Protocol for mathematical prediction of patient response and survival to immune checkpoint inhibitor immunotherapy

Joseph D. Butner; Maguy Farhat; Vittorio Cristini; Caroline Chung; Zhihui Wang

doi:10.1016/j.xpro.2022.101886

Protocol for mathematical prediction of patient response and survival to immune checkpoint inhibitor immunotherapy

Joseph D. Butner, Maguy Farhat, Vittorio Cristini, Caroline Chung, Zhihui Wang

Research output: Contribution to journal › Article › peer-review

Abstract

This protocol describes the application of a mechanistic mathematical model of immune checkpoint inhibitor (ICI) immunotherapy to patient tumor imaging data for predicting solid tumor response and patient survival under ICI intervention. We describe steps for data collection and processing, data pipelines, and approaches to increase precision. The protocol is highly predictive as early as the first restaging after treatment start and can be used with standard-of-care imaging measures. For complete details on the use and execution of this protocol, please refer to Butner et al. (2020)¹ and Butner et al. (2021).²

Original language	English (US)
Article number	101886
Pages (from-to)	101886
Journal	STAR Protocols
Volume	3
Issue number	4
DOIs	https://doi.org/10.1016/j.xpro.2022.101886 https://doi.org/10.1016/j.xpro.2022.101886
State	Published - Dec 16 2022

Keywords

Biotechnology and bioengineering
Cancer
Health Sciences
Immunology
Systems biology
Immune Checkpoint Inhibitors/pharmacology
Humans
Immunotherapy
Data Collection

ASJC Scopus subject areas

Biochemistry, Genetics and Molecular Biology(all)
Immunology and Microbiology(all)
Neuroscience(all)

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title = "Protocol for mathematical prediction of patient response and survival to immune checkpoint inhibitor immunotherapy",

abstract = "This protocol describes the application of a mechanistic mathematical model of immune checkpoint inhibitor (ICI) immunotherapy to patient tumor imaging data for predicting solid tumor response and patient survival under ICI intervention. We describe steps for data collection and processing, data pipelines, and approaches to increase precision. The protocol is highly predictive as early as the first restaging after treatment start and can be used with standard-of-care imaging measures. For complete details on the use and execution of this protocol, please refer to Butner et al. (2020)1 and Butner et al. (2021).2",

keywords = "Biotechnology and bioengineering, Cancer, Health Sciences, Immunology, Systems biology, Immune Checkpoint Inhibitors/pharmacology, Humans, Immunotherapy, Data Collection",

author = "Butner, {Joseph D.} and Maguy Farhat and Vittorio Cristini and Caroline Chung and Zhihui Wang",

note = "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 (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: {\textcopyright} 2022 The Author(s)",

year = "2022",

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day = "16",

doi = "10.1016/j.xpro.2022.101886",

language = "English (US)",

volume = "3",

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T1 - Protocol for mathematical prediction of patient response and survival to immune checkpoint inhibitor immunotherapy

AU - Butner, Joseph D.

AU - Farhat, Maguy

AU - Cristini, Vittorio

AU - Chung, Caroline

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 (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 The Author(s)

PY - 2022/12/16

Y1 - 2022/12/16

N2 - This protocol describes the application of a mechanistic mathematical model of immune checkpoint inhibitor (ICI) immunotherapy to patient tumor imaging data for predicting solid tumor response and patient survival under ICI intervention. We describe steps for data collection and processing, data pipelines, and approaches to increase precision. The protocol is highly predictive as early as the first restaging after treatment start and can be used with standard-of-care imaging measures. For complete details on the use and execution of this protocol, please refer to Butner et al. (2020)1 and Butner et al. (2021).2

AB - This protocol describes the application of a mechanistic mathematical model of immune checkpoint inhibitor (ICI) immunotherapy to patient tumor imaging data for predicting solid tumor response and patient survival under ICI intervention. We describe steps for data collection and processing, data pipelines, and approaches to increase precision. The protocol is highly predictive as early as the first restaging after treatment start and can be used with standard-of-care imaging measures. For complete details on the use and execution of this protocol, please refer to Butner et al. (2020)1 and Butner et al. (2021).2

KW - Biotechnology and bioengineering

KW - Cancer

KW - Health Sciences

KW - Immunology

KW - Systems biology

KW - Immune Checkpoint Inhibitors/pharmacology

KW - Humans

KW - Immunotherapy

KW - Data Collection

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AN - SCOPUS:85143535828

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JO - STAR Protocols

JF - STAR Protocols

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ER -

Protocol for mathematical prediction of patient response and survival to immune checkpoint inhibitor immunotherapy

Abstract

Keywords

ASJC Scopus subject areas

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