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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

Patterns of DNA methylation across UC methylation subgroups. The methylation levels for subgroup-specific DMRs are shown for 98 UC samples (columns) divided into the four methylation subgroups. The subgroup-specific DMRs (rows) are grouped into patterns 1 to 3. Relative DNA methylation levels are shown as a heat map pseudo-colored blue (unmethylated) to red (methylated). The Lund gene expression subtypes of the UC samples are shown at the top of the heat map. Characteristics of DMRs are shown in five panels to the right of the heatmap: DMR methylation profiles in four normal urothelium samples (Normal); CpG Island (CGI; green); NCEC overlap (GERP; yellow); Lee et al. 2006 Polycomb targets (PCG; red); chromatin states according to Ernst et al. [17] (HMM; 1, ‘Active promoter’; 2, ‘Weak promoter’; 3, ‘Inactive/poised promoter’; 4 to 5, ‘Strong enhancer’; 6 to 7, ‘Weak enhancer’; 8, ‘Insulator’; 9, ‘Transcriptional transition’; 10, ‘Trancriptional elongation’; 11, ‘Weak transcribed’; 12, ‘Polycomb repressed’; 13, ‘Heterochromatin/low signal’; 14 to 15, ‘Repetitive/CNV’).
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Fig1: Patterns of DNA methylation across UC methylation subgroups. The methylation levels for subgroup-specific DMRs are shown for 98 UC samples (columns) divided into the four methylation subgroups. The subgroup-specific DMRs (rows) are grouped into patterns 1 to 3. Relative DNA methylation levels are shown as a heat map pseudo-colored blue (unmethylated) to red (methylated). The Lund gene expression subtypes of the UC samples are shown at the top of the heat map. Characteristics of DMRs are shown in five panels to the right of the heatmap: DMR methylation profiles in four normal urothelium samples (Normal); CpG Island (CGI; green); NCEC overlap (GERP; yellow); Lee et al. 2006 Polycomb targets (PCG; red); chromatin states according to Ernst et al. [17] (HMM; 1, ‘Active promoter’; 2, ‘Weak promoter’; 3, ‘Inactive/poised promoter’; 4 to 5, ‘Strong enhancer’; 6 to 7, ‘Weak enhancer’; 8, ‘Insulator’; 9, ‘Transcriptional transition’; 10, ‘Trancriptional elongation’; 11, ‘Weak transcribed’; 12, ‘Polycomb repressed’; 13, ‘Heterochromatin/low signal’; 14 to 15, ‘Repetitive/CNV’).

Mentions: We then applied ANOVA on the full set of 5,453 UC DMRs to identify regions with subgroup-specific methylation patterns. ANOVA significant DMRs (N = 2,697, P <0.05, FDR corrected) exhibited a higher median CpG density compared to DMRs without subgroup-specific methylation patterns (median = 0.022 and 0.011 CpG/bp, respectively, P <7 × 10-82, Mann-Whitney U test, Additional file 4: Figure S1A). We observed a significant difference in CpG density between UC DMRs that are hyper- (high CpG density) and hypomethylated (low CpG density) in tumor samples compared to normal urothelium (Additional file 4: Figure S1B). Hierarchical clustering of the subgroup specific DMRs revealed three main methylation patterns across the data (Figure 1 and Additional file 5: Figure S2A to C), hereafter referred to as methylation pattern 1 (672 DMRs), 2 (650 DMRs), and 3 (1,375 DMRs).Figure 1


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)

Patterns of DNA methylation across UC methylation subgroups. The methylation levels for subgroup-specific DMRs are shown for 98 UC samples (columns) divided into the four methylation subgroups. The subgroup-specific DMRs (rows) are grouped into patterns 1 to 3. Relative DNA methylation levels are shown as a heat map pseudo-colored blue (unmethylated) to red (methylated). The Lund gene expression subtypes of the UC samples are shown at the top of the heat map. Characteristics of DMRs are shown in five panels to the right of the heatmap: DMR methylation profiles in four normal urothelium samples (Normal); CpG Island (CGI; green); NCEC overlap (GERP; yellow); Lee et al. 2006 Polycomb targets (PCG; red); chromatin states according to Ernst et al. [17] (HMM; 1, ‘Active promoter’; 2, ‘Weak promoter’; 3, ‘Inactive/poised promoter’; 4 to 5, ‘Strong enhancer’; 6 to 7, ‘Weak enhancer’; 8, ‘Insulator’; 9, ‘Transcriptional transition’; 10, ‘Trancriptional elongation’; 11, ‘Weak transcribed’; 12, ‘Polycomb repressed’; 13, ‘Heterochromatin/low signal’; 14 to 15, ‘Repetitive/CNV’).
© Copyright Policy - open-access
Related In: Results  -  Collection

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Fig1: Patterns of DNA methylation across UC methylation subgroups. The methylation levels for subgroup-specific DMRs are shown for 98 UC samples (columns) divided into the four methylation subgroups. The subgroup-specific DMRs (rows) are grouped into patterns 1 to 3. Relative DNA methylation levels are shown as a heat map pseudo-colored blue (unmethylated) to red (methylated). The Lund gene expression subtypes of the UC samples are shown at the top of the heat map. Characteristics of DMRs are shown in five panels to the right of the heatmap: DMR methylation profiles in four normal urothelium samples (Normal); CpG Island (CGI; green); NCEC overlap (GERP; yellow); Lee et al. 2006 Polycomb targets (PCG; red); chromatin states according to Ernst et al. [17] (HMM; 1, ‘Active promoter’; 2, ‘Weak promoter’; 3, ‘Inactive/poised promoter’; 4 to 5, ‘Strong enhancer’; 6 to 7, ‘Weak enhancer’; 8, ‘Insulator’; 9, ‘Transcriptional transition’; 10, ‘Trancriptional elongation’; 11, ‘Weak transcribed’; 12, ‘Polycomb repressed’; 13, ‘Heterochromatin/low signal’; 14 to 15, ‘Repetitive/CNV’).
Mentions: We then applied ANOVA on the full set of 5,453 UC DMRs to identify regions with subgroup-specific methylation patterns. ANOVA significant DMRs (N = 2,697, P <0.05, FDR corrected) exhibited a higher median CpG density compared to DMRs without subgroup-specific methylation patterns (median = 0.022 and 0.011 CpG/bp, respectively, P <7 × 10-82, Mann-Whitney U test, Additional file 4: Figure S1A). We observed a significant difference in CpG density between UC DMRs that are hyper- (high CpG density) and hypomethylated (low CpG density) in tumor samples compared to normal urothelium (Additional file 4: Figure S1B). Hierarchical clustering of the subgroup specific DMRs revealed three main methylation patterns across the data (Figure 1 and Additional file 5: Figure S2A to C), hereafter referred to as methylation pattern 1 (672 DMRs), 2 (650 DMRs), and 3 (1,375 DMRs).Figure 1

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