Turning Cold into Hot: Firing up the Tumor Microenvironment
Abstract
Cancers develop within complex tissue environments consisting of diverse innate and adaptive immune cells, along with stromal cells, vascular networks, and many other cellular and noncellular components. The high heterogeneity within the tumor microenvironment (TME) remains a key obstacle in understanding and treating cancer. Understanding the dynamic functional interplay within this intricate ecosystem will provide important insights into the design of effective combinatorial strategies against cancer. Here, we present recent technical advances to explore the complexity of the TME. Then, we discuss how innate immune sensing machinery, genetic alterations of oncogenic signaling, cellular metabolism, and epigenetic factors are involved in modulating the TME. Finally, we summarize the potential strategies to boost antitumor immunity by therapeutically exploiting the TME.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The RF achieves precise identification of 12 key TME cell types (e.g., CD8+ T cells, macrophages, NK cells).
“Leveraging these strengths, the RF achieves precise identification of 12 key TME cell types (e.g., CD8+ T cells, macrophages, NK cells).17”
From SpaHE-Infil: A spatial heterogeneity framework for decoding TME infiltration from H&E-stained slides (2026) · cites this paper as [17]
Cites
The emerging hallmarks of cancer metabolism
Pavlova, N. N., & Thompson, C. B. (2016) · Cell Metabolism · doi:10.1016/j.cmet.2015.12.006 · Cited by 5,926 · see citing works
The immune contexture in human tumours: impact on clinical outcome
Fridman, W. H., Pagès, F., Sautès-Fridman, C., & Galon, J. (2012) · Nature reviews. Cancer · doi:10.1038/nrc3245 · Cited by 4,981 · see citing works
CSF-1R inhibition alters macrophage polarization and blocks glioma progression
Pyonteck, S. M., Akkari, L., Schuhmacher, A. J., Bowman, R. L., Sevenich, L., Quail, D. F., Olson, O. C., Quick, M. L., Huse, J. T., Teijeiro, V., et al. (2013) · Nature Medicine · doi:10.1038/nm.3337 · Cited by 2,463 · see citing works
Hildner, K., Edelson, B. T., Purtha, W. E., Diamond, M., Matsushita, H., Kohyama, M., Calderon, B., Schraml, B. U., Unanue, E. R., Diamond, M. S., et al. (2008) · Science · doi:10.1126/science.1164206 · Cited by 2,081 · see citing works
Macrophages and metabolism in the tumor microenvironment
Vitale, I., Manic, G., Coussens, L. M., Kroemer, G., & Galluzzi, L. (2019) · Cell Metabolism · doi:10.1016/j.cmet.2019.06.001 · Cited by 1,837 · see citing works
TOX transcriptionally and epigenetically programs CD8+ T cell exhaustion
Khan, O., Giles, J. R., McDonald, S., Manne, S., Ngiow, S. F., Patel, K. P., Werner, M. T., Huang, A. C., Alexander, K. A., Wu, J. E., et al. (2019) · Nature · doi:10.1038/s41586-019-1325-x · Cited by 1,646 · see citing works
Organoid modeling of the tumor immune microenvironment
Neal, J. T., Li, X., Zhu, J., Giangarra, V., Grzeskowiak, C. L., Ju, J., Liu, I. H., Chiou, S.-H., Salahudeen, A. A., Smith, A. R., et al. (2018) · Cell · doi:10.1016/j.cell.2018.11.021 · Cited by 1,556 · see citing works
Spranger, S., Dai, D., Horton, B., & Gajewski, T. F. (2017) · Cancer Cell · doi:10.1016/j.ccell.2017.04.003 · Cited by 1,441 · see citing works
Dijkstra, K. K., Cattaneo, C. M., Weeber, F., Chalabi, M., van de Haar, J., Fanchi, L. F., Slagter, M., van der Velden, D. L., Kaing, S., Kelderman, S., et al. (2018) · Cell · doi:10.1016/j.cell.2018.07.009 · Cited by 1,135 · see citing works
Garris, C. S., Arlauckas, S. P., Kohler, R. H., Trefny, M. P., Garren, S., Piot, C., Engblom, C., Pfirschke, C., Siwicki, M., Gungabeesoon, J., et al. (2018) · Immunity · doi:10.1016/j.immuni.2018.09.024 · Cited by 966 · see citing works
Chromatin states define tumour-specific T cell dysfunction and reprogramming
Philip, M., Fairchild, L., Sun, L., Horste, E. L., Camara, S., Shakiba, M., Scott, A. C., Viale, A., Lauer, P., Merghoub, T., et al. (2017) · Nature · doi:10.1038/nature22367 · Cited by 933 · see citing works
Scharping, N. E., Menk, A. V., Moreci, R. S., Whetstone, R. D., Dadey, R. E., Watkins, S. C., Ferris, R. L., & Delgoffe, G. M. (2016) · Immunity · doi:10.1016/j.immuni.2016.07.009 · Cited by 848 · see citing works
Pantelidou, C., Sonzogni, O., De Oliveria Taveira, M., Mehta, A. K., Kothari, A., Wang, D., Visal, T., Li, M. K., Pinto, J., Castrillon, J. A., et al. (2019) · Cancer Discovery · doi:10.1158/2159-8290.cd-18-1218 · Cited by 743 · see citing works
Chow, M. T., Ozga, A. J., Servis, R. L., Frederick, D. T., Lo, J. A., Fisher, D. E., Freeman, G. J., Boland, G. M., & Luster, A. D. (2019) · Immunity · doi:10.1016/j.immuni.2019.04.010 · Cited by 716 · see citing works
Siska, P. J., Beckermann, K. E., Mason, F. M., Andrejeva, G., Greenplate, A. R., Sendor, A. B., Chiang, Y.-C. J., Corona, A. L., Gemta, L. F., Vincent, B. G., et al. (2017) · JCI Insight · doi:10.1172/jci.insight.93411 · Cited by 389 · see citing works
DuPage, M., Chopra, G., Quiros, J., Rosenthal, W. L., Morar, M. M., Holohan, D., Zhang, R., Turka, L., Marson, A., & Bluestone, J. A. (2015) · Immunity · doi:10.1016/j.immuni.2015.01.007 · Cited by 332 · see citing works
Quail, D. F., Olson, O. C., Bhardwaj, P., Walsh, L. A., Akkari, L., Quick, M. L., Chen, I.-C., Wendel, N., Ben-Chetrit, N., Walker, J., et al. (2017) · Nature Cell Biology · doi:10.1038/ncb3578 · Cited by 277 · see citing works
Luo, N., Nixon, M. J., Gonzalez-Ericsson, P. I., Sanchez, V., Opalenik, S. R., Li, H., Zahnow, C. A., Nickels, M. L., Liu, F., Tantawy, M. N., et al. (2018) · Nature Communications · doi:10.1038/s41467-017-02630-w · Cited by 275 · see citing works
Huang, S., Wang, Z., Zhou, J., Huang, J., Zhou, L., Luo, J., Wan, Y. Y., Long, H., & Zhu, B. (2019) · Cancer Research · doi:10.1158/0008-5472.can-18-2395 · Cited by 185 · see citing works
Cao, Y., Trillo-Tinoco, J., Sierra, R. A., Anadon, C., Dai, W., Mohamed, E., Cen, L., Costich, T. L., Magliocco, A., Marchion, D., et al. (2019) · Nature Communications · doi:10.1038/s41467-019-09263-1 · Cited by 161 · see citing works
Chon, H. J., Lee, W. S., Yang, H., Kong, S. J., Lee, N. K., Moon, E. S., Choi, J., Han, E. C., Kim, J. H., Ahn, J. B., et al. (2019) · Clinical Cancer Research · doi:10.1158/1078-0432.ccr-18-1932 · Cited by 158 · see citing works
Immune-based antitumor effects of BRAF inhibitors rely on signaling by CD40L and IFNγ
Ho, P.-C., Meeth, K. M., Tsui, Y.-C., Srivastava, B., Bosenberg, M. W., & Kaech, S. M. (2014) · Cancer Research · doi:10.1158/0008-5472.can-13-3461 · Cited by 126 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Spatial genomics enables multi-modal study of clonal heterogeneity in tissues
Zhao, T., Chiang, Z. D., Morriss, J. W., LaFave, L. M., Murray, E. M., Del Priore, I., Meli, K., Lareau, C. A., Nadaf, N. M., Li, J., et al. (2022) · Nature · doi:10.1038/s41586-021-04217-4 · Cited by 268 · see citing works
Rah, B., Rather, R. A., Bhat, G. R., Baba, A. B., Mushtaq, I., Farooq, M., Yousuf, T., Dar, S. B., Parveen, S., Hassan, R., et al. (2022) · Frontiers in Pharmacology · doi:10.3389/fphar.2022.821344 · Cited by 202 · see citing works
Xiang, X., Liu, Z., Zhang, C., Li, Z., Gao, J., Zhang, C., Cao, Q., Cheng, J., Liu, H., Chen, D., et al. (2021) · Advanced Science · doi:10.1002/advs.202101230 · Cited by 71 · see citing works
Huang, H., Cai, Y., Hong, X., Gao, W., Tang, J., Zhang, S., & Xu, Z. (2022) · Frontiers in Genetics · doi:10.3389/fgene.2022.948734 · Cited by 6 · see citing works
Wang, F., Xu, H., Wang, X., Wu, M., Ding, S., Duan, L., Yang, C., Li, J., Ge, X., Qin, Y., et al. (2026) · iScience · doi:10.1016/j.isci.2026.115155
“17 Particularly noteworthy is its TME_adjust dynamic calibration mechanism.”
- Authors
- Duan, Q., Zhang, H., Zheng, J., & Zhang, L.
- Affiliations
- Chinese Academy of Medical Sciences & Peking Union Medical College (CN)
- Affiliated Hospital of Qingdao University (CN)
- Xuzhou Medical College (CN)
- Corresponding author
- Zheng, Junnian
- Zhang, Lianjun
- Venue
- Trends in Cancer
- Publisher
- Elsevier BV
- Published
- 2020-07
- Volume
- 6
- Issue
- 7
- Pages
- 605-618
- ISSN
- 2405-8025 (ISSN-L)
- 2405-8033
- OpenAlex
- W3012408003
- Cited by
- 934
- References
- 22
- Citations by year
- 2026: 59 · 2025: 109 · 2024: 186 · 2023: 205 · 2022: 195 · 2021: 160 · 2020: 20
- FWCI
- 15.92
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Trends in cancer (publishedVersion)
- PubMed (publishedVersion)
- Funders
- Chinese Academy of Medical Sciences
- Ministry of Science and Technology of the People's Republic of China
- National Natural Science Foundation of China
- Topics
- Health Sciences › Medicine › Oncology › Cancer Immunotherapy and Biomarkers
- Life Sciences › Immunology and Microbiology › Immunology › Immune cells in cancer
- Health Sciences › Medicine › Oncology › Cancer Cells and Metastasis
- Keywords
- tumor microenvironment, antitumor immunity, innate immune recognition, oncogenic signaling, cellular metabolism, epigenetic regulation, stromal cells, immune cell heterogeneity, cancer immunotherapy, combination cancer therapy
- MeSH terms
- Ferroptosis, RNA-Seq, Immune Checkpoint Inhibitors, Cell Transformation, Neoplastic, Combined Modality Therapy, Humans, Immunity, Innate, Immunotherapy, Neoplasms, Biomarkers, Tumor, Signal Transduction, Gene Expression Regulation, Neoplastic, Tumor Escape, Epigenesis, Genetic, Oncolytic Virotherapy, Oncolytic Viruses, Metabolomics, Single-Cell Analysis, Tumor Microenvironment
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
