Abstract
With the recognition of the heterogeneity within complex diseases, such as cancer, there is an accompanying understanding of the need for a stratified approach to treatment. Patients with different underlying biologies originating at the genomic, epigenetic, or transcriptomics levels may present with similar phenotypes at diagnosis. The same treatment may thus result in different outcomes. Using the wealth of public information that is available, particularly from high-throughput experiments, regarding the behavior of approved drugs may facilitate the discovery of novel treatments for subgroups of patients. In silico approaches to drug repositioning have been developed over the past 15 years with a view to enabling this process, with a focus on mapping compounds to patient phenotypes and uncovering novel mechanisms of action. An understanding of the core structure and design of each of these tools, possible applications, and how different inputs can influence results is essential in order that users can maximize the potential of such in silico analyses. This in turn will accelerate the preclinical stage of the biomarker translational pipeline, often perceived as a key bottleneck.
This is a preview of subscription content, log in via an institution to check access.
References and Further Reading
Adams RA, D’Souza MMA, Pierce CJ et al (2015) Ectopic expression of protein kinase C-β sensitizes head and neck squamous cell carcinoma to diterpene esters. Anticancer Res 35:1291–1296
Alex A, Harris J, Smith DA (2015) Attrition in the pharmaceutical industry: reasons, implications, and pathways forward. Wiley, Hoboken, pp 1–356. https://doi.org/10.1002/9781118819586
Ashburn TT, Thor KB (2004) Drug repositioning: identifying and developing new uses for existing drugs. Nat Rev Drug Discov 3:673–683
Aslam B, Wang W, Arshad MI et al (2018) Antibiotic resistance: a rundown of a global crisis. Infect Drug Resist 11:1645–1658. https://doi.org/10.2147/IDR.S173867
Athar A, Füllgrabe A, George N et al (2019) ArrayExpress update – from bulk to single-cell expression data. Nucleic Acids Res 47:D711–D715. https://doi.org/10.1093/nar/gky964
Boolell M, Allen MJ, Ballard SA et al (1996) Sildenafil: an orally active type 5 cyclic GMP-specific phosphodiesterase inhibitor for the treatment of penile erectile dysfunction. Int J Impot Res 8:47–52
Broad Institute (2019) CLUE (Connectivity Map). https://clue.io/
Camidge DR, Pao W, Sequist LV (2014) Acquired resistance to TKIs in solid tumours: learning from lung cancer. Nat Rev Clin Oncol 11:473–481. https://doi.org/10.1038/nrclinonc.2014.104
Chae YK, Arya A, Malecek M-K et al (2016) Repurposing metformin for cancer treatment: current clinical studies. Oncotarget 7. https://doi.org/10.18632/oncotarget.8194
Chiang AP, Dudley JT, Shenoy M et al (2011) Computational repositioning of the anticonvulsant topiramate for inflammatory bowel disease. Sci Transl Med 3:96ra76–96ra76. https://doi.org/10.1126/scitranslmed.3002648
Edgar R, Domrachev MLA (2002) Gene expression omnibus: NCBI gene expression and hybridization array data repository. Nucleic Acids Res 30:207–210
Evans JMM, Donnelly LA, Emslie-Smith AM et al (2005) Metformin and reduced risk of cancer in diabetic patients. Br Med J 330:1304–1305. https://doi.org/10.1136/bmj.38415.708634.F7
Karapetis CS, Khambata-Ford S, Jonker DJ et al (2008) K-ras mutations and benefit from cetuximab in advanced colorectal cancer. N Engl J Med 359(17):1757–65. https://doi.org/10.1056/NEJMoa0804385
Gerhard I, Schindler AE, Buhler K et al (1992) Treatment of endometriosis with leuprorelin acetate depot: a German multicentre study. Clin Ther 14:3–16
Guinney J, Dienstmann R, Wang X et al (2015) The consensus molecular subtypes of colorectal cancer. Nat Med 21:1350–1356. https://doi.org/10.1038/nm.3967
He L, Gao L, Shay C et al (2019) Histone deacetylase inhibitors suppress aggressiveness of head and neck squamous cell carcinoma via histone acetylation-independent blockade of the EGFR-Arf1 axis 06 Biological Sciences 0601 Biochemistry and Cell Biology. J Exp Clin Cancer Res 38. https://doi.org/10.1186/s13046-019-1080-8
Hoadley KA, Yau C, Hinoue T et al (2018) Cell-of-origin patterns dominate the molecular classification of 10,000 tumors from 33 types of cancer. Cell 173:291–304.e6. https://doi.org/10.1016/j.cell.2018.03.022
Holzinger ER, Ritchie MD (2012) Integrating heterogeneous high-throughput data for meta-dimensional pharmacogenomics and disease-related studies. Pharmacogenomics 13:213–222. https://doi.org/10.2217/pgs.11.145
Huang DW, Sherman BT, Lempicki RA (2009) Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nat Protoc 4:44–57. https://doi.org/10.1038/nprot.2008.211
Huang S, Chaudhary K, Garmire LX (2017) More is better: recent progress in multi-omics data integration methods. Front Genet 8. https://doi.org/10.3389/fgene.2017.00084
Iorio F, Bosotti R, Scacheri E et al (2010) Discovery of drug mode of action and drug repositioning from transcriptional responses. Proc Natl Acad Sci 107:14621–14626. https://doi.org/10.1073/pnas.1000138107
Kaitin KI, Dimasi JA (2011) Pharmaceutical innovation in the 21st century: new drug approvals in the first decade, 2000–2009. Clin Pharmacol Ther 89:183–188. https://doi.org/10.1038/clpt.2010.286
Kim W-J, Shim M-S, Kim J-Y (2013) Management of hyperglycemia in type 2 diabetes: a patient-centered approach developed by the American Diabetes Association and the European Association for the Study of Diabetes. J Korean Diab 13:172. https://doi.org/10.4093/jkd.2012.13.4.172
Laboratory of Human Retrovirology and Immunoinformatics (LHRI) (2019) DAVID. https://david.ncifcrf.gov/. Accessed 26 Jul 2019
Laifenfeld D, Cha Y, Erez T et al (2017) Drug repurposing from the perspective of pharmaceutical companies. Br J Pharmacol. https://doi.org/10.1111/bph.13798
Lamb J, Crawford ED, Peck D et al (2006) The connectivity map: using gene-expression signatures to connect small molecules, genes, and disease. Science (80-) 313:1929–1935. https://doi.org/10.1126/science.1132939
Lee JH, Kim DG, Bae TJ et al (2012) CDA: combinatorial drug discovery using transcriptional response modules. PLoS One 7. https://doi.org/10.1371/journal.pone.0042573
Liu F, Yan L, Wang Z et al (2017) Metformin therapy and risk of colorectal adenomas and colorectal cancer in type 2 diabetes mellitus patients: a systematic review and meta-analysis. Oncotarget 8. https://doi.org/10.18632/oncotarget.13762
Malcomson B, Wilson H, Veglia E et al (2016) Connectivity mapping (ssCMap) to predict A20-inducing drugs and their antiinflammatory action in cystic fibrosis. Proc Natl Acad Sci 113:E3725–E3734. https://doi.org/10.1073/pnas.1520289113
Napolitano F, Sirci F, Carrella D, Di Bernardo D (2016) Drug-set enrichment analysis: a novel tool to investigate drug mode of action. Bioinformatics 32:235–241. https://doi.org/10.1093/bioinformatics/btv536
Napolitano F, Carrella D, Mandriani B et al (2018) Gene2drug: a computational tool for pathway-based rational drug repositioning. Bioinformatics 34:1498–1505. https://doi.org/10.1093/bioinformatics/btx800
NCBI (2019) GEO2R. https://www.ncbi.nlm.nih.gov/geo/geo2r/. Accessed 26 Jul 2019
O’Reilly P (2016) QUADrATiC: software. http://go.qub.ac.uk/QUADrATiC
Peyvandipour A, Saberian N, Shafi A et al (2018) Systems biology: a novel computational approach for drug repurposing using systems biology. Bioinformatics 34:2817–2825. https://doi.org/10.1093/bioinformatics/bty133
Raghavan R, Hyter S, Pathak HB et al (2016) Drug discovery using clinical outcome-based connectivity mapping: application to ovarian cancer. BMC Genomics 17. https://doi.org/10.1186/s12864-016-3149-5
Rena G, Hardie DG, Pearson ER (2017) The mechanisms of action of metformin. Diabetologia 60:1577–1585. https://doi.org/10.1007/s00125-017-4342-z
Ritchie MD, Holzinger ER, Li R et al (2015) Methods of integrating data to uncover genotype-phenotype interactions. Nat Rev Genet 16:85–97. https://doi.org/10.1038/nrg3868
Schally A, Kastin A, Coy D (1976) Edward T. Tyler Prize Oration: LH-releasing hormone and its analogues: recent basic and clinical investigations. Int J Fertil 21:1–30
Setoain J, Franch M, Martínez M et al (2015) NFFinder: an online bioinformatics tool for searching similar transcriptomics experiments in the context of drug repositioning. Nucleic Acids Res 43:W193–W199. https://doi.org/10.1093/nar/gkv445
Subramanian A, Tamayo P, Mootha VK et al (2005) Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc Natl Acad Sci 102:15545–15550. https://doi.org/10.1073/pnas.0506580102
Subramanian A, Narayan R, Corsello SM et al (2017) A next generation connectivity map: L1000 platform and the first 1,000,000 profiles. Cell 171:1437–1452.e17. https://doi.org/10.1016/j.cell.2017.10.049
The Comprehensive R Network (2019) The Comprehensive R Archive Network. https://cran.r-project.org/. Accessed 26 Jul 2019
Tomar S, Graves C, Altomare D et al (2016) Human papillomavirus status and gene expression profiles of oropharyngeal and oral cancers from European American and African American patients. Head Neck 38:E694–E704. https://doi.org/10.1002/hed.24072
University of California SC (2016) Xena. https://tcga.xenahubs.net. Accessed 26 Jul 2019
Wellcome Sanger Institute (2019) GDSC. http://www.cancerrxgene.org. Accessed 27 Jul 2019
Wu H, Miller E, Wijegunawardana D et al (2017) MD-Miner: a network-based approach for personalized drug repositioning. BMC Syst Biol 11. https://doi.org/10.1186/s12918-017-0462-9
Yang W, Soares J, Greninger P et al (2013) Genomics of Drug Sensitivity in Cancer (GDSC): a resource for therapeutic biomarker discovery in cancer cells. Nucleic Acids Res 41. https://doi.org/10.1093/nar/gks1111
Yuen HF, McCrudden CM, Huang YH et al (2013) TAZ expression as a prognostic indicator in colorectal cancer. PLoS One 8. https://doi.org/10.1371/journal.pone.0054211
Zhang S-D, Gant TW (2008) A simple and robust method for connecting small-molecule drugs using gene-expression signatures. BMC Bioinf 9:258. https://doi.org/10.1186/1471-2105-9-258
Zhang M, Lee S, Yao B et al (2019) DIGREM: an integrated web-based platform for detecting effective multi-drug combinations. Bioinformatics 35:1792–1794. https://doi.org/10.1093/bioinformatics/bty860
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this entry
Cite this entry
Scanlon, E.E., Blayney, J.K. (2019). In silico Drug Repositioning Using Omics Data: The Potential and Pitfalls. In: Hock, F., Gralinski, M. (eds) Drug Discovery and Evaluation: Methods in Clinical Pharmacology. Springer, Cham. https://doi.org/10.1007/978-3-319-56637-5_20-1
Download citation
DOI: https://doi.org/10.1007/978-3-319-56637-5_20-1
Received:
Accepted:
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-56637-5
Online ISBN: 978-3-319-56637-5
eBook Packages: Springer Reference Biomedicine and Life SciencesReference Module Biomedical and Life Sciences