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

TL;DR

This study uses mathematical modeling and hierarchical Bayesian inference to identify the minimal effective dose of IFNα therapy for treating Myeloproliferative Neoplasms, aiming to optimize personalized treatment strategies.

Contribution

It introduces a novel model selection and Bayesian inference approach to determine patient-specific minimal doses of IFNα for MPN treatment.

Findings

IFNα induces differentiation of mutated stem cells into progenitors.

Higher doses lead to greater therapeutic effects.

A patient-specific minimal dose can induce long-term remission.

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 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 the treatment can induce long-term remission when a sufficient (patient-dependent) dose is reached. 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.