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Cited by 599

The DOI matches its Crossref record; 55 of the 55 references it cites were found in a registry or an index.

The Clinical Relevance of Cancer Cell Lines

Open access
Gillet, J.-P., Varma, S., Gottesman, M. M.·2013·JNCI Journal of the National Cancer Institute

Abstract

Although advances in genomics during the last decade have opened new avenues for translational research and allowed the direct evaluation of clinical samples, there is still a need for reliable preclinical models to test therapeutic strategies. Human cancer-derived cell lines are the most widely used models to study the biology of cancer and to test hypotheses to improve the efficacy of cancer treatment. Since the development of the first cancer cell line, the clinical relevance of these models has been continuously questioned. Based upon recent studies that have fueled the debate, we review the major events in the development of the in vitro models and the emergence of new technologies that have revealed important issues and limitations concerning human cancer cell lines as models. All cancer cell lines do not have equal value as tumor models. Some have been successful, whereas others have failed. However, the success stories should not obscure the growing body of data that motivates us to develop new in vitro preclinical models that would substantially increase the success rate of new in vitro-assessed cancer treatments.

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Crossref · 10.1093/jnci/djt007

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Metadata

Authors
Gillet, J.-P., Varma, S., & Gottesman, M. M.
Venue
JNCI Journal of the National Cancer Institute
Published
2013-02-21
Volume
105
Issue
7
Pages
452-458
ISSN
  • 0027-8874 (ISSN-L)
  • 1460-2105
OpenAlex
W2168102484
Cited by
599
References
55
FWCI
13.75
Type
journal article
Language
en
Open access
green

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CoChat. (2026). Citation check: The Clinical Relevance of Cancer Cell Lines. https://app.cochat.ai/papers/10.1093/jnci/djt007
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Gillet, J.-P., Varma, S., & Gottesman, M. M. (2013). The Clinical Relevance of Cancer Cell Lines. JNCI Journal of the National Cancer Institute. https://doi.org/10.1093/jnci/djt007

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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.