Landscapes of cellular phenotypic diversity in breast cancer xenografts and their impact on drug response

Funder: Cancer Research UK (CRUK); doi: https://doi.org/10.13039/501100000289Funder: AstraZeneca; doi: https://doi.org/10.13039/100004325Abstract: The heterogeneity of breast cancer plays a major role in drug response and resistance and has been extensively characterized at the genomic level. Here, a single-cell breast cancer mass cytometry (BCMC) panel is optimized to identify cell phenotypes and their oncogenic signalling states in a biobank of patient-derived tumour xenograft (PDTX) models representing the diversity of human breast cancer. The BCMC panel identifies 13 cellular phenotypes (11 human and 2 murine), associated with both breast cancer subtypes and specific genomic features. Pre-treatment cellular phenotypic composition is a determinant of response to anticancer therapies. Single-cell profiling also reveals drug-induced cellular phenotypic dynamics, unravelling previously unnoticed intra-tumour response diversity. The comprehensive view of the landscapes of cellular phenotypic heterogeneity in PDTXs uncovered by the BCMC panel, which is mirrored in primary human tumours, has profound implications for understanding and predicting therapy response and resistance

Development and application of an imaging and spatially-driven toolkit to elucidate vasculogenic mimicry in breast cancer mouse models

Vasculogenic mimicry (VM) is one form of tumour vascularisation that describes tumour cells that have acquired endothelial-like features, endowing them with the ability to form vessel-like structures. VM networks have been postulated to be functional, enabling them to play a critical role tumour survival and metastatic disease. These de novo vessels are independent of angiogenesis processes, in which host endothelial cells form vessels from pre-existing vasculature, and instead have been shown to underpin therapeutic resistance to anti-angiogenic therapies (AATs). Although molecular data largely supported by in vitro work has immensely contributed to our understanding of VM, the spatial features that define the VM phenotype remain largely unknown and understudied. How these vessels present in space and in 3D, has, until recently, been unknown. This underpins the scarcity of in vivo evidence and subsequent imaging and spatial data which would help further illuminate VM. Therefore, the overarching goal of this dissertation was to curate a novel toolkit that enables a revolutionary approach to better capturing and understanding VM, in vivo. The toolkit has largely entailed the optimisation of a vascular perfusion assay and the integration of two state-of-the-art technologies: a 3D two-photon imaging modality and a single cell, multiplexed immune-labelling proteomics platform. The former has greatly improved our ability to confidently capture genuine VM networks in their natural environment whilst the latter has enabled a novel approach to better resolving the spatial features of these networks and VM-tagged tumours more broadly. Upon successful development, optimisation and validation of the VM toolkit, the final phase of this project was to apply it first to mouse models of VM, followed by human cancer cell line-derived mouse models, all in the triple negative breast cancer setting. The culmination of this PhD has yielded three impactful achievements. First is an optimised and validated novel toolkit that enables VM networks to be confidently and reliably captured and better understood, spatially. 3D evidence for VM can now be directly interrogated with intricate spatial technologies for further molecular and spatial characterisation. Second is the application of this toolkit to mouse models of VM, illuminating a complex vasculature across 3D models and the prominent role of anti-angiogenic pathways in these VM-tagged tumours. Third is the application of this tool kit to VM-competent human cancer cell line-derived mouse models of VM, enabling additional in vivo models to be established. In these models, genuine VM networks were captured, encapsulating some of the most convincing in vivo and 3D evidence for VM across all models supporting this PhD and arguably across much of the current VM literature. This PhD has enabled the elusive VM phenotype to be more robustly captured and comprehensively resolved spatially, using a bespoke toolkit in addition to identifying and exploring additional in vivo models of VM. These are pivotal accomplishments that will directly impact the field and enable the biological importance and relevance of this mechanism to be further supported. The implications that the toolkit developed and the insights gathered in support of this project can be clearly defined and are highlighted throughout this dissertation

Catastrophic expenditures and impoverishment due to out-of-pocket health payments in Kosovo

Abstract Background The current health system reforms in Kosovo aim to improve health status through universal health coverage. Risk pooling and ensuring access to necessary care without financial hardship are envisaged through compulsory health insurance. We measure the level of financial risk protection through two commonly applied concepts: catastrophic health expenditures and impoverishment. Methods Data from the 2014 Kosovo Household Budget Survey were used to estimate catastrophic health expenditures as a percentage of household consumption expenditures at different thresholds. Poverty head counts and gaps were estimated before and after out-of-pocket (OOP) health payments. Results Approximately 80% of the households in Kosovo incurred OOP health payments. Most of these expenditures were for medicine, pharmaceutical products and medical devices, followed by diagnostic and outpatient services. Hospital services and treatment abroad were less frequent but highly costly. Although households from the upper consumption groups spent more, households from the lower consumption groups spent a greater share of their consumption expenditures on healthcare. The catastrophic health expenditure head count showed an increase, while the impoverishment and poverty gap remained stable compared to 2011. Regression analysis showed that age of the household head, insurance coverage, household size, belonging to the lowest consumption expenditure quintiles, and having disabled and aged household members were significant predictors of the probability of experiencing catastrophic health expenditures. Conclusions Ongoing financing reforms should target the lower income quintiles and vulnerable groups, pharmaceutical policies should be revisited, and the internal referral system should be strengthened to overcome excessive spending for treatment abroad

Prevalence of Perceived Stress, Anxiety, and Depression in HCW in Kosovo during the COVID-19 Pandemic: A Cross-Sectional Survey

A pandemic may have a negative impact on healthcare workers’ (HCW) mental health. In this cross-sectional study, we assess the self-reported prevalence of stress, anxiety, and depression and identify their predictive factors among HCW in Kosovo. The online questionnaire collected data on socio-demographics (sex, age, occupation, education, workplace) and the presence and severity of depression, anxiety, and stress through the 21-item Depression, Anxiety, and Stress Scale (DASS-21) questionnaire. Descriptive statistics, t-test, and linear logistic regression were used to analyze the data. Of the 545 respondents, the majority were male (53.0%), under 60 years of age (94.7%), and married (81.7%). Most of them were physicians (78.2%), while the remaining were nurses, midwives, and other health professionals (22%). Prevalence rates for moderate to extremely high stress, anxiety, and depressive symptoms were 21.9%, 13.0%, and 13.9%, respectively. The nurses reported significantly higher mean scores for depression and anxiety than the physicians (p < 0.05). Being married, having poor health, not exercising, and reporting “burnout” from work significantly predicted higher levels of depressive, anxiety, and stress symptoms among health workers (p < 0.05). Most HCWs (71.6%) reported a mild, moderate, or severe mental health burden, and certain factors predicted higher levels of such burden

FOXC2 promotes vasculogenic mimicry and resistance to anti-angiogenic therapy.

Vasculogenic mimicry (VM) describes the formation of pseudo blood vessels constructed of tumor cells that have acquired endothelial-like properties. VM channels endow the tumor with a tumor-derived vascular system that directly connects to host blood vessels, and their presence is generally associated with poor patient prognosis. Here we show that the transcription factor, Foxc2, promotes VM in diverse solid tumor types by driving ectopic expression of endothelial genes in tumor cells, a process that is stimulated by hypoxia. VM-proficient tumors are resistant to anti-angiogenic therapy, and suppression of Foxc2 augments response. This work establishes co-option of an embryonic endothelial transcription factor by tumor cells as a key mechanism driving VM proclivity and motivates the search for VM-inhibitory agents that could form the basis of combination therapies with anti-angiogenics

Exploration and analysis of molecularly annotated, 3D models of breast cancer at single-cell resolution using virtual reality

A set of increasingly powerful approaches are enabling spatially resolved measurements of growing numbers of molecular features in biological samples. While important insights can be derived from the two-dimensional data that many of these technologies generate, it is clear that extending these approaches into the third and fourth dimensions will magnify their impact. Realizing biological insights from datasets where thousands to millions of cells are annotated with tens to hundreds of parameters in space will require the development of new computational and visualization strategies. Here, we describe Theia, a virtual reality-based platform, which enables exploration and analysis of either volumetric or segmented, molecularly-annotated, three-dimensional datasets, with the option to extend the analysis to time-series data. We also describe our pipeline for generating annotated 3D models of breast cancer and supply several datasets to enable users to explore the utility of Theia for understanding cancer biology in three dimensions

Landscapes of cellular phenotypic diversity in breast cancer xenografts and their impact on drug response.

The heterogeneity of breast cancer plays a major role in drug response and resistance and has been extensively characterized at the genomic level. Here, a single-cell breast cancer mass cytometry (BCMC) panel is optimized to identify cell phenotypes and their oncogenic signalling states in a biobank of patient-derived tumour xenograft (PDTX) models representing the diversity of human breast cancer. The BCMC panel identifies 13 cellular phenotypes (11 human and 2 murine), associated with both breast cancer subtypes and specific genomic features. Pre-treatment cellular phenotypic composition is a determinant of response to anticancer therapies. Single-cell profiling also reveals drug-induced cellular phenotypic dynamics, unravelling previously unnoticed intra-tumour response diversity. The comprehensive view of the landscapes of cellular phenotypic heterogeneity in PDTXs uncovered by the BCMC panel, which is mirrored in primary human tumours, has profound implications for understanding and predicting therapy response and resistance