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

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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

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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.

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A bioinformatics approach to identify novel long, non-coding RNAs in breast cancer cell lines from an existing RNA-sequencing dataset
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LncAS2Cancer: a comprehensive database for alternative splicing of lncRNAs across human cancers
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Development and Validation of an RNA Sequencing Assay for Gene Fusion Detection in Formalin-Fixed, Paraffin-Embedded Tumors
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YY1 regulated transcription‐based stratification of gastric tumors and identification of potential therapeutic candidates
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Comprehensive CRISPR-Cas9 screens identify genetic determinants of drug responsiveness in multiple myeloma
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Comprehensive Pan-Cancer Analysis of Heat Shock Protein 110, 90, 70, and 60 Families
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Identification of a DNA Methylation-Driven Genes-Based Prognostic Model and Drug Targets in Breast Cancer: In silico Screening of Therapeutic Compounds and in vitro Characterization
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Are CRISPR Screens Providing the Next Generation of Therapeutic Targets?
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Self-citation
Epithelial-Mesenchymal Transition Induces GSDME Transcriptional Activation for Inflammatory Pyroptosis
Jia, C., Zhang, Z., Tang, J., Cai, M., Zang, J., Shi, K., Sun, Y., Wu, J., Shi, H., Shi, W., et al. (2021) · Frontiers in Cell and Developmental Biology · doi:10.3389/fcell.2021.781365 · Cited by 19
“(G) Scatterplots with linear regression line and shaded 95% confidence region for correlation estimation ofGSDME transcript expression and EMT ssGSEA scores in CCLE lung cancer cell lines.”
MethodInfluential
Identification of Pan-Cancer Biomarkers Based on the Gene Expression Profiles of Cancer Cell Lines
Ding, S., Li, H., Zhang, Y., Zhou, X., Feng, K., Li, Z., Chen, L., Huang, T., & Cai, Y. (2021) · Frontiers in Cell and Developmental Biology · doi:10.3389/fcell.2021.781285 · Cited by 20
“We applied several advanced computational methods to analyze the Cancer Cell Line Encyclopedia (CCLE) gene expression profiles which included 988 cell lines from 20 cancer types.”
MethodInfluentialSelf-citation
BRD4 inhibition induces synthetic lethality in ARID2-deficient hepatocellular carcinoma by increasing DNA damage
He, D.-D., Shang, X.-Y., Wang, N., Wang, G.-X., He, K.-Y., Wang, L., & Han, Z.-G. (2022) · Oncogene · doi:10.1038/s41388-022-02176-2 · Cited by 21
“Additionally, these cell lines are confirmed as ARID2 wildtype ones without somatic mutations according to Cancer Cell Line Encyclopedia [25].”
Background
RASGRF1 Fusions Activate Oncogenic RAS Signaling and Confer Sensitivity to MEK Inhibition
Hunihan, L., Zhao, D., Lazowski, H., Li, M., Qian, Y., Abriola, L., Surovtseva, Y. V., Muthusamy, V., Tanoue, L. T., Rothberg, B. E. G., et al. (2022) · Clinical Cancer Research · doi:10.1158/1078-0432.ccr-21-4291 · Cited by 19
Transcriptome‑based drug repositioning identifies TPCA‑1 as a potential selective inhibitor of esophagus squamous carcinoma cell viability
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High-throughput profiling of histone post-translational modifications and chromatin modifying proteins by reverse phase protein array
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Self-citation
Metabolic drug survey highlights cancer cell dependencies and vulnerabilities
Pemovska, T., Bigenzahn, J. W., Srndic, I., Lercher, A., Bergthaler, A., César‐Razquin, A., Kartnig, F., Kornauth, C., Valent, P., Staber, P. B., et al. (2021) · Nature Communications · doi:10.1038/s41467-021-27329-x · Cited by 19
CTPC, a combined transcriptome data set of human prostate cancer cell lines
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“Korkola's group were also included because both contain H660 samples.(2,7)”
Background
GBP2 promotes clear cell renal cell carcinoma progression through immune infiltration and regulation of PD‑L1 expression via STAT1 signaling
Ye, S., Li, S., Qin, L., Zheng, W., Liu, B., Li, X., Ren, Z., Zhao, H., Hu, X., Ye, N., et al. (2023) · Oncology Reports · doi:10.3892/or.2023.8486 · Cited by 19
Comparative transcriptome characterization of esophageal squamous cell carcinoma and adenocarcinoma
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“In this study, we collected and analyzed 551 transcriptomes from both normal and cancerous esophageal tissues, including 316 normal tissues,104 EAC and 131 ESCC from the Genotype-Tissue Expression (GTEx) [27], the Sequence Read Archive of National Center for Biotechnology Information (NCBI SRA) [28], the Broad Institu…”
MethodInfluential
Pan-Cancer Proteomics Analysis to Identify Tumor-Enriched and Highly Expressed Cell Surface Antigens as Potential Targets for Cancer Therapeutics
Wang, J., Yu, W., D’Anna, R. K., Przybyła, A., Wilson, M., Sung, M. S., Bullen, J., Hurt, E. M., D’Angelo, G., Sidders, B. S., et al. (2023) · Molecular & Cellular Proteomics · doi:10.1016/j.mcpro.2023.100626 · Cited by 17
Clinically conserved genomic subtypes of gastric adenocarcinoma
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“6A) [57].”
BPTF Drives Gastric Cancer Resistance to EGFR Inhibitor by Epigenetically Regulating the C‐MYC/PLCG1/Perk Axis
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UCHL1 contributes to insensitivity to endocrine therapy in triple-negative breast cancer by deubiquitinating and stabilizing KLF5
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“Using data from Cancer Cell Line Encyclopedia (CCLE) database (https:// sites.broadinstitute.org/ccle/datasets) [30], we confirmed positive expression of BAP1, USP3 and UCHL1 in two TNBC representative cell lines, MDA-MB-468 and SUM149 (Supplementary Fig.”
Method
Antibody–Drug Conjugates Targeting the EGFR Ligand Epiregulin Elicit Robust Antitumor Activity in Colorectal Cancer
Jacob, J., Anami, Y., High, P. C., Liang, Z., Subramanian, S., Ghosh, S. C., AghaAmiri, S., Guernsey-Biddle, C., Tran, H., Rowe, J. H., et al. (2024) · Cancer Research · doi:10.1158/0008-5472.can-24-0798 · Cited by 20
Visualizing Energy Landscapes through Manifold Learning
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Adaptation to spindle assembly checkpoint inhibition through the selection of specific aneuploidiesPreprint
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Sub-cellular Imaging of the Entire Protein-Coding Human Transcriptome (18933-plex) on FFPE Tissue Using Spatial Molecular ImagingPreprint
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A Deep Neural Network for Predicting Synergistic Drug Combinations on Cancer
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DARS2 Promotes Bladder Cancer Progression by Enhancing PINK1-Mediated Mitophagy
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CrossAttOmics: multiomics data integration with cross-attention
Beaude, A., Augé, F., Zehraoui, F., & Hanczar, B. (2025) · Bioinformatics · doi:10.1093/bioinformatics/btaf302 · Cited by 18
“However, the performances were still lower than AttOmics or MLP on single omics.”
Influential
Synergistic targeting of cancer cells through simultaneous inhibition of key metabolic enzymes
Dreute, J., Stengel, J., Becher, J., van den Borre, D. R., Pfisterer, M., Bartkuhn, M., Beutgen, V. M., Ndreshkjana, B., Gärtner, U., Graumann, J., et al. (2025) · Cell Death and Differentiation · doi:10.1038/s41418-025-01532-5 · Cited by 21
“To identify pathways associated with the early response of KRAS G12V /MYC cells to combination treatment, gene set enrichment analysis (GSEA) using the KEGG database was performed.”
PARP Inhibitors in Prostate and Urothelial Cancers
Garje, R., Vaddepally, R. K., & Zakharia, Y. (2020) · Frontiers in Oncology · doi:10.3389/fonc.2020.00114 · Cited by 18
Aggressive early-stage lung adenocarcinoma is characterized by epithelial cell plasticity with acquirement of stem-like traits and immune evasion phenotype
Melocchi, V., Dama, E., Mazzarelli, F., Cuttano, R., Colangelo, T., Candia, L. D., Lugli, E., Veronesi, G., Pelosi, G., Ferretti, G. M., et al. (2021) · Oncogene · doi:10.1038/s41388-021-01909-z · Cited by 18
Mutation Analysis of Radioresistant Early-Stage Cervical Cancer
Oike, T., Sekiguchi, Y., Yoshimoto, Y., Oike, T., Ando, K., Gu, W., Sasaki, Y., Tokino, T., Iwase, A., & Ohno, T. (2021) · International Journal of Molecular Sciences · doi:10.3390/ijms23010051 · Cited by 18
“RNAseq gene expression data from 1019 cell lines were download from CCLE [42].”
MethodInfluential
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Karaoğlanoğlu, F., Chauve, C., & Hach, F. (2022) · BMC Genomics · doi:10.1186/s12864-022-08339-5 · Cited by 18
“The Depmap database [43] contains an inversion event on top of ARHGAP15 and GTDC1, which supports this fusion event, however to the best of our knowledge, this fusion has not been validated.”
Background
Computational Drug Repurposing Based on a Recommendation System and Drug–Drug Functional Pathway Similarity
Shao, M., Jiang, L., Meng, Z., & Xu, J. (2022) · Molecules · doi:10.3390/molecules27041404 · Cited by 18
“csv”, which is available on the CCLE website [44] and includes IC50 data for 24 anti-cancer drugs.”
BackgroundMethod
PersonaDrive: a method for the identification and prioritization of personalized cancer drivers
Erten, C., Houdjedj, A., Kazan, H., & Bahmed, A. A. T. (2022) · Bioinformatics · doi:10.1093/bioinformatics/btac329 · Cited by 18
A nuclear cAMP microdomain suppresses tumor growth by Hippo pathway inactivation
Drozdz, M. M., Doane, A. S., Alkallas, R., Desman, G. T., Bareja, R., Reilly, M., Bang, J., Yusupova, M., You, J., Eraslan, Z., et al. (2022) · Cell Reports · doi:10.1016/j.celrep.2022.111412 · Cited by 18
“…and comparison of YAP and NLS-sAC activities in human melanoma cell lines Melanoma cell line data from the Cancer Cell Line Encyclopedia (CCLE)(Ghandi et al., 2019) were acquired from the DepMap data portal(Tsherniak et al., 2017) (release 20Q1; file names: sample_info.csv,…”
MethodInfluential
N6-methyladenosine-associated prognostic pseudogenes contribute to predicting immunotherapy benefits and therapeutic agents in head and neck squamous cell carcinoma
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Utilization of Cancer Cell Line Screening to Elucidate the Anticancer Activity and Biological Pathways Related to the Ruthenium-Based Therapeutic BOLD-100
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“Furthermore, the abundance of cell line molecular data available in public databases such as the Cancer Cell Line Encyclopedia (CCLE) allows for pharmacogenomic analyses to uncover molecular determinants of drug sensitivity [21].”
Background
Prognosis and personalized treatment prediction in lung adenocarcinoma: An in silico and in vitro strategy adopting cuproptosis related lncRNA towards precision oncology
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“org/ ccle) [31] and cBioPortal [32] were used as described in Additional file 1: Methods.”
Method
Nonmonotone invasion landscape by noise-aware control of metastasis activator levels
Wan, Y., Cohen, J., Szenk, M., Farquhar, K. S., Coraci, D., Krzysztoń, R., Azukas, J., Nest, N. V., Smashnov, A., Chern, Y., et al. (2023) · Nature Chemical Biology · doi:10.1038/s41589-023-01344-z · Cited by 20

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

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Next-generation characterization of the Cancer Cell Line Encyclopedia

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