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Cited by 194

The DOI matches its Crossref record; 57 of the 57 references it cites were found in a registry or an index.

Future prospects of immune checkpoint blockade in cancer: from response prediction to overcoming resistance

Open access
Park, Young-Jun, Kuen, Da-Sol, Chung, Yeonseok·2018·Experimental & Molecular Medicine

Abstract

Recent advances in the understating of tumor immunology suggest that cancer immunotherapy is an effective treatment against various types of cancer. In particular, the remarkable successes of immune checkpoint-blocking antibodies in clinical settings have encouraged researchers to focus on developing other various immunologic strategies to combat cancer. However, such immunotherapies still face difficulties in controlling malignancy in many patients due to the heterogeneity of both tumors and individual patients. Here, we discuss how tumor-intrinsic cues, tumor environmental metabolites, and host-derived immune cells might impact the efficacy and resistance often seen during immune checkpoint blockade treatment. Furthermore, we introduce biomarkers identified from human and mouse models that predict clinical benefits for immune checkpoint blockers in cancer.

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Metadata

Authors
Park, Y.-J., Kuen, D.-S., & Chung, Y.
Venue
Experimental & Molecular Medicine
Published
2018-08
Volume
50
Issue
8
Pages
1-13
ISSN
  • 1226-3613 (ISSN-L)
  • 2092-6413
OpenAlex
W2888038493
Cited by
194
References
57
FWCI
10.20
Type
journal article
Language
en
Open access
gold

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CoChat. (2026). Citation check: Future prospects of immune checkpoint blockade in cancer: from response prediction to overcoming resistance. https://app.cochat.ai/papers/10.1038/s12276-018-0130-1
The paper
Park, Y.-J., Kuen, D.-S., & Chung, Y. (2018). Future prospects of immune checkpoint blockade in cancer: from response prediction to overcoming resistance. Experimental & Molecular Medicine. https://doi.org/10.1038/s12276-018-0130-1

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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.