ScalpelSig Designs Targeted Genomic Panels from Data to Detect Activity of Mutational Signatures
ScalpelSig Designs Targeted Genomic Panels from Data to Detect Activity of Mutational Signatures
复制标题
ScalpelSig 根据数据设计靶向基因组面板以检测突变特征的活性
DOI：
10.1089/cmb.2021.0453
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发表时间：
2022
期刊：
影响因子：
--
通讯作者：
Mark D. M. Leiserson
中科院分区：
文献类型：
--
作者：
Nicholas Franzese;Jason Fan;R. Sharan;Mark D. M. Leiserson
在过去的十年里，一条很有前途的癌症研究领域利用机器学习来挖掘癌症基因组突变的统计模式，以获取信息。最近的工作表明，这些统计模式，通常被称为“突变特征”，具有不同的治疗潜力，作为癌症治疗的生物标记物。然而，将这一潜力转化为现实受到临床测序机会有限的阻碍。几乎所有的突变特征分析(MSA)方法都依赖于全基因组或全外显子组测序数据，而临床上的测序通常局限于小的基因小组。为了改善临床上获得MSA的机会，我们考虑了是否可以为突变特征检测的目的而设计靶向面板的问题。在这里，我们介绍了ScalpelSig，据我们所知，这是第一个自动设计基因组面板的算法，为检测给定的突变签名而优化。该算法从数据中学习，以确定特别指示签名活动的基因组区域。使用一组乳腺癌基因组作为训练数据，我们表明，与基线相比，ScalpelSig面板显著提高了签名检测的准确性。我们发现，一些扇贝Sig板甚至接近整个外显子组测序的性能，它观察到超过10 × 的基因组材料。我们在不同的条件下测试了我们的算法，表明它的性能适用于另一个乳腺癌数据集、较小的面板尺寸和较少的训练数据量。
Over the past decade, a promising line of cancer research has utilized machine learning to mine statistical patterns of mutations in cancer genomes for information. Recent work shows that these statistical patterns, commonly referred to as "mutational signatures," have diverse therapeutic potential as biomarkers for cancer therapies. However, translating this potential into reality is hindered by limited access to sequencing in the clinic. Almost all methods for mutational signature analysis (MSA) rely on whole genome or whole exome sequencing data, while sequencing in the clinic is typically limited to small gene panels. To improve clinical access to MSA, we considered the question of whether targeted panels could be designed for the purpose of mutational signature detection. Here we present ScalpelSig, to our knowledge the first algorithm that automatically designs genomic panels optimized for detection of a given mutational signature. The algorithm learns from data to identify genome regions that are particularly indicative of signature activity. Using a cohort of breast cancer genomes as training data, we show that ScalpelSig panels substantially improve accuracy of signature detection compared to baselines. We find that some ScalpelSig panels even approach the performance of whole exome sequencing, which observes over 10 × as much genomic material. We test our algorithm under a variety of conditions, showing that its performance generalizes to another dataset of breast cancers, to smaller panel sizes, and to lesser amounts of training data.
影响因子：
4.1
作者：
Cheng, Donavan T.;Mitchell, Talia N.;Berger, Michael F.
通讯作者：
Berger, Michael F.