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April 21, 2020  |  

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

Authors: Rossenkhan, Raabya and Rolland, Morgane and Labuschagne, Jan P L and Ferreira, Roux-Cil and Magaret, Craig A and Carpp, Lindsay N and Matsen Iv, Frederick A and Huang, Yunda and Rudnicki, Erika E and Zhang, Yuanyuan and Ndabambi, Nonkululeko and Logan, Murray and Holzman, Ted and Abrahams, Melissa-Rose and Anthony, Colin and Tovanabutra, Sodsai and Warth, Christopher and Botha, Gordon and Matten, David and Nitayaphan, Sorachai and Kibuuka, Hannah and Sawe, Fred K and Chopera, Denis and Eller, Leigh Anne and Travers, Simon and Robb, Merlin L and Williamson, Carolyn and Gilbert, Peter B and Edlefsen, Paul T

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.

Journal: Viruses
DOI: 10.3390/v11070607
Year: 2019

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