CancerSTFormer enables multi-scale analysis of spot-resolution spatial transcriptomes and dissects the gene and immune regulatory responses of targeted therapies

Abstract

The growing number of spot-resolution sequencing based spatial transcriptomic (ST) datasets provides an unprecedented opportunity to study multicellular spatial niches driving cancer transitions. However studying niche-level behavior of tumors remains challenging as it requires a multi-scale approach to modeling the spatial niches and the ability to predict possible effects of genetic perturbations on spatial niches. We propose CancerSTFormer, consisting of a pair of spatially aware transcriptomic foundation models to accommodate niche modeling at different length scales. These models, at the 50{micro}m-Local and 250{micro}m-Extended scales, possess unique capabilities to recover ligand-target gene relationships, niche-specific differentially expressed genes, and organ-specific metastasis associated genes in diverse cancer applications. CancerSTFormer can also reveal the regulatory effects of immune-checkpoint blockade therapies, and other targeted therapies, on patients tumors through perturbation analysis, and accurately recapitulated perturbation responses from a spatial Perturb-map experiment. By reusing existing spot-resolution ST studies at scale, this tool transforms the vast spot-resolution ST data into a resource for understanding how gene perturbation impact spatial niches in cancer, while also providing ST-driven, gene-based refinement of treatment-resistance and sensitivity signatures derived from existing bulk transcriptomic studies, enhancing signature interpretation.