# Exploiting the Host-HIV Interface To Identify Biomarkers Predicting Time to Viral Rebound after Treatment Interruption

> **NIH NIH P01** · J. DAVID GLADSTONE INSTITUTES · 2020 · $1,687,137

## Abstract

Project Summary/Abstract
The development and testing of potential HIV cure therapeutics would be greatly expedited by a robust set of
biomarkers predicting their clinical effectiveness. Biomarkers that can serve as surrogate endpoints remain
unidentified. Such biomarkers will: 1) accelerate progress in the HIV cure arena much like plasma viral load
testing propelled antiviral drug development; 2) afford patients participating in analytical treatment interruption
(ATI) trials a higher degree of clinical protection by both reducing the number of trials; 3) provide biological
clues into the molecular and biochemical pathways that control the latent reservoir; and 4) serve as a magnet
for attracting Biotech and Pharma to more vigorously engage in HIV cure research. The BioMark program
project team (Warner Greene, Gilad Doitsh, Garry Nolan, Katie Pollard, Satish Pillai, Nadia Roan, and Robert
Siliciano) will search for strong biomarkers that accurately predict time to rebound following treatment
interruption. Such biomarkers would be of great value for the cure field as they would allow clinicians to predict
the period of time a patient can remain off ART without viral recrudescence. Blood cells and plasma from 125
HIV-infected volunteers participating in four different ATI trials obtained before ATI and at the time of viral
rebound will be analyzed. These patients include 30 individuals treated during acute infection who are
expected to exhibit slower rebound times. To identify both virus- and host-derived biomarkers, the team will 1)
deploy an exciting “first in class” digital droplet PCR assay that selectively detects and quantitates intact
proviral DNAs (IPDA) in the reservoir––because it is this key small fraction of the total provirus population that
contains the infectious proviruses mediating rebound, a low number of intact proviruses might emerge as a
strong biomarker predicting a longer time to viral rebound; 2) utilize next-generation ultra-deep sequencing to
profile cellular RNAs and miRNAs in CD4 T and other immune cells and in parallel to sequence DNA, RNA and
miRNA circulating free in plasma (and in cerebrospinal fluid in a limited subset of subjects) or bound as
cargoes in extracellular vesicles to identify predictors of time of viral rebound; 3) use 7 validated CyTOF panels
comprising over >200 parameters to phenotypically study CD4 T cells and other immune cells under both
resting and stimulated conditions to identify single-cell signatures of time to viral rebound; 4) assess changes
in the titer and avidity of circulating anti-HIV antibodies or markers of lymphoid tissue inflammation (including
products of pyroptosis) as indicators of the size of the expressed reservoir, which can serve as predictors of
time to viral rebound. These studies will generate large bodies of high-dimensional data that will be compiled,
curated, and analyzed in BioMark's Bioinformatics and Biostatistics Core. Several biostatistical approaches
will be emplo...

## Key facts

- **NIH application ID:** 9984950
- **Project number:** 5P01AI131374-04
- **Recipient organization:** J. DAVID GLADSTONE INSTITUTES
- **Principal Investigator:** Warner C. Greene
- **Activity code:** P01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $1,687,137
- **Award type:** 5
- **Project period:** 2017-08-08 → 2022-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9984950

## Citation

> US National Institutes of Health, RePORTER application 9984950, Exploiting the Host-HIV Interface To Identify Biomarkers Predicting Time to Viral Rebound after Treatment Interruption (5P01AI131374-04). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/9984950. Licensed CC0.

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