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Myeloproliferative Neoplasms (MPN) are blood cancers that appear afteracquiring a driver mutation in a hematopoietic stem cell. These hematologicalmalignancies result in the overproduction of mature blood cells and, if nottreated, induce a risk of cardiovascular events and thrombosis. PegylatedIFN$\alpha$ is commonly used to treat MPN, but no clear guidelines existconcerning the dose prescribed to patients. We applied a model selectionprocedure and ran a hierarchical Bayesian inference method to decipher how dosevariations impact the response to the therapy. We inferred that IFN$\alpha$acts on mutated stem cells by inducing their differentiation into progenitorcells, the higher the dose, the higher the effect. We found that when asufficient (patient-dependent) dose is reached, the treatment can induce along-term remission. We determined this minimal dose for individuals in acohort of patients and estimated the most suitable starting dose to give to anew patient to increase the chances of being cured.

Mathematical modelling, selection and hierarchical inference to  determine the minimal dose in IFN$\alpha$ therapy against Myeloproliferative  Neoplasms