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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Chen, Y., He, L., Ianevski, A., Durán, P. A., Potdar, S., Saarela, J., Miettinen, J. J., Kytölä, S., Miettinen, S., Manninen, M. J., et al. (2023) · Nature Protocols · doi:10.1038/s41596-023-00903-x · Cited by 17
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Improving the Quality of ‘Personalized Medicine’ Research and Practice: Through an Ethical Lens
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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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