14 research outputs found
Hidden Patterns of Anti-HLA Class I Alloreactivity Revealed Through Machine Learning
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 intralocus
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 crossreactive
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 featurebased
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 crossreactive
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
T Cell Immunoprofiling of Patients with Relapsed and/or Refractory Myeloma Who Receive Daratumumab Monotherapy: Longitudinal Analysis during 7 Cycle Follow-up of the Rebuild Phase 2 Study
T Cell Immunoprofiling of Patients with Relapsed and/or Refractory Myeloma Who Receive Daratumumab Monotherapy: Longitudinal Analysis during 7 Cycle Follow-up of the Rebuild Phase 2 Study
An Approach to Identify HLA Class II Immunogenic Epitopes in the Greek Population through Machine Learning Algorithms
Current pre-transplantation routine matching involves serum anti-HLA antibodies quantification but cannot always preclude unfavorable graft outcomes. Epitope-based matching is proposed as a more precise approach, but to date no epitope-matching algorithm provides a satisfactory predictive tool for transplantation outcomes. In this study, anti-HLA-II loci responses from 1748 patients were analyzed with unsupervised machine learning algorithms, namely principal component analysis (PCA) and antigenic distances, projected as dendrograms. PCA for anti-HLA-DR anti-bodies revealed three main clusters of responses: anti-HLA-DR51 combined with anti-HLA-DRB1*01, anti-HLA-DR52 combined with anti-HLA-DRB1*08 and anti-HLA-DR53 combined with anti-HLA-DRB1*10. The dendrogram for anti-HLA-DR confirmed the pattern and showed further bisection of each cluster. Common epitopes present exclusively in all HLA molecules of each cluster were determined following the HLA epitope registry. Thus, we propose that 19 out of 123 HLA-DR epitopes are those that mainly lead anti-HLA-DR responses in the studied population. Likewise, we identified 22 out of 83 epitopes responsible for anti-HLA-DQ and 13 out of 62 responsible for anti-HLA-DP responses. Interpretation of these results may elucidate mechanisms of interlocus cross-reactivity, providing an alternative way of estimating the significance of each epitope in a population and thus suggesting a novel strategy towards optimal donor selection
Hidden Patterns of Anti-HLA Class I Alloreactivity Revealed Through Machine Learning
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 intralocus
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 crossreactive
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 featurebased
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 crossreactive
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
Hidden Patterns of Anti-HLA Class I Alloreactivity Revealed Through Machine Learning
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 intralocus
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 crossreactive
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 featurebased
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 crossreactive
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
P835: IN SILICO PREDICTION REVEALS PUTATIVE T-CELL CLASS I/II NEOEPITOPES WITHIN THE CLONOTYPIC IMMUNOGLOBULIN HEAVY AND LIGHT CHAINS IN PATIENTS WITH MULTIPLE MYELOMA
Hidden Patterns of Anti-HLA Class I Alloreactivity Revealed Through Machine Learning
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