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Transduction-Transplantation Mouse Model of Myeloproliferative Neoplasm
Published on: December 22, 2016
Classification and Personalized Prognosis in Myeloproliferative Neoplasms
Jacob Grinfeld1, Jyoti Nangalia1, E Joanna Baxter1
1From 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.).
Genomic analysis of myeloproliferative neoplasms (MPNs) identified distinct genetic subgroups and improved personalized risk prediction. This classification aids in understanding disease mechanisms and tailoring treatments for MPNs.
Area of Science:
- Hematology
- Genomics
- Oncology
Background:
- Myeloproliferative neoplasms (MPNs) are chronic blood cancers with variable progression.
- Genomic characterization offers potential for personalized diagnosis, risk stratification, and treatment of MPNs.
Purpose of the Study:
- To comprehensively characterize the genomics of MPNs.
- To develop and validate genomic classification and prognostic models for MPNs.
Main Methods:
- Sequenced coding exons of 69 myeloid cancer genes in 2035 MPN patients.
- Annotated driver mutations and copy-number changes.
- Developed and validated genomic classification and prognostic models.
Main Results:
- Identified driver mutations in 33 genes, with JAK2, CALR, or MPL mutations in 45% of patients.
- Defined eight genomic subgroups with distinct clinical phenotypes.
- Developed prognostic models integrating clinical and genomic data, showing strong predictive accuracy.
Conclusions:
- Comprehensive genomic characterization provides a classification of MPNs based on biologic mechanisms.
- Integration of genomic and clinical data enables personalized outcome predictions for MPN patients.
- This approach may support improved treatment strategies for MPNs.
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