Integrating histopathology and transcriptomics for spatial tumor microenvironment profiling in a melanoma case study

Abstract

Local structures formed by cells in the tumor microenvironment (TME) play an important role in tumor development and treatment response. This study introduces SPoTLIghT, a computational framework providing a quantitative description of the tumor architecture from hematoxylin and eosin (H&E) slides. We trained a weakly supervised machine learning model on melanoma patients linking tile-level imaging features extracted from H&E slides to sample-level cell type quantifications derived from RNA-sequencing data. Using this model, SPoTLIghT provides spatial cellular maps for any H&E image, and converts them in graphs to derive 96 interpretable features capturing TME cellular organization. We show how SPoTLIghT’s spatial features can distinguish microenvironment subtypes and reveal nuanced immune infiltration structures not apparent in molecular data alone. Finally, we use SPoTLIghT to effectively predict patients’ prognosis in an independent melanoma cohort. SPoTLIghT enhances computational histopathology providing a quantitative and interpretable characterization of the spatial contexture of tumors.