The DOI matches its Crossref record; 184 of the 185 references it cites were found in a registry or an index.
Understanding the tumor microenvironment for effective immunotherapy
Open accessAbstract
Advances in immunotherapy have led to durable and long‐term benefits in a subset of patients across a number of solid tumor types. Understanding of the subsets of patients that respond to immune checkpoint inhibitors at the cellular level, and in the context of their tumor microenvironment (TME) is becoming increasingly important. The TME is composed of a heterogeneous milieu of tumor and immune cells. The immune landscape of the TME can inhibit or promote tumor initiation and progression; thus, a deeper understanding of tumor immunity is necessary to develop immunotherapeutic strategies. Recent developments have focused on characterizing the TME immune contexture (type, density, and function) to discover mechanisms and biomarkers that may predict treatment outcomes. This has, in part, been powered by advancements in spatial characterization technologies. In this review article, we address the role of specific immune cells within the TME at various stages of tumor progression and how the immune contexture determinants affecting tumor growth are used therapeutically.
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![Figure 1 Different roles of TME‐driven cell populations in cancer progression. Cell populations within the TME modulate immune‐activating or immunosuppressive conditions to prevent or promote tumor growth. (A) T cell effector function and differentiation signals can be supported by the dendritic cell (DC) through the interleukin‐12 (IL‐12) secretion. (B) Cancer‐associated fibroblast (CAF) plays an immunosuppressive function by inhibiting CD8+ T cell infiltration and function via secreting transforming growth factor‐beta (TGF‐β). (C) Within the TME, tumor‐associated macrophage (TAM) consists primarily of the M2 macrophage, which is tumorigenic. TAM (M2) contributes to angiogenesis through the secretion of vascular endothelial growth factor (VEGF). (D) CD4+ T cell can play immunoactivative or immunosuppressive roles through the differentiation into T helper type 1 cell (Th1) and regulatory T cell (Treg), respectively. Th1 is able to induce CD8+ T cell by releasing cell‐activating cytokines such as interferon‐gamma (IFN‐γ); however, Treg has an immunosuppressive activity through the inhibition of CD8+ T cell function via releasing IL‐10 and TGF‐β. (E) Myeloid‐derived suppressor cell (MDSC) causes suppression of tumor‐specific CD8+ T cell response by increasing the levels of prostaglandin E2 (PGE2) and arginase (ARG). (F) Mast cell can promote tumor progression through angiogenesis development by secreting certain proteases, such as matrix metalloproteinase 2 (MMP‐2) and MMP‐9, and also by releasing VEGF and fibroblast growth factor (bFGF) from the extracellular matrix (ECM). (G) Similar to the mast cell, neutrophil is also capable of developing angiogenesis via secreting VEGF and MMPs. (H) Natural killer (NK) cell can induce innate and adaptive immune responses through the secretion of pro‐inflammatory cytokines, including IFN‐γ, tumor necrosis factor‐alpha (TNF‐α), and granulocyte/monocyte colony‐stimulating factor (GM‐CSF). TME, tumur microenvironment [Color figure can be viewed at wileyonlinelibrary.com]](/paper-figures/59370fe781b04f65892072e8c3a95cf7/understanding-the-tumor-microenvironment-for-effective-immunotherapy-figure-1.jpg)
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“Interactions among the various components of the TME play a key role in influencing immunotherapy responses49.”
From Predicting the tumor microenvironment composition and immunotherapy response in non-small cell lung cancer from digital histopathology images (2024) · cites this paper as [49]
In the citing paper
“Interestingly, tumor cells take advantage of this inhibitory effect in order to induce T-cell tolerance and exhaustion [86].”
From Current and Future Trends of Colorectal Cancer Treatment: Exploring Advances in Immunotherapy (2024) · cites this paper as [86]
In the citing paper
“T cell receptors Cytotoxic T lymphocyte antigen 4 (CTLA-4) and Programmed Cell Death 1 (PD-1) have a synergistic inhibitory effect on the T cell immune response.”
From Current and Future Trends of Colorectal Cancer Treatment: Exploring Advances in Immunotherapy (2024) · cites this paper as [86]
In the citing paper
“Tumour mutational burden (TMB), PDL-1 expression, microbiome, hypoxia, interferon-γ, extracellular matrix (ECM) and molecular and cellular characterization within the tumour microenvironment (TME) are all related to immunotherapy outcomes [2].”
From Immune Checkpoint Inhibitors in Cancer Therapy (2022) · cites this paper as [2]
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Citations
Cites
PD-1 and PD-L1 Immune Checkpoint Blockade to Treat Breast Cancer
Hartkopf, A. D., Taran, F., Wallwiener, M., Walter, C. B., Krämer, B., Grischke, E., & Brucker, S. Y. (2016) · Breast Care · doi:10.1159/000453569 · Cited by 30,989
Adverse Renal Effects of Immune Checkpoint Inhibitors: A Narrative Review
Wanchoo, R., Karam, S., Uppal, N. N., Barta, V. S., Deray, G., Devoe, C., Launay‐Vacher, V., Jhaveri, K. D., & on behalf of Cancer and Kidney International Network Workgroup on Immune Checkpoint Inhibitors (2017) · American Journal of Nephrology · doi:10.1159/000455014 · Cited by 15,320
Pembrolizumab versus Chemotherapy for PD-L1-Positive Non-Small-Cell Lung Cancer
Reck, M., Rodríguez-Abreu, D., Robinson, A. G., Hui, R., Csőszi, T., Fülöp, A., Gottfried, M., Peled, N., Tafreshi, A., Cuffe, S., et al. (2016) · New England Journal of Medicine · doi:10.1056/nejmoa1606774 · Cited by 10,089
Mutational landscape determines sensitivity to PD-1 blockade in non–small cell lung cancer
Rizvi, N. A., Hellmann, M. D., Snyder, A., Kvistborg, P., Makarov, V., Havel, J. J., Lee, W., Yuan, J., Wong, P., Ho, T. S., et al. (2015) · Science · doi:10.1126/science.aaa1348 · Cited by 7,802
Type, Density, and Location of Immune Cells Within Human Colorectal Tumors Predict Clinical Outcome
Galon, J., Costes, A., Sanchez-Cabo, F., Kirilovsky, A., Mlecnik, B., Lagorce-Pagès, C., Tosolini, M., Camus, M., Berger, A., Wind, P., et al. (2006) · Science · doi:10.1126/science.1129139 · Cited by 6,516
Pembrolizumab for the Treatment of Non–Small-Cell Lung Cancer
Garon, E. B., Rizvi, N. A., Hui, R., Leighl, N., Balmanoukian, A. S., Eder, J. P., Patnaik, A., Aggarwal, C., Gubens, M., Horn, L., et al. (2015) · New England Journal of Medicine · doi:10.1056/nejmoa1501824 · Cited by 6,198
Understanding the tumor immune microenvironment (TIME) for effective therapy
Binnewies, M., Roberts, E. W., Kersten, K., Chan, V., Fearon, D. F., Merad, M., Coussens, L. M., Gabrilovich, D. I., Ostrand-Rosenberg, S., Hedrick, C. C., et al. (2018) · Nature Medicine · doi:10.1038/s41591-018-0014-x · Cited by 6,145
New functions for the matrix metalloproteinases in cancer progression
Egeblad, M., & Werb, Z. (2002) · Nature Reviews Cancer · doi:10.1038/nrc745 · Cited by 6,108
Mutational heterogeneity in cancer and the search for new cancer-associated genes
Lawrence, M. S., Stojanov, P., Polak, P., Kryukov, G. V., Cibulskis, K., Sivachenko, A., Carter, S. L., Stewart, C., Mermel, C. H., Roberts, S. A., et al. (2013) · Nature · doi:10.1038/nature12213 · Cited by 5,461
TGFβ attenuates tumour response to PD-L1 blockade by contributing to exclusion of T cells
Mariathasan, S., Turley, S. J., Nickles, D., Castiglioni, A., Yuen, K., Wang, Y., Kadel III, E. E., Koeppen, H., Astarita, J. L., Cubas, R., et al. (2018) · Nature · doi:10.1038/nature25501 · Cited by 5,400
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Metadata
- Authors
- Sadeghi Rad, H., Monkman, J., Warkiani, M. E., Ladwa, R., O'Byrne, K., Rezaei, N., & Kulasinghe, A.
- Affiliations
- School of Medicine Tehran University of Medical Sciences Tehran Iran
- Translational Research Institute (AU)
- University of Technology Sydney (AU)
- Princess Alexandra Hospital (AU)
- The School of Biomedical Sciences, Institute of Health and Biomedical Innovation Queensland University of Technology Brisbane Queensland Australia
- Universal Scientific Education and Research Network (IR)
- Corresponding author
- Rezaei, Nima
- Kulasinghe, Arutha
- Venue
- Medicinal Research Reviews
- Publisher
- Wiley
- Published
- 2020-12-04
- Volume
- 41
- Issue
- 3
- Pages
- 1474-1498
- ISSN
- 0198-6325 (ISSN-L)
- 1098-1128
- PMC
- PMC8247330
- OpenAlex
- W3108853735
- Cited by
- 334
- References
- 185
- Citations by year
- 2026: 46 · 2025: 79 · 2024: 99 · 2023: 49 · 2022: 50 · 2021: 9 · 2020: 1
- FWCI
- 2.99
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Medicinal Research Reviews (publishedVersion, cc-by-nc)
- PubMed Central (publishedVersion, cc-by-nc)
- PubMed (publishedVersion)
- QUT ePrints (Queensland University of Technology) (submittedVersion, cc-by-nc)
- Funders
- Garnett Passe and Rodney Williams Memorial Foundation
- National Health and Medical Research Council (1157741)
- Topics
- Health Sciences › Medicine › Oncology › Cancer Immunotherapy and Biomarkers
- Life Sciences › Immunology and Microbiology › Immunology › Immune cells in cancer
- Life Sciences › Immunology and Microbiology › Immunology › Immunotherapy and Immune Responses
- Keywords
- tumor microenvironment, cancer immunotherapy, immune checkpoint inhibitors, immune contexture, tumor-infiltrating immune cells, predictive biomarkers, treatment response, tumor progression, antitumor immunity
- MeSH terms
- Humans, Immunotherapy, Neoplasms, Tumor Microenvironment
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