Identification of gene expression signatures in autoimmune disease without the influence of familial resemblance

Integrating Omics into Functional Biomarkers of Type 1 Diabetes

Biomarkers are critical to the staging and diagnosis of type 1 diabetes (T1D). Functional biomarkers offer insights into T1D immunopathogenesis and are often revealed using "omics" approaches that integrate multiple measures to identify involved pathways and functions. Application of the omics biomarker discovery may enable personalized medicine approaches to circumvent the more recently appreciated heterogeneity of T1D progression and treatment. Use of omics to define functional biomarkers is still in its early years, yet findings to date emphasize the role of cytokine signaling and adaptive immunity in biomarkers of progression and response to therapy. Here, we share examples of the use of omics to define functional biomarkers focusing on two signatures, T-cell exhaustion and T-cell help, which have been associated with outcomes in both the natural history and treatment contexts.

Moving towards a molecular taxonomy of autoimmune rheumatic diseases

Autoimmune rheumatic diseases pose many problems that have, in general, already been solved in the field of cancer. The heterogeneity of each disease, the clinical similarities and differences between different autoimmune rheumatic diseases and the large number of patients that remain without a diagnosis underline the need to reclassify these diseases via new approaches. Knowledge about the molecular basis of systemic autoimmune diseases, along with the availability of bioinformatics tools capable of handling and integrating large volumes of various types of molecular data at once, offer the possibility of reclassifying these diseases. A new taxonomy could lead to the discovery of new biomarkers for patient stratification and prognosis. Most importantly, this taxonomy might enable important changes in clinical trial design to reach the expected outcomes or the design of molecularly targeted therapies. In this Review, we discuss the basis for a new molecular taxonomy for autoimmune rheumatic diseases. We highlight the evidence surrounding the idea that these diseases share molecular features related to their pathogenesis and development and discuss previous attempts to classify these diseases. We evaluate the tools available to analyse and combine different types of molecular data. Finally, we introduce PRECISESADS, a project aimed at reclassifying the systemic autoimmune diseases.

New Attempts to Define and Clarify Lupus

Purpose of Review

The purpose is to discuss the advances that genetics and genomics have provided to better understand the molecular mechanisms behind SLE and how to solve its heterogeneity. I propose new ideas that can help us stratify lupus in order to find the best therapies for each patient, and the idea of substituting clinical diagnosis with molecular diagnosis according to their molecular patterns, an idea that may not only include lupus but also other diseases.

Recent Findings

The study of rare mutations may provide insight into groups of lupus patients where type I interferon signature is important and help understand those with an atypical clinical presentation. Recent papers used longitudinal blood transcriptome data correlating with disease activity scores to stratify lupus into molecular clusters. The implication of neutrophils in the risk to develop nephritis was established, but also that neutrophils and lymphocytes may correlate with activity differentiating the mechanisms of flares and separating patients into clinically separate groups.

Summary

The role of type I interferon signature is important; however, the stratification of SLE patients according to the genes and cellular compartments being modulated during disease activity may be even more important to define those patients who may benefit the most with new anti-type I IFN receptor therapies.

An Extensive Study of the Functional Polymorphisms of Kinin-Kallikrein System in Rheumatoid Arthritis Susceptibility

Objectives

This study aims to examine the following functional polymorphisms in rheumatoid arthritis (RA) susceptibility: (i) the 587C>T of kininogen gene, (ii) the 287 bp Alu repeat insertion of angiotensin converting enzyme gene, (iii) the 9 bp insertion of bradykinin receptor 2 gene, (iv) the -58T>C of bradykinin receptor 2 gene, and (v) the -699G>C of bradykinin receptor 1 gene.

Patients and methods

The study included 136 RA patients (27 males; 109 females; mean age 60.8 years; range 39 to 75 years) and 149 ethnic matching controls (30 males, 119 females; mean age 56.2 years; range 35 to 78 years). Polymerase chain reaction coupled with restriction assay was performed for 587C>T, -58T>C, and -699G>C.

Results

Rheumatoid arthritis patients and controls carried the wild type allele of 587C>T; therefore, produced the high molecular weight kininogen. No significant difference was observed in genotype or allele distribution of the studied functional polymorphisms between RA patients and controls.

Conclusion

Kinin-kallikrein system related genes might not be major RA susceptibility loci.

SLE redefined on the basis of molecular pathways

The implementation of precision medicine requires the recruiting of patients in statistically enough numbers, the possibility of obtaining enough materials, and the integration of data from various platforms, which are all real limitations. These types of studies have been performed extensively in cancer but barely on systemic lupus erythematosus (SLE) or other rheumatic diseases. To consider the practical use of the information obtained from such studies, we have to take into account the best biological fluid to use, the ease to perform the analysis in clinical practice, and its relevance to clinical practice. Here we review the most relevant studies that have performed analyses that attempt to classify or stratify SLE. We focus on two types of studies: those that stratify individuals diagnosed with SLE and those that compare SLE with other autoimmune diseases, defining differences and similarities that may be clinically relevant in the future.

Using biomarkers to predict progression from clinically isolated syndrome to multiple sclerosis

Background

Detection of brain lesions disseminated in space and time by magnetic resonance imaging remains a cornerstone for the diagnosis of clinically definite multiple sclerosis. We have sought to determine if gene expression biomarkers could contribute to the clinical diagnosis of multiple sclerosis.

Methods

We employed expression levels of 30 genes in blood from 199 subjects with multiple sclerosis, 203 subjects with other neurologic disorders, and 114 healthy control subjects to train ratioscore and support vector machine algorithms. Blood samples were obtained from 46 subjects coincident with clinically isolated syndrome who progressed to clinically definite multiple sclerosis determined by conventional methods. Gene expression levels from these subjects were inputted into ratioscore and support vector machine algorithms to determine if these methods also predicted that these subjects would develop multiple sclerosis. Standard calculations of sensitivity and specificity were employed to determine accuracy of these predictions.

Results

Our results demonstrate that ratioscore and support vector machine methods employing input gene transcript levels in blood can accurately identify subjects with clinically isolated syndrome that will progress to multiple sclerosis.

Conclusions

We conclude these approaches may be useful to predict progression from clinically isolated syndrome to multiple sclerosis.

Gene-expression signatures: biomarkers toward diagnosing multiple sclerosis

Identification of biomarkers contributing to disease diagnosis, classification or prognosis could be of considerable utility. For example, primary methods to diagnose multiple sclerosis (MS) include magnetic resonance imaging and detection of immunological abnormalities in cerebrospinal fluid. We determined whether gene-expression differences in blood discriminated MS subjects from comparator groups, and identified panels of ratios that performed with varying degrees of accuracy depending upon complexity of comparator groups. High levels of overall accuracy were achieved by comparing MS with homogeneous comparator groups. Overall accuracy was compromised when MS was compared with a heterogeneous comparator group. Results, validated in independent cohorts, indicate that gene-expression differences in blood accurately exclude or include a diagnosis of MS and suggest that these approaches may provide clinically useful prediction of MS.

Gene expression profiles from discordant monozygotic twins suggest that molecular pathways are shared among multiple systemic autoimmune diseases

INTRODUCTION

The objective of this study is to determine if multiple systemic autoimmune diseases (SAID) share gene expression pathways that could provide insights into pathogenic mechanisms common to these disorders.

METHODS

RNA microarray analyses (Agilent Human 1A(V2) 20K oligo arrays) were used to quantify gene expression in peripheral blood cells from 20 monozygotic (MZ) twin pairs discordant for SAID. Six affected probands with systemic lupus erythematosus (SLE), six with rheumatoid arthritis (RA), eight with idiopathic inflammatory myopathies (IIM), and their same-gendered unaffected twins, were enrolled. Comparisons were made between discordant twin pairs and these were also each compared to 40 unrelated control subjects (matched 2:1 to each twin by age, gender and ethnicity) using statistical and molecular pathway analyses. Relative quantitative PCR was used to verify independently measures of differential gene expression assessed by microarray analysis.

RESULTS

Probands and unrelated, matched controls differed significantly in gene expression for 104 probes corresponding to 92 identifiable genes (multiple-comparison adjusted P values < 0.1). Differentially expressed genes involved several overlapping pathways including immune responses (16%), signaling pathways (24%), transcription/translation regulators (26%), and metabolic functions (15%). Interferon (IFN)-response genes (IFI27, OASF, PLSCR1, EIF2AK2, TNFAIP6, and TNFSF10) were up-regulated in probands compared to unrelated controls. Many of the abnormally expressed genes played regulatory roles in multiple cellular pathways. We did not detect any probes expressed differentially in comparisons among the three SAID phenotypes. Similarly, we found no significant differences in gene expression when comparing probands to unaffected twins or unaffected twins to unrelated controls. Gene expression levels for unaffected twins appeared intermediate between that of probands and unrelated controls for 6535 probes (32% of the total probes) as would be expected by chance. By contrast, in unaffected twins intermediate ordering was observed for 84 of the 104 probes (81%) whose expression differed significantly between probands and unrelated controls.

CONCLUSIONS

Alterations in expression of a limited number of genes may influence the dysregulation of numerous, integrated immune response, cell signaling and regulatory pathways that are common to a number of SAID. Gene expression profiles in peripheral blood suggest that for genes in these critical pathways, unaffected twins may be in a transitional or intermediate state of immune dysregulation between twins with SAID and unrelated controls, perhaps predisposing them to the development of SAID given the necessary and sufficient environmental exposures.

Gene expression profiles in peripheral blood for the diagnosis of autoimmune diseases

Gene expression profiling in clinical genomics has yet to deliver robust and reliable approaches for developing diagnostics and contributing to personalized medicine. Owing to technological developments and the recent accumulation of expression profiles, it is a timely and relevant question whether peripheral blood gene expression profiling can be used routinely in clinical decision making. Here, we review the available gene expression profiling data of peripheral blood in autoimmune and chronic inflammatory diseases and suggest that peripheral blood mononuclear cells are suitable for descriptive and comparative gene expression analyses. A gene-disease interaction network in chronic inflammatory diseases, a general protocol for future studies and a decision tree for researchers are presented to facilitate standardization and adoption of this approach.

Peripheral blood gene expression profiles in metabolic syndrome, coronary artery disease and type 2 diabetes

To determine if individuals with metabolic disorders possess unique gene expression profiles, we compared transcript levels in peripheral blood from patients with coronary artery disease, type 2 diabetes and their precursor state, metabolic syndrome to those of control subjects and subjects with rheumatoid arthritis. The gene expression profile of each metabolic state was distinguishable from controls and correlated with other metabolic states more than with rheumatoid arthritis. Of note, subjects in the metabolic cohorts over-expressed gene sets that participate in the innate immune response. Genes involved in activation of the pro-inflammatory transcription factor, NF-κB, were over-expressed in coronary artery disease while genes differentially expressed in type 2 diabetes play key roles in T cell activation and signaling. RT-PCR validation confirmed microarray results. Furthermore, several genes differentially expressed in human metabolic disorders have been previously shown to participate in inflammatory responses in murine models of obesity and Type 2 diabetes. Taken together, these data demonstrate that peripheral blood from individuals with metabolic disorders display overlapping and non-overlapping patterns of gene expression indicative of unique, underlying immune processes.

Gene expression profiling-based identification of molecular subtypes in stage IV melanomas with different clinical outcome

PURPOSE

The incidence of malignant melanoma is increasing worldwide in fair-skinned populations. Melanomas respond poorly to systemic therapy, and metastatic melanomas inevitably become fatal. Although spontaneous regression, likely due to immune defense activation, rarely occurs, we lack a biological rationale and predictive markers in selecting patients for immune therapy.

EXPERIMENTAL DESIGN

We performed unsupervised hierarchical clustering of global gene expression data from stage IV melanomas in 57 patients. For further characterization, we used immunohistochemistry of selected markers, genome-wide DNA copy number analysis, genetic and epigenetic analysis of the CDKN2A locus, and NRAS/BRAF mutation screening.

RESULTS

The analysis revealed four distinct subtypes with gene signatures characterized by expression of immune response, pigmentation differentiation, proliferation, or stromal composition genes. Although all subtypes harbored NRAS and BRAF mutations, there was a significant difference between subtypes (P < 0.01), with no BRAF/NRAS wild-type samples in the proliferative subtype. Additionally, the proliferative subtype was characterized by a high frequency of CDKN2A homozygous deletions (P < 0.01). We observed a different prognosis between the subtypes (P = 0.01), with a particularly poor survival for patients harboring tumors of the proliferative subtype compared with the others (P = 0.003). Importantly, the clinical relevance of the subtypes was validated in an independent cohort of 44 stage III and IV melanomas. Moreover, low expression of an a priori defined gene set associated with immune response signaling was significantly associated with poor outcome (P = 0.001).

CONCLUSIONS

Our data reveal a biologically based taxonomy of malignant melanomas with prognostic effect and support an influence of the antitumoral immune response on outcome.

CD11c as a transcriptional biomarker to predict response to anti-TNF monotherapy with adalimumab in patients with rheumatoid arthritis

We performed transcription profiling using monocytes to identify predictive markers of response to anti-tumor necrosis factor (anti-TNF) therapy in patients with rheumatoid arthritis (RA). Several potential predictors of response were identified, including CD11c. Validation in samples from independent cohorts (total of n = 27 patients) using reverse transcription-PCR confirmed increased expression of CD11c in responders to adalimumab (100% sensitivity; 91.7% specificity, power 99.6%; alpha = 0.01). Pretherapy CD11c levels significantly correlated with the response criteria as defined by the American College of Rheumatology (ACR) (r = 0.656, P < 0.0001). However, CD11c was neither predictive of response to methotrexate (MTX) alone (n = 34) nor to MTX in combination with adalimumab (n = 16). Clinical responders revealed a reset to a normal expression pattern of resident/inflammatory monocyte markers, which was absent in nonresponders. Therefore, an analysis of key cell types identifies potentially predictive biomarkers that may help to restrict the use of adalimumab to therapy responders. Larger studies, including studies of monotherapy with other drugs, are now needed to confirm and validate the specificity of CD11c for anti-TNF biologics.

An integrative approach to characterize disease-specific pathways and their coordination: a case study in cancer

Background

The most common application of microarray technology in disease research is to identify genes differentially expressed in disease versus normal tissues. However, it is known that, in complex diseases, phenotypes are determined not only by genes, but also by the underlying structure of genetic networks. Often, it is the interaction of many genes that causes phenotypic variations.

Results

In this work, using cancer as an example, we develop graph-based methods to integrate multiple microarray datasets to discover disease-related co-expression network modules. We propose an unsupervised method that take into account both co-expression dynamics and network topological information to simultaneously infer network modules and phenotype conditions in which they are activated or de-activated. Using our method, we have discovered network modules specific to cancer or subtypes of cancers. Many of these modules are consistent with or supported by their functional annotations or their previously known involvement in cancer. In particular, we identified a module that is predominately activated in breast cancer and is involved in tumor suppression. While individual components of this module have been suggested to be associated with tumor suppression, their coordinated function has never been elucidated. Here by adopting a network perspective, we have identified their interrelationships and, particularly, a hub gene PDGFRL that may play an important role in this tumor suppressor network.

Conclusion

Using a network-based approach, our method provides new insights into the complex cellular mechanisms that characterize cancer and cancer subtypes. By incorporating co-expression dynamics information, our approach can not only extract more functionally homogeneous modules than those based solely on network topology, but also reveal pathway coordination beyond co-expression.

Understanding systemic sclerosis through gene expression profiling

PURPOSE OF REVIEW

Genomic analysis has rapidly become commonplace in the study and treatment of complex disease. Several recent studies of gene expression profiling in systemic sclerosis have demonstrated its value in diagnosis and illustrate the potential for this technique in prognostication, as well as the elucidation of the underlying pathogenesis.

RECENT FINDINGS

Skin biopsies from patients with systemic sclerosis show robust changes in gene profile that precede clinically detectable involvement. Current results suggest that clinically indistinguishable subgroups may be identified with different pathogenesis and outcome. Expression profiling studies of animal models of systemic sclerosis and explanted fibroblasts have helped to reveal the utility and deficiencies of these surrogates in the study of systemic sclerosis.

SUMMARY

Gene profiling is likely to provide valuable prognostic information in systemic sclerosis patients. Recent advances in sample collection and standardization of analysis mean that longitudinal collection of samples for gene profiling, even in small numbers of patients from different clinical centers, will contribute enormously to our understanding of the disease.

Identification of molecular biomarkers for multiple sclerosis

Multiple sclerosis is a demyelinating disease of the central nervous system with a presumed autoimmune etiology. Previous microarray analyses identified conserved gene expression signatures in peripheral blood mononuclear cells of patients with autoimmune diseases. We used quantitative real-time polymerase chain reaction analysis to identify a minimum number of genes of which transcript levels discriminated multiple sclerosis patients from patients with other chronic diseases and from controls. We used a computer program to search quantitative transcript levels to identify optimum ratios that distinguished among the different categories. A combination of a 4-ratio equation using expression levels of five genes segregated the multiple sclerosis cohort (n=55) from the control cohort (n=49) with a sensitivity of 91% and specificity of 98%. When autoimmune and other chronic disease groups were included (n=78), this discriminator still performed with a sensitivity of 79% and a specificity of 87%. This approach may have diagnostic utility not only for multiple sclerosis but also for other clinically complex autoimmune diseases.