Assessing the tumor immune landscape across multiple spatial scales to differentiate immunotherapy response in metastatic non-small cell lung cancer

While immune checkpoint inhibitor-based (ICI) therapy has shown promising results in non-small cell lung cancer (NSCLC) patients with high programmed death ligand 1 (PD-L1) expression, not all patients respond to therapy. The tumor microenvironment (TME) is complex and heterogeneous, making it challenging to understand the key agents and features which influence response to therapies. In this study, we leverage multiplex fluorescent immunohistochemistry (mfIHC) to quantitatively assess interactions between tumor and immune cells in an effort to identify patterns occurring at multiple spatial levels of the TME. To do so, we introduce several computational methods novel to a dataset of 1,269 mfIHC images from a cohort of 52 patients with metastatic NSCLC. With the spatial G-cross function, we quantify the degree of cell interaction at an entire image level, where we see significantly increased activity of cytotoxic T-cells (CTLs) and helper T-cells (HTLs) with epithelial tumor cells (ECs) in responders to ICI (p = .022 and p < .001, respectively), and decreased activity of T-regulatory cells (Tregs) with ECs compared to non-responders (p = .010). By leveraging spatial overlap methods, we define tumor subregions (which we call the tumor "periphery", "edge" and "center") and discover more localized immune-immune interactions influencing positive response, including those between CTLs and HTLs with antigen presenting cells (APCs) in these subregions specifically. Lastly, we trained an interpretable deep learning model which identified key cellular regions of interest that most influenced response classification (AUC = 0.71±0.02). Assessing spatial interactions within these subregions further revealed new insights not significant at the whole image level, particularly the elevated association of APCs and Tregs with one another in responder groups (p = 0.024). Altogether, we demonstrate that elucidating patterns of cell composition and interplay across multiple levels of spatial analyses can improve our understanding of the TME and better differentiate patient responses to immunotherapy.