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Sala-Torra O, Reddy S, Hung LH, Beppu L, Wu D, Radich J, Yeung KY, Yeung CCS. Rapid detection of myeloid neoplasm fusions using single-molecule long-read sequencing. PLOS GLOBAL PUBLIC HEALTH 2023; 3:e0002267. [PMID: 37699001 PMCID: PMC10497132 DOI: 10.1371/journal.pgph.0002267] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/31/2022] [Accepted: 07/17/2023] [Indexed: 09/14/2023]
Abstract
Recurrent gene fusions are common drivers of disease pathophysiology in leukemias. Identifying these structural variants helps stratify disease by risk and assists with therapy choice. Precise molecular diagnosis in low-and-middle-income countries (LMIC) is challenging given the complexity of assays, trained technical support, and the availability of reliable electricity. Current fusion detection methods require a long turnaround time (7-10 days) or advance knowledge of the genes involved in the fusions. Recent technology developments have made sequencing possible without a sophisticated molecular laboratory, potentially making molecular diagnosis accessible to remote areas and low-income settings. We describe a long-read sequencing DNA assay designed with CRISPR guides to select and enrich for recurrent leukemia fusion genes, that does not need a priori knowledge of the abnormality present. By applying rapid sequencing technology based on nanopores, we sequenced long pieces of genomic DNA and successfully detected fusion genes in cell lines and primary specimens (e.g., BCR::ABL1, PML::RARA, CBFB::MYH11, KMT2A::AFF1) using cloud-based bioinformatics workflows with novel custom fusion finder software. We detected fusion genes in 100% of cell lines with the expected breakpoints and confirmed the presence or absence of a recurrent fusion gene in 12 of 14 patient cases. With our optimized assay and cloud-based bioinformatics workflow, these assays and analyses could be performed in under 8 hours. The platform's portability, potential for adaptation to lower-cost devices, and integrated cloud analysis make this assay a candidate to be placed in settings like LMIC to bridge the need of bedside rapid molecular diagnostics.
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Affiliation(s)
- Olga Sala-Torra
- Translational Science and Therapeutics Division, Fred Hutchinson Cancer Center, Seattle, Washington, United States of America
- University of Washington, Seattle, Washington, United States of America
| | - Shishir Reddy
- University of Washington, Seattle, Washington, United States of America
| | - Ling-Hong Hung
- University of Washington, Seattle, Washington, United States of America
| | - Lan Beppu
- Translational Science and Therapeutics Division, Fred Hutchinson Cancer Center, Seattle, Washington, United States of America
| | - David Wu
- School of Engineering and Technology, University of Washington Tacoma, Tacoma, Washington, United States of America
| | - Jerald Radich
- Translational Science and Therapeutics Division, Fred Hutchinson Cancer Center, Seattle, Washington, United States of America
- School of Engineering and Technology, University of Washington Tacoma, Tacoma, Washington, United States of America
| | - Ka Yee Yeung
- University of Washington, Seattle, Washington, United States of America
| | - Cecilia C. S. Yeung
- Translational Science and Therapeutics Division, Fred Hutchinson Cancer Center, Seattle, Washington, United States of America
- School of Engineering and Technology, University of Washington Tacoma, Tacoma, Washington, United States of America
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