Data_Sheet_1_Integrative Clinical, Molecular, and Computational Analysis Identify Novel Biomarkers and Differential Profiles of Anti-TNF Response in R.doc (2.52 MB)
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Data_Sheet_1_Integrative Clinical, Molecular, and Computational Analysis Identify Novel Biomarkers and Differential Profiles of Anti-TNF Response in Rheumatoid Arthritis.doc

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posted on 2021-03-23, 08:46 authored by Maria Luque-Tévar, Carlos Perez-Sanchez, Alejandra Mª Patiño-Trives, Nuria Barbarroja, Ivan Arias de la Rosa, Mª Carmen Abalos-Aguilera, Juan Antonio Marin-Sanz, Desiree Ruiz-Vilchez, Rafaela Ortega-Castro, Pilar Font, Clementina Lopez-Medina, Montserrat Romero-Gomez, Carlos Rodriguez-Escalera, Jose Perez-Venegas, Mª Dolores Ruiz-Montesinos, Carmen Dominguez, Carmen Romero-Barco, Antonio Fernandez-Nebro, Natalia Mena-Vazquez, Jose Luis Marenco, Julia Uceda-Montañez, Mª Dolores Toledo-Coello, M. Angeles Aguirre, Alejandro Escudero-Contreras, Eduardo Collantes-Estevez, Chary Lopez-Pedrera

Background: This prospective multicenter study developed an integrative clinical and molecular longitudinal study in Rheumatoid Arthritis (RA) patients to explore changes in serologic parameters following anti-TNF therapy (TNF inhibitors, TNFi) and built on machine-learning algorithms aimed at the prediction of TNFi response, based on clinical and molecular profiles of RA patients.

Methods: A total of 104 RA patients from two independent cohorts undergoing TNFi and 29 healthy donors (HD) were enrolled for the discovery and validation of prediction biomarkers. Serum samples were obtained at baseline and 6 months after treatment, and therapeutic efficacy was evaluated. Serum inflammatory profile, oxidative stress markers and NETosis-derived bioproducts were quantified and miRNomes were recognized by next-generation sequencing. Then, clinical and molecular changes induced by TNFi were delineated. Clinical and molecular signatures predictors of clinical response were assessed with supervised machine learning methods, using regularized logistic regressions.

Results: Altered inflammatory, oxidative and NETosis-derived biomolecules were found in RA patients vs. HD, closely interconnected and associated with specific miRNA profiles. This altered molecular profile allowed the unsupervised division of three clusters of RA patients, showing distinctive clinical phenotypes, further linked to the TNFi effectiveness. Moreover, TNFi treatment reversed the molecular alterations in parallel to the clinical outcome. Machine-learning algorithms in the discovery cohort identified both, clinical and molecular signatures as potential predictors of response to TNFi treatment with high accuracy, which was further increased when both features were integrated in a mixed model (AUC: 0.91). These results were confirmed in the validation cohort.

Conclusions: Our overall data suggest that: 1. RA patients undergoing anti-TNF-therapy conform distinctive clusters based on altered molecular profiles, which are directly linked to their clinical status at baseline. 2. Clinical effectiveness of anti-TNF therapy was divergent among these molecular clusters and associated with a specific modulation of the inflammatory response, the reestablishment of the altered oxidative status, the reduction of NETosis, and the reversion of related altered miRNAs. 3. The integrative analysis of the clinical and molecular profiles using machine learning allows the identification of novel signatures as potential predictors of therapeutic response to TNFi therapy.