Molecular evidence-based medicine: evolution and integration of information in the genomic era

Sample size evolution in neuroimaging research: An evaluation of highly-cited studies (1990-2012) and of latest practices (2017-2018) in high-impact journals

We evaluated 1038 of the most cited structural and functional (fMRI) magnetic resonance brain imaging papers (1161 studies) published during 1990-2012 and 270 papers (300 studies) published in top neuroimaging journals in 2017 and 2018. 96% of highly cited experimental fMRI studies had a single group of participants and these studies had median sample size of 12, highly cited clinical fMRI studies (with patient participants) had median sample size of 14.5, and clinical structural MRI studies had median sample size of 50. The sample size of highly cited experimental fMRI studies increased at a rate of 0.74 participant/year and this rate of increase was commensurate with the median sample sizes of neuroimaging studies published in top neuroimaging journals in 2017 (23 participants) and 2018 (24 participants). Only 4 of 131 papers in 2017 and 5 of 142 papers in 2018 had pre-study power calculations, most for single t-tests and correlations. Only 14% of highly cited papers reported the number of excluded participants whereas 49% of papers with their own data in 2017 and 2018 reported excluded participants. Publishers and funders should require pre-study power calculations necessitating the specification of effect sizes. The field should agree on universally required reporting standards. Reporting formats should be standardized so that crucial study parameters could be identified unequivocally.

Talking the talk, but not walking the walk: RT-qPCR as a paradigm for the lack of reproducibility in molecular research

Poorly executed and inadequately reported molecular measurement methods are amongst the causes underlying the lack of reproducibility of much biomedical research. Although several high impact factor journals have acknowledged their past failure to scrutinise adequately the technical soundness of manuscripts, there is a perplexing reluctance to implement basic corrective measures. The reverse transcription real-time quantitative PCR (RT-qPCR) is probably the most straightforward measurement technique available for RNA quantification and is widely used in research, diagnostic, forensic and biotechnology applications. Despite the impact of the minimum information for the publication of quantitative PCR experiments (MIQE) guidelines, which aim to improve the robustness and the transparency of reporting of RT-qPCR data, we demonstrate that elementary protocol errors, inappropriate data analysis and inadequate reporting continue to be rife and conclude that the majority of published RT-qPCR data are likely to represent technical noise.

Use FlowRepository to share your clinical data upon study publication

A fundamental tenet of scientific research is that published results including underlying data should be open to independent validation and refutation. Data sharing encourages collaboration, facilitates quality and reduces redundancy in data production. Authors submitting manuscripts to several journals have already adopted the habit of sharing their underlying flow cytometry data by deposition to FlowRepository-a data repository that is jointly supported by the International Society for Advancement of Cytometry, the International Clinical Cytometry Society and the European Society for Clinical Cell Analysis. De-identification is required for publishing data from clinical studies and we discuss ways to satisfy data sharing requirements and patient privacy requirements simultaneously. Scientific communities in the fields of microarray, proteomics, and sequencing have been benefiting from reuse and re-exploration of data in public repositories for over decade. We believe it is time that clinicians follow suit and that de-identified clinical data also become routinely available along with published cytometry-based findings. © 2016 International Clinical Cytometry Society.

Predictive tests for individualization of pharmacological cancer treatment

BACKGROUND

The selection of cancer drugs for an individual patient is still based mostly on cancer type and stage. Predictive tests are needed to make individualized and more efficient pharmacological cancer treatment possible.

OBJECTIVE

To provide an overview of available, possible future development and principles for development of predictive tests for individualized selection of cancer drugs.

METHODS

Overview of published data.

RESULTS/CONCLUSION

Despite increased knowledge in cancer biology, only limited progress has been made in the development and use of predictive tests. However, rapid progress in this field will be possible using already available and emerging technologies, but requires a paradigm shift in principles for the development and use of cancer drugs. Assessment of drug activity in intact tumor cells and tumor cell gene expression signatures are considered to have greatest potential for the development of versatile predictive tests.

Comprehensive behavioral testing in the R6/2 mouse model of Huntington's disease shows no benefit from CoQ10 or minocycline

Previous studies of the effects of coenzyme Q10 and minocycline on mouse models of Huntington's disease have produced conflicting results regarding their efficacy in behavioral tests. Using our recently published best practices for husbandry and testing for mouse models of Huntington's disease, we report that neither coenzyme Q10 nor minocycline had significant beneficial effects on measures of motor function, general health (open field, rotarod, grip strength, rearing-climbing, body weight and survival) in the R6/2 mouse model. The higher doses of minocycline, on the contrary, reduced survival. We were thus unable to confirm the previously reported benefits for these two drugs, and we discuss potential reasons for these discrepancies, such as the effects of husbandry and nutrition.

Validating, augmenting and refining genome-wide association signals

Studies using genome-wide platforms have yielded an unprecedented number of promising signals of association between genomic variants and human traits. This Review addresses the steps required to validate, augment and refine such signals to identify underlying causal variants for well-defined phenotypes. These steps include: large-scale exact replication across both similar and diverse populations; fine mapping and resequencing; determination of the most informative markers and multiple independent informative loci; incorporation of functional information; and improved phenotype mapping of the implicated genetic effects. Even in cases for which replication proves that an effect exists, confident localization of the causal variant often remains elusive.

Searching for genes that matter in acute kidney injury: a systematic review

BACKGROUND AND OBJECTIVES

Identifying patients who may develop acute kidney injury (AKI) remains challenging, as clinical determinants explain only a portion of individual risk. Another factor that likely affects risk is intrinsic genetic variability. Therefore, a systematic review of studies was performed that related the development or prognosis of AKI to genetic variation.

DESIGN, SETTING, PARTICIPANTS, AND MEASUREMENTS

MEDLINE, EMBASE, HuGEnet, SCOPUS, and Web of Science were searched for articles from 1950 to Dec 2007. Two independent researchers screened articles using predetermined criteria. Studies were assessed for methodological quality via an aggregate scoring system.

RESULTS

The 16 included studies were of cohort or case-cohort design and investigated 35 polymorphisms in 21 genes in association with AKI. Fifteen gene-gene interactions were also investigated in four separate studies. Study populations were primarily premature infants or adults who were critically ill or postcardiac bypass patients. Among the studies, five different definitions of AKI were used. Only one polymorphism, APO E e2/e3/e4, had greater than one study showing a significant impact (P < 0.05) on AKI incidence. The mean quality score of 5.8/10 (range four to nine), heterogeneity in the studies, and the dearth of studies precluded additional meta-analysis of the results.

CONCLUSIONS

Current association studies are unable to provide definitive evidence linking genetic variation to AKI. Future success will require a narrow consensus definition of AKI, rigorous epidemiologic techniques, and a shift from a priori hypothesis-driven to genome-wide association studies.

Pharmacogenetics of the response to beta 2 agonist drugs: a systematic overview of the field

The response to beta2-agonist treatment shows large repeatability within individuals and may thus be determined by genetic influences. Here we present a systematic overview of the available genetic association and linkage data for beta2-agonist treatment response. Systematic searches identified 66 eligible articles, as of March 2007, pertaining either to B2AR gene polymorphisms and short-acting or long-acting beta2-agonists or to another 29 different genes. We systematize these study results according to gene, agent and type of outcomes addressed. The systematic review highlights major challenges in the field, including extreme multiplicity of analyses; lack of consensus for main phenotypes of interest; typically small sample sizes; and poor replicability of the proposed genetic variants. Future studies will benefit from standardization of analyses and outcomes, hypothesis-free genome-wide association testing platforms, potentially additional fine mapping around new discovered variants, and large-scale collaborative studies with prospective plans for replication among several teams, with transparent public recording of all data.

Repeatability of published microarray gene expression analyses

Given the complexity of microarray-based gene expression studies, guidelines encourage transparent design and public data availability. Several journals require public data deposition and several public databases exist. However, not all data are publicly available, and even when available, it is unknown whether the published results are reproducible by independent scientists. Here we evaluated the replication of data analyses in 18 articles on microarray-based gene expression profiling published in Nature Genetics in 2005-2006. One table or figure from each article was independently evaluated by two teams of analysts. We reproduced two analyses in principle and six partially or with some discrepancies; ten could not be reproduced. The main reason for failure to reproduce was data unavailability, and discrepancies were mostly due to incomplete data annotation or specification of data processing and analysis. Repeatability of published microarray studies is apparently limited. More strict publication rules enforcing public data availability and explicit description of data processing and analysis should be considered.

Radiological interpretation 2020: toward quantitative image assessment

The interpretation of medical images by radiologists is primarily and fundamentally a subjective activity, but there are a number of clinical applications such as tumor imaging where quantitative imaging (QI) metrics (such as tumor growth rate) would be valuable to the patient's care. It is predicted that the subjective interpretive environment of the past will, over the next decade, evolve toward the increased use of quantitative metrics for evaluating patient health from images. The increasing sophistication and resolution of modern tomographic scanners promote the development of meaningful quantitative end points, determined from images which are in turn produced using well-controlled imaging protocols. For the QI environment to expand, medical physicists, physicians, other researchers and equipment vendors need to work collaboratively to develop the quantitative protocols for imaging, scanner calibrations, and robust analytical software that will lead to the routine inclusion of quantitative parameters in the diagnosis and therapeutic assessment of human health. Most importantly, quantitative metrics need to be developed which have genuine impact on patient diagnosis and welfare, and only then will QI techniques become integrated into the clinical environment.

Methods for meta-analysis in genetic association studies: a review of their potential and pitfalls

Meta-analysis offers the opportunity to combine evidence from retrospectively accumulated or prospectively generated data. Meta-analyses may provide summary estimates and can help in detecting and addressing potential inconsistency between the combined datasets. Application of meta-analysis in genetic associations presents considerable potential and several pitfalls. In this review, we present basic principles of meta-analytic methods, adapted for human genome epidemiology. We describe issues that arise in the retrospective or the prospective collection of relevant data through various sources, common traps to consider in the appraisal of evidence and potential biases that may interfere. We describe the relative merits and caveats for common methods used to trace inconsistency across studies along with possible reasons for non-replication of proposed associations. Different statistical models may be employed to combine data and some common misconceptions may arise in the process. Several meta-analysis diagnostics are often applied or misapplied in the literature, and we comment on their use and limitations. An alternative to overcome limitations arising from retrospective combination of data from published studies is to create networks of research teams working in the same field and perform collaborative meta-analyses of individual participant data, ideally on a prospective basis. We discuss the advantages and the challenges inherent in such collaborative approaches. Meta-analysis can be a useful tool in dissecting the genetics of complex diseases and traits, provided its methods are properly applied and interpreted.