Clinical Pathology - Theses

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    Genetic and Epigenetic Risk Factors for Invasive Lobular Breast Cancer
    Suman, Medha ( 2021)
    Invasive lobular breast cancer (ILBC) is the second most common histological subtype of breast cancer and accounts for 10-15% of all cases. Loss of e-cadherin protein is a hallmark of ILBC and contributes to its characteristic discohesive morphology. In addition to distinct histological features, several subtype-specific molecular and clinical features have been described. However, ILBC remains understudied relative to other breast cancer subtypes, despite its frequency. In this era of precision medicine, there is a growing interest in further refining breast cancer tumour subtyping by identifying additional discriminating molecular features. However, there is limited data to pursue this for ILBC as it is often not well-represented in study samples. For instance, the seminal work on breast cancer classification based on gene-expression levels by Perou et al., (2000) included only two ILBC cases. It is important to identify ways to refine the subtyping of ILBC tumours so that women with ILBC can benefit from a more precise treatment plan, prognosis and targeted therapy options. The main objectives of this PhD project were: i) to examine the distinguishing methylation patterns between ILBC (n=151) and non-ILBC (n=341) tumours ii) to investigate the ILBC methylome to identify methylation signatures for prognostication (n=130) and iii) to subclassify ILBC into subgroups with increased homogeneity based on their genome-wide DNA methylation profiles (ILBC, n=151, non-ILBC=341) and to further characterise these subgroups by investigating their somatic mutational signatures (n=15). Three subgroups of ILBC were defined via unsupervised cluster analysis of genome-wide DNA methylation measured using the Infinium HumanMethylation450K assay. Of these, Subgroup 1 was identified as the most distinct ILBC subgroup, characterised by a predominant hypomethylation across 27,675 CpGs compared with Subgroup 2 and across 13,067 CpGs compared with Subgroup 3. Subgroup 1 showed more similarity to the TNBC (non-ILBC) cases compared with the other two methylation-defined subgroups in terms of their genome-wide methylation pattern. Survival analysis showed that women with ILBC tumours in Subgroup 1 had the poorest overall survival when compared with women in Subgroup 2 (hazard ratio (HR): 0.59, 95% confidence interval (CI): 0.19-1.79) and Subgroup 3 (HR: 0.16, 95% CI: 0.03-0.88), after adjusting for age and year of diagnosis. Subgroup 3 had an enrichment for women who had a first-degree relative with a history of any cancer. Both Subgroup 2 and Subgroup 3 were enriched with women who had a female relative with a history of breast cancer. This suggests that women in Subgroup 2 and Subgroup 3 may be genetically or epigenetically predisposed to developing breast cancer. The somatic genetic variant profiles of the ILBC DNA methylation-defined subgroups were further investigated by performing whole-exome sequencing (WES) on five ILBC tumours representing each of the three subgroups (n=15). The mismatch repair deficiency (MMRd) associated mutational signature SBS6 was the most frequently observed mutational signature in the ILBC tumours, detected in 12/15 (80%) cases. Microsatellite instability (MSI) was also predicted in 13/15 (87%) of the cases, including all 12 tumours with SBS6. Although distinct somatic (genetic) characteristics for tumours of individual subgroups were not observed, this research highlighted the potential role of MMRd in ILBC tumourigenesis and progression. DNA methylation of ILBC was also investigated as a possible prognostic biomarker. The analysis revealed 2,771 variably methylated regions within the ILBC tumours (n=130). A pooled survival analysis of the study set and TCGA data identified APC, TMEM101, HCG4P3 and CELF2 promoter methylation as potential prognostic biomarkers for women with ILBC. Comparing the DNA methylation profiles of ILBC (n=151) and non-ILBC (n=341) tumours, 13,763 genes and 8,456 intergenic regions showing statistically significant differences in DNA methylation (false discovery rate (fdr), P-value < 0.01) were identified. Gene set enrichment analysis revealed that the differentially methylated genes were found to be involved in biological pathways related to metabolism of RNA (R-HSA-8953854), mRNA processing (GO:0006397), RNA splicing (GO:0008380), cell cycle (R-HSA-1640170) and DNA repair (GO:0006281). This study brings together several lines of evidence to indicate that distinct molecular features of ILBC can enable further subtyping, identify important features for targeted therapies (e.g., MMRd) and provide additional information for prognostication. This research identified Subgroup 1 as an important subgroup with similarities to TNBC and more aggressive clinical behaviour. Further investigation of samples from Subgroup 1 may identify additional important targets for precision medicine.
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    The genetics of gene expression: from simulations to the early-life origins of immune diseases
    Huang, Qinqin ( 2019)
    Human complex traits and diseases are often highly polygenic. Genome-wide association studies (GWAS) have been successful in identifying the underlying genetic components. However, challenges still remain and one of them is the biological interpretation of these findings. Genetic variants that are associated with diseases or traits are enriched in regulatory regions of the genome, suggesting that they may have a role in the regulation of intermediate molecular phenotypes, such as mRNA gene expression. Studies investigating the genetic architecture of gene expression variation, or expression quantitative trait loci (eQTLs), have aided the interpretation of GWAS findings by providing potential mechanisms through which the genetic variants contribute to higher-order phenotypes. In addition, eQTLs identified in disease-relevant tissues, or those that are specific to certain cell types or conditions are more informative in disease pathogenesis. This thesis first explored eQTL study design and analysis choices using extensive, empirically driven simulations with varying sample sizes, true effect sizes, and allele frequencies of true eQTLs. False discovery rate (FDR) control applied to the entire collection of tests had inflated FDR of genes with eQTLs (eGenes) in most scenarios; in contrast, hierarchical correction procedures had well-calibrated FDR. Significant eQTLs with low allele frequencies identified using small sample sizes were enriched for false positives. Overestimation of eQTL effect sizes was common in scenarios with low statistical power, and a bootstrap method (BootstrapQTL) which can lead to more accurate effect size estimation was developed. Based on the insights of the eQTL simulation study, optimal strategies were selected for the following eQTL analysis in two types of neonatal immune cells (monocytes and T cells) under resting and stimulated conditions. A great proportion of cis-eQTLs were specific to a certain cell type or condition, and the majority of them were observed only upon stimulation. Response eQTLs (reQTLs), with effects on gene expression modified by immune responses, were identified for 31% of the eGenes in monocytes and 52% of the eGenes in T cells. Trans-eQTL effects that were mediated through expression of cis-eGenes were observed. Lastly, integrative analyses were performed, using the early-life eQTLs, as well as GWAS variants associated with immune-related diseases obtained from external large cohorts. Significant overlaps between neonatal eQTLs and postnatal disease-associated variants were observed. Some cell type- or condition-specific cis-eQTLs colocalised with disease associations, suggesting that the potential risk genes involved in disease pathogenesis are linked to the stimulation of certain immune cells. Causal effects of genes were evaluated using Mendelian randomisation, and changes in expression levels (e.g. BTN3A2) were identified to have causal associations with multiple immune-related diseases. Taken together, it demonstrates that the early-life genetic variants and gene expression might contribute to later disease development. In conclusion, this thesis provides a strong evidence base for eQTL study design and guidance for analysis strategies in future studies. The characterisation of genetic regulation of neonatal immune responses and the interaction between regulatory variants and stimulatory conditions is a useful resource, and generates insights on the early-life origins of immune-related diseases that develop later in life.