1. Proteomic Profiling of Hepatocellular Adenomas Paves the Way to Diagnostic and Prognostic Approaches
- Author
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Laurence Chiche, Brigitte Le Bail, Anne-Aurélie Raymond, Frédéric Saltel, Céline Julien, Cyril Dourthe, Jean-William Dupuy, Sylvaine Di Tommaso, Alexandre Brochard, Nathalie Dugot-Senant, Jean-Frédéric Blanc, Charles Balabaud, Paulette Bioulac-Sage, Bordeaux Research In Translational Oncology [Bordeaux] (BaRITOn), Université de Bordeaux (UB)-CHU Bordeaux [Bordeaux]-Institut National de la Santé et de la Recherche Médicale (INSERM), Plateforme Protéome [Bordeaux], Centre Génomique Fonctionnelle Bordeaux [Bordeaux] (CGFB), Institut Polytechnique de Bordeaux-Université de Bordeaux Ségalen [Bordeaux 2]-Institut Polytechnique de Bordeaux-Université de Bordeaux Ségalen [Bordeaux 2], INSERM, Neurocentre Magendie, U1215, Physiopathologie de la Plasticité Neuronale, F-33000 Bordeaux, France, European Project: 32078,OrPal, Neurocentre Magendie : Physiopathologie de la Plasticité Neuronale (U1215 Inserm - UB), and Université de Bordeaux (UB)-Institut François Magendie-Institut National de la Santé et de la Recherche Médicale (INSERM)
- Subjects
Adult ,Male ,Proteomics ,Adolescent ,Databases, Factual ,Carcinogenesis ,Hemorrhage ,Computational biology ,Malignancy ,Risk Assessment ,Adenoma, Liver Cell ,Malignant transformation ,Machine Learning ,Young Adult ,03 medical and health sciences ,0302 clinical medicine ,medicine ,Humans ,Diagnostic biomarker ,030304 developmental biology ,Laser capture microdissection ,0303 health sciences ,Proteomic Profile ,Hepatology ,Proteomic Profiling ,business.industry ,Liver Neoplasms ,Middle Aged ,medicine.disease ,Patient management ,Female ,[SDV.NEU]Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC] ,030211 gastroenterology & hepatology ,business - Abstract
Background and Aims : Through an exploratory proteomic approach based on typical hepatocellular adenomas (HCAs), we previously identified a diagnostic biomarker for a distinctive subtype of HCA with high risk of bleeding, already validated on a multicenter cohort. We hypothesized that the whole protein expression deregulation profile could deliver much more informative data for tumor characterization. Therefore, we pursued our analysis with the characterization of HCA proteomic profiles, evaluating their correspondence with the established genotype/phenotype classification and assessing whether they could provide added diagnosis and prognosis values. Approach and Results : From a collection of 260 cases, we selected 52 typical cases of all different subgroups on which we built a reference HCA proteomics database. Combining laser microdissection and mass-spectrometry–based proteomic analysis, we compared the relative protein abundances between tumoral (T) and nontumoral (NT) liver tissues from each patient and we defined a specific proteomic profile of each of the HCA subgroups. Next, we built a matching algorithm comparing the proteomic profile extracted from a patient with our reference HCA database. Proteomic profiles allowed HCA classification and made diagnosis possible, even for complex cases with immunohistological or genomic analysis that did not lead to a formal conclusion. Despite a well-established pathomolecular classification, clinical practices have not substantially changed and the HCA management link to the assessment of the malignant transformation risk remains delicate for many surgeons. That is why we also identified and validated a proteomic profile that would directly evaluate malignant transformation risk regardless of HCA subtype. Conclusions : This work proposes a proteomic-based machine learning tool, operational on fixed biopsies, that can improve diagnosis and prognosis and therefore patient management for HCAs.
- Published
- 2021