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.

Consistency in large pharmacogenomic studies

This article has been updated

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

Relevant articles

Open Access articles citing this article.

Access options

Buy article

Get time limited or full article access on ReadCube.

$32.00

All prices are NET prices.

Figure 1: Limitations of using a correlation metric for the assessment of concordance between the CGP and CCLE drug sensitivity data.

Change history

  • 13 December 2016

    The received date and affiliation details for author P.G. were corrected in the HTML.

References

  1. Haibe-Kains, B. et al. Inconsistency in large pharmacogenomic studies. Nature 504, 389–393 (2013)

    Article  ADS  CAS  Google Scholar 

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

    Article  ADS  CAS  Google Scholar 

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

    Article  ADS  CAS  Google Scholar 

  4. 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 

  5. 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 

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

    Article  ADS  CAS  Google Scholar 

  7. 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 

  8. 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 

  9. 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 

  10. Timm, A. & Kolesar, J. M. Crizotinib for the treatment of non-small-cell lung cancer. Am. J. Health Syst. Pharm. 70, 943–947 (2013)

    Article  CAS  Google Scholar 

  11. Geeleher, P., Cox, N. J. & Huang, R. S. Clinical drug response can be predicted using baseline gene expression levels and in vitro drug sensitivity in cell lines. Genome Biol. 15, R47 (2014)

    Article  Google Scholar 

  12. Falgreen, S. et al. Predicting response to multidrug regimens in cancer patients using cell line experiments and regularised regression models. BMC Cancer 15, 235 (2015)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding authors

Correspondence to Nancy J. Cox or R. Stephanie Huang.

Ethics declarations

Competing interests

The authors declare no competing financial interests.

Supplementary information

Supplementary Table 1

This file shows a summary of the results reported with the Supplementary Data of CGP and CCLE for the 15 compounds assessed by Haibe-Kains et al. These results were obtained from either the CGP web portal (www.cancerrxgene.org) or the supplementary tables S6 & S7 or supplementary figure 9 provided with the publication of the CCLE data. These already published results show a very clear trend of both studies reliably identifying many canonical drug targets. (XLSX 15 kb)

PowerPoint slides

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Geeleher, P., Gamazon, E., Seoighe, C. et al. Consistency in large pharmacogenomic studies. Nature 540, E1–E2 (2016). https://doi.org/10.1038/nature19838

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/nature19838

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

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing