Clinical Pathology - Theses
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ItemA Proteomic Exploration of Chemotherapeutic Resistance Mechanisms in Metastatic Colorectal Cancer( 2022)There is a substantial global burden of colorectal cancer with many patients developing or presenting with distant metastases, most commonly within the liver. Though curative surgery can be attempted, modern clinical management of metastatic colorectal cancer is heavily reliant on systemic chemotherapy. The most administered chemotherapeutic regimen in Australia is FOLFOX, whose constituent drugs are the pyrimidine antimetabolite 5-Fluorouracil and the platinum based alkylating agent Oxaliplatin. Despite being highly cytotoxic, their clinical efficacy is limited by mechanisms of chemotherapeutic resistance within cancer cells, which contributes to poor patient outcome. Protein level research of these mechanisms has traditionally been limited in scope and often restricted to two-dimensional cell experimental models. Hence, there is an unmet need for proteomic analyses of chemotherapeutic response in models with greater physiological relevance, like three-dimensionally cultured organoids. In this thesis, we optimised a mass spectrometry sample preparation method to permit the proteomic analysis of metastatic colorectal cancer patient derived tumour organoids and their supernatants. We then analysed the proteomes of organoids with differential responses to 5-Fluorouracil and Oxaliplatin treatment using mass spectrometry, finding proteins associated with a mitotic cell cycle theme to be of greater abundance in relatively 5-FU/Oxal resistant samples compared to sensitive ones. Using western blotting,CDK1 expression was shown to be significantly increased in relatively resistant organoid samples during 5-FU/Oxal exposure compared to sensitive ones, though its inhibition did not re-sensitise them to chemotherapy. A proteomic analysis of organoid supernatants also revealed significantly greater levels of GSTP1 within the supernatants of sensitive organoids, when compared to relatively resistant ones. These discoveries help describe mechanisms by which metastatic colorectal cancer cells can evade the cytotoxic effects of chemotherapeutic drugs.
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ItemImproving the Differentiation of Inherited from Sporadic Causes of DNA Mismatch Repair Deficient Cancers( 2022)People with Lynch syndrome (LS), the most common cancer predisposition syndrome, have an increased risk of developing colorectal (CRC), endometrial (EC) and sebaceous skin (SST) tumours, among other cancer types. LS is caused by germline (likely) pathogenic variants in the DNA mismatch repair (MMR) genes, leading to a characteristic MMR-deficient (dMMR) / microsatellite unstable (MSI) tumour phenotype. However, dMMR / MSI is not only caused by LS and can also result from somatic causes of MMR gene inactivation. Identification of the dMMR / MSI tumour phenotype is of clinical significance for cancer prevention, prognosis, and response to immune checkpoint inhibitor treatment. However, diagnostic challenges still remain for accurate dMMR identification using current clinical tools. Therefore, the aim of my thesis is to address these two clinically relevant challenges: 1) can we improve detection of dMMR / MSI status in CRCs, ECs, and SSTs using next generation sequencing (NGS) derived tumour features and bioinformatic tools, and 2) can we improve differentiation of inherited (high risk) from sporadic (low risk) dMMR in CRCs, ECs, and SSTs using NGS? With the increased adoption of NGS in clinical practice for precision oncology and cancer genetics, the opportunity exists to develop a tumour-focused approach to improve the identification of LS. In Chapter 3, I investigated whether routinely collected clinicopathological tumour features could differentiate LS (inherited) from MLH1 methylated (sporadic) and suspected LS (unknown) subtypes in over 631 dMMR CRCs from the Colon Cancer Family Registry. Although age at diagnosis, sex and other variables were different between these subtypes, their power to differentiate was limited. Significantly, MLH1me tumours presented with a higher mortality rate than LS and suspected LS (SLS) tumours. These findings confirmed the need to examine new NGS-based approaches for differentiation purposes. In Chapter 4, I developed a novel model using NGS data to accurately determine dMMR from MMR-proficient (pMMR) tumour status in CRC and tested this model on EC and SST tumours. In total, 104 tumour features derived from whole exome sequencing of 300 CRCs were tested for their ability to identify dMMR with 10 features displaying >80% prediction accuracies. I established a novel model which combines the best predictive features for improved dMMR detection in CRC, EC, and SST sequenced tumours. In Chapter 5, using a custom-designed panel sequencing assay that I developed, tumour and blood-derived DNA were assessed from 134 SLS dMMR tumours (79 CRCs, 32 ECs, and 23 SSTs) in one of the largest investigations of SLS in these tumour types to date. Using the model determined in Chapter 4, I found 8.2% were not dMMR as clinically reported. The predominant cause of dMMR in SLS was biallelic somatic MMR mutations (61.9%) while only 1.5% were identified as LS that were missed by clinical testing. The findings from my thesis provide an evidence base to implement NGS-based diagnostic approaches for accurately detecting dMMR status and to resolve an SLS diagnosis in CRCs, ECs, and SSTs using a single test, which will ultimately improve utilisation of limited clinical resources for improved clinical management and cancer prevention.
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ItemEvaluating the clinical applicability of tumour mutational signatures in colorectal cancer and related syndromes( 2022)Colorectal cancer (CRC) poses a major health burden. It is the second most common cause of cancer death, with the impact of CRC incidence and mortality continuing to grow worldwide. Early detection of CRC substantially improves outcomes, motivating the adoption of screening programs aimed at identifying high-risk individuals for ongoing surveillance. However, efforts to identify individuals predisposed to developing CRC have been hampered by the complexity and heterogeneity of CRC. Recent advances in DNA sequencing technology enable the genome to be studied in high resolution, providing the ability to detect a wide array of somatic mutations and rearrangements in the DNA of cancer cells. Certain mutagenic processes leave identifiable mutational patterns in cancer genomes. The advent of cost-effective large-scale DNA sequencing enables systematic detection of these patterns, known collectively as tumour mutational signatures. To date the main application of mutational signatures has been research focused, where they have been used to determine cancer subtypes and categorise the underlying changes to DNA associated with those subtypes. However, their applicability to clinical contexts have not been sufficiently explored. A limitation to the adoption of mutational signatures clinically is the prevalence of FFPE-preserved tissue in conjunction with whole-exome and panel-sequenced data, in contrast to the use of fresh-frozen whole-genome sequenced data typical in research settings. Formalin is mutagenic, which can result in artefactual variants, while at least an order of magnitude fewer mutations are detected with whole-exome and panel-sequenced data compared to whole-genome. We assess the utility of mutational signatures generated from both whole-exome and panel-sequenced data derived from FFPE-preserved tissue. Specifically, we show that inherited predispositions to CRC, including Lynch syndrome and MUTYH-associated polyposis, can be accurately identified with whole-exome sequenced data from FFPE-preserved tumour tissue, and that, with the correct methodology, biallelic MUTYH carriers can be identified from panel-sequenced FFPE-preserved tumour tissue. Understanding the relationship between environmental exposures and CRC development has implications for both prevention and screening. We consider the ability of mutational signatures to detect mutation patterns arising from exposure to colibactin, the genotoxic compound synthesised by pathogenic E. coli and a potential cause of sporadic (non-inherited) CRC. Demonstrating distinct genomic, clinic-pathological and epidemiological characteristics, we show the potential existence of a distinct subtype of CRC based on the presence of the colibactin-associated mutational signature. The effectiveness of mutational signatures depends on the environment in which they are calculated. We analyse the impact of key analytical parameters and recommend specific filtering settings for variant allele fraction and sequencing depth. We identify situations where mutational signatures are less effective, recommending minimum mutation counts and maximum signature reconstruction error, enabling confidence in mutational signature results to be based on their specific application. The results presented in this thesis have clinical applications. We show that applying mutational signatures to individual tumours provides direct evidence suggesting a particular aetiology. More broadly, mutational signatures provide evidence indicating the likely pathogenicity of co-occurring mutations. Mutational signatures are an important technique for extracting information from sequencing data. This thesis demonstrates clinical applications of mutational signatures in CRC and related syndromes.
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ItemStatistical and Functional Genomics of Host-Microbial Interactions( 2022)The continuous refinement of sequencing technologies has allowed bioinformatics to evolve as a field, as new data types enable the analysis of multiple levels of biological dimensions. Yet, an issue shared between these “omic” types persists in the form of variations introduced by either technical or biological confounders that are ultimately unwanted, as they may conflate true biological signals from the condition of interest and result in misleading conclusions. Technical confounders are introduced from inconsistencies during experimental design, while biological confounders are inherent and a product of the genetic structure of individuals. This thesis aims to capture the complexities of host-microbial dynamics in a manner that accurately captures biological variance of interest, describing separate approaches to minimizing the impact of unwanted variations from technical and biological confounders in metagenomic and transcriptomic data, respectively. This thesis addresses confounders in two different aspects of the host-microbial dynamics: the presence of microbial life in the host, as well as the host molecular response towards microbial life in the body. Chapter 2 touches on the first aspect as it delves into the method of removing variations introduced by technical batches in metagenomic data. Using a method originally designed for single-cell RNAseq called RUV-III-NB, unwanted technical variations were assessed and removed from microbiome profiles of pig faecal samples that underwent various storage options and sample treatments. The study identified storage conditions and freeze-thaw cycle among the highest contributors to unwanted variations in microbiome abundance, particularly affecting high-abundance bacterial taxa. RUV-III-NB’s consistently robust corrective performance was also shown when benchmarked with other popular batch correction methods. This chapter describes the importance of preventative measures during experimental design and at the same time offers a robust corrective measure in-silico. In Chapter 3, biological confounding in the form of population stratification is addressed in transcriptomic data analysis using a dataset from a Salmonella Typhi infection study from Nepal. A bioinformatics pipeline to capture genetic structure named RGStraP (RNAseq-based Genetic Stratification PCs) was developed for this study to capture genetic structure solely based on RNAseq samples. This chapter demonstrates RGStraP’s capability as a robust alternative for capturing genetic structure based on its performance when compared to paired array genotypes, as shown by SNP-level genetic concordance and canonical correlations between two sets of genetic principal components. The effect of population stratification on gene expression data is also shown, as the lack of adjustment based on genetic structure in downstream RNAseq analysis may result in possible exaggeration of significant associations. Using the same RNAseq dataset from Nepal, Chapter 4 profiles the host gene expression signature towards S. Typhi infection. Both technical and biological confounders were addressed in downstream RNAseq analyses using results from RUVg and RGStraP, respectively. This chapter presents a distinct gene expression signature between confirmed cases and healthy controls, from which subclades of samples could be determined using unsupervised hierarchical clustering. A typhoid disease classifier was constructed from the S. Typhi-specific gene signature and was tested on external validation sets, showing promising potential in diagnosing S. Typhi infection from patient’s gene expression. Finally, Chapter 5 discusses the overarching takeaway from the studies, current limitations, as well as future directions for the studies involved.
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ItemClinical outcome prediction using biomedical data and machine learning approaches( 2022)Identifying asymptomatic individuals with increased susceptibility to disease provides substantial opportunities for preventative interventions. Over the past few years, the advances in sequencing and computing technologies have enabled omics-driven disease prediction modelling which may aid in the exploration of new biomarkers and future clinical utility. Recent studies have revealed evidence linking human gut microbiota with the pathogenesis of various complex diseases. However, previous studies have been limited by cross-sectional study design and there are limited data regarding the longitudinal association between baseline gut microbiome and incident diseases. In addition, there are few published studies on incident disease prediction combing genetic risk and gut microbial risk factors. To address this, we designed a longitudinal study to examine the predictive utility of clinical metadata, the gut metagenomics and genomics data for a series of complex diseases, using statistical and machine learning approaches in a large population-based cohort with ~15 years of electronic health records follow-up. Chapter 1 provides a comprehensive review on advances and challenges in complex disease prediction. Emerging prediction methods and novel biomarkers are highlighted, including the polygenic risk scores, gut metagenomics, and machine learning approaches in the context of disease prediction. Recent progress in clinical utility of the advancements in multi-omics-based prediction, and future challenges and potential opportunities for clinical translation are discussed. In Chapter 2, the potential of gut microbiota for prospective risk prediction of liver disease was investigated using machine learning approaches. The predictive capacity of the baseline gut microbiota was evaluated individually and in combination with conventional risk factors. The results demonstrated that the microbiome augmentation of conventional risk factors using gradient boosting classifiers significantly improved prediction performance. Investigation of predictive microbial signatures revealed previously unknown bacterial taxa for incident liver disease, as well as those previously associated with hepatic function and disease. In Chapter 3, the associations with baseline gut microbiome were tested for incident respiratory diseases, including COPD and adult-onset asthma. The gut microbial alterations and variations at each taxonomic level were compared between disease cases and non-cases. Machine learning models demonstrated moderate predictive capacities of baseline gut microbiome for incident asthma/COPD. Subgroup analyses indicated gut microbiome was significantly associated with incident COPD in both current smokers and non-smokers, as well as in individuals who reported never smoking. In Chapter 4, the predictive utility of genetic risk factors, gut microbial risk factors, and lifestyle risk factors was investigated for multiple complex diseases, including myocardial infarction, coronary heart disease, prostate cancer, Type 2 diabetes and Alzheimer’s disease. Since the gut microbiome is involved in numerous host physiological processes and linked to all vital organs, it was hypothesized that the gut microbiome can reflect host environmental risk factors for relevant diseases. It was also hypothesized that the inclusion of genetic susceptibility could improve the prediction performance over clinical risk factors for complex diseases. The findings demonstrated the individual and combined impact of polygenic predisposition and variations in baseline gut microbiota on disease incidence. This thesis presents a comprehensive investigation of the integrative use of clinical metadata and multi-omics data, the human gut metagenomics in particular, for incident disease prediction. The findings of this work provide an evidence base for the translation of omics and machine learning to risk prediction of multiple diseases, and support further investigation into identification of new biomarkers for disease risk assessment and prevention.
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ItemHeritable DNA methylation marks associated with familial breast and prostate cancer( 2022)The known genetic risk factors for breast and prostate cancer account for less than 50% and 35% of the familial risk respectively. Early candidate gene studies provided evidence that DNA methylation measured in peripheral blood can mimic the effect of cancer predisposition associated with germline pathogenic variants. Key examples included the increased risk of breast cancer associated with both BRCA1 promoter hypermethylation and intragenic DNA methylation in ATM. More recently, the association between heritable DNA methylation marks and breast and/or prostate cancer risk has been reported by genome-wide studies. However, it is not clear how most of these modifications in DNA methylation mediate phenotypes resulting in increased cancer risk. In this thesis, a recently developed analytic approach, based on complex segregation analysis and family structure, was applied to identify 1,000 heritable DNA methylation marks in 57 families with multiple cases of breast and prostate cancer. Using Cox segregation analysis, we found that 229 and 140 methylation marks were associated with breast and prostate cancer respectively and 63 were associated with increased risk of both cancer types. We developed a Clustered regularly interspaced short palindromic repeats (CRISPR)-based strategy using a dCas9-DNA methyltransferase enzyme (DNMT) and a modified sgRNA containing two PP7 hairpins fused with DNMT3A in the synergistic activation mediator (SAM) system to assess whether induction of DNA methylation at these methylation sites resulted in a recognised cancer phenotype (such as DNA damage and proliferation). We first optimised CRISPR mediated DNA methylation by testing three combinations, including fusion of dCas9-DNMT3A, dCas9-DNMT3B and dCas9-DNMT1 with our modified sgRNA and found that SAM-DNMT3A which involves fusion of dCas9-DNMT3A with our modified sgRNA induced highly robust DNA methylation even compared to the previously developed approach, SunTag system, via transfection of HEK293FT cells targeting BRCA1 promoter region. In addition, we performed lentiviral transduction of human mammary breast cell lines using SAM-DNMT3A and sgRNAs targeting BRCA1, PTEN and NF1 regions and demonstrated a significant DNA methylation induction at these regions compared to GFP- sgRNA, targeting non-human genes. We found that the SAM-DNMT3A could also induce higher gene silencing in comparison with CRISPR-inhibition (CRISPRi) tool, involving dCas9-KRAB. A lentiviral delivered sgRNA pooled library, including 10 sgRNAs for each of the 1,000 heritable DNA methylation sites, was generated to perform phenotypic screens in the human mammary epithelial cells, B80-T5 and K5+/K19+ cells using SAM-DNNT3A and CRISPRi tools.We measured the effect of DNA methylation on proliferation and the DNA damage response using a PARP inhibitor synthetic lethal screen. K5+/K19+ cells, showed very low phenotypic changes. Overall, the SAM-DNMT3A tool enabled a systemic high throughput pooled screen of heritable DNA methylation sites mediating breast cancer related phenotypes including proliferation and DNA damage. The findings of this study further characterise the non-genetic component of familial risk of breast and prostate cancer, provides new opportunities to elaborate how tumorigenesis can be affected by DNA methylation and develop epigenetic therapeutics targeting these risk factors, ultimately advancing both precision prevention and medicine.