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Updated: Feb 2, 2026
Author Spotlight: Investigating Immune Cell Dynamics in the Tumor Microenvironment — Challenges and Innovations in Cancer Prognosis
Published on: April 12, 2024
Immunophenotypic profile as a predictor of prognosis in advanced ovarian carcinoma
Ako Yokoi1, Toshihide Matsumoto1, Go Watanabe2
1Department of Pathology, Kitasato University School of Medicine, Sagamihara, Kanagawa 252-0374, Japan.
Abstract:
Although proteomic profiles for ovarian epithelial carcinoma (OECa) have been widely investigated, no single marker or set of predictors has been clinically implemented mainly because their reliability and validity have not yet been well established. To establish immunohistochemical (IHC) panels for prognosis prediction of OECa for use in daily pathology practice, the expression patterns of 12 IHC markers, p53, HNF-1β, ARID1A, estrogen receptor-α, progesterone receptor, vimentin, PTEN, PIK3CA, WT1, left-right determination factor, β-catenin, and Ki-67 were investigated using 282 OECas. Hierarchical clustering analysis revealed 7 major immunoprofile groups (IPGs I-VII) that could be used to categorize OECa tumors independent of histotypes. Based on the results of the cluster analysis and protein expression statuses, we further demonstrated the effective classification of OECa tumors into simplified immunoprofile panels using only 4 IHC markers including HNF-1β, p53, ARID1A, and WT1. The tumors in IPG VII with HNF1β+/p53+/ARID1A+ immunophenotype demonstrated a significantly worse overall survival and progression-free survival as compared with the other IPGs. Multivariate Cox regression analysis also revealed that the immunophenotype (HNF1β+/p53+/ARID1A+) and clinical stage were significant and independent prognostic factors for overall survival and progression-free survival in advanced OECa. In conclusion, we identified immunoprofiles in OECa using a panel of 4 IHC markers, which could identify tumors by the immunophenotype that is associated with the most unfavorable prognosis and thus facilitate prognosis prediction of advanced OECa.
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