Molecular subtyping of bladder cancer using Kohonen self‐organizing maps

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...

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Published in:Cancer medicine (Malden, MA) MA), 2014-10, Vol.3 (5), p.1225-1234
Main Authors: Borkowska, Edyta M., Kruk, Andrzej, Jedrzejczyk, Adam, Rozniecki, Marek, Jablonowski, Zbigniew, Traczyk, Magdalena, Constantinou, Maria, Banaszkiewicz, Monika, Pietrusinski, Michal, Sosnowski, Marek, Hamdy, Freddie C., Peter, Stefan, Catto, James W.F., Kaluzewski, Bogdan
Format: Article
Language:English
Subjects:
Adult
Aged
Aged, 80 and over
Biomarkers, Tumor - genetics
Bladder cancer
Cancer Biology
Cluster Analysis
Disease Progression
Female
Gene Expression
Humans
Kohonen self‐organizing map
Male
Middle Aged
Models, Biological
molecular markers
Mutation
Neoplasm Recurrence, Local
Neoplasm Staging
Neural Networks (Computer)
progression
Risk Factors
Urinary Bladder Neoplasms - genetics
Urinary Bladder Neoplasms - mortality
Urinary Bladder Neoplasms - pathology
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Summary: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 
ISSN:2045-7634
2045-7634
DOI:10.1002/cam4.217