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We identified significantly hypermethylated genes in clear cell renal cell carcinoma.We previously identified a set of under expressed genes in renal cell carcinoma tissue through transcriptional profiling and a robust computational screen. We selected 19 of these genes for hypermethylation analysis using a rigorous search for the best candidate regions, considering CpG islands and transcription factor binding sites. The genes were analyzed for hypermethylation in the DNA of 38 matched clear cell renal cell carcinoma and normal samples using matrix assisted laser desorption ionization time-of-flight mass spectrometry. The significance of hypermethylation was assessed using 3 statistical tests. We validated the down-regulation of significantly hypermethylated genes at the RNA and protein levels in a separate set of patients using reverse transcriptase-polymerase chain reaction, immunohistochemistry and Western blots.We found 7 significantly hypermethylated regions from 6 down-regulated genes, including SFRP1, which was previously shown to be hypermethylated in renal cell carcinoma and other cancer types.To our knowledge we report for the first time that another 5 genes (SCNN1B, SYT6, DACH1, and the tumor suppressors TFAP2A and MT1G) are hypermethylated in renal cell carcinoma. Robust computational screens and the high throughput methylation assay resulted in an enriched set of novel genes that are epigenetically altered in clear cell renal cell carcinoma. Overall the detection of hypermethylation in these highly down-regulated genes suggests that assaying for their methylation using cells from urine or blood could provide the basis for a viable diagnostic test.

Gene expression analysis has identified biologically relevant subclasses of breast cancer. However, most classification schemes do not robustly cluster all HER2+ breast cancers, in part due to limitations and bias of clustering techniques used. In this article, we propose an alternative approach that first separates the HER2+ tumors using a gene amplification signal for Her2/neu amplicon genes and then applies consensus ensemble clustering separately to the HER2+ and HER2− clusters to look for further substructure. We applied this procedure to a microarray data set of 286 early-stage breast cancers treated only with surgery and radiation and identified two basal and four luminal subtypes in the HER2− tumors, as well as two novel and robust HER2+ subtypes. HER2+ subtypes had median distant metastasis-free survival of 99 months [95% confidence interval (95% CI), 83–118 months] and 33 months (95% CI, 11–54 months), respectively, and recurrence rates of 11% and 58%, respectively. The low recurrence subtype had a strong relative overexpression of lymphocyte-associated genes and was also associated with a prominent lymphocytic infiltration on histologic analysis. These data suggest that early-stage HER2+ cancers associated with lymphocytic infiltration are a biologically distinct subtype with an improved natural history. [Cancer Res 2007;67(22):10669–76]

We develop a new technique to analyse microarray data which uses a combination of principal components analysis and consensus ensemble k-clustering to find robust clusters and gene markers in the data. We apply our method to a public microarray breast cancer dataset which has expression levels of genes in normal samples as well as in three pathological stages of disease; namely, atypical ductal hyperplasia or ADH, ductal carcinoma in situ or DCIS and invasive ductal carcinoma or IDC. Our method averages over clustering techniques and data perturbation to find stable, robust clusters and gene markers. We identify the clusters and their pathways with distinct subtypes of breast cancer (Luminal, Basal and Her2+). We confirm that the cancer phenotype develops early (in early hyperplasia or ADH stage) and find from our analysis that each subtype progresses from ADH to DCIS to IDC along its own specific pathway, as if each was a distinct disease.