Network-based clustering for drug sensitivity prediction in cancer cell lines

1. Shoemaker RH. The NCI60 human tumour cell line anticancer drug screen. Nature Reviews Cancer. 2006;6(10):813{823. doi:10.1038/nrc1951.

2. Iorio F, Knijnenburg TA, Vis DJ, Bignell GR, Menden MP, Schubert M, et al. A Landscape of Pharmacogenomic Interactions in Cancer. Cell. 2016;166(3):740{754. doi:10.1016/j.cell.2016.06.017. [OpenAIRE]

3. Barretina J, Caponigro G, Stransky N, Venkatesan K, Margolin AA, Kim S, et al. The Cancer Cell Line Encyclopedia enables predictive modelling of anticancer drug sensitivity. Nature. 2012;483(7391):603{607. doi:10.1038/nature11003. [OpenAIRE]

4. Yang W, Soares J, Greninger P, Edelman EJ, Lightfoot H, Forbes S, et al. Genomics of Drug Sensitivity in Cancer (GDSC): a resource for therapeutic biomarker discovery in cancer cells. Nucleic Acids Research. 2012;41(D1):D955{D961. doi:10.1093/nar/gks1111. [OpenAIRE]

5. Garnett MJ, Edelman EJ, Heidorn SJ, Greenman CD, Dastur A, Lau KW, et al. Systematic identification of genomic markers of drug sensitivity in cancer cells. Nature. 2012;483(7391):570{575. doi:10.1038/nature11005.

6. Chabner BA. NCI-60 Cell Line Screening: A Radical Departure in its Time. Journal of the National Cancer Institute. 2016;108(5):djv388. doi:10.1093/jnci/djv388. [OpenAIRE]

7. Boyd MR, Paull KD. Some practical considerations and applications of the national cancer institute in vitro anticancer drug discovery screen. Drug Development Research. 1995;34(2):91{109. doi:10.1002/ddr.430340203.

8. Weinstein JN. Integromic Analysis of the NCI-60 Cancer Cell Lines. Breast Disease. 2004;19(1):11{22. doi:10.3233/BD-2004-19103.

9. Staunton JE, Slonim DK, Coller HA, Tamayo P, Angelo MJ, Park J, et al. Chemosensitivity prediction by transcriptional profiling. Proceedings of the National Academy of Sciences. 2001;98(19):10787{10792. doi:10.1073/pnas.191368598. [OpenAIRE]

10. Azuaje F. Computational models for predicting drug responses in cancer research. Briefings in Bioinformatics. 2016;18(5):820{829. doi:10.1093/bib/bbw065. [OpenAIRE]

11. Dong Z, Zhang N, Li C, Wang H, Fang Y, Wang J, et al. Anticancer drug sensitivity prediction in cell lines from baseline gene expression through recursive feature selection. BMC Cancer. 2015;15(1). doi:10.1186/s12885-015-1492-6.

12. Daemen A, Griffith OL, Heiser LM, Wang NJ, Enache OM, Sanborn Z, et al. Modeling precision treatment of breast cancer. Genome Biology. 2013;14(10):R110. doi:10.1186/gb-2013-14-10-r110.

13. Menden MP, Iorio F, Garnett M, McDermott U, Benes CH, Ballester PJ, et al. Machine Learning Prediction of Cancer Cell Sensitivity to Drugs Based on Genomic and Chemical Properties. PLoS ONE. 2013;8(4):e61318. doi:10.1371/journal.pone.0061318. [OpenAIRE]

14. Geeleher P, Cox NJ, Huang R. Clinical drug response can be predicted using baseline gene expression levels and in vitro drug sensitivity in cell lines. Genome Biology. 2014;15(3):R47. doi:10.1186/gb-2014-15-3-r47.

15. Riddick G, Song H, Ahn S, Walling J, Borges-Rivera D, Zhang W, et al. Predicting in vitro drug sensitivity using Random Forests. Bioinformatics. 2010;27(2):220{224. doi:10.1093/bioinformatics/btq628.