Dataset Information

Aberrant transcriptional regulations in cancers: genome, transcriptome and epigenome analysis of lung adenocarcinoma cell lines.

ABSTRACT: Here we conducted an integrative multi-omics analysis to understand how cancers harbor various types of aberrations at the genomic, epigenomic and transcriptional levels. In order to elucidate biological relevance of the aberrations and their mutual relations, we performed whole-genome sequencing, RNA-Seq, bisulfite sequencing and ChIP-Seq of 26 lung adenocarcinoma cell lines. The collected multi-omics data allowed us to associate an average of 536 coding mutations and 13,573 mutations in promoter or enhancer regions with aberrant transcriptional regulations. We detected the 385 splice site mutations and 552 chromosomal rearrangements, representative cases of which were validated to cause aberrant transcripts. Averages of 61, 217, 3687 and 3112 mutations are located in the regulatory regions which showed differential DNA methylation, H3K4me3, H3K4me1 and H3K27ac marks, respectively. We detected distinct patterns of aberrations in transcriptional regulations depending on genes. We found that the irregular histone marks were characteristic to EGFR and CDKN1A, while a large genomic deletion and hyper-DNA methylation were most frequent for CDKN2A. We also used the multi-omics data to classify the cell lines regarding their hallmarks of carcinogenesis. Our datasets should provide a valuable foundation for biological interpretations of interlaced genomic and epigenomic aberrations.

SUBMITTER: Suzuki A

PROVIDER: S-EPMC4267666 | biostudies-literature | 2014 Dec

REPOSITORIES: biostudies-literature

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Aberrant transcriptional regulations in cancers: genome, transcriptome and epigenome analysis of lung adenocarcinoma cell lines.

Suzuki Ayako A Makinoshima Hideki H Wakaguri Hiroyuki H Esumi Hiroyasu H Sugano Sumio S Kohno Takashi T Tsuchihara Katsuya K Suzuki Yutaka Y

Nucleic acids research 20141106 22

Here we conducted an integrative multi-omics analysis to understand how cancers harbor various types of aberrations at the genomic, epigenomic and transcriptional levels. In order to elucidate biological relevance of the aberrations and their mutual relations, we performed whole-genome sequencing, RNA-Seq, bisulfite sequencing and ChIP-Seq of 26 lung adenocarcinoma cell lines. The collected multi-omics data allowed us to associate an average of 536 coding mutations and 13,573 mutations in promot ...[more]

PMID: 25378332

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Project description:BackgroundHi-C and its high nucleosome resolution variant Micro-C provide a window into the spatial packing of a genome in 3D within the cell. Even though both techniques do not directly depend on the binding of specific antibodies, previous work has revealed enriched interactions and domain structures around multiple chromatin marks; epigenetic modifications and transcription factor binding sites. However, the joint impact of chromatin marks in Hi-C and Micro-C interactions have not been globally characterized, which limits our understanding of 3D genome characteristics. An emerging question is whether it is possible to deduce 3D genome characteristics and interactions by integrative analysis of multiple chromatin marks and associate interactions to functionality of the interacting loci.ResultWe come up with a probabilistic method PROBC to decompose Hi-C and Micro-C interactions by known chromatin marks. PROBC is based on convex likelihood optimization, which can directly take into account both interaction existence and nonexistence. Through PROBC, we discover histone modifications (H3K27ac, H3K9me3, H3K4me3, H3K4me1) and CTCF as particularly predictive of Hi-C and Micro-C contacts across cell types and species. Moreover, histone modifications are more effective than transcription factor binding sites in explaining the genome's 3D shape through these interactions. PROBC can successfully predict Hi-C and Micro-C interactions in given species, while it is trained on different cell types or species. For instance, it can predict missing nucleosome resolution Micro-C interactions in human ES cells trained on mouse ES cells only from these 5 chromatin marks with above 0.75 AUC. Additionally, PROBC outperforms the existing methods in predicting interactions across almost all chromosomes.ConclusionVia our proposed method, we optimally decompose Hi-C interactions in terms of these chromatin marks at genome and chromosome levels. We find a subset of histone modifications and transcription factor binding sites to be predictive of both Hi-C and Micro-C interactions and TADs across human, mouse, and different cell types. Through learned models, we can predict interactions on species just from chromatin marks for which Hi-C data may be limited.

Dataset Information

Aberrant transcriptional regulations in cancers: genome, transcriptome and epigenome analysis of lung adenocarcinoma cell lines.

Publications

Aberrant transcriptional regulations in cancers: genome, transcriptome and epigenome analysis of lung adenocarcinoma cell lines.

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