研究动态
Articles below are published ahead of final publication in an issue. Please cite articles in the following format: authors, (year), title, journal, DOI.

检测微卫星不稳定性的计算工具的性能评估。

Performance assessment of computational tools to detect microsatellite instability.

发表日期:2024 Jul 25
作者: Harrison Anthony, Cathal Seoighe
来源: BRIEFINGS IN BIOINFORMATICS

摘要:

微卫星不稳定性 (MSI) 是多种癌症类型中常见的一种现象,可用作帮助指导免疫检查点抑制剂治疗的生物标志物。为了促进这一点,研究人员开发了计算工具,使用下一代测序数据将样本分类为具有高微卫星不稳定性或微卫星稳定。大多数这些工具的发布范围和用途不明确,并且尚未进行独立基准测试。为了解决这些问题,我们评估了八种领先的 MSI 工具在包含多种测序方法的几个独特数据集上的性能。虽然我们能够在全外显子组测序数据上复制每个工具的原始发现,但大多数工具在全基因组测序数据上的曲线值下具有较差的接收者操作特性和精确回忆面积。我们还发现他们彼此之间以及与商业 MSI 软件在基因组数据上缺乏一致性,并且最佳阈值截止值因测序类型而异。最后,我们测试了专门为 RNA 测序数据设计的工具,发现它们的性能优于为 DNA 测序数据设计的工具。总之,两种工具(MSIsensor2、MANTIS)在几乎所有数据集上都表现良好,但当所有数据集组合在一起时,它们的精度会下降。我们的结果警告说,MSI 工具在除最初评估数据集以外的数据集上的性能可能要低得多,对于 RNA 测序工具来说,甚至在创建它们的数据类型上表现不佳。© 作者(s) 2024。由牛津大学出版社出版。
Microsatellite instability (MSI) is a phenomenon seen in several cancer types, which can be used as a biomarker to help guide immune checkpoint inhibitor treatment. To facilitate this, researchers have developed computational tools to categorize samples as having high microsatellite instability, or as being microsatellite stable using next-generation sequencing data. Most of these tools were published with unclear scope and usage, and they have yet to be independently benchmarked. To address these issues, we assessed the performance of eight leading MSI tools across several unique datasets that encompass a wide variety of sequencing methods. While we were able to replicate the original findings of each tool on whole exome sequencing data, most tools had worse receiver operating characteristic and precision-recall area under the curve values on whole genome sequencing data. We also found that they lacked agreement with one another and with commercial MSI software on gene panel data, and that optimal threshold cut-offs vary by sequencing type. Lastly, we tested tools made specifically for RNA sequencing data and found they were outperformed by tools designed for use with DNA sequencing data. Out of all, two tools (MSIsensor2, MANTIS) performed well across nearly all datasets, but when all datasets were combined, their precision decreased. Our results caution that MSI tools can have much lower performance on datasets other than those on which they were originally evaluated, and in the case of RNA sequencing tools, can even perform poorly on the type of data for which they were created.© The Author(s) 2024. Published by Oxford University Press.