TUCAN: Ultra-fast methylation-based classification of pediatric solid tumors and lymphomas
Jongmans, M.; van Tuil, M.; de Ruijter, E.; Hiemcke-Jiwa, L.; Flucke, U.; de Krijger, R.; Scheijde-Vermeulen, M.; Kusters, P.; van Ewijk, R.; Merks, H.; van Noesel, M.; Pages-Gallego, M.; Vermeulen, C.; Tops, B.; de Ridder, J.; Kester, L.
Show abstract
The high heterogeneity of pediatric cancers presents significant diagnostic challenges, underscoring the need for accurate classification. Although molecular profiling supports first-line diagnostics and guides treatment, it can delay final diagnosis. While Nanopore-based methylation analysis has enabled rapid CNS tumor diagnosis, its application to pediatric solid tumors and lymphomas has remained largely unexplored. We developed Tucan, a deep-learning classifier trained on 3,818 methylation array profiles representing 84 subtypes, designed to classify tumors from sparse Nanopore methylation data. In retrospective validation (n=514), Tucan generated confident predictions (CFT[≥] 0.7) within 30 minutes of sequencing in 385 cases, achieving 372 correct diagnoses (F1-score: 0.98). In prospective testing (n=74; 63 classifiable), 52 samples reached the confidence threshold with 96% accuracy, confirming the original diagnosis in 47 cases and correctly refining or revising it in three. Together, Tucan enables rapid, high-confidence molecular classification of pediatric solid tumors and lymphomas.
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