Pre-defined gene co-expression modules in rheumatoid arthritis transition towards molecular health following anti-TNF therapy.

BACKGROUND: No reliable biomarkers to predict response to TNF inhibitors (TNFi) in RA patients currently exist. The aims of this study were to replicate changes in gene co-expression modules that were previously reported in response to TNFi therapy in RA; to test if changes in module expression are specific to TNFi therapy; and to determine whether module expression transitions towards a disease-free state in responding patients. METHOD: Published transcriptomic data from the whole blood of disease-free controls (n = 10) and RA patients, treated with the TNFi adalimumab (n = 70) or methotrexate (n = 85), were studied. Treatment response was assessed using the EULAR response criteria following 3 or 6 months of treatment. Change in transcript expression between pre- and post-treatment was recorded for previously defined modules. Linear mixed models tested whether modular expression after treatment transitioned towards a disease-free state. RESULTS: For 25 of the 27 modules, change in expression between pre- and post-treatment in the adalimumab cohort replicated published findings. Of these 25 modules, six transitioned towards a disease-free state by 3 months (P < 0.05), irrespective of clinical response. One module (M3.2), related to inflammation and TNF biology, significantly correlated with response to adalimumab. Similar patterns of modular expression, with reduced magnitude, were observed in the methotrexate cohort. CONCLUSION: This study provides independent validation of changes in module expression in response to therapy in RA. However, these effects are not specific to TNFi. Further studies are required to determine whether specific modules could assist molecular classification of therapeutic response.

Automated clustering reveals CD4+ T cell subset imbalances in rheumatoid arthritis.

Background: Despite the report of an imbalance between CD4+ T helper (Th) cell subsets in rheumatoid arthritis (RA), patient stratification for precision medicine has been hindered by the discovery of ever more Th cell subsets, as well as contradictory association results. Objectives: To capture previously reported Th imbalance in RA with deep immunophenotyping techniques; to compare hypothesis-free unsupervised automated clustering with hypothesis-driven conventional biaxial gating and explore if Th cell heterogeneity accounts for conflicting association results. Methods: Unstimulated and stimulated peripheral blood mononuclear cells from 10 patients with RA and 10 controls were immunophenotyped with a 37-marker panel by mass cytometry (chemokine receptors, intra-cellular cytokines, intra-nuclear transcription factors). First, conventional biaxial gating and standard definitions of Th cell subsets were applied to compare subset frequencies between cases and controls. Second, unsupervised clustering was performed with FlowSOM and analysed using mixed-effects modelling of Associations of Single Cells (MASC). Results: Conventional analytical techniques fail to identify classical Th subset imbalance, while unsupervised automated clustering, by allowing for unusual marker combinations, identified an imbalance between pro- and anti-inflammatory subsets. For example, a pro-inflammatory Th1-like (IL-2+ T-bet+) subset and an unconventional but pro-inflammatory IL-17+ T-bet+ subset were significantly enriched in RA (odds ratio=5.7, p=2.2 x 10-3; odds ratio=9.7, p=1.5x10-3, respectively). In contrast, a FoxP3+ IL-2+ HLA-DR+ Treg-like subset was reduced in RA (odds ratio=0.1, p=7.7x10-7). Conclusion: Taking an unbiased approach to large dataset analysis using automated clustering algorithms captures non-canonical CD4+ T cell subset imbalances in RA blood.

Personalized medicine in rheumatic diseases: how close are we to being able to use genetic biomarkers to predict response to TNF inhibitors?

Introduction: A genetic biomarker to select which drug will work best for which patients with rheumatic diseases is a goal of pharmacogenetic precision medicine approaches and one that patients and the public support. However, studies to date have yielded inconsistent findings with no robustly replicated or clinically useful genetic biomarkers emerging.Areas covered: Using studies investigating biomarkers to predict response to tumor necrosis factor inhibitor therapies in rheumatoid arthritis as an exemplar, we consider factors that reduce the power to detect such predictive biomarkers, including non-adherence, immunogenicity, the use of clinical outcome measures comprising composite scores and sample size. We argue that the biologic therapies were developed to target joint inflammation and so the outcome measure should be closer to the biology and, ideally, should be a biological measure. Given that heritability studies have shown a substantial genetic contribution, there is merit in designing studies to optimize the chance of identifying robust genetic markers and that includes testing drug levels for adherence.Expert opinion: Ultimately, we think that genetics will be used as part of an algorithm to assess likely response to individual drugs but that other factors will also be important including clinical and environmental factors.