Dataset Information

Conjunctival melanoma copy number alterations and correlation with mutation status, tumor features, and clinical outcome.

ABSTRACT: Relatively little is known about the genetic aberrations of conjunctival melanomas (CoM) and their correlation with clinical and histomorphological features as well as prognosis. The aim of this large collaborative multicenter study was to determine potential key biomarkers for metastatic risk and any druggable targets for high metastatic risk CoM. Using Affymetrix single nucleotide polymorphism genotyping arrays on 59 CoM, we detected frequent amplifications on chromosome (chr) 6p and deletions on 7q, and characterized mutation-specific copy number alterations. Deletions on chr 10q11.21-26.2, a region harboring the tumor suppressor genes, PDCD4, SUFU, NEURL1, PTEN, RASSF4, DMBT1, and C10orf90 and C10orf99, significantly correlated with metastasis (Fisher's exact, p ? 0.04), lymphatic invasion (Fisher's exact, p ? 0.02), increasing tumor thickness (Mann-Whitney, p ? 0.02), and BRAF mutation (Fisher's exact, p ? 0.05). This enhanced insight into CoM biology is a step toward identifying patients at risk of metastasis and potential therapeutic targets for systemic disease.

SUBMITTER: Kenawy N

PROVIDER: S-EPMC6849808 | biostudies-literature | 2019 Jul

REPOSITORIES: biostudies-literature

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Publications

Conjunctival melanoma copy number alterations and correlation with mutation status, tumor features, and clinical outcome.

Kenawy Nihal N Kalirai Helen H Sacco Joseph J JJ Lake Sarah L SL Heegaard Steffen S Larsen Ann-Cathrine AC Finger Paul T PT Milman Tatyana T Chin Kimberly K Mosci Carlo C Lanza Francesco F Moulin Alexandre A Schmitt Caroline A CA Caujolle Jean Pierre JP Maschi Célia C Marinkovic Marina M Taktak Azzam F AF Heimann Heinrich H Damato Bertil E BE Coupland Sarah E SE

Pigment cell & melanoma research 20190219 4

Relatively little is known about the genetic aberrations of conjunctival melanomas (CoM) and their correlation with clinical and histomorphological features as well as prognosis. The aim of this large collaborative multicenter study was to determine potential key biomarkers for metastatic risk and any druggable targets for high metastatic risk CoM. Using Affymetrix single nucleotide polymorphism genotyping arrays on 59 CoM, we detected frequent amplifications on chromosome (chr) 6p and deletions ...[more]

PMID: 30672666

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Project description:BackgroundWith the rapid development of new genetic measurement methods, several types of genetic alterations can be quantified in a high-throughput manner. While the initial focus has been on investigating each data set separately, there is an increasing interest in studying the correlation structure between two or more data sets. Multivariate methods based on Canonical Correlation Analysis (CCA) have been proposed for integrating paired genetic data sets. The high dimensionality of microarray data imposes computational difficulties, which have been addressed for instance by studying the covariance structure of the data, or by reducing the number of variables prior to applying the CCA. In this work, we propose a new method for analyzing high-dimensional paired genetic data sets, which mainly emphasizes the correlation structure and still permits efficient application to very large data sets. The method is implemented by translating a regularized CCA to its dual form, where the computational complexity depends mainly on the number of samples instead of the number of variables. The optimal regularization parameters are chosen by cross-validation. We apply the regularized dual CCA, as well as a classical CCA preceded by a dimension-reducing Principal Components Analysis (PCA), to a paired data set of gene expression changes and copy number alterations in leukemia.ResultsUsing the correlation-maximizing methods, regularized dual CCA and PCA+CCA, we show that without pre-selection of known disease-relevant genes, and without using information about clinical class membership, an exploratory analysis singles out two patient groups, corresponding to well-known leukemia subtypes. Furthermore, the variables showing the highest relevance to the extracted features agree with previous biological knowledge concerning copy number alterations and gene expression changes in these subtypes. Finally, the correlation-maximizing methods are shown to yield results which are more biologically interpretable than those resulting from a covariance-maximizing method, and provide different insight compared to when each variable set is studied separately using PCA.ConclusionsWe conclude that regularized dual CCA as well as PCA+CCA are useful methods for exploratory analysis of paired genetic data sets, and can be efficiently implemented also when the number of variables is very large.

Dataset Information

Conjunctival melanoma copy number alterations and correlation with mutation status, tumor features, and clinical outcome.

Publications

Conjunctival melanoma copy number alterations and correlation with mutation status, tumor features, and clinical outcome.

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