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Motivation A single monoclonal broadly neutralizing antibody (bnAb) regimen was recently evaluated in two randomized trials for prevention efficacy against HIV-1 infection. Subsequent trials will evaluate combination bnAb regimens (e.g. cocktails, multi-specific antibodies), which demonstrate higher potency and breadth in vitro compared to single bnAbs. Given the large number of potential regimens, methods for down-selecting these regimens into efficacy trials are of great interest. Results We developed Super LeArner Prediction of NAb Panels (SLAPNAP), a software tool for training and evaluating machine learning models that predict in vitro neutralization sensitivity of HIV Envelope (Env) pseudoviruses to a given single or combination bnAb regimen, based on Env amino acid sequence features. SLAPNAP also provides measures of variable importance of sequence features. By predicting bnAb coverage of circulating sequences, SLAPNAP can improve ranking of bnAb regimens by their potential prevention efficacy. In addition, SLAPNAP can improve sieve analysis by defining sequence features that impact bnAb prevention efficacy. Availabilityand implementation SLAPNAP is a freely available docker image that can be downloaded from DockerHub (https://hub.docker.com/r/slapnap/slapnap). Source code and documentation are available at GitHub (https://github.com/benkeser/slapnap and https://benkeser.github.io/slapnap/). [ABSTRACT FROM AUTHOR]

The broadly neutralizing antibody (bnAb) VRC01 is being evaluated for its efficacy to prevent HIV-1 infection in the Antibody Mediated Prevention (AMP) trials. A secondary objective of AMP utilizes sieve analysis to investigate how VRC01 prevention efficacy (PE) varies with HIV-1 envelope (Env) amino acid (AA) sequence features. An exhaustive analysis that tests how PE depends on every AA feature with sufficient variation would have low statistical power. To design an adequately powered primary sieve analysis for AMP, we modeled VRC01 neutralization as a function of Env AA sequence features of 611 HIV-1 gp160 pseudoviruses from the CATNAP database, with objectives: (1) to develop models that best predict the neutralization readouts; and (2) to rank AA features by their predictive importance with classification and regression methods. The dataset was split in half, and machine learning algorithms were applied to each half, each analyzed separately using cross-validation and hold-out validation. We selected Super Learner, a nonparametric ensemble-based cross-validated learning method, for advancement to the primary sieve analysis. This method predicted the dichotomous resistance outcome of whether the IC50 neutralization titer of VRC01 for a given Env pseudovirus is right-censored (indicating resistance) with an average validated AUC of 0.868 across the two hold-out datasets. Quantitative log IC50 was predicted with an average validated R2 of 0.355. Features predicting neutralization sensitivity or resistance included 26 surface-accessible residues in the VRC01 and CD4 binding footprints, the length of gp120, the length of Env, the number of cysteines in gp120, the number of cysteines in Env, and 4 potential N-linked glycosylation sites; the top features will be advanced to the primary sieve analysis. This modeling framework may also inform the study of VRC01 in the treatment of HIV-infected persons. [ABSTRACT FROM AUTHOR]

Two phase 3 placebo-controlled trials of the CYD-TDV vaccine, evaluated in children aged 2-14 y (CYD14) and 9-16 y (CYD15), demonstrated vaccine efficacy (VE) of 56.5% and 60.8%, respectively, against symptomatic virologically confirmed dengue (VCD). Sieve analyses were conducted to evaluate whether and how VE varied with amino acid sequence features of dengue viruses (DENVs). DENV premembrane/envelope amino acid sequences from VCD endpoint cases were aligned with the vaccine insert sequences, and extensions of the proportional hazards model were applied to assess variation in VE with amino acid mismatch proportion distances from vaccine strains, individual amino acid residues, and phylogenetic genotypes. In CYD14, VE against VCD of any serotype (DENV-Any) decreased significantly with increasing amino acid distance from the vaccine, whereas in CYD15, VE against DENV-Any was distance-invariant. Restricting to the common age range and amino acid distance range between the trials and accounting for differential VE by serotype, however, showed no evidence of VE variation with distance in either trial. In serotype-specific analyses, VE against DENV4 decreased significantly with increasing amino acid distance from the DENV4 vaccine insert and was significantly greater against residuematched DENV4 at eight signature positions. These effects were restricted to 2- to 8-y-olds, potentially because greater seropositivity of older children at baseline might facilitate a broader protective immune response. The relevance of an antigenicmatch between vaccine strains and circulating DENVs was also supported by greater estimated VE against serotypes and genotypes for which the circulating DENVs had shorter amino acid sequence distances from the vaccine. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Background Hybrid immunity is associated with more durable protection against coronavirus disease 2019 (COVID-19). We describe the antibody responses following severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection in vaccinated and unvaccinated individuals. Methods The 55 vaccine arm COVID-19 cases diagnosed during the blinded phase of the Coronavirus Efficacy trial were matched with 55 placebo arm COVID-19 cases. Pseudovirus neutralizing antibody (nAb) activity to the ancestral strain and binding antibody (bAb) responses to nucleocapsid and spike antigens (ancestral and variants of concern [VOCs]) were assessed on disease day 1 (DD1) and 28 days later (DD29). Results The primary analysis set was 46 vaccine cases and 49 placebo cases with COVID-19 at least 57 days post–first dose. For vaccine group cases, there was a 1.88-fold rise in ancestral antispike bAbs 1 month post–disease onset, although 47% had no increase. The vaccine-to-placebo geometric mean ratios for DD29 antispike and antinucleocapsid bAbs were 6.9 and 0.04, respectively. DD29 mean bAb levels were higher for vaccine vs placebo cases for all VOCs. DD1 nasal viral load positively correlated with bAb levels in the vaccine group. Conclusions Following COVID-19, vaccinated participants had higher levels and greater breadth of antispike bAbs and higher nAb titers than unvaccinated participants. These were largely attributable to the primary immunization series. [ABSTRACT FROM AUTHOR]

Background: Noninvasive models to predict the presence of coronary artery disease (CAD) may help reduce the societal burden of CAD.Objectives: From a prospective registry of patients referred for coronary angiography, the goal of this study was to develop a clinical and biomarker score to predict the presence of significant CAD.Methods: In a training cohort of 649 subjects, predictors of ≥70% stenosis in at least 1 major coronary vessel were identified from >200 candidate variables, including 109 biomarkers. The final model was then validated in a separate cohort (n = 278).Results: The scoring system consisted of clinical variables (male sex and previous percutaneous coronary intervention) and 4 biomarkers (midkine, adiponectin, apolipoprotein C-I, and kidney injury molecule-1). In the training cohort, elevated scores were predictive of ≥70% stenosis in all subjects (odds ratio [OR]: 9.74; p < 0.001), men (OR: 7.88; p <0.001), women (OR: 24.8; p < 0.001), and those with no previous CAD (OR: 8.67; p < 0.001). In the validation cohort, the score had an area under the receiver-operating characteristic curve of 0.87 (p < 0.001) for coronary stenosis ≥70%. Higher scores were associated with greater severity of angiographic stenosis. At optimal cutoff, the score had 77% sensitivity, 84% specificity, and a positive predictive value of 90% for ≥70% stenosis. Partitioning the score into 5 levels allowed for identifying or excluding CAD with >90% predictive value in 42% of subjects. An elevated score predicted incident acute myocardial infarction during 3.6 years of follow up (hazard ratio: 2.39; p < 0.001).Conclusions: We described a clinical and biomarker score with high accuracy for predicting the presence of anatomically significant CAD. (The CASABLANCA Study: Catheter Sampled Blood Archive in Cardiovascular Diseases; NCT00842868). [ABSTRACT FROM AUTHOR]

Patients with chronic kidney disease (CKD) undergoing coronary catheterization are at increased risk of cardiovascular events (CVE). Measuring biomarkers before the procedure may guide clinicians in identifying patients at higher risk of future cardiovascular events. In this sub-study the Catheter Sampled Blood Archive in Cardiovascular Diseases (CASABLANCA), 927 patients underwent coronary catheterization and were followed up for two years. Using machine learning algorithm and targeted proteomics from samples of patients with CKD, 4 biomarkers (kidney injury molecule-1, N-terminal pro B-type natriuretic peptide, osteopontin, and tissue inhibitor of metalloproteinase-1) were integrated into a prognostic algorithm to predict CVE. Results from the panel are expressed in a graded fashion (CVE higher risk and lower risk) using a data-driven cutoff optimized for balanced sensitivity and specificity. During the 2-year follow-up, 74 CVE were ascertained. 51 (rate: 51/378 = 13.5%) events occurred in stage 1–2 CKD and 23 (rate: 23/68 = 33.8%) events occurred in stage 3–5 CKD. The C-statistic for predicting 2-years cardiovascular events in all 446 patients was 0.77 (0.72, 0.82). The model was well-calibrated (Hosmer-Lemeshow test p -value >0.40). Considering patients at CVE lower-risk within each CKD staging group as a reference, the hazard ratio (95% confidence interval) of cardiovascular events was 2.82 (1.53, 5.22) for CKD stage 1–2/CVE higher-risk, and 8.32 (1.12, 61.76) for CKD stage 3–5/CVE higher-risk. Measuring biomarker panel prior to coronary catheterization may be useful to individualize CVE risk assessment among patients with CKD. HART-CVE panel can help clinicians recognize individuals at high risk of cardiovascular events prior to angiographic procedures among patients at different stages of chronic kidney disease. *Incidence rates are presented as cases per 100 person years. Abbreviations: IR: incidence rate, CKD: chronic kidney disease, CVE: cardiovascular event, HR: hazard ratio, CI: confidence interval, KIM-1: kidney injury molecule-1, NTproBNP: N terminal pro B-type natriuretic peptides, TIMP-1: Tissue inhibitor matrix metalloproteinase-1. [Display omitted] • Patients with chronic kidney disease are at increased risk of cardiovascular events following coronary catheterization. Preceding coronary catheterization is an ideal time to measure biomarkers and estimate risk for future cardiovascular events. • Using a machine learning algorithm and targeted proteomics, we demonstrated that measuring four biomarkers (kidney injury molecule-1, N-terminal pro B-type natriuretic peptide, osteopontin, and tissue inhibitor of metalloproteinase-1) can individualize cardiovascular events risk assessment among patients with chronic kidney disease • Future clinical trials may assess the efficacy of implementing the HART CVE model in lowering cardiovascular events compared to the standard of care [ABSTRACT FROM AUTHOR]

• This correlates analysis integrated host immune response & pathogen sequence data. • The nAb correlate of risk depends on dengue sequence in baseline seropositive participants. • Dependency of correlates on sequence-distance stronger in the vaccine (vs placebo) arm. • Infection- vs vaccine-induced nAbs may affect dengue risk differently. • nAb titer approximate surrogate endpoint of dengue disease in baseline-seropositive 9–14-year-olds. In the CYD14 trial of the CYD-TDV dengue vaccine in 2–14 year-olds, neutralizing antibody (nAb) titers to the vaccine-insert dengue strains correlated inversely with symptomatic, virologically-confirmed dengue (VCD). Also, vaccine efficacy against VCD was higher against dengue prM/E amino acid sequences closer to the vaccine inserts. We integrated the nAb and sequence data types by assessing nAb titers as a correlate of sequence-specific VCD separately in the vaccine arm and in the placebo arm. In both vaccine and placebo recipients the correlation of nAb titer with sequence-specific VCD was stronger for dengue nAb contact site sequences closer to the vaccine (p = 0.005 and p = 0.012, respectively). The risk of VCD in vaccine (placebo) recipients was 6.7- (1.80)-fold lower at the 90th vs 10th percentile of nAb for viruses perfectly matched to CYD-TDV, compared to 2.1- (0.78)-fold lower at the 90th vs 10th percentile for viruses with five amino acid mismatches. The evidence for a stronger sequence-distance dependent correlate of risk for the vaccine arm indicates departure from the Prentice criteria for a valid sequence-distance specific surrogate endpoint and suggests that the nAb marker may affect dengue risk differently depending on whether nAbs arise from infection or also by vaccination. However, when restricting to baseline-seropositive 9–14 year-olds, the correlation pattern became more similar between the vaccine and placebo arms, supporting nAb titers as an approximate surrogate endpoint in this population. No sequence-specific nAb titer correlates of VCD were seen in baseline-seronegative participants. Integrated immune response/pathogen sequence data correlates analyses could help increase knowledge of correlates of risk and surrogate endpoints for other vaccines against genetically diverse pathogens. Trial registration: EU Clinical Trials Register 2014-001708-24; registration date 2014-05-26. [ABSTRACT FROM AUTHOR]

Background: HIV diversity may be a useful biomarker for discriminating between recent and non-recent HIV infection. The high resolution melting (HRM) diversity assay was developed to quantify HIV diversity in viral populations without sequencing. In this assay, HIV diversity is expressed as a single numeric HRM score that represents the width of a melting peak. HRM scores are highly associated with diversity measures obtained with next generation sequencing. In this report, a software package, the HRM Diversity Assay Analysis Tool (DivMelt), was developed to automate calculation of HRM scores from melting curve data. Methods: DivMelt uses computational algorithms to calculate HRM scores by identifying the start (T1) and end (T2) melting temperatures for a DNA sample and subtracting them (T2-T1 =HRM score). DivMelt contains many user-supplied analysis parameters to allow analyses to be tailored to different contexts. DivMelt analysis options were optimized to discriminate between recent and non-recent HIV infection and to maximize HRM score reproducibility. HRM scores calculated using DivMelt were compared to HRM scores obtained using a manual method that is based on visual inspection of DNA melting curves. Results: HRM scores generated with DivMelt agreed with manually generated HRM scores obtained from the same DNA melting data. Optimal parameters for discriminating between recent and non-recent HIV infection were identified. DivMelt provided greater discrimination between recent and non-recent HIV infection than the manual method. Conclusion: DivMelt provides a rapid, accurate method of determining HRM scores from melting curve data, facilitating use of the HRM diversity assay for large-scale studies. [ABSTRACT FROM AUTHOR]

Although the HVTN 505 DNA/recombinant adenovirus type 5 vector HIV-1 vaccine trial showed no overall efficacy, analysis of breakthrough HIV-1 sequences in participants can help determine whether vaccine-induced immune responses impacted viruses that caused infection. We analyzed 480 HIV-1 genomes sampled from 27 vaccine and 20 placebo recipients and found that intra-host HIV-1 diversity was significantly lower in vaccine recipients (P ≤ 0.04, Q-values ≤ 0.09) in Gag, Pol, Vif and envelope glycoprotein gp120 (Env-gp120). Furthermore, Env-gp120 sequences from vaccine recipients were significantly more distant from the subtype B vaccine insert than sequences from placebo recipients (P = 0.01, Q-value = 0.12). These vaccine effects were associated with signatures mapping to CD4 binding site and CD4-induced monoclonal antibody footprints. These results suggest either (i) no vaccine efficacy to block acquisition of any viral genotype but vaccine-accelerated Env evolution post-acquisition; or (ii) vaccine efficacy against HIV-1s with Env sequences closest to the vaccine insert combined with increased acquisition due to other factors, potentially including the vaccine vector. [ABSTRACT FROM AUTHOR]

Background: It is important to identify vaccine-induced immune responses that predict the preventative efficacy of a human immunodeficiency virus (HIV)-1 vaccine. We assessed T-cell response markers as correlates of risk in the HIV Vaccine Trials Network (HVTN) 505 HIV-1 vaccine efficacy trial.Methods: 2504 participants were randomized to DNA/rAd5 vaccine or placebo, administered at weeks 0, 4, 8, and 24. Peripheral blood mononuclear cells were obtained at week 26 from all 25 primary endpoint vaccine cases and 125 matched vaccine controls, and stimulated with vaccine-insert-matched peptides. Primary variables were total HIV-1-specific CD4+ T-cell magnitude and Env-specific CD4+ polyfunctionality. Four secondary variables were also assessed. Immune responses were evaluated as predictors of HIV-1 infection among vaccinees using Cox proportional hazards models. Machine learning analyses identified immune response combinations best predicting HIV-1 infection.Results: We observed an unexpectedly strong inverse correlation between Env-specific CD8+ immune response magnitude and HIV-1 infection risk (hazard ratio [HR] = 0.18 per SD increment; P = .04) and between Env-specific CD8+ polyfunctionality and infection risk (HR = 0.34 per SD increment; P < .01).Conclusions: Further research is needed to determine if these immune responses are predictors of vaccine efficacy or markers of natural resistance to HIV-1 infection. [ABSTRACT FROM AUTHOR]

Background: Pritelivir is a novel helicase-primase inhibitor in clinical development for treatment of herpes simplex virus type 2 (HSV-2) infections. In preclinical work, resistance-mediating mutations were identified in the HSV-2 genome at 3 loci in the UL5 gene and 1 locus in UL52.Methods: To evaluate whether daily pritelivir treatment results in emergence of resistance-mediating mutations, we analyzed HSV-2 strains detected in genital swab specimens from trial participants who were randomly assigned to receive different dosages of pritelivir. We sequenced resistance regions from 87 participants' samples, the UL5 gene in 73 samples from 44 participants, and the UL52 gene in 71 samples from 43 participants.Results: We found no evidence that pritelivir induced known resistance-mediating mutations or for amino acid variation at other loci. In one participant's HSV-2 isolate, we found a previously unidentified mutation close to the putative resistance-mediating region in UL5 and subsequently determined in vitro susceptibility to pritelivir. We characterized mutations from 32 cultivated HSV-2 isolates previously found to be susceptible to pritelivir in vitro and identified several novel mutations that most likely reflect preexisting variation in circulating HSV-2.Conclusions: This study demonstrates evidence of retained susceptibility of HSV-2 to pritelivir in immunocompetent persons following daily therapy for up to 28 days. [ABSTRACT FROM AUTHOR]

Summary: Analysis of HIV-1 virions from participants infected in a randomized controlled preventive HIV-1 vaccine efficacy trial can help elucidate mechanisms of partial protection. By comparing the genetic sequence of viruses from vaccine and placebo recipients to the sequence of the vaccine itself, a technique called 'sieve analysis', one can identify functional specificities of vaccine-induced immune responses. We have created an interactive web-based visualization and data access tool for exploring the results of sieve analyses performed on four major preventive HIV-1 vaccine efficacy trials: (i) the HIV Vaccine Trial Network (HVTN) 502/Step trial, (ii) the RV144/Thai trial, (iii) the HVTN 503/Phambili trial and (iv) the HVTN 505 trial. The tool acts simultaneously as a platform for rapid reinterpretation of sieve effects and as a portal for organizing and sharing the viral sequence data. Access to these valuable datasets also enables the development of novel methodology for future sieve analyses. [ABSTRACT FROM AUTHOR]

The RV144 clinical trial showed the partial efficacy of a vaccine regimen with an estimated vaccine efficacy (VE) of 31% for protecting low-risk Thai volunteers against acquisition of HIV-1. The impact of vaccine-induced immune responses can be investigated through sieve analysis of HIV-1 breakthrough infections (infected vaccine and placebo recipients). A V1/V2-targeted comparison of the genomes of HIV-1 breakthrough viruses identified two V2 amino acid sites that differed between the vaccine and placebo groups. Here we extended the V1/V2 analysis to the entire HIV-1 genome using an array of methods based on individual sites, k-mers and genes/proteins. We identified 56 amino acid sites or “signatures” and 119 k-mers that differed between the vaccine and placebo groups. Of those, 19 sites and 38 k-mers were located in the regions comprising the RV144 vaccine (Env-gp120, Gag, and Pro). The nine signature sites in Env-gp120 were significantly enriched for known antibody-associated sites (p = 0.0021). In particular, site 317 in the third variable loop (V3) overlapped with a hotspot of antibody recognition, and sites 369 and 424 were linked to CD4 binding site neutralization. The identified signature sites significantly covaried with other sites across the genome (mean = 32.1) more than did non-signature sites (mean = 0.9) (p < 0.0001), suggesting functional and/or structural relevance of the signature sites. Since signature sites were not preferentially restricted to the vaccine immunogens and because most of the associations were insignificant following correction for multiple testing, we predict that few of the genetic differences are strongly linked to the RV144 vaccine-induced immune pressure. In addition to presenting results of the first complete-genome analysis of the breakthrough infections in the RV144 trial, this work describes a set of statistical methods and tools applicable to analysis of breakthrough infection genomes in general vaccine efficacy trials for diverse pathogens. [ABSTRACT FROM AUTHOR]

The RV144 HIV-1 vaccine trial demonstrated partial efficacy of 31% against HIV-1 infection. Studies into possible correlates of protection found that antibodies specific to the V1 and V2 (V1/V2) region of envelope correlated inversely with infection risk and that viruses isolated from trial participants contained genetic signatures of vaccine-induced pressure in the V1/V2 region. We explored the hypothesis that the genetic signatures in V1 and V2 could be partly attributed to selection by vaccine-primed T cells. We performed a T-cellbased sieve analysis of breakthrough viruses in the RV144 trial and found evidence of predicted HLA binding escape that was greater in vaccine versus placebo recipients. The predicted escape depended on class I HLA A*02- and A*11-restricted epitopes in the MNstrain rgp120 vaccine immunogen. Though we hypothesized that this was indicative of postacquisition selection pressure, we also found that vaccine efficacy (VE) was greater in A*02-positive (A*02+) participants than in A*02- participants (VE=54% versus 3%, P=0.05). Vaccine efficacy against viruses with a lysine residue at site 169, important to antibody binding and implicated in vaccine-induced immune pressure, was also greater in A*02+ participants (VE=74% versus 15%, P=0.02). Additionally, a reanalysis of vaccine-induced immune responses that focused on those that were shown to correlate with infection risk suggested that the humoral responses may have differed in A*02+ participants. These exploratory and hypothesis-generating analyses indicate there may be an association between a class I HLA allele and vaccine efficacy, highlighting the importance of considering HLA alleles and host immune genetics in HIV vaccine trials. [ABSTRACT FROM AUTHOR]

This article calls for broader use of outcome-adaptive randomization when designing clinical trials to test COVID-19 interventions, which may reduce the number of adverse outcomes. [ABSTRACT FROM AUTHOR]

The RV144 trial demonstrated 31% vaccine efficacy at preventing human immunodeficiency virus (HIV)-1 infection. Antibodies against the HIV-1 envelope variable loops 1 and 2 (Env V1 and V2) correlated inversely with infection risk. We proposed that vaccine-induced immune responses against V1/V2 would have a selective effect against, or sieve, HIV-1 breakthrough viruses. A total of 936 HIV-1 genome sequences from 44 vaccine and 66 placebo recipients were examined. We show that vaccine-induced immune responses were associated with two signatures in V2 at amino acid positions 169 and 181. Vaccine efficacy against viruses matching the vaccine at position 169 was 48% (confidence interval 18% to 66%; P = 0.0036), whereas vaccine efficacy against viruses mismatching the vaccine at position 181 was 78% (confidence interval 35% to 93%; P = 0.0028). Residue 169 is in a cationic glycosylated region recognized by broadly neutralizing and RV144-derived antibodies. The predicted distance between the two signature sites (21?±?7?Å) and their match/mismatch dichotomy indicate that multiple factors may be involved in the protection observed in RV144. Genetic signatures of RV144 vaccination in V2 complement the finding of an association between high V1/V2-binding antibodies and reduced risk of HIV-1 acquisition, and provide evidence that vaccine-induced V2 responses plausibly had a role in the partial protection conferred by the RV144 regimen. [ABSTRACT FROM AUTHOR]

We analyzed HIV-1 genome sequences from 68 newly infected volunteers in the STEP HIV-1 vaccine trial. To determine whether the vaccine exerted selective T cell pressure on breakthrough viruses, we identified potential T cell epitopes in the founder sequences and compared them to epitopes in the vaccine. We found greater distances to the vaccine sequence for sequences from vaccine recipients than from placebo recipients. The most significant signature site distinguishing vaccine from placebo recipients was Gag amino acid 84, a site encompassed by several epitopes contained in the vaccine and restricted by human leukocyte antigen (HLA) alleles common in the study cohort. Moreover, the extended divergence was confined to the vaccine components of the virus (HIV-1 Gag, Pol and Nef) and not found in other HIV-1 proteins. These results represent what is to our knowledge the first evidence of selective pressure from vaccine-induced T cell responses on HIV-1 infection in humans. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Background: We previously developed a clinical/proteomics panel for predicting procedural acute kidney injury (AKI) and now examine its performance in those with diabetes (DM). Methods: We measured 109 biomarkers in blood from patients undergoing coronary angiography. Procedural AKI defined as increase in serum creatinine ≥0.3 mg/dL, increase in serum creatinine of ≥50%, or documented oliguria ≤7 days after procedure. Clinical and biomarker predictors of AKI identified using least-angle regression; a final prognostic model was developed with LASSO; the model was then measured in those with DM. Results: Besides DM, 5 predictors were in the final model: 3 (blood urea nitrogen to creatinine ratio, C-reactive protein and osteopontin) had positive association, while 2 (CD5 antigen-like and Factor VII) had negative association with AKI risk. Among 217 patients with DM, 18 (8.3%) developed AKI. The final model had an AUC of 0.87 for predicting procedural AKI (P<0.001). The optimal score cut-off had 100% sensitivity and a negative predictive value (NPV) of 100% for procedural AKI. Conclusions: We describe a clinical and proteomics-supported biomarker model with high sensitivity/NPV for AKI in patients with DM following coronary angiography. Disclosure: N.E. Ibrahim: Other Relationship; Self; Novartis Pharmaceuticals Corporation. C.P. McCarthy: None. S. Shrestha: None. H. Gaggin: Other Relationship; Self; Dr. Gaggin has received research grant support from Roche Diagnostics, Jana Care, Ortho Clinical, Novartis; consulting income from Merck, Roche Diagnostics; research payments for clinical endpoint com. R. Mukai: None. C.A. Magaret: Consultant; Self; Prevencio. R.F. Rhyne: Board Member; Self; Prevencio. Employee; Self; Prevencio. Stock/Shareholder; Self; Prevencio. J. Januzzi: Consultant; Self; Abbott, Roche Diagnostic USA. Research Support; Self; Abbott, Prevencio, Quest Diagnostics, Roche Diagnostic USA. Funding: Prevencio, Inc. [ABSTRACT FROM AUTHOR]