Predicting drug response of tumors from integrated genomic profiles by deep neural networks
Predicting drug response of tumors from integrated genomic profiles by deep neural networks
复制标题
DOI：
10.1186/s12920-018-0460-9
复制
发表时间：
2019-01-31
影响因子：
2.7
通讯作者：
Chen, Yidong
中科院分区：
文献类型：
--
作者：
Chiu, Yu-Chiao;Chen, Hung-I Harry;Chen, Yidong
背景从药物基因组学的角度对高通量基因组图谱的研究为了解调节药物反应的致癌特征提供了前所未有的视角。最近的一项研究筛选了一千种人类癌细胞系对广泛的抗癌药物的反应，并阐明了细胞基因型和脆弱性之间的联系。然而，由于细胞系和肿瘤之间的本质差异，迄今为止，预测肿瘤中的药物反应的翻译仍然具有挑战性。最近，深度学习的进步彻底改变了生物信息学，并为基因组数据的整合引入了新技术。它在药物基因组学中的应用可能会填补基因组学与药物反应之间的差距，提高肿瘤药物反应的预测能力。ResultsWe提出了一种基于癌细胞或肿瘤的突变和表达谱的深度学习模型来预测药物反应（DeepDR）。该模型包含三个深度神经网络（DNN），i）使用大型泛癌症数据集（癌症基因组图谱; TCGA）预训练的突变编码器，以抽象高维突变数据的核心表示，ii）预训练的表达编码器，以及iii）整合前两个子网络的药物反应预测器网络。给定一对突变和表达谱，该模型预测265种药物的IC 50值。我们在622个癌细胞系的数据集上训练和测试了该模型，并实现了均方误差为1.96（对数尺度IC 50值）的总体预测性能。其性能在预测误差或稳定性方面上级两种经典方法（线性回归和支持向量机）和DeepDR的四种模拟DNN模型，包括未经TCGA预训练构建的DNN，部分被主成分取代，并建立在各种类型的输入数据上。然后，我们应用该模型预测了33种癌症类型的9059种肿瘤的药物反应。使用癌前和泛癌环境，该模型预测了已知的两种药物，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，以及新的药物靶点，如TTN突变肿瘤的长春瑞滨。全面的分析进一步揭示了潜在的耐药性的化疗药物多西他赛在泛癌症的设置和抗癌潜力的一种新的代理，CX-5461，在治疗胶质瘤和造血系统malignances.ConclusionsHere我们目前，据我们所知，第一DNN模型翻译的药物基因组学功能从体外药物筛选，预测肿瘤的反应。结果涵盖了充分研究和新的耐药机制和药物靶点。我们的模型和研究结果提高了药物反应的预测和新的治疗方案的识别。
BackgroundThe 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.ResultsWe 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.ConclusionsHere 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.