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Dissecting the Genome for Drug Response Prediction

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Data Mining Techniques for the Life Sciences

Part of the book series: Methods in Molecular Biology ((MIMB,volume 2449))

Abstract

The prediction of the cancer cell lines sensitivity to a specific treatment is one of the current challenges in precision medicine. With omics and pharmacogenomics data being available for over 1000 cancer cell lines, several machine learning and deep learning algorithms have been proposed for drug sensitivity prediction. However, deciding which omics data to use and which computational methods can efficiently incorporate data from different sources is the challenge which several research groups are working on. In this review, we summarize recent advances in the representative computational methods that have been developed in the last 2 years on three public datasets: COSMIC, CCLE, NCI-60. These methods aim to improve the prediction of the cancer cell lines sensitivity to a given treatment by incorporating drug’s chemical information in the input or using a priori feature selection. Finally, we discuss the latest published method which aims to improve the prediction of clinical drug response of real patients starting from cancer cell line molecular profiles.

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References

  1. Reuter JA, Spacek DV, Snyder MP (2015) High-throughput sequencing technologies. Mol Cell 58:586–597

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. Iorio F, Knijnenburg TA, Vis DJ et al (2016) A landscape of pharmacogenomic interactions in cancer. Cell 166:740–754

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Garnett MJ, Edelman EJ, Heidorn SJ et al (2012) Systematic identification of genomic markers of drug sensitivity in cancer cells. Nature 483:570–575

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Azuaje F (2017) Computational models for predicting drug responses in cancer research. Brief Bioinform 18:820–829

    CAS  PubMed  Google Scholar 

  5. Menden MP, AstraZeneca-Sanger Drug Combination DREAM Consortium, Wang D et al (2019) Community assessment to advance computational prediction of cancer drug combinations in a pharmacogenomic screen. Nat Commun 10

    Google Scholar 

  6. Huang C, Mezencev R, McDonald JF, Vannberg F (2017) Open source machine-learning algorithms for the prediction of optimal cancer drug therapies. PLoS One 12:e0186906

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  7. Weinstein JN (2012) Drug discovery: cell lines battle cancer. Nature 483:544–545

    Article  CAS  PubMed  Google Scholar 

  8. Wilding JL, Bodmer WF (2014) Cancer cell lines for drug discovery and development. Cancer Res 74:2377–2384

    Article  CAS  PubMed  Google Scholar 

  9. Yamori T (2003) Panel of human cancer cell lines provides valuable database for drug discovery and bioinformatics. Cancer Chemother Pharmacol 52:74–79

    Article  CAS  Google Scholar 

  10. Barretina J, Caponigro G, Stransky N et al (2012) The cancer cell line Encyclopedia enables predictive modelling of anticancer drug sensitivity. Nature 483:603–607

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Forbes SA, Beare D, Boutselakis H et al (2017) COSMIC: somatic cancer genetics at high-resolution. Nucleic Acids Res 45:D777–D783

    Article  CAS  PubMed  Google Scholar 

  12. Alley MC, Scudiero DA, Monks A et al (1988) Feasibility of drug screening with panels of human tumor cell lines using a microculture tetrazolium assay. Cancer Res 48:589–601

    CAS  PubMed  Google Scholar 

  13. Shoemaker RH (2006) The NCI60 human tumour cell line anticancer drug screen. Nat Rev Cancer 6:813–823

    Article  CAS  PubMed  Google Scholar 

  14. Nguyen T, Nguyen GTT, Nguyen T, Le D-H (2021) Graph convolutional networks for drug response prediction. bioRxiv. 2020.04.07.030908

    Google Scholar 

  15. Blessie EC, Chandra Blessie E, Karthikeyan E (2012) Sigmis: a feature selection algorithm using correlation based method. J Algorithms Computl Technol 6:385–394

    Article  Google Scholar 

  16. Parca L, Pepe G, Pietrosanto M et al (2019) Modeling cancer drug response through drug-specific informative genes. Sci Rep 9:15222

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  17. Sánchez-Maroño N, Caamaño-Fernández M, Castillo E, Alonso-Betanzos A (2006) Functional networks and analysis of variance for feature selection. Intell Data Eng Autom Learn 2006:1031–1038

    Google Scholar 

  18. Chang Y, Park H, Yang H-J et al (2018) Cancer drug response profile scan (CDRscan): a deep learning model that predicts drug effectiveness from cancer genomic signature. Sci Rep 8:8857

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  19. Chiu Y-C, Chen H-IH, Zhang T et al (2019) Predicting drug response of tumors from integrated genomic profiles by deep neural networks. BMC Med Genet 12:18

    Google Scholar 

  20. Li M, Wang Y, Zheng R et al (2021) DeepDSC: a deep learning method to predict drug sensitivity of cancer cell lines. IEEE/ACM Trans Comput Biol Bioinform 18:575–582

    Article  CAS  PubMed  Google Scholar 

  21. Ali M, Aittokallio T (2019) Machine learning and feature selection for drug response prediction in precision oncology applications. Biophys Rev 11:31–39

    Article  CAS  PubMed  Google Scholar 

  22. Gillet J-P, Varma S, Gottesman MM (2013) The clinical relevance of cancer cell lines. J Natl Cancer Inst 105:452–458

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Geeleher P, Cox NJ, Huang R (2014) Clinical drug response can be predicted using baseline gene expression levels and in vitro drug sensitivity in cell lines. Genome Biol 15:R47

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  24. Huang H-H, Dai J-G, Liang Y (2018) clinical drug response prediction by using a Lq penalized network-constrained logistic regression method. Cell Physiol Biochem 51:2073–2084

    Article  CAS  PubMed  Google Scholar 

  25. Liu P, Li H, Li S, Leung K-S (2019) Improving prediction of phenotypic drug response on cancer cell lines using deep convolutional network. BMC Bioinformatics 20:408

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  26. Moughari FA, Eslahchi C (2020) Author correction: ADRML: anticancer drug response prediction using manifold learning. Sci Rep 10:22360

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Ma Y, Fu Y (2011) Manifold learning theory and applications. CRC Press

    Book  Google Scholar 

  28. Wang JJ-Y, Huang JZ, Sun Y, Gao X (2015) Feature selection and multi-kernel learning for adaptive graph regularized nonnegative matrix factorization. Expert Syst Appl 42:1278–1286

    Article  Google Scholar 

  29. Wei D, Liu C, Zheng X, Li Y (2019) Comprehensive anticancer drug response prediction based on a simple cell line-drug complex network model. BMC Bioinformatics 20:44

    Article  PubMed  PubMed Central  Google Scholar 

  30. Wang L, Li X, Zhang L, Gao Q (2017) Improved anticancer drug response prediction in cell lines using matrix factorization with similarity regularization. BMC Cancer 17:513

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Suphavilai C, Bertrand D, Nagarajan N (2018) Predicting cancer drug response using a recommender system. Bioinformatics 34:3907–3914

    Article  CAS  PubMed  Google Scholar 

  32. Garreta R, Moncecchi G (2013) Learning scikit-learn: machine Learning in Python. Packt Publishing Ltd.

    Google Scholar 

  33. Koras K, Juraeva D, Kreis J et al (2020) Feature selection strategies for drug sensitivity prediction. Sci Rep 10:9377

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Kursa MB, Jankowski A, Rudnicki WR (2010) Boruta – a system for feature selection. Fundamenta Inform 101:271–285

    Article  Google Scholar 

  35. Xu X, Gu H, Wang Y et al (2019) Autoencoder based feature selection method for classification of anticancer drug response. Front Genet 10:233

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Dong Z, Zhang N, Li C et al (2015) Anticancer drug sensitivity prediction in cell lines from baseline gene expression through recursive feature selection. BMC Cancer 15:489

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  37. Emdadi A, Eslahchi C (2021) Auto-HMM-LMF: feature selection based method for prediction of drug response via autoencoder and hidden Markov model. BMC Bioinformatics 22:33

    Article  PubMed  PubMed Central  Google Scholar 

  38. Neumann U, Genze N, Heider D (2017) EFS: an ensemble feature selection tool implemented as R-package and web-application. BioData Min 10:21

    Article  PubMed  PubMed Central  Google Scholar 

  39. Ahmed KT, Park S, Jiang Q et al (2020) Network-based drug sensitivity prediction. BMC Med Genet 13:193

    CAS  Google Scholar 

  40. An B, Zhang Q, Fang Y et al (2020) Iterative sure independent ranking and screening for drug response prediction. BMC Med Inform Decis Mak 20:224

    Article  PubMed  PubMed Central  Google Scholar 

  41. Zhu L, Li L, Li R, Zhu L (2011) Model-free feature screening for ultrahigh dimensional data. J Am Stat Assoc 106:1464–1475

    Article  CAS  PubMed  Google Scholar 

  42. Fang Y, Qin Y, Zhang N et al (2015) DISIS: prediction of drug response through an iterative sure independence screening. PLoS One 10:e0120408

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  43. Majumder B, Baraneedharan U, Thiyagarajan S et al (2015) Predicting clinical response to anticancer drugs using an ex vivo platform that captures tumour heterogeneity. Nat Commun 6(1):1–14

    Article  CAS  Google Scholar 

  44. Ding Z, Zu S, Gu J (2016) Evaluating the molecule-based prediction of clinical drug responses in cancer. Bioinformatics 32:2891–2895

    Article  CAS  PubMed  Google Scholar 

  45. Turki T, Wei Z, Wang JTL (2018) A transfer learning approach via procrustes analysis and mean shift for cancer drug sensitivity prediction. J Bioinforma Comput Biol 16:1840014

    Article  CAS  Google Scholar 

  46. Huang EW, Bhope A, Lim J et al (2020) Tissue-guided LASSO for prediction of clinical drug response using preclinical samples. PLoS Comput Biol 16:e1007607

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Johnson WE, Evan Johnson W, Li C Adjusting batch effects in microarray experiments with small sample size using empirical Bayes methods. Batch Effects Noise Microarray Exp:113–129

    Google Scholar 

  48. Marangoni E, Poupon M-F (2014) Patient-derived tumour xenografts as models for breast cancer drug development. Curr Opin Oncol 26:556–561

    Article  CAS  PubMed  Google Scholar 

  49. Tentler JJ, Tan AC, Weekes CD et al (2012) Patient-derived tumour xenografts as models for oncology drug development. Nat Rev Clin Oncol 9:338–350

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Weeber F, Ooft SN, Dijkstra KK, Voest EE (2017) Tumor organoids as a pre-clinical cancer model for drug discovery. Cell Chem Biol 24:1092–1100

    Article  CAS  PubMed  Google Scholar 

  51. Rae C, Amato F, Braconi C (2021) Patient-derived organoids as a model for cancer drug discovery. Int J Mol Sci 22:3483

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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Author information

Authors and Affiliations

  1. Department of Biology, Centro di Bioinformatica Molecolare, University of Rome “Tor Vergata”, Rome, Italy

    Gerardo Pepe, Chiara Carrino & Manuela Helmer-Citterich

  2. Italian Space Agency, Via del Politecnico snc, Rome, Italy

    Luca Parca

Authors
  1. Gerardo Pepe
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  2. Chiara Carrino
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  3. Luca Parca
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  4. Manuela Helmer-Citterich
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Corresponding author

Correspondence to Manuela Helmer-Citterich .

Editor information

Editors and Affiliations

  1. University of Pavia, Pavia, Italy

    Oliviero Carugo

  2. Genome Institute and Bioinformatics Institute, Singapore, Singapore

    Frank Eisenhaber

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© 2022 The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature

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Pepe, G., Carrino, C., Parca, L., Helmer-Citterich, M. (2022). Dissecting the Genome for Drug Response Prediction. In: Carugo, O., Eisenhaber, F. (eds) Data Mining Techniques for the Life Sciences. Methods in Molecular Biology, vol 2449. Humana, New York, NY. https://doi.org/10.1007/978-1-0716-2095-3_7

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  • DOI: https://doi.org/10.1007/978-1-0716-2095-3_7

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  • Publisher Name: Humana, New York, NY

  • Print ISBN: 978-1-0716-2094-6

  • Online ISBN: 978-1-0716-2095-3

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