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Open AccessArticle

Data Fusion Techniques for the Integration of Multi-Domain Genomic Data from Uveal Melanoma

1
Max Planck Institute for Mathematics in the Sciences, 04103 Leipzig, Germany
2
IRCCS Ospedale Policlinico San Martino, 16132 Genova, Italy
*
Authors to whom correspondence should be addressed.
Cancers 2019, 11(10), 1434; https://doi.org/10.3390/cancers11101434
Received: 20 June 2019 / Revised: 29 August 2019 / Accepted: 15 September 2019 / Published: 26 September 2019
(This article belongs to the Special Issue Uveal Melanoma)
Uveal melanoma (UM) is a rare cancer that is well characterized at the molecular level. Two to four classes have been identified by the analyses of gene expression (mRNA, ncRNA), DNA copy number, DNA-methylation and somatic mutations yet no factual integration of these data has been reported. We therefore applied novel algorithms for data fusion, joint Singular Value Decomposition (jSVD) and joint Constrained Matrix Factorization (jCMF), as well as similarity network fusion (SNF), for the integration of gene expression, methylation and copy number data that we applied to the Cancer Genome Atlas (TCGA) UM dataset. Variant features that most strongly impact on definition of classes were extracted for biological interpretation of the classes. Data fusion allows for the identification of the two to four classes previously described. Not all of these classes are evident at all levels indicating that integrative analyses add to genomic discrimination power. The classes are also characterized by different frequencies of somatic mutations in putative driver genes (GNAQ, GNA11, SF3B1, BAP1). Innovative data fusion techniques confirm, as expected, the existence of two main types of uveal melanoma mainly characterized by copy number alterations. Subtypes were also confirmed but are somewhat less defined. Data fusion allows for real integration of multi-domain genomic data. View Full-Text
Keywords: DNA-methylation; copy number alteration; gene expression profile; metastasis; tumor classification; tumor subtypes; data fusion; singular value decomposition; constrained matrix factorization; similarity network fusion DNA-methylation; copy number alteration; gene expression profile; metastasis; tumor classification; tumor subtypes; data fusion; singular value decomposition; constrained matrix factorization; similarity network fusion
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MDPI and ACS Style

Pfeffer, M.; Uschmajew, A.; Amaro, A.; Pfeffer, U. Data Fusion Techniques for the Integration of Multi-Domain Genomic Data from Uveal Melanoma. Cancers 2019, 11, 1434. https://doi.org/10.3390/cancers11101434

AMA Style

Pfeffer M, Uschmajew A, Amaro A, Pfeffer U. Data Fusion Techniques for the Integration of Multi-Domain Genomic Data from Uveal Melanoma. Cancers. 2019; 11(10):1434. https://doi.org/10.3390/cancers11101434

Chicago/Turabian Style

Pfeffer, Max; Uschmajew, André; Amaro, Adriana; Pfeffer, Ulrich. 2019. "Data Fusion Techniques for the Integration of Multi-Domain Genomic Data from Uveal Melanoma" Cancers 11, no. 10: 1434. https://doi.org/10.3390/cancers11101434

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