The DOI matches its Crossref record; 39 of the 39 references it cites were found in a registry or an index.
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.
Overview
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
Verification
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?
In the citing paper
“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]
In the citing paper
“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]
In the citing paper
“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]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
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
Cited by
Sorted by citations
Items 201–300 of 1,004
A single-cell analysis of breast cancer cell lines to study tumour heterogeneity and drug response
Gambardella, G., Viscido, G., Tumaini, B., Isacchi, A., Bosotti, R., & di Bernardo, D. (2022) · Nature Communications · doi:10.1038/s41467-022-29358-6 · Cited by 204
McFarland, J. M., Paolella, B. R., Warren, A., Geiger-Schuller, K., Shibue, T., Rothberg, M., Kuksenko, O., Colgan, W. N., Jones, A., Chambers, E., et al. (2020) · Nature Communications · doi:10.1038/s41467-020-17440-w · Cited by 173
van der Meer, D., Barthorpe, S., Yang, W., Lightfoot, H., Hall, C., Gilbert, J., Francies, H. E., & Garnett, M. J. (2019) · Nucleic Acids Research · doi:10.1093/nar/gky872 · Cited by 195
Self-citationZhang, W., Mao, J., Zhu, W., Jain, A. K., Liu, K., Brown, J. B., & Karpen, G. H. (2016) · Nature Communications · doi:10.1038/ncomms12619 · Cited by 195
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 195
Self-citationApplications of organoids for cancer biology and precision medicine
Lo, Y.-H., Karlsson, K., & Kuo, C. J. (2020) · Nature Cancer · doi:10.1038/s43018-020-0102-y · Cited by 182
Molecular subtyping of colorectal cancer: Recent progress, new challenges and emerging opportunities
Wang, W., Kandimalla, R., Huang, H., Zhu, L., Li, Y., Gao, F., Goel, A., & Wang, X. (2018) · Seminars in Cancer Biology · doi:10.1016/j.semcancer.2018.05.002 · Cited by 192
Caruso, S., Calatayud, A.-L., Pilet, J., Bella, T. L., Rekik, S., Imbeaud, S., Letouzé, É., Meunier, L., Bayard, Q., Rohr‐Udilova, N., et al. (2019) · Gastroenterology · doi:10.1053/j.gastro.2019.05.001 · Cited by 186
Evolution and dynamics of pancreatic cancer progression
Yachida, S., & Iacobuzio–Donahue, C. A. (2013) · Oncogene · doi:10.1038/onc.2013.29 · Cited by 185
Wu, Y., & Xie, L. (2025) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2024.12.030 · Cited by 220
Bunn, P. A., Minna, J. D., Augustyn, A., Gazdar, A. F., Ouadah, Y., Krasnow, M. A., Berns, A. J. M., Brambilla, E. M., Rekhtman, N., Massion, P. P., et al. (2016) · Journal of Thoracic Oncology · doi:10.1016/j.jtho.2016.01.012 · Cited by 180
Targeting the DNA Repair Pathway in Ewing Sarcoma
Stewart, E. A., Goshorn, D. R., Bradley, C. L., Griffiths, L. M., Benavente, C. A., Twarog, N. R., Miller, G. M., Caufield, W. V., Freeman, B. B., Bahrami, A., et al. (2014) · Cell Reports · doi:10.1016/j.celrep.2014.09.028 · Cited by 183
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
Gruosso, T., Mieulet, V., Cardon, M., Bourachot, B., Kieffer, Y., Devun, F., Dubois, T., Dutreix, M., Vincent‐Salomon, A., Miller, K. M., et al. (2016) · EMBO Molecular Medicine · doi:10.15252/emmm.201505891 · Cited by 179
Emerging targeted agents in metastatic breast cancer
Zardavas, D., Baselga, J. M., & Piccart, M. J. (2013) · Nature Reviews Clinical Oncology · doi:10.1038/nrclinonc.2013.29 · Cited by 184
Self-citationA framework for identification of actionable cancer genome dependencies in small cell lung cancer
Sos, M. L., Dietlein, F., Peifer, M., Schöttle, J., Balke‐Want, H., Müller, C., Koker, M., Richters, A., Heynck, S., Malchers, F., et al. (2012) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1207310109 · Cited by 179
Wang, L., Li, X., Zhang, L., & Gao, Q. (2017) · BMC Cancer · doi:10.1186/s12885-017-3500-5 · Cited by 180
Consensus molecular subtypes of colorectal cancer are recapitulated in in vitro and in vivo models
Linnekamp, J. F., van Hooff, S. R., Prasetyanti, P. R., Kandimalla, R., Buikhuisen, J. Y., Fessler, E., Ramesh, P., Lee, K. A. S. T., Bochove, G. G. W., de Jong, J. H., et al. (2018) · Cell Death and Differentiation · doi:10.1038/s41418-017-0011-5 · Cited by 182
Liu, P., Li, H., Li, S., & Leung, K. (2019) · BMC Bioinformatics · doi:10.1186/s12859-019-2910-6 · Cited by 189
Jin, H., Zhang, C., Zwahlen, M., von Feilitzen, K., Karlsson, M., Shi, M., Yuan, M., Song, X., Li, X., Yang, H., et al. (2023) · Nature Communications · doi:10.1038/s41467-023-41132-w · Cited by 188
Choy, E., Butrynski, J. E., Harmon, D. C., Morgan, J. A., George, S., Wagner, A. J., D’Adamo, D. R., Cote, G. M., Flamand, Y., Benes, C. H., et al. (2014) · BMC Cancer · doi:10.1186/1471-2407-14-813 · Cited by 176
Self-citationMultiscale Agent-Based and Hybrid Modeling of the Tumor Immune Microenvironment
Norton, K., Gong, C., Jamalian, S., & Popel, A. S. (2019) · Processes · doi:10.3390/pr7010037 · Cited by 178
Kim, H. S., Mendiratta, S., Kim, J., Pecot, C. V., Larsen, J. E., Zubovych, I., Seo, B. Y., Kim, J., Eskiocak, B., Chung, H., et al. (2013) · Cell · doi:10.1016/j.cell.2013.09.041 · Cited by 172
EWS/FLI confers tumor cell synthetic lethality to CDK12 inhibition in Ewing sarcoma
Iniguez, A. B., Stolte, B., Wang, E. J., Conway, A. S., Alexe, G., Dharia, N. V., Kwiatkowski, N., Zhang, T., Abraham, B. J., Mora, J., et al. (2018) · Cancer Cell · doi:10.1016/j.ccell.2017.12.009 · Cited by 173
Self-citationIn 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 177
Michaut, M., Chin, S., Majewski, I. J., Severson, T., Bismeijer, T., de Koning, L., Peeters, J. K., Schouten, P. C., Rueda, O. M., Bosma, A. J., et al. (2016) · Scientific Reports · doi:10.1038/srep18517 · Cited by 174
Self-citationImaging windows for long-term intravital imaging
Alieva, M., Ritsma, L., Giedt, R. J., Weissleder, R., & van Rheenen, J. (2014) · IntraVital · doi:10.4161/intv.29917 · Cited by 172
Venere, M., Hamerlik, P., Wu, Q., Rasmussen, R. D., Song, L. A., Vasanji, A., Tenley, N., Flavahan, W. A., Hjelmeland, A. B., Bártek, J., et al. (2013) · Cell Death and Differentiation · doi:10.1038/cdd.2013.136 · Cited by 181
Reprogramming cellular events by poly(ADP-ribose)-binding proteins
Krietsch, J., Rouleau, M., Pic, É., Ethier, C., Dawson, T. M., Dawson, V. L., Masson, J., Poirier, G. G., & Gagné, J. (2012) · Molecular Aspects of Medicine · doi:10.1016/j.mam.2012.12.005 · Cited by 185
Tivantinib (ARQ197) Displays Cytotoxic Activity That Is Independent of Its Ability to Bind MET
Basilico, C., Pennacchietti, S., Vigna, E., Chiriaco, C., Arena, S., Bardelli, A., Valdembri, D., Serini, G., & Michieli, P. (2013) · Clinical Cancer Research · doi:10.1158/1078-0432.ccr-12-3459 · Cited by 171
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 171
Jang, I. S., Neto, E. C., Guinney, J., Friend, S. H., & Margolin, A. A. (2014) · PubMed · Cited by 170
McMullin, R. P., Wittner, B. S., Yang, C., Denton-Schneider, B. R., Hicks, D. F., Singavarapu, R., Moulis, S., Lee, J., Akbari, M. R., Narod, S. A., et al. (2014) · Breast Cancer Research · doi:10.1186/bcr3625 · Cited by 168
Self-citationActivating HER2 mutations as emerging targets in multiple solid cancers
Connell, C. M., & Doherty, G. J. (2017) · ESMO Open · doi:10.1136/esmoopen-2017-000279 · Cited by 169
Multiplexed imaging for diagnosis and therapy
Heinzmann, K., Carter, L. M., Lewis, J. S., & Aboagye, E. O. (2017) · Nature Biomedical Engineering · doi:10.1038/s41551-017-0131-8 · Cited by 169
Jones, S., Stransky, N., McCord, C. L., Cerami, E. G., Lagowski, J. P., Kelly, D. D., Angiuoli, S. V., Sausen, M., Kann, L. M., Shukla, M., et al. (2014) · Nature Communications · doi:10.1038/ncomms6006 · Cited by 169
Molina-Arcas, M., Hancock, D. C., Sheridan, C., Kumar, M. S., & Downward, J. (2013) · Cancer Discovery · doi:10.1158/2159-8290.cd-12-0446 · Cited by 157
RAS Synthetic Lethal Screens Revisited: Still Seeking the Elusive Prize?
Downward, J. (2015) · Clinical Cancer Research · doi:10.1158/1078-0432.ccr-14-2180 · Cited by 168
Tlemsani, C., Pongor, L. S., Elloumi, F., Girard, L., Huffman, K. E., Roper, N., Varma, S., Luna, A., Rajapakse, V. N., Sebastian, R., et al. (2020) · Cell Reports · doi:10.1016/j.celrep.2020.108296 · Cited by 170
Differential network entropy reveals cancer system hallmarks
West, J., Bianconi, G., Simone Severini, B., & Teschendorff, A. E. (2012) · Scientific Reports · doi:10.1038/srep00802 · Cited by 164
Computational models for predicting drug responses in cancer research
Azuaje, F. (2016) · Briefings in Bioinformatics · doi:10.1093/bib/bbw065 · Cited by 59
Analysis of Racial/Ethnic Representation in Select Basic and Applied Cancer Research Studies
Guerrero, S. X., López‐Cortés, A., Indacochea, A., García-Cárdenas, J. M., Zambrano, A. K., Cabrera‐Andrade, A., Guevara‐Ramírez, P., González, D. A., Leone, P., & Paz‐y‐Miño, C. (2018) · Scientific Reports · doi:10.1038/s41598-018-32264-x · Cited by 166
The UCSC Cancer Genomics Browser: update 2013
Goldman, M. J., Craft, B., Swatloski, T., Ellrott, K., Cline, M., Diekhans, M., Ma, S., Wilks, C., Stuart, J. M., Haussler, D. H., et al. (2012) · Nucleic Acids Research · doi:10.1093/nar/gks1008 · Cited by 163
Looking beyond the hype: applied AI and machine learning in translational medicine
Toh, T. S., Dondelinger, F., & Wang, D. (2019) · EBioMedicine · doi:10.1016/j.ebiom.2019.08.027 · Cited by 159
Biology of Bone Sarcomas and New Therapeutic Developments
Brown, H. K., Schiavone, K., Gouin, F., Heymann, M.-F., & Heymann, D. (2018) · Calcified Tissue International · doi:10.1007/s00223-017-0372-2 · Cited by 163
Saha, S. K., Gordan, J. D., Kleinstiver, B. P., Vu, P., Najem, M. S., Yeo, J.-C., Shi, L., Kato, Y., Levin, R. S., Webber, J. T., et al. (2016) · Cancer Discovery · doi:10.1158/2159-8290.cd-15-1442 · Cited by 160
Self-citationDeepDSC: 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 162
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
Organoid cultures for cancer modeling
Yan, H. H. N., Chan, A., Lai, F. P.-L., & Leung, S. Y. (2023) · Cell stem cell · doi:10.1016/j.stem.2023.05.012 · Cited by 176
Transient CDK4/6 inhibition protects hematopoietic stem cells from chemotherapy-induced exhaustion
He, S., Roberts, P. J., Sorrentino, J. A., Bisi, J., White, H. S., Tiessen, R. G., Makhuli, K. M., Wargin, W. A., Tadema, H., van Hoogdalem, E., et al. (2017) · Science Translational Medicine · doi:10.1126/scitranslmed.aal3986 · Cited by 165
Lapek, J. D., Greninger, P., Morris, R. T., Amzallag, A., Pruteanu-Malinici, I., Benes, C. H., & Haas, W. (2017) · Nature Biotechnology · doi:10.1038/nbt.3955 · Cited by 152
Self-citationEvaluating the molecule-based prediction of clinical drug responses in cancer
Ding, Z., Zu, S., & Gu, J. (2016) · Bioinformatics · doi:10.1093/bioinformatics/btw344 · Cited by 162
Meyer, U. A., Zanger, U. M., & Schwab, M. (2013) · Annual Review of Pharmacology and Toxicology · doi:10.1146/annurev-pharmtox-010510-100502 · Cited by 157
Liu, Y., Marks, K. M., Cowley, G. S., Carretero, J., Liu, Q., Nieland, T. J. F., Xu, C., Cohoon, T. J., Gao, P., Zhang, Y., et al. (2013) · Cancer Discovery · doi:10.1158/2159-8290.cd-13-0015 · Cited by 152
Self-citationPharmacoDB: 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
Managing drug resistance in cancer: lessons from HIV therapy
Bock, C., & Lengauer, T. (2012) · Nature reviews. Cancer · doi:10.1038/nrc3297 · Cited by 160
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 156
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
Epidemiology and therapies for metastatic sarcoma
Reed, D. R., Amankwah, & Conley, A. P. (2013) · Clinical Epidemiology · doi:10.2147/clep.s28390 · Cited by 154
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
Self-citationMolecular subtyping of cancer: current status and moving toward clinical applications
Zhao, L., Lee, V. H.-F., Ng, M. K., Yan, H., & Bijlsma, M. F. (2018) · Briefings in Bioinformatics · doi:10.1093/bib/bby026 · Cited by 156
Using CellMiner 1.6 for Systems Pharmacology and Genomic Analysis of the NCI-60
Reinhold, W. C., Sunshine, M., Varma, S., Doroshow, J. H., & Pommier, Y. G. (2015) · Clinical Cancer Research · doi:10.1158/1078-0432.ccr-15-0335 · Cited by 147
Qin, H.-D., Liao, X., Chen, Y., Huang, S.-Y., Xue, W., Li, F.-F., Ge, X.-S., Liu, D.-Q., Cai, Q., Long, J., et al. (2016) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2016.02.021 · Cited by 152
Differential effector engagement by oncogenic KRAS
Yuan, T. L., Amzallag, A., Bagni, R., Yi, M., Afghani, S., Burgan, W., Fer, N., Strathern, L. A., Powell, K., Smith, B., et al. (2018) · Cell Reports · doi:10.1016/j.celrep.2018.01.051 · Cited by 150
Self-citationRobust meta-analysis of gene expression using the elastic net
Hughey, J., & Butte, A. J. (2015) · Nucleic Acids Research · doi:10.1093/nar/gkv229 · Cited by 149
DNA damage induces p53-independent apoptosis through ribosome stalling
Boon, N. J., Oliveira, R. A., Körner, P.-R., Kochavi, A., Mertens, S., Malka, Y., Voogd, R., van der Horst, S. E. M., Huismans, M. A., Smabers, L. P., et al. (2024) · Science · doi:10.1126/science.adh7950 · Cited by 161
Antonysamy, S., Pustilnik, A., Manglicmot, D., Froning, K., Weichert, K., & Wasserman, S. (2018) · Worldwide Protein Data Bank · doi:10.2210/pdb6c2r/pdb · Cited by 148
Truong, D. D., Kratz, A., Park, J. G., Barrientos, E. S., Saini, H., Nguyen, T., Pockaj, B., Mouneimne, G., LaBaer, J., & Nikkhah, M. (2019) · Cancer Research · doi:10.1158/0008-5472.can-18-2293 · Cited by 149
Methods, Tools and Current Perspectives in Proteogenomics
Ruggles, K. V., Krug, K., Wang, X., Clauser, K. R., Wang, J., Payne, S., Fenyö, D., Zhang, B., & Mani, D. R. (2017) · Molecular & Cellular Proteomics · doi:10.1074/mcp.mr117.000024 · Cited by 152
LKB1/AMPK Pathway and Drug Response in Cancer: A Therapeutic Perspective
Ciccarese, F., Zulato, E., & Indraccolo, S. (2019) · Oxidative Medicine and Cellular Longevity · doi:10.1155/2019/8730816 · Cited by 147
Proteogenomic integration reveals therapeutic targets in breast cancer xenografts
Huang, K., Li, S., Mertins, P., Cao, S., Gunawardena, H. P., Ruggles, K. V., Mani, D. R., Clauser, K. R., Tanioka, M., Usary, J., et al. (2017) · Nature Communications · doi:10.1038/ncomms14864 · Cited by 152
Manica, M., Oskooei, A., Born, J., Subramanian, V., Sáez-Rodríguez, J., & Martínez, M. R. (2019) · Molecular Pharmaceutics · doi:10.1021/acs.molpharmaceut.9b00520 · Cited by 149
Singh, M. P., Rai, S., Pandey, A., Singh, N. K., & Srivastava, S. (2019) · Genes & Diseases · doi:10.1016/j.gendis.2019.10.013 · Cited by 167
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 151
Pharmacological blockade of TEAD–YAP reveals its therapeutic limitation in cancer cells
Sun, Y., Hu, L., Tao, Z., Jarugumilli, G. K., Erb, H., Singh, A., Li, Q., Cotton, J. L., Greninger, P., Egan, R. K., et al. (2022) · Nature Communications · doi:10.1038/s41467-022-34559-0 · Cited by 145
Self-citationBoehnke, K., Iversen, P. W., Schumacher, D., Lallena, M. J., Haro, R., Amat, J., Haybaeck, J., Liebs, S., Lange, M., Schäfer, R., et al. (2016) · SLAS DISCOVERY · doi:10.1177/1087057116650965 · Cited by 145
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
Chemistry-First Approach for Nomination of Personalized Treatment in Lung Cancer
McMillan, E. A., Ryu, M.-J., Diep, C. H., Mendiratta, S., Clemenceau, J. R., Vaden, R. M., Kim, J.-H., Motoyaji, T., Covington, K. R., Peyton, M., et al. (2018) · Cell · doi:10.1016/j.cell.2018.03.028 · Cited by 141
A review of soft-tissue sarcomas: translation of biological advances into treatment measures
Hoang, N. T., Acevedo, L. A., Mann, M. J., & Tolani, B. (2018) · Cancer Management and Research · doi:10.2147/cmar.s159641 · Cited by 141
Nguyen, E. M., Taniguchi, H., Chan, J. M., Zhan, Y. A., Chen, X., Qiu, J. J., de Stanchina, E., Allaj, V., Shah, N., Uddin, F. Z., et al. (2022) · Journal of Thoracic Oncology · doi:10.1016/j.jtho.2022.05.014 · Cited by 144
MYC Is a Major Determinant of Mitotic Cell Fate
Topham, C. H., Tighe, A., Ly, P., Bennett, A., Sloss, O., Nelson, L., Ridgway, R. A., Huels, D. J., Littler, S., Schandl, C., et al. (2015) · Cancer Cell · doi:10.1016/j.ccell.2015.06.001 · Cited by 144
canSAR: an updated cancer research and drug discovery knowledgebase
Tym, J. E., Mitsopoulos, C., Coker, E. A., Razaz, P., Schierz, A. C., Antolín, A. A., & Al‐Lazikani, B. (2015) · Nucleic Acids Research · doi:10.1093/nar/gkv1030 · Cited by 136
Modeling cancer drug response through drug-specific informative genes
Parca, L., Pepe, G., Pietrosanto, M., Galvan, G., Galli, L., Palmeri, A., Sciandrone, M., Ferrè, F., Ausiello, G., & Helmer‐Citterich, M. (2019) · Scientific Reports · doi:10.1038/s41598-019-50720-0 · Cited by 147
Translational applications of adult stem cell-derived organoids
Drost, J., & Clevers, H. C. (2017) · Development · doi:10.1242/dev.140566 · Cited by 144
Patient-derived organoid (PDO) platforms to facilitate clinical decision making
Liu, L., Yu, L., Li, Z., Li, W., & Huang, W. (2021) · Journal of Translational Medicine · doi:10.1186/s12967-020-02677-2 · Cited by 145
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 142
PI3K Pathway Dependencies in Endometrioid Endometrial Cancer Cell Lines
Weigelt, B., Warne, P. H., Lambros, M. B. K., Reis-Filho, J. S., & Downward, J. (2013) · Clinical Cancer Research · doi:10.1158/1078-0432.ccr-12-3815 · Cited by 138
Morphology and gene expression profiling provide complementary information for mapping cell state
Way, G. P., Natoli, T. E., Adeboye, A., Litichevskiy, L., Yang, A. X., Lü, X., Caicedo, J. C., Cimini, B. A., Karhohs, K. W., Logan, D. J., et al. (2022) · Cell Systems · doi:10.1016/j.cels.2022.10.001 · Cited by 146
Lind, A. P., & Anderson, P. C. (2019) · PLoS ONE · doi:10.1371/journal.pone.0219774 · Cited by 141
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
Phenotype-based cell-specific metabolic modeling reveals metabolic liabilities of cancer
Yizhak, K., Gaude, E., Dévédec, S. E. L., Waldman, Y. Y., Stein, G. Y., van de Water, B., Frezza, C., & Ruppin, E. (2014) · eLife · doi:10.7554/elife.03641 · Cited by 140
Mathematical modeling and computational prediction of cancer drug resistance
Sun, X., & Hu, B. X. (2017) · Briefings in Bioinformatics · doi:10.1093/bib/bbx065 · Cited by 142
BRAF inhibitor resistance mediated by the AKT pathway in an oncogenic BRAF mouse melanoma model
Perna, D., Karreth, F. A., Rust, A. G., Pérez-Mancera, P. A., Rashid, M., Iorio, F., Alifrangis, C., Arends, M. J., Bosenberg, M., Bollag, G. E., et al. (2015) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1418163112 · Cited by 139
Self-citationInactivating CUX1 mutations promote tumorigenesis
Wong, C. C., Martin, S., Rust, A. G., Rashid, M., Alifrangis, C., Alexandrov, L. B., Tiffen, J. C., Kober, C., Green, A. R., Massie, C. E., et al. (2013) · Nature Genetics · doi:10.1038/ng.2846 · Cited by 135
Self-citationCecco, L. D., Nicolau, M., Giannoccaro, M., Daidone, M. G., Bossi, P., Locati, L. D., Licitra, L. F., & Canevari, S. (2015) · Oncotarget · doi:10.18632/oncotarget.3301 · Cited by 138
Bisi, J., Sorrentino, J. A., Roberts, P. J., Tavares, F. X., & Strum, J. C. (2016) · Molecular Cancer Therapeutics · doi:10.1158/1535-7163.mct-15-0775 · Cited by 132
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
Self-citationA 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
Mar, V., Wong, S. Q., Li, J., Scolyer, R. A., McLean, C., Papenfuss, A. T., Tothill, R. W., Kakavand, H., Mann, G. J., Thompson, J. F., et al. (2013) · Clinical Cancer Research · doi:10.1158/1078-0432.ccr-13-0398 · Cited by 135
Faber, A. C., Farago, A. F., Costa, C., Dastur, A., Gomez-Caraballo, M., Robbins, R., Wagner, B. L., Rideout, W. M., Jakubik, C. T., Ham, J., et al. (2015) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1411848112 · Cited by 127
Self-citation
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
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