Chromosomal regions in prostatic carcinomas studied by comparative genomic hybridization, hierarchical cluster analysis and self-organizing feature maps, Anal

Abstract. 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

TESTING HISTOLOGICAL IMAGES OF MAMMARY TISSUES ON COMPATIBILITY WITH THE BOOLEAN MODEL OF RANDOM SETS

Methods for testing the Boolean model assumption from binary images are briefly reviewed. Two hundred binary images of mammary cancer tissue and 200 images of mastopathic tissue were tested individually on the Boolean model assumption. In a previous paper, it had been found that a Monte Carlo method based on the approximation of the envelopes by a multi-normal distribution with the normalized intrinsic volume densities of parallel sets as a summary statistics had the highest power for this purpose. Hence, this method was used here as its first application to real biomedical data. It was found that mastopathic tissue deviates from the Boolean model significantly more strongly than mammary cancer tissue does

MULTICLASS PATTERN RECOGNITION OF THE GLEASON SCORE OF PROSTATIC CARCINOMAS USING METHODS OF SPATIAL STATISTICS

The Gleason score of a prostatic carcinoma is generally considered as one of the most important prognostic parameters of this tumour type. In the present study, it was attempted to study the relation between the Gleason score and objective data of spatial statistics, and to predict this score from such data. For this purpose, 25 T1 incidental prostatic carcinomas, 50 pT2N0, and 28 pT3N0 prostatic adenocarcinomas were characterized by a histological texture analysis based on principles of spatial statistics. On sectional images, progression from low grade to high grade prostatic cancer in terms of the Gleason score is correlated with complex changes of the epithelial cells and their lumina with respect to their area, boundary length and Euler number per unit area. The central finding was a highly significant negative correlation between the Gleason score and the Euler number of the epithelial cell phase per unit area. The Gleason score of all individual cases was predicted from the spatial statistical variables by multivariate linear regression. This approach means to perform a multiclass pattern recognition, as opposed to the usual problem of binary pattern recognition. A prediction was considered as acceptable when its deviation from the human classification was no more than 1 point. This was achieved in 79 of these 103 cases when only the Euler number density was used as predictor variable. The accuracy could be risen slightly to 84 of the 103 cases, when 7 input variables were used for prediction of the Gleason score, which means an accuracy of 81.5%

CHARACTERIZATION OF MAMMARY GLAND TISSUE USING JOINT ESTIMATORS OF MINKOWSKI FUNCTIONALS

A theoretical approach to estimate the Minkowski functionals, i.e., area fraction, specifc boundary length and specifc Euler number in 2D, and their asymptotic covariance matrix proposed by Spodarev and Schmidt (2005) and Pantle et al. (2006a;b) is applied to real image data. These two-dimensional images show mammary gland tissue and should be classifed automatically as tumor-free or mammary cancer, respectively. The estimation procedure is illustrated step-by-step and the calculations are described in detail. To reduce dependencies from chosen parameters, a least-squares approach is considered as recommended by Klenk et al. (2006). Emphasis is placed on the detailed description of the estimation procedure and the application of the theory to real image data

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 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