Gene profiling predicts rheumatoid arthritis responsiveness to IL-1Ra (anakinra)

A Bidens pilosa L. Non-Polar Extract Modulates the Polarization of Human Macrophages and Dendritic Cells into an Anti-Inflammatory Phenotype

Different communities around the world traditionally use Bidens pilosa L. for medicinal purposes, mainly for its anti-inflammatory, antinociceptive, and antioxidant properties; it is used as an ingredient in teas or herbal medicines for the treatment of pain, inflammation, and immunological disorders. Several studies have been conducted that prove the immunomodulatory properties of this plant; however, it is not known whether the immunomodulatory properties of B. pilosa are mediated by its ability to modulate antigen-presenting cells (APCs) such as macrophages (MØs) and dendritic cells (DCs) (through polarization or the maturation state, respectively). Different polar and non-polar extracts and fractions were prepared from the aerial part of B. pilosa. Their cytotoxic and immunomodulatory effects were first tested on human peripheral blood mononuclear cells (PBMCs) and phytohemagglutinin (PHA)-stimulated PBMCs, respectively, via an MTT assay. Then, the non-cytotoxic plant extracts and fractions that showed the highest immunomodulatory activity were selected to evaluate their effects on human MØ polarization and DC maturation (cell surface phenotype and cytokine secretion) through multiparametric flow cytometry. Finally, the chemical compounds of the B. pilosa extract that showed the most significant immunomodulatory effects on human APCs were identified using gas chromatography coupled with mass spectrometry. The petroleum ether extract and the ethyl acetate and hydroalcoholic fractions obtained from B. pilosa showed low cytotoxicity and modulated the PHA-stimulated proliferation of PBMCs. Furthermore, the B. pilosa petroleum ether extract induced M2 polarization or a hybrid M1/M2 phenotype in MØs and a semi-mature status in DCs, regardless of exposure to a maturation stimulus. The immunomodulatory activity of the non-polar (petroleum ether) extract of B. pilosa on human PBMC proliferation, M2 polarization of MØs, and semi-mature status in DCs might be attributed to the low-medium polarity components in the extract, such as phytosterol terpenes and fatty acid esters.

Integrating microRNA expression, miRNA-mRNA regulation network and signal pathway: a novel strategy for lung cancer biomarker discovery

Background

Since there are inextricably connections among molecules in the biological networks, it would be a more efficient and accurate research strategy to screen microRNA (miRNA) markers combining with miRNA-mRNA regulatory networks. The independent regulation mode is more “fragile” and “influential” than the co-regulation mode. miRNAs can be used as biomarkers if they can independently regulate hub genes with important roles in the PPI network, simultaneously the expression products of the regulated hub genes play important roles in the signaling pathways of related tissue diseases.

Methods

We collected miRNA expression of non-small cell lung cancer (NSCLC) from The Cancer Genome Atlas (TCGA) database and the Gene Expression Omnibus (GEO) database. Volcano plot and signal-to-noise ratio (SNR) methods were used to obtain significant differentially expressed (SDE) miRNAs from the TCGA database and GEO database, respectively. A human miRNA-mRNA regulatory network was constructed and the number of genes uniquely targeted (NOG) by a certain miRNA was calculated. The area under the curve (AUC) values were used to screen for clinical sensitivity and specificity. The candidate markers were obtained using the criteria of the top five maximum AUC values and NOG ≥ 3. The protein–protein interaction (PPI) network was constructed and independently regulated hub genes were obtained. Gene Ontology (GO) analysis and KEGG pathway analysis were used to identify genes involved in cancer-related pathways. Finally, the miRNA which can independently regulate a hub gene and the hub gene can participate in an important cancer-related pathway was considered as a biomarker. The AUC values and gene expression profile analysis from two external GEO datasets as well as literature validation were used to verify the screening capability and reliability of this marker.

Results

Fifteen SDE miRNAs in lung cancer were obtained from the intersection of volcano plot and SNR based on the GEO database and the TCGA database. Five miRNAs with the top five maximum AUC values and NOG ≥ 3 were screened out. A total of 61 hub genes were obtained from the PPI network. It was found that the hub gene GTF2F2 was independently regulated by miR-708-5p. Further pathway analysis indicated that GTF2F2 participates in protein expression by binding with polymerase II, and it can regulate transcription and accelerate tumor growth. Hence, miR-708-5p could be used as a biomarker. The good screening capability and reliability of miR-708-5p as a lung cancer marker were confirmed by AUC values and gene expression profiling of external datasets, and experimental literature. The potential mechanism of miR-708-5p was proposed.

Conclusions

This study proposes a new idea for lung cancer marker screening by integrating microRNA expression, regulation network and signal pathway. miR-708-5p was identified as a biomarker using this novel strategy. This study may provide some help for cancer marker screening.

NF-κB: At the Borders of Autoimmunity and Inflammation

The transcription factor NF-κB regulates multiple aspects of innate and adaptive immune functions and serves as a pivotal mediator of inflammatory response. In the first part of this review, we discuss the NF-κB inducers, signaling pathways, and regulators involved in immune homeostasis as well as detail the importance of post-translational regulation by ubiquitination in NF-κB function. We also indicate the stages of central and peripheral tolerance where NF-κB plays a fundamental role. With respect to central tolerance, we detail how NF-κB regulates medullary thymic epithelial cell (mTEC) development, homeostasis, and function. Moreover, we elaborate on its role in the migration of double-positive (DP) thymocytes from the thymic cortex to the medulla. With respect to peripheral tolerance, we outline how NF-κB contributes to the inactivation and destruction of autoreactive T and B lymphocytes as well as the differentiation of CD4⁺-T cell subsets that are implicated in immune tolerance. In the latter half of the review, we describe the contribution of NF-κB to the pathogenesis of autoimmunity and autoinflammation. The recent discovery of mutations involving components of the pathway has both deepened our understanding of autoimmune disease and informed new therapeutic approaches to treat these illnesses.

Genome-wide association study of response to methotrexate in early rheumatoid arthritis patients

Methotrexate (MTX) monotherapy is a common first treatment for rheumatoid arthritis (RA), but many patients do not respond adequately. In order to identify genetic predictors of response, we have combined data from two consortia to carry out a genome-wide study of response to MTX in 1424 early RA patients of European ancestry. Clinical endpoints were change from baseline to 6 months after starting treatment in swollen 28-joint count, tender 28-joint count, C-reactive protein and the overall 3-component disease activity score (DAS28). No single nucleotide polymorphism (SNP) reached genome-wide statistical significance for any outcome measure. The strongest evidence for association was with rs168201 in NRG3 (p = 10^-7 for change in DAS28). Some support was also seen for association with ZMIZ1, previously highlighted in a study of response to MTX in juvenile idiopathic arthritis. Follow-up in two smaller cohorts of 429 and 177 RA patients did not support these findings, although these cohorts were more heterogeneous.

Pre-silencing of genes involved in the electron transport chain (ETC) pathway is associated with responsiveness to abatacept in rheumatoid arthritis

Background

In the current context of personalized medicine, one of the major challenges in the management of rheumatoid arthritis (RA) is to identify biomarkers that predict drug responsiveness. From the European APPRAISE trial, our main objective was to identify a gene expression profile associated with responsiveness to abatacept (ABA) + methotrexate (MTX) and to understand the involvement of this signature in the pathophysiology of RA.

Methods

Whole human genome microarrays (4 × 44 K) were performed from a first subset of 36 patients with RA. Data validation by quantitative reverse-transcription (qRT)-PCR was performed from a second independent subset of 32 patients with RA. Gene Ontology and WikiPathways database allowed us to highlight the specific biological mechanisms involved in predicting response to ABA/MTX.

Results

From the first subset of 36 patients with RA, a combination including 87 transcripts allowed almost perfect separation between responders and non-responders to ABA/MTX. Next, the second subset of patients 32 with RA allowed validation by qRT-PCR of a minimal signature with only four genes. This latter signature categorized 81% of patients with RA with 75% sensitivity, 85% specificity and 85% negative predictive value. This combination showed a significant enrichment of genes involved in electron transport chain (ETC) pathways. Seven transcripts from ETC pathways (NDUFA6, NDUFA4, UQCRQ, ATP5J, COX7A2, COX7B, COX6A1) were significantly downregulated in responders versus non-responders to ABA/MTX. Moreover, dysregulation of these genes was independent of inflammation and was specific to ABA response.

Conclusion

Pre-silencing of ETC genes is associated with future response to ABA/MTX and might be a crucial key to susceptibility to ABA response.

Electronic supplementary material

The online version of this article (doi:10.1186/s13075-017-1319-8) contains supplementary material, which is available to authorized users.

Personalized biological treatment for rheumatoid arthritis: a systematic review with a focus on clinical applicability

OBJECTIVES

To review studies that address prediction of response to biologic treatment in RA and to explore the clinical utility of the studied (bio)markers.

METHODS

A search for relevant articles was performed in PubMed, Embase and Cochrane databases. Studies that presented predictive values or in which these could be calculated were selected. The added value was determined by the added value on prior probability for each (bio)marker. Only an increase/decrease in chance of response ⩾15% was considered clinically relevant, whereas in oncology values >25% are common.

RESULTS

Of the 57 eligible studies, 14 (bio)markers were studied in more than one cohort and an overview of the added predictive value of each marker is presented. Of the replicated predictors, none consistently showed an increase/decrease in probability of response ⩾15%. However, positivity of RF and ACPA in case of rituximab and the presence of the TNF-α promoter 308 GG genotype for TNF inhibitor therapy were consistently predictive, yet low in added predictive value. Besides these, 65 (bio)markers studied once showed remarkably high (but not validated) predictive values.

CONCLUSION

We were unable to address clinically useful baseline (bio)markers for use in individually tailored treatment. Some predictors are consistently predictive, yet low in added predictive value, while several others are promising but await replication. The challenge now is to design studies to validate all explored and promising findings individually and in combination to make these (bio)markers relevant to clinical practice.

Factors affecting the accuracy of a class prediction model in gene expression data

BACKGROUND

Class prediction models have been shown to have varying performances in clinical gene expression datasets. Previous evaluation studies, mostly done in the field of cancer, showed that the accuracy of class prediction models differs from dataset to dataset and depends on the type of classification function. While a substantial amount of information is known about the characteristics of classification functions, little has been done to determine which characteristics of gene expression data have impact on the performance of a classifier. This study aims to empirically identify data characteristics that affect the predictive accuracy of classification models, outside of the field of cancer.

RESULTS

Datasets from twenty five studies meeting predefined inclusion and exclusion criteria were downloaded. Nine classification functions were chosen, falling within the categories: discriminant analyses or Bayes classifiers, tree based, regularization and shrinkage and nearest neighbors methods. Consequently, nine class prediction models were built for each dataset using the same procedure and their performances were evaluated by calculating their accuracies. The characteristics of each experiment were recorded, (i.e., observed disease, medical question, tissue/cell types and sample size) together with characteristics of the gene expression data, namely the number of differentially expressed genes, the fold changes and the within-class correlations. Their effects on the accuracy of a class prediction model were statistically assessed by random effects logistic regression. The number of differentially expressed genes and the average fold change had significant impact on the accuracy of a classification model and gave individual explained-variation in prediction accuracy of up to 72% and 57%, respectively. Multivariable random effects logistic regression with forward selection yielded the two aforementioned study factors and the within class correlation as factors affecting the accuracy of classification functions, explaining 91.5% of the between study variation.

CONCLUSIONS

We evaluated study- and data-related factors that might explain the varying performances of classification functions in non-cancerous datasets. Our results showed that the number of differentially expressed genes, the fold change, and the correlation in gene expression data significantly affect the accuracy of class prediction models.

Blood-based identification of non-responders to anti-TNF therapy in rheumatoid arthritis

BACKGROUND

Faced with an increasing number of choices for biologic therapies, rheumatologists have a critical need for better tools to inform rheumatoid arthritis (RA) disease management. The ability to identify patients who are unlikely to respond to first-line biologic anti-TNF therapies prior to their treatment would allow these patients to seek alternative therapies, providing faster relief and avoiding complications of disease.

METHODS

We identified a gene expression classifier to predict, pre-treatment, which RA patients are unlikely to respond to the anti-TNF infliximab. The classifier was trained and independently evaluated using four published whole blood gene expression data sets, in which RA patients (n = 116 = 44 + 15 + 30 + 27) were treated with infliximab, and their response assessed 14-16 months post treatment according to the European League Against Rheumatism (EULAR) response criteria. For each patient, prior knowledge was used to group gene expression measurements into disease-relevant biological signaling mechanisms that were used as the input features for regularized logistic regression.

RESULTS

The classifier produced a substantial enrichment of non-responders (59 %, given by the cross validated test precision) compared to the full population (27 % non-responders), while identifying nearly a third of non-responders. Given this classifier performance, treatment of predicted non-responders with alternative biologics would decrease their chance of non-response by between a third and a half, substantially improving their odds of effective treatment and stemming further disease progression. The classifier consisted of 18 signaling mechanisms, which together indicated that higher inflammatory signaling mediated by TNF and other cytokines was present pre-treatment in the blood of patients who responded to infliximab treatment. In contrast, non-responders were classified by relatively higher levels of specific metabolic activities in the blood prior to treatment.

CONCLUSIONS

We were able to successfully produce a classifier to identify a population of RA patients significantly enriched in anti-TNF non-responders across four different patient cohorts. Additional prospective studies are needed to validate and refine the classifier for clinical use.

Interleukin-1 as a common denominator from autoinflammatory to autoimmune disorders: premises, perils, and perspectives

A complex web of dynamic relationships between innate and adaptive immunity is now evident for many autoinflammatory and autoimmune disorders, the first deriving from abnormal activation of innate immune system without any conventional danger triggers and the latter from self-/non-self-discrimination loss of tolerance, and systemic inflammation. Due to clinical and pathophysiologic similarities giving a crucial role to the multifunctional cytokine interleukin-1, the concept of autoinflammation has been expanded to include nonhereditary collagen-like diseases, idiopathic inflammatory diseases, and metabolic diseases. As more patients are reported to have clinical features of autoinflammation and autoimmunity, the boundary between these two pathologic ends is becoming blurred. An overview of monogenic autoinflammatory disorders, PFAPA syndrome, rheumatoid arthritis, type 2 diabetes mellitus, uveitis, pericarditis, Behçet's disease, gout, Sjögren's syndrome, interstitial lung diseases, and Still's disease is presented to highlight the fundamental points that interleukin-1 displays in the cryptic interplay between innate and adaptive immune systems.

Fluorescently activated cell sorting followed by microarray profiling of helper T cell subtypes from human peripheral blood

Background

Peripheral blood samples have been subjected to comprehensive gene expression profiling to identify biomarkers for a wide range of diseases. However, blood samples include red blood cells, white blood cells, and platelets. White blood cells comprise polymorphonuclear leukocytes, monocytes, and various types of lymphocytes. Blood is not distinguishable, irrespective of whether the expression profiles reflect alterations in (a) gene expression patterns in each cell type or (b) the proportion of cell types in blood. CD4⁺ Th cells are classified into two functionally distinct subclasses, namely Th1 and Th2 cells, on the basis of the unique characteristics of their secreted cytokines and their roles in the immune system. Th1 and Th2 cells play an important role not only in the pathogenesis of human inflammatory, allergic, and autoimmune diseases, but also in diseases that are not considered to be immune or inflammatory disorders. However, analyses of minor cellular components such as CD4⁺ cell subpopulations have not been performed, partly because of the limited number of these cells in collected samples.

Methodology/Principal Findings

We describe fluorescently activated cell sorting followed by microarray (FACS–array) technology as a useful experimental strategy for characterizing the expression profiles of specific immune cells in the circulation. We performed reproducible gene expression profiling of Th1 and Th2, respectively. Our data suggest that this procedure provides reliable information on the gene expression profiles of certain small immune cell populations. Moreover, our data suggest that GZMK, GZMH, EOMES, IGFBP3, and STOM may be novel markers for distinguishing Th1 cells from Th2 cells, whereas IL17RB and CNTNAP1 can be Th2-specific markers.

Conclusions/Significance

Our approach may help in identifying aberrations and novel therapeutic or diagnostic targets for diseases that affect Th1 or Th2 responses and elucidating the involvement of a subpopulation of immune cells in some diseases.

Urinary proteome profile predictive of disease activity in rheumatoid arthritis

Current serum biomarkers for rheumatoid arthritis (RA) are not highly sensitive or specific to changes of disease activities. Thus, other complementary biomarkers have been needed to improve assessment of RA activities. In many diseases, urine has been studied as a window to provide complementary information to serum measures. Here, we conducted quantitative urinary proteome profiling using liquid chromatography-tandem mass spectrometry (LC-MS/MS) and identified 134 differentially expressed proteins (DEPs) between RA and osteoarthritis (OA) urine samples. By integrating the DEPs with gene expression profiles in joints and mononuclear cells, we initially selected 12 biomarker candidates related to joint pathology and then tested their altered expression in independent RA and OA samples using enzyme-linked immunosorbent assay. Of the initial candidates, we selected four DEPs as final candidates that were abundant in RA patients and consistent with those observed in LC-MS/MS analysis. Among them, we further focused on urinary soluble CD14 (sCD14) and examined its diagnostic value and association with disease activity. Urinary sCD14 had a diagnostic value comparable to conventional serum measures and an even higher predictive power for disease activity when combined with serum C-reactive protein. Thus, our urinary proteome provides a diagnostic window complementary to current serum parameters for the disease activity of RA.

Evaluation of gene expression classification studies: factors associated with classification performance

Classification methods used in microarray studies for gene expression are diverse in the way they deal with the underlying complexity of the data, as well as in the technique used to build the classification model. The MAQC II study on cancer classification problems has found that performance was affected by factors such as the classification algorithm, cross validation method, number of genes, and gene selection method. In this paper, we study the hypothesis that the disease under study significantly determines which method is optimal, and that additionally sample size, class imbalance, type of medical question (diagnostic, prognostic or treatment response), and microarray platform are potentially influential. A systematic literature review was used to extract the information from 48 published articles on non-cancer microarray classification studies. The impact of the various factors on the reported classification accuracy was analyzed through random-intercept logistic regression. The type of medical question and method of cross validation dominated the explained variation in accuracy among studies, followed by disease category and microarray platform. In total, 42% of the between study variation was explained by all the study specific and problem specific factors that we studied together.

Gene expression analysis in RA: towards personalized medicine

Gene expression has recently been at the forefront of advance in personalized medicine, notably in the field of cancer and transplantation, providing a rational for a similar approach in rheumatoid arthritis (RA). RA is a prototypic inflammatory autoimmune disease with a poorly understood etiopathogenesis. Inflammation is the main feature of RA; however, many biological processes are involved at different stages of the disease. Gene expression signatures offer management tools to meet the current needs for personalization of RA patients' care. This review analyses currently available information with respect to RA diagnostic, prognostic and prediction of response to therapy with a view to highlight the abundance of data, whose comparison is often inconclusive due to the mixed use of material source, experimental methodologies and analysis tools, reinforcing the need for harmonization if gene expression signatures are to become a useful clinical tool in personalized medicine for RA patients.

Pharmacogenetics and pharmacogenomics for rheumatoid arthritis responsiveness to methotrexate treatment: the 2013 update

Rheumatoid arthritis (RA) is a complex, systemic autoimmune disease characterized by chronic inflammation of multiple peripheral joints, which leads to serious destruction of cartilage and bone, progressive deformity and severe disability. Methotrexate (MTX) is one of the first-line drugs commonly used in RA therapy owing to its excellent long-term efficacy and cheapness. However, the efficacy and toxicity of MTX treatment have significant interpatient variability. Genetic factors contribute to this variability. In this review, we have summarized and updated the progress of RA response to MTX treatment since 2009 by focusing on the fields of pharmacogenetics and pharmacogenomics. Identification of genetic factors involved in MTX treatment response will increase the understanding of RA pathology and the development of new personalized treatments.

The peripheral whole-blood transcriptome of acute pyelonephritis in human pregnancya

OBJECTIVE

Human pregnancy is characterized by activation of the innate immune response and suppression of adaptive immunity. The former is thought to provide protection against infection for the mother, and the latter, tolerance against paternal antigens expressed in fetal cells. Acute pyelonephritis is associated with an increased risk of acute respiratory distress syndrome and sepsis in pregnant (vs. nonpregnant) women. The objective of this study was to describe the gene expression profile (transcriptome) of maternal whole blood in acute pyelonephritis.

METHOD

A case-control study was conducted to include pregnant women with acute pyelonephritis (n=15) and women with a normal pregnancy (n=34). Affymetrix HG-U133 Plus 2.0 arrays (Affymetrix, Santa Clara, CA, USA) were used for gene expression profiling. A linear model was used to test the association between the presence of pyelonephritis and gene expression levels while controlling for white blood cell count and gestational age. A fold change of 1.5 was considered significant at a false discovery rate of 0.1. A subset of differentially expressed genes (n=56) was tested with real-time quantitative reverse transcription-polymerase chain reaction (qRT-PCR) (cases, n=19; controls, n=59). Gene ontology and pathway analyses were applied.

RESULTS

A total of 983 genes were differentially expressed in acute pyelonephritis: 457 were upregulated and 526 were downregulated. Significant enrichment of 300 biological processes and 63 molecular functions was found in pyelonephritis. Significantly impacted pathways in pyelonephritis included (a) cytokine-cytokine receptor interaction, (b) T-cell receptor signaling, (c) Jak-STAT signaling, and (d) complement and coagulation cascades. Of 56 genes tested by qRT-PCR, 48 (85.7%) had confirmation of differential expression.

CONCLUSION

This is the first study of the transcriptomic signature of whole blood in pregnant women with acute pyelonephritis. Acute infection during pregnancy is associated with the increased expression of genes involved in innate immunity and the decreased expression of genes involved in lymphocyte function.

Differences and similarities in the transcriptional profile of peripheral whole blood in early and late-onset preeclampsia: insights into the molecular basis of the phenotype of preeclampsiaa

OBJECTIVE

Preeclampsia (PE) can be sub-divided into early- and late-onset phenotypes. The pathogenesis of these two phenotypes has not been elucidated. To gain insight into the mechanisms of disease, the transcriptional profiles of whole blood from women with early- and late-onset PE were examined.

METHODS

A cross-sectional study was conducted to include women with: i) early-onset PE (diagnosed prior to 34 weeks, n=25); ii) late-onset PE (after 34 weeks, n=47); and iii) uncomplicated pregnancy (n=61). Microarray analysis of mRNA expression in peripheral whole blood was undertaken using Affymetrix microarrays. Differential gene expression was evaluated using a moderated t-test (false discovery rate <0.1 and fold change >1.5), adjusting for maternal white blood cell count and gestational age. Validation by real-time qRT-PCR was performed in a larger sample size [early PE (n=31), late PE (n=72) and controls (n=99)] in all differentially expressed genes. Gene ontology analysis and pathway analysis were performed.

RESULTS

i) 43 and 28 genes were differentially expressed in early- and late-onset PE compared to the control group, respectively; ii) qRT-PCR confirmed the microarray results for early and late-onset PE in 77% (33/43) and 71% (20/28) of genes, respectively; iii) 20 genes that are involved in coagulation (SERPINI2), immune regulation (VSIG4, CD24), developmental process (H19) and inflammation (S100A10) were differentially expressed in early-onset PE alone. In contrast, only seven genes that encoded proteins involved in innate immunity (LTF, ELANE) and cell-to-cell recognition in the nervous system (CNTNAP3) were differentially expressed in late-onset PE alone. Thirteen genes that encode proteins involved in host defense (DEFA4, BPI, CTSG, LCN2), tight junctions in blood-brain barrier (EMP1) and liver regeneration (ECT2) were differentially expressed in both early- and late-onset PE.

CONCLUSION

Early- and late-onset PE are characterized by a common signature in the transcriptional profile of whole blood. A small set of genes were differentially regulated in early- and late-onset PE. Future studies of the biological function, expression timetable and protein expression of these genes may provide insight into the pathophysiology of PE.

Peripheral blood derived gene panels predict response to infliximab in rheumatoid arthritis and Crohn's disease

Background

Biological therapies have been introduced for the treatment of chronic inflammatory diseases including rheumatoid arthritis (RA) and Crohn's disease (CD). The efficacy of biologics differs from patient to patient. Moreover these therapies are rather expensive, therefore treatment of primary non-responders should be avoided.

Method

We addressed this issue by combining gene expression profiling and biostatistical approaches. We performed peripheral blood global gene expression profiling in order to filter the genome for target genes in cohorts of 20 CD and 19 RA patients. Then RT-quantitative PCR validation was performed, followed by multivariate analyses of genes in independent cohorts of 20 CD and 15 RA patients, in order to identify sets ofinterrelated genes that can separate responders from non-responders to the humanized chimeric anti-TNFalpha antibody infliximab at baseline.

Results

Gene panels separating responders from non-responders were identified using leave-one-out cross-validation test, and a pool of genes that should be tested on larger cohorts was created in both conditions.

Conclusions

Our data show that peripheral blood gene expression profiles are suitable for determining gene panels with high discriminatory power to differentiate responders from non-responders in infliximab therapy at baseline in CD and RA, which could be cross-validated successfully. Biostatistical analysis of peripheral blood gene expression data leads to the identification of gene panels that can help predict responsiveness of therapy and support the clinical decision-making process.

The potential use of expression profiling: implications for predicting treatment response in rheumatoid arthritis

Whole genome expression profiling, or transcriptomics, is a high throughput technology with the potential for major impacts in both clinical settings and drug discovery and diagnostics. In particular, there is much interest in this technique as a mechanism for predicting treatment response. Gene expression profiling entails the quantitative measurement of messenger RNA levels for thousands of genes simultaneously with the inherent possibility of identifying biomarkers of response to a particular therapy or by singling out those at risk of serious adverse events. This technology should contribute to the era of stratified medicine, in which patient specific populations are matched to potentially beneficial drugs via clinical tests. Indeed, in the oncology field, gene expression testing is already recommended to allow rational use of therapies to treat breast cancer. However, there are still many issues surrounding the use of the various testing platforms available and the statistical analysis associated with the interpretation of results generated. This review will discuss the implications this promising technology has in predicting treatment response and outline the various advantages and pitfalls associated with its use.