Pathology - Theses
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ItemThe genomic landscape of phaeochromocytoma( 2015)Phaeochromocytomas (PCC) and paragangliomas (PGL) (collectively PPGL) are rare neural crest-derived tumours originating from adrenal chromaffin cells or extra-adrenal sympathetic and parasympathetic tissues. More than a third of PPGL cases are associated with heritable syndromes involving 18 or more known genes. These genes have been broadly partitioned into two groups based on pseudo-hypoxic and receptor tyrosine kinase (RTK) signalling pathways. Many of these genes can also become somatically mutated, although up to one third of sporadic cases have no known genetic driver. Furthermore, little is known of the genes that co-operate with known driver genes to initiate and drive tumourigenesis. To explore the genomic landscape of PPGL, exome sequencing, high-density SNP-array analysis, and RNA sequencing was applied to 36 PCCs and four PGL tumours. All tumours displayed a low mutation frequency in combination with frequent large segmental copy-number alterations and aneuploidy, with evidence for chromothripsis seen in a single case. Thirty-one of forty (77.5%) cases could be explained by germline or somatic mutations or structural alterations affecting known PPGL genes. Deleterious somatic mutations were also identified in known tumour-suppressor genes associated with genome maintenance and epigenetic modulation (e.g. TP53, STAG2, KMT2D). A multitude of other genes were also found mutated that are likely important for normal neuroendocrine cell function (e.g. ASCL1, NCAM1, GOLGA1). In addition, the existing paradigm for gene-expression subtyping of PPGL was further refined by applying consensus clustering to a compendium of previously published microarray data, enabling the identification of six robust gene-expression subtypes and subsequent cross-platform classification of RNA-seq data. The majority of cases in the cohort with no identifiable driver mutation were classified into a gene-expression subtype bearing similarity to MAX mutant PPGL, suggesting there are yet unknown PPGL cancer genes that can phenocopy MAX mutations. The cross-platform classification model was then further refined to develop a 46-gene Nanostring-based diagnostic tool capable of classifying PPGL tumours into gene-expression subtypes. The strong genotype-to-subtype relationship in PPGL makes subtyping a powerful tool that can be used clinically to guide and interpret genetic testing, determine surveillance programs and aid in better elucidation of PPGL biology. In applying the diagnostic assay to a test set of 38 cases, correct classification into one of the six subtypes was achieved for 34 (90%) samples based on the known genotype to gene-expression subtype association. The observation that at least one of the six subtypes is likely defined by the presence of non-neoplastic cells led to further refinement into five, four, and three-class architectures, further improving classification accuracy. Increasingly tumour heterogeneity is being recognised as one of the most significant challenges facing modern oncology. Genomically diverse tumour regions create additional complexity in predicting treatment response and metastatic potential through biopsy. Multi-region sampling of multiple synchronous primaries from patients with a predisposing germline mutation was used to explore tumour evolution and heterogeneity in PPGL and concomitant medullary thyroid carcinoma. Evolutionary reconstruction of a single primary PPGL demonstrated periods of both branched and linear evolution resulting in a high degree of intratumoural heterogeneity. Comparison of multiple synchronous primaries provided strong evidence of convergent evolution through recurrent chromosomal aberrations, indicating these may be obligate events in tumourigenesis, and as such, may indicate potential novel therapeutic targets.
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ItemInvestigating stemness and plasticity models as sources of intra-tumoural heterogeneity in Glioblastoma multiforme( 2015-09-09)Glioblastoma mutiforme (GBM) is a heterogeneous tumour of the brain with a poor prognosis. Genome-wide profiling has revealed four molecular subtypes, yet there is no significant difference in long-term survival between subtypes. Recurrence and resistance to GBM therapy is believed to be due to an underlying Glioma Stem Cell (GSC) subpopulation. To identify gene expression differences with the ability to predict patient survival, the cancer stem cell subpopulation of Patient Derived Glioma Cells (PDGCs) was isolated based on the expression of the putative stem cell marker CD133. RNA-seq libraries were prepared from six different PDGCs with the identification of 37 differentially expressed genes. Downstream characterisation of the cellular phenotypes exhibited by CD133+ and CD133- cells indicated that CD133 does not enrich for stem and malignant phenotypes. Genes coexpressed with GSCs markers were used to build a gene signature that classified patients based on a CD133 coexpression module signature (CD133-M) or a CD44 coexpression module signature (CD44-M) subtype. CD133-M tumours were enriched for the Proneural GBM subtype and correspondingly CD44-M tumours were enriched for the Mesenchymal subtype. Gene set enrichment identified proliferative pathways as activated in CD133-M and invasion/ migration pathways as enriched in CD44-M. CD133-M patients benefitted most from radiotherapy while CD44-M classified patients responded equally well to temozolomide or radiotherapy. In different PDGCs there was a culture specific equilibrium of distinct PN and MES subpopulations, potentially due to the genetic background of the original patient. The distribution of Proneural and Mesenchymal cells in the same tumour was measured in GBM sections using the expression of Olig2 and CD44 proteins as markers. Heterogeneous expression of these markers in tumours was observed, consistent with the heterogeneity observed in cell cultures. The influence of oxygen tension and chemoradiotherapy on the intra-tumoural equilibrium of PN and MES cells in PDGCs was investigated, with hypoxia inducing a MES to PN shift and chemoradiotherapy inducing a PN to MES shift respectively. The results of this study favour a cellular plasticity model over a hierarchical cancer stem cell model and is in agreement with accumulating evidence that CD133 and CD44 expression are markers of PN and MES molecular subtypes respectively. Both PN and MES subtypes coexist in the same tumours while rare cells that transiently express both CD44 and CD133 (having PN and MES properties) may be cancer stem cells. The results of this thesis suggest the tumour specific proportion of these states is determined by a combination of genetic and environmental factors. Surveillance and modulation of intra-tumoural heterogeneity could be of benefit in the clinical management of GBM.