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Projeto de investigação
Data-driven models for Progression Of Neurological Disease
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Spatiotemporal analysis for detection of pre-symptomatic shape changes in neurodegenerative diseases: initial application to the GENFI cohort
Publication . Cury, Claire; Durrleman, Stanley; Cash, David M.; Lorenzi, Marco; Nicholas, Jennifer M.; Bocchetta, Martina; van Swieten, John C.; Borroni, Barbara; Galimberti, Daniela; Masellis, Mario; Tartaglia, Maria Carmela; Rowe, James B.; Graff, Caroline; Tagliavini, Fabrizio; Frisoni, Giovanni B.; Laforce, Robert; Finger, Elizabeth; De Mendonça, Alexandre; Sorbi, Sandro; Ourselin, Sebastien; Rohrer, Jonathan D.; Modat, Marc; Andersson, Christin; Archetti, Silvana; Arighi, Andrea; Benussi, Luisa; Black, Sandra; Cosseddu, Maura; Fallstrm, Marie; Ferreira, Carlos; Fenoglio, Chiara; Fox, Nick; Freedman, Morris; Fumagalli, Giorgio; Gazzina, Stefano; Ghidoni, Roberta; Grisoli, Marina; Jelic, Vesna; Jiskoot, Lize; Keren, Ron; Lombardi, Gemma; Maruta, Carolina; Meeter, Lieke; van Minkelen, Rick; Nacmias, Benedetta; ijerstedt, Linn; Padovani, Alessandro; Panman, Jessica; Pievani, Michela; Polito, Cristina; Premi, Enrico; Prioni, Sara; Rademakers, Rosa; Redaelli, Veronica; Rogaeva, Ekaterina; Rossi, Giacomina; Rossor, Martin; Scarpini, Elio; Tang-Wai, David; Tartaglia, Carmela; Thonberg, Hakan; Tiraboschi, Pietro; Verdelho, Ana; Warren, Jason
Brain atrophy as measured from structural MR images, is one of the primary imaging biomarkers used to track neurodegenerative disease progression. In diseases such as frontotemporal dementia or Alzheimer's disease, atrophy can be observed in key brain structures years before any clinical symptoms are present. Atrophy is most commonly captured as volume change of key structures and the shape changes of these structures are typically not analysed despite being potentially more sensitive than summary volume statistics over the entire structure. In this paper we propose a spatiotemporal analysis pipeline based on Large Diffeomorphic Deformation Metric Mapping (LDDMM) to detect shape changes from volumetric MRI scans. We applied our framework to a cohort of individuals with genetic variants of frontotemporal dementia and healthy controls from the Genetic FTD Initiative (GENFI) study. Our method, take full advantage of the LDDMM framework, and relies on the creation of a population specific average spatiotemporal trajectory of a relevant brain structure of interest, the thalamus in our case. The residuals from each patient data to the average spatiotemporal trajectory are then clustered and studied to assess when presymptomatic mutation carriers differ from healthy control subjects. We found statistical differences in shape in the anterior region of the thalamus at least five years before the mutation carrier subjects develop any clinical symptoms. This region of the thalamus has been shown to be predominantly connected to the frontal lobe, consistent with the pattern of cortical atrophy seen in the disease.
A data-driven disease progression model of fluid biomarkers in genetic frontotemporal dementia
Publication . van der Ende, Emma L.; Bron, Esther E.; Poos, Jackie M.; Jiskoot, Lize C.; Panman, Jessica L.; Papma, Janne M.; Meeter, Lieke H.; Dopper, Elise G. P.; Wilke, Carlo; Synofzik, Matthis; Heller, Carolin; Miltenberger-Miltenyi, Gabriel; van Minkelen, Rick; Mitchell, Sara; Moore, Katrina; Nacmias, Benedetta; Nicholas, Jennifer; Öijerstedt, Linn; Olives, Jaume; Ourselin, Sebastien; Padovani, Alessandro; Swift, Imogen J.; Peakman, Georgia; Pievani, Michela; Pijnenburg, Yolande; Polito, Cristina; Premi, Enrico; Prioni, Sara; Prix, Catharina; Rademakers, Rosa; Redaelli, Veronica; Rittman, Tim; Sogorb-Esteve, Aitana; Rogaeva, Ekaterina; Rosa-Neto, Pedro; Rossi, Giacomina; Rosser, Martin; Santiago, Beatriz; Scarpini, Elio; Schönecker, Sonja; Semler, Elisa; Shafei, Rachelle; Shoesmith, Christen; Bouzigues, Arabella; Tábuas-Pereira, Miguel; Tainta, Mikel; Taipa, Ricardo; Tang-Wai, David; Thomas, David L.; Thompson, Paul; Thonberg, Hakan; Timberlake, Carolyn; Tiraboschi, Pietro; Todd, Emily; Borroni, Barbara; Van Damme, Philip; Vandenbulcke, Mathieu; Veldsman, Michele; Verdelho, Ana; Villanua, Jorge; Warren, Jason; Woollacott, Ione; Wlasich, Elisabeth; Zulaica, Miren; Sanchez-Valle, Raquel; Moreno, Fermin; Graff, Caroline; Laforce, Robert; Galimberti, Daniela; Masellis, Mario; Tartaglia, Maria Carmela; Finger, Elizabeth; Vandenberghe, Rik; Rowe, James B.; De Mendonça, Alexandre; Tagliavini, Fabrizio; Santana, Isabel; Ducharme, Simon; Butler, Christopher R.; Gerhard, Alexander; Levin, Johannes; Danek, Adrian; Otto, Markus; Pijnenburg, Yolande A. L.; Sorbi, Sandro; Zetterberg, Henrik; Niessen, Wiro J.; Rohrer, Jonathan D.; Klein, Stefan; van Swieten, John C.; Venkatraghavan, Vikram; Seelaar, Harro; Afonso, Sónia; Almeida, Maria Rosario; Anderl-Straub, Sarah; Andersson, Christin; Antonell, Anna; Archetti, Silvana; Arighi, Andrea; Balasa, Mircea; Barandiaran, Myriam; Bargalló, Nuria; Bartha, Robart; Bender, Benjamin; Benussi, Alberto; Benussi, Luisa; Bessi, Valentina; Binetti, Giuliano; Black, Sandra; Bocchetta, Martina; Borrego-Ecija, Sergi; Bras, Jose; Bruffaerts, Rose; Cañada, Marta; Cantoni, Valentina; Caroppo, Paola; Cash, David; Castelo-Branco, Miguel; Convery, Rhian; Cope, Thomas; Di Fede, Giuseppe; Díez, Alina; Duro, Diana; Fenoglio, Chiara; Ferrari, Camilla; Ferreira, Catarina B.; Fox, Nick; Freedman, Morris; Fumagalli, Giorgio; Gabilondo, Alazne; Gasparotti, Roberto; Gauthier, Serge; Gazzina, Stefano; Giaccone, Giorgio; Gorostidi, Ana; Greaves, Caroline; Guerreiro, Rita; Hoegen, Tobias; Indakoetxea, Begoña; Jelic, Vesna; Karnath, Hans-Otto; Keren, Ron; Langheinrich, Tobias; Leitão, Maria João; Lladó, Albert; Lombardi, Gemma; Loosli, Sandra; Maruta, Carolina; Mead, Simon
Several CSF and blood biomarkers for genetic frontotemporal dementia have been proposed, including those reflecting neuroaxonal loss (neurofilament light chain and phosphorylated neurofilament heavy chain), synapse dysfunction [neuronal pentraxin 2 (NPTX2)], astrogliosis (glial fibrillary acidic protein) and complement activation (C1q, C3b). Determining the sequence in which biomarkers become abnormal over the course of disease could facilitate disease staging and help identify mutation carriers with prodromal or early-stage frontotemporal dementia, which is especially important as pharmaceutical trials emerge. We aimed to model the sequence of biomarker abnormalities in presymptomatic and symptomatic genetic frontotemporal dementia using cross-sectional data from the Genetic Frontotemporal dementia Initiative (GENFI), a longitudinal cohort study. Two-hundred and seventy-five presymptomatic and 127 symptomatic carriers of mutations in GRN, C9orf72 or MAPT, as well as 247 non-carriers, were selected from the GENFI cohort based on availability of one or more of the aforementioned biomarkers. Nine presymptomatic carriers developed symptoms within 18 months of sample collection ('converters'). Sequences of biomarker abnormalities were modelled for the entire group using discriminative event-based modelling (DEBM) and for each genetic subgroup using co-initialized DEBM. These models estimate probabilistic biomarker abnormalities in a data-driven way and do not rely on previous diagnostic information or biomarker cut-off points. Using cross-validation, subjects were subsequently assigned a disease stage based on their position along the disease progression timeline. CSF NPTX2 was the first biomarker to become abnormal, followed by blood and CSF neurofilament light chain, blood phosphorylated neurofilament heavy chain, blood glial fibrillary acidic protein and finally CSF C3b and C1q. Biomarker orderings did not differ significantly between genetic subgroups, but more uncertainty was noted in the C9orf72 and MAPT groups than for GRN. Estimated disease stages could distinguish symptomatic from presymptomatic carriers and non-carriers with areas under the curve of 0.84 (95% confidence interval 0.80-0.89) and 0.90 (0.86-0.94) respectively. The areas under the curve to distinguish converters from non-converting presymptomatic carriers was 0.85 (0.75-0.95). Our data-driven model of genetic frontotemporal dementia revealed that NPTX2 and neurofilament light chain are the earliest to change among the selected biomarkers. Further research should investigate their utility as candidate selection tools for pharmaceutical trials. The model's ability to accurately estimate individual disease stages could improve patient stratification and track the efficacy of therapeutic interventions.
Development of a sensitive trial-ready poly(GP) CSF biomarker assay for C9orf72-associated frontotemporal dementia and amyotrophic lateral sclerosis
Publication . Wilson, Katherine M.; Katona, Eszter; Glaria, Idoia; Carcolé, Mireia; Swift, Imogen J.; Sogorb-Esteve, Aitana; Heller, Carolin; Bouzigues, Arabella; Heslegrave, Amanda J.; Keshavan, Ashvini; Knowles, Kathryn; Patil, Saurabh; Mohapatra, Susovan; Liu, Yuanjing; Goyal, Jaya; Sanchez-Valle, Raquel; Laforce, Robert Jr.; Synofzik, Matthis; Rowe, James B.; Finger, Elizabeth; Vandenberghe, Rik; Butler, Christopher R.; Gerhard, Alexander; Van Swieten, John C.; Seelaar, Harro; Borroni, Barbara; Galimberti, Daniela; De Mendonça, Alexandre; Masellis, Mario; Tartaglia, M. Carmela; Otto, Markus; Graff, Caroline; Ducharme, Simon; Schott, Jonathan M.; Malaspina, Andrea; Zetterberg, Henrik; Boyanapalli, Ramakrishna; Rohrer, Jonathan D.; Isaacs, Adrian M.; Maruta, Carolina; Ferreira, Catarina B.; Verdelho, Ana
Objective: A GGGGCC repeat expansion in the C9orf72 gene is the most common cause of genetic frontotemporal dementia (FTD) and amyotrophic lateral sclerosis (ALS). As potential therapies targeting the repeat expansion are now entering clinical trials, sensitive biomarker assays of target engagement are urgently required. Our objective was to develop such an assay.
Methods: We used the single molecule array (Simoa) platform to develop an immunoassay for measuring poly(GP) dipeptide repeat proteins (DPRs) generated by the C9orf72 repeat expansion in cerebrospinal fluid (CSF) of people with C9orf72-associated FTD/ALS.
Results and conclusions: We show the assay to be highly sensitive and robust, passing extensive qualification criteria including low intraplate and interplate variability, a high precision and accuracy in measuring both calibrators and samples, dilutional parallelism, tolerance to sample and standard freeze-thaw and no haemoglobin interference. We used this assay to measure poly(GP) in CSF samples collected through the Genetic FTD Initiative (N=40 C9orf72 and 15 controls). We found it had 100% specificity and 100% sensitivity and a large window for detecting target engagement, as the C9orf72 CSF sample with the lowest poly(GP) signal had eightfold higher signal than controls and on average values from C9orf72 samples were 38-fold higher than controls, which all fell below the lower limit of quantification of the assay. These data indicate that a Simoa-based poly(GP) DPR assay is suitable for use in clinical trials to determine target engagement of therapeutics aimed at reducing C9orf72 repeat-containing transcripts.
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Entidade financiadora
European Commission
Programa de financiamento
H2020
Número da atribuição
666992