TY - JOUR
T1 - Mathematical modeling of cancer immunotherapy for personalized clinical translation
AU - Butner, Joseph D.
AU - Dogra, Prashant
AU - Chung, Caroline
AU - Pasqualini, Renata
AU - Arap, Wadih
AU - Lowengrub, John
AU - Cristini, Vittorio
AU - Wang, Zhihui
N1 - Funding Information:
This research has been supported in part by the National Science Foundation Grant DMS-1930583 (V.C. and Z.W.), the National Institutes of Health (NIH) Grants 1R01CA253865 (V.C. and Z.W.), 1R01CA226537 (R.P., W.A., V.C. and Z.W.), 1R01CA222007 (V.C. and Z.W.), 1R01AI165372 (Z.W.), 1R01DK132104 (Z.W.) and 1R01DK133610 (Z.W.). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Publisher Copyright:
© 2022, Springer Nature America, Inc.
PY - 2022/12
Y1 - 2022/12
N2 - 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.
AB - 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.
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U2 - 10.1038/s43588-022-00377-z
DO - 10.1038/s43588-022-00377-z
M3 - Review article
AN - SCOPUS:85144254019
VL - 2
SP - 785
EP - 796
JO - Nature Computational Science
JF - Nature Computational Science
SN - 2662-8457
IS - 12
ER -