Vickram Sundaram, Infant Shofia Saghya, Manikandan S, Jenila Rani D, Mathan Muthu C M, Chopra Hitesh
Department of Biotechnology, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Chennai, India.
Department of Biotechnology, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Chennai, India.
Pathol Res Pract. 2025 Jan;265:155743. doi: 10.1016/j.prp.2024.155743. Epub 2024 Nov 26.
Gastric cancer is a malignant disease with a poor prognosis and few therapeutic options once it has advanced. Immunotherapy using ICIs has emerged as a viable therapeutic method; nevertheless, reliable immunological biomarkers are required to identify who may benefit from these therapies. It focuses on key immune biomarkers and predictive signatures in gastric cancer, such as PD-L1 expression, microsatellite instability (MSI), tumor mutational burden (TMB), and Epstein-Barr virus (EBV) status, to optimize gastric cancer patients' immunotherapy responses. PD-L1 expression is a popular biomarker for ICI effectiveness. Tumors with high MSI-H and TMB are the most susceptible to ICIs because they are highly immunogenic. EBV-positive stomach tumors are highly immunogenic, and immunotherapy has a high response rate. Combining composite biomarker panels with multi-omics-based techniques improved patient selection accuracy. In recent years, machine learning models have been integrated into next-generation sequencing. Dynamic, real-time-monitorable biomarkers for real-time immune response monitoring are also being considered. Thus, enhancing biomarker-driven immunotherapy is critical for improving clinical outcomes with gastric cancer. There is still more work to be done in this field, and verifying developing biomarkers will be an important component in the future of customized cancer therapy.
胃癌是一种预后较差的恶性疾病，一旦进展，治疗选择有限。使用免疫检查点抑制剂（ICIs）的免疫疗法已成为一种可行的治疗方法；然而，需要可靠的免疫生物标志物来确定哪些患者可能从这些疗法中获益。本文重点关注胃癌中的关键免疫生物标志物和预测特征，如程序性死亡受体配体1（PD-L1）表达、微卫星不稳定性（MSI）、肿瘤突变负荷（TMB）和爱泼斯坦-巴尔病毒（EBV）状态，以优化胃癌患者的免疫治疗反应。PD-L1表达是评估ICIs疗效常用的生物标志物。高度微卫星高度不稳定（MSI-H）和高肿瘤突变负荷的肿瘤对ICIs最为敏感，因为它们具有高度免疫原性。EBV阳性的胃肿瘤具有高度免疫原性，免疫治疗的反应率很高。将复合生物标志物组合与基于多组学的技术相结合可提高患者选择的准确性。近年来，机器学习模型已被整合到下一代测序中。也正在考虑用于实时免疫反应监测的动态、可实时监测的生物标志物。因此，加强生物标志物驱动的免疫治疗对于改善胃癌的临床结局至关重要。该领域仍有更多工作要做，验证正在开发的生物标志物将是未来个性化癌症治疗的重要组成部分。
