Details

Autor(en) / Beteiligte

• Subramanian, Jyothi
• Simon, Richard

Titel

Gene Expression–Based Prognostic Signatures in Lung Cancer: Ready for Clinical Use?

Ist Teil von

• JNCI : Journal of the National Cancer Institute, 2010-04, Vol.102 (7), p.464-474

Ort / Verlag

Cary, NC: Oxford University Press

Erscheinungsjahr

2010

Link zum Volltext

Quelle

Oxford Journals 2020 Medicine

Beschreibungen/Notizen

• A substantial number of studies have reported the development of gene expression–based prognostic signatures for lung cancer. The ultimate aim of such studies should be the development of well-validated clinically useful prognostic signatures that improve therapeutic decision making beyond current practice standards. We critically reviewed published studies reporting the development of gene expression–based prognostic signatures for non–small cell lung cancer to assess the progress made toward this objective. Studies published between January 1, 2002, and February 28, 2009, were identified through a PubMed search. Following hand-screening of abstracts of the identified articles, 16 were selected as relevant. Those publications were evaluated in detail for appropriateness of the study design, statistical validation of the prognostic signature on independent datasets, presentation of results in an unbiased manner, and demonstration of medical utility for the new signature beyond that obtained using existing treatment guidelines. Based on this review, we found little evidence that any of the reported gene expression signatures are ready for clinical application. We also found serious problems in the design and analysis of many of the studies. We suggest a set of guidelines to aid the design, analysis, and evaluation of prognostic signature studies. These guidelines emphasize the importance of focused study planning to address specific medically important questions and the use of unbiased analysis methods to evaluate whether the resulting signatures provide evidence of medical utility beyond standard of care–based prognostic factors.