Gao Yang, Jones Allison, Fasching Peter A, Ruebner Matthias, Beckmann Matthias W, Widschwendter Martin, Teschendorff Andrew E
CAS 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.
Clin Epigenetics. 2015 Dec 9;7:126. doi: 10.1186/s13148-015-0159-0. eCollection 2015.
While 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.
Here, 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.
Our 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.
虽然最近对乳腺癌中拷贝数和基因表达数据的综合分析揭示了一个具有多种亚型和许多致癌/抑癌驱动事件的复杂分子格局，但关于DNA甲基化在塑造乳腺癌分类和定义驱动事件中的作用却知之甚少。
在此，我们应用一种强大的综合网络算法，对来自癌症基因组图谱（TCGA）项目的724例雌激素受体（ER）阳性（ER+）乳腺癌和111例正常相邻组织标本的匹配DNA甲基化和RNA测序数据进行分析，以识别推定的表观遗传驱动事件并探索由此产生的分子分类。这揭示了存在9个功能失调的表观遗传热点，共包含146个基因，我们能够在包含超过1000例ER+乳腺癌的独立数据集中对其进行验证。对这些基因上匹配的信使RNA（mRNA）和DNA甲基化数据进行综合聚类，仅产生两个聚类，这与腔面A型和腔面B型亚型密切相关。总体而言，腔面A型和腔面B型乳腺癌共享相同的表观遗传失调热点，但腔面B型癌症相对于正常组织表现出更多的异常DNA甲基化模式。我们表明，DNA甲基化水平的增加和mRNA表达偏离正常状态定义了预后不良的标志物。我们的数据进一步表明，WNT信号拮抗剂和骨形态发生蛋白（BMP）的表观遗传沉默是两种腔面亚型尤其是腔面B型乳腺癌的关键潜在事件。最后，我们表明，在同一癌症样本中，所识别的表观遗传相互作用组热点内的DNA甲基化变化并不呈现相互排斥的模式，而是在样本内呈现协同变化。
我们的结果表明，ER+乳腺癌的综合DNA甲基化和转录组格局出人意料地均匀，定义了两个主要亚型，它们与腔面A型/ B型亚型状态密切相关。特别是，我们将WNT和BMP信号识别为腔面ER+乳腺癌中关键的表观遗传失调的肿瘤抑制途径，在腔面B型乳腺癌中失调增加。
