Revealing the Impact of Genomic Alterations on Cancer Cell Signaling with an Interpretable Deep Learning Model.
Revealing the Impact of Genomic Alterations on Cancer Cell Signaling with an Interpretable Deep Learning Model.
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癌症是由体细胞基因组改变（SGAs）引起的异常细胞信号传导引起的。然而，推断SGAs如何引起细胞信号传导的畸变并导致癌症仍然具有挑战性。我们设计了一个可解释的深度学习模型来编码SGA对细胞信号系统（由模型中的隐藏节点表示）以及最终对肿瘤基因表达的影响。透明的深度学习架构使模型能够发现影响常见信号通路的驱动因素，并部分解析信号蛋白的因果结构。这是使用透明的深度学习模型的早期尝试，与传统的“黑匣子”方法相反，它可以学习对癌细胞信号系统的可解释的见解。更好地表示癌细胞的信号系统有助于阐明癌症的疾病机制，并可以指导精准医学。癌症是由体细胞基因组改变（SGA）引起的异常细胞信号传导的疾病。肿瘤中的异质性SGA事件导致肿瘤特异性信号传导系统畸变。我们将癌症信号系统解释为因果图模型，其中SGAs影响信号蛋白，通过信号转导传播其作用，并最终改变基因表达。为了表示这样的系统，我们开发了一个称为冗余输入神经网络（RINN）的深度学习模型，它具有透明的冗余输入架构。我们的研究结果表明，通过利用SGAs作为输入，RINN可以编码它们对信号传导系统的影响，并在ROC曲线下的面积测量时准确地预测基因表达。此外，RINN可以发现SGAs的共享功能影响（类似嵌入），这些影响扰乱了共同的信号传导途径（例如，PI3K、Nrf2和TGF）。此外，RINN表现出发现细胞信号系统中已知关系的能力。
Cancer results from aberrant cellular signaling caused by somatic genomic alterations (SGAs). However, inferring how SGAs cause aberrations in cellular signaling and lead to cancer remains challenging. We designed an interpretable deep learning model to encode the impact of SGAs on cellular signaling systems (represented by hidden nodes in the model) and eventually on tumor gene expression. The transparent deep learning architecture enabled the model to discover drivers affecting common signaling pathways and partially resolve the causal structure of signaling proteins. This is an early attempt to use transparent deep learning model, in contrast to conventional "black box" approach, to learn interpretable insights into cancer cell signaling systems. A better representation of signaling system of a cancer cell sheds light on the disease mechanisms of the cancer and can guide precision medicine. Cancer is a disease of aberrant cellular signaling resulting from somatic genomic alterations (SGAs). Heterogeneous SGA events in tumors lead to tumor-specific signaling system aberrations. We interpret the cancer signaling system as a causal graphical model, where SGAs affect signaling proteins, propagate their effects through signal transduction, and ultimately change gene expression. To represent such a system, we developed a deep learning model called redundant-input neural network (RINN) with a transparent redundant-input architecture. Our findings demonstrate that by utilizing SGAs as inputs, the RINN can encode their impact on the signaling system and predict gene expression accurately when measured as the area under ROC curves. Moreover, the RINN can discover the shared functional impact (similar embeddings) of SGAs that perturb a common signaling pathway (e.g., PI3K, Nrf2, and TGF). Furthermore, the RINN exhibits the ability to discover known relationships in cellular signaling systems.
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影响因子：
5.8
作者：
Chen, Lujia;Cai, Chunhui;Lu, Xinghua
通讯作者：
Lu, Xinghua
影响因子：
22.7
作者：
Lee, Honglak;Grosse, Roger;Ng, Andrew Y.
通讯作者：
Ng, Andrew Y.
影响因子：
1.7
作者：
Murray-Watters, Alexander;Glymour, Clark
通讯作者：
Glymour, Clark
影响因子：
6
作者：
Narayanan, A;Keedwell, EC;Tatineni, S
通讯作者：
Tatineni, S
DOI：
10.1088/1742-5468/2008/10/p10008
发表时间：
2008-10-01
影响因子：
2.4
作者：
Blondel, Vincent D.;Guillaume, Jean-Loup;Lefebvre, Etienne
通讯作者：
Lefebvre, Etienne