通过深度神经网络框架发现癌症中的隐剪接突变。

NAR Cancer Pub Date : 2023-06-01 DOI:10.1093/narcan/zcad014
Raphaël Teboul, Michalina Grabias, Jessica Zucman-Rossi, Eric Letouzé
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引用次数: 0

摘要

体细胞突变可以破坏剪接调节元件并对癌症基因产生巨大影响,然而位于扩展剪接区域的突变的功能后果很难预测。在这里,我们使用深度神经网络(SpliceAI)来表征癌症中剪接改变突变的景观。在我们的401例肝癌的内部系列中,SpliceAI发现了1244个隐剪接突变,位于必要剪接位点之外,在匹配的RNA-seq数据中验证率很高(66%)。然后,我们将分析扩展到17714个肿瘤的大型泛癌症队列,揭示了>10万个隐剪接突变。考虑到这些突变增加了发现驱动基因的能力,揭示了126个新的候选驱动基因。它还揭示了已知癌症基因中新的驱动突变,使肿瘤抑制基因剪接改变的频率增加了一倍。突变特征分析表明,突变过程可能在每种癌症类型中优先引起剪接突变,与时钟样过程和DNA修复缺陷相关的特征丰富。总之,这项工作揭示了癌症中隐剪接突变的原因和影响,并强调了深度学习方法在更好地注释肿瘤突变的功能后果方面的力量。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Discovering cryptic splice mutations in cancers via a deep neural network framework.

Discovering cryptic splice mutations in cancers via a deep neural network framework.

Discovering cryptic splice mutations in cancers via a deep neural network framework.

Discovering cryptic splice mutations in cancers via a deep neural network framework.

Somatic mutations can disrupt splicing regulatory elements and have dramatic effects on cancer genes, yet the functional consequences of mutations located in extended splice regions is difficult to predict. Here, we use a deep neural network (SpliceAI) to characterize the landscape of splice-altering mutations in cancer. In our in-house series of 401 liver cancers, SpliceAI uncovers 1244 cryptic splice mutations, located outside essential splice sites, that validate at a high rate (66%) in matched RNA-seq data. We then extend the analysis to a large pan-cancer cohort of 17 714 tumors, revealing >100 000 cryptic splice mutations. Taking into account these mutations increases the power of driver gene discovery, revealing 126 new candidate driver genes. It also reveals new driver mutations in known cancer genes, doubling the frequency of splice alterations in tumor suppressor genes. Mutational signature analysis suggests mutational processes that could give rise preferentially to splice mutations in each cancer type, with an enrichment of signatures related to clock-like processes and DNA repair deficiency. Altogether, this work sheds light on the causes and impact of cryptic splice mutations in cancer, and highlights the power of deep learning approaches to better annotate the functional consequences of mutations in oncology.

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