Issues and Opportunities on Implementing an Online Faculty Review System.

Intensifying accountability pressures have led to an increased attention to assessments of teaching, but teaching generally represents only a portion of faculty duties. Less attention has been paid to how evaluations of faculty members can be used to gather data on teaching, research, clinical work, and outreach to integrate clinical and academic contributions and fill in information gaps in strategic areas such as technology transfer and commercialization where universities are being pressed to do more. Online reporting systems can enable departments to gather comprehensive data on faculty activities that can be aggregated for accreditation assessments, program reviews, and strategic planning. As detailed in our case study of implementing such a system at a research university, online annual reviews can also be used to publicize faculty achievements, to document departmental achievements, foster interdisciplinary and community collaborations, recognize service contributions (and disparities), and provide a comprehensive baseline for salary and budgetary investments.

Accurate classification of diffuse large B-cell lymphoma into germinal center and activated B-cell subtypes using a nuclease protection assay on formalin-fixed, paraffin-embedded tissues.

Classification of diffuse large B-cell lymphoma (DLBCL) into cell-of-origin (COO) subtypes based on gene expression profiles has well-established prognostic value. These subtypes, termed germinal center B cell (GCB) and activated B cell (ABC) also have different genetic alterations and overexpression of different pathways that may serve as therapeutic targets. Thus, accurate classification is essential for analysis of clinical trial results and planning new trials by using targeted agents. The current standard for COO classification uses gene expression profiling (GEP) of snap frozen tissues, and a Bayesian predictor algorithm. However, this is generally not feasible. In this study, we investigated whether the qNPA technique could be used for accurate classification of COO by using formalin-fixed, paraffin-embedded (FFPE) tissues. We analyzed expression levels of 14 genes in 121 cases of R-CHOP-treated DLBCL that had previously undergone GEP by using the Affymetrix U133 Plus 2.0 microarray and had matching FFPE blocks. Results were evaluated by using the previously published algorithm with a leave-one-out cross-validation approach. These results were compared with COO classification based on frozen tissue GEP profiles. For each case, a probability statistic was generated indicating the likelihood that the classification by using qNPA was accurate. When data were dichotomized into GCB or non-GCB, overall accuracy was 92%. The qNPA technique accurately categorized DLBCL into GCB and ABC subtypes, as defined by GEP. This approach is quantifiable, applicable to FFPE tissues with no technical failures, and has potential for significant impact on DLBCL research and clinical trial development.