DNA methylation at enhancers identifies distinct breast cancer lineages

Fleischer, Thomas; Tekpli, Xavier; Mathelier, Anthony; Wang, Shixiong; Nebdal, Daniel J. H.; Dhakal, Hari Prasad; Sahlberg, Kristine Kleivi; Schlichting, Ellen; Sauer, Torill; Geisler, Jürgen; Hofvind, Solveig; Bathen, Tone Frost; Engebraaten, Olav; Garred, Øystein; Geitvik, Gry Aarum; Langerød, Anita; Kåresen, Rolf; Mælandsmo, Gunhild Mari; Russnes, Hege G.; Sørlie, Thérese; Lingjærde, Ole Christian; Skjerven, Helle; Park, Daehoon; Fritzman, Britt; Børresen‐Dale, Anne‐Lise; Borgen, Elin; Naume, Bjørn; Eskeland, Ragnhild; Frigessi, Arnoldo; Tost, Jörg; Hurtado, Antoni; Kristensen, Vessela N.

doi:10.1038/s41467-017-00510-x

Cited by 112 publications

(142 citation statements)

References 59 publications

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“…However, little attention has been given to DNA methylation at other non-coding regions. We recently showed that DNA methylation at enhancers identifies distinct breast cancer lineages [2]. The epigenome and the chromatin landscape are important features to explain breast cancer development and also progression, as recently demonstrated for endocrine resistance in breast cancer [4].…”

Section: Introductionmentioning

confidence: 79%

“…ATAC-seq data from TCGA confirmed that the regions surrounding these CpGs were more accessible (open) in ER positive than in ER negative tumors (Wilcoxon p-value = 6.38e-5; Figure 2b). Furthermore, using the UniBind [28] database storing direct TF-DNA interactions for 231 TFs using 1983 human ChIP-seq data sets, we found cluster 1 CpGs enriched at FOXA1/2, GATA2/3, TFAP2C, and ESR1 (encoding ER-alpha) binding sites; these TFs are known to drive ER positive breast cancers [2,30] (Figure 2c). Unsupervised clustering of the DNA methylation values Enhancer=Biv_Enh, Active Intergenic Enhancer=Act_Intergen_Enh.…”

Section: Cluster 1 Cpgsmentioning

confidence: 98%

“…Breast cancers are highly heterogeneous at the clinical and molecular level. Alterations of methylation at CpGs are found already in breast pre-invasive lesions [1] and are thought to shape the methylation patterns found in the different clinical and molecular breast cancer subtypes [2,3]. The epigenome contributes to the cancer cells' phenotype by regulating gene expression and the accessibility of regulatory regions.…”

Section: Introductionmentioning

confidence: 99%

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Crosstalk between microRNA expression and DNA methylation drive the hormone-dependent phenotype of breast cancer

Aure

Fleischer

Bjørklund

et al. 2020

Preprint

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Background:Abnormal DNA methylation is observed as an early event in breast carcinogenesis. However, how such alterations arise is still poorly understood. microRNAs (miRNAs) regulate gene expression at the post-transcriptional level and have been shown to play key roles in various biological processes. Here, we integrate miRNA expression and DNA methylation at CpGs to study how miRNAs may affect the breast cancer methylome and how DNA methylation may regulate miRNA expression. ResultsmiRNA expression and DNA methylation data from two breast cancer cohorts were subjected to genome-wide correlation analysis. Clustering of the miRNA expression-DNA methylation association pairs significant in both cohorts identified distinct clusters of miRNAs and CpGs. These clusters recapitulated important biological processes associated with breast cancer pathogenesis. Notably, two major clusters were related to immune or fibroblast infiltration, hence identifying miRNAs associated with cells of the tumor microenvironment, while another large cluster was related to estrogen receptor (ER) signaling. Studying the chromatin landscape surrounding the CpGs associated with the estrogensignaling cluster, we found that miRNAs from this cluster are likely to be regulated through DNA methylation of enhancers bound by FOXA1, GATA2 and ER-alpha. Further, at the hub of the estrogen-cluster, we identified hsa-miR-29c-5p as negatively correlated with the mRNA and protein expression of the DNA methyltransferase DNMT3A, a key enzyme regulating DNA methylation. We found deregulation of hsa-miR-29c-5p already in pre-invasive breast lesions and postulate that hsa-miR-29c-5p may trigger early event abnormal DNA methylation in ER positive breast cancer. ConclusionsWe describe how miRNA expression and DNA methylation interact and associate with distinct breast cancer phenotypes.

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Section: Introductionmentioning

confidence: 79%

Section: Cluster 1 Cpgsmentioning

confidence: 98%

Section: Introductionmentioning

confidence: 99%

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Crosstalk between microRNA expression and DNA methylation drive the hormone-dependent phenotype of breast cancer

Aure

Fleischer

Bjørklund

et al. 2020

Preprint

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“…Of the co-regulatory factors, cohesin could be used as a mark of an enhancer [65,4]. DNA methylation data has also been used to predict enhancers [66]. Other potential useful features could be the information about the CpG islands, phastCons evolutionary conservation scores, TF motifs or DNA sequence itself.…”

Section: Discussionmentioning

confidence: 99%

Enhancer prediction in the human genome by probabilistic modelling of the chromatin feature patterns

Osmala

Lähdesmäki

2019

Preprint

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Background: The human genome is far from completely annotated. Specifically, the locations ofgenedistal regulatory enhancers are difficult to locate. Enhancers are binding sites of transcription factors and occupied by nucleosomes with modified histones. The binding sites of transcription factors (TFs) and the localization of histone modifications can be quantified by the chromatin immunoprecipitation assay coupled with next generation sequencing (ChIP-seq). The resulting data has been successfully adopted for genomewide enhancer identification by several unsupervised and supervised machine learning methods. However, the current methods predict different numbers and different sets of enhancers for the same cell type, and they do not utilize the pattern of the ChIP-seq coverage profiles efficiently. It is also difficult to estimate the accuracy and specificity of the genome-wide enhancer predictions. Results:We developed PREPRINT, a PRobabilistic Enhancer PRedictIoN Tool. We considered the pattern of, for example, the ChIP-seq coverage profile around the enhancers. The data at the positive and negative examples of enhancers was utilized to probabilistically model the enhancer coverage pattern and to train a kernel-based classifier. We demonstrated the performance of the method using ENCODE data from two cell lines. The predicted enhancers were computationally validated based on the TFs and co-regulatory factor binding sites. We compared our enhancer predictions to the ones obtained by other methods. The effects of different parameter choices during training, testing and validation were studied, and finally, the approach to validate the genome-wide predictions was investigated. Conclusion: PREPRINT performed comparably to the state-of-the-art methods and provided probabilistic interpretation (i.e. uncertainty) for the predictions. PREPRINT generalized to data from cell type not utilized for training, and often the performance of PREPRINT was superior to RFECS. We observed that the choice of training data and the choice of parameter values at different steps of the enhancer prediction and validation influenced on the final set of predictions. PREPRINT identifed biologically validated enhancers not predicted by the competing methods. The enhancers predicted by PREPRINT can aid the genome interpretation in functional genomics and clinical studies.

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“…For example, using RNA-seq and ChIP-seq analysis of the acetylation of lysine 91 27 on histone 3 (H3K27ac), an established active enhancer marker, revealed that epigenetic 92 activation of the cholesterol biosynthesis pathway causes activation of ERα resulting in 93 resistance (10). DNA methylation has also been shown to be perturbed during breast cancer 94 development and may largely affect gene expression (4,11) Since DNA methylation has also 95 been shown to be altered in endocrine resistant tumours (12) the identification of methylation 96 markers for disease diagnosis, prognosis, and treatment outcome is receiving increased 97 attention. Moreover, breast cancer treatment might benefit from the regulation of methylation 98 activity by using DNA methyltransferase inhibitors (4).…”

Section: Introduction 69mentioning

confidence: 99%

Candidate methylation sites associated with endocrine therapy resistance in the TCGA ER+/HER2- breast cancer cohort

Soleimani

Borgoni

Sofyalı

et al. 2019

Preprint

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35Background: Estrogen receptor (ER) positive breast cancer is often effectively treated with 36 drugs that inhibit ER signaling, i.e., tamoxifen (TAM) and aromatase inhibitors (AIs). 37However, about 30% of ER+ breast cancer patients develop resistance to therapy leading to 38 tumour recurrence. Changes in the methylation profile have been implicated as one of the 39 mechanisms through which therapy resistance develops. Therefore, we aimed to identify 40 methylation loci associated with endocrine therapy resistance. 41Methods: We used genome-wide DNA methylation profiles of primary ER+ tumors from The 42Cancer Genome Atlas in combination with curated data on survival and treatment to predict 43 development of endocrine resistance. Association of individual DNA methylation markers 44 with survival was assessed using Cox proportional hazards models in a cohort of 45 ER+/HER2-tumours (N=552) and two sub-cohorts corresponding to the endocrine treatment 46 (AIs or TAM) that patients received (N=210 and N=172, respectively). Models were adjusted 47 for clinical variables tumour stage, age, AI treatment and luminal subtype. We also identified 48 signatures of multiple methylation loci associated with survival using Cox proportional 49 hazards models with elastic net regularization. Individual markers and multivariable 50 signatures were compared with DNA methylation profiles generated in a time course 51 experiment using the T47D ER+ breast cancer cell line treated with tamoxifen or deprived 52 from estrogen. 53 Results:We identified 132, 9 and 1 CpGs for which DNA methylation is significantly 54 associated with survival in the ER+/HER2-, TAM and AI cohorts respectively. Corresponding 55 multi-locus signatures consisted of 171, 50 and 160 CpGs and showed a large overlap with 56 the corresponding single-locus signatures. Single-locus signatures for the ER+/HER2-and 57 TAM cohorts were conserved among the loci that were differentially methylated in endocrine-58 resistant T47D cells. Similarly, multi-locus signatures for the ER+/HER2-and AI cohorts were 59 conserved in endocrine-resistant T47D cells. 60 Conclusions:We identified individual and multivariable DNA methylation markers 61 associated with therapy resistance independently of luminal status. Our results suggest that 62 these markers identified from primary tumours and prior to any endocrine treatment are 63 associated with development of endocrine resistance. 64 65 Keywords (three to ten keywords representing the main content of the article): breast 66 cancer, DNA methylation, endocrine therapy, resistance, survival, T47D 67 68

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DNA methylation at enhancers identifies distinct breast cancer lineages

Cited by 112 publications

References 59 publications

Crosstalk between microRNA expression and DNA methylation drive the hormone-dependent phenotype of breast cancer

Crosstalk between microRNA expression and DNA methylation drive the hormone-dependent phenotype of breast cancer

Enhancer prediction in the human genome by probabilistic modelling of the chromatin feature patterns

Candidate methylation sites associated with endocrine therapy resistance in the TCGA ER+/HER2- breast cancer cohort

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