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OBJECTIVETo develop a highly sensitive and specific blood biomarker panel that identifies febrile children with Kawasaki disease (KD).METHODSWe tested blood samples from a single-center cohort of KD (n = 50) and control febrile children (n = 100) to develop a biomarker panel from 11 candidates selected by their assay clinical availability. We used machine learning with least absolute shrinkage and selection operator regression to identify 11 blood markers with values incorporated into a model, which provided a binary predictive risk score for KD determined with Youden’s index. We further reduced the model using least angle regression.RESULTSUsing 10-fold cross-validation with least absolute shrinkage and selection operator regression on these 11 readouts plus patient age resulted in an area under the receiver operating characteristic curve of 0.94 (95% confidence interval [CI]: 0.90–0.98; P CONCLUSIONSMachine learning identified a highly accurate diagnostic model for KD. The reduced model employs 3 biomarkers currently approved by regulatory bodies and performed on platforms commonly used by certified diagnostic laboratories.

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.

BackgroundHybrid 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.MethodsThe 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).ResultsThe 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.ConclusionsFollowing 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.

Key Points Question What are the characteristics of SARS-CoV-2 G614 viral shedding in incident infections in association with COVID-19 symptom onset and severity? Findings In a cohort study of persons who tested positive for SARS-CoV-2 after recent exposure, viral RNA trajectory was characterized by a rapid peak followed by slower decay. Peak viral load correlated positively with symptom severity and generally occurred within 1 day of symptom onset if the patient was symptomatic. Meaning A detailed description of the SARS-CoV-2 G614 viral shedding trajectory serves as a baseline for comparison with new viral variants of concern and informs models to plan clinical trials to end the pandemic., Importance The SARS-CoV-2 viral trajectory has not been well characterized in incident infections. These data are needed to inform natural history, prevention practices, and therapeutic development. Objective To characterize early SARS-CoV-2 viral RNA load (hereafter referred to as viral load) in individuals with incident infections in association with COVID-19 symptom onset and severity. Design, Setting, and Participants This prospective cohort study was a secondary data analysis of a remotely conducted study that enrolled 829 asymptomatic community-based participants recently exposed (, This cohort study characterizes early SARS-CoV-2 viral RNA load in individuals with incident infections in association with COVID-19 symptom onset and severity.

The Antibody Mediated Prevention trials showed that the broadly neutralizing antibody (bnAb) VRC01 prevented acquisition of human immunodeficiency virus-1 (HIV-1) sensitive to VRC01. Using AMP trial data, here we show that the predicted serum neutralization 80% inhibitory dilution titer (PT80) biomarker—which quantifies the neutralization potency of antibodies in an individual’s serum against an HIV-1 isolate—can be used to predict HIV-1 prevention efficacy. Similar to the results of nonhuman primate studies, an average PT80 of 200 (meaning a bnAb concentration 200-fold higher than that required to reduce infection by 80% in vitro) against a population of probable exposing viruses was estimated to be required for 90% prevention efficacy against acquisition of these viruses. Based on this result, we suggest that the goal of sustained PT80 >200 against 90% of circulating viruses can be achieved by promising bnAb regimens engineered for long half-lives. We propose the PT80 biomarker as a surrogate endpoint for evaluation of bnAb regimens, and as a tool for benchmarking candidate bnAb-inducing vaccines.

Background Undetected obstructive coronary artery disease (oCAD) is a global health problem associated with significant morbidity and mortality. A need exists for an accurate and easily accessible diagnostic test for oCAD. Using machine learning, a multi-biomarker blood diagnostic test for oCAD based on high-sensitivity cardiac troponin-I (hs-cTnI) has been developed. Purpose To validate the performance of a previously developed, algorithmically weighted, multiple protein diagnostic panel to diagnose oCAD in a pooled multi-national cohort and to compare the diagnostic panel's performance to predict oCAD to hs-cTnI alone. Methods Three clinical factors (sex, age, and previous coronary percutaneous intervention) and three biomarkers (hs-cTnI, Adiponectin, and Kidney Injury Molecule-1) were combined. hs-cTnI blood samples were assayed on the Siemens Atellica and Abbott Diagnostics ARCHITECT immunoassay platforms. Adiponectin and Kidney Injury Molecule-1 were measured with a multiplex assay on blood samples via the Luminex 100/200 xMAP platform. Individual data from a total of 924 patients with a mixture of acute and lesser acute presentations from three centers were pooled (Table 1). oCAD was defined as >50% coronary obstruction in at least one coronary artery (for the University Hospital Hamburg-Eppendorf cohort) or >70% coronary obstruction in at least one coronary artery (for the other two cohorts). The multiple biomarker diagnostic panel's performance to predict oCAD was also compared to hs-cTnI alone. Results The multiple protein panel had an area under the receiver-operating characteristic curve of 0.80 (95% CI, 0.77, 0.83, p Conclusions In this multinational pooled cohort, a previously described novel machine learning, multiple biomarker panel provided high accuracy to diagnose patients for oCAD. Funding Acknowledgement Type of funding sources: Private company. Main funding source(s): Prevencio, Inc. Table 1. Pooled Variable DataFigure 1. ROC for HART CADhs and hs-cTnI

SummarySARS-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.

Introduction: Increased systemic inflammation is one of the key triggers in the initiation and progression of atherosclerosis. We investigated interleukin 18 binding protein (IL-18BP), a new inflammatory marker of the IL-1 cytokine family, on its predictive value for cardiovascular disease in subjects under investigation for stenotic atherosclerotic disease. Methods: We performed a prospective, single-center, investigator-initiated, observational cohort study in 927 subjects who underwent coronary and/or peripheral angiography. The cohort was randomly split into a discovery (n=627) and a validation set (n =267). Predictive value for incident cardiovascular events (composite of cardiovascular death, myocardial infarction and stroke), cardiovascular mortality and all-cause mortality was investigated using Cox proportional hazard models, including concordance index analyses and assessment of net reclassification for 10-year cardiovascular risk categories using Weibull modelling. Results: An one standard deviation increase in log-transformed IL-18BP was strongly associated with cardiovascular events (HR 1.96, 95% CI 1.54-2.51), cardiovascular mortality (HR 1.59, 95% CI 1.04-2.44) and all-cause mortality (HR 1.72, 95 CI 1.43-2.07), independent of the traditional cardiovascular risk factors, including body mass index. C-index of a clinical model for prediction of cardiovascular events improved from 0.69 (95% CI to 0.62-0.75) to 0.73 (95% CI 0.67-0.79) with inclusion of IL-18BP in the model; in doing so, a reclassification of 35.9% (p < 0.001) for events was demonstrated. Conclusions: Among a population of patient undergoing coronary angiography, results of IL18-BP measurement improved cardiovascular risk prediction beyond the traditional risk factors. Kaplan-Meier survival curves for CV events during one-year follow up comparing tertiles IL-18BP (discovery set)

We propose platform trials with outcome-adaptive randomization to efficiently select the most effective coronavirus disease 2019 (COVID-19) treatments. The global spread of severe acute respiratory syndrome coronavirus 2 infection is alarming in its geographic scope and in the number of associated deaths. There are currently no treatments proven to decrease mortality from COVID-19 further than what can be achieved through supportive care. Thus far, the choice of therapeutics has been limited to existing, repurposed medications. Given that some of the medications are perceived to have low toxicity, many have been embraced without evidence. Although remdesivir was recently found to shorten time to symptom resolution, evidence for survival benefit is inconclusive

Background Current noninvasive modalities to diagnose coronary artery disease (CAD) have several limitations. We sought to derive and externally validate a hs‐cTn (high‐sensitivity cardiac troponin)–based proteomic model to diagnose obstructive coronary artery disease. Methods and Results In a derivation cohort of 636 patients referred for coronary angiography, predictors of ≥70% coronary stenosis were identified from 6 clinical variables and 109 biomarkers. The final model was first internally validated on a separate cohort (n=275) and then externally validated on a cohort of 241 patients presenting to the ED with suspected acute myocardial infarction where ≥50% coronary stenosis was considered significant. The resulting model consisted of 3 clinical variables (male sex, age, and previous percutaneous coronary intervention) and 3 biomarkers (hs‐cTnI [high‐sensitivity cardiac troponin I], adiponectin, and kidney injury molecule‐1). In the internal validation cohort, the model yielded an area under the receiver operating characteristic curve of 0.85 for coronary stenosis ≥70% ( P P Conclusions A model including hs‐cTnI can predict the presence of obstructive coronary artery disease with high accuracy including in those with indeterminate hs‐cTnI concentrations.

SummarySingle broadly neutralizing antibody (bnAb) regimens are currently being evaluated in 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 in the research pipeline, methods for down-selecting these regimens into efficacy trials are of great interest. To aid the down-selection process, we developed Super LeArner Prediction of NAb Panels (SLAPNAP), a software tool for training and evaluating machine learning models that predict in vitro neutralization resistance of HIV Envelope pseudoviruses to a given single or combination bnAb regimen, based on Envelope amino acid sequence features. SLAPNAP also provides measures of variable importance of sequence features. These results can rank bnAb regimens by their potential prevention efficacy and aid assessments of how prevention efficacy depends on sequence features.Availability and ImplementationSLAPNAP 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 (respectively,https://github.com/benkeser/slapnapandhttps://benkeser.github.io/slapnap/).ContactDavid Benkeser,benkeser@emory.edu

Background: In patients with suspected myocardial infarction (MI), we sought to validate a machine learning-driven, multibiomarker panel for prediction of incident major adverse cardiovascular events (MACE). Methodology & results: A previously described prognostic panel for MACE consisting of four biomarkers was measured in 748 patients with suspected MI. The investigated end point was incident MACE within 1 year. The prognostic value of a continuous score and an optimal cut-off was investigated. The area under the curve was 0.86 for the overall model. Using the optimal cut-off resulted in a negative predictive value of 99.4% for incident MACE. Patients with an elevated prognostic score were at high risk for MACE. Conclusion: Among patients with suspected MI, we validated a multibiomarker panel for predicting 1-year MACE. Clinical Trial Registration: NCT02355457 (ClinicalTrials.gov)

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.

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 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.

BackgroundPatients 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 resultsThe 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.ConclusionA clinical/biomarker model can predict with high accuracy the presence of PAD among patients with DM.Trial registration numberNCT00842868.

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. Availability and Implementation: Visualization: http://sieve.fredhutch.org/viz. Source code: https://github.com/nkullman/SIEVE. Data API: http://sieve.fredhutch.org/data. Contact: agartlan@fredhutch.org

Undetected coronary artery disease (CAD) carries significant morbidity and mortality. A novel biomarker-based model to predict the presence of CAD has the potential to non-invasively diagnose CAD. We assessed the performance of a previously developed algorithm to diagnose obstructive CAD versus

Background Standard measures of kidney function are only modestly useful for accurate prediction of risk for acute kidney injury (AKI). Hypothesis Clinical and biomarker data can predict AKI more accurately. Methods Using Luminex xMAP technology, we measured 109 biomarkers in blood from 889 patients prior to undergoing coronary angiography. Procedural AKI was defined as an absolute increase in serum creatinine of ≥0.3 mg/dL, a percentage increase in serum creatinine of ≥50%, or a reduction in urine output (documented oliguria of 6 hours) within 7 days after contrast exposure. Clinical and biomarker predictors of AKI were identified using machine learning and a final prognostic model was developed with least absolute shrinkage and selection operator (LASSO). Results Forty‐three (4.8%) patients developed procedural AKI. Six predictors were present in the final model: four (history of diabetes, blood urea nitrogen to creatinine ratio, C‐reactive protein, and osteopontin) had a positive association with AKI risk, while two (CD5 antigen‐like and Factor VII) had a negative association with AKI risk. The final model had a cross‐validated area under the receiver operating characteristic curve (AUC) of 0.79 for predicting procedural AKI, and an in‐sample AUC of 0.82 (P < 0.001). The optimal score cutoff had 77% sensitivity, 75% specificity, and a negative predictive value of 98% for procedural AKI. An elevated score was predictive of procedural AKI in all subjects (odds ratio = 9.87; P < 0.001). Conclusions We describe a clinical and proteomics‐supported biomarker model with high accuracy for predicting procedural AKI in patients undergoing coronary angiography.

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 residue-matched 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 antigenic match 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.

Background: Patients with diabetes mellitus (DM) are at increased risk for prevalent coronary artery disease (CAD) and incident major adverse cardiac events (MACE) such as cardiovascular death, myocardial infarction, and stroke. Prediction of risk in those with DM may be challenging. Using unbiased proteomics we recently described biomarker panels to predict prevalent CAD (HART-CAD) and incident MACE (HART-CVE; both Prevencio, Kirkland, WA) in unselected patients undergoing diagnostic coronary angiography. In this study, we sought to compare accuracy of these panels in patients with and without DM. Methods: The HART-CAD panel includes 2 clinical variables (male sex and prior PCI) and 4 biomarkers: adiponectin, apolipoprotein CI, kidney injury molecule 1 (KIM 1), and midkine. The HART-CVE panel includes 4 biomarkers (amino-terminal pro-B type natriuretic peptide, KIM 1, osteopontin, and tissue inhibitor of matrix metalloproteinase-1). These panels were derived in 167 DM and 482 non-DM patients; performance of each was validated in a second cohort of patients (N=278) with and without DM. Results: In patients with DM, the HART-CAD panel had excellent performance for prediction of coronary stenosis ≥ 70%, with area under the curve (AUC) of 0.81 (p Conclusion: Previously described multivariate proteomic biomarker panels effectively predict prevalent CAD and risk for incident MACE in at-risk patients with DM. Disclosure S. Shrestha: None. C.P. McCarthy: None. N.E. Ibrahim: None. R. van Kimmenade: None. H. Gaggin: Research Support; Self; Roche Diagnostics Corporation, Portola. Consultant; Self; Roche Diagnostics Corporation, Amgen Inc., Ortho clinical. Other Relationship; Self; EchoSense, Radiometer. R. Mukai: None. C.A. Magaret: Consultant; Self; Prevencio. G. Barnes: Employee; Self; Prevencio. R.F. Rhyne: None. J. Januzzi: Research Support; Self; Boehringer Ingelheim Pharmaceuticals, Inc., Janssen Pharmaceuticals, Inc.. Board Member; Self; Jana Care Inc.. Research Support; Self; Prevencio.

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.

Contains fulltext : 196098.pdf (Publisher’s version ) (Open Access) BACKGROUND: Peripheral arterial disease (PAD) is a global health problem that is frequently underdiagnosed and undertreated. Noninvasive tools to predict the presence and severity of PAD have limitations including inaccuracy, cost, or need for intravenous contrast and ionizing radiation. HYPOTHESIS: A clinical/biomarker score may offer an attractive alternative diagnostic method for PAD. METHODS: In a prospective cohort of 354 patients referred for diagnostic peripheral and/or coronary angiography, predictors of >/=50% stenosis in >/=1 peripheral vessel (carotid/subclavian, renal, or lower extremity arteries) were identified from >50 clinical variables and 109 biomarkers. Machine learning identified variables predictive of obstructive PAD; a score derived from the final model was developed. RESULTS: The score consisted of 1 clinical variable (history of hypertension) and 6 biomarkers (midkine, kidney injury molecule-1, interleukin-23, follicle-stimulating hormone, angiopoietin-1, and eotaxin-1). The model had an in-sample area under the receiver operating characteristic curve of 0.85 for obstructive PAD and a cross-validated area under the curve of 0.84; higher scores were associated with greater severity of angiographic stenosis. At optimal cutoff, the score had 65% sensitivity, 88% specificity, 76% positive predictive value (PPV), and 81% negative predictive value (NPV) for obstructive PAD and performed consistently across vascular territories. Partitioning the score into 5 levels resulted in a PPV of 86% and NPV of 98% in the highest and lowest levels, respectively. Elevated score was associated with shorter time to revascularization during 4.3 years of follow-up. CONCLUSIONS: A clinical/biomarker score demonstrates high accuracy for predicting the presence of PAD.

ObjectiveSevere 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.MethodsWe 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.ResultsOf 1244 subjects (age 66.4±11.5 years, 28.7% female), 80 (6.4%) had severe AS (defined as aortic valve area (AVA) 2). 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, pConclusionsWe describe a novel, multiple biomarker approach for diagnostic evaluation of severe AS.Trial registration numberNCT00842868.

BackgroundAnti-HIV-1 broadly neutralizing antibodies (bnAbs) have been developed as potential agents for prevention of HIV-1 infection. The HIV Vaccine Trials Network and the HIV Prevention Trials Network are conducting the Antibody Mediated Prevention (AMP) trials to assess whether, and how, intravenous infusion of the anti-CD4 binding site bnAb, VRC01, prevents HIV-1 infection. These are the first test-of-concept studies to assess HIV-1 bnAb prevention efficacy in humans.MethodsThe AMP trials are two parallel phase 2b HIV-1 prevention efficacy trials conducted in two cohorts: 2700 HIV-uninfected men and transgender persons who have sex with men in the United States, Peru, Brazil, and Switzerland; and 1500 HIV-uninfected sexually active women in seven countries in sub-Saharan Africa. Participants are randomized 1:1:1 to receive an intravenous infusion of 10 mg/kg VRC01, 30 mg/kg VRC01, or a control preparation every 8 weeks for a total of 10 infusions. Each trial is designed (1) to assess overall prevention efficacy (PE) pooled over the two VRC01 dose groups vs. control and (2) to assess VRC01 dose and laboratory markers as correlates of protection (CoPs) against overall and genotype- and phenotype-specific infection.ResultsEach AMP trial is designed to have 90 % power to detect PE > 0 % if PE is ≥ 60 %. The AMP trials are also designed to identify VRC01 properties (i. e., concentration and effector functions) that correlate with protection and to provide insight into mechanistic CoPs. CoPs are assessed using data from breakthrough HIV-1 infections, including genetic sequences and sensitivities to VRC01-mediated neutralization and Fc effector functions.ConclusionsThe AMP trials test whether VRC01 can prevent HIV-1 infection in two study populations. If affirmative, they will provide information for estimating the optimal dosage of VRC01 (or subsequent derivatives) and identify threshold levels of neutralization and Fc effector functions associated with high-level protection, setting a benchmark for future vaccine evaluation and constituting a bridge to other bnAb approaches for HIV-1 prevention.

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.

Item does not contain fulltext We sought to develop a multiple biomarker approach for prediction of incident major adverse cardiac events (MACE; composite of cardiovascular death, myocardial infarction, and stroke) in patients referred for coronary angiography. In a 649-participant training cohort, predictors of MACE within 1 year were identified using least-angle regression; over 50 clinical variables and 109 biomarkers were analyzed. Predictive models were generated using least absolute shrinkage and selection operator with logistic regression. A score derived from the final model was developed and evaluated with a 278-patient validation set during a median of 3.6 years follow-up. The scoring system consisted of N-terminal pro B-type natriuretic peptide (NT-proBNP), kidney injury molecule-1, osteopontin, and tissue inhibitor of metalloproteinase-1; no clinical variables were retained in the predictive model. In the validation cohort, each biomarker improved model discrimination or calibration for MACE; the final model had an area under the curve (AUC) of 0.79 (p

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.

Undetected coronary artery disease (CAD) carries significant morbidity and mortality. A novel biomarker-based model to predict presence of CAD has potential to non-invasively diagnose CAD. To validate a previously developed algorithm to diagnose obstructive CAD in a prospective cohort undergoing

Viral variants that predominate during early infection may exhibit constrained diversity compared with those found during chronic infection and could contain amino acid signature patterns that may enhance transmission, establish productive infection, and influence early events that modulate the infection course. We compared amino acid distributions in 17 patients recently infected with HIV-1C with patients with chronic infection. We found significantly lower entropy in inferred transmitted/founder (t/f) compared with chronic viruses and identified signature patterns in Vif and Vpr from inferred t/f viruses. We investigated sequence evolution longitudinally up to 500 days postseroconversion and compared the impact of selected substitutions on predicted human leukocyte antigen (HLA) binding affinities of published and predicted cytotoxic T-lymphocyte epitopes. Polymorphisms in Vif and Vpr during early infection occurred more frequently at epitope-HLA anchor residues and significantly decreased predicted epitope-HLA binding. Transmission-associated sequence signatures may have implications for novel strategies to prevent HIV-1 transmission.

The Merck Adenovirus-5 Gag/Pol/Nef HIV-1 subtype-B vaccine evaluated in predominately subtype B epidemic regions (Step Study), while not preventing infection, exerted vaccine-induced immune pressure on HIV-1 breakthrough infections. Here we investigated if the same vaccine exerted immune pressure when tested in the Phambili Phase 2b study in a subtype C epidemic.A sieve analysis, which compares breakthrough viruses from placebo and vaccine arms, was performed on 277 near full-length genomes generated from 23 vaccine and 20 placebo recipients. Vaccine coverage was estimated by computing the percentage of 9-mers that were exact matches to the vaccine insert.There was significantly greater protein distances from the vaccine immunogen sequence in Gag (p=0.045) and Nef (p=0.021) in viruses infecting vaccine recipients compared to placebo recipients. Twenty-seven putative sites of vaccine-induced pressure were identified (p0.05) in Gag (n=10), Pol (n=7) and Nef (n=10), although they did not remain significant after adjustment for multiple comparisons. We found the epitope sieve effect in Step was driven by HLA A∗02:01; an allele which was found in low frequency in Phambili participants compared to Step participants. Furthermore, the coverage of the vaccine against subtype C Phambili viruses was 31%, 46% and 14% for Gag, Pol and Nef, respectively, compared to subtype B Step virus coverage of 56%, 61% and 26%, respectively.This study presents evidence of sieve effects in Gag and Nef; however could not confirm effects on specific amino acid sites. We propose that this weaker signal of vaccine immune pressure detected in the Phambili study compared to the Step study may have been influenced by differences in host genetics (HLA allele frequency) and reduced impact of vaccine-induced immune responses due to mismatch between the viral subtype in the vaccine and infecting subtypes.

Genetic analysis of breakthrough infections in people vaccinated against HIV-1 show that vaccine efficacy increased by up to 80% against viruses carrying two mutations in Env V2, but also raises the possibility of population-level adaptation to the vaccine. A major clinical trial involving more than 16,000 volunteers, known as the RV144 trial, tested a combination of two vaccines (ALVAC-HIV and AIDSVAX B/E gp120) for its ability to prevent HIV infection, as well as for safety. The vaccine was 31% effective against HIV-1 infection, and antibodies against the HIV-1 envelope variable loop 1 and 2 (V1/V2) domain correlated inversely with infection risk. Rolland et al. present a genetic analysis of breakthrough infections in RV144 trial participants and identify signatures associated with vaccine-induced immune pressure, thereby gaining support for a causal relationship between vaccination and protection. Viral amino-acid changes at positions 169 and 181 in the second variable loop of the viral envelope are shown to be most associated with efficacy — and represent possible targets for future vaccines. The RV144 trial demonstrated 31% vaccine efficacy at preventing human immunodeficiency virus (HIV)-1 infection1. Antibodies against the HIV-1 envelope variable loops 1 and 2 (Env V1 and V2) correlated inversely with infection risk2. 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 A) 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.

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.

Pritelivir (also known as BAY 57–1293 and AIC316) belongs to a new class of antiviral compounds, the helicase-primase inhibitors (HPIs), that prevent the de novo synthesis of virus DNA through inhibition of the helicase-primase complex [1]. Functionally, both UL5 and UL52, together with the accessory protein UL8, form the helicase-primase enzyme complex, the molecular target of pritelivir [2]. Unlike nucleoside analogues, which are currently the mainstay of therapy for herpes simplex virus (HSV) infections, pritelivir does not require activation by viral thymidine kinase within an HSV-infected cell. HSV infections resistant to nucleoside analogues are rare in immunocompetent persons, but their frequency is increased in immunocompromised patients [3]. Mutations mediating resistance to nucleoside analogs are located in the gene encoding the thymidine kinase and/or the gene encoding the DNA polymerase. All resistance-mediating mutations to pritelivir identified so far in vitro are located either at a single amino acid position in the viral UL52 primase (amino acid 906) or within/downstream of the fourth functional motif of the viral UL5 helicase (the conserved helicase motif; amino acids 341–355; Figure ​Figure1)1) [4, 5]. To date, no resistant virus isolate or strain has been identified for which drug resistance against pritelivir or other HPIs was mediated by an amino acid change in UL8. Figure 1. Locations of known resistance DNA mutations in UL5 and UL52 versus mutations identified by DNA sequence analysis of swab specimens from trial participants. These mutations are relative to the consensus. Four different DNA mutations in the region in UL5 ... In a dosage-finding trial in persons with genital HSV type 2 (HSV-2) infection, pritelivir was shown to reduce the level of viral shedding and the recurrence of genital lesions [6]. Here we report the results of a secondary objective of this trial: the monitoring of molecular signals of drug resistance over 28 days of therapy.

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., Author Summary We present an analysis of the genomes of the HIV viruses that infected some participants of the RV144 Thai trial, which was the first study to show efficacy of a vaccine to prevent HIV infection. We analyzed the HIV genomes of infected vaccine recipients and infected placebo recipients, and found differences between them. These differences coincide with previously-studied genetic features that are relevant to the biology of HIV infection, including features involved in immune recognition of the virus. The findings presented here generate testable hypotheses about the mechanism of the partial protection seen in the Thai trial, and may ultimately lead to improved vaccines. The article also presents a toolkit of methods for computational analyses that can be applied to other vaccine efficacy trials.