Graham, David George; (2021) Identifying individuals at-risk of developing oesophageal adenocarcinoma through symptom, risk factor and salivary biomarker analysis. Doctoral thesis (Ph.D), UCL (University College London).

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Abstract

Background: Oesophageal adenocarcinoma (OAC) carries a grave prognosis. Existing early detection strategies are flawed predominately because of reliance upon symptoms known to occur late when the disease is often incurable. Detection of individuals with Barrett’s Oesophagus (BO), a known pre-malignant condition, is problematic and the vast majority will not develop OAC. Aim: To explore novel methods of identifying patients with or at risk of OAC through machine learning (ML) techniques and biomarker identification. Materials and Methods: Initial work utilised novel ML on two existing patient symptom and risk factor questionnaire datasets. Additionally, targeted expression analysis was performed to establish whether transcriptomic biomarkers were present in blood and saliva of affected patients. Optimal RNA extraction techniques and saliva collection strategies for sufficient quality and quantity RNA were determined. Whole mRNA sequencing was performed on patient salivary RNA to identify biomarkers for future assessment. Epigenetic analysis was performed on salivary DNA to identify biomarkers. ML techniques analysed these data to derive a risk prediction tool. Results: ML techniques on questionnaire data produced satisfactory sensitivity (90%), but accuracy not appropriate for population screening (AUC 0.77). Blood and saliva extraction and collection methods were established and samples found to contain biomarkers. Targeted transcriptomic expression analysis demonstrated 12 / 22 tested genes were significantly aberrantly expressed in patients. 5 genes, combined with 6 questionnaire data-points, identified those with or at risk of OAC 93% sensitivity, AUC 0.88. Whole mRNA sequencing identified a further 134 genes implicated in OAC pathogenesis requiring future testing. Epigenetic analysis found 25 differentially methylated regions, when combined, identified those with or at risk of OAC to 99.9% accuracy. 5 Conclusion: Utilisation of salivary biomarkers is a potentially effective means to identify individuals with or at risk of OAC. Further work exploring transcriptomic and epigenetic data established in this thesis should be performed.

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