A functional genomic model for predicting prognosis in idiopathic pulmonary fibrosis

Background: The course of disease for patients with idiopathic pulmonary fibrosis (IPF) is highly heterogeneous. Prognostic models rely on demographic and clinical characteristics and are not reproducible. Integrating data from genomic analyses may identify novel prognostic models and provide mechanistic insights into IPF. Methods: Total RNA of peripheral blood mononuclear cells was subjected to microarray profiling in a training (45 IPF individuals) and two independent validation cohorts (21 IPF/10 controls, and 75 IPF individuals, respectively). To identify a gene set predictive of IPF prognosis, we incorporated genomic, clinical, and outcome data from the training cohort. Predictor genes were selected if all the following criteria were met: 1) Present in a gene co-expression module from Weighted Gene Co-expression Network Analysis (WGCNA) that correlated with pulmonary function (p 1.5 and false discovery rate (FDR) < 2 %; and 3) Predictive of mortality (p < 0.05) in univariate Cox regression analysis. "Survival risk group prediction" was adopted to construct a functional genomic model that used the IPF prognostic predictor gene set to derive a prognostic index (PI) for each patient into either high or low risk for survival outcomes. Prediction accuracy was assessed with a repeated 10-fold cross-validation algorithm and independently assessed in two validation cohorts through multivariate Cox regression survival analysis. Results: A set of 118 IPF prognostic predictor genes was used to derive the functional genomic model and PI. In the training cohort, high-risk IPF patients predicted by PI had significantly shorter survival compared to those labeled as low-risk patients (log rank p < 0.001). The prediction accuracy was further validated in two independent cohorts (log rank p < 0.001 and 0.002). Functional pathway analysis revealed that the canonical pathways enriched with the IPF prognostic predictor gene set were involved in T-cell biology, including iCOS, T-cell receptor, and CD28 signaling. Conclusions: Using supervised and unsupervised analyses, we identified a set of IPF prognostic predictor genes and derived a functional genomic model that predicted high and low-risk IPF patients with high accuracy. This genomic model may complement current prognostic tools to deliver more personalized care for IPF patients

Biomarkers in the evaluation and management of idiopathic pulmonary fibrosis

Idiopathic Pulmonary Fibrosis (IPF) is a chronic, progressive, debilitating disease of unknown etiology and a median survival from diagnosis of 3-5 years. Despite extensive research efforts, its etiology in humans still remains largely unknown, and no curative drug therapies are available. With a gradually increasing worldwide incidence, IPF still presents a major challenge in clinical research due to its appreciable heterogeneity among individual patients in disease course and the lack of easily reproducible surrogate markers for patient relevant outcomes. Currently clinicians and researchers apply a panel of functional, radiological and histopathological indices to stratify patients into distinct phenotypic patterns of disease progression. However, none of these indicators can reliably predict not only treatment responsiveness but more importantly disease behavior, thus allowing clinicians to promptly apply aggressive therapeutic approaches to prevent or ameliorate acute exacerbation. Furthermore, on the contrary to molecular biomarkers, physiologic prognosticators provide no insights into disease mechanism and thus are unlikely to identify distinct molecular phenotypes of the disease. In the dawn of the “fibromics” era the need for disease stratification based on molecular phenotypes and implementation of personalized medicine therapeutic approaches is still unmet. Molecular biomarkers lie in the core of personalized medicine and therefore represent the main focus of this review article. Limitations that hamper their widespread clinical applicability along with future perspectives on how to address these major caveats and launch IPF biomarkers to the same trajectory as to tumor biomarkers in oncology are also discussed. © 2016 Bentham Science Publishers

Regularized latent class model for joint analysis of high dimensional longitudinal biomarkers and a time-to-event outcome

Although many modeling approaches have been developed to jointly analyze longitudinal biomarkers and a time-to-event outcome, most of these methods can only handle one or a few biomarkers. In this article, we propose a novel joint latent class model to deal with high dimensional longitudinal biomarkers. Our model has three components: a class membership model, a survival submodel, and a longitudinal submodel. In our model, we assume that covariates can potentially affect biomarkers and class membership. We adopt a penalized likelihood approach to infer which covariates have random effects and/or fixed effects on biomarkers, and which covariates are informative for the latent classes. Through extensive simulation studies, we show that our proposed method has improved performance in prediction and assigning subjects to the correct classes over other joint modeling methods and that bootstrap can be used to do inference for our model. We then apply our method to a dataset of patients with idiopathic pulmonary fibrosis, for whom gene expression profiles were measured longitudinally.We are able to identify four interesting latent classes with one class being at much higher risk of death compared to the other classes. We also find that each of the latent classes has unique trajectories in some genes, yielding novel biological insights. This article is protected by copyright. All rights reserved

50-gene risk profiles in peripheral blood predict COVID-19 outcomes: A retrospective, multicenter cohort study

BACKGROUND: COVID-19 has been associated with Interstitial Lung Disease features. The immune transcriptomic overlap between Idiopathic Pulmonary Fibrosis (IPF) and COVID-19 has not been investigated. METHODS: we analyzed blood transcript levels of 50 genes known to predict IPF mortality in three COVID-19 and two IPF cohorts. The Scoring Algorithm of Molecular Subphenotypes (SAMS) was applied to distinguish high versus low-risk profiles in all cohorts. SAMS cutoffs derived from the COVID-19 Discovery cohort were used to predict intensive care unit (ICU) status, need for mechanical ventilation, and in-hospital mortality in the COVID-19 Validation cohort. A COVID-19 Single-cell RNA-sequencing cohort was used to identify the cellular sources of the 50-gene risk profiles. The same COVID-19 SAMS cutoffs were used to predict mortality in the IPF cohorts. FINDINGS: 50-gene risk profiles discriminated severe from mild COVID-19 in the Discovery cohort (P = 0·015) and predicted ICU admission, need for mechanical ventilation, and in-hospital mortality (AUC: 0·77, 0·75, and 0·74, respectively, P < 0·001) in the COVID-19 Validation cohort. In COVID-19, 50-gene expressing cells with a high-risk profile included monocytes, dendritic cells, and neutrophils, while low-risk profile-expressing cells included CD4+, CD8+ T lymphocytes, IgG producing plasmablasts, B cells, NK, and gamma/delta T cells. Same COVID-19 SAMS cutoffs were also predictive of mortality in the University of Chicago (HR:5·26, 95%CI:1·81-15·27, P = 0·0013) and Imperial College of London (HR:4·31, 95%CI:1·81-10·23, P = 0·0016) IPF cohorts. INTERPRETATION: 50-gene risk profiles in peripheral blood predict COVID-19 and IPF outcomes. The cellular sources of these gene expression changes suggest common innate and adaptive immune responses in both diseases

Stanje pridelave hmelja (Humulus lupulus L.) v Sloveniji

Background\ud The increased multi-omics information on carefully phenotyped patients in studies of complex diseases requires novel methods for data integration. Unlike continuous intensity measurements from most omics data sets, phenome data contain clinical variables that are binary, ordinal and categorical.\ud \ud Results\ud In this paper we introduce an integrative phenotyping framework (iPF) for disease subtype discovery. A feature topology plot was developed for effective dimension reduction and visualization of multi-omics data. The approach is free of model assumption and robust to data noises or missingness. We developed a workflow to integrate homogeneous patient clustering from different omics data in an agglomerative manner and then visualized heterogeneous clustering of pairwise omics sources. We applied the framework to two batches of lung samples obtained from patients diagnosed with chronic obstructive lung disease (COPD) or interstitial lung disease (ILD) with well-characterized clinical (phenomic) data, mRNA and microRNA expression profiles. Application of iPF to the first training batch identified clusters of patients consisting of homogenous disease phenotypes as well as clusters with intermediate disease characteristics. Analysis of the second batch revealed a similar data structure, confirming the presence of intermediate clusters. Genes in the intermediate clusters were enriched with inflammatory and immune functional annotations, suggesting that they represent mechanistically distinct disease subphenotypes that may response to immunomodulatory therapies. The iPF software package and all source codes are publicly available.\ud \ud Conclusions\ud Identification of subclusters with distinct clinical and biomolecular characteristics suggests that integration of phenomic and other omics information could lead to identification of novel mechanism-based disease sub-phenotypes

Local and Systemic CD4+ T Cell Exhaustion Reverses with Clinical Resolution of Pulmonary Sarcoidosis

Investigation of the Th1 immune response in sarcoidosis CD4+ T cells has revealed reduced proliferative capacity and cytokine expression upon TCR stimulation. In other disease models, such cellular dysfunction has been associated with a step-wise, progressive loss of T cell function that results from chronic antigenic stimulation. T cell exhaustion is defined by decreased cytokine production upon TCR activation, decreased proliferation, increased expression of inhibitory cell surface receptors, and increased susceptibility to apoptosis. We characterized sarcoidosis CD4+ T cell immune function in systemic and local environments among subjects undergoing disease progression compared to those experiencing disease resolution. Spontaneous and TCR-stimulated Th1 cytokine expression and proliferation assays were performed in 53 sarcoidosis subjects and 30 healthy controls. PD-1 expression and apoptosis were assessed by flow cytometry. Compared to healthy controls, sarcoidosis CD4+ T cells demonstrated reductions in Th1 cytokine expression, proliferative capacity (p<0.05), enhanced apoptosis (p<0.01), and increased PD-1 expression (p<0.001). BAL-derived CD4+ T cells also demonstrated multiple facets of T cell exhaustion (p<0.05). Reversal of CD4+ T cell exhaustion was observed in subjects undergoing spontaneous resolution (p<0.05). Sarcoidosis CD4+ T cells exhibit loss of cellular function during progressive disease that follows the archetype of T cell exhaustion