Abstract
BackgroundDNA methylation (DNAm) age has been widely accepted as an epigenetic biomarker for biological aging. Emerging evidence suggests that DNAm age can be tissue-specific and female breast tissue ages faster than other parts of the body. The Horvath clock, which estimates DNAm age across multiple tissues, has been shown to be poorly calibrated in breast issue. We aim to develop a model to estimate breast tissue-specific DNAm age.MethodsGenome-wide DNA methylation sequencing data were generated for 459 normal, 107 tumor, and 45 paired adjacent-normal breast tissue samples. We determined a novel set of 286 breast tissue-specific clock CpGs using penalized linear regression and developed a model to estimate breast tissue-specific DNAm age. The model was applied to estimate breast tissue-specific DNAm age in different breast tissue types and in tumors with distinct clinical characteristics to investigate cancer-related aging effects.ResultsOur estimated breast tissue-specific DNAm age was highly correlated with chronological age (r = 0.88; p = 2.9 × 10−31) in normal breast tissue. Breast tumor tissue samples exhibited a positive epigenetic age acceleration, where DNAm age was on average 7 years older than respective chronological age (p = 1.8 × 10−8). In age-matched analyses, tumor breast tissue appeared 12 and 13 years older in DNAm age than adjacent-normal and normal breast tissue (p = 4.0 × 10−6 and 1.0 × 10−6, respectively). Both HER2+ and hormone-receptor positive subtypes demonstrated significant acceleration in DNAm ages (p = 0.04 and 3.8 × 10−6, respectively), while no apparent DNAm age acceleration was observed for triple-negative breast tumors. We observed a non-linear pattern of epigenetic age acceleration with breast tumor grade. In addition, early-staged tumors showed a positive epigenetic age acceleration (p = 0.003) while late-staged tumors exhibited a non-significant negative epigenetic age acceleration (p = 0.10).ConclusionsThe intended applications for this model are wide-spread and have been shown to provide biologically meaningful results for cancer-related aging effects in breast tumor tissue. Future studies are warranted to explore whether breast tissue-specific epigenetic age acceleration is predictive of breast cancer development, treatment response, and survival as well as the clinical utility of whether this model can be extended to blood samples.
Highlights
DNA methylation (DNAm) levels in specific sets of cytosine-phosphate-guanines (CpGs) in the human genome can be used to establish an epigenetic biomarker of biological age [1,2,3,4,5,6], known as an “epigenetic clock,” where the resulting age estimate is commonly referred to as “epigenetic age” or “DNAm age.” Increasing evidence suggests that many facets of aging are epigenetic [7, 8] and that DNAm age captures both the genetic and environmental influences across time on cellular functions [1, 2, 4, 6]
Breast tissue-specific DNAm age was calculated based on the methylation of a novel set of 286 clock CpGs that were selected by a penalized regression model trained on data from normal breast tissue
We found that the estimated DNAm age was highly correlated with chronological age with a minimal error
Summary
DNA methylation (DNAm) levels in specific sets of cytosine-phosphate-guanines (CpGs) in the human genome can be used to establish an epigenetic biomarker of biological age [1,2,3,4,5,6], known as an “epigenetic clock,” where the resulting age estimate is commonly referred to as “epigenetic age” or “DNAm age.” Increasing evidence suggests that many facets of aging are epigenetic [7, 8] and that DNAm age captures both the genetic and environmental influences across time on cellular functions [1, 2, 4, 6]. DNA methylation (DNAm) levels in specific sets of cytosine-phosphate-guanines (CpGs) in the human genome can be used to establish an epigenetic biomarker of biological age [1,2,3,4,5,6], known as an “epigenetic clock,” where the resulting age estimate is commonly referred to as “epigenetic age” or “DNAm age.”. A multi-tissue DNAm age estimator was developed recently using a large dataset of DNAm profiles measured on the Illumina Methylation 27K and 450K microarray platforms (Illumina Inc., San Diego, CA, USA) This model, known as the Horvath clock model, displays a remarkable accuracy in predicting chronological age across multiple tissue types using the methylation levels of only 353 CpG loci in the human genome [6]; DNAm ages estimated with this model were not well calibrated in several tissue types, including breast tissue, uterine endometrium, dermal fibroblasts, skeletal muscle tissue, and heart tissue [6]. We aim to develop a model to estimate breast tissue-specific DNAm age
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