Biology subjects

Tseng, G.

Publications and source records attributed to Tseng, G..

25 records · Page 2Linked to original sources

bioRxiv2021

Hotspot ESR1 mutations are multimodal and contextual drivers of breast cancer metastasis

Constitutively active estrogen receptor- (ER/ESR1) mutations have been identified in approximately one third of ER+ metastatic breast cancer. Although these mutations are known mediators of endocrine resistance, their potential role in promoting metastatic disease has not yet been mechanistically addressed. In this study, we show the presence of ESR1 mutations exclusively in distant, but not local recurrences. In concordance with transcriptomic profiling of ESR1 mutant tumors, genome-edited Y537S and D538G cell models have a reprogrammed cell adhesive gene network via alterations in desmosome/gap junction genes and the TIMP3/MMP axis, which functionally confers enhanced cell-cell contacts while decreased cell-ECM adhesion. Context-dependent migratory phenotypes revealed co-targeting of Wnt and ER as vulnerability. Mutant ESR1 exhibits non-canonical regulation of several metastatic pathways including secondary transactivation and de novo FOXA1-driven chromatin remodeling. Collectively, our data supports evidence for ESR1 mutation-driven metastases and provides insight for future preclinical therapeutic strategies. SignificanceContext and allele-dependent transcriptome and cistrome reprogramming in genome-edited ESR1 mutation cell models elicit diverse metastatic phenotypes, including but not limited to alterations in cell adhesion and migration. The gain-of-function mutations can be pharmacologically targeted, and thus may be key components of novel therapeutic treatment strategies for ER-mutant metastatic breast cancer.

cancer biology↗

bioRxiv2020

Transcriptional alterations in opioid use disorder reveal an interplay between neuroinflammation and synaptic remodeling

BackgroundPrevalence rates of opioid use disorder (OUD) have increased dramatically, accompanied by a surge of overdose deaths. While opioid dependence has been extensively studied in preclinical models, an understanding of the biological alterations that occur in the brains of people who chronically use opioids and who are diagnosed with OUD remains limited. To address this limitation, RNA-sequencing (RNA-seq) was conducted on the dorsolateral prefrontal cortex (DLPFC) and nucleus accumbens (NAc), regions heavily implicated in OUD, from postmortem brains in subjects with OUD. MethodsWe performed RNA-seq on the DLPFC and NAc from unaffected comparison subjects (n=20) and subjects diagnosed with OUD (n=20). Our transcriptomic analyses identified differentially expressed (DE) transcripts and investigated the transcriptional coherence between brain regions using rank-rank hypergeometric ordering (RRHO). Weighted gene co-expression analyses (WGCNA) also identified OUD-specific modules and gene networks. Integrative analyses between DE transcripts and GWAS datasets using linkage disequilibrium score (LDSC) assessed the genetic liability psychiatric-related phenotypes. ResultsRRHO analyses revealed extensive overlap in transcripts between DLPFC and NAc in OUD, primarily relating to synaptic remodeling and neuroinflammation. Identified transcripts were enriched for factors that control pro-inflammatory cytokine-mediated, chondroitin sulfate, and extracellular matrix signaling. Cell-type deconvolution implicated a role for microglia as a critical driver for opioid-induced neuroplasticity. Using LDSC, we discovered genetic liabilities for risky behavior, attention deficit hyperactivity disorder, and depression. ConclusionsOverall, our findings reveal new connections between the brains immune system and opioid dependence in the human brain.

neuroscience↗

bioRxiv2019

Unpaired Data Empowers Association Tests

To achieve a holistic view of the underlying mechanisms of human diseases, the biomedical research community is moving toward harvesting retrospective data available in Electronic Healthcare Records (EHRs). The first step for causal understanding is to perform association tests between types of potentially high-dimensional biomedical data, such as genetic, blood biomarkers, and imaging data. To obtain a reasonable power, current methods require a substantial sample size of individuals with both data modalities. This prevents researchers from using much larger EHR samples that include individuals with at least one data type, limits the power of the association test, and may result in higher false discovery rate. We present a new method called the Semi-paired Association Test (SAT) that makes use of both paired and unpaired data. In contrast to classical approaches, incorporating unpaired data allows SAT to produce better control of false discovery and, under some conditions, improve the association test power. We study the properties of SAT theoretically and empirically, through simulations and application to real studies in the context of Chronic Obstructive Pulmonary Disease. Our method identifies an association between the high-dimensional characterization of Computed Tomography (CT) chest images and blood biomarkers as well as the expression of dozens of genes involved in the immune system.

bioinformatics↗

bioRxiv2019

BayICE: A hierarchical Bayesian deconvolution model with stochastic search variable selection

Gene expression deconvolution is a powerful tool for exploring the microenvironment of complex tissues comprised of multiple cell groups using transcriptomic data. Characterizing cell activities for a particular condition has been regarded as a primary mission against diseases. For example, cancer immunology aims to clarify the role of the immune system in the progression and development of cancer through analyzing the immune cell components of tumors. To that end, many deconvolution methods have been proposed for inferring cell subpopulations within tissues. Nevertheless, two problems limit the practicality of current approaches. First, all approaches use external purified data to preselect cell type-specific genes that contribute to deconvolution. However, some types of cells cannot be found in purified profiles and the genes specifically over- or under-expressed in them cannot be identified. This is particularly a problem in cancer studies. Hence, a preselection strategy that is independent from deconvolution is inappropriate. The second problem is that existing approaches do not recover the expression profiles of unknown cells present in bulk tissues, which results in biased estimation of unknown cell proportions. Furthermore, it causes the shift-invariant property of deconvolution to fail, which then affects the estimation performance. To address these two problems, we propose a novel deconvolution approach, BayICE, which employs hierarchical Bayesian modeling with stochastic search variable selection. We develop a comprehensive Markov chain Monte Carlo procedure through Gibbs sampling to estimate cell proportions, gene expression profiles, and signature genes. Simulation and validation studies illustrate that BayICE outperforms existing deconvolution approaches in estimating cell proportions. Subsequently, we demonstrate an application of BayICE in the RNA sequencing of patients with non-small cell lung cancer. The model is implemented in the R package \"BayICE\" and the algorithm is available for download.

genomics↗

bioRxiv2019

A sparse negative binomial classifier with covariateadjustment for RNA-seq data

Supervised machine learning methods have been increasingly used in biomedical research and in clinical practice. In transcriptomic applications, RNA-seq data have become dominating and have gradually replaced traditional microarray due to its reduced background noise and increased digital precision. Most existing machine learning methods are, however, designed for continuous intensities of microarray and are not suitable for RNA-seq count data. In this paper, we develop a negative binomial model via generalized linear model framework with double regularization for gene and covariate sparsity to accommodate three key elements: adequate modeling of count data with overdispersion, gene selection and adjustment for covariate effect. The proposed method is evaluated in simulations and two real applications using cervical tumor miRNA-seq data and schizophrenia post-mortem brain tissue RNA-seq data to demonstrate its superior performance in prediction accuracy and feature selection.

genomics↗

bioRxiv2019

Metastatic breast cancers have reduced immune cell recruitment but harbor increased macrophages relative to their matched primary tumors

The interplay between the immune system and tumor progression is well recognized. However, current human breast cancer immunophenotyping studies are mostly focused on primary tumors with metastatic breast cancer lesions remaining largely understudied. To address this gap, we examined exome-capture RNA sequencing data from 50 primary breast tumors (PBTs) and their patient-matched metastatic tumors (METs) in brain, ovary, bone and gastrointestinal tract. We used gene expression signatures as surrogates for tumor infiltrating lymphocytes (TIL) and compared TIL patterns in PBTs and METs. Enrichment analysis and deconvolution methods both revealed that METs have a significantly lower abundance of total immune cells, including CD8+ T cells, regulatory T cells and dendritic cells. An exception was M2-like macrophages, which were significantly higher in METs across the organ sites examined. Multiplex immunohistochemistry results were consistent with data from the in-silico analysis and showed increased macrophages in METs. We confirmed the finding of a significant reduction in immune cells in brain (BRM) METs by pathologic assessment of TILs in a set of 49 patient-matched pairs of PBT/BRMs. These findings indicate that METs have an overall lower infiltration of immune cells relative to their matched PBTs, possibly due to immune escape. RNAseq analysis suggests that the relative levels of M2-like macrophages are increased in METs, and their potential role in promoting breast cancer metastasis warrants further study.

immunology↗

bioRxiv2018

Comparative Pathway Integrator: a framework of meta-analytic integration of multiple transcriptomic studies for consensual and differential pathway analysis

MotivationPathway analysis provides a knowledge-driven approach to interpret differentially expressed genes associated with disease status. Many tools have been developed to analyze a single study. When multiple studies of different conditions are jointly analyzed, novel integrative tools are needed. In addition, pathway redundancy issue introduced by combining public pathway databases hinders knowledge discovery.\n\nMethods and ResultsWe present a meta-analytic integration tool, Comparative Pathway Integrator (CPI), to address these issues using adaptively weighted Fishers method to discover consensual and differential enrichment patterns, consensus clustering to reduce pathway redundancy, and a novel text mining algorithm to assist interpretation of the pathway clusters. We applied CPI to jointly analyze six psychiatric disorder transcriptomic studies to demonstrate its effectiveness, and found functions confirmed by previous biological studies as well novel enrichment patterns.\n\nAvailabilityCPI is accessible online: http://tsenglab.biostat.pitt.edu/software.htm.\n\nContactxiangruz@andrew.cmu.edu

bioinformatics↗