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Combining Viral Genetics and Statistical Modeling to Improve HIV-1 Time-of-Infection Estimation towards Enhanced Vaccine Efficacy Assessment

1
Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA
2
U.S. Military HIV Research Program, Walter Reed Army Institute of Research, Silver Spring, MD 20910, USA
3
Henry M. Jackson Foundation for the Advancement of Military Medicine, Inc., Bethesda, MD 20817, USA
4
South African Medical Research Council Bioinformatics Unit, South African National Bioinformatics Institute, University of the Western Cape, Cape Town 7535, South Africa
5
Department of Pathology, Faculty of Health Sciences, Institute of Infectious Disease and Molecular Medicine, University of Cape Town, Cape Town 7925, South Africa
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Armed Forces Research Institute of Medical Sciences, Bangkok 10400, Thailand
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Makerere University Walter Reed Project, Kampala, Uganda
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Kenya Medical Research Institute/U.S. Army Medical Research Directorate-Africa/Kenya-Henry Jackson Foundation MRI, Kericho 20200, Kenya
9
Sub-Saharan African Network for TB/HIV Research Excellence (SANTHE), Africa Health Research Institute, Durban 4001, South Africa
10
Department of Biostatistics, University of Washington, Seattle, WA 98195, USA
*
Author to whom correspondence should be addressed.
Viruses 2019, 11(7), 607; https://doi.org/10.3390/v11070607
Received: 11 April 2019 / Revised: 19 June 2019 / Accepted: 27 June 2019 / Published: 3 July 2019
(This article belongs to the Special Issue HIV Molecular Epidemiology for Prevention)
Knowledge of the time of HIV-1 infection and the multiplicity of viruses that establish HIV-1 infection is crucial for the in-depth analysis of clinical prevention efficacy trial outcomes. Better estimation methods would improve the ability to characterize immunological and genetic sequence correlates of efficacy within preventive efficacy trials of HIV-1 vaccines and monoclonal antibodies. We developed new methods for infection timing and multiplicity estimation using maximum likelihood estimators that shift and scale (calibrate) estimates by fitting true infection times and founder virus multiplicities to a linear regression model with independent variables defined by data on HIV-1 sequences, viral load, diagnostics, and sequence alignment statistics. Using Poisson models of measured mutation counts and phylogenetic trees, we analyzed longitudinal HIV-1 sequence data together with diagnostic and viral load data from the RV217 and CAPRISA 002 acute HIV-1 infection cohort studies. We used leave-one-out cross validation to evaluate the prediction error of these calibrated estimators versus that of existing estimators and found that both infection time and founder multiplicity can be estimated with improved accuracy and precision by calibration. Calibration considerably improved all estimators of time since HIV-1 infection, in terms of reducing bias to near zero and reducing root mean squared error (RMSE) to 5–10 days for sequences collected 1–2 months after infection. The calibration of multiplicity assessments yielded strong improvements with accurate predictions (ROC-AUC above 0.85) in all cases. These results have not yet been validated on external data, and the best-fitting models are likely to be less robust than simpler models to variation in sequencing conditions. For all evaluated models, these results demonstrate the value of calibration for improved estimation of founder multiplicity and of time since HIV-1 infection. View Full-Text
Keywords: HIV-1; infection time; founder multiplicity; leave-one-out-cross-validation (LOOCV); HIV-1 primary infection; sequence analysis; vaccine efficacy assessment; acute and early HIV-1 infection HIV-1; infection time; founder multiplicity; leave-one-out-cross-validation (LOOCV); HIV-1 primary infection; sequence analysis; vaccine efficacy assessment; acute and early HIV-1 infection
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Rossenkhan, R.; Rolland, M.; Labuschagne, J.P.; Ferreira, R.-C.; Magaret, C.A.; Carpp, L.N.; Matsen IV, F.A.; Huang, Y.; Rudnicki, E.E.; Zhang, Y.; Ndabambi, N.; Logan, M.; Holzman, T.; Abrahams, M.-R.; Anthony, C.; Tovanabutra, S.; Warth, C.; Botha, G.; Matten, D.; Nitayaphan, S.; Kibuuka, H.; Sawe, F.K.; Chopera, D.; Eller, L.A.; Travers, S.; Robb, M.L.; Williamson, C.; Gilbert, P.B.; Edlefsen, P.T. Combining Viral Genetics and Statistical Modeling to Improve HIV-1 Time-of-Infection Estimation towards Enhanced Vaccine Efficacy Assessment. Viruses 2019, 11, 607.

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