A blood-based prognostic biomarker in IBD
ObjectiveWe have previously described a prognostic transcriptional signature in CD8 T cells that separates patients with IBD into two phenotypically distinct subgroups, termed IBD1 and IBD2. Here we sought to develop a blood-based test that could identify these subgroups without cell separation, and...
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| Veröffentlicht in: | Gut 2019-08, Vol.68 (8), p.1386-1395 |
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| creator | Biasci, Daniele Lee, James C Noor, Nurulamin M Pombal, Diana R Hou, Monica Lewis, Nina Ahmad, Tariq Hart, Ailsa Parkes, Miles McKinney, Eoin F Lyons, Paul A Smith, Kenneth G C |
| description | ObjectiveWe have previously described a prognostic transcriptional signature in CD8 T cells that separates patients with IBD into two phenotypically distinct subgroups, termed IBD1 and IBD2. Here we sought to develop a blood-based test that could identify these subgroups without cell separation, and thus be suitable for clinical use in Crohn’s disease (CD) and ulcerative colitis (UC).DesignPatients with active IBD were recruited before treatment. Transcriptomic analyses were performed on purified CD8 T cells and/or whole blood. Phenotype data were collected prospectively. IBD1/IBD2 patient subgroups were identified by consensus clustering of CD8 T cell transcriptomes. In a training cohort, machine learning was used to identify groups of genes (‘classifiers’) whose differential expression in whole blood recreated the IBD1/IBD2 subgroups. Genes from the best classifiers were quantitative (q)PCR optimised, and further machine learning was used to identify the optimal qPCR classifier, which was locked down for further testing. Independent validation was sought in separate cohorts of patients with CD (n=66) and UC (n=57).ResultsIn both validation cohorts, a 17-gene qPCR-based classifier stratified patients into two distinct subgroups. Irrespective of the underlying diagnosis, IBDhi patients (analogous to the poor prognosis IBD1 subgroup) experienced significantly more aggressive disease than IBDlo patients (analogous to IBD2), with earlier need for treatment escalation (hazard ratio=2.65 (CD), 3.12 (UC)) and more escalations over time (for multiple escalations within 18 months: sensitivity=72.7% (CD), 100% (UC); negative predictive value=90.9% (CD), 100% (UC)).ConclusionThis is the first validated prognostic biomarker that can predict prognosis in newly diagnosed patients with IBD and represents a step towards personalised therapy. |
| doi_str_mv | 10.1136/gutjnl-2019-318343 |
| format | Article |
| fullrecord | <record><control><sourceid>proquest_pubme</sourceid><recordid>TN_cdi_pubmedcentral_primary_oai_pubmedcentral_nih_gov_6691955</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2252643809</sourcerecordid><originalsourceid>FETCH-LOGICAL-b573t-14da96f19c31b39059fa8d1734b4a2d5a942cc9b5fcbbb34276b94049e4c955f3</originalsourceid><addsrcrecordid>eNqNkU9P3DAQxS1UVBbaL8ABReoFDgaP7Tj2pdJCaUFaqZf2bNmOs2TJxoudIPXb1yjb5c-h4jSH-b2nN_MQOgZyDsDExXIcVn2HKQGFGUjG2R6aARcSMyrlBzQjBCpcVlwdoMOUVoQQKRV8RAcMCMsqmKGzeWG7EGpsTfJ1sYlh2Yc0tK6wbVibeO9j0fbF7eW3T2i_MV3yn7fzCP3-fv3r6gYvfv64vZovsC0rNmDgtVGiAeUYWKZIqRoja6gYt9zQujSKU-eULRtnrWWcVsIqTrjy3KmybNgR-jr5bka79rXz_RBNpzexzXH-6GBa_XrTt3d6GR61EAqyQzY43RrE8DD6NOh1m5zvOtP7MCZNKYiqYrISGf3yBl2FMfb5vEyVVHAmicoUnSgXQ0rRN7swQPRTE3pqQj81oacmsujk5Rk7yb_XZwBPgF2v3md4_szvYv5H8BcGAKFO</addsrcrecordid><sourcetype>Open Access Repository</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2252643809</pqid></control><display><type>article</type><title>A blood-based prognostic biomarker in IBD</title><source>MEDLINE</source><source>PubMed Central</source><creator>Biasci, Daniele ; Lee, James C ; Noor, Nurulamin M ; Pombal, Diana R ; Hou, Monica ; Lewis, Nina ; Ahmad, Tariq ; Hart, Ailsa ; Parkes, Miles ; McKinney, Eoin F ; Lyons, Paul A ; Smith, Kenneth G C</creator><creatorcontrib>Biasci, Daniele ; Lee, James C ; Noor, Nurulamin M ; Pombal, Diana R ; Hou, Monica ; Lewis, Nina ; Ahmad, Tariq ; Hart, Ailsa ; Parkes, Miles ; McKinney, Eoin F ; Lyons, Paul A ; Smith, Kenneth G C</creatorcontrib><description>ObjectiveWe have previously described a prognostic transcriptional signature in CD8 T cells that separates patients with IBD into two phenotypically distinct subgroups, termed IBD1 and IBD2. Here we sought to develop a blood-based test that could identify these subgroups without cell separation, and thus be suitable for clinical use in Crohn’s disease (CD) and ulcerative colitis (UC).DesignPatients with active IBD were recruited before treatment. Transcriptomic analyses were performed on purified CD8 T cells and/or whole blood. Phenotype data were collected prospectively. IBD1/IBD2 patient subgroups were identified by consensus clustering of CD8 T cell transcriptomes. In a training cohort, machine learning was used to identify groups of genes (‘classifiers’) whose differential expression in whole blood recreated the IBD1/IBD2 subgroups. Genes from the best classifiers were quantitative (q)PCR optimised, and further machine learning was used to identify the optimal qPCR classifier, which was locked down for further testing. Independent validation was sought in separate cohorts of patients with CD (n=66) and UC (n=57).ResultsIn both validation cohorts, a 17-gene qPCR-based classifier stratified patients into two distinct subgroups. Irrespective of the underlying diagnosis, IBDhi patients (analogous to the poor prognosis IBD1 subgroup) experienced significantly more aggressive disease than IBDlo patients (analogous to IBD2), with earlier need for treatment escalation (hazard ratio=2.65 (CD), 3.12 (UC)) and more escalations over time (for multiple escalations within 18 months: sensitivity=72.7% (CD), 100% (UC); negative predictive value=90.9% (CD), 100% (UC)).ConclusionThis is the first validated prognostic biomarker that can predict prognosis in newly diagnosed patients with IBD and represents a step towards personalised therapy.</description><identifier>ISSN: 0017-5749</identifier><identifier>EISSN: 1468-3288</identifier><identifier>DOI: 10.1136/gutjnl-2019-318343</identifier><identifier>PMID: 31030191</identifier><language>eng</language><publisher>England: BMJ Publishing Group Ltd and British Society of Gastroenterology</publisher><subject>Adult ; Antigens ; Bioinformatics ; Biomarkers ; Biomarkers - blood ; Blood ; Breast cancer ; Cancer therapies ; CD8 antigen ; CD8-Positive T-Lymphocytes - metabolism ; Clinical medicine ; Colitis, Ulcerative - blood ; Colitis, Ulcerative - diagnosis ; Crohn Disease - blood ; Crohn Disease - diagnosis ; Crohn's disease ; Diagnosis, Differential ; Endoscopy ; Female ; Gastroenterology ; Gene Expression ; Gene Expression Profiling - methods ; Generalized linear models ; Humans ; Ibd basic besearch ; Ibd clinical ; Inflammatory Bowel Disease ; Learning algorithms ; Lymphocytes ; Lymphocytes T ; Machine Learning ; Male ; Middle Aged ; Patients ; Phenotype ; Phenotypes ; Predictive Value of Tests ; Prognosis ; Reproducibility of Results ; Sensitivity and Specificity ; Severity of Illness Index ; Transcription ; Tumor necrosis factor-TNF ; Ulcerative colitis</subject><ispartof>Gut, 2019-08, Vol.68 (8), p.1386-1395</ispartof><rights>Author(s) (or their employer(s)) 2019. Re-use permitted under CC BY. Published by BMJ.</rights><rights>2019 Author(s) (or their employer(s)) 2019. Re-use permitted under CC BY. Published by BMJ. This is an open access article distributed in accordance with the Creative Commons Attribution 4.0 Unported (CC BY 4.0) license, which permits others to copy, redistribute, remix, transform and build upon this work for any purpose, provided the original work is properly cited, a link to the licence is given, and indication of whether changes were made. See: https://creativecommons.org/licenses/by/4.0/ . Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><rights>Author(s) (or their employer(s)) 2019. Re-use permitted under CC BY. Published by BMJ. 2019</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-b573t-14da96f19c31b39059fa8d1734b4a2d5a942cc9b5fcbbb34276b94049e4c955f3</citedby><cites>FETCH-LOGICAL-b573t-14da96f19c31b39059fa8d1734b4a2d5a942cc9b5fcbbb34276b94049e4c955f3</cites><orcidid>0000-0001-5711-9385</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/PMC6691955/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC6691955/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,314,723,776,780,881,27901,27902,53766,53768</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/31030191$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Biasci, Daniele</creatorcontrib><creatorcontrib>Lee, James C</creatorcontrib><creatorcontrib>Noor, Nurulamin M</creatorcontrib><creatorcontrib>Pombal, Diana R</creatorcontrib><creatorcontrib>Hou, Monica</creatorcontrib><creatorcontrib>Lewis, Nina</creatorcontrib><creatorcontrib>Ahmad, Tariq</creatorcontrib><creatorcontrib>Hart, Ailsa</creatorcontrib><creatorcontrib>Parkes, Miles</creatorcontrib><creatorcontrib>McKinney, Eoin F</creatorcontrib><creatorcontrib>Lyons, Paul A</creatorcontrib><creatorcontrib>Smith, Kenneth G C</creatorcontrib><title>A blood-based prognostic biomarker in IBD</title><title>Gut</title><addtitle>Gut</addtitle><addtitle>Gut</addtitle><description>ObjectiveWe have previously described a prognostic transcriptional signature in CD8 T cells that separates patients with IBD into two phenotypically distinct subgroups, termed IBD1 and IBD2. Here we sought to develop a blood-based test that could identify these subgroups without cell separation, and thus be suitable for clinical use in Crohn’s disease (CD) and ulcerative colitis (UC).DesignPatients with active IBD were recruited before treatment. Transcriptomic analyses were performed on purified CD8 T cells and/or whole blood. Phenotype data were collected prospectively. IBD1/IBD2 patient subgroups were identified by consensus clustering of CD8 T cell transcriptomes. In a training cohort, machine learning was used to identify groups of genes (‘classifiers’) whose differential expression in whole blood recreated the IBD1/IBD2 subgroups. Genes from the best classifiers were quantitative (q)PCR optimised, and further machine learning was used to identify the optimal qPCR classifier, which was locked down for further testing. Independent validation was sought in separate cohorts of patients with CD (n=66) and UC (n=57).ResultsIn both validation cohorts, a 17-gene qPCR-based classifier stratified patients into two distinct subgroups. Irrespective of the underlying diagnosis, IBDhi patients (analogous to the poor prognosis IBD1 subgroup) experienced significantly more aggressive disease than IBDlo patients (analogous to IBD2), with earlier need for treatment escalation (hazard ratio=2.65 (CD), 3.12 (UC)) and more escalations over time (for multiple escalations within 18 months: sensitivity=72.7% (CD), 100% (UC); negative predictive value=90.9% (CD), 100% (UC)).ConclusionThis is the first validated prognostic biomarker that can predict prognosis in newly diagnosed patients with IBD and represents a step towards personalised therapy.</description><subject>Adult</subject><subject>Antigens</subject><subject>Bioinformatics</subject><subject>Biomarkers</subject><subject>Biomarkers - blood</subject><subject>Blood</subject><subject>Breast cancer</subject><subject>Cancer therapies</subject><subject>CD8 antigen</subject><subject>CD8-Positive T-Lymphocytes - metabolism</subject><subject>Clinical medicine</subject><subject>Colitis, Ulcerative - blood</subject><subject>Colitis, Ulcerative - diagnosis</subject><subject>Crohn Disease - blood</subject><subject>Crohn Disease - diagnosis</subject><subject>Crohn's disease</subject><subject>Diagnosis, Differential</subject><subject>Endoscopy</subject><subject>Female</subject><subject>Gastroenterology</subject><subject>Gene Expression</subject><subject>Gene Expression Profiling - methods</subject><subject>Generalized linear models</subject><subject>Humans</subject><subject>Ibd basic besearch</subject><subject>Ibd clinical</subject><subject>Inflammatory Bowel Disease</subject><subject>Learning algorithms</subject><subject>Lymphocytes</subject><subject>Lymphocytes T</subject><subject>Machine Learning</subject><subject>Male</subject><subject>Middle Aged</subject><subject>Patients</subject><subject>Phenotype</subject><subject>Phenotypes</subject><subject>Predictive Value of Tests</subject><subject>Prognosis</subject><subject>Reproducibility of Results</subject><subject>Sensitivity and Specificity</subject><subject>Severity of Illness Index</subject><subject>Transcription</subject><subject>Tumor necrosis factor-TNF</subject><subject>Ulcerative colitis</subject><issn>0017-5749</issn><issn>1468-3288</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2019</creationdate><recordtype>article</recordtype><sourceid>9YT</sourceid><sourceid>ACMMV</sourceid><sourceid>EIF</sourceid><sourceid>BENPR</sourceid><recordid>eNqNkU9P3DAQxS1UVBbaL8ABReoFDgaP7Tj2pdJCaUFaqZf2bNmOs2TJxoudIPXb1yjb5c-h4jSH-b2nN_MQOgZyDsDExXIcVn2HKQGFGUjG2R6aARcSMyrlBzQjBCpcVlwdoMOUVoQQKRV8RAcMCMsqmKGzeWG7EGpsTfJ1sYlh2Yc0tK6wbVibeO9j0fbF7eW3T2i_MV3yn7fzCP3-fv3r6gYvfv64vZovsC0rNmDgtVGiAeUYWKZIqRoja6gYt9zQujSKU-eULRtnrWWcVsIqTrjy3KmybNgR-jr5bka79rXz_RBNpzexzXH-6GBa_XrTt3d6GR61EAqyQzY43RrE8DD6NOh1m5zvOtP7MCZNKYiqYrISGf3yBl2FMfb5vEyVVHAmicoUnSgXQ0rRN7swQPRTE3pqQj81oacmsujk5Rk7yb_XZwBPgF2v3md4_szvYv5H8BcGAKFO</recordid><startdate>20190801</startdate><enddate>20190801</enddate><creator>Biasci, Daniele</creator><creator>Lee, James C</creator><creator>Noor, Nurulamin M</creator><creator>Pombal, Diana R</creator><creator>Hou, Monica</creator><creator>Lewis, Nina</creator><creator>Ahmad, Tariq</creator><creator>Hart, Ailsa</creator><creator>Parkes, Miles</creator><creator>McKinney, Eoin F</creator><creator>Lyons, Paul A</creator><creator>Smith, Kenneth G C</creator><general>BMJ Publishing Group Ltd and British Society of Gastroenterology</general><general>BMJ Publishing Group LTD</general><general>BMJ Publishing Group</general><scope>9YT</scope><scope>ACMMV</scope><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>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>LK8</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><scope>7X8</scope><scope>5PM</scope><orcidid>https://orcid.org/0000-0001-5711-9385</orcidid></search><sort><creationdate>20190801</creationdate><title>A blood-based prognostic biomarker in IBD</title><author>Biasci, Daniele ; Lee, James C ; Noor, Nurulamin M ; Pombal, Diana R ; Hou, Monica ; Lewis, Nina ; Ahmad, Tariq ; Hart, Ailsa ; Parkes, Miles ; McKinney, Eoin F ; Lyons, Paul A ; Smith, Kenneth G C</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-b573t-14da96f19c31b39059fa8d1734b4a2d5a942cc9b5fcbbb34276b94049e4c955f3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2019</creationdate><topic>Adult</topic><topic>Antigens</topic><topic>Bioinformatics</topic><topic>Biomarkers</topic><topic>Biomarkers - blood</topic><topic>Blood</topic><topic>Breast cancer</topic><topic>Cancer therapies</topic><topic>CD8 antigen</topic><topic>CD8-Positive T-Lymphocytes - metabolism</topic><topic>Clinical medicine</topic><topic>Colitis, Ulcerative - blood</topic><topic>Colitis, Ulcerative - diagnosis</topic><topic>Crohn Disease - blood</topic><topic>Crohn Disease - diagnosis</topic><topic>Crohn's disease</topic><topic>Diagnosis, Differential</topic><topic>Endoscopy</topic><topic>Female</topic><topic>Gastroenterology</topic><topic>Gene Expression</topic><topic>Gene Expression Profiling - methods</topic><topic>Generalized linear models</topic><topic>Humans</topic><topic>Ibd basic besearch</topic><topic>Ibd clinical</topic><topic>Inflammatory Bowel Disease</topic><topic>Learning algorithms</topic><topic>Lymphocytes</topic><topic>Lymphocytes T</topic><topic>Machine Learning</topic><topic>Male</topic><topic>Middle Aged</topic><topic>Patients</topic><topic>Phenotype</topic><topic>Phenotypes</topic><topic>Predictive Value of Tests</topic><topic>Prognosis</topic><topic>Reproducibility of Results</topic><topic>Sensitivity and Specificity</topic><topic>Severity of Illness Index</topic><topic>Transcription</topic><topic>Tumor necrosis factor-TNF</topic><topic>Ulcerative colitis</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Biasci, Daniele</creatorcontrib><creatorcontrib>Lee, James C</creatorcontrib><creatorcontrib>Noor, Nurulamin M</creatorcontrib><creatorcontrib>Pombal, Diana R</creatorcontrib><creatorcontrib>Hou, Monica</creatorcontrib><creatorcontrib>Lewis, Nina</creatorcontrib><creatorcontrib>Ahmad, Tariq</creatorcontrib><creatorcontrib>Hart, Ailsa</creatorcontrib><creatorcontrib>Parkes, Miles</creatorcontrib><creatorcontrib>McKinney, Eoin F</creatorcontrib><creatorcontrib>Lyons, Paul A</creatorcontrib><creatorcontrib>Smith, Kenneth G C</creatorcontrib><collection>BMJ Open Access Journals</collection><collection>BMJ Journals:Open Access</collection><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><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>ProQuest Health & Medical Complete (Alumni)</collection><collection>ProQuest Biological Science Collection</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><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><jtitle>Gut</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Biasci, Daniele</au><au>Lee, James C</au><au>Noor, Nurulamin M</au><au>Pombal, Diana R</au><au>Hou, Monica</au><au>Lewis, Nina</au><au>Ahmad, Tariq</au><au>Hart, Ailsa</au><au>Parkes, Miles</au><au>McKinney, Eoin F</au><au>Lyons, Paul A</au><au>Smith, Kenneth G C</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>A blood-based prognostic biomarker in IBD</atitle><jtitle>Gut</jtitle><stitle>Gut</stitle><addtitle>Gut</addtitle><date>2019-08-01</date><risdate>2019</risdate><volume>68</volume><issue>8</issue><spage>1386</spage><epage>1395</epage><pages>1386-1395</pages><issn>0017-5749</issn><eissn>1468-3288</eissn><abstract>ObjectiveWe have previously described a prognostic transcriptional signature in CD8 T cells that separates patients with IBD into two phenotypically distinct subgroups, termed IBD1 and IBD2. Here we sought to develop a blood-based test that could identify these subgroups without cell separation, and thus be suitable for clinical use in Crohn’s disease (CD) and ulcerative colitis (UC).DesignPatients with active IBD were recruited before treatment. Transcriptomic analyses were performed on purified CD8 T cells and/or whole blood. Phenotype data were collected prospectively. IBD1/IBD2 patient subgroups were identified by consensus clustering of CD8 T cell transcriptomes. In a training cohort, machine learning was used to identify groups of genes (‘classifiers’) whose differential expression in whole blood recreated the IBD1/IBD2 subgroups. Genes from the best classifiers were quantitative (q)PCR optimised, and further machine learning was used to identify the optimal qPCR classifier, which was locked down for further testing. Independent validation was sought in separate cohorts of patients with CD (n=66) and UC (n=57).ResultsIn both validation cohorts, a 17-gene qPCR-based classifier stratified patients into two distinct subgroups. Irrespective of the underlying diagnosis, IBDhi patients (analogous to the poor prognosis IBD1 subgroup) experienced significantly more aggressive disease than IBDlo patients (analogous to IBD2), with earlier need for treatment escalation (hazard ratio=2.65 (CD), 3.12 (UC)) and more escalations over time (for multiple escalations within 18 months: sensitivity=72.7% (CD), 100% (UC); negative predictive value=90.9% (CD), 100% (UC)).ConclusionThis is the first validated prognostic biomarker that can predict prognosis in newly diagnosed patients with IBD and represents a step towards personalised therapy.</abstract><cop>England</cop><pub>BMJ Publishing Group Ltd and British Society of Gastroenterology</pub><pmid>31030191</pmid><doi>10.1136/gutjnl-2019-318343</doi><tpages>10</tpages><orcidid>https://orcid.org/0000-0001-5711-9385</orcidid><oa>free_for_read</oa></addata></record> |
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| ispartof | Gut, 2019-08, Vol.68 (8), p.1386-1395 |
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| subjects | Adult Antigens Bioinformatics Biomarkers Biomarkers - blood Blood Breast cancer Cancer therapies CD8 antigen CD8-Positive T-Lymphocytes - metabolism Clinical medicine Colitis, Ulcerative - blood Colitis, Ulcerative - diagnosis Crohn Disease - blood Crohn Disease - diagnosis Crohn's disease Diagnosis, Differential Endoscopy Female Gastroenterology Gene Expression Gene Expression Profiling - methods Generalized linear models Humans Ibd basic besearch Ibd clinical Inflammatory Bowel Disease Learning algorithms Lymphocytes Lymphocytes T Machine Learning Male Middle Aged Patients Phenotype Phenotypes Predictive Value of Tests Prognosis Reproducibility of Results Sensitivity and Specificity Severity of Illness Index Transcription Tumor necrosis factor-TNF Ulcerative colitis |
| title | A blood-based prognostic biomarker in IBD |
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