Background/Purpose: Abatacept (CTLA4-Ig) is an approved biological therapy for the treatment of rheumatoid arthritis (RA). Similar to other biological agents, most patients (50-60%) respond significantly to this therapy. To date, however, the biological mechanisms underlying the lack of efficacy for this drug are unknown.The objectives of the present study were to generate insights into the biological processes that underly the differential response to abatacept and to evaluate the blood transcriptome as a valid source for drug response prediction.

Methods: A total of n=57 patients diagnosed with RA according to the ACR-EULAR criteria where recruited for this study from the rheumatology departments of 16 different university hospitals from Spain. All patients had >6 months of disease evolution and started therapy with a baseline DAS28 > 3.2. The primary clinical response was defined at week 12 of therapy using the EULAR criteria. Good and moderate responders were aggregated into a single response group and compared to the remaining (no response) group of patients. Whole blood RNA was collected from all patients at baseline using Paxgene tubes. From a subgroup of these patients (n=31), blood RNA was also obtained at weeks 12, 24 and 48 of treatment with abatacept. Gene expression levels were determined using paired-end RNA-seq with the NovaSeq 6000 platform (Illumina). Differential gene expression, association to biological processes, longitudinal association analysis and building of the multigenic predictor were performed using the R software. The Random Forest algorithm was used to build the predictor and the prediction accuracy was evaluated using the ROC AUC.

Results: From the 57 patients treated with abatacept, n=10 (17.5%) were good EULAR responders, n=24 (42%) moderate EULAR responders and n=23 (40.5%) non-responders at week 12 of therapy. Two significantly distinct biological profiles were identified between responders and non-responders to abatacept. In responders, we found an association to pathways associated with the effector phase of T cells (e.g. interleukin IL-15 and IL-22 signalling, adjusted P < 0.05). Non-responder patients showed instead a strong association to biological processes associated with antigen presentation and activation of T cells (adjusted P < 0.005). Using the baseline gene expression profiles, we built a multigenic predictor of response to abatacept with an AUC = 75%. In the longitudinal cohort, patients were stratified based on reaching an inactive state at week 48 (DAS28 < 3.2). Using this endpoint measure, the longitudinal analysis of the 4 time points corroborated the association with antigen presentation activation and the lack of response to this drug (adjusted P < 0.01).

Conclusion: The analysis of longitudinal blood RNA-seq profiles of RA patients starting abatacept therapy, has enabled the identification of specific immunological processes associated with the efficacy of B7 costimulation inhibition. Also, we demonstrate that blood expression profiles could be predictive of the response abatacept. The results from this study contribute to the advancement of precision medicine in RA and the understanding of the underlying disease heterogeneity.

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ACR Meeting Abstracts - https://acrabstracts.org/abstract/identification-of-immunological-processes-associated-with-the-response-to-abatacept-in-rheumatoid-arthritis-using-longitudinal-blood-rna-seq-analysis/