The Intra-Tumour Heterogeneity Landscape of Human Cancers

Tumours accumulate many somatic mutations in their lifetime. Some of these mutations, drivers, convey a selective advantage and can induce clonal expansions. Incomplete clonal expansions give rise to intra-tumour heterogeneity. Somatic mutations can be measured through massively parallel sequencing, where mutations that are supporting incomplete expansions will appear as subclonal. These mutations can be used as a marker of the existence of the expansion and allow for a window into the clonal and subclonal architecture of the tumour at diagnosis. During my Ph.D. I have developed computational methods to infer intra-tumour hetero- geneity from massively parallel sequencing data and applied these to the 2,778 tumour whole genome sequences in the International Cancer Genome Consortium Pan-Cancer Analysis of Whole Genomes initiative to paint the pan-cancer landscape of intra-tumour heterogeneity. I will first introduce the methods; a method to call somatic copy number alterations (Battenberg) and a method to infer subclones from single nucleotide variants (DPClust). Both are extensively validated on simulated and on real data, and I describe a rigorous quality control procedure. The methods are then applied to a single sample to showcase what can be learned about the life history of a cancer, before introducing additional computational methods for a pan-cancer study of heterogeneity. Finally, I describe the findings. I find that nearly all cancers, for which there is sufficient power, contain at least one subclone (96.7% of 1,801 primary tumours). The subclones contain driver mutations that are under positive selection, and known cancer genes contain subclonal driver mutations in low proportions. 9.5% of tumours contain only subclonal drivers that are clinically actionable, suggesting that heterogeneity could inform treatment choices. Finally, the analysis reveals that activity of smoking and UV-light associated mutational signatures goes down as the tumour evolves, while activity of the APOBEC associated signatures goes up.This thesis was funded by the Wellcome Trust

Pervasive lesion segregation shapes cancer genome evolution

Cancers arise through the acquisition of oncogenic mutations and grow through clonal expansion. Here we reveal that most mutagenic DNA lesions are not resolved as mutations within a single cell-cycle. Instead, DNA lesions segregate unrepaired into daughter cells for multiple cell generations, resulting in the chromosome-scale phasing of subsequent mutations. We characterise this process in mutagen-induced mouse liver tumours and show that DNA replication across persisting lesions can produce multiple alternative alleles in successive cell divisions, thereby generating both multi-allelic and combinatorial genetic diversity. The phasing of lesions enables the accurate measurement of strand biased repair processes, quantification of oncogenic selection, and fine mapping of sister chromatid exchange events. Finally, we demonstrate that lesion segregation is a unifying property of exogenous mutagens, including UV light and chemotherapy agents in human cells and tumours, which has profound implications for the evolution and adaptation of cancer genomes.This work was supported by: Cancer Research UK (20412, 22398), the European Research Council (615584, 682398), the Wellcome Trust (WT108749/Z/15/Z, WT106563/Z/14/A, WT202878/B/16/Z), the European Molecular Biology Laboratory, the MRC Human Genetics Unit core funding programme grants (MC_UU_00007/11, MC_UU_00007/16), and the ERDF/Spanish Ministry of Science, Innovation and Universities-Spanish State Research Agency/DamReMap Project (RTI2018-094095-B-I00)

Methods and practice of detecting selection in human cancers

Cancer development and progression is an evolutionary process, understanding these evolutionary dynamics is important for treatment and diagnosis as how a cancer evolves determines its future prognosis. This thesis focuses on elucidating selective evolutionary pressures in cancers and somatic tissues using population genetics models and cancer genomics data. First a model for the expected diversity in the absence of selection was developed. This neutral model of evolution predicts that under neutrality the frequency of subclonal mutations is expected to follow a power law distribution. Surprisingly more than 30% of cancer across multiple cohorts fitted this model. The next part of the thesis develops models to explore the effects of selection given these should be observable as deviations from the neutral prediction. For this I developed two approaches. The first approach investigated selection at the level of individual samples and showed that a characteristic pattern of clusters of mutations is observed in deep sequencing experiments. Using a mathematical model, information encoded within these clusters can be used to measure the relative fitness of subclones and the time they emerge during tumour evolution. With this I observed strikingly high fitness advantages for subclones of above 20%. The second approach enables measuring recurrent patterns of selection in cohorts of sequenced cancers using dN/dS, the ratio of non-synonymous to synonymous mutations, a method originally developed for molecular species evolution. This approach demonstrates how selection coefficients can be extracted by combining measurements of dN/dS with the size of mutational lineages. With this approach selection coefficients were again observed to be strikingly high. Finally I looked at population dynamics in normal colonic tissue given that many mutations accumulate in physiologically normal tissue. I found that the current view of stem cell dynamics was unable to explain sequencing data from individual colonic crypts. Some new models were proposed that introduce a longer time scale evolution that suppresses the accumulation of mutations which appear consistent with the data

Investigating intratumour heterogeneity analysis methods and their application in GBM

Glioblastoma (GBM) is an incurable cancer with a median survival of 15 months. Despite debulking surgery, cancer cells are inevitably left behind in the surrounding brain, with a minority able to resist subsequent chemoradiotherapy and eventually form a recurrent tumour. This resistance is likely influenced by the cells’ genotypes, which show high variability (intratumour heterogeneity), as a result of tumour evolution. Characterising changes in the genetic architecture of tumours through therapy, may allow us to understand the effect that different mutations and pathways have on cell survival, and potentially identify novel targets for counteracting resistance in GBM. Such analyses involve detection of mutations from bulk tumour samples, and then delineating them into individual genetically distinct ‘subclones’, through subclonal deconvolution. This is a complex process, with no reliable guidelines for the best pipelines to use. I therefore developed methods to allow simulation and in silico sequencing of genomes from realistically complex, artificial tumour samples, so that I could benchmark such pipelines. This revealed that no tested pipelines, using single bulk samples, showed a high level of accuracy, though mutation calling with Mutect2 and FACETS, followed by subclonal deconvolution with Ccube, showed the best results. I then used alternative approaches with the largest longitudinal GBM dataset investigated to date. I found that evidence of strong subclonal selection is absent in many samples, and not associated with therapy. Nonetheless, this does not negate the possibility of smaller, or less frequent, pockets of altered fitness. Using pathway analysis combined with variants that are informative of tumour progression, I identified processes that may confer increased resistance, or sensitisation to therapy, and which warrant further investigation. Lastly, I apply subclonal deconvolution to investigate mouse-specific evolution in GBM patient-derived orthotopic xenografts and found no clear evidence to suggest these models are unsuitable for investigations relevant to humans

Evolution and lineage dynamics of a transmissible cancer in Tasmanian devils.

Devil facial tumour 1 (DFT1) is a transmissible cancer clone endangering the Tasmanian devil. The expansion of DFT1 across Tasmania has been documented, but little is known of its evolutionary history. We analysed genomes of 648 DFT1 tumours collected throughout the disease range between 2003 and 2018. DFT1 diverged early into five clades, three spreading widely and two failing to persist. One clade has replaced others at several sites, and rates of DFT1 co-infection are high. DFT1 gradually accumulates copy number variants (CNVs) and its telomere lengths are short but constant. Recurrent CNVs reveal genes under positive selection, sites of genome instability and repeated loss of a small derived chromosome. Cultured DFT1 cell lines have increased CNV frequency and undergo highly reproducible convergent evolution. Overall, DFT1 is a remarkably stable lineage whose genome illustrates how cancer cells adapt to diverse environments and persist in a parasitic niche.This work was supported by grants from Wellcome (102942/Z/13/A), the National Science Foundation (DEB-1316549), the University of Tasmania Foundation (Eric Guiler Tasmanian Devil Research Grants), the Australian Research Council (DE 170101116) and a Philip Leverhulme Prize from the Leverhulme Trust. YMK was supported by a Herchel Smith Postgraduate Fellowship

Somatic evolution in healthy and chronically inflamed colon and skin

The human body is made up of trillions of cells which cooperate to reproduce their genetic material. While all the cells are a part of a whole, each is also an individual and will selfishly give rise to a clonal expansion of cells within a tissue given the chance, even to the detriment of the organism. This thesis discusses the evolutionary forces acting on cells within the body, specifically on epithelial cells in the colon and skin. After a general introduction of the evolutionary forces acting on normal cells and the methods used to study them, Chapter 2 focuses specifically on genetic drift within the colon, where clones expand through the process of crypt fission. I apply a statistical framework called Approximate Bayesian Computation to estimate the crypt fission rate in the normal colon and in individuals with Familial adenomatous polyposis (FAP). I estimate the rate of crypt fission to be one every 27 years in the normal colon and one every 13 years in (FAP). In Chapter 3, I describe somatic evolution in the colon under conditions of chronic inflam- mation. I used whole-genome sequencing of individual colonic crypts from patients with inflammatory bowel disease (IBD) to show that the IBD-colon is characterized by a higher mutation burden and larger clonal expansions than the healthy colon. I also show that muta- tions in immune-related genes, including PIGR, ZC3H12A and genes in the interleuking 17 and toll-like receptor pathways, are under positive selection in the colons of IBD patients and may contribute to the disease pathogenesis. In Chapter 4, I focus on the skin. I performed whole-exome sequencing of microbiopsies of epidermis from patients with psoriasis, a second chronic inflammatory disease. In contrast to IBD, I did not find increased mutation burden and clonal spread in psoriasis, except when the skin had been treated with psoralens + UVA (PUVA) phototreatment. The selection landscape of psoriatic skin resembles that of normal skin, and mutations in NOTCH1, FAT1, TP53, PPM1D and NOTCH2 are positively selected. ZFP36L2 was the only gene found to be enriched in mutations that has not been previously reported in normal skin, but it is as yet uncertain if selection of ZFP36L2 mutant cells is a feature specific to psoriatic skin or not. Finally, Chapter 5 discusses my findings in the broader context of cancer and complex-trait genomics. I discuss how a causal relationship between somatic evolution and non-neoplastic diseases may be established and the different ways somatic evolution may affect disease progression for good or ill. I further discuss how to design a study to search for germline determinants of somatic evolution and the need for developing methods to enable such studies to be conducted at scale.Wellcome Trus

Somatic evolution in human blood and colon

All cancers were once normal cells. They became cancerous through the chance acquisition of particular somatic mutations that gave them a selective advantage over their neighbours. Thus, the mutations that initiate cancer occur in normal cells, and the normal clonal dynamics of the tissue determine a mutant cell’s ability to establish a malignant clone; yet these remain poorly understood in humans. One tissue was selected for the exploration of each of these two facets of somatic evolution: blood for clonal dynamics; colon for mutational processes. Blood presents an opportunity to study normal human clonal dynamics, as clones mix spatially and longitudinal samples can be taken. We isolated 140 single haematopoietic stem and progenitor cells from a healthy 59 year-old and grew them in vitro into colonies that were whole genome sequenced. Population genetics approaches were applied to this dataset, allowing us to elucidate for the first time the number of active haematopoietic stem cells, the rate at which clones grow and shrink, and the cellular output of stem cell clones. Colonic epithelium is organised into crypts, at the base of which sit a small number of stem cells. All cells in a crypt ultimately share an ancestor in one stem cell that existed recently, and consequently share the mutations that were present in this ancestor. We exploited this natural clonal unit, isolating single colonic crypts through laser capture microdissection. 570 colonic crypts from 42 individuals were whole genome sequenced. We describe the burden and pattern of somatic mutations in these genomes and their variability across and within different people, identifying some mutational processes that are ubiquitous and others that are sporadic. Targeted sequencing of an additional 1,500 crypts allowed us to quantify the frequency of driver mutations in normal human colon. Together, these two studies inform on the somatic evolution of normal tissues, describing new biology in human tissue homeostasis and providing a window into the processes that govern cancer incidence.Funded by the Wellcome Sanger Institute

Evolution and lineage dynamics of a transmissible cancer in Tasmanian devils

Devil facial tumour 1 (DFT1) is a transmissible cancer clone endangering the Tasmanian devil. The expansion of DFT1 across Tasmania has been documented, but little is known of its evolutionary history. We analysed genomes of 648 DFT1 tumours collected throughout the disease range between 2003 and 2018. DFT1 diverged early into five clades, three spreading widely and two failing to persist. One clade has replaced others at several sites, and rates of DFT1 coinfection are high. DFT1 gradually accumulates copy number variants (CNVs), and its telomere lengths are short but constant. Recurrent CNVs reveal genes under positive selection, sites of genome instability, and repeated loss of a small derived chromosome. Cultured DFT1 cell lines have increased CNV frequency and undergo highly reproducible convergent evolution. Overall, DFT1 is a remarkably stable lineage whose genome illustrates how cancer cells adapt to diverse environments and persist in a parasitic niche

Single-cell droplet microfluidics for metagenomics and cancer multiomics

Cellular heterogeneity is inherent to many biological systems, across both normal and disease states. For example, diverse ensembles of microbes in the natural environment fulfill distinct roles related to nutrient metabolism and gas fixation. In human cancers, genetic and phenotypic heterogeneity is observed among cells originating from a common oncogenic clone. Understanding biological heterogeneity, whether for metabolic engineering applications or the design of cancer therapeutics, begins at the fundamental unit of the organism: a single cell. Droplet microfluidics enables analyses of single cells at a biologically-relevant scale through rapid compartmentalization and manipulation of millions of parallel reactions. In this thesis, I describe the development and application of single-cell genomics platforms leveraging droplet microfluidics to interrogate many individual genomes. These technologies enable single-cell metagenomics and multiomic analysis of single cancer cells, providing new insights into the extent of cellular heterogeneity and its implications across biology