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

Brooks SA, Brannon AR, Parker JS, Fisher JC, Sen O, Kattan MW, Hakimi AA, Hsieh JJ, Choueiri TK, Tamboli P, Maranchie JK, Hinds P, Miller CR, Nielsen ME, Rathmell WK
Eur Urol. 2014 66 (1): 77-84

PMID: 24613583 · PMCID: PMC4058355 · DOI:10.1016/j.eururo.2014.02.035

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.

Copyright © 2014 European Association of Urology. Published by Elsevier B.V. All rights reserved.

MeSH Terms (19)

Adult Aged Aged, 80 and over Animals Biomarkers, Tumor Carcinoma, Renal Cell Disease-Free Survival Female Gene Expression Gene Expression Profiling Humans Kidney Neoplasms Male Middle Aged Neoplasm Recurrence, Local Risk Assessment RNA, Neoplasm Sequence Analysis, RNA Survival Rate

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