MILVA:An interactive tool for the exploration of multidimensional microarray data

Clustering techniques such as k-means and hierarchical clustering are commonly used to analyze DNA microarray derived gene expression data. However, the interactions between processes underlying the cell activity suggest that the complexity of the microarray data structure may not be fully represented with discrete clustering methods.

Uncertainty and topographic visualisations

This thesis is a study of low-dimensional visualisation methods for data visualisation under certainty of the input data. It focuses on the two main feed-forward neural network algorithms which are NeuroScale and Generative Topographic Mapping (GTM) by trying to make both algorithms able to accommodate the uncertainty. The two models are shown not to work well under high levels of noise within the data and need to be modified. The modification of both models, NeuroScale and GTM, are verified by using synthetic data to show their ability to accommodate the noise. The thesis is interested in the controversy surrounding the non-uniqueness of predictive gene lists (PGL) of predicting prognosis outcome of breast cancer patients as available in DNA microarray experiments. Many of these studies have ignored the uncertainty issue resulting in random correlations of sparse model selection in high dimensional spaces. The visualisation techniques are used to confirm that the patients involved in such medical studies are intrinsically unclassifiable on the basis of provided PGL evidence. This additional category of ‘unclassifiable’ should be accommodated within medical decision support systems if serious errors and unnecessary adjuvant therapy are to be avoided.

Predictive gene lists for breast cancer prognosis: a topographic visualisation study

Background: The controversy surrounding the non-uniqueness of predictive gene lists (PGL) of small selected subsets of genes from very large potential candidates as available in DNA microarray experiments is now widely acknowledged 1. Many of these studies have focused on constructing discriminative semi-parametric models and as such are also subject to the issue of random correlations of sparse model selection in high dimensional spaces. In this work we outline a different approach based around an unsupervised patient-specific nonlinear topographic projection in predictive gene lists. Methods: We construct nonlinear topographic projection maps based on inter-patient gene-list relative dissimilarities. The Neuroscale, the Stochastic Neighbor Embedding(SNE) and the Locally Linear Embedding(LLE) techniques have been used to construct two-dimensional projective visualisation plots of 70 dimensional PGLs per patient, classifiers are also constructed to identify the prognosis indicator of each patient using the resulting projections from those visualisation techniques and investigate whether a-posteriori two prognosis groups are separable on the evidence of the gene lists. A literature-proposed predictive gene list for breast cancer is benchmarked against a separate gene list using the above methods. Generalisation ability is investigated by using the mapping capability of Neuroscale to visualise the follow-up study, but based on the projections derived from the original dataset. Results: The results indicate that small subsets of patient-specific PGLs have insufficient prognostic dissimilarity to permit a distinction between two prognosis patients. Uncertainty and diversity across multiple gene expressions prevents unambiguous or even confident patient grouping. Comparative projections across different PGLs provide similar results. Conclusion: The random correlation effect to an arbitrary outcome induced by small subset selection from very high dimensional interrelated gene expression profiles leads to an outcome with associated uncertainty. This continuum and uncertainty precludes any attempts at constructing discriminative classifiers. However a patient's gene expression profile could possibly be used in treatment planning, based on knowledge of other patients' responses. We conclude that many of the patients involved in such medical studies are intrinsically unclassifiable on the basis of provided PGL evidence. This additional category of 'unclassifiable' should be accommodated within medical decision support systems if serious errors and unnecessary adjuvant therapy are to be avoided.

Aspirin and alterations in DNA repair proteins in the SW480 colorectal cancer cell line

Regular aspirin intake is associated with a reduction in the incidence of colorectal cancer. Aspirin has been shown to be cytotoxic to colorectal cancer cells in vitro. The molecular basis for this cytotoxicity is controversial, with a number of competing hypotheses in circulation. One suggestion is that the protective effect is related to the induction of expression of the DNA mismatch repair (MMR) proteins hMLH1, hMSH2, hMSH6 and hPMS2 in DNA MMR proficient cells. We report that treatment of the DNA MMR competent/p53 mutant colorectal cancer cell line SW480 with 1 mM aspirin for 48 h caused changes in mRNA expression of several key genes involved in DNA damage signalling pathways, including a significant down-regulation in transcription of the genes ATR, BRCA1 and MAPK12. Increases in the transcription of XRCC3 and GADD45alpha genes are also reported. Regulation of these genes could potentially have profound effects on colorectal cancer cells and may play a role in the observed chemo-protective effect of aspirin in vivo. Although a correlation was not seen between transcript and protein levels of ATR, BRCA1 and GADD45alpha, an increase in XRCC3 encoded protein expression upon aspirin treatment in SW480 cells was observed by immunoblotting, immunofluorescence and immunohistochemical analysis. This is the first report of XRCC3 gene transcription and encoded protein expression being susceptible to exposure to the non-steroidal anti-inflammatory drug, aspirin. Furthermore, this study indicates that alterations in gene transcription seen in microarray studies must be verified at the protein level.