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

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 IFN$\alpha$ 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 IFN$\alpha$ 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.