Unified classification and risk-stratification in Acute Myeloid Leukemia.

Autor: Tazi Y; Computational Oncology Service, Department of Epidemiology & Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA.; Center for Hematologic Malignancies, Memorial Sloan Kettering Cancer Center, New York, NY, USA.; Tri-Institutional Computational Biology and Medicine PhD Program, Weill Cornell Medicine of Cornell University and Rockefeller University, New York, NY, USA.; The Rockefeller University, New York, NY, USA., Arango-Ossa JE; Computational Oncology Service, Department of Epidemiology & Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA.; Center for Hematologic Malignancies, Memorial Sloan Kettering Cancer Center, New York, NY, USA., Zhou Y; Computational Oncology Service, Department of Epidemiology & Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA.; Center for Hematologic Malignancies, Memorial Sloan Kettering Cancer Center, New York, NY, USA., Bernard E; Computational Oncology Service, Department of Epidemiology & Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA.; Center for Hematologic Malignancies, Memorial Sloan Kettering Cancer Center, New York, NY, USA., Thomas I; Centre for Trials Research, School of Medicine, Cardiff University, Cardiff, UK., Gilkes A; Department of Haematology, School of Medicine, Cardiff University, Cardiff, UK., Freeman S; Institute of Immunology and Immunotherapy, University of Birmingham, Birmingham, UK., Pradat Y; Computational Oncology Service, Department of Epidemiology & Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA., Johnson SJ; Centre for Trials Research, School of Medicine, Cardiff University, Cardiff, UK., Hills R; Nuffield Department of Population Health, University of Oxford, Oxford, UK., Dillon R; Department of Medical and Molecular Genetics, King's College, London, UK., Levine MF; Computational Oncology Service, Department of Epidemiology & Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA., Leongamornlert D; Cancer, Ageing and Somatic Mutation Programme, Wellcome Sanger Institute, Hinxton, UK., Butler A; Cancer, Ageing and Somatic Mutation Programme, Wellcome Sanger Institute, Hinxton, UK., Ganser A; Department of Hematology, Hemostasis, Oncology, and Stem Cell Transplantation, Hannover Medical School, Hannover, Germany., Bullinger L; Department of Hematology, Oncology, and Tumorimmunology, Campus Virchow Klinikum, Berlin, Charité-Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany., Döhner K; Department of Internal Medicine III, Ulm University, Ulm, Germany., Ottmann O; Department of Haematology, School of Medicine, Cardiff University, Cardiff, UK., Adams R; Centre for Trials Research, School of Medicine, Cardiff University, Cardiff, UK., Döhner H; Department of Internal Medicine III, Ulm University, Ulm, Germany., Campbell PJ; Cancer, Ageing and Somatic Mutation Programme, Wellcome Sanger Institute, Hinxton, UK., Burnett AK; Visiting Professor University of Glasgow, formerly Cardiff University, Cardiff, UK., Dennis M; The Christie NHS Foundation Trust, Manchester, UK., Russell NH; Department of Haematology, Nottingham University Hospital, Nottingham, UK., Devlin SM; Computational Oncology Service, Department of Epidemiology & Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA., Huntly BJP; Department of Haematology and Wellcome Trust-MRC Cambridge Stem Cell Institute, University of Cambridge, Cambridge, UK., Papaemmanuil E; Computational Oncology Service, Department of Epidemiology & Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA. papaemme@mskcc.org.; Center for Hematologic Malignancies, Memorial Sloan Kettering Cancer Center, New York, NY, USA. papaemme@mskcc.org.
Jazyk: angličtina
Zdroj: Nature communications [Nat Commun] 2022 Aug 08; Vol. 13 (1), pp. 4622. Date of Electronic Publication: 2022 Aug 08.
DOI: 10.1038/s41467-022-32103-8
Abstrakt: Clinical recommendations for Acute Myeloid Leukemia (AML) classification and risk-stratification remain heavily reliant on cytogenetic findings at diagnosis, which are present in <50% of patients. Using comprehensive molecular profiling data from 3,653 patients we characterize and validate 16 molecular classes describing 100% of AML patients. Each class represents diverse biological AML subgroups, and is associated with distinct clinical presentation, likelihood of response to induction chemotherapy, risk of relapse and death over time. Secondary AML-2, emerges as the second largest class (24%), associates with high-risk disease, poor prognosis irrespective of flow Minimal Residual Disease (MRD) negativity, and derives significant benefit from transplantation. Guided by class membership we derive a 3-tier risk-stratification score that re-stratifies 26% of patients as compared to standard of care. This results in a unified framework for disease classification and risk-stratification in AML that relies on information from cytogenetics and 32 genes. Last, we develop an open-access patient-tailored clinical decision support tool.
(© 2022. The Author(s).)
Databáze: MEDLINE
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