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突变签名赋值异质性很普遍,可以通过整体方法来解决

Andy J Wu^1,2, Akila Perera^1,3, Linganesan Kularatnarajah¹

¹Cancer Science Institute of Singapore, National University of Singapore, Singapore, Singapore.

Briefings in bioinformatics

|September 24, 2023

概括

癌症基因组学中的参考签名分配显示了显著的工具可变性. 一种集体方法,EnsembleFit,提高了单基替代 (SBS) 突变特征的准确性,特别是对于像SBS3.3这样具有挑战性的突变特征.

科学领域:

癌症基因组学癌症基因组学
计算生物学是一种计算生物学.
生物信息学是一种生物信息学.

背景情况:

单基替代 (SBS) 突变特征在癌症基因组学中对于理解瘤进化至关重要.
参考签名分配工具估计恶性瘤中预先建立的SBS签名活动,但缺乏标准化.
签名分配的工具间的变化可能会影响下游的生物解释.

研究的目的:

深入描述不同SBS签名分配策略和工具的性能.
在现有方法中识别签名分配变化的原因.
开发和验证一个改进的整体方法,用于准确的SBS签名分配.

主要方法:

对三种赋值策略和五种SBS签名赋值工具进行比较分析.
使用诸如Jaccard指数和Kendall tau-b.等指标对工具性能进行评估.
使用合成和真实突变数据开发和测试一个合并方法 (EnsembleFit).
在BRCA1/2-缺陷乳腺癌中对同类重组缺陷特征 (SBS3) 进行EnsembleFit的验证.

主要成果:

分配策略显著影响结果;重新装配策略在减少过度装配方面表现出色.
在各种工具中观察到大量的定性和定量变化,即使采用统一的 Refit 策略.
EnsembleFit方法提高了SBS3签名分配在相关癌症类型的准确性.
在全癌症合成数据集中,EnsembleFit平均减少了15.924.7%的签名活动赋值错误.

结论:

跨工具和策略的SBS签名分配的异质性是癌症基因组学的一个重大挑战.
EnsembleFit方法提供了一个强大而准确的解决方案,以减轻分配变化.
EnsembleFit提供了一个用户友好的网络门户,用于生成基于集团的签名分配,以促进更广泛的采用.

关键词:

一个基准的基准指标.癌症癌症癌症癌症癌症总的来说,一个团队就是一个团队.基因组学就是基因组学.突变的签名突变的签名网站网站网站网站网站网站

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