A Comparison of E6H4 and G175-405 p16-specific Monoclonal Antibodies in Oropharyngeal Squamous Cell Carcinoma.

The purpose of this investigation is to directly compare G175-405 and E6H4 p16-specific antibodies as immunomarkers of HPV-driven oropharyngeal carcinoma. The investigators designed a retrospective analysis using specimens from an archived tissue bank with known in situ hybridization and polymerase chain reaction status for HPV DNA. Fifty randomly selected oropharyngeal specimens were evaluated with both the G175-405 and E6H4 p16-specific monoclonal antibodies. Two pathologists, blinded to the HPV-specific testing status, evaluated p16 positivity for both antibody clones. Interrater agreement was determined using a Cohen κ coefficient. Sensitivity and specificity values were calculated using a standard 2×2 contingency table, then compared using McNemar test. Interrater agreement for interpretation of p16 expression was 92% (κ=0.84) for the G175-405 clone and 100% for the E6H4 clone (κ=1.0). The G175-405 stain had a sensitivity of 0.917 and specificity of 0.846. The E6H4 stain had a sensitivity of 1.000 and specificity of 0.769. Using McNemar test, there were no significant differences found for sensitivity (P=0.480) or specificity (P=0.480) values. The results of this study suggest that though both G175-405 and E6H4 antibody stains are statistically comparable immunomarkers for HPV-driven oropharyngeal carcinoma, the E6H4 clone offers improved interobserver reliability.

Max Margin General Linear Modeling for Neuroimage Analyses.

General linear modeling (GLM) is one of the most commonly used approaches to perform voxel based analyses (VBA) for hypotheses testing in neuroimaging. In this paper we tie support vector machine based regression (SVR) and classical significance testing to provide the benefits of max margin estimation in the GLM setting. Using Welch-Satterthwaite approximations, we compute degrees of freedom (df) of error (also known as residual df) for ε-SVR. We demonstrate that ε-SVR can result not only in robustness of estimation but also improved residual df compared to the very commonly used ordinary least squares (OLS) estimation. This can result in higher sensitivity to signal in neuroimaging studies and also allow for better control of confounding effects of nuisance covariates. We demonstrate the application of our approach in white matter analyses using diffusion tensor imaging (DTI) data from autism and emotion-regulation studies.

A diffusion tensor brain template for rhesus macaques.

Diffusion tensor imaging (DTI) is a powerful and noninvasive imaging method for characterizing tissue microstructure and white matter organization in the brain. While it has been applied extensively in research studies of the human brain, DTI studies of non-human primates have been performed only recently. The growing application of DTI in rhesus monkey studies would significantly benefit from a standardized framework to compare findings across different studies. A very common strategy for image analysis is to spatially normalize (co-register) the individual scans to a representative template space. This paper presents the development of a DTI brain template, UWRMAC-DTI271, for adolescent Rhesus Macaque (Macaca mulatta) monkeys. The template was generated from 271 rhesus monkeys, collected as part of a unique brain imaging genetics study. It is the largest number of animals ever used to generate a computational brain template, which enables the generation of a template that has high image quality and accounts for variability in the species. The quality of the template is further ensured with the use of DTI-TK, a well-tested and high-performance DTI spatial normalization method in human studies. We demonstrated its efficacy in monkey studies for the first time by comparing it to other commonly used scalar-methods for DTI normalization. It is anticipated that this template will play an important role in facilitating cross-site voxelwise DTI analyses in Rhesus Macaques. Such analyses are crucial in investigating the role of white matter structure in brain function, development, and other psychopathological disorders for which there are well-validated non-human primate models.