The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
Generation of a new therapeutic peptide that depletes myeloid-derived suppressor cells in tumor-bearing mice
Open accessAbstract
Immune evasion is an emerging hallmark of cancer progression. However, functional studies to understand the role of myeloid-derived suppressor cells (MDSCs) in the tumor microenvironment are limited by the lack of available specific cell surface markers. We adapted a competitive peptide phage display platform to identify candidate peptides binding MDSCs specifically and generated peptide-Fc fusion proteins (peptibodies). In multiple tumor models, intravenous peptibody injection completely depleted blood, splenic and intratumoral MDSCs in tumor-bearing mice without affecting proinflammatory immune cell types, such as dendritic cells. Whereas control Gr-1–specific antibody primarily depleted granulocytic MDSCs, peptibodies depleted both granulocytic and monocytic MDSC subsets. Peptibody treatment was associated with inhibition of tumor growth in vivo, which was superior to that achieved with Gr-1–specific antibody. Immunoprecipitation of MDSC membrane proteins identified S100 family proteins as candidate targets. Our strategy may be useful to identify new diagnostic and therapeutic surface targets on rare cell subtypes, including human MDSCs.
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Citations
Cites
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Cited by
Myeloid-Derived Suppressor Cells
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Metadata
- Authors
- Qin, H., Lerman, B., Sakamaki, I., Wei, G., Cha, S. C., Rao, S. S., Qian, J., Hailemichael, Y., Nurieva, R., Dwyer, K. C., Roth, J., Yi, Q., Overwijk, W. W., & Kwak, L. W.
- Venue
- Nature Medicine
- Published
- 2014-05-25
- Volume
- 20
- Issue
- 6
- Pages
- 676-681
- ISSN
- 1078-8956 (ISSN-L)
- 1546-170X
- OpenAlex
- W1970880147
- Cited by
- 233
- References
- 3
- FWCI
- 9.21
- Type
- journal article
- Language
- en
- Open access
- green
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
