Clinical Evaluation of the Utility of Machine Learning-Based Subtype Classifiers of Acute Lymphoblastic Leukemia in Identifying Cryptic and Novel Fusions

This is the authors' abstract. We don't add key points for this paper.

Background: B-acute lymphoblastic leukemia (B-ALL) is a heterogeneous disease comprising more than 20 molecular subtypes defined by distinct gene expression profiles (GEPs) and genetic alterations. Subtypes primarily defined by GEPs remain challenging to identify using conventional cytogenetic and targeted molecular assays. Although GEP-based classifiers can predict B-ALL subtypes, their clinical performance has not been validated using independent cohorts, and reported accuracies may be inflated due to overlapping training and test datasets. Therefore, we evaluated the clinical performance of four open-source GEP-based classifiers using an independent cohort. Methods: We retrospectively analyzed RNA-sequencing data from 92 patients with B-ALL to evaluate the performance of four open-source GEP-based classifiers (ALLCatchR, ALLSorts, ALLIUM GEX, and Allspice). To ensure fair comparisons across models with heterogeneous subtype definitions and output formats, prediction results were unified using a multilabel encoding framework, followed by label-based performance evaluation. Results: fusions, neither of which has been previously reported in B-ALL. Conclusions: GEP-based classifiers provide valuable support for B-ALL subtype classification and show potential as adjunct tools in routine diagnostics.