Rapid, Comprehensive Methylation-Based Classification of Hematologic Malignancies by Nanopore Sequencing
Achterberg, T.; Vermeulen, C.; van der Ent, H.; Jongmans, M.; Cammel, K.; de Ruijter, E.; Groenewegen, N.; Kranenburg, C.; van Tuil, M.; Waanders, E.; Parihar, M.; Islam, R.; Aijaz, J.; Goemans, B.; Calkoen, F.; van der Sluis, I.; den Boer, M. L.; Boer, J. M.; de Haas, V.; Triche, T.; Alexander, T. B.; Wang, J. R.; Bhakta, N.; Pieters, R.; Kester, L.; Tops, B.; de Ridder, J.
Show abstract
Hematologic malignancies are diagnosed through a fragmented, sequential workup of morphology, immunophenotyping, cytogenetics, and molecular testing that can take days to weeks and is unavailable at many centers. DNA methylation profiling has transformed central nervous system tumor diagnosis, yet hematologic classifiers have remained confined to narrow acute leukemia panels. Here we present Lamprey, a deep-learning methylation classifier spanning 86 hematologic malignancy entities, trained on a reference cohort of 8,544 patients and deployed directly from nanopore sequencing. A depth-aware training framework allows confident classification from the first minutes of a run. Against blinded integrated reference diagnoses across retrospective, external, and prospective cohorts, Lamprey exceeded 98% accuracy among classified cases. Lamprey reaches a confident call within minutes, and cost as little as $82 per sample. Lamprey consolidates a sequential diagnostic workup into a single, rapid, same-day molecular readout.
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