Integrative Clinical, Molecular, and Computational Analysis Identify Novel Biomarkers and Differential Profiles of Anti-TNF Response in Rheumatoid Arthritis

Frontiers in Immunology

Clementina Lopez-Medina¹  Carmen Romero-Barco²  Natalia Mena-Vazquez³  Antonio Fernandez-Nebro³  Mª Dolores Ruiz-Montesinos⁴  Carmen Dominguez⁴  Jose Perez-Venegas⁴  Jose Luis Marenco⁵  Julia Uceda-Montañez⁵  Carlos Rodriguez-Escalera⁶  Mª Dolores Toledo-Coello⁷  Ivan Arias de la Rosa⁸  Desiree Ruiz-Vilchez⁸  Maria Luque-Tévar⁸  Montserrat Romero-Gomez⁸  Alejandra Mª Patiño-Trives⁸  Carlos Perez-Sanchez⁸  M. Angeles Aguirre⁸  Eduardo Collantes-Estevez⁸  Juan Antonio Marin-Sanz⁸  Pilar Font⁸  Mª Carmen Abalos-Aguilera⁸  Chary Lopez-Pedrera⁸  Rafaela Ortega-Castro⁸  Nuria Barbarroja⁸  Alejandro Escudero-Contreras⁸ 

[1] ;Hospital Clínico Universitario, Malaga, Spain;Hospital Regional Universitario de Malaga, Malaga, Spain;Hospital Universitario Virgen Macarena, Sevilla, Spain;Hospital Universitario Virgen de Valme, Sevilla, Spain;Hospital Universitario de Jaen, Jaén, Spain;Hospital Universitario de Jerez de la Frontera, Cádiz, Spain;Instituto Maimónides de Investigación Biomédica de Córdoba (IMIBIC), Hospital Reina Sofia, Universidad de Cordoba, Córdoba, Spain;

关键词: rheumatoid arthritis;    anti-TNF agents;    inflammation;    NEtosis;    microRNAs;    machine learning;   

DOI  :  10.3389/fimmu.2021.631662

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown