Mobile website
Archive > Volume 32 Issue 11 > 2025,32(11):1105-1114. DOI:10.3872/j.issn.1007-385X.2025.11.001 Prev Next
Expert Forum
Dynamic monitoring approach for predicting the efficacy of immune checkpoint inhibitors: fine typing of circulating T lymphocyte subsets
- Article
- Figures
- Metrics
- Preview PDF
- Reference
- Related
- Cited by
- Materials
Abstract:
[Abstract] Immune checkpoint inhibitors (ICIs) have been widely used in the treatment of various solid tumors, and the fine typing of circulating T lymphocyte subsets has attracted considerable attention due to its potential as a predictive biomarker for treatment efficacy. Currently, commonly used ICI efficacy predictive biomarkers mostly rely on tumor tissue samples, with limitations such as difficulty in sampling and dynamic monitoring. In contrast, the fine typing of circulating T lymphocyte subsets can not only reflect the functional status of T cells to predict the response to ICI treatment but also serve as a feasible dynamic detection method due to its advantages of convenience in sampling and minimal invasiveness. Based on its functional status, the fine typing of circulating T lymphocyte subsets can be mainly classified into activated, proliferative, senescent, and exhausted phenotypes. The activated and proliferative phenotypes usually indicate the activation, proliferation, and functional activation status of T cells, while the senescent and exhausted phenotypes reflect the state of T cells with reduced reserves, decreased proliferative and survival capacities, shortened lifespan, and impaired or incompetent effector functions. These functional states are closely related to the efficacy of ICIs. This article will systematically review the latest research progress in the application of fine typing based on the functional status of peripheral blood T cells in predicting ICI treatment efficacy, explore the predictive value of different functional states, and discuss the clinical application prospects and practical difficulties of this approach as a potential predictive tool, providing references for subsequent technical optimization and clinical transformation.
Keywords:
Project Supported:
Clc Number:
