FRI0444 Early Detection of Psoriatic Arthritis: A Multi-Omic Approach To Developing A New Blood Test
BackgroundPsoriatic Arthritis (PsA) is a form of seronegative inflammatory arthritis (IA) frequently associated with psoriasis. Similar to other forms of IA, it is a complex heterogeneous disease. While PsA can be characterised by distinct clinical manifestations, at early onset, it often resembles...
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| Veröffentlicht in: | Annals of the rheumatic diseases 2016-06, Vol.75 (Suppl 2), p.596 |
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| container_title | Annals of the rheumatic diseases |
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| creator | Mc Ardle, A.F. Szentpetery, A. De Rook, S. De Ganck, A.D.G. Hernandez, B. FitzGerlad, O. Pennington, S.R. |
| description | BackgroundPsoriatic Arthritis (PsA) is a form of seronegative inflammatory arthritis (IA) frequently associated with psoriasis. Similar to other forms of IA, it is a complex heterogeneous disease. While PsA can be characterised by distinct clinical manifestations, at early onset, it often resembles other disease types - especially rheumatoid arthritis (RA). During the treatment and management of the disease important clinical decisions are made and these have a significant impact on patient outcomes. Making an accurate and early diagnosis is particularly important to ensuring that individual patients receive effective and safe medication. Thus, it is widely acknowledged by physicians and patients alike that a new test is needed to facilitate the early and specific diagnosis of PsA [1].ObjectivesTo (i) identify candidate biomarkers with the potential to segregate patients with PsA from those with RA (ii) explore the value of combining different omic discovery platforms.MethodsSerum samples were obtained from a cohort of 64 patients (32 PsA and 32 RA) defined as early onset ( |
| doi_str_mv | 10.1136/annrheumdis-2016-eular.4324 |
| format | Article |
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Similar to other forms of IA, it is a complex heterogeneous disease. While PsA can be characterised by distinct clinical manifestations, at early onset, it often resembles other disease types - especially rheumatoid arthritis (RA). During the treatment and management of the disease important clinical decisions are made and these have a significant impact on patient outcomes. Making an accurate and early diagnosis is particularly important to ensuring that individual patients receive effective and safe medication. Thus, it is widely acknowledged by physicians and patients alike that a new test is needed to facilitate the early and specific diagnosis of PsA [1].ObjectivesTo (i) identify candidate biomarkers with the potential to segregate patients with PsA from those with RA (ii) explore the value of combining different omic discovery platforms.MethodsSerum samples were obtained from a cohort of 64 patients (32 PsA and 32 RA) defined as early onset (<12 months) and DMARD naïve. Individual baseline samples were analysed using microRNA (miRNA) (n=63), Luminex xMAP (n=62), an aptamer based platform called SOMAscan (n=36) and label free LC-MS/MS (n=64), platforms. Data was imported into Perseus (version 1.5.0.8) for statistical analysis (p≤0.05). Additionally, data was analysed using a random forest model in R software (version 3.1.0).ResultsIn this study, by combining 4 different omic technologies it was possible to quantify 376, 48, 1129, 387 analytes by miRNA, Luminex, SOMAscan and LC-MS/MS analysis, respectively. Statistical analysis revealed that a total of 287 analytes were differentially expressed across the two conditions (p≤0.05). Furthermore, random forest analysis revealed the most discriminatory biomarkers from the miRNA, Luminex and SOMAscan analysis. Figure 1 A-C illustrates the receiver operating characteristic (ROC) curves generated from these analyses. The application of the random forest model to the LC-MS/MS data is part of ongoing work.ConclusionsBy combining the most discriminatory analytes from each type of analysis into a single algorithm, a biomarker signature with increased predictive potency should be identified. Thus, these molecules represent candidates for inclusion into blood based mulit-analyte test that could be used in the diagnosis of PsA.ReferencesMc Ardle. A. Butt, AQ. Szentpetery, De Jager, Wilco. De Rook, Sytze. FitzGerald, O. Pennington, SR.Developing Clincially Relevant Biomarerks In Inflammatory Arthritis: A Mutli-Platform Approach for Serum Candidate Protein Discovery. Clincal Proteomics 2015, doi: 10.1oo2/prca.201500046AcknowledgementKieran Wynne is thanked for the technical support provided during this project.Disclosure of InterestNone declared</description><identifier>ISSN: 0003-4967</identifier><identifier>EISSN: 1468-2060</identifier><identifier>DOI: 10.1136/annrheumdis-2016-eular.4324</identifier><identifier>CODEN: ARDIAO</identifier><language>eng</language><publisher>Kidlington: Elsevier Limited</publisher><ispartof>Annals of the rheumatic diseases, 2016-06, Vol.75 (Suppl 2), p.596</ispartof><rights>2016, Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://group.bmj.com/group/rights-licensing/permissions</rights><rights>Copyright: 2016 (c) 2016, Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://group.bmj.com/group/rights-licensing/permissions</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttp://ard.bmj.com/content/75/Suppl_2/596.2.full.pdf$$EPDF$$P50$$Gbmj$$H</linktopdf><linktohtml>$$Uhttp://ard.bmj.com/content/75/Suppl_2/596.2.full$$EHTML$$P50$$Gbmj$$H</linktohtml><link.rule.ids>114,115,314,776,780,3182,23551,27903,27904,77347,77378</link.rule.ids></links><search><creatorcontrib>Mc Ardle, A.F.</creatorcontrib><creatorcontrib>Szentpetery, A.</creatorcontrib><creatorcontrib>De Rook, S.</creatorcontrib><creatorcontrib>De Ganck, A.D.G.</creatorcontrib><creatorcontrib>Hernandez, B.</creatorcontrib><creatorcontrib>FitzGerlad, O.</creatorcontrib><creatorcontrib>Pennington, S.R.</creatorcontrib><title>FRI0444 Early Detection of Psoriatic Arthritis: A Multi-Omic Approach To Developing A New Blood Test</title><title>Annals of the rheumatic diseases</title><description>BackgroundPsoriatic Arthritis (PsA) is a form of seronegative inflammatory arthritis (IA) frequently associated with psoriasis. Similar to other forms of IA, it is a complex heterogeneous disease. While PsA can be characterised by distinct clinical manifestations, at early onset, it often resembles other disease types - especially rheumatoid arthritis (RA). During the treatment and management of the disease important clinical decisions are made and these have a significant impact on patient outcomes. Making an accurate and early diagnosis is particularly important to ensuring that individual patients receive effective and safe medication. Thus, it is widely acknowledged by physicians and patients alike that a new test is needed to facilitate the early and specific diagnosis of PsA [1].ObjectivesTo (i) identify candidate biomarkers with the potential to segregate patients with PsA from those with RA (ii) explore the value of combining different omic discovery platforms.MethodsSerum samples were obtained from a cohort of 64 patients (32 PsA and 32 RA) defined as early onset (<12 months) and DMARD naïve. Individual baseline samples were analysed using microRNA (miRNA) (n=63), Luminex xMAP (n=62), an aptamer based platform called SOMAscan (n=36) and label free LC-MS/MS (n=64), platforms. Data was imported into Perseus (version 1.5.0.8) for statistical analysis (p≤0.05). Additionally, data was analysed using a random forest model in R software (version 3.1.0).ResultsIn this study, by combining 4 different omic technologies it was possible to quantify 376, 48, 1129, 387 analytes by miRNA, Luminex, SOMAscan and LC-MS/MS analysis, respectively. Statistical analysis revealed that a total of 287 analytes were differentially expressed across the two conditions (p≤0.05). Furthermore, random forest analysis revealed the most discriminatory biomarkers from the miRNA, Luminex and SOMAscan analysis. Figure 1 A-C illustrates the receiver operating characteristic (ROC) curves generated from these analyses. The application of the random forest model to the LC-MS/MS data is part of ongoing work.ConclusionsBy combining the most discriminatory analytes from each type of analysis into a single algorithm, a biomarker signature with increased predictive potency should be identified. Thus, these molecules represent candidates for inclusion into blood based mulit-analyte test that could be used in the diagnosis of PsA.ReferencesMc Ardle. A. Butt, AQ. Szentpetery, De Jager, Wilco. De Rook, Sytze. FitzGerald, O. Pennington, SR.Developing Clincially Relevant Biomarerks In Inflammatory Arthritis: A Mutli-Platform Approach for Serum Candidate Protein Discovery. Clincal Proteomics 2015, doi: 10.1oo2/prca.201500046AcknowledgementKieran Wynne is thanked for the technical support provided during this project.Disclosure of InterestNone declared</description><issn>0003-4967</issn><issn>1468-2060</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2016</creationdate><recordtype>article</recordtype><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><recordid>eNqVkMFOwzAQRC0EEqXwD5Z6TvE6ThrDqZQWKhWKUO-Wk9jUVRoHOwH1xoUf5UtwKQeunFY7mtlZPYQGQIYAcXop69qtVbctjY8ogTRSXSXdkMWUHaEesDQLckqOUY8QEkeMp6NTdOb9Jqwkg6yH9Ox5ThhjXx-fU-mqHb5VrSpaY2tsNX7y1hnZmgKPXbt2pjX-Co_xQ1e1Jlpu93rTOCuLNV7ZEH1TlW1M_RI8j-od31TWlnilfHuOTrSsvLr4nX20mk1Xk_tosbybT8aLKAc6ohGUkCckTzVPdA6Ml4UmNC8yIlkpQdFSyYLoROWKx5LqDHhSxgxyDqzgqoz7aHA4G5567UKv2NjO1aFRACeQJTylNLiuD67CWe-d0qJxZivdTgARe67iD1ex5yp-uIo915BOD-l8u_lX8Bv7V4R7</recordid><startdate>201606</startdate><enddate>201606</enddate><creator>Mc Ardle, A.F.</creator><creator>Szentpetery, A.</creator><creator>De Rook, S.</creator><creator>De Ganck, A.D.G.</creator><creator>Hernandez, B.</creator><creator>FitzGerlad, O.</creator><creator>Pennington, S.R.</creator><general>Elsevier Limited</general><scope>AAYXX</scope><scope>CITATION</scope><scope>3V.</scope><scope>7X7</scope><scope>7XB</scope><scope>88E</scope><scope>88I</scope><scope>8AF</scope><scope>8FE</scope><scope>8FH</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>AZQEC</scope><scope>BBNVY</scope><scope>BENPR</scope><scope>BHPHI</scope><scope>BTHHO</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>FYUFA</scope><scope>GHDGH</scope><scope>GNUQQ</scope><scope>HCIFZ</scope><scope>K9-</scope><scope>K9.</scope><scope>LK8</scope><scope>M0R</scope><scope>M0S</scope><scope>M1P</scope><scope>M2P</scope><scope>M7P</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>Q9U</scope></search><sort><creationdate>201606</creationdate><title>FRI0444 Early Detection of Psoriatic Arthritis: A Multi-Omic Approach To Developing A New Blood Test</title><author>Mc Ardle, A.F. ; Szentpetery, A. ; De Rook, S. ; De Ganck, A.D.G. ; Hernandez, B. ; FitzGerlad, O. ; Pennington, S.R.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-b1272-1d1b50b6f95fb149dcf02bc80a4da1e2deac0f5ebe93a2f8195d341b914c9ed3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2016</creationdate><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Mc Ardle, A.F.</creatorcontrib><creatorcontrib>Szentpetery, A.</creatorcontrib><creatorcontrib>De Rook, S.</creatorcontrib><creatorcontrib>De Ganck, A.D.G.</creatorcontrib><creatorcontrib>Hernandez, B.</creatorcontrib><creatorcontrib>FitzGerlad, O.</creatorcontrib><creatorcontrib>Pennington, S.R.</creatorcontrib><collection>CrossRef</collection><collection>ProQuest Central (Corporate)</collection><collection>Health & Medical Collection</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Medical Database (Alumni Edition)</collection><collection>Science Database (Alumni Edition)</collection><collection>STEM Database</collection><collection>ProQuest SciTech 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 Central UK/Ireland</collection><collection>ProQuest Central Essentials</collection><collection>Biological Science Collection</collection><collection>ProQuest Central</collection><collection>Natural Science Collection</collection><collection>BMJ Journals</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</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>Consumer Health Database (Alumni Edition)</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>ProQuest Biological Science Collection</collection><collection>Consumer Health Database</collection><collection>Health & Medical Collection (Alumni Edition)</collection><collection>Medical Database</collection><collection>Science Database</collection><collection>Biological Science 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><jtitle>Annals of the rheumatic diseases</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Mc Ardle, A.F.</au><au>Szentpetery, A.</au><au>De Rook, S.</au><au>De Ganck, A.D.G.</au><au>Hernandez, B.</au><au>FitzGerlad, O.</au><au>Pennington, S.R.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>FRI0444 Early Detection of Psoriatic Arthritis: A Multi-Omic Approach To Developing A New Blood Test</atitle><jtitle>Annals of the rheumatic diseases</jtitle><date>2016-06</date><risdate>2016</risdate><volume>75</volume><issue>Suppl 2</issue><spage>596</spage><pages>596-</pages><issn>0003-4967</issn><eissn>1468-2060</eissn><coden>ARDIAO</coden><abstract>BackgroundPsoriatic Arthritis (PsA) is a form of seronegative inflammatory arthritis (IA) frequently associated with psoriasis. Similar to other forms of IA, it is a complex heterogeneous disease. While PsA can be characterised by distinct clinical manifestations, at early onset, it often resembles other disease types - especially rheumatoid arthritis (RA). During the treatment and management of the disease important clinical decisions are made and these have a significant impact on patient outcomes. Making an accurate and early diagnosis is particularly important to ensuring that individual patients receive effective and safe medication. Thus, it is widely acknowledged by physicians and patients alike that a new test is needed to facilitate the early and specific diagnosis of PsA [1].ObjectivesTo (i) identify candidate biomarkers with the potential to segregate patients with PsA from those with RA (ii) explore the value of combining different omic discovery platforms.MethodsSerum samples were obtained from a cohort of 64 patients (32 PsA and 32 RA) defined as early onset (<12 months) and DMARD naïve. Individual baseline samples were analysed using microRNA (miRNA) (n=63), Luminex xMAP (n=62), an aptamer based platform called SOMAscan (n=36) and label free LC-MS/MS (n=64), platforms. Data was imported into Perseus (version 1.5.0.8) for statistical analysis (p≤0.05). Additionally, data was analysed using a random forest model in R software (version 3.1.0).ResultsIn this study, by combining 4 different omic technologies it was possible to quantify 376, 48, 1129, 387 analytes by miRNA, Luminex, SOMAscan and LC-MS/MS analysis, respectively. Statistical analysis revealed that a total of 287 analytes were differentially expressed across the two conditions (p≤0.05). Furthermore, random forest analysis revealed the most discriminatory biomarkers from the miRNA, Luminex and SOMAscan analysis. Figure 1 A-C illustrates the receiver operating characteristic (ROC) curves generated from these analyses. The application of the random forest model to the LC-MS/MS data is part of ongoing work.ConclusionsBy combining the most discriminatory analytes from each type of analysis into a single algorithm, a biomarker signature with increased predictive potency should be identified. Thus, these molecules represent candidates for inclusion into blood based mulit-analyte test that could be used in the diagnosis of PsA.ReferencesMc Ardle. A. Butt, AQ. Szentpetery, De Jager, Wilco. De Rook, Sytze. FitzGerald, O. Pennington, SR.Developing Clincially Relevant Biomarerks In Inflammatory Arthritis: A Mutli-Platform Approach for Serum Candidate Protein Discovery. Clincal Proteomics 2015, doi: 10.1oo2/prca.201500046AcknowledgementKieran Wynne is thanked for the technical support provided during this project.Disclosure of InterestNone declared</abstract><cop>Kidlington</cop><pub>Elsevier Limited</pub><doi>10.1136/annrheumdis-2016-eular.4324</doi></addata></record> |
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| title | FRI0444 Early Detection of Psoriatic Arthritis: A Multi-Omic Approach To Developing A New Blood Test |
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