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使用主要编辑和深度学习对所有ATM SNV进行功能评估

Kwang Seob Lee¹, Joon-Goo Min², Yumin Cheong³

¹Department of Pharmacology, Yonsei University College of Medicine, Seoul 03722, Republic of Korea.

|June 28, 2025

概括

研究人员评估了Ataxia telangiectasia突变 (ATM) 基因中的所有可能的单核酸变体 (SNV). 他们确定了影响细胞适应性的关键残留物,并开发了一个深度学习模型来预测变异的影响,帮助癌症风险和预后评估.

科学领域:

遗传学和基因组学
癌症生物学
生物信息学

背景情况:

在DNA损伤反应中,Ataxia telangiectasia突变 (ATM) 基因至关重要.
失去ATM功能会增加癌症的风险,并影响患者的预后.
解释ATM变体的功能意义,特别是不确定意义的变体 (VUS),是一个重大挑战.

研究的目的:

综合评估ATM基因中所有可能的单核体变异 (SNV) 的功能影响.
在ATM中确定其功能所必需的关键残留物.
开发ATM变种致病性的预测模型,以支持临床应用.

主要方法:

使用原始编辑对23,092个ATMSNV进行实验评估,以评估olaparib存在的细胞适应性.
对癌症遗传学数据和英国生物库数据的分析,以将变异影响与临床结果相关联.
开发和应用深度学习模型 (DeepATM) 来预测剩余的4421个ATM SNV的功能影响.

主要成果:

实验验证确定了ATM基因中的关键残留物.
该研究成功评估了大量的ATM SNV的功能影响.
深度ATM模型在预测以前未经评估的ATM变体的功能影响方面表现出很高的准确性.
使用现实数据发现ATM变种影响与癌症风险/预后之间的相关性.

结论:

这种全面的ATM变体功能评估为DNA损伤反应和癌症倾向提供了宝贵的见解.
开发的DeepATM模型为预测ATM变种的致病性提供了一个强大的工具,支持精准医学.
这项研究为处理ATM中的VUS和潜在的其他基因建立了框架,改善了临床决策.

关键词:

机器人一种PARP抑制剂容易患上癌症深度学习精准医学主编和基因组编辑不确定意义的变种

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