A data mining approach for identifying pathway-gene biomarkers for predicting clinical outcome: A case study of erlotinib and sorafenib
A novel data mining procedure is proposed for identifying potential pathway-gene biomarkers from preclinical drug sensitivity data for predicting clinical responses to erlotinib or sorafenib. The analysis applies linear ridge regression modeling to generate a small (N~1000) set of baseline gene expr...
Gespeichert in:
| Veröffentlicht in: | PloS one 2017-08, Vol.12 (8), p.e0181991 |
|---|---|
| 1. Verfasser: | |
| Format: | Artikel |
| Sprache: | eng |
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| container_end_page | |
|---|---|
| container_issue | 8 |
| container_start_page | e0181991 |
| container_title | PloS one |
| container_volume | 12 |
| creator | Covell, David G |
| description | A novel data mining procedure is proposed for identifying potential pathway-gene biomarkers from preclinical drug sensitivity data for predicting clinical responses to erlotinib or sorafenib. The analysis applies linear ridge regression modeling to generate a small (N~1000) set of baseline gene expressions that jointly yield quality predictions of preclinical drug sensitivity data and clinical responses. Standard clustering of the pathway-gene combinations from gene set enrichment analysis of this initial gene set, according to their shared appearance in molecular function pathways, yields a reduced (N~300) set of potential pathway-gene biomarkers. A modified method for quantifying pathway fitness is used to determine smaller numbers of over and under expressed genes that correspond with favorable and unfavorable clinical responses. Detailed literature-based evidence is provided in support of the roles of these under and over expressed genes in compound efficacy. RandomForest analysis of potential pathway-gene biomarkers finds average treatment prediction errors of 10% and 22%, respectively, for patients receiving erlotinib or sorafenib that had a favorable clinical response. Higher errors were found for both compounds when predicting an unfavorable clinical response. Collectively these results suggest complementary roles for biomarker genes and biomarker pathways when predicting clinical responses from preclinical data. |
| doi_str_mv | 10.1371/journal.pone.0181991 |
| format | Article |
| fullrecord | <record><control><sourceid>gale_plos_</sourceid><recordid>TN_cdi_plos_journals_1927152229</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><galeid>A500114365</galeid><doaj_id>oai_doaj_org_article_a3a68a291d8147518fc343e3ac54dd61</doaj_id><sourcerecordid>A500114365</sourcerecordid><originalsourceid>FETCH-LOGICAL-c692t-dfc4f6e2ca50bf69b79a047ce038eeee41ce8c3e852744b939cf333e8bfa40ce3</originalsourceid><addsrcrecordid>eNqNk1uLEzEUxwdR3HX1G4gGBMGH1lzmFh-EsngpLCx4ew1nMidt1ulkNsmo_QR-bdPt7NKCgslDcpLf-Sf5k5NlTxmdM1Gx11du9D1088H1OKesZlKye9kpk4LPSk7F_YP5SfYohCtKC1GX5cPshNeV5AUvTrPfC9JCBLKxve1XBIbBO9BrYpwntsU-WrPdbQwQ1z9hO1thj6SxbgP-O_pwww0eW6vjDtNd0tHQETdG7Tb4hiyIhoAkxLHdEmcI-s4l1DYE-pYE58Fgih5nDwx0AZ9M41n29f27L-cfZxeXH5bni4uZLiWPs9bo3JTINRS0MaVsKgk0rzRSUWNqOdNYa4F1was8b6SQ2giR4sZATjWKs-z5XnfoXFCTh0ExyStWcM5lIpZ7onVwpQZv01O3yoFVNwvOrxT4aHWHCgSUNXDJ2prlVcFqo0UuUIAu8rYtWdJ6O502NhtsdfLTQ3ckerzT27VauR-qKHJZ0TIJvJgEvLseMcR_XHmiVpBuZXvjkpje2KDVoqCUsVyURaLmf6FSb3FjdfpFxqb1o4RXRwmJifgrrmAMQS0_f_p_9vLbMfvygF0jdHEdXDdG6_pwDOZ7UHsXgkdz5xyjalcEt26oXRGoqQhS2rND1--Sbn-9-APN3QSL</addsrcrecordid><sourcetype>Open Website</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>1927152229</pqid></control><display><type>article</type><title>A data mining approach for identifying pathway-gene biomarkers for predicting clinical outcome: A case study of erlotinib and sorafenib</title><source>MEDLINE</source><source>DOAJ Directory of Open Access Journals</source><source>Elektronische Zeitschriftenbibliothek - Frei zugängliche E-Journals</source><source>Public Library of Science (PLoS)</source><source>PubMed Central</source><source>Free Full-Text Journals in Chemistry</source><creator>Covell, David G</creator><contributor>Nath, Aritro</contributor><creatorcontrib>Covell, David G ; Nath, Aritro</creatorcontrib><description>A novel data mining procedure is proposed for identifying potential pathway-gene biomarkers from preclinical drug sensitivity data for predicting clinical responses to erlotinib or sorafenib. The analysis applies linear ridge regression modeling to generate a small (N~1000) set of baseline gene expressions that jointly yield quality predictions of preclinical drug sensitivity data and clinical responses. Standard clustering of the pathway-gene combinations from gene set enrichment analysis of this initial gene set, according to their shared appearance in molecular function pathways, yields a reduced (N~300) set of potential pathway-gene biomarkers. A modified method for quantifying pathway fitness is used to determine smaller numbers of over and under expressed genes that correspond with favorable and unfavorable clinical responses. Detailed literature-based evidence is provided in support of the roles of these under and over expressed genes in compound efficacy. RandomForest analysis of potential pathway-gene biomarkers finds average treatment prediction errors of 10% and 22%, respectively, for patients receiving erlotinib or sorafenib that had a favorable clinical response. Higher errors were found for both compounds when predicting an unfavorable clinical response. Collectively these results suggest complementary roles for biomarker genes and biomarker pathways when predicting clinical responses from preclinical data.</description><identifier>ISSN: 1932-6203</identifier><identifier>EISSN: 1932-6203</identifier><identifier>DOI: 10.1371/journal.pone.0181991</identifier><identifier>PMID: 28792525</identifier><language>eng</language><publisher>United States: Public Library of Science</publisher><subject>Analysis ; Antineoplastic Agents - therapeutic use ; Apoptosis ; Bioindicators ; Biological markers ; Biology and Life Sciences ; Biomarkers ; Biomarkers, Pharmacological - metabolism ; Biomarkers, Tumor - genetics ; Biomarkers, Tumor - metabolism ; Case reports ; Case studies ; Clinical outcomes ; Cluster Analysis ; Clustering ; Cyclin-dependent kinases ; Data mining ; Data Mining - methods ; Data processing ; Databases, Factual ; Dosage and administration ; Erlotinib ; Erlotinib Hydrochloride - therapeutic use ; Fitness ; Gene expression ; Gene Expression Regulation, Neoplastic - drug effects ; Gene set enrichment analysis ; Genes ; Genomes ; Humans ; Inhibitor drugs ; Kinases ; Leukemia ; Linear Models ; Lung cancer ; Medical research ; Medicine and Health Sciences ; Microarray Analysis ; Mutation ; Neoplasms - drug therapy ; Neoplasms - genetics ; Neoplasms - metabolism ; Niacinamide - analogs & derivatives ; Niacinamide - therapeutic use ; Phenylurea Compounds - therapeutic use ; Physical Sciences ; Predictions ; Prognosis ; Regression analysis ; Research and Analysis Methods ; Sensitivity ; Sensitivity analysis ; Single nucleotide polymorphisms ; Sorafenib ; Studies ; Targeted cancer therapy ; Treatment Outcome</subject><ispartof>PloS one, 2017-08, Vol.12 (8), p.e0181991</ispartof><rights>COPYRIGHT 2017 Public Library of Science</rights><rights>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: https://creativecommons.org/publicdomain/zero/1.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c692t-dfc4f6e2ca50bf69b79a047ce038eeee41ce8c3e852744b939cf333e8bfa40ce3</citedby><cites>FETCH-LOGICAL-c692t-dfc4f6e2ca50bf69b79a047ce038eeee41ce8c3e852744b939cf333e8bfa40ce3</cites><orcidid>0000-0002-0952-6776</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC5549706/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC5549706/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,314,727,780,784,864,885,2102,2928,23866,27924,27925,53791,53793,79600,79601</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/28792525$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><contributor>Nath, Aritro</contributor><creatorcontrib>Covell, David G</creatorcontrib><title>A data mining approach for identifying pathway-gene biomarkers for predicting clinical outcome: A case study of erlotinib and sorafenib</title><title>PloS one</title><addtitle>PLoS One</addtitle><description>A novel data mining procedure is proposed for identifying potential pathway-gene biomarkers from preclinical drug sensitivity data for predicting clinical responses to erlotinib or sorafenib. The analysis applies linear ridge regression modeling to generate a small (N~1000) set of baseline gene expressions that jointly yield quality predictions of preclinical drug sensitivity data and clinical responses. Standard clustering of the pathway-gene combinations from gene set enrichment analysis of this initial gene set, according to their shared appearance in molecular function pathways, yields a reduced (N~300) set of potential pathway-gene biomarkers. A modified method for quantifying pathway fitness is used to determine smaller numbers of over and under expressed genes that correspond with favorable and unfavorable clinical responses. Detailed literature-based evidence is provided in support of the roles of these under and over expressed genes in compound efficacy. RandomForest analysis of potential pathway-gene biomarkers finds average treatment prediction errors of 10% and 22%, respectively, for patients receiving erlotinib or sorafenib that had a favorable clinical response. Higher errors were found for both compounds when predicting an unfavorable clinical response. Collectively these results suggest complementary roles for biomarker genes and biomarker pathways when predicting clinical responses from preclinical data.</description><subject>Analysis</subject><subject>Antineoplastic Agents - therapeutic use</subject><subject>Apoptosis</subject><subject>Bioindicators</subject><subject>Biological markers</subject><subject>Biology and Life Sciences</subject><subject>Biomarkers</subject><subject>Biomarkers, Pharmacological - metabolism</subject><subject>Biomarkers, Tumor - genetics</subject><subject>Biomarkers, Tumor - metabolism</subject><subject>Case reports</subject><subject>Case studies</subject><subject>Clinical outcomes</subject><subject>Cluster Analysis</subject><subject>Clustering</subject><subject>Cyclin-dependent kinases</subject><subject>Data mining</subject><subject>Data Mining - methods</subject><subject>Data processing</subject><subject>Databases, Factual</subject><subject>Dosage and administration</subject><subject>Erlotinib</subject><subject>Erlotinib Hydrochloride - therapeutic use</subject><subject>Fitness</subject><subject>Gene expression</subject><subject>Gene Expression Regulation, Neoplastic - drug effects</subject><subject>Gene set enrichment analysis</subject><subject>Genes</subject><subject>Genomes</subject><subject>Humans</subject><subject>Inhibitor drugs</subject><subject>Kinases</subject><subject>Leukemia</subject><subject>Linear Models</subject><subject>Lung cancer</subject><subject>Medical research</subject><subject>Medicine and Health Sciences</subject><subject>Microarray Analysis</subject><subject>Mutation</subject><subject>Neoplasms - drug therapy</subject><subject>Neoplasms - genetics</subject><subject>Neoplasms - metabolism</subject><subject>Niacinamide - analogs & derivatives</subject><subject>Niacinamide - therapeutic use</subject><subject>Phenylurea Compounds - therapeutic use</subject><subject>Physical Sciences</subject><subject>Predictions</subject><subject>Prognosis</subject><subject>Regression analysis</subject><subject>Research and Analysis Methods</subject><subject>Sensitivity</subject><subject>Sensitivity analysis</subject><subject>Single nucleotide polymorphisms</subject><subject>Sorafenib</subject><subject>Studies</subject><subject>Targeted cancer therapy</subject><subject>Treatment Outcome</subject><issn>1932-6203</issn><issn>1932-6203</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2017</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><sourceid>DOA</sourceid><recordid>eNqNk1uLEzEUxwdR3HX1G4gGBMGH1lzmFh-EsngpLCx4ew1nMidt1ulkNsmo_QR-bdPt7NKCgslDcpLf-Sf5k5NlTxmdM1Gx11du9D1088H1OKesZlKye9kpk4LPSk7F_YP5SfYohCtKC1GX5cPshNeV5AUvTrPfC9JCBLKxve1XBIbBO9BrYpwntsU-WrPdbQwQ1z9hO1thj6SxbgP-O_pwww0eW6vjDtNd0tHQETdG7Tb4hiyIhoAkxLHdEmcI-s4l1DYE-pYE58Fgih5nDwx0AZ9M41n29f27L-cfZxeXH5bni4uZLiWPs9bo3JTINRS0MaVsKgk0rzRSUWNqOdNYa4F1was8b6SQ2giR4sZATjWKs-z5XnfoXFCTh0ExyStWcM5lIpZ7onVwpQZv01O3yoFVNwvOrxT4aHWHCgSUNXDJ2prlVcFqo0UuUIAu8rYtWdJ6O502NhtsdfLTQ3ckerzT27VauR-qKHJZ0TIJvJgEvLseMcR_XHmiVpBuZXvjkpje2KDVoqCUsVyURaLmf6FSb3FjdfpFxqb1o4RXRwmJifgrrmAMQS0_f_p_9vLbMfvygF0jdHEdXDdG6_pwDOZ7UHsXgkdz5xyjalcEt26oXRGoqQhS2rND1--Sbn-9-APN3QSL</recordid><startdate>20170808</startdate><enddate>20170808</enddate><creator>Covell, David G</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>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>PTHSS</scope><scope>PYCSY</scope><scope>RC3</scope><scope>5PM</scope><scope>DOA</scope><orcidid>https://orcid.org/0000-0002-0952-6776</orcidid></search><sort><creationdate>20170808</creationdate><title>A data mining approach for identifying pathway-gene biomarkers for predicting clinical outcome: A case study of erlotinib and sorafenib</title><author>Covell, David G</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c692t-dfc4f6e2ca50bf69b79a047ce038eeee41ce8c3e852744b939cf333e8bfa40ce3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2017</creationdate><topic>Analysis</topic><topic>Antineoplastic Agents - therapeutic use</topic><topic>Apoptosis</topic><topic>Bioindicators</topic><topic>Biological markers</topic><topic>Biology and Life Sciences</topic><topic>Biomarkers</topic><topic>Biomarkers, Pharmacological - metabolism</topic><topic>Biomarkers, Tumor - genetics</topic><topic>Biomarkers, Tumor - metabolism</topic><topic>Case reports</topic><topic>Case studies</topic><topic>Clinical outcomes</topic><topic>Cluster Analysis</topic><topic>Clustering</topic><topic>Cyclin-dependent kinases</topic><topic>Data mining</topic><topic>Data Mining - methods</topic><topic>Data processing</topic><topic>Databases, Factual</topic><topic>Dosage and administration</topic><topic>Erlotinib</topic><topic>Erlotinib Hydrochloride - therapeutic use</topic><topic>Fitness</topic><topic>Gene expression</topic><topic>Gene Expression Regulation, Neoplastic - drug effects</topic><topic>Gene set enrichment analysis</topic><topic>Genes</topic><topic>Genomes</topic><topic>Humans</topic><topic>Inhibitor drugs</topic><topic>Kinases</topic><topic>Leukemia</topic><topic>Linear Models</topic><topic>Lung cancer</topic><topic>Medical research</topic><topic>Medicine and Health Sciences</topic><topic>Microarray Analysis</topic><topic>Mutation</topic><topic>Neoplasms - drug therapy</topic><topic>Neoplasms - genetics</topic><topic>Neoplasms - metabolism</topic><topic>Niacinamide - analogs & derivatives</topic><topic>Niacinamide - therapeutic use</topic><topic>Phenylurea Compounds - therapeutic use</topic><topic>Physical Sciences</topic><topic>Predictions</topic><topic>Prognosis</topic><topic>Regression analysis</topic><topic>Research and Analysis Methods</topic><topic>Sensitivity</topic><topic>Sensitivity analysis</topic><topic>Single nucleotide polymorphisms</topic><topic>Sorafenib</topic><topic>Studies</topic><topic>Targeted cancer therapy</topic><topic>Treatment Outcome</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Covell, David G</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>Proquest Nursing & Allied Health Source</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 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>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>Covell, David G</au><au>Nath, Aritro</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>A data mining approach for identifying pathway-gene biomarkers for predicting clinical outcome: A case study of erlotinib and sorafenib</atitle><jtitle>PloS one</jtitle><addtitle>PLoS One</addtitle><date>2017-08-08</date><risdate>2017</risdate><volume>12</volume><issue>8</issue><spage>e0181991</spage><pages>e0181991-</pages><issn>1932-6203</issn><eissn>1932-6203</eissn><abstract>A novel data mining procedure is proposed for identifying potential pathway-gene biomarkers from preclinical drug sensitivity data for predicting clinical responses to erlotinib or sorafenib. The analysis applies linear ridge regression modeling to generate a small (N~1000) set of baseline gene expressions that jointly yield quality predictions of preclinical drug sensitivity data and clinical responses. Standard clustering of the pathway-gene combinations from gene set enrichment analysis of this initial gene set, according to their shared appearance in molecular function pathways, yields a reduced (N~300) set of potential pathway-gene biomarkers. A modified method for quantifying pathway fitness is used to determine smaller numbers of over and under expressed genes that correspond with favorable and unfavorable clinical responses. Detailed literature-based evidence is provided in support of the roles of these under and over expressed genes in compound efficacy. RandomForest analysis of potential pathway-gene biomarkers finds average treatment prediction errors of 10% and 22%, respectively, for patients receiving erlotinib or sorafenib that had a favorable clinical response. Higher errors were found for both compounds when predicting an unfavorable clinical response. Collectively these results suggest complementary roles for biomarker genes and biomarker pathways when predicting clinical responses from preclinical data.</abstract><cop>United States</cop><pub>Public Library of Science</pub><pmid>28792525</pmid><doi>10.1371/journal.pone.0181991</doi><tpages>e0181991</tpages><orcidid>https://orcid.org/0000-0002-0952-6776</orcidid><oa>free_for_read</oa></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 1932-6203 |
| ispartof | PloS one, 2017-08, Vol.12 (8), p.e0181991 |
| issn | 1932-6203 1932-6203 |
| language | eng |
| recordid | cdi_plos_journals_1927152229 |
| source | MEDLINE; DOAJ Directory of Open Access Journals; Elektronische Zeitschriftenbibliothek - Frei zugängliche E-Journals; Public Library of Science (PLoS); PubMed Central; Free Full-Text Journals in Chemistry |
| subjects | Analysis Antineoplastic Agents - therapeutic use Apoptosis Bioindicators Biological markers Biology and Life Sciences Biomarkers Biomarkers, Pharmacological - metabolism Biomarkers, Tumor - genetics Biomarkers, Tumor - metabolism Case reports Case studies Clinical outcomes Cluster Analysis Clustering Cyclin-dependent kinases Data mining Data Mining - methods Data processing Databases, Factual Dosage and administration Erlotinib Erlotinib Hydrochloride - therapeutic use Fitness Gene expression Gene Expression Regulation, Neoplastic - drug effects Gene set enrichment analysis Genes Genomes Humans Inhibitor drugs Kinases Leukemia Linear Models Lung cancer Medical research Medicine and Health Sciences Microarray Analysis Mutation Neoplasms - drug therapy Neoplasms - genetics Neoplasms - metabolism Niacinamide - analogs & derivatives Niacinamide - therapeutic use Phenylurea Compounds - therapeutic use Physical Sciences Predictions Prognosis Regression analysis Research and Analysis Methods Sensitivity Sensitivity analysis Single nucleotide polymorphisms Sorafenib Studies Targeted cancer therapy Treatment Outcome |
| title | A data mining approach for identifying pathway-gene biomarkers for predicting clinical outcome: A case study of erlotinib and sorafenib |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-07T00%3A24%3A57IST&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=A%20data%20mining%20approach%20for%20identifying%20pathway-gene%20biomarkers%20for%20predicting%20clinical%20outcome:%20A%20case%20study%20of%20erlotinib%20and%20sorafenib&rft.jtitle=PloS%20one&rft.au=Covell,%20David%20G&rft.date=2017-08-08&rft.volume=12&rft.issue=8&rft.spage=e0181991&rft.pages=e0181991-&rft.issn=1932-6203&rft.eissn=1932-6203&rft_id=info:doi/10.1371/journal.pone.0181991&rft_dat=%3Cgale_plos_%3EA500114365%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=1927152229&rft_id=info:pmid/28792525&rft_galeid=A500114365&rft_doaj_id=oai_doaj_org_article_a3a68a291d8147518fc343e3ac54dd61&rfr_iscdi=true |