The alterations in peripheral lymphocyte subsets predict the efficacy and prognosis of immune checkpoint inhibitors in hepatocellular carcinoma.

Abstract

Background: Immune checkpoint inhibitor (ICI) treatments are promising therapies for hepatocellular carcinoma (HCC) patients. However, not all HCC patients benefit from immunotherapy. Therefore, it is urgent to explore markers for the clinical efficacy and prognosis of immunotherapy for liver cancer. This study aimed to investigate changes in peripheral blood lymphocyte subsets after immunotherapy and to assess their predictive and prognostic value. Methods: Sixty-one patients with advanced HCC were enrolled. Peripheral blood samples were collected from HCC patients before and after ICI treatment, and lymphocytes were detected by flow cytometry. The rank sum test, chi-square test, Kaplan‒Meier curve, and Cox regression model were used to determine the relationship between the changes in the percentages of peripheral blood lymphocyte subsets and clinicopathological characteristics, clinical efficacy, progression-free survival (PFS) and overall survival (OS). Results: After ICI treatment, the percentage of CD3+CD8+ T cells increased, and the percentage of B cells decreased. The changes in memory T cells percentages varied according to different immune efficacy groups. Age, history of hepatitis B infection, first-line therapy, and distant metastasis influenced the proportion of peripheral blood lymphocyte subsets in patients with advanced HCC. Furthermore, univariate analysis demonstrated that high percentage changes in the natural killer (NK) cells percentage change predicted longer PFS and OS. Conclusions: ICI treatment alters the percentage of peripheral blood lymphocyte subsets in immunotherapy-treated HCC patients. Changes in the proportion of lymphocyte subsets are influenced by variances in immunological response and clinicopathological features. A high degree of NK cells percentage change in HCC patients treated with ICI represents an independent prognostic predictor.