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Kohonen self-organizing maps (SOMs) are unsupervised Artificial Neural Networks (ANNs) that are good for low-density data visualization. They easily deal with complex and nonlinear relationships between variables. We evaluated molecular events that characterize high- and low-grade BC pathways in the tumors from 104 patients. We compared the ability of statistical clustering with a SOM to stratify tumors according to the risk of progression to more advanced disease. In univariable analysis, tumor stage (log rank P = 0.006) and grade (P < 0.001), HPV DNA (P < 0.004), Chromosome 9 loss (P = 0.04) and the A148T polymorphism (rs 3731249) in CDKN2A (P = 0.02) were associated with progression. Multivariable analysis of these parameters identified that tumor grade (Cox regression, P = 0.001, OR.2.9 (95% CI 1.6-5.2)) and the presence of HPV DNA (P = 0.017, OR 3.8 (95% CI 1.3-11.4)) were the only independent predictors of progression. Unsupervised hierarchical clustering grouped the tumors into discreet branches but did not stratify according to progression free survival (log rank P = 0.39). These genetic variables were presented to SOM input neurons. SOMs are suitable for complex data integration, allow easy visualization of outcomes, and may stratify BC progression more robustly than hierarchical clustering.

Original publication

DOI

10.1002/cam4.217

Type

Journal article

Journal

Cancer Med

Publication Date

10/2014

Volume

3

Pages

1225 - 1234

Keywords

Bladder cancer, Kohonen self-organizing map, molecular markers, progression, Adult, Aged, Aged, 80 and over, Biomarkers, Tumor, Cluster Analysis, Disease Progression, Female, Gene Expression, Humans, Male, Middle Aged, Models, Biological, Mutation, Neoplasm Recurrence, Local, Neoplasm Staging, Neural Networks (Computer), Risk Factors, Urinary Bladder Neoplasms