Dual bronchodilators in Bronchiectasis study (DIBS): protocol for a pragmatic, multicentre, placebo-controlled, three-arm, double-blinded, randomised controlled trial studying bronchodilators in preventing exacerbations of bronchiectasis.

INTRODUCTION: Bronchiectasis is a long-term lung condition, with dilated bronchi, chronic inflammation, chronic infection and acute exacerbations. Recurrent exacerbations are associated with poorer clinical outcomes such as increased severity of lung disease, further exacerbations, hospitalisations, reduced quality of life and increased risk of death. Despite an increasing prevalence of bronchiectasis, there is a critical lack of high-quality studies into the disease and no treatments specifically approved for its treatment. This trial aims to establish whether inhaled dual bronchodilators (long acting beta agonist (LABA) and long acting muscarinic antagonist (LAMA)) taken as either a stand-alone therapy or in combination with inhaled corticosteroid (ICS) reduce the number of exacerbations of bronchiectasis requiring treatment with antibiotics during a 12 month treatment period. METHODS: This is a multicentre, pragmatic, double-blind, randomised controlled trial, incorporating an internal pilot and embedded economic evaluation. 600 adult patients (≥18 years) with CT confirmed bronchiectasis will be recruited and randomised to either inhaled dual therapy (LABA+LAMA), triple therapy (LABA+LAMA+ICS) or matched placebo, in a 2:2:1 ratio (respectively). The primary outcome is the number of protocol defined exacerbations requiring treatment with antibiotics during the 12 month treatment period. ETHICS AND DISSEMINATION: Favourable ethical opinion was received from the North East-Newcastle and North Tyneside 2 Research Ethics Committee (reference: 21/NE/0020). Results will be disseminated in peer-reviewed publications, at national and international conferences, in the NIHR Health Technology Assessments journal and to participants and the public (using lay language). TRIAL REGISTRATION NUMBER: ISRCTN15988757.

Developing a predictive signature for two trial endpoints using the cross-validated risk scores method.

The existing cross-validated risk scores (CVRS) design has been proposed for developing and testing the efficacy of a treatment in a high-efficacy patient group (the sensitive group) using high-dimensional data (such as genetic data). The design is based on computing a risk score for each patient and dividing them into clusters using a nonparametric clustering procedure. In some settings, it is desirable to consider the tradeoff between two outcomes, such as efficacy and toxicity, or cost and effectiveness. With this motivation, we extend the CVRS design (CVRS2) to consider two outcomes. The design employs bivariate risk scores that are divided into clusters. We assess the properties of the CVRS2 using simulated data and illustrate its application on a randomized psychiatry trial. We show that CVRS2 is able to reliably identify the sensitive group (the group for which the new treatment provides benefit on both outcomes) in the simulated data. We apply the CVRS2 design to a psychology clinical trial that had offender status and substance use status as two outcomes and collected a large number of baseline covariates. The CVRS2 design yields a significant treatment effect for both outcomes, while the CVRS approach identified a significant effect for the offender status only after prefiltering the covariates.

Innovative trial approaches in immune-mediated inflammatory diseases: current use and future potential.

BACKGROUND: Despite progress that has been made in the treatment of many immune-mediated inflammatory diseases (IMIDs), there remains a need for improved treatments. Randomised controlled trials (RCTs) provide the highest form of evidence on the effectiveness of a potential new treatment regimen, but they are extremely expensive and time consuming to conduct. Consequently, much focus has been given in recent years to innovative design and analysis methods that could improve the efficiency of RCTs. In this article, we review the current use and future potential of these methods within the context of IMID trials. METHODS: We provide a review of several innovative methods that would provide utility in IMID research. These include novel study designs (adaptive trials, Sequential Multi-Assignment Randomised Trials, basket, and umbrella trials) and data analysis methodologies (augmented analyses of composite responder endpoints, using high-dimensional biomarker information to stratify patients, and emulation of RCTs from routinely collected data). IMID trials are now well-placed to embrace innovative methods. For example, well-developed statistical frameworks for adaptive trial design are ready for implementation, whilst the growing availability of historical datasets makes the use of Bayesian methods particularly applicable. To assess whether and how these innovative methods have been used in practice, we conducted a review via PubMed of clinical trials pertaining to any of 51 IMIDs that were published between 2018 and 20 in five high impact factor clinical journals. RESULTS: Amongst 97 articles included in the review, 19 (19.6%) used an innovative design method, but most of these were relatively straightforward examples of innovative approaches. Only two (2.1%) reported the use of evidence from routinely collected data, cohorts, or biobanks. Eight (9.2%) collected high-dimensional data. CONCLUSIONS: Application of innovative statistical methodology to IMID trials has the potential to greatly improve efficiency, to generalise and extrapolate trial results, and to further personalise treatment strategies. Currently, such methods are infrequently utilised in practice. New research is required to ensure that IMID trials can benefit from the most suitable methods.

Multi-arm Trial of Inflammatory Signal Inhibitors (MATIS) for hospitalised patients with mild or moderate COVID-19 pneumonia: a structured summary of a study protocol for a randomised controlled trial.

OBJECTIVES: The primary objective of MATIS is to determine the efficacy of ruxolitinib (RUX) or fostamatinib (FOS) compared to standard of care (SOC) with respect to reducing the proportion of hospitalised patients progressing from mild or moderate to severe COVID-19 pneumonia. Secondary objectives, at 14 and 28 days, are to: Determine the efficacy of RUX or FOS to reduce mortality Determine the efficacy of RUX or FOS to reduce the need for invasive ventilation or ECMO Determine the efficacy of RUX or FOS to reduce the need for non-invasive ventilation Determine the efficacy of RUX or FOS to reduce the proportion of participants suffering significant oxygen desaturation Determine the efficacy of RUX or FOS to reduce the need for renal replacement therapy Determine the efficacy of RUX and FOS to reduce the incidence of venous thromboembolism Determine the efficacy of RUX and FOS to reduce the severity of COVID-19 pneumonia [graded by a 9-point modified WHO Ordinal Scale* Determine the efficacy of RUX or FOS to reduce systemic inflammation Determine the efficacy of RUX or FOS to the incidence of renal impairment Determine the efficacy of RUX or FOS to reduce duration of hospital stay Evaluate the safety of RUX and FOS for treatment of COVID-19 pneumonia. TRIAL DESIGN: A multi-arm, multi-stage (3-arm parallel-group, 2-stage) randomised controlled trial that allocates participants 1:1:1 and tests for superiority in experimental arms versus standard of care. PARTICIPANTS: Patients will be recruited while inpatients during hospitalisation for COVID-19 in multiple centres throughout the UK including Imperial College Healthcare NHS Trust. INCLUSION: Patients age ≥ 18 years at screening Patients with mild or moderate COVID-19 pneumonia, defined as Grade 3 or 4 severity by the WHO COVID-19 Ordinal Scale Patients meeting criteria: Hospitalization AND SARS-CoV2 infection (clinically suspected or laboratory confirmed) AND Radiological change consistent with COVID-19 disease CRP ≥ 30mg/L at any time point Informed consent from patient or personal or professional representative Agreement to abstain from sexual intercourse or use contraception that is >99% effective for all participants of childbearing potential for 42 days after the last dose of study drug. For male participants, agreement to abstain from sperm donation for 42 days after the last dose of study drug. EXCLUSION: Requiring either invasive or non-invasive ventilation including CPAP or high flow nasal oxygen at any point after hospital admission but before baseline, not related to a pre-existing condition (e.g., obstructive sleep apnoea) Grade ≥ 5 severity on the modified WHO COVID-19 Ordinal Scale, i.e. SpO2 < 90% on ≥ 60% inspired oxygen by facemask at baseline; non-invasive ventilation; or invasive mechanical ventilation In the opinion of the investigator, progression to death is inevitable within the next 24 hours, irrespective of the provision of therapy Known severe allergic reactions to the investigational agents Child-Pugh B or C grade hepatic dysfunction Use of drugs within the preceding 14 days that are known to interact with any study treatment (FOS or RUX), as listed in the Summary of Product Characteristics Pregnant or breastfeeding Any medical condition or concomitant medication that in the opinion of the investigator would compromise subjects' safety or compliance with study procedures. Any medical condition which in the opinion of the principal investigator would compromise the scientific integrity of the study Non-English speakers will be able to join the study. If participants are unable to understand verbal or written information in English, then hospital translation services will be requested at the participating site for the participant where possible. INTERVENTION AND COMPARATOR: RUXOLITINIB (RUX) (14 days): An oral selective and potent inhibitor of Janus Associated Kinases (JAK1 and JAK2) and cell proliferation (Verstovek, 2010). It is approved for the treatment of disease-related splenomegaly or constitutional symptoms in myelofibrosis, polycythaemia vera and graft-versus-host-disease. RUX will be administered orally 10mg bd Day 1-7 and 5mg bd Day 8-14. FOSTAMATINIB (FOS) (14 days): An oral spleen tyrosine kinase inhibitor approved for the treatment of thrombocytopenia in adult participants with chronic immune thrombocytopenia. FOS will be administered orally 150mg bd Day 1-7 and 100mg bd Day 8-14. Please see protocol for recommended dose modifications where required. COMPARATOR (Standard of Care, SOC): experimental arms will be compared to participants receiving standard of care. It is accepted that SOC may change during a rapidly evolving pandemic. Co-enrolment to other trials and rescue therapy, either pre- or post-randomisation, is permitted and will be accounted for in the statistical analysis. MAIN OUTCOMES: Pairwise comparison (RUX vs SOC and FOS vs SOC) of the proportion of participants diagnosed with severe COVID-19 pneumonia within 14 days. Severe COVID-19 pneumonia is defined by a score ≥ 5 on a modified WHO COVID-19 Ordinal Scale, comprising the following indicators of disease severity: Death OR Requirement for invasive ventilation OR Requirement for non-invasive ventilation including CPAP or high flow oxygen OR O2 saturation < 90% on ≥60% inspired oxygen RANDOMISATION: Participants will be allocated to interventions using a central web-based randomisation service that generates random sequences using random permuted blocks (1:1:1), with stratification by age (<65 and ≥65 years) and site. BLINDING (MASKING): No participants or caregivers are blinded to group assignment. Clinical outcomes will be compared blind to group assignment. NUMBERS TO BE RANDOMISED (SAMPLE SIZE): For an early informal dose examination by the Data Monitoring Committee a minimum of 30 participants will be recruited. For Stage 1 of this multi-arm multi-stage study, 171 participants will be randomised, with 57 participants in each arm. If at least one experimental intervention shows promise, then Stage 2 will recruit a further 95 participants per arm. Sample size calculations are given in the protocol. TRIAL STATUS: Recruitment is ongoing and started 2nd October 2020. We anticipate completion of Stage 1 by July 2021 and Stage 2 by April 2022. The current protocol version 2.0 of 11th February 2021 is appended. TRIAL REGISTRATION: EudraCT: 2020-001750-22 , 9th July 2020 ClinicalTrials.gov: NCT04581954 , 9th October 2020 FULL PROTOCOL: The full protocol is attached as an additional file, accessible from the Trials website (Additional file 1). In the interest of expediting dissemination of this material, familiar formatting has been eliminated; this Letter serves as a summary of the key elements of the full protocol.

A Genome-wide Association Study Identifies SERPINB10, CRLF3, STX7, LAMP3, IFNG-AS1, and KRT80 As Risk Loci Contributing to Cutaneous Leishmaniasis in Brazil.

BACKGROUND: Our goal was to identify genetic risk factors for cutaneous leishmaniasis (CL) caused by Leishmania braziliensis. METHODS: Genotyping 2066 CL cases and 2046 controls using Illumina HumanCoreExomeBeadChips provided data for 4 498 586 imputed single-nucleotide variants (SNVs). A genome-wide association study (GWAS) using linear mixed models took account of genetic diversity/ethnicity/admixture. Post-GWAS positional, expression quantitative trait locus (eQTL) and chromatin interaction mapping was performed in Functional Mapping and Annotation (FUMA). Transcriptional data were compared between lesions and normal skin, and cytokines measured using flow cytometry and Bioplex assay. RESULTS: Positional mapping identified 32 genomic loci associated with CL, none achieving genome-wide significance (P < 5 × 10-8). Lead SNVs at 23 loci occurred at protein coding or noncoding RNA genes, 15 with eQTLs for functionally relevant cells/tissues and/or showing differential expression in lesions. Of these, the 6 most plausible genetic risk loci were SERPINB10 (Pimputed_1000G = 2.67 × 10-6), CRLF3 (Pimputed_1000G = 5.12 × 10-6), STX7 (Pimputed_1000G = 6.06 × 10-6), KRT80 (Pimputed_1000G = 6.58 × 10-6), LAMP3 (Pimputed_1000G = 6.54 × 10-6), and IFNG-AS1 (Pimputed_1000G = 1.32 × 10-5). LAMP3 (Padjusted = 9.25 × 10-12; +6-fold), STX7 (Padjusted = 7.62 × 10-3; +1.3-fold), and CRLF3 (Padjusted = 9.19 × 10-9; +1.97-fold) were expressed more highly in CL biopsies compared to normal skin; KRT80 (Padjusted = 3.07 × 10-8; -3-fold) was lower. Multiple cis-eQTLs across SERPINB10 mapped to chromatin interaction regions of transcriptional/enhancer activity in neutrophils, monocytes, B cells, and hematopoietic stem cells. Those at IFNG-AS1 mapped to transcriptional/enhancer regions in T, natural killer, and B cells. The percentage of peripheral blood CD3+ T cells making antigen-specific interferon-γ differed significantly by IFNG-AS1 genotype. CONCLUSIONS: This first GWAS for CL identified multiple genetic risk loci including a novel lead to understanding CL pathogenesis through regulation of interferon-γ by IFNG antisense RNA 1.

Investigation of genetically regulated gene expression and response to treatment in rheumatoid arthritis highlights an association between IL18RAP expression and treatment response.

OBJECTIVES: In this study, we sought to investigate whether there was any association between genetically regulated gene expression (as predicted using various reference panels) and anti-tumour necrosis factor (anti-TNF) treatment response (change in erythrocyte sedimentation rate (ESR)) using 3158 European ancestry patients with rheumatoid arthritis. METHODS: The genetically regulated portion of gene expression was estimated in the full cohort of 3158 subjects (as well as within a subcohort consisting of 1575 UK patients) using the PrediXcan software package with three different reference panels. Estimated expression was tested for association with anti-TNF treatment response. As a replication/validation experiment, we also investigated the correlation between change in ESR with measured gene expression at the Interleukin 18 Receptor Accessory Protein (IL18RAP) gene in whole blood and synovial tissue, using an independent replication data set of patients receiving conventional synthetic disease modifying anti-rheumatic drugs, with directly measured (via RNA sequencing) gene expression. RESULTS: We found that predicted expression of IL18RAP showed a consistent signal of association with treatment response across the reference panels. In our independent replication data set, IL18RAP expression in whole blood showed correlation with the change in ESR between baseline and follow-up (r=-0.35, p=0.0091). Change in ESR was also correlated with the expression of IL18RAP in synovial tissue (r=-0.28, p=0.02). CONCLUSION: Our results suggest that IL18RAP expression is worthy of further investigation as a potential predictor of treatment response in rheumatoid arthritis that is not specific to a particular drug type.

Developing and testing high-efficacy patient subgroups within a clinical trial using risk scores.

There is the potential for high-dimensional information about patients collected in clinical trials (such as genomic, imaging, and data from wearable technologies) to be informative for the efficacy of a new treatment in situations where only a subset of patients benefits from the treatment. The adaptive signature design (ASD) method has been proposed for developing and testing the efficacy of a treatment in a high-efficacy patient group (the sensitive group) using genetic data. The method requires selection of three tuning parameters which may be highly computationally expensive. We propose a variation to the ASD method, the cross-validated risk scores (CVRS) design method, that does not require selection of any tuning parameters. The method is based on computing a risk score for each patient and dividing them into clusters using a nonparametric clustering procedure. We assess the properties of CVRS against the originally proposed cross-validated ASD using simulation data and a real psychiatry trial. CVRS, as assessed for various sample sizes and response rates, has a substantial reduction in the computational time required. In many simulation scenarios, there is a substantial improvement in the ability to correctly identify the sensitive group and the power of the design to detect a treatment effect in the sensitive group. We illustrate the application of the CVRS method on the psychiatry trial.

Prediction of treatment response in rheumatoid arthritis patients using genome-wide SNP data.

Although a number of treatments are available for rheumatoid arthritis (RA), each of them shows a significant nonresponse rate in patients. Therefore, predicting a priori the likelihood of treatment response would be of great patient benefit. Here, we conducted a comparison of a variety of statistical methods for predicting three measures of treatment response, between baseline and 3 or 6 months, using genome-wide SNP data from RA patients available from the MAximising Therapeutic Utility in Rheumatoid Arthritis (MATURA) consortium. Two different treatments and 11 different statistical methods were evaluated. We used 10-fold cross validation to assess predictive performance, with nested 10-fold cross validation used to tune the model hyperparameters when required. Overall, we found that SNPs added very little prediction information to that obtained using clinical characteristics only, such as baseline trait value. This observation can be explained by the lack of strong genetic effects and the relatively small sample sizes available; in analysis of simulated and real data, with larger effects and/or larger sample sizes, prediction performance was much improved. Overall, methods that were consistent with the genetic architecture of the trait were able to achieve better predictive ability than methods that were not. For treatment response in RA, methods that assumed a complex underlying genetic architecture achieved slightly better prediction performance than methods that assumed a simplified genetic architecture.

Using penalized regression to predict phenotype from SNP data.

BACKGROUND: In a typical genome-enabled prediction problem there are many more predictor variables than response variables. This prohibits the application of multiple linear regression, because the unique ordinary least squares estimators of the regression coefficients are not defined. To overcome this problem, penalized regression methods have been proposed, aiming at shrinking the coefficients toward zero. METHODS: We explore prediction of phenotype from single nucleotide polymorphism (SNP) data in the GAW20 data set using a penalized regression approach (LASSO [least absolute shrinkage and selection operator] regression). We use 10-fold cross-validation to assess predictive performance and 10-fold nested cross-validation to specify a penalty parameter. RESULTS: By analyzing approximately 600,000 SNPs we find that, when the sample size comprises a few hundred individuals, SNP effects are heavily penalized, resulting in a poor predictive performance. Increasing the sample size to a few thousand individuals results in a much smaller penalization of the true effects, thus greatly improving the prediction. CONCLUSIONS: LASSO regression results in a heavy shrinkage of the regression coefficients, and also requires large sample sizes (several thousand individuals) to achieve good prediction.

Detecting responses to treatment with fenofibrate in pedigrees.

BACKGROUND: Fenofibrate (Fb) is a known treatment for elevated triglyceride (TG) levels. The Genetics of Lipid Lowering Drugs and Diet Network (GOLDN) study was designed to investigate potential contributors to the effects of Fb on TG levels. Here, we summarize the analyses of 8 papers whose authors had access to the GOLDN data and were grouped together because they pursued investigations into Fb treatment responses as part of GAW20. These papers report explorations of a variety of genetics, epigenetics, and study design questions. Data regarding treatment with 160 mg of micronized Fb per day for 3 weeks included pretreatment and posttreatment TG and methylation levels (ML) at approximately 450,000 epigenetic markers (cytosine-phosphate-guanine [CpG] sites). In addition, approximately 1 million single-nucleotide polymorphisms (SNPs) were genotyped or imputed in each of the study participants, drawn from 188 pedigrees. RESULTS: The analyses of a variety of subsets of the GOLDN data used a number of analytic approaches such as linear mixed models, a kernel score test, penalized regression, and artificial neural networks. CONCLUSIONS: Results indicate that (a) CpG ML are responsive to Fb; (b) CpG ML should be included in models predicting the TG level responses to Fb;

The Effect of Nonreversibility on Inferring Rooted Phylogenies.

Most phylogenetic models assume that the evolutionary process is stationary and reversible. In addition to being biologically improbable, these assumptions also impair inference by generating models under which the likelihood does not depend on the position of the root. Consequently, the root of the tree cannot be inferred as part of the analysis. Yet identifying the root position is a key component of phylogenetic inference because it provides a point of reference for polarizing ancestor-descendant relationships and therefore interpreting the tree. In this paper, we investigate the effect of relaxing the unrealistic reversibility assumption and allowing the position of the root to be another unknown. We propose two hierarchical models that are centered on a reversible model but perturbed to allow nonreversibility. The models differ in the degree of structure imposed on the perturbations. The analysis is performed in the Bayesian framework using Markov chain Monte Carlo methods for which software is provided. We illustrate the performance of the two nonreversible models in analyses of simulated data using two types of topological priors. We then apply the models to a real biological data set, the radiation of polyploid yeasts, for which there is robust biological opinion about the root position. Finally, we apply the models to a second biological alignment for which the rooted tree is controversial: the ribosomal tree of life. We compare the two nonreversible models and conclude that both are useful in inferring the position of the root from real biological data.

New substitution models for rooting phylogenetic trees.

The root of a phylogenetic tree is fundamental to its biological interpretation, but standard substitution models do not provide any information on its position. Here, we describe two recently developed models that relax the usual assumptions of stationarity and reversibility, thereby facilitating root inference without the need for an outgroup. We compare the performance of these models on a classic test case for phylogenetic methods, before considering two highly topical questions in evolutionary biology: the deep structure of the tree of life and the root of the archaeal radiation. We show that all three alignments contain meaningful rooting information that can be harnessed by these new models, thus complementing and extending previous work based on outgroup rooting. In particular, our analyses exclude the root of the tree of life from the eukaryotes or Archaea, placing it on the bacterial stem or within the Bacteria. They also exclude the root of the archaeal radiation from several major clades, consistent with analyses using other rooting methods. Overall, our results demonstrate the utility of non-reversible and non-stationary models for rooting phylogenetic trees, and identify areas where further progress can be made.