Therapeutic interventions for childhood cancer: An umbrella review of randomized evidence

Treatment advancements in pediatric cancer have improved prognosis, but the strength of supporting evidence has not been thoroughly evaluated. To critically appraise it, we performed an umbrella review of meta-analyses of randomized controlled trials examining the efficacy and safety of therapeutic interventions for pediatric malignancies. Fourteen publications (68 meta-analyses, 31,496 participants) were eligible. Acute lymphoblastic leukemia (ALL) was investigated at most. Substantial heterogeneity was detected in 10 associations, with limited indications for small-study effects and excess-significance bias. The most concrete evidence pertained to the use of methotrexate and vincristine-prednisone pulses for ALL, improving event-free survival. Evidence regarding other cancers was relatively weak. Conclusively, we found few small meta-analyses focusing mainly on ALL. Randomized evidence stemming from adult populations still seems to serve as valuable indirect evidence backup. More randomized evidence and individual patient data meta-analyses are needed to increase certainty and precision in the care of pediatric cancer patients

The Role of SNP Interactions when Determining Independence of Novel Signals in Genetic Association Studies—An Application to <i>ARG1</i> and Bronchodilator Response

Genome-wide association studies (GWAS) play a critical role in identifying many loci for common diseases and traits. There has been a rapid increase in the number of GWAS over the past decade. As additional GWAS are being conducted, it is unclear whether a novel signal associated with the trait of interest is independent of single nucleotide polymorphisms (SNPs) in the same region that has been previously associated with the trait of interest. The general approach to determining whether the novel association is independent of previous signals is to examine the association of the novel SNP with the trait of interest conditional on the previously identified SNP and/or calculate linkage disequilibrium (LD) between the two SNPs. However, the role of epistasis and SNP by SNP interactions are rarely considered. Through simulation studies, we examined the role of SNP by SNP interactions when determining the independence of two genetic association signals. We have created an R package on Github called gxgRC to generate these simulation studies based on user input. In genetic association studies of asthma, we considered the role of SNP by SNP interactions when determining independence of signals for SNPs in the ARG1 gene and bronchodilator response

Asthma exacerbations and eosinophilia in the UK Biobank: a genome-wide association study.

BackgroundAsthma exacerbations reflect disease severity, affect morbidity and mortality, and may lead to declining lung function. Inflammatory endotypes (e.g. T2-high (eosinophilic)) may play a key role in asthma exacerbations. We aimed to assess whether genetic susceptibility underlies asthma exacerbation risk and additionally tested for an interaction between genetic variants and eosinophilia on exacerbation risk.MethodsUK Biobank data were used to perform a genome-wide association study of individuals with asthma and at least one exacerbation compared to individuals with asthma and no history of exacerbations. Individuals with asthma were identified using self-reported data, hospitalisation data and general practitioner records. Exacerbations were identified as either asthma-related hospitalisation, general practitioner record of asthma exacerbation or an oral corticosteroid burst prescription. A logistic regression model adjusted for age, sex, smoking status and genetic ancestry via principal components was used to assess the association between genetic variants and asthma exacerbations. We sought replication for suggestive associations (p-6) in the GERA cohort.ResultsIn the UK Biobank, we identified 11 604 cases and 37 890 controls. While no variants reached genome-wide significance (p-8) in the primary analysis, 116 signals were suggestively significant (p-6). In GERA, two single nucleotide polymorphisms (rs34643691 and rs149721630) replicated (pConclusionsOur study has identified reproducible associations with asthma exacerbations in the UK Biobank and GERA cohorts. Confirmation of these findings in different asthma subphenotypes in diverse ancestries and functional investigation will be required to understand their mechanisms of action and potentially inform therapeutic development

A robust and adaptive framework for interaction testing in quantitative traits between multiple genetic loci and exposure variables.

The identification and understanding of gene-environment interactions can provide insights into the pathways and mechanisms underlying complex diseases. However, testing for gene-environment interaction remains a challenge since a.) statistical power is often limited and b.) modeling of environmental effects is nontrivial and such model misspecifications can lead to false positive interaction findings. To address the lack of statistical power, recent methods aim to identify interactions on an aggregated level using, for example, polygenic risk scores. While this strategy can increase the power to detect interactions, identifying contributing genes and pathways is difficult based on these relatively global results. Here, we propose RITSS (Robust Interaction Testing using Sample Splitting), a gene-environment interaction testing framework for quantitative traits that is based on sample splitting and robust test statistics. RITSS can incorporate sets of genetic variants and/or multiple environmental factors. Based on the user's choice of statistical/machine learning approaches, a screening step selects and combines potential interactions into scores with improved interpretability. In the testing step, the application of robust statistics minimizes the susceptibility to main effect misspecifications. Using extensive simulation studies, we demonstrate that RITSS controls the type 1 error rate in a wide range of scenarios, and we show how the screening strategy influences statistical power. In an application to lung function phenotypes and human height in the UK Biobank, RITSS identified highly significant interactions based on subcomponents of genetic risk scores. While the contributing single variant interaction signals are weak, our results indicate interaction patterns that result in strong aggregated effects, providing potential insights into underlying gene-environment interaction mechanisms

Power curves for RITSS1, RITSS2, GESAT, and GAMsv over increasing signal density <i>p</i>_<i>XE</i>.

Significance level α = 0.005, μXE = 0.05, and results based on 1,000 replicates. Power was simulated for pXE = 0.05, 0.1, 0.2, 0.3, 0.4, 0.5.</p

Power curves for RITSS1, RITSS2, GESAT, and GAMsv over increasing signal density <i>p</i>_<i>XE</i>.

Significance level α = 0.005, μXE = 0.1, and results based on 1,000 replicates. Power was simulated for pXE = 0.05, 0.1, 0.2, 0.3, 0.4, 0.5.</p

Results of the interaction testing using the two approaches RITSS1 and RITSS2 in the UK Biobank.

The environmental factor tested for interaction is denoted by Eit. |m| is the number of total SNPs in the analysis, |m4| and |m3| are the number of SNPs that are shared by all four and exactly three interaction scores, respectively. P-Y-S: pack-years of smoking, E-S: ever-smoking.</p

Visualization of the RITSS algorithm.

Visualization of the RITSS algorithm.</p