• Je něco špatně v tomto záznamu ?

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

T. Beder, BT. Hansen, AM. Hartmann, J. Zimmermann, E. Amelunxen, N. Wolgast, W. Walter, M. Zaliova, Ž. Antić, P. Chouvarine, L. Bartsch, MJ. Barz, M. Bultmann, J. Horns, S. Bendig, J. Kässens, C. Kaleta, G. Cario, M. Schrappe, M. Neumann, N....

. 2023 ; 7 (9) : e939. [pub] 20230825

Status neindexováno Jazyk angličtina Země Spojené státy americké

Typ dokumentu časopisecké články

Perzistentní odkaz   https://www.medvik.cz/link/bmc23015347

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.

Citace poskytuje Crossref.org

000      
00000naa a2200000 a 4500
001      
bmc23015347
003      
CZ-PrNML
005      
20231020093622.0
007      
ta
008      
231010s2023 xxu f 000 0|eng||
009      
AR
024    7_
$a 10.1097/HS9.0000000000000939 $2 doi
035    __
$a (PubMed)37645423
040    __
$a ABA008 $b cze $d ABA008 $e AACR2
041    0_
$a eng
044    __
$a xxu
100    1_
$a Beder, Thomas $u Medical Department II, Hematology and Oncology, University Hospital Schleswig-Holstein, Kiel, Germany
245    14
$a The Gene Expression Classifier ALLCatchR Identifies B-cell Precursor ALL Subtypes and Underlying Developmental Trajectories Across Age / $c T. Beder, BT. Hansen, AM. Hartmann, J. Zimmermann, E. Amelunxen, N. Wolgast, W. Walter, M. Zaliova, Ž. Antić, P. Chouvarine, L. Bartsch, MJ. Barz, M. Bultmann, J. Horns, S. Bendig, J. Kässens, C. Kaleta, G. Cario, M. Schrappe, M. Neumann, N. Gökbuget, AK. Bergmann, J. Trka, C. Haferlach, M. Brüggemann, CD. Baldus, L. Bastian
520    9_
$a 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.
590    __
$a NEINDEXOVÁNO
655    _2
$a časopisecké články $7 D016428
700    1_
$a Hansen, Björn-Thore $u Medical Department II, Hematology and Oncology, University Hospital Schleswig-Holstein, Kiel, Germany
700    1_
$a Hartmann, Alina M $u Medical Department II, Hematology and Oncology, University Hospital Schleswig-Holstein, Kiel, Germany $u Clinical Research Unit "CATCH ALL" (KFO 5010/1) funded by the Deutsche Forschungsgemeinschaft, Bonn, Germany
700    1_
$a Zimmermann, Johannes $u Institute of Experimental Medicine, Research Group Medical Systems Biology, Christian-Albrechts-University Kiel, Germany
700    1_
$a Amelunxen, Eric $u Medical Department II, Hematology and Oncology, University Hospital Schleswig-Holstein, Kiel, Germany
700    1_
$a Wolgast, Nadine $u Medical Department II, Hematology and Oncology, University Hospital Schleswig-Holstein, Kiel, Germany $u Clinical Research Unit "CATCH ALL" (KFO 5010/1) funded by the Deutsche Forschungsgemeinschaft, Bonn, Germany
700    1_
$a Walter, Wencke $u MLL Munich Leukemia Laboratory, Munich, Germany
700    1_
$a Zaliova, Marketa $u Childhood Leukaemia Investigation Prague, Second Faculty of Medicine, Charles University and University Hospital Motol, Prague, Czech Republic
700    1_
$a Antić, Željko $u Department of Human Genetics, Hannover Medical School (MHH), Hannover, Germany
700    1_
$a Chouvarine, Philippe $u Department of Human Genetics, Hannover Medical School (MHH), Hannover, Germany
700    1_
$a Bartsch, Lorenz $u Medical Department II, Hematology and Oncology, University Hospital Schleswig-Holstein, Kiel, Germany
700    1_
$a Barz, Malwine J $u Medical Department II, Hematology and Oncology, University Hospital Schleswig-Holstein, Kiel, Germany $u Clinical Research Unit "CATCH ALL" (KFO 5010/1) funded by the Deutsche Forschungsgemeinschaft, Bonn, Germany
700    1_
$a Bultmann, Miriam $u Medical Department II, Hematology and Oncology, University Hospital Schleswig-Holstein, Kiel, Germany
700    1_
$a Horns, Johanna $u Medical Department II, Hematology and Oncology, University Hospital Schleswig-Holstein, Kiel, Germany
700    1_
$a Bendig, Sonja $u Medical Department II, Hematology and Oncology, University Hospital Schleswig-Holstein, Kiel, Germany $u Clinical Research Unit "CATCH ALL" (KFO 5010/1) funded by the Deutsche Forschungsgemeinschaft, Bonn, Germany
700    1_
$a Kässens, Jan $u Medical Department II, Hematology and Oncology, University Hospital Schleswig-Holstein, Kiel, Germany
700    1_
$a Kaleta, Christoph $u Institute of Experimental Medicine, Research Group Medical Systems Biology, Christian-Albrechts-University Kiel, Germany
700    1_
$a Cario, Gunnar $u Clinical Research Unit "CATCH ALL" (KFO 5010/1) funded by the Deutsche Forschungsgemeinschaft, Bonn, Germany $u Department of Pediatrics, University Hospital Schleswig-Holstein Kiel, Germany
700    1_
$a Schrappe, Martin $u Clinical Research Unit "CATCH ALL" (KFO 5010/1) funded by the Deutsche Forschungsgemeinschaft, Bonn, Germany $u Department of Pediatrics, University Hospital Schleswig-Holstein Kiel, Germany
700    1_
$a Neumann, Martin $u Medical Department II, Hematology and Oncology, University Hospital Schleswig-Holstein, Kiel, Germany $u Clinical Research Unit "CATCH ALL" (KFO 5010/1) funded by the Deutsche Forschungsgemeinschaft, Bonn, Germany
700    1_
$a Gökbuget, Nicola $u Department of Medicine II, Hematology/Oncology, Goethe University Hospital, Frankfurt/M., Germany
700    1_
$a Bergmann, Anke Katharina $u Department of Human Genetics, Hannover Medical School (MHH), Hannover, Germany
700    1_
$a Trka, Jan $u Childhood Leukaemia Investigation Prague, Second Faculty of Medicine, Charles University and University Hospital Motol, Prague, Czech Republic
700    1_
$a Haferlach, Claudia $u MLL Munich Leukemia Laboratory, Munich, Germany
700    1_
$a Brüggemann, Monika $u Medical Department II, Hematology and Oncology, University Hospital Schleswig-Holstein, Kiel, Germany $u Clinical Research Unit "CATCH ALL" (KFO 5010/1) funded by the Deutsche Forschungsgemeinschaft, Bonn, Germany
700    1_
$a Baldus, Claudia D $u Medical Department II, Hematology and Oncology, University Hospital Schleswig-Holstein, Kiel, Germany $u Clinical Research Unit "CATCH ALL" (KFO 5010/1) funded by the Deutsche Forschungsgemeinschaft, Bonn, Germany
700    1_
$a Bastian, Lorenz $u Medical Department II, Hematology and Oncology, University Hospital Schleswig-Holstein, Kiel, Germany $u Clinical Research Unit "CATCH ALL" (KFO 5010/1) funded by the Deutsche Forschungsgemeinschaft, Bonn, Germany
773    0_
$w MED00201259 $t HemaSphere $x 2572-9241 $g Roč. 7, č. 9 (2023), s. e939
856    41
$u https://pubmed.ncbi.nlm.nih.gov/37645423 $y Pubmed
910    __
$a ABA008 $b sig $c sign $y - $z 0
990    __
$a 20231010 $b ABA008
991    __
$a 20231020093616 $b ABA008
999    __
$a ok $b bmc $g 1997104 $s 1201709
BAS    __
$a 3
BAS    __
$a PreBMC-PubMed-not-MEDLINE
BMC    __
$a 2023 $b 7 $c 9 $d e939 $e 20230825 $i 2572-9241 $m HemaSphere $n Hemasphere $x MED00201259
LZP    __
$a Pubmed-20231010

Najít záznam

Citační ukazatele

Možnosti archivace