Landscapes of binding antibody and T-cell responses to pox-protein HIV vaccines in Thais and South Africans

Abstract

Background: HIV vaccine trials routinely measure multiple vaccine-elicited immune responses to compare regimens and study their potential associations with protection. Here we employ unsupervised learning tools facilitated by a bidirectional power transformation to explore the multivariate binding antibody and T-cell response patterns of immune responses elicited by two pox-protein HIV vaccine regimens. Both regimens utilized a recombinant canarypox vector (ALVAC-HIV) prime and a bivalent recombinant HIV-1 Envelope glycoprotein 120 subunit boost. We hypothesized that within each trial, there were participant subgroups sharing similar immune responses and that their frequencies differed across trials.

Methods and findings: We analyzed data from three trials-RV144 (NCT00223080), HVTN 097 (NCT02109354), and HVTN 100 (NCT02404311), the latter of which was pivotal in advancing the tested pox-protein HIV vaccine regimen to the HVTN 702 Phase 2b/3 efficacy trial. We found that bivariate CD4+ T-cell and anti-V1V2 IgG/IgG3 antibody response patterns were similar by age, sex-at-birth, and body mass index, but differed for the pox-protein clade AE/B alum-adjuvanted regimen studied in RV144 and HVTN 097 (PAE/B/alum) compared to the pox-protein clade C/C MF59-adjuvanted regimen studied in HVTN 100 (PC/MF59). Specifically, more PAE/B/alum recipients had low CD4+ T-cell and high anti-V1V2 IgG/IgG3 responses, and more PC/MF59 recipients had broad responses of both types. Analyses limited to "vaccine-matched" antigens suggested that some of the differences in responses between the regimens could have been due to antigens in the assays that did not match the vaccine immunogens. Our approach was also useful in identifying subgroups with unusually absent or high co-responses across assay types, flagging individuals for further characterization by functional assays. We also found that co-responses of anti-V1V2 IgG/IgG3 and CD4+ T cells had broad variability. As additional immune response assays are standardized and validated, we anticipate our framework will be increasingly valuable for multivariate analysis.

Conclusions: Our approach can be used to advance vaccine development objectives, including the characterization and comparison of candidate vaccine multivariate immune responses and improved design of studies to identify correlates of protection. For instance, results suggested that HVTN 702 will have adequate power to interrogate immune correlates involving anti-V1V2 IgG/IgG3 and CD4+ T-cell co-readouts, but will have lower power to study anti-gp120/gp140 IgG/IgG3 due to their lower dynamic ranges. The findings also generate hypotheses for future testing in experimental and computational analyses aimed at achieving a mechanistic understanding of vaccine-elicited immune response heterogeneity.

Fig 1. Landscape of immune responses to the P_AE/B/alum and P_C/MF59 regimens.

The heatmap was generated by two-way hierarchical clustering of participants and immune response variables based on their similarity after bi-directional power transformation of the immune response measurements. The 64 immune response measurements shared across RV144, HVTN 097, and HVTN 100 participants were used. Columns designate immune responses; cluster, response, gene, and clade information are given in the top 4 color-coded bars. Rows designate participants; cluster, study, and treatment assignment information are given in the three color-coded bars on the left. Dendrograms on the top and left illustrate column and participant clustering, respectively. Immune response measurement values are designated by color according to the key shown in the upper left.

Fig 2. Summary of participant cluster immune response patterns and distribution of participant clusters across studies.

A) Radar plot showing the distribution of immune responses in each of the four participant clusters shown in Fig 1. Each colored line represents one participant cluster and each vertex represents an immune response. B) Distribution of participant clusters across the RV144, HVTN 097, and HVTN 100 trials. Each column represents one trial; participant clusters are designated on the left-hand side and are color-coded.

Fig 3. Landscape of immune responses in Thai and South African populations to the P_AE/B/alum regimen.

The heatmap shows 29 selected immune response measurements of RV144 and HVTN 097 vaccine recipients. Columns designate immune responses; cluster, response, gene, and clade information are given in the top 4 color-coded bars. Rows designate participants; cluster and study information are given in the two color-coded bars on the left. Dendrograms on the top and left illustrate column and participant clustering, respectively. Immune response measurement values are designated by color according to the key shown in the upper left.

Fig 4. Immune response landscapes for the P_AE/B/alum and P_C/MF59 regimens.

A) Heatmap of the same 29 immune response measurements combining RV144 and HVTN 097 vaccine recipients (pooled) and HVTN 100 vaccine recipients. Columns designate immune responses; cluster, response, gene, and clade information are given in the top 4 color-coded bars. Rows designate participants; cluster and study information are given in the two color-coded bars on the left. Dendrograms on the top and left illustrate column and participant clustering, respectively. Immune response measurement values are designated by color according to the key shown in the upper left. Scatterplots of participant-specific average values of CD4+ T-cell and anti-V1V2 antibody responses in (B) RV144 and HVTN 097 and (C) HVTN 100. Ellipsoids of bivariate normal distributions, based on respective empirical means and co-variances, are used to approximately cover 90% empirical bivariate observations. Panels B and C are shown on BDPT-transformed scales.

Fig 5. Distribution of CD4+ T−cell response magnitude (BDPT-scaled) among 100 participants in all three trials with strong CD4+ T−cell responses.

The densities of the different CD4+ T-cell responses (color coded) and the average CD4+ T-cell response are shown by response level.

Fig 6. Dynamic ranges of the 64 immune responses shared across the three trials.

The ranges of the immune responses after bi-directional power transformation are shown (25^th percentile to 75^th percentile, thicker lines; 10^th percentile to 90^th percentile, thinner lines). Lines are color-coded by response type.

Fig 7. Distributions of study-adjusted rank-based Spearman correlations between immune variables within each of the three immune response clusters.

Correlations were computed across participants in all three studies. The density (y-axis) indicates the relative number of immune response variable pairs with a given correlation (x-axis). “High correlation” was defined as a correlation greater than the threshold value of 0.75 (dashed line).

Fig 8. Immune response landscape of vaccine-matched responses and participant clustering based on vaccine-matched immune responses.

A) Heatmap showing the vaccine-matched CD8+ T-cell polyfunctionality, anti-V1V2 IgG3 antibody binding, CD4+ T-cell polyfunctionality, anti-gp120 IgG3 antibody binding, anti-V1V2 IgG antibody binding, and anti-gp120 IgG antibody binding measurements used to organize participants into the six row clusters. B) The distribution of participants in each trial (column) in each cluster is represented by the row height. Box size is proportional to the number of participants.