Biomarkers for response to immune checkpoint inhibitors in gastrointestinal cancers

被引:2
|
作者
Li, Meng [1 ]
Kaili, Denis [2 ]
Shi, Lei [1 ]
机构
[1] Chongqing Univ, Sch Life Sci, 174 Shazheng St, Chongqing 400044, Peoples R China
[2] Ohio State Univ, Wexner Med Ctr, Dept Surg, Columbus, OH 43210 USA
关键词
Immunotherapy; Immune checkpoint inhibitor; Biomarker; Predictive response; Gastrointestinal cancer; TUMOR MUTATIONAL BURDEN; CELL LUNG-CANCER; PROOFREADING DOMAIN MUTATIONS; COLORECTAL-CANCER; PD-1; BLOCKADE; PREDICTIVE BIOMARKERS; ANTI-PD-1; THERAPY; LYMPHOCYTE RATIO; GASTRIC-CANCER; IMMUNOTHERAPY;
D O I
10.4251/wjgo.v14.i1.19
中图分类号
R73 [肿瘤学];
学科分类号
100214 ;
摘要
Gastrointestinal (GI) cancers account for a large proportion of cancer deaths worldwide and pose a major public health challenge. Immunotherapy is considered to be one of the prominent and successful approaches in cancer treatment in recent years. Among them, immune checkpoint inhibitor (ICI) therapy, has received widespread attention, and many clinical findings support the feasibility of ICIs, with sustained responses and significantly prolonged lifespan observed in a wide range of tumors. However, patients treated with ICIs have not fully benefited, and therefore, the identification and development of biomarkers for predicting ICI treatment response have received further attention and exploration. From tumor genome to molecular interactions in the tumor microenvironment, and further expanding to circulating biomarkers and patient characteristics, the exploration of biomarkers is evolving with high-throughput sequencing as well as bioinformatics. More large-scale prospective and specific studies are needed to explore biomarkers in GI cancers. In this review, we summarize the known biomarkers used in ICI therapy for GI tumors. In addition, some ICI biomarkers applied to other tumors are included to provide insights and further validation for GI tumors. Moreover, we present single-cell analysis and machine learning approaches that have emerged in recent years. Although there are no clear applications yet, it can be expected that these techniques will play an important role in the application of biomarker prediction.
引用
收藏
页码:19 / 37
页数:19
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