Émilie Pichette

Abstract Single-cell technologies have revealed the complexity of the tumour immune microenvironment with unparalleled resolution 1–9 . 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-mm 2 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.

Rare diseases are collectively common, and thus very likely to be encountered in clinical practice. However, due in large part to deficits in medical training specific to these conditions, rare disease patients all-too often find themselves facing inadequate care. We are medical students representing institutions from the United States and Canada who believe that trainees can drive change in the landscape of rare disease care. In addition to highlighting a need for medical education to inculcate the knowledge and skills to effectively care for rare disease patients, we describe our efforts including a combination of peer-assisted learning, patient-oriented outreach, and interprofessional collaboration, which are intended to improve awareness of rare disease among future medical professionals.