Machine learning approaches identify immunologic signatures of total and intact HIV DNA during long-term antiretroviral therapy

Abstract

AbstractAntiretroviral therapy (ART) halts HIV replication; however, cellular / immue cell viral reservoirs persist despite ART. Understanding the interplay between the HIV reservoir, immune perturbations, and HIV-specific immune responses on ART may yield insights into HIV persistence. A cross-sectional study of peripheral blood samples from 115 people with HIV (PWH) on long-term ART was conducted. High-dimensional immunophenotyping, quantification of HIV-specific T cell responses, and the intact proviral DNA assay (IPDA) were performed. Total and intact HIV DNA was positively correlated with T cell activation and exhaustion. Years of ART and select bifunctional HIV-specific CD4 T cell responses were negatively correlated with the percentage of intact proviruses. A Leave-One-Covariate-Out (LOCO) inference approach identified specific HIV reservoir and clinical-demographic parameters that were particularly important in predicting select immunophenotypes. Dimension reduction revealed two main clusters of PWH with distinct reservoirs. Additionally, machine learning approaches identified specific combinations of immune and clinical-demographic parameters that predicted with approximately 70% accuracy whether a given participant had qualitatively high or low levels of total or intact HIV DNA. The techniques described here may be useful for assessing global patterns within the increasingly high-dimensional data used in HIV reservoir and other studies of complex biology.