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Single-cell RNA sequencing can reveal valuable insights into cellular heterogeneity within tumour microenvironments (TMEs), paving the way for a deep understanding of cellular mechanisms contributing to cancer. However, high heterogeneity among the same cancer types and low transcriptomic variation in immune cell subsets present challenges for accurate, high-resolution confirmation of cells’ identities. Here we present scATOMIC; a modular annotation tool for malignant and non-malignant cells. We trained scATOMIC on >300,000 cancer, immune, and stromal cells defining a pan-cancer reference across 19 common cancers and employ a hierarchical approach, outperforming current classification methods. We extensively confirm scATOMIC’s accuracy on 225 tumour biopsies encompassing >350,000 cancer and a variety of TME cells. Lastly, we demonstrate scATOMIC’s practical significance to accurately subset breast cancers into clinically relevant subtypes and predict tumours’ primary origin across metastatic cancers. Our approach represents a broadly applicable strategy to analyse multicellular cancer TMEs. The accuracy and granularity of classifying cell types in the tumour microenvironment (TME) from single-cell RNA-seq data is impacted by heterogeneity among cancer cells and similarities among functionally related immune cells. Here, the authors develop scATOMIC, a tumour and TME cell type classifier based on a hierarchical approach that can be applied to pan-cancer datasets.

Understanding the complex interaction of risk factors that increase the likelihood of developing common diseases is challenging. The Canadian Partnership for Tomorrow Project (CPTP) is a prospective cohort study created as a population-health research platform for assessing the effect of genetics, behaviour, family health history and environment (among other factors) on chronic diseases. Volunteer participants were recruited from the general Canadian population for a confederation of 5 regional cohorts. Participants were enrolled in the study and core information obtained using 2 approaches: attendance at a study assessment centre for all study measures (questionnaire, venous blood sample and physical measurements) or completion of the core questionnaire (online or paper), with later collection of other study measures where possible. Physical measurements included height, weight, percentage body fat and blood pressure. Participants consented to passive follow-up through linkage with administrative health databases and active follow-up through recontact. All participant data across the 5 regional cohorts were harmonized. A total of 307 017 participants aged 30–74 from 8 provinces were recruited. More than half provided a venous blood sample and/or other biological sample, and 33% completed physical measurements. A total of 709 harmonized variables were created; almost 25% are available for all participants and 60% for at least 220 000 participants. Primary recruitment for the CPTP is complete, and data and biosamples are available to Canadian and international researchers through a data-access process. The CPTP will support research into how modifiable risk factors, genetics and the environment interact to affect the development of cancer and other chronic diseases, ultimately contributing evidence to reduce the global burden of chronic disease.

Uncovering the interaction between genomes and the environment is a principal challenge of modern genomics and preventive medicine. While theoretical models are well defined, little is known of the G × E interactions in humans. We used an integrative approach to comprehensively assess the interactions between 1.6 million data points, encompassing a range of environmental exposures, health, and gene expression levels, coupled with whole-genome genetic variation. From ∼1000 individuals of a founder population in Quebec, we reveal a substantial impact of the environment on the transcriptome and clinical endophenotypes, overpowering that of genetic ancestry. Air pollution impacts gene expression and pathways affecting cardio-metabolic and respiratory traits, when controlling for genetic ancestry. Finally, we capture four expression quantitative trait loci that interact with the environment (air pollution). Our findings demonstrate how the local environment directly affects disease risk phenotypes and that genetic variation, including less common variants, can modulate individual’s response to environmental challenges. Individuals with different genotypes may respond differently to environmental variation. Here, Favé et al. find substantial impacts of different environment exposures on the transcriptome and clinical endophenotypes when controlling for genetic ancestry by analyzing data from ∼1000 individuals from a founder population in Quebec.

Key Points The frequency and rate of genetic recombination in several species, specifically in microbes and pathogens, is unknown. For pathogens, the rate at which populations recombine can help to explain the dynamics of drug resistance and pathogenicity. Furthermore, recombination is necessary for genetic mapping and for the ability of population genetic studies to locate genes that underlie important phenotypes (for example, genes that are associated with virulence, transmission and immune evasion). Finally, although almost all organisms engage in some form of recombination, our understanding of why recombination occurs and is maintained remains controversial. Recombination allows genomic sites or regions to have different evolutionary histories. As a result, the presence of recombination complicates phylogenetic reconstruction and several phylogenetic methods that are used to infer population parameters. Several non-parametric methods are available to detect and estimate recombination in systems that do not conform to standard assumptions, such as having constant population size and an infinite number of sites. Many of these methods have successfully revealed the action of recombination in several viruses, and in bacterial and protozoan species. Unlike non-parametric methods, model-based approaches allow the population recombination rate to be inferred. Model-based estimates of the population recombination rate seem to be consistent with experimental estimates, at least in bacteria. Although species certainly vary, there seem to be some phylogenetic consistencies between recombination rates, relative to the population mutation rates, across broad phylogenetic groupings of taxa. The rates of recombination are often substantial and are correlated with life history, such as endemicity in a population. It is reasonable to suggest that recombination has an active role in the life history and fitness of many pathogens. A pressing problem in studying the evolution of microbial pathogens is to determine the extent to which these genomes recombine. This information is essential for locating pathogenicity loci by using association studies or population genetic approaches. Recombination also complicates the use of phylogenetic approaches to estimate evolutionary parameters such as selection pressures. Reliable methods that detect and estimate the rate of recombination are, therefore, vital. This article reviews the approaches that are available for detecting and estimating recombination in microbial pathogens and how they can be used to understand pathogen evolution and to identify medically relevant loci.

Metastatic prostate cancer remains a major clinical challenge and metastatic lesions are highly heterogeneous and difficult to biopsy. Liquid biopsy provides opportunities to gain insights into the underlying biology. Here, using the highly sensitive enrichment-based sequencing technology, we provide analysis of 60 and 175 plasma DNA methylomes from patients with localized and metastatic prostate cancer, respectively. We show that the cell-free DNA methylome can capture variations beyond the tumor. A global hypermethylation in metastatic samples is observed, coupled with hypomethylation in the pericentromeric regions. Hypermethylation at the promoter of a glucocorticoid receptor gene NR3C1 is associated with a decreased immune signature. The cell-free DNA methylome is reflective of clinical outcomes and can distinguish different disease types with 0.989 prediction accuracy. Finally, we show the ability of predicting copy number alterations from the data, providing opportunities for joint genetic and epigenetic analysis on limited biological samples. Metastatic prostate cancer can be difficult to biopsy and characterise. Here, the authors use cell-free DNA methylation analysis to illustrate changes in hypermethylation in metastatic disease.

During the first year of the COVID-19 pandemic, the proportion of reported cases of COVID-19 among Canadians was under 6%. Although high vaccine coverage was achieved in Canada by fall 2021, the Omicron variant caused unprecedented numbers of infections, overwhelming testing capacity and making it difficult to quantify the trajectory of population immunity. Using a time-series approach and data from more than 900 000 samples collected by 7 research studies collaborating with the COVID-19 Immunity Task Force (CITF), we estimated trends in SARS-CoV-2 seroprevalence owing to infection and vaccination for the Canadian population over 3 intervals: prevaccination (March to November 2020), vaccine roll-out (December 2020 to November 2021), and the arrival of the Omicron variant (December 2021 to March 2023). We also estimated seroprevalence by geographical region and age. By November 2021, 9.0% (95% credible interval [CrI] 7.3%–11%) of people in Canada had humoral immunity to SARS-CoV-2 from an infection. Seroprevalence increased rapidly after the arrival of the Omicron variant — by Mar. 15, 2023, 76% (95% CrI 74%–79%) of the population had detectable antibodies from infections. The rapid rise in infection-induced antibodies occurred across Canada and was most pronounced in younger age groups and in the Western provinces: Manitoba, Saskatchewan, Alberta and British Columbia. Data up to March 2023 indicate that most people in Canada had acquired antibodies against SARS-CoV-2 through natural infection and vaccination. However, given variations in population seropositivity by age and geography, the potential for waning antibody levels, and new variants that may escape immunity, public health policy and clinical decisions should be tailored to local patterns of population immunity.

Variant allele frequencies (VAF) encode ongoing evolution and subclonal selection in growing tumours. However, existing methods that utilize VAF information for cancer evolutionary inference are compressive, slow, or incorrectly specify the underlying cancer evolutionary dynamics. Here, we provide a proof-of-principle synthetic supervised learning method, TumE, that integrates simulated models of cancer evolution with Bayesian neural networks, to infer ongoing selection in bulk-sequenced single tumour biopsies. Analyses in synthetic and patient tumours show that TumE significantly improves both accuracy and inference time per sample when detecting positive selection, deconvoluting selected subclonal populations, and estimating subclone frequency. Importantly, we show how transfer learning can leverage stored knowledge within TumE models for related evolutionary inference tasks—substantially reducing data and computational time for further model development and providing a library of recyclable deep learning models for the cancer evolution community. This extensible framework provides a foundation and future directions for harnessing progressive computational methods for the benefit of cancer genomics and, in turn, the cancer patient.

Age-related clonal hematopoiesis (ARCH) is characterized by age-associated accumulation of somatic mutations in hematopoietic stem cells (HSCs) or their pluripotent descendants. HSCs harboring driver mutations will be positively selected and cells carrying these mutations will rise in frequency. While ARCH is a known risk factor for blood malignancies, such as Acute Myeloid Leukemia (AML), why some people who harbor ARCH driver mutations do not progress to AML remains unclear. Here, we model the interaction of positive and negative selection in deeply sequenced blood samples from individuals who subsequently progressed to AML, compared to healthy controls, using deep learning and population genetics. Our modeling allows us to discriminate amongst evolutionary classes with high accuracy and captures signatures of purifying selection in most individuals. Purifying selection, acting on benign or mildly damaging passenger mutations, appears to play a critical role in preventing disease-predisposing clones from rising to dominance and is associated with longer disease-free survival. Through exploring a range of evolutionary models, we show how different classes of selection shape clonal dynamics and health outcomes thus enabling us to better identify individuals at a high risk of malignancy. Age-related clonal hematopoiesis is associated with risk for diseases like acute myeloid leukemia (AML), yet it is unclear why some individuals do not progress despite having AML driver mutations. Here, the authors use deep learning and population genetics models to investigate how the interplay of positive and negative selection influences AML progression.

PurposeTo address emerging nutritional epidemiological research questions, data from contemporary cohorts are needed. CARTaGENE is the largest ongoing prospective cohort study of men and women in Québec, Canada. Dietary information was collected making it a rich resource for the exploration of diet in the aetiology of many health outcomes.ParticipantsCARTaGENE recruited over 43 000 men and women aged 40–69 in two phases (A and B). In phase A, a total of 19 784 men and women were enrolled between 2009 and 2010. In 2011–2012, phase A participants of CARTaGENE were recontacted and invited to complete the self-administered Canadian Diet History Questionnaire II, which assessed usual intake over the past 12 months of a comprehensive array of foods, beverages and supplements; 9379 participants with non-missing age and sex data and with plausible total energy intake comprise the CARTaGENE Cohort Nutrition Study (4212 men; 5167 women).Findings to dateAvailable dietary data include intake of total energy, macronutrients and micronutrients, food group equivalents and a measure of diet quality based on the Canadian Healthy Eating Index 2005 (C-HEI 2005). Intake and diet quality varied among participants though they generally met the recommended dietary reference intakes for most nutrients. The mean C-HEI 2005 score was 61.5 (SD=14.0; max score=100), comparable to the general Canadian population. The mean (SD) scores for men and women separately were 57.0 (14.1) and 65.2 (12.8), respectively. C-HEI scores were higher for never smokers (61.6), those who had attained more than a high school education (61.4) and those with high physical activity (60.4) compared with current smokers (55.8), less than high school education level (56.2) and low physical activity (57.6), respectively (p values<0.01).Future plansThe CARTaGENE Cohort Nutrition Study is an additional resource of the CARTaGENE platform and is available internationally to examine research questions related to diet and health among contemporary populations. Starting in 2024, annual diet assessments using two 24-hour dietary recalls over a 30-day period will take place, further expanding the cohort as a resource for dietary research.

Despite the growing utility of cardiovascular magnetic resonance (CMR) for cardiac morphology and function, sex and age-specific normal reference values derived from large, multi-ethnic data sets are lacking. Furthermore, most available studies use a simplified tracing methodology. Using a large cohort of participants without history of cardiovascular disease (CVD) or risk factors from the Canadian Alliance for Healthy Heart and Minds, we sought to establish a robust set of reference values for ventricular and atrial parameters using an anatomically correct contouring method, and to determine the influence of age and sex on ventricular parameters. Participants (n = 3206, 65% females; age 55.2 ± 8.4 years for females and 55.1 ± 8.8 years for men) underwent CMR using standard methods for quantitative measurements of cardiac parameters. Normal ventricular and atrial reference values are provided: (1) for males and females, (2) stratified by four age categories, and (3) for different races/ethnicities. Values are reported as absolute, indexed to body surface area, or height. Ventricular volumes and mass were significantly larger for males than females (p < 0.001). Ventricular ejection fraction was significantly diminished in males as compared to females (p < 0.001). Indexed left ventricular (LV) end-systolic, end-diastolic volumes, mass and right ventricular (RV) parameters significantly decreased as age increased for both sexes (p < 0.001). For females, but not men, mean LV and RVEF significantly increased with age (p < 0.001). Using anatomically correct contouring methodology, we provide accurate sex and age-specific normal reference values for CMR parameters derived from the largest, multi-ethnic population free of CVD to date. ClinicalTrials.gov, NCT02220582. Registered 20 August 2014—Retrospectively registered, https://clinicaltrials.gov/ct2/show/NCT02220582.