Summary

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Vaccination with the RV144 regimen (ALVAC-HIV and AIDSVAX B/E gp120) afforded an estimated 31% protection against HIV-1 acquisition¹. Two immune correlates of infection risk were identified: plasma IgA antibodies to Env were associated with increased risk, and IgG binding to Env-V1/V2 with decreased risk². These analyses compared HIV-1 infected and uninfected vaccine recipients, and established correlates of risk^3,4, which are not necessarily predictive of protection as immune responses are not randomized among vaccinees. In contrast, sieve analyses^5–7 compare breakthrough viruses in vaccine and placebo recipients, leveraging the randomization to causally attribute observed differences between HIV-1 sequences to the vaccine. Sieve analyses look for evidence that vaccine-induced immune responses selectively block certain viruses and/or drive escape mutations post-infection, and interrogate treatment differences in HIV-1 sequences derived at the time of HIV-1 diagnosis as evidence for this effect.

We hypothesized that RV144 vaccine-induced antibodies to Env-V1/V2 could selectively prevent HIV-1 infections by certain variants, and that this effect would be evident in the V1/V2 region of breakthrough viruses. To test this hypothesis, we examined the relationship between vaccine status and V1/V2 sequence characteristics using 936 HIV-1 sequences from 110 breakthrough infections: 44 vaccine and 66 placebo recipients (focusing on subjects infected with HIV-1 CRF01_AE, i.e. 110 of the 121 characterized infections that occurred after the first immunization). One epidemiologically-known transmission pair was confirmed by phylogenetic reconstructions; thus, sensitivity analyses were performed after removing sequences from the individual who was the second to become HIV-1 infected to preserve independence of infection events.

Our analysis focused on HIV-1 sequences corresponding to the glycoprotein 70 gp70-V1/V2 clade B reagent used to identify the correlate of risk (AA120 to 204 of the reference sequence HXB2). Supplementary Methods S1 summarizes the pre-filtering of sites that was performed as pre-specified to increase statistical power. First, sites that were invariant or where there was little confidence in the alignment were excluded. Then, sites were selected following two approaches. The first approach, termed `contact residues', required that sites were both a) known contact residues for monoclonal antibodies or had been implicated as such in neutralization sensitivity assays<sup>8–11</sup>, and b) `hotspots' of vaccine-induced binding antibody reactivity from a linear peptide binding microarray analysis of uninfected RV144 vaccine recipients. The `contact residues' approach yielded eight sites for analysis: HXB2-positions 120, 124, 165, 166, 168, 169, 171, 181. The second approach, called EPIMAP (Epitope Prediction by Interrogating conformational ensembles of glycosylated antigens with a Multi-oriented Antibody Probe), was based on structural predictions of antibody epitopes: thousands of potential antibody epitopes (centered on gp120 surface residues for the three Env in the vaccine) were predicted and used to rank V1/V2 sites by their likelihood of being antibody targets. The EPIMAP approach yielded 12 high-ranking sites: HXB2-positions 160, 166, 168–173, 178, 179, 181, 197.

To test if vaccination (through vaccine-induced antibodies to Env-V1/V2) protected against acquisition of certain HIV-1 variants, we adapted the primary analysis method of Rerks-Ngarm and colleagues¹ to assess VE against HIV-1 genotypes differing at the 15 selected sites identified by either the `contact residues' or EPIMAP approaches (Supplementary Table S1 and Figure S2; note that VE is estimated based on all RV144 participants, i.e. 8,197 vaccine and 8,198 placebo recipients). We found that VE significantly differed for HIV-1 genotypes defined by whether they presented a residue matching the vaccine insert (or not) at position 169 (p=0.034) and 181 (p=0.024) (Table 1). The estimated cumulative HIV-1 incidence curves in the vaccine and placebo groups illustrate VE for two genotypes (K169 and I181X) and no efficacy for the opposite residues (Supplementary Figure S2) (diagnostic tests did not show significant evidence of violation of the proportional hazards assumption) – and the cumulative HIV-1 incidence curves showed a trend toward waning vaccine effect on genotype over time, analogous to the temporal effect on efficacy seen in RV144<sup>1,12</sup>. The estimated VE against viruses matching the vaccine at position 169 (K169) was 48% (p=0.0036; 95% CI: 18%, 66%), whereas VE versus 169-mismatched viruses was not significant. VE against viruses differing from the vaccine insert at 181 (181X) was 78% (p=0.0028; 95% CI: 35%, 93%), whereas VE versus vaccine-matched viruses at that site was not significant. Furthermore, VE against viruses that were both 169-matched and 181-mismatched was 80% (p=0.0046; CI: 31%, 94%). These results suggest that vaccine-induced immune responses to the Env-V2 may have blocked infections with viruses matching the vaccine at K169 and differing from the vaccine at I181. We applied the statistical method of Haynes and colleagues<sup>2</sup> to test whether gp70-V1/V2 antibodies and V2 `hotspot' antibodies were correlates of risk of infection for specific HIV-1 genotypes. The estimated association of these antibodies with genotype-specific infection risk was similar across the genotypes, such that the sieve effects are not explained by these antibody correlates. However, there is low power to detect differences by genotype because only 34 infected vaccinees could be included in the analysis (see Supplementary Table S2).

To further evaluate site-specific differences between viruses from vaccine and placebo recipients, we applied three additional site-scanning methods to both the `contact residues' and `EPIMAP' sets of sites: the nonparametric weighted distance comparison test (GWJ)¹³, the Mismatch Bootstrap method (MMBootstrap)⁷, and a model-based method that is more sensitive to differences in non-insert AA frequencies¹⁴. Based on both the `contact residues' and `EPIMAP'-derived sets of sites, the most general method GWJ identified positions 169 and 181 as significantly distinguishing HIV-1 sequences from vaccine and placebo recipients (Figure 2). These two sites showed significant or borderline significant results with the additional site-scanning methods (MMBootstrap and Model-based) (Supplementary Table S3). These results were corroborated after excluding a subject from the transmission pair: results were consistent although with weaker statistical support. As pre-specified, corrections for multiple testing were performed separately for each analysis method and for each of the three vaccine insert sequences; no correction for multiple tests was applied across the tests because these are considered sensitivity analyses meant to evaluate congruence.

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Figure 2

Bar graphs representing the mutations at positions 169 and 181 based on sequences from 44 vaccine and 66 placebo recipients, with p- and q-values corresponding to the site-scanning sieve analysis method GWJ. The values correspond to comparisons against the 92TH023 vaccine insert based on the selected sites identified through the `contact residues' approach. Pre-specified p-value and q-value significance thresholds of .05 and .2, respectively. Full results in Supplementary Table S3.

The fact that our sequence data were obtained through an effectively implemented randomized trial¹⁵ reduces the need for phylogenetic corrections as performed in observational studies (Supplementary Note S1). Nonetheless, to potentially assess mechanisms behind sieve effects, we tested the impact of shared ancestry among viruses in our dataset with phylogenetic dependency networks¹⁶ and independent contrasts¹⁷. Both approaches found that the sieve effect at site 181 was independent of the tree topology, whereas that of site 169 was not (Supplementary Tables S4 and S5); these results must be interpreted with caution because the sequence data were used to both infer the phylogenetic tree and to evaluate sieve effects¹⁷.

In agreement with the VE results, the consensus AA K169 was more frequently different from the CRF01_AE vaccine sequence among viruses from vaccinees, whereas site 181, the third position of the putative tri-peptide α₄β₇ integrin binding motif, was more likely to be similar to the vaccine sequence in vaccinees. When we tested whether sites in the two groups were evolving under different selective pressures along internal tree branches, likelihood ratio test results¹⁸ provided evidence that site 169 was under differential pressure across treatment arms (p = 0.043 based on dataset with 110 subjects; p = 0.064 based on 109 subjects) (Supplementary Table S6).

HIV-1 variants with K169X might have had longer V2 loops [Mean = 43.2 (IQR: 40–46) vs 41.9 (IQR: 39–44), p=0.11] and more potential N-linked glycosylation sites (PNGS) [Mean = 2.44 (IQR: 2–3) vs 2.12 (IQR: 2–3), p=0.08] than K169 viruses (Supplementary Table S7). Although the latter p-values were not significant, longer loops and increased PNGS have been associated with reduced sensitivity to neutralization¹⁹. The trend toward longer V2 and more PNGS in individuals with K169X was apparently not explained by a longer duration of HIV-1 infection (no difference in the time since the last HIV-1 negative visit used as a proxy for the duration of HIV-1 infection (K169X vs K169: Median = 181 vs 179, Mean = 250 vs 207, p =0.41) (Supplementary Table S7). Furthermore, studies with quaternary-structure-preferring (QSP) antibodies (e.g., PG9, PG16, CH01–04 and PGT141–145) showed that mutations at positions 169 and 181 were associated with significant alterations in neutralization^10,20. Some strains became sensitive to QSP antibodies by mutating residue 169 to K²¹. These results suggest that K169X mutations could be a mechanism for avoidance of QSP antibodies and potentially other V2-specific antibodies. Indeed, the epitope of CH58, an anti-V2 mAb isolated from an RV144 vaccine recipient, spanned AA167-180 and position 169 was critical for binding²².

To explore whether the sieve effects at sites 169 and 181 were linked, we looked at structural data and co-variability. We found no conclusive evidence suggesting that the two sites were contained within the same epitope. The Cα-Cα distance between residues 169 and 181 was 21±7 Å (mean±St.dev) based on the 3000 low-energy all-atom models computed for the three vaccine inserts (Supplementary Table S8), while sites 169 and 181 were more than 30Å apart in the crystal structure of J08 scaffolded HIV-1 gp120 V1/V2 bound to the broadly neutralizing mAb PG9²³ (Supplementary Figure S3). The two sites could lie in a single antibody epitope because the average diameter of antibody-bound epitopes was predicted to be 30.5 Å (based on 32 pairs of bound antigen structures²⁴). Furthermore, we found no evidence that the sieve effect at site 181 was linked to site 169 by analyzing co-variation on pre-selected V1/V2 sites ^25,26. We noted co-variability in the mid-V2 loop, which corresponds to the binding site of the V1/V2-specific mAb from RV144^2,22 (Supplementary Table S9).

The unexpected sieve effect at site 181 showing greater VE against mismatched HIV-1, suggests a vaccine-induced constraint that either hindered the establishment of infection with I181× variants or promoted infections with I181 variants. While it is plausible that vaccine-induced antibodies enhanced HIV-1 infections with I181 variants, there was no statistical support for such enhancement because there was no evidence of negative VE against I181 viruses: Est. VE = 17%, 95% CI = −26% to 45%, p=0.38 (Table 1). Other hypotheses include: i) the stricter conservation at site 181 may be a marker of a non-identified sequence characteristic (possibly linked to the variable loop that starts a few AA downstream of 181), and ii) specific variants may be unable to establish infection due to steric/quaternary structure constraints with vaccine-induced antibodies; iii) I181× viruses may be preferentially targeted by vaccine induced antibodies.

Sieve analysis is an important component of assessing immune correlates of protection because it compares vaccine to placebo recipients and could identify selective pressures below the level of detection of standard immune assays⁷. Our V1/V2-focused analysis leveraged randomized treatment assignments to establish a causal connection between vaccination and a selective filtering of V1/V2 variants. The identification of signatures in V2 provides a corroborative virological determinant to the V1/V2 antibodies correlate of risk² and highlights the mid-V2 loop as an important target for antibody-mediated prevention of HIV-1 infection, but also suggests that VE is ablated by viral sequences with signature sequence variants, raising the possibility of population-level adaptation to the vaccine. Given the 31% efficacy against infection afforded by the vaccine and the suspected mechanism of action of V1/V2 Ab, our analysis suggests that specific HIV-1 variants were blocked from establishing HIV-1 infection (an acquisition sieve effect); however, viral sequences may also have mutated in response to vaccine-induced immune pressure, corresponding to a post-infection sieve effect. Further studies with infectious molecular clones carrying V1/V2 signature mutations are needed to clarify their role in viral infectivity, fitness, and escape. Together, these results vouch for sieve analysis as an integral part of the search for immune correlates of protection in vaccine studies and show that sieve analysis can be a powerful tool to assess the efficacy of new vaccine candidates.

Methods

Supplementary Figure S1 provides a schematic description of the study.

Supplementary Material

Acknowledgments

Footnotes

References