Transforming a hospital nursing research fellowship into an evidence-based practice fellowship

An established hospital-based nursing research fellowship program was transformed into an evidence-based practice fellowship despite its previous high satisfaction ratings from nursing leaders and nurse fellow participants. The faculty for the fellowship program determined that the long-term outcomes of the research program were insufficient in light of the hospital resources committed to the fellowship program. An evidence-based practice approach was then created in anticipation that greater short-term and more sustained longer-term benefits for the hospital would be realized. The transformation of the fellowship and the short-term outcomes are described.     

ClearCode34: A Prognostic Risk Predictor for Localized Clear Cell Renal Cell Carcinoma

Background

Gene expression signatures have proven to be useful tools in many cancers to identify distinct subtypes of disease based on molecular features that drive pathogenesis, and to aid in predicting clinical outcomes. However, there are no current signatures for kidney cancer that are applicable in a clinical setting.

Objective

To generate a signature biomarker for the clear cell renal cell carcinoma (ccRCC) good risk (ccA) and poor risk (ccB) subtype classification that could be readily applied to clinical samples to develop an integrated model for biologically defined risk stratification.

Design, setting, and participants

A set of 72 ccRCC sample standards was used to develop a 34-gene classifier (ClearCode34) for assigning ccRCC tumors to subtypes. The classifier was applied to RNA-sequencing data from 380 nonmetastatic ccRCC samples from the Cancer Genome Atlas (TCGA), and to 157 formalin-fixed clinical samples collected at the University of North Carolina.

Outcome measurements and statistical analysis

Kaplan-Meier analyses were performed on the individual cohorts to calculate recurrence-free survival (RFS), cancer-specific survival (CSS), and overall survival (OS). Training and test sets were randomly selected from the combined cohorts to assemble a risk prediction model for disease recurrence.

Results and limitations

The subtypes were significantly associated with RFS (p < 0.01), CSS (p < 0.01), and OS (p < 0.01). Hazard ratios for subtype classification were similar to those of stage and grade in association with recurrence risk, and remained significant in multivariate analyses. An integrated molecular/clinical model for RFS to assign patients to risk groups was able to accurately predict CSS above established, clinical risk-prediction algorithms.

Conclusions

The ClearCode34-based model provides prognostic stratification that improves upon established algorithms to assess risk for recurrence and death for nonmetastatic ccRCC patients.

Patient summary

We developed a 34-gene subtype predictor to classify clear cell renal cell carcinoma tumors according to ccA or ccB subtypes and built a subtype-inclusive model to analyze patient survival outcomes.