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Comment
. 2013 Apr 1;19(7):1634-6.
doi: 10.1158/1078-0432.CCR-13-0169. Epub 2013 Feb 12.

Power in numbers: meta-analysis to identify inhibitor-sensitive tumor genotypes

Geoffrey R Oxnard  1 , Pasi A Jänne
Affiliations
Comment

Power in numbers: meta-analysis to identify inhibitor-sensitive tumor genotypes

Geoffrey R Oxnard et al. Clin Cancer Res. .

Abstract

Widespread tumor genotyping has increased the complexity of lung cancer care, often identifying mutations of uncertain clinical significance. In the accompanying article, the authors carried out a meta-analysis of the published literature on EGF receptor (EGFR) genotype and erlotinib/gefitinib sensitivity to develop a publicly accessible database to inform patient care.

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Conflict of interest statement

Disclosure of Potential Conflicts of Interest

G.R. Oxnard is a consultant/advisory board member of Genentech and Boehringer Ingelheim. P.A. Jänne is a consultant/advisory board member of Astra Zeneca, Boehringer Ingelheim, Pfizer, Roche, Genentech, and Sanofi and receives licensing fees from Lab Corp related to intellectual property.

Figures

Figure 1
Figure 1
Approaches to studying and interpreting tumor genotypes. Genotyping carried out as part of routine clinical care occasionally identifies uncommon genotypes, which may lend sensitivity to targeted therapy (top). However, not all data on specific genotypes and their association with drug sensitivity reaches the published literature (middle). Common genotypes are often included in prospective trials (aqua), less common genotypes may be described in observational series (red), and the rarest genotypes may only be presented as case reports (purple). Meta-analysis can allow collective study of these individual published experiences (bottom), though the completeness of the data inherently depends upon selection criteria as well as publication biases.
See this image and copyright information in PMC

Comment on

References

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