Integrative Clinical, Molecular, and Computational Analysis Identify Novel Biomarkers and Differential Profiles of Anti-TNF Response in Rheumatoid Arthritis
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 p...
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Frontiers Media S.A.
2021-03-01
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| Series: | Frontiers in Immunology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fimmu.2021.631662/full |
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| author | 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 |
| author_facet | 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 |
| author_sort | Maria Luque-Tévar |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-17T21:29:45Z |
| format | Article |
| id | doaj.art-bb017e91ba5d4f1ebf7fe17dc8217b1e |
| institution | Directory Open Access Journal |
| issn | 1664-3224 |
| language | English |
| last_indexed | 2024-12-17T21:29:45Z |
| publishDate | 2021-03-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Immunology |
| spelling | doaj.art-bb017e91ba5d4f1ebf7fe17dc8217b1e2022-12-21T21:31:56ZengFrontiers Media S.A.Frontiers in Immunology1664-32242021-03-011210.3389/fimmu.2021.631662631662Integrative Clinical, Molecular, and Computational Analysis Identify Novel Biomarkers and Differential Profiles of Anti-TNF Response in Rheumatoid ArthritisMaria Luque-Tévar0Carlos Perez-Sanchez1Alejandra Mª Patiño-Trives2Nuria Barbarroja3Ivan Arias de la Rosa4Mª Carmen Abalos-Aguilera5Juan Antonio Marin-Sanz6Desiree Ruiz-Vilchez7Rafaela Ortega-Castro8Pilar Font9Clementina Lopez-MedinaMontserrat Romero-Gomez10Carlos Rodriguez-Escalera11Jose Perez-Venegas12Mª Dolores Ruiz-Montesinos13Carmen Dominguez14Carmen Romero-Barco15Antonio Fernandez-Nebro16Natalia Mena-Vazquez17Jose Luis Marenco18Julia Uceda-Montañez19Mª Dolores Toledo-Coello20M. Angeles Aguirre21Alejandro Escudero-Contreras22Eduardo Collantes-Estevez23Chary Lopez-Pedrera24Instituto Maimónides de Investigación Biomédica de Córdoba (IMIBIC), Hospital Reina Sofia, Universidad de Cordoba, Córdoba, SpainInstituto Maimónides de Investigación Biomédica de Córdoba (IMIBIC), Hospital Reina Sofia, Universidad de Cordoba, Córdoba, SpainInstituto Maimónides de Investigación Biomédica de Córdoba (IMIBIC), Hospital Reina Sofia, Universidad de Cordoba, Córdoba, SpainInstituto Maimónides de Investigación Biomédica de Córdoba (IMIBIC), Hospital Reina Sofia, Universidad de Cordoba, Córdoba, SpainInstituto Maimónides de Investigación Biomédica de Córdoba (IMIBIC), Hospital Reina Sofia, Universidad de Cordoba, Córdoba, SpainInstituto Maimónides de Investigación Biomédica de Córdoba (IMIBIC), Hospital Reina Sofia, Universidad de Cordoba, Córdoba, SpainInstituto Maimónides de Investigación Biomédica de Córdoba (IMIBIC), Hospital Reina Sofia, Universidad de Cordoba, Córdoba, SpainInstituto Maimónides de Investigación Biomédica de Córdoba (IMIBIC), Hospital Reina Sofia, Universidad de Cordoba, Córdoba, SpainInstituto Maimónides de Investigación Biomédica de Córdoba (IMIBIC), Hospital Reina Sofia, Universidad de Cordoba, Córdoba, SpainInstituto Maimónides de Investigación Biomédica de Córdoba (IMIBIC), Hospital Reina Sofia, Universidad de Cordoba, Córdoba, SpainInstituto Maimónides de Investigación Biomédica de Córdoba (IMIBIC), Hospital Reina Sofia, Universidad de Cordoba, Córdoba, SpainHospital Universitario de Jaen, Jaén, SpainHospital Universitario Virgen Macarena, Sevilla, SpainHospital Universitario Virgen Macarena, Sevilla, SpainHospital Universitario Virgen Macarena, Sevilla, SpainHospital Clínico Universitario, Malaga, SpainHospital Regional Universitario de Malaga, Malaga, SpainHospital Regional Universitario de Malaga, Malaga, SpainHospital Universitario Virgen de Valme, Sevilla, SpainHospital Universitario Virgen de Valme, Sevilla, SpainHospital Universitario de Jerez de la Frontera, Cádiz, SpainInstituto Maimónides de Investigación Biomédica de Córdoba (IMIBIC), Hospital Reina Sofia, Universidad de Cordoba, Córdoba, SpainInstituto Maimónides de Investigación Biomédica de Córdoba (IMIBIC), Hospital Reina Sofia, Universidad de Cordoba, Córdoba, SpainInstituto Maimónides de Investigación Biomédica de Córdoba (IMIBIC), Hospital Reina Sofia, Universidad de Cordoba, Córdoba, SpainInstituto Maimónides de Investigación Biomédica de Córdoba (IMIBIC), Hospital Reina Sofia, Universidad de Cordoba, Córdoba, SpainBackground: 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.https://www.frontiersin.org/articles/10.3389/fimmu.2021.631662/fullrheumatoid arthritisanti-TNF agentsinflammationNEtosismicroRNAsmachine learning |
| spellingShingle | 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 Integrative Clinical, Molecular, and Computational Analysis Identify Novel Biomarkers and Differential Profiles of Anti-TNF Response in Rheumatoid Arthritis Frontiers in Immunology rheumatoid arthritis anti-TNF agents inflammation NEtosis microRNAs machine learning |
| title | Integrative Clinical, Molecular, and Computational Analysis Identify Novel Biomarkers and Differential Profiles of Anti-TNF Response in Rheumatoid Arthritis |
| title_full | Integrative Clinical, Molecular, and Computational Analysis Identify Novel Biomarkers and Differential Profiles of Anti-TNF Response in Rheumatoid Arthritis |
| title_fullStr | Integrative Clinical, Molecular, and Computational Analysis Identify Novel Biomarkers and Differential Profiles of Anti-TNF Response in Rheumatoid Arthritis |
| title_full_unstemmed | Integrative Clinical, Molecular, and Computational Analysis Identify Novel Biomarkers and Differential Profiles of Anti-TNF Response in Rheumatoid Arthritis |
| title_short | Integrative Clinical, Molecular, and Computational Analysis Identify Novel Biomarkers and Differential Profiles of Anti-TNF Response in Rheumatoid Arthritis |
| title_sort | integrative clinical molecular and computational analysis identify novel biomarkers and differential profiles of anti tnf response in rheumatoid arthritis |
| topic | rheumatoid arthritis anti-TNF agents inflammation NEtosis microRNAs machine learning |
| url | https://www.frontiersin.org/articles/10.3389/fimmu.2021.631662/full |
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