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Batcha AMN, Bamopoulos SA, Kerbs P, Kumar A, Jurinovic V, Rothenberg-Thurley M, Ksienzyk B, Philippou-Massier J, Krebs S, Blum H, Schneider S, Konstandin N, Bohlander SK, Heckman C, Kontro M, Hiddemann W, Spiekermann K, Braess J, Metzeler KH, Greif PA, Mansmann U, Herold T. Allelic Imbalance of Recurrently Mutated Genes in Acute Myeloid Leukaemia. Sci Rep 2019; 9:11796. [PMID: 31409822 PMCID: PMC6692371 DOI: 10.1038/s41598-019-48167-4] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2019] [Accepted: 07/29/2019] [Indexed: 12/24/2022] Open
Abstract
The patho-mechanism of somatic driver mutations in cancer usually involves transcription, but the proportion of mutations and wild-type alleles transcribed from DNA to RNA is largely unknown. We systematically compared the variant allele frequencies of recurrently mutated genes in DNA and RNA sequencing data of 246 acute myeloid leukaemia (AML) patients. We observed that 95% of all detected variants were transcribed while the rest were not detectable in RNA sequencing with a minimum read-depth cut-off (10x). Our analysis focusing on 11 genes harbouring recurring mutations demonstrated allelic imbalance (AI) in most patients. GATA2, RUNX1, TET2, SRSF2, IDH2, PTPN11, WT1, NPM1 and CEBPA showed significant AIs. While the effect size was small in general, GATA2 exhibited the largest allelic imbalance. By pooling heterogeneous data from three independent AML cohorts with paired DNA and RNA sequencing (N = 253), we could validate the preferential transcription of GATA2-mutated alleles. Differential expression analysis of the genes with significant AI showed no significant differential gene and isoform expression for the mutated genes, between mutated and wild-type patients. In conclusion, our analyses identified AI in nine out of eleven recurrently mutated genes. AI might be a common phenomenon in AML which potentially contributes to leukaemogenesis.
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Affiliation(s)
- Aarif M N Batcha
- Institute of Medical Data Processing, Biometrics and Epidemiology (IBE), Faculty of Medicine, LMU Munich, Munich, Germany. .,Data Integration for Future Medicine (DiFuture, www.difuture.de), LMU Munich, Munich, Germany.
| | - Stefanos A Bamopoulos
- Laboratory for Leukemia Diagnostics, Department of Medicine III, University Hospital, LMU Munich, Munich, Germany
| | - Paul Kerbs
- Laboratory for Leukemia Diagnostics, Department of Medicine III, University Hospital, LMU Munich, Munich, Germany
| | - Ashwini Kumar
- Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, Finland
| | - Vindi Jurinovic
- Institute of Medical Data Processing, Biometrics and Epidemiology (IBE), Faculty of Medicine, LMU Munich, Munich, Germany.,Laboratory for Leukemia Diagnostics, Department of Medicine III, University Hospital, LMU Munich, Munich, Germany
| | - Maja Rothenberg-Thurley
- Laboratory for Leukemia Diagnostics, Department of Medicine III, University Hospital, LMU Munich, Munich, Germany
| | - Bianka Ksienzyk
- Laboratory for Leukemia Diagnostics, Department of Medicine III, University Hospital, LMU Munich, Munich, Germany
| | - Julia Philippou-Massier
- Laboratory for Functional Genome Analysis (LAFUGA), Gene Center, University of Munich, Munich, Germany
| | - Stefan Krebs
- Laboratory for Functional Genome Analysis (LAFUGA), Gene Center, University of Munich, Munich, Germany
| | - Helmut Blum
- Laboratory for Functional Genome Analysis (LAFUGA), Gene Center, University of Munich, Munich, Germany
| | - Stephanie Schneider
- Laboratory for Leukemia Diagnostics, Department of Medicine III, University Hospital, LMU Munich, Munich, Germany.,Institute of Human Genetics, University Hospital, LMU Munich, Munich, Germany
| | - Nikola Konstandin
- Laboratory for Leukemia Diagnostics, Department of Medicine III, University Hospital, LMU Munich, Munich, Germany
| | - Stefan K Bohlander
- Leukaemia and Blood Cancer Research Unit, Department of Molecular Medicine and Pathology, University of Auckland, Auckland, New Zealand
| | - Caroline Heckman
- Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, Finland
| | - Mika Kontro
- Department of Haematology, Helsinki University Hospital Comprehensive Cancer Center, Helsinki, Finland
| | - Wolfgang Hiddemann
- Laboratory for Leukemia Diagnostics, Department of Medicine III, University Hospital, LMU Munich, Munich, Germany.,German Cancer Consortium (DKTK), Partner Site Munich, Munich, Germany.,German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Karsten Spiekermann
- Laboratory for Leukemia Diagnostics, Department of Medicine III, University Hospital, LMU Munich, Munich, Germany.,German Cancer Consortium (DKTK), Partner Site Munich, Munich, Germany.,German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Jan Braess
- Department of Oncology and Hematology, Hospital Barmherzige Brüder, Regensburg, Germany
| | - Klaus H Metzeler
- Laboratory for Leukemia Diagnostics, Department of Medicine III, University Hospital, LMU Munich, Munich, Germany.,German Cancer Consortium (DKTK), Partner Site Munich, Munich, Germany.,German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Philipp A Greif
- Laboratory for Leukemia Diagnostics, Department of Medicine III, University Hospital, LMU Munich, Munich, Germany.,German Cancer Consortium (DKTK), Partner Site Munich, Munich, Germany.,German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Ulrich Mansmann
- Institute of Medical Data Processing, Biometrics and Epidemiology (IBE), Faculty of Medicine, LMU Munich, Munich, Germany.,Data Integration for Future Medicine (DiFuture, www.difuture.de), LMU Munich, Munich, Germany.,German Cancer Consortium (DKTK), Partner Site Munich, Munich, Germany.,German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Tobias Herold
- Laboratory for Leukemia Diagnostics, Department of Medicine III, University Hospital, LMU Munich, Munich, Germany. .,German Cancer Consortium (DKTK), Partner Site Munich, Munich, Germany. .,German Cancer Research Center (DKFZ), Heidelberg, Germany. .,Research Unit Apoptosis in Hematopoietic Stem Cells, Helmholtz Zentrum München, German Research Center for Environmental Health (HMGU), Munich, Germany.
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Herold T, Jurinovic V, Batcha AMN, Bamopoulos SA, Rothenberg-Thurley M, Ksienzyk B, Hartmann L, Greif PA, Phillippou-Massier J, Krebs S, Blum H, Amler S, Schneider S, Konstandin N, Sauerland MC, Görlich D, Berdel WE, Wörmann BJ, Tischer J, Subklewe M, Bohlander SK, Braess J, Hiddemann W, Metzeler KH, Mansmann U, Spiekermann K. A 29-gene and cytogenetic score for the prediction of resistance to induction treatment in acute myeloid leukemia. Haematologica 2017; 103:456-465. [PMID: 29242298 PMCID: PMC5830382 DOI: 10.3324/haematol.2017.178442] [Citation(s) in RCA: 72] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2017] [Accepted: 12/07/2017] [Indexed: 01/15/2023] Open
Abstract
Primary therapy resistance is a major problem in acute myeloid leukemia treatment. We set out to develop a powerful and robust predictor for therapy resistance for intensively treated adult patients. We used two large gene expression data sets (n=856) to develop a predictor of therapy resistance, which was validated in an independent cohort analyzed by RNA sequencing (n=250). In addition to gene expression markers, standard clinical and laboratory variables as well as the mutation status of 68 genes were considered during construction of the model. The final predictor (PS29MRC) consisted of 29 gene expression markers and a cytogenetic risk classification. A continuous predictor is calculated as a weighted linear sum of the individual variables. In addition, a cut off was defined to divide patients into a high-risk and a low-risk group for resistant disease. PS29MRC was highly significant in the validation set, both as a continuous score (OR=2.39, P=8.63·10−9, AUC=0.76) and as a dichotomous classifier (OR=8.03, P=4.29·10−9); accuracy was 77%. In multivariable models, only TP53 mutation, age and PS29MRC (continuous: OR=1.75, P=0.0011; dichotomous: OR=4.44, P=0.00021) were left as significant variables. PS29MRC dominated all models when compared with currently used predictors, and also predicted overall survival independently of established markers. When integrated into the European LeukemiaNet (ELN) 2017 genetic risk stratification, four groups (median survival of 8, 18, 41 months, and not reached) could be defined (P=4.01·10−10). PS29MRC will make it possible to design trials which stratify induction treatment according to the probability of response, and refines the ELN 2017 classification.
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Affiliation(s)
- Tobias Herold
- Department of Internal Medicine III, University of Munich, Germany .,German Cancer Consortium (DKTK), Partner Site Munich, Munich, Germany.,German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Vindi Jurinovic
- Institute for Medical Informatics, Biometry and Epidemiology, University of Munich, Germany
| | - Aarif M N Batcha
- German Cancer Consortium (DKTK), Partner Site Munich, Munich, Germany.,German Cancer Research Center (DKFZ), Heidelberg, Germany.,Institute for Medical Informatics, Biometry and Epidemiology, University of Munich, Germany
| | | | | | - Bianka Ksienzyk
- Department of Internal Medicine III, University of Munich, Germany
| | - Luise Hartmann
- Department of Internal Medicine III, University of Munich, Germany.,German Cancer Consortium (DKTK), Partner Site Munich, Munich, Germany.,German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Philipp A Greif
- Department of Internal Medicine III, University of Munich, Germany.,German Cancer Consortium (DKTK), Partner Site Munich, Munich, Germany.,German Cancer Research Center (DKFZ), Heidelberg, Germany
| | | | - Stefan Krebs
- Institute of Biostatistics and Clinical Research, University of Münster, Germany
| | - Helmut Blum
- Institute of Biostatistics and Clinical Research, University of Münster, Germany
| | - Susanne Amler
- German Cancer Research Center (DKFZ), Heidelberg, Germany
| | | | | | | | - Dennis Görlich
- Institute of Biostatistics and Clinical Research, University of Munich, Germany
| | - Wolfgang E Berdel
- Department of Medicine, Hematology and Oncology, University of Münster, Germany
| | | | - Johanna Tischer
- Department of Internal Medicine III, University of Munich, Germany
| | - Marion Subklewe
- Department of Internal Medicine III, University of Munich, Germany
| | - Stefan K Bohlander
- Department of Molecular Medicine and Pathology, University of Auckland, Auckland, New Zealand
| | - Jan Braess
- Department of Oncology and Hematology, Hospital Barmherzige Brüder, Regensburg, Germany
| | - Wolfgang Hiddemann
- Department of Internal Medicine III, University of Munich, Germany.,German Cancer Consortium (DKTK), Partner Site Munich, Munich, Germany.,German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Klaus H Metzeler
- Department of Internal Medicine III, University of Munich, Germany.,German Cancer Consortium (DKTK), Partner Site Munich, Munich, Germany.,German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Ulrich Mansmann
- German Cancer Consortium (DKTK), Partner Site Munich, Munich, Germany.,German Cancer Research Center (DKFZ), Heidelberg, Germany.,Institute for Medical Informatics, Biometry and Epidemiology, University of Munich, Germany
| | - Karsten Spiekermann
- Department of Internal Medicine III, University of Munich, Germany.,German Cancer Consortium (DKTK), Partner Site Munich, Munich, Germany.,German Cancer Research Center (DKFZ), Heidelberg, Germany
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