The DOI matches its Crossref record; 82 of the 82 references it cites were found in a registry or an index.
Targeting the PI3K/Akt/mTOR pathway in non‐small cell lung cancer (NSCLC)
Open accessAbstract
The traditional classification of lung cancer into small cell lung cancer and non‐small cell lung cancer (NSCLC) has been transformed with the increased understanding of the molecular alterations and genomic biomarkers that drive the development of lung cancer. Increased activation of the phosphatidylinositol 3‐kinase (PI3K)/Akt/mechanistic target of rapamycin (mTOR) pathway leads to numerous hallmarks of cancer and this pathway represents an attractive target for novel anticancer therapies. In NSCLC, the PI3K/Akt/mTOR pathway has been heavily implicated in both tumorigenesis and the progression of disease. A number of specific inhibitors of PI3K, Akt and mTOR are currently under development and in various stages of preclinical investigation and in early phase clinical trials for NSCLC. Early evidence has yielded disappointing results. Clinical trials, however, have been performed on predominantly molecularly unselected populations, and patient enrichment strategies using high‐precision predictive biomarkers in future trials will increase the likelihood of success. A greater understanding of the underlying molecular biology including epigenetic alterations is also crucial to allow for the detection of appropriate biomarkers and guide combination approaches.
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Metadata
- Authors
- Tan, A. C.
- Venue
- Thoracic Cancer
- Publisher
- Wiley
- Published
- 2020-01-27
- Volume
- 11
- Issue
- 3
- Pages
- 511-518
- ISSN
- 1759-7706 (ISSN-L)
- 1759-7714
- OpenAlex
- W3003807413
- Cited by
- 528
- References
- 82
- FWCI
- 19.85
- Type
- journal article
- Language
- en
- Open access
- gold
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
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.
