Bladder Cancer Studies Using Clinical Specimens

Microarray analyses have been used to correlate changes in the expression of specific genes and groups of genes within distinct bladder subclasses. Such signature genes would ideally provide a molecular basis for classification, yielding insight into the molecular events underlying different clinical bladder cancer phenotypes.

The first report monitored the expression patterns of superficial and invasive tumor cell suspensions prepared from individuals and pools of normal and bladder tumors of tumors of different stages such as from pTA grade I and II and pT2 grade III and IV bladder cancer specimens.[15] Hierarchical clustering of gene expression levels grouped bladder cancer specimens based on tumor stage and grade. The most significant functional genes included those involved in cell cycle, cell growth, immunology, adhesion, transcription, and protein metabolism. Superficial papillary tumors showed increased transcription factor and ribosomal levels, as well as proteinase encoding genes up-regulation. In the invasive tumors, increased levels of cell cycle, growth factor networks, immunology-related and oncogene transcripts, and loss of cellular adhesion genes were observed.[15]

The combination of separate expression profiling studies of bladder tumors and bladder cancer cell lines has allowed the identification of the tumor suppressor role of KiSS-1 in bladder cancer progression.1-16-1 Lower transcript levels of KiSS-1 were observed in cells derived from advanced bladder tumors and bladder carcinomas as compared with superficial tumors, and these ratios provided prognostic information. The expression patterns of KiSS-1 analyzed by in situ hybridization on tissue microarrays were associated with tumor stage, grade, and overall survival. Thus gene expression profiling identified a novel target involved in bladder cancer progression with clinical relevance.[16]

The most extensive expression profiling study of bladder tumors reported to date has dealt with the development of a predictive classifier of Ta, T1, and T2+ bladder carcinoma subclasses. The use of a support vector machine algorithm allowed prediction with 75% accuracy of these tumor subclasses in an independent set of patients, using cross-validation strategies to evaluate the clinical impact of the classifier defined using independent series of tumors. Smad6 and cyclin G2 were also identified as Ta/T1 classifier genes and their immunostaining patterns were validated on tissue micro-arrays by immunohistochemistry.[17] This study represents the first attempt to predict recurrence within 2 years for patients with bladder cancer.

Gene profiling has also successfully classified bladder tumors based on their progression and clinical outcome. Early-stage tumors showing gene profiles similar to invasive disease were identified. More importantly, carcinoma in situ from papillary superficial lesions and subgroups within early stage and invasive tumors displaying different overall survival were separated. Different techniques were used to identify molecular biomarkers of clinical significance. For example, p33ING1 was found to be significantly associated with pathological stage, tumor grade, and overall survival using tissue microarrays. Analysis of the annotation of the most significant genes revealed the relevance of critical genes and pathways during bladder cancer progression.1-18-1

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