Identification of a Blood-Based Protein Biomarker Panel for Lung Cancer Detection
Lung cancer is the deadliest cancer worldwide, mainly due to its advanced stage at the time of diagnosis. A non-invasive method for its early detection remains mandatory to improve patients' survival. Plasma levels of 351 proteins were quantified by Liquid Chromatography-Parallel Reaction Monit...
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| Veröffentlicht in: | Cancers 2020-06, Vol.12 (6), p.1629 |
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| creator | El-Khoury, Victoria Schritz, Anna Kim, Sang-Yoon Lesur, Antoine Sertamo, Katriina Bernardin, François Petritis, Konstantinos Pirrotte, Patrick Selinsky, Cheryl Whiteaker, Jeffrey R Zhang, Haizhen Kennedy, Jacob J Lin, Chenwei Lee, Lik Wee Yan, Ping Tran, Nhan L Inge, Landon J Chalabi, Khaled Decker, Georges Bjerkvig, Rolf Paulovich, Amanda G Berchem, Guy Kim, Yeoun Jin |
| description | Lung cancer is the deadliest cancer worldwide, mainly due to its advanced stage at the time of diagnosis. A non-invasive method for its early detection remains mandatory to improve patients' survival. Plasma levels of 351 proteins were quantified by Liquid Chromatography-Parallel Reaction Monitoring (LC-PRM)-based mass spectrometry in 128 lung cancer patients and 93 healthy donors. Bootstrap sampling and least absolute shrinkage and selection operator (LASSO) penalization were used to find the best protein combination for outcome prediction. The PanelomiX platform was used to select the optimal biomarker thresholds. The panel was validated in 48 patients and 49 healthy volunteers. A 6-protein panel clearly distinguished lung cancer from healthy individuals. The panel displayed excellent performance: area under the receiver operating characteristic curve (AUC) = 0.999, positive predictive value (PPV) = 0.992, negative predictive value (NPV) = 0.989, specificity = 0.989 and sensitivity = 0.992. The panel detected lung cancer independently of the disease stage. The 6-protein panel and other sub-combinations displayed excellent results in the validation dataset. In conclusion, we identified a blood-based 6-protein panel as a diagnostic tool in lung cancer. Used as a routine test for high- and average-risk individuals, it may complement currently adopted techniques in lung cancer screening. |
| doi_str_mv | 10.3390/cancers12061629 |
| format | Article |
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A non-invasive method for its early detection remains mandatory to improve patients' survival. Plasma levels of 351 proteins were quantified by Liquid Chromatography-Parallel Reaction Monitoring (LC-PRM)-based mass spectrometry in 128 lung cancer patients and 93 healthy donors. Bootstrap sampling and least absolute shrinkage and selection operator (LASSO) penalization were used to find the best protein combination for outcome prediction. The PanelomiX platform was used to select the optimal biomarker thresholds. The panel was validated in 48 patients and 49 healthy volunteers. A 6-protein panel clearly distinguished lung cancer from healthy individuals. The panel displayed excellent performance: area under the receiver operating characteristic curve (AUC) = 0.999, positive predictive value (PPV) = 0.992, negative predictive value (NPV) = 0.989, specificity = 0.989 and sensitivity = 0.992. The panel detected lung cancer independently of the disease stage. The 6-protein panel and other sub-combinations displayed excellent results in the validation dataset. In conclusion, we identified a blood-based 6-protein panel as a diagnostic tool in lung cancer. Used as a routine test for high- and average-risk individuals, it may complement currently adopted techniques in lung cancer screening.</description><identifier>ISSN: 2072-6694</identifier><identifier>EISSN: 2072-6694</identifier><identifier>DOI: 10.3390/cancers12061629</identifier><identifier>PMID: 32575471</identifier><language>eng</language><publisher>Switzerland: MDPI AG</publisher><subject>Biomarkers ; Business metrics ; Cancer screening ; Growth factors ; Liquid chromatography ; Lung cancer ; Mass spectrometry ; Mass spectroscopy ; Medical prognosis ; Medical screening ; Mortality ; Patients ; Plasma ; Plasma levels ; Proteins ; Proteomics ; Scientific imaging ; Tumor necrosis factor-TNF ; Tumors ; Wound healing</subject><ispartof>Cancers, 2020-06, Vol.12 (6), p.1629</ispartof><rights>2020. This work is licensed under http://creativecommons.org/licenses/by/3.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><rights>2020 by the authors. 2020</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c421t-5a480dc4231480939ada240898767baa72aade862bc280eeb7cfb3e95696bff53</citedby><cites>FETCH-LOGICAL-c421t-5a480dc4231480939ada240898767baa72aade862bc280eeb7cfb3e95696bff53</cites><orcidid>0000-0003-0157-2257 ; 0000-0002-8478-8055 ; 0000-0002-6329-5542 ; 0000-0001-8660-8532</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/PMC7352295/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC7352295/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,314,727,780,784,885,27924,27925,53791,53793</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/32575471$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>El-Khoury, Victoria</creatorcontrib><creatorcontrib>Schritz, Anna</creatorcontrib><creatorcontrib>Kim, Sang-Yoon</creatorcontrib><creatorcontrib>Lesur, Antoine</creatorcontrib><creatorcontrib>Sertamo, Katriina</creatorcontrib><creatorcontrib>Bernardin, François</creatorcontrib><creatorcontrib>Petritis, Konstantinos</creatorcontrib><creatorcontrib>Pirrotte, Patrick</creatorcontrib><creatorcontrib>Selinsky, Cheryl</creatorcontrib><creatorcontrib>Whiteaker, Jeffrey R</creatorcontrib><creatorcontrib>Zhang, Haizhen</creatorcontrib><creatorcontrib>Kennedy, Jacob J</creatorcontrib><creatorcontrib>Lin, Chenwei</creatorcontrib><creatorcontrib>Lee, Lik Wee</creatorcontrib><creatorcontrib>Yan, Ping</creatorcontrib><creatorcontrib>Tran, Nhan L</creatorcontrib><creatorcontrib>Inge, Landon J</creatorcontrib><creatorcontrib>Chalabi, Khaled</creatorcontrib><creatorcontrib>Decker, Georges</creatorcontrib><creatorcontrib>Bjerkvig, Rolf</creatorcontrib><creatorcontrib>Paulovich, Amanda G</creatorcontrib><creatorcontrib>Berchem, Guy</creatorcontrib><creatorcontrib>Kim, Yeoun Jin</creatorcontrib><title>Identification of a Blood-Based Protein Biomarker Panel for Lung Cancer Detection</title><title>Cancers</title><addtitle>Cancers (Basel)</addtitle><description>Lung cancer is the deadliest cancer worldwide, mainly due to its advanced stage at the time of diagnosis. A non-invasive method for its early detection remains mandatory to improve patients' survival. Plasma levels of 351 proteins were quantified by Liquid Chromatography-Parallel Reaction Monitoring (LC-PRM)-based mass spectrometry in 128 lung cancer patients and 93 healthy donors. Bootstrap sampling and least absolute shrinkage and selection operator (LASSO) penalization were used to find the best protein combination for outcome prediction. The PanelomiX platform was used to select the optimal biomarker thresholds. The panel was validated in 48 patients and 49 healthy volunteers. A 6-protein panel clearly distinguished lung cancer from healthy individuals. The panel displayed excellent performance: area under the receiver operating characteristic curve (AUC) = 0.999, positive predictive value (PPV) = 0.992, negative predictive value (NPV) = 0.989, specificity = 0.989 and sensitivity = 0.992. The panel detected lung cancer independently of the disease stage. The 6-protein panel and other sub-combinations displayed excellent results in the validation dataset. In conclusion, we identified a blood-based 6-protein panel as a diagnostic tool in lung cancer. Used as a routine test for high- and average-risk individuals, it may complement currently adopted techniques in lung cancer screening.</description><subject>Biomarkers</subject><subject>Business metrics</subject><subject>Cancer screening</subject><subject>Growth factors</subject><subject>Liquid chromatography</subject><subject>Lung cancer</subject><subject>Mass spectrometry</subject><subject>Mass spectroscopy</subject><subject>Medical prognosis</subject><subject>Medical screening</subject><subject>Mortality</subject><subject>Patients</subject><subject>Plasma</subject><subject>Plasma levels</subject><subject>Proteins</subject><subject>Proteomics</subject><subject>Scientific imaging</subject><subject>Tumor necrosis factor-TNF</subject><subject>Tumors</subject><subject>Wound 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spectroscopy</topic><topic>Medical prognosis</topic><topic>Medical screening</topic><topic>Mortality</topic><topic>Patients</topic><topic>Plasma</topic><topic>Plasma levels</topic><topic>Proteins</topic><topic>Proteomics</topic><topic>Scientific imaging</topic><topic>Tumor necrosis factor-TNF</topic><topic>Tumors</topic><topic>Wound healing</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>El-Khoury, Victoria</creatorcontrib><creatorcontrib>Schritz, Anna</creatorcontrib><creatorcontrib>Kim, Sang-Yoon</creatorcontrib><creatorcontrib>Lesur, Antoine</creatorcontrib><creatorcontrib>Sertamo, Katriina</creatorcontrib><creatorcontrib>Bernardin, François</creatorcontrib><creatorcontrib>Petritis, Konstantinos</creatorcontrib><creatorcontrib>Pirrotte, Patrick</creatorcontrib><creatorcontrib>Selinsky, Cheryl</creatorcontrib><creatorcontrib>Whiteaker, Jeffrey R</creatorcontrib><creatorcontrib>Zhang, Haizhen</creatorcontrib><creatorcontrib>Kennedy, Jacob 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G</au><au>Berchem, Guy</au><au>Kim, Yeoun Jin</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Identification of a Blood-Based Protein Biomarker Panel for Lung Cancer Detection</atitle><jtitle>Cancers</jtitle><addtitle>Cancers (Basel)</addtitle><date>2020-06-19</date><risdate>2020</risdate><volume>12</volume><issue>6</issue><spage>1629</spage><pages>1629-</pages><issn>2072-6694</issn><eissn>2072-6694</eissn><abstract>Lung cancer is the deadliest cancer worldwide, mainly due to its advanced stage at the time of diagnosis. A non-invasive method for its early detection remains mandatory to improve patients' survival. Plasma levels of 351 proteins were quantified by Liquid Chromatography-Parallel Reaction Monitoring (LC-PRM)-based mass spectrometry in 128 lung cancer patients and 93 healthy donors. Bootstrap sampling and least absolute shrinkage and selection operator (LASSO) penalization were used to find the best protein combination for outcome prediction. The PanelomiX platform was used to select the optimal biomarker thresholds. The panel was validated in 48 patients and 49 healthy volunteers. A 6-protein panel clearly distinguished lung cancer from healthy individuals. The panel displayed excellent performance: area under the receiver operating characteristic curve (AUC) = 0.999, positive predictive value (PPV) = 0.992, negative predictive value (NPV) = 0.989, specificity = 0.989 and sensitivity = 0.992. The panel detected lung cancer independently of the disease stage. The 6-protein panel and other sub-combinations displayed excellent results in the validation dataset. In conclusion, we identified a blood-based 6-protein panel as a diagnostic tool in lung cancer. Used as a routine test for high- and average-risk individuals, it may complement currently adopted techniques in lung cancer screening.</abstract><cop>Switzerland</cop><pub>MDPI AG</pub><pmid>32575471</pmid><doi>10.3390/cancers12061629</doi><orcidid>https://orcid.org/0000-0003-0157-2257</orcidid><orcidid>https://orcid.org/0000-0002-8478-8055</orcidid><orcidid>https://orcid.org/0000-0002-6329-5542</orcidid><orcidid>https://orcid.org/0000-0001-8660-8532</orcidid><oa>free_for_read</oa></addata></record> |
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| source | PubMed Central Open Access; MDPI - Multidisciplinary Digital Publishing Institute; EZB-FREE-00999 freely available EZB journals; PubMed Central |
| subjects | Biomarkers Business metrics Cancer screening Growth factors Liquid chromatography Lung cancer Mass spectrometry Mass spectroscopy Medical prognosis Medical screening Mortality Patients Plasma Plasma levels Proteins Proteomics Scientific imaging Tumor necrosis factor-TNF Tumors Wound healing |
| title | Identification of a Blood-Based Protein Biomarker Panel for Lung Cancer Detection |
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