Faculty, Staff and Student Publications
Publication Date
12-1-2022
Abstract
Encouraging advances are being made in cancer immunotherapy modeling, especially in the key areas of developing personalized treatment strategies based on individual patient parameters, predicting treatment outcomes and optimizing immunotherapy synergy when used in combination with other treatment approaches. Here we present a focused review of the most recent mathematical modeling work on cancer immunotherapy with a focus on clinical translatability. It can be seen that this field is transitioning from pure basic science to applications that can make impactful differences in patients' lives. We discuss how researchers are integrating experimental and clinical data to fully inform models so that they can be applied for clinical predictions, and present the challenges that remain to be overcome if widespread clinical adaptation is to be realized. Lastly, we discuss the most promising future applications and areas that are expected to be the focus of extensive upcoming modeling studies.
DOI
10.1038/s43588-022-00377-z
PMID
38126024
PMCID
PMC10732566
PubMedCentral® Posted Date
12-20-2023
PubMedCentral® Full Text Version
Author MSS
Published Open-Access
yes
Included in
Bioinformatics Commons, Biomedical Informatics Commons, Medical Sciences Commons, Oncology Commons