Myelofibrosis and Survival Prognostic Models: A Journey between Past and Future

Abstract

Among the myeloproliferative diseases, myelofibrosis is a widely heterogeneous entity characterized by a highly variable prognosis. In this context, several prognostic models have been proposed to categorize these patients appropriately. Identifying who deserves more invasive treatments, such as bone marrow transplantation, is a critical clinical need. Age, complete blood count (above all, hemoglobin value), constitutional symptoms, driver mutations, and blast cells have always represented the milestones of the leading models still used worldwide (IPSS, DIPSS, MYSEC-PM). Recently, the advent of new diagnostic techniques (among all, next-generation sequencing) and the extensive use of JAK inhibitor drugs have allowed the development and validation of new models (MIPSS-70 and version 2.0, GIPSS, RR6), which are continuously updated. Finally, the new frontier of artificial intelligence promises to build models capable of drawing an overall survival perspective for each patient. This review aims to collect and summarize the existing standard prognostic models in myelofibrosis and examine the setting where each of these finds its best application.

Occurrence of lymphoproliferative disorders during ruxolitinib treatment: May fedratinib be the turning point?

“…The clinical approach to MF has radically changed after the development of Janus Kinase inhibitors (JAKi), since the progenitor ruxolitinib (RUX) to the more recent molecules. 1 Based on common prognostic scores, 2 high-risk patients are addressed to JAKi therapy, but during follow-up, they may interrupt RUX due to inadequate early response, or adverse events. 3 The principal factors leading to treatment discontinuation are hematological toxicities or immunological dysfunction due to inhibition of the JAK-STAT pathway.…”

ACVR1: A Novel Therapeutic Target to Treat Anemia in Myelofibrosis

“…In MF, the median life expectancy ranges between 2 and 6 years from the initial diagnosis [ 40 , 41 ]. Several prognostic models that take into consideration demographic, clinical, genetic, and molecular variables are available for prognostication in primary and secondary MF [ 42 ]. MF is considered a chronic disease, primarily occurring in the elderly.…”

AIPSS‐MF machine learning prognostic score validation in a cohort of myelofibrosis patients treated with ruxolitinib

“…This condition, divided into primary (PMF) or secondary (SMF), occurring after another MPN, is characterized by variable overall survival (OS), with a range from <2 to 20 years 1 . Several prognostic factors evaluated both at the time of diagnosis and later during the follow‐up of the disease are extensively considered in a few currently available prognostic scores, that is, International Prognostic Score System (IPSS), Dynamic IPSS (DIPSS) for PMF, and Myelofibrosis Secondary to PV and ET‐Prognostic Model (MYSEC‐PM) for SMF 2 . The more accurate prognostic models (Genetically Inspired Prognostic Scoring System (GIPSS), Mutation‐Enhanced International Prognostic Score System for Transplantation‐Age Patients (MIPSS70) and v.2) consider the molecular and genomic side, through the wide use of the next‐generation (NGS) able to identify the occurrence of high‐risk mutations 3 …”