publication . Other literature type . Preprint . 2019

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

James C. Mathews; Joseph O. Deasy; Caroline Moosmüller; Caroline Moosmüller; Maryam Pouryahya; Jung Hun Oh; Zehor Belkhatir; Allen Tannenbaum; Allen Tannenbaum;
Open Access
  • Published: 18 Sep 2019
  • Publisher: bioRxiv
Abstract
<jats:title>Abstract</jats:title><jats:p>The study of large-scale pharmacogenomics provides an unprecedented opportunity to develop computational models that can accurately predict large cohorts of cell lines and drugs. In this work, we present a novel method for predicting drug sensitivity in cancer cell lines which considers both cell line genomic features and drug chemical features. Our network-based approach combines the theory of optimal mass transport (OMT) with machine learning techniques. It starts with unsupervised clustering of both cell line and drug data, followed by the prediction of drug sensitivity in the paired cluster of cell lines and drugs. We...
Subjects
free text keywords: Interpretability, Computational model, Cancer cell lines, Pharmacogenomics, Mass transport, Drug, media_common.quotation_subject, media_common, Drug response, Computational biology, Computer science, Cluster analysis
Funded by
NIH| Glymphatic function in a transgenic rat model of Alzheimer's disease
Project
  • Funder: National Institutes of Health (NIH)
  • Project Code: 5R01AG048769-04
  • Funding stream: NATIONAL INSTITUTE ON AGING
,
NIH| MOUSE GENETICS
Project
  • Funder: National Institutes of Health (NIH)
  • Project Code: 2P30CA008748-43
  • Funding stream: NATIONAL CANCER INSTITUTE
35 references, page 1 of 3

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.

35 references, page 1 of 3
Abstract
<jats:title>Abstract</jats:title><jats:p>The study of large-scale pharmacogenomics provides an unprecedented opportunity to develop computational models that can accurately predict large cohorts of cell lines and drugs. In this work, we present a novel method for predicting drug sensitivity in cancer cell lines which considers both cell line genomic features and drug chemical features. Our network-based approach combines the theory of optimal mass transport (OMT) with machine learning techniques. It starts with unsupervised clustering of both cell line and drug data, followed by the prediction of drug sensitivity in the paired cluster of cell lines and drugs. We...
Subjects
free text keywords: Interpretability, Computational model, Cancer cell lines, Pharmacogenomics, Mass transport, Drug, media_common.quotation_subject, media_common, Drug response, Computational biology, Computer science, Cluster analysis
Funded by
NIH| Glymphatic function in a transgenic rat model of Alzheimer's disease
Project
  • Funder: National Institutes of Health (NIH)
  • Project Code: 5R01AG048769-04
  • Funding stream: NATIONAL INSTITUTE ON AGING
,
NIH| MOUSE GENETICS
Project
  • Funder: National Institutes of Health (NIH)
  • Project Code: 2P30CA008748-43
  • Funding stream: NATIONAL CANCER INSTITUTE
35 references, page 1 of 3

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.

35 references, page 1 of 3
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