Biologically-targeted discovery-replication scan identifies G×G interaction in relation to risk of Barrett's esophagus and esophageal adenocarcinoma.

Inherited genetics represents an important contributor to risk of esophageal adenocarcinoma (EAC), and its precursor Barrett's esophagus (BE). Genome-wide association studies have identified ∼30 susceptibility variants for BE/EAC, yet genetic interactions remain unexamined. To address challenges in large-scale G×G scans, we combined knowledge-guided filtering and machine learning approaches, focusing on genes with (A) known/plausible links to BE/EAC pathogenesis (n=493) or (B) prior evidence of biological interactions (n=4,196).

Structured testing of genetic association with mixed clinical outcomes.

Genetic factors play a fundamental role in disease development. Studying the genetic association with clinical outcomes is critical for understanding disease biology and devising novel treatment targets. However, the frequencies of genetic variations are often low, making it difficult to examine the variants one-by-one.

TCRpred: incorporating T-cell receptor repertoire for clinical outcome prediction.

T-cell receptor (TCR) plays critical roles in recognizing antigen peptides and mediating adaptive immune response against disease. High-throughput technologies have enabled the sequencing of TCR repertoire at the single nucleotide level, allowing researchers to characterize TCR sequences with high resolutions. The TCR sequences provide important information about patients' adaptive immune system, and have the potential to improve clinical outcome prediction.

Dietary Factors Differ Between Young-Onset and Older-Onset Colorectal Cancer Patients.

We aimed to evaluate differences in dietary factors between young-onset (diagnosed at ages <50) and older-onset colorectal cancer (CRC). CRC patients diagnosed from 1998 to 2018 reported to the Puget Sound Surveillance, Epidemiology, and End Results registry were recruited using mail and telephone. Consented patients completed questionnaires assessing demographics, medical history, and CRC risk factors, including dietary factors.

Alpha-Momorcharin Inhibits Proinflammatory Cytokine Expression by M1 Macrophages but Not Anti-Inflammatory Cytokine Expression by M2 Macrophages.

Background: Alpha-momorcharin (α-MMC) is a natural medicine derived from bitter melon and has been found to exert immunomodulatory effects. Our previous study indicated that α-MMC can regulate cytokine release from monocytes, but it remains unknown about its regulatory effect on different types of cytokines, such as inflammatory cytokines or anti-inflammatory cytokines.

Methods: LPS-induced M1-type macrophages model and IL-4-induced M2-type macrophages model were established, and the expression of proinflammatory cytokines and anti-inflammatory cytokines were assessed by ELISA after α-MMC was administered.

Combining FIB-4 and Liver Stiffness Into the FIB-5, a Single Model that Accurately Predicts Complications of Portal Hypertension.

Introduction: We aimed to combine the fibrosis (FIB)-4 score and fibroscan-derived liver stiffness (LS) into a single score (FIB-5) that predicts incident complications of portal hypertension (PH) in persons with compensated liver disease.

Methods: In this retrospective cohort study, we identified 5849 US veterans who underwent LS measurement from May 01, 2014 to June 30, 2019, and laboratory tests enabling FIB-4 calculation within 6 months of LS measurement. Patients were followed up from the LS measurement date until February 05, 2020, for incident complications of PH.

TCR-L: an analysis tool for evaluating the association between the T-cell receptor repertoire and clinical phenotypes.

Background: T cell receptors (TCRs) play critical roles in adaptive immune responses, and recent advances in genome technology have made it possible to examine the T cell receptor (TCR) repertoire at the individual sequence level. The analysis of the TCR repertoire with respect to clinical phenotypes can yield novel insights into the etiology and progression of immune-mediated diseases. However, methods for association analysis of the TCR repertoire have not been well developed.

A Phase 1/2 Trial Combining Avelumab and Trabectedin for Advanced Liposarcoma and Leiomyosarcoma.

Purpose: Leiomyosarcoma and liposarcoma frequently express PD-L1 but are generally resistant to PD-1/PD-L1 inhibition (immune checkpoint inhibitor). Trabectedin is FDA approved for leiomyosarcoma and liposarcoma. This study aimed to evaluate the safety and efficacy of trabectedin with anti-PD-L1 antibody avelumab in patients with advanced leiomyosarcoma and liposarcoma.

Statistical Inference for High-Dimensional Pathway Analysis with Multiple Responses.

Pathway analysis, i.e., grouping analysis, has important applications in genomic studies.

Neoadjuvant Therapy Induces a Potent Immune Response to Sarcoma, Dominated by Myeloid and B Cells.

Purpose: To characterize changes in the soft-tissue sarcoma (STS) tumor immune microenvironment induced by standard neoadjuvant therapy with the goal of informing neoadjuvant immunotherapy trial design.

Experimental Design: Paired pre- and postneoadjuvant therapy specimens were retrospectively identified for 32 patients with STSs and analyzed by three modalities: multiplexed IHC, NanoString, and RNA sequencing with ImmunoPrism analysis.

Results: All 32 patients, representing a variety of STS histologic subtypes, received neoadjuvant radiotherapy and 21 (66%) received chemotherapy prior to radiotherapy.

Noncoding RNAs and Deep Learning Neural Network Discriminate Multi-Cancer Types.

Detecting cancers at early stages can dramatically reduce mortality rates. Therefore, practical cancer screening at the population level is needed. To develop a comprehensive detection system to classify multiple cancer types, we integrated an artificial intelligence deep learning neural network and noncoding RNA biomarkers selected from massive data.

Estimation for the bivariate quantile varying coefficient model with application to diffusion tensor imaging data analysis.

Despite interest in the joint modeling of multiple functional responses such as diffusion properties in neuroimaging, robust statistical methods appropriate for this task are lacking. To address this need, we propose a varying coefficient quantile regression model able to handle bivariate functional responses. Our work supports innovative insights into biomedical data by modeling the joint distribution of functional variables over their domains and across clinical covariates.

Home Spirometry Telemonitoring for Early Detection of Bronchiolitis Obliterans Syndrome in Patients with Chronic Graft-versus-Host Disease.

Early detection of bronchiolitis obliterans syndrome (BOS) after allogeneic hematopoietic cell transplantation (HCT) depends on recognition of subclinical spirometric changes, which is possible only with frequent interval spirometry. We evaluated the feasibility of home monitoring of weekly spirometry via a wireless handheld device and a web monitoring portal in a cohort of high-risk patients for the detection of lung function changes preceding BOS diagnosis. In this observational study, 46 patients with chronic graft-versus-host disease or a decline in forced expiratory volume in 1 second (FEV) of unclear etiology after allogeneic HCT were enrolled to perform weekly home spirometry with a wireless portable spirometer for a period of 1 year.

A method for subtype analysis with somatic mutations.

Motivation: Cancer is a highly heterogeneous disease, and virtually all types of cancer have subtypes. Understanding the association between cancer subtypes and genetic variations is fundamental to the development of targeted therapies for patients. Somatic mutation plays important roles in tumor development and has emerged as a new type of genetic variations for studying the association with cancer subtypes.

Germline variation in the insulin-like growth factor pathway and risk of Barrett's esophagus and esophageal adenocarcinoma.

Genome-wide association studies (GWAS) of esophageal adenocarcinoma (EAC) and its precursor, Barrett's esophagus (BE), have uncovered significant genetic components of risk, but most heritability remains unexplained. Targeted assessment of genetic variation in biologically relevant pathways using novel analytical approaches may identify missed susceptibility signals. Central obesity, a key BE/EAC risk factor, is linked to systemic inflammation, altered hormonal signaling and insulin-like growth factor (IGF) axis dysfunction.

A general framework for functionally informed set-based analysis: Application to a large-scale colorectal cancer study.

Genome-wide association studies (GWAS) have successfully identified tens of thousands of genetic variants associated with various phenotypes, but together they explain only a fraction of heritability, suggesting many variants have yet to be discovered. Recently it has been recognized that incorporating functional information of genetic variants can improve power for identifying novel loci. For example, S-PrediXcan and TWAS tested the association of predicted gene expression with phenotypes based on GWAS summary statistics by leveraging the information on genetic regulation of gene expression and found many novel loci.

Genome-wide Modeling of Polygenic Risk Score in Colorectal Cancer Risk.

Accurate colorectal cancer (CRC) risk prediction models are critical for identifying individuals at low and high risk of developing CRC, as they can then be offered targeted screening and interventions to address their risks of developing disease (if they are in a high-risk group) and avoid unnecessary screening and interventions (if they are in a low-risk group). As it is likely that thousands of genetic variants contribute to CRC risk, it is clinically important to investigate whether these genetic variants can be used jointly for CRC risk prediction. In this paper, we derived and compared different approaches to generating predictive polygenic risk scores (PRS) from genome-wide association studies (GWASs) including 55,105 CRC-affected case subjects and 65,079 control subjects of European ancestry.

In-silico analysis of ligand-receptor binding patterns of α-MMC, TCS and MAP30 protein to LRP1 receptor.

Alpha-momorcharin (α-MMC), trichosanthin (TCS), and momordica anti-HIV protein of 30 kD (MAP30) are potential anti-tumor drug candidates but have cytotoxicity to normal cells. The binding of these proteins to LRP1 receptor and the subsequent endocytosis are essential to their cytotoxicity, but this binding process remains largely unknown. This study, in-silico analysis of the binding patterns, was conducted via the protein-protein docking software, ZDOCK 3.

Early Post-Transplantation Spirometry Is Associated with the Development of Bronchiolitis Obliterans Syndrome after Allogeneic Hematopoietic Cell Transplantation.

Bronchiolitis obliterans syndrome (BOS) after allogeneic hematopoietic cell transplantation (allo-HCT) is often diagnosed at a late stage when lung dysfunction is severe and irreversible. Identifying patients early after transplantation may offer improved strategies for early detection that could avert the morbidity and mortality of BOS. This study aimed to determine whether a decline in lung function before and early after (days +80 to +100) allo-HCT are associated with a risk of BOS beyond 6 months post-transplantation.

Azithromycin Use and Increased Cancer Risk among Patients with Bronchiolitis Obliterans after Hematopoietic Cell Transplantation.

Azithromycin exposure during the early phase of allogeneic hematopoietic cell transplantation (HCT) has been associated with an increased incidence of hematologic relapse. We assessed the impact of azithromycin exposure on the occurrence of relapse or new subsequent neoplasm (SN) in patients with bronchiolitis obliterans syndrome (BOS) after HCT who are commonly treated with azithromycin alone or in combination with other agents. In a retrospective study of patients with BOS from 2 large allograft centers, the effect of azithromycin exposure on the risk of relapse or SN was estimated from a Cox model with a time-dependent variable for treatment initiation.

Statistical inference of genetic pathway analysis in high dimensions.

Genetic pathway analysis has become an important tool for investigating the association between a group of genetic variants and traits. With dense genotyping and extensive imputation, the number of genetic variants in biological pathways has increased considerably and sometimes exceeds the sample size [Formula: see text]. Conducting genetic pathway analysis and statistical inference in such settings is challenging.