Alexander J. Titus

Cell-type deconvolution from DNA methylation: a review of recent applications

Abstract Recent advances in cell-type deconvolution approaches are adding to our understanding of the biology underlying disease development and progression. DNA methylation (DNAm) can be used as a biomarker of cell types, and through deconvolution approaches, to infer underlying cell type proportions. Cell-type deconvolution algorithms have two main categories: reference-based and reference-free. Reference-based algorithms are supervised methods that determine the underlying composition of cell types within a sample by leveraging differentially methylated regions (DMRs) specific to cell type, identified from DNAm measures of purified cell populations. Reference-free algorithms are unsupervised methods for use when cell-type specific DMRs are not available, allowing scientists to estimate putative cellular proportions or control for potential confounding from cell type. Reference-based deconvolution is typically applied to blood samples and has potentiated our understanding of the relation between immune profiles and disease by allowing estimation of immune cell proportions from archival DNA. Bioinformatic analyses using DNAm to infer immune cell proportions, part of a new field known as Immunomethylomics, provides a new direction for consideration in epigenome wide association studies (EWAS).

methyLiftover: cross-platform DNA methylation data integration

UNLABELLED : The public availability of high throughput molecular data provides new opportunities for researchers to advance discovery, replication and validation efforts. One common challenge in leveraging such data is the diversity of measurement approaches and platforms and a lack of utilities enabling cross-platform comparisons among data sources for analysis. We present a method to map DNA methylation data from bisulfite sequencing approaches to CpG sites measured with the widely used Illumina methylation bead-array platforms. Correlations and median absolute deviations support the validity of using bisulfite sequencing data in combination with Illumina bead-array methylation data. AVAILABILITY AND IMPLEMENTATION https://github.com/Christensen-Lab-Dartmouth/methyLiftover includes source, documentation and data references. CONTACT brock.c.christensen@dartmouth.edu SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.

Genome-scale identification of microRNA-related SNPs associated with risk of head and neck squamous cell carcinoma

We present a genome-scale analysis of the association between microRNA-related genetic variation and head and neck squamous cell carcinoma (HNSCC). Our findings identify miRNA-related genetic variation related with HNSCC risk and provide a framework for evaluating microRNA-related variants in other cancers.

Mortality Risk Along the Frailty Spectrum: Data from the National Health and Nutrition Examination Survey 1999 to 2004

To determine the relationship between frailty and overall and cardiovascular mortality.

Deconvolution of DNA methylation identifies differentially methylated gene regions on 1p36 across breast cancer subtypes

Breast cancer is a complex disease consisting of four distinct molecular subtypes. DNA methylation-based (DNAm) studies in tumors are complicated further by disease heterogeneity. In the present study, 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 to control for cellular heterogeneity. 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 to the chromosomal cytoband 1p36.3. Seventeen of these DMGRs were independently validated in a similar analysis of an external data set. The 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 discuss evidence of the specific importance and potential function of 1p36 in cancer.

Investigating Random Forest Classification on Publicly Available Tuberculosis Data to Uncover Robust Transcriptional Biomarkers.

There has been increasing concern amongst the scientific community of a reproducibility crisis, particularly in the field of bioinformatics. Often, published research results do not correlate with clinical success. One theory explaining this phenomenon is that findings from homogeneous cohort studies are not generalizable to an inherently heterogeneous population. In this work, we integrate data from 4 distinct tuberculosis (TB) cohorts, for a total of 1164 samples, to find common differentially regulated genes which may be used to diagnose active TB from latent TB, treated TB, other diseases, and healthy controls. We selected 25 genes using random forest to get an AUC of 0.89 in our training data, and 0.86 in our test data. A total of 18 out of 25 genes had been previously associated with TB in independent studies, suggesting that integrating data may be an important tool for increasing micro-array research reproducibility.

A New Dimension of Breast Cancer Epigenetics - Applications of Variational Autoencoders with DNA Methylation

In the era of precision medicine and cancer genomics, data are being generated so quickly that it is difficult to fully appreciate the extent of what is discoverable. DNA methylation, a chemical modification to DNA, has been shown to be a significant factor in many cancers and is a candidate data source with ample features for model traing. However, the black-box nature of non-linear models, such as those in deep learning, and a lack of accurately labeled ground truth data have limited the same rapid adoption in this space that other methods have experienced. In this article, we discuss the applications of unsupervised learning through the use of variational autoencoders using DNA methylation data and motivate further work with initial results using breast cancer data provided by The Cancer Genome Atlas. We show that a logistic regression classifier trained on the learned latent methylome accurately classifies disease subtype.

An unsupervised deep learning framework with variational autoencoders for genome-wide DNA methylation analysis and biologic feature extraction applied to breast cancer

Recent advances in deep learning, particularly unsupervised approaches, have shown promise for furthering our biological knowledge through their application to gene expression datasets, though applications to epigenomic data are lacking. Here, we employ an unsupervised deep learning framework with variational autoencoders (VAEs) to learn latent representations of the DNA methylation landscape from three independent breast tumor datasets. Through interrogation of methylation-based learned latent dimension activation values, we demonstrate the feasibility of VAEs to track representative differential methylation patterns among clinical subtypes of tumors. CpGs whose methylation was most correlated VAE latent dimension activation values were significantly enriched for CpG sparse regulatory regions of the genome including enhancer regions. In addition, through comparison with LASSO, we show the utility of the VAE approach for revealing novel information about CpG DNA methylation patterns in breast cancer.

SIG-DB: Leveraging homomorphic encryption to securely interrogate privately held genomic databases

Genomic data are becoming increasingly valuable as we develop methods to utilize the information at scale and gain a greater understanding of how genetic information relates to biological function. Advances in synthetic biology and the decreased cost of sequencing are increasing the amount of privately held genomic data. As the quantity and value of private genomic data grows, so does the incentive to acquire and protect such data, which creates a need to store and process these data securely. We present an algorithm for the Secure Interrogation of Genomic DataBases (SIG-DB). The SIG-DB algorithm enables databases of genomic sequences to be searched with an encrypted query sequence without revealing the query sequence to the Database Owner or any of the database sequences to the Querier. SIG-DB is the first application of its kind to take advantage of locality-sensitive hashing and homomorphic encryption to allow generalized sequence-to-sequence comparisons of genomic data.

Military Service and Decision Quality in the Management of Knee Osteoarthritis

Background Decision quality measures the degree to which care decisions are knowledge-based and value-aligned. Because military service emphasizes hierarchy, command, and mandates some healthcare decisions, military service may attenuate patient autonomy in healthcare decisions and lower decision quality. VA is the nations largest provider of orthopedic care. We compared decision quality in a sample of VA and non-VA patients seeking care for knee osteoarthritis. Methods Our study sample consisted of patients newly referred to our orthopedic clinic for the management of knee osteoarthritis. None of the study patients were exposed to a knee osteoarthritis decision aid. Consenting patients were administered the Hip/Knee Decision Quality Instrument (HK-DQI). In addition, they were surveyed about decision-making preferences and demographics. We compared results to a non-VA cohort from our academic institutions arthroplasty database. Results The HK-DQI Knowledge Score was lower in the VA cohort (45%, SD = 22, n = 25) compared with the non-VA cohort (53%, SD = 21, n = 177) (p = 0.04). The Concordance Score was lower in the VA cohort (36%, SD = 49%) compared with the control cohort (70%, SD 46%) (p = 0.003). Non-VA patients were more likely to make a high-quality decision (p = 0.05). Non-VA patients were more likely to favor a shared decision-making process (p = 0.002). Conclusions Decision quality is lower in Veterans with knee osteoarthritis compared with civilians, placing them at risk for lower treatment satisfaction and possibly unwarranted surgical utilization. Our future work will examine if this difference is from conditioned military service behaviors or confounding demographic factors, and if conventional shared decision-making techniques will correct this deficiency.