1. Integrative Clinical, Molecular, and Computational Analysis Identify Novel Biomarkers and Differential Profiles of Anti-TNF Response in Rheumatoid Arthritis
- Author
-
Alejandra M. Patiño-Trives, Chary López-Pedrera, José Luis Marenco, Rafaela Ortega-Castro, Carlos Perez-Sanchez, M. Romero-Gómez, Iván Arias de la Rosa, Alejandro Escudero-Contreras, Eduardo Collantes-Estevez, Natalia Mena-Vázquez, Nuria Barbarroja, Pilar Font, Antonio Fernández-Nebro, J.J. Pérez-Venegas, Desiree Ruiz-Vilchez, Carmen Dominguez, M. D. C. Abalos-Aguilera, C.M. Romero-Barco, Juan Antonio Marin-Sanz, M. Angeles Aguirre, Carlos Rodriguez-Escalera, Mª Dolores Ruiz-Montesinos, Julia Uceda-Montañez, María Luque-Tévar, Mª Dolores Toledo-Coello, Clementina López-Medina, [Luque-Tévar,M, Perez-Sanchez,C, Patiño-Trives,AM, Barbarroja,N, Arias de la Rosa,I, Abalos-Aguilera,MC, Marin-Sanz,JA, Ruiz-Vilchez,D, Ortega-Castro,R, Font,P, Lopez-Medina,C, Romero-Gomez,M, Aguirre,MA, Escudero-Contreras,A, Collantes-Estevez,E, Lopez-Pedrera,C] Instituto Maimónides de Investigación Biomédica de Córdoba (IMIBIC), Hospital Reina Sofia, Universidad de Cordoba, Córdoba, Spain. [Rodriguez-Escalera,C] Hospital Universitario de Jaen, Jaén, Spain. [Perez-Venegas,J, Ruiz-Montesinos,MD, Dominguez,C] Hospital Universitario Virgen Macarena, Sevilla, Spain. [Romero-Barco,C] Hospital Clínico Universitario, Malaga, Spain. [Fernandez-Nebro,A, Mena-Vazquez,N] Hospital Regional Universitario de Malaga, Malaga, Spain. [Marenco,JL, Uceda-Montañez,J] Hospital Universitario Virgen de Valme, Sevilla, Spain. [Toledo-Coello,MD] Hospital Universitario de Jerez de la Frontera, Cádiz, Spain., and This study was supported by grants from the Instituto de Salud Carlos III (PI18/00837), cofinanciado por el Fondo Europeo de Desarrollo Regional de la Unión Europea Una manera de hacer Europa, Spain, the Spanish Inflammatory and Rheumatic Diseases Network (RIER), Instituto de Salud Carlos III (RD16/0012/0015) and the Andalusian Regional Health System (ref. PI-0285-2017). CL-P was supported by a contract from the Spanish Junta de Andalucía (Nicolas Monardes program).
- Subjects
Male ,0301 basic medicine ,Oncology ,rheumatoid arthritis ,Longitudinal study ,Phenomena and Processes::Genetic Phenomena::Phenotype [Medical Subject Headings] ,Chemicals and Drugs::Amino Acids, Peptides, and Proteins::Peptides::Intercellular Signaling Peptides and Proteins::Cytokines::Tumor Necrosis Factors [Medical Subject Headings] ,efficacy ,Analytical, Diagnostic and Therapeutic Techniques and Equipment::Investigative Techniques::Genetic Techniques::Sequence Analysis::High-Throughput Nucleotide Sequencing [Medical Subject Headings] ,Logistic regression ,Extracellular Traps ,Organisms::Eukaryota::Animals::Chordata::Vertebrates::Mammals::Primates::Haplorhini::Catarrhini::Hominidae::Humans [Medical Subject Headings] ,Arthritis, Rheumatoid ,0302 clinical medicine ,Cluster Analysis ,Immunology and Allergy ,Longitudinal Studies ,Prospective Studies ,Computational analysis ,Phenomena and Processes::Metabolic Phenomena::Metabolism::Oxidative Stress [Medical Subject Headings] ,Original Research ,MicroARNs ,Estrés oxidativo ,Anti-TNF agents ,Middle Aged ,Phenotype ,Aprendizaje automático ,microRNAs ,Diseases::Musculoskeletal Diseases::Rheumatic Diseases::Arthritis, Rheumatoid [Medical Subject Headings] ,Analytical, Diagnostic and Therapeutic Techniques and Equipment::Investigative Techniques::Epidemiologic Methods::Epidemiologic Research Design::Sensitivity and Specificity::Predictive Value of Tests [Medical Subject Headings] ,Treatment Outcome ,machine learning ,Analytical, Diagnostic and Therapeutic Techniques and Equipment::Investigative Techniques::Epidemiologic Methods::Statistics as Topic::Models, Statistical::Logistic Models [Medical Subject Headings] ,Eficacia ,Antirheumatic Agents ,Rheumatoid arthritis ,Cohort ,Female ,Tumor necrosis factor alpha ,anti-TNF agents ,medicine.symptom ,Fenotipo ,Chemicals and Drugs::Biological Factors::Biological Markers::Biomarkers, Pharmacological [Medical Subject Headings] ,Adult ,lcsh:Immunologic diseases. Allergy ,medicine.medical_specialty ,Phenomena and Processes::Mathematical Concepts::Algorithms [Medical Subject Headings] ,Efficacy ,Analytical, Diagnostic and Therapeutic Techniques and Equipment::Investigative Techniques::Epidemiologic Methods::Epidemiologic Study Characteristics as Topic::Epidemiologic Studies::Cohort Studies::Longitudinal Studies::Prospective Studies [Medical Subject Headings] ,Immunology ,Check Tags::Male [Medical Subject Headings] ,Inflammation ,Artritis reumatoide ,Analytical, Diagnostic and Therapeutic Techniques and Equipment::Investigative Techniques::Epidemiologic Methods::Statistics as Topic::Cluster Analysis [Medical Subject Headings] ,Chemicals and Drugs::Chemical Actions and Uses::Pharmacologic Actions::Therapeutic Uses::Antirheumatic Agents [Medical Subject Headings] ,NEtosis ,03 medical and health sciences ,Predictive Value of Tests ,Analytical, Diagnostic and Therapeutic Techniques and Equipment::Investigative Techniques::Epidemiologic Methods::Epidemiologic Study Characteristics as Topic::Epidemiologic Studies::Cohort Studies::Longitudinal Studies [Medical Subject Headings] ,Internal medicine ,Machine learning ,medicine ,Humans ,Persons::Persons::Age Groups::Adult [Medical Subject Headings] ,Analytical, Diagnostic and Therapeutic Techniques and Equipment::Diagnosis::Prognosis::Treatment Outcome [Medical Subject Headings] ,030203 arthritis & rheumatology ,Inflamación ,Predictors ,business.industry ,Inhibidores del factor de necrosis tumoral ,Analytical, Diagnostic and Therapeutic Techniques and Equipment::Investigative Techniques::Epidemiologic Methods::Statistics as Topic::Area Under Curve [Medical Subject Headings] ,Persons::Persons::Age Groups::Adult::Middle Aged [Medical Subject Headings] ,medicine.disease ,Chemicals and Drugs::Nucleic Acids, Nucleotides, and Nucleosides::Antisense Elements (Genetics)::RNA, Antisense::MicroRNAs [Medical Subject Headings] ,MicroRNAs ,Oxidative Stress ,Biomarcadores ,predictors ,030104 developmental biology ,Check Tags::Female [Medical Subject Headings] ,Oxidative stress ,inflammation ,Tumor Necrosis Factor Inhibitors ,business ,lcsh:RC581-607 ,Diseases::Pathological Conditions, Signs and Symptoms::Pathologic Processes::Inflammation [Medical Subject Headings] ,Biomarkers - Abstract
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.
- Published
- 2021
- Full Text
- View/download PDF