Prognostic Integrated Image-Based Immune and Molecular Profiling in Early-Stage Endometrial Cancer

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 endometrial cancers from the PORTEC-1 and -2 trials.

The relationship between immune cell density and clinicopathologic/molecular factors was analysed by hierarchical clustering and multiple regression. The prognostic value of immune infiltrate by cell type and location was analysed by univariable and multivariable Cox regression, incorporating the molecular endometrial cancer 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 endometrial cancer subgroups between them. Univariable analysis revealed intraepithelial CD8⁺ cell density as the strongest predictor of endometrial cancer recurrence; multivariable analysis confirmed this was independent of pathologic 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 endometrial cancer classification in early-stage endometrial cancer.

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This article is related to Immuno-oncology