The search for clinically useful biomarkers of complex disease: A data analysis perspective

Unmet clinical diagnostic needs exist for many complex diseases, which it is hoped will be solved by the discovery of metabolomics biomarkers. However, as yet, no diagnostic tests based on metabolomics have yet been introduced to the clinic. This review is presented as a research perspective on how data analysis methods in metabolomics biomarker discovery may contribute to the failure of biomarker studies and suggests how such failures might be mitigated. The study design and data pretreatment steps are reviewed briefly in this context, and the actual data analysis step is examined more closely

Screening for Preterm Birth: Potential for a Metabolomics Biomarker Panel

The aim of this preliminary study was to investigate the potential of maternal serum to provide metabolomic biomarker candidates for the prediction of spontaneous preterm birth (SPTB) in asymptomatic pregnant women at 15 and/or 20 weeks’ gestation. Metabolomics LC-MS datasets from serum samples at 15- and 20-weeks’ gestation from a cohort of approximately 50 cases (GA < 37 weeks) and 55 controls (GA > 41weeks) were analysed for candidate biomarkers predictive of SPTB. Lists of the top ranked candidate biomarkers from both multivariate and univariate analyses were produced. At the 20 weeks’ GA time-point these lists had high concordance with each other (85%). A subset of 4 of these features produce a biomarker panel that predicts SPTB with a partial Area Under the Curve (pAUC) of 12.2, a sensitivity of 87.8%, a specificity of 57.7% and a p-value of 0.0013 upon 10-fold cross validation using PanelomiX software. This biomarker panel contained mostly features from groups already associated in the literature with preterm birth and consisted of 4 features from the biological groups of “Bile Acids”, “Prostaglandins”, “Vitamin D and derivatives” and “Fatty Acids and Conjugates”

Challenges of developing robust AI for intrapartum fetal heart rate monitoring

Background: CTG remains the only non-invasive tool available to the maternity team for continuous monitoring of fetal well-being during labour. Despite widespread use and investment in staff training, difficulty with CTG interpretation continues to be identified as a problem in cases of fetal hypoxia, which often results in permanent brain injury. Given the recent advances in AI, it is hoped that its application to CTG will offer a better, less subjective and more reliable method of CTG interpretation. Objectives: This mini-review examines the literature and discusses the impediments to the success of AI application to CTG thus far. Prior randomised control trials (RCTs) of CTG decision support systems are reviewed from technical and clinical perspectives. A selection of novel engineering approaches, not yet validated in RCTs, are also reviewed. The review presents the key challenges that need to be addressed in order to develop a robust AI tool to identify fetal distress in a timely manner so that appropriate intervention can be made. Results: The decision support systems used in three RCTs were reviewed, summarising the algorithms, the outcomes of the trials and the limitations. Preliminary work suggests that the inclusion of clinical data can improve the performance of AI-assisted CTG. Combined with newer approaches to the classification of traces, this offers promise for rewarding future development

Finishing the euchromatic sequence of the human genome

The sequence of the human genome encodes the genetic instructions for human physiology, as well as rich information about human evolution. In 2001, the International Human Genome Sequencing Consortium reported a draft sequence of the euchromatic portion of the human genome. Since then, the international collaboration has worked to convert this draft into a genome sequence with high accuracy and nearly complete coverage. Here, we report the result of this finishing process. The current genome sequence (Build 35) contains 2.85 billion nucleotides interrupted by only 341 gaps. It covers ∼99% of the euchromatic genome and is accurate to an error rate of ∼1 event per 100,000 bases. Many of the remaining euchromatic gaps are associated with segmental duplications and will require focused work with new methods. The near-complete sequence, the first for a vertebrate, greatly improves the precision of biological analyses of the human genome including studies of gene number, birth and death. Notably, the human enome seems to encode only 20,000-25,000 protein-coding genes. The genome sequence reported here should serve as a firm foundation for biomedical research in the decades ahead

The development of a molecular biomarker based screening test to predict spontaneous preterm birth in pregnancy

Background and Aims: This thesis has 2 major aims: 1) to address the issues and pitfalls in the data analysis step in metabolomics biomarker discovery such as poor reporting, lack of appropriate methods and inappropriately handled heterogeneity of disease and data; and 2) To discover clinically useful biomarkers of Spontaneous Preterm Birth (SPTB). Structure and Methods: This thesis commences with a substantial literature review on SPTB and the challenges associated with biomarker discovery for this disorder. Next the thesis contains a comprehensive critical review on reporting of the data analysis step in metabolomics biomarker discovery studies. This review carried out in a systematic fashion adhering to the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines in so far as is possible. Next this thesis presents an R Markdown authoring guideline containing a minimum reporting standard checklist to guide users to report fully a data analysis and to produce workflow diagrams. The major data analysis for this thesis is carried out in Chapter 6. A case-control LCMS (Liquid Chromatography Mass Spectrometry) dataset of serum from women from 15 weeks and 20 weeks gestation, who eventually experienced SPTB is analysed strategically to reveal biomarkers of SPTB. Chapter 7 employs a method on the same SPTB dataset, similar to methods that have been used on cancer microarry data, to find biomarkers of unknown subgroups of disease. This class of methods has not been applied to metabolomics data before. Chapter 5 contains a well thought out perspective on the pitfalls of metabolomics biomarker discovery, focussing on study design, data pretreatment and particularly on data analyis itself. Results: The results of the literature review reveal that the difficulties associated with discovering biomarkers for SPTB can be attributed largely to the heterogeneity of SPTB. The critical review highlights the extent of poor reporting in metabolomics biomarker discovery studies. The authoring tool presents a simple solution to facilitate and encourage the uptake of minimum reporting guidelines. Chapter 6 reveals potential candidate biomarkers for further targeted analysis. A subset of these produces a panel with excellent performance, at least on the discovery dataset. Chapter 7 shows that utilising a simple univariate method, similar to an established method used in cancer microarray data analysis, to find biomarkers of hidden subgroups reveals biomarker candidates that are biologically meaningful. These features overlap with the features found in Chapter 6. Chapter 5 reveals the major insights into biomarker discovery for metabolomics. Conclusions: The conclusions of this thesis are: 1). Data analysis reporting in metabolomics studies needs to be improved urgently; 2). successful biomarker discovery from heterogeneous disease requires data analysis that incorporates the heterogeneity of the dataset; 3). Biologically meaningful candidate biomarkers for SPTB are found from the biological classes of bile acids, prostaglandins, fatty acids and vitamin D and derivatives; and finally, 4). simpler models may be more suited to clinical biomarker discovery than complex models

A Tool to Encourage Minimum Reporting Guideline Uptake for Data Analysis in Metabolomics

Despite the proposal of minimum reporting guidelines for metabolomics over a decade ago, reporting on the data analysis step in metabolomics studies has been shown to be unclear and incomplete. Major omissions and a lack of logical flow render the data analysis’ sections in metabolomics studies impossible to follow, and therefore replicate or even imitate. Here, we propose possible reasons why the original reporting guidelines have had poor adherence and present an approach to improve their uptake. We present in this paper an R markdown reporting template file that guides the production of text and generates workflow diagrams based on user input. This R Markdown template contains, as an example in this instance, a set of minimum information requirements specifically for the data pre-treatment and data analysis section of biomarker discovery metabolomics studies, (gleaned directly from the original proposed guidelines by Goodacre at al). These minimum requirements are presented in the format of a questionnaire checklist in an R markdown template file. The R Markdown reporting template proposed here can be presented as a starting point to encourage the data analysis section of a metabolomics manuscript to have a more logical presentation and to contain enough information to be understandable and reusable. The idea is that these guidelines would be open to user feedback, modification and updating by the metabolomics community via GitHub