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

• Published in: BMC medical genomics Vol. 12; no. Suppl 1; pp. 18 - 155
• Main Authors: Chiu, Yu-Chiao, Chen, Hung-I Harry, Zhang, Tinghe, Zhang, Songyao, Gorthi, Aparna, Wang, Li-Ju, Huang, Yufei, Chen, Yidong
• Format: Journal Article
• Language: English
• Published: England BioMed Central Ltd 31.01.2019; BioMed Central; BMC
• Subjects: Antineoplastic agents; Antineoplastic Agents - pharmacology; Artificial neural networks; Benzothiazoles - pharmacology; Bioinformatics; Breast cancer; Cancer; Cancer cell line encyclopedia; Cancer research; Cancer therapies; Cell Line, Tumor; Chemotherapy; Computational biology; Data processing; Datasets; Deep Learning; Deep neural networks; Docetaxel - pharmacology; Drug evaluation; Drug resistance; Drug response prediction; Drug screening; Drugs; Encyclopedias; Epidermal growth factor receptors; Gene mutation; Genes; Genetic research; Genomes; Genomics; Genomics - methods; Genomics of Drug Sensitivity in Cancer; Genotypes; Gliomas; Humans; Lung cancer; Molecular modelling; Mutation; Naphthyridines - pharmacology; Neural networks; Non-small cell lung cancer; Non-small cell lung carcinoma; Novels; Pharmacogenomics; Regression analysis; RNA polymerase; Small cell lung cancer; Tamoxifen; The Cancer Genome Atlas; Transcriptome - drug effects; Tumor cell lines; Tumors; Vinorelbine; Cancer cell line encyclopedia; Deep neural networks; Drug response prediction; Genomics of Drug Sensitivity in Cancer; Pharmacogenomics; The Cancer Genome Atlas
• ISSN: 1755-8794; 1755-8794
• DOI: 10.1186/s12920-018-0460-9

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 IC 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 IC 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.