Interplay Between Immune Microenvironment CD8⁺ Tumor-Infiltrating Lymphocytes and PDL-1 Expression as Prognostic...

Background: Cervical cancer remains a major cause of cancer-related morbidity and mortality worldwide, with limited therapeutic options for advanced disease. As we better understand the fine mechanisms involved in the interaction between tumor cells and the tumor microenvironment, new paths and opportunities will emerge. Recent evidence highlights the prognostic and predictive roles of immune checkpoint markers and tumor-infiltrating lymphocytes (TILs), especially CD8⁺ TILs, in shaping treatment outcomes. Objectives: This study investigated the immunohistochemical expression of PD-L1 and CD8⁺ TILs in 48 newly diagnosed, treatment-naive cervical cancer cases and analyzed their associations with clinicopathological features and survival outcomes. Results: In our cohort, we observed that PD-L1 positivity was identified in 68.8% of cases, most frequently in advanced FIGO stages and in tumors with lympho-vascular invasion or with a high proliferation rate evaluated by the Ki-67 index. High levels of intra-tumoral CD8⁺ TILs were observed in 52.1% of cases and correlated positively with stromal TILs, lower proliferation rates, and absence of vascular invasion. A significant inverse relationship was found between PD-L1 expression and the density of CD8⁺ TILs (p = 0.047). Survival analysis showed that patients exhibiting a "cold" immunophenotype with low levels of CD8⁺ TILs and PD-L1-positive tumors had worse outcomes, while high levels of CD8⁺ TILs played a protective role. Conclusions: Our study highlights the importance of the immunohistochemical assessment of PD-L1 and CD8⁺ TILs biomarkers, which have a complementary inter-relationship and have a significant prognostic impact on cervical squamous cell carcinoma. PD-L1 positivity marks aggressive disease features, while higher intra-tumoral CD8⁺ TIL density is protective. Their combined evaluation may improve patient stratification and inform immunotherapy strategies.