Classification and Personalized Prognosis in Myeloproliferative Neoplasms.
Godfrey, Anna L
Teague, Jon W
Butler, Adam P
Andersen, Christen L
Hasselbalch, Hans C
McMullin, Mary F
Vannucchi, Alessandro M
Harrison, Claire N
Campbell, Peter J
The New England journal of medicine
Massachusetts Medical Society
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Grinfeld, J., Nangalia, J., Baxter, J., Wedge, D. C., Angelopoulos, N., Cantrill, R., Godfrey, A. L., et al. (2018). Classification and Personalized Prognosis in Myeloproliferative Neoplasms.. The New England journal of medicine, 379 (15), 1416-1430. https://doi.org/10.1056/NEJMoa1716614
BACKGROUND. Myeloproliferative neoplasms (MPN), comprising polycythemia vera, essential thrombocythemia and myelofibrosis, are chronic hematological malignancies with variable progression rates. Genomic characterization of MPN patients offers the potential for personalised diagnosis, risk stratification and management. METHODS. We sequenced coding exons from 69 myeloid cancer genes in 2035 MPN patients, comprehensively annotating driver mutations and copy number changes. We developed a genomic classification for MPNs and multistage prognostic models for predicting individual patient outcomes. Classification and prognostic models were validated on an external cohort. RESULTS. 33 genes carried driver mutations in >4 patients, with JAK2, CALR or MPL mutations being the sole abnormality in 45% patients. The number of driver mutations increased with age and advanced disease. Driver mutations, germline polymorphisms and demographic variables independently predicted whether patients were diagnosed with essential thrombocythemia versus polycythemia vera, and chronic phase disease versus myelofibrosis. We defined 8 genomic subgroups, exhibiting distinct clinical phenotypes, including diagnostic 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 chronic phase MPN or myelofibrosis. Predicted and observed outcomes correlated well using internal cross-validation and an independent external cohort. Even within individual categories of existing prognostic schemas, our models substantially improved predictive accuracy. CONCLUSIONS. Comprehensive genomic characterization identifies distinct genetic subgroups and provides an MPN classification based on causal biological mechanisms. Integration of genomic data with clinical parameters enables personalised predictions of patient outcome and will support management of MPN patients.
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
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.
Cancer Research UK (26718)
Wellcome Trust (203151/Z/16/Z)
External DOI: https://doi.org/10.1056/NEJMoa1716614
This record's URL: https://www.repository.cam.ac.uk/handle/1810/283087