Germline copy number variations are associated with breast cancer risk and prognosis

Kumaran, Mahalakshmi; Cass, Carol E.; Graham, Kathryn; Mackey, John R.; Hubaux, Roland; Lam, Wan L.; Yasui, Yutaka; Damaraju, Sambasivarao

doi:10.1038/s41598-017-14799-7

Cited by 60 publications

(81 citation statements)

References 81 publications

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“…Within the identified latent dimensions related to ER-positive tumors, the VAE91 CpG set was enriched for open sea context and for enhancers and the GSE analysis identified VAE91 as significantly associated with the set focused around neuroblastoma copy number variation. Copy number variation has been shown to play a major role in breast cancer [23] and are associated with risk and prognosis [25]. The VAE93 dimension was also enriched for open sea context, but in contrast to VAE91 enhancer enrichment, VAE93 is significantly depleted of enhancers.…”

Section: Discussionmentioning

confidence: 99%

Unsupervised deep learning with variational autoencoders applied to breast tumor genome-wide DNA methylation data with biologic feature extraction

Titus

Wilkins

Bobak

et al. 2018

Preprint

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Recent advances in deep learning, particularly unsupervised approaches, have shown promise for furthering our biological knowledge through their application to gene expression datasets, though applications to epigenomic data are lacking. Here, we employ an unsupervised deep learning framework with variational autoencoders (VAEs) to learn latent representations of the DNA methylation landscape from three independent breast tumor datasets. Through interrogation of methylation-based learned latent dimension activation values, we demonstrate the feasibility of VAEs to track representative differential methylation patterns among clinical subtypes of tumors. CpGs whose methylation was most correlated VAE latent dimension activation values were significantly enriched for CpG sparse regulatory regions of the genome including enhancer regions. In addition, through comparison with LASSO, we show the utility of the VAE approach for revealing novel information about CpG DNA methylation patterns in breast cancer.

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Section: Discussionmentioning

confidence: 99%

Unsupervised deep learning with variational autoencoders applied to breast tumor genome-wide DNA methylation data with biologic feature extraction

Titus

Wilkins

Bobak

et al. 2018

Preprint

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“…Specific CNVs can be used to assess prognosis 15. A meta-analysis showed that MET gene amplification is inversely related to the OS of non-small-cell lung cancer patients and increases the risk of death 16.…”

Section: Discussionmentioning

confidence: 99%

E2F3 promotes cancer growth and is overexpressed through copy number variation in human melanoma

Feng¹,

Peng²,

Li³

et al. 2018

OTT

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IntroductionMelanoma is a malignant tumor that seriously affects patients. The pathogenesis of malignant melanoma is complex, and the cell cycle is closely related to tumor progression. Based on the catalog of cancer somatic mutations, we found that overexpression of the E2F3 gene ranked first in percentage increase in not only melanoma but also in all human cancer tissues. However, there are few studies on the high expression of E2F3 and its carcinogenic mechanism in melanoma.Methods and resultsWe found that E2F3 showed extensive copy number amplification that was positively correlated with the expression level. Patients with high copy number had a significantly poorer prognosis. We also found that E2F3 levels were significantly negatively correlated with promoter methylation. However, we showed that the E2F3 promoter region is hypomethylated, and in normal cells or tumor cells, the methylation level did not correlate with expression. Finally, we knocked down the E2F3 gene in melanoma cells by shRNA. Colony formation, anchorage-dependent growth, and EdU cell proliferation experiments showed a significant decrease in proliferation. Flow cytometry showed a significant increase in the G0/G1 ratio.ConclusionIt can be speculated that copy number amplification and other mechanisms result in the high expression of E2F3 in melanoma, which promotes tumor progression by involving the cell cycle. E2F3 is a good target for the treatment of melanoma.

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“…Since copy number variation or the focal amplification/deletion of a region of gene, is associated with breast cancer risk and prognosis [ 49 – 53 ], we calculate the fraction of the genome altered (FGA) as a measure of copy number activity as described in the Supplementary Section VII of [ 2 ] (with copy number level threshold T = 0.15) for each cluster. Our results are summarized in Table 1 and visualized in Fig 3 .…”

Section: Resultsmentioning

confidence: 99%

Directionally dependent multi-view clustering using copula model

et al. 2020

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Recent developments in high-throughput methods have resulted in the collection of high-dimensional data types from multiple sources and technologies that measure distinct yet complementary information. Integrated clustering of such multiple data types or multi-view clustering is critical for revealing pathological insights. However, multi-view clustering is challenging due to the complex dependence structure between multiple data types, including directional dependency. Specifically, genomics data types have pre-specified directional dependencies known as the central dogma that describes the process of information flow from DNA to messenger RNA (mRNA) and then from mRNA to protein. Most of the existing multi-view clustering approaches assume an independent structure or pair-wise (non-directional) dependence between data types, thereby ignoring their directional relationship. Motivated by this, we propose a biology-inspired Bayesian integrated multi-view clustering model that uses an asymmetric copula to accommodate the directional dependencies between the data types. Via extensive simulation experiments, we demonstrate the negative impact of ignoring directional dependency on clustering performance. We also present an application of our model to a real-world dataset of breast cancer tumor samples collected from The Cancer Genome Altas program and provide comparative results.

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Germline copy number variations are associated with breast cancer risk and prognosis

Cited by 60 publications

References 81 publications

Unsupervised deep learning with variational autoencoders applied to breast tumor genome-wide DNA methylation data with biologic feature extraction

Unsupervised deep learning with variational autoencoders applied to breast tumor genome-wide DNA methylation data with biologic feature extraction

E2F3 promotes cancer growth and is overexpressed through copy number variation in human melanoma

Directionally dependent multi-view clustering using copula model

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