MALDI-TOF analysis of blood serum proteome can predict the presence of monoclonal gammopathy of undetermined significance
Monoclonal gammopathy of undetermined significance (MGUS) is a plasma cell dyscrasia that can progress to malignant multiple myeloma (MM). Specific molecular biomarkers to classify the MGUS status and discriminate the initial asymptomatic phase of MM have not been identified. We examined the serum p...
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| Veröffentlicht in: | PloS one 2018-08, Vol.13 (8), p.e0201793-e0201793 |
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| creator | Barceló, Francisca Gomila, Rosa de Paul, Ivan Gili, Xavier Segura, Jaume Pérez-Montaña, Albert Jimenez-Marco, Teresa Sampol, Antonia Portugal, José |
| description | Monoclonal gammopathy of undetermined significance (MGUS) is a plasma cell dyscrasia that can progress to malignant multiple myeloma (MM). Specific molecular biomarkers to classify the MGUS status and discriminate the initial asymptomatic phase of MM have not been identified. We examined the serum peptidome profile of MGUS patients and healthy volunteers using MALDI-TOF mass spectrometry and developed a predictive model for classifying serum samples. The predictive model was built using a support vector machine (SVM) supervised learning method tuned by applying a 20-fold cross-validation scheme. Predicting class labels in a blinded test set containing randomly selected MGUS and healthy control serum samples validated the model. The generalization performance of the predictive model was evaluated by a double cross-validation method that showed 88% average model accuracy, 89% average sensitivity and 86% average specificity. Our model, which classifies unknown serum samples as belonging to either MGUS patients or healthy individuals, can be applied to clinical diagnosis. |
| doi_str_mv | 10.1371/journal.pone.0201793 |
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Specific molecular biomarkers to classify the MGUS status and discriminate the initial asymptomatic phase of MM have not been identified. We examined the serum peptidome profile of MGUS patients and healthy volunteers using MALDI-TOF mass spectrometry and developed a predictive model for classifying serum samples. The predictive model was built using a support vector machine (SVM) supervised learning method tuned by applying a 20-fold cross-validation scheme. Predicting class labels in a blinded test set containing randomly selected MGUS and healthy control serum samples validated the model. The generalization performance of the predictive model was evaluated by a double cross-validation method that showed 88% average model accuracy, 89% average sensitivity and 86% average specificity. Our model, which classifies unknown serum samples as belonging to either MGUS patients or healthy individuals, can be applied to clinical diagnosis.</description><identifier>ISSN: 1932-6203</identifier><identifier>EISSN: 1932-6203</identifier><identifier>DOI: 10.1371/journal.pone.0201793</identifier><identifier>PMID: 30071092</identifier><language>eng</language><publisher>United States: Public Library of Science</publisher><subject>Benign monoclonal gammopathy ; Bioinformatics ; Biological markers ; Biology and Life Sciences ; Biomarkers ; Blood serum ; Bone marrow ; Classification ; Complications and side effects ; Computer and Information Sciences ; Diagnosis ; Engineering and Technology ; Gene expression ; Learning algorithms ; Mass spectrometry ; Mass spectroscopy ; Mathematical models ; Medicine and Health Sciences ; Model accuracy ; Monoclonal gammopathies ; Multiple myeloma ; Patients ; Performance prediction ; Physical Sciences ; Predictive control ; Proteins ; Proteomes ; Proteomics ; Research and Analysis Methods ; Risk factors ; Scientific imaging ; Support vector machines</subject><ispartof>PloS one, 2018-08, Vol.13 (8), p.e0201793-e0201793</ispartof><rights>COPYRIGHT 2018 Public Library of Science</rights><rights>2018 Barceló et al. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><rights>2018 Barceló et al 2018 Barceló et al</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c692t-5f1a5c69604f7ce0bc040daccdb2d824077450bf140f36a5799eefcc209d8b73</citedby><cites>FETCH-LOGICAL-c692t-5f1a5c69604f7ce0bc040daccdb2d824077450bf140f36a5799eefcc209d8b73</cites><orcidid>0000-0002-9158-099X</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/PMC6072114/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC6072114/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,314,727,780,784,864,885,2102,2928,23866,27924,27925,53791,53793</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/30071092$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><contributor>Roemer, Klaus</contributor><creatorcontrib>Barceló, Francisca</creatorcontrib><creatorcontrib>Gomila, Rosa</creatorcontrib><creatorcontrib>de Paul, Ivan</creatorcontrib><creatorcontrib>Gili, Xavier</creatorcontrib><creatorcontrib>Segura, Jaume</creatorcontrib><creatorcontrib>Pérez-Montaña, Albert</creatorcontrib><creatorcontrib>Jimenez-Marco, Teresa</creatorcontrib><creatorcontrib>Sampol, Antonia</creatorcontrib><creatorcontrib>Portugal, José</creatorcontrib><title>MALDI-TOF analysis of blood serum proteome can predict the presence of monoclonal gammopathy of undetermined significance</title><title>PloS one</title><addtitle>PLoS One</addtitle><description>Monoclonal gammopathy of undetermined significance (MGUS) is a plasma cell dyscrasia that can progress to malignant multiple myeloma (MM). Specific molecular biomarkers to classify the MGUS status and discriminate the initial asymptomatic phase of MM have not been identified. We examined the serum peptidome profile of MGUS patients and healthy volunteers using MALDI-TOF mass spectrometry and developed a predictive model for classifying serum samples. The predictive model was built using a support vector machine (SVM) supervised learning method tuned by applying a 20-fold cross-validation scheme. Predicting class labels in a blinded test set containing randomly selected MGUS and healthy control serum samples validated the model. The generalization performance of the predictive model was evaluated by a double cross-validation method that showed 88% average model accuracy, 89% average sensitivity and 86% average specificity. Our model, which classifies unknown serum samples as belonging to either MGUS patients or healthy individuals, can be applied to clinical diagnosis.</description><subject>Benign monoclonal gammopathy</subject><subject>Bioinformatics</subject><subject>Biological markers</subject><subject>Biology and Life Sciences</subject><subject>Biomarkers</subject><subject>Blood serum</subject><subject>Bone marrow</subject><subject>Classification</subject><subject>Complications and side effects</subject><subject>Computer and Information Sciences</subject><subject>Diagnosis</subject><subject>Engineering and Technology</subject><subject>Gene expression</subject><subject>Learning algorithms</subject><subject>Mass spectrometry</subject><subject>Mass spectroscopy</subject><subject>Mathematical models</subject><subject>Medicine and Health Sciences</subject><subject>Model accuracy</subject><subject>Monoclonal gammopathies</subject><subject>Multiple myeloma</subject><subject>Patients</subject><subject>Performance prediction</subject><subject>Physical Sciences</subject><subject>Predictive control</subject><subject>Proteins</subject><subject>Proteomes</subject><subject>Proteomics</subject><subject>Research and Analysis Methods</subject><subject>Risk factors</subject><subject>Scientific imaging</subject><subject>Support vector 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expression</topic><topic>Learning algorithms</topic><topic>Mass spectrometry</topic><topic>Mass spectroscopy</topic><topic>Mathematical models</topic><topic>Medicine and Health Sciences</topic><topic>Model accuracy</topic><topic>Monoclonal gammopathies</topic><topic>Multiple myeloma</topic><topic>Patients</topic><topic>Performance prediction</topic><topic>Physical Sciences</topic><topic>Predictive control</topic><topic>Proteins</topic><topic>Proteomes</topic><topic>Proteomics</topic><topic>Research and Analysis Methods</topic><topic>Risk factors</topic><topic>Scientific imaging</topic><topic>Support vector machines</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Barceló, Francisca</creatorcontrib><creatorcontrib>Gomila, Rosa</creatorcontrib><creatorcontrib>de Paul, Ivan</creatorcontrib><creatorcontrib>Gili, Xavier</creatorcontrib><creatorcontrib>Segura, Jaume</creatorcontrib><creatorcontrib>Pérez-Montaña, 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Klaus</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>MALDI-TOF analysis of blood serum proteome can predict the presence of monoclonal gammopathy of undetermined significance</atitle><jtitle>PloS one</jtitle><addtitle>PLoS One</addtitle><date>2018-08-02</date><risdate>2018</risdate><volume>13</volume><issue>8</issue><spage>e0201793</spage><epage>e0201793</epage><pages>e0201793-e0201793</pages><issn>1932-6203</issn><eissn>1932-6203</eissn><abstract>Monoclonal gammopathy of undetermined significance (MGUS) is a plasma cell dyscrasia that can progress to malignant multiple myeloma (MM). Specific molecular biomarkers to classify the MGUS status and discriminate the initial asymptomatic phase of MM have not been identified. We examined the serum peptidome profile of MGUS patients and healthy volunteers using MALDI-TOF mass spectrometry and developed a predictive model for classifying serum samples. The predictive model was built using a support vector machine (SVM) supervised learning method tuned by applying a 20-fold cross-validation scheme. Predicting class labels in a blinded test set containing randomly selected MGUS and healthy control serum samples validated the model. The generalization performance of the predictive model was evaluated by a double cross-validation method that showed 88% average model accuracy, 89% average sensitivity and 86% average specificity. Our model, which classifies unknown serum samples as belonging to either MGUS patients or healthy individuals, can be applied to clinical diagnosis.</abstract><cop>United States</cop><pub>Public Library of Science</pub><pmid>30071092</pmid><doi>10.1371/journal.pone.0201793</doi><tpages>e0201793</tpages><orcidid>https://orcid.org/0000-0002-9158-099X</orcidid><oa>free_for_read</oa></addata></record> |
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| subjects | Benign monoclonal gammopathy Bioinformatics Biological markers Biology and Life Sciences Biomarkers Blood serum Bone marrow Classification Complications and side effects Computer and Information Sciences Diagnosis Engineering and Technology Gene expression Learning algorithms Mass spectrometry Mass spectroscopy Mathematical models Medicine and Health Sciences Model accuracy Monoclonal gammopathies Multiple myeloma Patients Performance prediction Physical Sciences Predictive control Proteins Proteomes Proteomics Research and Analysis Methods Risk factors Scientific imaging Support vector machines |
| title | MALDI-TOF analysis of blood serum proteome can predict the presence of monoclonal gammopathy of undetermined significance |
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