Optimum risk stratification in early-stage endometrial cancer (EC) combines clinicopathological factors and the molecular EC classification defined by The Cancer Genome Atlas (TCGA). It is unclear whether analysis of intratumoral immune infiltrate improves this. We developed a machine-learning image-based algorithm to quantify density of CD8+ and CD103+ immune cells in tumor epithelium and stroma in 695 stage I endometrioid ECs from the PORTEC-1&-2 trials. The relationship between immune cell density and clinicopathological/molecular factors was analyzed by hierarchical clustering and multiple regression. The prognostic value of immune infiltrate by cell type and location was analyzed by univariable and multivariable Cox regression, incorporating the molecular EC classification. Tumor-infiltrating immune cell density varied substantially between cases, and more modestly by immune cell type and location. Clustering revealed three groups with high, intermediate and low densities, with highly significant variation in the proportion of molecular EC subgroups between them. Univariable analysis revealed intraepithelial CD8+ cell density as the strongest predictor of EC recurrence; multivariable analysis confirmed this was independent of pathological factors and molecular subgroup. Exploratory analysis suggested this association was not uniform across molecular subgroups, but greatest in tumors with mutant p53 and absent in DNA mismatch repair deficient cancers. Thus, this work identified that quantification of intraepithelial CD8+ cells improved upon the prognostic utility of the molecular EC classification in early-stage EC.
Cancer immunology research
Radiation Oncology, Leiden University Medical Center.