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

BACKGROUND: Cell-type heterogeneity of tumors is a key factor in tumor progression and response to chemotherapy. Tumor cell-type heterogeneity, defined as the proportion of the various cell-types in a tumor, can be inferred from DNA methylation of surgical specimens. However, confounding factors known to associate with methylation values, such as age and sex, complicate accurate inference of cell-type proportions. While reference-free algorithms have been developed to infer cell-type proportions from DNA methylation, a comparative evaluation of the performance of these methods is still lacking. RESULTS: Here we use simulations to evaluate several computational pipelines based on the software packages MeDeCom, EDec, and RefFreeEWAS. We identify that accounting for confounders, feature selection, and the choice of the number of estimated cell types are critical steps for inferring cell-type proportions. We find that removal of methylation probes which are correlated with confounder variables reduces the error of inference by 30-35%, and that selection of cell-type informative probes has similar effect. We show that Cattell's rule based on the scree plot is a powerful tool to determine the number of cell-types. Once the pre-processing steps are achieved, the three deconvolution methods provide comparable results. We observe that all the algorithms' performance improves when inter-sample variation of cell-type proportions is large or when the number of available samples is large. We find that under specific circumstances the methods are sensitive to the initialization method, suggesting that averaging different solutions or optimizing initialization is an avenue for future research. CONCLUSION: Based on the lessons learned, to facilitate pipeline validation and catalyze further pipeline improvement by the community, we develop a benchmark pipeline for inference of cell-type proportions and implement it in the R package medepir.

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. CONTACT: andres.houseman@oregonstate.edu SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

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.

Placental DNA methylation alterations associated with maternal tobacco smoking at the RUNX3 gene are also associated with gestational age.

AIMS: The developmental origins of health and disease hypothesis states that later-life disease may be influenced by the quality of the in utero environment. Environmental toxicants can have detrimental effects on fetal development, potentially through effects on placental development and function. Maternal smoking during pregnancy is associated with low birth weight, preterm birth and other complications, and exposure to cigarette smoke in utero has been linked to gross pathologic and molecular changes to the placenta, including differential DNA methylation in placental tissue. The aim of this study was to investigate the relationship between maternal smoking during pregnancy, methylation changes in the placenta and gestational age. MATERIALS & METHODS: We used Illumina(®)'s (CA, USA) Human Methylation27 BeadChip technology platform to investigate the methylation status of 21,551 autosomal, non-SNP-associated CpG loci in DNA extracted from 206 human placentas and examined loci whose variation in methylation was associated with maternal smoking during pregnancy. RESULTS: We found that methylation patterns of a number of loci within the RUNX3 gene were significantly associated with smoking during pregnancy, and one of these loci was associated with decreased gestational age (p = 0.04). CONCLUSION: Our findings, demonstrating maternal smoking-induced changes in DNA methylation at specific loci, suggest a mechanism by which in utero tobacco smoke exposure could exert its detrimental effects upon the health of the fetus.

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.

An optimized method to detect influenza virus and human rhinovirus from exhaled breath and the airborne environment.

Respiratory viruses are difficult to characterize in the airborne environment due to their low concentration and the presence of a wide range of inhibitors. As a first step in studying airborne viruses, we optimized molecular biology methods to quantify influenza viruses and human rhinovirus. Quantitative PCR was used as an endpoint to evaluate RNA extraction techniques and reverse transcription protocols. We found that a Trizol-chloroform extraction and MultiScribe RT increased virus detection 10-fold compared to methods used in published field studies of airborne respiratory viruses. Virus was recovered without inhibition from samples contaminated with up to 50 microg/sample of particulate matter. The methods developed can be used in studies of airborne respiratory viruses.