Search

Ovarian clear cell carcinoma (OCCC) is a gynaecological cancer of unmet need characterized by ARID1A and PPP2R1A mutations. Prior work identified a synthetic lethality between ARID1A tumour suppressor mutations in OCCC and inhibition of the ATR kinase, information that has led to phase II proof-of-concept clinical trials assessing an ATR inhibitor (ATRi) in ARID1A mutant cancers. Using a genome-wide CRISPR-Cas9 mutagenesis screen in an ARID1A mutant OCCC tumour cell line and subsequent genome-wide CRISPR-Cas9 mutagenesis and CRISPR-dCas9 interference screens, I identify multiple protein phosphatase 2A (PP2A) subunit-coding genes as determinants of ATRi sensitivity. Analysis of a cohort of OCCCs indicated that over approximately one half possessed ARID1A mutations as well as mutations in the PP2A scaffolding subunit coding gene, PPP2R1A. CRISPR-prime editing of PPP2R1A demonstrated that cancer-associated hotspot p.R183 missense mutations cause ATRi sensitivity in OCCC cells, even in the presence of ARID1A mutations. The synthetic lethality between PPP2R1A p.R183 missense mutation and ATRi operated in both in vitro and in vivo and was characterized by a decrease in cells in replicating S phase of the cell cycle, an increase in cells entering mitosis with sub-4n genomic content and the accumulation of 53BP1 bodies. Mechanistically, this synthetic lethal effect was dependent upon the kinase WNK1, which itself exhibits increased phosphorylation in PPP2R1A mutant OCCC cells. Depletion of WNK1 restored the replicating S phase population normally depleted by ATRi and reversed ATRi sensitivity in PPP2R1A p.R183 mutant OCCC cells. TOV21G PPP2R1A p.R183 mutant cells were also observed to be more sensitive to ATRi than PPP2R1A wild-type cells in an in vivo model system of OCCC. Together with the data presented here, the co-occurrence of PPP2R1A and ARID1A mutations in OCCC suggests that in addition to ARID1A, PPP2R1A status should be assessed as a biomarker of ATRi response in on-going clinical trials.

For a long time, hexagonal boron nitride (hBN) has been considered the silent sister material of graphene, but recently an appreciation for it is starting to grow within the scientific community for the properties it possesses and not just its functionality as graphene's supporting partner. The discovery of its ability to host quantum emitters from defect sites within its crystal structure has stimulated a recent increase in publications exploring their properties. The large bandgap hBN possesses, so juxtaposed to the conductive properties of graphene, and its ability to host energy levels at defects sites that are isolated from the band-edges, allows for single-photon emission (SPE) to occur at room temperature; thus, providing an exciting alternative to diamond for hosting quantum emission at these conditions. To date, most of the research on hBN and its emissive defects has revolved around thick, exfoliated hBN flakes. Theses samples are limited in area and combined with empirical SPE activation steps or from grown multi-layer hBN of typically, not well-defined structure and purity. The precise nature of such quantum emitters is still under debate because their generation or activation mechanisms remain poorly understood and their deterministic and scalable spatial positioning, both laterally and vertically, remains a scientific challenge. Spatial positioning is particularly important to enable effective and refined fundamental studies of the emissive defects, their stability and response to strain and coupling to e.g., external electric or magnetic fields, but also to open pathways to integrated device technology. Neither exfoliated hBN flakes or grown multilayers offer precise emitter position control (in zdirection) across the film thickness. Currently, investigations and characterisations of the material and emission sites are slow, utilising low-throughput techniques, which is why the development of fast analysis techniques combined with high-quality large-area material production are urgently required to better understand the material and emission sites. The work in this thesis begins by addressing the scalability issues of both characterisation and material size and it does it in two ways: first, by building upon previously developed chemical vapour deposition (CVD) growth procedures of monolayer (ML) hBN films, a large-area, continuous, and versatile SPE host material is created; second, to characterise it, a photoluminescence (PL) characterisation technique was newly applied that can simultaneously collect spatial, spectral, and temporal emitter information from 100s of emission sites at once. High-throughput scanning opens the possibility for comprehensive statistical behaviour analysis of emitters in hBN to be generated, elucidating in-depth information about their ensemble behaviour. Through the application of such a tool, the thesis next addresses salient issues that have plagued emission within ML hBN, namely its stability and contamination. First, the research presents pretreatment processes for CVD hBN MLs which are established to either fully suppress or activate emission, whilst removing the influence of process residues and environmental influences on emitter behaviour. Then, by being able to utilise such differently treated MLs as select building blocks and creating specific assembly protocols, the known emitter bleaching in air can be suppressed by sandwiching between two protecting (non-emissive) hBN MLs. The found stability lasts for months in air and allows for more sophisticated and intensive characterisation techniques to be used. Consequently, through second-order fluorescence intensity correlation measurements, the singlephoton nature of the emission sites are confirmed and comparisons of their structural origins to previous work can be commented on, which are based on the now-possible high-resolution spectral measurements. Finally, the thesis addresses the key technological challenge of scalable and deterministic SPE spatial position control, both laterally and vertically, whilst maintaining the 2D nature of the material. This is achieved through emitter localisation in hBN in all 3 dimensions via a ML engineering approach. The ML stacking process achieves vertical (z) emitter localisation at the atomic layer level, creating the thinnest, stable emissive structure. Such trilayer stacks and ultra-shallow z-localisation retains unique opportunities for external control of emission, quantum sensing and metrology, and efficient coupling to waveguides, fibres, or plasmonic and photonic cavities. Graphene is highly efficient in quenching fluorescence and by combining the trilayer hBN structure with a patterned CVD graphene mask, addressable emitter arrays and effective lateral (x-y) emitter localisation down to single emission sites is achieved. Such complete emitter site localisation is scalable and highly versatile, overcoming some of the major challenges hindering the materials advancement.

A spin system is a general framework in which the vertices of a graph are assigned spins from a finite set. The local interactions between neighbouring spins give rise to a global weight, which describes the probability that the system is in the given configuration. Spin systems provide a framework for sampling and counting problems in computer science, graph homomorphism problems in combinatorics, and phase transition phenomena in statistical physics. Two natural computational problems associated to a spin system are counting (computing the aggregate weight of all spin configurations) and sampling (constructing a random spin configuration with probability proportional to its weight). In general, the problem of exact counting is computationally hard, and our attention therefore turns towards approximate counting, and the closely related problem of approximate sampling. In computer science, approximate counting and sampling for spin systems, such as the hard-core model on independent sets or the Potts model on colourings, are well-studied problems. In recent years, on bounded-degree graph classes, an intimate connection has been established between the computational complexity of approximate sampling and counting, and the the existence of phase transitions in corresponding statistical physics models. Roughly speaking, there is a phase transition between a 'high-temperature' regime and a 'low temperature' regime. In the hightemperature regime, interactions between spins are weak and there is disorder in the model. Conversely, in the low temperature regime, interactions between spins are strong and the model is highly ordered - a typical configuration corresponds to one of some number of ground states, which limit the way that spins are assigned. This behaviour is reflected by the algorithmic properties of these models. At high-temperatures, there exists a variety of algorithmic techniques for approximate counting and sampling - examples include the Markov chain Monte Carlo (MCMC) method and correlation decay. At low temperatures, these approaches no longer succeed. Indeed, for many spin systems on many interesting classes of graphs, the problems of approximate counting and sampling are provably intractable. Recent research has sought to understand the algorithmic behaviour of spin systems such as the hard-core and Potts models on graphs such as the integer lattice and the random regular graph, the latter of which can be seen as an 'average case' instance of a regular graph. A detailed algorithmic and probabilistic understanding of these models has been developed, and has resulted in efficient low temperature algorithms. This is in contrast to the case of arbitrary bounded-degree graphs, where efficient algorithms can usually only be achieved in the high-temperature regime. These approaches typically use the truncated Taylor series approach of Barvinok to approximate the log partition function of a certain convergent series expansion. As such, they rely on tools from the complex analysis literature, and typically have running times of the form n O(log ∆) , where ∆ is the maximum degree of the input graph. The work in this thesis can be seen as part of this same program - we develop efficient algorithmic approaches for approximate counting and sampling in low temperature spin systems, in a variety of different settings. We present a Markov chain based approach to approximate counting and sampling from spin systems such as the hard-core and Potts models. Using combinatorial and probabilistic techniques, we prove fast running times for these algorithms. Our results apply to classes of graphs such as the integer lattice, the random regular graph, and bounded-degree expanders, and they apply in a similar range of parameters to existing results. We also generalise these techniques to any spin system in which the interactions between spins are sufficiently strong, and to classes of random graphs of possibly unbounded degree.