Chromosomal Regions in Prostatic Carcinomas Studied by Comparative Genomic Hybridization, Hierarchical Cluster Analysis and Self-Organizing Feature Maps
Comparative genomic hybridization (CGH) is an established 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 place. Therefore,...
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| Format: | Article |
| Language: | English |
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Wiley
2002-01-01
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| Series: | Analytical Cellular Pathology |
| Online Access: | http://dx.doi.org/10.1155/2002/902831 |
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| author | Torsten Mattfeldt Hubertus Wolter Danilo Trijic Hans‐Werner Gottfried Hans A. Kestler |
| author_facet | Torsten Mattfeldt Hubertus Wolter Danilo Trijic Hans‐Werner Gottfried Hans A. Kestler |
| author_sort | Torsten Mattfeldt |
| collection | DOAJ |
| description | Comparative genomic hybridization (CGH) is an established 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 place. Therefore, large amounts of data quickly accumulate which must be put into a logical order. Cluster analysis can be used to assign individual cases (samples) to different clusters of cases, which are similar and where each cluster may be related to a different tumour biology. Another approach consists in a clustering of chromosomal regions by rewriting the original data matrix, where the cases are written as rows and the chromosomal regions as columns, in a transposed form. In this paper we applied hierarchical cluster analysis as well as two implementations of self‐organizing feature maps as classical and neuronal tools for cluster analysis of CGH data from prostatic carcinomas to such transposed data sets. Self‐organizing maps are artificial neural networks with the capability to form clusters on the basis of an unsupervised learning rule. We studied a group of 48 cases of incidental carcinomas, a tumour category which has not been evaluated by CGH before. In addition we studied a group of 50 cases of pT2N0‐tumours and a group of 20 pT3N0‐carcinomas. The results show in all case groups three clusters of chromosomal regions, which are (i) normal or minimally affected by losses and gains, (ii) regions with many losses and few gains and (iii) regions with many gains and few losses. Moreover, for the pT2N0‐ and pT3N0‐groups, it could be shown that the regions 6q, 8p and 13q lay all on the same cluster (associated with losses), and that the regions 9q and 20q belonged to the same cluster (associated with gains). For the incidental cancers such clear correlations could not be demonstrated. |
| format | Article |
| id | doaj-art-7a9c6da88930419d8d316ce5472c1f4d |
| institution | Kabale University |
| issn | 0921-8912 1878-3651 |
| language | English |
| publishDate | 2002-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Analytical Cellular Pathology |
| spelling | doaj-art-7a9c6da88930419d8d316ce5472c1f4d2025-02-03T01:00:46ZengWileyAnalytical Cellular Pathology0921-89121878-36512002-01-01244-516717910.1155/2002/902831Chromosomal Regions in Prostatic Carcinomas Studied by Comparative Genomic Hybridization, Hierarchical Cluster Analysis and Self-Organizing Feature MapsTorsten Mattfeldt0Hubertus Wolter1Danilo Trijic2Hans‐Werner Gottfried3Hans A. Kestler4Department of Pathology, University of Ulm, Ulm, GermanyDepartment of Pathology, University of Ulm, Ulm, GermanyDepartment of Pathology, University of Ulm, Ulm, GermanyDepartment of Urology, University of Ulm, Ulm, GermanyDepartment of Neuroinformatics, University of Ulm, Ulm, GermanyComparative genomic hybridization (CGH) is an established 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 place. Therefore, large amounts of data quickly accumulate which must be put into a logical order. Cluster analysis can be used to assign individual cases (samples) to different clusters of cases, which are similar and where each cluster may be related to a different tumour biology. Another approach consists in a clustering of chromosomal regions by rewriting the original data matrix, where the cases are written as rows and the chromosomal regions as columns, in a transposed form. In this paper we applied hierarchical cluster analysis as well as two implementations of self‐organizing feature maps as classical and neuronal tools for cluster analysis of CGH data from prostatic carcinomas to such transposed data sets. Self‐organizing maps are artificial neural networks with the capability to form clusters on the basis of an unsupervised learning rule. We studied a group of 48 cases of incidental carcinomas, a tumour category which has not been evaluated by CGH before. In addition we studied a group of 50 cases of pT2N0‐tumours and a group of 20 pT3N0‐carcinomas. The results show in all case groups three clusters of chromosomal regions, which are (i) normal or minimally affected by losses and gains, (ii) regions with many losses and few gains and (iii) regions with many gains and few losses. Moreover, for the pT2N0‐ and pT3N0‐groups, it could be shown that the regions 6q, 8p and 13q lay all on the same cluster (associated with losses), and that the regions 9q and 20q belonged to the same cluster (associated with gains). For the incidental cancers such clear correlations could not be demonstrated.http://dx.doi.org/10.1155/2002/902831 |
| spellingShingle | Torsten Mattfeldt Hubertus Wolter Danilo Trijic Hans‐Werner Gottfried Hans A. Kestler Chromosomal Regions in Prostatic Carcinomas Studied by Comparative Genomic Hybridization, Hierarchical Cluster Analysis and Self-Organizing Feature Maps Analytical Cellular Pathology |
| title | Chromosomal Regions in Prostatic Carcinomas Studied by Comparative Genomic Hybridization, Hierarchical Cluster Analysis and Self-Organizing Feature Maps |
| title_full | Chromosomal Regions in Prostatic Carcinomas Studied by Comparative Genomic Hybridization, Hierarchical Cluster Analysis and Self-Organizing Feature Maps |
| title_fullStr | Chromosomal Regions in Prostatic Carcinomas Studied by Comparative Genomic Hybridization, Hierarchical Cluster Analysis and Self-Organizing Feature Maps |
| title_full_unstemmed | Chromosomal Regions in Prostatic Carcinomas Studied by Comparative Genomic Hybridization, Hierarchical Cluster Analysis and Self-Organizing Feature Maps |
| title_short | Chromosomal Regions in Prostatic Carcinomas Studied by Comparative Genomic Hybridization, Hierarchical Cluster Analysis and Self-Organizing Feature Maps |
| title_sort | chromosomal regions in prostatic carcinomas studied by comparative genomic hybridization hierarchical cluster analysis and self organizing feature maps |
| url | http://dx.doi.org/10.1155/2002/902831 |
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