Key points are not available for this paper at this time.
BACKGROUND: The authors previously showed that increased tumor expression levels of B7-H1, survivin, and Ki-67 are independent predictors of poor outcome for patients with clear cell renal cell carcinoma (ccRCC). In the current study, they described the creation of a scoring system based on this panel of biomarkers that can be used in tandem with existing clinicopathologic features and algorithms to improve ccRCC outcome prediction. METHODS: The authors used immunohistochemistry to determine tumor expression levels of B7-H1, survivin, and Ki-67 for 634 consecutive ccRCC patients. A multivariate model verified that each biomarker was independently associated with RCC-specific death after adjusting for the remaining 2. A biomarker-based panel, termed BioScore, was generated to predict the likelihood of RCC-specific death. BioScore was tested for its ability to enhance the performance of several clinicopathologic features and algorithms. RESULTS: Patients with high BioScores were 5 times more likely to die from RCC compared with patients with low BioScores (hazard ratio, 5.03; 95% confidence interval, 3.82-6.61; P < .001). Multivariate adjustment for individual clinicopathologic features or existing prognostic algorithms failed to attenuate this positive association. Moreover, an examination of concordance indexes revealed that BioScore significantly enhanced the prognostic ability of each of the individual prognostic features or algorithms studied. CONCLUSIONS: The authors described the creation of BioScore, a biomarker-based scoring system that can be used in tandem with established prognostic algorithms to further enhance ccRCC outcome prediction. The need for external validation notwithstanding, they envision that BioScore can be readily updated as new biomarkers are identified.
Parker et al. (Thu,) studied this question.