使用机器学习对患者特异性的3D染色体构造进行总结
Duo Xu1, Andre Neil Forbes2, Sandra Cohen3
1Sandra and Edward Meyer Cancer Center, Weill Cornell Medicine, New York, NY, USA; Institute for Computational Biomedicine, Weill Cornell Medical College, New York, NY, USA; Department of Physiology and Biophysics, Weill Cornell Medical College, New York, NY, USA; Englander Institute for Precision Medicine, Weill Cornell Medicine, New York, NY, USA.
Cell reports methods
|September 6, 2023
概括
研究人员开发了一种机器学习模型,从有限的患者活检数据中预测基因调节网络. 这种方法使用测定转移酶可访问的染色质使用测序 (ATAC-seq) 和RNA-seq,使得癌症基因在大型队列中被发现.
科学领域:
- 基因组学就是基因组学.
- 癌症生物学 癌症生物学
- 计算生物学 计算生物学
背景情况:
- 细胞状态是由调控网络定义的,这些网络将增强剂与基因联系起来.
- 现有的绘制这些网络的方法需要大量的数据,限制了对大患者队伍的应用.
- 染色体免疫沉测序 (ChIP-seq) 对于有限的活检材料是不可行的.
研究的目的:
- 从有限的活检样本开发一种机器学习方法,用于从有限的活检样本中预测增强剂基因调控网络.
- 克服基于关联的方法在区分目标基因和调节状态方面的局限性.
- 为了能够对癌症中的网络重新布线进行大规模分析.
主要方法:
- 训练有素的机器学习模型使用染色体相互作用分析与配对端标签测序 (ChIA-PET) 和高通量染色体构造捕获与染色体免疫沉 (HiChIP) 数据相结合.
- 通过测序 (ATAC-seq) 和RNA-seq数据作为输入,用于转化酶可访问的染色质的利用定量,适用于有限的活检材料.
- 将模型应用于22种癌症类型的371个样本.
主要成果:
- 对602个癌症基因确定了1,780个增强剂-基因连接.
- 该模型只使用ATAC-seq和RNA-seq数据准确预测监管连接.
- 使用CRISPR干扰 (CRISPRi) 验证了调节乳腺癌中的ESR1和肝癌中的A1CF的预测增强剂.
结论:
- 开发的方法可以从有限的患者活检中进行可扩展的增强器基因网络预测.
- 这种方法有助于研究癌症中的网络重新连接,并识别新的癌症基因调节器.
- 这些发现对理解癌症生物学和开发向治疗有意义.
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