Promoter hypermethylation is associated with tumor location, stage, and subsequent progression in transitional cell carcinoma.

PURPOSE: Transitional cell carcinoma (TCC) is a pan-urothelial disease characterized by multiplicity. Although little is known about the molecular events in upper-tract TCC, similar carcinogenic mechanisms are thought to occur throughout the urinary tract. However, we have previously shown that distinct patterns of microsatellite instability occur in upper and lower urinary tract TCC, suggesting biologic differences between these tumors. Here we investigate the extent of promoter hypermethylation in TCC throughout the urinary tract. PATIENTS AND METHODS: Tissue was obtained from 280 patients (median follow-up, 56 months) whose tumors comprised 116 bladder and 164 upper-tract tumors (UTT). Analysis for hypermethylation at 11 CpG islands, using methylation-sensitive polymerase chain reaction and bisulfite sequencing, was performed for each sample and compared with the tumor's clinicopathologic details, microsatellite instability status, and subsequent behavior. RESULTS: Promoter methylation was present in 86% of TCC and occurred both more frequently and more extensively in UTT (94%) than in bladder tumors (76%; P < .0001). Methylation was associated with advanced tumor stage (P = .0001) and higher tumor progression (P = .03) and mortality rates (P = .04), when compared with tumors without methylation. Multivariate analysis revealed that methylation at the RASSF1A and DAPK loci, in addition to tumor stage and grade, were associated with disease progression (P < .04). CONCLUSION: Despite morphologic similarities, there are genetic and epigenetic differences between TCC in the upper and lower urinary tracts. Methylation occurs commonly in urinary tract tumors, may affect carcinogenic mechanisms, and is a prognostic marker and a potential therapeutic target.

Distinct patterns of microsatellite instability are seen in tumours of the urinary tract.

To date, two forms of microsatellite instability (MSI) have been described in human cancer. MSI typical of hereditary nonpolyposis colon cancer (HNPCC), is due to deficient DNA mismatch repair (MMR) and is defined with mono- and dinucleotide repeat microsatellites. A second variety of instability is best seen at selective tetranucleotide repeats (EMAST; elevated microsatellite alterations at select tetranucleotides). While MSI occurs infrequently in bladder cancers, EMAST is common. Sporadic tumours with the largest proportion showing MSI are those found most frequently in HNPCC kindreds. While bladder cancer is not frequently seen in HNPCC, upper urinary tract tumours (UTTs) are. Having previously found a low frequency of MSI in bladder cancer, we sought to determine the relative levels of MSI and EMAST in transitional cell carcinoma (TCC) of the upper and lower urinary tracts. Microsatellite analysis was performed at 10 mono- and dinucleotide and eight tetranucleotide loci, in 89 bladder and 71 UTT TCC. Contrasting patterns of instability were seen in urinary tumours. In bladder cancer, MSI was rare and EMAST was common. The presence of EMAST was not related to tumour grade, stage, subsequent outcome or immunohistochemical expression of the MMR proteins. In UTT, while MSI occurred frequently, EMAST was seen less frequently than in bladder cancer. When TCC of the upper and lower urinary tracts are compared, MSI-H is more frequent in UTT and EMAST more frequent in bladder cancer. Our findings show that, as for colorectal cancer, the pattern of MSI varies with location in the urinary tract. In addition, we have confirmed that MSI and EMAST are discrete forms of MSI, and that the presence of EMAST does not affect tumour phenotype.

Artificial intelligence in predicting bladder cancer outcome: a comparison of neuro-fuzzy modeling and artificial neural networks.

PURPOSE: New techniques for the prediction of tumor behavior are needed, because statistical analysis has a poor accuracy and is not applicable to the individual. Artificial intelligence (AI) may provide these suitable methods. Whereas artificial neural networks (ANN), the best-studied form of AI, have been used successfully, its hidden networks remain an obstacle to its acceptance. Neuro-fuzzy modeling (NFM), another AI method, has a transparent functional layer and is without many of the drawbacks of ANN. We have compared the predictive accuracies of NFM, ANN, and traditional statistical methods, for the behavior of bladder cancer. EXPERIMENTAL DESIGN: Experimental molecular biomarkers, including p53 and the mismatch repair proteins, and conventional clinicopathological data were studied in a cohort of 109 patients with bladder cancer. For all three of the methods, models were produced to predict the presence and timing of a tumor relapse. RESULTS: Both methods of AI predicted relapse with an accuracy ranging from 88% to 95%. This was superior to statistical methods (71-77%; P < 0.0006). NFM appeared better than ANN at predicting the timing of relapse (P = 0.073). CONCLUSIONS: The use of AI can accurately predict cancer behavior. NFM has a similar or superior predictive accuracy to ANN. However, unlike the impenetrable "black-box" of a neural network, the rules of NFM are transparent, enabling validation from clinical knowledge and the manipulation of input variables to allow exploratory predictions. This technique could be used widely in a variety of areas of medicine.

Clinically localised prostate cancer is microsatellite stable.

OBJECTIVES: To determine the frequency of microsatellite instability (MSI) change with mono-, di- and tetranucleotide markers in clinically localized prostate cancer, and to correlate those markers with clinical and pathological variables. MATERIALS AND METHODS: Two forms of MSI have been described in human cancer: MSI typical of hereditary nonpolyposis colon cancer, defined with mono- and dinucleotide repeat MS; and a second variety of MSI is best seen at selective tetranucleotide repeats, i.e. elevated microsatellite alterations at select tetranucleotides (EMAST). Prostate specimens were taken from 50 patients. The MS analysis used the Bethesda consensus panel (BCP) and four tetranucleotide loci shown to detect the presence of EMAST. RESULTS: All but four tumours were stable for the 14 loci investigated. There were two (4%) cases with adenomatous polyposis coli (APC) instability among the BCP markers and the same instability rate (4%) amongst the EMAST markers. These four tumours were all unstable at one locus of the 10 markers of the BCP that classified them as MS stable. CONCLUSIONS: The MSI related to a mismatch repair deficiency or to the EMAST does not seem to be important in prostate cancer in the early stages of the disease.