Evidence map›Paper›PMID 26664652›Full record

ArticleClinical epigenetics2015

The integrative epigenomic-transcriptomic landscape of ER positive breast cancer.

Yang Gao, Allison Jones, Peter A Fasching, Matthias Ruebner, Matthias W Beckmann, Martin Widschwendter, Andrew E Teschendorff

Erratum issuedOpen access · goldAbstract read
In one paragraph

Article in Clinical epigenetics, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 18 papers.

0numbers the graph read from it
0cells of the map it votes in
18citing papers in PubMed
3.3field-weighted citation impact, top 7% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

18 citing papers in PubMed, 31 citations in OpenAlex.

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  5. On epigenetic stochasticity, entropy and cancer risk.Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2024
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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

7 authors at 3 institutions in 3 countries.

Yang GaoCAS Key Lab for Computational Biology, CAS-MPG Partner Institute for Computational Biology, Chinese Academy of Sciences, Shanghai Institute for Biological Sciences, 320 Yue Yang Road, 200031 Shanghai, China.
Allison JonesDepartment of Women's Cancer, University College London, 74 Huntley Street, London, WC1E 6BT UK.
Peter A FaschingUniversity Clinic Erlangen, Department of Gynaecology and Obstetrics, Friedrich-Alexander University Erlangen-Nuremberg, Erlangen, 91054 Germany.
Matthias RuebnerUniversity Clinic Erlangen, Department of Gynaecology and Obstetrics, Friedrich-Alexander University Erlangen-Nuremberg, Erlangen, 91054 Germany.
Matthias W BeckmannUniversity Clinic Erlangen, Department of Gynaecology and Obstetrics, Friedrich-Alexander University Erlangen-Nuremberg, Erlangen, 91054 Germany.
Martin WidschwendterDepartment of Women's Cancer, University College London, 74 Huntley Street, London, WC1E 6BT UK.
Andrew E TeschendorffCAS Key Lab for Computational Biology, CAS-MPG Partner Institute for Computational Biology, Chinese Academy of Sciences, Shanghai Institute for Biological Sciences, 320 Yue Yang Road, 200031 Shanghai, China ; Department of Women's Cancer, University College London, 74 Huntley Street, London, WC1E 6BT UK ; Statistical Genomics Group, Paul O'Gorman Building, UCL Cancer Institute, University College London, 72 Huntley Street, London, WC1E 6BT UK.
Friedrich-Alexander-Universität Erlangen-Nürnberg · DEUniversity College London · GBShanghai Institute of Nutrition and Health · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundWhile recent integrative analyses of copy number and gene expression data in breast cancer have revealed a complex molecular landscape with multiple subtypes and many oncogenic/tumour suppressor driver events, much less is known about the role of DNA methylation in shaping breast cancer taxonomy and defining driver events.

resultsHere, we applied a powerful integrative network algorithm to matched DNA methylation and RNA-Seq data for 724 estrogen receptor (ER)-positive (ER+) breast cancers and 111 normal adjacent tissue specimens from The Cancer Genome Atlas (TCGA) project, in order to identify putative epigenetic driver events and to explore the resulting molecular taxonomy. This revealed the existence of nine functionally deregulated epigenetic hotspots encompassing a total of 146 genes, which we were able to validate in independent data sets encompassing over 1000 ER+ breast cancers. Integrative clustering of the matched messenger RNA (mRNA) and DNA methylation data over these genes resulted in only two clusters, which correlated very strongly with the luminal-A and luminal B subtypes. Overall, luminal-A and luminal-B breast cancers shared the same epigenetically deregulated hotspots but with luminal-B cancers exhibiting increased aberrant DNA methylation patterns relative to normal tissue. We show that increased levels of DNA methylation and mRNA expression deviation from the normal state define a marker of poor prognosis. Our data further implicates epigenetic silencing of WNT signalling antagonists and bone morphogenetic proteins (BMP) as key events underlying both luminal subtypes but specially of luminal-B breast cancer. Finally, we show that DNA methylation changes within the identified epigenetic interactome hotspots do not exhibit mutually exclusive patterns within the same cancer sample, instead exhibiting coordinated changes within the sample.

conclusionsOur results indicate that the integrative DNA methylation and transcriptomic landscape of ER+ breast cancer is surprisingly homogeneous, defining two main subtypes which strongly correlate with luminal-A/B subtype status. In particular, we identify WNT and BMP signalling as key epigenetically deregulated tumour suppressor pathways in luminal ER+ breast cancer, with increased deregulation seen in luminal-B breast cancer.

Identifiers

PMID26664652
PMCPMC4673726
OpenAlexW2195191237

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.