Automated Pathomic Analysis of Angiogenesis and Immune Profiles Unveils an Interpretable Prognostic Biomarker in...

Computational pathology enables the automatic tissue analysis of Whole Slide Images (WSIs), offering unmatched possibilities to capture quantitative tumor microenvironment (TME) characteristics that are essential for patients' prognosis and therapy response. The existing clinical and digital biomarkers do not encompass the morphometric features and spatial interactions between vascular networks and immunological compartment in the TME. To address this challenge, this work presents a high-throughput quantitative framework for automatic segmentation and assessment of aberrant phenotypes of blood vessels and immune cell clusters in hematoxylin & eosin-stained WSIs, to construct the Vascular-Immune Pathomic (VIPath) biomarker. For our study, we utilized three public datasets of Colon Adenocarcinoma (COAD) and Stomach Adenocarcinoma (STAD) from The Cancer Genome Atlas (TCGA) and Clinical Proteomic Tumor Analysis Consortium (CPTAC) projects: TCGA-COAD, TCGA-STAD, and CPTAC-COAD. Additionally, we collected two in-house gastric cancer cohorts of 80 and 51 patients: DBGC-M0 and DBGC-M1. The VIPath biomarker was incorporated in a Cox proportional hazards model trained on the TCGA-COAD. Then, it was validated for predicting Overall Survival (OS) in TCGA-STAD, DBGC-M0, and DBGC-M1, Disease Free Survival in CPTAC-COAD and second-line therapy Progression-free Survival (PFS-2) in DBGC-M1. VIPath encompasses features from both vascular and immunological compartments interacting in the TME. Results proved that VIPath was capable to significantly stratify risk groups for OS TCGA-STAD (p=0.018), OS DBGC-M0 (p = 0.029), OS DBGC-M1 (p = 0.014), and PFS-2 DBGC-M1 (p<0.005). Furthermore, when inserted in a Cox model, it led to an improvement of C-index and R² over all other considered prognostic factors, i.e., p-TNM, ECOG, MSI, HER2.