Next-generation characterization of the Cancer Cell Line Encyclopedia
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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.
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Mining correlations between cytotoxicity of 4,518 clinical and preclinical small molecules and E3 ligase expression across hundreds of cancer cell lines identified CR8, a CDK inhibitor, as a molecular glue degrader.
“Through systematic mining of databases for correlations between the cytotoxicity of 4,518 clinical and pre-clinical small molecules and E3 ligase expression levels across hundreds of human cancer cell lines,3–5 we identified CR8, a cyclin-dependent kinase (CDK) inhibitor,6 as a compound that acts as a molecular glue degrader.”
From The CDK inhibitor CR8 acts as a molecular glue degrader that depletes cyclin K (2020) · cites this paper as [5]
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“…datasets from three different efforts to characterize panels of cancer cell lines through omics profiling (Cancer Cell Line Encyclopedia (CCLE)) 4,5 , drug screening (profiling relative inhibition simultaneously in mixtures (PRISM)) 6–8 and genetic perturbations (Project Achilles) 9,10 to…”
Self-citationThe landscape of cancer cell line metabolism
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The evolving metabolic landscape of chromatin biology and epigenetics
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Pan-cancer single-cell RNA-seq identifies recurring programs of cellular heterogeneity
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Necroptosis and tumor progression
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A metastasis map of human cancer cell lines
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Self-citationThe CDK inhibitor CR8 acts as a molecular glue degrader that depletes cyclin K
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Mammalian SWI/SNF Chromatin Remodeling Complexes: Emerging Mechanisms and Therapeutic Strategies
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ecDNA hubs drive cooperative intermolecular oncogene expression
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Molecular Glues for Targeted Protein Degradation: From Serendipity to Rational Discovery
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Epigenetic Silencing of CDR1as Drives IGF2BP3-Mediated Melanoma Invasion and Metastasis
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Massively parallel assessment of human variants with base editor screens
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Cancer Cell Lines Are Useful Model Systems for Medical Research
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Machine learning for multi-omics data integration in cancer
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“20.0) by using a common CCLE dataset.”
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A non-canonical tricarboxylic acid cycle underlies cellular identity
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Mitochondrial RNA modifications shape metabolic plasticity in metastasis
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Empowering biomedical discovery with AI agents
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Aneuploidy renders cancer cells vulnerable to mitotic checkpoint inhibition
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Self-citationNeurons release serine to support mRNA translation in pancreatic cancer
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High-speed fluorescence image–enabled cell sorting
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DNA Sensing in Mismatch Repair-Deficient Tumor Cells Is Essential for Anti-tumor Immunity
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Overcoming challenges and dogmas to understand the functions of pseudogenes
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Distinct Classes of Complex Structural Variation Uncovered across Thousands of Cancer Genome Graphs
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Self-citationTranscriptional landscape and clinical utility of enhancer RNAs for eRNA-targeted therapy in cancer
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Self-citationDeep learning-based approaches for multi-omics data integration and analysis
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Big data in basic and translational cancer research
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CCNE1 amplification is synthetic lethal with PKMYT1 kinase inhibition
Gallo, D., Young, J. T. F., Fourtounis, J., Martino, G., Álvarez-Quilón, A., Bernier, C., Duffy, N. M., Papp, R., Roulston, A., Stocco, R., et al. (2022) · Nature · doi:10.1038/s41586-022-04638-9 · Cited by 246 · see citing works
Deep learning for drug response prediction in cancer
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Recent trends in cancer therapy: A review on the current state of gene delivery
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Pan-cancer proteomic map of 949 human cell lines
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“To circumvent the limitations of using patient tissue samples for this type of approach, biomarkers of cancer vulnerabilities have been analyzed using cancer cell lines, together with deep molecular characterization, functional genetic and pharmacological screens (Ghandi et al., 2019; Iorio et al., 2016; Tsherniak et…”
BackgroundMethodMulti-omics data integration considerations and study design for biological systems and disease
Graw, S., Chappell, K., Washam, C. L., Gies, A. J., Bird, J. T., Robeson, M. S., & Byrum, S. D. (2020) · Molecular Omics · doi:10.1039/d0mo00041h · Cited by 223 · see citing works
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The PP2A-Integrator-CDK9 axis fine-tunes transcription and can be targeted therapeutically in cancer
Vervoort, S. J., Welsh, S. A., Devlin, J. R., Barbieri, E., Knight, D. A., Offley, S. R., Bjelosevic, S., Costacurta, M., Todorovski, I., Kearney, C. J., et al. (2021) · Cell · doi:10.1016/j.cell.2021.04.022 · Cited by 206 · see citing works
Ubiquitin ligases: guardians of mammalian development
Walma, D. A. C., Chen, Z., Bullock, A. N., & Yamada, K. M. (2022) · Nature Reviews Molecular Cell Biology · doi:10.1038/s41580-021-00448-5 · Cited by 212 · see citing works
Synthetic Lethality in Cancer Therapeutics: The Next Generation
Setton, J. S., Zinda, M. J., Riaz, N., Durocher, D., Zimmermann, M., Koehler, M. T., Reis-Filho, J. S., & Powell, S. N. (2021) · Cancer Discovery · doi:10.1158/2159-8290.cd-20-1503 · Cited by 211 · see citing works
Tumour-selective activity of RAS-GTP inhibition in pancreatic cancer
Wasko, U. N., Jiang, J., Dalton, T. C., Curiel-Garcia, A., Edwards, A. C., Wang, Y., Lee, B., Orlen, M., Tian, S., Stalnecker, C. A., et al. (2024) · Nature · doi:10.1038/s41586-024-07379-z · Cited by 209 · see citing works
“Gene mutation, gene expression and lineage data were downloaded from the 22Q4 release of the DepMap Data Portal 45 .”
MethodArtificial Intelligence (AI)-Based Systems Biology Approaches in Multi-Omics Data Analysis of Cancer
Biswas, N., & Chakrabarti, S. (2020) · Frontiers in Oncology · doi:10.3389/fonc.2020.588221 · Cited by 217 · see citing works
Luna, A., Elloumi, F., Varma, S., Wang, Y., Rajapakse, V. N., Aladjem, M. I., Robert, J., Sander, C., Pommier, Y. G., & Reinhold, W. C. (2020) · Nucleic Acids Research · doi:10.1093/nar/gkaa968 · Cited by 209 · see citing works
Shared Immunogenic Poly-Epitope Frameshift Mutations in Microsatellite Unstable Tumors
Roudko, V., Bozkus, C. C., Orfanelli, T., McClain, C. B., Carr, C. E., O’Donnell, T. J., Chakraborty, L., Samstein, R. M., Huang, K., Blank, S. V., et al. (2020) · Cell · doi:10.1016/j.cell.2020.11.004 · Cited by 212 · see citing works
Repeat expansions confer WRN dependence in microsatellite-unstable cancers
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Massively parallel pooled screening reveals genomic determinants of nanoparticle delivery
Boehnke, N., Straehla, J. P., Safford, H. C., Kocak, M., Rees, M. G., Ronan, M. M., Rosenberg, D., Adelmann, C. H., Chivukula, R. R., Nabar, N., et al. (2022) · Science · doi:10.1126/science.abm5551 · Cited by 204 · see citing works
“EGFR-targeting compounds identified relevant biomarkers with high confidence Using univariate analysis for all CCLE features, we identified EGFR gene expression and protein abundance as the two most significantly correlated hits (q = 4 × 10−100 and q = 4 × 10−76, respectively) with antibody to EGFR, but much less sign…”
MethodInfluentialSelf-citationGenomic–transcriptomic evolution in lung cancer and metastasis
Martínez‐Ruiz, C., Black, J. R., Puttick, C., Hill, M. S., Demeulemeester, J., Cadieux, E. L., Thol, K., Jones, T. P., Veeriah, S., Naceur‐Lombardelli, C., et al. (2023) · Nature · doi:10.1038/s41586-023-05706-4 · Cited by 203 · see citing works
Reexamining assumptions about miRNA-guided gene silencing
Kilikevicius, A., Meister, G., & Corey, D. R. (2022) · Nucleic Acids Research · doi:10.1093/nar/gkab1256 · Cited by 194 · see citing works
“A recent study of over 1000 cell lines (100) reported that HCT116 cells expressed miRNAs at levels that are typical.”
Zhao, W., Zhang, S., Zhu, Y., Xi, X., Bao, P., Ma, Z.-Y., Kapral, T. H., Chen, S., Žagrović, B., Yang, Y., et al. (2021) · Nucleic Acids Research · doi:10.1093/nar/gkab702 · Cited by 202 · see citing works
Landscape of helper and regulatory antitumour CD4+ T cells in melanoma
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Self-citationA 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 194 · see citing works
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 · see citing works
Self-citationExpanding control of the tumor cell cycle with a CDK2/4/6 inhibitor
Freeman‐Cook, K. D., Hoffman, R. L., Miller, N. L. G., Almaden, J., Chionis, J., Zhang, Q., Eisele, K. J., Liu, C., Zhang, C., Huser, N. H., et al. (2021) · Cancer Cell · doi:10.1016/j.ccell.2021.08.009 · Cited by 191 · see citing works
Immune heterogeneity in small-cell lung cancer and vulnerability to immune checkpoint blockade
Nabet, B. Y., Hamidi, H., Lee, M. C., Banchereau, R., Morris, S., Adler, L., Gayevskiy, V., Elhossiny, A. M., Srivastava, M. K., Patil, N. S., et al. (2024) · Cancer Cell · doi:10.1016/j.ccell.2024.01.010 · Cited by 196 · see citing works
Immunogenomic Landscape of Hematological Malignancies
Dufva, O., Pölönen, P., Brück, O. E., Keränen, M. A. I., Klievink, J., Mehtonen, J., Huuhtanen, J., Kumar, A., Malani, D., Siitonen, S. M., et al. (2020) · Cancer Cell · doi:10.1016/j.ccell.2020.06.002 · Cited by 188 · see citing works
A unified model for the G1/S cell cycle transition
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Pan-cancer analysis identifies tumor-specific antigens derived from transposable elements
Shah, N. M., Jang, H. J., Liang, Y., Maeng, J. H., Tzeng, S.-C., Wu, A., Basri, N. L., Qu, X., Fan, C., Li, A., et al. (2023) · Nature Genetics · doi:10.1038/s41588-023-01349-3 · Cited by 175 · see citing works
Aneuploidy-Driven Genome Instability Triggers Resistance to Chemotherapy
Ippolito, M. R., Martis, V., Martin, S., Tijhuis, A. E., Hong, C., Wardenaar, R., Dumont, M., Zerbib, J., Spierings, D. C. J., Fachinetti, D., et al. (2020) · Developmental Cell · doi:10.1016/j.devcel.2021.07.006 · Cited by 191 · see citing works
Targeting pan-essential genes in cancer: Challenges and opportunities
Chang, L., Ruiz, P. A., Ito, T., & Sellers, W. R. (2021) · Cancer Cell · doi:10.1016/j.ccell.2020.12.008 · Cited by 183 · see citing works
Self-citationRecent advances in the developmental origin of neuroblastoma: an overview
Ponzoni, M., Bachetti, T., Corrias, M. V., Brignole, C., Pastorino, F., Calarco, E., Bensa, V., Giusto, E., Ceccherini, I., & Perri, P. (2022) · Journal of Experimental & Clinical Cancer Research · doi:10.1186/s13046-022-02281-w · Cited by 186 · see citing works
“Knockdown profiles of 17 NB cell lines were compared with the profiles of 546 other cancer cells [109] to identify subtype-independent essential genes.”
ResultACSL4 and polyunsaturated lipids support metastatic extravasation and colonization
Wang, Y., Hu, M., Cao, J., Wang, F., Han, J. R., Wu, T. W., Li, L., Yu, J., Fan, Y., Xie, G., et al. (2024) · Cell · doi:10.1016/j.cell.2024.10.047 · Cited by 191 · see citing works
Wu, Y., & Xie, L. (2025) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2024.12.030 · Cited by 205 · see citing works
Bruno, P. M., Lu, M., Dennis, K. A., Inam, H., Moore, C. J., Sheehe, J., Elledge, S. J., Hemann, M. T., & Pritchard, J. R. (2020) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1921649117 · Cited by 175 · see citing works
From Donor to the Lab: A Fascinating Journey of Primary Cell Lines
Richter, M., Piwocka, O., Musielak, M., Piotrowski, I., Suchorska, W. M., & Trzeciak, T. (2021) · Frontiers in Cell and Developmental Biology · doi:10.3389/fcell.2021.711381 · Cited by 174 · see citing works
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Undisclosed, unmet and neglected challenges in multi-omics studies
Tarazona, S., Arzalluz-Luque, Á., & Conesa, A. (2021) · Nature Computational Science · doi:10.1038/s43588-021-00086-z · Cited by 177 · see citing works
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Santana-Codina, N., del Rey, M. Q., Kapner, K. S., Zhang, H., Gikandi, A., Malcolm, C., Poupault, C. L., Kuljanin, M., John, K. M., Biancur, D. E., et al. (2022) · Cancer Discovery · doi:10.1158/2159-8290.cd-22-0043 · Cited by 179 · see citing works
“Across multiple additional PDAC cell lines, we noted variability in the degree of proliferative block following NCOA4 depletion, as predicted by dependency scores in DepMap (Fig.”
InfluentialEP300 Selectively Controls the Enhancer Landscape of MYCN -Amplified Neuroblastoma
Durbin, A. D., Wang, T., Wimalasena, V. K., Zimmerman, M. W., Li, D., Dharia, N. V., Mariani, L., Shendy, N. A. M., Nance, S., Patel, A. G., et al. (2021) · Cancer Discovery · doi:10.1158/2159-8290.cd-21-0385 · Cited by 174 · see citing works
“CCLE analyses of RNA expression and proteomic expression were performed using the 20Q1 data release (67, 68).”
Comprehensive characterization of circular RNAs in ~ 1000 human cancer cell lines
Ruan, H., Xiang, Y., Ko, J., Li, S., Jing, Y., Zhu, X., Ye, Y., Zhang, Z., Mills, T., Feng, J. Q., et al. (2019) · Genome Medicine · doi:10.1186/s13073-019-0663-5 · Cited by 168 · see citing works
Self-citationRewiring cancer drivers to activate apoptosis
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New insights into abasic site repair and tolerance
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BackgroundMethodSelf-citation
- 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
- Published
- 2019-05
- Volume
- 569
- Issue
- 7757
- Pages
- 503-508
- ISSN
- 0028-0836 (ISSN-L)
- 1476-4687
- 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 · 2018: 3
- 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
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