人工智能癌症驱动突变预测在现实世界数据中是有效的
Thinh N Tran1, Chris Fong1, Karl Pichotta1
1Memorial Sloan Kettering Cancer Center, New York, NY, USA.
Nature communications
|September 26, 2025
概括
人工智能 (AI) 可以通过分析蛋白质结构和基因组数据来识别致癌突变. 经过验证的AI预测有助于了解瘤遗传学和患者生存率,特别是在未知意义的变体 (VUS) 中.
科学领域:
- 基因组学就是基因组学.
- 计算生物学 计算生物学
- 在瘤学瘤学.
背景情况:
- 识别癌症驱动突变是复杂的.
- 人工智能 (AI) 在癌症基因组学中显示出超越蛋白质结构预测的承诺.
- 人工智能在识别癌症驱动因素中的实用性需要进一步调查.
研究的目的:
- 评估用于识别癌症驱动突变的计算方法.
- 在非小细胞肺癌 (NSCLC) 中验证AI识别的未知意义的变异 (VUSs).
- 通过患者存活率和途径分析评估AI预测的生物有效性.
主要方法:
- 使用进化,蛋白质结构和功能基因组数据进行比较的计算方法.
- 通过评估它们与两个NSCLC患者队伍的整体存活率的关联,验证了AI注释的致病性VUS.
- 分析了致病性VUS与已知的瘤性改变在途径水平的相互排他性.
主要成果:
- 结合蛋白质结构或功能性基因组数据的方法超过了识别已知的癌症驱动因素的仅进化方法.
- 在KEAP1和SMARCA4中,人工智能识别的致病性VUS与患者存活率降低有关.
- 致病性VUSs在已知瘤变异的途径水平上表现出相互排他性,支持生物有效性.
结论:
- 人工智能驱动的计算方法可以有效地识别癌症驱动突变.
- 经过验证的AI预测提高了对瘤遗传学和临床相关性的理解.
- 人工智能有助于更全面地分析与癌症相关的遗传变异.
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