Integrative Modeling of Multiomics Data for Predicting Tumor Mutation Burden in Patients with Lung Cancer
Immunotherapy has been widely used in the treatment of lung cancer, and one of the most effective biomarkers for the prognosis of immunotherapy currently is tumor mutation burden (TMB). Although whole-exome sequencing (WES) could be utilized to assess TMB, several problems prevent its routine clinic...
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| description | Immunotherapy has been widely used in the treatment of lung cancer, and one of the most effective biomarkers for the prognosis of immunotherapy currently is tumor mutation burden (TMB). Although whole-exome sequencing (WES) could be utilized to assess TMB, several problems prevent its routine clinical application. To develop a simplified TMB prediction model, patients with lung adenocarcinoma (LUAD) in The Cancer Genome Atlas (TCGA) were randomly split into training and validation cohorts and categorized into the TMB-high (TMB-H) and TMB-low (TMB-L) groups, respectively. Based on the 610 differentially expressed genes, 50 differentially expressed miRNAs and 58 differentially methylated CpG sites between TMB-H and TMB-L patients, we constructed 4 predictive signatures and established TMB prediction model through machine learning methods that integrating the expression or methylation profiles of 7 genes, 7 miRNAs, and 6 CpG sites. The multiomics model exhibited excellent performance in predicting TMB with the area under curve (AUC) of 0.911 in the training cohort and 0.859 in the validation cohort. Besides, the significant correlation between the multiomics model score and TMB was observed. In summary, we developed a prognostic TMB prediction model by integrating multiomics data in patients with LUAD, which might facilitate the further development of quantitative real time-polymerase chain reaction- (qRT-PCR-) based TMB prediction assay. |
| doi_str_mv | 10.1155/2022/2698190 |
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Although whole-exome sequencing (WES) could be utilized to assess TMB, several problems prevent its routine clinical application. To develop a simplified TMB prediction model, patients with lung adenocarcinoma (LUAD) in The Cancer Genome Atlas (TCGA) were randomly split into training and validation cohorts and categorized into the TMB-high (TMB-H) and TMB-low (TMB-L) groups, respectively. Based on the 610 differentially expressed genes, 50 differentially expressed miRNAs and 58 differentially methylated CpG sites between TMB-H and TMB-L patients, we constructed 4 predictive signatures and established TMB prediction model through machine learning methods that integrating the expression or methylation profiles of 7 genes, 7 miRNAs, and 6 CpG sites. The multiomics model exhibited excellent performance in predicting TMB with the area under curve (AUC) of 0.911 in the training cohort and 0.859 in the validation cohort. Besides, the significant correlation between the multiomics model score and TMB was observed. In summary, we developed a prognostic TMB prediction model by integrating multiomics data in patients with LUAD, which might facilitate the further development of quantitative real time-polymerase chain reaction- (qRT-PCR-) based TMB prediction assay.</description><identifier>ISSN: 2314-6133</identifier><identifier>EISSN: 2314-6141</identifier><identifier>DOI: 10.1155/2022/2698190</identifier><identifier>PMID: 35097114</identifier><language>eng</language><publisher>United States: Hindawi</publisher><subject>Adenocarcinoma ; Adenocarcinoma of Lung - drug therapy ; Adenocarcinoma of Lung - genetics ; Apoptosis ; Biomarkers ; Biomarkers, Tumor - metabolism ; Biomedical research ; Cancer ; Cancer therapies ; Chemotherapy ; CpG islands ; Development and progression ; DNA methylation ; Gene expression ; Gene mutations ; Genes ; Genetic aspects ; Genomes ; Genomics ; Humans ; Immunotherapy ; Learning algorithms ; Lung cancer ; Lung Neoplasms - pathology ; Machine learning ; Methods ; MicroRNAs - therapeutic use ; Mutation ; Mutation - genetics ; Oncology, Experimental ; Patients ; Performance prediction ; Polymerase chain reaction ; Prediction models ; Prognosis ; Software ; Training ; Tumors</subject><ispartof>BioMed research international, 2022, Vol.2022 (1), p.2698190-2698190</ispartof><rights>Copyright © 2022 Jun Wang et al.</rights><rights>COPYRIGHT 2022 John Wiley & Sons, Inc.</rights><rights>Copyright © 2022 Jun Wang et al. This is an open access article distributed under the Creative Commons Attribution License (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. https://creativecommons.org/licenses/by/4.0</rights><rights>Copyright © 2022 Jun Wang et al. 2022</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c476t-937f7a0ac0c31899ab521849e19b507331f90c01a89458e94303c2234c33a7e83</citedby><cites>FETCH-LOGICAL-c476t-937f7a0ac0c31899ab521849e19b507331f90c01a89458e94303c2234c33a7e83</cites><orcidid>0000-0002-5470-4631 ; 0000-0002-3312-841X</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/PMC8794677/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC8794677/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,314,723,776,780,881,4009,27902,27903,27904,53769,53771</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/35097114$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><contributor>Moloney, Gerard M.</contributor><contributor>Gerard M Moloney</contributor><creatorcontrib>Wang, Jun</creatorcontrib><creatorcontrib>Chen, Peng</creatorcontrib><creatorcontrib>Su, Mingyang</creatorcontrib><creatorcontrib>Zhong, Guocheng</creatorcontrib><creatorcontrib>Zhang, Shasha</creatorcontrib><creatorcontrib>Gou, Deming</creatorcontrib><title>Integrative Modeling of Multiomics Data for Predicting Tumor Mutation Burden in Patients with Lung Cancer</title><title>BioMed research international</title><addtitle>Biomed Res Int</addtitle><description>Immunotherapy has been widely used in the treatment of lung cancer, and one of the most effective biomarkers for the prognosis of immunotherapy currently is tumor mutation burden (TMB). Although whole-exome sequencing (WES) could be utilized to assess TMB, several problems prevent its routine clinical application. To develop a simplified TMB prediction model, patients with lung adenocarcinoma (LUAD) in The Cancer Genome Atlas (TCGA) were randomly split into training and validation cohorts and categorized into the TMB-high (TMB-H) and TMB-low (TMB-L) groups, respectively. Based on the 610 differentially expressed genes, 50 differentially expressed miRNAs and 58 differentially methylated CpG sites between TMB-H and TMB-L patients, we constructed 4 predictive signatures and established TMB prediction model through machine learning methods that integrating the expression or methylation profiles of 7 genes, 7 miRNAs, and 6 CpG sites. The multiomics model exhibited excellent performance in predicting TMB with the area under curve (AUC) of 0.911 in the training cohort and 0.859 in the validation cohort. Besides, the significant correlation between the multiomics model score and TMB was observed. In summary, we developed a prognostic TMB prediction model by integrating multiomics data in patients with LUAD, which might facilitate the further development of quantitative real time-polymerase chain reaction- (qRT-PCR-) based TMB prediction assay.</description><subject>Adenocarcinoma</subject><subject>Adenocarcinoma of Lung - drug therapy</subject><subject>Adenocarcinoma of Lung - genetics</subject><subject>Apoptosis</subject><subject>Biomarkers</subject><subject>Biomarkers, Tumor - metabolism</subject><subject>Biomedical research</subject><subject>Cancer</subject><subject>Cancer therapies</subject><subject>Chemotherapy</subject><subject>CpG islands</subject><subject>Development and progression</subject><subject>DNA methylation</subject><subject>Gene expression</subject><subject>Gene mutations</subject><subject>Genes</subject><subject>Genetic aspects</subject><subject>Genomes</subject><subject>Genomics</subject><subject>Humans</subject><subject>Immunotherapy</subject><subject>Learning algorithms</subject><subject>Lung cancer</subject><subject>Lung Neoplasms - pathology</subject><subject>Machine learning</subject><subject>Methods</subject><subject>MicroRNAs - therapeutic use</subject><subject>Mutation</subject><subject>Mutation - genetics</subject><subject>Oncology, Experimental</subject><subject>Patients</subject><subject>Performance prediction</subject><subject>Polymerase chain reaction</subject><subject>Prediction models</subject><subject>Prognosis</subject><subject>Software</subject><subject>Training</subject><subject>Tumors</subject><issn>2314-6133</issn><issn>2314-6141</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2022</creationdate><recordtype>article</recordtype><sourceid>RHX</sourceid><sourceid>EIF</sourceid><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><recordid>eNp9kc1vFCEYh4nR2Kb25rkh8WJi1_I1MFya1K22TXZjD_VMWIbZpZmBlo82_vcy2XWtHuQCeXl4eN_8AHiP0WeMm-aMIELOCJctlugVOCQUsxnHDL_enyk9AMcp3aO6WsyR5G_BAW2QFBizQ-BufLbrqLN7snAZOjs4v4ahh8syZBdGZxK81FnDPkR4G23nTJ6IuzLWwrLk-jJ4-KXEznroPLytBetzgs8ub-CiVHauvbHxHXjT6yHZ491-BH58-3o3v54tvl_dzC8WM8MEzzNJRS800gYZilsp9aohuGXSYrlqkKAU9xIZhHUrWdNaySiihhDKDKVa2JYegfOt96GsRtuZ2kzUg3qIbtTxpwraqb9vvNuodXhSrZCMC1EFH3eCGB6LTVmNLhk7DNrbUJIinDCCKKO8oh_-Qe9Dib6ON1FUCCIQ-UOt9WCV832o_5pJqi64FFygRk7U6ZYyMaQUbb9vGSM1ha2msNUu7IqfvBxzD_-OtgKftsDG-U4_u__rfgFAUa8i</recordid><startdate>2022</startdate><enddate>2022</enddate><creator>Wang, Jun</creator><creator>Chen, Peng</creator><creator>Su, Mingyang</creator><creator>Zhong, Guocheng</creator><creator>Zhang, Shasha</creator><creator>Gou, Deming</creator><general>Hindawi</general><general>John Wiley & Sons, Inc</general><general>Hindawi Limited</general><scope>RHU</scope><scope>RHW</scope><scope>RHX</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>7QL</scope><scope>7QO</scope><scope>7T7</scope><scope>7TK</scope><scope>7U7</scope><scope>7U9</scope><scope>7X7</scope><scope>7XB</scope><scope>88E</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>AFKRA</scope><scope>ARAPS</scope><scope>AZQEC</scope><scope>BBNVY</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>BHPHI</scope><scope>C1K</scope><scope>CCPQU</scope><scope>CWDGH</scope><scope>DWQXO</scope><scope>FR3</scope><scope>FYUFA</scope><scope>GHDGH</scope><scope>GNUQQ</scope><scope>H94</scope><scope>HCIFZ</scope><scope>K9.</scope><scope>LK8</scope><scope>M0S</scope><scope>M1P</scope><scope>M7N</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>7X8</scope><scope>5PM</scope><orcidid>https://orcid.org/0000-0002-5470-4631</orcidid><orcidid>https://orcid.org/0000-0002-3312-841X</orcidid></search><sort><creationdate>2022</creationdate><title>Integrative Modeling of Multiomics Data for Predicting Tumor Mutation Burden in Patients with Lung Cancer</title><author>Wang, Jun ; Chen, Peng ; Su, Mingyang ; Zhong, Guocheng ; Zhang, Shasha ; Gou, Deming</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c476t-937f7a0ac0c31899ab521849e19b507331f90c01a89458e94303c2234c33a7e83</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2022</creationdate><topic>Adenocarcinoma</topic><topic>Adenocarcinoma of Lung - drug therapy</topic><topic>Adenocarcinoma of Lung - genetics</topic><topic>Apoptosis</topic><topic>Biomarkers</topic><topic>Biomarkers, Tumor - metabolism</topic><topic>Biomedical research</topic><topic>Cancer</topic><topic>Cancer therapies</topic><topic>Chemotherapy</topic><topic>CpG islands</topic><topic>Development and progression</topic><topic>DNA methylation</topic><topic>Gene expression</topic><topic>Gene mutations</topic><topic>Genes</topic><topic>Genetic aspects</topic><topic>Genomes</topic><topic>Genomics</topic><topic>Humans</topic><topic>Immunotherapy</topic><topic>Learning algorithms</topic><topic>Lung cancer</topic><topic>Lung Neoplasms - pathology</topic><topic>Machine learning</topic><topic>Methods</topic><topic>MicroRNAs - therapeutic use</topic><topic>Mutation</topic><topic>Mutation - genetics</topic><topic>Oncology, Experimental</topic><topic>Patients</topic><topic>Performance prediction</topic><topic>Polymerase chain reaction</topic><topic>Prediction models</topic><topic>Prognosis</topic><topic>Software</topic><topic>Training</topic><topic>Tumors</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Wang, Jun</creatorcontrib><creatorcontrib>Chen, Peng</creatorcontrib><creatorcontrib>Su, Mingyang</creatorcontrib><creatorcontrib>Zhong, Guocheng</creatorcontrib><creatorcontrib>Zhang, Shasha</creatorcontrib><creatorcontrib>Gou, Deming</creatorcontrib><collection>Hindawi Publishing Complete</collection><collection>Hindawi Publishing Subscription Journals</collection><collection>Hindawi Publishing 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>Bacteriology Abstracts (Microbiology B)</collection><collection>Biotechnology Research Abstracts</collection><collection>Industrial and Applied Microbiology Abstracts (Microbiology A)</collection><collection>Neurosciences Abstracts</collection><collection>Toxicology Abstracts</collection><collection>Virology and AIDS Abstracts</collection><collection>Health & Medical Collection</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Medical 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 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 (ProQuest)</collection><collection>Environmental Sciences and Pollution Management</collection><collection>ProQuest One Community College</collection><collection>Middle East & Africa Database</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>AIDS and Cancer Research Abstracts</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>Algology Mycology and Protozoology Abstracts (Microbiology C)</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>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><jtitle>BioMed research international</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Wang, Jun</au><au>Chen, Peng</au><au>Su, Mingyang</au><au>Zhong, Guocheng</au><au>Zhang, Shasha</au><au>Gou, Deming</au><au>Moloney, Gerard M.</au><au>Gerard M Moloney</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Integrative Modeling of Multiomics Data for Predicting Tumor Mutation Burden in Patients with Lung Cancer</atitle><jtitle>BioMed research international</jtitle><addtitle>Biomed Res Int</addtitle><date>2022</date><risdate>2022</risdate><volume>2022</volume><issue>1</issue><spage>2698190</spage><epage>2698190</epage><pages>2698190-2698190</pages><issn>2314-6133</issn><eissn>2314-6141</eissn><abstract>Immunotherapy has been widely used in the treatment of lung cancer, and one of the most effective biomarkers for the prognosis of immunotherapy currently is tumor mutation burden (TMB). Although whole-exome sequencing (WES) could be utilized to assess TMB, several problems prevent its routine clinical application. To develop a simplified TMB prediction model, patients with lung adenocarcinoma (LUAD) in The Cancer Genome Atlas (TCGA) were randomly split into training and validation cohorts and categorized into the TMB-high (TMB-H) and TMB-low (TMB-L) groups, respectively. Based on the 610 differentially expressed genes, 50 differentially expressed miRNAs and 58 differentially methylated CpG sites between TMB-H and TMB-L patients, we constructed 4 predictive signatures and established TMB prediction model through machine learning methods that integrating the expression or methylation profiles of 7 genes, 7 miRNAs, and 6 CpG sites. The multiomics model exhibited excellent performance in predicting TMB with the area under curve (AUC) of 0.911 in the training cohort and 0.859 in the validation cohort. Besides, the significant correlation between the multiomics model score and TMB was observed. In summary, we developed a prognostic TMB prediction model by integrating multiomics data in patients with LUAD, which might facilitate the further development of quantitative real time-polymerase chain reaction- (qRT-PCR-) based TMB prediction assay.</abstract><cop>United States</cop><pub>Hindawi</pub><pmid>35097114</pmid><doi>10.1155/2022/2698190</doi><tpages>1</tpages><orcidid>https://orcid.org/0000-0002-5470-4631</orcidid><orcidid>https://orcid.org/0000-0002-3312-841X</orcidid><oa>free_for_read</oa></addata></record> |
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| subjects | Adenocarcinoma Adenocarcinoma of Lung - drug therapy Adenocarcinoma of Lung - genetics Apoptosis Biomarkers Biomarkers, Tumor - metabolism Biomedical research Cancer Cancer therapies Chemotherapy CpG islands Development and progression DNA methylation Gene expression Gene mutations Genes Genetic aspects Genomes Genomics Humans Immunotherapy Learning algorithms Lung cancer Lung Neoplasms - pathology Machine learning Methods MicroRNAs - therapeutic use Mutation Mutation - genetics Oncology, Experimental Patients Performance prediction Polymerase chain reaction Prediction models Prognosis Software Training Tumors |
| title | Integrative Modeling of Multiomics Data for Predicting Tumor Mutation Burden in Patients with Lung Cancer |
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