Sir Peter MacCallum Department of Oncology - Theses
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ItemReal-world Management and Outcomes for Anaplastic Lymphoma Kinase (ALK)- rearranged Advanced Non-small Cell Lung Cancer and Impact of COVID-19 on Cancer Service Delivery( 2023-09)This thesis is divided into two parts. Part 1 - Real-world Management and Outcomes for Anaplastic Lymphoma Kinase (ALK)-rearranged Advanced Non-small Cell Lung Cancer (ALK+ aNSCLC) ALK-rearrangements are found in 4% of Non-small cell lung cancers (NSCLC). Although this condition remains incurable, survival appears to be improving over time, with a multitude of selective oral tyrosine kinase inhibitors (ALK-inhibitors) now available and with many patients receiving multiple lines of therapy. Whilst next-generation ALK-inhibitors are standard of care in the first line, how to best sequence available therapies beyond this remains unclear. This thesis examines outcomes for real-world patients with ALK+ aNSCLC, using cohorts from AURORA (Australia) and Flatiron health (United States). Key findings: median overall survival (mOS) of 84 months in the AURORA cohort (n=171) and 37 months in the Flatiron cohort (n=737). Positive prognostic factors: never-smoking history, treatment in an academic setting and initial early stage at diagnosis. Gender was not prognostic. Treatment patterns varied and changed over time. Initial treatment with 2nd generation ALK-inhibitor was associated with improved survival over chemotherapy; initial treatment with 1st generation ALK-inhibitor followed by 2nd generation ALK-inhibitor was associated with improved survival compared to initial chemotherapy followed by 1st generation ALK-inhibitor. These retrospective observational studies represent the largest for people with ALK+ aNSCLC in Australia (AURORA) and globally (Flatiron). Future research may focus on intensifying treatment for people with a smoking history. Further work is required to determine why treatment in a community setting correlated with poorer survival in the US. Identifying optimal treatment sequences will require larger contemporary patient databases; collaboration is required among research organisations and with pharmaceutical companies conducting post-marketing studies. Part 2 - Impact of COVID-19 on Cancer Service Delivery Amid the early stages of the COVID-19 pandemic, significant shifts in patient presentation and oncology health service provision for people with lung and other cancer-types were observed globally. This research aimed to obtain timely real-world data on how clinicians perceived alterations in cancer service delivery due to COVID-19. Surveys were distributed to oncology clinicians through international professional societies in 2020. Clinicians highlighted substantial changes in oncology services. In the early period (May-June 2020), 89% of clinicians reported altering their practice due to COVID-19; including being less likely to initiate and more likely to cease systemic therapy in palliative and curative settings. Telehealth use was rapidly expanded; many clinicians reported concerns that this may negatively impact patient outcomes. Clinicians reported seeing fewer new patients in clinic. In the later period (October-November 2020), clinicians reported more advanced disease presentations and a swing back towards pre-COVID practice. Clinicians’ reported concerns regarding potential negative impact on cancer-related outcomes are further substantiated by global reports of fewer cancer diagnoses across 2020 and modelling studies predicting increase cancer-related mortality and health-care costs due to such changes. For cancer-related outcomes to be optimised through future pandemic events, heath-systems and policy makers need to have implementable action plans to rapidly upscale mitigation strategies, such as public education campaigns, telehealth and hospital in the home.
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ItemDevelopment and Implementation of Robotic Colorectal Cancer Surgery( 2023-03)Robotic colorectal surgery is increasingly being used around the world, but its status in Australia has not been explored. Whilst retrospective studies and a few trials have demonstrated its feasibility and safety, there is limited research on the potential advantages of using a robotic platform for complex oncologic procedures, where the penetration of minimally invasive surgery remains anecdotal. In addition, current evidence on the costs of robotic colorectal surgery compared to conventional laparoscopic surgery is limited and potentially outdated. This thesis addresses these gaps by examining the adoption of robotic colorectal surgery in Australia, revealing a dramatic increase in its use, particularly in the private sector. It also confirms that the implementation of robotic surgery for complex cancer work, such as complete mesocolic excision for right-sided colon cancer and beyond total mesorectal excision surgery for advanced or recurrent pelvic malignancies, is feasible. This expands our limited understanding of how minimally invasive techniques can be applied to navigate complex oncological scenarios, providing valuable insights into the technical aspects involved. The thesis also sheds light on the costs associated with robotic colorectal surgery compared to a conventional laparoscopic approach. Whilst it highlights the increased total costs, it acknowledges the limitations of the current data in this evolving field. Ultimately, this work provides baseline data to inform future economic evaluations, which will be required to support the wider adoption of robotic colorectal surgery in the public sector.
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ItemA cell-based functional assay that accurately links genotype to phenotype in Familial HLH( 2023-04)Cytotoxic lymphocytes protect humans against viral pathogens and cancer by killing infected and transformed cells, through perforin-mediated mechanism. Mutations in perforin (PRF1) itself or in the secretory machinery responsible for its release (UNC13D, STX11, and STXBP2) are catastrophic, and lead to fatal immune dysregulation, an autosomal recessive disease called familial haemophagocytic lymphohistiocytosis (FHL). Traditionally, FHL has been associated with infant patients. However, it is now apparent that many patients remain disease-free for years, and then present with highly variable and often unexpected symptoms. They remain undiagnosed for a long time and, instead of receiving curative stem cell transplantation, they are treated symptomatically leading to high risk of severe neurological impairment, organ failure and/or death. While the pathogenicity of frame-shift/nonsense mutations is rarely in doubt, the effect of missense mutations on protein function can vary enormously. Yet, over the last two decades, the pathogenicity of missense mutations was almost invariably assumed, and invasive stem cell transplantation was considered without confirmed pathogenicity of mutations. Sadly, transplantation without genetically proven FHL results in a 20% increased mortality compared to patients with proven FHL. Therefore, early and accurate diagnosis of the disease is essential to determine the most appropriate treatment option. Due to the diversity of genetic causes of FHL, there was no test available to directly assess the effect of mutations on cytotoxic lymphocyte function, leading to delayed/erroneous diagnoses. To overcome this diagnostic problem, we have developed a simple, rapid, and robust method that takes advantage of the functional equivalence of the human and mouse orthologues of PRF1, UNC13D, STX11 and STXBP2 proteins. By knocking out endogenous mouse genes in CD8+ T cells and simultaneously expressing their mutated human orthologues, we can accurately assess the effect of mutations on cell function. The wide dynamic range of this novel system allowed us to understand the basis of otherwise cryptic cases of FHL/HLH and, in some instances, to demonstrate that previously reported mutations are unlikely to cause FHL. In addition to diagnosing patients, this unique approach will be paramount for assessing the prognosis of asymptomatic siblings and to guide genetic counselling advice for prospective parents.
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ItemA Comprehensive Machine Learning System for Cancer Classification Experiments( 2023-01)Cancer detection, classification/subtyping, grading, segmentation and prognostication are promising applications of machine learning to oncology which, if successful, would yield substantial clinical benefits. Merely knowing a cancer’s subtype provides a clinician with deep insights into its nature and likely progress, effective treatments regimes, and the patient’s prognosis. The volume of studies which make use of machine learning in medicine, including in oncology, is large and growing. A variety of clinical aspirations are evident from the literature, including: improving patient outcomes by increasing cancer detection and classification accuracy; reducing cancer diagnosis costs, and reducing the time required to diagnose cancer. However, researchers wishing to incorporate machine learning into their research face a high barrier, since it requires specialised data science/machine learning skills over and above biomedical expertise. Further, few software tools are available to assist a researcher wishing to undertake such studies. Researchers typically develop necessary software themselves: a difficult and time consuming prerequisite activity to conducting experiments. In response to these shortcomings, I developed ‘CLASSI’: an experiment pipeline for cancer classification/subtyping using whole slide images or RNA-Seq gene expression data. CLASSI makes it straightforward for researchers to incorporate machine learning into their research for one important class of oncology experiments, viz.: cancer classification and subtyping using oncopathology images or RNA-Seq data. CLASSI advances the field by providing an ‘off-the-shelf’ experiment platform to simplify and automate the conduct of histopathology image and RNA-Seq data machine learning experiments, and demonstrates that ‘machine learning enabled’ experiment pipelines are feasible, supporting the case for the development of other, more broadly scoped, experiment pipelines.
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ItemOptimising cancer risk management for women at increased risk of breast cancer( 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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ItemAutomated discovery of interacting genomic events that impact cancer survival by using data mining and machine learning techniques( 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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ItemInvestigating the mechanism of cytotoxic lymphocyte resistance to perforin( 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.