James Wason

Replication analysis identifies TYK2 as a multiple sclerosis susceptibility factor

In a recent genome-wide association study (GWAS) based on 12 374 non-synonymous single nucleotide polymorphisms we identified a number of candidate multiple sclerosis susceptibility genes. Here, we describe the extended analysis of 17 of these loci undertaken using an additional 4234 patients, 2983 controls and 2053 trio families. In the final analysis combining all available data, we found that evidence for association was substantially increased for one of the 17 loci, rs34536443 from the tyrosine kinase 2 (TYK2) gene (P=2.7 × 10−6, odds ratio=1.32 (1.17–1.47)). This single nucleotide polymorphism results in an amino acid substitution (proline to alanine) in the kinase domain of TYK2, which is predicted to influence the levels of phosphorylation and therefore activity of the protein and so is likely to have a functional role in multiple sclerosis.

What role for genetics in the prediction of multiple sclerosis

For most of us, the foundations of our understanding of genetics were laid by considering Mendelian diseases in which familial recurrence risks are high, and mutant alleles are both necessary and sufficient. One consequence of this deterministic teaching is that our conceptualization of genetics tends to be dominated by the notion that the genetic aspects of disease are caused by rare alleles exerting large effects. Unfortunately, the preconceptions that flow from this training are frequently erroneous and misleading in the context of common traits, where familial recurrence risks are modest, and for the most part the relevant alleles are neither rare, necessary, nor sufficient. For these common traits, the genetic architecture is far more complex, with susceptibility rather than causality resulting from the combined effects of many alleles, each exerting only a modest effect on risk. None of these alleles is sufficient to cause disease on its own, and none is essential for the development of disease. Furthermore, most are carried by large sections of the population, the vast majority of which does not develop the disease. One consequence of our innate belief in the Mendelian paradigm is that we have an inherent expectation that knowledge about the genetic basis for a disease should allow genetic testing and thereby accurate risk prediction. There is an inevitable feeling that the same should be true in complex disease, but is it? ANN NEUROL 2010;67:3–10

Optimal design of multi‐arm multi‐stage trials

In drug development, there is often uncertainty about the most promising among a set of different treatments. Multi-arm multi-stage (MAMS) trials provide large gains in efficiency over separate randomised trials of each treatment. They allow a shared control group, dropping of ineffective treatments before the end of the trial and stopping the trial early if sufficient evidence of a treatment being superior to control is found. In this paper, we discuss optimal design of MAMS trials. An optimal design has the required type I error rate and power but minimises the expected sample size at some set of treatment effects. Finding an optimal design requires searching over stopping boundaries and sample size, potentially a large number of parameters. We propose a method that combines quick evaluation of specific designs and an efficient stochastic search to find the optimal design parameters. We compare various potential designs motivated by the design of a phase II MAMS trial. We also consider allocating more patients to the control group, as has been carried out in real MAMS studies. We show that the optimal allocation to the control group, although greater than a 1:1 ratio, is smaller than previously advocated and that the gain in efficiency is generally small.

Confounding underlies the apparent month of birth effect in multiple sclerosis

Several groups have reported apparent association between month of birth and multiple sclerosis. We sought to test the extent to which such studies might be confounded by extraneous variables such as year and place of birth.

Correcting for multiple-testing in multi-arm trials: is it necessary and is it done?

BackgroundMulti-arm trials enable the evaluation of multiple treatments within a single trial. They provide a way of substantially increasing the efficiency of the clinical development process. However, since multi-arm trials test multiple hypotheses, some regulators require that a statistical correction be made to control the chance of making a type-1 error (false-positive). Several conflicting viewpoints are expressed in the literature regarding the circumstances in which a multiple-testing correction should be used. In this article we discuss these conflicting viewpoints and review the frequency with which correction methods are currently used in practice.MethodsWe identified all multi-arm clinical trials published in 2012 by four major medical journals. Summary data on several aspects of the trial design were extracted, including whether the trial was exploratory or confirmatory, whether a multiple-testing correction was applied and, if one was used, what type it was.ResultsWe found that almost half (49%) of published multi-arm trials report using a multiple-testing correction. The percentage that corrected was higher for trials in which the experimental arms included multiple doses or regimens of the same treatments (67%). The percentage that corrected was higher in exploratory than confirmatory trials, although this is explained by a greater proportion of exploratory trials testing multiple doses and regimens of the same treatment.ConclusionsA sizeable proportion of published multi-arm trials do not correct for multiple-testing. Clearer guidance about whether multiple-testing correction is needed for multi-arm trials that test separate treatments against a common control group is required.

The endoplasmic reticulum stress marker CHOP predicts survival in malignant mesothelioma

Background:Mesothelioma is an incurable cancer originating from the mesothelial cells that line the pleural, peritoneal and pericardial cavities. These cells synthesise large quantities of surface glycoproteins, rendering them dependent upon efficient endoplasmic reticulum (ER) function. When faced with elevated levels of secretory protein load, cells are said to experience ER stress, which has been implicated in the pathogenesis of many human diseases including cancer.Method:We set out to measure markers of ER stress in malignant mesothelioma and to determine whether ER stress signalling correlates with clinical parameters.Results:We observed that expression of the ER stress-responsive transcription factor C/EBP homologous protein (CHOP) correlated with patient survival and remained an independent prognostic variable in pairwise comparisons with all clinical variables tested. The most parsimonious multivariate model in our study comprised only performance status and CHOP staining. In contrast, expression of the ER stress-responsive phosphatase growth arrest and DNA damage 34 (GADD34) correlated with the degree of mesothelial differentiation, being lost progressively in biphasic and sarcomatoid mesotheliomas.Conclusion:Our findings suggest that staining for CHOP provides prognostic information that may be useful in the stratification of patients with mesothelioma. Staining for GADD34 may prove useful in classification of mesothelioma histopathology.

Some recommendations for multi-arm multi-stage trials:

Multi-arm multi-stage designs can improve the efficiency of the drug-development process by evaluating multiple experimental arms against a common control within one trial. This reduces the number of patients required compared to a series of trials testing each experimental arm separately against control. By allowing for multiple stages experimental treatments can be eliminated early from the study if they are unlikely to be significantly better than control. Using the TAILoR trial as a motivating example, we explore a broad range of statistical issues related to multi-arm multi-stage trials including a comparison of different ways to power a multi-arm multi-stage trial; choosing the allocation ratio to the control group compared to other experimental arms; the consequences of adding additional experimental arms during a multi-arm multi-stage trial, and how one might control the type-I error rate when this is necessary; and modifying the stopping boundaries of a multi-arm multi-stage design to account for unknown variance in the treatment outcome. Multi-arm multi-stage trials represent a large financial investment, and so considering their design carefully is important to ensure efficiency and that they have a good chance of succeeding.

A comparison of Bayesian adaptive randomization and multi‐stage designs for multi‐arm clinical trials

When several experimental treatments are available for testing, multi-arm trials provide gains in efficiency over separate trials. Including interim analyses allows the investigator to effectively use the data gathered during the trial. Bayesian adaptive randomization (AR) and multi-arm multi-stage (MAMS) designs are two distinct methods that use patient outcomes to improve the efficiency and ethics of the trial. AR allocates a greater proportion of future patients to treatments that have performed well; MAMS designs use pre-specified stopping boundaries to determine whether experimental treatments should be dropped. There is little consensus on which method is more suitable for clinical trials, and so in this paper, we compare the two under several simulation scenarios and in the context of a real multi-arm phase II breast cancer trial. We compare the methods in terms of their efficiency and ethical properties. We also consider the practical problem of a delay between recruitment of patients and assessment of their treatment response. Both methods are more efficient and ethical than a multi-arm trial without interim analyses. Delay between recruitment and response assessment attenuates this efficiency gain. We also consider futility stopping rules for response adaptive trials that add efficiency when all treatments are ineffective. Our comparisons show that AR is more efficient than MAMS designs when there is an effective experimental treatment, whereas if none of the experimental treatments is effective, then MAMS designs slightly outperform AR.

Optimal multistage designs for randomised clinical trials with continuous outcomes

Multistage designs allow considerable reductions in the expected sample size of a trial. When stopping for futility or efficacy is allowed at each stage, the expected sample size under different possible true treatment effects (δ) is of interest. The δ-minimax design is the one for which the maximum expected sample size is minimised amongst all designs that meet the types I and II error constraints. Previous work has compared a two-stage δ-minimax design with other optimal two-stage designs. Applying the δ-minimax design to designs with more than two stages was not previously considered because of computational issues. In this paper, we identify the δ-minimax designs with more than two stages through use of a novel application of simulated annealing. We compare them with other optimal multistage designs and the triangular design. We show that, as for two-stage designs, the δ-minimax design has good expected sample size properties across a broad range of treatment effects but generally has a higher maximum sample size. To overcome this drawback, we use the concept of admissible designs to find trials which balance the maximum expected sample size and maximum sample size. We show that such designs have good expected sample size properties and a reasonable maximum sample size and, thus, are very appealing for use in clinical trials. Copyright

A non-synonymous SNP within membrane metalloendopeptidase-like 1 (MMEL1) is associated with multiple sclerosis

Several single-nucleotide polymorphism (SNP) genome-wide association studies (GWASs) have been completed in multiple sclerosis (MS). Follow-up studies of the variants with the most promising rankings, especially when supplemented by informed candidate gene selection, have proven to be extremely successful. In this study we report the results of a multi-stage replication analysis of the putatively associated SNPs identified in the Wellcome Trust Case Control Consortium non-synonymous SNP (nsSNP) screen. In total, the replication sample consisted of 3444 patients and 2595 controls. A combined analysis of the nsSNP screen and replication data provides evidence implicating a novel additional locus, rs3748816 in membrane metalloendopeptidase-like 1 (MMEL1; odds ratio=1.16, P=3.54 × 10−6) in MS susceptibility.