Yang Bingyin, Cheng Cuie, Zhou Jingfang, Ni Haoxiang, Liu Haoran, Fu Yiwei, Li Rui
Department of Gastroenterology, The First Affiliated Hospital of Soochow University, Suzhou, Jiangsu, China.
Department of Gastroenterology, The Affiliated Huai'an Hospital of Xuzhou Medical University, Huai'an, Jiangsu, China.
Discov Oncol. 2024 Sep 29;15(1):507. doi: 10.1007/s12672-024-01400-7.
Immune checkpoint inhibitors (ICIs) have significantly transformed the treatment of gastroesophageal cancer (GEC). However, the lack of reliable prognostic biomarkers hinders the ability to predict patient response to ICI therapy.
In this study, we engineered and validated a genomic mutation signature (GMS) utilizing an innovative artificial intelligence (AI) algorithm to forecast ICI therapy outcomes in GEC patients. We further explored immune profiles across subtypes through comprehensive multiomics analysis. Our investigation of drug sensitivity data from the Genomics of Drug Sensitivity in Cancer (GDSC) database led to the identification of trametinib as a potential therapeutic agent. We subsequently evaluated trametinib's efficacy in AGS and MKN45 cell lines using Cell Counting Kit-8 (CCK8) assays and clonogenic experiments.
We developed a GMS by integrating 297 algorithms, enabling autonomous prognosis prediction for GEC patients. The GMS demonstrated consistent performance across three public cohorts, exhibiting high sensitivity and specificity for overall survival (OS) at 6, 12, and 18 months, as shown by Receiver Operator Characteristic Curve (ROC) analysis. Notably, the GMS surpassed traditional clinical and molecular features, including tumor mutational burden (TMB), programmed death-ligand 1 (PD-L1) expression, and microsatellite instability (MSI), in predictive accuracy. Low-risk samples exhibited elevated levels of cytolytic immune cells and heightened immunogenic potential compared to high-risk samples. Our investigation identified trametinib as a potential therapeutic agent. An inverse correlation was observed between GMS and trametinib IC50. Moreover, the high-risk-derived AGS cell line showed increased sensitivity to trametinib compared to the low-risk-derived MKN45 cell line.
The GMS utilized in this study successfully demonstrated the ability to reliably predict the survival advantage for patients with GECs undergoing ICI therapy.
免疫检查点抑制剂（ICIs）显著改变了胃癌和食管癌（GEC）的治疗方式。然而，缺乏可靠的预后生物标志物阻碍了预测患者对ICI治疗反应的能力。
在本研究中，我们设计并验证了一种基因组突变特征（GMS），利用创新的人工智能（AI）算法预测GEC患者的ICI治疗结果。我们通过全面的多组学分析进一步探索了各亚型的免疫特征。我们对癌症药物敏感性基因组学（GDSC）数据库中的药物敏感性数据进行调查，确定曲美替尼为一种潜在的治疗药物。随后，我们使用细胞计数试剂盒-8（CCK8）检测和克隆形成实验评估了曲美替尼在AGS和MKN45细胞系中的疗效。
我们通过整合297种算法开发了一种GMS，能够对GEC患者进行自主预后预测。GMS在三个公开队列中表现出一致的性能，如通过受试者工作特征曲线（ROC）分析所示，对6、12和18个月的总生存期（OS）具有高敏感性和特异性。值得注意的是，GMS在预测准确性方面超过了传统的临床和分子特征，包括肿瘤突变负荷（TMB）、程序性死亡配体1（PD-L1）表达和微卫星不稳定性（MSI）。与高风险样本相比，低风险样本表现出更高水平的细胞溶解性免疫细胞和更强的免疫原性潜力。我们的调查确定曲美替尼为一种潜在的治疗药物。观察到GMS与曲美替尼IC50之间呈负相关。此外，与低风险来源的MKN45细胞系相比，高风险来源的AGS细胞系对曲美替尼表现出更高的敏感性。
本研究中使用的GMS成功证明了能够可靠地预测接受ICI治疗的GEC患者的生存优势。
