The use of randomisation-based efficacy estimators in non-inferiority trials

Background In a non-inferiority (NI) trial, analysis based on the intention-to-treat (ITT) principle is anti-conservative, so current guidelines recommend analysing on a per-protocol (PP) population in addition. However, PP analysis relies on the often implausible assumption of no confounders. Randomisation-based efficacy estimators (RBEEs) allow for treatment non-adherence while maintaining a comparison of randomised groups. Fischer et al. have developed an approach for estimating RBEEs in randomised trials with two active treatments, a common feature of NI trials. The aim of this paper was to demonstrate the use of RBEEs in NI trials using this approach, and to appraise the feasibility of these estimators as the primary analysis in NI trials. Methods Two NI trials were used. One comparing two different dosing regimens for the maintenance of remission in people with ulcerative colitis (CODA), and the other comparing an orally administered treatment to an intravenously administered treatment in preventing skeletal-related events in patients with bone metastases from breast cancer (ZICE). Variables that predicted adherence in each of the trial arms, and were also independent of outcome, were sought in each of the studies. Structural mean models (SMMs) were fitted that conditioned on these variables, and the point estimates and confidence intervals compared to that found in the corresponding ITT and PP analyses. Results In the CODA study, no variables were found that differentially predicted treatment adherence while remaining independent of outcome. The SMM, using standard methodology, moved the point estimate closer to 0 (no difference between arms) compared to the ITT and PP analyses, but the confidence interval was still within the NI margin, indicating that the conclusions drawn would remain the same. In the ZICE study, cognitive functioning as measured by the corresponding domain of the QLQ-C30, and use of chemotherapy at baseline were both differentially associated with adherence while remaining independent of outcome. However, while the SMM again moved the point estimate closer to 0, the confidence interval was wide, overlapping with any NI margin that could be justified. Conclusion Deriving RBEEs in NI trials with two active treatments can provide a randomisation-respecting estimate of treatment efficacy that accounts for treatment adherence, is straightforward to implement, but requires thorough planning during the design stage of the study to ensure that strong baseline predictors of treatment are captured. Extension of the approach to handle nonlinear outcome variables is also required. Trial registration The CODA study: ClinicalTrials.gov, identifier: NCT00708656. Registered on 8 April 2008. The ZICE study trial: ClinicalTrials.gov, identifier: NCT00326820. Registered on 16 May 2006

Identifying Mendelian disease genes with the Variant Effect Scoring Tool

Background Whole exome sequencing studies identify hundreds to thousands of rare protein coding variants of ambiguous significance for human health. Computational tools are needed to accelerate the identification of specific variants and genes that contribute to human disease. Results We have developed the Variant Effect Scoring Tool (VEST), a supervised machine learning-based classifier, to prioritize rare missense variants with likely involvement in human disease. The VEST classifier training set comprised ~ 45,000 disease mutations from the latest Human Gene Mutation Database release and another ~45,000 high frequency (allele frequency > 1%) putatively neutral missense variants from the Exome Sequencing Project. VEST outperforms some of the most popular methods for prioritizing missense variants in carefully designed holdout benchmarking experiments (VEST ROC AUC = 0.91, PolyPhen2 ROC AUC = 0.86, SIFT4.0 ROC AUC = 0.84). VEST estimates variant score p-values against a null distribution of VEST scores for neutral variants not included in the VEST training set. These p-values can be aggregated at the gene level across multiple disease exomes to rank genes for probable disease involvement. We tested the ability of an aggregate VEST gene score to identify candidate Mendelian disease genes, based on whole-exome sequencing of a small number of disease cases. We used whole-exome data for two Mendelian disorders for which the causal gene is known. Considering only genes that contained variants in all cases, the VEST gene score ranked dihydroorotate dehydrogenase (DHODH) number 2 of 2253 genes in four cases of Miller syndrome, and myosin-3 (MYH3) number 2 of 2313 genes in three cases of Freeman Sheldon syndrome. Conclusions Our results demonstrate the potential power gain of aggregating bioinformatics variant scores into gene-level scores and the general utility of bioinformatics in assisting the search for disease genes in large-scale exome sequencing studies

Compound specific isotope analyses of harp seal teeth : tools for trophic ecology reconstruction

This work resulted from the ARISE project (NE/P006035/1 awarded to CM and RJ, and NE/P00623X/1 awarded to SS), part of the Changing Arctic Ocean programme, funded by the UKRI Natural Environment Research Council (NERC).As sentinels of ecosystem health, high trophic level predators integrate information through all levels of the food web. Their tissues can be used to investigate spatiotemporal variability in foraging behaviour, and with the appropriate analytical methods and tools, archived samples can be used to reconstruct past trophic interactions. Harp seal (Pagophilus groenlandicus) teeth collected in the 1990s from the Northwest Atlantic were analysed for bulk stable carbon and nitrogen isotopes (δ13Cbulk and δ15Nbulk), and compound specific stable nitrogen isotopes of amino acids (δ15NAA) for the first time. We developed a fine-scale, annual growth layer group (GLG) dentine sub-sampling method corresponding to their second and third year of life. In accordance with previous diet studies, while there was individual variability in δ15Nbulk, δ13Cbulk, and δ15NAA measurements, we did not detect significant differences in isotopic niche widths between males and females, or between GLGs. Relative trophic position was calculated as the baseline corrected δ15NAA values using trophic (glutamic acid) and source (phenylalanine and glycine) amino acids. Variability was measured between individuals in their relative trophic position, but within individual variability was low, suggesting that they fed at the same trophic level over these 2 years of life. These novel δ15NAA data may therefore suggest individual, specialist harp seal foraging behaviour in sub-adults. Our findings show that compound specific stable isotope signatures of archived, inert predator tissues can be used as tools for the retrospective reconstruction of trophic interactions on broad spatiotemporal scales.PostprintPeer reviewe

Assessing the pathogenicity of insertion and deletion variants with the Variant Effect Scoring Tool (VEST-Indel)

Insertion/deletion variants (indels) alter protein sequence and length, yet are highly prevalent in healthy populations, presenting a challenge to bioinformatics classifiers. Commonly used features—DNA and protein sequence conservation, indel length, and occurrence in repeat regions—are useful for inference of protein damage. However, these features can cause false positives when predicting the impact of indels on disease. Existing methods for indel classification suffer from low specificities, severely limiting clinical utility. Here, we further develop our variant effect scoring tool (VEST) to include the classification of in-frame and frameshift indels (VEST-indel) as pathogenic or benign. We apply 24 features, including a new “PubMed” feature, to estimate a gene's importance in human disease. When compared with four existing indel classifiers, our method achieves a drastically reduced false-positive rate, improving specificity by as much as 90%. This approach of estimating gene importance might be generally applicable to missense and other bioinformatics pathogenicity predictors, which often fail to achieve high specificity. Finally, we tested all possible meta-predictors that can be obtained from combining the four different indel classifiers using Boolean conjunctions and disjunctions, and derived a meta-predictor with improved performance over any individual method

Clinical trials in amyotrophic lateral sclerosis:a systematic review and perspective

Amyotrophic lateral sclerosis is a progressive and devastating neurodegenerative disease. Despite decades of clinical trials, effective disease modifying drugs remain scarce. To understand the challenges of trial design and delivery, we performed a systematic review of phase II, phase II/III and phase III amyotrophic lateral sclerosis clinical drug trials on trial registries and PubMed between 2008 and 2019. We identified 125 trials, investigating 76 drugs and recruiting more than 15000 people with amyotrophic lateral sclerosis. 90% of trials used traditional fixed designs. The limitations in understanding of disease biology, outcome measures, resources and barriers to trial participation in a rapidly progressive, disabling and heterogenous disease hindered timely and definitive evaluation of drugs in two-arm trials. Innovative trial designs, especially adaptive platform trials may offer significant efficiency gains to this end. We propose a flexible and scalable multi-arm, multi-stage trial platform where opportunities to participate in a clinical trial can become the default for people with amyotrophic lateral sclerosis

MRI-derived g-ratio and lesion severity in newly diagnosed multiple sclerosis

Myelin loss is associated with axonal damage in established multiple sclerosis. This relationship is challenging to study in vivo in early disease. Here, we ask whether myelin loss is associated with axonal damage at diagnosis, by combining non-invasive neuroimaging and blood biomarkers. We performed quantitative microstructural MRI and single molecule ELISA plasma neurofilament measurement in 73 patients with newly diagnosed, immunotherapy naïve relapsing-remitting multiple sclerosis. Myelin integrity was evaluated using aggregate g-ratios, derived from magnetization transfer saturation (MTsat) and neurite orientation dispersion and density imaging (NODDI) diffusion data. We found significantly higher g-ratios within cerebral white matter lesions (suggesting myelin loss) compared with normal-appearing white matter (0.61 vs 0.57, difference 0.036, 95% CI 0.029 to 0.043, p < 0.001). Lesion volume (Spearman’s rho rs= 0.38, p < 0.001) and g-ratio (rs= 0.24 p < 0.05) correlated independently with plasma neurofilament. In patients with substantial lesion load (n = 38), those with higher g-ratio (defined as greater than median) were more likely to have abnormally elevated plasma neurofilament than those with normal g-ratio (defined as less than median) (11/23 [48%] versus 2/15 [13%] p < 0.05). These data suggest that, even at multiple sclerosis diagnosis, reduced myelin integrity is associated with axonal damage. MRI-derived g-ratio may provide useful additional information regarding lesion severity, and help to identify individuals with a high degree of axonal damage at disease onset. York, Martin et al. simultaneously measured g-ratio and plasma neurofilament in 73 relapsing-remitting multiple sclerosis patients at diagnosis using advanced MRI and single molecule ELISA. They demonstrate that g-ratio of cerebral white matter lesions varies at diagnosis, and show that high g-ratio of lesions is associated with elevated plasma neurofilament

Mapping genetic variations to three- dimensional protein structures to enhance variant interpretation: a proposed framework

The translation of personal genomics to precision medicine depends on the accurate interpretation of the multitude of genetic variants observed for each individual. However, even when genetic variants are predicted to modify a protein, their functional implications may be unclear. Many diseases are caused by genetic variants affecting important protein features, such as enzyme active sites or interaction interfaces. The scientific community has catalogued millions of genetic variants in genomic databases and thousands of protein structures in the Protein Data Bank. Mapping mutations onto three-dimensional (3D) structures enables atomic-level analyses of protein positions that may be important for the stability or formation of interactions; these may explain the effect of mutations and in some cases even open a path for targeted drug development. To accelerate progress in the integration of these data types, we held a two-day Gene Variation to 3D (GVto3D) workshop to report on the latest advances and to discuss unmet needs. The overarching goal of the workshop was to address the question: what can be done together as a community to advance the integration of genetic variants and 3D protein structures that could not be done by a single investigator or laboratory? Here we describe the workshop outcomes, review the state of the field, and propose the development of a framework with which to promote progress in this arena. The framework will include a set of standard formats, common ontologies, a common application programming interface to enable interoperation of the resources, and a Tool Registry to make it easy to find and apply the tools to specific analysis problems. Interoperability will enable integration of diverse data sources and tools and collaborative development of variant effect prediction methods