• 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.

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Accessions

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. 1.

    & Advances in the preclinical testing of cancer therapeutic hypotheses. Nature Rev. Drug Discov. 10, 179–187 (2011)

  2. 2.

    & Clinical implications of the cancer genome. J. Clin. Oncol. 28, 5219–5228 (2010)

  3. 3.

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

  4. 4.

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

  5. 5.

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

  6. 6.

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

  7. 7.

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

  8. 8.

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

  9. 9.

    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)

  10. 10.

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

  11. 11.

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

  12. 12.

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

  13. 13.

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

  14. 14.

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

  15. 15.

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

  16. 16.

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

  17. 17.

    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)

  18. 18.

    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)

  19. 19.

    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)

  20. 20.

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

  21. 21.

    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)

  22. 22.

    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)

  23. 23.

    , , , & 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)

  24. 24.

    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)

  25. 25.

    , , , & PD98059 is an equipotent antagonist of the aryl hydrocarbon receptor and inhibitor of mitogen-activated protein kinase kinase. Mol. Pharmacol. 53, 438–445 (1998)

  26. 26.

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

  27. 27.

    et al. Systematic identification of genomic markers of drug sensitivity in cancer cells. Nature (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

Author notes

    • Jordi Barretina
    • , Adam A. Margolin
    •  & Michael F. Berger

    Present addresses: Novartis Institutes for Biomedical Research, Cambridge, Massachusetts 02139, USA (J.B.); Sage Bionetworks, 1100 Fairview Ave. N., Seattle, Washington 98109, USA (A.A.M.); Department of Pathology, Memorial Sloan-Kettering Cancer Center, New York, New York 10065, USA (M.F.B.).

    • Jordi Barretina
    • , Giordano Caponigro
    • , Nicolas Stransky
    • , Kavitha Venkatesan
    • , Adam A. Margolin
    • , Michael P. Morrissey
    • , William R. Sellers
    • , Robert Schlegel
    •  & Levi A. Garraway

    These authors contributed equally to this work.

Affiliations

  1. The Broad Institute of Harvard and MIT, Cambridge, Massachusetts 02142, USA

    • Jordi Barretina
    • , Nicolas Stransky
    • , Adam A. Margolin
    • , Gregory V. Kryukov
    • , Lauren Murray
    • , Michael F. Berger
    • , Paula Morais
    • , Adam Korejwa
    • , Judit Jané-Valbuena
    • , Supriya Gupta
    • , Scott Mahan
    • , Carrie Sougnez
    • , Robert C. Onofrio
    • , Ted Liefeld
    • , Wendy Winckler
    • , Michael Reich
    • , Jill P. Mesirov
    • , Stacey B. Gabriel
    • , Gad Getz
    • , Kristin Ardlie
    • , Matthew Meyerson
    • , Todd R. Golub
    •  & Levi A. Garraway
  2. Department of Medical Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, Massachusetts 02115, USA

    • Jordi Barretina
    • , Judit Jané-Valbuena
    • , Matthew Meyerson
    •  & Levi A. Garraway
  3. Center for Cancer Genome Discovery, Dana-Farber Cancer Institute, Harvard Medical School, Boston, Massachusetts 02115, USA

    • Jordi Barretina
    • , Charles Hatton
    • , Emanuele Palescandolo
    • , Laura MacConaill
    • , Matthew Meyerson
    • , Todd R. Golub
    •  & Levi A. Garraway
  4. Novartis Institutes for Biomedical Research, Cambridge, Massachusetts 02139, USA

    • Giordano Caponigro
    • , Kavitha Venkatesan
    • , Christopher J. Wilson
    • , Joseph Lehár
    • , Dmitriy Sonkin
    • , Anupama Reddy
    • , Manway Liu
    • , John E. Monahan
    • , Jodi Meltzer
    • , Felipa A. Mapa
    • , Eva Bric-Furlong
    • , Pichai Raman
    • , Peter Aspesi
    • , Melanie de Silva
    • , Kalpana Jagtap
    • , Michael D. Jones
    • , Li Wang
    • , Vic E. Myer
    • , Barbara L. Weber
    • , Jeff Porter
    • , Markus Warmuth
    • , Peter Finan
    • , Michael P. Morrissey
    • , William R. Sellers
    •  & Robert Schlegel
  5. Genomics Institute of the Novartis Research Foundation, San Diego, California 92121, USA

    • Sungjoon Kim
    • , Joseph Thibault
    • , Aaron Shipway
    • , Ingo H. Engels
    • , Nanxin Li
    •  & Jennifer L. Harris
  6. Novartis Institutes for Biomedical Research, Emeryville, California 94608, USA

    • Jill Cheng
    • , Guoying K. Yu
    • , Jianjun Yu
    •  & Vivien Chan
  7. Department of Pediatric Oncology, Dana-Farber Cancer Institute, Boston, Massachusetts 02115, USA

    • Todd R. Golub
  8. Howard Hughes Medical Institute, Chevy Chase, Maryland 20815, USA

    • Todd R. Golub

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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.

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.

Corresponding authors

Correspondence to Robert Schlegel or Levi A. Garraway.

Supplementary information

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    Supplementary Information 1

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    Supplementary Information 2

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https://doi.org/10.1038/nature11003

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