A statistical model to assess (allele-specific) associations between gene expression and epigenetic features using sequencing data

Sequencing techniques have been widely used to assess gene expression (i.e., RNA-seq) or the presence of epigenetic features (e.g., DNase-seq to identify open chromatin regions). In contrast to traditional microarray platforms, sequencing data are typically summarized in the form of discrete counts, and they are able to delineate allele-specific signals, which are not available from microarrays. The presence of epigenetic features are often associated with gene expression, both of which have been shown to be affected by DNA polymorphisms. However, joint models with the flexibility to assess interactions between gene expression, epigenetic features and DNA polymorphisms are currently lacking. In this paper, we develop a statistical model to assess the associations between gene expression and epigenetic features using sequencing data, while explicitly modeling the effects of DNA polymorphisms in either an allele-specific or nonallele-specific manner. We show that in doing so we provide the flexibility to detect associations between gene expression and epigenetic features, as well as conditional associations given DNA polymorphisms. We evaluate the performance of our method using simulations and apply our method to study the association between gene expression and the presence of DNase I Hypersensitive sites (DHSs) in HapMap individuals. Our model can be generalized to exploring the relationships between DNA polymorphisms and any two types of sequencing experiments, a useful feature as the variety of sequencing experiments continue to expand

Handling Non-ignorably Missing Features in Electronic Health Records Data Using Importance-Weighted Autoencoders

Electronic Health Records (EHRs) are commonly used to investigate relationships between patient health information and outcomes. Deep learning methods are emerging as powerful tools to learn such relationships, given the characteristic high dimension and large sample size of EHR datasets. The Physionet 2012 Challenge involves an EHR dataset pertaining to 12,000 ICU patients, where researchers investigated the relationships between clinical measurements, and in-hospital mortality. However, the prevalence and complexity of missing data in the Physionet data present significant challenges for the application of deep learning methods, such as Variational Autoencoders (VAEs). Although a rich literature exists regarding the treatment of missing data in traditional statistical models, it is unclear how this extends to deep learning architectures. To address these issues, we propose a novel extension of VAEs called Importance-Weighted Autoencoders (IWAEs) to flexibly handle Missing Not At Random (MNAR) patterns in the Physionet data. Our proposed method models the missingness mechanism using an embedded neural network, eliminating the need to specify the exact form of the missingness mechanism a priori. We show that the use of our method leads to more realistic imputed values relative to the state-of-the-art, as well as significant differences in fitted downstream models for mortality.Comment: 37 pages, 3 figures, 3 tables, under review (Journal of the American Statistical Association

Deeply-Learned Generalized Linear Models with Missing Data

Deep Learning (DL) methods have dramatically increased in popularity in recent years, with significant growth in their application to supervised learning problems in the biomedical sciences. However, the greater prevalence and complexity of missing data in modern biomedical datasets present significant challenges for DL methods. Here, we provide a formal treatment of missing data in the context of deeply learned generalized linear models, a supervised DL architecture for regression and classification problems. We propose a new architecture, \textit{dlglm}, that is one of the first to be able to flexibly account for both ignorable and non-ignorable patterns of missingness in input features and response at training time. We demonstrate through statistical simulation that our method outperforms existing approaches for supervised learning tasks in the presence of missing not at random (MNAR) missingness. We conclude with a case study of a Bank Marketing dataset from the UCI Machine Learning Repository, in which we predict whether clients subscribed to a product based on phone survey data

Prognostic and Predictive Value of Immune-Related Gene Expression Signatures vs Tumor-Infiltrating Lymphocytes in Early-Stage ERBB2/HER2-Positive Breast Cancer: A Correlative Analysis of the CALGB 40601 and PAMELA Trials

Càncer de mama; Expressió gènicaCáncer de mama; Expresión génicaBreast cancer; Gene ExpressionImportance Both tumor-infiltrating lymphocytes (TILs) assessment and immune-related gene expression signatures by RNA profiling predict higher pathologic complete response (pCR) and improved event-free survival (EFS) in patients with early-stage ERBB2/HER2-positive breast cancer. However, whether these 2 measures of immune activation provide similar or additive prognostic value is not known. Objective To examine the prognostic ability of TILs and immune-related gene expression signatures, alone and in combination, to predict pCR and EFS in patients with early-stage ERBB2/HER2-positive breast cancer treated in 2 clinical trials. Design, Setting, and Participants In this prognostic study, a correlative analysis was performed on the Cancer and Leukemia Group B (CALGB) 40601 trial and the PAMELA trial. In the CALGB 40601 trial, 305 patients were randomly assigned to weekly paclitaxel with trastuzumab, lapatinib, or both for 16 weeks. The primary end point was pCR, with a secondary end point of EFS. In the PAMELA trial, 151 patients received neoadjuvant treatment with trastuzumab and lapatinib for 18 weeks. The primary end point was the ability of the HER2-enriched subtype to predict pCR. The studies were conducted from October 2013 to November 2015 (PAMELA) and from December 2008 to February 2012 (CALGB 40601). Data analyses were performed from June 1, 2020, to January 1, 2022. Main Outcomes and Measures Immune-related gene expression profiling by RNA sequencing and TILs were assessed on 230 CALGB 40601 trial pretreatment tumors and 138 PAMELA trial pretreatment tumors. The association of these biomarkers with pCR (CALGB 40601 and PAMELA) and EFS (CALGB 40601) was studied by logistic regression and Cox analyses. Results The median age of the patients was 50 years (IQR, 42-50 years), and 305 (100%) were women. Of 202 immune signatures tested, 166 (82.2%) were significantly correlated with TILs. In both trials combined, TILs were significantly associated with pCR (odds ratio, 1.01; 95% CI, 1.01-1.02; P = .02). In addition to TILs, 36 immune signatures were significantly associated with higher pCR rates. Seven of these signatures outperformed TILs for predicting pCR, 6 of which were B-cell related. In a multivariable Cox model adjusted for clinicopathologic factors, including PAM50 intrinsic tumor subtype, the immunoglobulin G signature, but not TILs, was independently associated with EFS (immunoglobulin G signature–adjusted hazard ratio, 0.63; 95% CI, 0.42-0.93; P = .02; TIL-adjusted hazard ratio, 1.00; 95% CI, 0.98-1.02; P = .99). Conclusions and Relevance Results of this study suggest that multiple B-cell–related signatures were more strongly associated with pCR and EFS than TILs, which largely represent T cells. When both TILs and gene expression are available, the prognostic value of immune-related signatures appears to be superior

ZINBA integrates local covariates with DNA-seq data to identify broad and narrow regions of enrichment, even within amplified genomic regions

ZINBA (Zero-Inflated Negative Binomial Algorithm) identifies genomic regions enriched in a variety of ChIP-seq and related next-generation sequencing experiments (DNA-seq), calling both broad and narrow modes of enrichment across a range of signal-to-noise ratios. ZINBA models and accounts for factors that co-vary with background or experimental signal, such as G/C content, and identifies enrichment in genomes with complex local copy number variations. ZINBA provides a single unified framework for analyzing DNA-seq experiments in challenging genomic contexts

Mu Opioid Splice Variant MOR-1K Contributes to the Development of Opioid-Induced Hyperalgesia

A subset of the population receiving opioids for the treatment of acute and chronic clinical pain develops a paradoxical increase in pain sensitivity known as opioid-induced hyperalgesia. Given that opioid analgesics are one of few treatments available against clinical pain, it is critical to determine the key molecular mechanisms that drive opioid-induced hyperalgesia in order to reduce its prevalence. Recent evidence implicates a splice variant of the mu opioid receptor known as MOR-1K in the emergence of opioid-induced hyperalgesia. Results from human genetic association and cell signaling studies demonstrate that MOR-1K contributes to decreased opioid analgesic responses and produces increased cellular activity via Gs signaling. Here, we conducted the first study to directly test the role of MOR-1K in opioid-induced hyperalgesia

Serotonin-Induced Hypersensitivity via Inhibition of Catechol O-Methyltransferase Activity

Abstract The subcutaneous and systemic injection of serotonin reduces cutaneous and visceral pain thresholds and increases responses to noxious stimuli. Different subtypes of 5-hydroxytryptamine (5-HT) receptors are suggested to be associated with different types of pain responses. Here we show that serotonin also inhibits catechol O-methyltransferase (COMT), an enzyme that contributes to modultion the perception of pain, via non-competitive binding to the site bound by catechol substrates with a binding affinity comparable to the binding affinity of catechol itself (K i  = 44 μM). Using computational modeling, biochemical tests and cellular assays we show that serotonin actively competes with the methyl donor S-adenosyl-L-methionine (SAM) within the catalytic site. Binding of serotonin to the catalytic site inhibits the access of SAM, thus preventing methylation of COMT substrates. The results of in vivo animal studies show that serotonin-induced pain hypersensitivity in mice is reduced by either SAM pretreatment or by the combined administration of selective antagonists for β2- and β3-adrenergic receptors, which have been previously shown to mediate COMT-dependent pain signaling. Our results suggest that inhibition of COMT via serotonin binding contributes to pain hypersensitivity, providing additional strategies for the treatment of clinical pain conditions

Cytokine biomarkers and chronic pain: Association of genes, transcription, and circulating proteins with temporomandibular disorders and widespread palpation tenderness

For reasons unknown, temporomandibular disorder (TMD) can manifest as localized pain or in conjunction with widespread pain. We evaluated relationships between cytokines and TMD without or with widespread palpation tenderness (TMD−WPT or TMD+WPT, respectively), at protein, transcription factory activity, and gene levels. Additionally, we evaluated the relationship between cytokines and intermediate phenotypes characteristic of TMD and WPT. In a case-control study of 344 females, blood samples were analyzed for levels of 22 cytokines and activity of 48 transcription factors. Intermediate phenotypes were measured by quantitative sensory testing and questionnaires asking about pain, health, and psychological status. Single nucleotide polymorphisms (SNPs) coding cytokines and transcription factors were genotyped. TMD−WPT cases had elevated protein levels of pro-inflammatory cytokine MCP-1 and anti-inflammatory cytokine IL-1ra, whereas TMD+WPT cases had elevated levels of pro-inflammatory cytokine IL-8. MCP-1, IL-1ra, and IL-8 were differentially associated with experimental pain, self-rated pain, self-rated health, and psychological phenotypes. TMD−WPT and TMD+WPT cases had inhibited transcription activity of the anti-inflammatory cytokine TGFβ1. Interactions were observed between TGFβ1 and IL-8 SNPs: an additional copy of the TGFβ1 rs2241719 minor T allele was associated with twice the odds of TMD+WPT among individuals homozygous for the IL-8 rs4073 major A allele and half the odds of TMD+WPT among individuals heterozygous for rs4073. These results demonstrate how pro- and anti-inflammatory cytokines contribute to the pathophysiology of TMD and WPT in genetically-susceptible people. Furthermore, they identify MCP-1, IL-1ra, IL-8, and TGFβ1 as potential diagnostic markers and therapeutic targets for pain in patients with TMD

Pain modality- and sex-specific effects of COMT genetic functional variants

The enzyme catechol-O-methyltransferase (COMT) metabolizes catecholamine neurotransmitters involved in a number of physiological functions including pain perception. Both human and mouse COMT genes possess functional polymorphisms contributing to inter-individual variability in pain phenotypes such as sensitivity to noxious stimuli, severity of clinical pain and response to pain treatment. In this study, we found that the effects of Comt functional variation in mice are modality-specific. Spontaneous inflammatory nociception and thermal nociception behaviors were correlated the most with the presence of the B2 SINE transposon insertion residing in the 3’UTR mRNA region. Similarly, in humans, COMT functional haplotypes were associated with thermal pain perception and with capsaicin-induced pain. Furthermore, COMT genetic variations contributed to pain behaviors in mice and pain ratings in humans in a sex-specific manner. The ancestral Comt variant, without a B2 SINE insertion, was more strongly associated with sensitivity to capsaicin in female versus male mice. In humans, the haplotype coding for low COMT activity increased capsaicin-induced pain perception in women, but not men. These findings reemphasize the fundamental contribution of COMT to pain processes, and provide a fine-grained resolution of this contribution at the genetic level that can be used to guide future studies in the area of pain genetics