Quantifying how single dose Ad26.COV2.S vaccine efficacy depends on Spike sequence features

It is of interest to pinpoint SARS-CoV-2 sequence features defining vaccine resistance. In the ENSEMBLE randomized, placebo-controlled phase 3 trial, estimated single-dose Ad26.COV2.S vaccine efficacy (VE) was 56% against moderate to severe-critical COVID-19. SARS-CoV-2 Spike sequences were measured from 484 vaccine and 1,067 placebo recipients who acquired COVID-19 during the trial. In Latin America, where Spike diversity was greatest, VE was significantly lower against Lambda than against Reference and against all non-Lambda variants [family-wise error rate (FWER) p < 0.05]. VE also differed by residue match vs. mismatch to the vaccine-strain residue at 16 amino acid positions (4 FWER p < 0.05; 12 q-value ≤ 0.20). VE significantly decreased with physicochemical-weighted Hamming distance to the vaccine-strain sequence for Spike, receptor-binding domain, N-terminal domain, and S1 (FWER p < 0.001); differed (FWER ≤ 0.05) by distance to the vaccine strain measured by 9 different antibody-epitope escape scores and by 4 NTD neutralization-impacting features; and decreased (p = 0.011) with neutralization resistance level to vaccine recipient sera. VE against severe-critical COVID-19 was stable across most sequence features but lower against viruses with greatest distances. These results help map antigenic specificity of in vivo vaccine protection.

Estimation of vaccine efficacy for variants that emerge after the placebo group is vaccinated

SARS‐CoV‐2 continues to evolve and the vaccine efficacy against variants is challenging to estimate. It is now common in phase III vaccine trials to provide vaccine to those randomized to placebo once efficacy has been demonstrated, precluding a direct assessment of placebo controlled vaccine efficacy after placebo vaccination. In this work, we extend methods developed for estimating vaccine efficacy post placebo vaccination to allow variant specific time varying vaccine efficacy, where time is measured since vaccination. The key idea is to infer counterfactual strain specific placebo case counts by using surveillance data that provide the proportions of the different strains. This blending of clinical trial and observational data allows estimation of strain‐specific time varying vaccine efficacy, or sieve effects, including for strains that emerge after placebo vaccination. The key requirements are that the surveillance strain distribution accurately reflects the strain distribution for a placebo group throughout follow‐up after placebo group vaccination, and that at least one strain is present before and after placebo vaccination. For illustration, we develop a Poisson approach for an idealized design under a rare disease assumption and then use a proportional hazards model to address staggered entry, staggered crossover, and smoothly varying strain specific vaccine efficacy. We evaluate these methods by theoretical work and simulations, and demonstrate that useful estimation of the efficacy profile is possible for strains that emerge after vaccination of the placebo group. An important principle is to incorporate sensitivity analyses to guard against misspecification of the strain distribution.

Estimation of Vaccine Efficacy for Variants that Emerge After the Placebo Group Is Vaccinated

SARS-CoV-2 continues to evolve and the vaccine efficacy against variants is challenging to estimate. It is now common in phase III vaccine trials to provide vaccine to those randomized to placebo once efficacy has been demonstrated, precluding a direct assessment of placebo controlled vaccine efficacy after placebo vaccination. In this work we extend methods developed for estimating vaccine efficacy post placebo vaccination to allow variant specific time varying vaccine efficacy, where time is measured since vaccination. The key idea is to infer counterfactual strain specific placebo case counts by using surveillance data that provide the proportions of the different strains. This blending of clinical trial and observational data allows estimation of strain-specific time varying vaccine efficacy, or sieve effects, including for strains that emergent after placebo vaccination. The key requirements are that surveillance strain distribution accurately reflect the strain distribution for a placebo group, throughout follow-up after placebo group vaccination and that at least one strain is present before and after placebo vaccination. For illustration, we develop a Poisson approach for an idealized design under a rare disease assumption and then use a proportional hazards modeling to better reflect the complexities of field trials with staggered entry, crossover, and smoothly varying strain specific vaccine efficacy We evaluate these by theoretical work and simulations, and demonstrate that useful estimation of the efficacy profile is possible for strains that emerge after vaccination of the placebo group. An important principle is to incorporate sensitivity analyses to guard against mis-specfication of the strain distribution. We also provide an approach for use when genotyping of the infecting strains of the trial participants has not been done.

Performance of a clinical/proteomic panel to predict obstructive peripheral artery disease in patients with and without diabetes mellitus

Background

Patients with diabetes mellitus (DM) are at substantial risk of developing peripheral artery disease (PAD). We recently developed a clinical/proteomic panel to predict obstructive PAD. In this study, we compare the accuracy of this panel for the diagnosis of PAD in patients with and without DM.

Methods and results

The HART PAD panel consists of one clinical variable (history of hypertension) and concentrations of six biomarkers (midkine, kidney injury molecule-1, interleukin-23, follicle-stimulating hormone, angiopoietin-1 and eotaxin-1). In a prospective cohort of 354 patients undergoing peripheral and/or coronary angiography, performance of this diagnostic panel to detect ≥50% stenosis in at least one peripheral vessel was assessed in patients with (n=94) and without DM (n=260). The model had an area under the receiver operating characteristic curve (AUC) of 0.85 for obstructive PAD. At optimal cut-off, the model had 84% sensitivity, 75% specificity, positive predictive value (PPV) of 84% and negative predictive value (NPV) of 75% for detection of PAD among patients with DM, similar as in those without DM. In those with DM, partitioning the model into five levels resulted in a PPV of 95% and NPV of 100% in the highest and lowest levels, respectively. Abnormal scores were associated with a shorter time to revascularisation during 4.3 years of follow-up.

Conclusion

A clinical/biomarker model can predict with high accuracy the presence of PAD among patients with DM.

Trial registration number

NCT00842868.

Multiple biomarker panel to screen for severe aortic stenosis: results from the CASABLANCA study

Objective

Severe aortic valve stenosis (AS) develops via insidious processes and can be challenging to correctly diagnose. We sought to develop a circulating biomarker panel to identify patients with severe AS.

Methods

We enrolled study participants undergoing coronary or peripheral angiography for a variety of cardiovascular diseases at a single academic medical centre. A panel of 109 proteins were measured in blood obtained at the time of the procedure. Statistical learning methods were used to identify biomarkers and clinical parameters that associate with severe AS. A diagnostic model incorporating clinical and biomarker results was developed and evaluated using Monte Carlo cross-validation.

Results

Of 1244 subjects (age 66.4±11.5  years, 28.7% female), 80 (6.4%) had severe AS (defined as aortic valve area (AVA) <1.0  cm²). A final model included age, N-terminal pro-B-type natriuretic peptide, von Willebrand factor and fetuin-A. The model had good discrimination for severe AS (OR=5.9, 95% CI 3.5 to 10.1, p<0.001) with an area under the curve of 0.76 insample and 0.74 with cross-validation. A diagnostic score was generated. Higher prevalence of severe AS was noted in those with higher scores, such that 1.6% of those with a score of 1 had severe AS compared with 15.3% with a score of 5 (p<0.001), and score values were inversely correlated with AVA (r=−0.35; p<0.001). At optimal model cut-off, we found 76% sensitivity, 65% specificity, 13% positive predictive value and 98% negative predictive value.

Conclusions

We describe a novel, multiple biomarker approach for diagnostic evaluation of severe AS.

Trial registration number

NCT00842868.

The Thai Phase III HIV Type 1 Vaccine trial (RV144) regimen induces antibodies that target conserved regions within the V2 loop of gp120

The Thai Phase III clinical trial (RV144) showed modest efficacy in preventing HIV-1 acquisition. Plasma collected from HIV-1-uninfected trial participants completing all injections with ALVAC-HIV (vCP1521) prime and AIDSVAX B/E boost were tested for antibody responses against HIV-1 gp120 envelope (Env). Peptide microarray analysis from six HIV-1 subtypes and group M consensus showed that vaccination induced antibody responses to the second variable (V2) loop of gp120 of multiple subtypes. We further evaluated V2 responses by ELISA and surface plasmon resonance using cyclic (Cyc) and linear V2 loop peptides. Thirty-one of 32 vaccine recipients tested (97%) had antibody responses against Cyc V2 at 2 weeks postimmunization with a reciprocal geometric mean titer (GMT) of 1100 (range: 200-3200). The frequency of detecting plasma V2 antibodies declined to 19% at 28 weeks post-last injection (GMT: 110, range: 100-200). Antibody responses targeted the mid-region of the V2 loop that contains conserved epitopes and has the amino acid sequence KQKVHALFYKLDIVPI (HXB2 Numbering sequence 169-184). Valine at position 172 was critical for antibody binding. The frequency of V3 responses at 2 weeks postimmunization was modest (18/32, 56%) with a GMT of 185 (range: 100-800). In contrast, naturally infected HIV-1 individuals had a lower frequency of antibody responses to V2 (10/20, 50%; p=0.003) and a higher frequency of responses to V3 (19/20, 95%), with GMTs of 400 (range: 100-3200) and 3570 (range: 200-12,800), respectively. RV144 vaccination induced antibodies that targeted a region of the V2 loop that contains conserved epitopes. Early HIV-1 transmission events involve V2 loop interactions, raising the possibility that anti-V2 antibodies in RV144 may have contributed to viral inhibition.