...
  • 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>BMC Medical Genomics >Predicting drug response of tumors from integrated genomic profiles by deep neural networks
【24h】

Predicting drug response of tumors from integrated genomic profiles by deep neural networks

机译：通过深度神经网络从整合的基因组概况预测肿瘤的药物反应

获取原文
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

The study of high-throughput genomic profiles from a pharmacogenomics viewpoint has provided unprecedented insights into the oncogenic features modulating drug response. A recent study screened for the response of a thousand human cancer cell lines to a wide collection of anti-cancer drugs and illuminated the link between cellular genotypes and vulnerability. However, due to essential differences between cell lines and tumors, to date the translation into predicting drug response in tumors remains challenging. Recently, advances in deep learning have revolutionized bioinformatics and introduced new techniques to the integration of genomic data. Its application on pharmacogenomics may fill the gap between genomics and drug response and improve the prediction of drug response in tumors. We proposed a deep learning model to predict drug response (DeepDR) based on mutation and expression profiles of a cancer cell or a tumor. The model contains three deep neural networks (DNNs), i) a mutation encoder pre-trained using a large pan-cancer dataset?(The Cancer Genome Atlas; TCGA) to abstract core representations of high-dimension mutation data, ii) a pre-trained expression encoder, and iii) a drug response predictor network integrating the first two subnetworks. Given a pair of mutation and expression profiles, the model predicts IC50 values of 265 drugs. We trained and tested the model on a dataset of 622 cancer cell lines and achieved an overall prediction performance of mean squared error at 1.96 (log-scale IC50 values). The performance was superior in prediction error or stability than two classical methods (linear regression and support vector machine) and four analog DNN models of DeepDR, including DNNs built without TCGA pre-training, partly replaced by principal components, and built on individual types of input data. We then applied the model to predict drug response of 9059 tumors of 33 cancer types. Using per-cancer and pan-cancer settings, the model predicted both known, including EGFR inhibitors in non-small cell lung cancer and tamoxifen in ER+ breast cancer, and novel drug targets, such as vinorelbine for TTN-mutated tumors. The comprehensive analysis further revealed the molecular mechanisms underlying the resistance to a chemotherapeutic drug docetaxel in a pan-cancer setting and the anti-cancer potential of a novel agent, CX-5461, in treating gliomas and hematopoietic malignancies. Here we present, as far as we know, the first DNN model to translate pharmacogenomics features identified from in vitro drug screening to predict the response of tumors. The results covered both well-studied and novel mechanisms of drug resistance and drug targets. Our model and findings improve the prediction of drug response and the identification of novel therapeutic options.
机译：从药物基因组学的观点对高通量基因组谱的研究为调节药物反应的致癌特征提供了空前的见识。最近的一项研究筛选了上千种人类癌细胞系对多种抗癌药物的反应，并阐明了细胞基因型与脆弱性之间的联系。然而，由于细胞系和肿瘤之间的本质差异，迄今为止，转化为预测肿瘤中的药物反应仍然具有挑战性。最近，深度学习的进步彻底改变了生物信息学，并向基因组数据的集成引入了新技术。它在药物基因组学上的应用可能会填补基因组学与药物反应之间的空白，并改善肿瘤中药物反应的预测。我们提出了一种深度学习模型，可根据癌细胞或肿瘤的突变和表达谱预测药物反应（DeepDR）。该模型包含三个深层神经网络（DNN），i）使用大型泛癌数据集进行预训练的突变编码器？（癌症基因组图集; TCGA），以提取高维突变数据的核心表示形式; ii） -训练后的表情编码器，以及iii）整合前两个子网的药物反应预测器网络。给定一对突变和表达特征，该模型可预测265种药物的IC50值。我们在622个癌细胞系的数据集上训练和测试了该模型，并在1.96（对数刻度IC50值）上实现了均方误差的整体预测性能。在预测误差或稳定性方面，该性能优于DeepDR的两种经典方法（线性回归和支持向量机）和DeepDR的四种模拟DNN模型，其中包括未经TCGA预训练而构建，部分被主要成分取代并基于个别类型的DNN构建的DNN。输入数据。然后，我们将模型应用于预测33种癌症类型的9059个肿瘤的药物反应。使用每个癌症和全癌的环境，该模型预测了两种药物的已知作用，包括非小细胞肺癌中的EGFR抑制剂和ER +乳腺癌中的他莫昔芬，以及新型药物靶标，例如长春瑞滨用于TTN突变的肿瘤。全面的分析进一步揭示了在全癌环境中对化学治疗药物多西紫杉醇的耐药性以及新型药物CX-5461在治疗神经胶质瘤和造血系统恶性肿瘤中的抗癌潜力的分子机制。在这里，据我们所知，这是第一个翻译神经网络模型，该模型可以转换从体外药物筛选中鉴定出的药物基因组学特征以预测肿瘤的反应。结果涵盖了耐药性和药物靶点的经过充分研究和新颖的机制。我们的模型和发现改善了药物反应的预测和新型治疗选择的识别。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号