Therapeutic targeting of tumour myeloid cells
Open accessAbstract
Myeloid cells are pivotal within the immunosuppressive tumour microenvironment. The accumulation of tumour-modified myeloid cells derived from monocytes or neutrophils - termed 'myeloid-derived suppressor cells' - and tumour-associated macrophages is associated with poor outcome and resistance to treatments such as chemotherapy and immune checkpoint inhibitors. Unfortunately, there has been little success in large-scale clinical trials of myeloid cell modulators, and only a few distinct strategies have been used to target suppressive myeloid cells clinically so far. Preclinical and translational studies have now elucidated specific functions for different myeloid cell subpopulations within the tumour microenvironment, revealing context-specific roles of different myeloid cell populations in disease progression and influencing response to therapy. To improve the success of myeloid cell-targeted therapies, it will be important to target tumour types and patient subsets in which myeloid cells represent the dominant driver of therapy resistance, as well as to determine the most efficacious treatment regimens and combination partners. This Review discusses what we can learn from work with the first generation of myeloid modulators and highlights recent developments in modelling context-specific roles for different myeloid cell subtypes, which can ultimately inform how to drive more successful clinical trials.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
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?
Accumulation and activation of immunosuppressive myeloid cells are strongly correlated with reduced efficacy of immunotherapies and poor prognosis across various cancers.
“Their accumulation and activation are strongly correlated with reduced efficacy of immunotherapies and poor prognosis across various cancers, such as colorectal cancer, pancreatic cancer, glioblastoma, breast cancer, liver cancer, head and neck cancer, lung cancer, and prostate cancer13.”
From Oligomeric cystatin C supports the immunosuppressive activity of myeloid cells through interaction with inhibitory receptors (2025) · cites this paper as [13]
The available passages (Dosing Strategy > M-MDSC; page not given) list myeloid-cell markers and treatment trials in several cancers, but do not link myeloid-cell accumulation or activation to immunotherapy efficacy or prognosis. Searches for “poor prognosis,” “immunotherapy resistance,” and the named cancer types did not find a passage establishing the claimed correlation. (LLM, conf=0.72)
Experimental — accuracy not yet measured
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Accessories to the Crime: Functions of Cells Recruited to the Tumor Microenvironment
Hanahan, D., & Coussens, L. M. (2012) · Cancer Cell · doi:10.1016/j.ccr.2012.02.022 · Cited by 4,632 · see citing works
Genomic analyses identify molecular subtypes of pancreatic cancer
Bailey, P., Chang, D. K., Nones, K., Johns, A. L., Patch, A.-M., Gingras, M.-C., Miller, D. K., Christ, A. N., Bruxner, T. J. C., Quinn, M. C., et al. (2016) · Nature · doi:10.1038/nature16965 · Cited by 3,621 · see citing works
Self-citationRecommendations for myeloid-derived suppressor cell nomenclature and characterization standards
Bronte, V., Brandau, S., Chen, S.-H., Colombo, M. P., Frey, A. B., Greten, T. F., Mandruzzato, S., Murray, P. J., Ochoa, A., Ostrand-Rosenberg, S., et al. (2016) · Nature Communications · doi:10.1038/ncomms12150 · Cited by 2,751 · see citing works
Self-citationWhole genomes redefine the mutational landscape of pancreatic cancer
Initiative, A. P. C. G., Waddell, N., Pajic, M., Patch, A.-M., Chang, D. K., Kassahn, K. S., Bailey, P., Johns, A. L., Miller, D., Nones, K., et al. (2015) · Nature · doi:10.1038/nature14169 · Cited by 2,642 · 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
TGFβ drives immune evasion in genetically reconstituted colon cancer metastasis
Tauriello, D. V. F., Palomo-Ponce, S., Stork, D., Berenguer-Llergo, A., Badia-Ramentol, J., Iglesias, M., Sevillano, M., Ibiza, S., Cañellas, A., Hernando-Momblona, X., et al. (2018) · Nature · doi:10.1038/nature25492 · Cited by 1,851 · see citing works
Majeti, R., Chao, M. P., Alizadeh, A. A., Pang, W. W., Jaiswal, S., Gibbs, K. D., van Rooijen, N., & Weissman, I. L. (2009) · Cell · doi:10.1016/j.cell.2009.05.045 · Cited by 1,752 · see citing works
IL-17-producing γδ T cells and neutrophils conspire to promote breast cancer metastasis
Coffelt, S. B., Kersten, K., Doornebal, C. W., Weiden, J., Vrijland, K., Hau, C.-S., Verstegen, N. J. M., Ciampricotti, M., Hawinkels, L. J. A. C., Jonkers, J., et al. (2015) · Nature · doi:10.1038/nature14282 · Cited by 1,703 · see citing works
<i>STK11/LKB1</i> Mutations and PD-1 Inhibitor Resistance in <i>KRAS</i> -Mutant Lung Adenocarcinoma
Skoulidis, F., Goldberg, M. E., Greenawalt, D. M., Hellmann, M. D., Awad, M. M., Gainor, J. F., Schrock, A. B., Hartmaier, R. J., Trabucco, S. E., Gay, L., et al. (2018) · Cancer Discovery · doi:10.1158/2159-8290.cd-18-0099 · Cited by 1,641 · see citing works
Resistance to checkpoint blockade therapy through inactivation of antigen presentation
Sade-Feldman, M., Jiao, Y. J., Chen, J. H., Rooney, M. S., Barzily-Rokni, M., Eliane, J.-P., Bjorgaard, S. L., Hammond, M. R., Vitzthum, H., Blackmon, S. M., et al. (2017) · Nature Communications · doi:10.1038/s41467-017-01062-w · Cited by 1,005 · see citing works
Metabolism of immune cells in cancer
Leone, R. D., & Powell, J. D. (2020) · Nature Reviews Cancer · doi:10.1038/s41568-020-0273-y · Cited by 877 · see citing works
Disruption of CXCR2-Mediated MDSC Tumor Trafficking Enhances Anti-PD1 Efficacy
Highfill, S. L., Cui, Y., Giles, A. J., Smith, J. P., Zhang, H., Morse, E., Kaplan, R. N., & Mackall, C. L. (2014) · Science Translational Medicine · doi:10.1126/scitranslmed.3007974 · Cited by 767 · see citing works
An orally available non-nucleotide STING agonist with antitumor activity
Pan, B.-S., Perera, S. A., Piesvaux, J. A., Presland, J. P., Schroeder, G. K., Cumming, J. N., Trotter, B. W., Altman, M. D., Buevich, A. V., Cash, B., et al. (2020) · Science · doi:10.1126/science.aba6098 · Cited by 694 · see citing works
TREM2 Modulation Remodels the Tumor Myeloid Landscape Enhancing Anti-PD-1 Immunotherapy
Molgora, M., Esaulova, E., Vermi, W., Hou, J., Chen, Y., Luo, J., Brioschi, S., Bugatti, M., Omodei, A. S., Ricci, B., et al. (2020) · Cell · doi:10.1016/j.cell.2020.07.013 · Cited by 656 · see citing works
KRAS-IRF2 axis drives immune suppression and immune therapy resistance in colorectal cancer
Liao, W., Overman, M. J., Boutin, A. T., Shang, X., Zhao, D., Dey, P., Li, J., Wang, G., Lan, Z., Li, J., et al. (2019) · Cancer Cell · doi:10.1016/j.ccell.2019.02.008 · Cited by 617 · see citing works
Class IIa HDAC inhibition reduces breast tumours and metastases through anti-tumour macrophages
Guerriero, J. L., Sotayo, A., Ponichtera, H. E., Castrillon, J. A., Pourzia, A. L., Schad, S., Johnson, S. F., Carrasco, R. D., Lazo, S., Bronson, R. T., et al. (2017) · Nature · doi:10.1038/nature21409 · Cited by 558 · see citing works
Genetic Mechanisms of Immune Evasion in Colorectal Cancer
Grasso, C. S., Giannakis, M., Wells, D. K., Hamada, T., Mu, X. J., Quist, M., Nowak, J. A., Nishihara, R., Qian, Z. R., Inamura, K., et al. (2018) · Cancer Discovery · doi:10.1158/2159-8290.cd-17-1327 · Cited by 535 · see citing works
McAllister, F., Bailey, J. M., Alsina, J., Nirschl, C. J., Sharma, R., Fan, H., Rattigan, Y., Roeser, J. C., Lankapalli, R. H., Zhang, H., et al. (2014) · Cancer Cell · doi:10.1016/j.ccr.2014.03.014 · Cited by 405 · see citing works
Padrón, L. J., Maurer, D. M., O’Hara, M. H., O’Reilly, E. M., Wolff, R. A., Wainberg, Z. A., Ko, A. H., Fisher, G., Rahma, O., Lyman, J. P., et al. (2022) · Nature Medicine · doi:10.1038/s41591-022-01829-9 · Cited by 384 · see citing works
DNA Sensing in Mismatch Repair-Deficient Tumor Cells Is Essential for Anti-tumor Immunity
Lu, C., Guan, J., Lu, S., Jin, Q., Rousseau, B., Lu, T., Stephens, D., Zhang, H., Zhu, J., Yang, M., et al. (2021) · Cancer Cell · doi:10.1016/j.ccell.2020.11.006 · Cited by 285 · see citing works
CD40 stimulation obviates innate sensors and drives T cell immunity in cancer
Byrne, K. T., & Vonderheide, R. H. (2016) · Cell Reports · doi:10.1016/j.celrep.2016.05.058 · Cited by 282 · see citing works
Candido, J. B., Morton, J. P., Bailey, P., Campbell, A. D., Karim, S. A., Jamieson, T., Lapienyte, L., Gopinathan, A., Clark, W., McGhee, E. J., et al. (2018) · Cell Reports · doi:10.1016/j.celrep.2018.03.131 · Cited by 224 · see citing works
Self-citationLiudahl, S. M., Betts, C. B., Sivagnanam, S., Morales-Oyarvide, V., da Silva, A., Yuan, C., Hwang, S., Grossblatt-Wait, A., Leis, K. R., Larson, W., et al. (2021) · Cancer Discovery · doi:10.1158/2159-8290.cd-20-0841 · Cited by 164 · see citing works
Tumor Microenvironment Remodeling Enables Bypass of Oncogenic KRAS Dependency in Pancreatic Cancer
Hou, P., Kapoor, A., Zhang, Q., Li, J., Wu, C.-J., Li, J., Lan, Z., Tang, M., Ma, X., Ackroyd, J. J., et al. (2020) · Cancer Discovery · doi:10.1158/2159-8290.cd-19-0597 · Cited by 154 · see citing works
Combined Inhibition of SHP2 and CXCR1/2 Promotes Antitumor T-cell Response in NSCLC
Tang, K. H., Li, S., Khodadadi-Jamayran, A., Jen, J., Han, H., Guidry, K., Chen, T., Hao, Y., Fedele, C., Zebala, J. A., et al. (2022) · Cancer Discovery · doi:10.1158/2159-8290.cd-21-0369 · Cited by 105 · see citing works
Pore, N., Wu, S., Standifer, N., Jure-Kunkel, M., de los Reyes, M., Shrestha, Y., Halpin, R., Rothstein, R., Mulgrew, K., Blackmore, S., et al. (2021) · Cancer Discovery · doi:10.1158/2159-8290.cd-20-1543 · Cited by 88 · see citing works
Boyer, S., Lee, H.-J., Steele, N., Zhang, L., Sajjakulnukit, P., Andren, A., Ward, M. H., Singh, R., Basrur, V., Zhang, Y., et al. (2022) · eLife · doi:10.7554/elife.73796 · Cited by 56 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Melanoma and microbiota: Current understanding and future directions
Routy, B., Jackson, T., Mählmann, L., Baumgartner, C. K., Blaser, M., Byrd, A., Corvaia, N., Couts, K., Davar, D., Derosa, L., et al. (2024) · Cancer Cell · doi:10.1016/j.ccell.2023.12.003 · Cited by 89 · see citing works
Zhang, C., He, Y., Liu, X., Xie, J., Fang, M., Yang, X., Huang, R., Lou, Q., Li, B., Gupta, A., et al. (2025) · Signal Transduction and Targeted Therapy · doi:10.1038/s41392-025-02462-x · Cited by 2 · see citing works
- Authors
- Barry, S. T., Gabrilovich, D. I., Sansom, O. J., Campbell, A. D., & Morton, J. P.
- Affiliations
- AstraZeneca (United Kingdom) (GB)
- AstraZeneca (United States) (US)
- Cancer Research UK Scotland Institute (GB)
- Corresponding author
- Barry, Simon T.
- Venue
- Nature Reviews Cancer
- Published
- 2023-02-06
- Volume
- 23
- Issue
- 4
- Pages
- 216-237
- ISSN
- 1474-175X (ISSN-L)
- 1474-1768
- OpenAlex
- W4319298155
- Cited by
- 305
- References
- 27
- Citations by year
- 2026: 71 · 2025: 104 · 2024: 96 · 2023: 34
- FWCI
- 8.48
- Type
- journal article
- Language
- en
- Open access
- green
- eprints.gla.ac.uk
- Licence
- Locations
- Nature reviews. Cancer (publishedVersion)
- PubMed (publishedVersion)
- ENLIGHTEN (Jurnal Bimbingan dan Konseling Islam) (submittedVersion, cc-by-sa)
- Funders
- Cancer Research UK (DRCQQR-May21\100002, 29996, 25233)
- Topics
- Life Sciences › Immunology and Microbiology › Immunology › Immune cells in cancer
- Health Sciences › Medicine › Oncology › Cancer Immunotherapy and Biomarkers
- Life Sciences › Immunology and Microbiology › Immunology › Immunotherapy and Immune Responses
- Keywords
- myeloid-derived suppressor cells, tumour-associated macrophages, tumor microenvironment, immunosuppression, treatment resistance, chemoresistance, immune checkpoint inhibitors, clinical trial design
- MeSH terms
- Myeloid-Derived Suppressor Cells, Humans, Immunotherapy, Neoplasms, Neutrophils, Myeloid Cells, 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