PD-1+ TIM-3+ T cells in malignant ascites predict prognosis of gastrointestinal cancer

Michitaka Nakano, Mamoru Ito, Risa Tanaka, Kyoko Yamaguchi, hiroshi ariyama, Kenji Mitsugi, Tomoyasu Yoshihiro, Hirofumi Ohmura, Nobuhiro Tsuruta, Fumiyasu Hanamura, Kosuke Sagara, Yuta Okumura, Kenta Nio, Kenji Tsuchihashi, Shuji Arita, Hitoshi Kusaba, Koichi Akashi, Eishi Baba

Research output: Contribution to journalArticlepeer-review

26 Citations (Scopus)

Abstract

The liquid biopsy of ascites fluid could be an excellent source of tumor and microenvironment for the study of prognostic biomarkers because of its accessibility. Tumor-infiltrating lymphocytes (TILs) can predict prognosis in multiple malignancies, including the response to immune checkpoint inhibitors, a breakthrough cancer therapy. However, TILs’ profiles from malignant ascites have not been extensively studied. Using flow cytometric analysis, we quantified the proportion of exhausted T cells and memory/naive/effector T-cell subsets, among the CD4+ and CD8+ T-cell populations of paired TILs and peripheral blood T cell samples (n = 22). The correlation between CD4+ and CD8+ subset profiles suggested that the combined analysis of CD4+ and CD8+ cells in malignant ascites was clinically significant. We found that cells positive for the exhaustion markers programmed cell death-1 (PD-1), and T-cell immunoglobulin and mucin domain 3 (TIM-3), and cells coexpressing PD-1 and TIM-3 abundantly exist among malignant ascites TILs. Furthermore, patients with high frequency of PD-1+ TIM-3+ cells among the CD4+ and CD8+ T-cell population showed worse clinical outcome in multivariate analysis (n = 27). We propose that exhausted ascites TILs represent a clinically significant prognostic biomarker in advanced gastrointestinal cancer and represent an important target for immune checkpoint inhibitors.

Original languageEnglish
Pages (from-to)2986-2992
Number of pages7
JournalCancer Science
Volume109
Issue number9
DOIs
Publication statusPublished - Sept 2018

All Science Journal Classification (ASJC) codes

  • Oncology
  • Cancer Research

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