scholarly journals Epigenetic clock detected a breast cancer mitosis subtype with improved immunotherapy

2021 ◽  
Author(s):  
Shipeng Shang ◽  
Xin Li ◽  
Yue Gao ◽  
Shuang Guo ◽  
Hanxiao Zhou ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Dna Methylation ◽  
Breast Tissue ◽  
Pearson Correlation ◽  
Breast Cancer Subtypes ◽  
Chronological Age ◽  
Epigenetic Clock ◽  
Methylation Data ◽  
Cancer Subtypes ◽  
Epigenetic Age

Abstract Background Epigenetic clock based on DNA methylation can estimate the epigenetic age of tissue and cell that can describe the process of aging. However, the exploration of diseases by the epigenetic clock is still an uncharted territory. Our objective was to assess the role of the epigenetic clock in breast cancer. Methods In this study, DNA methylation data of breast tissue sample was download from TCGA and GEO database. DNA methylation level of CpG sites and age of samples was calculated by using pearson correlation test. Differentially expressed genes were identified using the limma package and Kruskal-Wallis test was used for the difference between cancer subtypes. Results We developed a workflow to construct the Breast Epigenetic Clock (BEpiC) that could accurately predict the chronological age of normal breast tissue. Furthermore, the BEpiC was applied to breast cancer to identify three breast cancer subtypes (including development, homeostasis, and mitosis) by using the deviation between epigenetic age and chronological age. Interestingly, the prognosis of the three breast cancer subtypes is significantly different. In addition, the three breast cancer subtypes had distinct differences in multiple immune cells and the mitosis subtype had the highest tumor mutation burden that was used to estimate response to checkpoint inhibitors. Conclusion Our model highlights that epigenetic age of breast cancer samples had an important impact on immunotherapy. We constructed a BEpiC web server (http://bio-bigdata.hrbmu.edu.cn/BEpiC/) where users submit DNA methylation data and age information to predict the epigenetic age of breast tissue and breast cancer subtypes. Trial registration Not applicable

scholarly journals Epigenetic clock and DNA methylation studies of roe deer in the wild

2020 ◽  
Author(s):  
Jean-François Lemaître ◽  
Benjamin Rey ◽  
Jean-Michel Gaillard ◽  
Corinne Régis ◽  
Emmanuelle Gilot ◽  
...  
Keyword(s):  
Dna Methylation ◽  
Evolutionary Biology ◽  
Roe Deer ◽  
Chronological Age ◽  
Epigenetic Clock ◽  
Aging Effects ◽  
Methylation Array ◽  
Biomarkers Of Aging ◽  
Epigenetic Age ◽  
The Relationship

AbstractDNA methylation-based biomarkers of aging (epigenetic clocks) promise to lead to new insights in the evolutionary biology of ageing. Relatively little is known about how the natural environment affects epigenetic aging effects in wild species. In this study, we took advantage of a unique long-term (>40 years) longitudinal monitoring of individual roe deer (Capreolus capreolus) living in two wild populations (Chizé and Trois Fontaines, France) facing different ecological contexts to investigate the relationship between chronological age and levels of DNA methylation (DNAm). We generated novel DNA methylation data from n=90 blood samples using a custom methylation array (HorvathMammalMethylChip40). We present three DNA methylation-based estimators of age (DNAm or epigenetic age), which were trained in males, females, and both sexes combined. We investigated how sex differences influenced the relationship between DNAm age and chronological age through the use of sex-specific epigenetic clocks. Our results highlight that both populations and sex influence the epigenetic age, with the bias toward a stronger male average age acceleration (i.e. differences between epigenetic age and chronological ages) particularly pronounced in the population facing harsh environmental conditions. Further, we identify the main sites of epigenetic alteration that have distinct aging patterns across the two sexes. These findings open the door to promising avenues of research at the crossroad of evolutionary biology and biogerontology.

Epigenetic aspects of triple-negative in patients with breast cancer.

2012 ◽  
Vol 30 (15_suppl) ◽  
pp. 1041-1041
Author(s):  
Joaquina Martínez-Galan ◽  
Sandra Rios ◽  
Juan Ramon Delgado ◽  
Blanca Torres-Torres ◽  
Jesus Lopez-Peñalver ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Dna Methylation ◽  
Triple Negative ◽  
Breast Cancer Subtype ◽  
Regulation Of Gene Expression ◽  
Breast Cancer Subtypes ◽  
Luminal A ◽  
Cancer Subtypes ◽  
Luminal B

1041 Background: Identification of gene expression-based breast cancer subtypes is considered a critical means of prognostication. Genetic mutations along with epigenetic alterations contribute to gene-expression changes occurring in breast cancer. However, the reproducibility of differential DNA methylation discoveries for cancer and the relationship between DNA methylation and aberrant gene expression have not been systematically analysed. The present study was undertaken to dissect the breast cancer methylome and to deliver specific epigenotypes associated with particular breast cancer subtypes. Methods: By using Real Time QMSPCR SYBR green we analyzed DNA methylation in regulatory regions of 107 pts with breast cancer and analyzed association with prognostics factor in triple negative breast cancer and methylation promoter ESR1, APC, E-Cadherin, Rar B and 14-3-3 sigma. Results: We identified novel subtype-specific epigenotypes that clearly demonstrate the differences in the methylation profiles of basal-like and human epidermal growth factor 2 (HER2)-overexpressing tumors. Of the cases, 37pts (40%) were Luminal A (LA), 32pts (33%) Luminal B (LB), 14pts (15%) Triple-negative (TN), and 9pts (10%) HER2+. DNA hypermethylation was highly inversely correlated with the down-regulation of gene expression. Methylation of this panel of promoter was found more frequently in triple negative and HER2 phenotype. ESR1 was preferably associated with TN(80%) and HER2+(60%) subtype. With a median follow up of 6 years, we found worse overall survival (OS) with more frequent ESR1 methylation gene(p>0.05), Luminal A;ESR1 Methylation OS at 5 years 81% vs 93% when was ESR1 Unmethylation. Luminal B;ESR1 Methylation 86% SG at 5 years vs 92% in Unmethylation ESR1. Triple negative;ESR1 Methylation SG at 5 years 75% vs 80% in unmethylation ESR1. HER2;ESR1 Methylation SG at 5 years was 66.7% vs 75% in unmethylation ESR1. Conclusions: Our results provide evidence that well-defined DNA methylation profiles enable breast cancer subtype prediction and support the utilization of this biomarker for prognostication and therapeutic stratification of patients with breast cancer.

scholarly journals A DNA methylation-based definition of biologically distinct breast cancer subtypes

2014 ◽  
Vol 9 (3) ◽  
pp. 555-568 ◽  
Author(s):  
Olafur A. Stefansson ◽  
Sebastian Moran ◽  
Antonio Gomez ◽  
Sergi Sayols ◽  
Carlos Arribas-Jorba ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Dna Methylation ◽  
Breast Cancer Subtypes ◽  
Cancer Subtypes ◽  
Definition Of

scholarly journals Recalibrating the epigenetic clock: implications for assessing biological age in the human cortex

Brain ◽  
2020 ◽  
Author(s):  
Gemma L Shireby ◽  
Jonathan P Davies ◽  
Paul T Francis ◽  
Joe Burrage ◽  
Emma M Walker ◽  
...  
Keyword(s):  
Dna Methylation ◽  
Age Distribution ◽  
Biological Age ◽  
Supervised Machine Learning ◽  
Tissue Type ◽  
Chronological Age ◽  
Epigenetic Clock ◽  
Methylation Data ◽  
Human Cortex ◽  
Dna Methylation Age

Abstract Human DNA methylation data have been used to develop biomarkers of ageing, referred to as ‘epigenetic clocks’, which have been widely used to identify differences between chronological age and biological age in health and disease including neurodegeneration, dementia and other brain phenotypes. Existing DNA methylation clocks have been shown to be highly accurate in blood but are less precise when used in older samples or in tissue types not included in training the model, including brain. We aimed to develop a novel epigenetic clock that performs optimally in human cortex tissue and has the potential to identify phenotypes associated with biological ageing in the brain. We generated an extensive dataset of human cortex DNA methylation data spanning the life course (n = 1397, ages = 1 to 108 years). This dataset was split into ‘training’ and ‘testing’ samples (training: n = 1047; testing: n = 350). DNA methylation age estimators were derived using a transformed version of chronological age on DNA methylation at specific sites using elastic net regression, a supervised machine learning method. The cortical clock was subsequently validated in a novel independent human cortex dataset (n = 1221, ages = 41 to 104 years) and tested for specificity in a large whole blood dataset (n = 1175, ages = 28 to 98 years). We identified a set of 347 DNA methylation sites that, in combination, optimally predict age in the human cortex. The sum of DNA methylation levels at these sites weighted by their regression coefficients provide the cortical DNA methylation clock age estimate. The novel clock dramatically outperformed previously reported clocks in additional cortical datasets. Our findings suggest that previous associations between predicted DNA methylation age and neurodegenerative phenotypes might represent false positives resulting from clocks not robustly calibrated to the tissue being tested and for phenotypes that become manifest in older ages. The age distribution and tissue type of samples included in training datasets need to be considered when building and applying epigenetic clock algorithms to human epidemiological or disease cohorts.

scholarly journals Dysfunctional epigenetic aging of the normal colon and colorectal cancer risk

2020 ◽  
Vol 12 (1) ◽  
Author(s):  
Ting Wang ◽  
Sean K. Maden ◽  
Georg E. Luebeck ◽  
Christopher I. Li ◽  
Polly A. Newcomb ◽  
...  
Keyword(s):  
Dna Methylation ◽  
Risk Group ◽  
Risk Groups ◽  
Biological Age ◽  
Chronological Age ◽  
Epigenetic Clock ◽  
Normal Colon ◽  
Epigenetic Aging ◽  
Epigenetic Age ◽  
Epigenetic Age Acceleration

Abstract Background Chronological age is a prominent risk factor for many types of cancers including colorectal cancer (CRC). Yet, the risk of CRC varies substantially between individuals, even within the same age group, which may reflect heterogeneity in biological tissue aging between people. Epigenetic clocks based on DNA methylation are a useful measure of the biological aging process with the potential to serve as a biomarker of an individual’s susceptibility to age-related diseases such as CRC. Methods We conducted a genome-wide DNA methylation study on samples of normal colon mucosa (N = 334). Subjects were assigned to three cancer risk groups (low, medium, and high) based on their personal adenoma or cancer history. Using previously established epigenetic clocks (Hannum, Horvath, PhenoAge, and EpiTOC), we estimated the biological age of each sample and assessed for epigenetic age acceleration in the samples by regressing the estimated biological age on the individual’s chronological age. We compared the epigenetic age acceleration between different risk groups using a multivariate linear regression model with the adjustment for gender and cell-type fractions for each epigenetic clock. An epigenome-wide association study (EWAS) was performed to identify differential methylation changes associated with CRC risk. Results Each epigenetic clock was significantly correlated with the chronological age of the subjects, and the Horvath clock exhibited the strongest correlation in all risk groups (r > 0.8, p < 1 × 10−30). The PhenoAge clock (p = 0.0012) revealed epigenetic age deceleration in the high-risk group compared to the low-risk group. Conclusions Among the four DNA methylation-based measures of biological age, the Horvath clock is the most accurate for estimating the chronological age of individuals. Individuals with a high risk for CRC have epigenetic age deceleration in their normal colons measured by the PhenoAge clock, which may reflect a dysfunctional epigenetic aging process.

scholarly journals Aberrant DNA methylation impacts gene expression and prognosis in breast cancer subtypes

2015 ◽  
Vol 138 (1) ◽  
pp. 87-97 ◽  
Author(s):  
Balázs Győrffy ◽  
Giulia Bottai ◽  
Thomas Fleischer ◽  
Gyöngyi Munkácsy ◽  
Jan Budczies ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Dna Methylation ◽  
Breast Cancer Subtypes ◽  
Cancer Subtypes ◽  
Aberrant Dna Methylation

scholarly journals A deep embedded refined clustering approach for breast cancer distinction based on DNA methylation

2021 ◽  
Author(s):  
Rocío del Amor ◽  
Adrián Colomer ◽  
Carlos Monteagudo ◽  
Valery Naranjo
Keyword(s):  
Breast Cancer ◽  
Dna Methylation ◽  
Deep Learning ◽  
Dimensionality Reduction ◽  
Error Rate ◽  
Breast Tissue ◽  
Cpg Island ◽  
Learning System ◽  
Methylation Data ◽  
Tissue Samples

AbstractEpigenetic alterations have an important role in the development of several types of cancer. Epigenetic studies generate a large amount of data, which makes it essential to develop novel models capable of dealing with large-scale data. In this work, we propose a deep embedded refined clustering method for breast cancer differentiation based on DNA methylation. In concrete, the deep learning system presented here uses the levels of CpG island methylation between 0 and 1. The proposed approach is composed of two main stages. The first stage consists in the dimensionality reduction of the methylation data based on an autoencoder. The second stage is a clustering algorithm based on the soft assignment of the latent space provided by the autoencoder. The whole method is optimized through a weighted loss function composed of two terms: reconstruction and classification terms. To the best of the authors’ knowledge, no previous studies have focused on the dimensionality reduction algorithms linked to classification trained end-to-end for DNA methylation analysis. The proposed method achieves an unsupervised clustering accuracy of 0.9927 and an error rate (%) of 0.73 on 137 breast tissue samples. After a second test of the deep-learning-based method using a different methylation database, an accuracy of 0.9343 and an error rate (%) of 6.57 on 45 breast tissue samples are obtained. Based on these results, the proposed algorithm outperforms other state-of-the-art methods evaluated under the same conditions for breast cancer classification based on DNA methylation data.

243 Aberrant DNA methylation impacts gene expression and prognosis in breast cancer subtypes

2015 ◽  
Vol 51 ◽  
pp. S41
Author(s):  
B. Gyorffy ◽  
G. Bottai ◽  
T. Fleischer ◽  
G. Munkacsy ◽  
L. Paladini ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Dna Methylation ◽  
Breast Cancer Subtypes ◽  
Cancer Subtypes ◽  
Aberrant Dna Methylation
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