Quantifying the HIV reservoir with dilution assays and deep viral sequencing.

Autor:	Lotspeich SC; Department of Statistical Sciences, Wake Forest University, Winston-Salem, NC 27109, United States.; Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, United States., Richardson BD; Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, United States., Baldoni PL; Bioinformatics Division, The Walter and Eliza Hall Institute of Medical Research, Parkville, Victoria, 3052, Australia.; Department of Medical Biology, The University of Melbourne, Parkville, Victoria, 3052, Australia., Enders KP; Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, United States., Hudgens MG; Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, United States.
Jazyk:	angličtina
Zdroj:	Biometrics [Biometrics] 2024 Jan 29; Vol. 80 (1).
DOI:	10.1093/biomtc/ujad018
Abstrakt:	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. The quantitative viral outgrowth assay (QVOA) is commonly used to estimate the reservoir size, that is, the infectious units per million (IUPM) of HIV-persistent resting CD4+ T cells. A new variation of the QVOA, the ultra deep sequencing assay of the outgrowth virus (UDSA), was recently developed that further quantifies the number of viral lineages within a subset of infected wells. Performing the UDSA on a subset of wells provides additional information that can improve IUPM estimation. This paper considers statistical inference about the IUPM from combined dilution assay (QVOA) and deep viral sequencing (UDSA) data, even when some deep sequencing data are missing. Methods are proposed to accommodate assays with wells sequenced at multiple dilution levels and with imperfect sensitivity and specificity, and a novel bias-corrected estimator is included 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. (© The Author(s) 2024. Published by Oxford University Press on behalf of The International Biometric Society.)
Databáze:	MEDLINE
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