AUTHOR=Vittoraki Angeliki G. , Fylaktou Asimina , Tarassi Katerina , Tsinaris Zafeiris , Siorenta Alexandra , Petasis George Ch. , Gerogiannis Demetris , Lehmann Claudia , Carmagnat Maryvonnick , Doxiadis Ilias , Iniotaki Aliki G. , Theodorou Ioannis 

TITLE=Hidden Patterns of Anti-HLA Class I Alloreactivity Revealed Through Machine Learning

JOURNAL=Frontiers in Immunology

VOLUME=Volume 12 - 2021

YEAR=2021

URL=https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2021.670956

DOI=10.3389/fimmu.2021.670956

ISSN=1664-3224

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 1066 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 show that anti-HLA-A and anti-HLA-C are the most distantly projected responses in the population with the anti-HLA-B responses to be projected between them. When PCA is applied on the responses against antigens belonging to a single locus, some already known groupings are confirmed while several new cross-reactive patterns of alloreactivity are detected. Anti-HLA-A responses projected through PCA suggest that three cross-reactive groups account for about 70% of the variance observed in the population, while anti-HLA-B responses are 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 can 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, generates 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 are 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.