Identification of clinically predictive metagenes that encode components of a network coupling cell shape to transcription by image-omics.
Identification of clinically predictive metagenes that encode components of a network coupling cell shape to transcription by image-omics.
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DOI：
10.1101/gr.202028.115
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发表时间：
2017-02
期刊：
影响因子：
7
通讯作者：
Bakal C
中科院分区：
文献类型：
--
作者：
Sailem HZ;Bakal C
临床表型（肿瘤分级、存活率）和细胞表型（如形状、信号活性和基因表达）之间的关联是癌症病理学的基础，但解释这些关系的机制并不总是清楚的。包含有关细胞表型和临床数据的信息的大型数据集的生成提供了描述这些机制的机会。在这里，我们开发了一种图像组学的方法来整合定量细胞成像数据，基因表达和蛋白质-蛋白质相互作用的数据，系统地描述了一个“形状-基因网络”，耦合乳腺癌细胞形状的特定方面的信号和转录事件。该网络的作用集中在NF-κB上，支持NF-κB对机械刺激有反应的观点。通过整合RNA干扰筛选数据，我们确定了响应细胞形状变化调节NF-κB的形状基因网络的组成部分。该网络还用于生成预测NF-κB活性和形态学方面（如细胞面积、伸长和膨胀性）的元基因模型。重要的是，这些多基因对肿瘤分级和患者预后也有预测价值。总之，这些数据有力地表明，由基因表达和/或机械力驱动的细胞形状的变化可以通过调节NF-κB活化来促进乳腺癌的进展。我们的研究结果强调了将分子水平（信号传导和基因表达）的表型数据与细胞和组织水平的表型数据相结合的重要性，以更好地了解乳腺癌的发生。
The associations between clinical phenotypes (tumor grade, survival) and cell phenotypes, such as shape, signaling activity, and gene expression, are the basis for cancer pathology, but the mechanisms explaining these relationships are not always clear. The generation of large data sets containing information regarding cell phenotypes and clinical data provides an opportunity to describe these mechanisms. Here, we develop an image-omics approach to integrate quantitative cell imaging data, gene expression, and protein–protein interaction data to systematically describe a “shape-gene network” that couples specific aspects of breast cancer cell shape to signaling and transcriptional events. The actions of this network converge on NF-κB, and support the idea that NF-κB is responsive to mechanical stimuli. By integrating RNAi screening data, we identify components of the shape-gene network that regulate NF-κB in response to cell shape changes. This network was also used to generate metagene models that predict NF-κB activity and aspects of morphology such as cell area, elongation, and protrusiveness. Critically, these metagenes also have predictive value regarding tumor grade and patient outcomes. Taken together, these data strongly suggest that changes in cell shape, driven by gene expression and/or mechanical forces, can promote breast cancer progression by modulating NF-κB activation. Our findings highlight the importance of integrating phenotypic data at the molecular level (signaling and gene expression) with those at the cellular and tissue levels to better understand breast cancer oncogenesis.