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Regulators associated with clinical outcomes revealed by DNA methylation data in breast cancer.

Ung MH, Varn FS, Lou S, Cheng C - PLoS Comput. Biol. (2015)

Bottom Line: This dysfunctional process typically involves additional regulatory modulators including DNA methylation.Our analysis identified TFs known to be associated with clinical outcomes of p53 and ER (estrogen receptor) subtypes of breast cancer, while also predicting new TFs that may also be involved.Overall, this study provides a comprehensive analysis that links DNA methylation to TF binding to patient prognosis.

View Article: PubMed Central - PubMed

Affiliation: Department of Genetics, Geisel School of Medicine at Dartmouth, Hanover, New Hampshire, United States of America.

ABSTRACT
The regulatory architecture of breast cancer is extraordinarily complex and gene misregulation can occur at many levels, with transcriptional malfunction being a major cause. This dysfunctional process typically involves additional regulatory modulators including DNA methylation. Thus, the interplay between transcription factor (TF) binding and DNA methylation are two components of a cancer regulatory interactome presumed to display correlated signals. As proof of concept, we performed a systematic motif-based in silico analysis to infer all potential TFs that are involved in breast cancer prognosis through an association with DNA methylation changes. Using breast cancer DNA methylation and clinical data derived from The Cancer Genome Atlas (TCGA), we carried out a systematic inference of TFs whose misregulation underlie different clinical subtypes of breast cancer. Our analysis identified TFs known to be associated with clinical outcomes of p53 and ER (estrogen receptor) subtypes of breast cancer, while also predicting new TFs that may also be involved. Furthermore, our results suggest that misregulation in breast cancer can be caused by the binding of alternative factors to the binding sites of TFs whose activity has been ablated. Overall, this study provides a comprehensive analysis that links DNA methylation to TF binding to patient prognosis.

No MeSH data available.


Related in: MedlinePlus

TF enriched in CpG subtypes of breast cancer.A) K-means clustering of CpG β-values into 5 distinct clusters. B) Ordering of significantly enriched/depleted (P<0.05) TF binding motifs relative to their -log10(P-value) ordering in cluster 1 (column 1). All -log10(P-values) greater than 10 were set to 10 and all values less than 3 were set to 0. Enrichment of motifs is shown in blue and depletion is shown as yellow. C) Enrichment values of TR-NFY_01 in each CpG subtype (C1–C5) D) Enrichment values of TR-E47_01 in each CpG subtype (C1–C5).
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pcbi.1004269.g006: TF enriched in CpG subtypes of breast cancer.A) K-means clustering of CpG β-values into 5 distinct clusters. B) Ordering of significantly enriched/depleted (P<0.05) TF binding motifs relative to their -log10(P-value) ordering in cluster 1 (column 1). All -log10(P-values) greater than 10 were set to 10 and all values less than 3 were set to 0. Enrichment of motifs is shown in blue and depletion is shown as yellow. C) Enrichment values of TR-NFY_01 in each CpG subtype (C1–C5) D) Enrichment values of TR-E47_01 in each CpG subtype (C1–C5).

Mentions: When analyzing TF-DNA methylation relationships in breast cancer subtypes, we build upon conventional methods of cancer stratification. However, in order to analyze TF motif enrichment within a classification scheme focused on DNA methylation, we adopted a bottom-up approach by first classifying all CpGs into subtypes based on their intensity levels. Since many cancers show genome-wide changes in DNA methylation, this approach may be able to identify TFs that are directly related to distinct intensity levels of DNA methylation. Therefore, we created a class of subtypes based on the clustering of CpG β-values and calculated TF binding motif enrichment in these subtypes. Fig 6A shows CpGs organized into 5 clusters based on β-values, with high intensity clusters on top and low intensity clusters on the bottom. From C1 to C5, the clusters are enriched in 68 (50 TFs), 45 (31 TFs), 6 (6 TFs), 6 (5 TFs), and 87 (59 TFs) TF binding motifs, respectively (P<0.05) (Fig 6A). Furthermore, we identified 119 (80 TFs), 38 (24 TFs), 3 (3 TFs), 1 (1 TFs), and 10 (8 TFs) TF binding motifs that were significantly depleted from C1 to C5, respectively (P<0.05) (S18 Table). Like histological and intrinsic subtypes of breast cancer, certain TF binding motifs exhibit different levels of enrichment across CpG subtypes. To globally illustrate the variation in TF motif enrichment between CpG subtypes, we sorted significant motifs in cluster 1 (C1) (P<0.01) from most enriched to most depleted (Fig 6B). We then ordered the TFs in the other 4 clusters relative to those belonging to cluster 1 (Fig 6B). From this, it is clear that related clusters share common patterns of enrichment (i.e. patterns in cluster 1 are more similar to that of cluster 2 than cluster 5) (Fig 6B). Interestingly, cluster C1, which contains highly methylated CpGs, is both enriched and depleted in TF binding motifs (Fig 6A and 6B). In contrast, cluster C5, which contains lowly methylated CpGs, is characterized mainly by TF binding motif enrichment events and few TF binding motif depletion events. This suggests that TF binding is generally associated with reduced methylation levels. Additionally, clusters C3 and C4 contain very few high-significance enriched/depleted TF binding motifs, suggesting that mid-intensity methylation are stochastic events and are not as informative for identifying important breast cancer-associated regulators.


Regulators associated with clinical outcomes revealed by DNA methylation data in breast cancer.

Ung MH, Varn FS, Lou S, Cheng C - PLoS Comput. Biol. (2015)

TF enriched in CpG subtypes of breast cancer.A) K-means clustering of CpG β-values into 5 distinct clusters. B) Ordering of significantly enriched/depleted (P<0.05) TF binding motifs relative to their -log10(P-value) ordering in cluster 1 (column 1). All -log10(P-values) greater than 10 were set to 10 and all values less than 3 were set to 0. Enrichment of motifs is shown in blue and depletion is shown as yellow. C) Enrichment values of TR-NFY_01 in each CpG subtype (C1–C5) D) Enrichment values of TR-E47_01 in each CpG subtype (C1–C5).
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pcbi.1004269.g006: TF enriched in CpG subtypes of breast cancer.A) K-means clustering of CpG β-values into 5 distinct clusters. B) Ordering of significantly enriched/depleted (P<0.05) TF binding motifs relative to their -log10(P-value) ordering in cluster 1 (column 1). All -log10(P-values) greater than 10 were set to 10 and all values less than 3 were set to 0. Enrichment of motifs is shown in blue and depletion is shown as yellow. C) Enrichment values of TR-NFY_01 in each CpG subtype (C1–C5) D) Enrichment values of TR-E47_01 in each CpG subtype (C1–C5).
Mentions: When analyzing TF-DNA methylation relationships in breast cancer subtypes, we build upon conventional methods of cancer stratification. However, in order to analyze TF motif enrichment within a classification scheme focused on DNA methylation, we adopted a bottom-up approach by first classifying all CpGs into subtypes based on their intensity levels. Since many cancers show genome-wide changes in DNA methylation, this approach may be able to identify TFs that are directly related to distinct intensity levels of DNA methylation. Therefore, we created a class of subtypes based on the clustering of CpG β-values and calculated TF binding motif enrichment in these subtypes. Fig 6A shows CpGs organized into 5 clusters based on β-values, with high intensity clusters on top and low intensity clusters on the bottom. From C1 to C5, the clusters are enriched in 68 (50 TFs), 45 (31 TFs), 6 (6 TFs), 6 (5 TFs), and 87 (59 TFs) TF binding motifs, respectively (P<0.05) (Fig 6A). Furthermore, we identified 119 (80 TFs), 38 (24 TFs), 3 (3 TFs), 1 (1 TFs), and 10 (8 TFs) TF binding motifs that were significantly depleted from C1 to C5, respectively (P<0.05) (S18 Table). Like histological and intrinsic subtypes of breast cancer, certain TF binding motifs exhibit different levels of enrichment across CpG subtypes. To globally illustrate the variation in TF motif enrichment between CpG subtypes, we sorted significant motifs in cluster 1 (C1) (P<0.01) from most enriched to most depleted (Fig 6B). We then ordered the TFs in the other 4 clusters relative to those belonging to cluster 1 (Fig 6B). From this, it is clear that related clusters share common patterns of enrichment (i.e. patterns in cluster 1 are more similar to that of cluster 2 than cluster 5) (Fig 6B). Interestingly, cluster C1, which contains highly methylated CpGs, is both enriched and depleted in TF binding motifs (Fig 6A and 6B). In contrast, cluster C5, which contains lowly methylated CpGs, is characterized mainly by TF binding motif enrichment events and few TF binding motif depletion events. This suggests that TF binding is generally associated with reduced methylation levels. Additionally, clusters C3 and C4 contain very few high-significance enriched/depleted TF binding motifs, suggesting that mid-intensity methylation are stochastic events and are not as informative for identifying important breast cancer-associated regulators.

Bottom Line: This dysfunctional process typically involves additional regulatory modulators including DNA methylation.Our analysis identified TFs known to be associated with clinical outcomes of p53 and ER (estrogen receptor) subtypes of breast cancer, while also predicting new TFs that may also be involved.Overall, this study provides a comprehensive analysis that links DNA methylation to TF binding to patient prognosis.

View Article: PubMed Central - PubMed

Affiliation: Department of Genetics, Geisel School of Medicine at Dartmouth, Hanover, New Hampshire, United States of America.

ABSTRACT
The regulatory architecture of breast cancer is extraordinarily complex and gene misregulation can occur at many levels, with transcriptional malfunction being a major cause. This dysfunctional process typically involves additional regulatory modulators including DNA methylation. Thus, the interplay between transcription factor (TF) binding and DNA methylation are two components of a cancer regulatory interactome presumed to display correlated signals. As proof of concept, we performed a systematic motif-based in silico analysis to infer all potential TFs that are involved in breast cancer prognosis through an association with DNA methylation changes. Using breast cancer DNA methylation and clinical data derived from The Cancer Genome Atlas (TCGA), we carried out a systematic inference of TFs whose misregulation underlie different clinical subtypes of breast cancer. Our analysis identified TFs known to be associated with clinical outcomes of p53 and ER (estrogen receptor) subtypes of breast cancer, while also predicting new TFs that may also be involved. Furthermore, our results suggest that misregulation in breast cancer can be caused by the binding of alternative factors to the binding sites of TFs whose activity has been ablated. Overall, this study provides a comprehensive analysis that links DNA methylation to TF binding to patient prognosis.

No MeSH data available.


Related in: MedlinePlus