This is a preview of subscription content, access via your institution
Access options
Subscribe to this journal
Receive 51 print issues and online access
$199.00 per year
only $3.90 per issue
Rent or buy this article
Prices vary by article type
from$1.95
to$39.95
Prices may be subject to local taxes which are calculated during checkout
References
Geeleher, P., Gamazon, E. R., Seoighe, C., Cox, N. J. & Huang, R. S. Consistency in large pharmacogenomic studies. Nature 540, http://dx.doi.org/10.1038/nature19838 (2016)
Garnett, M. J. et al. Systematic identification of genomic markers of drug sensitivity in cancer cells. Nature 483, 570–575 (2012)
Barretina, J. et al. The Cancer Cell Line Encyclopedia enables predictive modelling of anticancer drug sensitivity. Nature 483, 603–607 (2012)
Haibe-Kains, B. et al. Inconsistency in large pharmacogenomic studies. Nature 504, 389–393 (2013)
Safikhani, Z. et al. Revisiting inconsistency in large pharmacogenomic studies. F1000Research http://dx.doi.org/10.12688/f1000research.9611.1 (2016)
Haverty, P. M. et al. Reproducible pharmacogenomic profiling of cancer cell line panels. Nature 533, 333–337 (2016)
The Cancer Cell Line Encyclopedia Consortium and Genomics of Drug Sensitivity in Cancer Investigators. Pharmacogenomic agreement between two cancer cell line data sets. Nature 528, 84–87 (2015)
Matthews, B. W. Comparison of the predicted and observed secondary structure of T4 phage lysozyme. Biochim. Biophys. Acta 405, 442–451 (1975)
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)
Papillon-Cavanagh, S. et al. Comparison and validation of genomic predictors for anticancer drug sensitivity. J. Am. Med. Inform. Assoc. 20, 597–602 (2013)
Dong, Z. et al. Anticancer drug sensitivity prediction in cell lines from baseline gene expression through recursive feature selection. BMC Cancer 15, 489 (2015)
Jang, I. S., Neto, E. C., Guinney, J., Friend, S. H. & Margolin, A. A. Systematic assessment of analytical methods for drug sensitivity prediction from cancer cell line data. Pac. Symp. Biocomput. 2014, 63–74 (2014)
Cortés-Ciriano, I. et al. Improved large-scale prediction of growth inhibition patterns using the NCI60 cancer cell line panel. Bioinformatics 32, 85–95 (2015)
Goodspeed, A., Heiser, L. M., Gray, J. W. & Costello, J. C. Tumor-derived cell lines as molecular models of cancer pharmacogenomics. Mol. Cancer Res. 14, 3–13 (2015)
Sandve, G. K, Nekrutenko, A., Taylor, J. & Hovig, E. Ten simple rules for reproducible computational research. PLoS Comput. Biol.9, e1003285 (2013).
Author information
Authors and Affiliations
Corresponding author
Supplementary information
Supplementary Information
This file contains Supplementary Text and Data, Supplementary Methods, Supplementary Tables 1-2, Supplementary Figures 1-7 and additional references. (PDF 1357 kb)
Rights and permissions
About this article
Cite this article
Safikhani, Z., El-Hachem, N., Smirnov, P. et al. Safikhani et al. reply. Nature 540, E2–E4 (2016). https://doi.org/10.1038/nature19839
Published:
Issue Date:
DOI: https://doi.org/10.1038/nature19839
This article is cited by
-
Bayesian multi-source regression and monocyte-associated gene expression predict BCL-2 inhibitor resistance in acute myeloid leukemia
npj Precision Oncology (2021)
-
Remember why we work on cancer
Nature (2017)
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.