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Advances in the diagnosis and classification of B-ALL: comparative insights from updated guidelines
Abstract:
Accurate molecular classification is essential for diagnosis, risk stratification, and treatment selection in B-cell lymphoblastic leukemia (B-ALL). In this study, we performed a comprehensive, real-world reclassification of 1015 consecutively diagnosed B-ALL patients using the fifth edition of the World Health Organization Classification of Haematolymphoid Tumours (WHO-HAEM5) and the International Consensus Classification (ICC). An integrative genomic strategy that combined whole transcriptome sequencing, fusion detection, mutational analysis, and cytogenetics enabled reclassification according to both the WHO-HAEM5 and ICC frameworks, thereby substantially reducing the proportion of unclassifiable B-ALL from 41.9% (2016 WHO revision [WHO-HAEM4R]) to 15.9% (WHO-HAEM5) and 11.9% (ICC). Distinct clinical and prognostic features were identified across newly defined subtypes. Multivariable analysis confirmed that this genomic classification is a robust, independent predictor of survival after adjusting for age, minimal residual disease status, and transplant intervention. Specifically, HLF-rearranged and MEF2D-rearranged B-ALL conferred a persistently poor prognosis across all age groups despite allogeneic hematopoietic stem cell transplantation, highlighting an urgent need for novel therapeutic strategies. Gene expression profiling resolved cryptic subtypes, including ETV6::RUNX1-like, ZNF384-rearranged-like, and BCR::ABL1-like B-ALL, and uncovered diagnostic ambiguity in patients with concurrent lesions. In addition, we report emerging high-risk groups, including IDH1/2- and ZEB2 Q1072-mutated B-ALL, that may warrant recognition as distinct molecular entities. Our findings demonstrate the clinical use of integrative transcriptomic profiling in refining B-ALL taxonomy in guiding risk-adapted therapies and informing future revisions of diagnostic standards. This study supports the incorporation of high-throughput molecular diagnostics into routine leukemia classification and precision treatment planning.
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