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Identifying functional CpG island methylation markers in clear cell renal cell cancer cell lines

ABSTRACT: Clear cell renal cell carcinoma (ccRCC) is the most common adult renal cancer. Although the molecular characteristics of ccRCC are currently being studied, the biologically and clinically relevant ccRCC methylome remains to be elucidated. To explore the RCC hypermethylome we employed massive sequencing of methyl-binding protein enriched DNA and validated the biological relevance of the identified hypermethylated sites by pharmacological inhibition of DNA methylation. We identified four candidate tumor suppressor genes (GREM1, LAD1, NEFH, and NEURL) of which promoter CpG island hypermethylation was strongly predictive for ccRCC survival in two independent series (n=150 and n=185) of ccRCC primary samples. The four markers combined are strongly associated with risk for cancer-related death in the test series (HR 3.64, 95% CI 1.02-13.01) as well as, independently of other clinicopathological characteristics, in the validation series (HR 7.54, 95% CI 2.68-21.19) using Cox proportional hazard models. According to Harrell’s C statistics and the Akaike Information Criterion (AIC) the four marker panel provides the best predictive capacity with the best fit of the model as assessed for the tested markers. These results provide novel insights into the ccRCC hypermethylome and identify a strong methylation marker panel to potentially guide personalized ccRCC patient management. Preliminary to implementation of this marker panel into clinical practice, enrollment of these patients in a Phase-III trial to study adjuvant treatment efficacy is essential.

ORGANISM(S): Homo sapiens

PROVIDER: GSE33916 | GEO | 2013/11/22

SECONDARY ACCESSION(S): PRJNA148235

REPOSITORIES: GEO

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Project description:Background: Accurate survival stratification in early-stage NSCLC could inform the use of adjuvant therapy. We developed a clinically-implementable mortality risk score incorporating distinct tumor microenvironmental gene expression signatures and clinical variables. Methods: Gene expression profiles from 1106 non-squamous NSCLCs were used for generation and internal validation of a 9-gene molecular prognostic index (MPI). Expression of the MPI genes was determined within sorted tumor cell subpopulations. A quantitative PCR (qPCR) assay was developed and validated on an independent cohort of FFPE tissues. A prognostic score using clinical variables was generated using Surveillance Epidemiology and End Results (SEER) data and combined with the MPI. Results: The MPI stratified stage I patients into prognostic categories in four independent validation datasets, including three microarray and one FFPE qPCR cohorts (HR=2.4, 95% CI, 1.8-3.3, P=7x10-9 in the largest microarray cohort; and HR=2.5, 95% CI 1.1-6.0, P=.03 in stage I patients of the qPCR validation cohort). Prognostic genes were expressed in distinct tumor cell subpopulations and expression of genes implicated in cellular proliferation and stem cells portended poor outcomes, while expression of genes involved in normal lung differentiation and immune infiltration was associated with superior survival. Integrating the MPI with clinical variables conferred greatest prognostic power (HR=3.3, 95% CI 2.4-4.6; P=2x10-15 in the largest microarray cohort; and HR=3.6, 95% CI 1.5-8.8, P=.003 in stage I patients of the qPCR validation cohort). Finally, the MPI was prognostic irrespective of somatic alterations in EGFR, KRAS, TP53, and ALK. Conclusion: The MPI incorporates genes expressed in the tumor and its microenvironment, and designates risk of death for patients with early-stage non-squamous NSCLC. The MPI can be implemented clinically using qPCR assays on FFPE tissues and a composite model integrating the MPI with clinical variables provides the most accurate risk stratification.

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Identifying functional CpG island methylation markers in clear cell renal cell cancer cell lines

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