Cluster Analysis of Comparative Genomic Hybridization (CGH) Data Using Self-Organizing Maps: Application to Prostate Carcinomas
Comparative genomic hybridization (CGH) is a modern genetic method which enables a genome‐wide survey of chromosomal imbalances. For each chromosome region, one obtains the information whether there is a loss or gain of genetic material, or whether there is no change at that region. Usually it is no...
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| Format: | Article |
| Language: | English |
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Wiley
2001-01-01
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| Series: | Analytical Cellular Pathology |
| Online Access: | http://dx.doi.org/10.1155/2001/852674 |
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| author | Torsten Mattfeldt Hubertus Wolter Ralf Kemmerling Hans‐Werner Gottfried Hans A. Kestler |
| author_facet | Torsten Mattfeldt Hubertus Wolter Ralf Kemmerling Hans‐Werner Gottfried Hans A. Kestler |
| author_sort | Torsten Mattfeldt |
| collection | DOAJ |
| description | Comparative genomic hybridization (CGH) is a modern genetic method which enables a genome‐wide survey of chromosomal imbalances. For each chromosome region, one obtains the information whether there is a loss or gain of genetic material, or whether there is no change at that region. Usually it is not possible to evaluate all 46 chromosomes of a metaphase, therefore several (up to 20 or more) metaphases are analyzed per individual, and expressed as average. Mostly one does not study one individual alone but groups of 20–30 individuals. Therefore, large amounts of data quickly accumulate which must be put into a logical order. In this paper we present the application of a self‐organizing map (Genecluster) as a tool for cluster analysis of data from pT2N0 prostate cancer cases studied by CGH. Self‐organizing maps are artificial neural networks with the capability to form clusters on the basis of an unsupervised learning rule, i.e., in our examples it gets the CGH data as only information (no clinical data). We studied a group of 40 recent cases without follow‐up, an older group of 20 cases with follow‐up, and the data set obtained by pooling both groups. In all groups good clusterings were found in the sense that clinically similar cases were placed into the same clusters on the basis of the genetic information only. The data indicate that losses on chromosome arms 6q, 8p and 13q are all frequent in pT2N0 prostatic cancer, but the loss on 8p has probably the largest prognostic importance. |
| format | Article |
| id | doaj-art-313250f860ba4a35a093e9b0bcf554df |
| institution | OA Journals |
| issn | 0921-8912 1878-3651 |
| language | English |
| publishDate | 2001-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Analytical Cellular Pathology |
| spelling | doaj-art-313250f860ba4a35a093e9b0bcf554df2025-08-20T02:38:53ZengWileyAnalytical Cellular Pathology0921-89121878-36512001-01-01231293710.1155/2001/852674Cluster Analysis of Comparative Genomic Hybridization (CGH) Data Using Self-Organizing Maps: Application to Prostate CarcinomasTorsten Mattfeldt0Hubertus Wolter1Ralf Kemmerling2Hans‐Werner Gottfried3Hans A. Kestler4Department of Pathology, University of Ulm, Ulm, GermanyDepartment of Pathology, University of Ulm, Ulm, GermanyDepartment of Medical Genetics, University of Ulm, Ulm, GermanyDepartment of Urology, University of Ulm, Ulm, GermanyDepartment of Neural Information Processing, University of Ulm, Ulm, GermanyComparative genomic hybridization (CGH) is a modern genetic method which enables a genome‐wide survey of chromosomal imbalances. For each chromosome region, one obtains the information whether there is a loss or gain of genetic material, or whether there is no change at that region. Usually it is not possible to evaluate all 46 chromosomes of a metaphase, therefore several (up to 20 or more) metaphases are analyzed per individual, and expressed as average. Mostly one does not study one individual alone but groups of 20–30 individuals. Therefore, large amounts of data quickly accumulate which must be put into a logical order. In this paper we present the application of a self‐organizing map (Genecluster) as a tool for cluster analysis of data from pT2N0 prostate cancer cases studied by CGH. Self‐organizing maps are artificial neural networks with the capability to form clusters on the basis of an unsupervised learning rule, i.e., in our examples it gets the CGH data as only information (no clinical data). We studied a group of 40 recent cases without follow‐up, an older group of 20 cases with follow‐up, and the data set obtained by pooling both groups. In all groups good clusterings were found in the sense that clinically similar cases were placed into the same clusters on the basis of the genetic information only. The data indicate that losses on chromosome arms 6q, 8p and 13q are all frequent in pT2N0 prostatic cancer, but the loss on 8p has probably the largest prognostic importance.http://dx.doi.org/10.1155/2001/852674 |
| spellingShingle | Torsten Mattfeldt Hubertus Wolter Ralf Kemmerling Hans‐Werner Gottfried Hans A. Kestler Cluster Analysis of Comparative Genomic Hybridization (CGH) Data Using Self-Organizing Maps: Application to Prostate Carcinomas Analytical Cellular Pathology |
| title | Cluster Analysis of Comparative Genomic Hybridization (CGH) Data Using Self-Organizing Maps: Application to Prostate Carcinomas |
| title_full | Cluster Analysis of Comparative Genomic Hybridization (CGH) Data Using Self-Organizing Maps: Application to Prostate Carcinomas |
| title_fullStr | Cluster Analysis of Comparative Genomic Hybridization (CGH) Data Using Self-Organizing Maps: Application to Prostate Carcinomas |
| title_full_unstemmed | Cluster Analysis of Comparative Genomic Hybridization (CGH) Data Using Self-Organizing Maps: Application to Prostate Carcinomas |
| title_short | Cluster Analysis of Comparative Genomic Hybridization (CGH) Data Using Self-Organizing Maps: Application to Prostate Carcinomas |
| title_sort | cluster analysis of comparative genomic hybridization cgh data using self organizing maps application to prostate carcinomas |
| url | http://dx.doi.org/10.1155/2001/852674 |
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