Sequencing technologies have advanced the discovery of cancer-related genes. However, the functional impact of mutations in these genes and the effect of their mutated forms on a cell’s ability to withstand additional perturbations is unclear. Tumour suppressor genes are cancer-associated genes frequently acquiring loss-of-function (LOF) alterations, rendering them ineffective drug targets. Therefore, alternative methods for identifying druggable targets in cancer cells harbouring LOF alterations in tumour suppressor genes are needed. Genetic networks, namely essentiality and genetic interaction (GI) networks, have been used to attribute novel biological functions to genes and identify genotype-specific vulnerabilities, respectively. However, the low-throughput and laborious nature of in vitro genetic screens have hampered efforts to characterise cancer-associated genetic networks. In this thesis, I explored the use of in silico genetic network mapping to characterise biological functions of tumour suppressor genes and reveal possible vulnerabilities in cells harbouring LOF mutations in these genes. First, I developed a computational tool for generating genetic network maps and provided proof-of-concept using a case study on ARID1A, whose GI and protein complex interactions have been well characterised. Next, I mapped the in silico genetic networks of two tumour suppressor genes, CIC and KMT2D. To characterise CIC’s biological functions, I collaborated to perform multi-omic analyses and revealed new interactions with SWItch/Sucrose Non-Fermentable (SWI/SNF) complex members and a novel potential role in maintaining mitotic integrity. I also characterised KMT2D’s genetic and proteomic interaction networks, identifying potential roles associated with mitotic processes, metabolism, DNA repair, and immune response and predicting several synthetic lethal genetic interactors that are targets of approved or preclinical drugs, including WRN, MDM2, NDUFB5, and TUBA1B. Additionally, I found that markers for immune checkpoint inhibitor (ICI) response, including TUBA1B expression, were elevated in KMT2D-LOF cases with microsatellite instability (MSI) but not in KMT2D wildtype MSI cases in two cancer patient cohorts, indicating that KMT2D-LOF could be a potential biomarker to stratify patients to ICI treatment. The research presented in this thesis thus shows the value of interrogating genetic networks of cancer-associated genes and contributes to our understanding of tumour suppressor gene functions and cancer cell vulnerabilities.Science, Faculty ofGraduat
