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

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Considine, Elizabeth C.
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University College Cork
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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.
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Biomarkers , Preterm birth , Metabolomics , Precision medicine , Biomarker discovery , Data analysis , Diagnostics
Considine, E. C. 2020. The development of a molecular biomarker based screening test to predict spontaneous preterm birth in pregnancy. PhD Thesis, University College Cork.