Abstract
Polygenic risk scores (PRS) have been successfully developed for the prediction of human diseases and complex traits in the past years. For drug response prediction in randomized clinical ...trials, a common practice is to apply PRS built from a disease genome-wide association study (GWAS) directly to a corresponding pharmacogenomics (PGx) setting. Here, we show that such an approach relies on stringent assumptions about the prognostic and predictive effects of the selected genetic variants. We propose a shift from disease PRS to PGx PRS approaches by simultaneously modeling both the prognostic and predictive effects and further make this shift possible by developing a series of PRS-PGx methods, including a novel Bayesian regression approach (PRS-PGx-Bayes). Simulation studies show that PRS-PGx methods generally outperform the disease PRS methods and PRS-PGx-Bayes is superior to all other PRS-PGx methods. We further apply the PRS-PGx methods to PGx GWAS data from a large cardiovascular randomized clinical trial (IMPROVE-IT) to predict treatment related LDL cholesterol reduction. The results demonstrate substantial improvement of PRS-PGx-Bayes in both prediction accuracy and the capability of capturing the treatment-specific predictive effects while compared with the disease PRS approaches.
Randomized clinical trials are often designed to assess whether a test treatment prolongs survival relative to a control treatment. Increased patient heterogeneity, while desirable for ...generalizability of results, can weaken the ability of common statistical approaches to detect treatment differences, potentially hampering the regulatory approval of safe and efficacious therapies. A novel solution to this problem is proposed. A list of baseline covariates that have the potential to be prognostic for survival under either treatment is pre‐specified in the analysis plan. At the analysis stage, using all observed survival times but blinded to patient‐level treatment assignment, “noise” covariates are removed with elastic net Cox regression. The shortened covariate list is used by a conditional inference tree algorithm to segment the heterogeneous trial population into subpopulations of prognostically homogeneous patients (risk strata). After patient‐level treatment unblinding, a treatment comparison is done within each formed risk stratum and stratum‐level results are combined for overall statistical inference. The impressive power‐boosting performance of our proposed 5‐step stratified testing and amalgamation routine (5‐STAR), relative to that of the logrank test and other common approaches that do not leverage inherently structured patient heterogeneity, is illustrated using a hypothetical and two real datasets along with simulation results. Furthermore, the importance of reporting stratum‐level comparative treatment effects (time ratios from accelerated failure time model fits in conjunction with model averaging and, as needed, hazard ratios from Cox proportional hazard model fits) is highlighted as a potential enabler of personalized medicine. An R package is available at
https://github.com/rmarceauwest/fiveSTAR.
Abstract
Motivation
Pharmacogenomics (PGx) research holds the promise for detecting association between genetic variants and drug responses in randomized clinical trials, but it is limited by small ...populations and thus has low power to detect signals. It is critical to increase the power of PGx genome-wide association studies (GWAS) with small sample sizes so that variant–drug-response association discoveries are not limited to common variants with extremely large effect.
Results
In this article, we first discuss the challenges of PGx GWAS studies and then propose the adaptively weighted joint test (AWOT) and Cauchy Weighted jOint Test (CWOT), which are two flexible and robust joint tests of the single nucleotide polymorphism main effect and genotype-by-treatment interaction effect for continuous and binary endpoints. Two analytic procedures are proposed to accurately calculate the joint test P-value. We evaluate AWOT and CWOT through extensive simulations under various scenarios. The results show that the proposed AWOT and CWOT control type I error well and outperform existing methods in detecting the most interesting signal patterns in PGx settings (i.e. with strong genotype-by-treatment interaction effects, but weak genotype main effects). We demonstrate the value of AWOT and CWOT by applying them to the PGx GWAS from the Bezlotoxumab Clostridium difficile MODIFY I/II Phase 3 trials.
Availability and implementation
The R package COWT is publicly available on CRAN https://cran.r-project.org/web/packages/cwot/index.html.
Supplementary information
Supplementary data are available at Bioinformatics online.
Abstract
Polygenic risk score (PRS) has been recently developed for predicting complex traits and drug responses. It remains unknown whether multi-trait PRS (mtPRS) methods, by integrating ...information from multiple genetically correlated traits, can improve prediction accuracy and power for PRS analysis compared with single-trait PRS (stPRS) methods. In this paper, we first review commonly used mtPRS methods and find that they do not directly model the underlying genetic correlations among traits, which has been shown to be useful in guiding multi-trait association analysis in the literature. To overcome this limitation, we propose a mtPRS-PCA method to combine PRSs from multiple traits with weights obtained from performing principal component analysis (PCA) on the genetic correlation matrix. To accommodate various genetic architectures covering different effect directions, signal sparseness and across-trait correlation structures, we further propose an omnibus mtPRS method (mtPRS-O) by combining P values from mtPRS-PCA, mtPRS-ML (mtPRS based on machine learning) and stPRSs using Cauchy Combination Test. Our extensive simulation studies show that mtPRS-PCA outperforms other mtPRS methods in both disease and pharmacogenomics (PGx) genome-wide association studies (GWAS) contexts when traits are similarly correlated, with dense signal effects and in similar effect directions, and mtPRS-O is consistently superior to most other methods due to its robustness under various genetic architectures. We further apply mtPRS-PCA, mtPRS-O and other methods to PGx GWAS data from a randomized clinical trial in the cardiovascular domain and demonstrate performance improvement of mtPRS-PCA in both prediction accuracy and patient stratification as well as the robustness of mtPRS-O in PRS association test.
Polygenic risk scores (PRSs) have emerged as promising tools for the prediction of human diseases and complex traits in disease genome-wide association studies (GWAS). Applying PRSs to ...pharmacogenomics (PGx) studies has begun to show great potential for improving patient stratification and drug response prediction. However, there are unique challenges that arise when applying PRSs to PGx GWAS beyond those typically encountered in disease GWAS (e.g. Eurocentric or trans-ethnic bias). These challenges include: (i) the lack of knowledge about whether PGx or disease GWAS/variants should be used in the base cohort (BC); (ii) the small sample sizes in PGx GWAS with corresponding low power and (iii) the more complex PRS statistical modeling required for handling both prognostic and predictive effects simultaneously. To gain insights in this landscape about the general trends, challenges and possible solutions, we first conduct a systematic review of both PRS applications and PRS method development in PGx GWAS. To further address the challenges, we propose (i) a novel PRS application strategy by leveraging both PGx and disease GWAS summary statistics in the BC for PRS construction and (ii) a new Bayesian method (PRS-PGx-Bayesx) to reduce Eurocentric or cross-population PRS prediction bias. Extensive simulations are conducted to demonstrate their advantages over existing PRS methods applied in PGx GWAS. Our systematic review and methodology research work not only highlights current gaps and key considerations while applying PRS methods to PGx GWAS, but also provides possible solutions for better PGx PRS applications and future research.
Summary Background Observational data and non-human primate challenge studies suggest that cell-mediated immune responses might provide control of HIV replication. The Step Study directly assessed ...the efficacy of a cell-mediated immunity vaccine to protect against HIV-1 infection or change in early plasma HIV-1 levels. Methods We undertook a double-blind, phase II, test-of-concept study at 34 sites in North America, the Caribbean, South America, and Australia. We randomly assigned 3000 HIV-1-seronegative participants by computer-generated assignments to receive three injections of MRKAd5 HIV-1 gag/pol/nef vaccine (n=1494) or placebo (n=1506). Randomisation was prestratified by sex, adenovirus type 5 (Ad5) antibody titre at baseline, and study site. Primary objective was a reduction in HIV-1 acquisition rates (tested every 6 months) or a decrease in HIV-1 viral-load setpoint (early plasma HIV-1 RNA measured 3 months after HIV-1 diagnosis). Analyses were per protocol and modified intention to treat. The study was stopped early because it unexpectedly met the prespecified futility boundaries at the first interim analysis. This study is registered with ClinicalTrials.gov , number NCT00095576. Findings In a prespecified interim analysis in participants with baseline Ad5 antibody titre 200 or less, 24 (3%) of 741 vaccine recipients became HIV-1 infected versus 21 (3%) of 762 placebo recipients (hazard ratio HR 1·2 95% CI 0·6–2·2). All but one infection occurred in men. The corresponding geometric mean plasma HIV-1 RNA was comparable in infected male vaccine and placebo recipients (4·61 vs 4·41 log10 copies per mL, one tailed p value for potential benefit 0·66). The vaccine elicited interferon-γ ELISPOT responses in 75% (267) of the 25% random sample of all vaccine recipients (including both low and high Ad5 antibody titres) on whose specimens this testing was done (n=354). In exploratory analyses of all study volunteers, irrespective of baseline Ad5 antibody titre, the HR of HIV-1 infection between vaccine and placebo recipients was higher in Ad5 seropositive men (HR 2·3 95% CI 1·2–4·3) and uncircumcised men (3·8 1·5–9·3), but was not increased in Ad5 seronegative (1·0 0·5–1·9) or circumcised (1·0 0·6–1·7) men. Interpretation This cell-mediated immunity vaccine did not prevent HIV-1 infection or reduce early viral level. Mechanisms for insufficient efficacy of the vaccine and the increased HIV-1 infection rates in subgroups of vaccine recipients are being explored. Funding Merck Research Laboratories; the Division of AIDS, National Institute of Allergy and Infectious Diseases, in the US National Institutes of Health (NIH); and the NIH-sponsored HIV Vaccine Trials Network (HVTN).
Integrating association evidence across multiple traits can improve the power of gene discovery and reveal pleiotropy. Most multi-trait analysis methods focus on individual common variants in ...genome-wide association studies. Here, we introduce multi-trait analysis of rare-variant associations (MTAR), a framework for joint analysis of association summary statistics between multiple rare variants and different traits. MTAR achieves substantial power gain by leveraging the genome-wide genetic correlation measure to inform the degree of gene-level effect heterogeneity across traits. We apply MTAR to rare-variant summary statistics for three lipid traits in the Global Lipids Genetics Consortium. 99 genome-wide significant genes were identified in the single-trait-based tests, and MTAR increases this to 139. Among the 11 novel lipid-associated genes discovered by MTAR, 7 are replicated in an independent UK Biobank GWAS analysis. Our study demonstrates that MTAR is substantially more powerful than single-trait-based tests and highlights the value of MTAR for novel gene discovery.
In randomized clinical trials, analyses of time-to-event data without risk stratification, or with stratification based on pre-selected factors revealed at the end of the trial to be at most weakly ...associated with risk, are quite common. We caution that such analyses are likely delivering hazard ratio estimates that unwittingly dilute the evidence of benefit for the test relative to the control treatment. To make our case, first, we use a hypothetical scenario to contrast risk-unstratified and risk-stratified hazard ratios. Thereafter, we draw attention to the previously published 5-step stratified testing and amalgamation routine (5-STAR) approach in which a pre-specified treatment-blinded algorithm is applied to survival times from the trial to partition patients into well-separated risk strata using baseline covariates determined to be jointly strongly prognostic for event risk. After treatment unblinding, a treatment comparison is done within each risk stratum and stratum-level results are averaged for overall inference. For illustration, we use 5-STAR to reanalyze data for the primary and key secondary time-to-event endpoints from three published cardiovascular outcomes trials. The results show that the 5-STAR estimate is typically smaller (i.e. more in favor of the test treatment) than the originally reported (traditional) estimate. This is not surprising because 5-STAR mitigates the presumed dilution bias in the traditional hazard ratio estimate caused by no or inadequate risk stratification, as evidenced by two detailed examples. Pre-selection of stratification factors at the trial design stage to achieve adequate risk stratification for the analysis will often be challenging. In such settings, an objective risk stratification approach such as 5-STAR, which is partly aligned with guidance from the US Food and Drug Administration on covariate-adjustment in clinical trials, is worthy of consideration.
In some randomized (drug versus placebo) clinical trials, the estimand of interest is the between‐treatment difference in population means of a clinical endpoint that is free from the confounding ...effects of “rescue” medication (e.g., HbA1c change from baseline at 24 weeks that would be observed without rescue medication regardless of whether or when the assigned treatment was discontinued). In such settings, a missing data problem arises if some patients prematurely discontinue from the trial or initiate rescue medication while in the trial, the latter necessitating the discarding of post‐rescue data. We caution that the commonly used mixed‐effects model repeated measures analysis with the embedded missing at random assumption can deliver an exaggerated estimate of the aforementioned estimand of interest. This happens, in part, due to implicit imputation of an overly optimistic mean for “dropouts” (i.e., patients with missing endpoint data of interest) in the drug arm. We propose an alternative approach in which the missing mean for the drug arm dropouts is explicitly replaced with either the estimated mean of the entire endpoint distribution under placebo (primary analysis) or a sequence of increasingly more conservative means within a tipping point framework (sensitivity analysis); patient‐level imputation is not required. A supplemental “dropout = failure” analysis is considered in which a common poor outcome is imputed for all dropouts followed by a between‐treatment comparison using quantile regression. All analyses address the same estimand and can adjust for baseline covariates. Three examples and simulation results are used to support our recommendations.
Several vaccine candidates to protect against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection or coronavirus disease 2019 (COVID-19) have entered or will soon enter ...large-scale, phase 3, placebo-controlled randomized clinical trials. To facilitate harmonized evaluation and comparison of the efficacy of these vaccines, a general set of clinical endpoints is proposed, along with considerations to guide the selection of the primary endpoints on the basis of clinical and statistical reasoning. The plausibility that vaccine protection against symptomatic COVID-19 could be accompanied by a shift toward more SARS-CoV-2 infections that are asymptomatic is highlighted, as well as the potential implications of such a shift.
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