Luque-Tévar, MariaPerez-Sanchez, CarlosPatiño-Trives, Alejandra MªBarbarroja, NuriaArias de la Rosa, IvanAbalos-Aguilera, Mª CarmenMarin-Sanz, Juan AntonioRuiz-Vilchez, DesireeOrtega-Castro, RafaelaFont, PilarLopez-Medina, ClementinaRomero-Gomez, MontserratRodriguez-Escalera, CarlosPerez-Venegas, JoseRuiz-Montesinos, Mª DoloresDominguez, CarmenRomero-Barco, CarmenFernandez-Nebro, AntonioMena-Vázquez, NataliaMarenco, Jose LuisUceda-Montañez, JuliaToledo-Coello, Mª DoloresAguirre, M. AngelesEscudero-Contreras, AlejandroCollantes-Estevez, EduardoLopez-Pedrera, Chary2022-11-092022-11-092021-03-23Luque-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;12:631662http://hdl.handle.net/10668/4348Background: 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.enAtribución 4.0 Internacionalhttp://creativecommons.org/licenses/by/4.0/Rheumatoid arthritisAnti-TNF agentsInflammationNEtosisMicroRNAsMachine learningPredictorsEfficacyBiomarkersPhenotypeOxidative stressArtritis reumatoideInhibidores del factor de necrosis tumoralInflamaciónMicroARNsAprendizaje automáticoEficaciaBiomarcadoresFenotipoEstrés oxidativoMedical Subject Headings::Persons::Persons::Age Groups::AdultMedical Subject Headings::Chemicals and Drugs::Chemical Actions and Uses::Pharmacologic Actions::Therapeutic Uses::Antirheumatic AgentsMedical Subject Headings::Diseases::Musculoskeletal Diseases::Rheumatic Diseases::Arthritis, RheumatoidMedical Subject Headings::Chemicals and Drugs::Biological Factors::Biological Markers::Biomarkers, PharmacologicalMedical Subject Headings::Analytical, Diagnostic and Therapeutic Techniques and Equipment::Investigative Techniques::Epidemiologic Methods::Statistics as Topic::Cluster AnalysisMedical Subject Headings::Check Tags::FemaleMedical Subject Headings::Organisms::Eukaryota::Animals::Chordata::Vertebrates::Mammals::Primates::Haplorhini::Catarrhini::Hominidae::HumansMedical Subject Headings::Diseases::Pathological Conditions, Signs and Symptoms::Pathologic Processes::InflammationMedical Subject Headings::Analytical, Diagnostic and Therapeutic Techniques and Equipment::Investigative Techniques::Epidemiologic Methods::Epidemiologic Study Characteristics as Topic::Epidemiologic Studies::Cohort Studies::Longitudinal StudiesMedical Subject Headings::Check Tags::MaleMedical Subject Headings::Chemicals and Drugs::Nucleic Acids, Nucleotides, and Nucleosides::Antisense Elements (Genetics)::RNA, Antisense::MicroRNAsMedical Subject Headings::Persons::Persons::Age Groups::Adult::Middle AgedMedical Subject Headings::Phenomena and Processes::Metabolic Phenomena::Metabolism::Oxidative StressMedical Subject Headings::Phenomena and Processes::Genetic Phenomena::PhenotypeMedical Subject Headings::Analytical, Diagnostic and Therapeutic Techniques and Equipment::Investigative Techniques::Epidemiologic Methods::Epidemiologic Research Design::Sensitivity and Specificity::Predictive Value of TestsMedical Subject Headings::Analytical, Diagnostic and Therapeutic Techniques and Equipment::Investigative Techniques::Epidemiologic Methods::Epidemiologic Study Characteristics as Topic::Epidemiologic Studies::Cohort Studies::Longitudinal Studies::Prospective StudiesMedical Subject Headings::Analytical, Diagnostic and Therapeutic Techniques and Equipment::Diagnosis::Prognosis::Treatment OutcomeMedical Subject Headings::Chemicals and Drugs::Amino Acids, Peptides, and Proteins::Peptides::Intercellular Signaling Peptides and Proteins::Cytokines::Tumor Necrosis FactorsMedical Subject Headings::Analytical, Diagnostic and Therapeutic Techniques and Equipment::Investigative Techniques::Epidemiologic Methods::Statistics as Topic::Models, Statistical::Logistic ModelsMedical Subject Headings::Analytical, Diagnostic and Therapeutic Techniques and Equipment::Investigative Techniques::Epidemiologic Methods::Statistics as Topic::Area Under CurveMedical Subject Headings::Analytical, Diagnostic and Therapeutic Techniques and Equipment::Investigative Techniques::Genetic Techniques::Sequence Analysis::High-Throughput Nucleotide SequencingMedical Subject Headings::Phenomena and Processes::Mathematical Concepts::Algorithmsresearch article33833756open access10.3389/fimmu.2021.6316621664-3224PMC8022208