Biology subjects

Christensen, B. C.

Publications and source records attributed to Christensen, B. C..

4 recordsLinked to original sources

bioRxiv2018

Genome-wide abundance of 5-hydroxymethylcytosine in breast tissue reveals unique function in dynamic gene regulation and carcinogenesis

5-hydroxymethylcytosine (5hmC) is generated by oxidation of 5-methylcytosine (5mC), however little is understood regarding the distribution and functions of 5hmC in mammalian cells. We determined the genome-wide distribution of 5hmC and 5mC in normal breast tissue from disease-free women. Although less abundant than 5mC, 5hmC is differentially distributed, and consistently enriched among breast-specific enhancers and transcriptionally active chromatin. In contrast, regulatory regions associated with transcriptional inactivity were relatively depleted of 5hmC. Gene regions containing abundant 5hmC were significantly associated with lactate oxidation, immune cell function, and prolactin signaling pathways. In independent data sets, normal breast tissue 5hmC was significantly enriched among CpG loci demonstrated to have altered methylation in pre-invasive breast cancer and invasive breast tumors. Our findings provide a genome-wide map of nucleotide-level 5hmC in normal breast tissue and demonstrate that 5hmC is positioned to contribute to gene regulatory functions which protect against carcinogenesis.

genomics↗

bioRxiv2017

Methylation-To-Expression Feature Models of Breast Cancer Accurately Predict Overall Survival, Distant-Recurrence Free Survival, And Pathologic Complete Response in Multiple Cohorts

BackgroundApproaches that capitalize on the benefits of multi-omic data integration in invasive breast carcinoma to define prognostic biomarkers for precision medicine have been slow to emerge. In this work, we examined the efficacy of our methylation-to-expression feature model (M2EFM) approach to combining molecular and clinical predictors as part of a single analysis to create prognostic risk scores for overall survival, distant metastasis, and chemosensitivity.\n\nMethodsGene expression and DNA methylation values as well as clinical variables were integrated via M2EFM to build prognostic models of overall survival using 1028 breast tumor samples and further applied to external validation cohorts of 61 and 327 samples. Data-integrated prognostic models of distant recurrence-free survival and pathologic complete response were built using 306 samples and validated on 182 samples of external validation data. Additionally, we compared the discrimination and calibration of M2EFM models to other approaches.\n\nResultsDespite different populations and assays, M2EFM models validated with good accuracy (C-index or AUC [≥] .7) for all outcomes in all validation data. M2EFM models had the most consistent performance overall and superior calibration, suggesting a greater likelihood of clinical utility. Finally, we demonstrated that M2EFM identifies functionally relevant genes, which could be useful in translating an M2EFM biomarker to the clinic.\n\nConclusionM2EFM uses multiple levels of genomic data to infer disrupted regulatory patterns, thus providing a gene signature that connects loss of regulatory control with cancer prognosis.\n\nFundingThe analyses described in this report were supported by NIH grants R01ES022222, P30CA138292, P30ES019776, and R01DE022772.\n\nConflicts of InterestThe authors declare no potential conflicts of interest.

bioinformatics↗

bioRxiv2017

Reference-free deconvolution of DNA methylation signatures identifies common differentially methylated gene regions on 1p36 across breast cancer subtypes

Breast cancer is a complex disease and studying DNA methylation (DNAm) in tumors is complicated by disease heterogeneity. We compared DNAm in breast tumors with normal-adjacent breast samples from The Cancer Genome Atlas (TCGA). We constructed models stratified by tumor stage and PAM50 molecular subtype and performed cell-type reference-free deconvolution on each model. We identified nineteen differentially methylated gene regions (DMGRs) in early stage tumors across eleven genes (AGRN, C1orf170, FAM41C, FLJ39609, HES4, ISG15, KLHL17, NOC2L, PLEKHN1, SAMD11, WASH5P). These regions were consistently differentially methylated in every subtype and all implicated genes are localized on chromosome 1p36.3. We also validated seventeen DMGRs in an independent data set. Identification and validation of shared DNAm alterations across tumor subtypes in early stage tumors advances our understanding of common biology underlying breast carcinogenesis and may contribute to biomarker development. We also provide evidence on the importance and potential function of 1p36 in cancer.

genomics↗

bioRxiv2017

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

BackgroundThe 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.\n\nResultsHere 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).\n\nConclusionsDNA 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.

epidemiology↗