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Cited by 312

The DOI matches its Crossref record; 6 of the 6 references it cites were found in a registry or an index.

Cell transfer immunotherapy for metastatic solid cancer—what clinicians need to know

Open access
Rosenberg, Steven A.·2011·Nature Reviews Clinical Oncology

Abstract

Cancer immunotherapy using the adoptive transfer of autologous tumor-infiltrating lymphocytes results in objective cancer regression in 49–72% of patients with metastatic melanoma. In a pilot trial combining cell transfer with a maximum lymphodepleting regimen, complete durable responses were seen in 40% of patients, with complete responses ongoing beyond 3 to 7 years. Current approaches to cell transfer therapy using autologous cells genetically engineered to express conventional or chimeric T-cell receptors have mediated cancer regression in patients with metastatic melanoma, synovial sarcoma, neuroblastoma and refractory lymphoma. Adoptive cell transfer immunotherapy is a rapidly developing new approach to the therapy of metastatic cancer in humans. This Review will emphasize the current available applications of cell transfer immunotherapy for patients with cancer.

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Crossref · 10.1038/nrclinonc.2011.116

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Metadata

Authors
Rosenberg, S. A.
Venue
Nature Reviews Clinical Oncology
Published
2011-08-02
Volume
8
Issue
10
Pages
577-585
ISSN
  • 1759-4774 (ISSN-L)
  • 1759-4782
OpenAlex
W2013328070
Cited by
312
References
6
FWCI
3.86
Type
journal article
Language
en
Open access
green

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CoChat. (2026). Citation check: Cell transfer immunotherapy for metastatic solid cancer—what clinicians need to know. https://app.cochat.ai/papers/10.1038/nrclinonc.2011.116
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Rosenberg, S. A. (2011). Cell transfer immunotherapy for metastatic solid cancer—what clinicians need to know. Nature Reviews Clinical Oncology. https://doi.org/10.1038/nrclinonc.2011.116

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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.