Molecular Subtypes Improve Prognostic Value of International Metastatic Renal Cell Carcinoma Database Consortium Prognostic Model.

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.

Management and outcomes of patients with renal medullary carcinoma: a multicentre collaborative study.

OBJECTIVE: To describe the management strategies and outcomes of patients with renal medullary carcinoma (RMC) and characterise predictors of overall survival (OS). PATIENTS AND METHODS: RMC is a rare and aggressive malignancy that afflicts young patients with sickle cell trait; there are limited data on management to date. This is a study of patients with RMC who were treated in 2000-2015 at eight academic institutions in North America and France. The Kaplan-Meier method was used to estimate OS, measured from initial RMC diagnosis to date of death. Cox regression analysis was used to determine predictors of OS. RESULTS: In all, 52 patients (37 males) were identified. The median (range) age at diagnosis was 28 (9-48) years and 49 patients (94%) had stage III/IV. The median OS for all patients was 13.0 months and 38 patients (75%) had nephrectomy. Patients who underwent nephrectomy had superior OS compared to patients who were treated with systemic therapy only (median OS 16.4 vs 7.0 months, P < 0.001). In all, 45 patients received chemotherapy and 13 (29%) had an objective response; 28 patients received targeted therapies, with 8-week median therapy duration and no objective responses. Only seven patients (13%) survived for >24 months. CONCLUSIONS: RMC carries a poor prognosis. Chemotherapy provides palliation and remains the mainstay of therapy, but <20% of patients survive for >24 months, underscoring the need to develop more effective therapy for this rare tumour. In this study, nephrectomy was associated with improved OS.

Modeling clear cell renal cell carcinoma and therapeutic implications.

Renal cell carcinoma (RCC) comprises a diverse group of malignancies arising from the nephron. The most prevalent type, clear cell renal cell carcinoma (ccRCC), is characterized by genetic mutations in factors governing the hypoxia signaling pathway, resulting in metabolic dysregulation, heightened angiogenesis, intratumoral heterogeneity, and deleterious tumor microenvironmental (TME) crosstalk. Identification of specific genetic variances has led to therapeutic innovation and improved survival for patients with ccRCC. Current barriers to effective long-term therapeutic success highlight the need for continued drug development using improved modeling systems. ccRCC preclinical models can be grouped into three broad categories: cell line, mouse, and 3D models. Yet, the breadth of important unanswered questions in ccRCC research far exceeds the accessibility of model systems capable of carrying them out. Accordingly, we review the strengths, weaknesses, and therapeutic implications of each model system that are relied upon today.