title: Molecular subtyping of bladder cancer using Kohonen self-organizing maps.
abstract: 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.
authors.affiliation: Department of Clinical Genetics, Medical University of Lodz, 3 Sterlinga Street, Lodz, 91-425, Poland; Institute for Cancer Studies and Academic Urology Unit, University of Sheffield, Beech Hill Road, Sheffield, S10 2RX, UK.
authors.forename: Edyta M
authors.initials: EM
authors.lastname: Borkowska
authors.forename: Andrzej
authors.initials: A
authors.lastname: Kruk
authors.forename: Adam
authors.initials: A
authors.lastname: Jedrzejczyk
authors.forename: Marek
authors.initials: M
authors.lastname: Rozniecki
authors.forename: Zbigniew
authors.initials: Z
authors.lastname: Jablonowski
authors.forename: Magdalena
authors.initials: M
authors.lastname: Traczyk
authors.forename: Maria
authors.initials: M
authors.lastname: Constantinou
authors.forename: Monika
authors.initials: M
authors.lastname: Banaszkiewicz
authors.forename: Michal
authors.initials: M
authors.lastname: Pietrusinski
authors.forename: Marek
authors.initials: M
authors.lastname: Sosnowski
authors.forename: Freddie C
authors.initials: FC
authors.lastname: Hamdy
authors.forename: Stefan
authors.initials: S
authors.lastname: Peter
authors.forename: James W F
authors.initials: JW
authors.lastname: Catto
authors.forename: Bogdan
authors.initials: B
authors.lastname: Kaluzewski
mesh_terms: D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D014408:Biomarkers, Tumor / Q000235:genetics; D016000:Cluster Analysis; D018450:Disease Progression; D005260:Female; D015870:Gene Expression; D006801:Humans; D008297:Male; D008875:Middle Aged; D008954:Models, Biological; D009154:Mutation; D009364:Neoplasm Recurrence, Local; D009367:Neoplasm Staging; D016571:Neural Networks, Computer; D012307:Risk Factors; D001749:Urinary Bladder Neoplasms / Q000235:genetics* / Q000401:mortality / Q000473:pathology
chemical_list: D014408:Biomarkers, Tumor
keywords: Bladder cancer; Kohonen self-organizing map; molecular markers; progression