Laboratory-based evaluation of legionellosis epidemiology in Ontario, Canada, 1978 to 2006

BACKGROUND: Legionellosis is a common cause of severe community acquired pneumonia and respiratory disease outbreaks. The Ontario Public Health Laboratory (OPHL) has conducted most testing for Legionella species in the Canadian province of Ontario since 1978, and represents a multi-decade repository of population-based data on legionellosis epidemiology. We sought to provide a laboratory-based review of the epidemiology of legionellosis in Ontario over the past 3 decades, with a focus on changing rates of disease and species associated with legionellosis during that time period. METHODS: We analyzed cases that were submitted and tested positive for legionellosis from 1978 to 2006 using Poisson regression models incorporating temporal, spatial, and demographic covariates. Predictors of infection with culture-confirmed L. pneumophila serogroup 1 (LP1) were evaluated with logistic regression models. Results: 1,401 cases of legionellosis tested positive from 1978 to 2006. As in other studies, we found a late summer to early autumn seasonality in disease occurrence with disease risk increasing with age and in males. In contrast to other studies, we found a decreasing trend in cases in the recent decade (IRR 0.93, 95% CI 0.91 to 0.95, P-value = 0.001); only 66% of culture-confirmed isolates were found to be LP1. CONCLUSION: Despite similarities with disease epidemiology in other regions, legionellosis appears to have declined in the past decade in Ontario, in contrast to trends observed in the United States and parts of Europe. Furthermore, a different range of Legionella species is responsible for illness, suggesting a distinctive legionellosis epidemiology in this North American region

Targeting DNA Damage Response and Replication Stress in Pancreatic Cancer

Background and aims: Continuing recalcitrance to therapy cements pancreatic cancer (PC) as the most lethal malignancy, which is set to become the second leading cause of cancer death in our society. The study aim was to investigate the association between DNA damage response (DDR), replication stress and novel therapeutic response in PC to develop a biomarker driven therapeutic strategy targeting DDR and replication stress in PC. Methods: We interrogated the transcriptome, genome, proteome and functional characteristics of 61 novel PC patient-derived cell lines to define novel therapeutic strategies targeting DDR and replication stress. Validation was done in patient derived xenografts and human PC organoids. Results: Patient-derived cell lines faithfully recapitulate the epithelial component of pancreatic tumors including previously described molecular subtypes. Biomarkers of DDR deficiency, including a novel signature of homologous recombination deficiency, co-segregates with response to platinum (P < 0.001) and PARP inhibitor therapy (P < 0.001) in vitro and in vivo. We generated a novel signature of replication stress with which predicts response to ATR (P < 0.018) and WEE1 inhibitor (P < 0.029) treatment in both cell lines and human PC organoids. Replication stress was enriched in the squamous subtype of PC (P < 0.001) but not associated with DDR deficiency. Conclusions: Replication stress and DDR deficiency are independent of each other, creating opportunities for therapy in DDR proficient PC, and post-platinum therapy

Inverting the model of genomics data sharing with the NHGRI Genomic Data Science Analysis, Visualization, and Informatics Lab-space

The NHGRI Genomic Data Science Analysis, Visualization, and Informatics Lab-space (AnVIL; https://anvilproject.org) was developed to address a widespread community need for a unified computing environment for genomics data storage, management, and analysis. In this perspective, we present AnVIL, describe its ecosystem and interoperability with other platforms, and highlight how this platform and associated initiatives contribute to improved genomic data sharing efforts. The AnVIL is a federated cloud platform designed to manage and store genomics and related data, enable population-scale analysis, and facilitate collaboration through the sharing of data, code, and analysis results. By inverting the traditional model of data sharing, the AnVIL eliminates the need for data movement while also adding security measures for active threat detection and monitoring and provides scalable, shared computing resources for any researcher. We describe the core data management and analysis components of the AnVIL, which currently consists of Terra, Gen3, Galaxy, RStudio/Bioconductor, Dockstore, and Jupyter, and describe several flagship genomics datasets available within the AnVIL. We continue to extend and innovate the AnVIL ecosystem by implementing new capabilities, including mechanisms for interoperability and responsible data sharing, while streamlining access management. The AnVIL opens many new opportunities for analysis, collaboration, and data sharing that are needed to drive research and to make discoveries through the joint analysis of hundreds of thousands to millions of genomes along with associated clinical and molecular data types