1. UNC Lineberger Cancer Center, Chapel Hill, NC, USA;2. Memorial Sloan Kettering Cancer Center, New York, NY, USA;3. Department of Quantitative Health Sciences, Cleveland Clinic, Cleveland, OH, USA;4. Department of Surgery, Urology Service, Memorial Sloan Kettering Cancer Center, New York, USA;5. Department of Medical Oncology and Kidney Cancer Center, Dana Farber Cancer Institute, Boston, MA, USA;6. Department of Pathology, MD Anderson Cancer Center, Houston, TX, USA;g Department of Urologic Oncology, University of Pittsburgh Medical Center, Pittsburgh, PA, USA;h Department of Pathology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA;i Department of Urology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA;j Department of Epidemiology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA;k Department of Medicine, Division of Hematology and Oncology, and Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
Abstract:
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.
