1. Mathematical Modeling of MPNs Offers Understanding and Decision Support for Personalized Treatment
- Author
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Marc John Bordier Dam, Lasse Kjær, Morten Andersen, Trine Alma Knudsen, Johnny T. Ottesen, Vibe Skov, and Rasmus Kristoffer Pedersen
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0301 basic medicine ,Oncology ,Cancer Research ,medicine.medical_specialty ,Personalized treatment ,lcsh:RC254-282 ,Article ,myeloproliferative neoplasms ,03 medical and health sciences ,0302 clinical medicine ,Immune system ,Internal medicine ,hemic and lymphatic diseases ,JAK2V617F dynamics ,medicine ,Allele ,business.industry ,Mechanism (biology) ,blood cancer ,mathematical modeling ,lcsh:Neoplasms. Tumors. Oncology. Including cancer and carcinogens ,Response to treatment ,personalized treatment ,Haematopoiesis ,030104 developmental biology ,Threshold dose ,030220 oncology & carcinogenesis ,Stem cell ,business - 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-&alpha, 2 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.
- Published
- 2020
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