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Cited by 239

The DOI matches its Crossref record; 17 of the 17 references it cites were found in a registry or an index.

Lung Cancer Cell Lines as Tools for Biomedical Discovery and Research

Open access
Gazdar, Adi F., Girard, Luc, Lockwood, William W., Lam, Wan L., Minna, John D.·2010·JNCI: Journal of the National Cancer Institute

Abstract

Lung cancer cell lines have made a substantial contribution to lung cancer translational research and biomedical discovery. A systematic approach to initiating and characterizing cell lines from small cell and non-small cell lung carcinomas has led to the current collection of more than 200 lung cancer cell lines, a number that exceeds those for other common epithelial cancers combined. The ready availability and widespread dissemination of the lines to investigators worldwide have resulted in more than 9000 citations, including multiple examples of important biomedical discoveries. The high (but not perfect) genomic similarities between lung cancer cell lines and the lung tumor type from which they were derived provide evidence of the relevance of their use. However, major problems including misidentification or cell line contamination remain. Ongoing studies and new approaches are expected to reveal the full potential of the lung cancer cell line panel.

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Crossref · 10.1093/jnci/djq279

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Metadata

Authors
Gazdar, A. F., Girard, L., Lockwood, W. W., Lam, W. L., & Minna, J. D.
Venue
JNCI: Journal of the National Cancer Institute
Published
2010-09-08
Volume
102
Issue
17
Pages
1310-1321
ISSN
  • 0027-8874 (ISSN-L)
  • 1460-2105
OpenAlex
W2117960515
Cited by
239
References
17
FWCI
4.13
Type
journal article
Language
en
Open access
bronze

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CoChat. (2026). Citation check: Lung Cancer Cell Lines as Tools for Biomedical Discovery and Research. https://app.cochat.ai/papers/10.1093/jnci/djq279
The paper
Gazdar, A. F., Girard, L., Lockwood, W. W., Lam, W. L., & Minna, J. D. (2010). Lung Cancer Cell Lines as Tools for Biomedical Discovery and Research. JNCI: Journal of the National Cancer Institute. https://doi.org/10.1093/jnci/djq279

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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.