Multi-centre validation of a flow cytometry method to identify optimal responders to interferon-beta in multiple sclerosis

Villarrubia, Noelia; Rodrı́guez-Martı́n, Eulalia; Alari-Pahissa, Elisenda; Aragón, Larraitz; Castillo-Triviño, Tamara; Eixarch, Herena; Ferrer, Joana M.; Martínez-Rodríguez, José Enrique; Massot, Margarita; Pinto-Medel, María Jesús; Prada, Álvaro; Rodríguez‐Acevedo, Breogán; Hurtado-Guerrero, Isaac; Gascón-Giménez, Francisco; Herrera, Guadalupe; Hernández-Clares, R.; Salgado, María Gema; Oterino, Agustı́n; Segundo, David San; Cuello, Juan Pablo; Gil-Herrera, Juana; Cámara, Carmen; Gómez-Gutiérrez, M.; Martínez‐Hernández, Eugenia; Meca-Lallana, Virginia; Moga, Esther; Muñoz‐Calleja, Cecilia; Querol, Luís; Presas-Rodríguez, Silvia; Teniente-Serra, Aina; Vlagea, Alexandru; Muriel, Alfonso; Roldán, Ernesto; Villar, Luisa María

doi:10.1016/j.cca.2018.11.008

Cited by 3 publications

(3 citation statements)

References 20 publications

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“…To address the performance of all participants, we evaluated intraoperator repeatability (i.e., variability of individual measurements on the same donor, expressed as CV in Supplementary Fig. S2), as well as standardised residuals (i.e., Z-score; Figure 4) [29], on both cPBMC and WB specimens, for each analysed parameter.…”

Section: Intraoperator Variability/operatormentioning

confidence: 99%

Multicentre Harmonisation of a Six-Colour Flow Cytometry Panel for Naïve/Memory T Cell Immunomonitoring

Macchia

Sorsa²,

Ruspantini³

et al. 2020

Journal of Immunology Research

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Background. Personalised medicine in oncology needs standardised immunological assays. Flow cytometry (FCM) methods represent an essential tool for immunomonitoring, and their harmonisation is crucial to obtain comparable data in multicentre clinical trials. The objective of this study was to design a harmonisation workflow able to address the most effective issues contributing to intraand interoperator variabilities in a multicentre project. Methods. The Italian National Institute of Health (Istituto Superiore di Sanità, ISS) managed a multiparametric flow cytometric panel harmonisation among thirteen operators belonging to five clinical and research centres of Lazio region (Italy). The panel was based on a backbone mixture of dried antibodies (anti-CD3, anti-CD4, anti-CD8, anti-CD45RA, and anti-CCR7) to detect naïve/memory T cells, recognised as potential prognostic/predictive immunological biomarkers in cancer immunotherapies. The coordinating centre distributed frozen peripheral blood mononuclear cells (PBMCs) and fresh whole blood (WB) samples from healthy donors, reagents, and Standard Operating Procedures (SOPs) to participants who performed experiments by their own equipment, in order to mimic a real-life scenario. Operators returned raw and locally analysed data to ISS for central analysis and statistical elaboration. Results. Harmonised and reproducible results were obtained by sharing experimental set-up and procedures along with centralising data analysis, leading to a reduction of crosscentre variability for naïve/memory subset frequencies particularly in the whole blood setting. Conclusion. Our experimental and analytical working process proved to be suitable for the harmonisation of FCM assays in a multicentre setting, where high-quality data are required to evaluate potential immunological markers, which may contribute to select better therapeutic options.

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Section: Intraoperator Variability/operatormentioning

confidence: 99%

Multicentre Harmonisation of a Six-Colour Flow Cytometry Panel for Naïve/Memory T Cell Immunomonitoring

Macchia

Sorsa²,

Ruspantini³

et al. 2020

Journal of Immunology Research

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“…Regarding the use of DMD as a surrogate marker of disease activity, we analyzed the ability to predict the start of the DMD or the switch to high-e cacy therapies, two relevant milestones in MS care. It is well described that disease activity and age are strong predictors of response to therapy 21 , but also differences in cell populations such as B (CD19 + CD5+) and CD8 (perforin+) T cells are associated with a differential response to some therapies such as INFB 22 , natalizumab or ngolimod 18 . Indeed, the recently developed Individual Treatment Response (ITR) score for MS therapies also identi ed clinical disability, quality of life, and some imaging outcomes as the main predictors of response to therapy 23 .…”

Section: Discussionmentioning

confidence: 99%

Predicting disease severity in Multiple Sclerosis using multimodal data and machine learning

Andorrà

Freire

Zubizarreta

et al. 2023

Preprint

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Background Multiple Sclerosis patients would benefit from machine learning algorithms that integrates clinical, imaging, and multimodal biomarkers to define the risk of disease activity. Methods We have analyzed a prospective multi-centric cohort of 322 MS patients and 98 healthy controls from four MS centers, collecting disability scales at baseline and 2 years later. Imaging data included brain MRI and optical coherence tomography, and omics included genotyping, cytomics and phosphoproteomic data from peripheral blood mononuclear cells. Predictors of clinical outcomes were searched using Random Forest algorithms. Validation was conducted in an independent prospective cohort of 271 MS patients from a single center. Results We found algorithms for predicting confirmed disability accumulation for the different scales, No Evidence of Disease Activity (NEDA), onset of immunotherapy and the escalation from low- to high-efficacy therapy with intermediate to high-accuracy. This accuracy was achieved for most of the predictors by using clinical data alone or in combination with imaging data. Still, in some cases, the addition of omics data slightly increased algorithm performance. Accuracies were comparable in the discovery and validation cohorts. Conclusion Combining clinical, imaging, and omics data with machine learning helps to identify MS patients at risk of disability worsening.

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“…Regarding the use of DMD as a surrogate marker of disease activity, we analysed the ability to predict the start of the DMD or the switch to high-efficacy therapies, two relevant milestones in MS care. It is well described that disease activity and age are strong predictors of response to therapy [ 46 ], but also differences in cell populations, such as B (CD19 + CD5 +) and CD8 (perforin +) T cells, are associated with a differential response to some therapies, such as INFB [ 47 ], natalizumab or fingolimod [ 23 ]. Indeed, the recently developed Individual Treatment Response (ITR) score for MS therapies also identified clinical disability, quality of life and some imaging outcomes as the main predictors of response to therapy [ 48 ].…”

Section: Discussionmentioning

confidence: 99%

Predicting disease severity in multiple sclerosis using multimodal data and machine learning

Andorra,

Freire,

Zubizarreta

et al. 2023

J Neurol

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Background Multiple sclerosis patients would benefit from machine learning algorithms that integrates clinical, imaging and multimodal biomarkers to define the risk of disease activity. Methods We have analysed a prospective multi-centric cohort of 322 MS patients and 98 healthy controls from four MS centres, collecting disability scales at baseline and 2 years later. Imaging data included brain MRI and optical coherence tomography, and omics included genotyping, cytomics and phosphoproteomic data from peripheral blood mononuclear cells. Predictors of clinical outcomes were searched using Random Forest algorithms. Assessment of the algorithm performance was conducted in an independent prospective cohort of 271 MS patients from a single centre. Results We found algorithms for predicting confirmed disability accumulation for the different scales, no evidence of disease activity (NEDA), onset of immunotherapy and the escalation from low- to high-efficacy therapy with intermediate to high-accuracy. This accuracy was achieved for most of the predictors using clinical data alone or in combination with imaging data. Still, in some cases, the addition of omics data slightly increased algorithm performance. Accuracies were comparable in both cohorts. Conclusion Combining clinical, imaging and omics data with machine learning helps identify MS patients at risk of disability worsening.

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Multi-centre validation of a flow cytometry method to identify optimal responders to interferon-beta in multiple sclerosis

Cited by 3 publications

References 20 publications

Multicentre Harmonisation of a Six-Colour Flow Cytometry Panel for Naïve/Memory T Cell Immunomonitoring

Multicentre Harmonisation of a Six-Colour Flow Cytometry Panel for Naïve/Memory T Cell Immunomonitoring

Predicting disease severity in Multiple Sclerosis using multimodal data and machine learning

Predicting disease severity in multiple sclerosis using multimodal data and machine learning

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