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

(1) Background: myeloproliferative neoplasms (MPNs) are slowly developing hematological cancers characterized by few driver mutations, with <i>JAK2</i>V617F being the most prevalent. (2) Methods: using mechanism-based mathematical modeling (MM) of hematopoietic stem cells, mutated hemato...

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Bibliographic Details
Main Authors: Johnny T. Ottesen, Rasmus K. Pedersen, Marc J. B. Dam, Trine A. Knudsen, Vibe Skov, Lasse Kjær, Morten Andersen
Format: Article
Language:English
Published: MDPI AG 2020-07-01
Series:Cancers
Subjects:
Online Access:https://www.mdpi.com/2072-6694/12/8/2119
Description
Summary:(1) Background: myeloproliferative neoplasms (MPNs) are slowly developing hematological cancers characterized by few driver mutations, with <i>JAK2</i>V617F 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 <i>JAK2</i>V617F allele burden is halved every 25 months. At the individual level, MM describes and predicts the <i>JAK2</i>V617F 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.
ISSN:2072-6694