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Cancer immunotherapy: Opportunities and challenges in the rapidly evolving clinical landscape
Abstract
Cancer immunotherapy is now established as a powerful way to treat cancer. The recent clinical success of immune checkpoint blockade (antagonists of CTLA-4, PD-1 and PD-L1) highlights both the universal power of treating the immune system across tumour types and the unique features of cancer immunotherapy. Immune-related adverse events, atypical clinical response patterns, durable responses, and clear overall survival benefit distinguish cancer immunotherapy from cytotoxic cancer therapy. Combination immunotherapies that transform non-responders to responders are under rapid development. Current challenges facing the field include incorporating immunotherapy into adjuvant and neoadjuvant cancer therapy, refining dose, schedule and duration of treatment and developing novel surrogate endpoints that accurately capture overall survival benefit early in treatment. As the field rapidly evolves, we must prioritise the development of biomarkers to guide the use of immunotherapies in the most appropriate patients. Immunotherapy is already transforming cancer from a death sentence to a chronic disease for some patients. By making smart, evidence-based decisions in developing next generation immunotherapies, cancer should become an imminently treatable, curable and even preventable disease.
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Metadata
- Authors
- Emens, L. A., Ascierto, P. A., Darcy, P. K., Demaria, S., Eggermont, A. M. M., Redmond, W. L., Seliger, B., & Marincola, F. M.
- Venue
- European Journal of Cancer
- Publisher
- Elsevier BV
- Published
- 2017-08
- Volume
- 81
- Pages
- 116-129
- ISSN
- 0959-8049 (ISSN-L)
- OpenAlex
- W2625574648
- Cited by
- 566
- References
- 131
- FWCI
- 22.19
- Type
- journal article
- Language
- en
- Open access
- closed
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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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.