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Classification and Personalized Prognosis in Myeloproliferative Neoplasms

Grinfeld, Jacob and Nangalia, Jyoti and Baxter, Joanna and Wedge, David C and Angelopoulos, Nicos and Cantrill, Rob and Godfrey, Anna L and Papaemmanuil, Elli and Gundem, Gunes and MacLean, Cathy and Cook, Julia and Mudie, Laura and Meara, Sarah O and Teague, Jon W and Butler, Adam P and Massie, Charlie E and Williams, Nicholas and Nice, Francesca L and Andersen, Christen L and Hasselbalch, Hans C and Guglielmelli, Paola and McMullin, Mary Frances and Vannucchi, Alessandro M and Harrison, Claire N and Gerstung, Moritz and Green, Anthony R and Campbell, Peter J (2018) 'Classification and Personalized Prognosis in Myeloproliferative Neoplasms.' The New England Journal of Medicine, 379. 1416 - 1430. ISSN 0028-4793

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Abstract

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.)

Item Type: Article
Uncontrolled Keywords: Myeloproliferative Disorders, Cancer, Precision Medicine, Bayesian networks, Proportional Hazards Models
Divisions: Faculty of Science and Health > Computer Science and Electronic Engineering, School of
Depositing User: Elements
Date Deposited: 23 Apr 2020 15:14
Last Modified: 23 Apr 2020 16:15
URI: http://repository.essex.ac.uk/id/eprint/26771

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