TASC: A transcriptome-driven machine learning classifier to explore molecular heterogeneity and relapse-associated programs in T-cell Acute Lymphoblastic Leukemia

Benetti, C.; Almolla, O.; Gambi, G.; Massa, A.; Aifantis, I.; Tsirigos, A.; Boccalatte, F.

doi:10.64898/2025.12.23.696007

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bioRxiv · 10.64898/2025.12.23.696007

TASC: A transcriptome-driven machine learning classifier to explore molecular heterogeneity and relapse-associated programs in T-cell Acute Lymphoblastic Leukemia

Benetti, C.·Almolla, O.·Gambi, G.·Massa, A.·Aifantis, I.·Tsirigos, A.·Boccalatte, F.

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Abstract

T-cell acute lymphoblastic leukemia is a biologically heterogeneous malignancy characterized by diverse transcriptional and genomic alterations. Recent studies have defined a set of recurrent molecular subtypes associated with distinct differentiation stages and clinical outcomes. However, no unified framework currently exists for assigning these subtypes in a standardized and accessible manner. Existing approaches often rely on mutation or fusion detection and may overlook broader transcriptional programs. The lack of a comprehensive, transcriptome-based classification tool has hampered the use of subtype-specific insights in both research and clinical settings. Here, we present a machine learning-based classifier trained on transcriptomic data to predict previously defined multi-omic subtypes of T-cell acute lymphoblastic leukemia. The model accurately assigns subtype identity across patient samples and cell lines, and provides a practical tool for standardized molecular stratification, supporting future integration into diagnostic and translational workflows, as demonstrated by its ability to reveal subtype-specific patterns of relapse.

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Benetti, C., Almolla, O., Gambi, G., Massa, A., Aifantis, I., Tsirigos, A., Boccalatte, F.. 2025-12-25. TASC: A transcriptome-driven machine learning classifier to explore molecular heterogeneity and relapse-associated programs in T-cell Acute Lymphoblastic Leukemia. https://doi.org/10.64898/2025.12.23.696007

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