The DOI matches its Crossref record; 108 of the 108 references it cites were found in a registry or an index.
Enhancing anti-tumour efficacy with immunotherapy combinations
Abstract
Several tumour types are responsive to immunotherapy, as shown by regulatory approvals for immune checkpoint inhibitors. However, many patients either do not respond or do not have durable clinical benefit. Thus, there is great interest in developing predictors of response to immunotherapy and rational combination therapies that can enhance efficacy by overcoming primary and acquired resistance. In this Review, we provide an assessment of immunotherapy response biomarkers that can identify patients who will benefit from monotherapy rather than from combinations. We review the rationale for combination therapy and different strategies, including combinations with chemotherapy, targeted therapy, radiation therapy, intratumoural therapies, other immunomodulators, and adaptive cell therapy, including chimeric antigen T-cell receptors and other novel T-cell receptor-based therapies. There are many combination partners in development; therefore, a programmatic approach is needed to develop a framework for biomarker-driven combination therapy selection.
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Cites
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Sharma, P., Goswami, S., Raychaudhuri, D., Siddiqui, B. A., Singh, P., Nagarajan, A., Liu, J., Subudhi, S. K., Poon, C., Gant, K. L., et al. (2023) · Cell · doi:10.1016/j.cell.2023.03.006 · Cited by 1,206
Type I interferon-mediated tumor immunity and its role in immunotherapy
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Metadata
- Authors
- Meric-Bernstam, F., Larkin, J., Tabernero, J., & Bonini, C.
- Venue
- The Lancet
- Publisher
- Elsevier BV
- Published
- 2021-03
- Volume
- 397
- Issue
- 10278
- Pages
- 1010-1022
- ISSN
- 0099-5355 (ISSN-L)
- 0140-6736
- OpenAlex
- W3109638148
- Cited by
- 409
- References
- 108
- FWCI
- 15.24
- Type
- journal article
- Language
- en
- Open access
- closed
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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.
