Acute lymphoblastic leukemia expressing the gamma delta T-cell receptor (γδ T-ALL) is a poorly understood disease. We studied 200 children with γδ T-ALL from 13 clinical study groups to understand the clinical and genetic features of this disease. We found age and genetic drivers were significantly associated with outcome. γδ T-ALL diagnosed in children under 3 years of age was extremely high-risk and enriched for genetic alterations that result in both LMO2 activation and STAG2 inactivation. Mechanistically, using patient samples and isogenic cell lines, we show that inactivation of STAG2 profoundly perturbs chromatin organization by altering enhancer-promoter looping, resulting in deregulation of gene expression associated with T-cell differentiation. High-throughput drug screening identified a vulnerability in DNA repair pathways arising from STAG2 inactivation, which can be targeted by poly(ADP-ribose) polymerase inhibition. These data provide a diagnostic framework for classification and risk stratification of pediatric γδ T-ALL. Significance: Patients with acute lymphoblastic leukemia expressing the gamma delta T-cell receptor under 3 years old or measurable residual disease ≥1% at end of induction showed dismal outcomes and should be classified as having high-risk disease. The STAG2/LMO2 subtype was enriched in this very young age group. STAG2 inactivation may perturb chromatin conformation and cell differentiation and confer vulnerability to poly(ADP-ribose) polymerase inhibition.

• MeSH
• adaptorové proteiny signální transdukční * genetika   metabolismus   MeSH
• dítě MeSH
• genová přestavba MeSH
• kojenec MeSH
• lidé MeSH
• lymfoblastická leukemie-lymfom z prekurzorových T-buněk genetika   patologie   MeSH
• předškolní dítě MeSH
• proteiny buněčného cyklu genetika   metabolismus   MeSH
• proteiny s doménou LIM * genetika   MeSH
• protoonkogenní proteiny MeSH
• Check Tag
• dítě MeSH
• kojenec MeSH
• lidé MeSH
• mužské pohlaví MeSH
• předškolní dítě MeSH
• ženské pohlaví MeSH
• Publikační typ
• časopisecké články MeSH
• Názvy látek
• adaptorové proteiny signální transdukční * MeSH
• LMO2 protein, human MeSH Prohlížeč
• proteiny buněčného cyklu MeSH
• proteiny s doménou LIM * MeSH
• protoonkogenní proteiny MeSH

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.

• Publikační typ
• časopisecké články MeSH