The Gene Expression Classifier ALLCatchR Identifies B-cell Precursor ALL Subtypes and Underlying Developmental Trajectories Across Age

Author:

Beder Thomas1,Hansen Björn-Thore1,Hartmann Alina M.12,Zimmermann Johannes3,Amelunxen Eric1,Wolgast Nadine12,Walter Wencke4,Zaliova Marketa5,Antić Željko6,Chouvarine Philippe6,Bartsch Lorenz1,Barz Malwine J.12,Bultmann Miriam1,Horns Johanna1,Bendig Sonja12,Kässens Jan1,Kaleta Christoph3,Cario Gunnar27,Schrappe Martin27,Neumann Martin12,Gökbuget Nicola8,Bergmann Anke Katharina6,Trka Jan5,Haferlach Claudia4,Brüggemann Monika12,Baldus Claudia D.12,Bastian Lorenz12

Affiliation:

1. Medical Department II, Hematology and Oncology, University Hospital Schleswig-Holstein, Kiel, Germany

2. Clinical Research Unit “CATCH ALL” (KFO 5010/1) funded by the Deutsche Forschungsgemeinschaft, Bonn, Germany

3. Institute of Experimental Medicine, Research Group Medical Systems Biology, Christian-Albrechts-University Kiel, Germany

4. MLL Munich Leukemia Laboratory, Munich, Germany

5. Childhood Leukaemia Investigation Prague, Second Faculty of Medicine, Charles University and University Hospital Motol, Prague, Czech Republic

6. Department of Human Genetics, Hannover Medical School (MHH), Hannover, Germany

7. Department of Pediatrics, University Hospital Schleswig-Holstein Kiel, Germany

8. Department of Medicine II, Hematology/Oncology, Goethe University Hospital, Frankfurt/M., Germany

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.

Publisher

Wiley

Subject

Hematology

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