Missing and misclassified wells: Challenges in quantifying the HIV reservoir from dilution assays

People living with HIV on antiretroviral therapy often have undetectable virus levels by standard assays, but latent" HIV still persists in viral reservoirs. Eliminating these reservoirs is the goal of HIV cure research. Dilution assays, including the quantitative viral outgrowth assay (QVOA) and more detailed Ultra Deep Sequencing Assay of the outgrowth virus (UDSA), are commonly used to estimate the reservoir size, i.e., the infectious units per million (IUPM) of HIV-persistent resting CD4+ T cells. This paper considers efficient statistical inference about the IUPM from combined dilution assay (QVOA) and deep viral sequencing (UDSA) data, even when some deep sequencing data are missing. Moreover, existing inference methods for the IUPM assumed that the assays are "perfect" (i.e., they have 100% sensitivity and specificity), which can be unrealistic in practice. The proposed methods accommodate assays with imperfect sensitivity and specificity, wells sequenced at multiple dilution levels, and include a novel bias-corrected estimator for small samples. The proposed methods are evaluated in a simulation study, applied to data from the University of North Carolina HIV Cure Center, and implemented in the open-source R package SLDeepAssay.

Bias-corrected estimator

Deep sequencing

Maximum likelihood estimation

Misclassification

Missing data

Poisson distribution