与BRCA1/2相关的同源重组异常 机器学习预测的目标下一代测序数据的突变
Maher Albitar1, Hong Zhang1, Andrew Pecora2
1Genomic Testing Cooperative, Irvine, CA, USA.
Breast cancer : basic and clinical research
|October 4, 2023
概括
这项研究开发了一种机器学习模型,使用下一代测序数据准确预测瘤中的同源重组缺陷 (HRD). 该模型显示出高灵敏度和特异性,有助于识别对双链破裂诱导药物的潜在反应.
科学领域:
- 在瘤学瘤学.
- 基因组学就是基因组学.
- 生物信息学是一种生物信息学.
背景情况:
- 同源重组缺陷 (HRD) 是预测对双链断裂 (DSB) 诱导药物的反应的关键生物标志物,特别是在BRCA1/2-突变癌症中.
- 在其他同类重组修复 (HRR) 基因或野生型 (WT) 瘤中突变的瘤中识别HRD对于扩大治疗选择至关重要.
研究的目的:
- 开发一种基于下一代测序 (NGS) 的实用方法,使用机器学习 (ML) 来预测任何瘤中的HRD.
- 建立一种识别HRD的方法,类似于在各种瘤类型的BRCA1/2突变中观察到的方法.
主要方法:
- 利用了来自434个基因的向NGS的拷贝数改变 (CNA) 数据.
- 在HRD预测中使用了修改后的原始贝叶斯模型,对CNA log2值训练了ML系统.
- 使用乳腺,卵巢和其他野生类型癌症的独立队列验证了该方法.
主要成果:
- 在预测HRD时,ML模型实现了高灵敏度 (90%) 和特异性 (98%).
- 在HRR基因突变的瘤中 (不包括BRCA1 / 2) 和32%的WT癌症中显示出39%的HRD阳性.
- 显示了与异合性丧失 (LOH) 的90%一致性,并在1300多个样本中得到验证.
结论:
- 副本数量的改变与ML相结合可靠地预测高特异性的BRCA1/2级HRD.
- 开发的ML模型可以有效地预测非BRCA1/2 HRR基因突变的癌症和WT瘤中的HRD.
- 这种方法为识别可能受益于DSB诱导疗法的患者提供了一个有希望的工具.
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