Holly Janes

Immune-Correlates Analysis of an HIV-1 Vaccine Efficacy Trial

BACKGROUND In the RV144 trial, the estimated efficacy of a vaccine regimen against human immunodeficiency virus type 1 (HIV-1) was 31.2%. We performed a case-control analysis to identify antibody and cellular immune correlates of infection risk. METHODS In pilot studies conducted with RV144 blood samples, 17 antibody or cellular assays met prespecified criteria, of which 6 were chosen for primary analysis to determine the roles of T-cell, IgG antibody, and IgA antibody responses in the modulation of infection risk. Assays were performed on samples from 41 vaccinees who became infected and 205 uninfected vaccinees, obtained 2 weeks after final immunization, to evaluate whether immune-response variables predicted HIV-1 infection through 42 months of follow-up. RESULTS Of six primary variables, two correlated significantly with infection risk: the binding of IgG antibodies to variable regions 1 and 2 (V1V2) of HIV-1 envelope proteins (Env) correlated inversely with the rate of HIV-1 infection (estimated odds ratio, 0.57 per 1-SD increase; P=0.02; q=0.08), and the binding of plasma IgA antibodies to Env correlated directly with the rate of infection (estimated odds ratio, 1.54 per 1-SD increase; P=0.03; q=0.08). Neither low levels of V1V2 antibodies nor high levels of Env-specific IgA antibodies were associated with higher rates of infection than were found in the placebo group. Secondary analyses suggested that Env-specific IgA antibodies may mitigate the effects of potentially protective antibodies. CONCLUSIONS This immune-correlates study generated the hypotheses that V1V2 antibodies may have contributed to protection against HIV-1 infection, whereas high levels of Env-specific IgA antibodies may have mitigated the effects of protective antibodies. Vaccines that are designed to induce higher levels of V1V2 antibodies and lower levels of Env-specific IgA antibodies than are induced by the RV144 vaccine may have improved efficacy against HIV-1 infection.

HIV-1 vaccine-induced immunity in the test-of-concept Step Study: a case–cohort analysis

BACKGROUND In the Step Study, the MRKAd5 HIV-1 gag/pol/nef vaccine did not reduce plasma viraemia after infection, and HIV-1 incidence was higher in vaccine-treated than in placebo-treated men with pre-existing adenovirus serotype 5 (Ad5) immunity. We assessed vaccine-induced immunity and its potential contributions to infection risk. METHODS To assess immunogenicity, we characterised HIV-specific T cells ex vivo with validated interferon-gamma ELISPOT and intracellular cytokine staining assays, using a case-cohort design. To establish effects of vaccine and pre-existing Ad5 immunity on infection risk, we undertook flow cytometric studies to measure Ad5-specific T cells and circulating activated (Ki-67+/BcL-2(lo)) CD4+ T cells expressing CCR5. FINDINGS We detected interferon-gamma-secreting HIV-specific T cells (range 163/10(6) to 686/10(6) peripheral blood mononuclear cells) ex vivo by ELISPOT in 77% (258/354) of people receiving vaccine; 218 of 354 (62%) recognised two to three HIV proteins. We identified HIV-specific CD4+ T cells by intracellular cytokine staining in 58 of 142 (41%) people. In those with reactive CD4+ T cells, the median percentage of CD4+ T cells expressing interleukin 2 was 88%, and the median co-expression of interferon gamma or tumor necrosis factor alpha (TNFalpha), or both, was 72%. We noted HIV-specific CD8+ T cells (range 0.4-1.0%) in 117 of 160 (73%) participants, expressing predominantly either interferon gamma alone or with TNFalpha. Vaccine-induced HIV-specific immunity, including response rate, magnitude, and cytokine profile, did not differ between vaccinated male cases (before infection) and non-cases. Ad5-specific T cells were lower in cases than in non-cases in several subgroup analyses. The percentage of circulating Ki-67+BcL-2(lo)/CCR5+CD4+ T cells did not differ between cases and non-cases. INTERPRETATION Consistent with previous trials, the MRKAd5 HIV-1 gag/pol/nef vaccine was highly immunogenic for inducing HIV-specific CD8+ T cells. Our findings suggest that future candidate vaccines have to elicit responses that either exceed in magnitude or differ in breadth or function from those recorded in this trial.

Tiered Categorization of a Diverse Panel of HIV-1 Env Pseudoviruses for Assessment of Neutralizing Antibodies

ABSTRACT The restricted neutralization breadth of vaccine-elicited antibodies is a major limitation of current human immunodeficiency virus-1 (HIV-1) candidate vaccines. In order to permit the efficient identification of vaccines with enhanced capacity for eliciting cross-reactive neutralizing antibodies (NAbs) and to assess the overall breadth and potency of vaccine-elicited NAb reactivity, we assembled a panel of 109 molecularly cloned HIV-1 Env pseudoviruses representing a broad range of genetic and geographic diversity. Viral isolates from all major circulating genetic subtypes were included, as were viruses derived shortly after transmission and during the early and chronic stages of infection. We assembled a panel of genetically diverse HIV-1-positive (HIV-1+) plasma pools to assess the neutralization sensitivities of the entire virus panel. When the viruses were rank ordered according to the average sensitivity to neutralization by the HIV-1+ plasmas, a continuum of average sensitivity was observed. Clustering analysis of the patterns of sensitivity defined four subgroups of viruses: those having very high (tier 1A), above-average (tier 1B), moderate (tier 2), or low (tier 3) sensitivity to antibody-mediated neutralization. We also investigated potential associations between characteristics of the viral isolates (clade, stage of infection, and source of virus) and sensitivity to NAb. In particular, higher levels of NAb activity were observed when the virus and plasma pool were matched in clade. These data provide the first systematic assessment of the overall neutralization sensitivities of a genetically and geographically diverse panel of circulating HIV-1 strains. These reference viruses can facilitate the systematic characterization of NAb responses elicited by candidate vaccine immunogens.

Pivotal evaluation of the accuracy of a biomarker used for classification or prediction: standards for study design.

Research methods for biomarker evaluation lag behind those for evaluating therapeutic treatments. Although a phased approach to development of biomarkers exists and guidelines are available for reporting study results, a coherent and comprehensive set of guidelines for study design has not been delineated. We describe a nested case–control study design that involves prospective collection of specimens before outcome ascertainment from a study cohort that is relevant to the clinical application. The biomarker is assayed in a blinded fashion on specimens from randomly selected case patients and control subjects in the study cohort. We separately describe aspects of the design that relate to the clinical context, biomarker performance criteria, the biomarker test, and study size. The design can be applied to studies of biomarkers intended for use in disease diagnosis, screening, or prognosis. Common biases that pervade the biomarker research literature would be eliminated if these rigorous standards were followed.

Case-crossover analyses of air pollution exposure data: referent selection strategies and their implications for bias.

The case–crossover design has been widely used to study the association between short-term air pollution exposure and the risk of an acute adverse health event. The design uses cases only; for each individual case, exposure just before the event is compared with exposure at other control (or “referent”) times. Time-invariant confounders are controlled by making within-subject comparisons. Even more important in the air pollution setting is that time-varying confounders can also be controlled by design by matching referents to the index time. The referent selection strategy is important for reasons in addition to control of confounding. The case–crossover design makes the implicit assumption that there is no trend in exposure across the referent times. In addition, the statistical method that is used—conditional logistic regression—is unbiased only with certain referent strategies. We review here the case–crossover literature in the air pollution context, focusing on key issues regarding referent selection. We conclude with a set of recommendations for choosing a referent strategy with air pollution exposure data. Specifically, we advocate the time-stratified approach to referent selection because it ensures unbiased conditional logistic regression estimates, avoids bias resulting from time trend in the exposure series, and can be tailored to match on specific time-varying confounders.

Efficacy Trial of a DNA/rAd5 HIV-1 Preventive Vaccine

BACKGROUND A safe and effective vaccine for the prevention of human immunodeficiency virus type 1 (HIV-1) infection is a global priority. We tested the efficacy of a DNA prime-recombinant adenovirus type 5 boost (DNA/rAd5) vaccine regimen in persons at increased risk for HIV-1 infection in the United States. METHODS At 21 sites, we randomly assigned 2504 men or transgender women who have sex with men to receive the DNA/rAd5 vaccine (1253 participants) or placebo (1251 participants). We assessed HIV-1 acquisition from week 28 through month 24 (termed week 28+ infection), viral-load set point (mean plasma HIV-1 RNA level 10 to 20 weeks after diagnosis), and safety. The 6-plasmid DNA vaccine (expressing clade B Gag, Pol, and Nef and Env proteins from clades A, B, and C) was administered at weeks 0, 4, and 8. The rAd5 vector boost (expressing clade B Gag-Pol fusion protein and Env glycoproteins from clades A, B, and C) was administered at week 24. RESULTS In April 2013, the data and safety monitoring board recommended halting vaccinations for lack of efficacy. The primary analysis showed that week 28+ infection had been diagnosed in 27 participants in the vaccine group and 21 in the placebo group (vaccine efficacy, -25.0%; 95% confidence interval, -121.2 to 29.3; P=0.44), with mean viral-load set points of 4.46 and 4.47 HIV-1 RNA log10 copies per milliliter, respectively. Analysis of all infections during the study period (41 in the vaccine group and 31 in the placebo group) also showed lack of vaccine efficacy (P=0.28). The vaccine regimen had an acceptable side-effect profile. CONCLUSIONS The DNA/rAd5 vaccine regimen did not reduce either the rate of HIV-1 acquisition or the viral-load set point in the population studied. (Funded by the National Institute of Allergy and Infectious Diseases; ClinicalTrials.gov number, NCT00865566.).

Net Reclassification Indices for Evaluating Risk Prediction Instruments: A Critical Review

Net reclassification indices have recently become popular statistics for measuring the prediction increment of new biomarkers. We review the various types of net reclassification indices and their correct interpretations. We evaluate the advantages and disadvantages of quantifying the prediction increment with these indices. For predefined risk categories, we relate net reclassification indices to existing measures of the prediction increment. We also consider statistical methodology for constructing confidence intervals for net reclassification indices and evaluate the merits of hypothesis testing based on such indices. We recommend that investigators using net reclassification indices should report them separately for events (cases) and nonevents (controls). When there are two risk categories, the components of net reclassification indices are the same as the changes in the true- and false-positive rates. We advocate the use of true- and false-positive rates and suggest it is more useful for investigators to retain the existing, descriptive terms. When there are three or more risk categories, we recommend against net reclassification indices because they do not adequately account for clinically important differences in shifts among risk categories. The category-free net reclassification index is a new descriptive device designed to avoid predefined risk categories. However, it experiences many of the same problems as other measures such as the area under the receiver operating characteristic curve. In addition, the category-free index can mislead investigators by overstating the incremental value of a biomarker, even in independent validation data. When investigators want to test a null hypothesis of no prediction increment, the well-established tests for coefficients in the regression model are superior to the net reclassification index. If investigators want to use net reclassification indices, confidence intervals should be calculated using bootstrap methods rather than published variance formulas. The preferred single-number summary of the prediction increment is the improvement in net benefit.

Adjusting for Covariates in Studies of Diagnostic, Screening, or Prognostic Markers: An Old Concept in a New Setting

The concept of covariate adjustment is well established in therapeutic and etiologic studies. However, it has received little attention in the growing area of medical research devoted to the development of markers for disease diagnosis, screening, or prognosis, where classification accuracy, rather than association, is of primary interest. In this paper, the authors demonstrate the need for covariate adjustment in studies of classification accuracy, discuss methods for adjusting for covariates, and distinguish covariate adjustment from several other related, but fundamentally different, uses for covariates. They draw analogies and contrasts throughout with studies of association.

Fine particulate matter and mortality: a comparison of the six cities and American Cancer Society cohorts with a medicare cohort.

Background: The American Cancer Society study and the Harvard Six Cities study are 2 landmark cohort studies for estimating the chronic effects of fine particulate air pollution (PM2.5) on mortality. Using Medicare data, we assessed the association of PM2.5 with mortality for the same locations included in these studies. Methods: We estimated the chronic effects of PM2.5 on mortality for the period 2000–2002 using mortality data for cohorts of Medicare participants and average PM2.5 levels from monitors in the same counties included in the 2 studies. We estimated mortality risk associated with air pollution adjusting for individual-level (age and sex) and area-level covariates (education, income level, poverty, and employment). We controlled for potential confounding by cigarette smoking by including standardized mortality ratios for lung cancer and chronic obstructive pulmonary disease. Results: Using the Medicare data, we estimated that a 10 &mgr;g/m3 increase in the yearly average PM2.5 concentration is associated with 10.9% (95% confidence interval = 9.0–12.8) and with 20.8% (14.8–27.1) increases in all-cause mortality for the American Cancer Society and Harvard Six Cities study counties, respectively. The estimates are somewhat higher than those reported by the original investigators. Conclusion: Although Medicare data lack information on some potential confounding factors, we estimated risks similar to those in the previously published reports, which incorporated more extensive information on individual-level confounders. We propose that the Medicare files can be used to construct on-going cohorts for tracking the risk of air pollution over time.

Measuring the Performance of Markers for Guiding Treatment Decisions

Treatment selection markers, sometimes called predictive markers, are factors that help clinicians select therapies that maximize good outcomes and minimize adverse outcomes for patients. Existing statistical methods for evaluating a treatment selection marker include assessing its prognostic value, evaluating treatment effects in patients with a restricted range of marker values, and testing for a statistical interaction between marker value and treatment. These methods are inadequate, because they give misleading measures of performance that do not answer key clinical questions about how the marker might help patients choose treatment, how treatment decisions should be made on the basis of a continuous marker measurement, what effect using the marker to select treatment would have on the population, or what proportion of patients would have treatment changes on the basis of marker measurement. Marker-by-treatment predictiveness curves are proposed as a more useful aid to answering these clinically relevant questions, because they illustrate treatment effects as a function of marker value, outcomes when using or not using the marker to select treatment, and the proportion of patients for whom treatment recommendations change after marker measurement. Randomized therapeutic clinical trials, in which entry criteria and treatment regimens are not restricted by the marker, are also proposed as the basis for constructing the curves and evaluating and comparing markers.