Dataset Information

Multi-omics and Machine Learning Accurately Predicts Clinical Response to Adalimumab and Etanercept Therapy in Patients with Rheumatoid Arthritis [Methylation array]

ABSTRACT: Objectives: To predict response prior to anti-TNF treatment and comprehensively understand the mechanism how patients respond differently to anti-TNF treatment in rheumatoid arthritis (RA). Methods: Gene expression and/or DNA methylation profiling on PBMC, monocytes, and CD4+ T cells, from 80 RA patients before initiating either adalimumab (ADA) or etanercept (ETN) therapy was studied. After 6-month treatment response was evaluated according to the EULAR criteria of disease response. Differential expression and methylation analyses were performed to identify the response-associated transcriptional and epigenetic signatures. Machine learning models were built using these signatures by random forest algorithm to predict response prior to anti-TNF treatment and were further validated by a follow-up study. Results: Transcriptional signatures in ADA and ETN responders are divergent in PBMCs, and this phenomenon was reproduced in monocytes and CD4+ T cells. The genes upregulated in CD4+ T cells of ADA responders were enriched in the TNF signaling pathway, while very few pathways were differential in monocytes. Differential methylation positions (DMPs) of responders to ETN but not to ADA are majorly hypermethylated. The machine learning models to predict the response to ADA and ETN using differential genes reached overall accuracy of 85.9% and 79%, respectively. The models using DMPs reached overall accuracy of 84.7% and 88% for ADA and ETN, respectively. A follow-up study validated the high performance of these models. Conclusions: Machine learning models based on these molecular signatures could accurately predict response before ADA and ETN treatment, paving the path towards personalized anti-TNF treatment.

ORGANISM(S): Homo sapiens

PROVIDER: GSE138653 | GEO | 2020/08/24

REPOSITORIES: GEO

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Project description:Tumor necrosis factor (TNF) is elevated in human abdominal aortic aneurysms (AAA). Non-selective TNF inhibition has been linked to several severe side effects. Compounds that target the proinflammatory soluble TNF (solTNF) while preserving the immunomodulatory capabilities of transmembrane TNF (tmTNF) may prevent such side effects. We hypothesize that inhibition of solTNF signaling by XPro1595 is just as effective as non-selective TNF inhibition by etanercept (ETN) in preventing AAA expansion. The effect of XPro1595 and ETN was examined in porcine pancreatic elastase (PPE) induced AAA mice and findings of XPro1595 was confirmed in Angiotensin II (AngII) induced AAA in hyperlipidemic apolipoprotein E (Apoe) -/- mice. XPro1595 treatment significantly reduced AAA expansion in both models, while a similar trend (p=0.06) was observed in ETN-treated mice using the PPE model. In the PPE aneurysm wall, elastin integrity scores were significantly improved in the XPro1595 group. In the aneurysms, mean TNFR1wasreduced no-significantly (p=0.07) by 50% non-significant after TNF inhibition, but the cellular location in murine AAAs were unaffected and like the localization in human AAAs. Systemic TNF levels were elevated in XPro1595-treated mice, while systemic IL-10 levels were significantly increased after ETN-treatment, while in AngII induced AAA mice systemic TNF and IL5 levels were elevated after XPro1595 treatment. In early AAA development, proteomic analyses revealed that components of the fibrinogen complex were elevated while prostaglandin E2 synthase was down regulated by Xpro1595 (unadjusted p-values). David’s ontology analyses revealed that several pathways including cell matrix adhesion, extracellular space, fibrinogen complex, blood microparticles and glycoproteins were elevated by XPro1595 treatment. In conclusion, selective inhibition of solTNF reduces aneurysm expansion. This supports targeting solTNF as an attractive treatment option for AAA patients.

Project description:Objective: use comprehensive molecular profiling to understand the molecular mechanisms that affect clinical response to anti-TNF therapy in rheumatoid arthritis (RA) and to identify predictive markers to differentiate good responders and non-responders. Methods: two independent cohorts of 40 and 36 biologic-naïve RA patients were selected from the Corrona (Comparative Effectiveness Registry to study Therapies for Arthritis and Inflammatory coNditions) CERTAIN registry and categorized by EULAR response criteria. Whole-blood RNA and plasma samples from baseline and after 3 months of anti-TNF treatment were profiled using RNA-seq, shotgun proteomics and glycopeptide analysis. A cell type-specific transcriptional data analysis was applied to RNA-seq data to evaluate the impact of the most common immune cell sub-populations. Results: a treatment-related molecular signature was identified that showed a high level of correlation (ρ=0.62; permutation p<0.01) between cohorts. Treatment led to a reduction of neutrophils, independent of the status of response. Gene expression differences between good responders and non-responders at baseline did not manifest statistically significant concordance genome-wide between the two cohorts. However, a cell type-specific analysis indicated increased representation of innate cell type signatures in good responders and, conversely, increased expression of adaptive cell type signatures in non-responders at baseline in both cohorts. This result was confirmed by applying the cell-type specific analysis to other publicly available RA datasets. Evaluation of the neutrophil to lymphocyte ratio (NLR) at baseline in the remaining patients (n=1962) from the CERTAIN database using a logistic regression model further confirmed the observation (odds ratio of good/moderate response = 1.20 [95% CI = 1.03 – 1.41; p = 0.02]). Conclusion: differences in innate/adaptive immune cell type composition at baseline may be a major contributor to response to anti-TNF treatment within the first 3 months of therapy.

Dataset Information

Multi-omics and Machine Learning Accurately Predicts Clinical Response to Adalimumab and Etanercept Therapy in Patients with Rheumatoid Arthritis [Methylation array]

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