RESULTS: Here we report that the treatment of RCC cell lines with azacitidine suppressed cell proliferation in all 15 lines tested. A marked response to azacitidine therapy (>50% reduction in colony formation assay) was detected in the three cell lines with VHL promoter methylation but some RCC cell lines without VHL TSG methylation also demonstrated a similar response suggesting that multiple methylated TSGs might determine the response to demethylating therapies. To identify novel candidate methylated TSGs implicated in RCC we undertook a combined analysis of copy number and CpG methylation array data. Candidate novel epigenetically inactivated TSGs were further prioritised by expression analysis of RCC cell lines pre and post-azacitidine therapy and comparative expression analysis of tumour/normal pairs. Thus, with subsequent investigation two candidate genes were found to be methylated in more than 25% of our series and in the TCGA methylation dataset for 199 RCC samples: RGS7 (25.6% and 35.2% of tumours respectively) and NEFM in (25.6% and 30.2%). In addition three candidate genes were methylated in >10% of both datasets (TMEM74 (15.4% and 14.6%), GCM2 (41.0% and 14.6%) and AEBP1 (30.8% and 13.1%)). Methylation of GCM2 (P = 0.0324), NEFM (P = 0.0024) and RGS7 (P = 0.0067) was associated with prognosis.
CONCLUSIONS: These findings provide preclinical evidence that treatment with demethylating agents such as azacitidine might be useful for the treatment of advanced RCC and further insights into the role of epigenetic changes in the pathogenesis of RCC.
METHODS: Studying breast cancer, we established genome-scale DNA methylation profiles of prospectively collected buffy coat samples (n = 702) from a case-control study nested within the EPIC-Heidelberg cohort using reduced representation bisulphite sequencing (RRBS).
RESULTS: We observed cancer-specific DNA methylation events in buffy coat samples. Increased DNA methylation in genomic regions associated with SURF6 and REXO1/CTB31O20.3 was linked to the length of time to diagnosis in the prospectively collected buffy coat DNA from individuals who subsequently developed breast cancer. Using machine learning methods, we piloted a DNA methylation-based classifier that predicted case-control status in a held-out validation set with 76.5% accuracy, in some cases up to 15 years before clinical diagnosis of the disease.
CONCLUSIONS: Taken together, our findings suggest a model of gradual accumulation of cancer-associated DNA methylation patterns in peripheral blood, which may be detected long before clinical manifestation of cancer. Such changes may provide useful markers for risk stratification and, ultimately, personalized cancer prevention.