Sir Peter MacCallum Department of Oncology - Theses

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    Optimising cancer risk management for women at increased risk of breast cancer
    Macdonald, Courtney ( 2022)
    Breast cancer is the most common cancer (after skin cancer) affecting women and is the second most common cause of cancer-related death for Australian women. Though breast cancer survival has improved in the last three decades, breast cancer incidence continues to rise and the disease now affects 1 in 7 women in their lifetime. In Australia, women are stratified into breast cancer risk categories (high, moderate or low) based on their lifetime risk of breast cancer. This categorisation helps guide a personalised approach to screening and breast cancer prevention with the aim of moving away from a “one size fits all” approach and towards precision prevention and screening based on personal risk level. This thesis aims to add to the personalised approach to management of breast cancer risk by: 1) contributing to a better understanding of barriers to effective risk management interventions that are currently available to women at elevated breast cancer risk and 2) reporting on the use of interventions offered to women that are not supported by evidence. Two studies presented in this thesis address the first aim. The first evaluates the current use of risk-reducing medication in Australia and the potential predictors of use. The second identifies barriers and facilitators to breast cancer risk-reducing medication from the perspectives of women and their clinicians. These studies demonstrate that the use of risk-reducing medication in Australia is very low, even compared to international standards. Novel barriers to risk-reducing medication use were identified, including women not having enough information to make a decision and clinicians being unaware of risk-reducing medications. The application of behavioural change theory to these results suggests the effective interventions to address these barriers would be: targeted education to clinicians and the public about the role and effectiveness of risk-reducing medications; campaigns to increase awareness of individualised breast cancer risk assessment; and policy change to facilitate routine breast cancer risk assessment. The second aim was addressed through two studies, the first evaluating the use of ineffective ovarian cancer screening in Australia and the second assessing the effectiveness of clinical breast examination as a component of breast cancer surveillance programs for women who carry a pathogenic variant in BRCA1 or BRCA2. The first study identified that ovarian cancer screening continues despite strong evidence and national guidelines not supporting its use. The facilitators of screening identified in this study included difficulty discontinuing screening, ordering screening tests for patient peace of mind and the lack of other available screening tests, highlighting the challenges of de-implementation of ineffective screening tests. Linking these identified facilitators with a validated behaviour change model pointed to interventions including education for clinicians and women on the ineffectiveness of ovarian cancer screening and a public campaign illustrating why high-profile women in the community do not screen for ovarian cancer. The second study demonstrated that clinical breast examination in carriers of a pathogenic variant in BRCA1 and BRCA2 has a very low clinical yield when used within a screening program that includes breast MRI, suggesting that clinical breast examination may be safely omitted in that setting. This thesis concludes that effective risk management interventions are underused in Australia, while there is continued widespread use of ineffective screening tests. Precision prevention for breast cancer requires the harnessing of available effective interventions and not offering tests that are not of benefit. Changes are required in our approach to breast cancer risk reduction to be successful in reducing breast cancer incidence in Australia.
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    Automated discovery of interacting genomic events that impact cancer survival by using data mining and machine learning techniques
    Lupat, Richard ( 2020)
    Rapid advancement in genomic technologies has driven down the cost of sequencing significantly. This efficiency has enabled large-scale cancer genomic studies to be conducted, generating a vast amount of data across different levels of omics variables. However, the tasks to extract new knowledge and information from this enormous volume of data present unique challenges. These analyses often require the application of specialised techniques for data mining, integration and interpretation to provide valuable insights. With the rise of machine learning adoption in recent decades, many advanced computational algorithms based on artificial intelligence techniques have also been proposed to analyse these genomics data. Although some of these applications have led to clinically relevant conclusions, many others are still relying on incomplete prior knowledge, or limited to only a selected number of features. These limitations raise the general question about the broader applicability of machine learning in the field of cancer genomics. This research addresses this question by assessing the application of machine learning techniques in the context of breast cancer genomics data. This assessment includes a comprehensive evaluation of computational methods for predicting cancer driver genes and the development of a novel deep learning approach for identifying breast cancer subtypes. The evaluation result of driver gene prediction algorithms suggests that the selection of the best method to be applied to a dataset will primarily be driven by the objectives of the study and the characteristics of the dataset. All of the evaluated approaches could identify well- studied genes, but not all of them performed as well on smaller datasets, subtype-specific cohorts, and in discovering novel genes. To examine the benefit of a more complex machine learning model, this thesis also presents a novel deep learning approach that integrates multi-omics data for predicting various breast cancer’ biomarkers and molecular subtypes. This method combines a semi-supervised autoencoder for dimensionality reduction, and a supervised multitask learning setup for the classifications. Taking an input of gene expression, somatic point mutation and copy number data, the algorithm predicts the ER-Status, HER2-Status and molecular subtypes of breast cancer samples. Further survival analysis of the outputs from this deep learning approach indicates that the predicted subtypes show a stronger correlation with patient prognosis compared to the original PAM50 label. While the outputs from machine learning algorithms still require further validation, the adoption of these complex computational methods in cancer genomics will become increasingly common. Collectively, the results from this thesis suggest that the machine learning analysis of ‘omics data hold great potential in automating the discovery of clinically- relevant molecular features.
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    Investigating the mechanism of cytotoxic lymphocyte resistance to perforin
    Rudd-Schmidt, Jesse Alexander ( 2020)
    Cytotoxic lymphocytes are highly efficient killer cells of the immune system. They destroy cognate target cells by secreting highly toxic effector molecules, the pore-forming protein perforin and pro-apoptotic serine proteases granzymes, into the confines of the immune synapse. Despite both the lymphocyte and target cell plasma membrane being equally exposed to the perforin and granzymes, the lymphocytes invariably survive that encounter as they remain resistant to perforin pores. This project investigates the mechanisms behind this unique phenomenon.