Mathematical modelling, selection and hierarchical inference to determine the minimal dose in IFNα therapy against Myeloproliferative Neoplasms

Abstract: Myeloproliferative Neoplasms (MPN) are blood cancers that appear after acquiring a driver mutation in a hematopoietic stem cell. These hematological malignancies result in the overproduction of mature blood cells and, if not treated, induce a risk of cardiovascular events and thrombosis. Pegylated IFNα is commonly used to treat MPN, but no clear guidelines exist concerning the dose prescribed to patients. We applied a model selection procedure and ran a hierarchical Bayesian inference method to decipher how dose variations impact the response to the therapy. We inferred that IFNα acts on mutated stem cells by inducing their differentiation into progenitor cells, the higher the dose, the higher the effect. We found that when a sufficient (patient-dependent) dose is reached, the treatment can induce a long-term remission. We determined this minimal dose for individuals in a cohort of patients and estimated the most suitable starting dose to give to a new patient to increase the chances of being cured.