Cluster Analysis of Comparative Genomic Hybridization (CGH) Data Using Self‐Organizing Maps: Application to Prostate Carcinomas

Mattfeldt, Torsten; Wolter, H.; Kemmerling, Ralf; Gottfried, Hans‐Werner; Kestler, Hans A.

doi:10.1155/2001/852674

Cited by 21 publications

(24 citation statements)

References 29 publications

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“…This observation is consistent with the published literature [for review, see Abate-Shen and Shen (2000)] that loss of one or more genes residing on 8p is most critical for prostate carcinogenesis. In addition, a recent cluster analysis of CGH data from stage T2N0 tumors by Mattfeldt et al (2001) found that, although losses on chromosome arms 6q, 8p, and 13q were frequent, only the loss of 8p has the largest prognostic importance based on clinical follow-up. An additional five steps in the model increased the percentage correct prediction of the stage of tumors to over 90%.…”

Section: Discussionmentioning

confidence: 99%

Genetic markers useful for distinguishing between organ‐confined and locally advanced prostate cancer

Chu

Troncoso

Johnston

et al. 2003

Genes Chromosomes & Cancer

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Prostate cancer is one of the most commonly diagnosed cancers and the second leading cause of cancer deaths among men in the United States. In this study, we performed comparative genomic hybridization (CGH) on 45 primary prostate adenocarcinomas to determine genetic markers that could be useful for distinguishing between organ-confined and locally advanced prostate cancer. Of these tumors, 24 were pT2 stage, 21 were pT3b; 20 had low Gleason scores (GS), 25 had high GS. The most common chromosomal alterations in all 45 tumors included losses on 8p (57.8%), 10q21-->qter (40.0%), 16q (35.6%), 11q21-->qter (28.9%), 16p (22.2%), 6q22-->24 (22.2%), 10p (20.0%), 5q31-->qter (17.8%), 6p (17.8%), 15q22-->qter (15.6%), and 17p (15.6%) as well as gains on 7cen-->p14 (20.0%), 7cen-->q22 (20.0%), and Xcen-->q21 (17.8%). Contingency table analysis showed that losses of 8p, 10q25-->qter, 6p21, 6q24-->qter, and 15q22-->qter were significantly increased in frequency (P < 0.05) with increasing stage and/or GS. A model was created following multivariate logistic regression analysis that was predictive of tumor stage in approximately 90% of the tumors studied. This model suggests that loss of 8p is the most valuable predictor of stage. These findings suggest that chromosomal regions identified in this study may be useful for distinguishing between organ-confined and locally advanced prostate tumors.

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Section: Discussionmentioning

confidence: 99%

Genetic markers useful for distinguishing between organ‐confined and locally advanced prostate cancer

Chu

Troncoso

Johnston

et al. 2003

Genes Chromosomes & Cancer

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“…it can be considered as the supervised counterpart to self-organising maps (SGMs) (KOHONEN, 1997;MATTFELDT et al, 2001b;2002;2004a). When we have decided on a set of k variables to evaluate, the data of a single case can be represented as a vector or point in the corresponding k-dimensional input space.…”

Section: Learning Vector Quantisation (Lvq)mentioning

confidence: 99%

Prediction of the axillary lymph node status in mammary cancer on the basis of clinicopathological data and flow cytometry

Mattfeldt

Kestler

Sinn

2004

Med. Biol. Eng. Comput.

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Axillary lymph node status is a major prognostic factor in mammary carcinoma. It is clinically desirable to predict the axillary lymph node status from data from the mammary cancer specimen. In the study, the axillary lymph node status, routine histological parameters and flow-cytometric data were retrospectively obtained from 1139 specimens of invasive mammary cancer. The ten variables: age, tumour type, tumour grade, tumour size, skin infiltration, lymphangiosis carcinomatosa, pT4 category, percentage of tumour cells in G2/M- and S-phases of the cell cycle, and ploidy index were considered as predictor variables, and the single variable lymph node metastasis pN (0 for pN0, or 1 for pN1 or pN2) was used as an output variable. A stepwise logistic regression analysis, with the axillary lymph node as a dependent variable, was used for feature selection. Only lymphangiosis carcinomatosa and tumour size proved to be significant as independent predictor variables; the other variables were non-contributory. Three paradigms with supervised learning rules (multilayer perceptron, learning vector quantisation and support vector machines) were used for the purpose of prediction. If any of these paradigms was used with the information from all ten input variables, 73% of cases could be correctly predicted, with specificity ranging from 82 to 84% and sensitivity ranging from 60 to 63%. If only the two significant input variables were used, lymphangiosis carcinomatosa and tumour diameter, the prediction accuracy was no worse. Nearly identical results were obtained by two different techniques of cross-validation (leave-one-out against ten-fold cross validation). It was concluded that: artificial neural networks can be used for risk stratification on the basis of routine data in individual cases of mammary cancer; and lymphangiosis carcinomatosa and tumour size are independent predictors of axillary lymph node metastasis in mammary cancer.

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“…For the present study the source files were compiled and used under Linux [10,11]. The result consists of tables where each case is assigned to one cluster.…”

Section: Somsmentioning

confidence: 99%

“…The classical implementation of a SOM is the package SOM_PAK [8,9], which can be obtained as free academic software from the internet at: http://www.cis.hut.fi/research/ som-research/nnrc-programs.shtml. For the present study the source files were compiled and used under Linux [10,11]. The result consists of tables where each case is assigned to one cluster.…”

Section: Somsmentioning

confidence: 99%

“…SOMs have been applied to a variety of problems and have been extensively studied empirically [8,9]. In a recent investigation on pT2N0 prostatic carcinomas, a biologically meaningful partition into three clusters of cases with different grades of malignancy was possible using SOMs [10]; it was furthermore feasible to apply SOMs for a cluster analysis of variables in the application to CGH data [11]. Related networks of the same group with a supervised learning rule (LVQ networks) were applied for predictive purposes in prostate carcinoma research [12][13][14].…”

Section: Introductionmentioning

confidence: 99%

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Incidental carcinoma of the prostate: clinicopathological, stereological and immunohistochemical findings studied with logistic regression and self‐organizing feature maps

Mattfeldt¹,

Trijic²,

Gottfried³

et al. 2004

BJU International

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OBJECTIVE To identify significant predictive factors determining category T1a and T1b in incidental prostatic carcinoma with classical and neural multivariate data analysis methods. MATERIALS AND METHODS Incidental prostatic carcinomas diagnosed in our department during 1990–99 (66 cases) were re‐examined. Besides acquiring routine clinical and pathological data the tumours were assessed by scoring immunohistochemistry for proliferative activity and p53‐overexpression. Tumour vascularization (angiogenesis) and epithelial texture variables were investigated by quantitative stereology. The data were evaluated by classical statistical methods (t‐test, correlation analysis, logistic regression). Moreover, self‐organizing feature maps (SOMs) were applied as an exploratory approach to unsupervised data analysis by artificial neural networks. RESULTS The proliferative fraction, p53 overexpression of tumour cell nuclei, preoperative prostate‐specific antigen value and density of capillary vascularization correlated with the Gleason score in incidental prostatic carcinoma. In a stepwise logistic regression analysis with the tumour categories T1a and T1b as dependent variables, the Gleason score and the volume fraction of epithelial cells were significant independent predictors of the tumour category. The cases could be grouped into clusters of different degrees of malignancy using SOMs. CONCLUSIONS Texture variables of tumour cells are of central importance for the extent of propagation in the prostate in incidental prostatic adenocarcinomas. Gleason score and quantitative stereological estimates of the volume fraction of tumour cells are significant predictors of T1a and T1b categories of incidental prostatic carcinoma. Unsupervised clustering of T1 prostate carcinoma cases by SOMs correlates well with the dichotomous classification into T1a and T1b according to the UICC.

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Cluster Analysis of Comparative Genomic Hybridization (CGH) Data Using Self‐Organizing Maps: Application to Prostate Carcinomas

Cited by 21 publications

References 29 publications

Genetic markers useful for distinguishing between organ‐confined and locally advanced prostate cancer

Genetic markers useful for distinguishing between organ‐confined and locally advanced prostate cancer

Prediction of the axillary lymph node status in mammary cancer on the basis of clinicopathological data and flow cytometry

Incidental carcinoma of the prostate: clinicopathological, stereological and immunohistochemical findings studied with logistic regression and self‐organizing feature maps

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