TY - JOUR
T1 - Mathematical Modeling of MPNs Offers Understanding and Decision Support for Personalized Treatment
AU - Ottesen, Johnny T.
AU - Pedersen, Rasmus Kristoffer
AU - Dam, Marc John Bordier
AU - Knudsen, Trine A.
AU - Skov, Vibe
AU - Kjær, Lasse
AU - Wienecke Andersen, Morten
N1 - This article belongs to the Special Issue New Insights into Myeloproliferative Neoplasms
PY - 2020/8
Y1 - 2020/8
N2 - (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.
AB - (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.
KW - Blood cancer
KW - JAK2V617F dynamics
KW - Mathematical modeling
KW - Myeloproliferative neoplasms
KW - Personalized treatment
U2 - 10.3390/cancers12082119
DO - 10.3390/cancers12082119
M3 - Journal article
SN - 2072-6694
VL - 12
SP - 1
EP - 15
JO - Cancers
JF - Cancers
IS - 8
M1 - 2119
ER -