Genomic models of short-term exposure accurately predict long-term chemical carcinogenicity and identify putative mechanisms of action
Despite an overall decrease in incidence of and mortality from cancer, about 40% of Americans will be diagnosed with the disease in their lifetime, and around 20% will die of it. Current approaches to test carcinogenic chemicals adopt the 2-year rodent bioassay, which is costly and time-consuming. A...
Gespeichert in:
| Veröffentlicht in: | PloS one 2014-07, Vol.9 (7), p.e102579 |
|---|---|
| Hauptverfasser: | , , , , , |
| Format: | Artikel |
| Sprache: | eng |
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| container_end_page | |
|---|---|
| container_issue | 7 |
| container_start_page | e102579 |
| container_title | PloS one |
| container_volume | 9 |
| creator | Gusenleitner, Daniel Auerbach, Scott S Melia, Tisha Gómez, Harold F Sherr, David H Monti, Stefano |
| description | Despite an overall decrease in incidence of and mortality from cancer, about 40% of Americans will be diagnosed with the disease in their lifetime, and around 20% will die of it. Current approaches to test carcinogenic chemicals adopt the 2-year rodent bioassay, which is costly and time-consuming. As a result, fewer than 2% of the chemicals on the market have actually been tested. However, evidence accumulated to date suggests that gene expression profiles from model organisms exposed to chemical compounds reflect underlying mechanisms of action, and that these toxicogenomic models could be used in the prediction of chemical carcinogenicity.
In this study, we used a rat-based microarray dataset from the NTP DrugMatrix Database to test the ability of toxicogenomics to model carcinogenicity. We analyzed 1,221 gene-expression profiles obtained from rats treated with 127 well-characterized compounds, including genotoxic and non-genotoxic carcinogens. We built a classifier that predicts a chemical's carcinogenic potential with an AUC of 0.78, and validated it on an independent dataset from the Japanese Toxicogenomics Project consisting of 2,065 profiles from 72 compounds. Finally, we identified differentially expressed genes associated with chemical carcinogenesis, and developed novel data-driven approaches for the molecular characterization of the response to chemical stressors.
Here, we validate a toxicogenomic approach to predict carcinogenicity and provide strong evidence that, with a larger set of compounds, we should be able to improve the sensitivity and specificity of the predictions. We found that the prediction of carcinogenicity is tissue-dependent and that the results also confirm and expand upon previous studies implicating DNA damage, the peroxisome proliferator-activated receptor, the aryl hydrocarbon receptor, and regenerative pathology in the response to carcinogen exposure. |
| doi_str_mv | 10.1371/journal.pone.0102579 |
| format | Article |
| fullrecord | <record><control><sourceid>gale_plos_</sourceid><recordid>TN_cdi_plos_journals_1548239839</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><galeid>A418127201</galeid><doaj_id>oai_doaj_org_article_844fbd201a39442d9f1ba02cc68aa821</doaj_id><sourcerecordid>A418127201</sourcerecordid><originalsourceid>FETCH-LOGICAL-c758t-d443756230a47dd8bab265a8dcb4d28d0ede7b73614f6eb55669ff89e533a1cb3</originalsourceid><addsrcrecordid>eNqNk91q3DAQhU1padK0b1BaQ6HQi93qz7Z8EwihTRcCgf7dirEkr7XY0laSQ_YF-tzVZp2whhaKL2Sk7xwNRzNZ9hqjJaYV_rhxo7fQL7fO6iXCiBRV_SQ7xTUli5Ig-vTo_yR7EcIGoYLysnyenZACFRxRdJr9vtLWDUbmg1O6D7lr89A5HxdR-yHXd1sXRq9zkHL0EHW_y7deKyNj3ju7PlCy08kB-lyCl8a6tbZGmrjLwarcKG2jaZNujBDNrc4HLTuwJgz3t4GMxtmX2bMW-qBfTetZ9uPzp--XXxbXN1ery4vrhawKHheKMVoVJaEIWKUUb6AhZQFcyYYpwhXSSldNRUvM2lI3RVGWddvyWheUApYNPcveHny3vQtiijAIXDBOaM1pnYjVgVAONmLrzQB-JxwYcb_h_FqAj0b2WnDG2kYRhIHWjBFVt7gBRKQsOQAnOHmdT7eNzaCVTEl46Gem8xNrOrF2t4JhVNeEJoN3k4F3v0Yd4j9Knqg1pKqMbV0yk4MJUlwwzDGpUo2JWv6FSp_av15qotak_Zngw0yQmKjv4hrGEMTq29f_Z29-ztn3R2ynoY9dcP2474MwB9kBlN6F4HX7mBxGYj8DD2mI_QyIaQaS7M1x6o-ih6anfwBR8wSx</addsrcrecordid><sourcetype>Open Website</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>1548239839</pqid></control><display><type>article</type><title>Genomic models of short-term exposure accurately predict long-term chemical carcinogenicity and identify putative mechanisms of action</title><source>Public Library of Science (PLoS) Journals Open Access</source><source>MEDLINE</source><source>DOAJ Directory of Open Access Journals</source><source>EZB-FREE-00999 freely available EZB journals</source><source>PubMed Central</source><source>Free Full-Text Journals in Chemistry</source><creator>Gusenleitner, Daniel ; Auerbach, Scott S ; Melia, Tisha ; Gómez, Harold F ; Sherr, David H ; Monti, Stefano</creator><creatorcontrib>Gusenleitner, Daniel ; Auerbach, Scott S ; Melia, Tisha ; Gómez, Harold F ; Sherr, David H ; Monti, Stefano</creatorcontrib><description>Despite an overall decrease in incidence of and mortality from cancer, about 40% of Americans will be diagnosed with the disease in their lifetime, and around 20% will die of it. Current approaches to test carcinogenic chemicals adopt the 2-year rodent bioassay, which is costly and time-consuming. As a result, fewer than 2% of the chemicals on the market have actually been tested. However, evidence accumulated to date suggests that gene expression profiles from model organisms exposed to chemical compounds reflect underlying mechanisms of action, and that these toxicogenomic models could be used in the prediction of chemical carcinogenicity.
In this study, we used a rat-based microarray dataset from the NTP DrugMatrix Database to test the ability of toxicogenomics to model carcinogenicity. We analyzed 1,221 gene-expression profiles obtained from rats treated with 127 well-characterized compounds, including genotoxic and non-genotoxic carcinogens. We built a classifier that predicts a chemical's carcinogenic potential with an AUC of 0.78, and validated it on an independent dataset from the Japanese Toxicogenomics Project consisting of 2,065 profiles from 72 compounds. Finally, we identified differentially expressed genes associated with chemical carcinogenesis, and developed novel data-driven approaches for the molecular characterization of the response to chemical stressors.
Here, we validate a toxicogenomic approach to predict carcinogenicity and provide strong evidence that, with a larger set of compounds, we should be able to improve the sensitivity and specificity of the predictions. We found that the prediction of carcinogenicity is tissue-dependent and that the results also confirm and expand upon previous studies implicating DNA damage, the peroxisome proliferator-activated receptor, the aryl hydrocarbon receptor, and regenerative pathology in the response to carcinogen exposure.</description><identifier>ISSN: 1932-6203</identifier><identifier>EISSN: 1932-6203</identifier><identifier>DOI: 10.1371/journal.pone.0102579</identifier><identifier>PMID: 25058030</identifier><language>eng</language><publisher>United States: Public Library of Science</publisher><subject>Animal models ; Animals ; Area Under Curve ; Aromatic compounds ; Bioassays ; Bioinformatics ; Biology and Life Sciences ; Breast cancer ; Cancer ; Carcinogenesis ; Carcinogenicity ; Carcinogenicity Tests - methods ; Carcinogens ; Carcinogens - toxicity ; Chemical compounds ; Chemicals ; Databases, Factual ; Datasets ; Decision theory ; Deoxyribonucleic acid ; DNA ; DNA Damage ; DNA microarrays ; DNA Repair ; Drugs, Investigational - toxicity ; Environmental health ; Exposure ; Gene expression ; Gene Expression - drug effects ; Gene Expression Profiling ; Genomes ; Genomics ; Genotoxicity ; Health aspects ; Male ; Mathematical models ; Medicine and Health Sciences ; Models, Genetic ; Organ Specificity ; Peroxisome Proliferator-Activated Receptors - genetics ; Peroxisome Proliferator-Activated Receptors - metabolism ; Pollutants ; Principal components analysis ; Quantitative Structure-Activity Relationship ; Rats ; Receptors, Aryl Hydrocarbon - genetics ; Receptors, Aryl Hydrocarbon - metabolism ; Rodents ; Sensitivity and Specificity ; Short term ; Toxicogenetics</subject><ispartof>PloS one, 2014-07, Vol.9 (7), p.e102579</ispartof><rights>COPYRIGHT 2014 Public Library of Science</rights><rights>2014. This is an open-access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. The work is made available under the Creative Commons CC0 public domain dedication. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><rights>2014</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c758t-d443756230a47dd8bab265a8dcb4d28d0ede7b73614f6eb55669ff89e533a1cb3</citedby><cites>FETCH-LOGICAL-c758t-d443756230a47dd8bab265a8dcb4d28d0ede7b73614f6eb55669ff89e533a1cb3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC4109923/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC4109923/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,314,723,776,780,860,881,2096,2915,23845,27901,27902,53766,53768,79342,79343</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/25058030$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Gusenleitner, Daniel</creatorcontrib><creatorcontrib>Auerbach, Scott S</creatorcontrib><creatorcontrib>Melia, Tisha</creatorcontrib><creatorcontrib>Gómez, Harold F</creatorcontrib><creatorcontrib>Sherr, David H</creatorcontrib><creatorcontrib>Monti, Stefano</creatorcontrib><title>Genomic models of short-term exposure accurately predict long-term chemical carcinogenicity and identify putative mechanisms of action</title><title>PloS one</title><addtitle>PLoS One</addtitle><description>Despite an overall decrease in incidence of and mortality from cancer, about 40% of Americans will be diagnosed with the disease in their lifetime, and around 20% will die of it. Current approaches to test carcinogenic chemicals adopt the 2-year rodent bioassay, which is costly and time-consuming. As a result, fewer than 2% of the chemicals on the market have actually been tested. However, evidence accumulated to date suggests that gene expression profiles from model organisms exposed to chemical compounds reflect underlying mechanisms of action, and that these toxicogenomic models could be used in the prediction of chemical carcinogenicity.
In this study, we used a rat-based microarray dataset from the NTP DrugMatrix Database to test the ability of toxicogenomics to model carcinogenicity. We analyzed 1,221 gene-expression profiles obtained from rats treated with 127 well-characterized compounds, including genotoxic and non-genotoxic carcinogens. We built a classifier that predicts a chemical's carcinogenic potential with an AUC of 0.78, and validated it on an independent dataset from the Japanese Toxicogenomics Project consisting of 2,065 profiles from 72 compounds. Finally, we identified differentially expressed genes associated with chemical carcinogenesis, and developed novel data-driven approaches for the molecular characterization of the response to chemical stressors.
Here, we validate a toxicogenomic approach to predict carcinogenicity and provide strong evidence that, with a larger set of compounds, we should be able to improve the sensitivity and specificity of the predictions. We found that the prediction of carcinogenicity is tissue-dependent and that the results also confirm and expand upon previous studies implicating DNA damage, the peroxisome proliferator-activated receptor, the aryl hydrocarbon receptor, and regenerative pathology in the response to carcinogen exposure.</description><subject>Animal models</subject><subject>Animals</subject><subject>Area Under Curve</subject><subject>Aromatic compounds</subject><subject>Bioassays</subject><subject>Bioinformatics</subject><subject>Biology and Life Sciences</subject><subject>Breast cancer</subject><subject>Cancer</subject><subject>Carcinogenesis</subject><subject>Carcinogenicity</subject><subject>Carcinogenicity Tests - methods</subject><subject>Carcinogens</subject><subject>Carcinogens - toxicity</subject><subject>Chemical compounds</subject><subject>Chemicals</subject><subject>Databases, Factual</subject><subject>Datasets</subject><subject>Decision theory</subject><subject>Deoxyribonucleic acid</subject><subject>DNA</subject><subject>DNA Damage</subject><subject>DNA microarrays</subject><subject>DNA Repair</subject><subject>Drugs, Investigational - toxicity</subject><subject>Environmental health</subject><subject>Exposure</subject><subject>Gene expression</subject><subject>Gene Expression - drug effects</subject><subject>Gene Expression Profiling</subject><subject>Genomes</subject><subject>Genomics</subject><subject>Genotoxicity</subject><subject>Health aspects</subject><subject>Male</subject><subject>Mathematical models</subject><subject>Medicine and Health Sciences</subject><subject>Models, Genetic</subject><subject>Organ Specificity</subject><subject>Peroxisome Proliferator-Activated Receptors - genetics</subject><subject>Peroxisome Proliferator-Activated Receptors - metabolism</subject><subject>Pollutants</subject><subject>Principal components analysis</subject><subject>Quantitative Structure-Activity Relationship</subject><subject>Rats</subject><subject>Receptors, Aryl Hydrocarbon - genetics</subject><subject>Receptors, Aryl Hydrocarbon - metabolism</subject><subject>Rodents</subject><subject>Sensitivity and Specificity</subject><subject>Short term</subject><subject>Toxicogenetics</subject><issn>1932-6203</issn><issn>1932-6203</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2014</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><sourceid>BENPR</sourceid><sourceid>DOA</sourceid><recordid>eNqNk91q3DAQhU1padK0b1BaQ6HQi93qz7Z8EwihTRcCgf7dirEkr7XY0laSQ_YF-tzVZp2whhaKL2Sk7xwNRzNZ9hqjJaYV_rhxo7fQL7fO6iXCiBRV_SQ7xTUli5Ig-vTo_yR7EcIGoYLysnyenZACFRxRdJr9vtLWDUbmg1O6D7lr89A5HxdR-yHXd1sXRq9zkHL0EHW_y7deKyNj3ju7PlCy08kB-lyCl8a6tbZGmrjLwarcKG2jaZNujBDNrc4HLTuwJgz3t4GMxtmX2bMW-qBfTetZ9uPzp--XXxbXN1ery4vrhawKHheKMVoVJaEIWKUUb6AhZQFcyYYpwhXSSldNRUvM2lI3RVGWddvyWheUApYNPcveHny3vQtiijAIXDBOaM1pnYjVgVAONmLrzQB-JxwYcb_h_FqAj0b2WnDG2kYRhIHWjBFVt7gBRKQsOQAnOHmdT7eNzaCVTEl46Gem8xNrOrF2t4JhVNeEJoN3k4F3v0Yd4j9Knqg1pKqMbV0yk4MJUlwwzDGpUo2JWv6FSp_av15qotak_Zngw0yQmKjv4hrGEMTq29f_Z29-ztn3R2ynoY9dcP2474MwB9kBlN6F4HX7mBxGYj8DD2mI_QyIaQaS7M1x6o-ih6anfwBR8wSx</recordid><startdate>20140724</startdate><enddate>20140724</enddate><creator>Gusenleitner, Daniel</creator><creator>Auerbach, Scott S</creator><creator>Melia, Tisha</creator><creator>Gómez, Harold F</creator><creator>Sherr, David H</creator><creator>Monti, Stefano</creator><general>Public Library of Science</general><general>Public Library of Science (PLoS)</general><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>IOV</scope><scope>ISR</scope><scope>3V.</scope><scope>7QG</scope><scope>7QL</scope><scope>7QO</scope><scope>7RV</scope><scope>7SN</scope><scope>7SS</scope><scope>7T5</scope><scope>7TG</scope><scope>7TM</scope><scope>7U9</scope><scope>7X2</scope><scope>7X7</scope><scope>7XB</scope><scope>88E</scope><scope>8AO</scope><scope>8C1</scope><scope>8FD</scope><scope>8FE</scope><scope>8FG</scope><scope>8FH</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>ABJCF</scope><scope>ABUWG</scope><scope>AEUYN</scope><scope>AFKRA</scope><scope>ARAPS</scope><scope>ATCPS</scope><scope>AZQEC</scope><scope>BBNVY</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>BHPHI</scope><scope>C1K</scope><scope>CCPQU</scope><scope>D1I</scope><scope>DWQXO</scope><scope>FR3</scope><scope>FYUFA</scope><scope>GHDGH</scope><scope>GNUQQ</scope><scope>H94</scope><scope>HCIFZ</scope><scope>K9.</scope><scope>KB.</scope><scope>KB0</scope><scope>KL.</scope><scope>L6V</scope><scope>LK8</scope><scope>M0K</scope><scope>M0S</scope><scope>M1P</scope><scope>M7N</scope><scope>M7P</scope><scope>M7S</scope><scope>NAPCQ</scope><scope>P5Z</scope><scope>P62</scope><scope>P64</scope><scope>PATMY</scope><scope>PDBOC</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>PTHSS</scope><scope>PYCSY</scope><scope>RC3</scope><scope>5PM</scope><scope>DOA</scope></search><sort><creationdate>20140724</creationdate><title>Genomic models of short-term exposure accurately predict long-term chemical carcinogenicity and identify putative mechanisms of action</title><author>Gusenleitner, Daniel ; Auerbach, Scott S ; Melia, Tisha ; Gómez, Harold F ; Sherr, David H ; Monti, Stefano</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c758t-d443756230a47dd8bab265a8dcb4d28d0ede7b73614f6eb55669ff89e533a1cb3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2014</creationdate><topic>Animal models</topic><topic>Animals</topic><topic>Area Under Curve</topic><topic>Aromatic compounds</topic><topic>Bioassays</topic><topic>Bioinformatics</topic><topic>Biology and Life Sciences</topic><topic>Breast cancer</topic><topic>Cancer</topic><topic>Carcinogenesis</topic><topic>Carcinogenicity</topic><topic>Carcinogenicity Tests - methods</topic><topic>Carcinogens</topic><topic>Carcinogens - toxicity</topic><topic>Chemical compounds</topic><topic>Chemicals</topic><topic>Databases, Factual</topic><topic>Datasets</topic><topic>Decision theory</topic><topic>Deoxyribonucleic acid</topic><topic>DNA</topic><topic>DNA Damage</topic><topic>DNA microarrays</topic><topic>DNA Repair</topic><topic>Drugs, Investigational - toxicity</topic><topic>Environmental health</topic><topic>Exposure</topic><topic>Gene expression</topic><topic>Gene Expression - drug effects</topic><topic>Gene Expression Profiling</topic><topic>Genomes</topic><topic>Genomics</topic><topic>Genotoxicity</topic><topic>Health aspects</topic><topic>Male</topic><topic>Mathematical models</topic><topic>Medicine and Health Sciences</topic><topic>Models, Genetic</topic><topic>Organ Specificity</topic><topic>Peroxisome Proliferator-Activated Receptors - genetics</topic><topic>Peroxisome Proliferator-Activated Receptors - metabolism</topic><topic>Pollutants</topic><topic>Principal components analysis</topic><topic>Quantitative Structure-Activity Relationship</topic><topic>Rats</topic><topic>Receptors, Aryl Hydrocarbon - genetics</topic><topic>Receptors, Aryl Hydrocarbon - metabolism</topic><topic>Rodents</topic><topic>Sensitivity and Specificity</topic><topic>Short term</topic><topic>Toxicogenetics</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Gusenleitner, Daniel</creatorcontrib><creatorcontrib>Auerbach, Scott S</creatorcontrib><creatorcontrib>Melia, Tisha</creatorcontrib><creatorcontrib>Gómez, Harold F</creatorcontrib><creatorcontrib>Sherr, David H</creatorcontrib><creatorcontrib>Monti, Stefano</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>Gale In Context: Opposing Viewpoints</collection><collection>Gale In Context: Science</collection><collection>ProQuest Central (Corporate)</collection><collection>Animal Behavior Abstracts</collection><collection>Bacteriology Abstracts (Microbiology B)</collection><collection>Biotechnology Research Abstracts</collection><collection>Nursing & Allied Health Database</collection><collection>Ecology Abstracts</collection><collection>Entomology Abstracts (Full archive)</collection><collection>Immunology Abstracts</collection><collection>Meteorological & Geoastrophysical Abstracts</collection><collection>Nucleic Acids Abstracts</collection><collection>Virology and AIDS Abstracts</collection><collection>Agricultural Science Collection</collection><collection>Health & Medical Collection</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Medical Database (Alumni Edition)</collection><collection>ProQuest Pharma Collection</collection><collection>Public Health Database</collection><collection>Technology Research Database</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>ProQuest Natural Science Collection</collection><collection>Hospital Premium Collection</collection><collection>Hospital Premium Collection (Alumni Edition)</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>Materials Science & Engineering Collection</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest One Sustainability</collection><collection>ProQuest Central UK/Ireland</collection><collection>Advanced Technologies & Aerospace Collection</collection><collection>Agricultural & Environmental Science Collection</collection><collection>ProQuest Central Essentials</collection><collection>Biological Science Collection</collection><collection>ProQuest Central</collection><collection>Technology Collection</collection><collection>Natural Science Collection</collection><collection>Environmental Sciences and Pollution Management</collection><collection>ProQuest One Community College</collection><collection>ProQuest Materials Science Collection</collection><collection>ProQuest Central Korea</collection><collection>Engineering Research Database</collection><collection>Health Research Premium Collection</collection><collection>Health Research Premium Collection (Alumni)</collection><collection>ProQuest Central Student</collection><collection>AIDS and Cancer Research Abstracts</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>Materials Science Database</collection><collection>Nursing & Allied Health Database (Alumni Edition)</collection><collection>Meteorological & Geoastrophysical Abstracts - Academic</collection><collection>ProQuest Engineering Collection</collection><collection>ProQuest Biological Science Collection</collection><collection>Agricultural Science Database</collection><collection>Health & Medical Collection (Alumni Edition)</collection><collection>Medical Database</collection><collection>Algology Mycology and Protozoology Abstracts (Microbiology C)</collection><collection>Biological Science Database</collection><collection>Engineering Database</collection><collection>Nursing & Allied Health Premium</collection><collection>Advanced Technologies & Aerospace Database</collection><collection>ProQuest Advanced Technologies & Aerospace Collection</collection><collection>Biotechnology and BioEngineering Abstracts</collection><collection>Environmental Science Database</collection><collection>Materials Science Collection</collection><collection>Publicly Available Content Database</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><collection>Engineering Collection</collection><collection>Environmental Science Collection</collection><collection>Genetics Abstracts</collection><collection>PubMed Central (Full Participant titles)</collection><collection>DOAJ Directory of Open Access Journals</collection><jtitle>PloS one</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Gusenleitner, Daniel</au><au>Auerbach, Scott S</au><au>Melia, Tisha</au><au>Gómez, Harold F</au><au>Sherr, David H</au><au>Monti, Stefano</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Genomic models of short-term exposure accurately predict long-term chemical carcinogenicity and identify putative mechanisms of action</atitle><jtitle>PloS one</jtitle><addtitle>PLoS One</addtitle><date>2014-07-24</date><risdate>2014</risdate><volume>9</volume><issue>7</issue><spage>e102579</spage><pages>e102579-</pages><issn>1932-6203</issn><eissn>1932-6203</eissn><abstract>Despite an overall decrease in incidence of and mortality from cancer, about 40% of Americans will be diagnosed with the disease in their lifetime, and around 20% will die of it. Current approaches to test carcinogenic chemicals adopt the 2-year rodent bioassay, which is costly and time-consuming. As a result, fewer than 2% of the chemicals on the market have actually been tested. However, evidence accumulated to date suggests that gene expression profiles from model organisms exposed to chemical compounds reflect underlying mechanisms of action, and that these toxicogenomic models could be used in the prediction of chemical carcinogenicity.
In this study, we used a rat-based microarray dataset from the NTP DrugMatrix Database to test the ability of toxicogenomics to model carcinogenicity. We analyzed 1,221 gene-expression profiles obtained from rats treated with 127 well-characterized compounds, including genotoxic and non-genotoxic carcinogens. We built a classifier that predicts a chemical's carcinogenic potential with an AUC of 0.78, and validated it on an independent dataset from the Japanese Toxicogenomics Project consisting of 2,065 profiles from 72 compounds. Finally, we identified differentially expressed genes associated with chemical carcinogenesis, and developed novel data-driven approaches for the molecular characterization of the response to chemical stressors.
Here, we validate a toxicogenomic approach to predict carcinogenicity and provide strong evidence that, with a larger set of compounds, we should be able to improve the sensitivity and specificity of the predictions. We found that the prediction of carcinogenicity is tissue-dependent and that the results also confirm and expand upon previous studies implicating DNA damage, the peroxisome proliferator-activated receptor, the aryl hydrocarbon receptor, and regenerative pathology in the response to carcinogen exposure.</abstract><cop>United States</cop><pub>Public Library of Science</pub><pmid>25058030</pmid><doi>10.1371/journal.pone.0102579</doi><oa>free_for_read</oa></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 1932-6203 |
| ispartof | PloS one, 2014-07, Vol.9 (7), p.e102579 |
| issn | 1932-6203 1932-6203 |
| language | eng |
| recordid | cdi_plos_journals_1548239839 |
| source | Public Library of Science (PLoS) Journals Open Access; MEDLINE; DOAJ Directory of Open Access Journals; EZB-FREE-00999 freely available EZB journals; PubMed Central; Free Full-Text Journals in Chemistry |
| subjects | Animal models Animals Area Under Curve Aromatic compounds Bioassays Bioinformatics Biology and Life Sciences Breast cancer Cancer Carcinogenesis Carcinogenicity Carcinogenicity Tests - methods Carcinogens Carcinogens - toxicity Chemical compounds Chemicals Databases, Factual Datasets Decision theory Deoxyribonucleic acid DNA DNA Damage DNA microarrays DNA Repair Drugs, Investigational - toxicity Environmental health Exposure Gene expression Gene Expression - drug effects Gene Expression Profiling Genomes Genomics Genotoxicity Health aspects Male Mathematical models Medicine and Health Sciences Models, Genetic Organ Specificity Peroxisome Proliferator-Activated Receptors - genetics Peroxisome Proliferator-Activated Receptors - metabolism Pollutants Principal components analysis Quantitative Structure-Activity Relationship Rats Receptors, Aryl Hydrocarbon - genetics Receptors, Aryl Hydrocarbon - metabolism Rodents Sensitivity and Specificity Short term Toxicogenetics |
| title | Genomic models of short-term exposure accurately predict long-term chemical carcinogenicity and identify putative mechanisms of action |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-02-08T19%3A41%3A38IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-gale_plos_&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Genomic%20models%20of%20short-term%20exposure%20accurately%20predict%20long-term%20chemical%20carcinogenicity%20and%20identify%20putative%20mechanisms%20of%20action&rft.jtitle=PloS%20one&rft.au=Gusenleitner,%20Daniel&rft.date=2014-07-24&rft.volume=9&rft.issue=7&rft.spage=e102579&rft.pages=e102579-&rft.issn=1932-6203&rft.eissn=1932-6203&rft_id=info:doi/10.1371/journal.pone.0102579&rft_dat=%3Cgale_plos_%3EA418127201%3C/gale_plos_%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=1548239839&rft_id=info:pmid/25058030&rft_galeid=A418127201&rft_doaj_id=oai_doaj_org_article_844fbd201a39442d9f1ba02cc68aa821&rfr_iscdi=true |