Repository logo

Classification and Personalized Prognosis in Myeloproliferative Neoplasms.

Accepted version

Change log


Grinfeld, Jacob 
Baxter, E Joanna 
Wedge, David C 
Angelopoulos, Nicos 


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.).



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

Journal Title

N Engl J Med

Conference Name

Journal ISSN


Volume Title



Massachusetts Medical Society
Cancer Research UK (26718)
Medical Research Council (MC_PC_12009)
Wellcome Trust (203151/Z/16/Z)
Leukaemia & Lymphoma Research (13058)
Supported by the Leukemia and Lymphoma Society of America, Cancer Research UK (including a fellowship to J.N), Bloodwise (including a fellowship to J.G), the Wellcome Trust (including a fellowship to P.J.C), the Kay Kendall Leukaemia Fund (including a fellowship to J.G), the European Haematology Association (research grant to J.N), the Li Ka Shing foundation (D.C.W), and the Medical Research Council, UK. A.M.V. and P.G. were supported by a grant from Associazione Italiana per la Ricerca sul Cancro (AIRC; Milan, Italy), to AIRC-Gruppo Italiano Malattie Mieloproliferative- AGIMM (project #1005). P.G. was supported also by a Progetto Ministero della Salute GR-2011-02352109. Samples were provided by the Cambridge Blood and Stem Cell Biobank, which is supported by the NIHR Cambridge Biomedical Research Centre, Wellcome - MRC Stem Cell Institute and the Cancer Research UK - Cambridge Cancer Centre, UK. We thank members of the Cambridge Blood and Stem Cell Bank (Cambridge) and the Cancer Genome Project laboratory (Hinxton) for technical assistance. We thank clinicians and centres who participated in the PT1 studies and Vorinostat trials (details listed in the supplementary appendix). We thank all patients who participated in this study.