PR001053 (Project)

Description:Non-invasive biomarkers that predict HIV remission after antiretroviral therapy (ART) interruption are urgently needed. Such biomarkers can improve the safety of analytic treatment interruption (ATI) and provide mechanistic insights into the pathways involved in post-ART HIV control. We identified plasma glycomic and metabolic signatures of time-to-viral-rebound and probability-of-viral-rebound using samples from two independent cohorts. These samples include a large number of post-treatment controllers, a rare population demonstrating sustained virologic suppression after ART-cessation. The signatures remained significant after adjusting for key demographic and clinical confounders. We also confirmed a mechanistic link between biomarkers and HIV latency reactivation and myeloid inflammation in vitro. Finally, machine learning algorithms selected sets of biomarkers that predict time-to-viral-rebound with 74-76% capacity and probability-of-viral-rebound with 97.5% capacity. In summary, we fill a major gap in HIV cure research by identifying non-invasive biomarkers, with potential functional significance, that predict duration and probability of viral remission after treatment interruption.