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Background: Metastasized clear cell renal cell carcinoma (ccRCC) is associated with a poor prognosis. Almost one-third of patients with non-metastatic tumors at diagnosis will later progress with metastatic disease. These patients need to be identified already at diagnosis, to undertake closer follow up and/or adjuvant treatment. Today, clinicopathological variables are used to risk classify patients, but molecular biomarkers are needed to improve risk classification to identify the high-risk patients which will benefit most from modern adjuvant therapies. Interestingly, DNA methylation profiling has emerged as a promising prognostic biomarker in ccRCC. This study aimed to derive a model for prediction of tumor progression after nephrectomy in non-metastatic ccRCC by combining DNA methylation profiling with clinicopathological variables., Methods: A novel cluster analysis approach (Directed Cluster Analysis) was used to identify molecular biomarkers from genome-wide methylation array data. These novel DNA methylation biomarkers, together with previously identified CpG-site biomarkers and clinicopathological variables, were used to derive predictive classifiers for tumor progression., Results: The "triple classifier" which included both novel and previously identified DNA methylation biomarkers together with clinicopathological variables predicted tumor progression more accurately than the currently used Mayo scoring system, by increasing the specificity from 50% in Mayo to 64% in our triple classifier at 85% fixed sensitivity. The cumulative incidence of progress ( p CIP 5yr ) was 7.5% in low-risk vs 44.7% in high-risk in M0 patients classified by the triple classifier at diagnosis., Conclusions: The triple classifier panel that combines clinicopathological variables with genome-wide methylation data has the potential to improve specificity in prognosis prediction for patients with non-metastatic ccRCC.

Background: Clear cell renal cell carcinoma (ccRCC) is the most common subtype among renal cancer and is associated with poor prognosis if metastasized. Up to one third of patients with local disease at diagnosis will develop metastasis after nephrectomy, and there is a need for new molecular markers to identify patients with high risk of tumor progression. In the present study, we performed genome-wide promoter DNA methylation analysis at diagnosis to identify DNA methylation profiles associated with risk for progress., Method: Diagnostic tissue samples from 115 ccRCC patients were analysed by Illumina HumanMethylation450K arrays and methylation status of 155,931 promoter associated CpGs were related to genetic aberrations, gene expression and clinicopathological parameters., Results: The ccRCC samples separated into two clusters (cluster A/B) based on genome-wide promoter methylation status. The samples in these clusters differed in tumor diameter (p < 0.001), TNM stage (p < 0.001), morphological grade (p < 0.001), and patients outcome (5 year cancer specific survival (pCSS 5yr ) p < 0.001 and cumulative incidence of progress (pCIP 5yr ) p < 0.001. An integrated genomic and epigenomic analysis in the ccRCCs, revealed significant correlations between the total number of genetic aberrations and total number of hypermethylated CpGs (R = 0.435, p < 0.001), and predicted mitotic age (R = 0.407, p < 0.001). We identified a promoter methylation classifier (PMC) panel consisting of 172 differently methylated CpGs accompanying progress of disease. Classifying non-metastatic patients using the PMC panel showed that PMC high tumors had a worse prognosis compared with the PMC low tumors (pCIP 5yr 38% vs. 8%, p = 0.001), which was confirmed in non-metastatic ccRCCs in the publically available TCGA-KIRC dataset (pCIP 5yr 39% vs. 16%, p < 0.001)., Conclusion: DNA methylation analysis at diagnosis in ccRCC has the potential to improve outcome-prediction in non-metastatic patients at diagnosis.

Epigenetic alterations in the methylome have been associated with tumor development and progression in renal cell carcinoma (RCC). In this study, 45 tumor samples, 12 tumor-free kidney cortex tissues, and 24 peripheral blood samples from patients with clear cell RCC (ccRCC) were analyzed by genome-wide promoter-directed methylation arrays and related to clinicopathological parameters. Unsupervised hierarchical clustering separated the tumors into two distinct methylation groups (clusters A and B), where cluster B had higher average methylation and increased number of hypermethylated CpG sites (CpGs). Furthermore, tumors in cluster B had, compared with cluster A, a larger tumor diameter (p = 0.033), a higher morphologic grade (p < 0.001), a higher tumor-node-metastasis (TNM) stage (p < 0.001), and a worse prognosis (p = 0.005). Higher TNM stage was correlated to an increase in average methylation level (p = 0.003) and number of hypermethylated CpGs (p = 0.003), whereas a number of hypomethylated CpGs were mainly unchanged. However, the predicted age of the tumors based on methylation profile did not correlate with TNM stage, morphological grade, or methylation cluster. Differently methylated (DM) genes (n = 840) in ccRCC samples compared with tumor-free kidney cortex samples were predominantly hypermethylated and a high proportion were identified as polycomb target genes. The DM genes were overrepresented by transcription factors, ligands, and receptors, indicating functional alterations of significance for ccRCC progression. To conclude, increased number of hypermethylated genes was associated with increased TNM stage of the tumors. DNA methylation classification of ccRCC tumor samples at diagnosis can serve as a clinically applicable prognostic marker in ccRCC.

Background: Wilms' tumour 1 (WT1) gene was discovered as a tumour suppressor gene. Later findings have suggested that WT1 also can be oncogenic. This complexity is partly explained by the fact that WT1 has a number of target genes., Method: WT1 and its target gene human telomerase reverse transcriptase (hTERT) were analysed in clear cell renal cell carcinoma (ccRCC). In vitro experiments were performed to examine the functional link between WT1 and hTERT by overexpression of WT1 isoforms in the ccRCC cell line, TK-10., Results: WT1 demonstrated lower RNA expression in ccRCC compared with renal cortical tissue, whereas hTERT was increased, showing a negative correlation between WT1 and hTERT (P=0.005). These findings were experimentally confirmed in vitro. The WT1 generated effect on hTERT promoter activity seemed complex, as several negative regulators of hTERT transcription, such as SMAD3, JUN (AP-1) and ETS1, were activated by WT1 overexpression. Downregulation of potential positive hTERT regulators, such as cMyc, AP-2α, AP-2γ, IRF1, NFX1 and GM-CSF, were also observed. Chromatin immunoprecipitation analysis verified WT1 binding to the hTERT, cMyc and SMAD3 promoters., Conclusion: The collected data strongly indicate multiple pathways for hTERT regulation by WT1 in ccRCC.