Cited by 499Computational Drug Discovery MethodsCancer Genomics and DiagnosticsGene expression and cancer classification

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Inconsistency in large pharmacogenomic studies

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Haibe-Kains, Benjamin, El-Hachem, Nehme, Birkbak, Nicolai Juul, Jin, Andrew C., Beck, Andrew H., Aerts, Hugo J. W. L., Quackenbush, John·2013·Nature

DOI 10.1038/nature12831PMC PMC4237165

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Abstract

Two large-scale pharmacogenomic studies were published recently in this journal. Genomic data are well correlated between studies; however, the measured drug response data are highly discordant. Although the source of inconsistencies remains uncertain, it has potential implications for using these outcome measures to assess gene-drug associations or select potential anticancer drugs on the basis of their reported results.

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Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles
Subramanian, A., Tamayo, P., Mootha, V. K., Mukherjee, S., Ebert, B. L., Gillette, M. A., Paulovich, A., Pomeroy, S. L., Golub, T. R., Lander, E. S., et al. (2005) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0506580102 · Cited by 53,625
Gene Ontology: tool for the unification of biology
Ashburner, M., Ball, C. A., Blake, J. A., Botstein, D., Butler, H., Cherry, J. M., Davis, A. P., Dolinski, K., Dwight, S. S., Eppig, J. T., et al. (2000) · Nature Genetics · doi:10.1038/75556 · Cited by 43,206
The Cancer Cell Line Encyclopedia enables predictive modelling of anticancer drug sensitivity
Barretina, J., Caponigro, G., Stransky, N., Venkatesan, K., Margolin, A. A., Kim, S., Wilson, C. J., Lehár, J., Kryukov, G. V., Sonkin, D., et al. (2012) · Nature · doi:10.1038/nature11003 · Cited by 7,950
The proof and measurement of association between two things.
Spearman, C. (2015) · International Journal of Epidemiology · doi:10.1093/ije/dyq191 · Cited by 7,777
The kappa statistic in reliability studies: use, interpretation, and sample size requirements
Sim, J., & Wright, C. C. (2005) · Physical Therapy · doi:10.1093/ptj/85.3.257 · Cited by 4,093
The NCI60 human tumour cell line anticancer drug screen
Shoemaker, R. H. (2006) · Nature reviews. Cancer · doi:10.1038/nrc1951 · Cited by 2,673
Systematic identification of genomic markers of drug sensitivity in cancer cells
Garnett, M. J., Edelman, E. J., Heidorn, S. J., Greenman, C. D., Dastur, A., Lau, K. W., Greninger, P., Thompson, I. R., Luo, X., Soares, J., et al. (2012) · Nature · doi:10.1038/nature11005 · Cited by 2,456
ArrayExpress--a public database of microarray experiments and gene expression profiles
Parkinson, H., Kapushesky, M., Shojatalab, M., Abeygunawardena, N., Coulson, R., Farne, A., Holloway, E., Kolesnykov, N., Lilja, P., Lukk, M., et al. (2006) · Nucleic Acids Research · doi:10.1093/nar/gkl995 · Cited by 754
Frozen robust multiarray analysis (fRMA)
McCall, M. N., Bolstad, B. M., & Irizarry, R. A. (2010) · Biostatistics · doi:10.1093/biostatistics/kxp059 · Cited by 594
Subtype and pathway specific responses to anticancer compounds in breast cancer
Heiser, L. M., Sadanandam, A., Kuo, W., Benz, S. C., Goldstein, T. C., Ng, S., Gibb, W. J., Wang, N. J., Ziyad, S., Tong, F. K., et al. (2011) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1018854108 · Cited by 464

Show all 18 references

Research works printed but not linked: 1

Roden, D. M. & George, A. L., Jr The genetic basis of drug response. Nature 1, 37–44 (2002)

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Yuan, H., Paskov, I., Paskov, H. S., González, A. J., & Leslie, C. S. (2016) · Scientific Reports · doi:10.1038/srep31619 · Cited by 104
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Safikhani, Z., Freeman, M., Smirnov, P., El-Hachem, N., She, A., Quevedo, R., Goldenberg, A., Birkbak, N. J., Hatzis, C., Shi, L. M., et al. (2016) · F1000Research · doi:10.12688/f1000research.9611.3 · Cited by 106
Self-citation
EPMA position paper in cancer: current overview and future perspectives
Grech, G., Zhan, X., Yoo, B. C., Bubnov, R. V., Hagan, S., Danesi, R., Vittadini, G., & Desiderio, D. M. (2015) · The EPMA Journal · doi:10.1186/s13167-015-0030-6 · Cited by 104
Gene Expression Analysis in Ovarian Cancer – Faults and Hints from DNA Microarray Study
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Comparative Study of Multicellular Tumor Spheroid Formation Methods and Implications for Drug Screening
Gencoglu, M. F., Barney, L. E., Hall, C. L., Brooks, E. A., Schwartz, A. D., Corbett, D. C., Stevens, K. R., & Peyton, S. R. (2017) · ACS Biomaterials Science & Engineering · doi:10.1021/acsbiomaterials.7b00069 · Cited by 102
A cross-study analysis of drug response prediction in cancer cell lines
Xia, F., Allen, J., Balaprakash, P., Brettin, T. S., García–Cardona, C., Clyde, A., Cohn, J. D., Doroshow, J. H., Duan, X., Dubinkina, V. B., et al. (2021) · Briefings in Bioinformatics · doi:10.1093/bib/bbab356 · Cited by 97
Cancer research needs a better map
Boehm, J. S., Garnett, M. J., Adams, D. J., Francies, H. E., Golub, T. R., Hahn, W. C., Iorio, F., McFarland, J. M., Parts, L., & Vázquez, F. (2021) · Nature · doi:10.1038/d41586-021-00182-0 · Cited by 96
Enhancing Reproducibility in Cancer Drug Screening: How Do We Move Forward?
Hatzis, C., Bedard, P. L., Birkbak, N. J., Beck, A. H., Aerts, H. J. W. L., Stern, D. F., Shi, L. M., Clarke, R. C., Quackenbush, J. F., & Haibe‐Kains, B. (2014) · Cancer Research · doi:10.1158/0008-5472.can-14-0725 · Cited by 96
Self-citation
Lessons Learned from Two Decades of Anticancer Drugs
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Discovery of novel drug sensitivities in T-PLL by high-throughput ex vivo drug testing and mutation profiling
Andersson, E. I., Pützer, S., Yadav, B., Dufva, O., Khan, S., He, L., Sellner, L., Schrader, A., Crispatzu, G., Oleś, M., et al. (2017) · Leukemia · doi:10.1038/leu.2017.252 · Cited by 94
Understanding the Genetic Mechanisms of Cancer Drug Resistance Using Genomic Approaches
Hu, X., & Zhang, Z. (2015) · Trends in Genetics · doi:10.1016/j.tig.2015.11.003 · Cited by 94
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Niepel, M., Hafner, M. A., Duan, Q., Wang, Z., Paull, E., Chung, M., Lü, X., Stuart, J. M., Golub, T. R., Subramanian, A., et al. (2017) · Nature Communications · doi:10.1038/s41467-017-01383-w · Cited by 93
Patient-derived cell line, xenograft and organoid models in lung cancer therapy
Huo, K.-G., D’Arcangelo, E., & Tsao, M. (2020) · Translational Lung Cancer Research · doi:10.21037/tlcr-20-154 · Cited by 98
Hippo pathway mediates resistance to cytotoxic drugs
Gujral, T. S., & Kirschner, M. W. (2017) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1703096114 · Cited by 91
Standard Operating Procedure to Optimize Resazurin-Based Viability Assays
Petiti, J., Revel, L., & Divieto, C. (2024) · Biosensors · doi:10.3390/bios14040156 · Cited by 89
A survey and systematic assessment of computational methods for drug response prediction
Chen, J., & Zhang, L. (2019) · Briefings in Bioinformatics · doi:10.1093/bib/bbz164 · Cited by 86
A cancer drug atlas enables synergistic targeting of independent drug vulnerabilities
Narayan, R. S., Molenaar, P., Teng, J., Cornelissen, F. M. G., Roelofs, I., de Menezes, R. X., Dik, R., Lagerweij, T., Broersma, Y., Petersen, N., et al. (2020) · Nature Communications · doi:10.1038/s41467-020-16735-2 · Cited by 89
Consistency in drug response profiling
Mpindi, J. P., Yadav, B., Östling, P., Gautam, P., Malani, D., Murumägi, A., Hirasawa, A., Kangaspeska, S., Wennerberg, K., Kallioniemi, O., et al. (2016) · Nature · doi:10.1038/nature20171 · Cited by 85
Alternative drug sensitivity metrics improve preclinical cancer pharmacogenomics
Hafner, M. A., Niepel, M., & Sorger, P. K. (2017) · Nature Biotechnology · doi:10.1038/nbt.3882 · Cited by 83
CRISPR knockout screening identifies combinatorial drug targets in pancreatic cancer and models cellular drug response
Szlachta, K. A., Kuscu, C., Tufan, T., Adair, S. J., Shang, S., Michaels, A. D., Mullen, M. G., Fischer, N. L., Yang, J., Liu, L., et al. (2018) · Nature Communications · doi:10.1038/s41467-018-06676-2 · Cited by 83
Colorectal Cancer Cell Line Proteomes Are Representative of Primary Tumors and Predict Drug Sensitivity
Wang, J., Mouradov, D., Wang, X., Jorissen, R. N., Chambers, M., Zimmerman, L. J., Vasaikar, S. V., Love, C. G., Li, S., Lowes, K. N., et al. (2017) · Gastroenterology · doi:10.1053/j.gastro.2017.06.008 · Cited by 79
Mutational signatures are markers of drug sensitivity of cancer cells
Levatić, J., Salvadores, M., Fuster‐Tormo, F., & Supek, F. (2022) · Nature Communications · doi:10.1038/s41467-022-30582-3 · Cited by 79
Dose-dependent intracellular reactive oxygen and nitrogen species (ROS/RNS) production from particulate matter exposure: comparison to oxidative potential and chemical composition
Tuet, W. Y., Fok, S., Verma, V., Rodriguez, M. S. T., Grosberg, A., Champion, J. A., & Ng, N. L. (2016) · Atmospheric Environment · doi:10.1016/j.atmosenv.2016.09.005 · Cited by 76
Drug response consistency in CCLE and CGP
Bouhaddou, M., DiStefano, M. S., Riesel, E. A., Carrasco, E., Holzapfel, H. Y., Jones, D. C., Smith, G. R., Stern, A. D., Somani, S. S., Thompson, T. V., et al. (2016) · Nature · doi:10.1038/nature20580 · Cited by 76
Integrative Analysis of Hypoxia-Associated Signature in Pan-Cancer
Zhang, Q., Huang, R., Hu, H., Yu, L., Tang, Q., Tao, Y., Liu, Z., Li, J., & Wang, G. (2020) · iScience · doi:10.1016/j.isci.2020.101460 · Cited by 75
Effect of Spheroidal Age on Sorafenib Diffusivity and Toxicity in a 3D HepG2 Spheroid Model
Eilenberger, C., Rothbauer, M., Ehmoser, E.-K., Ertl, P., & Küpcü, S. (2019) · Scientific Reports · doi:10.1038/s41598-019-41273-3 · Cited by 74

Authors: Haibe-Kains, B., El-Hachem, N., Birkbak, N. J., Jin, A. C., Beck, A. H., Aerts, H. J. W. L., & Quackenbush, J.
Affiliations: Technical University of Denmark (DK)
Beth Israel Deaconess Medical Center (US)
Brigham and Women's Hospital (US)
Dana-Farber Cancer Institute (US)
ORCID: Birkbak, Nicolai Juul
Beck, Andrew H.
Aerts, Hugo J. W. L.
Quackenbush, John
Venue: Nature
Publisher: Springer Science and Business Media LLC
Published: 2013-11-27
Volume: 504
Issue: 7480
Pages: 389-393
ISSN: 0028-0836 (ISSN-L)
1476-4687
DOI: 10.1038/nature12831
PMC: PMC4237165
OpenAlex: W2011474181
Cited by: 499
References: 18
Citations by year: 2026: 16 · 2025: 16 · 2024: 14 · 2023: 30 · 2022: 33 · 2021: 44 · 2020: 41 · 2019: 43 · 2018: 51 · 2017: 58 · 2016: 60 · 2015: 46 · 2014: 43 · 2013: 4
FWCI: 55.38
Type: journal article
Language: en
Open access: green
www.ncbi.nlm.nih.gov
Licence: www.springernature.com/gp/researchers/text-and-data-mining (tdm)
www.springernature.com/gp/researchers/text-and-data-mining (vor)
Locations: Nature (publishedVersion)
PubMed (publishedVersion)
Research Publications (Maastricht University) (publishedVersion)
PubMed Central (acceptedVersion)
RePEc: Research Papers in Economics (submittedVersion)
UCL Discovery (University College London) (submittedVersion)
Technical University of Denmark, DTU Orbit (Technical University of Denmark, DTU) (submittedVersion)
Research Publications (Maastricht University) (submittedVersion)
Funders: National Cancer Institute (CA 087969, U19CA148065, P01CA087969)
Topics: Life Sciences › Pharmacology, Toxicology and Pharmaceutics › Drug Discovery › Computational Drug Discovery Methods
Life Sciences › Biochemistry, Genetics and Molecular Biology › Cancer Research › Cancer Genomics and Diagnostics
Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
Keywords: pharmacogenomics, cancer cell lines, drug sensitivity, data inconsistency, drug response biomarkers, anticancer drug selection, drug-gene interactions
MeSH terms: Antineoplastic Agents, Cell Line, Humans, Neoplasms, Pharmacogenetics, Reproducibility of Results, Genome, Human, Drug Resistance, Neoplasm, Area Under Curve, Inhibitory Concentration 50, Gene Expression Profiling

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