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BACKGROUND:Myeloproliferative neoplasms, such as polycythemia vera, essential thrombocythemia, and myelofibrosis, are chronic hematologic cancers with varied progression rates. The genomic characterization of patients with myeloproliferative neoplasms offers the potential for personalized diagnosis, risk stratification, and treatment. METHODS:We sequenced coding exons from 69 myeloid cancer genes in patients with myeloproliferative neoplasms, comprehensively annotating driver mutations and copy-number changes. We developed a genomic classification for myeloproliferative neoplasms and multistage prognostic models for predicting outcomes in individual patients. Classification and prognostic models were validated in an external cohort. RESULTS:A total of 2035 patients were included in the analysis. A total of 33 genes had driver mutations in at least 5 patients, with mutations in JAK2, CALR, or MPL being the sole abnormality in 45% of the patients. The numbers of driver mutations increased with age and advanced disease. Driver mutations, germline polymorphisms, and demographic variables independently predicted whether patients received a diagnosis of essential thrombocythemia as compared with polycythemia vera or a diagnosis of chronic-phase disease as compared with myelofibrosis. We defined eight genomic subgroups that showed distinct clinical phenotypes, including blood counts, risk of leukemic transformation, and event-free survival. Integrating 63 clinical and genomic variables, we created prognostic models capable of generating personally tailored predictions of clinical outcomes in patients with chronic-phase myeloproliferative neoplasms and myelofibrosis. The predicted and observed outcomes correlated well in internal cross-validation of a training cohort and in an independent external cohort. Even within individual categories of existing prognostic schemas, our models substantially improved predictive accuracy. CONCLUSIONS:Comprehensive genomic characterization identified distinct genetic subgroups and provided a classification of myeloproliferative neoplasms on the basis of causal biologic mechanisms. Integration of genomic data with clinical variables enabled the personalized predictions of patients' outcomes and may support the treatment of patients with myeloproliferative neoplasms. (Funded by the Wellcome Trust and others.).

Original publication

DOI

10.1056/nejmoa1716614

Type

Journal article

Journal

The New England journal of medicine

Publication Date

10/2018

Volume

379

Pages

1416 - 1430

Addresses

From the Wellcome-MRC Cambridge Stem Cell Institute and Cambridge Institute for Medical Research (J.G., C.E.M., F.L.N., A.R.G., P.J.C.), the Department of Haematology, University of Cambridge (J.G., E.J.B., C.M., J.C., C.E.M., F.L.N., A.R.G.), and the Department of Haematology, Cambridge University Hospitals NHS Foundation Trust (J.G., E.J.B., A.L.G., C.M., J.C., A.R.G.), Cambridge, Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus (J.N., D.C.W., N.A., E.P., G.G., L.O., S.O., J.W.T., A.P.B., N.W., P.J.C.), and the European Molecular Biology Laboratory, European Bioinformatics Institute (R.C., M.G.), Hinxton, Big Data Institute, University of Oxford, Oxford (D.C.W.), the Department of Haematology, Queen's University Belfast, Belfast (M.F.M.), and the Department of Haematology, Guy's and St. Thomas' NHS Foundation Trust, London (C.N.H.) - all in the United Kingdom; the Center for Molecular Oncology and the Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York (E.P., G.G.); the Department of Hematology, Zealand University Hospital, Roskilde, and the University of Copenhagen, Copenhagen (C.L.A., H.C.H.); and the Department of Experimental and Clinical Medicine, Center of Research and Innovation of Myeloproliferative Neoplasms, Azienda Ospedaliera Universitaria Careggi, University of Florence, Florence, Italy (P.G., A.M.V.).

Keywords

Humans, Myeloproliferative Disorders, Disease Progression, Calreticulin, DNA, Neoplasm, Prognosis, Disease-Free Survival, Multivariate Analysis, Proportional Hazards Models, Bayes Theorem, Sequence Analysis, DNA, Phenotype, Mutation, Receptors, Thrombopoietin, Janus Kinase 2, Precision Medicine