Reverse-Engineering of Genetic Regulatory Pathways in Human Cancer
Microarray-based gene expression profiling, and more recently RNA sequencing, have been widely used in cancer research and have provided valuable insights into the molecular mechanisms underlying cancer. The research presented in this thesis uses data-driven computational models to interpret tumour gene expression information in the context of regulatory network inference, identification of modulators of regulation and tumour classification. Firstly, an ordinary differential equation (ODE) regression-based reverse-engineering algorithm, MIKANA, is extended to reconstruct gene regulatory interactions from both steady-state and time-series measurements simultaneously. Inferring gene networks from a combination of steady-state and time-series data is found to be especially advantageous when using noisy time-series measurements collected with either lower sampling rates or limited number of experimental replicates. When applied to human datasets this approach is found to reveal biology that cannot be revealed by steady-state or dynamic models individually. By incorporating combinatorial interactions, in which the action of one regulating gene on its downstream target is modified by another ‘modulator’ gene, the method is further extended to identify both molecular and clinical factors that may control the activities of transcription factors (TFs). This new method adopts the concept of three-way interactions to identify candidate modulators of TF-target genes interaction from gene expression data without making any prior biological assumptions. The method is applied to cancer-related transcription factors, and the inferred modulators are shown to be statistically and biologically significant for the corresponding transcriptional modules. Finally, a previously published biclustering approach, cMonkey, is adopted to identify molecular-based tumour subclasses (MetaChips) by searching for similarity in the expression of subsets of genes across subsets of tumours. Application of the method to breast cancer data shows that tumours in the same MetaChip present similar clinico-pathological features. Tumour samples in different MetaChips are molecularly and clinico-pathologically distinct. A conditional inference tree-based survival prediction model is built from the combination of clinical information and the membership of MetaChips. It is shown that prediction of patient‘s early relapse is improved by incorporating these molecular-based tumour subclasses, compared with the prediction from the model with conventional clinical variables only.
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