Comprehensive next generation sequencing assay and bioinformatic pipeline for identifying pathogenic variants associated with hereditary cancers.

Abstract

e13105 Background: Diagnosis of hereditary cancer syndromes involves time-consuming comprehensive clinical and laboratory work-up, however, timely and accurate diagnosis is pivotal to the clinical management of cancer patients. Germline genetic testing has shown to facilitate the diagnostic process, allowing for identification and management of individuals at risk for inherited cancers. However, the laboratory diagnostics process requires not only development and validation of comprehensive gene panels to improve diagnostic yields, but a quality driven workflow including an end-to-end bioinformatics pipeline, and a robust process for variant classification. We will present a gene panel for the evaluation of hereditary cancer syndromes, conducted utilizing our novel end-to-end workflow, and validated in the CLIA-approved environment. Methods: A targeted Next-Generation Sequencing (NGS) panel consisting of 130 genes, including exons, promoters, 5’-UTRs, 3’-UTRs and selected introns, was designed to include genes associated with hereditary cancers. The assay was validated using samples from the 1000 genomes project and samples with known pathogenic variants. Elements software was utilized for end-to-end bioinformatic process ensuring adherence with the CLIA quality standards, and supporting manual curation of sequence variants. Results: Preliminary data from our current panel of genes associated with hereditary cancer syndromes revealed high sensitivity, specificity, and positive predictive value. Accuracy was confirmed by analysis of known SNVs, indels, and CNVs using 1000 Genomes and samples carrying pathogenic variants. The bioinformatics software allowed for an end-to-end quality controlled process of handling and analyzing of the NGS data, showing applicability for a clinical laboratory workflow. Conclusions: We have developed a comprehensive and accurate genetic testing process based on an automated and quality driven bioinformatics workflow that can be used to identify clinically important variants in genes associated with hereditary cancers. It's performance allows for implementation in the clinical laboratory setting.