ClearCode34: A prognostic risk predictor for localized clear cell renal cell carcinoma

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.

Keywords: Biomarker; Kidney cancer; Prognosis; Renal cell carcinoma; TCGA; ccRCC.

Fig. 1

Workflow for biomarker discovery: steps taken to identify the 34 genes that classify ccA and ccB tumors. LAD = logical analysis of data.

Fig. 2

Order of analyses to develop and validate the relapse risk model. Diagram of analyses to validate the efficiency of the biomarkers to classify tumors and predict prognostic outcomes. TCGA = the Cancer Genome Atlas; UNC = University of North Carolina.

Fig. 3

Tumor classification from the Cancer Genome Atlas (TCGA) shows distinct prognostic outcomes. Prediction analysis for microarray classified 380 untreated, nonmetastatic clear cell renal cell carcinoma tumors from TCGA as either ccA or ccB, using the 34-gene classifier, ClearCode34. Kaplan-Meier curves were used to calculate (a) recurrence-free survival (RFS), (b) cancer-specific survival (CSS), and(c) overall survival (OS) for ccA and ccB patients. ccB-typed patients had a median RFS and OS of 53 and 65 mo, respectively, while patients with ccA-typed tumors had a 50% survival probability of 91 and 94 mo for RFS and OS, respectively. HR = hazard ratio; CI = confidence interval.

Fig. 4

Clear cell renal cell carcinoma (ccRCC) classifier recapitulates survival outcomes for subtypes in clinical cohort. Whole lysates from 157 nonmetastatic, archived ccRCC primary tumor samples were subjected to NanoString gene expression analysis (NanoString Technologies Inc, Seattle, WA, USA). Kaplan-Meier plots of the independent cohort using ClearCode34 show ccB patients have significantly lower probabilities of (a) recurrence-free survival (RFS), (b) cancer-specific survival (CSS), and (c) overall survival (OS) compared to ccA. HR = hazard ratio; CI = confidence interval.

Fig. 5

ClearCode34 prognostic model can evaluate patient risk. A randomized training set of 265 patients from the Cancer Genome Atlas (TCGA) project and clinical cohorts were used to train a model to identify low-, intermediate-, and high-risk groups for tumor recurrence using clear cell renal cell carcinoma (ccRCC) subtype status (ccA/ccB), tumor stage, and histologic Fuhrman grade. The model was applied to the test set (n = 266) to predict (a) recurrence and (b) cancer-specific death, revealing a highly significant risk profile integrating clinical and biologic features. (c and e) Co-occurrence index (C-index) and (d and f) multivariate analysis validated the efficacy of the model using the three risk groups to predict risk of ccRCC death over the established algorithms University of California, Los Angeles Integrated Staging System (UISS) and Mayo Clinic Stage, Size, Grade, and Necrosis (SSIGN) score. Chi-square statistic values resulting from multivariate regression depict the additive value of the three risk models. *p < 0.05.