Machine Learning Prediction of Cancer Cell Sensitivity to Drugs Based on Genomic and Chemical Properties

Predicting the response of a specific cancer to a therapy is a major goal in modern oncology that should ultimately lead to a personalised treatment. High-throughput screenings of potentially active compounds against a panel of genomically heterogeneous cancer cell lines have unveiled multiple relat...

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Published in:	PloS one Vol. 8; no. 4; p. e61318
Main Authors:	Menden, Michael P., Iorio, Francesco, Garnett, Mathew, McDermott, Ultan, Benes, Cyril H., Ballester, Pedro J., Saez-Rodriguez, Julio
Format:	Journal Article
Language:	English
Published:	United States Public Library of Science 30.04.2013 Public Library of Science (PLoS)
Subjects:	Analysis of Variance Antineoplastic Agents - pharmacology Antineoplastic Agents - therapeutic use Artificial Intelligence Bioinformatics Biology Biomarkers Biotechnology Cancer Chemical properties Computation Computer applications Computer Science Computer Simulation Design optimization Drug development Drug Resistance, Neoplasm - genetics Drug screening Drugs Experimental design Gene amplification Gene expression Genomes Genomics Genomics - methods Humans Inhibitory Concentration 50 Learning algorithms Machine learning Mathematical models Medical screening Medicine Multivariate analysis Mutation Neoplasms - drug therapy Neoplasms - genetics Pharmacogenetics - methods Precision medicine Predictions Sensitivity Tumor cell lines Tumors Workflow United States > US Massachusetts
ISSN:	1932-6203, 1932-6203
Online Access:	Get full text
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Abstract	Predicting the response of a specific cancer to a therapy is a major goal in modern oncology that should ultimately lead to a personalised treatment. High-throughput screenings of potentially active compounds against a panel of genomically heterogeneous cancer cell lines have unveiled multiple relationships between genomic alterations and drug responses. Various computational approaches have been proposed to predict sensitivity based on genomic features, while others have used the chemical properties of the drugs to ascertain their effect. In an effort to integrate these complementary approaches, we developed machine learning models to predict the response of cancer cell lines to drug treatment, quantified through IC₅₀ values, based on both the genomic features of the cell lines and the chemical properties of the considered drugs. Models predicted IC₅₀ values in a 8-fold cross-validation and an independent blind test with coefficient of determination R² of 0.72 and 0.64 respectively. Furthermore, models were able to predict with comparable accuracy (R² of 0.61) IC50s of cell lines from a tissue not used in the training stage. Our in silico models can be used to optimise the experimental design of drug-cell screenings by estimating a large proportion of missing IC₅₀ values rather than experimentally measuring them. The implications of our results go beyond virtual drug screening design: potentially thousands of drugs could be probed in silico to systematically test their potential efficacy as anti-tumour agents based on their structure, thus providing a computational framework to identify new drug repositioning opportunities as well as ultimately be useful for personalized medicine by linking the genomic traits of patients to drug sensitivity.
AbstractList	Predicting the response of a specific cancer to a therapy is a major goal in modern oncology that should ultimately lead to a personalised treatment. High-throughput screenings of potentially active compounds against a panel of genomically heterogeneous cancer cell lines have unveiled multiple relationships between genomic alterations and drug responses. Various computational approaches have been proposed to predict sensitivity based on genomic features, while others have used the chemical properties of the drugs to ascertain their effect. In an effort to integrate these complementary approaches, we developed machine learning models to predict the response of cancer cell lines to drug treatment, quantified through IC50 values, based on both the genomic features of the cell lines and the chemical properties of the considered drugs. Models predicted IC50 values in a 8-fold cross-validation and an independent blind test with coefficient of determination R2 of 0.72 and 0.64 respectively. Furthermore, models were able to predict with comparable accuracy (R2 of 0.61) IC50s of cell lines from a tissue not used in the training stage. Our in silico models can be used to optimise the experimental design of drug-cell screenings by estimating a large proportion of missing IC50 values rather than experimentally measuring them. The implications of our results go beyond virtual drug screening design: potentially thousands of drugs could be probed in silico to systematically test their potential efficacy as anti-tumour agents based on their structure, thus providing a computational framework to identify new drug repositioning opportunities as well as ultimately be useful for personalized medicine by linking the genomic traits of patients to drug sensitivity. Predicting the response of a specific cancer to a therapy is a major goal in modern oncology that should ultimately lead to a personalised treatment. High-throughput screenings of potentially active compounds against a panel of genomically heterogeneous cancer cell lines have unveiled multiple relationships between genomic alterations and drug responses. Various computational approaches have been proposed to predict sensitivity based on genomic features, while others have used the chemical properties of the drugs to ascertain their effect. In an effort to integrate these complementary approaches, we developed machine learning models to predict the response of cancer cell lines to drug treatment, quantified through IC₅₀ values, based on both the genomic features of the cell lines and the chemical properties of the considered drugs. Models predicted IC₅₀ values in a 8-fold cross-validation and an independent blind test with coefficient of determination R² of 0.72 and 0.64 respectively. Furthermore, models were able to predict with comparable accuracy (R² of 0.61) IC50s of cell lines from a tissue not used in the training stage. Our in silico models can be used to optimise the experimental design of drug-cell screenings by estimating a large proportion of missing IC₅₀ values rather than experimentally measuring them. The implications of our results go beyond virtual drug screening design: potentially thousands of drugs could be probed in silico to systematically test their potential efficacy as anti-tumour agents based on their structure, thus providing a computational framework to identify new drug repositioning opportunities as well as ultimately be useful for personalized medicine by linking the genomic traits of patients to drug sensitivity. Predicting the response of a specific cancer to a therapy is a major goal in modern oncology that should ultimately lead to a personalised treatment. High-throughput screenings of potentially active compounds against a panel of genomically heterogeneous cancer cell lines have unveiled multiple relationships between genomic alterations and drug responses. Various computational approaches have been proposed to predict sensitivity based on genomic features, while others have used the chemical properties of the drugs to ascertain their effect. In an effort to integrate these complementary approaches, we developed machine learning models to predict the response of cancer cell lines to drug treatment, quantified through IC₅₀ values, based on both the genomic features of the cell lines and the chemical properties of the considered drugs. Models predicted IC₅₀ values in a 8-fold cross-validation and an independent blind test with coefficient of determination R² of 0.72 and 0.64 respectively. Furthermore, models were able to predict with comparable accuracy (R² of 0.61) IC50s of cell lines from a tissue not used in the training stage. Our in silico models can be used to optimise the experimental design of drug-cell screenings by estimating a large proportion of missing IC₅₀ values rather than experimentally measuring them. The implications of our results go beyond virtual drug screening design: potentially thousands of drugs could be probed in silico to systematically test their potential efficacy as anti-tumour agents based on their structure, thus providing a computational framework to identify new drug repositioning opportunities as well as ultimately be useful for personalized medicine by linking the genomic traits of patients to drug sensitivity.Predicting the response of a specific cancer to a therapy is a major goal in modern oncology that should ultimately lead to a personalised treatment. High-throughput screenings of potentially active compounds against a panel of genomically heterogeneous cancer cell lines have unveiled multiple relationships between genomic alterations and drug responses. Various computational approaches have been proposed to predict sensitivity based on genomic features, while others have used the chemical properties of the drugs to ascertain their effect. In an effort to integrate these complementary approaches, we developed machine learning models to predict the response of cancer cell lines to drug treatment, quantified through IC₅₀ values, based on both the genomic features of the cell lines and the chemical properties of the considered drugs. Models predicted IC₅₀ values in a 8-fold cross-validation and an independent blind test with coefficient of determination R² of 0.72 and 0.64 respectively. Furthermore, models were able to predict with comparable accuracy (R² of 0.61) IC50s of cell lines from a tissue not used in the training stage. Our in silico models can be used to optimise the experimental design of drug-cell screenings by estimating a large proportion of missing IC₅₀ values rather than experimentally measuring them. The implications of our results go beyond virtual drug screening design: potentially thousands of drugs could be probed in silico to systematically test their potential efficacy as anti-tumour agents based on their structure, thus providing a computational framework to identify new drug repositioning opportunities as well as ultimately be useful for personalized medicine by linking the genomic traits of patients to drug sensitivity.
Author	Menden, Michael P. Benes, Cyril H. Ballester, Pedro J. McDermott, Ultan Iorio, Francesco Garnett, Mathew Saez-Rodriguez, Julio
AuthorAffiliation	2 Cancer Genome Project, Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus-Cambridge, Cambridge, United Kingdom 1 European Bioinformatics Institute, Wellcome Trust Genome Campus–Cambridge, Cambridge, United Kingdom 3 Center for Molecular Therapeutics, Massachusetts General Hospital Cancer Center and Harvard Medical School, Charlestown, Massachusetts, United States of America CSIR-Institute of Microbial Technology, India
AuthorAffiliation_xml	– name: CSIR-Institute of Microbial Technology, India – name: 2 Cancer Genome Project, Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus-Cambridge, Cambridge, United Kingdom – name: 3 Center for Molecular Therapeutics, Massachusetts General Hospital Cancer Center and Harvard Medical School, Charlestown, Massachusetts, United States of America – name: 1 European Bioinformatics Institute, Wellcome Trust Genome Campus–Cambridge, Cambridge, United Kingdom
Author_xml	– sequence: 1 givenname: Michael P. surname: Menden fullname: Menden, Michael P. – sequence: 2 givenname: Francesco surname: Iorio fullname: Iorio, Francesco – sequence: 3 givenname: Mathew surname: Garnett fullname: Garnett, Mathew – sequence: 4 givenname: Ultan surname: McDermott fullname: McDermott, Ultan – sequence: 5 givenname: Cyril H. surname: Benes fullname: Benes, Cyril H. – sequence: 6 givenname: Pedro J. surname: Ballester fullname: Ballester, Pedro J. – sequence: 7 givenname: Julio surname: Saez-Rodriguez fullname: Saez-Rodriguez, Julio
BackLink	https://www.ncbi.nlm.nih.gov/pubmed/23646105$$D View this record in MEDLINE/PubMed
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Notes	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23 Designed the software and implementation of different approaches: MPM FI PJB. Conceived and designed the experiments: MPM PJB JSR. Performed the experiments: MPM FI PJB. Analyzed the data: MPM FI MG UM CHB PJB JSR. Contributed reagents/materials/analysis tools: MPM FI MG UM CHB PJB. Wrote the paper: MPM CHB PJB JSR. Competing Interests: The authors have declared that no competing interests exist.
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SubjectTerms	Analysis of Variance Antineoplastic Agents - pharmacology Antineoplastic Agents - therapeutic use Artificial Intelligence Bioinformatics Biology Biomarkers Biotechnology Cancer Chemical properties Computation Computer applications Computer Science Computer Simulation Design optimization Drug development Drug Resistance, Neoplasm - genetics Drug screening Drugs Experimental design Gene amplification Gene expression Genomes Genomics Genomics - methods Humans Inhibitory Concentration 50 Learning algorithms Machine learning Mathematical models Medical screening Medicine Multivariate analysis Mutation Neoplasms - drug therapy Neoplasms - genetics Pharmacogenetics - methods Precision medicine Predictions Sensitivity Tumor cell lines Tumors Workflow
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Title	Machine Learning Prediction of Cancer Cell Sensitivity to Drugs Based on Genomic and Chemical Properties
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