Investigator

Oscar Bruck

Helsinki University Hospital

OBOscar Bruck
Papers(1)
Prognostic implicatio…
Collaborators(4)
Riku TurkkiTeijo PellinenAlberto BerjónMarta Mendiola
Institutions(5)
Helsinki University H…Karolinska InstitutetUniversity of HelsinkiHospital Universitari…Hospital La Paz Insti…

Papers

Prognostic implications of tumor-infiltrating T cells in early-stage endometrial cancer

Patients with endometrial cancer differ in terms of the extent of T-cell infiltration; however, the association between T-cell subpopulations and patient outcomes remains unexplored. We characterized 285 early-stage endometrial carcinoma samples for T-cell infiltrates in a tissue microarray format using multiplex fluorescent immunohistochemistry. The proportion of T cells and their subpopulations were associated with clinicopathological features and relapse-free survival outcomes. CD3+ CD4+ infiltrates were more abundant in the patients with higher grade or non-endometrioid histology. Cytotoxic T cells (CD25+, PD-1+, and PD-L1+) were strongly associated with longer relapse-free survival. Moreover, CD3+ PD-1+ stromal cells were independent of other immune T-cell populations and clinicopathological factors in predicting relapses. Patients with high stromal T-cell fraction of CD3+ PD-1+ cells were associated with a 5-year relapse-free survival rate of 93.7% compared to 79.0% in patients with low CD3+ PD-1+ fraction. Moreover, in patients classically linked to a favorable outcome (such as endometrioid subtype and low-grade tumors), the stromal CD3+ PD-1+ T-cell fraction remained prognostically significant. This study supports that T-cell infiltrates play a significant prognostic role in early-stage endometrial carcinoma. Specifically, CD3+ PD-1+ stromal cells emerge as a promising novel prognostic biomarker.

66Works
1Papers
4Collaborators
PrognosisCarcinoma, Renal CellKidney NeoplasmsLymphocytes, Tumor-InfiltratingHematologic NeoplasmsAntigens, NeoplasmLeukemia, Myeloid

Positions

Researcher

Helsinki University Hospital

Researcher

University of Helsinki

Education

University of Helsinki

Country

FI

Keywords
deep learningimage analysisdata sciencehematologycell morphology