A bridge over troubled questions. (July 2008)
- Record Type:
- Journal Article
- Title:
- A bridge over troubled questions. (July 2008)
- Main Title:
- A bridge over troubled questions
- Authors:
- Daidone, Maria Grazia
Pierotti, Marco A - Abstract:
- Evaluation of: Acharya CR, Hsu DS, Anders CK et al. : Gene expression signatures, clinicopathological features, and individualized therapy in breast cancer. JAMA 299, 1574––1587 (2008). The impressive knowledge derived from gene-expression profiling studies challenges the currently used breast cancer classification and the existing theories of metastatic progression and breast cancer biology. The proposed prognostic gene-expression signatures outperform traditional clinical risk-classification systems, suggesting the possibility to reduce over treatment in early breast cancer. Nevertheless, the ability to identify high-risk patients and markers/signatures predictive of treatment response still needs to be improved. In this retrospective study carried out on 964 patients with early tumors, gene-expression signatures based on functional criteria proved to better define the prognostic classification based on the clinicopathological model of Adjuvant! Online. The novel findings, however, originate from the integration of these results with those that the same authors obtained on cell lines, identifying genomic signatures that predict individual sensitivity to commonly used cytotoxic agents. When combined, these signatures are also indicative of sensitivity to multidrug therapies. If validated in independent patient populations, signatures defining breast cancers according to their biologic behavior may help in the decision making of treatment, thus bridging the gap betweenEvaluation of: Acharya CR, Hsu DS, Anders CK et al. : Gene expression signatures, clinicopathological features, and individualized therapy in breast cancer. JAMA 299, 1574––1587 (2008). The impressive knowledge derived from gene-expression profiling studies challenges the currently used breast cancer classification and the existing theories of metastatic progression and breast cancer biology. The proposed prognostic gene-expression signatures outperform traditional clinical risk-classification systems, suggesting the possibility to reduce over treatment in early breast cancer. Nevertheless, the ability to identify high-risk patients and markers/signatures predictive of treatment response still needs to be improved. In this retrospective study carried out on 964 patients with early tumors, gene-expression signatures based on functional criteria proved to better define the prognostic classification based on the clinicopathological model of Adjuvant! Online. The novel findings, however, originate from the integration of these results with those that the same authors obtained on cell lines, identifying genomic signatures that predict individual sensitivity to commonly used cytotoxic agents. When combined, these signatures are also indicative of sensitivity to multidrug therapies. If validated in independent patient populations, signatures defining breast cancers according to their biologic behavior may help in the decision making of treatment, thus bridging the gap between prognosis and treatment response. … (more)
- Is Part Of:
- Personalized medicine. Volume 5:Number 4(2008)
- Journal:
- Personalized medicine
- Issue:
- Volume 5:Number 4(2008)
- Issue Display:
- Volume 5, Issue 4 (2008)
- Year:
- 2008
- Volume:
- 5
- Issue:
- 4
- Issue Sort Value:
- 2008-0005-0004-0000
- Page Start:
- 405
- Page End:
- 407
- Publication Date:
- 2008-07
- Subjects:
- breast cancer -- gene-expression profile -- prognosis -- treatment response
Pharmacogenomics -- Periodicals
Pharmacogenetics -- Periodicals
615.19 - Journal URLs:
- http://www.futuremedicine.com/loi/pme ↗
http://www.futuremedicine.com/ ↗ - DOI:
- 10.2217/17410541.5.4.405 ↗
- Languages:
- English
- ISSNs:
- 1741-0541
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - 6428.011710
British Library DSC - BLDSS-3PM
British Library HMNTS - Digital store
British Library HMNTS - ELD Digital store - Ingest File:
- 16509.xml