clinical topic updates

Genomics and Personalized Prognosis in Myeloproliferative Neoplasms

by Rami Komrokji, MD


Driver mutations correlating with phenotype and prognosis have become increasingly important in predicting clinical outcomes in patients with myeloproliferative neoplasms (MPN). Other key prognostic genomic variables may include the order of acquisition of mutations and the total number of mutations.

Expert Commentary

Rami S. Komrokji, MD

Senior Member
Section Head, Leukemia and MDS
Vice Chair, Department of Malignant Hematology
Moffitt Cancer Center
Professor of Medicine & Oncologic Sciences
University of South Florida Health Morsani College of Medicine
Tampa, FL

“In the past few years, the genomic landscape of MPN has become much more apparent to us.”

Rami Komrokji, MD

We have identified somatic mutations that correlate with the clinical phenotype of MPN and other mutations associated with disease initiation or progression. We currently recognize the following 3 mutation types that predominate as phenotype drivers in MPN: mutations in JAK2, CALR, and MPL. The Janus kinase/signal transducer and activator of transcription signaling pathway is overactive in patients with MPN, sometimes in response to excess cytokine activity and not necessarily because of mutations. JAK2 mutations are found in approximately 90% of patients with polycythemia vera (PV) and approximately 50% of those with essential thrombocythemia (ET) or primary myelofibrosis (PMF). Those with PV may harbor a mutation in the JAK2 exon 12, with the majority of individuals harboring the classic JAK2 V617F mutation. Patients with ET who do not have the JAK2 mutation or prior thrombotic events are considered to be lower risk. It is important to recognize that JAK2 inhibition has activity in patients with PMF without regard to JAK2 mutation status. Somatic mutations in the CALR gene are detected in approximately one-third of patients with PMF and ET but are rare in those with PV. W515L and W515K represent the vast majority of MPL mutations reported in the literature. We refer to patients in whom we cannot find any phenotype driver mutations as triple-negative patients; these individuals tend to have poorer outcomes. Other somatic mutations are associated with different stages of MPN as disease initiating or progression related. ASXL1 or SRSF2 mutations are associated with poor outcomes. IDH mutations are associated with higher rates of acute myeloid leukemia transformation. Finally, although TP53 mutations are uncommon in MPN, those who do have these mutations are at an increased risk for disease progression and very poor outcomes. 

Genomic mutations are now being integrated into the newer prognostic risk stratification models. The Mutation-Enhanced International Prognostic Scoring System for Transplant-Age Patients model is the most contemporary prognostic system for PMF and includes genomic mutations and karyotype, in addition to clinical risk factors. Thus, in the past few years, the genomic landscape of MPN has become much more apparent to us. In the future, there could be therapeutic implications based on some of these mutations.


Grinfeld J, Nangalia J, Baxter EJ, et al. Classification and personalized prognosis in myeloproliferative neoplasms. N Engl J Med. 2018;379(15):1416-1430.

Nangalia J, Massie CE, Baxter EJ, et al. Somatic CALR mutations in myeloproliferative neoplasms with nonmutated JAK2. N Engl J Med. 2013;369(25):2391-2405.

Tefferi A, Guglielmelli P, Pardanani A, Vannucchi AM. Myelofibrosis Treatment Algorithm 2018. Blood Cancer J. 2018;8(8):72.

Vainchenker W, Kralovics R. Genetic basis and molecular pathophysiology of classical myeloproliferative neoplasms. Blood. 2017;129(6):667-679.

Verstovsek S, Mesa RA, Gotlib J, et al. The clinical benefit of ruxolitinib across patient subgroups: analysis of a placebo-controlled, phase III study in patients with myelofibrosis. Br J Haematol. 2013;161(4):508-516.

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