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Graduate Student Supervision
Doctoral Student Supervision (Jan 2008 - Mar 2019)
No abstract available.
Traditional classifications and treatment of human cancers have operated with limitations surrounding tumor homogeneity and mutational stasis. Clinical metrics of malignant tumors focused on descriptive and behavioral properties such as tissue of origin, cellular morphologic features and extent of spread. Missing has been an understanding of the dynamics of cellular subpopulations that underpin divergent functional properties in space and time. This dissertation is focused on the development and application of methods, including next generation DNA sequencing, computational modeling, and single-cell genotyping protocols to elucidate breast tumor heterogeneity and clonal evolution at single nucleotide and single-cell resolution. First, I present advances in our knowledge of the mutational spectrum that may occur and evolve in an individual epithelial cancer, namely a lobular breast cancer metastases and matched primary tumor separated by a nine year interval. This seminal study demonstrated clonal evolution in a patient’s breast cancer and the successful application of targeted deep sequencing for determining digital allelic prevalences and clonal genotypes in bulk tumors. Second, I describe the diversity of genomic sequence and clonal heterogeneity in tumors of the triple-negative breast cancer subtype. The study uncovered wide clonal diversity in these primary tumors at first diagnosis. Third, I demonstrate via genotyping single tumor cells, that computational inferences of tumor clonal architecture can be made reliably from bulk tissue-derived data sets. This was performed using both somatic point mutations and loss of heterozygosity loci as clonal marks. And fourth, I applied single-cell analysis to study the clonal evolution in breast tumor murine xenografts following engraftment and serial passaging. This research uncovered a range of outcomes in tumor clonal composition upon initial engraftment and serial passaging. The same clonal groups were found to arise independently in separate xenografts derived from the same primary tumor, suggesting selection of functionally significant genotypes. Comprehensive capabilities in the measurement and analysis of clonal structure in cancers offers improved classification and combinatorial treatments of subpopulations in heterogeneous tumors and better use of murine xenograft models. Functionally relevant subpopulations of tumor cells, irrespective of numerical abundance or spatiotemporal persistence, can thereby be targeted using clonally informative genomic profiles.
PPP2R2A is a regulatory subunit of protein phosphatase 2A (PP2A). Genomic analysis based on 2000 breast cancer cases identified PPP2R2A as one of the deletion hotspots in breast cancer genomes and 63% of PPP2R2A deletions were found in estrogen receptor positive (ER+) breast cancers. We hypothesized that the high frequency deletion of PPP2R2A, as well as its correlation with ER expression status, implies its functional roles in breast cancer development. In this thesis, I investigated the functional impacts of PPP2R2A deletion in breast cancer development from the aspects of ER signaling, PP2A complex composition, and cell morphological changes. First, I studied ER signaling activity and mechanism in T47D cells with the analysis of transcriptome, ER binding specificity and ER co-factor recruitment, etc. Second, I studied PP2A complex composition in three breast cell models with targeted quantitative mass spectrometry (MRM). Last, I studied morphological changes in mammary breast cell models, 184-hTERT and MCF10A, in terms of cell proliferation, surface protein localization, and cell mobility based on transcriptome and signaling network analysis. As a result, reduced expression of PPP2R2A led to differential expression of ER response genes, alterations in ER binding specificity, and changes in ER co-factor composition. SPDEF was particularly up regulated and recruited in ER transcriptional machinery. Analysis in clinical samples also confirmed the correlation of SPDEF up regulation with ER expression status and PPP2R2A copy number loss. In addition, MRM analysis revealed changes in PP2A complex composition after PPP2R2A knockdown with increased relative abundance of STRN in PP2A complexes in ER positive cell models. Finally, my morphological studies demonstrated mislocalization of cell surface proteins and enhanced cell mobility in breast mammary epithelial cell models with reduced PPP2R2A expression. In conclusion, PPP2R2A plays an important role in regulating cell signaling activity and cellular morphology and its genomic deletion would significantly contribute to the development of breast cancer.
Genomic aberrations such as copy number alterations (CNA) and loss of heterozygosity (LOH) are hallmarks of human malignancies. These genomic abnormalities can have a measurable effect on the structure and dosage of chromosomal regions. Tumour suppressors and oncogenes altered by CNAs often contribute to a tumourigenic phenotype of increased proliferation. CNA and LOH can accrue through the process of branched evolution, resulting in the emergence of divergent clones with distinct aberrations present at diagnosis. Therefore, measuring and modeling how CNA/LOH distribute in cell populations can elucidate the abundance of specific clones and, ultimately, enable the study of clonal evolution. CNA/LOH events in tumours can be profiled using SNP genotyping arrays and whole genome sequencing (WGS). However, to maximize biological interpretability from these data, accurate and statistically robust computational methods for inferring CNA/LOH are necessary.I present three novel probabilistic approaches that apply hidden Markov models (HMM) to analyze CNA/LOH in tumour genomes. The first method is HMM-Dosage, which distinguishes somatic and germline copy number events. This tool was used to profile 2000 breast cancers, the largest study of this kind in the world. The second method is APOLLOH, which was one of the earliest methods developed to profile LOH in tumour WGS data. Its application to WGS of 23 triple negative breast cancers (TNBC) represents the first time that LOH and its effects on allelic expression were jointly analyzed from sequencing data. The third method is TITAN, which simultaneously infers CNA/LOH and the clonal population dynamics from tumour WGS data. This method provides an analytical route to studying the degree of clonal evolution driven by CNA/LOH. I applied TITAN to a novel set of primary breast tumours and corresponding mouse xenografts, presenting the results of distinct modes of temporal clonal selection patterns. In conclusion, this dissertation presents a suite of novel approaches and their application to real-world cancer datasets, contributing to significant discoveries in breast and ovarian cancers. Future applications of these approaches will further facilitate the elucidation of cancer evolution, the genetic basis of metastatic potential, and therapeutic response and resistance.
The processes involved in mammary gland development are intimately linked withthose that drive breast oncogenesis. Regulation of growth, tissue polarity and genomestability are a few of the factors the maintain homeostasis in breast epithelium and preventmalignant progression. In this work, a series of clonal 184-hTERT cell lines were generatedthat modeled the in vitro growth characteristics of bi-potent mammary progenitor cells; theywere dependent upon fibroblasts for low-density growth and formed dual-lineage acini in 3Dculture. These lines were subsequently used in a genome-wide siRNA screen to identify thefactors that regulate fibroblast-driven epithelial cell growth. Fibroblasts constitute themajority of cells within stroma, which plays a major role in supporting mammary progenitorcell growth. From this screen, 49 surface and secreted factors were identified that putativelytransduce the signals emanating from the fibroblasts that are required for epithelial cellgrowth. These factors were more potent than any of the previously described growth factorreceptors. When assessed in primary tissue, Gpr39, Scarb2, Ntn1, Efna4, Nptx1, and Ctnna1were found to have the greatest effect on overall progenitor cell growth, while SerpinH1differentially suppressed luminal progenitor cells, and Nkain4 and Kcnj5 differentiallysuppressed bi-potent progenitor cells. Further profiling of these lines identified the planarcell polarity protein Celsr1 as differentially regulated under fibroblast-dependent conditions.Silencing of Celsr1 increased the number of bi-potent progenitor cells detected in the colonyformingassay. Furthermore, it induced branching morphogenesis within normally sphericalacini and disrupted the apical polarity of these structures in 3D culture. Within this system,Celsr1 is suspected of signaling through Shisa4. This is the first description of a noncanonicalCelsr1 interactor. Finally, a curious variant line was identified amongst thecollection of 184-hTERT cells generated for this work. This line harbours mitotic spindleand cell cycle checkpoint defects, and rapidly gains chromosome 20 during passaging.Through an elimination process, de novo promoter hypomethylation and subsequentoverexpression of CENPI was identified as likely being responsible for this phenotype. Thisis the first description of CENPI deregulation and one of a few descriptions of gene promoterhypomethylation resulting in genome instability.
Kisspeptins and their receptor, GPR54, mediate sex hormone release through stimulation of the hypothalamic-pituitary-gonadal axis and have been implicated as metastasis suppressors. Expression of kisspeptin and GPR54 has been associated with less invasive cancers as determined by RNA expression, and a multitude of in vitro studies has consistently shown that overexpression of either ligand or receptor in malignant cell lines results in a less invasive phenotype. We hypothesized that expression of GPR54/kisspeptin in epithelial malignancies is predictive of disease outcome and altering endogenous GPR54 signalling in malignant breast and ovarian epithelial cells could alter their metastatic properties. We have determined by immunohistochemistry that kisspeptin and GPR54 are independent favourable prognostic markers for ovarian carcinoma and are specific for the clear cell cancer subtype; the least characterized of the subtypes. Additionally, loss of GPR54 is associated with poor prognosis in node positive breast cancer patients and is also lost in prostate cancer and testicular germ cell nonseminomas as compared to more benign disease. Moreover, secreted kisspeptin is elevated above physiological levels in the plasma of women with gynaecological cancers, including ovarian cancer. We evaluated GPR54 expression across a panel of breast and ovarian cancer cell lines to create an in vitro model system with which to knockdown GPR54 expression using RNA interference. However, we discovered that endogenous GPR54 was internalized rather than localized to the plasma membrane of these cancer cell lines. Consequently, internal GPR54 was unable to signal through its canonical Gαq pathway. To discover novel genes involved in kisspeptin-GPR54 signalling, we assessed gene expression differences between the Gpr54 and Kiss1 knockout mice as compared to wildtype mice. Our novel candidate list provides insight into physiological signalling in the hypothalamus that can then be applied to epithelial anti-metastatic signalling. Our results also support the sex hormone negative feedback effect on kisspeptin expression as reported in the current literature.In summary, we have confirmed kisspeptin and GPR54 as favourable prognostic markers, are the first to report the intracellular localization of GPR54 in endogenously expressing cancer cell lines, and we have introduced a list of novel genes involved in signalling.
Master's Student Supervision (2010-2017)
Tumours comprise (epi)genetically and phenotypically diverse cellular subpopulationsevolving over space and time. This heterogeneity can be observed as differences in morphologyor immunohistology of tumour sections, gene expression levels, genomic sequence and structure,proliferative potential, or metastatic ability. In order to unravel how this heterogeneity persists,one may study the clonal structure and evolution of tumours. Understanding intra-tumorheterogeneity, or the differences amongst cells in a single tumour, is of particular importance tofacilitate treatment combinations that effectively target all clinically relevant subclones. Thisclonality-informed approach requires identification and monitoring of clonal cell populationswithin a tumour. In this study I simulated tumour heterogeneity using cancer cells lines inidealised mixtures. Deep allelic measurements using next generation DNA amplicon sequencingwere integrated in a computational modelling program (PyClone), to retrieve cellularprevalence’s and clonal structure within these cell line mixtures. This approach to modelingheterogeneity employed both diploid and aneuploid cell lines with genomic analysis focused onheterozygous alleles and changes in the prevalence of theses alleles when cell lines were mixedin different proportions. As a result I first identified that using NGS and PyClone modelingenables elucidation of population clonal structure as predicted from idealised mixtures of diploidcell lines. However, the aneuploidy cell line mixtures demonstrated a requirement for copynumber information to be included as a prior input to clonality modeling in order to avoidmisleading interpretations of cellular prevalence and clonal structure. Defining and monitoringclonal heterogeneity in patient tumours is of importance to track functionally relevantsubpopulations of tumour cells, enabling oncologists to administer cocktails of therapeutic agentstargeting relevant clones irrespective of their numerical abundance. This clonality-informediiitreatment approach is a promising development to tackle the growing challenge of therapy resistantsubclones and thus limit cancer recurrence.
Next generation sequencing has now enabled a cost-effective enumeration of the full mutational complement of a tumour genome - in particular single nucleotide variants (SNVs). However, most current computational and statistical models for analyzing next generation sequencing data do not account for cancer-specific biological properties, including somatic segmental copy number alterations (CNAs), which require special treatment of the data. Here we present CoNAn-SNV (Copy Number Annotated –SNV): a novel algorithm for the inference of single nucleotide variants (SNVs) that overlap copy number alterations. The method is based on modelling the notion that genomic regions of segmental duplication and amplification induce an extended ‘genotype space’ where a subset of genotypes will exhibit heavily skewed allelic distributions in SNVs (and therefore render them undetectable by methods that assume diploidy). CoNAn-SNV introduces the concept of modelling allelic counts from sequencing data using a panel of Binomial mixture models where the number of mixtures for a given locus in the genome is informed by a discrete copy number state given as input. We applied CoNAn-SNV to a previously published whole genome shotgun data set obtained from a lobular breast cancer and show that it is able to detect 24 experimentally revalidated somatic non-synonymous mutations that were not detected using copy number insensitive SNV discovery algorithms. Importantly, ROC analysis shows that the increased sensitivity of CoNAn-SNV does not result in disproportionate loss of specificity. Our results indicate that in genomically unstable tumours, copy number annotation for SNV detection will be critical to fully characterize the mutational landscape of cancer genomes. The Binomial mixture model framework, however, is unable to fully cope with the complexity of tumour sequence data. We explore substituting the Binomial mixture model framework with the Beta-Binomial framework to overcome this limitation. The effectiveness of this substitution is compared against the lobular breast carcinoma and the 30 exon capture data sets all derived from triple negative breast cancers. The performance of Binomial and Beta-Binomial mixture model is evaluated against a cohort of exon capture test cases and we report ROC and f-measures.