• OGM achieves diagnostic outcomes equivalent to, and in 13% of cases better than, standard-of-care technologies in a real-world setting

  • OGM may uncover findings that would alter recommended clinical care (4% of cases) or render cases eligible for clinical trials (8% of cases)

Detection of hallmark genomic aberrations in acute myeloid leukemia (AML) is essential for diagnostic subtyping, prognosis and patient management. However, cytogenetic/cytogenomic techniques used to identify those aberrations, such as karyotyping, fluorescence in situ hybridization (FISH) or chromosomal microarray analysis (CMA), are limited by the need for skilled personnel as well as significant time, cost and labor. Optical genome mapping (OGM) provides in a single, cost-effective assay significantly higher resolution than karyotyping with comprehensive genome-wide analysis comparable to CMA and the added unique ability to detect balanced structural variants (SVs). Here, we report in a real-world setting the performance of OGM in a cohort of 100 AML cases, which were previously characterized by karyotype alone or karyotype and FISH or CMA. OGM identified all clinically relevant SVs and copy number variants (CNVs) reported by these standard cytogenetic methods when representative clones were present in >5% allelic fraction. Importantly, OGM identified clinically relevant information in 13% of cases that had been missed by the routine methods. Three cases reported with normal karyotypes were shown to have cryptic translocations involving gene fusions. In 4% of cases, OGM findings would have altered recommended clinical management and in an additional 8%, OGM would have rendered the cases potentially eligible for clinical trials. The results from this multi-institutional study indicate that OGM effectively recovers clinically relevant SVs and CNVs found by standard of care methods and reveals additional SVs not reported. Furthermore, OGM minimizes the need for labor-intensive multiple cytogenetic tests while concomitantly maximizing diagnostic detection through a standardized workflow.

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