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Integrative epigenomic analysis of differential DNA methylation in urothelial carcinoma.

Aine M, Sjödahl G, Eriksson P, Veerla S, Lindgren D, Ringnér M, Höglund M - Genome Med (2015)

Bottom Line: We identify 5,453 between-tumor DMRs and derive four DNA methylation subgroups of UC with distinct associations to clinicopathological features and gene expression subtypes.Genome-wide DMR methylation patterns are reflected in the gene expression subtypes of UC.UC DMRs display three distinct methylation patterns, each associated with intrinsic features of the genome and differential regulatory factor binding preferences.

View Article: PubMed Central - PubMed

Affiliation: Division of Oncology and Pathology, Department of Clinical Sciences Lund, Lund University, Lund, Sweden.

ABSTRACT

Background: Urothelial carcinoma of the bladder (UC) is a common malignancy. Although extensive transcriptome analysis has provided insights into the gene expression patterns of this tumor type, the mechanistic underpinnings of differential methylation remain poorly understood. Multi-level genomic data may be used to profile the regulatory potential and landscape of differential methylation in cancer and gain understanding of the processes underlying epigenetic and phenotypic characteristics of tumors.

Methods: We perform genome-wide DNA methylation profiling of 98 gene-expression subtyped tumors to identify between-tumor differentially methylated regions (DMRs). We integrate multi-level publically available genomic data generated by the ENCODE consortium to characterize the regulatory potential of UC DMRs.

Results: We identify 5,453 between-tumor DMRs and derive four DNA methylation subgroups of UC with distinct associations to clinicopathological features and gene expression subtypes. We characterize three distinct patterns of differential methylation and use ENCODE data to show that tumor subgroup-defining DMRs display differential chromatin state, and regulatory factor binding preferences. Finally, we characterize an epigenetic switch involving the HOXA-genes with associations to tumor differentiation states and patient prognosis.

Conclusions: Genome-wide DMR methylation patterns are reflected in the gene expression subtypes of UC. UC DMRs display three distinct methylation patterns, each associated with intrinsic features of the genome and differential regulatory factor binding preferences. Epigenetic inactivation of HOX-genes correlates with tumor differentiation states and may present an actionable epigenetic alteration in UC.

No MeSH data available.


Related in: MedlinePlus

External validation in TCGA data. (A) Standard deviation of probes overlapping UC DMRs and those that are outside of UC DMRs. (B) Differences in mean methylation levels between tumors and adjacent histologically normal tissue stratified by methylation patterns. (C) Heatmap of co-clustering frequencies of tumors derived by K-means consensus clustering of the 2,000 most variable probes in the TCGA data. (D) Tumor subgroupings derived using K-means clustering on the 25% most varying DMR-overlapping probes or the 2,000 most variable probes. (E) Hierarchical clustering of probes and heat map visualization of the three methylation clusters. The plot shows four main clusters of differentially methylated probes (leftmost annotation bar) and the associated chromatin states in H1ESC (middle bar, states as in Figure 1). The average methylation level in adjacent normal bladder tissue is indicated in the rightmost bar.
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Fig2: External validation in TCGA data. (A) Standard deviation of probes overlapping UC DMRs and those that are outside of UC DMRs. (B) Differences in mean methylation levels between tumors and adjacent histologically normal tissue stratified by methylation patterns. (C) Heatmap of co-clustering frequencies of tumors derived by K-means consensus clustering of the 2,000 most variable probes in the TCGA data. (D) Tumor subgroupings derived using K-means clustering on the 25% most varying DMR-overlapping probes or the 2,000 most variable probes. (E) Hierarchical clustering of probes and heat map visualization of the three methylation clusters. The plot shows four main clusters of differentially methylated probes (leftmost annotation bar) and the associated chromatin states in H1ESC (middle bar, states as in Figure 1). The average methylation level in adjacent normal bladder tissue is indicated in the rightmost bar.

Mentions: We sought to validate the main observed methylation patterns in independent data generated by TCGA. We obtained methylation data for 234 MI tumors as well as 21 adjacent normal samples. We confirmed the high variance nature of UC DMR methylation by comparing the standard deviation of DMR overlapping (N = 9,969) and non-overlapping probes (N = 308,871). Probes within DMRs exhibited substantially higher variability in the external dataset (Figure 2A, P <2.2 × 10-16, Mann-Whitney U test).Figure 2


Integrative epigenomic analysis of differential DNA methylation in urothelial carcinoma.

Aine M, Sjödahl G, Eriksson P, Veerla S, Lindgren D, Ringnér M, Höglund M - Genome Med (2015)

External validation in TCGA data. (A) Standard deviation of probes overlapping UC DMRs and those that are outside of UC DMRs. (B) Differences in mean methylation levels between tumors and adjacent histologically normal tissue stratified by methylation patterns. (C) Heatmap of co-clustering frequencies of tumors derived by K-means consensus clustering of the 2,000 most variable probes in the TCGA data. (D) Tumor subgroupings derived using K-means clustering on the 25% most varying DMR-overlapping probes or the 2,000 most variable probes. (E) Hierarchical clustering of probes and heat map visualization of the three methylation clusters. The plot shows four main clusters of differentially methylated probes (leftmost annotation bar) and the associated chromatin states in H1ESC (middle bar, states as in Figure 1). The average methylation level in adjacent normal bladder tissue is indicated in the rightmost bar.
© Copyright Policy - open-access
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC4373102&req=5

Fig2: External validation in TCGA data. (A) Standard deviation of probes overlapping UC DMRs and those that are outside of UC DMRs. (B) Differences in mean methylation levels between tumors and adjacent histologically normal tissue stratified by methylation patterns. (C) Heatmap of co-clustering frequencies of tumors derived by K-means consensus clustering of the 2,000 most variable probes in the TCGA data. (D) Tumor subgroupings derived using K-means clustering on the 25% most varying DMR-overlapping probes or the 2,000 most variable probes. (E) Hierarchical clustering of probes and heat map visualization of the three methylation clusters. The plot shows four main clusters of differentially methylated probes (leftmost annotation bar) and the associated chromatin states in H1ESC (middle bar, states as in Figure 1). The average methylation level in adjacent normal bladder tissue is indicated in the rightmost bar.
Mentions: We sought to validate the main observed methylation patterns in independent data generated by TCGA. We obtained methylation data for 234 MI tumors as well as 21 adjacent normal samples. We confirmed the high variance nature of UC DMR methylation by comparing the standard deviation of DMR overlapping (N = 9,969) and non-overlapping probes (N = 308,871). Probes within DMRs exhibited substantially higher variability in the external dataset (Figure 2A, P <2.2 × 10-16, Mann-Whitney U test).Figure 2

Bottom Line: We identify 5,453 between-tumor DMRs and derive four DNA methylation subgroups of UC with distinct associations to clinicopathological features and gene expression subtypes.Genome-wide DMR methylation patterns are reflected in the gene expression subtypes of UC.UC DMRs display three distinct methylation patterns, each associated with intrinsic features of the genome and differential regulatory factor binding preferences.

View Article: PubMed Central - PubMed

Affiliation: Division of Oncology and Pathology, Department of Clinical Sciences Lund, Lund University, Lund, Sweden.

ABSTRACT

Background: Urothelial carcinoma of the bladder (UC) is a common malignancy. Although extensive transcriptome analysis has provided insights into the gene expression patterns of this tumor type, the mechanistic underpinnings of differential methylation remain poorly understood. Multi-level genomic data may be used to profile the regulatory potential and landscape of differential methylation in cancer and gain understanding of the processes underlying epigenetic and phenotypic characteristics of tumors.

Methods: We perform genome-wide DNA methylation profiling of 98 gene-expression subtyped tumors to identify between-tumor differentially methylated regions (DMRs). We integrate multi-level publically available genomic data generated by the ENCODE consortium to characterize the regulatory potential of UC DMRs.

Results: We identify 5,453 between-tumor DMRs and derive four DNA methylation subgroups of UC with distinct associations to clinicopathological features and gene expression subtypes. We characterize three distinct patterns of differential methylation and use ENCODE data to show that tumor subgroup-defining DMRs display differential chromatin state, and regulatory factor binding preferences. Finally, we characterize an epigenetic switch involving the HOXA-genes with associations to tumor differentiation states and patient prognosis.

Conclusions: Genome-wide DMR methylation patterns are reflected in the gene expression subtypes of UC. UC DMRs display three distinct methylation patterns, each associated with intrinsic features of the genome and differential regulatory factor binding preferences. Epigenetic inactivation of HOX-genes correlates with tumor differentiation states and may present an actionable epigenetic alteration in UC.

No MeSH data available.


Related in: MedlinePlus