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.
The presence of tumor-infiltrating lymphocytes (TILs) is correlated with outcome and prognosis in epithelial ovarian cancer (EOC). In this study, automated image analysis was used to analyze the association between overall survival (OS) and TIL spatial arrangement and density in a multi-site cohort of 102 EOC patients who received adjuvant chemotherapy following debulking surgery. Features of the spatial arrangement of TILs (SpaTIL) were used to quantify the spatial co-localization of TILs and tumor cells on digitized pathology slides of the malignant neoplasm of excised specimens. A multivariable Cox regression model of SpaTIL features was fit on the n1 = 51 patient training set and was evaluated in the n2 = 51 patient validation set. The SpaTIL signature was significantly associated with OS, both in the training set (hazard ratio (HR) = 2.81, 95% confidence interval (CI) = 1.33 − 5.92, and p = 0.003) and the validation set (HR = 2.06, 95% CI = 1.04 − 4.07, and p = 0.008). In addition, fusing our spaTIL risk score and the clinical staging further improved the results of the predictive model (HR = 4.045, 95% CI = 4.11−5.41, and p = 0.0002 in the validation set) and outperformed clinical staging alone. This finding illustrates that a spaTIL risk score is not only able to predict OS independent of clinical data, but also offers prognostic value complementary to current clinical standard-of-care. Patients with longer survival times had significantly higher heterogeneity of non-TIL cluster area, while shorter time survivors had mostly same-sized, evenly-distributed non-TIL clusters and smaller average TIL cluster area. These findings suggest that dispersion of TILs throughout the tumor is associated with better treatment response to post-treatment adjuvant chemotherapy, and therefore longer survival time.
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