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. 2015 Apr 13;11(4):e1004185.
doi: 10.1371/journal.pcbi.1004185. eCollection 2015 Apr.

Machine Learning Methods Enable Predictive Modeling of Antibody Feature:function Relationships in RV144 Vaccinees

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Free PMC article

Machine Learning Methods Enable Predictive Modeling of Antibody Feature:function Relationships in RV144 Vaccinees

Ickwon Choi et al. PLoS Comput Biol. .
Free PMC article

Abstract

The adaptive immune response to vaccination or infection can lead to the production of specific antibodies to neutralize the pathogen or recruit innate immune effector cells for help. The non-neutralizing role of antibodies in stimulating effector cell responses may have been a key mechanism of the protection observed in the RV144 HIV vaccine trial. In an extensive investigation of a rich set of data collected from RV144 vaccine recipients, we here employ machine learning methods to identify and model associations between antibody features (IgG subclass and antigen specificity) and effector function activities (antibody dependent cellular phagocytosis, cellular cytotoxicity, and cytokine release). We demonstrate via cross-validation that classification and regression approaches can effectively use the antibody features to robustly predict qualitative and quantitative functional outcomes. This integration of antibody feature and function data within a machine learning framework provides a new, objective approach to discovering and assessing multivariate immune correlates.

Conflict of interest statement

The authors have declared that no competing interests exist.

Figures

Fig 1
Fig 1. Input data.
For each of 80 vaccinated subjects (rows), measurements of (A) 20 antibody features (4 IgG subclasses with 5 antigen specificities) and (B) 3 effector functions. The heatmap colors indicate relative values within each column, standardized to a mean of 0, a standard deviation of 1, and truncated at 6σ. Color blocks above the antibody feature columns indicate IgG subclass and antigen specificity.
Fig 2
Fig 2. Unsupervised analysis of antibody features and functions.
(A) Antibody feature:function correlations. IgG subclass and antigen specificity are indicated by color blocks. Cell colors indicate Pearson correlation coefficients (PCC), and p-values are represented by asterisks (* < = 0.05; ** < = 0.01; *** < = 0.001). (B) Feature:feature correlations, hierarchically clustered. Antibody feature color blocks, PCCs, and significances are denoted as in (A). Bisecting the dendrogram, as shown by the red line, results in 6 antigen.subclass clusters, each also denoted in the figure by a box. For each function, one feature was selected (starred: blue-ADCP; yellow-ADCC; green-cytokines) from each cluster to yield the filtered feature set. (C) Eigenvectors from principal component analysis. Cell colors indicate feature coefficients in the eigenvectors. Antibody feature color blocks are as in (A).
Fig 3
Fig 3. Classification of ADCP from antibody features by penalized logistic regression.
(A-F) Prediction results by 200-replicate five-fold cross-validation, illustrating PLR values (>0.5 predicted high ADCP; <0.5 predicted low) for one replicate (A,C,E) and providing area under the ROC curve (AUC) over all 200 replicates (B,D,F). Box & whisker plots show the median (thick center line), upper and lower quartiles (box), and 1.5 times the interquartile range (whiskers); all points are also plotted in a jittered stripchart. Colors for the classification examples indicate high (red) and low (blue) observed ADCP. (G-I) Coefficients and p-values of the features for a model trained on all subjects. Different input features were used in classification: (A,B,G) the complete set; (C,D,H) the filtered set; (E,F,I) the principal components. Colors for the feature coefficients indicate antibody subclass and antigen-specificity. For convenience, a red line is drawn at p = 0.05.
Fig 4
Fig 4. Regression modeling of ADCP from antibody features by Lars.
(A,C,E) Representative regression scatterplot based on leave-one-out cross-validation, (B,D,F) PCCs for 200-replicate five-fold cross-validation. (G-I) Coefficients and p-values of the features for a model trained on all subjects. Different input features were used: (A,B,G) the complete set; (C,D,H) the filtered set; (E,F,I) the principal components. Box & whisker plots show the median (thick center line), upper and lower quartiles (box), and 1.5 times the interquartile range (whiskers); all points are also plotted in a jittered stripchart. Colors for the feature coefficients indicate antibody subclass and antigen-specificity.

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