Immune cellular patterns of distribution affect outcomes of patients with non-small cell lung cancer

Studying the cellular geographic distribution in non-small cell lung cancer is essential to understand the roles of cell populations in this type of tumor. In this study, we characterize the spatial cellular distribution of immune cell populations using 23 makers placed in five multiplex immunofluorescence panels and their associations with clinicopathologic variables and outcomes. Our results demonstrate two cellular distribution patterns-an unmixed pattern mostly related to immunoprotective cells and a mixed pattern mostly related to immunosuppressive cells. Distance analysis shows that T-cells expressing immune checkpoints are closer to malignant cells than other cells. Combining the cellular distribution patterns with cellular distances, we can identify four groups related to inflamed and not-inflamed tumors. Cellular distribution patterns and distance are associated with survival in univariate and multivariable analyses. Spatial distribution is a tool to better understand the tumor microenvironment, predict outcomes, and may can help select therapeutic interventions. The spatial distribution of cellular compartments within the tumour microenvironment in non-small cell lung cancer (NSCLC) remains to be investigated. Here, the authors identify distinct cell populations of tumour cells and tumour-associated immune cell phenotypes with different spatial distributions in NSCLC.

Automated summary

Studying the cellular geographic distribution in non-small cell lung cancer is crucial for understanding the roles of cell populations in this tumor. This study characterizes the spatial cellular distribution of immune cell populations and their associations with clinicopathologic variables and outcomes. The results show two cellular distribution patterns related to immunoprotective and immunosuppressive cells, respectively, and reveal the proximity of immune checkpoint-expressing T-cells to malignant cells. The combination of cellular distribution patterns and distances can identify different tumor groups and predict survival.