1. Hidden Patterns of Anti-HLA Class I Alloreactivity Revealed Through Machine Learning
- Author
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Angeliki G. Vittoraki, Asimina Fylaktou, Katerina Tarassi, Zafeiris Tsinaris, Alexandra Siorenta, George Ch. Petasis, Demetris Gerogiannis, Claudia Lehmann, Maryvonnick Carmagnat, Ilias Doxiadis, Aliki G. Iniotaki, Ioannis Theodorou, ‘George Gennimatas' General Hospital, Hippokration General Hospital, Evangelismos Athens General Hospital, University of Ioannina, University Hospital Leipzig, Hopital Saint-Louis [AP-HP] (AP-HP), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP), Laikon Hospital, University of Athens School of Medicine, Centre d'Immunologie et de Maladies Infectieuses (CIMI), and Institut National de la Santé et de la Recherche Médicale (INSERM)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)
- Subjects
0301 basic medicine ,MESH: Registries ,sensitization ,Epitope ,Cohort Studies ,MESH: Cross Reactions ,Epitopes ,0302 clinical medicine ,Isoantibodies ,Feature (machine learning) ,Immunology and Allergy ,bead array test ,Registries ,MESH: Cohort Studies ,Original Research ,MESH: Aged ,Principal Component Analysis ,education.field_of_study ,MESH: Middle Aged ,MESH: Machine Learning ,biology ,Middle Aged ,3. Good health ,machine learning ,Principal component analysis ,[SDV.IMM]Life Sciences [q-bio]/Immunology ,Antibody ,MESH: Computational Biology ,Adult ,MESH: Epitopes ,Immunology ,Population ,MESH: Organ Transplantation ,HLA-C Antigens ,Human leukocyte antigen ,Computational biology ,Cross Reactions ,03 medical and health sciences ,Antigen ,Transplantation Immunology ,Humans ,machine learning, antigenic epitopes, alloimmune response, translational research, sensitization, bead array test, anti-HLA alloantibodies ,ddc:610 ,MESH: Transplantation Immunology ,Allele ,education ,MESH: HLA-A Antigens ,Aged ,MESH: Principal Component Analysis ,MESH: Humans ,HLA-A Antigens ,Computational Biology ,antigenic epitopes ,alloimmune response ,MESH: Adult ,Organ Transplantation ,RC581-607 ,anti-HLA alloantibodies ,MESH: Isoantibodies ,MESH: HLA-C Antigens ,030104 developmental biology ,translational research ,HLA-B Antigens ,MESH: HLA-B Antigens ,biology.protein ,Immunologic diseases. Allergy ,030215 immunology - Abstract
Detection of alloreactive anti-HLA antibodies is a frequent and mandatory test before and after organ transplantation to determine the antigenic targets of the antibodies. Nowadays, this test involves the measurement of fluorescent signals generated through antibody–antigen reactions on multi-beads flow cytometers. In this study, in a cohort of 1,066 patients from one country, anti-HLA class I responses were analyzed on a panel of 98 different antigens. Knowing that the immune system responds typically to “shared” antigenic targets, we studied the clustering patterns of antibody responses against HLA class I antigens without any a priori hypothesis, applying two unsupervised machine learning approaches. At first, the principal component analysis (PCA) projections of intra-locus specific responses showed that anti-HLA-A and anti-HLA-C were the most distantly projected responses in the population with the anti-HLA-B responses to be projected between them. When PCA was applied on the responses against antigens belonging to a single locus, some already known groupings were confirmed while several new cross-reactive patterns of alloreactivity were detected. Anti-HLA-A responses projected through PCA suggested that three cross-reactive groups accounted for about 70% of the variance observed in the population, while anti-HLA-B responses were mainly characterized by a distinction between previously described Bw4 and Bw6 cross-reactive groups followed by several yet undocumented or poorly described ones. Furthermore, anti-HLA-C responses could be explained by two major cross-reactive groups completely overlapping with previously described C1 and C2 allelic groups. A second feature-based analysis of all antigenic specificities, projected as a dendrogram, generated a robust measure of allelic antigenic distances depicting bead-array defined cross reactive groups. Finally, amino acid combinations explaining major population specific cross-reactive groups were described. The interpretation of the results was based on the current knowledge of the antigenic targets of the antibodies as they have been characterized either experimentally or computationally and appear at the HLA epitope registry.
- Published
- 2021
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