Development of a prognostic risk model for clear cell renal cell carcinoma by systematic evaluation of DNA methylation markers

Abstract

Background Current risk models for renal cell carcinoma (RCC) based on clinicopathological factors are sub-optimal in accurately identifying high-risk patients. Here, we perform a head-to-head comparison of previously published DNA methylation markers and propose a potential prognostic model for clear cell RCC (ccRCC). Patients and methods Promoter methylation of PCDH8, BNC1, SCUBE3, GREM1, LAD1, NEFH, RASSF1A, GATA5, SFRP1, CDO1, and NEURL was determined by nested methylation-specific PCR. To identify clinically relevant methylated regions, The Cancer Genome Atlas (TCGA) was used to guide primer design. Formalin-fixed paraffin-embedded (FFPE) tissue samples from 336 non-metastatic ccRCC patients from the prospective Netherlands Cohort Study (NLCS) were used to develop a Cox proportional hazards model using stepwise backward elimination and bootstrapping to correct for optimism. For validation purposes, FFPE ccRCC tissue of 64 patients from the University Hospitals Leuven and a series of 232 cases from The Cancer Genome Atlas (TCGA) were used. Results Methylation of GREM1, GATA5, LAD1, NEFH, NEURL, and SFRP1 was associated with poor ccRCC-specific survival, independent of age, sex, tumor size, TNM stage or tumor grade. Moreover, the association between GREM1, NEFH, and NEURL methylation and outcome was shown to be dependent on the genomic region. A prognostic biomarker model containing GREM1, GATA5, LAD1, NEFH and NEURL methylation in combination with clinicopathological characteristics, performed better compared to the model with clinicopathological characteristics only (clinical model), in both the NLCS and the validation population with a c-statistic of 0.71 versus 0.65 and a c-statistic of 0.95 versus 0.86 consecutively. However, the biomarker model had limited added prognostic value in the TCGA series with a c-statistic of 0.76 versus 0.75 for the clinical model. Conclusion In this study we performed a head-to-head comparison of potential prognostic methylation markers for ccRCC using a novel approach to guide primers design which utilizes the optimal location for measuring DNA methylation. Using this approach, we identified five methylation markers that potentially show prognostic value in addition to currently known clinicopathological factors.

Technical considerations in PCR-based assay design for diagnostic DNA methylation cancer biomarkers

“…Next to these variations in genomic location, we also observed a large variation in diagnostic performance even within the same genes. As previously postulated, the exact studied genomic location could influence the diagnostic performance of a biomarker, emphasizing the importance of considering genomic location of the assay upfront [ 1 , 7 , 8 ]. Currently, to our knowledge, no guidelines for identifying the optimal genomic location for diagnostic DNA methylation biomarkers are described.…”

“…These data can be used to identify the genomic location with the largest methylation differences between sample groups, associated with the clinical outcome of interest. For example, we assume that the genomic locations with the largest difference in methylation between normal and tumor samples can be used to discriminate tumor tissue/patients from normal tissue/healthy individuals, as suggested in several of our previous publications [ 1 , 7 , 8 ].…”

DNA Methylation in INA, NHLH2, and THBS4 Is Associated with Metastatic Disease in Renal Cell Carcinoma

“…Methylation panels combining the CpG methylation of different candidate genes were previously reported to be associated with patient survival, with more robust statistical results and improved performance compared to clinical prognostic models [ 25 , 63 , 64 , 65 ]. Moreover, in view of the complex molecular architecture of alterations observed during tumorigenesis and metastasis and the individual variation of tumors, biomarker signatures that are subject to subsequent continuous extension, reevaluation, and reselection, rather than single markers, may be useful for a specific diagnostic task.…”

“…The potential value of such measurements has decisively improved with recent findings showing that adjuvant treatment with pembrolizumab is beneficial for RCC patients with high-risk tumors [ 10 ]. Thus, future involvement of methylation-based signatures in prognostic models have the potential to improve risk stratification for adjuvant treatments [ 10 , 65 ].…”

Identification of a Somatic Mutation-Derived Long Non-Coding RNA Signatures of Genomic Instability in Renal Cell Carcinoma

“…With the development of next-generation sequencing (NGS) technologies, the detection of DNA, microRNA and lncRNA in the blood or tissues of patients had become more and more convenient and accessible. There were also studies that systematically evaluated the expression of DNA methylation markers (45), microRNA (46), and lncRNA (47,48) to predict the prognosis of patients with RCC. However, these models were not relate to genomic instability, and some models could only predict the prognosis of specific patients, such as metastatic or non-metastatic RCC.…”