Molecular Epidemiological Approaches for Pancreatic Ductal Adenocarcinoma & Esophageal Squamous Cell Carcinoma: From Bioinformatics to Drug Repositioning

基於生物信息學和藥物重定位分子流行病學方法探索胰腺癌和食管鱗狀細胞癌生物學機制

Student thesis: Doctoral Thesis

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Award date25 Aug 2023

Abstract

Cancer is a complex and heterogenous group of diseases that are caused by the uncontrolled growth of abnormal cells. Due to the increasing number of cancer cases and deaths each year, cancers continue to pose a significant burden on global health. Despite this trend, however, several cancers have begun to see increasingly favourable prognoses in recent decades. Indeed, the five-year survival rate for cancer has rapidly increased in the UK from 24% to 50% over the last 40 years. This has largely been due to the combination of early detection and the development of effective therapies. These include targeted therapies; therapeutics which specifically inhibit or modulate the activity of the aberrant molecules that drive oncogenesis. The development of such therapies has been possible due to the significant advances in the biological sciences in recent years, which has subsequently revolutionised our understanding of biology, including human wellbeing and disease. In particular, due to modern techniques, such as high-throughput sequencing, we have unprecedented insights into the genetic, epigenetic, and metabolic underpinnings of complex diseases. This has ultimately led to breakthroughs in the diagnosis and treatment of diseases, including multiple cancers. Specifically, many cancer types can now be grouped into sub-types on the basis of molecular phenotypes, such as the presence of an (in)activating mutation in a particular gene (e.g. BRCA2). Hence, upon diagnosis of certain cancers, patients can be stratified according to their sub-type and thereby receive the best treatment option available to them. As a result, multiple cancers now have drastically reduced morbidity and mortality. Despite these advances, however, molecular sub-typing and targeted therapy for some cancers and has proved extremely difficult, and thus they have a poor prognosis. This includes both pancreatic and esophageal cancer, which are amongst the most lethal cancers worldwide. In-order-to increase patient survival, novel therapeutic strategies for patients must be devised. Unfortunately, the development of novel drugs also presents its own significant challenges as most novel drugs do not make it to market due to lack of efficacy or safety concerns. Therefore, it is more time and cost effective to identify existing FDA approved drugs which can be repurposed to treat cancers without effective treatment options. This can be achieved using a drugs gene signature, the alterations in gene expression as a result of exposure to the drug. The gene signature of a drug indicates the underlying biological pathways and mechanisms that are involved in the therapeutic effect of the drug. With this knowledge, we can then identify candidate drugs which have gene signatures capable of reversing aberrant gene expression patterns observed in disease-states to those observed in normal cells. This gene signature-based approach has been adopted by previous research to identify drugs that can be repositioned to treat a variety of diseases including, but not limited to, cancer, Alzheimer’s, hyperlipidaemia, hypertension, and inflammatory disease. In summary, the aim of this thesis is to analyse large-scale genomic data, using a bioinformatics approach, to identify novel therapeutic strategies for pancreatic ductal adenocarcinoma and esophageal squamous cell carcinoma. Chapter 1 will provide relevant background information pertaining to the material discussed in this thesis. Chapter 2 will discuss ‘Canary’, a singularity-based tool, that can automatically convert file types for GWAS analysis. Chapters 3 and 4 will focus on computational drug repositioning for esophageal squamous cell carcinoma and pancreatic ductal adenocarcinoma, respectively. Chapter 5 will provide a summary of this work and discuss future directions.