Combining Viral Genetics and Statistical Modeling to Improve HIV-1 Time-of-Infection Estimation towards Enhanced Vaccine Efficacy Assessment
Abstract
:1. Introduction
- (1)
- Time-dependent marker correlates of risk (CoR) of HIV-1 infection: For studying the correlates of HIV-1 risk, a case-cohort or case-control study design can be used to measure a time-varying potential correlate (marker) of interest as near as possible prior to the time of HIV-1 acquisition for all HIV-1 infected cases. Moreover, for a random sample of participants who complete follow-up testing as HIV-1-negative, the marker(s) is measured at all longitudinal sample time points (e.g., this design was employed in the VaxGen HIV-1 VE trial [20,28] and the Partners in PrEP prevention efficacy trial [29] and is planned for the AMP prevention efficacy trials [27] as well as for the HVTN 702 and HVTN 705 VE trials). In AMP one key marker of interest is VRC01 serum concentration measured by ELISA or serum neutralization titer against a standard panel of viruses by a neutralization assay; population pharmacokinetics/pharmacodynamics (PK/PD) models can be used to provide low-error unbiased estimates for the VRC01 concentration in infected individuals [30], given an accurate estimate of the date of infection. An important goal of the AMP trials is to characterize the relationship between a person’s VRC01 concentration and their instantaneous risk of HIV-1 infection. Identification of a serum neutralization threshold associated with (very) low risk of HIV-1 infection would provide valuable guidance for future vaccine development. What makes it challenging to pinpoint a marker’s value at infection is uncertainty in the date of infection. Even with monthly HIV-1 testing with high adherence to the testing schedule, the estimation methodologies that we previously employed for evaluation of HIV-1 VE trials are inadequate for the requirements of the AMP studies. In Supplementary Section A we illustrate the amount of increase in statistical power to detect such a CoR in the AMP studies that we expect to result from reducing the error in the infection time estimator (Supplementary Figure S1) using our previously applied approach [31].
- (2)
- “Sieve analysis”: How the level of vaccine/prevention efficacy depends on genotypic characteristics of HIV-1 at the time of acquisition: Sieve analysis provides another tool to detect and evaluate correlates of vaccine protection, based on the comparison of viruses that infect placebo recipients with the viruses that infect vaccine recipients, despite the protective barrier induced by vaccination [32]. An ongoing challenge for sieve analysis is that the determination of HIV-1 genetics at the time of HIV-1 acquisition is of fundamental importance for discriminating true sieve effects from post-acquisition effects. That is, whether observed viral genetic differences (across treatment groups, vaccine vs. placebo) can be interpreted as differential blockage of acquisition of incoming variants (a true “sieve effect”) vs. as resulting from differential evolution post-infection of similar starting viruses, resulting for example from effects in which vaccine-induced anamnestic responses impact the early evolution of HIV-1 prior to diagnosis (and sampling for sequencing) [33]. This issue has been critically important in the interpretation of sieve effects for all HIV-1 sieve reports to date [34,35,36,37,38]. Statistical methods have been developed that require the ability to determine which HIV-1 infection events are diagnosed very early prior to significant post-infection evolution [39,40,41]; additional research is needed to ensure that the methods optimally incorporate state-of-the-art infection time estimators.
2. Materials and Methods
2.1. Studies, Participants, Diagnostic Testing and HIV-1 Sequencing
2.2. Sequence Data Pre-Processing, Hypermutation Detection, and Recombination Detection
2.3. Infection Time
2.4. True and Artificial Diagnostic Bounds on the Date of Infection
2.5. PFitter Estimate of Days Since Infection
2.6. Variations on the PFitter Estimator of t: (syn) and (w/in clusts)
2.7. Clustering Sequences for the Within-Clusters PFitter Method
2.8. PrankenBeast
2.9. Founder Multiplicity Characterization
2.10. Rolland HVTN Method for Determining Founder Multiplicity
2.11. Tests for Star-Like Phylogeny or Founder Multiplicity
2.12. Statistical Methods for Calibrating Predictors of the Indicator of a Multiple-Founder Infection
2.13. Statistical Methods for Calibrating Predictors of Infection Timing
2.14. Software Pipeline
3. Results
3.1. RMSE and Bias of Center-of-Bounds (COB) Estimates of Infection Time
3.2. Prediction Error of Sequence-Based Estimators of Time Since HIV-1 Infection is Improved with Calibration
3.3. Multiplicity Assessment is Improved by Calibration with LASSO
3.4. Calibration, Considerations and Results Summary
4. Discussion
Supplementary Materials
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
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Study Feature | RV 217 (ECHO) | CAPRISA 002 |
---|---|---|
HIV-1 subtype(s) | CRF01_AE (MSM); A1/D/C and Recombinants (WSM) | C (WSM) |
Sequencing strategy | Single genome amplification and sequencing | Next generation sequencing (Illumina w/PrimerID) |
HIV-1 genomic region | Near full length genome (NFLG) | V3 variable loop of the gp120 envelope protein |
Median bases per HIV-1 sequence (min, IQR, max) | NFLG: 8813 (8624, 8753-8841, 8891); LH:5057 (5027, 5051-5063, 5209); RH:5061 (4898, 5040-5092, 5141) | 498 (495, 498-498, 501) |
Median HIV-1 sequences per participant after removing recombination and hypermutation (min, IQR, max) | 9.5 (2.6, 8.4-10, 11) NFLG: 10 (2, 8-10, 11) LH: 10 (2, 8-10,10) RH: 10 (3, 8-10, 11) | 352 (26, 142.3-640, 2764) |
Median HIV-1 sequences removed per participant (min, IQR, max) | 0 (0, 0-1, 8) NFLG: 0 (0, 0-1.3, 8) LH: 0 (0, 0-0, 4) RH: 0 (0, 0-1, 4) | 0 (0, 0-1, 356) |
Total number of participants | 36 | 21 |
Number of MSM | 17 | 0 |
Number of WSM | 19 | 21 |
N participants with 1-2M sample | 36 | 20 |
N participants with ~6M sample | 34 | 18 |
Mean Gold days 1-2M (SD) | 47 (4.3) | 62 (4.9) |
Mean Gold days ~6M (SD) | 184 (11.3) | 180 (12.1) |
N Gold isMultiple 1-2M (%) | 10 (28%) | 5 (25%) |
N Gold isMultiple ~6M (%) | 10 (29%) | 6 (33%) |
Median bounds width in days 1-2M (min, IQR, max) | 48 (20, 34-76, 308) | 54 (27, 41-70, 108) |
Median bounds width in days ~6M (min, IQR, max) | 146 (18, 91-195, 369) | 120 (30, 86-170, 183) |
Mean lPVL 1-2M (SD) | 4.5 (0.8) | 4.9 (0.7) |
Mean lPVL ~6M (SD) | 4.1 (1.0) | 4.5 (0.8) |
© 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
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Rossenkhan, R.; Rolland, M.; Labuschagne, J.P.L.; Ferreira, R.-C.; Magaret, C.A.; Carpp, L.N.; Matsen IV, F.A.; Huang, Y.; Rudnicki, E.E.; Zhang, Y.; et al. Combining Viral Genetics and Statistical Modeling to Improve HIV-1 Time-of-Infection Estimation towards Enhanced Vaccine Efficacy Assessment. Viruses 2019, 11, 607. https://doi.org/10.3390/v11070607
Rossenkhan R, Rolland M, Labuschagne JPL, Ferreira R-C, Magaret CA, Carpp LN, Matsen IV FA, Huang Y, Rudnicki EE, Zhang Y, et al. Combining Viral Genetics and Statistical Modeling to Improve HIV-1 Time-of-Infection Estimation towards Enhanced Vaccine Efficacy Assessment. Viruses. 2019; 11(7):607. https://doi.org/10.3390/v11070607
Chicago/Turabian StyleRossenkhan, Raabya, Morgane Rolland, Jan P.L. Labuschagne, Roux-Cil Ferreira, Craig A. Magaret, Lindsay N. Carpp, Frederick A. Matsen IV, Yunda Huang, Erika E. Rudnicki, Yuanyuan Zhang, and et al. 2019. "Combining Viral Genetics and Statistical Modeling to Improve HIV-1 Time-of-Infection Estimation towards Enhanced Vaccine Efficacy Assessment" Viruses 11, no. 7: 607. https://doi.org/10.3390/v11070607