Deep learning on image-omics data in identifying prognostic immune biomarkers for ovarian cancer

Abstract Although stromal and immune cells in the tumor microenvironment have been shown to directly affect tumor growth and chemoresistance, how the interactions among stromal and immune cells and their spatially resolved cell heterogeneity would influence ovarian patients’ survival remains largely unknown. To fill this gap, we developed a new imageomics method that (i) incorporates an artificial intelligence–based analytics pipeline for imaging mass cytometry (IMC) to increase the accuracy of cell segmentation and spatial information extraction in order to identify immune biomarkers and their interactions that can predict overall survival rates in patients with treatment-naïve ovarian cancer, and (ii) integrates quantitated spatial IMC image data with microdissected tumor and stromal transcriptomic data from the same patients as well as single-cell RNA sequencing data to detect genes correlated with the prognostic features and postulate novel mechanisms by which these genes contribute to the prognostic features.