ArticleInternational journal of molecular sciences2022
Multi-Cohort Transcriptomic Subtyping of B-Cell Acute Lymphoblastic Leukemia.
Article in International journal of molecular sciences, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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Who cites it
20 citing papers in PubMed, 20 citations in OpenAlex.
- Deep learning based individualized cross-platform molecular subtype classification of B-lineage acute lymphoblastic leukemia.NPJ precision oncology · 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
- 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
- Artificial Intelligence and Machine Learning in Pediatric Endocrine Tumors: Opportunities, Pitfalls, and a Roadmap for Trustworthy Clinical Translation.Biomedicines · 2026Review
- Comprehensive omics-based classification system in adult patients with B-cell acute lymphoblastic leukemia.Molecular oncology · 2025Article
- Data-driven discovery of gene expression markers distinguishing pediatric acute lymphoblastic leukemia subtypes.Molecular oncology · 2025Article
- Identification of Molecular Subtypes of B-Cell Acute Lymphoblastic Leukemia in Mexican Children by Whole-Transcriptome Analysis.International journal of molecular sciences · 2025Article
- T-cell acute lymphoblastic leukaemia: subtype prevalence, clinical outcome, and emerging targeted treatments.Leukemia · 2025Review
- Deciphering IGH rearrangement complexity and detection strategies in acute lymphoblastic leukaemia.NPJ precision oncology · 2025Review
- Review
- Diagnostic genomic analysis is prognostic in AYA patients with ALL treated on an MRD-stratified pediatric protocol.Blood neoplasia · 2025Article
- RanBALL: An Ensemble Random Projection Model for Identifying Subtypes of B-Cell Acute Lymphoblastic Leukemia.bioRxiv : the preprint server for biology · 2025Article
- Transcriptome analysis of primary adult B-cell lineage acute lymphoblastic leukemia identifies pathogenic variants and gene fusions, and predicts subtypes for in depth molecular diagnosis.European journal of haematology · 2024Article
- Transcriptome Sequencing Allows Comprehensive Genomic Characterization of Pediatric B-Acute Lymphoblastic Leukemia in an Academic Clinical Laboratory.The Journal of molecular diagnostics : JMD · 2024Article
- Multimodal classification of molecular subtypes in pediatric acute lymphoblastic leukemia.NPJ precision oncology · 2023Article
- The Gene Expression Classifier ALLCatchR Identifies B-cell Precursor ALL Subtypes and Underlying Developmental Trajectories Across Age.HemaSphere · 2023Article
- Diagnostic utility of whole genome sequencing in adults with B-other acute lymphoblastic leukemia.Blood advances · 2023Article
- MD-ALL: an Integrative Platform for Molecular Diagnosis of B-cell Acute Lymphoblastic Leukemia.Research square · 2023Article
- Artificial intelligence applications in pediatric oncology diagnosis.Exploration of targeted anti-tumor therapy · 2023Review
Corrections and comments
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Authors and funding
5 authors at 4 institutions in 2 countries.
Funding
Abstract
RNA sequencing provides a snapshot of the functional consequences of genomic lesions that drive acute lymphoblastic leukemia (ALL). The aims of this study were to elucidate diagnostic associations (via machine learning) between mRNA-seq profiles, independently verify ALL lesions and develop easy-to-interpret transcriptome-wide biomarkers for ALL subtyping in the clinical setting. A training dataset of 1279 ALL patients from six North American cohorts was used for developing machine learning models. Results were validated in 767 patients from Australia with a quality control dataset across 31 tissues from 1160 non-ALL donors. A novel batch correction method was introduced and applied to adjust for cohort differences. Out of 18,503 genes with usable expression, 11,830 (64%) were confounded by cohort effects and excluded. Six ALL subtypes (ETV6::RUNX1, KMT2A, DUX4, PAX5 P80R, TCF3::PBX1, ZNF384) that covered 32% of patients were robustly detected by mRNA-seq (positive predictive value ≥ 87%). Five other frequent subtypes (CRLF2, hypodiploid, hyperdiploid, PAX5 alterations and Ph-positive) were distinguishable in 40% of patients at lower accuracy (52% ≤ positive predictive value ≤ 73%). Based on these findings, we introduce the Allspice R package to predict ALL subtypes and driver genes from unadjusted mRNA-seq read counts as encountered in real-world settings. Two examples of Allspice applied to previously unseen ALL patient samples with atypical lesions are included.
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Registered trials
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