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EBNA3A and EBNA3C are oncoproteins that are essential for EBV-driven transformation of B-cells, whereas in vivo studies have demonstrated that EBNA3B has tumour suppressive properties. Previous exon-microarray studies performed by our group have identified a subset of cellular genes that may be regulated by EBNA3B. In order to investigate the gene regulatory mechanisms of EBNA3B, I established a Doxycycline (Dox)-inducible EBNA3B system by transfecting EBNA3B-null (3BKO) lymphoblastoid cell lines (LCLs) with a pRTS vector expressing EBNA3B with Dox treatment. Using this system I tested 43 predicted EBNA3B-regulated genes using Taqman Low Density Array cards and confirmed that EBNA3B rapidly and robustly represses 7 genes, and induces 1 gene. I have additionally established LCLs expressing epitope (FLAG-Strep-Strep)-tagged EBNA3B and used these for Chromatin Immunoprecipitation linked to high throughput sequencing (ChIP-seq). ChIP-seq analyses uncovered 1931 EBNA3B peaks and revealed that EBNA3B localises distal to its predicted target genes, in enhancer regions that are positive for active histone marks (H3K4me1 ± H3K27Ac). EBNA3B frequently overlaps with EBNA3C, but EBNA3B-only binding sites are found within currently uncharacterised genomic regions. EBNA3B binds within the same looped chromatin domain as TERT and TNFSF10 genes, and qPCR analyses revealed that induction of EBNA3B using the Dox-inducible system robustly alters the expression of these genes. Focussed analyses revealed that EBNA3B binds to a putative enhancer 17kb upstream of the TERT promoter and strongly represses the expression of TERT, a gene encoding the catalytic subunit of the telomere-lengthening telomerase complex. EBNA3B mediates gene repression by facilitating the removal of active histone marks (H3K27Ac, H3K9Ac and H3K4me3) from the TERT promoter and EBNA3B-bound region and by recruiting Polycomb Repressive Complex to deposit repressive H3K27me3 at the TERT promoter. However, deletion of EBNA3B does not have an obvious effect on stabilising telomere lengths during the first 120 days of B-cell outgrowth.

Colorectal cancer (CRC) is the second largest cause of cancer deaths in the UK, with approximately 16,000 per year. Over 41,000 people are diagnosed annually, and 43% of those will die within ten years of diagnosis. The treatment of CRC patients relies on pathological examination of the disease to identify visual features that predict growth and spread, and response to chemoradiotherapy. These prognostic features are identified manually, and are subject to inter and intra-scorer variability. This variability stems from the subjectivity in interpreting large images which can have very varied appearances, as well as the time consuming and laborious methodology of visually inspecting cancer cells. The work in this thesis presents a systematic approach to developing a solution to address this problem for one such prognostic indicator, the Tumour:Stroma Ratio (TSR). The steps taken are presented sequentially through the chapters, in order of the work carried out. These specifically involve the acquisition and assessment of a dataset of 2.4 million expert-classified images of CRC, and multiple iterations of algorithm development, to automate the process of generating TSRs for patient cases. The algorithm improvements are made using conclusions from observer studies, conducted on a psychophysics experiment platform developed as part of this work, and further work is undertaken to identify issues of image quality that affect automated solutions. The developed algorithm is then applied to a clinical trial dataset with survival data, meaning that the algorithm is validated against two separate pathologist-scored, clinical trial datasets, as well as being able to test its suitability for generating independent prognostic markers.

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