Molecular profiling and classification of sporadic renal cell carcinoma by quantitative methylation analysis

Mark L Gonzalgo, Srinivasan Yegnasubramanian, Gai Yan, Craig G. Rogers, Theresa L. Nicol, William G. Nelson, Christian P. Pavlovich

Research output: Contribution to journalArticle

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Abstract

Purpose: Preoperative histologic classification of solid renal masses remains limited with current technology. We determine the utility of molecular profiling based on quantitative methylation analysis for characterization of sporadic renal cell carcinoma. Experimental Design: Primary renal cell carcinomas representing three different histologic subtypes were obtained from a total of 38 patients who underwent radical nephrectomy for suspected malignant disease. Genomic DNA was isolated from tumors and was subjected to sodium bisulfite modification. The normalized index of methylation (NIM) for each sample was determined by quantitative real-time methylation-specific PCR at 17 different gene promoters. Hierarchical cluster analysis was performed by using an unsupervised neural network with binary tree topology. Results: The majority of gene promoters that were analyzed in this study demonstrated very low levels of methylation (NIM < 1.0). The RASSF1A gene promoter, however, was methylated in 30 of 38 (79%) cases. The frequency of RASSF1A methylation in papillary, clear-cell, and oncocytoma subtypes was 100, 90, and 25%, respectively. The highest levels of RASSF1A methylation were observed in the papillary (mean NIM = 78.9) and clear-cell (mean NIM = 13.4) subtypes. The vast majority of oncocytomas were completely unmethylated, and none demonstrated > 1% methylation (mean NIM = 0.11). Hierarchical cluster analysis based on quantitative methylation levels resulted in stratification of sporadic renal cell carcinomas into their discrete histologic subtypes. Conclusions: Classification of sporadic renal cell carcinomas into histologic subtypes can be accomplished via multigene quantitative methylation profiling. Validation of this approach and selection of appropriate methylation markers may ultimately lead to use of this technology in the preoperative assessment of suspicious renal masses.

Original languageEnglish (US)
Pages (from-to)7276-7283
Number of pages8
JournalClinical Cancer Research
Volume10
Issue number21
DOIs
StatePublished - Nov 1 2004
Externally publishedYes

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Renal Cell Carcinoma
Methylation
Cluster Analysis
Technology
Kidney
Nephrectomy
Genes
Research Design
Polymerase Chain Reaction
DNA

ASJC Scopus subject areas

  • Cancer Research
  • Oncology

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Molecular profiling and classification of sporadic renal cell carcinoma by quantitative methylation analysis. / Gonzalgo, Mark L; Yegnasubramanian, Srinivasan; Yan, Gai; Rogers, Craig G.; Nicol, Theresa L.; Nelson, William G.; Pavlovich, Christian P.

In: Clinical Cancer Research, Vol. 10, No. 21, 01.11.2004, p. 7276-7283.

Research output: Contribution to journalArticle

Gonzalgo, Mark L ; Yegnasubramanian, Srinivasan ; Yan, Gai ; Rogers, Craig G. ; Nicol, Theresa L. ; Nelson, William G. ; Pavlovich, Christian P. / Molecular profiling and classification of sporadic renal cell carcinoma by quantitative methylation analysis. In: Clinical Cancer Research. 2004 ; Vol. 10, No. 21. pp. 7276-7283.
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abstract = "Purpose: Preoperative histologic classification of solid renal masses remains limited with current technology. We determine the utility of molecular profiling based on quantitative methylation analysis for characterization of sporadic renal cell carcinoma. Experimental Design: Primary renal cell carcinomas representing three different histologic subtypes were obtained from a total of 38 patients who underwent radical nephrectomy for suspected malignant disease. Genomic DNA was isolated from tumors and was subjected to sodium bisulfite modification. The normalized index of methylation (NIM) for each sample was determined by quantitative real-time methylation-specific PCR at 17 different gene promoters. Hierarchical cluster analysis was performed by using an unsupervised neural network with binary tree topology. Results: The majority of gene promoters that were analyzed in this study demonstrated very low levels of methylation (NIM < 1.0). The RASSF1A gene promoter, however, was methylated in 30 of 38 (79{\%}) cases. The frequency of RASSF1A methylation in papillary, clear-cell, and oncocytoma subtypes was 100, 90, and 25{\%}, respectively. The highest levels of RASSF1A methylation were observed in the papillary (mean NIM = 78.9) and clear-cell (mean NIM = 13.4) subtypes. The vast majority of oncocytomas were completely unmethylated, and none demonstrated > 1{\%} methylation (mean NIM = 0.11). Hierarchical cluster analysis based on quantitative methylation levels resulted in stratification of sporadic renal cell carcinomas into their discrete histologic subtypes. Conclusions: Classification of sporadic renal cell carcinomas into histologic subtypes can be accomplished via multigene quantitative methylation profiling. Validation of this approach and selection of appropriate methylation markers may ultimately lead to use of this technology in the preoperative assessment of suspicious renal masses.",
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