Isabelle Fournier (Presenter)
PRISM INSERM U1192 - University of Lille
Bio: Isabelle Fournier, Professor. Isabelle Fournier is professor at the University of Lille since 2009 and co-director of PRISM Lab INSERM U1192 since 2012. Prof. Fournier is a specialist of Mass Spectrometry. After a PhD dedicated to fundamental of mass spectrometry, Prof. Fournier is interested since 2000 in the methodology development of MS applied to biology and clinics. This has led her to be involved in the development of MALDI MS Imaging since 2001. Main topics of Prof. Fournier are related to MS Imaging and its application into clinics for oncology and neurosciences. More recently she is involved in the development of a new methodology for in-vivo real-time analysis using MS based technology for application to surgery.
Authorship: Duhamel Marie(1), Le Rhun Emilie(1,2), Wisztorski Maxence(1), Gimeno Jean-Pascal(3), Zairi Fahed(1,2), Fabienne Escande(4), Maurage Claude-Alain(4), Reyns Nicolas(2), Salzet Michel(1), Fournier Isabelle(1)
(1) Lille University, INSERM U1192, Laboratoire PRISM, France (2) Department of Neuro-oncology and Neurosurgery, University Hospital, Lille, France (3) ONCOLille, Maison Régionale de la Recherche Clinique, Lille, France (4) Pathology Department, University Hospital, Lille, France
| Short Abstract High Grade Gliomas remains very difficult to classify which is a real concern for diagnosis, prognosis and patient uptake. Currently, the WHO 2007classification used for gliomas is under revision replacing morphological criteria by a classification based on patients mutation status. In this context, we have been evaluating for high grade glioma the classification based on unsupervised large scale molecular data obtained by combining MALDI MS Imaging to MicroProteomics. Unsupervised classification based on MS Imaging data give access to the molecular phenotyping within the tissues from which large scale proteins identification and relative quantification can be deduced. Using this strategy we can associate specific proteins pathways to biopsies and correlate these classification to the clinical data of the patients including the mutation status in order to improve Glioma classification. |
Long Abstract
Mass Spectrometry Imaging has now gained its letters of nobility demonstrating to be a robust technology to become the new pathologist tool. MSI coupled with Spatially-Resolved Large Scale proteomics has revealed to give access to tumor microenvironment allowing for markers hunting and studies of physio-pathological mechanisms through proteins pathways and networks. This strategy was apply here for studying High Grade Glioma (HGG). High Grade Gliomas (grade 3 and 4) remains very difficult to classify leading to a real concern for the establishment of the diagnostic, prognostic and for patient uptake. Currently, the WHO 2007 classification used for gliomas is under revision and morphological criteria used by the pathologist based on histology are to be replaced by a classification based on patient’s mutation status. In this context, we have been evaluating on high grade glioma the classification based on unsupervised large scale molecular data obtained combining MALDI MS Imaging to MicroProteomics.
Patients with grade 3 and 4 gliomas were analyzed after tumor resection (NCT 02473484). Mutation status of the patients for Isocitrate dehydrogenase 1 (IHD1), EGFR and 1p19q was established. IDH1 mutation was found in 80% of the cases, a 1p19q codeletion in 40% and an expression of EGFR in 60%. MALDI MS Imaging was performed on frozen tissue sections of 12 µm thickness. Proteins were imaged at 70 µm spatial resolution based on their enzymatic digestion peptides. This was achieved by performing in-situ trypsin digestion after tissue section washing with organic solvents followed by MALDI matrix deposition using an automatic micro-sprayer. After molecular image acquisition, matrix was removed, and tissues were stained for annotation by the pathologist. The imaging datasets including all the biopsy sections were spatially segmented all together or individually by clustering all spectra by their molecular profiles similarity. The Regions of Interest (ROIs) resulting from the clustering were used for protein large identification by performing microextraction followed by Shot-Gun proteomics analyses. After identification and relative quantification under label free conditions all the samples were submitted to statistical analysis. For statistical analysis of identified proteins, samples were grouped according to the ROIs found from the imaging datasets. The specific and upregulated proteins were then related to proteins pathways and further correlated to patient’s clinical data including the mutation status.
MALDI MS Imaging of proteins based on their digestion peptides reveals a high molecular heterogeneity of the glioma tumors. The unsupervised clustering of each patient tissue individually based on the MALDI MS Imaging data reveals differences with the pathologist annotation based on the morphological criteria. A low concordance between the histological annotations and molecular data was found. Individual segmentations from peptides imaging showed different regions on each tumor biopsies which in most cases were not observed in the histological annotations. On the other hand, the global unsupervised clustering of all the biopsies allows to classify patients or region within patient’s tissues into several groups. For example, grade 3 glioma classification gives 3 main groups of distinct molecular profiles. The ROIs defined by the global clustering can then serve as a basis for Spatially-Resolved MicroProteomics performing localized in-situ trypsin digestion followed by microextraction. After tissue microproteomics allowing for large scale proteins identification and relative quantification, more than 2500 proteins were identified. Each group demonstrates both specific proteins and the upregulation of specific proteins involved in different signaling pathways. Identification of the proteins in each of these 3 specific molecular region of Grade 3. Group 1 demonstrates notably the presence of proteins involved in neoplasia with proteins sharing activities in innate immune response, autophaghy, response to oxydative stress, mRNA metabolism, protein folding, spliceosome, regulation of translation. The second group is attributed to Glioma with inflammation, microglia activation, neoplasma and metastasis, cell migration, motility, microtubule cytoskeleton assembly which is line with aggressive Glioma. The last group of proteins is related to neuronal stem cells with proteins implicated in nerve cell differenciation, neurite outgrowth, axon guidance, filopodia formation, secretory pathway Using this strategy we can associate the specific proteins pathways and correlate these classification to the clinical data of the patients including the mutation status in order to improve Glioma classification. This analysis provides new insights into HGG tissue organization. The data obtained from MALDI MSI analysis and tissue microproteomics provide specific information and allow a more accurate classification of the biopsies for diagnosis.
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