Morteza Rezanejad

Abstract Single-cell technologies have enabled the characterization of the tumour microenvironment at unprecedented depth and have revealed vast cellular diversity among tumour cells and their niche. Anti-tumour immunity relies on cell–cell relationships within the tumour microenvironment 1,2 , yet many single-cell studies lack spatial context and rely on dissociated tissues 3 . Here we applied imaging mass cytometry to characterize the immunological landscape of 139 high-grade glioma and 46 brain metastasis tumours from patients. Single-cell analysis of more than 1.1 million cells across 389 high-dimensional histopathology images enabled the spatial resolution of immune lineages and activation states, revealing differences in immune landscapes between primary tumours and brain metastases from diverse solid cancers. These analyses revealed cellular neighbourhoods associated with survival in patients with glioblastoma, which we leveraged to identify a unique population of myeloperoxidase (MPO)-positive macrophages associated with long-term survival. Our findings provide insight into the biology of primary and metastatic brain tumours, reinforcing the value of integrating spatial resolution to single-cell datasets to dissect the microenvironmental contexture of cancer.

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.

BACKGROUND: Immunotherapy has revolutionized clinical outcomes for patients suffering from lung cancer, yet relatively few patients sustain long-term durable responses. Recent studies have demonstrated that the tumor immune microenvironment fosters tumorous heterogeneity and mediates both disease progression and response to immune checkpoint inhibitors (ICI). As such, there is an unmet need to elucidate the spatially defined single-cell landscape of the lung cancer microenvironment to understand the mechanisms of disease progression and identify biomarkers of response to ICI. METHODS: Here, in this study, we applied imaging mass cytometry to characterize the tumor and immunological landscape of immunotherapy response in non-small cell lung cancer by describing activated cell states, cellular interactions and neighborhoods associated with improved efficacy. We functionally validated our findings using preclinical mouse models of cancer treated with anti-programmed cell death protein-1 (PD-1) immune checkpoint blockade. RESULTS: We resolved 114,524 single cells in 27 patients treated with ICI, enabling spatial resolution of immune lineages and activation states with distinct clinical outcomes. We demonstrated that CXCL13 expression is associated with ICI efficacy in patients, and that recombinant CXCL13 potentiates anti-PD-1 response in vivo in association with increased antigen experienced T cell subsets and reduced CCR2+ monocytes. DISCUSSION: Our results provide a high-resolution molecular resource and illustrate the importance of major immune lineages as well as their functional substates in understanding the role of the tumor immune microenvironment in response to ICIs.

Lung cancer is the leading cause of cancer-related deaths. An enhanced understanding of the immune microenvironments within these tumours may foster more precise and efficient treatment, particularly for immune-targeted therapies. The spatial architectural differences between lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC) are relatively unexplored. Here, we applied imaging mass cytometry to a balanced cohort of LUAD and LUSC patients, matched for clinical factors such as age, sex, and smoking history, to analyze 204 histopathology images of tumours from 102 individuals with non-small cell lung cancer (NSCLC). By analyzing interactions and broader cellular networks, we interrogate the tumour microenvironment to understand how immune cells are spatially organized in clinically matched adenocarcinoma and squamous cell carcinoma subsets. This spatial analysis revealed distinct patterns of immune cell aggregation, particularly among macrophage populations, that correlated with patient prognosis differentially in adenocarcinoma and squamous cell carcinoma, suggesting potential new strategies for therapeutic intervention. Our findings underscore the importance of analyzing NSCLC histological subtypes separately when investigating the spatial immune landscape, as microenvironmental characteristics and cellular interactions differed by subtype. Recognizing these distinctions is essential for designing precision therapies tailored to each subtype’s unique immune architecture, ultimately enhancing patient outcomes. The spatial architecture of the lung cancer microenvironment could influence the survival outlook and therapeutic options of patients. Here the authors find distinct patterns and composition of immune cells when comparing lung adenocarcinomas and lung squamous cell carcinomas, paving way for precision immune therapy according to histological subtype.