Mathematical modeling of cancer immunotherapy for personalized clinical translation

Joseph D Butner, Prashant Dogra, Caroline Chung, Renata Pasqualini, Wadih Arap, John Lowengrub, Vittorio Cristini, Zhihui Wang

Research output: Contribution to journalArticlepeer-review


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.

Original languageEnglish (US)
Pages (from-to)785-796
Number of pages12
JournalNature Computational Science
Issue number12
StatePublished - Dec 2022

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