanian Hossein 
Published: September 2020

Abstract
The goal of tumor genomic profiling is to identify somatic mutations. However, most implementations lack patient-matched control DNA sequence to enable determination of variant source (somatic or germline) and resolution of allelic status, including possible loss of heterozygosity (LOH), without additional analyses based on accurate estimates of specimens' tumor purity. Here, we present the validation of an information-theoretic algorithm for identifying germline variants detected in tumor-only sequencing using cancer patient clinical germline test data. 1,631 adult patients consented for the PROFILE study at DFCI between 2014 and 2018, and also underwent clinical germline testing. DNA was isolated from tissue containing &gt;20% tumor nuclei and analyzed using Agilent SureSelect hybrid capture kit. All exons and 191 introns in 447 genes were interrogated for single nucleotide variants, small indels, copy number alterations, and structural variants using standard pipelines. Germline testing was performed at CLIA-certified laboratories. Specimen tumor purity was estimated using computational approaches developed at DFCI and Rutgers with further manual curation. LOHGIC (LOH-Germline Inference Calculator), developed on a model-selection scheme using Akaike Information Criterion weighting, was applied to individual variants to infer the most consistent germline-v-somatic model and LOH status, incorporating biases in clinical sequencing and purity estimation. 427 patients had 676 variants detected in high-penetrance cancer predisposition genes: BRCA1, BRCA2, MLH1, MSH2, MSH6 and PMS2. Comparison of LOHGIC's results with germline test data showed 91.4% accuracy, 67.6% precision, and 67.0% recall, with other performance measures reported in Table 1. LOHGIC showed evidence of LOH for 35% of variants with concordant inference and germline testing results. Special challenges with TP53 variants will also be discussed. Our results indicate that tumor-only sequencing can provide excellent power for statistical approaches to identify likely germline mutations and infer LOH. Our analyses may be confounded by the presence of reversion mutations, poor specimen quality, inaccurate purity estimates, low sequencing depths, and/or germline call variability. This work further demonstrates the need for a systematic effort to interpret clinical-grade sequencing results.
Table 1.Summary of resultsGeneBRCA1BRCA2MLH1MSH2MSH6PMS2OverallGermline Test: Positive/Negative28/10445/1753/6010/6514/12112/39112/564Germline Inference: Positive/Negative/Ambiguous24/94/1448/138/347/46/1013/51/1115/105/154/37/10111/471/94Positive Predictive Value (PPV)79.270.842.969.246.775.067.6Negative Predictive Value (NPV)95.798.6100.0100.096.286.596.9True Positive Rate (TPR)67.975.6100.090.050.025.067.0False Omission Rate (FOR)4.31.40.00.03.813.53.1Sensitivity82.694.4100.0100.063.637.583.3Specificity94.790.792.092.792.797.092.8Accuracy92.491.492.593.890.085.491.4
Citation Format: Israel Gomy, Nahed Jalloul, Samantha Stokes, Alexander Gusev, Shridar Ganesan, Judy Garber, Hossein Khiabanian. Validating models of imputing germline versus somatic status for variants detected by tumor-only genomic profiling using germline clinical testing data [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr LB-245.