JAK2V617F clonality profile: identification of cell types involved in myeloproliferative disorders-associated thrombosis – ACTOR

Myeloproliferative disorders (MPD) are acquired haematological diseases often due to the acquisition of the activating JAK2V617F mutation in a hematopoietic stem cell, that leads to increased production of platelets, red cells and leukocytes. Thrombosis reveals MPD in about 30% of patients and is the main cause of morbidity and mortality. Understanding the mechanisms underlying the MPD thrombotic diathesis is therefore crucial to develop efficient anti thrombotic strategies. JAK2V617F red cells, platelets, neutrophils and endothelial cells (EC) were shown to participate in the pathogenesis of thrombosis in MPDs but, it is still unclear which of these actors is the most relevant and thus needs to be targeted. Our research hypothesis is that certain cell types are more important than others in the occurrence of thrombosis in JAK2V617F MPD. We will first quantify the JAK2V617F allele burden in each cell type in patients with and without thrombosis and compare this “clonality profile” with the occurrence of thrombosis and the type of thrombosis (arterial, venous, splanchnic thrombosis). Our second objective will be to generate mouse models with different “clonality profiles”, and analyze the occurrence of thrombosis using various mouse models of thrombosis (ie, stroke, inferior vena cava stenosis and cerebral venous thrombosis). Our third objective will be to test different therapies to find the most efficient ones to prevent thrombosis depending on the thrombus localization and the clonality profile of the mouse.
The ACTOR project gathers one team specialized in MPD-associated thrombosis and one with an international expertise in platelet biology and mouse models of hemostasis and thrombosis. The project will use state-of-the art techniques and a bed to benchside strategy to identify the most relevant actors of thrombosis in JAK2V617F MPDs. It will allow the development of preclinical mouse models to precisely identify the best antithrombotic strategy