Spatial Connectivity of Tumor and Associated Cells (SpaCell): A Novel Computational Pathology Biomarker

The tumor microenvironment (TME) is comprised of multiple cell types, with their spatial organization having been previously studied to identify associations with disease progression and response to therapy. These works, however, have focused on spatial interactions of a single cell type, ignoring spatial interplay between the remaining cells. Here, we introduce a framework to quantify complex spatial interactions on H&E-stained image between multiple cell families simultaneously within the TME, called spatial connectivity of tumor and associated cells (SpaCell). First, nuclei are segmented and classified into different families (e.g., cancerous cells and lymphocytes) using a combination of image processing and machine learning techniques. Local clusters of proximal nuclei are then built for each family. Next, quantitative metrics are extracted from these clusters to capture inter- and intra-family relationships, namely: density of clusters, area intersected between clusters, diversity of clusters surrounding a cluster, architecture of clusters, among others. When evaluated for predicting risk of recurrence in HPV-associated oropharyngeal squamous cell carcinoma (n=233, 107 vs 126 patients for training vs testing) and non-small cell lung cancer (n=186, 70 vs 116 patients for training vs for testing), SpaCell was able to differentiate between patients at high and low risk of recurrence (p=0.03 and p=0.02, respectively). SpaCell was compared against a deep learning and a state-of-the-art approach that uses single-family cell cluster graphs (CGG). CCG extracted metrics were not prognostic of disease-free survival (DFS) for oropharyngeal (p=0.98) nor lung (p=0.15) cancer, and deep learning was prognostic of DFS for lung (p=0.03) but not for oropharyngeal cancer (p=0.26). SpaCell was not only prognostic for both cancer types but also provides more explainability in terms of tumor biology.