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Raynaud M, Al-Awadhi S, Louis K, Zhang H, Su X, Goutaudier V, Wang J, Demir Z, Wei Y, Truchot A, Bouquegneau A, Del Bello A, Bailly É, Lombardi Y, Maanaoui M, Giarraputo A, Naser S, Divard G, Aubert O, Murad MH, Wang C, Liu L, Bestard O, Naesens M, Friedewald JJ, Lefaucheur C, Riella L, Collins G, Ioannidis JP, Loupy A. Prognostic Biomarkers in Kidney Transplantation: A Systematic Review and Critical Appraisal. J Am Soc Nephrol 2024; 35:177-188. [PMID: 38053242 PMCID: PMC10843205 DOI: 10.1681/asn.0000000000000260] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2023] [Accepted: 10/08/2023] [Indexed: 12/07/2023] Open
Abstract
SIGNIFICANCE STATEMENT Why are there so few biomarkers accepted by health authorities and implemented in clinical practice, despite the high and growing number of biomaker studies in medical research ? In this meta-epidemiological study, including 804 studies that were critically appraised by expert reviewers, the authors have identified all prognostic kidney transplant biomarkers and showed overall suboptimal study designs, methods, results, interpretation, reproducible research standards, and transparency. The authors also demonstrated for the first time that the limited number of studies challenged the added value of their candidate biomarkers against standard-of-care routine patient monitoring parameters. Most biomarker studies tended to be single-center, retrospective studies with a small number of patients and clinical events. Less than 5% of the studies performed an external validation. The authors also showed the poor transparency reporting and identified a data beautification phenomenon. These findings suggest that there is much wasted research effort in transplant biomarker medical research and highlight the need to produce more rigorous studies so that more biomarkers may be validated and successfully implemented in clinical practice. BACKGROUND Despite the increasing number of biomarker studies published in the transplant literature over the past 20 years, demonstrations of their clinical benefit and their implementation in routine clinical practice are lacking. We hypothesized that suboptimal design, data, methodology, and reporting might contribute to this phenomenon. METHODS We formed a consortium of experts in systematic reviews, nephrologists, methodologists, and epidemiologists. A systematic literature search was performed in PubMed, Embase, Scopus, Web of Science, and Cochrane Library between January 1, 2005, and November 12, 2022 (PROSPERO ID: CRD42020154747). All English language, original studies investigating the association between a biomarker and kidney allograft outcome were included. The final set of publications was assessed by expert reviewers. After data collection, two independent reviewers randomly evaluated the inconsistencies for 30% of the references for each reviewer. If more than 5% of inconsistencies were observed for one given reviewer, a re-evaluation was conducted for all the references of the reviewer. The biomarkers were categorized according to their type and the biological milieu from which they were measured. The study characteristics related to the design, methods, results, and their interpretation were assessed, as well as reproducible research practices and transparency indicators. RESULTS A total of 7372 publications were screened and 804 studies met the inclusion criteria. A total of 1143 biomarkers were assessed among the included studies from blood ( n =821, 71.8%), intragraft ( n =169, 14.8%), or urine ( n =81, 7.1%) compartments. The number of studies significantly increased, with a median, yearly number of 31.5 studies (interquartile range [IQR], 23.8-35.5) between 2005 and 2012 and 57.5 (IQR, 53.3-59.8) between 2013 and 2022 ( P < 0.001). A total of 655 studies (81.5%) were retrospective, while 595 (74.0%) used data from a single center. The median number of patients included was 232 (IQR, 96-629) with a median follow-up post-transplant of 4.8 years (IQR, 3.0-6.2). Only 4.7% of studies were externally validated. A total of 346 studies (43.0%) did not adjust their biomarker for key prognostic factors, while only 3.1% of studies adjusted the biomarker for standard-of-care patient monitoring factors. Data sharing, code sharing, and registration occurred in 8.8%, 1.1%, and 4.6% of studies, respectively. A total of 158 studies (20.0%) emphasized the clinical relevance of the biomarker, despite the reported nonsignificant association of the biomarker with the outcome measure. A total of 288 studies assessed rejection as an outcome. We showed that these rejection studies shared the same characteristics as other studies. CONCLUSIONS Biomarker studies in kidney transplantation lack validation, rigorous design and methodology, accurate interpretation, and transparency. Higher standards are needed in biomarker research to prove the clinical utility and support clinical use.
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Affiliation(s)
- Marc Raynaud
- INSERM, PARCC, Paris Institute for Transplantation and Organ Regeneration, Université de Paris Cité, Paris, France
| | - Solaf Al-Awadhi
- INSERM, PARCC, Paris Institute for Transplantation and Organ Regeneration, Université de Paris Cité, Paris, France
| | - Kevin Louis
- INSERM, PARCC, Paris Institute for Transplantation and Organ Regeneration, Université de Paris Cité, Paris, France
| | - Huanxi Zhang
- The First Affiliated Hospital of Sun Yat-Sen University, Guangzhou, China
| | - Xiaojun Su
- The First Affiliated Hospital of Sun Yat-Sen University, Guangzhou, China
| | - Valentin Goutaudier
- INSERM, PARCC, Paris Institute for Transplantation and Organ Regeneration, Université de Paris Cité, Paris, France
| | - Jiali Wang
- The First Affiliated Hospital of Sun Yat-Sen University, Guangzhou, China
| | - Zeynep Demir
- INSERM, PARCC, Paris Institute for Transplantation and Organ Regeneration, Université de Paris Cité, Paris, France
| | - Yongcheng Wei
- The First Affiliated Hospital of Sun Yat-Sen University, Guangzhou, China
| | - Agathe Truchot
- INSERM, PARCC, Paris Institute for Transplantation and Organ Regeneration, Université de Paris Cité, Paris, France
| | - Antoine Bouquegneau
- Department of Nephrology-Dialysis-Transplantation, University Hospital of Liège, Liège, Belgium
| | - Arnaud Del Bello
- Department of Nephrology and Organ Transplantation, INSERM, CHU Rangueil & Purpan, Université Paul Sabatier, Toulouse, France
| | - Élodie Bailly
- INSERM, PARCC, Paris Institute for Transplantation and Organ Regeneration, Université de Paris Cité, Paris, France
- Thomas E. Starzl Transplantation Institute, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania
| | - Yannis Lombardi
- Kidney Transplant Department, Tenon Hospital, Assistance Publique – Hôpitaux de Paris, Paris, France
| | - Mehdi Maanaoui
- Nephrology Department, CHU Lille, Lille University, Lille, France
- INSERM U1190, Translational Research for Diabetes, Lille, France
| | - Alessia Giarraputo
- INSERM, PARCC, Paris Institute for Transplantation and Organ Regeneration, Université de Paris Cité, Paris, France
- Department of Cardiac, Thoracic, Vascular Sciences and Public Health, University of Padua, Padua, Italy
| | - Sofia Naser
- INSERM, PARCC, Paris Institute for Transplantation and Organ Regeneration, Université de Paris Cité, Paris, France
| | - Gillian Divard
- INSERM, PARCC, Paris Institute for Transplantation and Organ Regeneration, Université de Paris Cité, Paris, France
| | - Olivier Aubert
- INSERM, PARCC, Paris Institute for Transplantation and Organ Regeneration, Université de Paris Cité, Paris, France
| | | | - Changxi Wang
- The First Affiliated Hospital of Sun Yat-Sen University, Guangzhou, China
| | - Longshan Liu
- The First Affiliated Hospital of Sun Yat-Sen University, Guangzhou, China
| | - Oriol Bestard
- Nephrology Department, Hospital de Vall d'Hebron, Barcelona, Spain
| | - Maarten Naesens
- Department of Microbiology, Immunology and Transplantation, Nephrology and Renal Transplantation Research Group, KU Leuven, Leuven, Belgium
| | - John J. Friedewald
- Division of Transplantation, Feinberg School of Medicine, Northwestern University, Chicago, Illinois
| | - Carmen Lefaucheur
- Kidney Transplant Department, Saint-Louis Hospital, Assistance Publique - Hôpitaux de Paris, Paris, France
| | - Leonardo Riella
- Renal Division, Schuster Family Transplantation Research Center, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts
| | - Gary Collins
- Center for Statistics in Medicine, NDORMS, Botnar Research Center, University of Oxford, Oxford, United Kingdom
| | - John P.A. Ioannidis
- Departments of Medicine, of Epidemiology and Population Health, of Biomedical Data Science, and of Statistics and Meta-Research Innovation Center at Stanford (METRICS), Stanford University, Stanford, California
| | - Alexandre Loupy
- INSERM, PARCC, Paris Institute for Transplantation and Organ Regeneration, Université de Paris Cité, Paris, France
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Truchot A, Raynaud M, Loupy A. The authors reply. Kidney Int 2023; 104:1036. [PMID: 37863625 DOI: 10.1016/j.kint.2023.07.017] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2023] [Accepted: 07/26/2023] [Indexed: 10/22/2023]
Affiliation(s)
- Agathe Truchot
- Paris Institute for Transplantation and Organ Regeneration, Université Paris Cité, Institut National de la Santé de Et de la Recherche Médicale (INSERM), U-970, AP-HP, Paris, France
| | - Marc Raynaud
- Paris Institute for Transplantation and Organ Regeneration, Université Paris Cité, Institut National de la Santé de Et de la Recherche Médicale (INSERM), U-970, AP-HP, Paris, France
| | - Alexandre Loupy
- Paris Institute for Transplantation and Organ Regeneration, Université Paris Cité, Institut National de la Santé de Et de la Recherche Médicale (INSERM), U-970, AP-HP, Paris, France.
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Truchot A, Raynaud M, Loupy A. Excess mortality after kidney transplantation: does sex matter? Kidney Int 2023; 103:1023-1024. [PMID: 37210193 DOI: 10.1016/j.kint.2023.03.011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2023] [Revised: 03/13/2023] [Accepted: 03/17/2023] [Indexed: 05/22/2023]
Abstract
Understanding sex differences in graft outcomes within the course of kidney transplantation is needed to unravel factors leading to the observed disparities and further improve patient management. In this issue, Vinson et al. presented a relative survival analysis comparing the excess risk of mortality in female and male recipients after kidney transplantation. This commentary discusses the major findings but also the challenges of the use of registry data to conduct large-scale analyses.
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Affiliation(s)
- Agathe Truchot
- Paris Institute for Transplantation and Organ Regeneration, Université Paris Cité, Institut National de la Santé et de la Recherche Médicale, U-970, Assistance Publique-Hôpitaux de Paris, Paris, France
| | - Marc Raynaud
- Paris Institute for Transplantation and Organ Regeneration, Université Paris Cité, Institut National de la Santé et de la Recherche Médicale, U-970, Assistance Publique-Hôpitaux de Paris, Paris, France
| | - Alexandre Loupy
- Paris Institute for Transplantation and Organ Regeneration, Université Paris Cité, Institut National de la Santé et de la Recherche Médicale, U-970, Assistance Publique-Hôpitaux de Paris, Paris, France.
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Truchot A, Raynaud M, Kamar N, Naesens M, Legendre C, Delahousse M, Thaunat O, Buchler M, Crespo M, Linhares K, Orandi BJ, Akalin E, Pujol GS, Silva HT, Gupta G, Segev DL, Jouven X, Bentall AJ, Stegall MD, Lefaucheur C, Aubert O, Loupy A. Machine learning does not outperform traditional statistical modelling for kidney allograft failure prediction. Kidney Int 2023; 103:936-948. [PMID: 36572246 DOI: 10.1016/j.kint.2022.12.011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2022] [Revised: 11/04/2022] [Accepted: 12/15/2022] [Indexed: 12/24/2022]
Abstract
Machine learning (ML) models have recently shown potential for predicting kidney allograft outcomes. However, their ability to outperform traditional approaches remains poorly investigated. Therefore, using large cohorts of kidney transplant recipients from 14 centers worldwide, we developed ML-based prediction models for kidney allograft survival and compared their prediction performances to those achieved by a validated Cox-Based Prognostication System (CBPS). In a French derivation cohort of 4000 patients, candidate determinants of allograft failure including donor, recipient and transplant-related parameters were used as predictors to develop tree-based models (RSF, RSF-ERT, CIF), Support Vector Machine models (LK-SVM, AK-SVM) and a gradient boosting model (XGBoost). Models were externally validated with cohorts of 2214 patients from Europe, 1537 from North America, and 671 from South America. Among these 8422 kidney transplant recipients, 1081 (12.84%) lost their grafts after a median post-transplant follow-up time of 6.25 years (Inter Quartile Range 4.33-8.73). At seven years post-risk evaluation, the ML models achieved a C-index of 0.788 (95% bootstrap percentile confidence interval 0.736-0.833), 0.779 (0.724-0.825), 0.786 (0.735-0.832), 0.527 (0.456-0.602), 0.704 (0.648-0.759) and 0.767 (0.711-0.815) for RSF, RSF-ERT, CIF, LK-SVM, AK-SVM and XGBoost respectively, compared with 0.808 (0.792-0.829) for the CBPS. In validation cohorts, ML models' discrimination performances were in a similar range of those of the CBPS. Calibrations of the ML models were similar or less accurate than those of the CBPS. Thus, when using a transparent methodological pipeline in validated international cohorts, ML models, despite overall good performances, do not outperform a traditional CBPS in predicting kidney allograft failure. Hence, our current study supports the continued use of traditional statistical approaches for kidney graft prognostication.
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Affiliation(s)
- Agathe Truchot
- Université de Paris, INSERM, PARCC, Paris Translational Research Centre for Organ Transplantation, Paris, France
| | - Marc Raynaud
- Université de Paris, INSERM, PARCC, Paris Translational Research Centre for Organ Transplantation, Paris, France
| | - Nassim Kamar
- Université Paul Sabatier, INSERM, Department of Nephrology and Organ Transplantation, CHU Rangueil and Purpan, Toulouse, France
| | - Maarten Naesens
- Department of Microbiology, Immunology and Transplantation, KU Leuven, Leuven, Belgium
| | - Christophe Legendre
- Université de Paris, INSERM, PARCC, Paris Translational Research Centre for Organ Transplantation, Paris, France; Kidney Transplant Department, Necker Hospital, Assistance Publique-Hôpitaux de Paris, Paris, France
| | - Michel Delahousse
- Department of Transplantation, Nephrology and Clinical Immunology, Foch Hospital, Suresnes, France
| | - Olivier Thaunat
- Department of Transplantation, Nephrology and Clinical Immunology, Hospices Civils de Lyon, Lyon, France
| | - Matthias Buchler
- Nephrology and Immunology Department, Bretonneau Hospital, Tours, France
| | - Marta Crespo
- Department of Nephrology, Hospital del Mar Barcelona, Barcelona, Spain
| | - Kamilla Linhares
- Hospital do Rim, Escola Paulista de Medicina, Universidade Federal de São Paulo, São Paulo, Brazil
| | - Babak J Orandi
- University of Alabama at Birmingham Heersink School of Medicine, Birmingham, Alabama, USA
| | - Enver Akalin
- Renal Division, Montefiore Medical Centre, Kidney Transplantation Program, Albert Einstein College of Medicine, New York, New York, USA
| | - Gervacio Soler Pujol
- Unidad de Trasplante Renopancreas, Centro de Educacion Medica e Investigaciones Clinicas Buenos Aires, Buenos Aires, Argentina
| | - Helio Tedesco Silva
- Hospital do Rim, Escola Paulista de Medicina, Universidade Federal de São Paulo, São Paulo, Brazil
| | - Gaurav Gupta
- Division of Nephrology, Department of Internal Medicine, Virginia Commonwealth University School of Medicine, Richmond, Virginia, USA
| | - Dorry L Segev
- Department of Surgery, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Xavier Jouven
- Université de Paris, INSERM, PARCC, Paris Translational Research Centre for Organ Transplantation, Paris, France; Cardiology Department, European Georges Pompidou Hospital, Paris, France
| | - Andrew J Bentall
- William J von Liebig Centre for Transplantation and Clinical Regeneration, Mayo Clinic, Rochester, Minnesota, USA
| | - Mark D Stegall
- William J von Liebig Centre for Transplantation and Clinical Regeneration, Mayo Clinic, Rochester, Minnesota, USA
| | - Carmen Lefaucheur
- Université de Paris, INSERM, PARCC, Paris Translational Research Centre for Organ Transplantation, Paris, France; Kidney Transplant Department, Saint-Louis Hospital, Assistance Publique-Hôpitaux de Paris, Paris, France
| | - Olivier Aubert
- Université de Paris, INSERM, PARCC, Paris Translational Research Centre for Organ Transplantation, Paris, France; Kidney Transplant Department, Necker Hospital, Assistance Publique-Hôpitaux de Paris, Paris, France
| | - Alexandre Loupy
- Université de Paris, INSERM, PARCC, Paris Translational Research Centre for Organ Transplantation, Paris, France; Kidney Transplant Department, Necker Hospital, Assistance Publique-Hôpitaux de Paris, Paris, France.
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Raynaud M, Goutaudier V, Louis K, Al-Awadhi S, Dubourg Q, Truchot A, Brousse R, Saleh N, Giarraputo A, Debiais C, Demir Z, Certain A, Tacafred F, Cortes-Garcia E, Yanes S, Dagobert J, Naser S, Robin B, Bailly É, Jouven X, Reese PP, Loupy A. Impact of the COVID-19 pandemic on publication dynamics and non-COVID-19 research production. BMC Med Res Methodol 2021; 21:255. [PMID: 34809561 PMCID: PMC8607966 DOI: 10.1186/s12874-021-01404-9] [Citation(s) in RCA: 34] [Impact Index Per Article: 11.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2021] [Accepted: 09/17/2021] [Indexed: 12/11/2022] Open
Abstract
BACKGROUND The COVID-19 pandemic has severely affected health systems and medical research worldwide but its impact on the global publication dynamics and non-COVID-19 research has not been measured. We hypothesized that the COVID-19 pandemic may have impacted the scientific production of non-COVID-19 research. METHODS We conducted a comprehensive meta-research on studies (original articles, research letters and case reports) published between 01/01/2019 and 01/01/2021 in 10 high-impact medical and infectious disease journals (New England Journal of Medicine, Lancet, Journal of the American Medical Association, Nature Medicine, British Medical Journal, Annals of Internal Medicine, Lancet Global Health, Lancet Public Health, Lancet Infectious Disease and Clinical Infectious Disease). For each publication, we recorded publication date, publication type, number of authors, whether the publication was related to COVID-19, whether the publication was based on a case series, and the number of patients included in the study if the publication was based on a case report or a case series. We estimated the publication dynamics with a locally estimated scatterplot smoothing method. A Natural Language Processing algorithm was designed to calculate the number of authors for each publication. We simulated the number of non-COVID-19 studies that could have been published during the pandemic by extrapolating the publication dynamics of 2019 to 2020, and comparing the expected number to the observed number of studies. RESULTS Among the 22,525 studies assessed, 6319 met the inclusion criteria, of which 1022 (16.2%) were related to COVID-19 research. A dramatic increase in the number of publications in general journals was observed from February to April 2020 from a weekly median number of publications of 4.0 (IQR: 2.8-5.5) to 19.5 (IQR: 15.8-24.8) (p < 0.001), followed afterwards by a pattern of stability with a weekly median number of publications of 10.0 (IQR: 6.0-14.0) until December 2020 (p = 0.045 in comparison with April). Two prototypical editorial strategies were found: 1) journals that maintained the volume of non-COVID-19 publications while integrating COVID-19 research and thus increased their overall scientific production, and 2) journals that decreased the volume of non-COVID-19 publications while integrating COVID-19 publications. We estimated using simulation models that the COVID pandemic was associated with a 18% decrease in the production of non-COVID-19 research. We also found a significant change of the publication type in COVID-19 research as compared with non-COVID-19 research illustrated by a decrease in the number of original articles, (47.9% in COVID-19 publications vs 71.3% in non-COVID-19 publications, p < 0.001). Last, COVID-19 publications showed a higher number of authors, especially for case reports with a median of 9.0 authors (IQR: 6.0-13.0) in COVID-19 publications, compared to a median of 4.0 authors (IQR: 3.0-6.0) in non-COVID-19 publications (p < 0.001). CONCLUSION In this meta-research gathering publications from high-impact medical journals, we have shown that the dramatic rise in COVID-19 publications was accompanied by a substantial decrease of non-COVID-19 research. META-RESEARCH REGISTRATION: https://osf.io/9vtzp/ .
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Affiliation(s)
- Marc Raynaud
- Paris Translational Research Epidemiology and Biostatistics Department, Université de Paris, INSERM U970, PARCC, 56 rue Leblanc, 75015, Paris, France
| | - Valentin Goutaudier
- Paris Translational Research Epidemiology and Biostatistics Department, Université de Paris, INSERM U970, PARCC, 56 rue Leblanc, 75015, Paris, France
| | - Kevin Louis
- Paris Translational Research Epidemiology and Biostatistics Department, Université de Paris, INSERM U970, PARCC, 56 rue Leblanc, 75015, Paris, France
| | - Solaf Al-Awadhi
- Paris Translational Research Epidemiology and Biostatistics Department, Université de Paris, INSERM U970, PARCC, 56 rue Leblanc, 75015, Paris, France
| | - Quentin Dubourg
- Pitié-Salpêtrière University Hospital, Assistance Publique-Hôpitaux de Paris, Sorbonne University, Paris, France
| | - Agathe Truchot
- Paris Translational Research Epidemiology and Biostatistics Department, Université de Paris, INSERM U970, PARCC, 56 rue Leblanc, 75015, Paris, France
| | - Romain Brousse
- Paris Translational Research Epidemiology and Biostatistics Department, Université de Paris, INSERM U970, PARCC, 56 rue Leblanc, 75015, Paris, France
- Kidney Transplantation Department, Necker Hospital, Assistance Publique - Hôpitaux de Paris, Paris, France
| | - Nouredine Saleh
- Paris Translational Research Epidemiology and Biostatistics Department, Université de Paris, INSERM U970, PARCC, 56 rue Leblanc, 75015, Paris, France
| | - Alessia Giarraputo
- Paris Translational Research Epidemiology and Biostatistics Department, Université de Paris, INSERM U970, PARCC, 56 rue Leblanc, 75015, Paris, France
| | - Charlotte Debiais
- Paris Translational Research Epidemiology and Biostatistics Department, Université de Paris, INSERM U970, PARCC, 56 rue Leblanc, 75015, Paris, France
| | - Zeynep Demir
- Paris Translational Research Epidemiology and Biostatistics Department, Université de Paris, INSERM U970, PARCC, 56 rue Leblanc, 75015, Paris, France
- Paediatrics Unit, Necker University Hospital, Paris, France
| | - Anaïs Certain
- Paris Translational Research Epidemiology and Biostatistics Department, Université de Paris, INSERM U970, PARCC, 56 rue Leblanc, 75015, Paris, France
| | - Francine Tacafred
- Paris Translational Research Epidemiology and Biostatistics Department, Université de Paris, INSERM U970, PARCC, 56 rue Leblanc, 75015, Paris, France
| | - Esteban Cortes-Garcia
- Paris Translational Research Epidemiology and Biostatistics Department, Université de Paris, INSERM U970, PARCC, 56 rue Leblanc, 75015, Paris, France
| | - Safia Yanes
- Kidney Transplantation Department, Necker Hospital, Assistance Publique - Hôpitaux de Paris, Paris, France
| | - Jessy Dagobert
- Paris Translational Research Epidemiology and Biostatistics Department, Université de Paris, INSERM U970, PARCC, 56 rue Leblanc, 75015, Paris, France
| | - Sofia Naser
- Nephrology, Dialysis and Transplantation Department, Hospital Privado Universitario de Cordoba, Cordoba, Argentina
| | - Blaise Robin
- Paris Translational Research Epidemiology and Biostatistics Department, Université de Paris, INSERM U970, PARCC, 56 rue Leblanc, 75015, Paris, France
| | - Élodie Bailly
- Paris Translational Research Epidemiology and Biostatistics Department, Université de Paris, INSERM U970, PARCC, 56 rue Leblanc, 75015, Paris, France
- Thomas E. Starzl Transplantation Institute, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
| | - Xavier Jouven
- Paris Translational Research Epidemiology and Biostatistics Department, Université de Paris, INSERM U970, PARCC, 56 rue Leblanc, 75015, Paris, France
- Cardiology Departement, Hôpital Européen Georges Pompidou, Paris, France
| | - Peter P Reese
- University of Pennsylvania School of Medicine, Philadelphia, PA, USA
| | - Alexandre Loupy
- Paris Translational Research Epidemiology and Biostatistics Department, Université de Paris, INSERM U970, PARCC, 56 rue Leblanc, 75015, Paris, France.
- Kidney Transplantation Department, Necker Hospital, Assistance Publique - Hôpitaux de Paris, Paris, France.
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Morales D, Lombart F, Truchot A, Maire P, Hussein M, Hamitou W, Vigneron P, Galmiche A, Lok C, Vayssade M. 3D Coculture Models Underline Metastatic Melanoma Cell Sensitivity to Vemurafenib. Tissue Eng Part A 2019; 25:1116-1126. [PMID: 30501565 DOI: 10.1089/ten.tea.2018.0210] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/17/2023] Open
Abstract
IMPACT STATEMENT Three dimensional in vitro cell culture systems better reflect the native structural architecture of tissues and are attractive to investigate cancer cell sensitivity to drugs. We have developed and compared several metastatic melanoma (MM) models cultured as a monolayer (2D) and cocultured on three dimensional (3D) dermal equivalents with fibroblasts to better unravel factors modulating cell sensitivity to vemurafenib, a BRAF inhibitor. The heterotypic 3D melanoma model we have established summarizes paracrine signalization by stromal cells and type I collagen matrix, mimicking the natural microenvironment of cutaneous MM, and allows for the identification of potent sensitive melanoma cells to the drug. This model could be a powerful tool for predicting drug efficiency.
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Affiliation(s)
- Delphine Morales
- 1Sorbonne University, Université de Technologie de Compiègne, CNRS, UMR 7338 Biomechanics and Bioengineering, Centre de Recherche Royallieu, Compiègne Cedex, France
| | - Florian Lombart
- 2Department of Dermatology, CHU Amiens Picardie-Site Nord, Amiens, France
| | - Agathe Truchot
- 1Sorbonne University, Université de Technologie de Compiègne, CNRS, UMR 7338 Biomechanics and Bioengineering, Centre de Recherche Royallieu, Compiègne Cedex, France
| | - Pauline Maire
- 1Sorbonne University, Université de Technologie de Compiègne, CNRS, UMR 7338 Biomechanics and Bioengineering, Centre de Recherche Royallieu, Compiègne Cedex, France
- 3Department of Biochemistry, CHU Amiens Picardie, Amiens, France
| | - Marwa Hussein
- 1Sorbonne University, Université de Technologie de Compiègne, CNRS, UMR 7338 Biomechanics and Bioengineering, Centre de Recherche Royallieu, Compiègne Cedex, France
| | - Warda Hamitou
- 1Sorbonne University, Université de Technologie de Compiègne, CNRS, UMR 7338 Biomechanics and Bioengineering, Centre de Recherche Royallieu, Compiègne Cedex, France
| | - Pascale Vigneron
- 1Sorbonne University, Université de Technologie de Compiègne, CNRS, UMR 7338 Biomechanics and Bioengineering, Centre de Recherche Royallieu, Compiègne Cedex, France
| | - Antoine Galmiche
- 3Department of Biochemistry, CHU Amiens Picardie, Amiens, France
- 4Research Unit EA7516 CHIMERE, Université de Picardie Jules Verne, Amiens, France
| | - Catherine Lok
- 2Department of Dermatology, CHU Amiens Picardie-Site Nord, Amiens, France
| | - Muriel Vayssade
- 1Sorbonne University, Université de Technologie de Compiègne, CNRS, UMR 7338 Biomechanics and Bioengineering, Centre de Recherche Royallieu, Compiègne Cedex, France
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