Modelling tumour heterogeneity with patient-specific networks

Author: John Salamon

Salamon, John, 2022 Modelling tumour heterogeneity with patient-specific networks, Flinders University, College of Medicine and Public Health

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The molecular heterogeneity of cancers such as colorectal cancer (CRC) hinders the effectiveness of treatments. To overcome this heterogeneity and identify better thera- peutic targets, we require patient-specific models which can more accurately predict the characteristics of individual tumours. Despite the availability of large scale patient- specific data from projects such as The Cancer Genome Atlas (TCGA), integrating data of this scale into a biologically meaningful model is a complex task. In this thesis, I investigated multiple patient-specific and network approaches to modelling tumour heterogeneity. I first took the approach of identifying patient-specific differ- entially expressed genes from TCGA transcriptomics data. From these, I was able to define prognostically relevant patient subgroups. I performed patient-specific pathway enrichment analysis and pathway level patient clustering, identifying novel patient clusters with significant differences in survival. Using the same patient-specific data, I combined transcriptomic and genomic data with protein-protein interaction (PPI) data to create patient-specific network models. I used the epidermal growth factor receptor (EGFR) PPI network, a network critical to the progression of CRC, to demon- strate this approach. I determined that while patient-specific network topology in the EGFR network was not directly linked to patient survival, it did differ significantly between patient subtypes. I developed SIFFIN, a novel tool to simulate the flow of biological information through these patient-specific networks, which predicted sub- stantive alterations between patients. Finally, I explored tumour heterogeneity from a spatial perspective, aiming to develop novel network-based tools to integrate spatially- resolved patient data. I developed InsituNet, a tool for spatial transcriptomics analysis, enabling spatially-resolved analysis of tumour heterogeneity, and further adapted this tool to support spatial metabolomics data.

Keywords: protein-protein networks, colorectal cancer, bioinformatics, computational biology

Subject: Biotechnology thesis

Thesis type: Doctor of Philosophy
Completed: 2022
School: College of Medicine and Public Health
Supervisor: David Lynn