Inconsistency in large pharmacogenomic studies
Open accessAbstract
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.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Marker-based predictions do not guarantee patient response because many are correlations from population-based studies.
“Even when marker-based predictions can be made, they do not guarantee patient response, as many are the result of correlations from population-based studies4, 5.”
From Drug sensitivity of single cancer cells is predicted by changes in mass accumulation rate (2016) · cites this paper as [5]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
The NCI60 human tumour cell line anticancer drug screen
Shoemaker, R. H. (2006) · Nature reviews. Cancer · doi:10.1038/nrc1951 · Cited by 2,644 · see citing works
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 · see citing works
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 · see citing works
Frozen robust multiarray analysis (fRMA)
McCall, M. N., Bolstad, B. M., & Irizarry, R. A. (2010) · Biostatistics · doi:10.1093/biostatistics/kxp059 · Cited by 592 · see citing works
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 · see citing works
Jetset: selecting the optimal microarray probe set to represent a gene
Li, Q., Birkbak, N. J., Győrffy, B., Szállási, Z., & Eklund, A. C. (2011) · BMC Bioinformatics · doi:10.1186/1471-2105-12-474 · Cited by 324 · see citing works
Self-citationThe genetic basis of variability in drug responses
Roden, D. M., & George, A. L. (2002) · Nature Reviews Drug Discovery · doi:10.1038/nrd705 · Cited by 293 · see citing works
Panel of human cancer cell lines provides valuable database for drug discovery and bioinformatics
Yamori, T. (2003) · Cancer Chemotherapy and Pharmacology · doi:10.1007/s00280-003-0649-1 · Cited by 146 · see citing works
Molecular Target Class Is Predictive of In vitro Response Profile
Greshock, J. D., Bachman, K. E., Degenhardt, Y., Jing, J., Wen, Y. H., Eastman, S. D., McNeil, E., Moy, C. H., Wegrzyn, R. J., Auger, K. R., et al. (2010) · Cancer Research · doi:10.1158/0008-5472.can-09-3788 · Cited by 124 · see citing works
Weinstein, J. N. (2012) · Nature · doi:10.1038/483544a · Cited by 107 · see citing works
Comparison and validation of genomic predictors for anticancer drug sensitivity
Papillon‐Cavanagh, S., Jay, N. D., El-Hachem, N., Olsen, C., Bontempi, G., Aerts, H. J. W. L., Quackenbush, J. F., & Haibe‐Kains, B. (2013) · Journal of the American Medical Informatics Association · doi:10.1136/amiajnl-2012-001442 · Cited by 65 · see citing works
Self-citationBarretina, J. G., 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/nature11735 · Cited by 44 · see citing works
Statistical and Computational Pharmacogenomics
Wu, R., & Lin, M. J. (2008) · Chapman & Hall/CRC eBooks · doi:10.1201/9781584888291 · Cited by 34 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Next-generation characterization of the Cancer Cell Line Encyclopedia
Ghandi, M., Huang, F. W., Jané-Valbuena, J., Kryukov, G. V., Lo, C. C., McDonald, E. R., Barretina, J., Gelfand, E. T., Bielski, C. M., Li, H., et al. (2019) · Nature · doi:10.1038/s41586-019-1186-3 · Cited by 3,394 · see citing works
Geeleher, P., Cox, N. J., & Huang, R. S. (2014) · PLoS ONE · doi:10.1371/journal.pone.0107468 · Cited by 2,444 · see citing works
Rouillard, A. D., Gundersen, G. W., Fernandez, N. F., Wang, Z., Monteiro, C. D., McDermott, M. G., & Ma’ayan, A. (2016) · Database · doi:10.1093/database/baw100 · Cited by 1,515 · see citing works
Reproducibility in Science: Improving the Standard for Basic and Preclinical Research
Begley, C. G., & Ioannidis, J. P. A. (2015) · Circulation Research · doi:10.1161/circresaha.114.303819 · Cited by 1,035 · see citing works
Genetic and transcriptional evolution alters cancer cell line drug response
Ben-David, U., Siranosian, B., Ha, G., Tang, H., Oren, Y., Hinohara, K., Strathdee, C. A., Dempster, J., Lyons, N. J., Burns, R., et al. (2018) · Nature · doi:10.1038/s41586-018-0409-3 · Cited by 931 · see citing works
Correlating chemical sensitivity and basal gene expression reveals mechanism of action
Rees, M. G., Seashore-Ludlow, B. A., Cheah, J. H., Adams, D., Price, E. V., Gill, S., Javaid, S., Coletti, M. E., Jones, V., Bodycombe, N. E., et al. (2015) · Nature Chemical Biology · doi:10.1038/nchembio.1986 · Cited by 874 · see citing works
Harnessing Connectivity in a Large-Scale Small-Molecule Sensitivity Dataset
Seashore-Ludlow, B., Rees, M. G., Cheah, J. H., Cokol, M., Price, E. V., Coletti, M. E., Jones, V., Bodycombe, N. E., Soule, C. K., Gould, J., et al. (2015) · Cancer Discovery · doi:10.1158/2159-8290.cd-15-0235 · Cited by 813 · see citing works
Tan, T. Z., Miow, Q. H., Miki, Y., Noda, T., Mori, S., Huang, R. Y., & Thiery, J. P. (2014) · EMBO Molecular Medicine · doi:10.15252/emmm.201404208 · Cited by 792 · see citing works
Growth rate inhibition metrics correct for confounders in measuring sensitivity to cancer drugs
Hafner, M., Niepel, M., Chung, M., & Sorger, P. K. (2016) · Nature Methods · doi:10.1038/nmeth.3853 · Cited by 681 · see citing works
Synthetic lethality and cancer
O’Neil, N. J., Bailey, M. L., & Hieter, P. (2017) · Nature Reviews Genetics · doi:10.1038/nrg.2017.47 · Cited by 658 · see citing works
Establishment of patient-derived cancer organoids for drug-screening applications
Driehuis, E., Kretzschmar, K., & Clevers, H. C. (2020) · Nature Protocols · doi:10.1038/s41596-020-0379-4 · Cited by 656 · see citing works
A comprehensive transcriptional portrait of human cancer cell lines
Klijn, C. N., Durinck, S., Stawiski, E. W., Haverty, P. M., Jiang, Z., Liu, H., Degenhardt, J. D., Mayba, O., Gnad, F., Liu, J., et al. (2014) · Nature Biotechnology · doi:10.1038/nbt.3080 · Cited by 650 · see citing works
Precision medicine for cancer with next-generation functional diagnostics
Friedman, A. A., Letai, A., Fisher, D. E., & Flaherty, K. T. (2015) · Nature Reviews Cancer · doi:10.1038/nrc4015 · Cited by 570 · see citing works
Screening out irrelevant cell-based models of disease
Horvath, P., Aulner, N., Bickle, M., Davies, A. M., Nery, E. D., Ebner, D., Montoya, M. C., Östling, P., Pietiäinen, V., Price, L. S., et al. (2016) · Nature Reviews Drug Discovery · doi:10.1038/nrd.2016.175 · Cited by 525 · see citing works
Pharmacogenomic agreement between two cancer cell line data sets
The Cancer Cell Line Encyclopedia Consortium, & The Genomics of Drug Sensitivity in Cancer Consortium (2015) · Nature · doi:10.1038/nature15736 · Cited by 418 · see citing works
Hickman, J. A., Graeser, R., Hoogt, R. D., Vidic, S., Brito, C., Gutekunst, M., & van der Kuip, H. (2014) · Biotechnology Journal · doi:10.1002/biot.201300492 · Cited by 379 · see citing works
Drug-Induced Death Signaling Strategy Rapidly Predicts Cancer Response to Chemotherapy
Montero, J., Sarosiek, K. A., DeAngelo, J. D., Maertens, O., Ryan, J., Ercan, D., Piao, H., Horowitz, N. S., Berkowitz, R. S., Matulonis, U., et al. (2015) · Cell · doi:10.1016/j.cell.2015.01.042 · Cited by 378 · see citing works
Menden, M. P., Wang, D. Y. Q., Mason, M. J., Szalai, B., Bulusu, K. C., Guan, Y., Yu, T. V., Kang, J., Jeon, M., Wolfinger, R. D., et al. (2019) · Nature Communications · doi:10.1038/s41467-019-09799-2 · Cited by 360 · see citing works
Yadav, B., Pemovska, T., Szwajda, A., Kulesskiy, E., Kontro, M., Karjalainen, R., Majumder, M. M., Malani, D., Murumägi, A., Knowles, J., et al. (2014) · Scientific Reports · doi:10.1038/srep05193 · Cited by 350 · see citing works
A machine learning approach to integrate big data for precision medicine in acute myeloid leukemia
Lee, S., Çelik, S., Logsdon, B. A., Lundberg, S., Martins, T. J., Oehler, V. G., Estey, E. H., Miller, C. P., Chien, S. S., Dai, J., et al. (2017) · Nature Communications · doi:10.1038/s41467-017-02465-5 · Cited by 332 · see citing works
An open investigation of the reproducibility of cancer biology research
Errington, T. M., Iorns, E., Gunn, W., Tan, F. E., Lomax, J., & Nosek, B. A. (2014) · eLife · doi:10.7554/elife.04333 · Cited by 312 · see citing works
Shimada, K., Hayano, M., Pagano, N. C., & Stockwell, B. R. (2016) · Cell chemical biology · doi:10.1016/j.chembiol.2015.11.016 · Cited by 300 · see citing works
Reproducible pharmacogenomic profiling of cancer cell line panels
Haverty, P. M., Lin, E., Tan, J., Yu, Y., Lam, B., Lianoglou, S., Neve, R. M., Martin, S. E., Settleman, J., Yauch, R. L., et al. (2016) · Nature · doi:10.1038/nature17987 · Cited by 279 · see citing works
When Quality Beats Quantity: Decision Theory, Drug Discovery, and the Reproducibility Crisis
Scannell, J. W., & Bosley, J. (2016) · PLOS ONE · doi:10.1371/journal.pone.0147215 · Cited by 279 · see citing works
Tumor-Derived Cell Lines as Molecular Models of Cancer Pharmacogenomics
Goodspeed, A. E., Heiser, L. M., Gray, J. W., & Costello, J. C. (2015) · Molecular Cancer Research · doi:10.1158/1541-7786.mcr-15-0189 · Cited by 271 · see citing works
PharmacoGx: an R package for analysis of large pharmacogenomic datasets
Smirnov, P., Safikhani, Z., El-Hachem, N., Wang, D., She, A., Olsen, C., Freeman, M., Selby, H. M., Gendoo, D. M. A., Großmann, P., et al. (2015) · Bioinformatics · doi:10.1093/bioinformatics/btv723 · Cited by 255 · see citing works
Self-citationAgreement between two large pan-cancer CRISPR-Cas9 gene dependency data sets
Dempster, J. M., Pacini, C., Pantel, S., Behan, F. M., Green, T., Krill-Burger, J. M., Beaver, C., Younger, S. T., Zhivich, V., Najgebauer, H., et al. (2019) · Nature Communications · doi:10.1038/s41467-019-13805-y · Cited by 250 · see citing works
Deep learning for drug response prediction in cancer
Baptista, D., Ferreira, P. G., & Rocha, M. (2019) · Briefings in Bioinformatics · doi:10.1093/bib/bbz171 · Cited by 239 · see citing works
Larsson, P., Engqvist, H., Biermann, J., Rönnerman, E. W., Forssell-Aronsson, E. B., Kovács, A., Karlsson, P., Helou, K., & Parris, T. Z. (2020) · Scientific Reports · doi:10.1038/s41598-020-62848-5 · Cited by 231 · see citing works
Genomic evolution of cancer models: perils and opportunities
Ben‐David, U., Beroukhim, R., & Golub, T. R. (2018) · Nature reviews. Cancer · doi:10.1038/s41568-018-0095-3 · Cited by 220 · see citing works
Reproducibility and replicability of rodent phenotyping in preclinical studies
Kafkafi, N., Agassi, J., Chesler, E. J., Crabbe, J. C., Crusio, W. E., Eilam, D., Gerlai, R., Golani, I., Gomez-Marin, A., Heller, R., et al. (2018) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2018.01.003 · Cited by 215 · see citing works
Reversal of cancer gene expression correlates with drug efficacy and reveals therapeutic targets
Chen, B., Ma, L., Paik, H., Sirota, M., Wei, W., Chua, M., So, S. K., & Butte, A. J. (2017) · Nature Communications · doi:10.1038/ncomms16022 · Cited by 209 · see citing works
Luna, A., Elloumi, F., Varma, S., Wang, Y., Rajapakse, V. N., Aladjem, M. I., Robert, J., Sander, C., Pommier, Y. G., & Reinhold, W. C. (2020) · Nucleic Acids Research · doi:10.1093/nar/gkaa968 · Cited by 209 · see citing works
Predicting Anticancer Drug Responses Using a Dual-Layer Integrated Cell Line-Drug Network Model
Zhang, N., Wang, H., Fang, Y., Wang, J., Zheng, X., & Liu, X. S. (2015) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1004498 · Cited by 208 · see citing works
Characterization of Human Cancer Cell Lines by Reverse-Phase Protein Arrays
Li, J., Zhao, W., Akbani, R., Liu, W.-B., Ju, Z., Ling, S., Vellano, C., Roebuck, P., Yu, Q., Eterovic, A., et al. (2017) · Cancer Cell · doi:10.1016/j.ccell.2017.01.005 · Cited by 195 · see citing works
Drugging PI3K in cancer: refining targets and therapeutic strategies
Yap, T. A., Bjerke, L. M., Clarke, P. A., & Workman, P. (2015) · Current Opinion in Pharmacology · doi:10.1016/j.coph.2015.05.016 · Cited by 204 · see citing works
Jabs, J., Zickgraf, F. M., Park, J., Wagner, S., Jiang, X., Jechow, K., Kleinheinz, K., Toprak, U. H., Schneider, M. A., Meister, M. A., et al. (2017) · Molecular Systems Biology · doi:10.15252/msb.20177697 · Cited by 201 · see citing works
Progress with covalent small-molecule kinase inhibitors
Zhao, Z., & Bourne, P. E. (2018) · Drug Discovery Today · doi:10.1016/j.drudis.2018.01.035 · Cited by 200 · see citing works
A Decision Support Framework for Genomically Informed Investigational Cancer Therapy
Meric‐Bernstam, F., Johnson, A. M., Holla, V. R., Bailey, A. M., Brusco, L., Chen, K., Routbort, M. J., Patel, K. P., Zeng, J., Kopetz, S., et al. (2015) · JNCI Journal of the National Cancer Institute · doi:10.1093/jnci/djv098 · Cited by 198 · see citing works
An automated fitting procedure and software for dose-response curves with multiphasic features
Veroli, G. Y. D., Fornari, C., Goldlust, I. S., Mills, G., Koh, S., Bramhall, J. L., Richards, F. M., & Jodrell, D. I. (2015) · Scientific Reports · doi:10.1038/srep14701 · Cited by 197 · see citing works
Ding, M. Q., Chen, L., Cooper, G. F., Young, J. D., & Lu, X. (2017) · Molecular Cancer Research · doi:10.1158/1541-7786.mcr-17-0378 · Cited by 191 · see citing works
Wang, L., Li, X., Zhang, L., & Gao, Q. (2017) · BMC Cancer · doi:10.1186/s12885-017-3500-5 · Cited by 180 · see citing works
In Vitro Human Cell Line Models to Predict Clinical Response to Anticancer Drugs
Niu, N., & Wang, L. (2015) · Pharmacogenomics · doi:10.2217/pgs.14.170 · Cited by 175 · see citing works
Dr.VAE: improving drug response prediction via modeling of drug perturbation effects
Rampášek, L., Hidru, D., Smirnov, P., Haibe‐Kains, B., & Goldenberg, A. (2019) · Bioinformatics · doi:10.1093/bioinformatics/btz158 · Cited by 174 · see citing works
Self-citationComputational models for predicting drug responses in cancer research
Azuaje, F. J. (2016) · Briefings in Bioinformatics · doi:10.1093/bib/bbw065 · Cited by 162 · see citing works
Rational Cell Culture Optimization Enhances Experimental Reproducibility in Cancer Cells
Wright Muelas, M., Ortega, F., Breitling, R., Bendtsen, C., & Westerhoff, H. V. (2018) · Scientific Reports · doi:10.1038/s41598-018-21050-4 · Cited by 160 · see citing works
DeepDSC: A Deep Learning Method to Predict Drug Sensitivity of Cancer Cell Lines
Li, M., Wang, Y., Zheng, R., Shi, X. M., Li, Y., Wu, F., & Wang, J. (2019) · IEEE Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbb.2019.2919581 · Cited by 159 · see citing works
Targeting the PI3K/AKT/mTOR Pathway in Bladder Cancer
Sathe, A., & Nawroth, R. (2017) · Methods in molecular biology · doi:10.1007/978-1-4939-7234-0_23 · Cited by 158 · see citing works
Rajapakse, V. N., Luna, A., Yamade, M., Loman, L., Varma, S., Sunshine, M., Iorio, F., Sousa, F. G., Elloumi, F., Aladjem, M. I., et al. (2018) · iScience · doi:10.1016/j.isci.2018.11.029 · Cited by 158 · see citing works
Machine learning in the prediction of cancer therapy
Rafique, R., Islam, S. M. R., & Kazi, J. U. (2021) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2021.07.003 · Cited by 154 · see citing works
Dong, Z., Zhang, N., Li, C., Wang, H., Fang, Y., Wang, J., & Zheng, X. (2015) · BMC Cancer · doi:10.1186/s12885-015-1492-6 · Cited by 151 · see citing works
Predicting Synergism of Cancer Drug Combinations Using NCI-ALMANAC Data
Sidorov, P., Naulaerts, S., Ariey-Bonnet, J., Pasquier, E., & Ballester, P. J. (2019) · Frontiers in Chemistry · doi:10.3389/fchem.2019.00509 · Cited by 151 · see citing works
Deep learning methods for drug response prediction in cancer: Predominant and emerging trends
Partin, A., Brettin, T. S., Zhu, Y., Narykov, O., Clyde, A., Overbeek, J., & Stevens, R. L. (2023) · Frontiers in Medicine · doi:10.3389/fmed.2023.1086097 · Cited by 149 · see citing works
Drug-perturbation-based stratification of blood cancer
Dietrich, S., Oleś, M., Lu, J., Sellner, L., Anders, S., Velten, B., Wu, B., Hüllein, J., Liberio, M. D. S., Walther, T., et al. (2017) · Journal of Clinical Investigation · doi:10.1172/jci93801 · Cited by 147 · see citing works
Predicting Cancer Drug Response using a Recommender System
Suphavilai, C., Bertrand, D., & Nagarajan, N. (2018) · Bioinformatics · doi:10.1093/bioinformatics/bty452 · Cited by 144 · see citing works
Fröhlich, F., Kessler, T., Weindl, D., Shadrin, A., Schmiester, L., Hache, H., Muradyan, A., Schütte, M., Lim, J.-H., Heinig, M., et al. (2018) · Cell Systems · doi:10.1016/j.cels.2018.10.013 · Cited by 143 · see citing works
Lind, A. P., & Anderson, P. C. (2019) · PLoS ONE · doi:10.1371/journal.pone.0219774 · Cited by 141 · see citing works
Geeleher, P., Zhang, Z., Wang, F., Gruener, R. F., Nath, A., Morrison, G., Bhutra, S., Grossman, R. L., & Huang, R. S. (2017) · Genome Research · doi:10.1101/gr.221077.117 · Cited by 137 · see citing works
Early phase clinical trials to identify optimal dosing and safety
Cook, N., Hansen, A. R., Siu, L. L., & Razak, A. R. A. (2014) · Molecular Oncology · doi:10.1016/j.molonc.2014.07.025 · Cited by 136 · see citing works
He, Y., Zhu, Q., Chen, M., Huang, Q., Wang, W., Li, Q., Huang, Y., & Di, W. J. (2016) · Oncotarget · doi:10.18632/oncotarget.12223 · Cited by 136 · see citing works
Biomimetic hydrogel supports initiation and growth of patient-derived breast tumor organoids
Prince, E., Cruickshank, J., Ba-Alawi, W., Hodgson, K., Haight, J., Tobin, C., Wakeman, A., Avoulov, A., Topolskaia, V., Elliott, M. J., et al. (2022) · Nature Communications · doi:10.1038/s41467-022-28788-6 · Cited by 135 · see citing works
Self-citationBioreactor technologies to support liver function in vitro
Ebrahimkhani, M. R., Neiman, J. A. S., Raredon, M. S. B., Hughes, D. J., & Griffith, L. G. (2014) · Advanced Drug Delivery Reviews · doi:10.1016/j.addr.2014.02.011 · Cited by 134 · see citing works
A Multi-center Study on the Reproducibility of Drug-Response Assays in Mammalian Cell Lines
Niepel, M., Hafner, M., Mills, C. E., Subramanian, K., Williams, E. H., Chung, M., Gaudio, B., Barrette, A. M., Stern, A. D., Hu, B., et al. (2019) · Cell Systems · doi:10.1016/j.cels.2019.06.005 · Cited by 133 · see citing works
Small cell lung cancer (SCLC): no treatment advances in recent years.
Koinis, F., Kotsakis, A. P., & Georgoulias, V. (2016) · Europe PMC (PubMed Central) · doi:10.3978/j.issn.2218-6751.2016.01.03 · Cited by 131 · see citing works
Leveraging multi-way interactions for systematic prediction of pre-clinical drug combination effects
Julkunen, H., Cichońska, A., Gautam, P., Szedmák, S., Douat, J., Pahikkala, T., Aittokallio, T. A., & Rousu, J. (2020) · Nature Communications · doi:10.1038/s41467-020-19950-z · Cited by 124 · see citing works
Predictive validity in drug discovery: what it is, why it matters and how to improve it
Scannell, J. W., Bosley, J., Hickman, J. A., Dawson, G. R., Truebel, H., Ferreira, G. S., Richards, D. B., & Treherne, J. M. (2022) · Nature Reviews Drug Discovery · doi:10.1038/s41573-022-00552-x · Cited by 122 · see citing works
Improved large-scale prediction of growth inhibition patterns using the NCI60 cancer cell line panel
Cortés‐Ciriano, I., van Westen, G. J. P., Bouvier, G., Nilges, M., Overington, J. P., Bender, A., & Malliavin, T. E. (2015) · Bioinformatics · doi:10.1093/bioinformatics/btv529 · Cited by 121 · see citing works
Systematic identification of non-coding pharmacogenomic landscape in cancer
Wang, Y., Wang, Z., Xu, J., Jiang, L., Song, L., Zhang, M., & Yang, D. (2018) · Nature Communications · doi:10.1038/s41467-018-05495-9 · Cited by 121 · see citing works
Open source machine-learning algorithms for the prediction of optimal cancer drug therapies
Huang, C., Mezencev, R., McDonald, J. F., & Vannberg, F. O. (2017) · PLoS ONE · doi:10.1371/journal.pone.0186906 · Cited by 119 · see citing works
Predictive approaches for drug combination discovery in cancer
Tonekaboni, S. A. M., Ghoraie, L. S., Manem, V. S., & Haibe‐Kains, B. (2016) · Briefings in Bioinformatics · doi:10.1093/bib/bbw104 · Cited by 118 · see citing works
Self-citationMalik, V., Kalakoti, Y., & Sundar, D. (2021) · BMC Genomics · doi:10.1186/s12864-021-07524-2 · Cited by 117 · see citing works
Cortés‐Ciriano, I., Ain, Q. U., Subramanian, V., Lenselink, E. B., Méndez‐Lucio, O., IJzerman, A. P., Wohlfahrt, G., Prūsis, P., Malliavin, T. E., van Westen, G. J. P., et al. (2014) · MedChemComm · doi:10.1039/c4md00216d · Cited by 115 · see citing works
Drug sensitivity of single cancer cells is predicted by changes in mass accumulation rate
Stevens, M. M., Maire, C. L., Chou, N., Murakami, M. A., Knoff, D. S., Kikuchi, Y., Kimmerling, R. J., Liu, H., Haidar, S., Calistri, N. L., et al. (2016) · Nature Biotechnology · doi:10.1038/nbt.3697 · Cited by 106 · see citing works
Uitdehaag, J. C. M., de Roos, J. A. D. M., van Doornmalen, A. M., Prinsen, M. B. W., de Man, J., Tanizawa, Y., Kawase, Y., Yoshino, K., Buijsman, R. C., & Zaman, G. J. R. (2014) · PLoS ONE · doi:10.1371/journal.pone.0092146 · Cited by 105 · see citing works
Multitask learning improves prediction of cancer drug sensitivity
Yuan, H., Paskov, I., Paskov, H. S., González, A. J., & Leslie, C. S. (2016) · Scientific Reports · doi:10.1038/srep31619 · Cited by 104 · see citing works
Revisiting inconsistency in large pharmacogenomic studies
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 104 · see citing works
Self-citationEPMA 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 101 · see citing works
Gene Expression Analysis in Ovarian Cancer – Faults and Hints from DNA Microarray Study
Lisowska, K. M., Olbryt, M., Dudaladava, V., Pamuła-Piłat, J., Kujawa, K., Grzybowska, E., Jarząb, M., Student, S., Rzepecka, I. K., Jarząb, B., et al. (2014) · Frontiers in Oncology · doi:10.3389/fonc.2014.00006 · Cited by 97 · see citing works
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 97 · see citing works
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 · see citing works
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 · see citing works
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 95 · see citing works
Self-citationLessons Learned from Two Decades of Anticancer Drugs
Liu, Z., Delavan, B., Roberts, R. A., & Tong, W. (2017) · Trends in Pharmacological Sciences · doi:10.1016/j.tips.2017.06.005 · Cited by 95 · see citing works
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 · see citing works
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 93 · see citing works
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 · see citing works
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 93 · see citing works
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 · see citing works
Standard Operating Procedure to Optimize Resazurin-Based Viability Assays
Petiti, J., Revel, L., & Divieto, C. (2024) · Biosensors · doi:10.3390/bios14040156 · Cited by 89 · see citing works
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 85 · see citing works
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 85 · see citing works
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 · see citing works
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 · see citing works
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 81 · see citing works
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 78 · see citing works
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 77 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 73 · see citing works
- 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)
- Venue
- Nature
- Published
- 2013-11-27
- Volume
- 504
- Issue
- 7480
- Pages
- 389-393
- ISSN
- 0028-0836 (ISSN-L)
- 1476-4687
- 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
- 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
- Physical Sciences › Computer Science › Computational Theory and Mathematics › 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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
