[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-124599-en":3,"doc-seo-124599-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},124599,1099513958762,"Logic","https://ap-avatar.wpscdn.com/avatar/1000023916a998db790?x-image-process=image/resize,m_fixed,w_180,h_180&k=1784791008015729253",7,"Healthcare","A Novel Genomic Approach to Multiple Cancer Diagnostics Using Mutations in Blood Bound EV RNA - Doctor of Philosophy Thesis","Identification of biomarkers is critical for early detection of cancers such as pancreatic ductal adenocarcinoma (PDAC), which is often diagnosed in later stages. Extracellular vesicles (EVs) released into the blood from tumour cells protect nucleotide cargo and reflect the state of the parent cell in real time. A pipeline was developed to analyse blood-based EV RNA mutations from patient RNAseq data for PDAC, colorectal carcinoma and invasive lobular carcinoma, producing biomarker panels and pathway insights. Machine learning models also assessed mutation severity and addressed cross-contamination and validation needs, enabling clinically relevant PDAC and ILC biomarker combinations.","A Novel Genomic Approach to Multiple Cancer Diagnostics Using  \nMutations in Blood Bound EV RNA  \nJamie Oliver  \nThis Thesis is submitted in partial fulfilment of the requirements of the award of  \nDoctor of Philosophy  \nAwarded by Oxford Brookes University  \nDepartment of Biological and Medical Sciences September 2022  \n2  \nAcknowledgements  \nI would like to thank Dr Ryan Pink and Dr Jon Lees and all staff for their guidance, support and encouragement throughout my time at Oxford Brookes. I would also like to thank all lab members who have provided technical details and the brains for which to bounce ideas off. Finally I would like to thank Harry, Stoney, Andy and Katie.  \nTo my friends and family, if you're not part of the solution you're part of the precipitate.  \n- - - - / - - - .- - / . .- . .- - . . . . . .- . - - . .- - / - - - - - -  \n . . . . . .- . / .- - . - . . / - - - - - . - .- -  \nTo my future self…hurry up.  \nAbstract  \nIdentification of biomarkers is critical for early detection of cancers such as pancreatic ductal adenocarcinoma (PDAC), which is often diagnosed in later stages. Extracellular vesicles (EVs) released into the blood from tumour cells make good biomarkers for this purpose by protecting their nucleotide cargo from the harsh environment, reflecting the state of the parent cell in real time.  \nIn a novel approach, blood based EV RNA mutations were analysed from patient RNAseq data for PDAC, colorectal carcinoma (CRC) and invasive lobular carcinoma (ILC) . A new pipeline, with the addition of machine learning methods, was developed to calculate the mutations across the EV RNA for each patient sample across all genes, offering original panels of cancer biomarkers. Associated pathways were also highlighted based on the mutation counts across genes and samples to offer clinical insight.  \nIn an attempt to analyse the complexity of EV RNA mutations on downstream proteins, machine learning was used to develop models capable of assessing the severity of a single amino acid mutation within a protein sequence. Multiple biological parameters were built into the machine learning models in an attempt to improve on state-of-the-art methods, such as Polyphen2 . Additionally, we took an original approach to protein cross-contamination between datasets to model performance assessment. As part of this process the need for stricter validation in order to stop machine learning models extracting biases within datasets was revealed.  \nOverall a novel pipeline and methodology for cancer biomarker discovery has been built, providing new combinations of candidate PDAC and ILC biomarkers with clinical relevance that could be expanded out to multiple cancers and clinical testing.  \nWord count 39,281 (excluding contents pages, appendix/supplementary information, data and bibliography) .  \nI declare that this thesis titled “A Novel Genomic Approach to Multiple Cancer Diagnostics Using Mutations in Blood Bound EV RNA” and submitted for assessment is my own original work. Any works of differing authors used within this thesis are correctly acknowledged. A Bibliography is provided at the end of  \nthe thesis.  \nTable of Contents  \nIntroduction 2  \n1.1-Breast Cancer, Pancreatic Cancer and Colorectal Cancer 2  \n1.1.1-A General Introduction to Cancer 2  \n1.1.2-Cancer Differences and Definitions 3  \n1.1.3-Causative Factors and Hallmarks in Cancer 7  \n1.1.4-The Role of Somatic Mutations in Cancer 9  \n1.2-Extracellular Vesicles and Cancer 10  \n1.2.1-Extracellular Vesicle Definitions 10  \n1.2.2-Extracellular Vesicle Function 11  \n1.2.3-Seeding the Metastatic Niche 12  \n1.2.4-The Role of Messenger RNA and Long Non-coding RNA in Extracellular Vesicles 14  \n1.2.5-Extracellular Vesicles and their Cell of Origin 17  \n1.3-RNA Sequencing 18  \n1.3.1-RNA Sequencing and Cancer 18  \n1.3.2-RNA sequencing and Extracellular Vesicles 19  \n1.4-Genetic mutations 20  \n1.4.1-Cancer Associated DNA Mutations 20  \n1.4.2-Extracellular Vesicle Messenger RNA and","cbCaij6EazIQUHmF","https://ap.wps.com/l/cbCaij6EazIQUHmF","pdf",10636394,1,374,"English","en",105,"# Abstract\n# Introduction\n## Breast Cancer, Pancreatic Cancer and Colorectal Cancer\n## Extracellular Vesicles and Cancer\n## RNA Sequencing\n## Genetic mutations\n## Blood Based Biomarkers for Cancer\n## Protein Biology\n## Protein Evolution\n## Machine learning\n## Aims\n# Chapter 2 - Materials and Methods\n## Obtaining Datasets\n## Sequencing Pipeline Differential Gene Expression Analysis\n## TissueEnrich\n## Mutation Pipeline Associated with Chapter 4\n## Protein Phenotype Prediction\n## Environment and Feature Engineering","[{\"question\":\"Why are extracellular vesicles (EVs) useful for cancer biomarker discovery?\",\"answer\":\"EVs released into blood from tumour cells protect their nucleotide cargo and reflect the state of the parent cell, enabling real-time biomarker signals.\"},{\"question\":\"How does the thesis analyse EV RNA mutations across multiple cancers?\",\"answer\":\"It analyses blood-based EV RNA mutations from patient RNAseq data for PDAC, colorectal carcinoma, and invasive lobular carcinoma, using a new pipeline with machine learning to compute mutation patterns across genes and samples.\"},{\"question\":\"What role does machine learning play in evaluating protein mutations?\",\"answer\":\"Machine learning models were built to assess the severity of single amino acid mutations within protein sequences, incorporating multiple biological parameters and using a cross-contamination modelling approach to evaluate performance and highlight the need for stricter validation.\"}]","A Novel Genomic Approach to Multiple Cancer Diagnostics Using Mutations in Blood Bound EV RNA - Doctor of Philosophy Thesis | PDF",1785893247,942,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"a-novel-genomic-approach-to-multiple-cancer-diagnostics-using-mutations-in-blood-bound-ev-rna-doctor-of-philosophy-thesis","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/healthcare/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/a-novel-genomic-approach-to-multiple-cancer-diagnostics-using-mutations-in-blood-bound-ev-rna-doctor-of-philosophy-thesis/124599/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-05",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"Why are extracellular vesicles (EVs) useful for cancer biomarker discovery?","Question",{"text":75,"@type":76},"EVs released into blood from tumour cells protect their nucleotide cargo and reflect the state of the parent cell, enabling real-time biomarker signals.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does the thesis analyse EV RNA mutations across multiple cancers?",{"text":80,"@type":76},"It analyses blood-based EV RNA mutations from patient RNAseq data for PDAC, colorectal carcinoma, and invasive lobular carcinoma, using a new pipeline with machine learning to compute mutation patterns across genes and samples.",{"name":82,"@type":73,"acceptedAnswer":83},"What role does machine learning play in evaluating protein mutations?",{"text":84,"@type":76},"Machine learning models were built to assess the severity of single amino acid mutations within protein sequences, incorporating multiple biological parameters and using a cross-contamination modelling approach to evaluate performance and highlight the need for stricter validation.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,118,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":116,"slug":117},40,"healthcare",{"id":119,"doc_module":4,"doc_module_name":46,"category_name":120,"show_sort_weight":121,"slug":122},8,"Research & Report",30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]