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基于必需性驱动的临床前和临床背景下抗癌药物反应预测

Essentiality-driven prediction of anticancer drug responses in preclinical and clinical contexts.

期刊
iScience
PMID
42565147
原文
PubMed ↗
发布日期

作者

  • Hongtu Cui — Department of Respiratory and Critical Care Medicine, Zhongnan Hospital of Wuhan University, School of Pharmaceutical Sciences, Wuhan University, Wuhan, China.
  • Xiaohui Du — Department of Respiratory and Critical Care Medicine, Zhongnan Hospital of Wuhan University, Wuhan, China.
  • Haixia Guo — Department of Respiratory and Critical Care Medicine, Zhongnan Hospital of Wuhan University, School of Pharmaceutical Sciences, Wuhan University, Wuhan, China.
  • Zhen Liao — Department of Respiratory and Critical Care Medicine, Zhongnan Hospital of Wuhan University, School of Pharmaceutical Sciences, Wuhan University, Wuhan, China.
  • Bingying Wang — Department of Respiratory and Critical Care Medicine, Zhongnan Hospital of Wuhan University, School of Pharmaceutical Sciences, Wuhan University, Wuhan, China.
  • Xiang Lian — Department of Respiratory and Critical Care Medicine, Zhongnan Hospital of Wuhan University, School of Pharmaceutical Sciences, Wuhan University, Wuhan, China.
  • Ji-Yun Zhang — Department of Respiratory and Critical Care Medicine, Zhongnan Hospital of Wuhan University, School of Pharmaceutical Sciences, Wuhan University, Wuhan, China.
  • Xing Lu — School of Cyber Science and Engineering, Wuhan University, Wuhan, China.
  • Dongdong Zhang — Department of Respiratory and Critical Care Medicine, Zhongnan Hospital of Wuhan University, School of Pharmaceutical Sciences, Wuhan University, Wuhan, China.
  • Anqiang Ye — Department of Respiratory and Critical Care Medicine, Zhongnan Hospital of Wuhan University, School of Pharmaceutical Sciences, Wuhan University, Wuhan, China.
  • Jing Feng — School of Artificial Intelligence, Wuhan University, Wuhan, China.
  • Jing Li — Department of Precision Medicine, Changhai Hospital, Second Military Medical University (Naval Medical University), Shanghai, China.
  • Zhenshun Cheng — Department of Respiratory and Critical Care Medicine, Zhongnan Hospital of Wuhan University, Wuhan, China.
  • Feng-Biao Guo — Department of Respiratory and Critical Care Medicine, Zhongnan Hospital of Wuhan University, School of Pharmaceutical Sciences, Wuhan University, Wuhan, China.

作者单位

  • Department of Respiratory and Critical Care Medicine, Zhongnan Hospital of Wuhan University, School of Pharmaceutical Sciences, Wuhan University, Wuhan, China.
  • Department of Respiratory and Critical Care Medicine, Zhongnan Hospital of Wuhan University, Wuhan, China.
  • School of Cyber Science and Engineering, Wuhan University, Wuhan, China.
  • School of Artificial Intelligence, Wuhan University, Wuhan, China.
  • Department of Precision Medicine, Changhai Hospital, Second Military Medical University (Naval Medical University), Shanghai, China.

摘要

中文

精准肿瘤学依赖于肿瘤分子谱来预测药物反应。我们不直接使用传统的分子特征，而是基于基因必需性构建预测特征。在这里，我们介绍DrGee，一个以必需性为中心的平台，仅通过基因表达谱推断药物敏感性。内置的DeepEEAA模型整合了基因表达、基因必需性、药物-蛋白质亲和力和药物-基因关联，以定量预测IC50值。DeepEEAA在独立细胞系数据集上取得了有竞争力的预测性能（R² = 0.764; MSE = 0.9345），优于近期基准深度学习方法。DrGee为95-D肺癌细胞系优先推荐了四种候选药物，其中BI-97C1和trimetrexate通过体外实验和小鼠异种移植实验得到验证。在OVCAR8卵巢癌细胞中进一步确认了稳健的预测性能。在TCGA队列中，基于必需性的预测将患者分为总生存期显著不同的亚组（AUC-PR = 0.825），突显了DrGee的转化潜力。

English

Precision oncology relies on tumor molecular profiles to predict drug responses. Instead of using conventional molecular features directly, we construct predictive signatures based on gene essentiality. Here, we present DrGee, an essentiality-centered platform that infers drug sensitivity solely from gene expression profiles. The built-in DeepEEAA model integrates gene expression, gene essentiality, drug-protein affinity, and drug-gene associations to quantitatively predict IC50 values. DeepEEAA achieved competitive predictive performance on independent cell line datasets (R2 = 0.764; MSE = 0.9345), outperforming recent benchmark deep learning methods. DrGee prioritized four candidate drugs for the 95-D lung cancer cell line, among which BI-97C1 and trimetrexate were validated by in vitro assays and mouse xenograft experiments. Robust predictive performance was further confirmed in OVCAR8 ovarian cancer cells. In TCGA cohorts, essentiality-driven predictions stratified patients with significantly different overall survival outcomes (AUC-PR = 0.825), highlighting the translational potential of DrGee.

分类与指标

研究类型
AI/ML
病种
肺癌
JCR 分区
Q1
影响因子
4.5
新锐分区
3区