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To develop a benchmark measure of US physicians' level of knowledge and extent of use of pharmacogenomic testing, we conducted an anonymous, cross-sectional, fax-based, national survey. Of 397,832 physicians receiving the survey questionnaire, 10,303 (3%) completed and returned it; the respondents were representative of the overall US physician population. The factors associated with the decision to test were evaluated using χ(2) and multivariate logistic regression. Overall, 97.6% of responding physicians agreed that genetic variations may influence drug response, but only 10.3% felt adequately informed about pharmacogenomic testing. Only 12.9% of physicians had ordered a test in the previous 6 months, and 26.4% anticipated ordering a test in the next 6 months. Early and future adopters of testing were more likely to have received training in pharmacogenomics, but only 29.0% of physicians overall had received any education in the field. Our findings highlight the need for more effective physician education on the clinical value, availability, and interpretation of pharmacogenomic tests.

In light of the meeting of the US Food and Drug Administration (FDA) in March 2011 to discuss the regulation of clinical direct-to-consumer (DTC) genetic tests, we have invited five experts to consider the best means of overseeing the ordering and interpretation of these tests. Should these tests be regulated? If so, who, if anyone, should communicate results to consumers?

Biologics can be seen as "designer" drugs whose mode of action in a specific disease mechanism is frequently well understood, making it often possible to predict better efficacy and safety profiles for biologics when compared with small molecule drugs. Biologics have been approved for the treatment of major disease classes, such as inflammatory disease, cardiovascular disease, and cancer. However, as it is true for small molecule drugs, often only a fraction of the treated population responds to biologics, and clinical markers for prediction of efficacy are seldom available. It is reasonable to expect that the use of genetic or genomic markers will contribute to improving the prediction of safety and efficacy of both biologics and small molecule drugs. In this paper, we will review the differences between biologics and small molecule drugs, focusing on studies highlighting the relevance of genetic and genomic information on safety and efficacy issues in therapies with biologics. The potential impact of these studies on the promotion of personalized medicine and on regulatory decisions will also be discussed.

Over the last decade, the introduction of microarray technology has had a profound impact on gene expression research. The publication of studies with dissimilar or altogether contradictory results, obtained using different microarray platforms to analyze identical RNA samples, has raised concerns about the reliability of this technology. The MicroArray Quality Control (MAQC) project was initiated to address these concerns, as well as other performance and data analysis issues. Expression data on four titration pools from two distinct reference RNA samples were generated at multiple test sites using a variety of microarray-based and alternative technology platforms. Here we describe the experimental design and probe mapping efforts behind the MAQC project. We show intraplatform consistency across test sites as well as a high level of interplatform concordance in terms of genes identified as differentially expressed. This study provides a resource that represents an important first step toward establishing a framework for the use of microarrays in clinical and regulatory settings.

Pharmacogenomics is changing the way drugs are being developed, approved and used. The Gurwitz et al article found in the previous issue is a timely 'call to arms' to ensure that this important field does not remain the wisdom of few, but becomes widely accepted and used. We are at a critical stage in pharmacogenomics: the science is not new, but has experienced a significant boost since the human genome project has been completed. Now is the time to capitalize on what basic science has provided and translate it into clinical practice. However, this can only happen if physicians and other health-care professionals, as well as patients, are being educated and become knowledgeable about pharmacogenomics.

Reproducibility is a fundamental requirement in scientific experiments and clinical contexts. Recent publications raise concerns about the reliability of microarray technology because of the apparent lack of agreement between lists of differentially expressed genes (DEGs). In this study we demonstrate that (1) such discordance may stem from ranking and selecting DEGs solely by statistical significance (P) derived from widely used simple t-tests; (2) when fold change (FC) is used as the ranking criterion, the lists become much more reproducible, especially when fewer genes are selected; and (3) the instability of short DEG lists based on P cutoffs is an expected mathematical consequence of the high variability of the t-values. We recommend the use of FC ranking plus a non-stringent P cutoff as a baseline practice in order to generate more reproducible DEG lists. The FC criterion enhances reproducibility while the P criterion balances sensitivity and specificity.

Reproducibility is a fundamental requirement in scientific experiments and clinical contexts. Recent publications raise concerns about the reliability of microarray technology because of the apparent lack of agreement between lists of differentially expressed genes (DEGs). In this study we demonstrate that (1) such discordance may stem from ranking and selecting DEGs solely by statistical significance (P) derived from widely used simple t-tests; (2) when fold change (FC) is used as the ranking criterion, the lists become much more reproducible, especially when fewer genes are selected; and (3) the instability of short DEG lists based on P cutoffs is an expected mathematical consequence of the high variability of the t-values. We recommend the use of FC ranking plus a non-stringent P cutoff as a baseline practice in order to generate more reproducible DEG lists. The FC criterion enhances reproducibility while the P criterion balances sensitivity and specificity.

Background The acceptance of microarray technology in regulatory decision-making is being challenged by the existence of various platforms and data analysis methods. A recent report (E. Marshall, Science, 306, 630–631, 2004), by extensively citing the study of Tan et al. (Nucleic Acids Res., 31, 5676–5684, 2003), portrays a disturbingly negative picture of the cross-platform comparability, and, hence, the reliability of microarray technology. Results We reanalyzed Tan's dataset and found that the intra-platform consistency was low, indicating a problem in experimental procedures from which the dataset was generated. Furthermore, by using three gene selection methods (i.e., p-value ranking, fold-change ranking, and Significance Analysis of Microarrays (SAM)) on the same dataset we found that p-value ranking (the method emphasized by Tan et al.) results in much lower cross-platform concordance compared to fold-change ranking or SAM. Therefore, the low cross-platform concordance reported in Tan's study appears to be mainly due to a combination of low intra-platform consistency and a poor choice of data analysis procedures, instead of inherent technical differences among different platforms, as suggested by Tan et al. and Marshall. Conclusion Our results illustrate the importance of establishing calibrated RNA samples and reference datasets to objectively assess the performance of different microarray platforms and the proficiency of individual laboratories as well as the merits of various data analysis procedures. Thus, we are progressively coordinating the MAQC project, a community-wide effort for microarray quality control.