28. Analytical validation of an optical genome mapping assay for structural variant detection in hematologic malignancies

Trilochan Sahoo

Abstract

Trilochan Sahoo, Karena Kosco, Andy Wing Chun Pang, Jen Hauenstein, Beth Matthews, Anusha Mylavarapu, Julia Brushett, Alex Hastie, Alka Chaubey

aBionano Laboratories, San Diego, CA, United States

Structural variations (SV) play a key role in the pathogenesis of hematologic malignancies. Standard-of-care cytogenetic methods (SOC) including chromosome (karyotyping) and fluorescence in situ hybridization (FISH) analysis have inherent limitations, while NGS technologies have limited ability to detect most SVs. Optical genome mapping (OGM) is a high-resolution SV detection method that overcomes multiple issues including culture or amplification biases. This study describes the validation of OGM as a laboratory developed test (LDT) for hematologic malignancies conducted at Bionano Laboratories. A total of 77 datasets from blood or bone marrow specimens from 57 patients harboring a broad spectrum of SVs (30 with SOC results), 18 normal controls and two cancer cell lines were included in this validation. DNA isolation and labeling were performed using manufacturer’s instructions. OGM data generated on the Bionano Saphyr¬© system was analyzed utilizing Bionano Access v1.7.2 and filtered based on disease or pan-cancer specific guideline files to identify variants known to be diagnostically or prognostically relevant. Accuracy studies showed high concordance with SOC results; sensitivity was 100% and specificity was 100% for guideline-based variants (NCCN, WHO, and NHS). Repeatability and reproducibility were 100% and 96%, respectively. Variant-specific lower limit of detection was also determined for 6 different SV types and ranged from 4.1% (inversions) to 11% (trisomy). In conclusion, in strong agreement¬† with recent publications, this study validates the clinical utility of OGM for diagnostic evaluation of hematologic malignancies and its implementation is predicted to enhance accurate diagnosis, classification, prognostication, therapy selection, and disease monitoring.