American Association for Cancer Research
10780432ccr042409-sup-unsupervised_normals_tumors.ppt (66 kB)

Figure S5 from Bladder Cancer Outcome and Subtype Classification by Gene Expression

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posted on 2023-03-31, 16:02 authored by Ekaterini Blaveri, Jeff P. Simko, James E. Korkola, Jeremy L. Brewer, Frederick Baehner, Kshama Mehta, Sandy DeVries, Theresa Koppie, Sunanda Pejavar, Peter Carroll, Frederic M. Waldman

Bladder normal tissue and tumors



Models of bladder tumor progression have suggested that genetic alterations may determine both phenotype and clinical course. We have applied expression microarray analysis to a divergent set of bladder tumors to further elucidate the course of disease progression and to classify tumors into more homogeneous and clinically relevant subgroups. cDNA microarrays containing 10,368 human gene elements were used to characterize the global gene expression patterns in 80 bladder tumors, 9 bladder cancer cell lines, and 3 normal bladder samples. Robust statistical approaches accounting for the multiple testing problem were used to identify differentially expressed genes. Unsupervised hierarchical clustering successfully separated the samples into two subgroups containing superficial (pTa and pT1) versus muscle-invasive (pT2-pT4) tumors. Supervised classification had a 90.5% success rate separating superficial from muscle-invasive tumors based on a limited subset of genes. Tumors could also be classified into transitional versus squamous subtypes (89% success rate) and good versus bad prognosis (78% success rate). The performance of our stage classifiers was confirmed in silico using data from an independent tumor set. Validation of differential expression was done using immunohistochemistry on tissue microarrays for cathepsin E, cyclin A2, and parathyroid hormone–related protein. Genes driving the separation between tumor subsets may prove to be important biomarkers for bladder cancer development and progression and eventually candidates for therapeutic targeting.

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