MSc in Clinical Embryology
The repertoire of serous ovarian cancer non-genetic heterogeneity revealed by single-cell sequencing of normal fallopian tube epithelial cells
SummaryThe inter-differentiation between cell states promotes cancer cell survival under stress and fosters non-genetic heterogeneity (NGH). NGH is, therefore, a surrogate of tumor resilience but its quantification is confounded by genetic heterogeneity. Here we show that NGH can be accurately measured when informed by the molecular signatures of the normal cells of origin. We surveyed the transcriptomes of ∼ 4000 normal fallopian tube epithelial (FTE) cells, the cells of origin of serous ovarian cancer (SOC), and identified six FTE subtypes. We used subtype signatures to deconvolute SOC expression data and found substantial intra-tumor NGH that was previously unrecognized. Importantly, NGH-based stratification of ∼1700 tumors robustly predicted survival. Our findings lay the foundation for accurate prognostic and therapeutic stratification of SOC.HighlightsThe projection of FTE subtypes refines the molecular classification of serous OCComprehensive single-cell profiling of FTE cells identifies 6 molecular subtypesSubstantial non-genetic heterogeneity of HGSOC identified in 1700 tumorsA mesenchymal-high HGSOC subtype is robustly correlated with poor prognosis
The remaining unknowns: a mixed methods study of the current and global health research priorities for COVID-19.
INTRODUCTION:In March 2020, the WHO released a Global Research Roadmap in an effort to coordinate and accelerate the global research response to combat COVID-19 based on deliberations of 400 experts across the world. Three months on, the disease and our understanding have both evolved significantly. As we now tackle a pandemic in very different contexts and with increased knowledge, we sought to build on the work of the WHO to gain a more current and global perspective on these initial priorities. METHODS:We undertook a mixed methods study seeking the views of the global research community to (1) assess which of the early WHO roadmap priorities are still most pressing; (2) understand whether they are still valid in different settings, regions or countries; and (3) identify any new emerging priorities. RESULTS:Thematic analysis of the significant body of combined data shows the WHO roadmap is globally relevant; however, new important priorities have emerged, in particular, pertinent to low and lower middle-income countries (less resourced countries), where health systems are under significant competing pressures. We also found a shift from prioritising vaccine and therapeutic development towards a focus on assessing the effectiveness, risks, benefits and trust in the variety of public health interventions and measures. Our findings also provide insight into temporal nature of these research priorities, highlighting the urgency of research that can only be undertaken within the period of virus transmission, as well as other important research questions but which can be answered outside the transmission period. Both types of studies are key to help combat this pandemic but also importantly to ensure we are better prepared for the future. CONCLUSION:We hope these findings will help guide decision-making across the broad research system including the multilateral partners, research funders, public health practitioners, clinicians and civil society.
A highly accurate platform for clone-specific mutation discovery enables the study of active mutational processes
Bulk whole genome sequencing (WGS) enables the analysis of tumor evolution but, because of depth limitations, can only identify old mutational events. The discovery of current mutational processes for predicting the tumor’s evolutionary trajectory requires dense sequencing of individual clones or single cells. Such studies, however, are inherently problematic because of the discovery of excessive false positive (FP) mutations when sequencing picogram quantities of DNA. Data pooling to increase the confidence in the discovered mutations, moves the discovery back in the past to a common ancestor. Here we report a robust WGS and analysis pipeline (DigiPico/MutLX) that virtually eliminates all F results while retaining an excellent proportion of true positives. Using our method, we identified, for the first time, a hyper-mutation (kataegis) event in a group of ∼30 cancer cells from a recurrent ovarian carcinoma. This was unidentifiable from the bulk WGS data. Overall, we propose DigiPico/MutLX method as a powerful framework for the identification of clone-specific variants at an unprecedented accuracy.
Reallocating time from device-measured sleep, sedentary behaviour or light physical activity to moderate-to-vigorous physical activity is associated with lower cardiovascular disease risk
AbstractBackgroundModerate-to-vigorous physical activity (MVPA), light physical activity, sedentary behaviour and sleep have all been associated with cardiovascular disease (CVD). Due to challenges in measuring and analysing movement behaviours, there is uncertainty about how the association with incident CVD varies with the time spent in these different movement behaviours.MethodsWe developed a machine-learning model (Random Forest smoothed by a Hidden Markov model) to classify sleep, sedentary behaviour, light physical activity and MVPA from accelerometer data. The model was developed using data from a free-living study of 152 participants who wore an Axivity AX3 accelerometer on the wrist while also wearing a camera and completing a time use diary. Participants in UK Biobank, a prospective cohort study, were asked to wear an accelerometer (of the same type) for seven days, and we applied our machine-learning model to classify their movement behaviours. Using Compositional Data Analysis Cox regression, we investigated how reallocating time between movement behaviours was associated with CVD incidence.FindingsWe classified accelerometer data as sleep, sedentary behaviour, light physical activity or MVPA with a mean accuracy of 88% (95% CI: 87, 89) and Cohen’s kappa of 0·80 (95% CI: 0·79, 0·82). Among 87,509 UK Biobank participants, there were 3,424 incident CVD events. Reallocating time from any behaviour to MVPA, or reallocating time from sedentary behaviour to any behaviour, was associated with a lower risk of CVD. For example, for a hypothetical average individual, reallocating 20 minutes/day to MVPA from all other behaviours proportionally was associated with 9% (7%, 10%) lower risk of incident CVD, while reallocating 1 hour/day to sedentary behaviour was associated with 5% (3%, 7%) higher risk.InterpretationReallocating time from light physical activity, sedentary behaviour or sleep to MVPA, or reallocating time from sedentary behaviour to other behaviours, was associated with lower risk of incident CVD. Accurate classification of movement behaviours using machine-learning and statistical methods to address the compositional nature of movement behaviours enabled these insights. Public health interventions and guidelines should promote reallocating time to MVPA from other behaviours, as well as reallocating time from sedentary behaviour to light physical activity.FundingMedical Research Council.