化学免疫疗法
医学
肿瘤科
内科学
回顾性队列研究
队列
转移
癌症
免疫疗法
作者
Pengfei Yu,Guangyu Ding,Xingmao Huang,Chenxuan Wang,Jingquan Fang,Ling Huang,Zeyao Ye,Qi Xu,Xiaoying Wu,Junrong Yan,Qiuxiang Ou,Ye Du,Xin‐Bing Cheng
标识
DOI:10.1097/js9.0000000000001281
摘要
Patients with peritoneal metastasis (PM) from gastric cancer (GC) exhibit poor prognosis. Chemoimmunotherapy offers promising clinical benefits; however, its efficacy and predictive biomarkers in a conversion therapy setting remain unclear. The authors aimed to retrospectively evaluate chemoimmunotherapy efficacy in a conversion therapy setting for GC patients with PM and establish a prediction model for assessing clinical benefits.A retrospective evaluation of clinical outcomes encompassed 55 GC patients with PM who underwent chemoimmunotherapy in a conversion therapy setting. Baseline PM specimens were collected for genomic and transcriptomic profiling. Clinicopathological factors, gene signatures, and tumor immune microenvironment were evaluated to identify predictive markers and develop a prediction model.Chemoimmunotherapy achieved a 41.8% objective response rate and 72.4% R0 resection rate in GC patients with PM. Patients with conversion surgery showed better overall survival (OS) than those without the surgery (median OS: not reached vs 7.82 m, P<0.0001). Responders to chemoimmunotherapy showed higher ERBB2 and ERBB3 mutation frequencies, CTLA4 and HLA-DQB1 expression, and CD8+ T cell infiltration, but lower CDH1 mutation and naïve CD4+ T cell infiltration, compared to nonresponders. A prediction model was established integrating CDH1 and ERBB3 mutations, HLA-DQB1 expression, and naïve CD4+ T cell infiltration (AUC=0.918), which were further tested using an independent external cohort (AUC=0.785).This exploratory study comprehensively evaluated clinicopathological, genomic, and immune features and developed a novel prediction model, providing a rational basis for the selection of GC patients with PM for chemoimmunotherapy-involved conversion therapy.
科研通智能强力驱动
Strongly Powered by AbleSci AI