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Cited by 336

The DOI matches its Crossref record; 248 of the 248 references it cites were found in a registry or an index.

Immunotherapy for glioblastoma: current state, challenges, and future perspectives

Open access
Liu, Yang, Zhou, Fei, Ali, Heba, Lathia, Justin D., Chen, Peiwen·2024·Cellular & Molecular Immunology

Abstract

Glioblastoma (GBM) is an aggressive and lethal type of brain tumor in human adults. The standard of care offers minimal clinical benefit, and most GBM patients experience tumor recurrence after treatment. In recent years, significant advancements have been made in the development of novel immunotherapies or other therapeutic strategies that can overcome immunotherapy resistance in many advanced cancers. However, the benefit of immune-based treatments in GBM is limited because of the unique brain immune profiles, GBM cell heterogeneity, and immunosuppressive tumor microenvironment. In this review, we present a detailed overview of current immunotherapeutic strategies and discuss the challenges and potential molecular mechanisms underlying immunotherapy resistance in GBM. Furthermore, we provide an in-depth discussion regarding the strategies that can overcome immunotherapy resistance in GBM, which will likely require combination therapies.

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Metadata

Authors
Liu, Y., Zhou, F., Ali, H., Lathia, J. D., & Chen, P.
Venue
Cellular & Molecular Immunology
Published
2024-10-15
Volume
21
Issue
12
Pages
1354-1375
ISSN
  • 1672-7681 (ISSN-L)
  • 2042-0226
OpenAlex
W4403432106
Cited by
336
References
248
FWCI
78.17
Type
journal article
Language
en
Open access
hybrid

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CoChat. (2026). Citation check: Immunotherapy for glioblastoma: current state, challenges, and future perspectives. https://app.cochat.ai/papers/10.1038/s41423-024-01226-x
The paper
Liu, Y., Zhou, F., Ali, H., Lathia, J. D., & Chen, P. (2024). Immunotherapy for glioblastoma: current state, challenges, and future perspectives. Cellular & Molecular Immunology. https://doi.org/10.1038/s41423-024-01226-x

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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.