Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

The Cancer Cell Line Encyclopedia enables predictive modelling of anticancer drug sensitivity

An Addendum to this article was published on 17 December 2018

An Addendum to this article was published on 28 November 2012

Abstract

The systematic translation of cancer genomic data into knowledge of tumour biology and therapeutic possibilities remains challenging. Such efforts should be greatly aided by robust preclinical model systems that reflect the genomic diversity of human cancers and for which detailed genetic and pharmacological annotation is available1. Here we describe the Cancer Cell Line Encyclopedia (CCLE): a compilation of gene expression, chromosomal copy number and massively parallel sequencing data from 947 human cancer cell lines. When coupled with pharmacological profiles for 24 anticancer drugs across 479 of the cell lines, this collection allowed identification of genetic, lineage, and gene-expression-based predictors of drug sensitivity. In addition to known predictors, we found that plasma cell lineage correlated with sensitivity to IGF1 receptor inhibitors; AHR expression was associated with MEK inhibitor efficacy in NRAS-mutant lines; and SLFN11 expression predicted sensitivity to topoisomerase inhibitors. Together, our results indicate that large, annotated cell-line collections may help to enable preclinical stratification schemata for anticancer agents. The generation of genetic predictions of drug response in the preclinical setting and their incorporation into cancer clinical trial design could speed the emergence of ‘personalized’ therapeutic regimens2.

This is a preview of subscription content, access via your institution

Access options

Rent or buy this article

Prices vary by article type

from$1.95

to$39.95

Prices may be subject to local taxes which are calculated during checkout

Figure 1: The Cancer Cell Line Encyclopedia.
Figure 2: Predictive modelling of pharmacological sensitivity using CCLE genomic data.
Figure 3: AHR expression may denote a tumour dependency targeted by MEK inhibitors in NRAS-mutant cell lines.
Figure 4: Predicting sensitivity to topoisomerase I inhibitors.

Accession codes

Primary accessions

Gene Expression Omnibus

Data deposits

Data have been deposited in the Gene ExpressionOmnibus (GEO) using accession number GSE36139 and are also available at http://www.broadinstitute.org/ccle.

References

  1. Caponigro, G. & Sellers, W. R. Advances in the preclinical testing of cancer therapeutic hypotheses. Nature Rev. Drug Discov. 10, 179–187 (2011)

    Article  CAS  Google Scholar 

  2. MacConaill, L. E. & Garraway, L. A. Clinical implications of the cancer genome. J. Clin. Oncol. 28, 5219–5228 (2010)

    Article  Google Scholar 

  3. Lin, W. M. et al. Modeling genomic diversity and tumor dependency in malignant melanoma. Cancer Res. 68, 664–673 (2008)

    Article  CAS  Google Scholar 

  4. Neve, R. M. et al. A collection of breast cancer cell lines for the study of functionally distinct cancer subtypes. Cancer Cell 10, 515–527 (2006)

    Article  CAS  Google Scholar 

  5. Sos, M. L. et al. Predicting drug susceptibility of non-small cell lung cancers based on genetic lesions. J. Clin. Invest. 119, 1727–1740 (2009)

    Article  CAS  Google Scholar 

  6. Dry, J. R. et al. Transcriptional pathway signatures predict MEK addiction and response to selumetinib (AZD6244). Cancer Res. 70, 2264–2273 (2010)

    Article  CAS  Google Scholar 

  7. Garraway, L. A. et al. Integrative genomic analyses identify MITF as a lineage survival oncogene amplified in malignant melanoma. Nature 436, 117–122 (2005)

    Article  ADS  CAS  Google Scholar 

  8. Greshock, J. et al. Molecular target class is predictive of in vitro response profile. Cancer Res. 70, 3677–3686 (2010)

    Article  CAS  Google Scholar 

  9. McDermott, U. et al. Identification of genotype-correlated sensitivity to selective kinase inhibitors by using high-throughput tumor cell line profiling. Proc. Natl Acad. Sci. USA 104, 19936–19941 (2007)

    Article  ADS  CAS  Google Scholar 

  10. Solit, D. B. et al. BRAF mutation predicts sensitivity to MEK inhibition. Nature 439, 358–362 (2006)

    Article  ADS  CAS  Google Scholar 

  11. Staunton, J. E. et al. Chemosensitivity prediction by transcriptional profiling. Proc. Natl Acad. Sci. USA 98, 10787–10792 (2001)

    Article  ADS  CAS  Google Scholar 

  12. Weinstein, J. N. et al. An information-intensive approach to the molecular pharmacology of cancer. Science 275, 343–349 (1997)

    Article  CAS  Google Scholar 

  13. Thomas, R. K. et al. High-throughput oncogene mutation profiling in human cancer. Nature Genet. 39, 347–351 (2007)

    Article  CAS  Google Scholar 

  14. Beroukhim, R. et al. The landscape of somatic copy-number alteration across human cancers. Nature 463, 899–905 (2010)

    Article  ADS  CAS  Google Scholar 

  15. Ross, D. T. et al. Systematic variation in gene expression patterns in human cancer cell lines. Nature Genet. 24, 227–235 (2000)

    Article  CAS  Google Scholar 

  16. Zou, H. & Hastie, T. Regularization and variable selection via the elastic net. J. R. Stat. Soc. B 67, 301–320 (2005)

    Article  MathSciNet  Google Scholar 

  17. Konecny, G. E. et al. Activity of the dual kinase inhibitor lapatinib (GW572016) against HER-2-overexpressing and trastuzumab-treated breast cancer cells. Cancer Res. 66, 1630–1639 (2006)

    Article  CAS  Google Scholar 

  18. Tsai, J. et al. Discovery of a selective inhibitor of oncogenic B-Raf kinase with potent antimelanoma activity. Proc. Natl Acad. Sci. USA 105, 3041–3046 (2008)

    Article  ADS  CAS  Google Scholar 

  19. Zou, H. Y. et al. An orally available small-molecule inhibitor of c-Met, PF-2341066, exhibits cytoreductive antitumor efficacy through antiproliferative and antiangiogenic mechanisms. Cancer Res. 67, 4408–4417 (2007)

    Article  CAS  Google Scholar 

  20. Müller, C. R. et al. Potential for treatment of liposarcomas with the MDM2 antagonist Nutlin-3A. Int. J. Cancer 121, 199–205 (2007)

    Article  Google Scholar 

  21. Nishio, M. et al. Serum heparan sulfate concentration is correlated with the failure of epidermal growth factor receptor tyrosine kinase inhibitor treatment in patients with lung adenocarcinoma. J. Thorac. Oncol. 6, 1889–1894 (2011)

    Article  Google Scholar 

  22. Guo, W. et al. Formation of 17-allylamino-demethoxygeldanamycin (17-AAG) hydroquinone by NAD(P)H:quinone oxidoreductase 1: role of 17-AAG hydroquinone in heat shock protein 90 inhibition. Cancer Res. 65, 10006–10015 (2005)

    Article  CAS  Google Scholar 

  23. Kelland, L. R., Sharp, S. Y., Rogers, P. M., Myers, T. G. & Workman, P. DT-Diaphorase expression and tumor cell sensitivity to 17-allylamino, 17-demethoxygeldanamycin, an inhibitor of heat shock protein 90. J. Natl Cancer Inst. 91, 1940–1949 (1999)

    Article  CAS  Google Scholar 

  24. Moreau, P. et al. Phase I study of the anti insulin-like growth factor 1 receptor (IGF-1R) monoclonal antibody, AVE1642, as single agent and in combination with bortezomib in patients with relapsed multiple myeloma. Leukemia 25, 872–874 (2011)

    Article  CAS  Google Scholar 

  25. Reiners, J. J., Jr, Lee, J. Y., Clift, R. E., Dudley, D. T. & Myrand, S. P. PD98059 is an equipotent antagonist of the aryl hydrocarbon receptor and inhibitor of mitogen-activated protein kinase kinase. Mol. Pharmacol. 53, 438–445 (1998)

    Article  CAS  Google Scholar 

  26. Wagner, L. M. et al. Temozolomide and intravenous irinotecan for treatment of advanced Ewing sarcoma. Pediatr. Blood Cancer 48, 132–139 (2007)

    Article  Google Scholar 

  27. Garnett, M. J. et al. Systematic identification of genomic markers of drug sensitivity in cancer cells. Nature http://dx.doi.org/10.1038/nature11005 (this issue)

Download references

Acknowledgements

We thank the staff of the Biological Samples Platform, the Genetic Analysis Platform and the Sequencing Platform at the Broad Institute. We thank S. Banerji, J. Che, C .M. Johannessen, A. Su and N. Wagle for advice and discussion. We are grateful for the technical assistance and support of G. Bonamy, R. Brusch III, E. Gelfand, K. Gravelin, T. Huynh, S. Kehoe, K. Matthews, J. Nedzel, L. Niu, R. Pinchback, D. Roby, J. Slind, T. R. Smith, L. Tan, V. Trinh, C. Vickers, G. Yang, Y. Yao and X. Zhang. The Cancer Cell Line Encyclopedia project was enabled by a grant from the Novartis Institutes for Biomedical Research. Additional funding support was provided by the National Cancer Institute (M.M., L.A.G.), the Starr Cancer Consortium (M.F.B., L.A.G.), and the NIH Director’s New Innovator Award (L.A.G.).

Author information

Authors and Affiliations

Authors

Contributions

For the work described herein, J.B. and G.C. were the lead research scientists; N.S., K.V. and A.M.M. were the lead computational biologists; M.P.M., W.R.S., R.S. and L.A.G. were the senior authors. J.B., G.C., S.K., P.M., J.M., J.T., A.S., N.L. and K.A. performed cell-line procural and processing; P.M. and K.A. performed or directed nucleic acid extraction and quality control; S.G., W.W. and S.B.G. performed or directed genomic data generation; C.J.W., F.A.M., E.B.-F., I.H.E., P.A., M.d.S., K.J. and V.E.M. performed pharmacological data generation; N.S., K.V., G.V.K., A.R., M.F.B., J.C., G.K.Y., M.D.J., T.L., M.R. and G.G. contributed to software development; N.S., K.V., A.A.M., J.L., G.V.K., D.S., A.R., M.L., M.F.B., A.K., P.R., J.C., G.K.Y., J.Y., M.D.J., L.W., C.H., E.P., J.P.M., V.C. and M.P.M. performed computational biology and bioinformatics analysis; J.B., G.C., N.S., L.M., J.E.M., J.J.-V., M.P.M., W.R.S., R.S. and L.A.G. performed biological analysis and interpretation; N.S., K.V., A.A.M., J.L., A.R., M.L., L.M., A.K., J.J.-V., J.C., G.K.Y. and J.Y. prepared figures and tables for the main text and Supplementary Information; J.B., G.C., N.S., K.V., A.A.M., J.L., G.V.K., J.J.-V., M.P.M. and L.A.G. wrote and edited the main text and Supplementary Information; J.B., G.C., N.S., K.V., S.K., C.J.W., J.L., S.M., C.S., R.C.O., T.L., L.McC., W.W., M.R., N.L., S.B.G., K.A. and V.C. performed project management; J.P.M., V.E.M., B.L.W., J.P., M.W., P.F., J.L.H., M.M. and T.R.G. contributed project oversight and advisory roles; and M.P.M., W.R.S., R.S. and L.A.G. provided overall project leadership.

Corresponding authors

Correspondence to Robert Schlegel or Levi A. Garraway.

Ethics declarations

Competing interests

Multiple authors are employees of Novartis, Inc., as noted in the affiliations. T.R.G., M.M. and L.A.G. are consultants for and equity holders in Foundation Medicine, Inc. M.M. and L.A.G. are consultants for and receive sponsored research from Novartis, Inc.

Supplementary information

Supplementary Information 1

This file contains Supplementary Figures 1-15 and legends for Supplementary Tables 1-12 (see separate file for Supplementary Tables). (PDF 5034 kb)

Supplementary Information 2

This file contains Supplementary Methods and additional references. (PDF 450 kb)

Supplementary Tables

This file contains Supplementary Tables 1-12 – see Supplementary Information 1 for legends. (XLS 5774 kb)

PowerPoint slides

Rights and permissions

Reprints and permissions

About this article

Cite this article

Barretina, J., Caponigro, G., Stransky, N. et al. The Cancer Cell Line Encyclopedia enables predictive modelling of anticancer drug sensitivity. Nature 483, 603–607 (2012). https://doi.org/10.1038/nature11003

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/nature11003

This article is cited by

Comments

By submitting a comment you agree to abide by our Terms and Community Guidelines. If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.

Search

Quick links

Nature Briefing: Cancer

Sign up for the Nature Briefing: Cancer newsletter — what matters in cancer research, free to your inbox weekly.

Get what matters in cancer research, free to your inbox weekly. Sign up for Nature Briefing: Cancer