Normal cell-type epigenetics and breast cancer classification: a case study of cell mixture-adjusted analysis of DNA methylation data from tumors

Historically, breast cancer classification has relied on prognostic subtypes. Thus, unlike hematopoietic cancers, breast tumor classification lacks phylogenetic rationale. The feasibility of phylogenetic classification of breast tumors has recently been demonstrated based on estrogen receptor (ER), androgen receptor (AR), vitamin D receptor (VDR) and Keratin 5 expression. Four hormonal states (HR0-3) comprising 11 cellular subtypes of breast cells have been proposed. This classification scheme has been shown to have relevance to clinical prognosis. We examine the implications of such phylogenetic classification on DNA methylation of both breast tumors and normal breast tissues by applying recently developed deconvolution algorithms to three DNA methylation data sets archived on Gene Expression Omnibus. We propose that breast tumors arising from a particular cell-of-origin essentially magnify the epigenetic state of their original cell type. We demonstrate that DNA methylation of tumors manifests patterns consistent with cell-specific epigenetic states, that these states correspond roughly to previously posited normal breast cell types, and that estimates of proportions of the underlying cell types are predictive of tumor phenotypes. Taken together, these findings suggest that the epigenetics of breast tumors is ultimately based on the underlying phylogeny of normal breast tissue

Enlarged leukocyte referent libraries can explain additional variance in blood-based epigenome-wide association studies

AIM: We examined whether variation in blood-based epigenome-wide association studies could be more completely explained by augmenting existing reference DNA methylation libraries. MATERIALS & METHODS: We compared existing and enhanced libraries in predicting variability in three publicly available 450K methylation datasets that collected whole-blood samples. Models were fit separately to each CpG site and used to estimate the additional variability when adjustments for cell composition were made with each library. RESULTS: Calculation of the mean difference in the CpG-specific residual sums of squares error between models for an arthritis, aging and metabolic syndrome dataset, indicated that an enhanced library explained significantly more variation across all three datasets (p < 10(-3)). CONCLUSION: Pathologically important immune cell subtypes can explain important variability in epigenome-wide association studies done in blood

Enlarged leukocyte referent libraries can explain additional variance in blood-based epigenome-wide association studies

AimWe examined whether variation in blood-based epigenome-wide association studies could be more completely explained by augmenting existing reference DNA methylation libraries.Materials &amp; methodsWe compared existing and enhanced libraries in predicting variability in three publicly available 450K methylation datasets that collected whole-blood samples. Models were fit separately to each CpG site and used to estimate the additional variability when adjustments for cell composition were made with each library.ResultsCalculation of the mean difference in the CpG-specific residual sums of squares error between models for an arthritis, aging and metabolic syndrome dataset, indicated that an enhanced library explained significantly more variation across all three datasets (p &lt; 10(-3)).ConclusionPathologically important immune cell subtypes can explain important variability in epigenome-wide association studies done in blood

Guidelines for cell-type heterogeneity quantification based on a comparative analysis of reference-free DNA methylation deconvolution software

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Agricultural Research Bulletins, Nos. 321-345

Volume 27, Bulletins 321-345. (321) Proper Size and Location of Corn Stabilization Stocks; (322) Some Farm Family Gardens Pay in Dollars; (323) Cost, Distribution and Utilization of Farm Machinery in Iowa; (324) Adjusting Crop Acreages for War Production to the Soil Resources of Iowa; (325) Bacteriology of Cheese: VII. Calcium and Phosphorus Contents of Various Cheeses, Including Relationship to Bacterial Action in the Manufacturing Procedures; (326) Nutritive Value of Corn Oil Meal and Feather Protein; (327) General Agriculture in the High Schools of Iowa; (328) Classification of the Organisms Important in Dairy Products: IV. Bacterium linens; (329) Statistical Investigations of Farm Sample Surveys Taken in Iowa, Florida and California; (330) Coordination of Wheat and Corn Price Controls; (331) Vegetative Development of Inbred and Hybrid Maize; (332) Soil-Inhabiting Fungi Attacking the Roots of Maize; (333) Design and Statistical Analysis of Some Confounded Factorial Experiments; (334) Testing the Quality of Seeds for Farm and Garden; (335) Teacher Supply and Demand in Home Economics in Iowa, 1935-1941; (336) Effect of Various Adjuvants to the Diet of Rats on the Changes in Body Fats Induced by Feeding Soybean Oil; (337) Materials-Balance Method for Determining Losses of Butterfat in the Creamery; (338) Epiphytology and Control of Sugar Beet Leaf Spot Caused by Cercospora beticola Sacc.; (339) Bacteriology of Butter: VIII. (IX). Salt Distribution in Butter and its Effect on Bacterial Growth; (340) Further Experiments with the Iowa Air Blast Seed Separator for the Analysis of Small-Seeded Grasses; (341) Pre-Harvest Sampling of Soybeans for Yield and Quality; (342) Retting of Hemp: I. Field Retting of Hemp in Iowa; (343) Retting of Hemp: II. Controlled Retting of Hemp; (344) Retting of Hemp: III. Biochemical Changes Accompanying Retting of Hemp; (345) Germinability of Treated and Untreated Lots of Vegetable Seed in Pythium-Infested Soil and in the Field</p

DNA methylation arrays as surrogate measures of cell mixture distribution

<p>Abstract</p> <p>Background</p> <p>There has been a long-standing need in biomedical research for a method that quantifies the normally mixed composition of leukocytes beyond what is possible by simple histological or flow cytometric assessments. The latter is restricted by the labile nature of protein epitopes, requirements for cell processing, and timely cell analysis. In a diverse array of diseases and following numerous immune-toxic exposures, leukocyte composition will critically inform the underlying immuno-biology to most chronic medical conditions. Emerging research demonstrates that DNA methylation is responsible for cellular differentiation, and when measured in whole peripheral blood, serves to distinguish cancer cases from controls.</p> <p>Results</p> <p>Here we present a method, similar to regression calibration, for inferring changes in the distribution of white blood cells between different subpopulations (e.g. cases and controls) using DNA methylation signatures, in combination with a previously obtained external validation set consisting of signatures from purified leukocyte samples. We validate the fundamental idea in a cell mixture reconstruction experiment, then demonstrate our method on DNA methylation data sets from several studies, including data from a Head and Neck Squamous Cell Carcinoma (HNSCC) study and an ovarian cancer study. Our method produces results consistent with prior biological findings, thereby validating the approach.</p> <p>Conclusions</p> <p>Our method, in combination with an appropriate external validation set, promises new opportunities for large-scale immunological studies of both disease states and noxious exposures.</p