Cited by 256Cancer Immunotherapy and BiomarkersImmunotherapy and Immune ResponsesRadiomics and Machine Learning in Medical Imaging

Assessing the interactions between radiotherapy and antitumour immunity

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Grassberger, Clemens, Ellsworth, Susannah G., Wilks, Moses Q., Keane, Florence K., Loeffler, Jay S.·2019·Nature Reviews Clinical Oncology

Abstract

Immunotherapy, specifically the introduction of immune checkpoint inhibitors, has transformed the treatment of cancer, enabling long-term tumour control even in individuals with advanced-stage disease. Unfortunately, only a small subset of patients show a response to currently available immunotherapies. Despite a growing consensus that combining immune checkpoint inhibitors with radiotherapy can increase response rates, this approach might be limited by the development of persistent radiation-induced immunosuppression. The ultimate goal of combining immunotherapy with radiotherapy is to induce a shift from an ineffective, pre-existing immune response to a long-lasting, therapy-induced immune response at all sites of disease. To achieve this goal and enable the adaptation and monitoring of individualized treatment approaches, assessment of the dynamic changes in the immune system at the patient level is essential. In this Review, we summarize the available clinical data, including forthcoming methods to assess the immune response to radiotherapy at the patient level, ranging from serum biomarkers to imaging techniques that enable investigation of immune cell dynamics in patients. Furthermore, we discuss modelling approaches that have been developed to predict the interaction of immunotherapy with radiotherapy, and highlight how they could be combined with biomarkers of antitumour immunity to optimize radiotherapy regimens and maximize their synergy with immunotherapy.

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  1. [160]Ballinger JR & Koziorowski J in Basic Science of PET Imaging (ed. Khalil MM) 127– 143 (Springer, 2016).
  2. [161]Saha GB. Basics of PET Imaging 179– 195 (Springer, 2016).

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Authors
Grassberger, C., Ellsworth, S. G., Wilks, M. Q., Keane, F. K., & Loeffler, J. S.
Affiliations
  • Harvard University (US)
  • Indiana University School of Medicine (US)
  • Gordon Center for Medical Imaging (US)
Corresponding author
Grassberger, Clemens
Venue
Nature Reviews Clinical Oncology
Published
2019-06-26
Volume
16
Issue
12
Pages
729-745
ISSN
  • 1759-4774 (ISSN-L)
  • 1759-4782
OpenAlex
W2954542121
Cited by
244
References
216
Citations by year
2026: 14 · 2025: 26 · 2024: 25 · 2023: 45 · 2022: 53 · 2021: 36 · 2020: 40 · 2019: 5
FWCI
3.19
Type
journal article
Language
en
Open access
Locations
  • Nature Reviews Clinical Oncology (publishedVersion)
  • PubMed (publishedVersion)
  • PubMed Central (submittedVersion)
Funders
  • Massachusetts General Hospital
  • National Cancer Institute (R21CA241918, P01CA021239)
Topics
  • Health Sciences › Medicine › Oncology › Cancer Immunotherapy and Biomarkers
  • Life Sciences › Immunology and Microbiology › Immunology › Immunotherapy and Immune Responses
  • Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiomics and Machine Learning in Medical Imaging
Keywords
radiotherapy, antitumor immunity, immune checkpoint inhibitors, radiation-induced immunosuppression, immune response assessment, serum biomarkers, immune cell imaging, personalized radiotherapy, predictive biomarkers
MeSH terms
Combined Modality Therapy, Humans, Immune System, Immunotherapy, Neoplasms, Radiotherapy, Precision Medicine

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