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Systematic identification of genomic markers of drug sensitivity in cancer cells
Open accessAbstract
Clinical responses to anticancer therapies are often restricted to a subset of patients. In some cases, mutated cancer genes are potent biomarkers for responses to targeted agents. Here, to uncover new biomarkers of sensitivity and resistance to cancer therapeutics, we screened a panel of several hundred cancer cell lines--which represent much of the tissue-type and genetic diversity of human cancers--with 130 drugs under clinical and preclinical investigation. In aggregate, we found that mutated cancer genes were associated with cellular response to most currently available cancer drugs. Classic oncogene addiction paradigms were modified by additional tissue-specific or expression biomarkers, and some frequently mutated genes were associated with sensitivity to a broad range of therapeutic agents. Unexpected relationships were revealed, including the marked sensitivity of Ewing's sarcoma cells harbouring the EWS (also known as EWSR1)-FLI1 gene translocation to poly(ADP-ribose) polymerase (PARP) inhibitors. By linking drug activity to the functional complexity of cancer genomes, systematic pharmacogenomic profiling in cancer cell lines provides a powerful biomarker discovery platform to guide rational cancer therapeutic strategies.
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“Large-scale efforts aimed at linking tumor genomic alterations with sensitivity to cytotoxic and targeted therapies have uncovered a wealth of pharmacogenomic interactions in NSCLC and other cancer types149–152.”
From Co-occurring genomic alterations in non-small-cell lung cancer biology and therapy (2019) · cites this paper as [150]
In the citing paper
“BRAF mutation was associated with nutlin 3a sensitivity in the same study130.”
From MDM2, MDMX and p53 in oncogenesis and cancer therapy (2013) · cites this paper as [130]
In the citing paper
“p53 mutation status was the strongest indicator of nutlin 3a resistance”
From MDM2, MDMX and p53 in oncogenesis and cancer therapy (2013) · cites this paper as [130]
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“Even with such a simple readout, genes and pathways that determine drug response can be identified,199,200”
From Advancing Targeted Protein Degradation via Multiomics Profiling and Artificial Intelligence (2023) · cites this paper as [199]
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“More than 1200 established cancer cell lines and newly derived organoid models comprise the foundational model set of the Passports, including all those utilized in the Sanger’s GDSC project (5, 18) as well as cell models generated as part of the Human Cancer Models Initiative.”
From Cell Model Passports-a hub for clinical, genetic and functional datasets of preclinical cancer models (2019) · cites this paper as [5]
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Cites
Cytoscape: A software Environment for integrated models of biomolecular interaction networks
Shannon, P., Markiel, A., Ozier, O., Baliga, N. S., Wang, J. T., Ramage, D., Amin, N., Schwikowski, B., & Ideker, T. (2003) · Genome Research · doi:10.1101/gr.1239303 · Cited by 51,888
Regularization and Variable Selection Via the Elastic Net
Zou, H., & Hastie, T. (2005) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.1467-9868.2005.00503.x · Cited by 20,957
Lynch, T. J., Bell, D. W., Sordella, R., Gurubhagavatula, S., Okimoto, R. A., Brannigan, B. W., Harris, P. L., Haserlat, S. M., Supko, J. G., Haluska, F. G., et al. (2004) · New England Journal of Medicine · doi:10.1056/nejmoa040938 · Cited by 11,486
Self-citationBolstad, B. M., Irizarry, R. A., Åstrand, M., & Speed, T. P. (2003) · Bioinformatics · doi:10.1093/bioinformatics/19.2.185 · Cited by 8,328
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
Improved survival with vemurafenib in melanoma with BRAF V600E mutation
Chapman, P. B., Hauschild, A., Robert, C., Haanen, J. B., Ascierto, P., Larkin, J., Dummer, R., Garbe, C., Testori, A., Maio, M., et al. (2011) · New England Journal of Medicine · doi:10.1056/nejmoa1103782 · Cited by 7,720
Clustering by Passing Messages Between Data Points
Frey, B. J., & Dueck, D. (2007) · Science · doi:10.1126/science.1136800 · Cited by 6,855
In Vivo Activation of the p53 Pathway by Small-Molecule Antagonists of MDM2
Vassilev, L. T., Vu, B. T., Graves, B., Carvajal, D., Podlaski, F., Filipovic, Z., Kong, N., Kammlott, U., Lukacs, C., Klein, C., et al. (2004) · Science · doi:10.1126/science.1092472 · Cited by 4,661
Anaplastic Lymphoma Kinase Inhibition in Non–Small-Cell Lung Cancer
Kwak, E. L., Bang, Y., Camidge, D. R., Shaw, A. T., Solomon, B., Maki, R. G., Ou, S. I., Dezube, B. J., Janne, P. A., Costa, D. B., et al. (2010) · New England Journal of Medicine · doi:10.1056/nejmoa1006448 · Cited by 4,477
Self-citationBoland, C. R., Thibodeau, S. N., Hamilton, S. R., Sidransky, D., Eshleman, J. R., Burt, R. W., Meltzer, S. J., Rodrı́guez-Bigas, M. A., Fodde, R., Ranzani, G. N., et al. (1998) · PubMed · Cited by 4,087
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Items 701–800 of 1,004
Abo, M. A., Sasanuma, H., Liu, X., Rajapakse, V. N., Huang, S.-Y., Kiselev, E., Takeda, S., Plunkett, W. K., & Pommier, Y. G. (2017) · Molecular Cancer Therapeutics · doi:10.1158/1535-7163.mct-17-0110 · Cited by 47
Fifteen years of irinotecan therapy for pediatric sarcoma: where to next?
Wagner, L. M. (2015) · Clinical Sarcoma Research · doi:10.1186/s13569-015-0035-x · Cited by 47
Ashraf, S. Q., Nicholls, A. M., Wilding, J. L., Ntouroupi, T. G., Mortensen, N., & Bodmer, W. F. (2012) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1218750110 · Cited by 49
“Death is my Heir”—Ferroptosis Connects Cancer Pharmacogenomics and Ischemia-Reperfusion Injury
Tonnus, W., & Linkermann, A. (2016) · Cell chemical biology · doi:10.1016/j.chembiol.2016.02.005 · Cited by 49
Consistency in large pharmacogenomic studies
Geeleher, P., Gamazon, E. R., Seoighe, C., Cox, N. J., & Huang, R. S. (2016) · Nature · doi:10.1038/nature19838 · Cited by 46
Zhang, L., Chen, X., Guan, N., Liu, H., & Li, J. (2018) · Frontiers in Pharmacology · doi:10.3389/fphar.2018.01017 · Cited by 47
Biomarker identification for statin sensitivity of cancer cell lines
Raghu, V. K., Beckwitt, C. H., Warita, K., Wells, A. H., Benos, P. V., & Oltvai, Z. N. (2017) · Biochemical and Biophysical Research Communications · doi:10.1016/j.bbrc.2017.11.065 · Cited by 47
Network integration of multi-tumour omics data suggests novel targeting strategies
do Valle, Í. F., Menichetti, G., Simonetti, G., Bruno, S., Zironi, I., Durso, D. F., Mombach, J. C. M., Martinelli, G., Castellani, G., & Remondini, D. (2018) · Nature Communications · doi:10.1038/s41467-018-06992-7 · Cited by 48
Chugh, R. K., Ballman, K. V., Helman, L. J., PATEL, S. R., Whelan, J., Widemann, B. C., Lu, Y., Hawkins, D. S., Mascarenhas, L., Glod, J. W., et al. (2020) · Cancer · doi:10.1002/cncr.33349 · Cited by 49
Comprehensive pharmacogenomic characterization of gastric cancer
Sa, J. K., Hong, J. Y., Lee, I.-K., Kim, J.-S., Sim, M.-H., Kim, H. J., An, J. Y., Sohn, T. S., Lee, J. H., Bae, J. M., et al. (2020) · Genome Medicine · doi:10.1186/s13073-020-0717-8 · Cited by 46
Can Drug Repurposing Accelerate Precision Oncology?
Schipper, L. J., Zeverijn, L. J., Garnett, M. J., & Voest, E. E. (2022) · Cancer Discovery · doi:10.1158/2159-8290.cd-21-0612 · Cited by 52
Self-citationAdvanced In Vitro Models for Preclinical Drug Safety: Recent Progress and Prospects
Nair, D. G., & Weiskirchen, R. (2024) · Current Issues in Molecular Biology · doi:10.3390/cimb47010007 · Cited by 50
BRCA1 regulates PIG3-mediated apoptosis in a p53-dependent manner
Zhang, W., Luo, J., Chen, F., Yang, F., Song, W., Zhu, A., & Guan, X. (2015) · Oncotarget · doi:10.18632/oncotarget.3263 · Cited by 46
Analysis of impedance-based cellular growth assays
Witzel, F., Fritsche‐Guenther, R., Lehmann, N., Sieber, A., & Blüthgen, N. (2015) · Bioinformatics · doi:10.1093/bioinformatics/btv216 · Cited by 46
HDAC1 and HDAC2 collectively regulate intestinal stem cell homeostasis
Zimberlin, C., Lancini, C., Sno, R., Rosekrans, S. L., McLean, C. M., Vlaming, H., van den Brink, G. R., Bots, M. L., Medema, J. P., & Dannenberg, J. (2015) · The FASEB Journal · doi:10.1096/fj.14-257931 · Cited by 49
A PTCH1 Homolog Transcriptionally Activated by p53 Suppresses Hedgehog Signaling
Chung, J., Larsen, A. R., Chen, E., & Bunz, F. (2014) · Journal of Biological Chemistry · doi:10.1074/jbc.m114.597203 · Cited by 46
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McCubrey, J. A., Steelman, L. S., Chappell, W. H., Sun, L. N., Davis, N. M., Abrams, S. L., Franklin, R. A., Cocco, L., Evangelisti, C., Chiarini, F., et al. (2012) · Oncotarget · doi:10.18632/oncotarget.802 · Cited by 46
Friedman, A. A., Amzallag, A., Pruteanu-Malinici, I., Baniya, S., Cooper, Z. A., Piris, A., Hargreaves, L., Igras, V., Frederick, D. T., Lawrence, D. P., et al. (2015) · PLoS ONE · doi:10.1371/journal.pone.0140310 · Cited by 47
Self-citationAmadoz, A., Sebastián-León, P., Vidal, E., Salavert, F., & Dopazo, J. (2015) · Scientific Reports · doi:10.1038/srep18494 · Cited by 47
Esfandiari, A., Hawthorne, T. A., Nakjang, S., & Lunec, J. (2016) · Molecular Cancer Therapeutics · doi:10.1158/1535-7163.mct-15-0651 · Cited by 49
Singh, H., Kumar, R., Singh, S., Chaudhary, K., Gautam, A., & Raghava, G. P. S. (2016) · BMC Cancer · doi:10.1186/s12885-016-2082-y · Cited by 46
Context Specificity in Causal Signaling Networks Revealed by Phosphoprotein Profiling
Hill, S. M., Nesser, N. K., Johnson-Camacho, K., Jeffress, M. L., Johnson, A. B., Boniface, C., Spencer, S. E. F., Lu, Y., Heiser, L. M., Lawrence, Y., et al. (2016) · Cell Systems · doi:10.1016/j.cels.2016.11.013 · Cited by 46
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Brown, D. K., & Bishop, Ö. T. (2017) · Global Heart · doi:10.1016/j.gheart.2017.01.009 · Cited by 47
Cancer Immunotherapy and Personalized Medicine: Emerging Technologies and Biomarker Based Approaches
Maciejko, L., Smalley, M., & Goldman, A. J. (2017) · Journal of Molecular Biomarkers & Diagnosis · doi:10.4172/2155-9929.1000350 · Cited by 48
Peters, T. L., Tillotson, J., Yeomans, A., Wilmore, S., Lemm, E. A., Jiménez-Romero, C., Amador, L. A., Li, L., Amin, A. D., Pongtornpipat, P., et al. (2018) · Clinical Cancer Research · doi:10.1158/1078-0432.ccr-17-3645 · Cited by 44
Self-citationSystematic functional identification of cancer multi-drug resistance genes
Lau, M.-T., Ghazanfar, S., Parkin, A., Chou, A. S. Y., Rouaen, J. R. C., Littleboy, J. B., Nessem, D., Khuong, T. M., Névoltris, D., Schofield, P., et al. (2020) · Genome biology · doi:10.1186/s13059-020-1940-8 · Cited by 45
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Price, S., Bhosle, S. G., Gonçalves, E., Li, X., McClurg, D. P., Barthorpe, S., Beck, A., Hall, C., Lightfoot, H. W., Farrow, L., et al. (2022) · Scientific Reports · doi:10.1038/s41598-022-09508-y · Cited by 47
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Reinhold, W. C., Varma, S., Sousa, F. G., Sunshine, M., Abaan, O. D., Davis, S. R., Reinhold, S. W., Kohn, K. W., Morris, J., Meltzer, P. S., et al. (2014) · PLoS ONE · doi:10.1371/journal.pone.0101670 · Cited by 45
Iorio, F., Shrestha, R. L., Levin, N., Boilot, V., Garnett, M. J., Sáez-Rodríguez, J., & Draviam, V. M. (2015) · PLoS ONE · doi:10.1371/journal.pone.0139446 · Cited by 44
Self-citationDrug sensitivity in cancer cell lines is not tissue-specific
Jaeger, S., Duran‐Frigola, M., & Aloy, P. (2015) · Molecular Cancer · doi:10.1186/s12943-015-0312-6 · Cited by 45
Zhu, J., Wang, M., Zhu, M., He, J., Wang, J.-C., Li, J., Wang, X. F., Xiang, J.-Q., & Wei, Q. (2015) · Scientific Reports · doi:10.1038/srep08250 · Cited by 45
VAV3 mediates resistance to breast cancer endocrine therapy
Aguilar, H., Urruticoechea, A., Halonen, P., Kiyotani, K., Mushiroda, T., Barril, X., Serra-Musach, J., Islam, A. B. M. M. K., Caizzi, L., Croce, L. D., et al. (2014) · Breast Cancer Research · doi:10.1186/bcr3664 · Cited by 45
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Hönes, J. M., Botezatu, L., Helness, A., Vadnais, C., Vaßen, L., Robert, F., Hergenhan, S. M., Thivakaran, A., Schütte, J., Al-Matary, Y. S., et al. (2016) · Leukemia · doi:10.1038/leu.2016.11 · Cited by 47
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Tan, M. (2016) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2016.09.004 · Cited by 45
Chauvin, C., Leruste, A., Tauziède‐Espariat, A., Andrianteranagna, M. J. D. D., Surdez, D., Lescure, A., Han, Z., Anthony, E., Richer, W., Baulande, S., et al. (2017) · Cell Reports · doi:10.1016/j.celrep.2017.10.076 · Cited by 45
Self-citationMEK inhibitors block growth of lung tumours with mutations in ataxia–telangiectasia mutated
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Ogunleye, A. Z., Piyawajanusorn, C., Gonçalvès, A., Ghislat, G., & Ballester, P. J. (2022) · Advanced Science · doi:10.1002/advs.202201501 · Cited by 43
Feng, C., Sun, Y., Ding, G., Wu, Z., Jiang, H., Wang, L., Ding, Q., & Wen, H. (2015) · Scientific Reports · doi:10.1038/srep09465 · Cited by 45
Discrepancies in drug sensitivity
Weinstein, J. N., & Lorenzi, P. L. (2013) · Nature · doi:10.1038/nature12839 · Cited by 42
Functional profiling of precursor microRNAs identifies microRNAs essential for glioma proliferation
Haapa-Paananen, S., Chen, P., Hellström, K., Kohonen, P., Hautaniemi, S., Kallioniemi, O., & Perälä, M. (2013) · PLoS ONE · doi:10.1371/journal.pone.0060930 · Cited by 44
Hart, T. D., Dider, S., Han, W., Xu, H., Zhao, Z., & Xie, L. (2016) · Scientific Reports · doi:10.1038/srep20441 · Cited by 44
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Jandu, H., Aluzaite, K., Fogh, L., Thrane, S. W., Noer, J. B., Proszek, J., Nguyen, K., Hansen, S., Damsgaard, B., Nielsen, S. L., et al. (2016) · BMC Cancer · doi:10.1186/s12885-016-2071-1 · Cited by 44
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Murakami, R., Matsumura, N., Brown, J. B., Wang, Z., Yamaguchi, K. D., Abiko, K., Yoshioka, Y., Hamanishi, J., Baba, T., Koshiyama, M., et al. (2016) · Gynecologic Oncology · doi:10.1016/j.ygyno.2016.02.027 · Cited by 44
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Salawu, A., Fernando, M. S., Hughes, D. J., Reed, M., Woll, P. J., Greaves, C. L., Day, C., Alhajimohammed, M., & Sisley, K. (2016) · British Journal of Cancer · doi:10.1038/bjc.2016.259 · Cited by 44
Hu, H., Zhao, X., Kaushik, S., Robillard, L., Barthelet, A., Lin, K., Shah, K. N., Simmons, A. D., Raponi, M., Harding, T. C., et al. (2018) · Cell Reports · doi:10.1016/j.celrep.2018.03.093 · Cited by 42
Lepikhova, T., Karhemo, P., Louhimo, R., Yadav, B., Murumägi, A., Kulesskiy, E., Kivento, M., Sihto, H. J., Grenman, R. A., Syrjänen, S., et al. (2018) · Molecular Cancer Therapeutics · doi:10.1158/1535-7163.mct-17-0733 · Cited by 44
Speck‐Planche, A. (2019) · ACS Omega · doi:10.1021/acsomega.8b03693 · Cited by 45
Old drugs, new uses: Drug repurposing in hematological malignancies
Kale, V. P., Habib, H., Chitren, R. J., Patel, M., Pramanik, K. C., Jonnalagadda, S. C., Challagundla, K. B., & Pandey, M. K. (2020) · Seminars in Cancer Biology · doi:10.1016/j.semcancer.2020.03.005 · Cited by 45
Deciphering the signaling network of breast cancer improves drug sensitivity prediction
Tognetti, M., Gábor, A., Yang, M., Cappelletti, V., Windhager, J., Rueda, O. M., Charmpi, K., Esmaeilishirazifard, E., Bruna, A., de Souza, N., et al. (2021) · Cell Systems · doi:10.1016/j.cels.2021.04.002 · Cited by 42
Large-scale Pan-cancer Cell Line Screening Identifies Actionable and Effective Drug Combinations
Bashi, A. C., Coker, E. A., Bulusu, K. C., Jaaks, P., Crafter, C., Lightfoot, H. W., Milo, M., McCarten, K., Jenkins, D. F., van der Meer, D. J., et al. (2024) · Cancer Discovery · doi:10.1158/2159-8290.cd-23-0388 · Cited by 43
Self-citationSaha, S. K., & Khuda‐Bukhsh, A. R. (2013) · European Journal of Pharmacology · doi:10.1016/j.ejphar.2013.06.009 · Cited by 43
Landmann, H., Proia, D. A., He, S., Ogawa, L. S., Krämer, F., Beißbarth, T., Grade, M., Gaedcke, J., Ghadimi, M. B., Moll, U. M., et al. (2014) · Cell Death and Disease · doi:10.1038/cddis.2014.378 · Cited by 44
Guinney, J., Ferté, C., Dry, J. R., McEwen, R. K., Manceau, G., Kao, K., Chang, K., Bendtsen, C., Hudson, K., Huang, E. S., et al. (2013) · Clinical Cancer Research · doi:10.1158/1078-0432.ccr-13-1943 · Cited by 42
Fast randomization of large genomic datasets while preserving alteration counts
Gobbi, A., Iorio, F., Dawson, K. J., Wedge, D. C., Tamborero, D., Alexandrov, L. B., López-Bigas, N., Garnett, M. J., Jurman, G., & Sáez-Rodríguez, J. (2014) · Bioinformatics · doi:10.1093/bioinformatics/btu474 · Cited by 43
Self-citationMeasuring Cancer Drug Sensitivity and Resistance in Cultured Cells
Niepel, M., Hafner, M. A., Chung, M., & Sorger, P. K. (2017) · Current Protocols in Chemical Biology · doi:10.1002/cpch.21 · Cited by 42
Jacobi, N., Seeboeck, R., Hofmann, E., Schweiger, H., Smolinska, V., Mohr, T., Boyer, A., Sommergruber, W., Lechner, P., Pichler-Huebschmann, C., et al. (2017) · Oncotarget · doi:10.18632/oncotarget.22475 · Cited by 43
Qi, C., Wang, X., Shen, Z., Chen, S., Yu, H., Williams, N., & Wang, G. (2018) · Cell Research · doi:10.1038/s41422-018-0018-6 · Cited by 41
An automated microfluidic gene-editing platform for deciphering cancer genes
Sinha, H., Quach, A. B. V., Vo, P. Q. N., & Shih, S. C. C. (2018) · Lab on a Chip · doi:10.1039/c8lc00470f · Cited by 44
Smith, M. P., Rana, S., Ferguson, J., Rowling, E. J., Flaherty, K. T., Wargo, J. A., Marais, R., & Wellbrock, C. (2018) · Pigment Cell & Melanoma Research · doi:10.1111/pcmr.12741 · Cited by 48
Ma, J., Wang, J., Ghoraie, L. S., Men, X., Haibe‐Kains, B., & Dai, P. (2019) · Frontiers in Pharmacology · doi:10.3389/fphar.2019.00109 · Cited by 43
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Najgebauer, H., Yang, M., Francies, H. E., Pacini, C., Stronach, E. A., Garnett, M. J., Sáez-Rodríguez, J., & Iorio, F. (2020) · Cell Systems · doi:10.1016/j.cels.2020.04.007 · Cited by 43
Self-citationKwon, O.-S., Kwon, E.-J., Kong, H., Choi, J., Kim, Y. J., Lee, E., Kim, W., Lee, H., & Cha, H. (2020) · Redox Biology · doi:10.1016/j.redox.2020.101719 · Cited by 44
Min, K., Kim, D., Son, B. K., Moon, K. M., Kim, S. M., Rahaman, M. I., Kim, S. W., Kim, E., Kwon, M. J., Koh, Y. W., et al. (2021) · PLoS ONE · doi:10.1371/journal.pone.0245075 · Cited by 44
Pfäffle, H. N., Wang, M., Gheorghiu, L., Ferraiolo, N., Greninger, P., Borgmann, K., Settleman, J. E., Benes, C. H., Sequist, L. V. D., Zou, L., et al. (2013) · Cancer Research · doi:10.1158/0008-5472.can-13-0044 · Cited by 43
Self-citationRicciuti, B., Son, J., Okoro, J. J., Mira, A., Patrucco, E., Eum, Y., Wang, X., Paranal, R., Wang, H., Lin, M., et al. (2022) · Clinical Cancer Research · doi:10.1158/1078-0432.ccr-21-2719 · Cited by 42
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Lou, K., Wassarman, D. R., Yang, T., Paung, Y., Zhang, Z., O’Loughlin, T. A., Moore, M. K., Egan, R. K., Greninger, P., Benes, C. H., et al. (2022) · Science · doi:10.1126/science.abl5829 · Cited by 42
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Cho, J. M., Johnson, D. E., & Grandis, J. R. (2017) · Seminars in Radiation Oncology · doi:10.1016/j.semradonc.2017.08.005 · Cited by 45
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Shen, Y., Chi, H., Xu, K., Li, Y., Yin, X., Chen, S., Yang, Q., He, M., Zhu, G., & Li, X. (2022) · Brain Sciences · doi:10.3390/brainsci12060700 · Cited by 43
Synthetic augmentation of cancer cell line multi-omic datasets using unsupervised deep learning
Cai, Z., Apolinário, S., Baião, A. R., Pacini, C., Sousa, M. D., Vinga, S., Reddel, R. R., Robinson, P. J., Garnett, M. J., Zhong, Q., et al. (2024) · Nature Communications · doi:10.1038/s41467-024-54771-4 · Cited by 49
Self-citationKRAS Genomic Status Predicts the Sensitivity of Ovarian Cancer Cells to Decitabine
Stewart, M. L., Tamayo, P., Wilson, A. J., Wang, S. Y., Chang, Y. M., Kim, J. W., Khabele, D., Shamji, A. F., & Schreiber, S. L. (2015) · Cancer Research · doi:10.1158/0008-5472.can-14-2860 · Cited by 42
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Cutillas, P. R. (2014) · PROTEOMICS - CLINICAL APPLICATIONS · doi:10.1002/prca.201400104 · Cited by 42
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Mitrofanova, A., Aytés, À., Zou, M., Shen, M. M., Abate-Shen, C. T., & Califano, A. (2015) · Cell Reports · doi:10.1016/j.celrep.2015.08.051 · Cited by 39
Hudson, K., Hancox, U. J., Trigwell, C. B., McEwen, R. K., Polanska, U. M., Nikolaou, M., Gutierrez, P. M., Avivar‐Valderas, A., Delpuech, O., Dudley, P., et al. (2016) · Molecular Cancer Therapeutics · doi:10.1158/1535-7163.mct-15-0687 · Cited by 41
van Maldegem, A. M., Bovée, J. V. M. G., Peterse, E., Hogendoorn, P. C. W., & Gelderblom, H. A. J. (2016) · European Journal of Cancer · doi:10.1016/j.ejca.2015.09.009 · Cited by 43
Cheng, F., Liu, C., Shen, B., & Zhao, Z. (2016) · BMC Systems Biology · doi:10.1186/s12918-016-0309-9 · Cited by 43
A CDK2 activity signature predicts outcome in CDK2-low cancers
McCurdy, S. R., Pacal, M., Ahmad, M. A. E.-R., & Bremner, R. (2016) · Oncogene · doi:10.1038/onc.2016.409 · Cited by 42
Vagapova, E. R., Lebedev, T. D., & Prassolov, V. (2021) · Scientific Reports · doi:10.1038/s41598-021-90701-w · Cited by 43
Lietz, C. E., Newman, E. T., Kelly, A. D., Xiang, D. H., Zhang, Z., Luscko, C. A., Lozano‐Calderón, S. A., Ebb, D. H., Raskin, K. A., Cote, G. M., et al. (2022) · Communications Biology · doi:10.1038/s42003-022-03117-1 · Cited by 45
False and mycoplasma-contaminated leukemia-lymphoma cell lines: time for a reappraisal
Drexler, H. G., Dirks, W. G., MacLeod, R. A. F., & Uphoff, C. C. (2017) · International Journal of Cancer · doi:10.1002/ijc.30530 · Cited by 45
DeepDRA: Drug repurposing using multi-omics data integration with autoencoders
Mohammadzadeh-Vardin, T., Ghareyazi, A., Gharizadeh, A., Abbasi, K., & Rabiee, H. R. (2024) · PLoS ONE · doi:10.1371/journal.pone.0307649 · Cited by 47
Goswami, C. P., Cheng, L., Alexander, P., Singal, A. G., & Li, L. (2015) · CPT Pharmacometrics & Systems Pharmacology · doi:10.1002/psp4.9 · Cited by 40
Shukla, N. N. (2013) · Frontiers in Oncology · doi:10.3389/fonc.2013.00141 · Cited by 41
Analysis of growth factor signaling in genetically diverse breast cancer lines
Niepel, M., Hafner, M. A., Pace, E. A., Chung, M., Chai, D. H., Zhou, L., Muhlich, J. L., Schoeberl, B., & Sorger, P. K. (2014) · BMC Biology · doi:10.1186/1741-7007-12-20 · Cited by 41
Integrated genomic analysis of recurrence-associated small non-coding RNAs in oesophageal cancer
Jang, H.-J., Lee, H., Burt, B. M., Lee, G. K., Yoon, K., Park, Y., Sohn, B. H., Kim, S. B., Kim, M. S., Lee, J. M., et al. (2016) · Gut · doi:10.1136/gutjnl-2015-311238 · Cited by 41
Prostate Cancer Cells in Different Androgen Receptor Status Employ Different Leucine Transporters
Otsuki, H., Kimura, T., Yamaga, T., Kosaka, T., Suehiro, J., & Sakurai, H. (2016) · The Prostate · doi:10.1002/pros.23263 · Cited by 41
Aldonza, M. B. D., Hong, J., & Lee, S. K. (2017) · Experimental & Molecular Medicine · doi:10.1038/emm.2016.131 · Cited by 42
Systematic identification of key genes and pathways in the development of invasive cervical cancer
Niu, G., Wang, D., Pei, Y., & Li, S. (2017) · Gene · doi:10.1016/j.gene.2017.03.018 · Cited by 41
Stromal SPOCK 1 supports invasive pancreatic cancer growth
Veenstra, V. L., Damhofer, H., Waasdorp, C., Steins, A., Kocher, H. M., Medema, J. P., van Laarhoven, H. W. M., & Bijlsma, M. F. (2017) · Molecular Oncology · doi:10.1002/1878-0261.12073 · Cited by 41
Coppé, J., Mori, M., Pan, B., Yau, C. S. F., Wolf, D. M., Ruiz-Sáenz, A., Brunen, D., Prahallad, A., Cornelissen‐Steijger, P., Kemper, K., et al. (2019) · Nature Cell Biology · doi:10.1038/s41556-019-0328-z · Cited by 43
Kleandrova, V. V., Scotti, M. T., Scotti, L., Nayarisseri, A., & Speck‐Planche, A. (2020) · SAR and QSAR in environmental research · doi:10.1080/1062936x.2020.1818617 · Cited by 44
Naulaerts, S., Dang, C. C., & Ballester, P. J. (2017) · Oncotarget · doi:10.18632/oncotarget.20923 · Cited by 41
Chong, M. L., Loh, M. C. S., Thakkar, B. M., Pang, B., Iacopetta, B. J., & Soong, R. (2013) · International Journal of Cancer · doi:10.1002/ijc.28444 · Cited by 40
Varma, S., Pommier, Y. G., Sunshine, M., Weinstein, J. N., & Reinhold, W. C. (2014) · PLoS ONE · doi:10.1371/journal.pone.0092047 · Cited by 40
Peng, S., Sen, B., Mazumdar, T., Byers, L. A., Diao, L., Wang, J., Tong, P., Giri, U. P., Heymach, J. V., Kadara, H. N., et al. (2015) · Oncotarget · doi:10.18632/oncotarget.6376 · Cited by 40
Geeleher, P., Cox, N. J., & Huang, R. S. (2016) · Genome biology · doi:10.1186/s13059-016-1050-9 · Cited by 44
Crosstalk and the evolvability of intracellular communication
Rowland, M. A., Greenbaum, J. M., & Deeds, E. J. (2017) · Nature Communications · doi:10.1038/ncomms16009 · Cited by 40
Genome‐wide prediction of synthetic rescue mediators of resistance to targeted and immunotherapy
Sahu, A. D., Lee, J. S., Wang, Z., Zhang, G., Iglesias‐Bartolomé, R., Tian, T., Wei, Z., Miao, B., Nair, N. U., Ponomarova, O., et al. (2019) · Molecular Systems Biology · doi:10.15252/msb.20188323 · Cited by 36
Self-citationEnsembled machine learning framework for drug sensitivity prediction
Sharma, A., & Rani, R. (2019) · IET Systems Biology · doi:10.1049/iet-syb.2018.5094 · Cited by 40
RETRACTED: Multiple strategies with the synergistic approach for addressing colorectal cancer
Karthika, C., Hari, B., Rahman, M. H., Akter, R., Najda, A., Albadrani, G. M., Sayed, A. A., Akhtar, M. F., & Abdel‐Daim, M. M. (2021) · Biomedicine & Pharmacotherapy · doi:10.1016/j.biopha.2021.111704 · Cited by 41
RetractedHow much can deep learning improve prediction of the responses to drugs in cancer cell lines?
Chen, Y., & Zhang, L. (2021) · Briefings in Bioinformatics · doi:10.1093/bib/bbab378 · Cited by 40
Metadata
- Authors
- 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., Liu, Q., Iorio, F., Surdez, D., Chen, L., Milano, R. J., Bignell, G. R., Tam, A. T., Davies, H., Stevenson, J. A., Barthorpe, S., Lutz, S. R., Kogera, F., Lawrence, K., McLaren-Douglas, A., Mitropoulos, X., Mironenko, T., Thi, H., Richardson, L., Zhou, W., Jewitt, F., Zhang, T., O’Brien, P., Boisvert, J. L., Price, S., Hur, W., Yang, W., Deng, X., Butler, A., Choi, H. G., Chang, J. W., Baselga, J., Stamenkovic, I., Engelman, J. A., Sharma, S. V., Delattre, O., Saez-Rodriguez, J., Gray, N. S., Settleman, J., Futreal, P. A., Haber, D. A., Stratton, M. R., Ramaswamy, S., McDermott, U., & Benes, C. H.
- Affiliations
- Wellcome Sanger Institute (GB)
- Harvard University (US)
- European Bioinformatics Institute (GB)
- Institut Curie (FR)
- Centre Hospitalier Universitaire Vaudois (CH)
- Howard Hughes Medical Institute (US)
- Corresponding author
- McDermott, Ultan
- Benes, Cyril H.
- ORCID
- Garnett, Mathew J.
- Greenman, Chris D.
- Dastur, Anahita
- Lau, King Wai
- Greninger, Patricia
- Thompson, I. Richard
- Luo, Xi
- Liu, Qingsong
- Iorio, Francesco
- Surdez, Didier
- Chen, Li
- Davies, Helen
- Barthorpe, Syd
- Lawrence, Karl
- Mironenko, Tatiana
- Richardson, Laura
- Zhou, Wenjun
- Zhang, Tinghu
- O’Brien, Patrick
- Hur, Wooyoung
- Yang, Wanjuan
- Deng, Xianming
- Choi, Hwan Geun
- Chang, Jae Won
- Baselga, Jose
- Stamenkovic, Ivan
- Engelman, Jeffrey A.
- Delattre, Olivier
- Gray, Nathanael S.
- Settleman, Jeffrey
- Futreal, P. Andrew
- Haber, Daniel A.
- Stratton, Michael R.
- McDermott, Ultan
- Benes, Cyril H.
- Venue
- Nature
- Published
- 2012-03
- Volume
- 483
- Issue
- 7391
- Pages
- 570-575
- ISSN
- 0028-0836 (ISSN-L)
- 1476-4687
- PMC
- PMC3349233
- OpenAlex
- W2125789330
- Cited by
- 2,456
- References
- 39
- Citations by year
- 2026: 106 · 2025: 101 · 2024: 114 · 2023: 145 · 2022: 161 · 2021: 228 · 2020: 162 · 2019: 183 · 2018: 212 · 2017: 198 · 2016: 214 · 2015: 199 · 2014: 197 · 2013: 153 · 2012: 82
- FWCI
- 66.78
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Nature (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- SERVAL (Université de Lausanne) (acceptedVersion, other-oa)
- IRIS (submittedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- UEA Digital Repository (University of East Anglia) (submittedVersion)
- Funders
- Howard Hughes Medical Institute
- Wellcome Trust (086357)
- Cancer Research UK
- National Institutes of Health (5u54hg006097-03)
- National Human Genome Research Institute (1U54HG006097-01, U54-HG006097)
- National Institute of General Medical Sciences (P41GM079575)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Cancer Research › Cancer Genomics and Diagnostics
- Health Sciences › Medicine › Pulmonary and Respiratory Medicine › Lung Cancer Treatments and Mutations
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Chromatin Remodeling and Cancer
- Keywords
- drug sensitivity, drug resistance, pharmacogenomics, cancer cell lines, genomic biomarkers, oncogene addiction, Ewing sarcoma, PARP inhibitors, topoisomerase inhibitors, SLFN11, tissue-specific biomarkers
- MeSH terms
- Poly(ADP-ribose) Polymerase Inhibitors, Cell Survival, Drug Screening Assays, Antitumor, Genetic Markers, Humans, Indoles, Neoplasms, Pharmacogenetics, Phthalazines, Piperazines, Sarcoma, Ewing, Oncogene Proteins, Fusion, Genome, Human, Gene Expression Regulation, Neoplastic, Drug Resistance, Neoplasm, Genomics, RNA-Binding Protein EWS, Cell Line, Tumor, Proto-Oncogene Protein c-fli-1, Genes, Neoplasm
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