Cited by 3,394Cancer Genomics and DiagnosticsChromatin Remodeling and CancerGene expression and cancer classification

The DOI matches its Crossref record; 67 of the 67 references it cites were found in a registry or an index.

Next-generation characterization of the Cancer Cell Line Encyclopedia

Open access

Ghandi, Mahmoud, Huang, Franklin W., Jané-Valbuena, Judit, Kryukov, Gregory V., Lo, Christopher C., McDonald, E. Robert, Barretina, Jordi, Gelfand, Ellen T., Bielski, Craig M., Li, Haoxin, Hu, Kevin, Andreev-Drakhlin, Alexander Y., Kim, Jaegil, Hess, Julian M., Haas, Brian J., Aguet, François, Weir, Barbara A., Rothberg, Michael V., Paolella, Brenton R., Lawrence, Michael S., Akbani, Rehan, Lu, Yiling, Tiv, Hong L., Gokhale, Prafulla C., de Weck, Antoine, Mansour, Ali Amin, Oh, Coyin, Shih, Juliann, Hadi, Kevin, Rosen, Yanay, Bistline, Jonathan, Venkatesan, Kavitha, Reddy, Anupama, Sonkin, Dmitriy, Liu, Manway, Lehar, Joseph, Korn, Joshua M., Porter, Dale A., Jones, Michael D., Golji, Javad, Caponigro, Giordano, Taylor, Jordan E., Dunning, Caitlin M., Creech, Amanda L., Warren, Allison C., McFarland, James M., Zamanighomi, Mahdi, Kauffmann, Audrey, Stransky, Nicolas, Imielinski, Marcin, Maruvka, Yosef E., Cherniack, Andrew D., Tsherniak, Aviad, Vazquez, Francisca, Jaffe, Jacob D., Lane, Andrew A., Weinstock, David M., Johannessen, Cory M., Morrissey, Michael P., Stegmeier, Frank, Schlegel, Robert, Hahn, William C., Getz, Gad, Mills, Gordon B., Boehm, Jesse S., Golub, Todd R., Garraway, Levi A., Sellers, William R.·2019·Nature

DOI 10.1038/s41586-019-1186-3PMC PMC6697103

Open this paper in CoChat — free account

Abstract

Large panels of comprehensively characterized human cancer models, including the Cancer Cell Line Encyclopedia (CCLE), have provided a rigorous framework with which to study genetic variants, candidate targets, and small-molecule and biological therapeutics and to identify new marker-driven cancer dependencies. To improve our understanding of the molecular features that contribute to cancer phenotypes, including drug responses, here we have expanded the characterizations of cancer cell lines to include genetic, RNA splicing, DNA methylation, histone H3 modification, microRNA expression and reverse-phase protein array data for 1,072 cell lines from individuals of various lineages and ethnicities. Integration of these data with functional characterizations such as drug-sensitivity, short hairpin RNA knockdown and CRISPR–Cas9 knockout data reveals potential targets for cancer drugs and associated biomarkers. Together, this dataset and an accompanying public data portal provide a resource for the acceleration of cancer research using model cancer cell lines. The original Cancer Cell Line Encyclopedia (CCLE) is expanded with deeper characterization of over 1,000 cell lines, including genomic, transcriptomic, and proteomic data, and integration with drug-sensitivity and gene-dependency data.

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

Crossref · 10.1038/s41586-019-1186-3

Cites

67 of 67 references verified0 citation errors · 0 not checkable

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?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

STAR: ultrafast universal RNA-seq aligner
Dobin, A., Davis, C. A., Schlesinger, F., Drenkow, J., Zaleski, C., Jha, S., Batut, P., Chaisson, M., & Gingeras, T. R. (2013) · Bioinformatics · doi:10.1093/bioinformatics/bts635 · Cited by 51,709
limma powers differential expression analyses for RNA-sequencing and microarray studies
Ritchie, M. E., Phipson, B., Wu, D., Hu, Y., Law, C. W., Shi, W., & Smyth, G. K. (2015) · Nucleic Acids Research · doi:10.1093/nar/gkv007 · Cited by 40,458
Untitled work
(2008) · Journal of Machine Learning Research · Cited by 33,051
Regularization Paths for Generalized Linear Models via Coordinate Descent
Friedman, J., Hastie, T., & Tibshirani, R. (2010) · Journal of Statistical Software · doi:10.18637/jss.v033.i01 · Cited by 19,927
The cBio Cancer Genomics Portal: An Open Platform for Exploring Multidimensional Cancer Genomics Data
Cerami, E., Gao, J., Dogrusoz, U., Gross, B. E., Sumer, S. O., Aksoy, B. A., Jacobsen, A., Byrne, C. J., Heuer, M. L., Larsson, E., et al. (2012) · Cancer Discovery · doi:10.1158/2159-8290.cd-12-0095 · Cited by 17,455
Integrative Analysis of Complex Cancer Genomics and Clinical Profiles Using the cBioPortal
Gao, J., Aksoy, B. A., Dogrusoz, U., Dresdner, G., Gross, B., Sumer, S. O., Sun, Y., Jacobsen, A., Sinha, R., Larsson, E., et al. (2013) · Science Signaling · doi:10.1126/scisignal.2004088 · Cited by 15,160
Linear Models and Empirical Bayes Methods for Assessing Differential Expression in Microarray Experiments
Smyth, G. K. (2004) · Statistical Applications in Genetics and Molecular Biology · doi:10.2202/1544-6115.1027 · Cited by 11,829
STRING v10: protein–protein interaction networks, integrated over the tree of life
Szklarczyk, D., Franceschini, A., Wyder, S., Forslund, K., Heller, D., Huerta-Cepas, J., Simonovic, M., Roth, A., Santos, A., Tsafou, K. P., et al. (2015) · Nucleic Acids Research · doi:10.1093/nar/gku1003 · Cited by 10,436
MicroRNA expression profiles classify human cancers
Lü, J., Getz, G. A., Miska, E. A., Alvarez-Saavedra, E., Lamb, J., Peck, D., Sweet-Cordero, A., Ebert, B. L., Mak, R. H., Ferrando, A. A., et al. (2005) · Nature · doi:10.1038/nature03702 · Cited by 9,637
Self-citation
Analysis of protein-coding genetic variation in 60,706 humans
Exome Aggregation Consortium, Lek, M., Karczewski, K. J., Minikel, E. V., Samocha, K. E., Banks, E., Fennell, T., O’Donnell-Luria, A. H., Ware, J. S., Hill, A. J., et al. (2016) · Nature · doi:10.1038/nature19057 · Cited by 9,587
Self-citation

Show all 67 references

Sorted by citations

Items 201–300 of 1,008

Stimuli‐Responsive PROTACs for Controlled Protein Degradation
An, K., Deng, X., Chi, H., Zhang, Y., Li, Y., Cheng, M., Ni, Z., Yang, Z., Wang, C., Chen, J., et al. (2023) · Angewandte Chemie International Edition · doi:10.1002/anie.202306824 · Cited by 95
URI alleviates tyrosine kinase inhibitors-induced ferroptosis by reprogramming lipid metabolism in p53 wild-type liver cancers
Ding, Z., Pan, Y., Shang, T., Jiang, T., Lin, Y., Yang, C., Pang, S., Cui, X., Wang, Y., Feng, X., et al. (2023) · Nature Communications · doi:10.1038/s41467-023-41852-z · Cited by 92
“In addition, most cancer cells with p53 mutation showed resistant to TKIs in CCLE database 41 , suggesting that wild-type p53 might sensitize cells to TKIs and transcriptional repression of SCD1 by p53 might be one of the mechanisms (Supplementary Fig.”
Single cell multi-omics reveal intra-cell-line heterogeneity across human cancer cell lines
Zhu, Q., Zhao, X., Zhang, Y., Li, Y., Liu, S., Han, J., Sun, Z., Wang, C., Deng, D., Tang, Y., et al. (2023) · Nature Communications · doi:10.1038/s41467-023-43991-9 · Cited by 100
“To validate the effectiveness of our assignment, weintegrated the bulk RNA-seq dataset generated by CCLE of three cell lines 13 , including A-253, HCT 116 and MCF7, with our scRNA-seq data.”
Molecular basis for substrate recruitment to the PRMT5 methylosome
Mulvaney, K. M., Blomquist, C., Acharya, N., Li, R., Ranaghan, M. J., O’Keefe, M., Rodriguez, D. J., Young, M. J., Kesar, D., Pal, D., et al. (2021) · Molecular Cell · doi:10.1016/j.molcel.2021.07.019 · Cited by 93
Self-citation
Myeloma-Modified Adipocytes Exhibit Metabolic Dysfunction and a Senescence-Associated Secretory Phenotype
Fairfield, H. E., Dudakovic, A., Khatib, C. M., Farrell, M. L., Costa, S., Falank, C. R., Hinge, M., Murphy, C. S., DeMambro, V., Pettitt, J. A., et al. (2020) · Cancer Research · doi:10.1158/0008-5472.can-20-1088 · Cited by 91
Integrated molecular characterisation of the MAPK pathways in human cancers reveals pharmacologically vulnerable mutations and gene dependencies
Sinkala, M., Nkhoma, P., Mulder, N., & Martin, D. P. (2021) · Communications Biology · doi:10.1038/s42003-020-01552-6 · Cited by 91
RNA‐binding motif protein 39 (RBM39): An emerging cancer target
Xu, Y., Nijhuis, A. M., & Keun, H. C. (2020) · British Journal of Pharmacology · doi:10.1111/bph.15331 · Cited by 90
Cooperation between melanoma cell states promotes metastasis through heterotypic cluster formation
Campbell, N. R., Rao, A. N., Hunter, M., Sznurkowska, M. K., Briker, L., Zhang, M., Baron, M., Heilmann, S., Déforet, M., Kenny, C., et al. (2021) · Developmental Cell · doi:10.1016/j.devcel.2021.08.018 · Cited by 94
Genome-wide mapping of somatic mutation rates uncovers drivers of cancer
Sherman, M. A., Yaari, A. U., Priebe, O., Dietlein, F., Loh, P.-R., & Berger, B. (2022) · Nature Biotechnology · doi:10.1038/s41587-022-01353-8 · Cited by 80
Illuminating the noncoding genome in cancer
Zhang, X., & Meyerson, M. M. (2020) · Nature Cancer · doi:10.1038/s43018-020-00114-3 · Cited by 86
Plasticity of Extrachromosomal and Intrachromosomal BRAF Amplifications in Overcoming Targeted Therapy Dosage Challenges
Song, K., Minami, J. K., Huang, A. J. Y., Dehkordi, S. R., Lomeli, S. H., Luebeck, J., Goodman, M. H., Moriceau, G., Krijgsman, O., Dharanipragada, P., et al. (2021) · Cancer Discovery · doi:10.1158/2159-8290.cd-20-0936 · Cited by 85
Biomedical Big Data Technologies, Applications, and Challenges for Precision Medicine: A Review
Yang, X., Huang, K., Yang, D., Zhao, W., & Zhou, X. (2023) · Global Challenges · doi:10.1002/gch2.202300163 · Cited by 99
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 89
RP-3500: A Novel, Potent, and Selective ATR Inhibitor that is Effective in Preclinical Models as a Monotherapy and in Combination with PARP Inhibitors
Roulston, A., Zimmermann, M., Papp, R., Skeldon, A. M., Pellerin, C., Dumas-Bérube, É., Dumais, V., Dorich, S., Fader, L. D., Fournier, S., et al. (2021) · Molecular Cancer Therapeutics · doi:10.1158/1535-7163.mct-21-0615 · Cited by 86
AMG 193, a Clinical Stage MTA-Cooperative PRMT5 Inhibitor, Drives Antitumor Activity Preclinically and in Patients with MTAP-Deleted Cancers
Belmontes, B., Slemmons, K. K., Su, C., Liu, S., Policheni, A. N., Moriguchi, J., Tan, H. L. E., Xie, F., Aiello, D. A., Yang, Y., et al. (2024) · Cancer Discovery · doi:10.1158/2159-8290.cd-24-0887 · Cited by 99
“To test this hypothesis, we analyzed the Cancer Cell Line Ency-clopedia RNA-seq dataset (28) for alternative splicing events in the 129 MTAP-deleted cell lines [Fig.”
R-loopBase: a knowledgebase for genome-wide R-loop formation and regulation
Lin, R., Zhong, X., Zhou, Y., Geng, H., Hu, Q., Huang, Z., Hu, J. S., Fu, X., Chen, L., & Chen, J. (2021) · Nucleic Acids Research · doi:10.1093/nar/gkab1103 · Cited by 86
“Expression profiles of R-loop regulators were based on GTEx (83), TCGA (84) and CCLE (85) data.”
Pan-cancer classification of single cells in the tumour microenvironment
Nofech-Mozes, I., Soave, D. M., Awadalla, P., & Abelson, S. (2023) · Nature Communications · doi:10.1038/s41467-023-37353-8 · Cited by 90
Controversies around the function of LARP1
Berman, A. J., Thoreen, C. C., Dedeić, Z., Chettle, J., Roux, P. P., & Blagden, S. P. (2020) · RNA Biology · doi:10.1080/15476286.2020.1733787 · Cited by 83
Immune evasion in HPV − head and neck precancer–cancer transition is driven by an aneuploid switch involving chromosome 9p loss
William, W. N., Zhao, X., Bianchi, J. J., Lin, H. Y., Cheng, P., Lee, J. J., Carter, H. K., Alexandrov, L. B., Abraham, J. P., Spetzler, D. B., et al. (2021) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2022655118 · Cited by 84
Aneuploid senescent cells activate NF-κB to promote their immune clearance by NK cells
Wang, R. W., Viganò, S., Ben‐David, U., Amon, A., & Santaguida, S. (2021) · EMBO Reports · doi:10.15252/embr.202052032 · Cited by 84
Global identification of SWI/SNF targets reveals compensation by EP400
Martin, B. J. E., Ablondi, E. F., Goglia, C., Mimoso, C. A., Espinel-Cabrera, P. R., & Adelman, K. (2023) · Cell · doi:10.1016/j.cell.2023.10.006 · Cited by 85
Epigenome-wide DNA methylation analysis of small cell lung cancer cell lines suggests potential chemotherapy targets
Krushkal, J., Silvers, T. R., Reinhold, W. C., Sonkin, D., Vural, S., Connelly, J., Varma, S., Meltzer, P. S., Kunkel, M. W., Rapisarda, A., et al. (2020) · Clinical Epigenetics · doi:10.1186/s13148-020-00876-8 · Cited by 84
Self-citation
Functional landscapes of POLE and POLD1 mutations in checkpoint blockade-dependent antitumor immunity
Ma, X., Riaz, N., Samstein, R. M., Lee, M., Makarov, V., Valero, C., Chowell, D., Kuo, F., Hoen, D. R., Fitzgerald, C. W. R., et al. (2022) · Nature Genetics · doi:10.1038/s41588-022-01108-w · Cited by 87
An ALYREF-MYCN coactivator complex drives neuroblastoma tumorigenesis through effects on USP3 and MYCN stability
Nagy, Z. S., Seneviratne, J. A., Kanikevich, M., Chang, W. K., Mayoh, C., Venkat, P., Du, Y., Jiang, C., Salib, A., Koach, J., et al. (2021) · Nature Communications · doi:10.1038/s41467-021-22143-x · Cited by 83
Predicting tumor response to drugs based on gene-expression biomarkers of sensitivity learned from cancer cell lines
Li, Y., Umbach, D. M., Krahn, J. M., Shats, I., Li, X., & Li, L. (2021) · BMC Genomics · doi:10.1186/s12864-021-07581-7 · Cited by 88
MS4A15 drives ferroptosis resistance through calcium-restricted lipid remodeling
Shan, X., Mueller, C., Pfeiffer, S., Kraft, V. A. N., Merl‐Pham, J., Bao, X., Feederle, R., Jin, X., Hauck, S. M., Schmitt‐Kopplin, P., et al. (2021) · Cell Death and Differentiation · doi:10.1038/s41418-021-00883-z · Cited by 83
Bayesian estimation of gene constraint from an evolutionary model with gene features
Zeng, T., Spence, J. P., Mostafavi, H., & Pritchard, J. K. (2024) · Nature Genetics · doi:10.1038/s41588-024-01820-9 · Cited by 91
“Genome-wide CRISPR growth screens have measured the effects of gene knockouts on cell survival or proliferation, quantifying the in vitro importance of each gene for fitness [37, 38].”
Inhibition of tumor progression and M2 microglial polarization by extracellular vesicle-mediated microRNA-124 in a 3D microfluidic glioblastoma microenvironment
Hong, S., You, J. Y., Paek, K., Park, J., Kang, S. J., Han, E. H., Choi, N., Chung, S. H., Rhee, W. J., & Kim, J. A. (2021) · Theranostics · doi:10.7150/thno.60851 · Cited by 83
Self-citation
Functional synapses between neurons and small cell lung cancer
Sakthivelu, V., Schmitt, A., Odenthal, F., Ndoci, K., Touet, M., Shaib, A., Chihab, A. W., Wani, G. A., Nieper, P., Hartmann, G. G., et al. (2025) · Nature · doi:10.1038/s41586-025-09434-9 · Cited by 97
Interpretation of cancer mutations using a multiscale map of protein systems
Zheng, F., Kelly, M. R., Ramms, D. J., Heintschel, M. L., Tao, K., Tutuncuoglu, B., Lee, J. J., Ono, K., Foussard, H., Chen, M., et al. (2021) · Science · doi:10.1126/science.abf3067 · Cited by 81
A Transcriptional Regulatory Loop of Master Regulator Transcription Factors, PPARG, and Fatty Acid Synthesis Promotes Esophageal Adenocarcinoma
Ma, S., Zhou, B., Yang, Q., Pan, Y., Yang, W., Freedland, S. J., Ding, L., Freeman, M. R., Breunig, J. J., Bhowmick, N. A., et al. (2021) · Cancer Research · doi:10.1158/0008-5472.can-20-0652 · Cited by 80
The ER stress response mediator ERO1 triggers cancer metastasis by favoring the angiogenic switch in hypoxic conditions
Varone, E., Decio, A., Chernorudskiy, A. L., Minoli, L., Brunelli, L., Ioli, F., Piotti, A., Pastorelli, R., Fratelli, M., Gobbi, M., et al. (2021) · Oncogene · doi:10.1038/s41388-021-01659-y · Cited by 85
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 79
GREM1 is required to maintain cellular heterogeneity in pancreatic cancer
Lan, L., Evan, T., Li, H., Hussain, A., Ruiz, E. J., Thin, M. Z., Ferreira, R. M. M., Hari, P. S., Riising, E. M., Zen, Y., et al. (2022) · Nature · doi:10.1038/s41586-022-04888-7 · Cited by 86
Self-citation
Experimental reproducibility limits the correlation between mRNA and protein abundances in tumor proteomic profiles
Upadhya, S. R., & Ryan, C. J. (2022) · Cell Reports Methods · doi:10.1016/j.crmeth.2022.100288 · Cited by 81
Autotaxin impedes anti-tumor immunity by suppressing chemotaxis and tumor infiltration of CD8+ T cells
Matas-Rico, E., Frijlink, E., Àvila, I. V. D. H., Menegakis, A., van Zon, M., Morris, A. J., Köster, J., Salgado-Polo, F., de Kivit, S., Lança, T., et al. (2021) · Cell Reports · doi:10.1016/j.celrep.2021.110013 · Cited by 85
Organoid screening reveals epigenetic vulnerabilities in human colorectal cancer
Toshimitsu, K., Takano, A., Fujii, M., Togasaki, K., Matano, M., Takahashi, S., Kanai∥, T., & Sato, T. (2022) · Nature Chemical Biology · doi:10.1038/s41589-022-00984-x · Cited by 84
Extensive protein dosage compensation in aneuploid human cancersPreprint
Schukken, K. M., & Sheltzer, J. M. (2022) · Genome Research · doi:10.1101/gr.276378.121 · Cited by 79
Characterization of the dual functional effects of heat shock proteins (HSPs) in cancer hallmarks to aid development of HSP inhibitors
Zhang, Z., Ji, J., Ye, Y., Chen, Z., Jing, Y., Li, S., Wei, H., Ruan, H., Liu, Y., Hu, Q., et al. (2020) · Genome Medicine · doi:10.1186/s13073-020-00795-6 · Cited by 81
Weighted Gene Co-expression Network Analysis Identifies a Cancer-Associated Fibroblast Signature for Predicting Prognosis and Therapeutic Responses in Gastric Cancer
Zheng, H., Liu, H., Li, H., Dou, W., & Wang, X. (2021) · Frontiers in Molecular Biosciences · doi:10.3389/fmolb.2021.744677 · Cited by 77
Self-citation
TERT accelerates BRAF mutant–induced thyroid cancer dedifferentiation and progression by regulating ribosome biogenesis
Yu, P., Qu, N., Zhu, R., Hu, J., Han, P., Wu, J., Tan, L., Gan, H., He, C., Fang, C., et al. (2023) · Science Advances · doi:10.1126/sciadv.adg7125 · Cited by 85
A Ubiquitination Cascade Regulating the Integrated Stress Response and Survival in Carcinomas
Cervia, L. D., Shibue, T., Borah, A. A., Gaeta, B., He, L., Leung, L., Li, N., Moyer, S. M., Shim, B. H., Dumont, N., et al. (2022) · Cancer Discovery · doi:10.1158/2159-8290.cd-22-1230 · Cited by 77
“Briefly, gains and losses of the chromosome arms were determined using the copy-number data of the genes calculated through the ABSOLUTE algorithm (102).”
MethodSelf-citation
Surface Proteomics Reveals CD72 as a Target for In Vitro –Evolved Nanobody-Based CAR-T Cells in KMT2A/MLL1 -Rearranged B-ALL
Nix, M. A., Mandal, K., Geng, H., Paranjape, N., Lin, Y.-H. T., Rivera, J. M., Marcoulis, M., White, K. L., Whitman, J. D., Bapat, S. P., et al. (2021) · Cancer Discovery · doi:10.1158/2159-8290.cd-20-0242 · Cited by 72
The exon-junction complex helicase eIF4A3 controls cell fate via coordinated regulation of ribosome biogenesis and translational output
Kanellis, D. C., Espinoza, J. A., Zisi, A., Sakkas, E., Bártková, J., Katsori, A., Boström, J., Dyrskjøt, L., Broholm, H., Altun, M., et al. (2021) · Science Advances · doi:10.1126/sciadv.abf7561 · Cited by 78
SMARCA4/2 loss inhibits chemotherapy-induced apoptosis by restricting IP3R3-mediated Ca2+ flux to mitochondria
Xue, Y., Morris, J. L., Yang, K., Fu, Z., Zhu, X., Johnson, F. D., Meehan, B., Witkowski, L., Yasmeen, A., Golenár, T., et al. (2021) · Nature Communications · doi:10.1038/s41467-021-25260-9 · Cited by 78
An Enhancer-Driven Stem Cell–Like Program Mediated by SOX9 Blocks Intestinal Differentiation in Colorectal Cancer
Liang, X., Duronio, G. N., Yang, Y., Bala, P., Hebbar, P., Spisák, S., Sahgal, P., Singh, H., Zhang, Y., Xie, Y., et al. (2021) · Gastroenterology · doi:10.1053/j.gastro.2021.09.044 · Cited by 76
TMPRSS4 promotes cancer stem–like properties in prostate cancer cells through upregulation of SOX2 by SLUG and TWIST1
Lee, Y., Yoon, J., Ko, D., Yu, M., Lee, S., & Kim, S. (2021) · Journal of Experimental & Clinical Cancer Research · doi:10.1186/s13046-021-02147-7 · Cited by 78
“We interrogated the CCLE data (Broad, 2019) [35] to determine the mRNA expression levels of SOX2 and EMT-inducing transcription factors in various cancer cell lines (n = 1156).”
Method
Acquired Cross-Resistance in Small Cell Lung Cancer due to Extrachromosomal DNA Amplification of MYC Paralogs
Choudhuri, S. P., Girard, L., Lim, J. Y. S., Wise, J. F., Freitas, B., Yang, D., Wong, E., Hamilton, S., Chien, V. D., Kim, Y. J., et al. (2024) · Cancer Discovery · doi:10.1158/2159-8290.cd-23-0656 · Cited by 78
“57–60).”
Self-citation
Distinct CDK6 complexes determine tumor cell response to CDK4/6 inhibitors and degraders
Wu, X., Yang, X., Xiong, Y., Li, R., Ito, T., Ahmed, T. A., Karoulia, Z., Adamopoulos, C., Wang, H., Wang, L., et al. (2021) · Nature Cancer · doi:10.1038/s43018-021-00174-z · Cited by 75
Self-citation
Dissolution of oncofusion transcription factor condensates for cancer therapy
Wang, Y., Yu, C., Pei, G., Jia, W., Li, T., & Li, P. (2023) · Nature Chemical Biology · doi:10.1038/s41589-023-01376-5 · Cited by 74
Relocalizing transcriptional kinases to activate apoptosis
Sarott, R. C., Gourisankar, S. P., Karim, B. A., Nettles, S. A., Yang, H., Dwyer, B. G., Simanauskaite, J., Tse, J., Abuzaid, H. Z., Krokhotin, A., et al. (2024) · Science · doi:10.1126/science.adl5361 · Cited by 80
N-substituted phthalimide–carboxylic acid hybrids as dual-targeted aldose reductase inhibitors: Synthesis, mechanistic insights, and cancer-relevant profiling
Zognjani, B., Nixha, A. R., Duran, H. E., Arslan, M., Yıldıztekin, G., Ece, A., & Türkeş, C. (2025) · Bioorganic Chemistry · doi:10.1016/j.bioorg.2025.108788 · Cited by 77
A pan-cancer transcriptome analysis of exitron splicing identifies novel cancer driver genes and neoepitopes
Wang, T.-Y., Liu, Q., Ren, Y., Alam, S. K., Wang, L., Zhu, Z., Hoeppner, L. H., Dehm, S. M., Cao, Q., & Yang, R. (2021) · Molecular Cell · doi:10.1016/j.molcel.2021.03.028 · Cited by 77
A comprehensive single-cell breast tumor atlas defines epithelial and immune heterogeneity and interactions predicting anti-PD-1 therapy response
Xu, L., Saunders, K., Huang, S.-P., Knútsdóttir, H., Martinez-Algarin, K., Terrazas, I., Chen, K., McArthur, H., Maués, J., Hodgdon, C., et al. (2024) · Cell Reports Medicine · doi:10.1016/j.xcrm.2024.101511 · Cited by 84
Pertpy: an end-to-end framework for perturbation analysis
Heumos, L., Ji, Y., May, L. T., Green, T. D., Peidli, S., Zhang, X., Wu, X., Ostner, J., Schumacher, A., Hrovatin, K., et al. (2025) · Nature Methods · doi:10.1038/s41592-025-02909-7 · Cited by 85
“Its metadata module annotates cell lines with tissue-of-origin, cancer type and bulk expression profiles from the disease ontology OncoTree 48 and the Cancer Cell Line Encyclopedia 49 (CCLE).”
Patient-Derived Xenografts and Matched Cell Lines Identify Pharmacogenomic Vulnerabilities in Colorectal Cancer
Lazzari, L., Corti, G., Picco, G., Isella, C., Montone, M., Arcella, P., Durinikova, E., Zanella, E. R., Novara, L., Barbosa, F., et al. (2019) · Clinical Cancer Research · doi:10.1158/1078-0432.ccr-18-3440 · Cited by 74
Cell line authentication: a necessity for reproducible biomedical research
Souren, N. Y., Fusenig, N. E., Heck, S., Dirks, W. G., Capes‐Davis, A., Bianchini, F., & Plass, C. (2022) · The EMBO Journal · doi:10.15252/embj.2022111307 · Cited by 73
“…genomics, epige-nomics, transcriptomics, proteomics, and metabolomics data) of more than 1,000 cancer cell lines are already publicly available (Ghandi et al , 2019; Nusinow et al , 2020), providing scientists with a wealth of information for direct analyses or for selecting the best models…”
Targeting melanoma’s MCL1 bias unleashes the apoptotic potential of BRAF and ERK1/2 pathway inhibitors
Sale, M. J., Minihane, E., Monks, N. R., Gilley, R., Richards, F. M., Schifferli, K. P., Andersen, C. L., Davies, E. J., Vicente, M. A., Ozono, E., et al. (2019) · Nature Communications · doi:10.1038/s41467-019-12409-w · Cited by 75
Patient similarity network of newly diagnosed multiple myeloma identifies patient subgroups with distinct genetic features and clinical implications
Bhalla, S., Melnekoff, D. T., Aleman, A., Leshchenko, V. V., Restrepo, P., Keats, J. J., Onel, K., Sawyer, J. R., Madduri, D., Richter, J. R., et al. (2021) · Science Advances · doi:10.1126/sciadv.abg9551 · Cited by 74
Discovery of two novel ALKBH5 selective inhibitors that exhibit uncompetitive or competitive type and suppress the growth activity of glioblastoma multiforme
Takahashi, H., Hase, H., Yoshida, T., Tashiro, J., Hirade, Y., Kitae, K., & Tsujikawa, K. (2022) · Chemical Biology & Drug Design · doi:10.1111/cbdd.14051 · Cited by 73
Phosphate dysregulation via the XPR1–KIDINS220 protein complex is a therapeutic vulnerability in ovarian cancer
Bondeson, D. P., Paolella, B. R., Asfaw, A., Rothberg, M. V., Skipper, T. A., Langan, C. R., Mesa, G., Gonzalez, A. E., Surface, L. E., Ito, K., et al. (2022) · Nature Cancer · doi:10.1038/s43018-022-00360-7 · Cited by 74
Self-citation
Modeling CRISPR-Cas13d on-target and off-target effects using machine learning approaches
Cheng, X., Li, Z., Shan, R., Li, Z., Wang, S., Zhao, W., Zhang, H., Chao, L., Peng, J., Teng, F., et al. (2023) · Nature Communications · doi:10.1038/s41467-023-36316-3 · Cited by 83
“The lncRNA expressions from A375 and A549 cells are fromthe Cancer Cell Line Encyclopedia (CCLE) and are downloaded from DepMap (version: 2019) 60 .”
Method
Circular extrachromosomal DNA promotes tumor heterogeneity in high-risk medulloblastoma
Chapman, O. S., Luebeck, J., Sridhar, S., Wong, I. T., Dixit, D., Wang, S., Prasad, G., Rajkumar, U., Pagadala, M., Larson, J. D., et al. (2023) · Nature Genetics · doi:10.1038/s41588-023-01551-3 · Cited by 77
Synthetic lethal strategies for the development of cancer therapeutics
Ngoi, N. Y. L., Gallo, D., Torrado, C., Nardo, M., Durocher, D., & Yap, T. A. (2024) · Nature Reviews Clinical Oncology · doi:10.1038/s41571-024-00966-z · Cited by 81
“In parallel, the Cancer Cell Line Encyclopedia project catalogued the mutational landscapes of common human cancer cell lines 13,14 .”
Functionalized Folate-Modified Graphene Oxide/PEI siRNA Nanocomplexes for Targeted Ovarian Cancer Gene Therapy
Wang, Y., Sun, G., Gong, Y., Zhang, Y., Liang, X., & Yang, L. (2020) · Nanoscale Research Letters · doi:10.1186/s11671-020-3281-7 · Cited by 72
Environmental robustness of the global yeast genetic interaction network
Costanzo, M., Hou, J., Messier, V., Nelson, J., Rahman, M., VanderSluis, B., Wang, W., Pons, C., Ross, C., Ušaj, M., et al. (2021) · Science · doi:10.1126/science.abf8424 · Cited by 75
Dynamic de novo heterochromatin assembly and disassembly at replication forks ensures fork stability
Gaggioli, V., Lo, C. S. Y., Reverón-Gómez, N., Jasencakova, Z., Domènech, H., Nguyen, H. N., Sidoli, S., Tvardovskiy, A., Uruci, S., Slotman, J. A., et al. (2023) · Nature Cell Biology · doi:10.1038/s41556-023-01167-z · Cited by 72
Self-citation
Molecular and Pathologic Characterization of YAP1-Expressing Small Cell Lung Cancer Cell Lines Leads to Reclassification as SMARCA4-Deficient Malignancies
Ng, J., Cai, L., Girard, L., Prall, O. W. J., Rajan, N., Khoo, C., Batrouney, A., Byrne, D. J., Boyd, D. K., Kersbergen, A. J., et al. (2023) · Clinical Cancer Research · doi:10.1158/1078-0432.ccr-23-2360 · Cited by 72
High-plex Multiomic Analysis in FFPE at Subcellular Level by Spatial Molecular ImagingPreprint
He, S., Bhatt, R., Brown, C., Brown, E., Buhr, D. L., Chantranuvatana, K., Danaher, P. J., Dunaway, D. L., Garrison, R. G., Geiss, G. K., et al. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.11.03.467020 · Cited by 75
TET2 as a tumor suppressor and therapeutic target in T-cell acute lymphoblastic leukemia
Bensberg, M., Rundquist, O., Selimović-Pašić, A., Lagerwall, C., Benson, M., Gustafsson, M., Vogt, H., Lentini, A., & Nestor, C. E. (2021) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2110758118 · Cited by 70
Whole-Genome Duplication Shapes the Aneuploidy Landscape of Human Cancers
Prasad, K., Bloomfield, M., Levi, H., Keuper, K., Bernhard, S. V., Baudoin, N. C., Leor, G., Eliezer, Y., Giam, M., Wong, C. K., et al. (2022) · Cancer Research · doi:10.1158/0008-5472.can-21-2065 · Cited by 74
Dysfunctional tRNA reprogramming and codon-biased translation in cancer
Dedon, P. C., & Begley, T. J. (2022) · Trends in Molecular Medicine · doi:10.1016/j.molmed.2022.09.007 · Cited by 68
LncRNA MIR200CHG inhibits EMT in gastric cancer by stabilizing miR-200c from target-directed miRNA degradation
Zhu, L., Huang, C., Zhang, C., Zhou, Y., Zhao, E., Zhang, Y., Pan, X., Huang, H., Liao, W., & Wang, X. (2023) · Nature Communications · doi:10.1038/s41467-023-43974-w · Cited by 71
UPP1 promotes lung adenocarcinoma progression through the induction of an immunosuppressive microenvironment
Li, Y., Jiang, M., Aye, L., Luo, L., Zhang, Y., Xu, F., Wei, Y., Peng, D., He, X., Gu, J., et al. (2024) · Nature Communications · doi:10.1038/s41467-024-45340-w · Cited by 71
“Using scRNA-seq datasets, bulk datasets, and CCLE mRNA and protein expression datasets 46,47 , correlations between UPP1 and various tumor immune checkpoints were assessed.”
Method
Malignant Melanoma: An Overview, New Perspectives, and Vitamin D Signaling
Slominski, R. M., Kim, T., Janjetović, Z., Brożyna, A. A., Podgórska, E., Dixon, K. M., Mason, R. S., Tuckey, R. C., Sharma, R., Crossman, D. K., et al. (2024) · Cancers · doi:10.3390/cancers16122262 · Cited by 79
A Strategy Based on the Enzyme-Catalyzed Polymerization Reaction of Asp-Phe-Tyr Tripeptide for Cancer Immunotherapy
Zhang, Q., Zheng, D., Dong, X., Pan, P., Zeng, S.-M., Gao, F., Cheng, S., & Zhang, X. (2021) · Journal of the American Chemical Society · doi:10.1021/jacs.1c00945 · Cited by 70
First Small-Molecule Inhibitors Targeting the RNA-Binding Protein IGF2BP2/IMP2 for Cancer Therapy
Dahlem, C., Abuhaliema, A., Kessler, S. M., Kröhler, T., Zoller, B. G. E., Chanda, S., Wu, Y., Both, S., Müller, F., Lepikhov, K., et al. (2022) · ACS Chemical Biology · doi:10.1021/acschembio.1c00833 · Cited by 70
Generation and multi-dimensional profiling of a childhood cancer cell line atlas defines new therapeutic opportunities
Sun, C. X., Daniel, P. M., Bradshaw, G., Shi, H., Loi, M., Chew, N. J., Parackal, S., Tsui, V., Liang, Y., Koptyra, M., et al. (2023) · Cancer Cell · doi:10.1016/j.ccell.2023.03.007 · Cited by 68
DNA double-strand break–capturing nuclear envelope tubules drive DNA repair
Shokrollahi, M., Stanić, M., Hundal, A. K., Chan, J. N. Y., Urman, D., Jordan, C. A., Hakem, A., Espín, R., Hao, J., Krishnan, R., et al. (2024) · Nature Structural & Molecular Biology · doi:10.1038/s41594-024-01286-7 · Cited by 71
“Rea-nalysis of genomic Cancer Dependency Map (DepMap; https://depmap. org/portal/home/#/) CRISPR screens confirmed a negative genetic interaction between BRCA1 and KIF5B (Extended Data Fig.”
High-throughput PRIME-editing screens identify functional DNA variants in the human genome
Ren, X., Yang, H., Nierenberg, J. L., Sun, Y., Chen, J., Beaman, C., Pham, T., Nobuhara, M., Takagi, M. A., Narayan, V., et al. (2023) · Molecular Cell · doi:10.1016/j.molcel.2023.11.021 · Cited by 70
miR-105-5p regulates PD-L1 expression and tumor immunogenicity in gastric cancer
Miliotis, C., & Slack, F. J. (2021) · Cancer Letters · doi:10.1016/j.canlet.2021.05.037 · Cited by 68
A Division of Labor between YAP and TAZ in Non–Small Cell Lung Cancer
Shreberk‐Shaked, M., Dassa, B., Sinha, S., Agostino, S. D., Azuri, I., Mukherjee, S., Aylon, Y., Blandino, G., Ruppin, E., & Oren, M. (2020) · Cancer Research · doi:10.1158/0008-5472.can-20-0125 · Cited by 70
The Capacity of the Ovarian Cancer Tumor Microenvironment to Integrate Inflammation Signaling Conveys a Shorter Disease-free Interval
Jordan, K. R., Sikora, M. J., Slansky, J. E., Minic, A. D., Richer, J. K., Moroney, M. R., Hu, J., Wolsky, R. J., Watson, Z. L., Yamamoto, T. M., et al. (2020) · Clinical Cancer Research · doi:10.1158/1078-0432.ccr-20-1762 · Cited by 68
Large-Scale Characterization of Drug Responses of Clinically Relevant Proteins in Cancer Cell Lines
Zhao, W., Li, J., Chen, M.-J. M., Luo, Y., Ju, Z., Nesser, N. K., Johnson-Camacho, K., Boniface, C. T., Lawrence, Y., Pande, N. T., et al. (2020) · Cancer Cell · doi:10.1016/j.ccell.2020.10.008 · Cited by 68
Self-citation
Comparative analysis of drug response and gene profiling of HER2-targeted tyrosine kinase inhibitors
Conlon, N. T., Kooijman, J. J., van Gerwen, S. J. C., Mulder, W. R., Zaman, G. J. R., Diala, I., Eli, L. D., Lalani, A. S., Crown, J. P., & Collins, D. M. (2021) · British Journal of Cancer · doi:10.1038/s41416-020-01257-x · Cited by 71
Small non-coding RNAs in human cancer: function, clinical utility, and characterization
Zhang, Z., Zhang, J., Diao, L., & Han, L. (2021) · Oncogene · doi:10.1038/s41388-020-01630-3 · Cited by 72
Functional inhibition of lactate dehydrogenase suppresses pancreatic adenocarcinoma progression
Cheng, C., Tan, H., Wang, N., Chen, L., Meng, Z., Chen, Z., & Feng, Y. (2021) · Clinical and Translational Medicine · doi:10.1002/ctm2.467 · Cited by 70
SWnet: a deep learning model for drug response prediction from cancer genomic signatures and compound chemical structures
Zuo, Z., Wang, P., Chen, X., Li, T., Ge, H., & Qian, D. (2021) · BMC Bioinformatics · doi:10.1186/s12859-021-04352-9 · Cited by 75
Comprehensive prediction of robust synthetic lethality between paralog pairs in cancer cell lines
Kegel, B. D., Quinn, N., Thompson, N., Adams, D. J., & Ryan, C. J. (2021) · Cell Systems · doi:10.1016/j.cels.2021.08.006 · Cited by 67
High-throughput functional evaluation of human cancer-associated mutations using base editors
Kim, Y., Lee, S., Cho, S., Park, J., Chae, D., Park, T., Minna, J. D., & Kim, S. (2022) · Nature Biotechnology · doi:10.1038/s41587-022-01276-4 · Cited by 66
Scaling of an antibody validation procedure enables quantification of antibody performance in major research applications
Ayoubi, R., Ryan, J., Biddle, M., Alshafie, W., Fotouhi, M., Bolívar, S. G., Moleón, V. R., Eckmann, P., Worrall, D., McDowell, I., et al. (2023) · eLife · doi:10.7554/elife.91645 · Cited by 110
DTSyn: a dual-transformer-based neural network to predict synergistic drug combinations
Hu, J., Gao, J., Fang, X., Liu, Z., Wang, F., Huang, W., Wu, H., & Zhao, G. (2022) · Briefings in Bioinformatics · doi:10.1093/bib/bbac302 · Cited by 65
Structural insights into p300 regulation and acetylation-dependent genome organisation
Ibrahim, Z., Wang, T., Destaing, O., Salvi, N., Hoghoughi, N., Chabert, C., Rusu, A., Gao, J., Feletto, L., Reynoird, N., et al. (2022) · Nature Communications · doi:10.1038/s41467-022-35375-2 · Cited by 64
“5k) leads to p300 HAT gain-of-function in disease 78,79 .”
Archetype tasks link intratumoral heterogeneity to plasticity and cancer hallmarks in small cell lung cancer
Groves, S. M., Ildefonso, G. V., McAtee, C. O., Ozawa, P. M. M., Ireland, A. S., Stauffer, P. E., Wasdin, P. T., Huang, X., Qiao, Y., Lim, J. S., et al. (2022) · Cell Systems · doi:10.1016/j.cels.2022.07.006 · Cited by 66
Cell-free multi-omics analysis reveals potential biomarkers in gastrointestinal cancer patients’ blood
Tao, Y., Xing, S., Zuo, S., Bao, P., Jin, Y., Li, Y., Li, M., Wu, Y., Chen, S., Wang, X., et al. (2023) · Cell Reports Medicine · doi:10.1016/j.xcrm.2023.101281 · Cited by 69
“We performed a comprehensive assessment of multi-omics correlations across plasma data (this study), cell line data (Cancer Cell Line Encyclopedia [CCLE] 20 ), and tissue data (The Cancer Genome Atlas [TCGA] 21 ) (Figure 1C).”
MethodResultInfluential
Single-cell functional genomics reveals determinants of sensitivity and resistance to natural killer cells in blood cancers
Dufva, O., Gandolfi, S., Huuhtanen, J., Dashevsky, O., Duàn, H., Saeed, K., Klievink, J., Nygren, P. J., Bouhlal, J., Lahtela, J., et al. (2023) · Immunity · doi:10.1016/j.immuni.2023.11.008 · Cited by 77
Using DNA sequencing data to quantify T cell fraction and therapy response
Bentham, R. B., Litchfield, K., Watkins, T. B. K., Lim, E. L., Rosenthal, R., Martínez‐Ruiz, C., Hiley, C. T., Bakir, M. A., Salgado, R., Moore, D. A., et al. (2021) · Nature · doi:10.1038/s41586-021-03894-5 · Cited by 63
A genetic disorder reveals a hematopoietic stem cell regulatory network co-opted in leukemia
Voit, R. A., Tao, L., Yu, F., Cato, L. D., Cohen, B., Fleming, T. J., Antoszewski, M., Liao, X., Fiorini, C., Nandakumar, S. K., et al. (2022) · Nature Immunology · doi:10.1038/s41590-022-01370-4 · Cited by 70
Short-term molecular consequences of chromosome mis-segregation for genome stability
Garribba, L., Feudis, G. D., Martis, V., Galli, M. G., Dumont, M., Eliezer, Y., Wardenaar, R., Ippolito, M. R., Iyer, D., Tijhuis, A. E., et al. (2023) · Nature Communications · doi:10.1038/s41467-023-37095-7 · Cited by 72
ATACdb: a comprehensive human chromatin accessibility database
Wang, F., Bai, X., Wang, Y., Jiang, Y., Ai, B., Zhang, Y., Liu, Y., Xu, M., Wang, Q., Han, X., et al. (2020) · Nucleic Acids Research · doi:10.1093/nar/gkaa943 · Cited by 66

Authors: 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., Hu, K., Andreev-Drakhlin, A. Y., Kim, J., Hess, J. M., Haas, B. J., Aguet, F., Weir, B. A., Rothberg, M. V., Paolella, B. R., Lawrence, M. S., Akbani, R., Lu, Y., Tiv, H. L., Gokhale, P. C., de Weck, A., Mansour, A. A., Oh, C., Shih, J., Hadi, K., Rosen, Y., Bistline, J., Venkatesan, K., Reddy, A., Sonkin, D., Liu, M., Lehar, J., Korn, J. M., Porter, D. A., Jones, M. D., Golji, J., Caponigro, G., Taylor, J. E., Dunning, C. M., Creech, A. L., Warren, A. C., McFarland, J. M., Zamanighomi, M., Kauffmann, A., Stransky, N., Imielinski, M., Maruvka, Y. E., Cherniack, A. D., Tsherniak, A., Vazquez, F., Jaffe, J. D., Lane, A. A., Weinstock, D. M., Johannessen, C. M., Morrissey, M. P., Stegmeier, F., Schlegel, R., Hahn, W. C., Getz, G., Mills, G. B., Boehm, J. S., Golub, T. R., Garraway, L. A., & Sellers, W. R.
Affiliations: Broad Institute (US)
The University of Texas MD Anderson Cancer Center (US)
Cornell University (US)
National Cancer Institute (US)
Novartis Institutes for BioMedical Research (CH)
Dana-Farber Cancer Institute (US)
Corresponding author: Sellers, William R.
ORCID: Ghandi, Mahmoud
Huang, Franklin W.
Kryukov, Gregory V.
McDonald, E. Robert
Barretina, Jordi
Li, Haoxin
Hu, Kevin
Andreev-Drakhlin, Alexander Y.
Kim, Jaegil
Hess, Julian M.
Haas, Brian J.
Aguet, François
Rothberg, Michael V.
Paolella, Brenton R.
Lawrence, Michael S.
Akbani, Rehan
Lu, Yiling
Tiv, Hong L.
Gokhale, Prafulla C.
Oh, Coyin
Shih, Juliann
Hadi, Kevin
Rosen, Yanay
Venkatesan, Kavitha
Reddy, Anupama
Sonkin, Dmitriy
Liu, Manway
Korn, Joshua M.
Jones, Michael D.
Taylor, Jordan E.
Creech, Amanda L.
McFarland, James M.
Zamanighomi, Mahdi
Kauffmann, Audrey
Stransky, Nicolas
Imielinski, Marcin
Maruvka, Yosef E.
Cherniack, Andrew D.
Tsherniak, Aviad
Jaffe, Jacob D.
Lane, Andrew A.
Weinstock, David M.
Johannessen, Cory M.
Stegmeier, Frank
Hahn, William C.
Getz, Gad
Mills, Gordon B.
Boehm, Jesse S.
Golub, Todd R.
Garraway, Levi A.
Sellers, William R.
Venue: Nature
Publisher: Springer Science and Business Media LLC
Published: 2019-05
Volume: 569
Issue: 7757
Pages: 503-508
ISSN: 0028-0836 (ISSN-L)
1476-4687
DOI: 10.1038/s41586-019-1186-3
PMC: PMC6697103
OpenAlex: W2944727022
Semantic Scholar: b3631d8d2656c0c72ad92f3b0a77af2afde649a5
Cited by: 3,394
References: 67
Citations by year: 2026: 297 · 2025: 472 · 2024: 560 · 2023: 551 · 2022: 550 · 2021: 594 · 2020: 306 · 2019: 59
FWCI: 121.99
Type: journal article
Language: en
Open access: green
www.ncbi.nlm.nih.gov
Licence: www.springer.com/tdm (tdm)
Locations: Nature (publishedVersion)
PubMed (publishedVersion)
PubMed Central (acceptedVersion)
RePEc: Research Papers in Economics (submittedVersion)
eScholarship (California Digital Library) (submittedVersion)
Funders: European Hematology Association (TRTH56)
National Institutes of Health (5r21da025720-02)
National Institute on Drug Abuse (R21-DA025720)
National Cancer Institute (R50CA221675, P50CA217685, R01CA219943, P50CA098258, U24CA180922, U54CA224068, U24CA210949, U24CA210950, R50CA211461, R37CA225191, U01CA217842, U01CA176058, P30CA016672)
Topics: 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
Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
Keywords: Cancer Cell Line Encyclopedia, cancer cell lines, drug sensitivity, gene dependency, RNA splicing, DNA methylation, histone H3 modifications, microRNA expression, reverse phase protein array, CRISPR/Cas9 knockout, shRNA knockdown, therapeutic target, biomarkers, multi-omics integration, public data portals
MeSH terms: Gene Editing, Antineoplastic Agents, Ethnicity, Histones, Humans, Neoplasms, RNA Splicing, Biomarkers, Tumor, Drug Resistance, Neoplasm, DNA Methylation, MicroRNAs, Protein Array Analysis, Cell Line, Tumor, Molecular Targeted Therapy

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

Next-generation characterization of the Cancer Cell Line Encyclopedia

Abstract

Overview

Integrity signals

Verification

Claims the paper makes

Claims other papers make about this one

Self-check

Citations

Cites

Cited by

Related

Metadata

Record

About these checks