Molecular Subtypes Improve Prognostic Value of International Metastatic Renal Cell Carcinoma Database Consortium Prognostic Model. (March 2017)

• Record Type: Journal Article
• Authors: de Velasco, Guillermo; Culhane, Aedín C.; Fay, André P.; Ari Hakimi, A.; Voss, Martin H.; Tannir, Nizar M.; Tamboli, Pheroze; Appleman, Leonard J.; Bellmunt, Joaquim; Kimryn Rathmell, W.; Albiges, Laurence; Hsieh, James J.; Heng, Daniel Y. C.; Signoretti, Sabina; Choueiri, Toni K.
• Is Part Of: Oncologist. Volume 22:Number 3(2017)
• Page Start: 286
• Page End: 292
• Publication Date: 2017-03
• Subjects: Genomics -- mRCC -- Prognosis -- Signature -- TCGA; Oncology -- Periodicals; Tumors -- Periodicals; Cancérologie -- Périodiques; Tumeurs -- Périodiques; Oncology; Tumors; Neoplasms; Electronic journals; Periodicals; Periodicals; 616.994
• DOI: 10.1634/theoncologist.2016-0078
• Languages: English
• ISSNs: 1083-7159

Abstract

Abstract: Introduction: Gene‐expression signatures for prognosis have been reported in localized renal cell carcinoma (RCC). The aim of this study was to test the predictive power of two different signatures, ClearCode34, a 34‐gene signature model [Eur Urol 2014;66:77–84], and an 8‐gene signature model [Eur Urol 2015;67:17–20], in the setting of systemic therapy for metastatic disease. Materials and Methods: Metastatic RCC (mRCC) patients from five institutions who were part of TCGA were identified and clinical data were retrieved. We trained and implemented each gene model as described by the original study. The latter was demonstrated by faithful regeneration of a figure and results from the original study. mRCC patients were dichotomized to good or poor prognostic risk groups using each gene model. Cox proportional hazard regression and concordance index (C‐Index) analysis were used to investigate an association between each prognostic risk model and overall survival (OS) from first‐line therapy. Results: Overall, 54 patients were included in the final analysis. The primary endpoint was OS. Applying the ClearCode34 model, median survival for the low‐risk—ccA ( n = 17)—and the high‐risk—ccB ( n = 37)—subtypes were 27.6 and 22.3 months (hazard ratio (HR): 2.33; p = .039), respectively. ClearCode34 ccA/ccB and International Metastatic Renal Cell Carcinoma Database Consortium (IMDC) classifications appear to represent distinct risk criteria in mRCC, and we observed no significant overlap in classification ( p > .05, chi‐square test). On multivariable analyses and adjusting for IMDC groups, ccB remained independently associated with a worse OS ( p = .044); the joint model of ccA/ccB and IMDC was significantly more accurate in predicting OS than a model with IMDC alone ( p = .045, F‐test). This was also observed in C‐Index analysis; a model with both ccA and ccB subtypes had higher accuracy (C‐Index 0.63, 95% confidence interval [CI] = 0.51–0.75) and 95% CIs of the C‐Index that did not include the null value of 0.5 in contrast to a model with IMDC alone (0.60, CI = 0.47–0.72). The 8‐gene signature molecular subtype model was a weak but insignificant predictor of survival in this cohort ( p = .13). A model that included both the 8‐gene signature and IMDC (C‐Index 0.62, CI = 0.49–0.76) was more prognostic than IMDC alone but did not reach significance, as the 95% CI included the null value of 0.5. These two genomic signatures share no genes in common and are enriched in different biological pathways. The ClearCode34 included genes ARNT and EPAS1 (also known as HIF2a), which are involved in regulation of gene expression by hypoxia‐inducible factor. Conclusion: The ClearCode34 but not the 8‐gene molecular model improved the prognostic predictive power of the IMDC model in this cohort of 54 patients with metastatic clear cell RCC. The Oncologist 2017;22:286–292 Implications for Practice: The clinical and laboratory factors included in the International Metastatic Renal Cell Carcinoma Database Consortium model provide prognostic information in metastatic renal cell carcinoma (mRCC). The present study shows that genomic signatures, originally validated in localized RCC, may add further complementary prognostic information in the metastatic setting. This study may provide new insights into the molecular basis of certain mRCC subgroups. The integration of clinical and molecular data has the potential to redefine mRCC classification, enhance the understanding of mRCC biology, and potentially predict response to treatment in the future. Abstract : Gene‐expression signatures for prognosis have been reported in localized renal cell carcinoma. The objective of this study was to test the predictive power of two different signatures, ClearCode34 (a 34‐gene signature model) and an 8‐gene signature model, in the setting of systemic therapy for metastatic disease.