Pharmacogenomic agreement between two cancer cell line data sets
Open accessAbstract
Large cancer cell line collections broadly capture the genomic diversity of human cancers and provide valuable insight into anti-cancer drug response. Here we show substantial agreement and biological consilience between drug sensitivity measurements and their associated genomic predictors from two publicly available large-scale pharmacogenomics resources: The Cancer Cell Line Encyclopedia and the Genomics of Drug Sensitivity in Cancer databases.
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?
Large-scale drug sensitivity studies using MEK and ERK inhibitors did not reveal the same degree of selectivity in KRAS mutant cells.
“However, large-scale drug sensitivity studies using the MEK and ERK inhibitors did not reveal the same degree of selectivity in KRAS mutant cells (69, 70) (Figure 4).”
From KRAS mutation in pancreatic cancer (2021) · cites this paper as [70]
Majority of KRAS-mutant CRC cell lines exhibit elevated ABCB1 expression.
“Notably, the majority of KRAS-mutant CRC cell lines exhibit elevated ABCB1 expression (39)”
From The drug efflux pump MDR1 promotes intrinsic and acquired resistance to PROTACs in cancer cells (2022) · cites this paper as [39]
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
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
A collection of breast cancer cell lines for the study of functionally distinct cancer subtypes
Neve, R. M., Chin, K., Fridlyand, J., Yeh, J., Baehner, F. L., Fevr, T., Clark, L., Bayani, N., Coppé, J., Tong, F. K., et al. (2006) · Cancer Cell · doi:10.1016/j.ccr.2006.10.008 · Cited by 3,191 · 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
Garraway, L. A., Widlund, H. R., Rubin, M. A., Getz, G., Berger, A. J., Ramaswamy, S., Beroukhim, R., Milner, D. A., Granter, S. R., Du, J., et al. (2005) · Nature · doi:10.1038/nature03664 · Cited by 1,548 · see citing works
BRAF mutation predicts sensitivity to MEK inhibition
Solit, D. B., Garraway, L. A., Pratilas, C. A., Sawai, A., Getz, G. A., Basso, A., Ye, Q., Lobo, J. M., She, Y., Osman, I., et al. (2005) · Nature · doi:10.1038/nature04304 · Cited by 1,317 · see citing works
Cell line-based platforms to evaluate the therapeutic efficacy of candidate anticancer agents
Sharma, S. V., Haber, D. A., & Settleman, J. (2010) · Nature reviews. Cancer · doi:10.1038/nrc2820 · Cited by 585 · see citing works
Inconsistency in large pharmacogenomic studies
Haibe-Kains, B., El-Hachem, N., Birkbak, N. J., Jin, A. C., Beck, A. H., Aerts, H. J. W. L., & Quackenbush, J. (2013) · Nature · doi:10.1038/nature12831 · Cited by 499 · see citing works
Predicting drug susceptibility of non–small cell lung cancers based on genetic lesions
Sos, M. L., Michel, K., Zander, T., Weiss, J. M., Frommolt, P., Peifer, M., Li, D., Ullrich, R. T., Koker, M., Fischer, F., et al. (2009) · Journal of Clinical Investigation · doi:10.1172/jci37127 · Cited by 255 · see citing works
Advances in the preclinical testing of cancer therapeutic hypotheses
Caponigro, G., & Sellers, W. R. (2011) · Nature Reviews Drug Discovery · doi:10.1038/nrd3385 · Cited by 165 · 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
Flagged first, then by citations
Cited by
Sorted by citations
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
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
A Landscape of Pharmacogenomic Interactions in Cancer
Iorio, F., Knijnenburg, T. A., Vis, D. J., Bignell, G. R., Menden, M. P., Schubert, M., Aben, N., Gonçalves, E., Barthorpe, S., Lightfoot, H., et al. (2016) · Cell · doi:10.1016/j.cell.2016.06.017 · Cited by 2,090 · 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
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
Repana, D., Nulsen, J., Dreßler, L., Bortolomeazzi, M., Venkata, S. K., Tourna, A., Yakovleva, A., Palmieri, T., & Ciccarelli, F. D. (2019) · Genome biology · doi:10.1186/s13059-018-1612-0 · Cited by 677 · 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
WRN helicase is a synthetic lethal target in microsatellite unstable cancers
Chan, E. M., Shibue, T., McFarland, J. M., Gaeta, B., Ghandi, M., Dumont, N., Gonzalez, A., McPartlan, J. S., Li, T., Zhang, Y., et al. (2019) · Nature · doi:10.1038/s41586-019-1102-x · Cited by 458 · see citing works
KRAS mutation in pancreatic cancer
Luo, J. (2021) · Seminars in Oncology · doi:10.1053/j.seminoncol.2021.02.003 · Cited by 360 · 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
Agreement 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
Predicting drug response of tumors from integrated genomic profiles by deep neural networks
Chiu, Y., Chen, H.-I. H., Zhang, T., Zhang, S., Gorthi, A., Wang, L., Huang, Y., & Chen, Y. (2019) · BMC Medical Genomics · doi:10.1186/s12920-018-0460-9 · Cited by 227 · 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
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
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
Choose and Use Your Chemical Probe Wisely to Explore Cancer Biology
Blagg, J., & Workman, P. (2017) · Cancer Cell · doi:10.1016/j.ccell.2017.06.005 · Cited by 203 · 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
PDXNET Consortium, Woo, X. Y., EurOPDX Consortium, Giordano, J., Srivastava, A., Zhao, Z.-M., Lloyd, M. W., de Bruijn, R., Suh, Y.-S., Patidar, R., et al. (2021) · Nature Genetics · doi:10.1038/s41588-020-00750-6 · Cited by 190 · see citing works
Liu, P., Li, H., Li, S., & Leung, K. (2019) · BMC Bioinformatics · doi:10.1186/s12859-019-2910-6 · Cited by 177 · see citing works
USP1 Is Required for Replication Fork Protection in BRCA1-Deficient Tumors
Lim, K. S., Li, H., Roberts, E. A., Gaudiano, E. F., Clairmont, C. S., Sambel, L. A., Ponnienselvan, K., Liu, J., Yang, C., Kozono, D., et al. (2018) · Molecular Cell · doi:10.1016/j.molcel.2018.10.045 · Cited by 174 · see citing works
Deubiquitinases Maintain Protein Homeostasis and Survival of Cancer Cells upon Glutathione Depletion
Harris, I. S., Endress, J. E., Coloff, J. L., Selfors, L. M., McBrayer, S. K., Rosenbluth, J. M., Takahashi, N., Dhakal, S., Koduri, V., Oser, M. G., et al. (2019) · Cell Metabolism · doi:10.1016/j.cmet.2019.01.020 · Cited by 168 · see citing works
Computational 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
Genome‐wide cooperation of EMT transcription factor ZEB1 with YAP and AP‐1 in breast cancer
Feldker, N., Ferrazzi, F., Schuhwerk, H., Widholz, S. A., Guenther, K., Frisch, I., Jakob, K., Kleemann, J., Riegel, D., Bönisch, U., et al. (2020) · The EMBO Journal · doi:10.15252/embj.2019103209 · Cited by 164 · see citing works
PharmacoDB: an integrative database for mining in vitro anticancer drug screening studies
Smirnov, P., Kofia, V., Maru, A., Freeman, M., Ho, C., El-Hachem, N., Adam, G.-A., Ba-alawi, W., Safikhani, Z., & Haibe-Kains, B. (2018) · Nucleic Acids Research · doi:10.1093/nar/gkx911 · Cited by 155 · 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
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
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
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
Schick, S., Rendeiro, A. F., Runggatscher, K., Ringler, A., Boidol, B., Hinkel, M., Májek, P., Vulliard, L., Penz, T., Parapatics, K., et al. (2019) · Nature Genetics · doi:10.1038/s41588-019-0477-9 · Cited by 145 · see citing works
Bioinformatics Approaches for Anti-cancer Drug Discovery
Li, K., Du, Y., Li, L., & Wei, D. (2019) · Current Drug Targets · doi:10.2174/1389450120666190923162203 · Cited by 143 · see citing works
Koromina, M., Pandi, M.-T., & Patrinos, G. P. (2019) · OMICS A Journal of Integrative Biology · doi:10.1089/omi.2019.0151 · Cited by 140 · 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
Liu, C., Dong, W. G., Xiang, J., Ren, F., Huang, L., Lang, J., Tian, G., Li, Y., & Yang, J. (2020) · Molecular Therapy — Nucleic Acids · doi:10.1016/j.omtn.2020.07.003 · Cited by 138 · see citing works
The drug efflux pump MDR1 promotes intrinsic and acquired resistance to PROTACs in cancer cells
Kurimchak, A. M., Herrera-Montávez, C., Montserrat-Sangrà, S., Araiza-Olivera, D., Hu, J., Neumann-Domer, R., Kuruvilla, M., Bellacosa, A., Testa, J. R., Jin, J., et al. (2022) · Science Signaling · doi:10.1126/scisignal.abn2707 · Cited by 136 · see citing works
Venetoclax Is Effective in Small-Cell Lung Cancers with High BCL-2 Expression
Lochmann, T. L., Floros, K. V., Naseri, M., Powell, K. M., Cook, W., March, R. J., Stein, G. T., Greninger, P., Maves, Y. K., Saunders, L. R., et al. (2017) · Clinical Cancer Research · doi:10.1158/1078-0432.ccr-17-1606 · 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
A widespread role for SLC transmembrane transporters in resistance to cytotoxic drugs
Girardi, E., César‐Razquin, A., Lindinger, S., Παπακώστας, Κ., Konecka, J., Hemmerich, J., Kickinger, S., Kartnig, F., Gürtl, B., Klavins, K., et al. (2020) · Nature Chemical Biology · doi:10.1038/s41589-020-0483-3 · Cited by 133 · see citing works
Kim, H., Kim, E., Lee, I., Bae, B., Park, M., & Nam, H. (2020) · Biotechnology and Bioprocess Engineering · doi:10.1007/s12257-020-0049-y · Cited by 138 · see citing works
Vougas, K. N., Sakellaropoulos, T., Kotsinas, A., Foukas, G.-R. P., Ntargaras, A., Koinis, F., Polyzos, A., Myrianthopoulos, V., Zhou, H., Narang, S., et al. (2019) · Pharmacology & Therapeutics · doi:10.1016/j.pharmthera.2019.107395 · Cited by 126 · see citing works
DeepTTA: a transformer-based model for predicting cancer drug response
Jiang, L., Jiang, C., Yu, X., Fu, R., Jin, S., & Liu, X. (2022) · Briefings in Bioinformatics · doi:10.1093/bib/bbac100 · Cited by 127 · see citing works
Interferon-γ Signaling in Melanocytes and Melanoma Cells Regulates Expression of CTLA-4
Mo, X., Zhang, H., Preston, S., Martin, K. A., Zhou, B., Vadalia, N., Gamero, A. M., Soboloff, J., Tempera, I., & Zaidi, M. R. (2017) · Cancer Research · doi:10.1158/0008-5472.can-17-1615 · 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
Salvage of ribose from uridine or RNA supports glycolysis in nutrient-limited conditions
Skinner, O. S., Blanco-Fernández, J., Goodman, R. P., Kawakami, A., Shen, H., Kemény, L. V., Joesch-Cohen, L. M., Rees, M. G., Roth, J. A., Fisher, D. E., et al. (2023) · Nature Metabolism · doi:10.1038/s42255-023-00774-2 · Cited by 120 · see citing works
An expanded landscape of human long noncoding RNA
Jiang, S., Cheng, S., Ren, L., Wang, Q., Kang, Y.-J., Ding, Y., Hou, M. L., Yang, X., Lin, Y., Liang, N., et al. (2019) · Nucleic Acids Research · doi:10.1093/nar/gkz621 · Cited by 114 · see citing works
Wooten, D. J., Groves, S. M., Tyson, D. R., Liu, Q., Lim, J. S., Albert, R., Lopez, C. F., Sage, J., & Quaranta, V. (2019) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1007343 · Cited by 114 · 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., Libério, M. S., Walther, T., et al. (2017) · Kölner Universitäts PublikationsServer (Universität zu Köln) · doi:10.17863/cam.20903 · Cited by 111 · see citing works
Barretina, 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. (2018) · Nature · doi:10.1038/s41586-018-0722-x · Cited by 109 · 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
Lessons 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
Liu, J., Kong, X., Huang, T., Wang, R., Li, W., & Chen, Q. (2020) · Frontiers in Immunology · doi:10.3389/fimmu.2020.02048 · Cited by 93 · see citing works
Paralog buffering contributes to the variable essentiality of genes in cancer cell lines
De Kegel, B., & Ryan, C. J. (2019) · PLOS Genetics · doi:10.1371/journal.pgen.1008466 · Cited by 92 · 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
Xu, S., Zhan, M., Jiang, C., He, M. Y., Yang, L., Shen, H., Huang, S., Huang, X., Lin, R., Shi, Y., et al. (2019) · Nature Communications · doi:10.1038/s41467-019-13420-x · Cited by 91 · see citing works
p53 Is a Master Regulator of Proteostasis in SMARCB1-Deficient Malignant Rhabdoid Tumors
Carugo, A., Minelli, R., Sapio, L., Soeung, M., Carbone, F., Robinson, F. S., Tepper, J., Chen, Z., Lovisa, S., Svelto, M., et al. (2019) · Cancer Cell · doi:10.1016/j.ccell.2019.01.006 · Cited by 90 · see citing works
Cooperation of the ATM and Fanconi Anemia/BRCA Pathways in Double-Strand Break End Resection
Cai, M., Dunn, C. E., Chen, W., Kochupurakkal, B. S., Nguyen, H. Q., Moreau, L. A., Shapiro, G. I., Parmar, K., Kozono, D., & D’Andrea, A. D. (2020) · Cell Reports · doi:10.1016/j.celrep.2020.01.052 · Cited by 87 · 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
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
Dammert, M. A., Brägelmann, J., Olsen, R. R., Böhm, S., Monhasery, N., Whitney, C. P., Chalishazar, M. D., Tumbrink, H. L., Guthrie, M. R., Klein, S., et al. (2019) · Nature Communications · doi:10.1038/s41467-019-11371-x · Cited by 82 · 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
Liu, L., Xiong, D., Lin, P., Yang, H., Dang, Y., & Chen, G. (2018) · International Journal of Oncology · doi:10.3892/ijo.2018.4531 · Cited by 80 · see citing works
Meta-GDBP: a high-level stacked regression model to improve anticancer drug response prediction
Su, R., Liu, X., Xiao, G., & Wei, L. (2019) · Briefings in Bioinformatics · doi:10.1093/bib/bbz022 · Cited by 80 · 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
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
Deep learning of pharmacogenomics resources: moving towards precision oncology
Chiu, Y., Chen, H.-I. H., Gorthi, A., Mostavi, M., Zheng, S., Huang, Y., & Chen, Y. (2019) · Briefings in Bioinformatics · doi:10.1093/bib/bbz144 · Cited by 77 · see citing works
Chang, A., Liu, L., Ashby, J. M., Wu, D., Chen, Y., O’Neill, S. S., HUANG, S. H., Wang, J., Wang, G., Cheng, D., et al. (2021) · Cancer Research · doi:10.1158/0008-5472.can-21-0688 · Cited by 77 · see citing works
Olfactory receptor gene abundance in invasive breast carcinoma
Masjedi, S., Zwiebel, L. J., & Giorgio, T. D. (2019) · Scientific Reports · doi:10.1038/s41598-019-50085-4 · Cited by 76 · see citing works
Chang, W., Chang, Y., Yang, Y., Lin, S.-K., Chang, P. M., & Hsiao, M. (2019) · Journal of Experimental & Clinical Cancer Research · doi:10.1186/s13046-019-1256-2 · Cited by 74 · see citing works
Marine Natural Products: A Potential Source of Anti-hepatocellular Carcinoma Drugs
Ren, X., Xie, X., Chen, B., Liu, L., Jiang, C., & Qian, Q. (2021) · Journal of Medicinal Chemistry · doi:10.1021/acs.jmedchem.0c02026 · Cited by 74 · see citing works
Lu, Q., Zhang, X., Liang, T., & Bai, X. (2022) · Molecular Medicine · doi:10.1186/s10020-022-00544-y · Cited by 71 · see citing works
Salvadores, M., Fuster‐Tormo, F., & Supek, F. (2020) · Science Advances · doi:10.1126/sciadv.aba1862 · Cited by 70 · see citing works
Li, Z., Chen, S., Feng, W., Luo, Y., Lai, H., Li, Q., Xiu, B., Li, Y., Li, Y., Huang, S., et al. (2020) · EBioMedicine · doi:10.1016/j.ebiom.2020.103074 · Cited by 71 · see citing works
PRMT7 ablation stimulates anti-tumor immunity and sensitizes melanoma to immune checkpoint blockade
Srour, N., Villarreal, O. D., Hardikar, S., Yu, Z., Preston, S. E. J., Miller, W. H., Szewczyk, M. M., Baršytė-Lovejoy, D., Xu, H., Chen, T., et al. (2022) · Cell Reports · doi:10.1016/j.celrep.2022.110582 · Cited by 70 · see citing works
Szalai, B., Subramanian, V., Holland, C. H., Alföldi, R., Puskás, L. G., & Sáez-Rodríguez, J. (2019) · Nucleic Acids Research · doi:10.1093/nar/gkz805 · Cited by 68 · see citing works
An overview of machine learning methods for monotherapy drug response prediction
Firoozbakht, F., Yousefi, B., & Schwikowski, B. (2021) · Briefings in Bioinformatics · doi:10.1093/bib/bbab408 · Cited by 69 · see citing works
Noncoding mutations target cis-regulatory elements of the FOXA1 plexus in prostate cancer
Zhou, S., Hawley, J. R., Soares, F., Grillo, G., Teng, M., Tonekaboni, S. A. M., Hua, J. T., Kron, K. J., Mazrooei, P., Ahmed, M., et al. (2020) · Nature Communications · doi:10.1038/s41467-020-14318-9 · Cited by 69 · see citing works
Genome-scale CRISPR-Cas9 screen identifies druggable dependencies in TP53 wild-type Ewing sarcoma
Stolte, B., Iniguez, A. B., Dharia, N. V., Robichaud, A. L., Conway, A. S., Morgan, A. M., Alexe, G., Schauer, N. J., Liu, X., Bird, G. H., et al. (2018) · The Journal of Experimental Medicine · doi:10.1084/jem.20171066 · Cited by 67 · see citing works
Sharifi-Noghabi, H., Jahangiri, S., Smirnov, P., Hon, C. S.-Y., Mammoliti, A., Nair, S. K., Mer, A. S., Ester, M., & Haibe‐Kains, B. (2021) · Briefings in Bioinformatics · doi:10.1093/bib/bbab294 · Cited by 65 · see citing works
Li, X., Li, J., Wu, P., Zhou, L., Lü, B., Ying, K., Chen, E., Lü, Y., & Liu, P. (2018) · OncoImmunology · doi:10.1080/2162402x.2018.1494677 · Cited by 65 · see citing works
Sperling, A. S., Burgess, M. W., Keshishian, H., Gasser, J. A., Bhatt, S., Jan, M., Slabicki, M. M., Sellar, R. S., Fink, E. C., Miller, P. G., et al. (2019) · Blood · doi:10.1182/blood.2019000789 · Cited by 65 · see citing works
CENPE promotes lung adenocarcinoma proliferation and is directly regulated by FOXM1
Shan, L., Zhao, M., Lu, Y., Ning, H., Ouyang, C., Song, Y., Chai, W., & Shi, X. (2019) · International Journal of Oncology · doi:10.3892/ijo.2019.4805 · Cited by 63 · see citing works
Comprehensive characterization of the rRNA metabolism-related genes in human cancer
Cui, K., Liu, C., Li, X., Zhang, Q., & Li, Y. (2019) · Oncogene · doi:10.1038/s41388-019-1026-9 · Cited by 63 · see citing works
Gene isoforms as expression-based biomarkers predictive of drug response in vitro
Safikhani, Z., Smirnov, P., Thu, K. L., Silvester, J., El-Hachem, N., Quevedo, R., Lupien, M., Mak, T. W., Cescon, D. W., & Haibe‐Kains, B. (2017) · Nature Communications · doi:10.1038/s41467-017-01153-8 · Cited by 59 · see citing works
Assessment of modelling strategies for drug response prediction in cell lines and xenografts
Kurilov, R., Haibe‐Kains, B., & Brors, B. (2020) · Scientific Reports · doi:10.1038/s41598-020-59656-2 · Cited by 62 · see citing works
The phosphatidylethanolamine biosynthesis pathway provides a new target for cancer chemotherapy
Guan, Y., Chen, X., Wu, M., Zhu, W., Arslan, A., Takeda, S., Nguyen, M. H., Majeti, R., Thomas, D., Zheng, M., et al. (2019) · Journal of Hepatology · doi:10.1016/j.jhep.2019.11.007 · Cited by 61 · see citing works
Fernández-Torras, A., Duran-Frigola, M., Bertoni, M., Locatelli, M., & Aloy, P. (2022) · Nature Communications · doi:10.1038/s41467-022-33026-0 · Cited by 59 · see citing works
Integrative Pharmacogenomics Analysis of Patient-Derived Xenografts
Mer, A. S., Ba-Alawi, W., Smirnov, P., Wang, Y. X., Brew, B., Ortmann, J., Tsao, M., Cescon, D. W., Goldenberg, A., & Haibe‐Kains, B. (2019) · Cancer Research · doi:10.1158/0008-5472.can-19-0349 · Cited by 57 · see citing works
Chou, J., Kaller, M., Jaeckel, S., Rokavec, M., & Hermeking, H. (2022) · Molecular Cancer · doi:10.1186/s12943-022-01581-1 · Cited by 57 · see citing works
Drug discovery and mechanism prediction with explainable graph neural networks
Wang, C., Kumar, G. A., & Rajapakse, J. C. (2025) · Scientific Reports · doi:10.1038/s41598-024-83090-3 · Cited by 58 · see citing works
Dong, W. G., Liu, C., Zheng, X., & Li, Y. (2019) · BMC Bioinformatics · doi:10.1186/s12859-019-2608-9 · Cited by 56 · see citing works
Roux, B., Vaganay, C., Vargas, J. D., Alexe, G., Benaksas, C., Pardieu, B., Fenouille, N., Ellegast, J. M., Małolepsza, E., Ling, F., et al. (2021) · Science Translational Medicine · doi:10.1126/scitranslmed.abg1168 · Cited by 59 · see citing works
Integrating heterogeneous drug sensitivity data from cancer pharmacogenomic studies
Pozdeyev, N. V., Yoo, M., Mackie, R., Schweppe, R. E., Tan, A. C., & Haugen, B. R. (2016) · Oncotarget · doi:10.18632/oncotarget.10010 · Cited by 55 · see citing works
Koumantou, D., Barnea, E., Martín‐Esteban, A., Maben, Z., Papakyriakou, A., Mpakali, A., Kokkala, P., Pratsinis, H., Georgiadis, D., Stern, L. J., et al. (2019) · Cancer Immunology Immunotherapy · doi:10.1007/s00262-019-02358-0 · Cited by 51 · see citing works
Hong, W., Zhang, Y., Wang, S., Zheng, D.-X., Hsu, S.-J., Zhou, J., Fan, J., Zeng, Z., Wang, N., Ding, Z., et al. (2023) · Cancer Letters · doi:10.1016/j.canlet.2023.216594 · Cited by 53 · see citing works
A p62-dependent rheostat dictates micronuclei catastrophe and chromosome rearrangements
Martin, S., Scorzoni, S., Cordone, S., Mazzagatti, A., Beznoussenko, G. V., Gunn, A., Bona, M. D., Eliezer, Y., Leor, G., Ben-Yishay, T., et al. (2024) · Science · doi:10.1126/science.adj7446 · Cited by 53 · see citing works
Feature selection strategies for drug sensitivity prediction
Koras, K., Juraeva, D., Kreis, J., Mazur, J., Staub, E., & Szczurek, E. (2020) · Scientific Reports · doi:10.1038/s41598-020-65927-9 · Cited by 55 · see citing works
In silico drug repositioning: from large-scale transcriptome data to therapeutics
Kwon, O., Kim, W., Cha, H., & Lee, H. (2019) · Archives of Pharmacal Research · doi:10.1007/s12272-019-01176-3 · Cited by 52 · see citing works
López‐Cortés, A., Paz‐y‐Miño, C., Guerrero, S. X., Cabrera‐Andrade, A., Barigye, S. J., Munteanu, C. R., González‐Díaz, H., Pazos, A., Pérez‐Castillo, Y., & Tejera, E. (2020) · Scientific Reports · doi:10.1038/s41598-020-62279-2 · Cited by 55 · see citing works
Trypsin‐Instructed Self‐Assembly on Endoplasmic Reticulum for Selectively Inhibiting Cancer Cells
Kim, B. J., Fang, Y., He, H., & Xu, B. (2020) · Advanced Healthcare Materials · doi:10.1002/adhm.202000416 · Cited by 52 · see citing works
- Authors
- The Cancer Cell Line Encyclopedia Consortium, & The Genomics of Drug Sensitivity in Cancer Consortium
- Venue
- Nature
- Published
- 2015-11-16
- Volume
- 528
- Issue
- 7580
- Pages
- 84-87
- ISSN
- 0028-0836 (ISSN-L)
- 1476-4687
- PMC
- PMC6343827
- OpenAlex
- W2318806991
- Cited by
- 418
- References
- 10
- Citations by year
- 2026: 17 · 2025: 26 · 2024: 33 · 2023: 37 · 2022: 49 · 2021: 47 · 2020: 38 · 2019: 78 · 2018: 36 · 2017: 37 · 2016: 19 · 2012: 1
- FWCI
- 31.30
- Type
- journal article
- Language
- en
- Open access
- green
- europepmc.org
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Nature (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- RWTH Publications (RWTH Aachen) (submittedVersion)
- Funders
- Wellcome Trust (086357, 102696STRATTON, 102696)
- Cancer Research UK (A16629)
- National Institutes of Health (5u54hg006097-03)
- National Human Genome Research Institute (1U54HG006097-01, U54-HG006097)
- 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 › Chromatin Remodeling and Cancer
- Keywords
- cancer cell lines, drug sensitivity, pharmacogenomics, GDSC, data concordance, anticancer drug response, drug library
- MeSH terms
- Humans, Neoplasms, Pharmacogenetics, Reproducibility of Results, Databases, Factual, Inhibitory Concentration 50, Cell Line, Tumor, Datasets as Topic
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