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Reproducible pharmacogenomic profiling of cancer cell line panels

Nature volume 533, pages 333337 (19 May 2016) | Download Citation

Abstract

The use of large-scale genomic and drug response screening of cancer cell lines depends crucially on the reproducibility of results. Here we consider two previously published screens, plus a later critique of these studies. Using independent data, we show that consistency is achievable, and provide a systematic description of the best laboratory and analysis practices for future studies.

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References

  1. 1.

    et al. The Cancer Cell Line Encyclopedia enables predictive modelling of anticancer drug sensitivity. Nature 483, 603–607 (2012)

  2. 2.

    et al. Systematic identification of genomic markers of drug sensitivity in cancer cells. Nature 483, 570–575 (2012)

  3. 3.

    et al. Inconsistency in large pharmacogenomic studies. Nature 504, 389–393 (2013)

  4. 4.

    , & Regularization paths for generalized linear models via coordinate descent. J. Stat. Softw. 33, 1–22 (2010)

  5. 5.

    , , , & Outcome signature genes in breast cancer: is there a unique set? Bioinformatics 21, 171–178 (2005)

  6. 6.

    , , & A simple high-content cell cycle assay reveals frequent discrepancies between cell number and ATP and MTS proliferation assays. PLoS ONE 8, e63583 (2013)

  7. 7.

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

  8. 8.

    Cancer Cell Line Encyclopedia Consortium & Genomics of Drug Sensitivity in Cancer Consortium. Pharmacogenomic agreement between two cancer cell line data sets. Nature 528, 84–87 (2015)

  9. 9.

    et al. A resource for cell line authentication, annotation and quality control. Nature 520, 307–311 (2015)

  10. 10.

    , , & A charged aerosol detector/chemiluminescent nitrogen detector/liquid chromatography/mass spectrometry system for regular and fragment compound analysis in drug discovery. J. Chromatogr. A 1411, 63–68 (2015)

  11. 11.

    et al. Compound transfer by acoustic droplet ejection promotes quality and efficiency in ultra-high-throughput screening campaigns. J. Lab. Autom. 21, 64–75 (2016)

  12. 12.

    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)

  13. 13.

    et al. A comprehensive transcriptional portrait of human cancer cell lines. Nature Biotechnol. 33, 306–312 (2015)

  14. 14.

    et al. Putative DNA/RNA helicase Schlafen-11 (SLFN11) sensitizes cancer cells to DNA-damaging agents. Proc. Natl Acad. Sci. USA 109, 15030–15035 (2012)

  15. 15.

    , , , & Description of paclitaxel resistance-associated genes in ovarian and breast cancer cell lines. Cancer Chemother. Pharmacol. 55, 277–285 (2005)

  16. 16.

    & Fast and SNP-tolerant detection of complex variants and splicing in short reads. Bioinformatics 26, 873–881 (2010)

  17. 17.

    & Differential expression analysis for sequence count data. Genome Biol. 11, R106 (2010)

  18. 18.

    et al. PICNIC: an algorithm to predict absolute allelic copy number variation with microarray cancer data. Biostatistics 11, 164–175 (2010)

  19. 19.

    et al. Comprehensive genomic analysis identifies SOX2 as a frequently amplified gene in small-cell lung cancer. Nature Genet. 44, 1111–1116 (2012)

  20. 20.

    & Spatial smoothing and hot spot detection for CGH data using the fused lasso. Biostatistics 9, 18–29 (2008)

  21. 21.

    , , , & Rich annotation of DNA sequencing variants by leveraging the Ensembl Variant Effect Predictor with plugins. Brief. Bioinform. 16, 255–264 (2015)

  22. 22.

    et al. COSMIC: mining complete cancer genomes in the Catalogue of Somatic Mutations in Cancer. Nucleic Acids Res. 39, D945–D950 (2011)

  23. 23.

    & Improving the assessment of the outcome of nonsynonymous SNVs with a consensus deleteriousness score, Condel. Am. J. Hum. Genet. 88, 440–449 (2011)

  24. 24.

    et al. Addendum: The Cancer Cell Line Encyclopedia enables predictive modelling of anticancer drug sensitivity. Nature 492, 290 (2012)

  25. 25.

    & Vigilance and validation: Keys to success in RNAi screening. ACS Chem. Biol. 6, 47–60 (2011)

  26. 26.

    & Model-based clustering, discriminant analysis and density estimation. J. Amer. Statist. Assoc. 97, 611–631 (2002)

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Acknowledgements

We thank G. Manning, T. Sandmann, B. Forrest and D. Stokoe for their valuable contributions to improving the manuscript. We also thank L. Shi and J. Wu for work on early phases of gCSI screening; R. Rodriguez, G. Yuen and D. Hascall for help preparing drug plates; Y. Jiang for assistance with drug plate quality control; and S. Selvaraj and M. Yu for banking and quality control of cell lines used in this study. This manuscript contains an analysis of data released by the Broad Institute (CCLE) and by the GDSC members. Those who carried out the original analysis and collection of these data bear no responsibility for the further analysis or interpretation of it. The relevant subset of the CCLE data are included in the supplementary software package with the explicit written consent of the CCLE group. The GDSC data has been included following the GDSC’s instructions for attribution and under their specified open-source license, Creative Commons 3.0.

Author information

Author notes

    • Peter M. Haverty
    •  & Eva Lin

    These authors contributed equally to this work.

Affiliations

  1. Department of Bioinformatics and Computational Biology, Genentech Inc., 1 DNA Way, South San Francisco, California 94080, USA

    • Peter M. Haverty
    • , Steve Lianoglou
    •  & Richard Bourgon
  2. Department of Discovery Oncology, Genentech Inc., 1 DNA Way, South San Francisco, California 94080, USA

    • Eva Lin
    • , Jenille Tan
    • , Yihong Yu
    • , Billy Lam
    • , Richard M. Neve
    • , Scott Martin
    • , Jeff Settleman
    •  & Robert L. Yauch

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Contributions

P.M.H. and R.B. drafted the manuscript, prepared figures and tables and designed the factorial screen. P.M.H. and R.B. performed computational analysis and interpreted results. S.L. contributed to the development of elastic net biomarker identification software. J.S. and R.L.Y. contributed to the experimental design, data analysis, and manuscript preparation. S.M. contributed to manuscript preparation and data interpretation. E.L., J.T., Y.Y. and B.L. performed primary cell-based screening experiments. E.L. and R.M.N. designed and managed the cell-based screening experiments.

Competing interests

All authors are employees of Genentech Inc. and may be stockholders of Roche Pharmaceuticals.

Corresponding author

Correspondence to Richard Bourgon.

Extended data

Supplementary information

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    Supplementary Information

    This file contains a Supplementary note and full legends for Supplementary Tables 1-6.

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    Supplementary Data

    This file contains Supplementary Tables 1-6, please refer to the Supplementary Information document for full legends.

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DOI

https://doi.org/10.1038/nature17987

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