PD-L1 and PD-1 Expression in Early Stage Uterine Endometrioid Carcinoma

Abstract

Background/aim: Immune checkpoint inhibitors (ICI) and tumor-infiltrating lymphocytes (TILs) for cancer treatment in clinical oncology have revolutionized patient care. However, no gold standard exists for the criteria of analytical validity of TILs of different types of cancer.

Materials and methods: Clinicopathological data from 60 patients with endometrioid carcinoma (EC) who had undergone surgical treatment at the Gyeongsang National University Hospital between January 2002 and December 2009, were investigated. The programmed cell death protein 1 (PD-1)/programmed cell death ligand 1 (PDL1) expression levels were characterized by immunohistochemical staining patterns, and the interpretations derived from machine learning morphometric analysis (Genie) and the pathologists' assessments were compared. In solid tumors, pathologists assessed the proportion of positive cells in each core of the tissue microarray. For Genie, the proportion of positive cells in the entire core and the number of positive cells per 1 mm² were used.

Results: Both the pathologists and Genie identified the same trend in association with tumor size, with significant differences (p=0.026, p=0.033). Genie expression showed a significant association with PD1 expression, and pathologists identified a significant association with PDL1 expression in immune cells.

Conclusion: The PD1 expression levels identified in immune cells of EC specimens were similar between the pathologists and Genie, suggesting that there is little resistance to the introduction of morphometric analysis. To our knowledge, this is the first study to introduce and validate machine learning as an integrated method for predicting prognosis and treatment based on PD1 expression in EC tumor microenvironments.

Keywords: PD1; PDL1; endometrioid carcinoma; machine learning.