Accelerated aging in normal breast tissue of women with breast cancer

Shoghag Panjarian,Jean-Pierre J Issa,Carolyn M Slater,Carmen Sapienza,Jaroslav Jelinek,Kelsey Keith,Jozef Madzo

doi:10.1186/s13058-021-01434-7

Abstract

BackgroundDNA methylation alterations have similar patterns in normal aging tissue and in cancer. In this study, we investigated breast tissue-specific age-related DNA methylation alterations and used those methylation sites to identify individuals with outlier phenotypes. Outlier phenotype is identified by unsupervised anomaly detection algorithms and is defined by individuals who have normal tissue age-dependent DNA methylation levels that vary dramatically from the population mean.MethodsWe generated whole-genome DNA methylation profiles (GSE160233) on purified epithelial cells and used publicly available Infinium HumanMethylation 450K array datasets (TCGA, GSE88883, GSE69914, GSE101961, and GSE74214) for discovery and validation.ResultsWe found that hypermethylation in normal breast tissue is the best predictor of hypermethylation in cancer. Using unsupervised anomaly detection approaches, we found that about 10% of the individuals (39/427) were outliers for DNA methylation from 6 DNA methylation datasets. We also found that there were significantly more outlier samples in normal-adjacent to cancer (24/139, 17.3%) than in normal samples (15/228, 5.2%). Additionally, we found significant differences between the predicted ages based on DNA methylation and the chronological ages among outliers and not-outliers. Additionally, we found that accelerated outliers (older predicted age) were more frequent in normal-adjacent to cancer (14/17, 82%) compared to normal samples from individuals without cancer (3/17, 18%). Furthermore, in matched samples, we found that the epigenome of the outliers in the pre-malignant tissue was as severely altered as in cancer.ConclusionsA subset of patients with breast cancer has severely altered epigenomes which are characterized by accelerated aging in their normal-appearing tissue. In the future, these DNA methylation sites should be studied further such as in cell-free DNA to determine their potential use as biomarkers for early detection of malignant transformation and preventive intervention in breast cancer.

Highlights

DNA methylation alterations have similar patterns in normal aging tissue and in cancer
We showed that 304 of the 1127 age-related sites that gained DNA methylation were enriched at CpG islands (CGI), at the promoter regions
In this study, we show age-dependent DNA methylation drifts in normal breast tissue and that these changes, Fig. 6 Outlier analysis of The Cancer Genome Atlas (TCGA) normal adjacent and breast cancer samples. a A least absolute shrinkage and selection operator (Lasso) regression model was built on the DNA methylation values of 146 aging sites in all non-outlier samples from the six DNA methylation datasets to predict the ages of all other samples: the outlier samples in normal adjacent, the not-outlier cancer samples, and the outlier cancer samples

Summary

Introduction

DNA methylation alterations have similar patterns in normal aging tissue and in cancer. We investigated breast tissue-specific age-related DNA methylation alterations and used those methylation sites to identify individuals with outlier phenotypes. It is well established that DNA methylation alters with age in normal healthy individuals and in disease states. Of interest are individuals with accelerated epigenetic aging, who have acquired altered methylation faster than expected based on their chronological age Exploring these extreme outlying variations in DNA methylation in normal tissues could help explain biological variations in disease states. These extreme DNA methylation alterations in normal tissues are infrequent events, making these stochastic outlier events that are difficult to identify. Several recent studies reported on different algorithms used to identify these rare events [11,12,13]

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