OBJECTIVE: The genetic testing for hereditary breast cancer that is most helpful in high-risk women is underused. Our objective was to quantify the risk factors for heritable breast and ovarian cancer contained in the electronic health record (EHR), to determine how many women meet national guidelines for referral to a cancer genetics professional but have no record of a referral. METHODS AND MATERIALS: We reviewed EHR records of a random sample of women to determine the presence and location of risk-factor information meeting National Comprehensive Cancer Network (NCCN) guidelines for a further genetic risk evaluation for breast and/or ovarian cancer, and determine whether the women were referred for such an evaluation. RESULTS: A thorough review of the EHR records of 299 women revealed that 24 (8%) met the NCCN criteria for referral for a further genetic risk evaluation; of these, 12 (50%) had no referral to a medical genetics clinic. CONCLUSIONS: Half of the women whose EHR records contain risk-factor information meeting the criteria for further genetic risk evaluation for heritable forms of breast and ovarian cancer were not referred.

Concerns about the reliability of expression data from microarrays inspire ongoing research into measurement error in these experiments. Error arises at both the technical level within the laboratory and the experimental level. In this paper, we will focus on estimating the spot-specific error, as there are few currently available models. This paper outlines two different approaches to quantify the reliability of spot-specific intensity estimates. In both cases, the spatial correlation between pixels and its impact on spot quality is accounted for. The first method is a straightforward parametric estimate of within-spot variance that assumes a Gaussian distribution and accounts for spatial correlation via an overdispersion factor. The second method employs a nonparametric quality estimate referred to throughout as the mean square prediction error (MSPE). The MSPE first smoothes a pixel region and then measures the difference between actual pixel values and the smoother. Both methods herein are compared for real and simulated data to assess numerical characteristics and the ability to describe poor spot quality. We conclude that both approaches capture noise in the microarray platform and highlight situations where one method or the other is superior.

Abstract Summary: Microarray technology is now routinely used to monitor genome-wide expression profiles. However, current microarray imaging and analysis packages typically require manual intervention and assumptions on alignments. Unfortunately, limitations and assumptions are typically undisclosed and methods are not published. To facilitate exploration of image data, we developed SignalViewer. This paper presents a description of the application. Availability: SignalViewer is available at http://qge.fhcrc.org/signalviewer/ Supplementary information: Screenshots are available at the above location, along with downloads for Windows 2000 and Linux (Redhat 7.2).