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Safikhani Z, Smirnov P, Freeman M, et al. Revisiting inconsistency in large pharmacogenomic studies. F1000Res. 2016;5:2333doi: 10.12688/f1000research.9611.3.
Safikhani, Z., Smirnov, P., Freeman, M., El-Hachem, N., She, A., Rene, Q., Goldenberg, A., Birkbak, N. J., Hatzis, C., Shi, L., Beck, A. H., Aerts, H. J. W. L., Quackenbush, J., & Haibe-Kains, B. (2016). Revisiting inconsistency in large pharmacogenomic studies. F1000Research, 52333. https://doi.org/10.12688/f1000research.9611.3
Safikhani, Zhaleh, et al. "Revisiting inconsistency in large pharmacogenomic studies." F1000Research vol. 5 (2016): 2333. doi: https://doi.org/10.12688/f1000research.9611.3
Safikhani Z, Smirnov P, Freeman M, El-Hachem N, She A, Rene Q, Goldenberg A, Birkbak NJ, Hatzis C, Shi L, Beck AH, Aerts HJWL, Quackenbush J, Haibe-Kains B. Revisiting inconsistency in large pharmacogenomic studies. F1000Res. 2016 Sep 16;5:2333. doi: 10.12688/f1000research.9611.3. eCollection 2016. PMID: 28928933; PMCID: PMC5580432.
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F1000Res. 2016 Sep 16;5:2333. doi: 10.12688/f1000research.9611.3. eCollection 2016.

Revisiting inconsistency in large pharmacogenomic studies.

F1000Research

Zhaleh Safikhani, Petr Smirnov, Mark Freeman, Nehme El-Hachem, Adrian She, Quevedo Rene, Anna Goldenberg, Nicolai J Birkbak, Christos Hatzis, Leming Shi, Andrew H Beck, Hugo J W L Aerts, John Quackenbush, Benjamin Haibe-Kains

Affiliations

  1. Department of Medical Biophysics, University of Toronto, Toronto, M5G 1L7, Canada.
  2. Princess Margaret Cancer Centre, University Health Network, Toronto, M5G 1L7, Canada.
  3. Institut de Recherches Cliniques de Montréal, Montréal, H2W 1R7, Canada.
  4. Department of Computer Science, University of Toronto, Toronto, M5S 2E4, Canada.
  5. Hospital for Sick Children, Toronto, M5G 1X8, Canada.
  6. University College London, London, WC1E 6BT, UK.
  7. Yale Cancer Center, Yale University, New Haven, CT, 06510, USA.
  8. Section of Medical Oncology, Yale University School of Medicine, New Haven, CT, 06520, USA.
  9. University of Arkansas for Medical Sciences, Little Rock, AR, 72205, USA.
  10. Fudan University, Shanghai City, 200135, China.
  11. Department of Pathology, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, MA, 02215, USA.
  12. Department of Biostatistics and Computational Biology and Center for Cancer Computational Biology, Boston, MA, 02215, USA.
  13. Department of Radiation Oncology and Radiology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, 02215, USA.
  14. Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA, 02215, USA.
  15. Ontario Institute of Cancer Research, Toronto, M5G 1L7, Canada.

PMID: 28928933 PMCID: PMC5580432 DOI: 10.12688/f1000research.9611.3

Abstract

In 2013, we published a comparative analysis of mutation and gene expression profiles and drug sensitivity measurements for 15 drugs characterized in the 471 cancer cell lines screened in the Genomics of Drug Sensitivity in Cancer (GDSC) and Cancer Cell Line Encyclopedia (CCLE). While we found good concordance in gene expression profiles, there was substantial inconsistency in the drug responses reported by the GDSC and CCLE projects. We received extensive feedback on the comparisons that we performed. This feedback, along with the release of new data, prompted us to revisit our initial analysis. We present a new analysis using these expanded data, where we address the most significant suggestions for improvements on our published analysis - that targeted therapies and broad cytotoxic drugs should have been treated differently in assessing consistency, that consistency of both molecular profiles and drug sensitivity measurements should be compared across cell lines, and that the software analysis tools provided should have been easier to run, particularly as the GDSC and CCLE released additional data. Our re-analysis supports our previous finding that gene expression data are significantly more consistent than drug sensitivity measurements. Using new statistics to assess data consistency allowed identification of two broad effect drugs and three targeted drugs with moderate to good consistency in drug sensitivity data between GDSC and CCLE. For three other targeted drugs, there were not enough sensitive cell lines to assess the consistency of the pharmacological profiles. We found evidence of inconsistencies in pharmacological phenotypes for the remaining eight drugs. Overall, our findings suggest that the drug sensitivity data in GDSC and CCLE continue to present challenges for robust biomarker discovery. This re-analysis provides additional support for the argument that experimental standardization and validation of pharmacogenomic response will be necessary to advance the broad use of large pharmacogenomic screens.

Keywords: cancer; consistency; drug sensitivity; pharmacogenomic agreement; pharmacogenomics

Conflict of interest statement

Competing interests: No competing interests were disclosed.

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