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Molecular subtyping of bladder cancer using K ohonen self‐organizing maps

2014; Wiley; Volume: 3; Issue: 5 Linguagem: Inglês

10.1002/cam4.217

2045-7634

Edyta Borkowska, Andrzej Kruk, Adam Jędrzejczyk, Marek Różniecki, Zbigniew Jabłonowski, M. Traczyk, Maria Constantinou, Monika Banaszkiewicz, Michał Pietrusiński, Marek Sosnowski, Freddie C. Hamdy, Stefán Péter, James W.F. Catto, Bogdan Kałużewski,

Metabolomics and Mass Spectrometry Studies

Abstract Kohonen self‐organizing maps ( SOM s) are unsupervised A rtificial N eural N etworks ( 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 CDKN 2A ( 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. SOM s are suitable for complex data integration, allow easy visualization of outcomes, and may stratify BC progression more robustly than hierarchical clustering.