The machine-learning classifier ALLCatchR2 identifies 20 T-ALL subtypes across cohorts and age groups

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Authors: Thomas Beder, Nadine Wolgast, Wencke Walter, Sonja Bendig, Alina Hartmann, Malwine J. Barz, Marketa Kubricanova Zaliova, Elisa Reitzel, David Baden, Stefan Schwartz, Nicola Gökbuget, Lennart A. Kester
+6 more Jan Trka, C. Haferlach, Monika Brüggemann, Claudia Dorothea Baldus, Martín Neumann, Lorenz Bastian
Cited by: 1
References: 32

Abstract

Abstract T-cell acute lymphoblastic leukemia (T-ALL) comprises molecularly diverse subtypes, but robust cross-cohort validations and operational gene-expression definitions are lacking. To establish a gene-expression-anchored framework for T-ALL subtyping, we aggregated 2,314 transcriptomes (15 cohorts, age: 0.8 to 90.8 years). An extended unsupervised approach defined 17 main clusters and 3 subclusters in samples with high blast fractions. Supervised analyses added an overarching immature T-ALL (ETP-like) definition and resolved the LMO2 γδ-like subtype. All clusters contained samples from at least two cohorts. Characteristic genomic driver enrichments were consistent across cohorts, while gene expression clusters did not correspond exclusively to single driver events but also reflected developmental origins. A machine learning classifier based on ALLCatchR, our B-ALL classifier, identified these 20 transcriptomic subtypes and the immature T-ALL (ETP-like) signature with 0.995-1.0 accuracy in a validation set (n=203). Testing the classifier on a second hold-out data set (n=265 samples) showed that 92.7% of predictions matched with corresponding driver alterations. Across all samples, 83.2% of cases received high-confidence predictions, 7.3% candidate predictions, and 9.5% remained unclassified, largely because of low blast fractions. We identified a novel gene expression cluster markedly enriched ( P 50 years. We advanced ALLCatchR, as a free R package that now enables B-/T-lineage separation, gene-expression subtyping, blast estimation, and developmental annotation to harmonize T-ALL classification across studies and clinical contexts. Key Points Established using 2,314 T-ALL transcriptomes, ALLCatchR2 assigns 20 RNA-Seq subtypes and their developmental underpinnings across ages. Clonal hematopoiesis-related T-ALL ( DNMT3A , IDH2 R140Q ) defines an immature gene-expression cluster in ∼40% of patients aged >50 years.

Fields of Study & Subjects

Acute Lymphoblastic Leukemia research Chronic Myeloid Leukemia Treatments Gene expression and cancer classification

Publication details

Journal / source
bioRxiv (Cold Spring Harbor Laboratory)
Publisher
openRxiv
Publication date
2 January 2026
Document type
Preprint