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Consistency in large pharmacogenomic studies

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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.


  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 

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

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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 ( 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)

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Geeleher, P., Gamazon, E., Seoighe, C. et al. Consistency in large pharmacogenomic studies. Nature 540, E1–E2 (2016).

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