大规模的机器学习分析揭示了DNA甲基化和基因表达反应特征,用于gemcitabine治疗的胰腺癌
Adeolu Ogunleye1, Chayanit Piyawajanusorn2, Ghita Ghislat3
1Department of Organismal Biology, Uppsala University, Uppsala, Sweden.
Health data science
|March 15, 2024
概括
机器学习模型预测胰腺腺癌患者对使用瘤分子形状的吉米西塔化疗的反应. 这些模型可以识别不响应者,从而实现个性化治疗,避免有毒副作用.
科学领域:
- 在瘤学瘤学.
- 生物信息学是一种生物信息学.
- 机器学习 机器学习
背景情况:
- 格姆西塔是胰腺腺癌 (PAAD) 的首要化疗.
- 许多PAAD患者表现出对gemcitabine的耐药性,需要预测生物标志物.
- 目前在PAAD中对gemcitabine反应的预测因素是有限的,并且不利用机器学习.
研究的目的:
- 开发和评估用于预测PAAD患者对gemcitabine反应的机器学习模型.
- 为了确定分子特征,可以区分响应者和不响应者对gemcitabine治疗.
- 评估机器学习模型的性能与现有的生物标志物 (如hENT1.1) 相比.
主要方法:
- 从基因组数据共享库收集了PAAD患者的多种分子概况和临床数据.
- 系统地将8个瘤配置文件与16个分类算法结合起来,生成128个机器学习模型.
- 通过多次10倍交叉验证评估每个模型,评估预测性表现指标,如马修斯相关系数 (MCC) 和ROC-AUC.
主要成果:
- 在128个机器学习模型中,有7个显示出预测能力.
- 使用4个mRNA的随机森林模型实现了0.44的MCC和0.785.78的ROC-AUC.
- 使用12个DNA甲基化探针的XGBoost模型实现了0.32的MCC和0.697.7的ROC-AUC.
- 这些机器学习模型还显示了预测患者预后 (总体和无进展生存) 的潜力.
结论:
- 在PAAD中开发了用于gemcitabine响应的新型预测机器学习模型.
- 这些模型的性能优于传统的标记器,如hENT1.1.
- 这些模型被释放用于前性验证,用于独立的gemcitabine治疗PAAD患者队列.
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