Integration of known DNA, RNA and protein biomarkers provides prediction of anti-TNF response in rheumatoid arthritis: results from the COMBINE study

Abstract

Objective: In rheumatoid arthritis (RA) several recent efforts have sought to discover means of predicting which patients would benefit from treatment. However, results have been discrepant with few successful replications. Our objective was to build a biobank with DNA, RNA and protein measurements to test the claim that the current state-of-the-art precision medicine will benefit RA patients.

Methods: We collected 451 blood samples from 61 healthy individuals and 185 RA patients initiating treatment, before treatment initiation and at a 3 month follow-up time. All samples were subjected to high-throughput RNA sequencing, DNA genotyping, extensive proteomics and flow cytometry measurements, as well as comprehensive clinical phenotyping. Literature review identified 2 proteins, 52 single-nucleotide polymorphisms (SNPs) and 72 gene-expression biomarkers that had previously been proposed as predictors of TNF inhibitor response (∆DAS28-CRP).

Results: From these published TNFi biomarkers we found that 2 protein, 2 SNP and 8 mRNA biomarkers could be replicated in the 59 TNF initiating patients. Combining these replicated biomarkers into a single signature we found that we could explain 51% of the variation in ∆DAS28-CRP. This corresponds to a sensitivity of 0.73 and specificity of 0.78 for the prediction of three month ∆DAS28-CRP better than -1.2.

Conclusions: The COMBINE biobank is currently the largest collection of multi-omics data from RA patients with high potential for discovery and replication. Taking advantage of this we surveyed the current state-of-the-art of drug-response stratification in RA, and identified a small set of previously published biomarkers available in peripheral blood which predicts clinical response to TNF blockade in this independent cohort.

Keywords: TNF-alpha; biobank; drug response; pharmacogenetics; rheumatoid arthritis.

Figure 1.

A) Overview of study design and cohort setup. B) DAS28-CRP score stratified by cohort and visit time, with an assumed value of 0 for the HC cohort. Each dot shows one sample. The Y-axis shows DAS28-CRP level and the X-axis shows cohort membership and time point. Grey lines connect paired time-points, and horizontal black bars indicate median value. Color-coding corresponds to figure 2C. C) Reproducibility of RNA-seq expression levels from technical replicates, in different library preparations with three months separation. Each dot shows the read count of one single gene. The Pearson correlation coefficent of log transformed counts was 0.991 (P<2e-16). D) Principal components analysis of genome-wide genotype profile and self-reported country of origin of individuals. Each dot shows one patient, color-coded by self-reported country of origin. TNFi: TNF inhibitor, MTX: Methotrexate, Rx naïve: no methotrexate initation.

Figure 2.

A) Replication of mRNA as stratification biomarkers for anti-TNF-response in 59 patients from cohort B. Each dot shows one gene, and the Y-axis shows association strength. For each of 7 published studies, all reported genes were investigated for association to anti-TNF-response. Four different models were calculated: three linear regressions with different covariate setups, and one binary-response/no-response setup set at ΔDAS28-CRP < −1.2. The strongest covariates reported in the respective studies are indicated by the color code in the legend. B) Comparison with mRNA expression data from Toonen et al. (GSE33377). The X- and Y-axis shows the log10(P)-value for association with TNFi response for each measured gene, from the Combine and the GSE33377 data, respectively. The compared P-values were calculated based on binary response/no-response. C) Replication of serum protein expression biomarkers sICAM1 and CXCL13 previously reported by Dennis et al. (14). Protein levels were divided by median values for each of the two proteins as indicated along X-axis. The P-value was calculated for a linear regression over the four levels. Each dot shows one patient. Color coding and symbol shape indicate baseline DAS28-CRP and gender. D) ROC plot illustrating the ability to estimate if a patient will have more or less than −1.2 ΔDAS28-CRP values, based on the fitted values of a model containing the 11 predictive biomarkers. This curve shows how different threshold levels will affect the rate of true and false positives predicted: the interpretation of the area under curve (AUC) is that 81.5% of randomly selected TNFi responders will have a higher test result than randomly selected non-responders.