Single-cell spatial landscapes of the lung tumour immune microenvironment.

Single-cell technologies have revealed the complexity of the tumour immune microenvironment with unparalleled resolution1-9. Most clinical strategies rely on histopathological stratification of tumour subtypes, yet the spatial context of single-cell phenotypes within these stratified subgroups is poorly understood. Here we apply imaging mass cytometry to characterize the tumour and immunological landscape of samples from 416 patients with lung adenocarcinoma across five histological patterns. We resolve more than 1.6 million cells, enabling spatial analysis of immune lineages and activation states with distinct clinical correlates, including survival. Using deep learning, we can predict with high accuracy those patients who will progress after surgery using a single 1-mm2 tumour core, which could be informative for clinical management following surgical resection. Our dataset represents a valuable resource for the non-small cell lung cancer research community and exemplifies the utility of spatial resolution within single-cell analyses. This study also highlights how artificial intelligence can improve our understanding of microenvironmental features that underlie cancer progression and may influence future clinical practice.

Next-generation sequencing of non-small cell lung cancer at a Quebec health care cancer centre.

BACKGROUND: Lung cancer is the leading cause of cancer death in both men and women. Quebec has the highest lung cancer mortality out of all provinces in Canada, believed to be caused by higher smoking rates. Molecular testing for lung cancer is standard of care due to the discovery of actionable driver mutations that can be targeted with tyrosine kinase inhibitors. To date, no detailed molecular testing characterization of Quebec patients with lung cancer using next generation sequencing (NGS) has been performed. MATERIALS AND METHODS: The aim of this study was to describe the genomic landscape of patients with lung cancer (n = 997) who underwent NGS molecular testing at a tertiary care center in Quebec and to correlate it with clinical and pathology variables. RESULTS: Compared to 10 other NGS studies found through a structured search strategy, our cohort had a higher prevalence of KRAS mutations (39.2%) compared to most geographical locations. Additionally, we observed a significant positive association between decreasing age and a higher proportion of KRAS G12C mutations. CONCLUSION: Overall, it remains important to assess institutional rates of actionable driver mutations to help guide governing bodies, fuel clinical trials and create benchmarks for expected rates as quality metrics.

Arterial stiffness measurements in pregnancy as a predictive tool for hypertensive disorders of pregnancy and preeclampsia: Protocol for a systematic review and meta-analysis.

Hypertensive disorders of pregnancy (HDPs) are a leading cause of maternal morbidity and mortality worldwide. Unfortunately, accurate early clinical screening methods for the development of these disorders are lacking. Arterial stiffness (AS) is an important hemodynamic indicator of vascular health that has shown promising results for the prediction of HDP onset. Past systematic reviews in the field have reported an increase in AS indices in women who develop HDPs and have highlighted the potential of AS measurements as a predictive tool early in pregnancy. The most recent systematic review, including papers up to 2015, assessed the differences in AS parameters between women with and without pregnancy complications. Since then, there has been a substantial influx of published research on the topic and a growing interest in the incorporation of AS measurements into clinical practice. Thus, we propose a systematic review and meta-analysis that is more inclusive to all HDP subsets and various hemodynamic indices of vascular health to provide a comprehensive overview of the current state of evidence. Specifically, we aim to evaluate these measures in women who develop HDPs compared to normotensive pregnancies to determine which measures are most associated with and/or can predict the development of HDPs. Major databases (Medline, Embase, The Cochrane Library, Web of Science, PubMed, and CINAHL), grey literature (Google Scholar) and clinical trials (clinicaltrials.gov) will be searched to identify studies that report AS and hemodynamic measurements in pregnant women with and without HDPs. No restrictions will be made on study type or year. Articles will be independently evaluated by three authors to determine eligibility based on inclusion and exclusion criteria. Methodological quality of included studies will be assessed. Pooled analyses will be conducted using a random-effects model. Publication bias and between-study heterogeneity will also be assessed. Sources of heterogeneity will be explored by sensitivity, subgroup, and/or meta-regression analyses. Results from this study will be shared through scientific conferences and publications in scientific journals. The analysis of potential AS and hemodynamic markers for HDP onset will help inform the development of screening guidelines and clinically relevant cut-off values of AS and hemodynamic markers for HDP risk, guiding future research. There are no applicable ethical considerations to the writing of this protocol.