Generic placeholder image

Current Medicinal Chemistry

Editor-in-Chief

ISSN (Print): 0929-8673
ISSN (Online): 1875-533X

General Review Article

Novel Computational Methods for Cancer Drug Design

Author(s): Sekhar Talluri*, Mohammad Amjad Kamal and Rama Rao Malla*

Volume 31, Issue 5, 2024

Published on: 19 June, 2023

Page: [554 - 572] Pages: 19

DOI: 10.2174/0929867330666230403100008

Price: $65

Abstract

Cancer is a complex and debilitating disease that is one of the leading causes of death in the modern world. Computational methods have contributed to the successful design and development of several drugs. The recent advances in computational methodology, coupled with the avalanche of data being acquired through high throughput genomics, proteomics, and metabolomics, are likely to increase the contribution of computational methods toward the development of more effective treatments for cancer. Recent advances in the application of neural networks for the prediction of the native conformation of proteins have provided structural information regarding the complete human proteome. In addition, advances in machine learning and network pharmacology have provided novel methods for target identification and for the utilization of biological, pharmacological, and clinical databases for the design and development of drugs. This is a review of the key advances in computational methods that have the potential for application in the design and development of drugs for cancer.

Keywords: Anti-cancer drug design, computational drug design, convolutional neural networks, generative adversarial networks, graph neural networks, reinforcement learning variational autoencoders.

[1]
Weinberg, R.A. How cancer arises. Sci. Am., 1996, 275(3), 62-70.
[http://dx.doi.org/10.1038/scientificamerican0996-62] [PMID: 8701295]
[2]
Deepak, K.G.K.; Vempati, R.; Nagaraju, G.P.; Dasari, V.R.; S, N.; Rao, D.N.; Malla, R.R. Tumor microenvironment: Challenges and opportunities in targeting metastasis of triple negative breast cancer. Pharmacol. Res., 2020, 153, 104683.
[http://dx.doi.org/10.1016/j.phrs.2020.104683] [PMID: 32050092]
[3]
Cao, C.; Moult, J. GWAS and drug targets. BMC Genomics, 2014, 15, S5.
[http://dx.doi.org/10.1186/1471-2164-15-S4-S5]
[4]
Liang, B.; Ding, H.; Huang, L.; Luo, H.; Zhu, X. GWAS in cancer: Progress and challenges. Mol. Genet. Genomics, 2020, 295(3), 537-561.
[http://dx.doi.org/10.1007/s00438-020-01647-z] [PMID: 32048005]
[5]
Boža, V.; Brejová, B.; Vinař, T. DeepNano: Deep recurrent neural networks for base calling in MinION nanopore reads. PLoS One, 2017, 12(6), e0178751.
[http://dx.doi.org/10.1371/journal.pone.0178751] [PMID: 28582401]
[6]
Wei, Q.; Ji, Z.; Li, Z.; Du, J.; Wang, J.; Xu, J.; Xiang, Y.; Tiryaki, F.; Wu, S.; Zhang, Y.; Tao, C.; Xu, H. A study of deep learning approaches for medication and adverse drug event extraction from clinical text. J. Am. Med. Inform. Assoc., 2020, 27(1), 13-21.
[http://dx.doi.org/10.1093/jamia/ocz063] [PMID: 31135882]
[7]
Gorostiola González, M.; Janssen, A.P.A.; IJzerman, A.P.; Heitman, L.H.; van Westen, G.J.P. Oncological drug discovery: AI meets structure-based computational research. Drug Discov. Today, 2022, 27(6), 1661-1670.
[http://dx.doi.org/10.1016/j.drudis.2022.03.005] [PMID: 35301149]
[8]
Rui Chang; Shoemaker, R.; Wei Wang A novel knowledge-driven systems biology approach for phenotype prediction upon genetic intervention. IEEE/ACM Trans. Comput. Biol. Bioinformatics, 2011, 8(5), 1170-1182.
[http://dx.doi.org/10.1109/TCBB.2011.18] [PMID: 21282866]
[9]
Lu, Y.; Bi, J.; Li, F.; Wang, G.; Zhu, J.; Jin, J.; Liu, Y. Differential gene analysis of trastuzumab in breast cancer based on network pharmacology and medical images. Front. Physiol., 2022, 13, 942049.
[http://dx.doi.org/10.3389/fphys.2022.942049] [PMID: 35874525]
[10]
Li, S.; Wu, S.; Wang, L.; Li, F.; Jiang, H.; Bai, F. Recent advances in predicting protein–protein interactions with the aid of artificial intelligence algorithms. Curr. Opin. Struct. Biol., 2022, 73, 102344.
[http://dx.doi.org/10.1016/j.sbi.2022.102344] [PMID: 35219216]
[11]
Tunyasuvunakool, K.; Adler, J.; Wu, Z.; Green, T.; Zielinski, M.; Žídek, A.; Bridgland, A.; Cowie, A.; Meyer, C.; Laydon, A.; Velankar, S.; Kleywegt, G.J.; Bateman, A.; Evans, R.; Pritzel, A.; Figurnov, M.; Ronneberger, O.; Bates, R.; Kohl, S.A.A.; Potapenko, A.; Ballard, A.J.; Romera-Paredes, B.; Nikolov, S.; Jain, R.; Clancy, E.; Reiman, D.; Petersen, S.; Senior, A.W.; Kavukcuoglu, K.; Birney, E.; Kohli, P.; Jumper, J.; Hassabis, D. Highly accurate protein structure prediction for the human proteome. Nature, 2021, 596(7873), 590-596.
[http://dx.doi.org/10.1038/s41586-021-03828-1] [PMID: 34293799]
[12]
Polanski, J. Unsupervised learning in drug design from self-organization to deep chemistry. Int. J. Mol. Sci., 2022, 23(5), 2797.
[http://dx.doi.org/10.3390/ijms23052797] [PMID: 35269939]
[13]
Nag, S.; Baidya, A.T.K.; Mandal, A.; Mathew, A.T.; Das, B.; Devi, B.; Kumar, R. Deep learning tools for advancing drug discovery and development. 3 Biotech, 2022, 12(5), 110.2022,
[14]
Liu, M.; Shen, X.; Pan, W. Deep reinforcement learning for personalized treatment recommendation. Stat. Med., 2022, 41(20), 4034-4056.
[http://dx.doi.org/10.1002/sim.9491] [PMID: 35716038]
[15]
Kabir, A.; Muth, A. Polypharmacology: The science of multi-targeting molecules. Pharmacol. Res., 2022, 176, 106055.
[http://dx.doi.org/10.1016/j.phrs.2021.106055] [PMID: 34990865]
[16]
Zhang, T.; Zhang, L.; Payne, P.R.O.; Li, F. Synergistic drug combination prediction by integrating multiomics data in deep learning models. Methods Mol. Biol., 2021, 2194, 223-238.
[http://dx.doi.org/10.1007/978-1-0716-0849-4_12] [PMID: 32926369]
[17]
Garofalo, M.; Grazioso, G.; Cavalli, A.; Sgrignani, J. How computational chemistry and drug delivery techniques can support the development of new anticancer drugs. Molecules, 2020, 25(7), 1756.
[http://dx.doi.org/10.3390/molecules25071756] [PMID: 32290224]
[18]
Bannigan, P.; Aldeghi, M.; Bao, Z.; Häse, F.; Aspuru-Guzik, A.; Allen, C. Machine learning directed drug formulation development. Adv. Drug Deliv. Rev., 2021, 175, 113806.
[http://dx.doi.org/10.1016/j.addr.2021.05.016] [PMID: 34019959]
[19]
Pantziarka, P.; Verbaanderd, C.; Huys, I.; Bouche, G.; Meheus, L. Repurposing drugs in oncology: From candidate selection to clinical adoption. Semin. Cancer Biol., 2021, 68, 186-191.
[http://dx.doi.org/10.1016/j.semcancer.2020.01.008] [PMID: 31982510]
[20]
Alaimo, S.; Pulvirenti, A. Network-based drug repositioning: Approaches, resources, and research directions. Methods Mol. Biol., 2019, 1903, 97-113.
[http://dx.doi.org/10.1007/978-1-4939-8955-3_6] [PMID: 30547438]
[21]
Prada-Gracia, D.; Huerta-Yépez, S.; Moreno-Vargas, L.M. Application of computational methods for anticancer drug discovery, design, and optimization. Bol. Méd. Hosp. Infant. México, 2016, 73(6), 411-423.
[http://dx.doi.org/10.1016/j.bmhimx.2016.10.006] [PMID: 29421286]
[22]
Ren, N.; Yu, L.; Qian, L.; Ye, G.; Zhu, Z.; Yu, J.; Sun, L.; Zhang, L. Exploring the pharmacological mechanism of the effective chinese medicines against gynecological cancer based on meta-analysis combined with network pharmacology analysis. Front. Oncol., 2022, 12, 817772.
[http://dx.doi.org/10.3389/fonc.2022.817772] [PMID: 35875080]
[23]
Eisenberg, M.C.; Jain, H.V. A confidence building exercise in data and identifiability: Modeling cancer chemotherapy as a case study. J. Theor. Biol., 2017, 431, 63-78.
[http://dx.doi.org/10.1016/j.jtbi.2017.07.018] [PMID: 28733187]
[24]
Gorgulla, C.; Jayaraj, A.; Fackeldey, K.; Arthanari, H. Emerging frontiers in virtual drug discovery: From quantum mechanical methods to deep learning approaches. Curr. Opin. Chem. Biol., 2022, 69, 102156.
[http://dx.doi.org/10.1016/j.cbpa.2022.102156] [PMID: 35576813]
[25]
Shreve, J.T.; Khanani, S.A.; Haddad, T.C. Artificial intelligence in oncology: Current capabilities, future opportunities, and ethical considerations. Am. Soc. Clin. Oncol. Educ. Book, 2022, 42(42), 842-851.
[http://dx.doi.org/10.1200/EDBK_350652] [PMID: 35687826]
[26]
Cui, W.; Aouidate, A.; Wang, S.; Yu, Q.; Li, Y.; Yuan, S. Discovering anti-cancer drugs via computational methods. Front. Pharmacol., 2020, 11, 733.
[http://dx.doi.org/10.3389/fphar.2020.00733] [PMID: 32508653]
[27]
Brady, R.; Enderling, H. Mathematical Models of Cancer: When to predict novel therapies, and when not to. Bull. Math. Biol., 2019, 81(10), 3722-3731.
[http://dx.doi.org/10.1007/s11538-019-00640-x] [PMID: 31338741]
[28]
Rahman, M.M.; Islam, M.R.; Rahman, F.; Rahaman, M.S.; Khan, M.S.; Abrar, S.; Ray, T.K.; Uddin, M.B.; Kali, M.S.K.; Dua, K.; Kamal, M.A.; Chellappan, D.K. Emerging promise of computational techniques in anti-cancer research: At a glance. Bioengineering, 2022, 9(8), 335.
[http://dx.doi.org/10.3390/bioengineering9080335] [PMID: 35892749]
[29]
Gupta, R.; Srivastava, D.; Sahu, M.; Tiwari, S.; Ambasta, R.K.; Kumar, P. Artificial intelligence to deep learning: machine intelligence approach for drug discovery. Mol. Divers., 2021, 25(3), 1315-1360.
[http://dx.doi.org/10.1007/s11030-021-10217-3] [PMID: 33844136]
[30]
Bossé, Y.; Amos, C.I. A decade of GWAS results in lung cancer. Cancer Epidemiol. Biomarkers Prev., 2018, 27(4), 363-379.
[http://dx.doi.org/10.1158/1055-9965.EPI-16-0794] [PMID: 28615365]
[31]
Fachal, L.; Dunning, A.M. From candidate gene studies to GWAS and post-GWAS analyses in breast cancer. Curr. Opin. Genet. Dev., 2015, 30, 32-41.
[http://dx.doi.org/10.1016/j.gde.2015.01.004] [PMID: 25727315]
[32]
Dezső, Z.; Ceccarelli, M. Machine learning prediction of oncology drug targets based on protein and network properties. BMC Bioinf., 2020, 21(1), 104.
[http://dx.doi.org/10.1186/s12859-020-3442-9] [PMID: 32171238]
[33]
Yeh, S.H.; Yeh, H.Y.; Soo, V.W. A network flow approach to predict drug targets from microarray data, disease genes and interactome network - case study on prostate cancer. J. Clin. Bioinf., 2012, 2(1), 1.
[http://dx.doi.org/10.1186/2043-9113-2-1] [PMID: 22239822]
[34]
Ciriello, G.; Cerami, E.; Sander, C.; Schultz, N. Mutual exclusivity analysis identifies oncogenic network modules. Genome Res., 2012, 22(2), 398-406.
[http://dx.doi.org/10.1101/gr.125567.111] [PMID: 21908773]
[35]
Singh, R.; Devkota, K.; Sledzieski, S.; Berger, B.; Cowen, L. Topsy-Turvy: Integrating a global view into sequence-based PPI prediction. Bioinformatics, 2022, 38(Suppl. 1), i264-i272.
[http://dx.doi.org/10.1093/bioinformatics/btac258] [PMID: 35758793]
[36]
Ghedira, K.; Hamdi, Y.; El Béji, A.; Othman, H. An integrative computational approach for the prediction of human-Plasmodium protein-protein interactions. BioMed Res. Int., 2020, 2020, 1-11.
[http://dx.doi.org/10.1155/2020/2082540] [PMID: 33426052]
[37]
Kanitkar, T.R.; Sen, N.; Nair, S.; Soni, N.; Amritkar, K.; Ramtirtha, Y.; Madhusudhan, M.S. Methods for molecular modelling of protein complexes. Methods Mol. Biol., 2021, 2305, 53-80.
[http://dx.doi.org/10.1007/978-1-0716-1406-8_3] [PMID: 33950384]
[38]
Hu, L.; Wang, X.; Huang, Y.A.; Hu, P.; You, Z.H. A survey on computational models for predicting protein–protein interactions. Brief. Bioinform., 2021, 22(5), bbab036.
[http://dx.doi.org/10.1093/bib/bbab036] [PMID: 33693513]
[39]
Yin, R.; Feng, B.Y.; Varshney, A.; Pierce, B.G. Benchmarking ALPHAFOLD for protein complex modeling reveals accuracy determinants. Protein Sci., 2022, 31(8), e4379.
[http://dx.doi.org/10.1002/pro.4379] [PMID: 35900023]
[40]
Jumper, J.; Evans, R.; Pritzel, A.; Green, T.; Figurnov, M.; Ronneberger, O.; Tunyasuvunakool, K.; Bates, R.; Žídek, A.; Potapenko, A.; Bridgland, A.; Meyer, C.; Kohl, S.A.A.; Ballard, A.J.; Cowie, A.; Romera-Paredes, B.; Nikolov, S.; Jain, R.; Adler, J.; Back, T.; Petersen, S.; Reiman, D.; Clancy, E.; Zielinski, M.; Steinegger, M.; Pacholska, M.; Berghammer, T.; Bodenstein, S.; Silver, D.; Vinyals, O.; Senior, A.W.; Kavukcuoglu, K.; Kohli, P.; Hassabis, D. Highly accurate protein structure prediction with AlphaFold. Nature, 2021, 596(7873), 583-589.
[http://dx.doi.org/10.1038/s41586-021-03819-2] [PMID: 34265844]
[41]
Wang, S.; Lin, H.; Huang, Z.; He, Y.; Deng, X.; Xu, Y.; Pei, J.; Lai, L. CavitySpace: A database of potential ligand binding sites in the human proteome. Biomolecules, 2022, 12(7), 967.
[http://dx.doi.org/10.3390/biom12070967] [PMID: 35883523]
[42]
Macari, G.; Toti, D.; Polticelli, F. Computational methods and tools for binding site recognition between proteins and small molecules: from classical geometrical approaches to modern machine learning strategies. J. Comput. Aided Mol. Des., 2019, 33(10), 887-903.
[http://dx.doi.org/10.1007/s10822-019-00235-7] [PMID: 31628659]
[43]
Dhakal, A.; McKay, C.; Tanner, J.J.; Cheng, J. Artificial intelligence in the prediction of protein–ligand interactions: recent advances and future directions. Brief. Bioinform., 2022, 23(1), bbab476.
[http://dx.doi.org/10.1093/bib/bbab476] [PMID: 34849575]
[44]
Pinzi, L.; Rastelli, G. Molecular docking: Shifting paradigms in drug discovery. Int. J. Mol. Sci., 2019, 20(18), 4331.
[http://dx.doi.org/10.3390/ijms20184331] [PMID: 31487867]
[45]
Trott, O.; Olson, A.J. AutoDock Vina: improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading. J. Comput. Chem., 2010, 31(2), 455-461.
[PMID: 19499576]
[46]
Eberhardt, J.; Santos-Martins, D.; Tillack, A.F.; Forli, S. AutoDock Vina 1.2.0: New docking methods, expanded force field, and python bindings. J. Chem. Inf. Model., 2021, 61(8), 3891-3898.
[http://dx.doi.org/10.1021/acs.jcim.1c00203] [PMID: 34278794]
[47]
Wang, C.; Zhang, Y. Improving scoring-docking-screening powers of protein-ligand scoring functions using random forest. J. Comput. Chem., 2017, 38(3), 169-177.
[http://dx.doi.org/10.1002/jcc.24667] [PMID: 27859414]
[48]
Zhang, B.; Li, H.; Yu, K.; Jin, Z. Molecular docking-based computational platform for high-throughput virtual screening. CCF Transactions on High Performance Computing, 2022, 4(1), 63-74.
[http://dx.doi.org/10.1007/s42514-021-00086-5] [PMID: 35039800]
[49]
Aziz, M.; Ejaz, S.A.; Zargar, S.; Akhtar, N.; Aborode, A.T.; A Wani, T.; Batiha, G.E.; Siddique, F.; Alqarni, M.; Akintola, A.A. Deep learning and structure-based virtual screening for drug discovery against NEK7: A novel target for the treatment of cancer. Molecules, 2022, 27(13), 4098.
[http://dx.doi.org/10.3390/molecules27134098] [PMID: 35807344]
[50]
Kerrigan, J.E. Molecular dynamics simulations in drug design. Methods Mol. Biol., 2013, 993, 95-113.
[http://dx.doi.org/10.1007/978-1-62703-342-8_7] [PMID: 23568466]
[51]
Cheng, Y.; Gong, Y.; Liu, Y.; Song, B.; Zou, Q. Molecular design in drug discovery: A comprehensive review of deep generative models. Brief. Bioinf., 2021, 22(6), bbab344.
[http://dx.doi.org/10.1093/bib/bbab344] [PMID: 34415297]
[52]
Segler, M.H.S.; Kogej, T.; Tyrchan, C.; Waller, M.P. Generating focused molecule libraries for drug discovery with recurrent neural networks. ACS Cent. Sci., 2018, 4(1), 120-131.
[http://dx.doi.org/10.1021/acscentsci.7b00512] [PMID: 29392184]
[53]
Joshi, R.P.; Kumar, N. Artificial intelligence for autonomous molecular design: A perspective. Molecules, 2021, 26(22), 6761.
[http://dx.doi.org/10.3390/molecules26226761] [PMID: 34833853]
[54]
Lim, J.; Hwang, S.Y.; Moon, S.; Kim, S.; Kim, W.Y. Scaffold-based molecular design with a graph generative model. Chem. Sci., 2020, 11(4), 1153-1164.
[http://dx.doi.org/10.1039/C9SC04503A] [PMID: 34084372]
[55]
Hong, S.H.; Ryu, S.; Lim, J.; Kim, W.Y. Molecular generative model based on an adversarially regularized autoencoder. J. Chem. Inf. Model., 2020, 60(1), 29-36.
[http://dx.doi.org/10.1021/acs.jcim.9b00694] [PMID: 31820983]
[56]
Putin, E.; Asadulaev, A.; Ivanenkov, Y.; Aladinskiy, V.; Sanchez-Lengeling, B.; Aspuru-Guzik, A.; Zhavoronkov, A. Reinforced adversarial neural computer for de novo molecular design. J. Chem. Inf. Model., 2018, 58(6), 1194-1204.
[http://dx.doi.org/10.1021/acs.jcim.7b00690] [PMID: 29762023]
[57]
Flam-Shepherd, D.; Zhu, K.; Aspuru-Guzik, A. Language models can learn complex molecular distributions. Nat. Commun., 2022, 13(1), 3293.
[http://dx.doi.org/10.1038/s41467-022-30839-x] [PMID: 35672310]
[58]
He, J.; You, H.; Sandström, E.; Nittinger, E.; Bjerrum, E.J.; Tyrchan, C.; Czechtizky, W.; Engkvist, O. Molecular optimization by capturing chemist’s intuition using deep neural networks. J. Cheminform., 2021, 13(1), 26.
[http://dx.doi.org/10.1186/s13321-021-00497-0] [PMID: 33743817]
[59]
Wang, M.; Hsieh, C.Y.; Wang, J.; Wang, D.; Weng, G.; Shen, C.; Yao, X.; Bing, Z.; Li, H.; Cao, D.; Hou, T. RELATION: A deep generative model for structure-based de novo drug design. J. Med. Chem., 2022, 65(13), 9478-9492.
[http://dx.doi.org/10.1021/acs.jmedchem.2c00732] [PMID: 35713420]
[60]
Li, Y.; Hu, J.; Wang, Y.; Zhou, J.; Zhang, L.; Liu, Z. DeepScaffold: A comprehensive tool for scaffold-based de novo drug discovery using deep learning. J. Chem. Inf. Model., 2020, 60(1), 77-91.
[http://dx.doi.org/10.1021/acs.jcim.9b00727] [PMID: 31809029]
[61]
Zheng, S.; Lei, Z.; Ai, H.; Chen, H.; Deng, D.; Yang, Y. Deep scaffold hopping with multimodal transformer neural networks. J. Cheminform., 2021, 13(1), 87.
[http://dx.doi.org/10.1186/s13321-021-00565-5] [PMID: 34774103]
[62]
Zhavoronkov, A.; Ivanenkov, Y.A.; Aliper, A.; Veselov, M.S.; Aladinskiy, V.A.; Aladinskaya, A.V.; Terentiev, V.A.; Polykovskiy, D.A.; Kuznetsov, M.D.; Asadulaev, A.; Volkov, Y.; Zholus, A.; Shayakhmetov, R.R.; Zhebrak, A.; Minaeva, L.I.; Zagribelnyy, B.A.; Lee, L.H.; Soll, R.; Madge, D.; Xing, L.; Guo, T.; Aspuru-Guzik, A. Deep learning enables rapid identification of potent DDR1 kinase inhibitors. Nat. Biotechnol., 2019, 37(9), 1038-1040.
[http://dx.doi.org/10.1038/s41587-019-0224-x] [PMID: 31477924]
[63]
Ongusaha, P.P.; Kim, J.I.; Fang, L.; Wong, T.W.; Yancopoulos, G.D.; Aaronson, S.A.; Lee, S.W. p53 induction and activation of DDR1 kinase counteract p53-mediated apoptosis and influence p53 regulation through a positive feedback loop. EMBO J., 2003, 22(6), 1289-1301.
[http://dx.doi.org/10.1093/emboj/cdg129] [PMID: 12628922]
[64]
Pereira, T.; Abbasi, M.; Oliveira, R.I.; Guedes, R.A.; Salvador, J.A.R.; Arrais, J.P. Deep generative model for therapeutic targets using transcriptomic disease-associated data-USP7 case study. Brief. Bioinform., 2022, 23(4), bbac270.
[http://dx.doi.org/10.1093/bib/bbac270] [PMID: 35789255]
[65]
Wang, J.; Chu, Y.; Mao, J.; Jeon, H.N.; Jin, H.; Zeb, A.; Jang, Y.; Cho, K.H.; Song, T.; No, K.T. De novo molecular design with deep molecular generative models for PPI inhibitors. Brief. Bioinform., 2022, 23(4), bbac285.
[http://dx.doi.org/10.1093/bib/bbac285] [PMID: 35830870]
[66]
Khan, M.F.; Verma, G.; Akhtar, W.; Shaquiquzzaman, M.; Akhter, M.; Rizvi, M.A.; Alam, M.M. Pharmacophore modeling, 3D-QSAR, docking study and ADME prediction of acyl 1,3,4-thiadiazole amides and sulfonamides as antitubulin agents. Arab. J. Chem., 2019, 12(8), 5000-5018.
[http://dx.doi.org/10.1016/j.arabjc.2016.11.004]
[67]
Elkaeed, E.B.; Yousef, R.G.; Elkady, H.; Gobaara, I.M.M.; Alsfouk, B.A.; Husein, D.Z.; Ibrahim, I.M.; Metwaly, A.M.; Eissa, I.H. Design, synthesis, docking, DFT, MD simulation studies of a new nicotinamide-based derivative: In vitro anticancer and VEGFR-2 inhibitory effects. Molecules, 2022, 27(14), 4606.
[http://dx.doi.org/10.3390/molecules27144606] [PMID: 35889478]
[68]
Iwaloye, O.; Elekofehinti, O.O.; Kikiowo, B.; Oluwarotimi, E.A.; Fadipe, T.M. Machine learning-based virtual screening strategy revealssome natural compounds as potential PAK4 inhibitors in triple negative breast cancer. Curr. Proteomics, 2021, 18(5), 753-769.
[http://dx.doi.org/10.2174/1570164618999201223092209]
[69]
Méndez-Lucio, O.; Baillif, B.; Clevert, D.A.; Rouquié, D.; Wichard, J. De novo generation of hit-like molecules from gene expression signatures using artificial intelligence. Nat. Commun., 2020, 11(1), 10.
[http://dx.doi.org/10.1038/s41467-019-13807-w] [PMID: 31900408]
[70]
Sharma, V.; Wakode, S.; Kumar, H. Structure-and ligand-based drug design: concepts, approaches, and challenges; Chemoinformatics and Bioinformatics in the Pharmaceutical Sciences, 2021, pp. 27-53.
[http://dx.doi.org/10.1016/B978-0-12-821748-1.00004-X]
[71]
Loganathan, L.; Muthusamy, K. Current Scenario in structure and ligand-based drug design on anti-colon cancer drugs. Curr. Pharm. Des., 2019, 24(32), 3829-3841.
[http://dx.doi.org/10.2174/1381612824666181114114513] [PMID: 30426891]
[72]
Sanyal, S.; Amin, S.A.; Adhikari, N.; Jha, T. Ligand-based design of anticancer MMP2 inhibitors: A review. Future Med. Chem., 2021, 13(22), 1987-2013.
[http://dx.doi.org/10.4155/fmc-2021-0262] [PMID: 34634916]
[73]
Hussin, S.K.; Omar, Y.M.; Abdelmageid, S.M.; Marie, M.I. Traditional machine learning and big data analytics in virtual screening: A comparative study. Int. J. Adv. Comput. Res., 2020, 10(47), 72-88.
[http://dx.doi.org/10.19101/IJACR.2019.940150]
[74]
Carracedo-Reboredo, P.; Liñares-Blanco, J.; Rodríguez-Fernández, N.; Cedrón, F.; Novoa, F.J.; Carballal, A.; Maojo, V.; Pazos, A.; Fernandez-Lozano, C. A review on machine learning approaches and trends in drug discovery. Comput. Struct. Biotechnol. J., 2021, 19, 4538-4558.
[http://dx.doi.org/10.1016/j.csbj.2021.08.011] [PMID: 34471498]
[75]
Kadurin, A.; Nikolenko, S.; Khrabrov, K.; Aliper, A.; Zhavoronkov, A. druGAN: An advanced generative adversarial autoencoder model for de novo generation of new molecules with desired molecular properties in silico. Mol. Pharm., 2017, 14(9), 3098-3104.
[http://dx.doi.org/10.1021/acs.molpharmaceut.7b00346] [PMID: 28703000]
[76]
Tsou, L.K.; Yeh, S.H.; Ueng, S.H.; Chang, C.P.; Song, J.S.; Wu, M.H.; Chang, H.F.; Chen, S.R.; Shih, C.; Chen, C.T.; Ke, Y.Y. Comparative study between deep learning and QSAR classifications for TNBC inhibitors and novel GPCR agonist discovery. Sci. Rep., 2020, 10(1), 16771.
[http://dx.doi.org/10.1038/s41598-020-73681-1] [PMID: 33033310]
[77]
Daoud, N.E.H.; Borah, P.; Deb, P.K.; Venugopala, K.N.; Hourani, W.; Alzweiri, M.; Bardaweel, S.K.; Tiwari, V. ADMET profiling in drug discovery and development: Perspectives of in silico, in vitro and integrated approaches. Curr. Drug Metab., 2021, 22(7), 503-522.
[http://dx.doi.org/10.2174/1389200222666210705122913] [PMID: 34225615]
[78]
Keyvanpour, M.R.; Shirzad, M.B. An analysis of QSAR research based on machine learning concepts. Curr. Drug Discov. Technol., 2021, 18(1), 17-30.
[http://dx.doi.org/10.2174/1570163817666200316104404] [PMID: 32178612]
[79]
Zhang, W.; Xue, Z.; Li, Z.; Yin, H. DCE-DForest: A deep forest model for the prediction of anticancer drug combination effects. Comput. Math. Methods Med., 2022, 2022, 1-5.
[http://dx.doi.org/10.1155/2022/8693746] [PMID: 35720022]
[80]
Celebi, R.; Bear Don’t Walk, O., IV; Movva, R.; Alpsoy, S.; Dumontier, M. In-silico prediction of synergistic anti- cancer drug combinations using multi-omics data. Sci. Rep., 2019, 9(1), 8949.
[http://dx.doi.org/10.1038/s41598-019-45236-6] [PMID: 31222109]
[81]
Liu, H.; Zhang, W.; Nie, L.; Ding, X.; Luo, J.; Zou, L. Predicting effective drug combinations using gradient tree boosting based on features extracted from drug-protein heterogeneous network. BMC Bioinformatics, 2019, 20(1), 645.
[http://dx.doi.org/10.1186/s12859-019-3288-1] [PMID: 31818267]
[82]
Alaparthi, S.; Mishra, M. Bidirectional Encoder Representations from Transformers (BERT): A sentiment analysis odyssey. 2007.011272020.
[83]
Wu, Z.; Jiang, D.; Wang, J.; Zhang, X.; Du, H.; Pan, L.; Hsieh, C.Y.; Cao, D.; Hou, T. Knowledge-based BERT: A method to extract molecular features like computational chemists. Brief. Bioinform., 2022, 23(3), bbac131.
[http://dx.doi.org/10.1093/bib/bbac131] [PMID: 35438145]
[84]
Khan, D.; Shedole, S. Leveraging deep learning techniques and integrated omics data for tailored treatment of breast cancer. J. Pers. Med., 2022, 12(5), 674.
[http://dx.doi.org/10.3390/jpm12050674] [PMID: 35629097]
[85]
Xia, F.; Shukla, M.; Brettin, T.; Garcia-Cardona, C.; Cohn, J.; Allen, J.E.; Maslov, S.; Holbeck, S.L.; Doroshow, J.H.; Evrard, Y.A.; Stahlberg, E.A.; Stevens, R.L. Predicting tumor cell line response to drug pairs with deep learning. BMC Bioinf., 2018, 19(Suppl. 18), 486.
[http://dx.doi.org/10.1186/s12859-018-2509-3] [PMID: 30577754]
[86]
Wang, J.; Liu, X.; Shen, S.; Deng, L.; Liu, H. DeepDDS: deep graph neural network with attention mechanism to predict synergistic drug combinations. Brief. Bioinf., 2022, 23(1), bbab390.
[http://dx.doi.org/10.1093/bib/bbab390] [PMID: 34571537]
[87]
Liu, Q.; Xie, L. TranSynergy: Mechanism-driven interpretable deep neural network for the synergistic prediction and pathway deconvolution of drug combinations. PLoS Comput. Biol., 2021, 17(2), e1008653.
[http://dx.doi.org/10.1371/journal.pcbi.1008653] [PMID: 33577560]
[88]
Schmucker, R.; Farina, G.; Faeder, J.; Fröhlich, F.; Saglam, A.S.; Sandholm, T. Combination treatment optimization using a pan-cancer pathway model. PLoS Comput. Biol., 2021, 17(12), e1009689.
[http://dx.doi.org/10.1371/journal.pcbi.1009689] [PMID: 34962919]
[89]
Enriquez-Navas, P.M.; Kam, Y.; Das, T.; Hassan, S.; Silva, A.; Foroutan, P.; Ruiz, E.; Martinez, G.; Minton, S.; Gillies, R.J.; Gatenby, R.A. Exploiting evolutionary principles to prolong tumor control in preclinical models of breast cancer. Sci. Transl. Med., 2016, 8(327), 327ra24.
[http://dx.doi.org/10.1126/scitranslmed.aad7842] [PMID: 26912903]
[90]
Zhang, J.; Cunningham, J.J.; Brown, J.S.; Gatenby, R.A. Integrating evolutionary dynamics into treatment of metastatic castrate-resistant prostate cancer. Nat. Commun., 2017, 8(1), 1816.
[http://dx.doi.org/10.1038/s41467-017-01968-5] [PMID: 29180633]
[91]
Galati, S.; Di Stefano, M.; Martinelli, E.; Poli, G.; Tuccinardi, T. Recent advances in in silico target fishing. Molecules, 2021, 26(17), 5124.
[http://dx.doi.org/10.3390/molecules26175124] [PMID: 34500568]
[92]
Mohanasundaram, N.; Sekhar, T. Computational studies of molecular targets regarding the adverse effects of isoniazid drug for tuberculosis. Curr. Pharmacogenomics Person. Med., 2019, 16(3), 210-218.
[http://dx.doi.org/10.2174/1875692116666181108145230]
[93]
Metzger, M.H.; Gadji, A.; Haj Salah, N.; Kane, W.; Boue, F. Deep learning methods for detecting side effects of cancer chemotherapies reported in a remote monitoring web application. Stud. Health Technol. Inform., 2022, 294, 880-881.
[http://dx.doi.org/10.3233/SHTI220616] [PMID: 35612235]
[94]
Blaschke, T.; Bajorath, J. Fine-tuning of a generative neural network for designing multi-target compounds. J. Comput. Aided Mol. Des., 2022, 36(5), 363-371.
[http://dx.doi.org/10.1007/s10822-021-00392-8] [PMID: 34046745]
[95]
Fan, Y.W.; Liu, W.H.; Chen, Y.T.; Hsu, Y.C.; Pathak, N.; Huang, Y.W.; Yang, J.M. Exploring kinase family inhibitors and their moiety preferences using deep SHapley additive exPlanations. BMC Bioinf., 2022, 23(Suppl. 4), 242.
[http://dx.doi.org/10.1186/s12859-022-04760-5] [PMID: 35725381]
[96]
Talluri, S. Molecular docking and virtual screening based prediction of drugs for COVID-19. Comb. Chem. High Throughput Screen., 2021, 24(5), 716-728.
[http://dx.doi.org/10.2174/13862073MTA5sMTEzz] [PMID: 32798373]
[97]
Lotfi Shahreza, M.; Ghadiri, N.; Mousavi, S.R.; Varshosaz, J.; Green, J.R. A review of network-based approaches to drug repositioning. Brief. Bioinf., 2018, 19(5), 878-892.
[http://dx.doi.org/10.1093/bib/bbx017] [PMID: 28334136]
[98]
Armando, R.G.; Mengual Gómez, D.L.; Gomez, D.E. New drugs are not enough-drug repositioning in oncology: An update. Int. J. Oncol., 2020, 56(3), 651-684.
[http://dx.doi.org/10.3892/ijo.2020.4966] [PMID: 32124955]
[99]
Marshall, G.R. Computer-aided drug design. Annu. Rev. Pharmacol. Toxicol., 1987, 27(1), 193-213.
[http://dx.doi.org/10.1146/annurev.pa.27.040187.001205] [PMID: 3555315]
[100]
Emig, D.; Ivliev, A.; Pustovalova, O.; Lancashire, L.; Bureeva, S.; Nikolsky, Y.; Bessarabova, M. Drug target prediction and repositioning using an integrated network-based approach. PLoS One, 2013, 8(4), e60618.
[http://dx.doi.org/10.1371/journal.pone.0060618] [PMID: 23593264]
[101]
Brown, A.S.; Kong, S.W.; Kohane, I.S.; Patel, C.J. ksRepo: A generalized platform for computational drug repositioning. BMC Bioinf., 2016, 17(1), 78.
[http://dx.doi.org/10.1186/s12859-016-0931-y] [PMID: 26860211]
[102]
Imoto, S.; Tamada, Y.; Savoie, C.J.; Miyano, S. Analysis of gene networks for drug target discovery and validation. Methods Mol. Biol., 2007, 360, 33-56.
[PMID: 17172724]
[103]
Chen, H.R.; Sherr, D.H.; Hu, Z.; DeLisi, C. A network based approach to drug repositioning identifies plausible candidates for breast cancer and prostate cancer. BMC Med. Genomics, 2016, 9(1), 51.
[http://dx.doi.org/10.1186/s12920-016-0212-7] [PMID: 27475327]
[104]
Zhao, Y.; Liu, Y.; Bai, H. Integrating LINCS data to evaluate cancer transcriptome modifying potential of small- molecule compounds for drug repositioning. Comb. Chem. High Throughput Screen., 2021, 24(9), 1340-1350.
[http://dx.doi.org/10.2174/1386207323666201027120149] [PMID: 33109034]
[105]
Yang, H.T.; Ju, J.H.; Wong, Y.T.; Shmulevich, I.; Chiang, J.H. Literature-based discovery of new candidates for drug repurposing. Brief. Bioinform., 2017, 18(3), 488-497.
[PMID: 27113728]
[106]
Iorio, F.; Bosotti, R.; Scacheri, E.; Belcastro, V.; Mithbaokar, P.; Ferriero, R.; Murino, L.; Tagliaferri, R.; Brunetti-Pierri, N.; Isacchi, A.; di Bernardo, D. Discovery of drug mode of action and drug repositioning from transcriptional responses. Proc. Natl. Acad. Sci. USA, 2010, 107(33), 14621-14626.
[http://dx.doi.org/10.1073/pnas.1000138107] [PMID: 20679242]
[107]
Folger, O.; Jerby, L.; Frezza, C.; Gottlieb, E.; Ruppin, E.; Shlomi, T. Predicting selective drug targets in cancer through metabolic networks. Mol. Syst. Biol., 2011, 7(1), 501.
[http://dx.doi.org/10.1038/msb.2011.35] [PMID: 21694718]
[108]
Wang, W.; Yang, S.; Zhang, X.; Li, J. Drug repositioning by integrating target information through a heterogeneous network model. Bioinformatics, 2014, 30(20), 2923-2930.
[http://dx.doi.org/10.1093/bioinformatics/btu403] [PMID: 24974205]
[109]
Lee, H.; Kang, S.; Kim, W. Drug repositioning for cancer therapy based on large-scale drug-induced transcriptional signatures. PLoS One, 2016, 11(3), e0150460.
[http://dx.doi.org/10.1371/journal.pone.0150460] [PMID: 26954019]
[110]
Tao, C.; Sun, J.; Zheng, W.J.; Chen, J.; Xu, H. Colorectal cancer drug target prediction using ontology-based inference and network analysis. Database, 2015, 2015
[http://dx.doi.org/10.1093/database/bav015]
[111]
Qin, Y.; Chen, M.; Wang, H.; Zheng, X. A network flow-based method to predict anticancer drug sensitivity. PLoS One, 2015, 10(5), e0127380.
[http://dx.doi.org/10.1371/journal.pone.0127380] [PMID: 25992881]
[112]
Ko, Y.K.; Gim, J.A. New drug development and clinical trial design by applying genomic information management. Pharmaceutics, 2022, 14(8), 1539.
[http://dx.doi.org/10.3390/pharmaceutics14081539] [PMID: 35893795]
[113]
Deamer, D.; Akeson, M.; Branton, D. Three decades of nanopore sequencing. Nat. Biotechnol., 2016, 34(5), 518-524.
[http://dx.doi.org/10.1038/nbt.3423] [PMID: 27153285]
[114]
Norris, A.L.; Workman, R.E.; Fan, Y.; Eshleman, J.R.; Timp, W. Nanopore sequencing detects structural variants in cancer. Cancer Biol. Ther., 2016, 17(3), 246-253.
[http://dx.doi.org/10.1080/15384047.2016.1139236] [PMID: 26787508]
[115]
Rang, F.J.; Kloosterman, W.P.; de Ridder, J. From squiggle to basepair: Computational approaches for improving nanopore sequencing read accuracy. Genome Biol., 2018, 19(1), 90.
[http://dx.doi.org/10.1186/s13059-018-1462-9] [PMID: 30005597]
[116]
Low, Z.Y.; Farouk, I.A.; Lal, S.K. Drug Repositioning: New approaches and future prospects for life-debilitating diseases and the COVID-19 pandemic outbreak. Viruses, 2020, 12(9), 1058.
[http://dx.doi.org/10.3390/v12091058] [PMID: 32972027]
[117]
Isik, Z.; Baldow, C.; Cannistraci, C.V.; Schroeder, M. Drug target prioritization by perturbed gene expression and network information. Sci. Rep., 2015, 5(1), 17417.
[http://dx.doi.org/10.1038/srep17417] [PMID: 26615774]
[118]
Kotsias, P.C.; Arús-Pous, J.; Chen, H.; Engkvist, O.; Tyrchan, C.; Bjerrum, E.J. Direct steering of de novo molecular generation with descriptor conditional recurrent neural networks. Nat. Mach. Intell., 2020, 2(5), 254-265.
[http://dx.doi.org/10.1038/s42256-020-0174-5]
[119]
Gebauer, N.W.A.; Gastegger, M.; Hessmann, S.S.P.; Müller, K.R.; Schütt, K.T. Inverse design of 3d molecular structures with conditional generative neural networks. Nat. Commun., 2022, 13(1), 973.
[http://dx.doi.org/10.1038/s41467-022-28526-y] [PMID: 35190542]
[120]
Galperin, M.Y.; Fernández-Suárez, X.M.; Rigden, D.J. The 24th annual Nucleic Acids Research database issue: a look back and upcoming changes. Nucleic Acids Res., 2017, 45(D1), D1-D11.
[http://dx.doi.org/10.1093/nar/gkw1188] [PMID: 28053160]
[121]
Rigden, D.J.; Fernández, X.M. The 2022 Nucleic Acids Research database issue and the online molecular biology database collection. Nucleic Acids Res., 2022, 50(D1), D1-D10.
[http://dx.doi.org/10.1093/nar/gkab1195] [PMID: 34986604]
[122]
Deng, J.; Yang, Z.; Ojima, I.; Samaras, D.; Wang, F. Artificial intelligence in drug discovery: Applications and techniques. Brief. Bioinf., 2022, 23(1), bbab430.
[http://dx.doi.org/10.1093/bib/bbab430] [PMID: 34734228]
[123]
David, A.; Islam, S.; Tankhilevich, E.; Sternberg, M.J.E. The alphafold database of protein structures: A Biologist’s guide. J. Mol. Biol., 2022, 434(2), 167336.
[http://dx.doi.org/10.1016/j.jmb.2021.167336] [PMID: 34757056]
[124]
Aaltonen, L.A.; Abascal, F.; Abeshouse, A.; Aburatani, H.; Adams, D.J.; Agrawal, N.; Ahn, K.S.; Ahn, S-M.; Aikata, H.; Akbani, R.; Akdemir, K.C.; Al-Ahmadie, H.; Al-Sedairy, S.T.; Al-Shahrour, F.; Alawi, M.; Albert, M.; Aldape, K.; Alexandrov, L.B.; Ally, A.; Alsop, K.; Alvarez, E.G.; Amary, F.; Amin, S.B.; Aminou, B.; Ammerpohl, O.; Anderson, M.J.; Ang, Y.; Antonello, D.; Anur, P.; Aparicio, S.; Appelbaum, E.L.; Arai, Y.; Aretz, A.; Arihiro, K.; Ariizumi, S.; Armenia, J.; Arnould, L.; Asa, S.; Assenov, Y.; Atwal, G.; Aukema, S.; Auman, J.T.; Aure, M.R.R.; Awadalla, P.; Aymerich, M.; Bader, G.D.; Baez-Ortega, A.; Bailey, M.H.; Bailey, P.J.; Balasundaram, M.; Balu, S.; Bandopadhayay, P.; Banks, R.E.; Barbi, S.; Barbour, A.P.; Barenboim, J.; Barnholtz-Sloan, J.; Barr, H.; Barrera, E.; Bartlett, J.; Bartolome, J.; Bassi, C.; Bathe, O.F.; Baumhoer, D.; Bavi, P.; Baylin, S.B.; Bazant, W.; Beardsmore, D.; Beck, T.A.; Behjati, S.; Behren, A.; Niu, B.; Bell, C.; Beltran, S.; Benz, C.; Berchuck, A.; Bergmann, A.K.; Bergstrom, E.N.; Berman, B.P.; Berney, D.M.; Bernhart, S.H.; Beroukhim, R.; Berrios, M.; Bersani, S.; Bertl, J.; Betancourt, M.; Bhandari, V.; Bhosle, S.G.; Biankin, A.V.; Bieg, M.; Bigner, D.; Binder, H.; Birney, E.; Birrer, M.; Biswas, N.K.; Bjerkehagen, B.; Bodenheimer, T.; Boice, L.; Bonizzato, G.; De Bono, J.S.; Boot, A.; Bootwalla, M.S.; Borg, A.; Borkhardt, A.; Boroevich, K.A.; Borozan, I.; Borst, C.; Bosenberg, M.; Bosio, M.; Boultwood, J.; Bourque, G.; Boutros, P.C.; Bova, G.S.; Bowen, D.T.; Bowlby, R.; Bowtell, D.D.L.; Boyault, S.; Boyce, R.; Boyd, J.; Brazma, A.; Brennan, P.; Brewer, D.S.; Brinkman, A.B.; Bristow, R.G.; Broaddus, R.R.; Brock, J.E.; Brock, M.; Broeks, A.; Brooks, A.N.; Brooks, D.; Brors, B.; Brunak, S.; Bruxner, T.J.C.; Bruzos, A.L.; Buchanan, A.; Buchhalter, I.; Buchholz, C.; Bullman, S.; Burke, H.; Burkhardt, B.; Burns, K.H.; Busanovich, J.; Bustamante, C.D.; Butler, A.P.; Butte, A.J.; Byrne, N.J.; Børresen-Dale, A-L.; Caesar-Johnson, S.J.; Cafferkey, A.; Cahill, D.; Calabrese, C.; Caldas, C.; Calvo, F.; Camacho, N.; Campbell, P.J.; Campo, E.; Cantù, C.; Cao, S.; Carey, T.E.; Carlevaro-Fita, J.; Carlsen, R.; Cataldo, I.; Cazzola, M.; Cebon, J.; Cerfolio, R.; Chadwick, D.E.; Chakravarty, D.; Chalmers, D.; Chan, C.W.Y.; Chan, K.; Chan-Seng-Yue, M.; Chandan, V.S.; Chang, D.K.; Chanock, S.J.; Chantrill, L.A.; Chateigner, A.; Chatterjee, N.; Chayama, K.; Chen, H-W.; Chen, J.; Chen, K.; Chen, Y.; Chen, Z.; Cherniack, A.D.; Chien, J.; Chiew, Y-E.; Chin, S-F.; Cho, J.; Cho, S.; Choi, J.K.; Choi, W.; Chomienne, C.; Chong, Z.; Choo, S.P.; Chou, A.; Christ, A.N.; Christie, E.L.; Chuah, E.; Cibulskis, C.; Cibulskis, K.; Cingarlini, S.; Clapham, P.; Claviez, A.; Cleary, S.; Cloonan, N.; Cmero, M.; Collins, C.C.; Connor, A.A.; Cooke, S.L.; Cooper, C.S.; Cope, L.; Corbo, V.; Cordes, M.G.; Cordner, S.M.; Cortés-Ciriano, I.; Covington, K.; Cowin, P.A.; Craft, B.; Craft, D.; Creighton, C.J.; Cun, Y.; Curley, E.; Cutcutache, I.; Czajka, K.; Czerniak, B.; Dagg, R.A.; Danilova, L.; Davi, M.V.; Davidson, N.R.; Davies, H.; Davis, I.J.; Davis-Dusenbery, B.N.; Dawson, K.J.; De La Vega, F.M.; De Paoli-Iseppi, R.; Defreitas, T.; Tos, A.P.D.; Delaneau, O.; Demchok, J.A.; Demeulemeester, J.; Demidov, G.M.; Demircioğlu, D.; Dennis, N.M.; Denroche, R.E.; Dentro, S.C.; Desai, N.; Deshpande, V.; Deshwar, A.G.; Desmedt, C.; Deu-Pons, J.; Dhalla, N.; Dhani, N.C.; Dhingra, P.; Dhir, R.; DiBiase, A.; Diamanti, K.; Ding, L.; Ding, S.; Dinh, H.Q.; Dirix, L.; Doddapaneni, H.V.; Donmez, N.; Dow, M.T.; Drapkin, R.; Drechsel, O.; Drews, R.M.; Serge, S.; Dudderidge, T.; Dueso-Barroso, A.; Dunford, A.J.; Dunn, M.; Dursi, L.J.; Duthie, F.R.; Dutton-Regester, K.; Eagles, J.; Easton, D.F.; Edmonds, S.; Edwards, P.A.; Edwards, S.E.; Eeles, R.A.; Ehinger, A.; Eils, J.; Eils, R.; El-Naggar, A.; Eldridge, M.; Ellrott, K.; Erkek, S.; Escaramis, G.; Espiritu, S.M.G.; Estivill, X.; Etemadmoghadam, D.; Eyfjord, J.E.; Faltas, B.M.; Fan, D.; Fan, Y.; Faquin, W.C.; Farcas, C.; Fassan, M.; Fatima, A.; Favero, F.; Fayzullaev, N.; Felau, I.; Fereday, S.; Ferguson, M.L.; Ferretti, V.; Feuerbach, L.; Field, M.A.; Fink, J.L.; Finocchiaro, G.; Fisher, C.; Fittall, M.W.; Fitzgerald, A.; Fitzgerald, R.C.; Flanagan, A.M.; Fleshner, N.E.; Flicek, P.; Foekens, J.A.; Fong, K.M.; Fonseca, N.A.; Foster, C.S.; Fox, N.S.; Fraser, M.; Frazer, S.; Frenkel-Morgenstern, M.; Friedman, W.; Frigola, J.; Fronick, C.C.; Fujimoto, A.; Fujita, M.; Fukayama, M.; Fulton, L.A.; Fulton, R.S.; Furuta, M.; Futreal, P.A.; Füllgrabe, A.; Gabriel, S.B.; Gallinger, S.; Gambacorti-Passerini, C.; Gao, J.; Gao, S.; Garraway, L.; Garred, Ø.; Garrison, E.; Garsed, D.W.; Gehlenborg, N.; Gelpi, J.L.L.; George, J.; Gerhard, D.S.; Gerhauser, C.; Gershenwald, J.E.; Gerstein, M.; Gerstung, M.; Getz, G.; Ghori, M.; Ghossein, R.; Giama, N.H.; Gibbs, R.A.; Gibson, B.; Gill, A.J.; Gill, P.; Giri, D.D.; Glodzik, D.; Gnanapragasam, V.J.; Goebler, M.E.; Goldman, M.J.; Gomez, C.; Gonzalez, S.; Gonzalez-Perez, A.; Gordenin, D.A.; Gossage, J.; Gotoh, K.; Govindan, R.; Grabau, D.; Graham, J.S.; Grant, R.C.; Green, A.R.; Green, E.; Greger, L.; Grehan, N.; Grimaldi, S.; Grimmond, S.M.; Grossman, R.L.; Grundhoff, A.; Gundem, G.; Guo, Q.; Gupta, M.; Gupta, S.; Gut, I.G.; Gut, M.; Göke, J.; Ha, G.; Haake, A.; Haan, D.; Haas, S.; Haase, K.; Haber, J.E.; Habermann, N.; Hach, F.; Haider, S.; Hama, N.; Hamdy, F.C.; Hamilton, A.; Hamilton, M.P.; Han, L.; Hanna, G.B.; Hansmann, M.; Haradhvala, N.J.; Harismendy, O.; Harliwong, I.; Harmanci, A.O.; Harrington, E.; Hasegawa, T.; Haussler, D.; Hawkins, S.; Hayami, S.; Hayashi, S.; Hayes, D.N.; Hayes, S.J.; Hayward, N.K.; Hazell, S.; He, Y.; Heath, A.P.; Heath, S.C.; Hedley, D.; Hegde, A.M.; Heiman, D.I.; Heinold, M.C.; Heins, Z.; Heisler, L.E.; Hellstrom-Lindberg, E.; Helmy, M.; Heo, S.G.; Hepperla, A.J.; Heredia-Genestar, J.M.; Herrmann, C.; Hersey, P.; Hess, J.M.; Hilmarsdottir, H.; Hinton, J.; Hirano, S.; Hiraoka, N.; Hoadley, K.A.; Hobolth, A.; Hodzic, E.; Hoell, J.I.; Hoffmann, S.; Hofmann, O.; Holbrook, A.; Holik, A.Z.; Hollingsworth, M.A.; Holmes, O.; Holt, R.A.; Hong, C.; Hong, E.P.; Hong, J.H.; Hooijer, G.K.; Hornshøj, H.; Hosoda, F.; Hou, Y.; Hovestadt, V.; Howat, W.; Hoyle, A.P.; Hruban, R.H.; Hu, J.; Hu, T.; Hua, X.; Huang, K.; Huang, M.; Huang, M.N.; Huang, V.; Huang, Y.; Huber, W.; Hudson, T.J.; Hummel, M.; Hung, J.A.; Huntsman, D.; Hupp, T.R.; Huse, J.; Huska, M.R.; Hutter, B.; Hutter, C.M.; Hübschmann, D.; Iacobuzio-Donahue, C.A.; Imbusch, C.D.; Imielinski, M.; Imoto, S.; Isaacs, W.B.; Isaev, K.; Ishikawa, S.; Iskar, M.; Islam, S.M.A.; Ittmann, M.; Ivkovic, S.; Izarzugaza, J.M.G.; Jacquemier, J.; Jakrot, V.; Jamieson, N.B.; Jang, G.H.; Jang, S.J.; Jayaseelan, J.C.; Jayasinghe, R.; Jefferys, S.R.; Jegalian, K.; Jennings, J.L.; Jeon, S-H.; Jerman, L.; Ji, Y.; Jiao, W.; Johansson, P.A.; Johns, A.L.; Johns, J.; Johnson, R.; Johnson, T.A.; Jolly, C.; Joly, Y.; Jonasson, J.G.; Jones, C.D.; Jones, D.R.; Jones, D.T.W.; Jones, N.; Jones, S.J.M.; Jonkers, J.; Ju, Y.S.; Juhl, H.; Jung, J.; Juul, M.; Juul, R.I.; Juul, S.; Jäger, N.; Kabbe, R.; Kahles, A.; Kahraman, A.; Kaiser, V.B.; Kakavand, H.; Kalimuthu, S.; von Kalle, C.; Kang, K.J.; Karaszi, K.; Karlan, B.; Karlić, R.; Karsch, D.; Kasaian, K.; Kassahn, K.S.; Katai, H.; Kato, M.; Katoh, H.; Kawakami, Y.; Kay, J.D.; Kazakoff, S.H.; Kazanov, M.D.; Keays, M.; Kebebew, E.; Kefford, R.F.; Kellis, M.; Kench, J.G.; Kennedy, C.J.; Kerssemakers, J.N.A.; Khoo, D.; Khoo, V.; Khuntikeo, N.; Khurana, E.; Kilpinen, H.; Kim, H.K.; Kim, H-L.; Kim, H-Y.; Kim, H.; Kim, J.; Kim, J.; Kim, J.K.; Kim, Y.; King, T.A.; Klapper, W.; Kleinheinz, K.; Klimczak, L.J.; Knappskog, S.; Kneba, M.; Knoppers, B.M.; Koh, Y.; Komorowski, J.; Komura, D.; Komura, M.; Kong, G.; Kool, M.; Korbel, J.O.; Korchina, V.; Korshunov, A.; Koscher, M.; Koster, R.; Kote-Jarai, Z.; Koures, A.; Kovacevic, M.; Kremeyer, B.; Kretzmer, H.; Kreuz, M.; Krishnamurthy, S.; Kube, D.; Kumar, K.; Kumar, P.; Kumar, S.; Kumar, Y.; Kundra, R.; Kübler, K.; Küppers, R.; Lagergren, J.; Lai, P.H.; Laird, P.W.; Lakhani, S.R.; Lalansingh, C.M.; Lalonde, E.; Lamaze, F.C.; Lambert, A.; Lander, E.; Landgraf, P.; Landoni, L.; Langerød, A.; Lanzós, A.; Larsimont, D.; Larsson, E.; Lathrop, M.; Lau, L.M.S.; Lawerenz, C.; Lawlor, R.T.; Lawrence, M.S.; Lazar, A.J.; Lazic, A.M.; Le, X.; Lee, D.; Lee, D.; Lee, E.A.; Lee, H.J.; Lee, J.J-K.; Lee, J-Y.; Lee, J.; Lee, M.T.M.; Lee-Six, H.; Lehmann, K-V.; Lehrach, H.; Lenze, D.; Leonard, C.R.; Leongamornlert, D.A.; Leshchiner, I.; Letourneau, L.; Letunic, I.; Levine, D.A.; Lewis, L.; Ley, T.; Li, C.; Li, C.H.; Li, H.I.; Li, J.; Li, L.; Li, S.; Li, S.; Li, X.; Li, X.; Li, X.; Li, Y.; Liang, H.; Liang, S-B.; Lichter, P.; Lin, P.; Lin, Z.; Linehan, W.M.; Lingjærde, O.C.; Liu, D.; Liu, E.M.; Liu, F-F.F.; Liu, F.; Liu, J.; Liu, X.; Livingstone, J.; Livitz, D.; Livni, N.; Lochovsky, L.; Loeffler, M.; Long, G.V.; Lopez-Guillermo, A.; Lou, S.; Louis, D.N.; Lovat, L.B.; Lu, Y.; Lu, Y-J.; Lu, Y.; Luchini, C.; Lungu, I.; Luo, X.; Luxton, H.J.; Lynch, A.G.; Lype, L.; López, C.; López-Otín, C.; Ma, E.Z.; Ma, Y.; MacGrogan, G.; MacRae, S.; Macintyre, G.; Madsen, T.; Maejima, K.; Mafficini, A.; Maglinte, D.T.; Maitra, A.; Majumder, P.P.; Malcovati, L.; Malikic, S.; Malleo, G.; Mann, G.J.; Mantovani-Löffler, L.; Marchal, K.; Marchegiani, G.; Mardis, E.R.; Margolin, A.A.; Marin, M.G.; Markowetz, F.; Markowski, J.; Marks, J.; Marques-Bonet, T.; Marra, M.A.; Marsden, L.; Martens, J.W.M.; Martin, S.; Martin-Subero, J.I.; Martincorena, I.; Martinez- Fundichely, A.; Maruvka, Y.E.; Mashl, R.J.; Massie, C.E.; Matthew, T.J.; Matthews, L.; Mayer, E.; Mayes, S.; Mayo, M.; Mbabaali, F.; McCune, K.; McDermott, U.; McGillivray, P.D.; McLellan, M.D.; McPherson, J.D.; McPherson, J.R.; McPherson, T.A.; Meier, S.R.; Meng, A.; Meng, S.; Menzies, A.; Merrett, N.D.; Merson, S.; Meyerson, M.; Meyerson, W.; Mieczkowski, P.A.; Mihaiescu, G.L.; Mijalkovic, S.; Mikkelsen, T.; Milella, M.; Mileshkin, L.; Miller, C.A.; Miller, D.K.; Miller, J.K.; Mills, G.B.; Milovanovic, A.; Minner, S.; Miotto, M.; Arnau, G.M.; Mirabello, L.; Mitchell, C.; Mitchell, T.J.; Miyano, S.; Miyoshi, N.; Mizuno, S.; Molnár-Gábor, F.; Moore, M.J.; Moore, R.A.; Morganella, S.; Morris, Q.D.; Morrison, C.; Mose, L.E.; Moser, C.D.; Muiños, F.; Mularoni, L.; Mungall, A.J.; Mungall, K.; Musgrove, E.A.; Mustonen, V.; Mutch, D.; Muyas, F.; Muzny, D.M.; Muñoz, A.; Myers, J.; Myklebost, O.; Möller, P.; Nagae, G.; Nagrial, A.M.; Nahal- Bose, H.K.; Nakagama, H.; Nakagawa, H.; Nakamura, H.; Nakamura, T.; Nakano, K.; Nandi, T.; Nangalia, J.; Nastic, M.; Navarro, A.; Navarro, F.C.P.; Neal, D.E.; Nettekoven, G.; Newell, F.; Newhouse, S.J.; Newton, Y.; Ng, A.W.T.; Ng, A.; Nicholson, J.; Nicol, D.; Nie, Y.; Nielsen, G.P.; Nielsen, M.M.; Nik-Zainal, S.; Noble, M.S.; Nones, K.; Northcott, P.A.; Notta, F.; O’Connor, B.D.; O’Donnell, P.; O’Donovan, M.; O’Meara, S.; O’Neill, B.P.; O’Neill, J.R.; Ocana, D.; Ochoa, A.; Oesper, L.; Ogden, C.; Ohdan, H.; Ohi, K.; Ohno-Machado, L.; Oien, K.A.; Ojesina, A.I.; Ojima, H.; Okusaka, T.; Omberg, L.; Ong, C.K.; Ossowski, S.; Ott, G.; Ouellette, B.F.F.; P’ng, C.; Paczkowska, M.; Paiella, S.; Pairojkul, C.; Pajic, M.; Pan-Hammarström, Q.; Papaemmanuil, E.; Papatheodorou, I.; Paramasivam, N.; Park, J.W.; Park, J-W.; Park, K.; Park, K.; Park, P.J.; Parker, J.S.; Parsons, S.L.; Pass, H.; Pasternack, D.; Pastore, A.; Patch, A-M.; Pauporté, I.; Pea, A.; Pearson, J.V.; Pedamallu, C.S.; Pedersen, J.S.; Pederzoli, P.; Peifer, M.; Pennell, N.A.; Perou, C.M.; Perry, M.D.; Petersen, G.M.; Peto, M.; Petrelli, N.; Petryszak, R.; Pfister, S.M.; Phillips, M.; Pich, O.; Pickett, H.A.; Pihl, T.D.; Pillay, N.; Pinder, S.; Pinese, M.; Pinho, A.V.; Pitkänen, E.; Pivot, X.; Piñeiro-Yáñez, E.; Planko, L.; Plass, C.; Polak, P.; Pons, T.; Popescu, I.; Potapova, O.; Prasad, A.; Preston, S.R.; Prinz, M.; Pritchard, A.L.; Prokopec, S.D.; Provenzano, E.; Puente, X.S.; Puig, S.; Puiggròs, M.; Pulido-Tamayo, S.; Pupo, G.M.; Purdie, C.A.; Quinn, M.C.; Rabionet, R.; Rader, J.S.; Radlwimmer, B.; Radovic, P.; Raeder, B.; Raine, K.M.; Ramakrishna, M.; Ramakrishnan, K.; Ramalingam, S.; Raphael, B.J.; Rathmell, W.K.; Rausch, T.; Reifenberger, G.; Reimand, J.; Reis-Filho, J.; Reuter, V.; Reyes-Salazar, I.; Reyna, M.A.; Reynolds, S.M.; Rheinbay, E.; Riazalhosseini, Y.; Richardson, A.L.; Richter, J.; Ringel, M.; Ringnér, M.; Rino, Y.; Rippe, K.; Roach, J.; Roberts, L.R.; Roberts, N.D.; Roberts, S.A.; Robertson, A.G.; Robertson, A.J.; Rodriguez, J.B.; Rodriguez-Martin, B.; Rodríguez-González, F.G.; Roehrl, M.H.A.; Rohde, M.; Rokutan, H.; Romieu, G.; Rooman, I.; Roques, T.; Rosebrock, D.; Rosenberg, M.; Rosenstiel, P.C.; Rosenwald, A.; Rowe, E.W.; Royo, R.; Rozen, S.G.; Rubanova, Y.; Rubin, M.A.; Rubio-Perez, C.; Rudneva, V.A.; Rusev, B.C.; Ruzzenente, A.; Rätsch, G.; Sabarinathan, R.; Sabelnykova, V.Y.; Sadeghi, S.; Sahinalp, S.C.; Saini, N.; Saito-Adachi, M.; Saksena, G.; Salcedo, A.; Salgado, R.; Salichos, L.; Sallari, R.; Saller, C.; Salvia, R.; Sam, M.; Samra, J.S.; Sanchez-Vega, F.; Sander, C.; Sanders, G.; Sarin, R.; Sarrafi, I.; Sasaki-Oku, A.; Sauer, T.; Sauter, G.; Saw, R.P.M.; Scardoni, M.; Scarlett, C.J.; Scarpa, A.; Scelo, G.; Schadendorf, D.; Schein, J.E.; Schilhabel, M.B.; Schlesner, M.; Schlomm, T.; Schmidt, H.K.; Schramm, S-J.; Schreiber, S.; Schultz, N.; Schumacher, S.E.; Schwarz, R.F.; Scolyer, R.A.; Scott, D.; Scully, R.; Seethala, R.; Segre, A.V.; Selander, I.; Semple, C.A.; Senbabaoglu, Y.; Sengupta, S.; Sereni, E.; Serra, S.; Sgroi, D.C.; Shackleton, M.; Shah, N.C.; Shahabi, S.; Shang, C.A.; Shang, P.; Shapira, O.; Shelton, T.; Shen, C.; Shen, H.; Shepherd, R.; Shi, R.; Shi, Y.; Shiah, Y-J.; Shibata, T.; Shih, J.; Shimizu, E.; Shimizu, K.; Shin, S.J.; Shiraishi, Y.; Shmaya, T.; Shmulevich, I.; Shorser, S.I.; Short, C.; Shrestha, R.; Shringarpure, S.S.; Shriver, C.; Shuai, S.; Sidiropoulos, N.; Siebert, R.; Sieuwerts, A.M.; Sieverling, L.; Signoretti, S.; Sikora, K.O.; Simbolo, M.; Simon, R.; Simons, J.V.; Simpson, J.T.; Simpson, P.T.; Singer, S.; Sinnott-Armstrong, N.; Sipahimalani, P.; Skelly, T.J.; Smid, M.; Smith, J.; Smith-McCune, K.; Socci, N.D.; Sofia, H.J.; Soloway, M.G.; Song, L.; Sood, A.K.; Sothi, S.; Sotiriou, C.; Soulette, C.M.; Span, P.N.; Spellman, P.T.; Sperandio, N.; Spillane, A.J.; Spiro, O.; Spring, J.; Staaf, J.; Stadler, P.F.; Staib, P.; Stark, S.G.; Stebbings, L.; Stefánsson, Ó.A.; Stegle, O.; Stein, L.D.; Stenhouse, A.; Stewart, C.; Stilgenbauer, S.; Stobbe, M.D.; Stratton, M.R.; Stretch, J.R.; Struck, A.J.; Stuart, J.M.; Stunnenberg, H.G.; Su, H.; Su, X.; Sun, R.X.; Sungalee, S.; Susak, H.; Suzuki, A.; Sweep, F.; Szczepanowski, M.; Sültmann, H.; Yugawa, T.; Tam, A.; Tamborero, D.; Tan, B.K.T.; Tan, D.; Tan, P.; Tanaka, H.; Taniguchi, H.; Tanskanen, T.J.; Tarabichi, M.; Tarnuzzer, R.; Tarpey, P.; Taschuk, M.L.; Tatsuno, K.; Tavaré, S.; Taylor, D.F.; Taylor-Weiner, A.; Teague, J.W.; Teh, B.T.; Tembe, V.; Temes, J.; Thai, K.; Thayer, S.P.; Thiessen, N.; Thomas, G.; Thomas, S.; Thompson, A.; Thompson, A.M.; Thompson, J.F.F.; Thompson, R.H.; Thorne, H.; Thorne, L.B.; Thorogood, A.; Tiao, G.; Tijanic, N.; Timms, L.E.; Tirabosco, R.; Tojo, M.; Tommasi, S.; Toon, C.W.; Toprak, U.H.; Torrents, D.; Tortora, G.; Tost, J.; Totoki, Y.; Townend, D.; Traficante, N.; Treilleux, I.; Trotta, J-R.; Trümper, L.H.P.; Tsao, M.; Tsunoda, T.; Tubio, J.M.C.; Tucker, O.; Turkington, R.; Turner, D.J.; Tutt, A.; Ueno, M.; Ueno, N.T.; Umbricht, C.; Umer, H.M.; Underwood, T.J.; Urban, L.; Urushidate, T.; Ushiku, T.; Uusküla-Reimand, L.; Valencia, A.; Van Den Berg, D.J.; Van Laere, S.; Van Loo, P.; Van Meir, E.G.; Van den Eynden, G.G.; Van der Kwast, T.; Vasudev, N.; Vazquez, M.; Vedururu, R.; Veluvolu, U.; Vembu, S.; Verbeke, L.P.C.; Vermeulen, P.; Verrill, C.; Viari, A.; Vicente, D.; Vicentini, C.; VijayRaghavan, K.; Viksna, J.; Vilain, R.E.; Villasante, I.; Vincent-Salomon, A.; Visakorpi, T.; Voet, D.; Vyas, P.; Vázquez-García, I.; Waddell, N.M.; Waddell, N.; Wadelius, C.; Wadi, L.; Wagener, R.; Wala, J.A.; Wang, J.; Wang, J.; Wang, L.; Wang, Q.; Wang, W.; Wang, Y.; Wang, Z.; Waring, P.M.; Warnatz, H-J.; Warrell, J.; Warren, A.Y.; Waszak, S.M.; Wedge, D.C.; Weichenhan, D.; Weinberger, P.; Weinstein, J.N.; Weischenfeldt, J.; Weisenberger, D.J.; Welch, I.; Wendl, M.C.; Werner, J.; Whalley, J.P.; Wheeler, D.A.; Whitaker, H.C.; Wigle, D.; Wilkerson, M.D.; Williams, A.; Wilmott, J.S.; Wilson, G.W.; Wilson, J.M.; Wilson, R.K.; Winterhoff, B.; Wintersinger, J.A.; Wiznerowicz, M.; Wolf, S.; Wong, B.H.; Wong, T.; Wong, W.; Woo, Y.; Wood, S.; Wouters, B.G.; Wright, A.J.; Wright, D.W.; Wright, M.H.; Wu, C-L.; Wu, D-Y.; Wu, G.; Wu, J.; Wu, K.; Wu, Y.; Wu, Z.; Xi, L.; Xia, T.; Xiang, Q.; Xiao, X.; Xing, R.; Xiong, H.; Xu, Q.; Xu, Y.; Xue, H.; Yachida, S.; Yakneen, S.; Yamaguchi, R.; Yamaguchi, T.N.; Yamamoto, M.; Yamamoto, S.; Yamaue, H.; Yang, F.; Yang, H.; Yang, J.Y.; Yang, L.; Yang, L.; Yang, S.; Yang, T-P.; Yang, Y.; Yao, X.; Yaspo, M-L.; Yates, L.; Yau, C.; Ye, C.; Ye, K.; Yellapantula, V.D.; Yoon, C.J.; Yoon, S-S.; Yousif, F.; Yu, J.; Yu, K.; Yu, W.; Yu, Y.; Yuan, K.; Yuan, Y.; Yuen, D.; Yung, C.K.; Zaikova, O.; Zamora, J.; Zapatka, M.; Zenklusen, J.C.; Zenz, T.; Zeps, N.; Zhang, C-Z.; Zhang, F.; Zhang, H.; Zhang, H.; Zhang, H.; Zhang, J.; Zhang, J.; Zhang, J.; Zhang, X.; Zhang, X.; Zhang, Y.; Zhang, Z.; Zhao, Z.; Zheng, L.; Zheng, X.; Zhou, W.; Zhou, Y.; Zhu, B.; Zhu, H.; Zhu, J.; Zhu, S.; Zou, L.; Zou, X.; deFazio, A.; van As, N.; van Deurzen, C.H.M.; van de Vijver, M.J.; van’t Veer, L.; von Mering, C. Pan-cancer analysis of whole genomes. Nature, 2020, 578(7793), 82-93.
[http://dx.doi.org/10.1038/s41586-020-1969-6] [PMID: 32025007]
[125]
Finck, A.; Gill, S.I.; June, C.H. Cancer immunotherapy comes of age and looks for maturity. Nat. Commun., 2020, 11(1), 3325.
[http://dx.doi.org/10.1038/s41467-020-17140-5] [PMID: 32620755]
[126]
Sparano, J.A.; Gray, R.J.; Makower, D.F.; Pritchard, K.I.; Albain, K.S.; Hayes, D.F.; Geyer, C.E., Jr; Dees, E.C.; Goetz, M.P.; Olson, J.A., Jr; Lively, T.; Badve, S.S.; Saphner, T.J.; Wagner, L.I.; Whelan, T.J.; Ellis, M.J.; Paik, S.; Wood, W.C.; Ravdin, P.M.; Keane, M.M.; Gomez Moreno, H.L.; Reddy, P.S.; Goggins, T.F.; Mayer, I.A.; Brufsky, A.M.; Toppmeyer, D.L.; Kaklamani, V.G.; Berenberg, J.L.; Abrams, J.; Sledge, G.W., Jr Adjuvant chemotherapy guided by a 21-Gene expression assay in breast cancer. N. Engl. J. Med., 2018, 379(2), 111-121.
[http://dx.doi.org/10.1056/NEJMoa1804710] [PMID: 29860917]
[127]
Hoadley, K.A.; Yau, C.; Hinoue, T.; Wolf, D.M.; Lazar, A.J.; Drill, E.; Shen, R.; Taylor, A.M.; Cherniack, A.D.; Thorsson, V.; Akbani, R.; Bowlby, R.; Wong, C.K.; Wiznerowicz, M.; Sanchez-Vega, F.; Robertson, A.G.; Schneider, B.G.; Lawrence, M.S.; Noushmehr, H.; Malta, T.M.; Stuart, J.M.; Benz, C.C.; Laird, P.W.; Caesar-Johnson, S.J.; Demchok, J.A.; Felau, I.; Kasapi, M.; Ferguson, M.L.; Hutter, C.M.; Sofia, H.J.; Tarnuzzer, R.; Wang, Z.; Yang, L.; Zenklusen, J.C.; Zhang, J.J.; Chudamani, S.; Liu, J.; Lolla, L.; Naresh, R.; Pihl, T.; Sun, Q.; Wan, Y.; Wu, Y.; Cho, J.; DeFreitas, T.; Frazer, S.; Gehlenborg, N.; Getz, G.; Heiman, D.I.; Kim, J.; Lawrence, M.S.; Lin, P.; Meier, S.; Noble, M.S.; Saksena, G.; Voet, D.; Zhang, H.; Bernard, B.; Chambwe, N.; Dhankani, V.; Knijnenburg, T.; Kramer, R.; Leinonen, K.; Liu, Y.; Miller, M.; Reynolds, S.; Shmulevich, I.; Thorsson, V.; Zhang, W.; Akbani, R.; Broom, B.M.; Hegde, A.M.; Ju, Z.; Kanchi, R.S.; Korkut, A.; Li, J.; Liang, H.; Ling, S.; Liu, W.; Lu, Y.; Mills, G.B.; Ng, K-S.; Rao, A.; Ryan, M.; Wang, J.; Weinstein, J.N.; Zhang, J.; Abeshouse, A.; Armenia, J.; Chakravarty, D.; Chatila, W.K.; de Bruijn, I.; Gao, J.; Gross, B.E.; Heins, Z.J.; Kundra, R.; La, K.; Ladanyi, M.; Luna, A.; Nissan, M.G.; Ochoa, A.; Phillips, S.M.; Reznik, E.; Sanchez-Vega, F.; Sander, C.; Schultz, N.; Sheridan, R.; Sumer, S.O.; Sun, Y.; Taylor, B.S.; Wang, J.; Zhang, H.; Anur, P.; Peto, M.; Spellman, P.; Benz, C.; Stuart, J.M.; Wong, C.K.; Yau, C.; Hayes, D.N.; Parker, J.S.; Wilkerson, M.D.; Ally, A.; Balasundaram, M.; Bowlby, R.; Brooks, D.; Carlsen, R.; Chuah, E.; Dhalla, N.; Holt, R.; Jones, S.J.M.; Kasaian, K.; Lee, D.; Ma, Y.; Marra, M.A.; Mayo, M.; Moore, R.A.; Mungall, A.J.; Mungall, K.; Robertson, A.G.; Sadeghi, S.; Schein, J.E.; Sipahimalani, P.; Tam, A.; Thiessen, N.; Tse, K.; Wong, T.; Berger, A.C.; Beroukhim, R.; Cherniack, A.D.; Cibulskis, C.; Gabriel, S.B.; Gao, G.F.; Ha, G.; Meyerson, M.; Schumacher, S.E.; Shih, J.; Kucherlapati, M.H.; Kucherlapati, R.S.; Baylin, S.; Cope, L.; Danilova, L.; Bootwalla, M.S.; Lai, P.H.; Maglinte, D.T.; Van Den Berg, D.J.; Weisenberger, D.J.; Auman, J.T.; Balu, S.; Bodenheimer, T.; Fan, C.; Hoadley, K.A.; Hoyle, A.P.; Jefferys, S.R.; Jones, C.D.; Meng, S.; Mieczkowski, P.A.; Mose, L.E.; Perou, A.H.; Perou, C.M.; Roach, J.; Shi, Y.; Simons, J.V.; Skelly, T.; Soloway, M.G.; Tan, D.; Veluvolu, U.; Fan, H.; Hinoue, T.; Laird, P.W.; Shen, H.; Zhou, W.; Bellair, M.; Chang, K.; Covington, K.; Creighton, C.J.; Dinh, H.; Doddapaneni, H.V.; Donehower, L.A.; Drummond, J.; Gibbs, R.A.; Glenn, R.; Hale, W.; Han, Y.; Hu, J.; Korchina, V.; Lee, S.; Lewis, L.; Li, W.; Liu, X.; Morgan, M.; Morton, D.; Muzny, D.; Santibanez, J.; Sheth, M.; Shinbrot, E.; Wang, L.; Wang, M.; Wheeler, D.A.; Xi, L.; Zhao, F.; Hess, J.; Appelbaum, E.L.; Bailey, M.; Cordes, M.G.; Ding, L.; Fronick, C.C.; Fulton, L.A.; Fulton, R.S.; Kandoth, C.; Mardis, E.R.; McLellan, M.D.; Miller, C.A.; Schmidt, H.K.; Wilson, R.K.; Crain, D.; Curley, E.; Gardner, J.; Lau, K.; Mallery, D.; Morris, S.; Paulauskis, J.; Penny, R.; Shelton, C.; Shelton, T.; Sherman, M.; Thompson, E.; Yena, P.; Bowen, J.; Gastier-Foster, J.M.; Gerken, M.; Leraas, K.M.; Lichtenberg, T.M.; Ramirez, N.C.; Wise, L.; Zmuda, E.; Corcoran, N.; Costello, T.; Hovens, C.; Carvalho, A.L.; de Carvalho, A.C.; Fregnani, J.H.; Longatto-Filho, A.; Reis, R.M.; Scapulatempo-Neto, C.; Silveira, H.C.S.; Vidal, D.O.; Burnette, A.; Eschbacher, J.; Hermes, B.; Noss, A.; Singh, R.; Anderson, M.L.; Castro, P.D.; Ittmann, M.; Huntsman, D.; Kohl, B.; Le, X.; Thorp, R.; Andry, C.; Duffy, E.R.; Lyadov, V.; Paklina, O.; Setdikova, G.; Shabunin, A.; Tavobilov, M.; McPherson, C.; Warnick, R.; Berkowitz, R.; Cramer, D.; Feltmate, C.; Horowitz, N.; Kibel, A.; Muto, M.; Raut, C.P.; Malykh, A.; Barnholtz-Sloan, J.S.; Barrett, W.; Devine, K.; Fulop, J.; Ostrom, Q.T.; Shimmel, K.; Wolinsky, Y.; Sloan, A.E.; De Rose, A.; Giuliante, F.; Goodman, M.; Karlan, B.Y.; Hagedorn, C.H.; Eckman, J.; Harr, J.; Myers, J.; Tucker, K.; Zach, L.A.; Deyarmin, B.; Hu, H.; Kvecher, L.; Larson, C.; Mural, R.J.; Somiari, S.; Vicha, A.; Zelinka, T.; Bennett, J.; Iacocca, M.; Rabeno, B.; Swanson, P.; Latour, M.; Lacombe, L.; Têtu, B.; Bergeron, A.; McGraw, M.; Staugaitis, S.M.; Chabot, J.; Hibshoosh, H.; Sepulveda, A.; Su, T.; Wang, T.; Potapova, O.; Voronina, O.; Desjardins, L.; Mariani, O.; Roman-Roman, S.; Sastre, X.; Stern, M-H.; Cheng, F.; Signoretti, S.; Berchuck, A.; Bigner, D.; Lipp, E.; Marks, J.; McCall, S.; McLendon, R.; Secord, A.; Sharp, A.; Behera, M.; Brat, D.J.; Chen, A.; Delman, K.; Force, S.; Khuri, F.; Magliocca, K.; Maithel, S.; Olson, J.J.; Owonikoko, T.; Pickens, A.; Ramalingam, S.; Shin, D.M.; Sica, G.; Van Meir, E.G.; Zhang, H.; Eijckenboom, W.; Gillis, A.; Korpershoek, E.; Looijenga, L.; Oosterhuis, W.; Stoop, H.; van Kessel, K.E.; Zwarthoff, E.C.; Calatozzolo, C.; Cuppini, L.; Cuzzubbo, S.; DiMeco, F.; Finocchiaro, G.; Mattei, L.; Perin, A.; Pollo, B.; Chen, C.; Houck, J.; Lohavanichbutr, P.; Hartmann, A.; Stoehr, C.; Stoehr, R.; Taubert, H.; Wach, S.; Wullich, B.; Kycler, W.; Murawa, D.; Wiznerowicz, M.; Chung, K.; Edenfield, W.J.; Martin, J.; Baudin, E.; Bubley, G.; Bueno, R.; De Rienzo, A.; Richards, W.G.; Kalkanis, S.; Mikkelsen, T.; Noushmehr, H.; Scarpace, L.; Girard, N.; Aymerich, M.; Campo, E.; Giné, E.; Guillermo, A.L.; Van Bang, N.; Hanh, P.T.; Phu, B.D.; Tang, Y.; Colman, H.; Evason, K.; Dottino, P.R.; Martignetti, J.A.; Gabra, H.; Juhl, H.; Akeredolu, T.; Stepa, S.; Hoon, D.; Ahn, K.; Kang, K.J.; Beuschlein, F.; Breggia, A.; Birrer, M.; Bell, D.; Borad, M.; Bryce, A.H.; Castle, E.; Chandan, V.; Cheville, J.; Copland, J.A.; Farnell, M.; Flotte, T.; Giama, N.; Ho, T.; Kendrick, M.; Kocher, J-P.; Kopp, K.; Moser, C.; Nagorney, D.; O’Brien, D.; O’Neill, B.P.; Patel, T.; Petersen, G.; Que, F.; Rivera, M.; Roberts, L.; Smallridge, R.; Smyrk, T.; Stanton, M.; Thompson, R.H.; Torbenson, M.; Yang, J.D.; Zhang, L.; Brimo, F.; Ajani, J.A.; Gonzalez, A.M.A.; Behrens, C.; Bondaruk; Broaddus, R.; Czerniak, B.; Esmaeli, B.; Fujimoto, J.; Gershenwald, J.; Guo, C.; Lazar, A.J.; Logothetis, C.; Meric-Bernstam, F.; Moran, C.; Ramondetta, L.; Rice, D.; Sood, A.; Tamboli, P.; Thompson, T.; Troncoso, P.; Tsao, A.; Wistuba, I.; Carter, C.; Haydu, L.; Hersey, P.; Jakrot, V.; Kakavand, H.; Kefford, R.; Lee, K.; Long, G.; Mann, G.; Quinn, M.; Saw, R.; Scolyer, R.; Shannon, K.; Spillane, A.; Stretch, J.; Synott, M.; Thompson, J.; Wilmott, J.; Al-Ahmadie, H.; Chan, T.A.; Ghossein, R.; Gopalan, A.; Levine, D.A.; Reuter, V.; Singer, S.; Singh, B.; Tien, N.V.; Broudy, T.; Mirsaidi, C.; Nair, P.; Drwiega, P.; Miller, J.; Smith, J.; Zaren, H.; Park, J-W.; Hung, N.P.; Kebebew, E.; Linehan, W.M.; Metwalli, A.R.; Pacak, K.; Pinto, P.A.; Schiffman, M.; Schmidt, L.S.; Vocke, C.D.; Wentzensen, N.; Worrell, R.; Yang, H.; Moncrieff, M.; Goparaju, C.; Melamed, J.; Pass, H.; Botnariuc, N.; Caraman, I.; Cernat, M.; Chemencedji, I.; Clipca, A.; Doruc, S.; Gorincioi, G.; Mura, S.; Pirtac, M.; Stancul, I.; Tcaciuc, D.; Albert, M.; Alexopoulou, I.; Arnaout, A.; Bartlett, J.; Engel, J.; Gilbert, S.; Parfitt, J.; Sekhon, H.; Thomas, G.; Rassl, D.M.; Rintoul, R.C.; Bifulco, C.; Tamakawa, R.; Urba, W.; Hayward, N.; Timmers, H.; Antenucci, A.; Facciolo, F.; Grazi, G.; Marino, M.; Merola, R.; de Krijger, R.; Gimenez-Roqueplo, A-P.; Piché, A.; Chevalier, S.; McKercher, G.; Birsoy, K.; Barnett, G.; Brewer, C.; Farver, C.; Naska, T.; Pennell, N.A.; Raymond, D.; Schilero, C.; Smolenski, K.; Williams, F.; Morrison, C.; Borgia, J.A.; Liptay, M.J.; Pool, M.; Seder, C.W.; Junker, K.; Omberg, L.; Dinkin, M.; Manikhas, G.; Alvaro, D.; Bragazzi, M.C.; Cardinale, V.; Carpino, G.; Gaudio, E.; Chesla, D.; Cottingham, S.; Dubina, M.; Moiseenko, F.; Dhanasekaran, R.; Becker, K-F.; Janssen, K-P.; Slotta-Huspenina, J.; Abdel-Rahman, M.H.; Aziz, D.; Bell, S.; Cebulla, C.M.; Davis, A.; Duell, R.; Elder, J.B.; Hilty, J.; Kumar, B.; Lang, J.; Lehman, N.L.; Mandt, R.; Nguyen, P.; Pilarski, R.; Rai, K.; Schoenfield, L.; Senecal, K.; Wakely, P.; Hansen, P.; Lechan, R.; Powers, J.; Tischler, A.; Grizzle, W.E.; Sexton, K.C.; Kastl, A.; Henderson, J.; Porten, S.; Waldmann, J.; Fassnacht, M.; Asa, S.L.; Schadendorf, D.; Couce, M.; Graefen, M.; Huland, H.; Sauter, G.; Schlomm, T.; Simon, R.; Tennstedt, P.; Olabode, O.; Nelson, M.; Bathe, O.; Carroll, P.R.; Chan, J.M.; Disaia, P.; Glenn, P.; Kelley, R.K.; Landen, C.N.; Phillips, J.; Prados, M.; Simko, J.; Smith-McCune, K.; VandenBerg, S.; Roggin, K.; Fehrenbach, A.; Kendler, A.; Sifri, S.; Steele, R.; Jimeno, A.; Carey, F.; Forgie, I.; Mannelli, M.; Carney, M.; Hernandez, B.; Campos, B.; Herold- Mende, C.; Jungk, C.; Unterberg, A.; von Deimling, A.; Bossler, A.; Galbraith, J.; Jacobus, L.; Knudson, M.; Knutson, T.; Ma, D.; Milhem, M.; Sigmund, R.; Godwin, A.K.; Madan, R.; Rosenthal, H.G.; Adebamowo, C.; Adebamowo, S.N.; Boussioutas, A.; Beer, D.; Giordano, T.; Mes- Masson, A-M.; Saad, F.; Bocklage, T.; Landrum, L.; Mannel, R.; Moore, K.; Moxley, K.; Postier, R.; Walker, J.; Zuna, R.; Feldman, M.; Valdivieso, F.; Dhir, R.; Luketich, J.; Pinero, E.M.M.; Quintero-Aguilo, M.; Carlotti, C.G., Jr; Dos Santos, J.S.; Kemp, R.; Sankarankuty, A.; Tirapelli, D.; Catto, J.; Agnew, K.; Swisher, E.; Creaney, J.; Robinson, B.; Shelley, C.S.; Godwin, E.M.; Kendall, S.; Shipman, C.; Bradford, C.; Carey, T.; Haddad, A.; Moyer, J.; Peterson, L.; Prince, M.; Rozek, L.; Wolf, G.; Bowman, R.; Fong, K.M.; Yang, I.; Korst, R.; Rathmell, W.K.; Fantacone-Campbell, J.L.; Hooke, J.A.; Kovatich, A.J.; Shriver, C.D.; DiPersio, J.; Drake, B.; Govindan, R.; Heath, S.; Ley, T.; Van Tine, B.; Westervelt, P.; Rubin, M.A.; Lee, J.I.; Aredes, N.D.; Mariamidze, A. Cell-of-origin patterns dominate the molecular classification of 10,000 tumors from 33 types of cancer. Cell, 2018, 173(2), 291-304.e6.
[http://dx.doi.org/10.1016/j.cell.2018.03.022] [PMID: 29625048]
[128]
Stanková, K.; Brown, J.S.; Dalton, W.S.; Gatenby, R.A. Optimizing cancer treatment using game theory. JAMA Oncol., 2019, 5(1), 96-103.
[http://dx.doi.org/10.1001/jamaoncol.2018.3395] [PMID: 30098166]
[129]
Zehir, A.; Benayed, R.; Shah, R.H.; Syed, A.; Middha, S.; Kim, H.R.; Srinivasan, P.; Gao, J.; Chakravarty, D.; Devlin, S.M.; Hellmann, M.D.; Barron, D.A.; Schram, A.M.; Hameed, M.; Dogan, S.; Ross, D.S.; Hechtman, J.F.; DeLair, D.F.; Yao, J.; Mandelker, D.L.; Cheng, D.T.; Chandramohan, R.; Mohanty, A.S.; Ptashkin, R.N.; Jayakumaran, G.; Prasad, M.; Syed, M.H.; Rema, A.B.; Liu, Z.Y.; Nafa, K.; Borsu, L.; Sadowska, J.; Casanova, J.; Bacares, R.; Kiecka, I.J.; Razumova, A.; Son, J.B.; Stewart, L.; Baldi, T.; Mullaney, K.A.; Al-Ahmadie, H.; Vakiani, E.; Abeshouse, A.A.; Penson, A.V.; Jonsson, P.; Camacho, N.; Chang, M.T.; Won, H.H.; Gross, B.E.; Kundra, R.; Heins, Z.J.; Chen, H.W.; Phillips, S.; Zhang, H.; Wang, J.; Ochoa, A.; Wills, J.; Eubank, M.; Thomas, S.B.; Gardos, S.M.; Reales, D.N.; Galle, J.; Durany, R.; Cambria, R.; Abida, W.; Cercek, A.; Feldman, D.R.; Gounder, M.M.; Hakimi, A.A.; Harding, J.J.; Iyer, G.; Janjigian, Y.Y.; Jordan, E.J.; Kelly, C.M.; Lowery, M.A.; Morris, L.G.T.; Omuro, A.M.; Raj, N.; Razavi, P.; Shoushtari, A.N.; Shukla, N.; Soumerai, T.E.; Varghese, A.M.; Yaeger, R.; Coleman, J.; Bochner, B.; Riely, G.J.; Saltz, L.B.; Scher, H.I.; Sabbatini, P.J.; Robson, M.E.; Klimstra, D.S.; Taylor, B.S.; Baselga, J.; Schultz, N.; Hyman, D.M.; Arcila, M.E.; Solit, D.B.; Ladanyi, M.; Berger, M.F. Mutational landscape of metastatic cancer revealed from prospective clinical sequencing of 10,000 patients. Nat. Med., 2017, 23(6), 703-713.
[http://dx.doi.org/10.1038/nm.4333] [PMID: 28481359]
[130]
Cao, Y.; Romero, J.; Aspuru-Guzik, A. Potential of quantum computing for drug discovery. IBM J. Res. Dev., 2018, 62(6), 6:1-6:20.
[http://dx.doi.org/10.1147/JRD.2018.2888987]
[131]
Lau, B.; Emani, P.S.; Chapman, J.; Yao, L.; Lam, T.; Merrill, P.; Warrell, J.; Gerstein, M.B.; Lam, H.Y.K. Insights from incorporating quantum computing into drug design workflows. Bioinformatics, 2023, 39(1), btac789.
[http://dx.doi.org/10.1093/bioinformatics/btac789] [PMID: 36477833]

Rights & Permissions Print Export Cite as
© 2024 Bentham Science Publishers | Privacy Policy