Automated next-generation profiling of genomic alterations in human cancers

• Published in: Nature communications Vol. 13; no. 1; p. 2830
• Main Authors: Keefer, Laurel A., White, James R., Wood, Derrick E., Gerding, Kelly M. R., Valkenburg, Kenneth C., Riley, David, Gault, Christopher, Papp, Eniko, Vollmer, Christine M., Greer, Amy, Hernandez, James, McGregor, Paul M., Zingone, Adriana, Ryan, Bríd M., Deak, Kristen, McCall, Shannon J., Datto, Michael B., Prescott, James L., Thompson, John F., Cerqueira, Gustavo C., Jones, Siân, Simmons, John K., McElhinny, Abigail, Dickey, Jennifer, Angiuoli, Samuel V., Diaz, Luis A., Velculescu, Victor E., Sausen, Mark
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 20.05.2022; Nature Publishing Group; Nature Portfolio
• Subjects: 45; 45/23; 631/114/1305; 631/61/514/2256; 631/67/1857; 631/67/69; Agreements; Automation; Biomarkers; Cancer; Deoxyribonucleic acid; DNA; Genomics; Humanities and Social Sciences; Machine learning; Microsatellite instability; multidisciplinary; Polymerase chain reaction; Precision medicine; Science; Science (multidisciplinary); Solid tumors; Tissues; Translocation; Tumors
• ISSN: 2041-1723; 2041-1723
• DOI: 10.1038/s41467-022-30380-x

The lack of validated, distributed comprehensive genomic profiling assays for patients with cancer inhibits access to precision oncology treatment. To address this, we describe elio tissue complete, which has been FDA-cleared for examination of 505 cancer-related genes. Independent analyses of clinically and biologically relevant sequence changes across 170 clinical tumor samples using MSK-IMPACT, FoundationOne, and PCR-based methods reveals a positive percent agreement of >97%. We observe high concordance with whole-exome sequencing for evaluation of tumor mutational burden for 307 solid tumors (Pearson r = 0.95) and comparison of the elio tissue complete microsatellite instability detection approach with an independent PCR assay for 223 samples displays a positive percent agreement of 99%. Finally, evaluation of amplifications and translocations against DNA- and RNA-based approaches exhibits >98% negative percent agreement and positive percent agreement of 86% and 82%, respectively. These methods provide an approach for pan-solid tumor comprehensive genomic profiling with high analytical performance. The genomic profiling of tumours has not been widely adopted in the clinic due to technical and practical hurdles. Here, the authors develop PGDx elio tissue complete, a scalable, standardised and FDA-cleared test comprising a targeted gene panel and automated machine-learning analysis, which detects clinically relevant sequence biomarkers in cancer samples.