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Predictive Modeling of Anti-Cancer Drug Sensitivity from Genetic Characterizations

  • Raziur Rahman
  • Ranadip PalEmail author
Protocol
Part of the Methods in Molecular Biology book series (MIMB, volume 1878)

Abstract

Accurately predicting sensitivity of tumor cells to anti-cancer drugs based on genetic characterizations is a significant challenge for personalized cancer therapy. This chapter provides a computational procedure to design predictive models from individual genomic characterizations and combine them to arrive at an integrated predictive model. Integrated modeling employs the complementary information from heterogeneous genetic characterizations to improve the prediction error as well as lowering the error confidence interval.

Key words

Integrated genomic modeling Random Forests Drug sensitivity prediction 

References

  1. 1.
    Costello JC et al (2014) A community effort to assess and improve drug sensitivity prediction algorithms. Nat Biotechnol.  https://doi.org/10.1038/nbt.2877CrossRefGoogle Scholar
  2. 2.
    Wan Q, Pal R (2014) An ensemble based top performing approach for NCI-dream drug sensitivity prediction challenge. PLoS One 9(6):e101183CrossRefGoogle Scholar
  3. 3.
    Sos ML, Michel K, Zander T, Weiss J, Frommolt P, Peifer M, Li D, Ullrich R, Koker M, Fischer F, Shimamura T, Rauh D, Mermel C, Fischer S, Stückrath I, Heynck S, Beroukhim R, Lin W, Winckler W, Shah K, LaFramboise T, Moriarty WF, Hanna M, Tolosi L, Rahnenführer J, Verhaak R, Chiang D, Getz G, Hellmich M, Wolf J, Girard L, Peyton M, Weir BA, Chen TH, Greulich H, Barretina J, Shapiro GI, Garraway LA, Gazdar AF, Minna JD, Meyerson M, Wong KK, Thomas RK (2009) Predicting drug susceptibility of non-small cell lung cancers based on genetic lesions. J Clin Invest 119(6):1727–1740CrossRefGoogle Scholar
  4. 4.
    Staunton JE, Slonim DK, Coller HA, Tamayo P, Angelo MJ, Park J, Scherf U, Lee JK, Reinhold WO, Weinstein JN, Mesirov JP, Lander ES, Golub TR (2001) Chemosensitivity prediction by transcriptional profiling. Proc Natl Acad Sci 98:10787–10792CrossRefGoogle Scholar
  5. 5.
    Lee JK, Havaleshko DM, Cho H, Weinstein JN, Kaldjian EP, Karpovich J, Grimshaw A, Theodorescu D (2007) A strategy for predicting the chemosensitivity of human cancers and its application to drug discovery. Proc Natl Acad Sci 104(32):13086–13091CrossRefGoogle Scholar
  6. 6.
    Mitsos A, Melas IN, Siminelakis P, Chairakaki AD, Saez-Rodriguez J, Alexopoulos LG (2009) Identifying drug effects via pathway alterations using an integer linear programming optimization formulation on phosphoproteomic data. PLoS Comput Biol 5(12):e1000591+CrossRefGoogle Scholar
  7. 7.
    Walther Z, Sklar J (2011) Molecular tumor profiling for prediction of response to anticancer therapies. Cancer J 17(2):71–79CrossRefGoogle Scholar
  8. 8.
    Zou H, Hastie T (2005) Regularization and variable selection via the elastic net. J R Stat Soc Ser B 67:301–320CrossRefGoogle Scholar
  9. 9.
    Barretina J et al (2012) The Cancer Cell Line Encyclopedia enables predictive modelling of anticancer drug sensitivity. Nature 483(7391):603–607CrossRefGoogle Scholar
  10. 10.
    Riddick G, Song H, Ahn S, Walling J, Borges-Rivera D, Zhang W, Fine HA (2011) Predicting in vitro drug sensitivity using Random Forests. Bioinformatics 27(2):220–224CrossRefGoogle Scholar
  11. 11.
    Bengtsson H, Simpson K, Bullard J, Hansen K (2008) aroma.affymetrix: a generic framework in R for analyzing small to very large Affymetrix data sets in bounded memory. Tech. Rep. 745, Department of Statistics, University of California, BerkeleyGoogle Scholar
  12. 12.
    Garber M, Grabherr MG, Guttman M, Trapnell C (2011) Computational methods for transcriptome annotation and quantification using RNA-seq. Nat Methods 8(6):469–477CrossRefGoogle Scholar
  13. 13.
    Wilhelm BT, Landry JR (2009) RNA-seq-quantitative measurement of expression through massively parallel RNA-sequencing. Methods 48(3):249–257CrossRefGoogle Scholar
  14. 14.
    Li S, Tighe SW, Nicolet CM, Grove D, Levy S, Farmerie W, Viale A, Wright C, Schweitzer PA, Gao Y, Kim D, Boland J, Hicks B, Kim R, Chhangawala S, Jafari N, Raghavachari N, Gandara J, Garcia-Reyero N, Hendrickson C, Roberson D, Rosenfeld J, Smith T, Underwood JG, Wang M, Zumbo P, Baldwin DA, Grills GS, Mason CE (2014) Multi-platform assessment of transcriptome profiling using RNA-seq in the ABRF next-generation sequencing study. Nat Biotechnol 32(9):915–925CrossRefGoogle Scholar
  15. 15.
    Xie F, Liu T, Qian WJ, Petyuk VA, Smith RD (2011) Liquid chromatography-mass spectrometry-based quantitative proteomics. J Biol Chem 286(29):25443–25449CrossRefGoogle Scholar
  16. 16.
    Li F, Gonzalez FJ, Ma X (2012) LC–MS-based metabolomics in profiling of drug metabolism and bioactivation. Acta Pharm Sin B 2(2):118–125. Drug Metabolism and TransportCrossRefGoogle Scholar
  17. 17.
    Troyanskaya OG, Cantor MN, Sherlock G, Brown PO, Hastie T, Tibshirani R, Botstein D, Altman RB (2001) Missing value estimation methods for DNA microarrays. Bioinformatics 17(6):520–525CrossRefGoogle Scholar
  18. 18.
    Berlow N, Haider S, Wan Q, Geltzeiler M, Davis LE, Keller C, Berlow RN (2014) An integrated approach to anti-cancer drugs sensitivity prediction. IEEE/ACM Trans Comput Biol Bioinform. https://doi.org/10.1155/2014/873436CrossRefGoogle Scholar
  19. 19.
    Berlow N, Davis LE, Cantor EL, Seguin B, Keller C, Pal R (2013) A new approach for prediction of tumor sensitivity to targeted drugs based on functional data. BMC Bioinformatics 14:239CrossRefGoogle Scholar
  20. 20.
    Robnik-Sikonja M, Kononenko I (1997) An adaptation of relief for attribute estimation in regression. In: Proceedings of the fourteenth international conference on machine learning (ICML ’97). Morgan Kaufmann Publishers Inc, San Francisco, pp 296–304Google Scholar
  21. 21.
    Šikonja MR, Kononenko I (2003) Theoretical and empirical analysis of ReliefF and RReliefF. Mach Learn 53(1–2):23–69CrossRefGoogle Scholar
  22. 22.
    Zarrinkar PP, Gunawardane RN, Cramer MD, Gardner MF, Brigham D, Belli B, Karaman MW, Pratz KW, Pallares G, Chao Q, Sprankle KG, Patel HK, Levis M, Armstrong RC, James J, Bhagwat SS (2009) AC220 is a uniquely potent and selective inhibitor of FLT3 for the treatment of acute myeloid leukemia (AML). Blood 114(14):2984–2992CrossRefGoogle Scholar
  23. 23.
    Fabian MA, Biggs WH, Treiber DK, Atteridge CE, Azimioara MD, Benedetti MG, Carter TA, Ciceri P, Edeen PT, Floyd M, Ford JM, Galvin M, Gerlach JL, Grotzfeld RM, Herrgard S, Insko DE, Insko MA, Lai AG, Lelias JM, Mehta SA, Milanov ZV, Velasco AM, Wodicka LM, Patel HK, Zarrinkar PP, Lockhart DJ (2005) A small molecule-kinase interaction map for clinical kinase inhibitors. Nat Biotechnol 23(3):329–336CrossRefGoogle Scholar
  24. 24.
    Karaman MW, Herrgard S, Treiber DK, Gallant P, Atteridge CE, Campbell BT, Chan KW, Ciceri P, Davis MI, Edeen PT, Faraoni R, Floyd M, Hunt JP, Lockhart DJ, Milanov ZV, Morrison MJ, Pallares G, Patel HK, Pritchard S, Wodicka LM, Zarrinkar PP (2008) A quantitative analysis of kinase inhibitor selectivity. Nat Biotechnol 26(1):127–132CrossRefGoogle Scholar
  25. 25.
    Breiman L (2001) Random forests. Mach Learn 45(1):5–32CrossRefGoogle Scholar
  26. 26.
    Meinshausen N (2006) Quantile regression forests. J Mach Learn Res 7:983–999Google Scholar
  27. 27.
    Biau G (2012) Analysis of a random forests model. J Mach Learn Res 98888:1063–1095Google Scholar
  28. 28.
    Shalabi LA, Shaaban Z, Kasasbeh B (2006) Data mining: a preprocessing engine. J Comput Sci 2(9):735CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Electrical and Computer EngineeringTexas Tech UniversityLubbockUSA

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