BACKGROUND Somatic mutations in the Janus kinase 2 gene (JAK2) occur in many myeloproliferative neoplasms, but the molecular pathogenesis of myeloproliferative neoplasms with nonmutated JAK2 is obscure, and the diagnosis of these neoplasms remains a challenge. METHODS We performed exome sequencing of samples obtained from 151 patients with myeloproliferative neoplasms. The mutation status of the gene encoding calreticulin (CALR) was assessed in an additional 1345 hematologic cancers, 1517 other cancers, and 550 controls. We established phylogenetic trees using hematopoietic colonies. We assessed calreticulin subcellular localization using immunofluorescence and flow cytometry. RESULTS Exome sequencing identified 1498 mutations in 151 patients, with medians of 6.5, 6.5, and 13.0 mutations per patient in samples of polycythemia vera, essential thrombocythemia, and myelofibrosis, respectively. Somatic CALR mutations were found in 70 to 84% of samples of myeloproliferative neoplasms with nonmutated JAK2, in 8% of myelodysplasia samples, in occasional samples of other myeloid cancers, and in none of the other cancers. A total of 148 CALR mutations were identified with 19 distinct variants. Mutations were located in exon 9 and generated a +1 base-pair frameshift, which would result in a mutant protein with a novel C-terminal. Mutant calreticulin was observed in the endoplasmic reticulum without increased cell-surface or Golgi accumulation. Patients with myeloproliferative neoplasms carrying CALR mutations presented with higher platelet counts and lower hemoglobin levels than patients with mutated JAK2. Mutation of CALR was detected in hematopoietic stem and progenitor cells. Clonal analyses showed CALR mutations in the earliest phylogenetic node, a finding consistent with its role as an initiating mutation in some patients. CONCLUSIONS Somatic mutations in the endoplasmic reticulum chaperone CALR were found in a majority of patients with myeloproliferative neoplasms with nonmutated JAK2. (Funded by the Kay Kendall Leukaemia Fund and others.)

Age older than 65 years, hemoglobin level lower than 100 g/L (10 g/dL), white blood cell count greater than 25 ؋ 10 9 /L, peripheral blood blasts 1% or higher, and constitutional symptoms have been shown to predict poor survival in primary myelofibrosis (PMF) at diagnosis. To investigate whether the acquisition of these factors during follow-up predicts survival, we studied 525 PMF patients regularly followed. All 5 variables had a significant impact on survival when analyzed as time- IntroductionPrimary myelofibrosis (PMF) is a Philadelphia-negative myeloproliferative neoplasm (MPN) whose diagnostic criteria have been recently updated. 1 Among MPNs, PMF has the most heterogeneous clinical presentation, which may encompass anemia, splenomegaly, leukocytosis or leukopenia, thrombocytosis or thrombocytopenia, and constitutional symptoms. The discovery of the activating mutation JAK2 (V617F) in more than 70% of patients with MPNs 2 led to the development of new biochemically selective JAK2 inhibitors. 3 These agents are currently being tested in clinical trials that usually include patients with long disease history.Advanced age, 4-7 anemia, 4-11 red blood cell transfusion need, 12 leukopenia, 8 leukocytosis, 8 thrombocytopenia, 9 peripheral blast count, 4,6 systemic symptoms, 6,10 degree of microvessel density, 13 and cytogenetic abnormalities 5,7,9,[14][15][16] were shown to be associated with poor outcome in patients with PMF. The presence of the JAK2 (V617F) mutation per se does not seem to imply worse survival, 17 although a low JAK2 (V617F) allele burden seems associated with poorer outcome. 18,19 Recently, Cervantes et al 17 on behalf of the International Working Group for Myeloproliferative Neoplasms Research and Treatment (IWG-MRT) developed a prognostic scoring system to estimate survival of PMF patients. This model uses 5 factors (age older than 65 years, hemoglobin level Ͻ 100 g/L [10 g/dL], white blood cell count Ͼ 25 ϫ 10 9 /L, peripheral blood blasts Ն 1%, and presence of constitutional symptoms) to identify 4 risk categories with different survival.Prognostic models for PMF developed so far are based on the evaluation of risk factors present at diagnosis. However, the acquisition of additional risk factors during the disease course may substantially modify the patients' outcome. A dynamic prognostic model that accounts for modifications of the risk profile after diagnosis may prove useful in clinical practice. On behalf of IWG-MRT, first we investigated whether the acquisition anytime during follow-up of one or more of the prognostic factors identified by Cervantes et al 17 predicts survival. Then, a new prognostic score based on a time-dependent risk evaluation was developed: the Dynamic International Prognostic Scoring System (DIPSS) for PMF. MethodsThe study was carried out through an international cooperation on behalf of the IWG-MRT. An ad hoc database was developed for data collection. For personal use only. on May 12, 2018. by guest www.bloodjournal.org From Study designThe Institutional Rev...

Patient outcome in primary myelofibrosis (PMF) is significantly influenced by karyotype. We studied 879 PMF patients to determine the individual and combinatorial prognostic relevance of somatic mutations. Analysis was performed in 483 European patients and the seminal observations were validated in 396 Mayo Clinic patients. Samples from the European cohort, collected at time of diagnosis, were analyzed for mutations in ASXL1, SRSF2, EZH2, TET2, DNMT3A, CBL, IDH1, IDH2, MPL and JAK2. Of these, ASXL1, SRSF2 and EZH2 mutations inter-independently predicted shortened survival. However, only ASXL1 mutations (HR: 2.02; P<0.001) remained significant in the context of the International Prognostic Scoring System (IPSS). These observations were validated in the Mayo Clinic cohort where mutation and survival analyses were performed from time of referral. ASXL1, SRSF2 and EZH2 mutations were independently associated with poor survival, but only ASXL1 mutations held their prognostic relevance (HR: 1.4; P=0.04) independent of the Dynamic IPSS (DIPSS)-plus model, which incorporates cytogenetic risk. In the European cohort, leukemia-free survival was negatively affected by IDH1/2, SRSF2 and ASXL1 mutations and in the Mayo cohort by IDH1 and SRSF2 mutations. Mutational profiling for ASXL1, EZH2, SRSF2 and IDH identifies PMF patients who are at risk for premature death or leukemic transformation.

Key Points Survival in ET is superior to that of PV, regardless of mutational status, but remains inferior to the sex- and age-matched US population. JAK2/CALR/MPL mutational status is prognostically informative in PMF, regarding overall and leukemia-free survival.

BACKGROUNDMyeloproliferative 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. METHODSWe sequenced coding exons from 69 myeloid cancer genes in patients with myeloproliferative neoplasms, comprehensively annotating driver mutations and copynumber 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. RESULTSA 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. CONCLUSIONSComprehensive 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.