Dataset Information

Classification and Personalized Prognosis in Myeloproliferative Neoplasms.

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.).

SUBMITTER: Grinfeld J

PROVIDER: S-EPMC7030948 | biostudies-literature | 2018 Oct

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Classification and Personalized Prognosis in Myeloproliferative Neoplasms.

Grinfeld Jacob J Nangalia Jyoti J Baxter E Joanna EJ Wedge David C DC Angelopoulos Nicos N Cantrill Robert R Godfrey Anna L AL Papaemmanuil Elli E Gundem Gunes G MacLean Cathy C Cook Julia J O'Neil Laura L O'Meara Sarah S Teague Jon W JW Butler Adam P AP Massie Charlie E CE Williams Nicholas N Nice Francesca L FL Andersen Christen L CL Hasselbalch Hans C HC Guglielmelli Paola P McMullin Mary F MF Vannucchi Alessandro M AM Harrison Claire N CN Gerstung Moritz M Green Anthony R AR Campbell Peter J PJ

The New England journal of medicine 20181001 15

<h4>Background</h4>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.<h4>Methods</h4>We sequenced coding exons from 69 myeloid cancer genes in patients with myeloproliferative neoplasms, comprehensively annotating driver muta ...[more]

PMID: 30304655

Similar Datasets

Project description:BackgroundPhiladelphia-negative myeloproliferative neoplasms (MPN) are most prevalent in the older population (median age at the diagnosis is above 60 years) and rarely diagnosed in pediatrics. Thus, our knowledge about the clinical presentation, mutational status, and complications of MPNs in pediatrics is limited.MethodsThe literature in English (PubMed, SCOPUS, and Google Scholar) was searched for studies, reviews, case series, and case reports of patients with Philadelphia-negative MPNs (including essential thrombocythemia, polycythemia vera, primary myelofibrosis, and profibrotic myelofibrosis) in the pediatrics age group (less than 18 years). Only studies that fulfilled WHO 2008 or 2016 criteria for MPNs were included. We aimed to describe the clinical characteristics, vascular and long-term complications, types of driver mutations, and treatment approaches in pediatric patients with MPNs.ResultsWe reviewed 33 articles of available published literature from 2008 to 2022 and collected data from a total of 196 patients of the pediatric population. Among the cohort of patients, 139 had essential thrombocythemia (ET), 20 had polycythemia vera (PV), and 37 had primary myelofibrosis (PMF). The median age at the time of diagnosis for each disease varied, with 8.8 years for ET, 10 years for PV, and 3.6 years for MF. There was a slight difference in gender prevalence between both gender groups and all three diseases. The presenting symptoms were not mentioned in more than 50% of studies. We found that JAK2 was the most prevalent among all mutations. Both bleeding and thrombosis were present equally in ET, with 9% of cases complicated by bleeding and 9% complicated by thrombosis. Hemorrhagic events did not occur in patients with PV; thrombosis in children with MF was also not found. The progression into AML occurred in two patients with PV and one with ET.ConclusionGiven the rarity of MPNs in pediatrics and their different characteristics compared with adults, we believe there is a need for unique diagnostic criteria to match the different molecular statuses in pediatrics. Based on our review, the incidence of MPN complications in pediatrics, including thrombotic events, hemorrhage, and leukemic transformation, differs from that in adults.

Dataset Information

Classification and Personalized Prognosis in Myeloproliferative Neoplasms.

Publications

Classification and Personalized Prognosis in Myeloproliferative Neoplasms.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets