The DOI matches its Crossref record; 50 of the 50 references it cites were found in a registry or an index.
Antigen presentation and tumor immunogenicity in cancer immunotherapy response prediction
Open accessAbstract
Immunotherapy, represented by immune checkpoint inhibitors (ICI), is transforming the treatment of cancer. However, only a small percentage of patients show response to ICI, and there is an unmet need for biomarkers that will identify patients who are more likely to respond to immunotherapy. The fundamental basis for ICI response is the immunogenicity of a tumor, which is primarily determined by tumor antigenicity and antigen presentation efficiency. Here, we propose a method to measure tumor immunogenicity score (TIGS), which combines tumor mutational burden (TMB) and an expression signature of the antigen processing and presenting machinery (APM). In both correlation with pan-cancer ICI objective response rates (ORR) and ICI clinical response prediction for individual patients, TIGS consistently showed improved performance compared to TMB and other known prediction biomarkers for ICI response. This study suggests that TIGS is an effective tumor-inherent biomarker for ICI-response prediction.
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Current advances in cancer vaccines targeting NY-ESO-1 for solid cancer treatment
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Lin, A., Zhang, H., Meng, H., Deng, Z., Gu, T., Luo, P., & Zhang, J. (2021) · Frontiers in Immunology · doi:10.3389/fimmu.2021.667875 · Cited by 27
Metadata
- Authors
- Wang, S., He, Z., Wang, X., Li, H., & Liu, X.-S.
- Venue
- eLife
- Publisher
- eLife Sciences Publications, Ltd
- Published
- 2019-11-26
- Volume
- 8
- ISSN
- 2050-084X (ISSN-L)
- OpenAlex
- W2989811984
- Cited by
- 312
- References
- 50
- FWCI
- 15.24
- Type
- journal article
- Language
- en
- Open access
- gold
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- Wang, S., He, Z., Wang, X., Li, H., & Liu, X.-S. (2019). Antigen presentation and tumor immunogenicity in cancer immunotherapy response prediction. eLife. https://doi.org/10.7554/elife.49020
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
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.
