A deep learning-based assessment pipeline for intraepithelial and stromal tumor-infiltrating lymphocytes in high-grade serous ovarian carcinoma.

Tumor-infiltrating lymphocytes (TILs) are associated with improved survival in patients with epithelial ovarian cancer. However, the evaluation of TILs has not been applied to routine clinical practice due to reproducibility issues. We developed two convolutional neural network models to detect TILs and to determine their spatial location in whole-slide images, and established a spatial assessment pipeline to objectively quantify intraepithelial and stromal TILs in patients with high-grade serous ovarian carcinoma. The predictions of the established models showed a significant positive correlation with the number of CD8+ T cells and immune gene expressions. We demonstrated that patients with a higher density of intraepithelial TILs had a significantly prolonged overall survival (OS) and progression-free survival (PFS) in multiple cohorts. Based on the density of intraepithelial and stromal TILs, we classified patients into three immunophenotypes: immune-inflamed, excluded, and desert. The immune-desert subgroup showed the worst prognosis. Gene expression analysis showed that the immune-desert subgroup had lower immune cytolytic activity (CYT) and T-cell-inflamed gene-expression profile (GEP) scores, whereas immune-excluded subgroup had higher expression of interferon-γ and programmed death 1 receptor (PD-1) signaling pathway. Our established evaluation method provides detailed and comprehensive quantification of intraepithelial and stromal TILs throughout hematoxylin and eosin (H&E)-stained slides, and has potential for clinical application for personalized treatment of patients with ovarian cancer.