2022年 第15卷 第8期
《工程（英文）》 >> 2022年 第15卷 第8期 doi: 10.1016/j.eng.2021.06.017
基于人工智能的肺癌NOG/PDX模型驱动基因匹配预测
a School of Medicine, Tongji University, Shanghai 200092, China
b Department of Medical Oncology, Shanghai Pulmonary Hospital, School of Medicine, Tongji University, Shanghai 200433, China
c Department of Automation, School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
d Spine Center, Orthopedic Department, Shanghai Changzheng Hospital, Shanghai 200003, China
e Department of Thoracic Surgery, Shanghai Pulmonary Hospital, School of Medicine, Tongji University, Shanghai 200433, China
f School of Materials Science and Engineering, Tongji University, Shanghai 201804, China
g Department of Thoracic Surgery, Allegheny General Hospital, Pittsburgh, PA 15212, USA
h Department of Thoracic Surgery and Minimally Invasive Thoracic Surgery Unit (UCTMI), Coruña University Hospital, Coruña 15006, Spain
i Oncology and Immunology BU, Research Service Division, WuXi Apptec, Shanghai 200131, China
j SJTU–Yale Joint Center for Biostatistics and Data Science, Department of Bioinformatics and Biostatistics, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, China
k Division of Medical Oncology, Department of Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA
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摘要
患者源性肿瘤异种移植物（PDX）是癌症药物发现和筛查的有力工具。然而，目前的研究对PDX的基因型错配知之甚少，导致PDX使用过程中产生巨大的经济损失。在此，本研究建立了53 例肺癌患者的PDX模型，基因型匹配率为79.2%（42/53）。此外，检查了17 个临床病理学特征，并基于最低赤池信息量准则（AIC）、最小绝对收缩和选择算子（LASSO）-逻辑回归（LR）、支持向量机（SVM）递归特征消除（SVM-RFE）、极端梯度增强（XGBoost）、梯度增强和分类特征（CatBoost），以及合成少数过采样技术（SMOTE）输入逐步逻辑回归模型。最后，通过100 个试验组的准确度、受试者工作特征曲线下面积（AUC）和F1 评分评价所有模型的性能。两个多变量 LR 模型显示，年龄、驱动基因突变的数量、表皮生长因子受体（EGFR）基因突变、既往化疗的类型、既往酪氨酸激酶抑制剂（TKI）治疗和样本来源是强有力的预测因素。此外，CatBoost （平均精度= 0.960；平均AUC = 0.939；平均F1 分数= 0.908）和八特征SVM-RFE（平均精度= 0.950；平均AUC = 0.934；平均F1 分数= 0.903）在算法中表现出最好的性能。同时，除CatBoost 外，SMOTE的应用提高了大多数模型的预测能力。基于SMOTE，单一模型的集成分类器达到了最高的准确度（平均值= 0.975）、AUC（平均值= 0.949）和F1 评分（平均值= 0.938）。总之，本文建立了一个最佳预测模型来筛选肺癌患者的NOD/Shi-scid白细胞介素-2受体（IL-2R） γnull（NOG）/PDX模型，并为建立预测模型提供了一种通用方法。
关键词
机器学习 ; 患者源性肿瘤异种移植物 ; NOG小鼠
补充材料
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参考文献
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