{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,9]],"date-time":"2026-04-09T01:12:27Z","timestamp":1775697147884,"version":"3.50.1"},"reference-count":35,"publisher":"Springer Science and Business Media LLC","issue":"S4","license":[{"start":{"date-parts":[[2022,4,15]],"date-time":"2022-04-15T00:00:00Z","timestamp":1649980800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2022,4,15]],"date-time":"2022-04-15T00:00:00Z","timestamp":1649980800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100001809","name":"NSFC","doi-asserted-by":"crossref","award":["61721003"],"award-info":[{"award-number":["61721003"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100001809","name":"NSFC","doi-asserted-by":"crossref","award":["62050178"],"award-info":[{"award-number":["62050178"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Tsinghua-Fuzhou Institute for Data Technology grant","award":["TFIDT2021005"],"award-info":[{"award-number":["TFIDT2021005"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["BMC Bioinformatics"],"abstract":"<jats:title>Abstract<\/jats:title><jats:sec>\n                <jats:title>Background<\/jats:title>\n                <jats:p>Drug resistance is a critical obstacle in cancer therapy. Discovering cancer drug response is important to improve anti-cancer drug treatment and guide anti-cancer drug design. Abundant genomic and drug response resources of cancer cell lines provide unprecedented opportunities for such study. However, cancer cell lines cannot fully reflect heterogeneous tumor microenvironments. Transferring knowledge studied from in vitro cell lines to single-cell and clinical data will be a promising direction to better understand drug resistance. Most current studies include single nucleotide variants (SNV) as features and focus on improving predictive ability of cancer drug response on cell lines. However, obtaining accurate SNVs from clinical tumor samples and single-cell data is not reliable. This makes it difficult to generalize such SNV-based models to clinical tumor data or single-cell level studies in the future.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Results<\/jats:title>\n                <jats:p>We present a new method, DualGCN, a unified Dual Graph Convolutional Network model to predict cancer drug response. DualGCN encodes both chemical structures of drugs and omics data of biological samples using graph convolutional networks. Then the two embeddings are fed into a multilayer perceptron to predict drug response. DualGCN incorporates prior knowledge on cancer-related genes and protein\u2013protein interactions, and outperforms most state-of-the-art methods while avoiding using large-scale SNV data.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Conclusions<\/jats:title>\n                <jats:p>The proposed method outperforms most state-of-the-art methods in predicting cancer drug response without the use of large-scale SNV data. These favorable results indicate its potential to be extended to clinical and single-cell tumor samples and advancements in precision medicine.\n<\/jats:p>\n              <\/jats:sec>","DOI":"10.1186\/s12859-022-04664-4","type":"journal-article","created":{"date-parts":[[2022,4,15]],"date-time":"2022-04-15T05:02:42Z","timestamp":1649998962000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":31,"title":["DualGCN: a dual graph convolutional network model to predict cancer drug response"],"prefix":"10.1186","volume":"23","author":[{"given":"Tianxing","family":"Ma","sequence":"first","affiliation":[]},{"given":"Qiao","family":"Liu","sequence":"additional","affiliation":[]},{"given":"Haochen","family":"Li","sequence":"additional","affiliation":[]},{"given":"Mu","family":"Zhou","sequence":"additional","affiliation":[]},{"given":"Rui","family":"Jiang","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9684-5643","authenticated-orcid":false,"given":"Xuegong","family":"Zhang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,4,15]]},"reference":[{"key":"4664_CR1","doi-asserted-by":"publisher","first-page":"299","DOI":"10.1038\/s41586-019-1730-1","volume":"575","author":"N Vasan","year":"2019","unstructured":"Vasan N, Baselga J, Hyman DM. A view on drug resistance in cancer. Nature. 2019;575:299\u2013309. https:\/\/doi.org\/10.1038\/s41586-019-1730-1.","journal-title":"Nature"},{"key":"4664_CR2","doi-asserted-by":"publisher","first-page":"603","DOI":"10.1038\/nature11003","volume":"483","author":"J Barretina","year":"2012","unstructured":"Barretina J, Caponigro G, Stransky N, Venkatesan K, Margolin AA, Kim S, et al. The Cancer Cell Line Encyclopedia enables predictive modelling of anticancer drug sensitivity. Nature. 2012;483:603\u20137. https:\/\/doi.org\/10.1038\/nature11003.","journal-title":"Nature"},{"key":"4664_CR3","doi-asserted-by":"publisher","first-page":"D777","DOI":"10.1093\/nar\/gkw1121","volume":"45","author":"SA Forbes","year":"2017","unstructured":"Forbes SA, Beare D, Boutselakis H, Bamford S, Bindal N, Tate J, et al. COSMIC: somatic cancer genetics at high-resolution. Nucleic Acids Res. 2017;45:D777\u201383. https:\/\/doi.org\/10.1093\/nar\/gkw1121.","journal-title":"Nucleic Acids Res"},{"key":"4664_CR4","doi-asserted-by":"publisher","first-page":"D955","DOI":"10.1093\/NAR\/GKS1111","volume":"41","author":"W Yang","year":"2013","unstructured":"Yang W, Soares J, Greninger P, Edelman EJ, Lightfoot H, Forbes S, et al. Genomics of Drug Sensitivity in Cancer (GDSC): a resource for therapeutic biomarker discovery in cancer cells. Nucleic Acids Res. 2013;41:D955\u201361. https:\/\/doi.org\/10.1093\/NAR\/GKS1111.","journal-title":"Nucleic Acids Res"},{"key":"4664_CR5","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/gb-2014-15-3-r47","volume":"15","author":"P Geeleher","year":"2014","unstructured":"Geeleher P, Cox NJ, Huang RS. Clinical drug response can be predicted using baseline gene expression levels and in vitro drug sensitivity in cell lines. Genome Biol. 2014;15:1\u201312. https:\/\/doi.org\/10.1186\/gb-2014-15-3-r47.","journal-title":"Genome Biol"},{"key":"4664_CR6","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/gb-2013-14-10-r110","volume":"14","author":"A Daemen","year":"2013","unstructured":"Daemen A, Griffith OL, Heiser LM, Wang NJ, Enache OM, Sanborn Z, et al. Modeling precision treatment of breast cancer. Genome Biol. 2013;14:1\u201314. https:\/\/doi.org\/10.1186\/gb-2013-14-10-r110.","journal-title":"Genome Biol"},{"key":"4664_CR7","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41598-018-27214-6","volume":"8","author":"Y Chang","year":"2018","unstructured":"Chang Y, Park H, Yang HJ, Lee S, Lee KY, Kim TS, et al. Cancer Drug Response Profile scan (CDRscan): a deep learning model that predicts drug effectiveness from cancer genomic signature. Sci Rep. 2018;8:1\u201311. https:\/\/doi.org\/10.1038\/s41598-018-27214-6.","journal-title":"Sci Rep"},{"key":"4664_CR8","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s12859-019-2910-6","volume":"20","author":"P Liu","year":"2019","unstructured":"Liu P, Li H, Li S, Leung KS. Improving prediction of phenotypic drug response on cancer cell lines using deep convolutional network. BMC Bioinform. 2019;20:1\u201314. https:\/\/doi.org\/10.1186\/s12859-019-2910-6.","journal-title":"BMC Bioinform"},{"issue":"Supplement_2","key":"4664_CR9","doi-asserted-by":"publisher","first-page":"I911","DOI":"10.1093\/bioinformatics\/btaa822","volume":"36","author":"Q Liu","year":"2020","unstructured":"Liu Q, Hu Z, Jiang R, Zhou M. DeepCDR: a hybrid graph convolutional network for predicting cancer drug response. Bioinformatics. 2020;36(Supplement_2):I911\u20138. https:\/\/doi.org\/10.1093\/bioinformatics\/btaa822.","journal-title":"Bioinformatics"},{"key":"4664_CR10","doi-asserted-by":"publisher","first-page":"81","DOI":"10.1038\/nrclinonc.2017.166","volume":"15","author":"I Dagogo-Jack","year":"2018","unstructured":"Dagogo-Jack I, Shaw AT. Tumour heterogeneity and resistance to cancer therapies. Nat Rev Clin Oncol. 2018;15:81\u201394. https:\/\/doi.org\/10.1038\/nrclinonc.2017.166.","journal-title":"Nat Rev Clin Oncol"},{"key":"4664_CR11","doi-asserted-by":"publisher","first-page":"4557","DOI":"10.1158\/0008-5472.CAN-18-3962","volume":"79","author":"DC Hinshaw","year":"2019","unstructured":"Hinshaw DC, Shevde LA. The tumor microenvironment innately modulates cancer progression. Cancer Res. 2019;79:4557\u201367. https:\/\/doi.org\/10.1158\/0008-5472.CAN-18-3962.","journal-title":"Cancer Res"},{"key":"4664_CR12","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41392-020-00449-4","volume":"6","author":"T Tang","year":"2021","unstructured":"Tang T, Huang X, Zhang G, Hong Z, Bai X, Liang T. Advantages of targeting the tumor immune microenvironment over blocking immune checkpoint in cancer immunotherapy. Signal Transduct Target Ther. 2021;6:1\u201313. https:\/\/doi.org\/10.1038\/s41392-020-00449-4.","journal-title":"Signal Transduct Target Ther"},{"key":"4664_CR13","doi-asserted-by":"publisher","first-page":"1206","DOI":"10.3389\/fcell.2021.637675","volume":"9","author":"Y Ni","year":"2021","unstructured":"Ni Y, Zhou X, Yang J, Shi H, Li H, Zhao X, et al. The role of tumor-stroma interactions in drug resistance within tumor microenvironment. Front Cell Dev Biol. 2021;9:1206.","journal-title":"Front Cell Dev Biol"},{"key":"4664_CR14","doi-asserted-by":"publisher","first-page":"1050","DOI":"10.1016\/j.tips.2020.10.004","volume":"41","author":"Z Wu","year":"2020","unstructured":"Wu Z, Lawrence PJ, Ma A, Zhu J, Xu D, Ma Q. Single-cell techniques and deep learning in predicting drug response. Trends Pharmacol Sci. 2020;41:1050\u201365. https:\/\/doi.org\/10.1016\/j.tips.2020.10.004.","journal-title":"Trends Pharmacol Sci"},{"key":"4664_CR15","doi-asserted-by":"publisher","first-page":"4412","DOI":"10.1158\/0008-5472.CAN-19-0122","volume":"79","author":"M Prieto-Vila","year":"2019","unstructured":"Prieto-Vila M, Usuba W, Takahashi RU, Shimomura I, Sasaki H, Ochiya T, et al. Single-cell analysis reveals a preexisting drug-resistant subpopulation in the luminal breast cancer subtype. Cancer Res. 2019;79:4412\u201325. https:\/\/doi.org\/10.1158\/0008-5472.CAN-19-0122.","journal-title":"Cancer Res"},{"key":"4664_CR16","doi-asserted-by":"publisher","first-page":"1353","DOI":"10.1101\/gr.234062.117","volume":"28","author":"YJ Ho","year":"2018","unstructured":"Ho YJ, Anaparthy N, Molik D, Mathew G, Aicher T, Patel A, et al. Single-cell RNA-seq analysis identifies markers of resistance to targeted BRAF inhibitors in melanoma cell populations. Genome Res. 2018;28:1353\u201363. https:\/\/doi.org\/10.1101\/gr.234062.117.","journal-title":"Genome Res"},{"key":"4664_CR17","doi-asserted-by":"publisher","first-page":"207","DOI":"10.1038\/nature12064","volume":"500","author":"A Adey","year":"2013","unstructured":"Adey A, Burton JN, Kitzman JO, Hiatt JB, Lewis AP, Martin BK, et al. The haplotype-resolved genome and epigenome of the aneuploid HeLa cancer cell line. Nature. 2013;500:207\u201311. https:\/\/doi.org\/10.1038\/nature12064.","journal-title":"Nature"},{"key":"4664_CR18","doi-asserted-by":"publisher","first-page":"175","DOI":"10.1038\/nrg.2015.16","volume":"17","author":"C Gawad","year":"2016","unstructured":"Gawad C, Koh W, Quake SR. Single-cell genome sequencing: current state of the science. Nat Rev Genet. 2016;17:175\u201388. https:\/\/doi.org\/10.1038\/nrg.2015.16.","journal-title":"Nat Rev Genet"},{"key":"4664_CR19","doi-asserted-by":"publisher","DOI":"10.1101\/2021.06.10.447906","author":"T Ma","year":"2021","unstructured":"Ma T, Li H, Zhang X. Discovering single-cell eQTLs from scRNA-seq data only. bioRxiv. 2021. https:\/\/doi.org\/10.1101\/2021.06.10.447906.","journal-title":"bioRxiv"},{"key":"4664_CR20","doi-asserted-by":"publisher","first-page":"71","DOI":"10.1038\/s41576-020-00292-x","volume":"22","author":"E Armingol","year":"2021","unstructured":"Armingol E, Officer A, Harismendy O, Lewis NE. Deciphering cell\u2013cell interactions and communication from gene expression. Nat Rev Genet. 2021;22:71\u201388. https:\/\/doi.org\/10.1038\/s41576-020-00292-x.","journal-title":"Nat Rev Genet"},{"key":"4664_CR21","doi-asserted-by":"publisher","first-page":"1458","DOI":"10.1016\/j.celrep.2018.10.047","volume":"25","author":"MP Kumar","year":"2018","unstructured":"Kumar MP, Du J, Lagoudas G, Jiao Y, Sawyer A, Drummond DC, et al. Analysis of single-cell RNA-Seq identifies cell\u2013cell communication associated with tumor characteristics. Cell Rep. 2018;25:1458-1468.e4. https:\/\/doi.org\/10.1016\/j.celrep.2018.10.047.","journal-title":"Cell Rep"},{"key":"4664_CR22","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41467-021-22801-0","volume":"12","author":"F Wu","year":"2021","unstructured":"Wu F, Fan J, He Y, Xiong A, Yu J, Li Y, et al. Single-cell profiling of tumor heterogeneity and the microenvironment in advanced non-small cell lung cancer. Nat Commun. 2021;12:1\u201311. https:\/\/doi.org\/10.1038\/s41467-021-22801-0.","journal-title":"Nat Commun"},{"key":"4664_CR23","unstructured":"Kipf TN, Welling M. Semi-supervised classification with graph convolutional networks. arXiv. 2017. https:\/\/arxiv.org\/abs\/1609.02907v4."},{"key":"4664_CR24","doi-asserted-by":"publisher","first-page":"1396","DOI":"10.1126\/science.1254257","volume":"344","author":"AP Patel","year":"2014","unstructured":"Patel AP, Tirosh I, Trombetta JJ, Shalek AK, Gillespie SM, Wakimoto H, et al. Single-cell RNA-seq highlights intratumoral heterogeneity in primary glioblastoma. Science. 2014;344:1396\u2013401.","journal-title":"Science"},{"key":"4664_CR25","doi-asserted-by":"publisher","first-page":"189","DOI":"10.1126\/science.aad0501","volume":"352","author":"I Tirosh","year":"2016","unstructured":"Tirosh I, Izar B, Prakadan SM, Wadsworth MH, Treacy D, Trombetta JJ, et al. Dissecting the multicellular ecosystem of metastatic melanoma by single-cell RNA-seq. Science. 2016;352:189\u201396. https:\/\/doi.org\/10.1126\/science.aad0501.","journal-title":"Science"},{"key":"4664_CR26","doi-asserted-by":"publisher","first-page":"1024","DOI":"10.1038\/s41422-020-0374-x","volume":"30","author":"YP Chen","year":"2020","unstructured":"Chen YP, Yin JH, Li WF, Li HJ, Chen DP, Zhang CJ, et al. Single-cell transcriptomics reveals regulators underlying immune cell diversity and immune subtypes associated with prognosis in nasopharyngeal carcinoma. Cell Res. 2020;30:1024\u201342. https:\/\/doi.org\/10.1038\/s41422-020-0374-x.","journal-title":"Cell Res"},{"key":"4664_CR27","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41467-020-16164-1","volume":"11","author":"N Kim","year":"2020","unstructured":"Kim N, Kim HK, Lee K, Hong Y, Cho JH, Choi JW, et al. Single-cell RNA sequencing demonstrates the molecular and cellular reprogramming of metastatic lung adenocarcinoma. Nat Commun. 2020;11:1\u201315. https:\/\/doi.org\/10.1038\/s41467-020-16164-1.","journal-title":"Nat Commun"},{"key":"4664_CR28","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s13073-020-00741-6","volume":"12","author":"HW Lee","year":"2020","unstructured":"Lee HW, Chung W, Lee HO, Jeong DE, Jo A, Lim JE, et al. Single-cell RNA sequencing reveals the tumor microenvironment and facilitates strategic choices to circumvent treatment failure in a chemorefractory bladder cancer patient. Genome Med. 2020;12:1\u201321. https:\/\/doi.org\/10.1186\/s13073-020-00741-6.","journal-title":"Genome Med"},{"key":"4664_CR29","unstructured":"Ramsudar B, Eastman P, Walters P, Pande V. Deep learning for life sciences. 2019."},{"key":"4664_CR30","unstructured":"Ioffe S, Szegedy C. Batch normalization: accelerating deep network training by reducing internal covariate shift. In: International conference on machine learning. PMLR; 2015. p. 448\u201356."},{"key":"4664_CR31","first-page":"1929","volume":"15","author":"N Srivastava","year":"2014","unstructured":"Srivastava N, Hinton G, Krizhevsky A, Sutskever I, Salakhutdinov R. Dropout: a simple way to prevent neural networks from overfitting. J Mach Learn Res. 2014;15:1929\u201358.","journal-title":"J Mach Learn Res"},{"key":"4664_CR32","doi-asserted-by":"publisher","first-page":"1113","DOI":"10.1038\/ng.2764","volume":"45","author":"JN Weinstein","year":"2013","unstructured":"Weinstein JN, Collisson EA, Mills GB, Shaw KRM, Ozenberger BA, Ellrott K, et al. The cancer genome atlas pan-cancer analysis project. Nat Genet. 2013;45:1113\u201320. https:\/\/doi.org\/10.1038\/ng.2764.","journal-title":"Nat Genet"},{"key":"4664_CR33","doi-asserted-by":"publisher","first-page":"D1102","DOI":"10.1093\/NAR\/GKY1033","volume":"47","author":"S Kim","year":"2019","unstructured":"Kim S, Chen J, Cheng T, Gindulyte A, He J, He S, et al. PubChem 2019 update: improved access to chemical data. Nucleic Acids Res. 2019;47:D1102\u20139. https:\/\/doi.org\/10.1093\/NAR\/GKY1033.","journal-title":"Nucleic Acids Res"},{"key":"4664_CR34","doi-asserted-by":"publisher","first-page":"D607","DOI":"10.1093\/NAR\/GKY1131","volume":"47","author":"D Szklarczyk","year":"2019","unstructured":"Szklarczyk D, Gable AL, Lyon D, Junge A, Wyder S, Huerta-Cepas J, et al. STRING v11: protein\u2013protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets. Nucleic Acids Res. 2019;47:D607\u201313. https:\/\/doi.org\/10.1093\/NAR\/GKY1131.","journal-title":"Nucleic Acids Res"},{"key":"4664_CR35","doi-asserted-by":"publisher","first-page":"D682","DOI":"10.1093\/NAR\/GKZ966","volume":"48","author":"AD Yates","year":"2020","unstructured":"Yates AD, Achuthan P, Akanni W, Allen J, Allen J, Alvarez-Jarreta J, et al. Ensembl 2020. Nucleic Acids Res. 2020;48:D682\u20138. https:\/\/doi.org\/10.1093\/NAR\/GKZ966.","journal-title":"Nucleic Acids Res"}],"container-title":["BMC Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12859-022-04664-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s12859-022-04664-4\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12859-022-04664-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,8,7]],"date-time":"2023-08-07T10:04:41Z","timestamp":1691402681000},"score":1,"resource":{"primary":{"URL":"https:\/\/bmcbioinformatics.biomedcentral.com\/articles\/10.1186\/s12859-022-04664-4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,4,15]]},"references-count":35,"journal-issue":{"issue":"S4","published-online":{"date-parts":[[2022,4]]}},"alternative-id":["4664"],"URL":"https:\/\/doi.org\/10.1186\/s12859-022-04664-4","relation":{},"ISSN":["1471-2105"],"issn-type":[{"value":"1471-2105","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,4,15]]},"assertion":[{"value":"24 March 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"4 April 2022","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"15 April 2022","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Not applicable.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"The authors declare that they have no competing interests.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"129"}}