Modelling and analysis of the tumour microenvironment of colorectal cancer

New bioimaging techniques have recently been proposed to visualise the colocation or interaction of several proteins within individual cells, displaying the heterogeneity of neighbouring cells within the same tissue specimen. Such techniques could hold the key to understanding complex biological systems such as the protein interactions involved in cancer. However, there is a need for new algorithmic approaches that analyse the large amounts of multi-tag bioimage data from cancerous and normal tissue specimens in order to begin to infer protein networks and unravel the cellular heterogeneity at a molecular level. In the firrst part of the thesis, we propose an approach to analyses cell phenotypes in normal and cancerous colon tissue imaged using the robotically controlled Toponome Imaging System (TIS) microscope. It involves segmenting the DAPI labelled image into cells and determining the cell phenotypes according to their protein-protein dependence profile. These were analysed using two new measures, Difference in Sums of Weighted cO-dependence/Anti-co-dependence profiles (DiSWOP and DiSWAP) for overall co-expression and anti-co-expression, respectively. This approach enables one to easily identify protein pairs which have significantly higher/lower co-dependence levels in cancerous tissue samples when compared to normal colon tissue. The proposed approach could identify potentially functional protein complexes active in cancer progression and cell differentiation. Due to the lack of ground truth data for bioimages, the objective evaluation of the methods developed for its analysis can be very challenging. To that end, in the second part of the thesis we propose a model of the healthy and cancerous colonic crypt microenvironments. Our model is designed to generate realistic synthetic fluorescence and histology image data with parameters that allow control over differentiation grade of cancer, crypt morphology, cellularity, cell overlap ratio, image resolution, and objective level. The model learns some of its parameters from real histology image data stained with standard Hematoxylin and Eosin (H&E) dyes in order to generate realistic chromatin texture, nuclei morphology, and crypt architecture. To the best of our knowledge, ours is the first model to simulate image data at subcellular level for healthy and cancerous colon tissue, where the cells are organised to mimic the microenvironment of tissue in situ rather than dispersed cells in a cultured environment. The simulated data could be used to validate techniques such as image restoration, cell segmentation, cell phenotyping, crypt segmentation, and differentiation grading, only to name a few. In addition, developing a detailed model of the tumour microenvironment can aid the understanding of the underpinning laws of tumour heterogeneity. In the third part of the thesis, we extend the model to include detailed models of protein expression to generate synthetic multi-tag fluorescence data. As a first step, we have developed models for various cell organelles that have been learned from real immunofluorescence data. We then develop models for five proteins associated with microsatellite instability, namely MLH1, PMS2, MSH2, MSH6 and p53. The protein models include subcellular location, which cells express the protein and under what conditions