基于日本临床基因组数据库的瘤单一测序的生殖系病原性变异预测模型
Masachika Ikegami1, Liuzhe Zhang2, Makoto Hirata3
1National Cancer Center Research Institute Tokyo, Tokyo Japan.
概括
一个新的算法比现有的标准更准确地预测日本癌症患者的生殖系致病变体 (GPV). 该工具通过分析瘤和正常DNA数据来帮助临床决策,改善遗传性癌症的个性化药物.
科学领域:
- 基因组学和精准医学精准医学
- 癌症遗传学 癌症遗传学
- 医疗保健中的机器学习
背景情况:
- 在癌症遗传检测过程中,经常偶然发现生殖系致病变体 (GPVs).
- 日本关于生殖线转换率 (GCR) 的有限数据需要更好的预测工具.
- 目前日本GPV的临床决定通常依赖于欧洲医学瘤学会 (ESMO) 的标准.
研究的目的:
- 开发一种变体级算法,预测日本人口中的GCRs.
- 为了开发算法,利用日本的瘤正常匹配面板数据库.
- 将新算法的临床实用性与ESMO标准等现有标准进行比较.
主要方法:
- 从NCC Oncopanel数据集分析了7,078例日本病例,重点关注32种遗传性癌症基因.
- 将临床特征,样本数据,序列结果和小等位基因频率 (MAF) 纳入机器学习模型和名录.
- 通过使用GenMineTOP数据集进行决策曲线分析和验证来评估临床效用.
主要成果:
- 开发的模型实现了高预测准确度 (c指数为0.96-0.97),显著超过ESMO标准 (0.88).
- 在BAP1,BRCA1 / 2和NF1基因中发现了高的疾病特异性GCRs,几个基因显示>50%的GCRs.
- 关键预测因素包括年龄,多种癌症,基因类型,癌症类型,MAF和瘤等位基因比率 (TAR).
结论:
- 结合TAR和MAF的变异级预测模型提高了日本癌症患者的GPV预测准确度.
- 与基因水平方法和ESMO标准相比,开发的算法提供了优越的临床实用性.
- 这种预测模型支持改善临床决策,并为遗传性癌症推进个性化医疗.
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