ArticleHemaSphere2023
The Gene Expression Classifier ALLCatchR Identifies B-cell Precursor ALL Subtypes and Underlying Developmental Trajectories Across Age.
Article in HemaSphere, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 41 papers.
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Who cites it
41 citing papers in PubMed.
- A genomic and epigenomic lens into the biology of acute lymphoblastic leukaemia.Nature reviews. Cancer · 2026Review
- The machine-learning classifier ALLCatchR2 identifies 20 T-ALL subtypes across cohorts and age groups.HemaSphere · 2026Article
- Myeloid cell-mediated killing of B-ALL by CD38 and CD20 IgA antibody variants is enhanced by CD47/SIRPα interference.Blood neoplasia · 2026Article
- Spontaneous regression of B-cell acute lymphoblastic leukemia with PAX5 alterations at relapse: a case report.Annals of hematology · 2026Article
- CD20 expression dynamics in adult B-cell acute lymphoblastic leukemia and impact on anti-CD20 treatment.Journal of hematology & oncology · 2026Observational
- Transcriptomic remodeling of bone marrow mesenchymal stromal cells in pediatric B-cell acute lymphoblastic leukemia: a four-gene signature.Translational pediatrics · 2026Article
- Deep learning based individualized cross-platform molecular subtype classification of B-lineage acute lymphoblastic leukemia.NPJ precision oncology · 2026Article
- TCF3::HLF-positive B-ALL: integrated clinical and molecular characterization of 34 cases from a single-center cohort.British journal of cancer · 2026Article
- AdultHemaSphere · 2026Article
- KMT2A-rearranged B-lymphoblastic lymphomas are skewed towards a more mature developmental stage.Leukemia · 2026Article
- An integrative molecular map of pediatric B-cell precursor acute lymphoblastic leukemia.Communications medicine · 2026Article
- IntegrateALL: An end-to-end RNA-seq analysis pipeline for multilevel data extraction and interpretable subtype classification in B-precursor ALL.HemaSphere · 2026Article
- Biological and clinical characteristics ofHemaSphere · 2026Article
- Article
- Venetoclax and Blinatumomab for adult patients with relapsed/refractory or MRD positive Ph-negative B-cell precursor ALL: phase I part of the GMALL-BLIVEN trial.Annals of hematology · 2026Article
- Intra-subtype heterogeneity shapes treatment response inHemaSphere · 2026Article
- RanBALL: An Ensemble Machine Learning Framework for Accurate Subtype Identification of Pediatric B-Cell Acute Lymphoblastic Leukemia.Advanced intelligent systems (Weinheim an der Bergstrasse, Germany) · 2026Article
- Utility of a Digital PCR-Based Gene Expression Panel for Detection of Leukemic Cells in Pediatric Acute Lymphoblastic Leukemia.International journal of molecular sciences · 2026Article
- Distribution of molecular subtypes in a Colombian cohort of patients with acute lymphoblastic leukemia using whole-transcriptome analysis.Frontiers in oncology · 2026Article
- Benchmarking standard-of-care and emerging genomic approaches to enhance diagnosis in pediatric acute lymphoblastic leukemia.British journal of cancer · 2025Article
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Authors and funding
27 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Current classifications (World Health Organization-HAEM5/ICC) define up to 26 molecular B-cell precursor acute lymphoblastic leukemia (BCP-ALL) disease subtypes by genomic driver aberrations and corresponding gene expression signatures. Identification of driver aberrations by transcriptome sequencing (RNA-Seq) is well established, while systematic approaches for gene expression analysis are less advanced. Therefore, we developed ALLCatchR, a machine learning-based classifier using RNA-Seq gene expression data to allocate BCP-ALL samples to all 21 gene expression-defined molecular subtypes. Trained on n = 1869 transcriptome profiles with established subtype definitions (4 cohorts; 55% pediatric / 45% adult), ALLCatchR allowed subtype allocation in 3 independent hold-out cohorts (n = 1018; 75% pediatric / 25% adult) with 95.7% accuracy (averaged sensitivity across subtypes: 91.1% / specificity: 99.8%). High-confidence predictions were achieved in 83.7% of samples with 98.9% accuracy. Only 1.2% of samples remained unclassified. ALLCatchR outperformed existing tools and identified novel driver candidates in previously unassigned samples. Additional modules provided predictions of samples blast counts, patient's sex, and immunophenotype, allowing the imputation in cases where these information are missing. We established a novel RNA-Seq reference of human B-lymphopoiesis using 7 FACS-sorted progenitor stages from healthy bone marrow donors. Implementation in ALLCatchR enabled projection of BCP-ALL samples to this trajectory. This identified shared proximity patterns of BCP-ALL subtypes to normal lymphopoiesis stages, extending immunophenotypic classifications with a novel framework for developmental comparisons of BCP-ALL. ALLCatchR enables RNA-Seq routine application for BCP-ALL diagnostics with systematic gene expression analysis for accurate subtype allocation and novel insights into underlying developmental trajectories.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.