Mathematical Modeling of MPNs Offers Understanding and Decision Support for Personalized Treatment

Johnny T. Ottesen*, Rasmus Kristoffer Pedersen*, Marc John Bordier Dam*, Trine A. Knudsen*, Vibe Skov*, Lasse Kjær*, Morten Wienecke Andersen*

*Corresponding author

Research output: Contribution to journalJournal articleResearchpeer-review

Abstract

(1) Background: myeloproliferative neoplasms (MPNs) are slowly developing hematological cancers characterized by few driver mutations, with JAK2V617F being the most prevalent. (2) Methods: using mechanism-based mathematical modeling (MM) of hematopoietic stem cells, mutated hematopoietic stem cells, differentiated blood cells, and immune response along with longitudinal data from the randomized Danish DALIAH trial, we investigate the effect of the treatment of MPNs with interferon-α2 on disease progression. (3) Results: At the population level, the JAK2V617F allele burden is halved every 25 months. At the individual level, MM describes and predicts the JAK2V617F kinetics and leukocyte- and thrombocyte counts over time. The model estimates the patient-specific treatment duration, relapse time, and threshold dose for achieving a good response to treatment. (4) Conclusions: MM in concert with clinical data is an important supplement to understand and predict the disease progression and impact of interventions at the individual level.
Original languageEnglish
Article number2119
JournalCancers
Volume12
Issue number8
Number of pages15
ISSN2072-6694
DOIs
Publication statusPublished - 2020

Bibliographical note

This article belongs to the Special Issue New Insights into Myeloproliferative Neoplasms

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