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Cited by 381

The DOI matches its Crossref record; 77 of the 77 references it cites were found in a registry or an index.

Lessons learned from the blockade of immune checkpoints in cancer immunotherapy

Open access
Li, Xiaolei, Shao, Changshun, Shi, Yufang, Han, Weidong·2018·Journal of Hematology & Oncology

Abstract

The advent of immunotherapy, especially checkpoint inhibitor-based immunotherapy, has provided novel and powerful weapons against cancer. Because only a subset of cancer patients exhibit durable responses, further exploration of the mechanisms underlying the resistance to immunotherapy in the bulk of cancer patients is merited. Such efforts may help to identify which patients could benefit from immune checkpoint blockade. Given the existence of a great number of pathways by which cancer can escape immune surveillance, and the complexity of tumor-immune system interaction, development of various combination therapies, including those that combine with conventional therapies, would be necessary. In this review, we summarize the current understanding of the mechanisms by which resistance to checkpoint blockade immunotherapy occurs, and outline how actionable combination strategies may be derived to improve clinical outcomes for patients.

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Crossref · 10.1186/s13045-018-0578-4

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Metadata

Authors
Li, X., Shao, C., Shi, Y., & Han, W.
Venue
Journal of Hematology & Oncology
Published
2018-02-27
Volume
11
Issue
1
Pages
31-31
ISSN
1756-8722 (ISSN-L)
OpenAlex
W2797580541
Cited by
381
References
77
FWCI
10.87
Type
journal article
Language
en
Open access
gold

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CoChat. (2026). Citation check: Lessons learned from the blockade of immune checkpoints in cancer immunotherapy. https://app.cochat.ai/papers/10.1186/s13045-018-0578-4
The paper
Li, X., Shao, C., Shi, Y., & Han, W. (2018). Lessons learned from the blockade of immune checkpoints in cancer immunotherapy. Journal of Hematology & Oncology. https://doi.org/10.1186/s13045-018-0578-4

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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.