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Safikhani et al. reply

Nature volume 540, pages E6–E8 (2016)Cite this article

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replying to J. P. Mpindi et al. Nature 540, 10.1038/nature20171 (2016).

The accompanying Comment1 by Mpindi et al. is an important contribution to the discussion of pharmacogenomic consistency for several reasons. Mpindi et al.1 were able to reproduce our initial finding2 of a substantial inconsistency between the pharmacological profiles generated within the Cancer Genome Project (CGP)3 and the Cancer Cell Line Encyclopedia (CCLE)4, and explored potential reasons behind the problem and possible solutions. To do this, they compared the CGP and CCLE to a new dataset generated by the Institute for Molecular Medicine Finland (FIMM) that includes 308 drugs that were tested across 106 cancer cell lines5. The authors shared the subset of the FIMM data overlapping with CGP and CCLE, including 52 drugs tested in up to 50 cell lines (drug dose–response curves and their comparison with CGP and CCLE curves are available in Supplementary Data). Overall, their comparative analysis1 of this newly released dataset supports our published finding2 of greater consistency between studies in which there is similarity in experimental methods. We agree with Mpindi et al.1 that harmonizing the readout, drug concentration range, and statistical estimator makes it possible to achieve greater consistency across pharmacogenomic studies. Here we provide specific responses to the main results reported by Mpindi et al.1

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Figure 1: Consistency between drug sensitivity measures (AUC) across FIMM, CGP and CCLE.

References

  1. Mpindi, J. P. et al. Consistency in drug response profiling. Nature 540 http://dx.doi.org/10.1038/nature20171 (2016)

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Authors and Affiliations

  1. Princess Margaret Cancer Centre, University Health Network, Toronto, M5G 2M9, Ontario, Canada

    Zhaleh Safikhani, Petr Smirnov, Mark Freeman & Benjamin Haibe-Kains

  2. Department of Medical Biophysics, University of Toronto, Toronto, M5G 1L7, Ontario, Canada

    Zhaleh Safikhani & Benjamin Haibe-Kains

  3. Institut de recherches cliniques de Montréal, Montreal, H2W 1R7, Quebec, Canada

    Nehme El-Hachem

  4. Hospital for Sick Children, Toronto, M5G 1X8, Ontario, Canada

    Anna Goldenberg

  5. Department of Computer Science, University of Toronto, Toronto, M5S 2E4, Ontario, Canada

    Anna Goldenberg & Benjamin Haibe-Kains

  6. The Francis Crick Institute, London, NW1 1AT, UK

    Nicolai J. Birkbak

  7. University College London Cancer Institute, London, WC1E 6BT, UK

    Nicolai J. Birkbak

  8. Beth Israel Deaconess Medical Center, Boston, 02215, Massachusetts, USA

    Andrew H. Beck

  9. Harvard Medical School, Boston, 02115, Massachusetts, USA

    Andrew H. Beck & Hugo J. W. L. Aerts

  10. Dana-Farber Cancer Institute, Boston, 02115, Massachusetts, USA

    Hugo J. W. L. Aerts & John Quackenbush

  11. Brigham and Women’s Hospital, Boston, 02115, Massachusetts, USA

    Hugo J. W. L. Aerts

  12. Harvard TH Chan School of Public Health, Boston, 02115, Massachusetts, USA

    John Quackenbush

  13. Ontario Institute of Cancer Research, Toronto, M5G 1L7, Ontario, Canada

    Benjamin Haibe-Kains

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  1. Zhaleh Safikhani
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Supplementary information

Supplementary Information

This file contains Supplementary Methods, Supplementary Text and Data, Supplementary Figure 1 and additional references. (PDF 305 kb)

Supplementary Data 1

This file shows drug dose-response curves from the FIMM dataset. When the same drug was tested on the same cell line in CGP and/or CCLE, the curves were also displayed for comparison. (PDF 1564 kb)

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Safikhani, Z., El-Hachem, N., Smirnov, P. et al. Safikhani et al. reply. Nature 540, E6–E8 (2016). https://doi.org/10.1038/nature20172

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