GraphTME: a framework for predicting response to immune checkpoint inhibitors by interpreting cell-cell interactions in the tumour microenvironment using spatial transcriptomics of tumour tissue

Immune checkpoint inhibitors (ICIs) have been used to treat cancer by reactivating T-cell responses against tumours, yet clinical efficacy remains limited due to low response rates and the lack of robust predictive biomarkers. The tumour microenvironment critically shapes ICI responsiveness by modulating antitumour immunity, necessitating a deeper understanding of spatially organised cell-cell interactions. Imaging-based spatial transcriptomics (ST) enables such analysis at single-cell resolution. We present GraphTME, a spatially informed and biologically interpretable framework that models pathway-specific directional cell-cell interactions to predict anti-PD-1 response. Ligand-receptor interactions are organised by signalling pathways and represented as a multi-relational directed graph, with edge weights inversely scaled by spatial distance. Using CD8+ T cells from single-cell RNA-seq data of ICI-treated NSCLC patients, we trained a relational graph convolutional network to infer immune responsiveness. GraphTME achieved an F1 score exceeding 0.83 in predicting ICI response and was validated using MERFISH data from an NSCLC patient with a partial response. Predicted responder CD8+ T cells exhibited higher abundance and stronger directional signalling to tumour cells. They also expressed genes associated with antitumour activity, while non-responders showed expression patterns linked to poor prognosis. GraphTME is the first framework to quantitatively model single-cell-level interactions within the spatial architecture of tumour tissues and predict ICI responses from these interactions. It offers a spatially resolved, biologically grounded biomarker for immunotherapy and a tool for dissecting immune dynamics in situ.