RT Journal Article
T1 Integrative Clinical, Molecular, and Computational Analysis Identify Novel Biomarkers and Differential Profiles of Anti-TNF Response in Rheumatoid Arthritis
A1 Luque-Tévar, Maria
A1 Perez-Sanchez, Carlos
A1 Patiño-Trives, Alejandra Mª
A1 Barbarroja, Nuria
A1 Arias de la Rosa, Ivan
A1 Abalos-Aguilera, Mª Carmen
A1 Marin-Sanz, Juan Antonio
A1 Ruiz-Vilchez, Desiree
A1 Ortega-Castro, Rafaela
A1 Font, Pilar
A1 Lopez-Medina, Clementina
A1 Romero-Gomez, Montserrat
A1 Rodriguez-Escalera, Carlos
A1 Perez-Venegas, Jose
A1 Ruiz-Montesinos, Mª Dolores
A1 Dominguez, Carmen
A1 Romero-Barco, Carmen
A1 Fernandez-Nebro, Antonio
A1 Mena-Vázquez, Natalia
A1 Marenco, Jose Luis
A1 Uceda-Montañez, Julia
A1 Toledo-Coello, Mª Dolores
A1 Aguirre, M. Angeles
A1 Escudero-Contreras, Alejandro
A1 Collantes-Estevez, Eduardo
A1 Lopez-Pedrera, Chary
K1 Rheumatoid arthritis
K1 Anti-TNF agents
K1 Inflammation
K1 NEtosis
K1 MicroRNAs
K1 Machine learning
K1 Predictors
K1 Efficacy
K1 Biomarkers
K1 Phenotype
K1 Oxidative stress
K1 Artritis reumatoide
K1 Inhibidores del factor de necrosis tumoral
K1 Inflamación
K1 MicroARNs
K1 Aprendizaje automático
K1 Eficacia
K1 Biomarcadores
K1 Fenotipo
K1 Estrés oxidativo
AB 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.
PB Frontiers
YR 2021
FD 2021-03-23
LK http://hdl.handle.net/10668/4348
UL http://hdl.handle.net/10668/4348
LA en
NO Luque-Tévar M, Perez-Sanchez C, Patiño-Trives AM, Barbarroja N, Arias de la Rosa I, Abalos-Aguilera MC, et al. Integrative Clinical, Molecular, and Computational Analysis Identify Novel Biomarkers and Differential Profiles of Anti-TNF Response in Rheumatoid Arthritis. Front Immunol. 2021 Mar 23;12:631662
DS RISalud
RD Apr 7, 2025