25 research outputs found
DNA Methylation Arrays as Surrogate Measures of Cell mixture Distribution
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
Note and Comment
Safeguarding the Criminal Defendant - Every now and then a new attack is made somewhere in the United States upon the rule prohibiting comment before the jury upon the fact that the defendant in a criminal case has not testified as a witness in his own behalf. At the present time an effort of this kind is being made in the Michigan legislature, and the introduction of the bill drew quite a little storm of protest from the State press as a dangerous inroad upon our ancient guarantees of personal liberty and security. In fact, however, it directly touches nothing more ancient than a statutory privilege which dates from the year i86i. By the Public Acts of that year the disability of parties to actions to testify as witnesses in this State was removed, but it was expressly provided that defendants in criminal cases could not be compelled to testify, but might do so or not at their own pleasure. (Act No. 125, §2). In 1881 an amendment to this statute was passed providing, as to the defendant in a criminal case, that his neglect to testify shall not create any presumption against him, nor shall the court permit any reference or comment to be made to or upon such neglect. (Pub. Acts, 188I, No. 245). And this is the form it retains in the Judicature Act. (Ch. 17, §64)
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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/
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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
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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
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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
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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
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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
Note and Comment
Safeguarding the Criminal Defendant - Every now and then a new attack is made somewhere in the United States upon the rule prohibiting comment before the jury upon the fact that the defendant in a criminal case has not testified as a witness in his own behalf. At the present time an effort of this kind is being made in the Michigan legislature, and the introduction of the bill drew quite a little storm of protest from the State press as a dangerous inroad upon our ancient guarantees of personal liberty and security. In fact, however, it directly touches nothing more ancient than a statutory privilege which dates from the year i86i. By the Public Acts of that year the disability of parties to actions to testify as witnesses in this State was removed, but it was expressly provided that defendants in criminal cases could not be compelled to testify, but might do so or not at their own pleasure. (Act No. 125, §2). In 1881 an amendment to this statute was passed providing, as to the defendant in a criminal case, that his neglect to testify shall not create any presumption against him, nor shall the court permit any reference or comment to be made to or upon such neglect. (Pub. Acts, 188I, No. 245). And this is the form it retains in the Judicature Act. (Ch. 17, §64)
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