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Cited by 613

The DOI matches its Crossref record; 13 of the 13 references it cites were found in a registry or an index.

Adaptive Immune Resistance: How Cancer Protects from Immune Attack

Open access
Ribas, Antoni·2015·Cancer Discovery

Abstract

Adaptive immune resistance is a process in which the cancer changes its phenotype in response to a cytotoxic or proinflammatory immune response, thereby evading it. This adaptive process is triggered by the specific recognition of cancer cells by T cells, which leads to the production of immune-activating cytokines. Cancers then hijack mechanisms developed to limit inflammatory and immune responses and protect themselves from the T-cell attack. Inhibiting adaptive immune resistance is the mechanistic basis of responses to PD-1 or PD-L1–blocking antibodies, and may be of relevance for the development of other cancer immunotherapy strategies. Significance: Several new immunotherapy strategies to treat cancer are based on inhibiting processes through which cancer adapts and evades from an immune response. Recognizing the specific adaptive resistance mechanisms in each case is likely to allow the personalized development of immunotherapies tailored to block how a particular cancer protects itself from the immune system. Cancer Discov; 5(9); 915–9. ©2015 AACR.

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Metadata

Authors
Ribas, A.
Venue
Cancer Discovery
Published
2015-09-01
Volume
5
Issue
9
Pages
915-919
ISSN
  • 2159-8274 (ISSN-L)
  • 2159-8290
OpenAlex
W2164001237
Cited by
613
References
13
FWCI
19.37
Type
journal article
Language
en
Open access
green

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CoChat. (2026). Citation check: Adaptive Immune Resistance: How Cancer Protects from Immune Attack. https://app.cochat.ai/papers/10.1158/2159-8290.cd-15-0563
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Ribas, A. (2015). Adaptive Immune Resistance: How Cancer Protects from Immune Attack. Cancer Discovery. https://doi.org/10.1158/2159-8290.cd-15-0563

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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.