Optical Genome Mapping improves detection and streamlines analysis of structural variants in myeloid neoplasms.
Raca, G.; Sahoo, T.; Iqbal, A.; Smolarek, T.; Levy, B.; Dupont, B.; Ryall, S.; Dubuc, A.; Sahajpal, N.; Liu, J.; Liao, J.; Wang, Z.-X.; Stence, A. A.; Guseva, N.; Broach, J. R.; Miller, C.; Ma, D.; Blachly, J.; Michaels, P. D.; Kolhe, R.; Kanagal-Shamanna, R.
Show abstract
Accurate diagnosis and risk stratification of hematological malignancies require disease-specific laboratory testing procedures involving the use of hematopathology, flow cytometry, molecular, and cytogenetic testing. While individual laboratories develop unique workflows to accommodate volume, clinical needs, and staffing, cytogenetic laboratories generally require a multitude of targeted and genome-wide tests that detect clinically relevant aberrations in hematologic malignancies. Specifically, the frequent use of multiple FISH panels coupled with concurrent chromosome analysis, can be both labor, and resource intensive. Optical Genome Mapping (OGM) is a comprehensive cytogenetic solution for detecting structural variants with high resolution and increased accuracy for hematological malignancy subtypes at the DNA level without need of any cell culture regimens. A new software tool for analysis of OGM data called VIA (Variant Intelligence Applications), provides an integrative analysis, interpretation, and reporting solution for OGM and other datatypes. In this study, we performed retrospective review of 56 datasets, representing 10 unique myeloid cases to assess multi-user (technologist and laboratory director) analyses and classification. Interpretation and reporting of OGM results were 100% concordant between reviewers for four cases with negative results by standard of care (SOC) testing. For the other six cases, five pathognomonic gene fusions identified by SOC assays were unanimously reported as Tier 1A classification was unanimous for five sentinel gene fusion rearrangements identified by SOC. OGM also found additional structural variants of clinical relevance in five of the six cases that were not found by SOC methods. Leveraging automatic pre-classification of variants and a custom decision tree, the VIA software enabled complete analysis with a mean technologist review time (variant analysis and initial tier determination) of 30.7 minutes. The analysis, interpretation, and reporting workflow described in this pilot study provides a framework for standardized and streamlined reporting of clinically significant variant in myeloid malignancies using VIA.
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