Reference-free cell mixture adjustments in analysis of DNA methylation data

MOTIVATION: Recently there has been increasing interest in the effects of cell mixture on the measurement of DNA methylation, specifically the extent to which small perturbations in cell mixture proportions can register as changes in DNA methylation. A recently published set of statistical methods exploits this association to infer changes in cell mixture proportions, and these methods are presently being applied to adjust for cell mixture effect in the context of epigenome-wide association studies. However, these adjustments require the existence of reference datasets, which may be laborious or expensive to collect. For some tissues such as placenta, saliva, adipose or tumor tissue, the relevant underlying cell types may not be known. RESULTS: We propose a method for conducting epigenome-wide association studies analysis when a reference dataset is unavailable, including a bootstrap method for estimating standard errors. We demonstrate via simulation study and several real data analyses that our proposed method can perform as well as or better than methods that make explicit use of reference datasets. In particular, it may adjust for detailed cell type differences that may be unavailable even in existing reference datasets. AVAILABILITY and IMPLEMENTATION: Software is available in the R package RefFreeEWAS. Data for three of four examples were obtained from Gene Expression Omnibus (GEO), accession numbers GSE37008, GSE42861 and GSE30601, while reference data were obtained from GEO accession number GSE39981.This is the publisher’s final pdf. The published article is copyrighted by the author(s) and published by Oxford University Press. The published article can be found at: http://bioinformatics.oxfordjournals.org/

Breast Cancer DNA Methylation Profiles Are Associated with Tumor Size and Alcohol and Folate Intake

Although tumor size and lymph node involvement are the current cornerstones of breast cancer prognosis, they have not been extensively explored in relation to tumor methylation attributes in conjunction with other tumor and patient dietary and hormonal characteristics. Using primary breast tumors from 162 (AJCC stage I–IV) women from the Kaiser Division of Research Pathways Study and the Illumina GoldenGate methylation bead-array platform, we measured 1,413 autosomal CpG loci associated with 773 cancer-related genes and validated select CpG loci with Sequenom EpiTYPER. Tumor grade, size, estrogen and progesterone receptor status, and triple negative status were significantly (Q-values <0.05) associated with altered methylation of 209, 74, 183, 69, and 130 loci, respectively. Unsupervised clustering, using a recursively partitioned mixture model (RPMM), of all autosomal CpG loci revealed eight distinct methylation classes. Methylation class membership was significantly associated with patient race (P<0.02) and tumor size (P<0.001) in univariate tests. Using multinomial logistic regression to adjust for potential confounders, patient age and tumor size, as well as known disease risk factors of alcohol intake and total dietary folate, were all significantly (P<0.0001) associated with methylation class membership. Breast cancer prognostic characteristics and risk-related exposures appear to be associated with gene-specific tumor methylation, as well as overall methylation patterns

DNA methylation arrays as surrogate measures of cell mixture distribution

Background: 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. Results: 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. Conclusions: 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.Keywords: Down syndrome, Absolute counts, Variable analysis, Lung cancer, Measurement error, Peripheral blood, Stem cells, Gene expression, T lymphocyte subsets, Ovarian cance

HousemanEugenePHHSReference-FreeCell.pdf

MOTIVATION: Recently there has been increasing interest in the effects of cell mixture on the measurement of DNA methylation, specifically the extent to which small perturbations in cell mixture proportions can register as changes in DNA methylation. A recently published set of statistical methods exploits this association to infer changes in cell mixture proportions, and these methods are presently being applied to adjust for cell mixture effect in the context of epigenome-wide association studies. However, these adjustments require the existence of reference datasets, which may be laborious or expensive to collect. For some tissues such as placenta, saliva, adipose or tumor tissue, the relevant underlying cell types may not be known. RESULTS: We propose a method for conducting epigenome-wide association studies analysis when a reference dataset is unavailable, including a bootstrap method for estimating standard errors. We demonstrate via simulation study and several real data analyses that our proposed method can perform as well as or better than methods that make explicit use of reference datasets. In particular, it may adjust for detailed cell type differences that may be unavailable even in existing reference datasets. AVAILABILITY and IMPLEMENTATION: Software is available in the R package RefFreeEWAS. Data for three of four examples were obtained from Gene Expression Omnibus (GEO), accession numbers GSE37008, GSE42861 and GSE30601, while reference data were obtained from GEO accession number GSE39981

HousemanEugenePHHSReference-FreeCell_SupplementaryData.pdf

MOTIVATION: Recently there has been increasing interest in the effects of cell mixture on the measurement of DNA methylation, specifically the extent to which small perturbations in cell mixture proportions can register as changes in DNA methylation. A recently published set of statistical methods exploits this association to infer changes in cell mixture proportions, and these methods are presently being applied to adjust for cell mixture effect in the context of epigenome-wide association studies. However, these adjustments require the existence of reference datasets, which may be laborious or expensive to collect. For some tissues such as placenta, saliva, adipose or tumor tissue, the relevant underlying cell types may not be known. RESULTS: We propose a method for conducting epigenome-wide association studies analysis when a reference dataset is unavailable, including a bootstrap method for estimating standard errors. We demonstrate via simulation study and several real data analyses that our proposed method can perform as well as or better than methods that make explicit use of reference datasets. In particular, it may adjust for detailed cell type differences that may be unavailable even in existing reference datasets. AVAILABILITY and IMPLEMENTATION: Software is available in the R package RefFreeEWAS. Data for three of four examples were obtained from Gene Expression Omnibus (GEO), accession numbers GSE37008, GSE42861 and GSE30601, while reference data were obtained from GEO accession number GSE39981

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

International audienc

Atteintes à la propriété, juste équilibre et compensations dans le système des droits de l'homme

BACKGROUND. Solid tumors, including head and neck squamous cell carcinomas (HNSCC), arise as a result of genetic and epigenetic alterations in a sustained stress environment. Little work has been done that simultaneously examines the spectrum of both types of changes in human tumors on a genome-wide scale and results so far have been limited and mixed. Since it has been hypothesized that epigenetic alterations may act by providing the second carcinogenic hit in gene silencing, we sought to identify genome-wide DNA copy number alterations and CpG dinucleotide methylation events and examine the global/local relationships between these types of alterations in HNSCC. METHODOLOGY/PRINCIPAL FINDINGS. We have extended a prior analysis of 1,413 cancer-associated loci for epigenetic changes in HNSCC by integrating DNA copy number alterations, measured at 500,000 polymorphic loci, in a case series of 19 primary HNSCC tumors. We have previously demonstrated that local copy number does not bias methylation measurements in this array platform. Importantly, we found that the global pattern of copy number alterations in these tumors was significantly associated with tumor methylation profiles (p<0.002). However at the local level, gene promoter regions did not exhibit a correlation between copy number and methylation (lowest q=0.3), and the spectrum of genes affected by each type of alteration was unique. CONCLUSION/SIGNIFICANCE. This work, using a novel and robust statistical approach demonstrates that, although a "second hit" mechanism is not likely the predominant mode of action for epigenetic dysregulation in cancer, the patterns of methylation events are associated with the patterns of allele loss. Our work further highlights the utility of integrative genomics approaches in exploring the driving somatic alterations in solid tumors.Flight Attendant Medical Research Institute; National Institutes of Health (5R01CA078609-10, 2R01CA100679-06A1, 5RO1ES006717-13

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

International audienc

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