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Cited by 639

The DOI matches its Crossref record; 116 of the 116 references it cites were found in a registry or an index.

Targeting the mTOR Signaling Network for Cancer Therapy

Open access
Meric-Bernstam, Funda, Gonzalez-Angulo, Ana Maria·2009·Journal of Clinical Oncology

Abstract

The serine-threonine kinase mammalian target of rapamycin (mTOR) plays a major role in the regulation of protein translation, cell growth, and metabolism. Alterations of the mTOR signaling pathway are common in cancer, and thus mTOR is being actively pursued as a therapeutic target. Rapamycin and its analogs (rapalogs) have proven effective as anticancer agents in a broad range of preclinical models. Clinical trials using rapalogs have demonstrated important clinical benefits in several cancer types; however, objective response rates achieved with single-agent therapy have been modest. Rapalogs may be more effective in combination with other anticancer agents, including chemotherapy and targeted therapies. It is increasingly apparent that the mTOR signaling network is quite complex, and rapamycin treatment leads to different signaling responses in different cell types. A better understanding of mTOR signaling, the mechanism of action of rapamycin, and the identification of biomarkers of response will lead to more optimal targeting of this pathway for cancer therapy.

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Registry

Crossref · 10.1200/jco.2008.20.0766

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Metadata

Authors
Meric-Bernstam, F., & Gonzalez-Angulo, A. M.
Venue
Journal of Clinical Oncology
Published
2009-05-01
Volume
27
Issue
13
Pages
2278-2287
ISSN
  • 0732-183X (ISSN-L)
  • 1527-7755
OpenAlex
W2071394735
Cited by
639
References
116
FWCI
26.05
Type
journal article
Language
en
Open access
green

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CoChat. (2026). Citation check: Targeting the mTOR Signaling Network for Cancer Therapy. https://app.cochat.ai/papers/10.1200/jco.2008.20.0766
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Meric-Bernstam, F., & Gonzalez-Angulo, A. M. (2009). Targeting the mTOR Signaling Network for Cancer Therapy. Journal of Clinical Oncology. https://doi.org/10.1200/jco.2008.20.0766

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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.