Single sample expression-anchored mechanisms predict survival in head and neck cancer
Gene expression signatures that are predictive of therapeutic response or prognosis are increasingly useful in clinical care; however, mechanistic (and intuitive) interpretation of expression arrays remains an unmet challenge. Additionally, there is surprisingly little gene overlap among distinct cl...
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| creator | Yang, Xinan Regan, Kelly Huang, Yong Zhang, Qingbei Li, Jianrong Seiwert, Tanguy Y Cohen, Ezra E W Xing, H Rosie Lussier, Yves A |
| description | Gene expression signatures that are predictive of therapeutic response or prognosis are increasingly useful in clinical care; however, mechanistic (and intuitive) interpretation of expression arrays remains an unmet challenge. Additionally, there is surprisingly little gene overlap among distinct clinically validated expression signatures. These "causality challenges" hinder the adoption of signatures as compared to functionally well-characterized single gene biomarkers. To increase the utility of multi-gene signatures in survival studies, we developed a novel approach to generate "personal mechanism signatures" of molecular pathways and functions from gene expression arrays. FAIME, the Functional Analysis of Individual Microarray Expression, computes mechanism scores using rank-weighted gene expression of an individual sample. By comparing head and neck squamous cell carcinoma (HNSCC) samples with non-tumor control tissues, the precision and recall of deregulated FAIME-derived mechanisms of pathways and molecular functions are comparable to those produced by conventional cohort-wide methods (e.g. GSEA). The overlap of "Oncogenic FAIME Features of HNSCC" (statistically significant and differentially regulated FAIME-derived genesets representing GO functions or KEGG pathways derived from HNSCC tissue) among three distinct HNSCC datasets (pathways:46%, p |
| doi_str_mv | 10.1371/journal.pcbi.1002350 |
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Additionally, there is surprisingly little gene overlap among distinct clinically validated expression signatures. These "causality challenges" hinder the adoption of signatures as compared to functionally well-characterized single gene biomarkers. To increase the utility of multi-gene signatures in survival studies, we developed a novel approach to generate "personal mechanism signatures" of molecular pathways and functions from gene expression arrays. FAIME, the Functional Analysis of Individual Microarray Expression, computes mechanism scores using rank-weighted gene expression of an individual sample. By comparing head and neck squamous cell carcinoma (HNSCC) samples with non-tumor control tissues, the precision and recall of deregulated FAIME-derived mechanisms of pathways and molecular functions are comparable to those produced by conventional cohort-wide methods (e.g. GSEA). The overlap of "Oncogenic FAIME Features of HNSCC" (statistically significant and differentially regulated FAIME-derived genesets representing GO functions or KEGG pathways derived from HNSCC tissue) among three distinct HNSCC datasets (pathways:46%, p<0.001) is more significant than the gene overlap (genes:4%). These Oncogenic FAIME Features of HNSCC can accurately discriminate tumors from control tissues in two additional HNSCC datasets (n = 35 and 91, F-accuracy = 100% and 97%, empirical p<0.001, area under the receiver operating characteristic curves = 99% and 92%), and stratify recurrence-free survival in patients from two independent studies (p = 0.0018 and p = 0.032, log-rank). Previous approaches depending on group assignment of individual samples before selecting features or learning a classifier are limited by design to discrete-class prediction. In contrast, FAIME calculates mechanism profiles for individual patients without requiring group assignment in validation sets. FAIME is more amenable for clinical deployment since it translates the gene-level measurements of each given sample into pathways and molecular function profiles that can be applied to analyze continuous phenotypes in clinical outcome studies (e.g. survival time, tumor volume).</description><identifier>ISSN: 1553-7358</identifier><identifier>ISSN: 1553-734X</identifier><identifier>EISSN: 1553-7358</identifier><identifier>DOI: 10.1371/journal.pcbi.1002350</identifier><identifier>PMID: 22291585</identifier><language>eng</language><publisher>United States: Public Library of Science</publisher><subject>Apoptosis ; Biology ; Biomarkers ; Carcinoma, Squamous Cell - genetics ; Carcinoma, Squamous Cell - metabolism ; Carcinoma, Squamous Cell - mortality ; Cohort Studies ; Development and progression ; DNA replication ; Gene expression ; Gene Expression Profiling ; Genetic aspects ; Genomics ; Head & neck cancer ; Head and neck cancer ; Head and Neck Neoplasms - genetics ; Head and Neck Neoplasms - metabolism ; Head and Neck Neoplasms - mortality ; Humans ; Lasers ; Medical research ; Medicine ; Patient outcomes ; Patients ; Physiological aspects ; Principal components analysis ; ROC Curve ; Statistical methods ; Studies ; Tumor proteins</subject><ispartof>PLoS computational biology, 2012-01, Vol.8 (1), p.e1002350-e1002350</ispartof><rights>COPYRIGHT 2012 Public Library of Science</rights><rights>2012 Yang et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited: Yang X, Regan K, Huang Y, Zhang Q, Li J, et al. (2012) Single Sample Expression-Anchored Mechanisms Predict Survival in Head and Neck Cancer. PLoS Comput Biol 8(1): e1002350. doi:10.1371/journal.pcbi.1002350</rights><rights>Yang et al. 2012</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c660t-7985ca7c730051d108637dbf23ccf16d8a1416cfc6b1559a390383b1a2b6d86c3</citedby><cites>FETCH-LOGICAL-c660t-7985ca7c730051d108637dbf23ccf16d8a1416cfc6b1559a390383b1a2b6d86c3</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/PMC3266878/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC3266878/$$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/22291585$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Yang, Xinan</creatorcontrib><creatorcontrib>Regan, Kelly</creatorcontrib><creatorcontrib>Huang, Yong</creatorcontrib><creatorcontrib>Zhang, Qingbei</creatorcontrib><creatorcontrib>Li, Jianrong</creatorcontrib><creatorcontrib>Seiwert, Tanguy Y</creatorcontrib><creatorcontrib>Cohen, Ezra E W</creatorcontrib><creatorcontrib>Xing, H Rosie</creatorcontrib><creatorcontrib>Lussier, Yves A</creatorcontrib><title>Single sample expression-anchored mechanisms predict survival in head and neck cancer</title><title>PLoS computational biology</title><addtitle>PLoS Comput Biol</addtitle><description>Gene expression signatures that are predictive of therapeutic response or prognosis are increasingly useful in clinical care; however, mechanistic (and intuitive) interpretation of expression arrays remains an unmet challenge. Additionally, there is surprisingly little gene overlap among distinct clinically validated expression signatures. These "causality challenges" hinder the adoption of signatures as compared to functionally well-characterized single gene biomarkers. To increase the utility of multi-gene signatures in survival studies, we developed a novel approach to generate "personal mechanism signatures" of molecular pathways and functions from gene expression arrays. FAIME, the Functional Analysis of Individual Microarray Expression, computes mechanism scores using rank-weighted gene expression of an individual sample. By comparing head and neck squamous cell carcinoma (HNSCC) samples with non-tumor control tissues, the precision and recall of deregulated FAIME-derived mechanisms of pathways and molecular functions are comparable to those produced by conventional cohort-wide methods (e.g. GSEA). The overlap of "Oncogenic FAIME Features of HNSCC" (statistically significant and differentially regulated FAIME-derived genesets representing GO functions or KEGG pathways derived from HNSCC tissue) among three distinct HNSCC datasets (pathways:46%, p<0.001) is more significant than the gene overlap (genes:4%). These Oncogenic FAIME Features of HNSCC can accurately discriminate tumors from control tissues in two additional HNSCC datasets (n = 35 and 91, F-accuracy = 100% and 97%, empirical p<0.001, area under the receiver operating characteristic curves = 99% and 92%), and stratify recurrence-free survival in patients from two independent studies (p = 0.0018 and p = 0.032, log-rank). Previous approaches depending on group assignment of individual samples before selecting features or learning a classifier are limited by design to discrete-class prediction. In contrast, FAIME calculates mechanism profiles for individual patients without requiring group assignment in validation sets. FAIME is more amenable for clinical deployment since it translates the gene-level measurements of each given sample into pathways and molecular function profiles that can be applied to analyze continuous phenotypes in clinical outcome studies (e.g. survival time, tumor volume).</description><subject>Apoptosis</subject><subject>Biology</subject><subject>Biomarkers</subject><subject>Carcinoma, Squamous Cell - genetics</subject><subject>Carcinoma, Squamous Cell - metabolism</subject><subject>Carcinoma, Squamous Cell - mortality</subject><subject>Cohort Studies</subject><subject>Development and progression</subject><subject>DNA replication</subject><subject>Gene expression</subject><subject>Gene Expression Profiling</subject><subject>Genetic aspects</subject><subject>Genomics</subject><subject>Head & neck cancer</subject><subject>Head and neck cancer</subject><subject>Head and Neck Neoplasms - genetics</subject><subject>Head and Neck Neoplasms - metabolism</subject><subject>Head and Neck Neoplasms - mortality</subject><subject>Humans</subject><subject>Lasers</subject><subject>Medical research</subject><subject>Medicine</subject><subject>Patient outcomes</subject><subject>Patients</subject><subject>Physiological aspects</subject><subject>Principal components analysis</subject><subject>ROC Curve</subject><subject>Statistical methods</subject><subject>Studies</subject><subject>Tumor proteins</subject><issn>1553-7358</issn><issn>1553-734X</issn><issn>1553-7358</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2012</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><sourceid>BENPR</sourceid><sourceid>DOA</sourceid><recordid>eNqVkk1v1DAQhiMEou3CP0AQiQPisIs_Ese5IFUVHytVIFF6tib2ZNdLYi92sir_vl42rbqoF-SDLc_zvuMZT5a9omRBeUU_bPwYHHSLrW7sghLCeEmeZKe0LPm84qV8-uB8kp3FuCEkHWvxPDthjNW0lOVpdn1l3arDPEK_TRvebAPGaL2bg9NrH9DkPeo1OBv7mKegsXrI4xh2dgddbl2-RjA5OJM71L9ynWQYXmTPWugivpz2WXb9-dPPi6_zy-9flhfnl3MtBBnmVS1LDZWuOCElNZRIwSvTtIxr3VJhJNCCCt1q0aRSauA14ZI3FFiTgkLzWfbm4LvtfFRTR6KiPC1ZVEWRiOWBMB42ahtsD-GP8mDV3wsfVgrCYHWHqjAgWMsElK0oaK0lr5sqvQGbApASTF4fp2xj06PR6IYA3ZHpccTZtVr5neJMCFnJZPBuMgj-94hxUL2NGrsOHPoxqprWjFX7r5xlb_8hHy9uolaQ3m9d61NavfdU50wSUjPKy0QtHqHSMthb7R22Nt0fCd4fCRIz4M2wgjFGtbz68R_st2O2OLA6-BgDtveto0TtZ_quSLWfaTXNdJK9ftj2e9HdEPNbv9PxpA</recordid><startdate>20120101</startdate><enddate>20120101</enddate><creator>Yang, Xinan</creator><creator>Regan, Kelly</creator><creator>Huang, Yong</creator><creator>Zhang, Qingbei</creator><creator>Li, Jianrong</creator><creator>Seiwert, Tanguy Y</creator><creator>Cohen, Ezra E W</creator><creator>Xing, H Rosie</creator><creator>Lussier, Yves A</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>ISN</scope><scope>ISR</scope><scope>3V.</scope><scope>7QO</scope><scope>7QP</scope><scope>7TK</scope><scope>7TM</scope><scope>7X7</scope><scope>7XB</scope><scope>88E</scope><scope>8AL</scope><scope>8FD</scope><scope>8FE</scope><scope>8FG</scope><scope>8FH</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>ABUWG</scope><scope>AEUYN</scope><scope>AFKRA</scope><scope>ARAPS</scope><scope>AZQEC</scope><scope>BBNVY</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>BHPHI</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>FR3</scope><scope>FYUFA</scope><scope>GHDGH</scope><scope>GNUQQ</scope><scope>HCIFZ</scope><scope>JQ2</scope><scope>K7-</scope><scope>K9.</scope><scope>LK8</scope><scope>M0N</scope><scope>M0S</scope><scope>M1P</scope><scope>M7P</scope><scope>P5Z</scope><scope>P62</scope><scope>P64</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>Q9U</scope><scope>RC3</scope><scope>7X8</scope><scope>5PM</scope><scope>DOA</scope></search><sort><creationdate>20120101</creationdate><title>Single sample expression-anchored mechanisms predict survival in head and neck cancer</title><author>Yang, Xinan ; Regan, Kelly ; Huang, Yong ; Zhang, Qingbei ; Li, Jianrong ; Seiwert, Tanguy Y ; Cohen, Ezra E W ; Xing, H Rosie ; Lussier, Yves A</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c660t-7985ca7c730051d108637dbf23ccf16d8a1416cfc6b1559a390383b1a2b6d86c3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2012</creationdate><topic>Apoptosis</topic><topic>Biology</topic><topic>Biomarkers</topic><topic>Carcinoma, Squamous Cell - genetics</topic><topic>Carcinoma, Squamous Cell - metabolism</topic><topic>Carcinoma, Squamous Cell - mortality</topic><topic>Cohort Studies</topic><topic>Development and progression</topic><topic>DNA replication</topic><topic>Gene expression</topic><topic>Gene Expression Profiling</topic><topic>Genetic aspects</topic><topic>Genomics</topic><topic>Head & neck cancer</topic><topic>Head and neck cancer</topic><topic>Head and Neck Neoplasms - genetics</topic><topic>Head and Neck Neoplasms - metabolism</topic><topic>Head and Neck Neoplasms - mortality</topic><topic>Humans</topic><topic>Lasers</topic><topic>Medical research</topic><topic>Medicine</topic><topic>Patient outcomes</topic><topic>Patients</topic><topic>Physiological aspects</topic><topic>Principal components analysis</topic><topic>ROC Curve</topic><topic>Statistical methods</topic><topic>Studies</topic><topic>Tumor proteins</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Yang, Xinan</creatorcontrib><creatorcontrib>Regan, Kelly</creatorcontrib><creatorcontrib>Huang, Yong</creatorcontrib><creatorcontrib>Zhang, Qingbei</creatorcontrib><creatorcontrib>Li, Jianrong</creatorcontrib><creatorcontrib>Seiwert, Tanguy Y</creatorcontrib><creatorcontrib>Cohen, Ezra E W</creatorcontrib><creatorcontrib>Xing, H Rosie</creatorcontrib><creatorcontrib>Lussier, Yves A</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: Canada</collection><collection>Gale In Context: Science</collection><collection>ProQuest Central (Corporate)</collection><collection>Biotechnology Research Abstracts</collection><collection>Calcium & Calcified Tissue Abstracts</collection><collection>Neurosciences Abstracts</collection><collection>Nucleic Acids Abstracts</collection><collection>Health & Medical Collection</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Medical Database (Alumni Edition)</collection><collection>Computing Database (Alumni Edition)</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>ProQuest Central (Alumni Edition)</collection><collection>ProQuest One Sustainability</collection><collection>ProQuest Central UK/Ireland</collection><collection>Advanced Technologies & Aerospace 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>ProQuest One Community College</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>SciTech Premium Collection</collection><collection>ProQuest Computer Science Collection</collection><collection>Computer Science Database</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>ProQuest Biological Science Collection</collection><collection>Computing Database</collection><collection>Health & Medical Collection (Alumni Edition)</collection><collection>Medical Database</collection><collection>Biological Science Database</collection><collection>Advanced Technologies & Aerospace Database</collection><collection>ProQuest Advanced Technologies & Aerospace Collection</collection><collection>Biotechnology and BioEngineering Abstracts</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>ProQuest Central Basic</collection><collection>Genetics Abstracts</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><collection>DOAJ Directory of Open Access Journals</collection><jtitle>PLoS computational biology</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Yang, Xinan</au><au>Regan, Kelly</au><au>Huang, Yong</au><au>Zhang, Qingbei</au><au>Li, Jianrong</au><au>Seiwert, Tanguy Y</au><au>Cohen, Ezra E W</au><au>Xing, H Rosie</au><au>Lussier, Yves A</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Single sample expression-anchored mechanisms predict survival in head and neck cancer</atitle><jtitle>PLoS computational biology</jtitle><addtitle>PLoS Comput Biol</addtitle><date>2012-01-01</date><risdate>2012</risdate><volume>8</volume><issue>1</issue><spage>e1002350</spage><epage>e1002350</epage><pages>e1002350-e1002350</pages><issn>1553-7358</issn><issn>1553-734X</issn><eissn>1553-7358</eissn><abstract>Gene expression signatures that are predictive of therapeutic response or prognosis are increasingly useful in clinical care; however, mechanistic (and intuitive) interpretation of expression arrays remains an unmet challenge. Additionally, there is surprisingly little gene overlap among distinct clinically validated expression signatures. These "causality challenges" hinder the adoption of signatures as compared to functionally well-characterized single gene biomarkers. To increase the utility of multi-gene signatures in survival studies, we developed a novel approach to generate "personal mechanism signatures" of molecular pathways and functions from gene expression arrays. FAIME, the Functional Analysis of Individual Microarray Expression, computes mechanism scores using rank-weighted gene expression of an individual sample. By comparing head and neck squamous cell carcinoma (HNSCC) samples with non-tumor control tissues, the precision and recall of deregulated FAIME-derived mechanisms of pathways and molecular functions are comparable to those produced by conventional cohort-wide methods (e.g. GSEA). The overlap of "Oncogenic FAIME Features of HNSCC" (statistically significant and differentially regulated FAIME-derived genesets representing GO functions or KEGG pathways derived from HNSCC tissue) among three distinct HNSCC datasets (pathways:46%, p<0.001) is more significant than the gene overlap (genes:4%). These Oncogenic FAIME Features of HNSCC can accurately discriminate tumors from control tissues in two additional HNSCC datasets (n = 35 and 91, F-accuracy = 100% and 97%, empirical p<0.001, area under the receiver operating characteristic curves = 99% and 92%), and stratify recurrence-free survival in patients from two independent studies (p = 0.0018 and p = 0.032, log-rank). Previous approaches depending on group assignment of individual samples before selecting features or learning a classifier are limited by design to discrete-class prediction. In contrast, FAIME calculates mechanism profiles for individual patients without requiring group assignment in validation sets. FAIME is more amenable for clinical deployment since it translates the gene-level measurements of each given sample into pathways and molecular function profiles that can be applied to analyze continuous phenotypes in clinical outcome studies (e.g. survival time, tumor volume).</abstract><cop>United States</cop><pub>Public Library of Science</pub><pmid>22291585</pmid><doi>10.1371/journal.pcbi.1002350</doi><oa>free_for_read</oa></addata></record> |
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| 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 |
| subjects | Apoptosis Biology Biomarkers Carcinoma, Squamous Cell - genetics Carcinoma, Squamous Cell - metabolism Carcinoma, Squamous Cell - mortality Cohort Studies Development and progression DNA replication Gene expression Gene Expression Profiling Genetic aspects Genomics Head & neck cancer Head and neck cancer Head and Neck Neoplasms - genetics Head and Neck Neoplasms - metabolism Head and Neck Neoplasms - mortality Humans Lasers Medical research Medicine Patient outcomes Patients Physiological aspects Principal components analysis ROC Curve Statistical methods Studies Tumor proteins |
| title | Single sample expression-anchored mechanisms predict survival in head and neck cancer |
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