MHCLSyn: Multi-View Hypergraph Contrastive Learning for Synergistic Drug Combination Prediction
Abstract
In the field of cancer treatment, drug combination therapy appears to be a promising treatment strategy compared to monotherapy. Recently, plenty of computational models are gradually applied to prioritize synergistic drug combinations. However, the existing prediction models have not fully exploited the multi-way relations between drug combinations and cell lines. Besides, the number of identified drug-drug-cell line triplets is insufficient owning to the high cost of in vitro screening, which affects the ability of models to capture and utilize multi-way relations. To address this challenge, we design the multi-view hypergraph contrastive learning model, termed MHCLSyn, for synergistic drug combination prediction. First, the synergistic drug-drug-cell line triplets are formulated as a drug synergy hypergraph, and three task-specific hypergraphs are designed based on the drug synergy hypergraph. Then, we design a multi-view hypergraph contrastive learning with enhancement schemes, which allows for more expressive and discriminative node representation learning on drug synergy hypergraph. After that, the representations of nodes indicating drug-drug-cell line triplets are inputted to fully connected network for making predictions. Extensive experiments show MHCLSyn achieves better performance than state-of-the-art prediction models on benchmark datasets and is applicable to unseen drug combinations or cell lines. Case study indicates that MHCLSyn is capable of detecting potential synergistic drug combinations.
Keywords
References
W. Jin, J. M. Stokes, R. T. Eastman, Z. Itkin, A. V. Zakharov, J. J. Collins, T. S. Jaakkola, and R. Barzilay, Deep learning identifies synergistic drug combinations for treating COVID-19, Proc. Natl. Acad. Sci. U. S. A., vol. 118, no. 39, p. e2105070118, 2021.
P. Csermely, T. Korcsmáros, H. J. M. Kiss, G. London, and R. Nussinov, Structure and dynamics of molecular networks: A novel paradigm of drug discovery, Pharmacol. Ther., vol. 138, no. 3, pp. 333–408, 2013.
S. Zhao, T. Nishimura, Y. Chen, E. U. Azeloglu, O. Gottesman, C. Giannarelli, M. U. Zafar, L. Benard, J. J. Badimon, R. J. Hajjar, et al., Systems pharmacology of adverse event mitigation by drug combinations, Sci. Transl. Med., vol. 5, no. 206, p. e3006548, 2013.
J. Lehár, A. S. Krueger, W. Avery, A. M. Heilbut, L. M. Johansen, E. R. Price, R. J. Rickles, G. F. Short III, J. E. Staunton, X. Jin, et al., Synergistic drug combinations tend to improve therapeutically relevant selectivity, Nat. Biotechnol., vol. 27, pp. 659–666, 2009.
A. A. Borisy, P. J. Elliott, N. W. Hurst, M. S. Lee, J. Lehár, E. R. Price, G. Serbedzija, G. R. Zimmermann, M. A. Foley, B. R. Stockwell, et al., Systematic discovery of multicomponent therapeutics, Proc. Natl. Acad. Sci. U. S. A., vol. 100, no. 13, pp. 7977–7982, 2003.
B. Mansoori, A. Mohammadi, S. Davudian, S. Shirjang, and B. Baradaran, The different mechanisms of cancer drug resistance: A brief review, Adv. Pharm. Bull., vol. 7, no. 3, pp. 339–348, 2017.
G. Housman, S. Byler, S. Heerboth, K. Lapinska, M. Longacre, N. Snyder, and S. Sarkar, Drug resistance in cancer: An overview, Cancers, vol. 6, no. 3, pp. 1769–1792, 2014.
M. Nikolaou, A. Pavlopoulou, A. G. Georgakilas, and E. Kyrodimos, The challenge of drugresistance in cancer treatment: A current overview, Clin. Exp. Metastasis, vol. 35, no. 4, pp. 309–318, 2018.
T. D. Giles, M. A. Weber, J. Basile, A. H. Gradman, D. B. Bharucha, W. Chen, and M. Pattathil, Efficacy and safety of nebivolol and valsartan as fixed-dose combination in hypertension: A randomised, multicentre study, Lancet, vol. 383, no. 9932, pp. 1889–1898, 2014.
W. Zheng, W. Sun, and A. Simeonov, Drug repurposing screens and synergistic drug-combinations for infectious diseases, Br. J. Pharmacol., vol. 175, no. 2, pp. 181–191, 2018.
Y. Kim, S. Zheng, J. Tang, W. Jim Zheng, Z. Li, and X. Jiang, Anticancer drug synergy prediction in understudied tissues using transfer learning, J. Am. Med. Inform. Assoc., vol. 28, no. 1, pp. 42–51, 2021.
P. P. Vitiello, G. Martini, L. Mele, E. F. Giunta, V. De Falco, D. Ciardiello, V. Belli, C. Cardone, N. Matrone, L. Poliero, et al., Vulnerability to low-dose combination of irinotecan and niraparib in ATM-mutated colorectal cancer, J. Exp. Clin. Cancer Res., vol. 40, no. 1, p. 15, 2021.
N. M. Raghavendra, D. Pingili, S. Kadasi, A. Mettu, and S. V. U. M. Prasad, Dual or multi-targeting inhibitors: The next generation anticancer agents, Eur. J. Med. Chem., vol. 143, pp. 1277–1300, 2018.
R. R. Ramsay, M. R. Popovic-Nikolic, K. Nikolic, E. Uliassi, and M. L. Bolognesi, A perspective on multi-target drug discovery and design for complex diseases, Clin. Transl. Med., vol. 7, no. 1, p. 3, 2018.
C. T. Keith, A. A. Borisy, and B. R. Stockwell, Multicomponent therapeutics for networked systems, Nat. Rev. Drug Discov., vol. 4, no. 1, pp. 71–78, 2005.
D. Q. Chong, I. B. Tan, S.-P. Choo, and H. C. Toh, The evolving landscape of therapeutic drug development for hepatocellular carcinoma, Contemp. Clin. Trials, vol. 36, no. 2, pp. 605–615, 2013.
F. Cheng, I. A. Kovács, and A.-L. Barabási, Network-based prediction of drug combinations, Nat. Commun., vol. 10, no. 1, p. 1197, 2019.
P. Sidorov, S. Naulaerts, J. Ariey-Bonnet, E. Pasquier, and P. J. Ballester, Predicting synergism of cancer drug combinations using NCI-ALMANAC data, Front. Chem., vol. 7, p. 509, 2019.
K. Preuer, R. P. I. Lewis, S. Hochreiter, A. Bender, K. C. Bulusu, and G. Klambauer, DeepSynergy: Predicting anti-cancer drug synergy with deep learning, Bioinformatics, vol. 34, no. 9, pp. 1538–1546, 2018.
Q. Liu and L. Xie, TranSynergy: Mechanism-driven interpretable deep neural network for the synergistic prediction and pathway deconvolution of drug combinations, PLoS Comput. Biol., vol. 17, no. 2, p. e1008653, 2021.
J. Wang, X. Liu, S. Shen, L. Deng, and H. Liu, DeepDDS: Deep graph neural network with attention mechanism to predict synergistic drug combinations, Brief. Bioinform., vol. 23, no. 1, p. bbab390, 2022.
X. Liu, C. Song, S. Liu, M. Li, X. Zhou, and W. Zhang, Multi-way relation-enhanced hypergraph representation learning for anti-cancer drug synergy prediction, Bioinformatics, vol. 38, no. 20, pp. 4782–4789, 2022.
Y. Feng, H. You, Z. Zhang, R. Ji, and Y. Gao, Hypergraph neural networks, Proc. AAAI Conf. Artif. Intell., vol. 33, no. 1, pp. 3558–3565, 2019.
J. O’Neil, Y. Benita, I. Feldman, M. Chenard, B. Roberts, Y. Liu, J. Li, A. Kral, S. Lejnine, A. Loboda, et al., An unbiased oncology compound screen to identify novel combination strategies, Mol. Cancer Ther., vol. 15, no. 6, pp. 1155–1162, 2016.
S. L. Holbeck, R. Camalier, J. A. Crowell, J. P. Govindharajulu, M. Hollingshead, L. W. Anderson, E. Polley, L. Rubinstein, A. Srivastava, D. Wilsker, et al., The national cancer institute ALMANAC: A comprehensive screening resource for the detection of anticancer drug pairs with enhanced therapeutic activity, Cancer Res., vol. 77, no. 13, pp. 3564–3576, 2017.
G. Y. Di Veroli, C. Fornari, D. Wang, S. Mollard, J. L. Bramhall, F. M. Richards, and D. I. Jodrell, Combenefit: an interactive platform for the analysis and visualization of drug combinations, Bioinformatics, vol. 32, no. 18, pp. 2866–2868, 2016.
S. Kim, J. Chen, T. Cheng, A. Gindulyte, J. He, S. He, Q. Li, B. A. Shoemaker, P. A. Thiessen, B. Yu, et al., PubChem 2019 update: Improved access to chemical data, Nucleic Acids Res., vol. 47, no. D1, pp. D1102–D1109, 2019.
J. Hu, L. Shen, S. Albanie, G. Sun, and E. Wu, Squeeze-and-excitation networks, IEEE Trans. Pattern Anal. Mach. Intell., vol. 42, no. 8, pp. 2011–2023, 2020.
Z. Zhou, Y. Hu, Y. Zhang, J. Chen, and H. Cai, Multiview deep graph infomax to achieve unsupervised graph embedding, IEEE Trans. Cybern., vol. 53, no. 10, pp. 6329–6339, 2023.
A. Zajdel, J. Nycz, and A. Wilczok, Lapatinib enhances paclitaxel toxicity in MCF-7, T47D, and MDA-MB-321 breast cancer cells, Toxicol. Vitro, vol. 75, p. 105200, 2021.
N. A. Patankar, J. Pritchard, M. van Grinsven, M. Osooly, and M. B. Bally, Topotecan and doxorubicin combination to treat recurrent ovarian cancer: The influence of drug exposure time and delivery systems to achieve optimum therapeutic activity, Clin. Cancer Res., vol. 19, no. 4, pp. 865–877, 2013.
Y. Liu, H. Tamam, and Y. Yeo, Mixed liposome approach for ratiometric and sequential delivery of paclitaxel and gemcitabine, AAPS PharmSciTech, vol. 19, no. 2, pp. 693–699, 2018.
S. F. Teixeira, I. d. S. Guimarães, K. P. Madeira, R. D. Daltoé, I. V. Silva, and L. B. A. Rangel, Metformin synergistically enhances antiproliferative effects of cisplatin and etoposide in NCI-H460 human lung cancer cells, J. Bras. Pneumol., vol. 39, no. 6, pp. 644–649, 2013.
京公网安备11010802044758号