Using sequential next-generation sequencing assays to identify germline cancer predisposition variants.

Abstract

1581 Background: Next-generation sequencing (NGS) increasingly guides clinical care in hematological malignancies by identifying DNA mutations that change dynamically over time. Clinical samples contain variable numbers of malignant and non-malignant cells. So, careful interpretation is required to determine if a particular variant is somatic, germline, or clonal hematopoietic in origin. Methods: The University of Chicago uses a targeted NGS assay of ~1200 genes, reporting 150 as a clinical test. We aimed to identify individuals with hereditary predisposition by detecting persistent variants on sequential assays regardless of disease state. Results: 943 NGS assays from July 2017 – Feb. 2020 on 711 patients [ages 1 mo – 95 yrs, median 65 yrs] were included. 2,320 variants in 33 genes were identified with 144 patients having the same variant identified on more than one assay. Single nucleotide variants (SNVs) with variant allele frequency (VAF) ≥ 0.3 were prioritized. The first candidate gene identified with potential germline SNVs was CSF3R. 28 unique SNVs in CSF3R were found, 14 were confirmed as germline, 6 somatic, and 8 were unconfirmed due to lack of available tissue. At least 2 confirmed germline CSF3R variants were likely deleterious based on functional testing. Sequential SNVs were quantified using the coefficient of variation, characterizing each by change in VAF over time. Using a worst-case-scenario analysis, in which unconfirmed variants were not counted as germline, a computer algorithm was designed to identify potential germline variants (specificity 0.89, PPV 0.75). Via an iterative method, the algorithm compares new assays to a pool of previously reported tests, flagging patients with potential germline mutations so that biopsies may be studied in the lab, records reviewed, and referrals placed to genetic counselors. To date, 61 patients with 89 likely germline variants have been identified. Known hereditary hematological malignancy genes, such as ATM, ASXL1, CHEK2, DDX41, TSC1, and RUNX1, had the most variants identified. Limitations include the challenge in distinguishing variants that do not change over time, reliance on a targeted NGS panel, and normalizing VAF data prior to analysis. Conclusions: These data highlight the utility of NGS of bone marrow and peripheral blood samples to identify patients suspected of having germline DNA variants. In addition to identifying known predisposition syndromes, one may discover new inherited cancer syndromes and help guide clinical practice in real time.