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Characterizing Mutational Load and Clonal Composition of Human Blood
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图形-ETMB:基于图形神经网络的模型,用于估计瘤突变负担.

Wanting Yang1, Yan Qiang1, Wei Wu2

  • 1College of Information and Computer, Taiyuan University of Technology, Taiyuan, Shanxi 030000, China.

Computational biology and chemistry
|June 7, 2023
PubMed
概括

瘤突变负担 (TMB) 预测免疫疗法反应,但整个外基因组测序是昂贵的. 一种新的图形神经网络方法开发了一个小的20基因小组,用于准确的TMB估计和成本有效的免疫治疗预测.

关键词:
数据表示数据表示.图表神经网络的神经网络免疫疗法的有效性 免疫疗法的有效性预测 预后 预测 预测瘤的突变负担

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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:

  • 瘤突变负担 (TMB) 是预测免疫治疗反应的关键生物标志物.
  • 对于TMB评估的整体外体序列 (WES) 被高成本,组织需求和长时间的周转时间所限制.
  • 癌症特异性突变场景需要定制方法来准确估计TMB.

研究的目的:

  • 开发一个具有成本效益的,癌症特异性的基因组,用于准确的TMB估计.
  • 利用图形神经网络框架来解决TMB癌症特异性的问题.
  • 用开发的TMB面板来预测免疫治疗反应.

主要方法:

  • 一个图形神经网络 (GNN) 框架,Graph-ETMB,被用来建模突变基因之间的相关性.
  • 在GNN中使用了消息传递和聚合算法.
  • 用半监督方法对肺腺癌数据进行了 GNN 的训练,以确定 TMB 估计的最小基因组.

主要成果:

  • 开发了一种新型的癌症特异性突变小组,包括20个基因 (0.16 Mb).
  • 这个小组比大多数当前的临床小组小得多.
  • 该小组在独立验证数据集上预测免疫治疗反应的有效性.

结论:

  • 开发的Graph-ETMB方法为TMB估计提供了具有成本效益和准确的方法.
  • 20基因小组促进了免疫治疗的精确临床决策.
  • 这一策略克服了WES的局限性,并改善了用于癌症治疗的TMB评估.