Patterns of 1,748 Unique Human Alloimmune Responses Seen by Simple Machine Learning Algorithms

Abstract

Allele specific antibody response against the polymorphic system of HLA is the allogeneic response marker determining the immunological risk for graft acceptance before and after organ transplantation and therefore routinely studied during the patient's workup. Experimentally, bead bound antigen- antibody reactions are detected using a special multicolor flow cytometer (Luminex). Routinely for each sample, antibody responses against 96 different HLA antigen groups are measured simultaneously and a 96-dimensional immune response vector is created. Under a common experimental protocol, using unsupervised clustering algorithms, we analyzed these immune intensity vectors of anti HLA class II responses from a dataset of 1,748 patients before or after renal transplantation residing in a single country. Each patient contributes only one serum sample in the analysis. A population view of linear correlations of hierarchically ordered fluorescence intensities reveals patterns in human immune responses with striking similarities with the previously described CREGs but also brings new information on the antigenic properties of class II HLA molecules. The same analysis affirms that "public" anti-DP antigenic responses are not correlated to anti DR and anti DQ responses which tend to cluster together. Principal Component Analysis (PCA) projections also demonstrate ordering patterns clearly differentiating anti DP responses from anti DR and DQ on several orthogonal planes. We conclude that a computer vision of human alloresponse by use of several dimensionality reduction algorithms rediscovers proven patterns of immune reactivity without any a priori assumption and might prove helpful for a more accurate definition of public immunogenic antigenic structures of HLA molecules. Furthermore, the use of Eigen decomposition on the Immune Response generates new hypotheses that may guide the design of more effective patient monitoring tests.

Keywords: HLA; PCA; allorecognition; descriptive statistics; machine learning; monitoring; patterns detection; transplantation.

Figure 1

Correlation patterns of anti HLA class II immune responses from patients awaiting a transplant. Correlation coefficient of MFIs on 96 different antigen coated bead assays were computed among all beads and all sera. The left triangular panel represents pairwise correlations of unscaled MFIs among antigen-bead specific reactions. Similarly, the right panel is a color scale representing the coefficient correlation. Rs values (Spearman's correlation) range from −1 (dark red, negative correlation) to 0 (white, no correlation) and then up to 1 (dark blue, positive correlation). Agglomerative ordering was applied with a bottom to top ascending hierarchical clustering based on Mc Quitty distances of the dissimilarity matrix.

Figure 2

Correlation patterns of anti HLA class II immune responses from patients followed after transplantation. Correlation coefficient of MFIs on 96 different antigen coated bead assays were computed among all beads and all sera. The left triangular panel represents pairwise correlations of unscaled MFIs among antigen-bead specific reactions. Similarly, the right panel is a color scale representing the coefficient correlation. Rs values (Spearman's correlation) range from −1 (dark red, negative correlation) to 0 (white, no correlation) and then up to 1 (dark blue, positive correlation). Agglomerative ordering was applied with a bottom to top ascending hierarchical clustering based on Mc Quitty distances of the dissimilarity matrix.

Figure 3

PCA biplots of anti HLA class II immune responses on patients immunized before the transplantation. Projections on Dim1 and Dim2. The points represent projections of individual reactions and arrows the corresponding variables according to the first and second principal components (referred to as Dim1 and Dim2 accordingly) of PCA. The color stripe on the right side exhibits the corresponding color vectors of explained variance, ranging from red color (indicating strong contribution on variance) to green (indicating weak contribution on variance).

Figure 4

PCA biplots of anti HLA class II immune responses on patients after transplantation. Projections on Dim1 and Dim2. The points represent projections of individual reactions and arrows the corresponding variables according to the first and second principal components (referred to as Dim1 and Dim2 accordingly) of PCA. The color stripe on the right side exhibits the corresponding color vectors of explained variance, ranging from red color (indicating strong contribution on variance) to green (indicating weak contribution on variance).

Figure 5

PCA biplots of anti HLA class II immune responses on patients immunized before the transplantation. Projections on Dim2 and Dim3. The points represent projections of individual reactions and arrows the corresponding variables according to the second and third principal components (referred to as Dim2 and Dim3 accordingly) of PCA. The color stripe on the right side exhibits the corresponding color vectors of explained variance, ranging from red color (indicating strong contribution on variance) to green (indicating weak contribution on variance).

Figure 6

PCA biplots of anti HLA class II immune responses on patients after transplantation. Projections on Dim2 and Dim3. The points represent projections of individual reactions and arrows the corresponding variables according to the second and third principal components (referred to as Dim2 and Dim3 accordingly) of PCA. The color stripe on the right side exhibits the corresponding color vectors of explained variance, ranging from red color (indicating strong contribution on variance) to green (indicating weak contribution on variance).