The evolution of SARS-CoV-2 seroprevalence in Canada: a time-series study, 2020-2023

BACKGROUND

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.

METHODS

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.

RESULTS

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.

INTERPRETATION

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.

Provincial variation in colorectal cancer screening adherence in Canada; evidence from the Canadian Partnership for Tomorrow's Health

Introduction

Although colorectal cancer (CRC) screening program is proven to reduce CRC incidence and mortality, understanding patterns and predictors of suboptimal adherence in screening program requires further investigation in Canada.

Methods

We used self-reported data from five regional cohorts of the Canadian Partnership for Tomorrow's Health (CanPath), namely the BC Generations Project (BCGP), Alberta's Tomorrow Project (ATP), the Ontario Health Study (OHS), Quebec's CARTaGENE, and the Atlantic Partnership for Tomorrow's Health Study (Atlantic PATH). We stratified participants into the following four risk categories: 1) age 50-74 years, 2) family history in a first-degree relative, 3) personal history of chronic inflammatory bowel disease and/or polyps, and 4) co-existence of personal risk and family history. Multivariable logistic regression was used to identify predictors of adherence to the screening guidelines.

Results

Adherence to CRC screening varied considerably between regions, ranging from 16.6% in CARTaGENE to 47.7% in OHS. Compared to the largest cohort OHS, the likelihood of non-adherence to CRC screening was significantly higher in BCGP (OR 1.15, 95% CI 1.11-1.19), the Atlantic PATH (OR 1.90, 95% CI 1.82-1.99) and CARTaGENE (OR 5.10, 95% CI 4.85-5.36). Low physical activity, current smoking, presence of personal risk, family history of CRC significantly reduced the likelihood of adherence to screening recommendations.

Discussion/conclusion

Compared to the national target of ≥ 60% for participation in CRC screening, adherence to regular CRC screening was suboptimal in this cohort of Canadians and varied by region. Further efforts are needed to identify the specific barriers to screening adherence in different provinces and across risk categories.

Abstract 2228: A multi-omic perspective of how selection shapes blood cancer risk phenotypes in aging populations

The genetic architecture of blood is highly complex with germline polymorphisms, somatic point mutations, and larger chromosomal alterations playing a role in shaping the fitness of the immune cells. Many advances have been made in understanding how the age associated acquisition of point mutations and somatic structural variants (SSVs) in blood, termed Age-Related Clonal Hematopoiesis (ARCH), predispose individuals to hematological cancer or cardiovascular disease. Yet, ARCH is commonly observed in healthy individuals and our ability to predict who is at risk of progressing to disease remains limited. A previous study which integrated deep learning and population genetics methods to evaluate the complex interplay of selection on point mutations in deeply sequenced blood samples highlighted the role that negative selection plays in prevention progression to hematological cancer. Here, we interrogate the mutational and selective pressures within blood to better understand how the full spectrum of somatic changes within an individual impact clonal fitness and disease outcomes. We evaluate how selection shapes the prevalence of large somatic structural variation in blood sampled from 15,910 individuals across over 20 different genetic ancestries, including the Thousand Genomes Study and the Canadian Partnership for Tomorrow’s Health study. Using dense genotyping arrays, we capture SSVs among 20 populations and find that ARCH attributed to somatic structural variation is three times as high as previously reported with up to 14% of individuals harbouring at least one large SSV in their blood. We estimate the rate at which SSVs accrue in blood cells and find that selection impacts the size and frequency of SSVs within individual blood populations. To determine the functional impact of clonal mutations on molecular phenotypes, we investigate the relationship between structural variation and the transcriptome. We show that gains, losses and copy number neutral variants impact gene expression distinctly, with stabilizing selection shaping the penetrance of copy number alterations in gene expression. Our work shows how different classes of selection shape clonal dynamics in blood thus enabling us to better understand why certain individuals are at a high risk of malignancy.

Citation Format: Kimberly Skead, David Soave, Marie-Julie Fave, Vanessa Bruat, Quaid Morris, Philip Awadalla. A multi-omic perspective of how selection shapes blood cancer risk phenotypes in aging populations [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 2228.

Abstract 3385: Pre-diagnosis plasma cell-free DNA methylation profiling reveals signatures of cancers up to7 years prior to clinical detection

Cancer survival rates are significantly improved when detected at early stages, particularly when the tumour is still localised to the tissue of origin. However, effective screening tools for early cancer detection is currently limited to a subset of cancer types. Profiling cell-free DNA (cfDNA) patterns has emerged as a prominent non-invasive biomarker for detection and subtyping of cancers. However, owing to difficulties in observing the early development of human malignancies as cancers are often detected once they become symptomatic, most cancer biomarker and evolution studies to date have primarily examined the genomics from solid tumour or liquid biopsies following a diagnosis. Utilizing cfDNA as a screening tool for early cancer detection requires profiling of blood plasma samples collected from asymptomatic individuals prior to the diagnosis of cancers to enable assessment of the earliest detectability and predictive performance of potential biomarkers. Here, we leverage the Canadian Partnership for Tomorrow’s Health Project (CanPaTH), to profile blood plasma collected prior to the clinical detection of underlying cancers. Specifically, we use cell-free methylated DNA immunoprecipitation and high-throughput sequencing (cfMeDIP-seq), a highly sensitive assay for profiling cfDNA methylomes, to profile over 300 blood plasma samples collected up to seven years prior to the detection of a breast, prostate or pancreatic cancer, in addition to matched controls with no history of cancer free through follow-up. We identified differentially methylated signatures in pre-diagnosis cfDNA that discriminated cancer-free controls from pre-diagnosis cancer cases up to seven years before diagnosis, and demonstrated that these markers were reflective of differentially methylated in cancer tissue relative to normal tissue and peripheral blood leukocytes. Further, predictive modelling reveals that cfDNA methylation markers in blood are predictive of pre-diagnosis breast cancer cases, achieving an average test AUROC of 0.75 (95% CI: 0.70 - 0.80). Predictive models trained solely with pre-diagnosis cfDNA methylation samples were also predictive of prostate and pancreatic cancer samples collected following diagnosis, achieving an average test AUROCs of 0.95 (95% CI: 0.93-0.96) and 0.96 (95% CI: 0.94-0.97) respectively. In our current studies, we focus specifically on breast, prostate and pancreatic cancer cases, and are extending this to further pan-cancer applications in subsequent investigations.

Citation Format: Nicholas Cheng, David Soave, Kimberly Skead, Tom Ouellette, Scott Bratman, Daniel De Carvalho, Philip Awadalla. Pre-diagnosis plasma cell-free DNA methylation profiling reveals signatures of cancers up to7 years prior to clinical detection [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 3385.

The politics of universal health coverage

The UN has declared universal health coverage an urgent global goal. Efforts to achieve this goal have been supported by rigorous research on the scientific, technical, and administrative aspects of health systems design. Yet a substantial portion of the world's population does not have access to essential health services. There is growing recognition that achieving universal health coverage is a political challenge. However, fundamental concepts from the political science discipline are often overlooked in the health literature. This Series paper draws on political science research to highlight the ways in which politics can facilitate, or stymie, policy reform. Specifically, we present a framework of analysis that explores how interests, ideas, and institutions shape universal health coverage. We then examine key considerations relating to the implementation of relevant policies. This Series paper shows that a political understanding of universal health coverage is needed to achieve health for all.

Interacting evolutionary pressures drive mutation dynamics and health outcomes in aging blood

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.

Abstract 2602: Leveraging cell-free methylome markers for early cancer detection

Cancer survival rates are significantly improved when detected at early stages, particularly when the tumor is still localized to the tissue of origin. However, effective screening tools for early cancer detection is currently limited to a subset of cancer types. The early development of human malignancies are difficult to observe as cancers are often detected once it becomes symptomatic, as such many cancer biomarker and evolution studies to date have primarily examined the genomics from solid tumor or liquid biopsies following a diagnosis. Investigating early tumor evolution in the pre-diagnosis context could allow us to better understand how to prevent or detect cancers in the earliest stage when survival rates are significantly higher, however this requires application of new technologies to biologics collected prior to a cancer diagnosis. Here, we leverage blood samples collected from participants in the Canadian Partnership for Tomorrow Project (CPTP), a longitudinal population cohort, prior to the onset of a cancer. Specifically, we utilize hybrid capture approaches to enrich for and characterize early mutations and methylation changes in circulating tumor DNA (ctDNA) of pre-cancer plasma samples collected from patients several months to years prior to clinical diagnosis. Here, we identify the earliest detectability of aberrant genetic and epigenetic events in ctDNA and describe the molecular evolution of these events at various stages prior to clinical detection of cancers. Further, we develop molecular biomarkers and implement machine learning tools to classify individuals with early cancers, and to develop risk scores from survival analyses predictive of cancer development up to 5 years prior to diagnosis. In our current study, we focus specifically on breast, prostate, lung and pancreatic cancer cases, and are extending this to pan-cancer applications in subsequent studies.

Citation Format: Nicholas Cheng, Kimberly Skead, David Soave, Jocelyn Meng, Elias Gbeha, Ilinca Lungu, Bernard Lam, Scott Bratman, Daniel De Carvalho, Philip Awadalla. Leveraging cell-free methylome markers for early cancer detection [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 2602.