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

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

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Garraway, L. A. (2017) · Nature · doi:10.1038/543613a · Cited by 3
Publisher's Note: Abstraction for data integration: Fusing mammalian molecular, cellular and phenotype big datasets for better knowledge extraction
Rouillard, A. D., Wang, Z., & Ma’ayan, A. (2015) · Computational Biology and Chemistry · doi:10.1016/j.compbiolchem.2015.06.003 · Cited by 3
Retracted
Using cell line and patient samples to improve predictions of patient drug responsePreprint
Zhao, C., Li, Y., Safikhani, Z., Haibe‐Kains, B., & Goldenberg, A. (2015) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/026534 · Cited by 3
Self-citation
Knowledge-guided gene prioritization reveals new insights into the mechanisms of chemoresistancePreprint
Emad, A., Woese, C. R., Cairns, J., Kalari, K. R., Liewei Wang, M. D., & Sinha, S. (2016) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/090027 · Cited by 3
Thunor: Visualization and Analysis of High-Throughput Dose-response DatasetsPreprint
Lubbock, A. L. R., Harris, L. A., Quaranta, V., Tyson, D. R., & Lopez, C. F. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/530329 · Cited by 3
Leveraging multi-source to resolve inconsistency across pharmacogenomic datasets in drug sensitivity predictionPreprint
Das, T., Bhattarai, K., Rajaganapathy, S., Wang, L.-W., Cerhan, J., & Zong, N. (2023) · medRxiv · doi:10.1101/2023.05.25.23290546
Ex Vivo Drug Screening: An Emerging Paradigm in the Treatment of Childhood Cancer
Ahmed, A., Cox, E., Lane, L., Rominiyi, O., Danson, S. J., Bryant, H. E., Wells, G., & King, D. A. (2025) · Journal of Pediatric Hematology/Oncology · doi:10.1097/mph.0000000000003017 · Cited by 2
Transformer-Based Multi-Omics Integration for Cancer Drug Response Prediction: A PRISMA 2020 Systematic Review
Habiba, U. (2026) · doi:10.63125/teryrs73 · Cited by 2
Gene Expression Analyses in Breast Cancer: Sample Matters
Haibe‐Kains, B., & Cescon, D. W. (2018) · JNCI Cancer Spectrum · doi:10.1093/jncics/pky019 · Cited by 2
Self-citation
Preclinical Solid Tumor Models to Study Novel Therapeutics in Brain Metastases
Singh, M., Dahal, A., & Brastianos, P. K. (2021) · Current Protocols · doi:10.1002/cpz1.284 · Cited by 2
Large-scale information retrieval and correction of noisy pharmacogenomic datasets through residual thresholded deep matrix factorization
Hu, Z. T., Yu, Y., Chen, R., Yeh, S., Chen, B., & Huang, H. (2025) · Briefings in Bioinformatics · doi:10.1093/bib/bbaf226 · Cited by 2
Methods: for studying pharmacogenetic profiles of combination chemotherapeutic drugs
Paolo, A. D., Polillo, M., Lastella, M., Bocci, G., Re, M. D., & Danesi, R. (2015) · Expert Opinion on Drug Metabolism & Toxicology · doi:10.1517/17425255.2015.1053460 · Cited by 2
Cross-Validation of Survival Bump Hunting by Recursive Peeling Methods.
Dazard, J.-E., Choe, M., LeBlanc, M. L., & Rao, J. S. (2014) · PubMed · Cited by 2
Nonlinear mixed effects dose response modeling in high throughput drug screens: application to melanoma cell line analysis
Ding, K.-F., Petricoin, E. F., Finlay, D., Yin, H. H., Hendricks, W. P. D., Sereduk, C., Kiefer, J., Sekulić, A., LoRusso, P. M., Vuori, K., et al. (2017) · Oncotarget · doi:10.18632/oncotarget.23495 · Cited by 2
Cross‐validation and peeling strategies for survival bump hunting using recursive peeling methods
Dazard, J.-E., Choe, M. J., LeBlanc, M. L., & Rao, J. S. (2016) · Statistical Analysis and Data Mining The ASA Data Science Journal · doi:10.1002/sam.11301 · Cited by 2
Computational Screening of Anti-Cancer Drugs Identifies a New BRCA Independent Gene Expression Signature to Predict Breast Cancer Sensitivity to Cisplatin
Berthelet, J., Foroutan, M., Bhuva, D. D., Whitfield, H. J., El-Saafin, F., Cursons, J., Serrano, A., Merdas, M., Lim, E., Charafe‐Jauffret, E., et al. (2022) · Cancers · doi:10.3390/cancers14102404 · Cited by 2
Finding common ground in cancer research
(2014) · Nature Medicine · doi:10.1038/nm.3456 · Cited by 2
Detection of circular RNAs and their potential as biomarkers predictive of drug responsePreprint
Nguyen, J., Mammoliti, A., Nair, S. K., So, E., Aghababazadeh, F. A., Eeles, C., Smith, I. C. P., Smirnov, P., He, H. H., Tsao, M., et al. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.01.08.522775 · Cited by 2
Self-citation
Monotherapy cancer drug-blind response prediction is limited to intraclass generalization
Herbert, W. G., Chia, N., Jensen, P. A., & Walther-Antonio, M. R. S. (2026) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1013232 · Cited by 2
Reassessing pharmacogenomic cell sensitivity with multilevel statistical models
Ploenzke, M., & Irizarry, R. A. (2022) · Biostatistics · doi:10.1093/biostatistics/kxac010 · Cited by 1

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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