Figure 5
Figure 5. Diagnostic tests based on integration of diverse HIV-1–specific CD8 T-cell functions into immunomonitoring models allows accurate discrimination of disease outcomes. (A-B) Concepts of binary classifiers and ROC curves. (A) The goal of the modeling component of this study was to establish a diagnostic test that seeks to determine whether a person is a controller. The models based on HIV-1–specific CD8 T-cell functions will yield a continuous random variable that will be used as classifier (horizontal axis). As the outcome is binary (2 classes: controller or noncontroller), the boundary must be determined by a threshold value (vertical line). There are therefore 4 possible outcomes summarized in the “contingency table,” true positive (TP), false positive (FP), true negative (TN), and false negative (FN), which define the true-positive rate (sensitivity) and false-positive rate (1-specificity). (B) A ROC is defined by the false-positive rate and true-positive rate, which depicts relative trade-offs resulting from changing the test threshold. The best possible prediction method would yield a “square curve” reaching the upper left corner and representing 100% sensitivity and 100% specificity. A completely random guess (chance) would give a point along the diagonal line (line of nondiscrimination). In the present study, ROC curves can be used to compare tests derived from different immunomonitoring models: here model B is better than model A. ROC curves can be further characterized by the AUC, which is a measure of test accuracy (1.0 = the best possible test and 0.5 = no discrimination). (C) Graphical representation of the ROC curve of discrimination between controllers and noncontrollers corresponding to the averaged LASSO logistic regression model with 10-fold cross-validation obtained with all 10 variables (CFSE-based proliferation; IFN-γ, IL-2, and TNF-α secretion at 6 hours; slopes of IFN-γ, IL-2, and TNF-α secretion; sex; and age). (D) Weight of the individual standardized features in regard to their contribution to the 10-variable LASSO model. Vertical lines correspond to the 2.5 and 97.5 percentiles, respectively. (E) Bootstrap analysis of the probability that each feature of the 10-variable LASSO model corresponds to a true correlate of spontaneous viral control. (F) Comparative ROC curves and corresponding AUC characteristics corresponding to the averaged LASSO logistic regression models with 10 variables, CFSE-based proliferation only, and 9 variables (all but proliferation). The 3 ROC curves are not statistically different (CFSE alone vs all, P = .55; CFSE alone vs all others, P = .32). Details of the 10-fold cross-validation analyses are presented in Table 3.

Diagnostic tests based on integration of diverse HIV-1–specific CD8 T-cell functions into immunomonitoring models allows accurate discrimination of disease outcomes. (A-B) Concepts of binary classifiers and ROC curves. (A) The goal of the modeling component of this study was to establish a diagnostic test that seeks to determine whether a person is a controller. The models based on HIV-1–specific CD8 T-cell functions will yield a continuous random variable that will be used as classifier (horizontal axis). As the outcome is binary (2 classes: controller or noncontroller), the boundary must be determined by a threshold value (vertical line). There are therefore 4 possible outcomes summarized in the “contingency table,” true positive (TP), false positive (FP), true negative (TN), and false negative (FN), which define the true-positive rate (sensitivity) and false-positive rate (1-specificity). (B) A ROC is defined by the false-positive rate and true-positive rate, which depicts relative trade-offs resulting from changing the test threshold. The best possible prediction method would yield a “square curve” reaching the upper left corner and representing 100% sensitivity and 100% specificity. A completely random guess (chance) would give a point along the diagonal line (line of nondiscrimination). In the present study, ROC curves can be used to compare tests derived from different immunomonitoring models: here model B is better than model A. ROC curves can be further characterized by the AUC, which is a measure of test accuracy (1.0 = the best possible test and 0.5 = no discrimination). (C) Graphical representation of the ROC curve of discrimination between controllers and noncontrollers corresponding to the averaged LASSO logistic regression model with 10-fold cross-validation obtained with all 10 variables (CFSE-based proliferation; IFN-γ, IL-2, and TNF-α secretion at 6 hours; slopes of IFN-γ, IL-2, and TNF-α secretion; sex; and age). (D) Weight of the individual standardized features in regard to their contribution to the 10-variable LASSO model. Vertical lines correspond to the 2.5 and 97.5 percentiles, respectively. (E) Bootstrap analysis of the probability that each feature of the 10-variable LASSO model corresponds to a true correlate of spontaneous viral control. (F) Comparative ROC curves and corresponding AUC characteristics corresponding to the averaged LASSO logistic regression models with 10 variables, CFSE-based proliferation only, and 9 variables (all but proliferation). The 3 ROC curves are not statistically different (CFSE alone vs all, P = .55; CFSE alone vs all others, P = .32). Details of the 10-fold cross-validation analyses are presented in Table 3.

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