Identification of key somatic oncogenic mutation based on a confounder-free causal inference model.
Identification of key somatic oncogenic mutation based on a confounder-free causal inference model.
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基于无混杂因素因果推理模型识别关键体细胞致癌突变
DOI：
10.1371/journal.pcbi.1010529
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发表时间：
2022-09
影响因子：
4.3
通讯作者：
中科院分区：
文献类型：
--
作者：
细胞异常增殖和上皮-间质转化（EMT）是肿瘤发生和发展的重要环节。癌症研究的一个基本目标是开发一种有效的方法来检测能够驱动癌症的突变基因。尽管已经提出了几种计算方法来识别这些关键突变，但其中许多方法都集中在基因突变与相关生物过程中的功能变化之间的关联，而不是它们的真实的因果关系。因果效应推断提供了一种方法来估计某一突变对癌症发生和发展的重要生物学过程的真实的诱导效应，通过解决由于中性突变和未观察到的潜在变量引起的混淆偏倚。在这项研究中，整合基因组和转录组数据，我们构建了一个新的因果推理模型的基础上，深度变分自动编码器，以确定关键的致癌体细胞突变。应用于10种癌症类型，我们的方法通过减少观察到的和未观察到的混淆偏倚来量化基因突变对细胞增殖和EMT的因果影响。实验结果表明，突变频率较高的基因并不一定意味着它们在诱发癌症和促进癌症发展方面更有效。此外，我们的研究填补了使用机器学习进行因果推理以识别致癌突变的空白。识别癌症的关键突变有助于更好地理解癌细胞转化的机制，对治疗方法至关重要。除了基于序列和结构的计算方法外，还开发了一些基于功能影响的方法来检测关键突变，这些方法考虑了突变事件与癌症相关生物过程活性之间的关联。然而，这些方法主要考虑了相关性，而忽略了由于观察到的和未观察到的混杂因素的存在，相关性与因果关系相距甚远。我们开发了一个无混淆的基于机器学习的因果推理框架，以估计突变对异常细胞增殖和上皮-间质转化（EMT）的因果影响。它填补了使用因果机制发现癌症生物系统中潜在驱动突变的空白。将我们的方法应用于10种癌症类型，识别出的关键突变与公共验证的突变高度一致。此外，还发现了一些新的关键突变。
Abnormal cell proliferation and epithelial-mesenchymal transition (EMT) are the essential events that induce cancer initiation and progression. A fundamental goal in cancer research is to develop an efficient method to detect mutational genes capable of driving cancer. Although several computational methods have been proposed to identify these key mutations, many of them focus on the association between genetic mutations and functional changes in relevant biological processes, but not their real causality. Causal effect inference provides a way to estimate the real induce effect of a certain mutation on vital biological processes of cancer initiation and progression, through addressing the confounder bias due to neutral mutations and unobserved latent variables. In this study, integrating genomic and transcriptomic data, we construct a novel causal inference model based on a deep variational autoencoder to identify key oncogenic somatic mutations. Applied to 10 cancer types, our method quantifies the causal effect of genetic mutations on cell proliferation and EMT by reducing both observed and unobserved confounding biases. The experimental results indicate that genes with higher mutation frequency do not necessarily mean they are more potent in inducing cancer and promoting cancer development. Moreover, our study fills a gap in the use of machine learning for causal inference to identify oncogenic mutations. Identifying key mutations of cancers is helpful to better understand the mechanisms of cancer cell transformation and is critical for therapeutic approaches. Besides sequence and structure-based computational approaches, some functional impact-based methods which consider the association between mutation events and the activity of cancer-related biological processes have also been developed to detect key mutations. However, these methods mainly consider the correlation but ignore that the correlation is far from causality due to the existence of observed and unobserved confounding factors. We develop a confounder-free machine learning-based causal inference framework to estimate the causal effect of mutations on abnormal cell proliferation and epithelial-mesenchymal transition (EMT). It fills a gap in the use of causal mechanisms to discover potential driver mutations in cancer biological systems. Applying our method to 10 cancer types, the identified key mutations are highly consistent with public well-verified ones. Additionally, some new key mutations have also been discovered.
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影响因子：
14.9
作者：
Gonzalez-Perez A;Lopez-Bigas N
通讯作者：
Lopez-Bigas N
影响因子：
12.3
作者：
Bashashati A;Haffari G;Ding J;Ha G;Lui K;Rosner J;Huntsman DG;Caldas C;Aparicio SA;Shah SP
通讯作者：
Shah SP
影响因子：
5.7
作者：
Adeli E;Kwon D;Zhao Q;Pfefferbaum A;Zahr NM;Sullivan EV;Pohl KM
通讯作者：
Pohl KM
影响因子：
14.9
作者：
Lan A;Smoly IY;Rapaport G;Lindquist S;Fraenkel E;Yeger-Lotem E
通讯作者：
Yeger-Lotem E
影响因子：
44.1
作者：
Cui Y;Chen H;Xi R;Cui H;Zhao Y;Xu E;Yan T;Lu X;Huang F;Kong P;Li Y;Zhu X;Wang J;Zhu W;Wang J;Ma Y;Zhou Y;Guo S;Zhang L;Liu Y;Wang B;Xi Y;Sun R;Yu X;Zhai Y;Wang F;Yang J;Yang B;Cheng C;Liu J;Song B;Li H;Wang Y;Zhang Y;Cheng X;Zhan Q;Li Y;Liu Z
通讯作者：
Liu Z