Performance evaluation for rapid detection of pan-cancer microsatellite instability with MANTIS.
Performance evaluation for rapid detection of pan-cancer microsatellite instability with MANTIS.
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DOI：
10.18632/oncotarget.13918
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发表时间：
2017-01-31
期刊：
影响因子：
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通讯作者：
Roychowdhury S
中科院分区：
文献类型：
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作者：
Kautto EA;Bonneville R;Miya J;Yu L;Krook MA;Reeser JW;Roychowdhury S
在目前的临床实践中，微卫星不稳定性（MSI）和错配修复缺陷的检测进行MSI-PCR和免疫组化。最近的研究已经产生了几种计算工具，用于利用下一代测序（NGS）数据进行MSI检测;然而，尚未对计算方法进行全面分析。在这项研究中，我们介绍了一种新的MSI检测工具，MANTIS，并证明其良好的性能相比，以前公布的工具mSINGS和MSISensor。我们评估了六种癌症亚型的458个正常肿瘤样本对，测试了10至2539个不同数量的靶位点的分类性能。所有三种计算方法都被认为是准确的，MANTIS表现出最高的准确性，所有六种疾病的样本中有98.91%被正确分类。MANTIS在这三种工具中表现出上级性能，在六种癌症类型中具有最高的总体灵敏度（MANTIS 97.18%、MSISensor 96.48%、mSINGS 76.06%）和特异性（MANTIS 99.68%、mSINGS 99.68%、MSISensor 98.73%），即使基因座组大小不同。此外，MANTIS还具有最低的资源消耗（<mSINGS所需空间的1%和<7%的内存）和最快的运行时间（分别为MSISensor和mSINGS的49.6%和8.7%）。这项研究强调了MANTIS在按MSI状态对样本进行分类方面的潜在效用，允许其纳入现有的NGS管道。
In current clinical practice, microsatellite instability (MSI) and mismatch repair deficiency detection is performed with MSI-PCR and immunohistochemistry. Recent research has produced several computational tools for MSI detection with next-generation sequencing (NGS) data; however a comprehensive analysis of computational methods has not yet been performed. In this study, we introduce a new MSI detection tool, MANTIS, and demonstrate its favorable performance compared to the previously published tools mSINGS and MSISensor. We evaluated 458 normal-tumor sample pairs across six cancer subtypes, testing classification performance on variable numbers of target loci ranging from 10 to 2539. All three computational methods were found to be accurate, with MANTIS exhibiting the highest accuracy with 98.91% of samples from all six diseases classified correctly. MANTIS displayed superior performance among the three tools, having the highest overall sensitivity (MANTIS 97.18%, MSISensor 96.48%, mSINGS 76.06%) and specificity (MANTIS 99.68%, mSINGS 99.68%, MSISensor 98.73%) across six cancer types, even with loci panels of varying size. Additionally, MANTIS also had the lowest resource consumption (<1% of the space and <7% of the memory required by mSINGS) and fastest running times (49.6% and 8.7% of the running times of MSISensor and mSINGS, respectively). This study highlights the potential utility of MANTIS in classifying samples by MSI-status, allowing its incorporation into existing NGS pipelines.