Dataset Information

Importance of correlation between gene expression levels: application to the type I interferon signature in rheumatoid arthritis.

ABSTRACT:

Background

The analysis of gene expression data shows that many genes display similarity in their expression profiles suggesting some co-regulation. Here, we investigated the co-expression patterns in gene expression data and proposed a correlation-based research method to stratify individuals.

Methodology/principal findings

Using blood from rheumatoid arthritis (RA) patients, we investigated the gene expression profiles from whole blood using Affymetrix microarray technology. Co-expressed genes were analyzed by a biclustering method, followed by gene ontology analysis of the relevant biclusters. Taking the type I interferon (IFN) pathway as an example, a classification algorithm was developed from the 102 RA patients and extended to 10 systemic lupus erythematosus (SLE) patients and 100 healthy volunteers to further characterize individuals. We developed a correlation-based algorithm referred to as Classification Algorithm Based on a Biological Signature (CABS), an alternative to other approaches focused specifically on the expression levels. This algorithm applied to the expression of 35 IFN-related genes showed that the IFN signature presented a heterogeneous expression between RA, SLE and healthy controls which could reflect the level of global IFN signature activation. Moreover, the monitoring of the IFN-related genes during the anti-TNF treatment identified changes in type I IFN gene activity induced in RA patients.

Conclusions

In conclusion, we have proposed an original method to analyze genes sharing an expression pattern and a biological function showing that the activation levels of a biological signature could be characterized by its overall state of correlation.

SUBMITTER: Reynier F

PROVIDER: S-EPMC3197194 | biostudies-literature | 2011

REPOSITORIES: biostudies-literature

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Publications

Importance of correlation between gene expression levels: application to the type I interferon signature in rheumatoid arthritis.

Reynier Frédéric F Petit Fabien F Paye Malick M Turrel-Davin Fanny F Imbert Pierre-Emmanuel PE Hot Arnaud A Mougin Bruno B Miossec Pierre P

PloS one 20111017 10

<h4>Background</h4>The analysis of gene expression data shows that many genes display similarity in their expression profiles suggesting some co-regulation. Here, we investigated the co-expression patterns in gene expression data and proposed a correlation-based research method to stratify individuals.<h4>Methodology/principal findings</h4>Using blood from rheumatoid arthritis (RA) patients, we investigated the gene expression profiles from whole blood using Affymetrix microarray technology. Co- ...[more]

PMID: 22043277

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Project description:The factors influencing long-term responses to a tumor necrosis factor inhibitor (TNFi) in rheumatoid arthritis (RA) patients currently remain unknown. Therefore, we herein conducted a multi-omics analysis of TNFi responses in a Japanese RA cohort. Blood samples were collected from 27 biological disease-modifying antirheumatic drug (DMARD)-naive RA patients at the initiation of and after three months of treatment with TNFi. Treatment responses were evaluated at one year. Differences in gene expression levels in peripheral blood mononuclear cells (PBMCs), plasma protein levels, drug concentrations, and the presence/absence of anti-drug antibodies were investigated, and a cell phenotypic analysis of PBMCs was performed using flow cytometry. After one year of treatment, thirteen patients achieved clinical remission (responders), while the others did not or switched to other biologics (non-responders). Differentially expressed genes related to treatment responses were enriched for the interferon (IFN) pathway. The expression of type I IFN signaling-related genes was higher in non-responders than in responders before and after treatment (P = 0.03, 0.005, respectively). The expression of type II IFN signaling-related genes did not significantly differ before treatment; however, it increased in non-responders and decreased in responders, with a significant difference being observed after three months of treatment (P = 1.2×10-3). The total number of lymphocytes and C-X-C Motif Chemokine Ligand 10 (CXCL10) protein levels were associated with the type I IFN signature (P = 6.7×10-7, 6.4×10-3, respectively). Hepatocyte growth factor (HGF) protein levels before treatment predicted fold increases in type II IFN (P = 0.03). These IFN signature-related indices (the number of lymphocytes, CXCL10, and HGF) significantly differed between responders and non-responders (P = 0.01, 0.01, and 0.04, respectively). A single-cell analysis revealed that the type I IFN signature was more highly enriched in monocytes than in other cell types. A deconvolution analysis of bulk-RNA sequence data identified CD4+ and CD8+ T cells as the main sources of the type II IFN signature in non-responders. Collectively, the present results demonstrated that the dynamics of the type I and II IFN pathways affected long-term responses to TNFi, providing information on its biological background and potential for clinical applications.

Project description:IntroductionB cell depletion therapy is efficacious in rheumatoid arthritis (RA) patients failing on tumor necrosis factor (TNF) blocking agents. However, approximately 40% to 50% of rituximab (RTX) treated RA patients have a poor response. We investigated whether baseline gene expression levels can discriminate between clinical non-responders and responders to RTX.MethodsIn 14 consecutive RA patients starting on RTX (test cohort), gene expression profiling on whole peripheral blood RNA was performed by Illumina® HumanHT beadchip microarrays. Supervised cluster analysis was used to identify genes expressed differentially at baseline between responders and non-responders based on both a difference in 28 joints disease activity score (?DAS28 < 1.2) and European League against Rheumatism (EULAR) response criteria after six months RTX. Genes of interest were measured by quantitative real-time PCR and tested for their predictive value using receiver operating characteristics (ROC) curves in an independent validation cohort (n = 26).ResultsGenome-wide microarray analysis revealed a marked variation in the peripheral blood cells between RA patients before the start of RTX treatment. Here, we demonstrated that only a cluster consisting of interferon (IFN) type I network genes, represented by a set of IFN type I response genes (IRGs), that is, LY6E, HERC5, IFI44L, ISG15, MxA, MxB, EPSTI1 and RSAD2, was associated with ?DAS28 and EULAR response outcome (P = 0.0074 and P = 0.0599, respectively). Based on the eight IRGs an IFN-score was calculated that reached an area under the curve (AUC) of 0.82 to separate non-responders from responders in an independent validation cohort of 26 patients using Receiver Operator Characteristics (ROC) curves analysis according to ?DAS28 < 1.2 criteria. Advanced classifier analysis yielded a three IRG-set that reached an AUC of 87%. Comparable findings applied to EULAR non-response criteria.ConclusionsThis study demonstrates clinical utility for the use of baseline IRG expression levels as a predictive biomarker for non-response to RTX in RA.

Project description:BACKGROUND: Mechanistic biosimulation can be used in drug development to form testable hypotheses, develop predictions of efficacy before clinical trial results are available, and elucidate clinical response to therapy. However, there is a lack of tools to simultaneously (1) calibrate the prevalence of mechanistically distinct, large sets of virtual patients so their simulated responses statistically match phenotypic variability reported in published clinical trial outcomes, and (2) explore alternate hypotheses of those prevalence weightings to reflect underlying uncertainty in population biology. Here, we report the development of an algorithm, MAPEL (Mechanistic Axes Population Ensemble Linkage), which utilizes a mechanistically-based weighting method to match clinical trial statistics. MAPEL is the first algorithm for developing weighted virtual populations based on biosimulation results that enables the rapid development of an ensemble of alternate virtual population hypotheses, each validated by a composite goodness-of-fit criterion. RESULTS: Virtual patient cohort mechanistic biosimulation results were successfully calibrated with an acceptable composite goodness-of-fit to clinical populations across multiple therapeutic interventions. The resulting virtual populations were employed to investigate the mechanistic underpinnings of variations in the response to rituximab. A comparison between virtual populations with a strong or weak American College of Rheumatology (ACR) score in response to rituximab suggested that interferon ? (IFN?) was an important mechanistic contributor to the disease state, a signature that has previously been identified though the underlying mechanisms remain unclear. Sensitivity analysis elucidated key anti-inflammatory properties of IFN? that modulated the pathophysiologic state, consistent with the observed prognostic correlation of baseline type I interferon measurements with clinical response. Specifically, the effects of IFN? on proliferation of fibroblast-like synoviocytes and interleukin-10 synthesis in macrophages each partially counteract reductions in synovial inflammation imparted by rituximab. A multianalyte biomarker panel predictive for virtual population therapeutic responses suggested population dependencies on B cell-dependent mediators as well as additional markers implicating fibroblast-like synoviocytes. CONCLUSIONS: The results illustrate how the MAPEL algorithm can leverage knowledge of cellular and molecular function through biosimulation to propose clear mechanistic hypotheses for differences in clinical populations. Furthermore, MAPEL facilitates the development of multianalyte biomarkers prognostic of patient responses in silico.

Project description:IntroductionAn increased expression of interferon (IFN)-responding genes (IRGs), the so-called IFN signature, has been reported in rheumatoid arthritis (RA). However, some controversy exists concerning its clinical relevance. The main aim of this study is to evaluate whether quantitative and qualitative differences in the activation of the IFN pathway may account for these findings.MethodsThe expression of IFN-induced protein 44 (IFI44), IFN-induced protein 44 like (IFI44L), IFN alpha inducible protein 6, and MX dynamin-like GTPase 1 (MX1) was determined in peripheral blood in 98 RA patients (IFI6) and 28 controls. RA patients were classified into groups according to their clinical stage and treatments received: very early RA (VERA), biological disease-modifying antirheumatic drug (bDMARD) naive, and bDMARD. An additional group of 13 RA patients candidates for tumor necrosis factor alpha (TNFα) blockade was also recruited. The associations among IRGs were evaluated by network and principal component analyses.ResultsThe expression of all IRGs was increased in RA to different levels. The IFN score was increased in all RA groups (VERA, bDMARD-naïve, and bDMARD), but important differences in their degree of activation and in the relationships among IRGs were observed. The IFN score correlated with the accumulated disease activity score 28-joints, and it was found to be a predictor of clinical outcome in VERA. No differences in the IFN score were observed between the bDMARD-naive and bDMARD groups, but opposite associations with the clinical parameters were noted. Interestingly, the correlations among IRGs delineate different pictures between these two groups. The IFN score at baseline predicted poor clinical outcome upon TNFα blockade. Although no absolute changes in the IFN score were found, TNFα-blockade shifted the associations among IRGs.ConclusionA certain heterogeneity within the IFN signature can be recognized in RA, depending on the clinical stage. The structure of the IFN signature may be a potential explanation for the controversy in this field and must be considered to decipher its clinical relevancein RA.

Project description:Lower levels of low-density lipoprotein (LDL) cholesterol may be associated with increased cardiovascular (CV) risk in rheumatoid arthritis (RA). This study was undertaken to determine whether the complex relationship between levels of LDL and high-density lipoprotein (HDL) cholesterol and CV risk is different in RA patients as compared to non-RA controls.Using data from a US health insurance plan (2003-2012), we conducted a cohort study that included patients with RA and non-RA control subjects matched with regard to age, sex, and index date. The nonlinearity of associations between lipid levels and incidence of major adverse CV events (MACE) was tested. We used multivariable Cox proportional hazards regression models to examine for an interaction between lipid levels and RA status in relation to the risk of MACE, after adjustment for CV risk factors.In total, 16,085 RA patients and 48,499 non-RA controls were studied. The mean age was 52.6 years and 78.6% were women. The relationship between LDL cholesterol levels and incidence of MACE was nonlinear and similar between RA patients and non-RA controls (P for interaction?=?0.72). No significant increase in CV risk was observed between the lowest quintile of LDL cholesterol levels (?91.0 mg/dl) and the second, third, or fourth quintiles, whereas the highest quintile (>190.0 mg/dl) conveyed a 40% increase in risk of MACE (hazard ratio [HR] 1.40, 95% confidence interval [95% CI] 1.17-1.68). The relationship between HDL cholesterol levels and incidence of MACE was also nonlinear and similar between RA patients and non-RA controls (P for interaction?=?0.39). Compared to the lowest quintile of HDL cholesterol levels, each successive quintile was associated with a reduced risk of MACE (HR 0.45, 95% CI 0.48-0.72 for lowest quintile [?43.0 mg/dl] versus highest quintile [>71.0 mg/dl]).The complex relationship between LDL cholesterol levels, HDL cholesterol levels, and risk of MACE was nonlinear in RA patients and also not statistically significantly different from that in an age- and sex-matched non-RA cohort.

Dataset Information

Importance of correlation between gene expression levels: application to the type I interferon signature in rheumatoid arthritis.

Background

Methodology/principal findings

Conclusions

Publications

Importance of correlation between gene expression levels: application to the type I interferon signature in rheumatoid arthritis.

Similar Datasets

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