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.

Comprehensive Assessment of PD-L1 Staining Heterogeneity in Pulmonary Adenocarcinomas Using Tissue Microarrays: Impact of the Architecture Pattern and the Number of Cores.

Checkpoint inhibitors directed against programmed death receptor 1 (PD-1) and its ligand (PD-L1) changed the treatment of advanced lung non-small cell carcinomas. The decision to treat patients is influenced by PD-L1 expression by tumor cells, but evidence indicates that this staining is heterogenous within a tumor. As PD-L1 staining is tested mostly on biopsies, false negative results can occur due to sampling issues. The clinical impact of this heterogeneity has not been established. We selected 241 patients who underwent pulmonary resection for adenocarcinoma. Tissue microarrays were constructed with five 1 mm cores representative of the histologic patterns observed in each tumor and stained for PD-L1. For each core, the histologic pattern and the percentage of PD-L1 positive tumor cells were noted. Staining heterogeneity was defined as cases with both positive and negative cores at positivity thresholds of 1%, 10%, and 50% of tumor cells. At the 50% cut-off, 37.8% of patients were PD-L1 positive, whereas 22.4% showed staining heterogeneity. Among patients with 1 negative core, 26.5% also had a positive core and could have been misclassified based on 1 biopsy. Mean staining of PD-L1 was higher in solid (47.9%) and micropapillary (24.2%) patterns and was lower in acinar (14.1%), papillary (3.4%), and lepidic (6.4%) architectures. A significant proportion of patients presented a heterogenous staining for PD-L1. A total of 26.5% of patients negative on 1 core turned out to be positive on another core, which raises the consideration of rebiopsy, in particular when lepidic, acinar, or papillary patterns are observed on a biopsy.