scholarly journals Accelerated aging in normal breast tissue of women with breast cancer

2021 ◽  
Vol 23 (1) ◽  
Author(s):  
Shoghag Panjarian ◽  
Jozef Madzo ◽  
Kelsey Keith ◽  
Carolyn M. Slater ◽  
Carmen Sapienza ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Dna Methylation ◽  
Anomaly Detection ◽  
Breast Tissue ◽  
Preventive Intervention ◽  
Normal Breast Tissue ◽  
Accelerated Aging ◽  
Normal Breast ◽  
Age Related ◽  
Unsupervised Anomaly Detection

Abstract Background DNA 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. Methods We 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. Results We 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. Conclusions A 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.

scholarly journals DNA Methylation and Breast Cancer Risk: An Epigenome-Wide Study of Normal Breast Tissue and Blood

Cancers ◽  
2020 ◽  
Vol 12 (11) ◽  
pp. 3088 ◽  
Author(s):  
Kaoutar Ennour-Idrissi ◽  
Dzevka Dragic ◽  
Elissar Issa ◽  
Annick Michaud ◽  
Sue-Ling Chang ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Dna Methylation ◽  
Breast Cancer Risk ◽  
Cancer Risk ◽  
Breast Tissue ◽  
Normal Breast Tissue ◽  
Normal Breast ◽  
Breast Epithelium ◽  
Normal Breast Epithelium ◽  
Differentially Methylated Genes

Differential DNA methylation is a potential marker of breast cancer risk. Few studies have investigated DNA methylation changes in normal breast tissue and were largely confounded by cancer field effects. To detect methylation changes in normal breast epithelium that are causally associated with breast cancer occurrence, we used a nested case–control study design based on a prospective cohort of patients diagnosed with a primary invasive hormone receptor-positive breast cancer. Twenty patients diagnosed with a contralateral breast cancer (CBC) were matched (1:1) with 20 patients who did not develop a CBC on relevant risk factors. Differentially methylated Cytosine-phosphate-Guanines (CpGs) and regions in normal breast epithelium were identified using an epigenome-wide DNA methylation assay and robust linear regressions. Analyses were replicated in two independent sets of normal breast tissue and blood. We identified 7315 CpGs (FDR < 0.05), 52 passing strict Bonferroni correction (p < 1.22 × 10−7) and 43 mapping to known genes involved in metabolic diseases with significant enrichment (p < 0.01) of pathways involving fatty acids metabolic processes. Four differentially methylated genes were detected in both site-specific and regions analyses (LHX2, TFAP2B, JAKMIP1, SEPT9), and three genes overlapped all three datasets (POM121L2, KCNQ1, CLEC4C). Once validated, the seven differentially methylated genes distinguishing women who developed and who did not develop a sporadic breast cancer could be used to enhance breast cancer risk-stratification, and allow implementation of targeted screening and preventive strategies that would ultimately improve breast cancer prognosis.

scholarly journals DNA methylation differences at regulatory elements are associated with the cancer risk factor age in normal breast tissue

2017 ◽  
Author(s):  
Kevin C. Johnson ◽  
E. Andres Houseman ◽  
Jessica E. King ◽  
Brock C. Christensen
Keyword(s):  
Breast Cancer ◽  
Risk Factors ◽  
Dna Methylation ◽  
Breast Cancer Risk ◽  
Cancer Risk ◽  
Breast Tissue ◽  
Normal Breast ◽  
Cancer Risk Factors ◽  
Age Related ◽  
Breast Cancer Risk Factors

AbstractBackgroundThe underlying biological mechanisms through which epidemiologically defined breast cancer risk factors contribute to disease risk remain poorly understood. Identification of the molecular changes associated with cancer risk factors in normal tissues may aid in determining the earliest events of carcinogenesis and informing cancer prevention strategies.ResultsHere we investigated the impact cancer risk factors have on the normal breast epigenome by analyzing DNA methylation genome-wide (Infinium 450K array) in cancer-free women from the Susan G. Komen Tissue Bank (n = 100). We tested the relation of established breast cancer risk factors: age, body mass index, parity, and family history of disease with DNA methylation adjusting for potential variation in cell-type proportions. We identified 787 CpG sites that demonstrated significant associations (Q-value < 0.01) with subject age. Notably, DNA methylation was not strongly associated with the other evaluated breast cancer risk factors. Age-related DNA methylation changes are primarily increases in methylation enriched at breast epithelial cell enhancer regions (P = 7.1E-20), and binding sites of chromatin remodelers (MYC and CTCF). We validated the age-related associations in two independent populations of normal breast tissue (n = 18) and normal-adjacent to tumor tissue (n = 97). The genomic regions classified as age-related were more likely to be regions altered in cancer in both pre-invasive (n = 40, P=3.0E-03) and invasive breast tumors (n = 731, P=1.1E-13).ConclusionsDNA methylation changes with age occur at regulatory regions, and are further exacerbated in cancer suggesting that age influences breast cancer risk in part through its contribution to epigenetic dysregulation in normal breast tissue.

Comparison of epigenetic aging in normal breast tissue from women with and without breast cancer.

2017 ◽  
Vol 35 (15_suppl) ◽  
pp. 1522-1522
Author(s):  
Erin Wysong Hofstatter ◽  
Steven Horvath ◽  
Anees B. Chagpar ◽  
Vikram Wali ◽  
Veerle Bossuyt ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Dna Methylation ◽  
Breast Tissue ◽  
Normal Breast Tissue ◽  
Tissue Bank ◽  
Normal Breast ◽  
Cancer Center ◽  
Epigenetic Clock ◽  
Epigenetic Aging ◽  
Tissue Aging

1522 Background: Age is one of the most important risk factors for developing breast cancer. However, why increasing age is associated with increasing incidence of breast cancer remains poorly understood. We hypothesize that accumulated epigenetic alterations in the breast contribute to the development of breast cancer, and that such changes accumulate more rapidly in the breast during the lifetime of women who develop breast cancer as compared to their healthy peers. We therefore sought to identify an epigenetic pattern of accelerated breast tissue “aging” in women with breast cancer. Methods: Samples of normal breast tissue were collected from four cohorts of women: age < 50 years with and without breast cancer, and age ≥50 years with and without breast cancer (BC). Samples were obtained from the Susan G. Komen Tissue Bank at IU Simon Cancer Center, reduction mammoplasties and adjuvant mastectomy specimens at Yale. The Illumina Human 450K BeadChip microarray was used to generate DNA methylation profiles. Data was analyzed using the “Epigenetic Clock”, a published biomarker of aging based on 353 specific CpGs in the human genome. Clinical data collected for each subject included: age, height, weight, ethnicity, medical and reproductive history, tobacco and alcohol use, family history of breast cancer, current medications, and tumor characteristics. Results: Normal breast tissue samples from 90 subjects were analyzed (age < 50 with BC = 22, age < 50 without BC = 30, age ≥50 with BC = 15, age ≥50 without BC = 23). Age range was 24-82 years and 18-82 years for cohorts with and without BC respectively. In the cohort with BC, 95% of tumors were estrogen receptor-positive. Overall, DNA methylation tissue age (DNAmAge) was strongly correlated with chronologic age (r = 0.88, p < 0.001). However, normal breast tissue from women with breast cancer demonstrated significantly accelerated DNAmAge as compared to healthy peers (p < 0.001). Conclusions: Normal breast tissue from women with breast cancer demonstrates evidence of an accelerated epigenetic "aging" process. DNAmAge of normal breast tissue may prove to be a useful tool in identifying those women at highest risk, and lend insight into novel mechanisms of breast cancer prevention.

scholarly journals Age-related DNA methylation in normal breast tissue and its relationship with invasive breast tumor methylation

Epigenetics ◽  
2013 ◽  
Vol 9 (2) ◽  
pp. 268-275 ◽  
Author(s):  
Kevin C Johnson ◽  
Devin C Koestler ◽  
Chao Cheng ◽  
Brock C Christensen
Keyword(s):  
Dna Methylation ◽  
Breast Tumor ◽  
Breast Tissue ◽  
Normal Breast Tissue ◽  
Normal Breast ◽  
Age Related ◽  
Invasive Breast Tumor

scholarly journals Targeted next-generation sequencing assays using triplet samples of normal breast tissue, primary breast cancer, and recurrent/metastatic lesions

2020 ◽  
Author(s):  
Toshiaki Akahane ◽  
Naoki Kanomata ◽  
Oi Harada ◽  
Tetsumasa Yamashita ◽  
Junichi Kurebayashi ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Primary Tumor ◽  
Primary Breast Cancer ◽  
Breast Tissue ◽  
Normal Breast Tissue ◽  
Metastatic Breast ◽  
Normal Breast ◽  
Human Breast ◽  
Metastatic Lesions ◽  
Generation Sequencing

Abstract Background: Next-generation sequencing (NGS) has shown that recurrent/metastatic breast cancer lesions may have additional genetic changes compared with the primary tumor. These additional changes may be related to tumor progression and/or drug resistance. However, breast cancer-targeted NGS is not still widely used in clinical practice to compare the genomic profiles of primary breast cancer and recurrent/metastatic lesions.Methods: Triplet samples of genomic DNA were extracted from each patient’s normal breast tissue, primary breast cancer, and recurrent/metastatic lesion(s). A DNA library was constructed using the QIAseq Human Breast Cancer Panel (93 genes, Qiagen) and then sequenced using MiSeq (Illumina). The Qiagen web portal was utilized for data analysis.Results: Successful results for three or four samples (normal breast tissue, primary tumor, and at least one metastatic/recurrent lesion) were obtained for 11 of 35 breast cancer patients with recurrence/metastases (36 samples). We detected shared somatic mutations in all but one patient, who had a germline mutation in TP53. Additional mutations that were detected in recurrent/metastatic lesions compared with primary tumor were in genes including TP53 (three patients) and one case each of ATR, BLM, CBFB, EP300, ERBB2, MUC16, PBRM1, and PIK3CA. Actionable mutations and/or copy number variations (CNVs) were detected in 73% (8/11) of recurrent/metastatic breast cancer lesions.Conclusions: The QIAseq Human Breast Cancer Panel assay showed that recurrent/metastatic breast cancers sometimes acquired additional mutations and CNV. Such additional genomic changes could provide therapeutic target.

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