Immunologic profiles distinguish aviremic HIV-infected adults

Abstract

Objective: Prior hypothesis-driven studies identified immunophenotypic characteristics associated with the control of HIV replication without antiretroviral therapy (HIV controllers) as well as with the degree of CD4 T-cell recovery during ART. We hypothesized that an unbiased 'discovery-based' approach might identify novel immunologic characteristics of these phenotypes.

Design: We performed immunophenotyping on four 'aviremic' patient groups: HIV controllers (n = 98), antiretroviral-treated immunologic nonresponders (CD4 < 350; n = 59), antiretroviral-treated immunologic responders (CD4 > 350, n = 142), and as a control group HIV-negative adults (n = 43). We measured levels of T-cell maturation, activation, dysfunction, senescence, functionality, and proliferation.

Methods: Supervised learning assessed the relative importance of immune parameters in predicting clinical phenotypes (controller, immunologic responder, or immunologic nonresponder). Unsupervised learning clustered immune parameters and examined if these clusters corresponded to clinical phenotypes.

Results: HIV controllers were characterized by high percentages of HIV-specific T-cell responses and decreased percentages of cells expressing human leukocytic antigen-antigen D related in naive, central memory, and effector T-cell subsets. Immunologic nonresponders were characterized by higher percentages of CD4 T cells that were TNFα+ or INFγ+, higher percentages of activated naive and central memory T cells, and higher percentages of cells expressing programmed cell death protein 1. Unsupervised learning found two distinct clusters of controllers and two distinct clusters of immunologic nonresponders, perhaps suggesting different mechanisms for the clinical outcomes.

Conclusion: Our discovery-based approach confirmed previously reported characteristics that distinguish aviremic individuals, but also identified novel immunologic phenotypes and distinct clinical subpopulations that should lead to more focused pathogenesis studies that might identify targets for novel therapeutic interventions.

Figure 1

Hierarchical clustering plot using Unsupervised Learning shows three “aviremic” HIV-positive phenotypes (HIV controllers, immunologic responders, and immunologic non-responders) have several discrete subgroups. Each branch in the tree represents a patient. The bar graph below shows the phenotype membership of each subject. HIV controllers are shown in blue and labeled Clusters A and B, immunologic responders are shown in yellow and labeled Cluster D, and immunologic non-responders are shown in red and labeled Clusters C and E.

Figure 2

a) Variable Importance Plots for the most important variables in predicting controllers from responders. b) Variable Importance Plots for the most important variables in predicting responders from immunologic non-responders. Variables are ranked in order of their importance, with the most important variable on top.

Figure 2

a) Variable Importance Plots for the most important variables in predicting controllers from responders. b) Variable Importance Plots for the most important variables in predicting responders from immunologic non-responders. Variables are ranked in order of their importance, with the most important variable on top.