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.

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.