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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

Regulatory factor occupancy at UC DMRs. (A) Ninety RF cell type combinations are clustered based on the occupancy of the RF in the respective cell type at UC DMRs. Binary overlaps between UC DMRs and 18 different RFs across the five cell types are shown in red. The top color bars indicate different cell types, and highlight CTCF/RAD21 binding (pink), POLR2A (olive), TAF1/TBP (turquoise), REST (light blue), and EZH2 (purple) binding, respectively. Characteristics of DMRs are shown in five panels to the right of the heatmap: CpG Island (CGI; green); H1ESC, GM12878, HEPG2, K562, chromatin states for four cell types (see color bar). The clustering highlights the cell type independent binding patterns at UC DMRs of RFs such as CTCF, RAD21 and POLR2A, as well as the similar but cell-type specific binding patterns of TBP and TAF1 across all cell lines. (B) The frequency of UC DMRs with RF binding is shown for selected RFs and combinations of RFs for each methylation pattern (1 to 3). Strong depletion (*D) or enrichment (*E) of RF binding is indicated (all P <4 × 10-6, Fisher’s exact test), the regulatory factor frequency bars are color coded as the factors in (A).
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Fig4: Regulatory factor occupancy at UC DMRs. (A) Ninety RF cell type combinations are clustered based on the occupancy of the RF in the respective cell type at UC DMRs. Binary overlaps between UC DMRs and 18 different RFs across the five cell types are shown in red. The top color bars indicate different cell types, and highlight CTCF/RAD21 binding (pink), POLR2A (olive), TAF1/TBP (turquoise), REST (light blue), and EZH2 (purple) binding, respectively. Characteristics of DMRs are shown in five panels to the right of the heatmap: CpG Island (CGI; green); H1ESC, GM12878, HEPG2, K562, chromatin states for four cell types (see color bar). The clustering highlights the cell type independent binding patterns at UC DMRs of RFs such as CTCF, RAD21 and POLR2A, as well as the similar but cell-type specific binding patterns of TBP and TAF1 across all cell lines. (B) The frequency of UC DMRs with RF binding is shown for selected RFs and combinations of RFs for each methylation pattern (1 to 3). Strong depletion (*D) or enrichment (*E) of RF binding is indicated (all P <4 × 10-6, Fisher’s exact test), the regulatory factor frequency bars are color coded as the factors in (A).

Mentions: We then utilized ChIP-seq data generated by the ENCODE consortium [30], to gain further insights into the basic gene regulatory and genomic context of regions associated with methylation changes in UC and to analyze our data from a regulatory factor (RF)-based perspective. We focused on the five cell lines for which the largest amount of data on RFs was available and restricted our analysis to UC DMRs (Methods). We extracted a core set of 18 RFs for which data were available in all five cell lines (90 RF-cell tracks in total) and constructed a binary matrix of DMR-ChIP-seq peak overlaps, which we subsequently clustered (Figure 4A, Methods). Fifty-five percent of all UC DMRs had at least one overlapping ChIP-seq peak. While 88% of pattern 2 and 71% of pattern 3 DMRs had at least one overlapping ChIP-seq peak, this was only the case for 27% of pattern 1 DMRs.Figure 4


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)

Regulatory factor occupancy at UC DMRs. (A) Ninety RF cell type combinations are clustered based on the occupancy of the RF in the respective cell type at UC DMRs. Binary overlaps between UC DMRs and 18 different RFs across the five cell types are shown in red. The top color bars indicate different cell types, and highlight CTCF/RAD21 binding (pink), POLR2A (olive), TAF1/TBP (turquoise), REST (light blue), and EZH2 (purple) binding, respectively. Characteristics of DMRs are shown in five panels to the right of the heatmap: CpG Island (CGI; green); H1ESC, GM12878, HEPG2, K562, chromatin states for four cell types (see color bar). The clustering highlights the cell type independent binding patterns at UC DMRs of RFs such as CTCF, RAD21 and POLR2A, as well as the similar but cell-type specific binding patterns of TBP and TAF1 across all cell lines. (B) The frequency of UC DMRs with RF binding is shown for selected RFs and combinations of RFs for each methylation pattern (1 to 3). Strong depletion (*D) or enrichment (*E) of RF binding is indicated (all P <4 × 10-6, Fisher’s exact test), the regulatory factor frequency bars are color coded as the factors in (A).
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Related In: Results  -  Collection

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Fig4: Regulatory factor occupancy at UC DMRs. (A) Ninety RF cell type combinations are clustered based on the occupancy of the RF in the respective cell type at UC DMRs. Binary overlaps between UC DMRs and 18 different RFs across the five cell types are shown in red. The top color bars indicate different cell types, and highlight CTCF/RAD21 binding (pink), POLR2A (olive), TAF1/TBP (turquoise), REST (light blue), and EZH2 (purple) binding, respectively. Characteristics of DMRs are shown in five panels to the right of the heatmap: CpG Island (CGI; green); H1ESC, GM12878, HEPG2, K562, chromatin states for four cell types (see color bar). The clustering highlights the cell type independent binding patterns at UC DMRs of RFs such as CTCF, RAD21 and POLR2A, as well as the similar but cell-type specific binding patterns of TBP and TAF1 across all cell lines. (B) The frequency of UC DMRs with RF binding is shown for selected RFs and combinations of RFs for each methylation pattern (1 to 3). Strong depletion (*D) or enrichment (*E) of RF binding is indicated (all P <4 × 10-6, Fisher’s exact test), the regulatory factor frequency bars are color coded as the factors in (A).
Mentions: We then utilized ChIP-seq data generated by the ENCODE consortium [30], to gain further insights into the basic gene regulatory and genomic context of regions associated with methylation changes in UC and to analyze our data from a regulatory factor (RF)-based perspective. We focused on the five cell lines for which the largest amount of data on RFs was available and restricted our analysis to UC DMRs (Methods). We extracted a core set of 18 RFs for which data were available in all five cell lines (90 RF-cell tracks in total) and constructed a binary matrix of DMR-ChIP-seq peak overlaps, which we subsequently clustered (Figure 4A, Methods). Fifty-five percent of all UC DMRs had at least one overlapping ChIP-seq peak. While 88% of pattern 2 and 71% of pattern 3 DMRs had at least one overlapping ChIP-seq peak, this was only the case for 27% of pattern 1 DMRs.Figure 4

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