Integration of tumor inflammation, cell proliferation, and traditional biomarkers improves prediction of immunotherapy resistance and response
Background Contemporary to the rapidly evolving landscape of cancer immunotherapy is the equally changing understanding of immune tumor microenvironments (TMEs) which is crucial to the success of these therapies. Their reliance on a robust host immune response necessitates clinical grade measurement...
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description | Background Contemporary to the rapidly evolving landscape of cancer immunotherapy is the equally changing understanding of immune tumor microenvironments (TMEs) which is crucial to the success of these therapies. Their reliance on a robust host immune response necessitates clinical grade measurements of immune TMEs at diagnosis. In this study, we describe a stable tumor immunogenic profile describing immune TMEs in multiple tumor types with ability to predict clinical benefit from immune checkpoint inhibitors (ICIs). Methods A tumor immunogenic signature (TIGS) was derived from targeted RNA-sequencing (RNA-seq) and gene expression analysis of 1323 clinical solid tumor cases spanning 35 histologies using unsupervised analysis. TIGS correlation with ICI response and survival was assessed in a retrospective cohort of NSCLC, melanoma and RCC tumor blocks, alone and combined with TMB, PD-L1 IHC and cell proliferation biomarkers. Results Unsupervised clustering of RNA-seq profiles uncovered a 161 gene signature where T cell and B cell activation, IFNg, chemokine, cytokine and interleukin pathways are over-represented. Mean expression of these genes produced three distinct TIGS score categories: strong (n = 384/1323; 29.02%), moderate (n = 354/1323; 26.76%), and weak (n = 585/1323; 44.22%). Strong TIGS tumors presented an improved ICI response rate of 37% (30/81); with highest response rate advantage occurring in NSCLC (ORR = 36.6%; 16/44; p = 0.051). Similarly, overall survival for strong TIGS tumors trended upward (median = 25 months; p = 0.19). Integrating the TIGS score categories with neoplastic influence quantified via cell proliferation showed highly proliferative and strong TIGS tumors correlate with significantly higher ICI ORR than poorly proliferative and weak TIGS tumors [14.28%; p = 0.0006]. Importantly, we noted that strong TIGS and highly [median = not achieved; p = 0.025] or moderately [median = 16.2 months; p = 0.025] proliferative tumors had significantly better survival compared to weak TIGS, highly proliferative tumors [median = 7.03 months]. Importantly, TIGS discriminates subpopulations of potential ICI responders that were considered negative for response by TMB and PD-L1. Conclusions TIGS is a comprehensive and informative measurement of immune TME that effectively characterizes host immune response to ICIs in multiple tumors. The results indicate that when combined with PD-L1, TMB and cell proliferation, TIGS provides greater context of b |
doi_str_mv | 10.1186/s40364-021-00308-6 |
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J. ; Van Roey, Erik ; Gao, Shuang ; Hoefer, Carrie ; Nesline, Mary K. ; DePietro, Paul ; Burgher, Blake ; Andreas, Jonathan ; Giamo, Vincent ; Wang, Yirong ; Lenzo, Felicia L. ; Schoenborn, Margot ; Zhang, Shengle ; Klein, Roger ; Glenn, Sean T. ; Conroy, Jeffrey M.</creator><creatorcontrib>Pabla, Sarabjot ; Seager, R. J. ; Van Roey, Erik ; Gao, Shuang ; Hoefer, Carrie ; Nesline, Mary K. ; DePietro, Paul ; Burgher, Blake ; Andreas, Jonathan ; Giamo, Vincent ; Wang, Yirong ; Lenzo, Felicia L. ; Schoenborn, Margot ; Zhang, Shengle ; Klein, Roger ; Glenn, Sean T. ; Conroy, Jeffrey M.</creatorcontrib><description>Background Contemporary to the rapidly evolving landscape of cancer immunotherapy is the equally changing understanding of immune tumor microenvironments (TMEs) which is crucial to the success of these therapies. Their reliance on a robust host immune response necessitates clinical grade measurements of immune TMEs at diagnosis. In this study, we describe a stable tumor immunogenic profile describing immune TMEs in multiple tumor types with ability to predict clinical benefit from immune checkpoint inhibitors (ICIs). Methods A tumor immunogenic signature (TIGS) was derived from targeted RNA-sequencing (RNA-seq) and gene expression analysis of 1323 clinical solid tumor cases spanning 35 histologies using unsupervised analysis. TIGS correlation with ICI response and survival was assessed in a retrospective cohort of NSCLC, melanoma and RCC tumor blocks, alone and combined with TMB, PD-L1 IHC and cell proliferation biomarkers. Results Unsupervised clustering of RNA-seq profiles uncovered a 161 gene signature where T cell and B cell activation, IFNg, chemokine, cytokine and interleukin pathways are over-represented. Mean expression of these genes produced three distinct TIGS score categories: strong (n = 384/1323; 29.02%), moderate (n = 354/1323; 26.76%), and weak (n = 585/1323; 44.22%). Strong TIGS tumors presented an improved ICI response rate of 37% (30/81); with highest response rate advantage occurring in NSCLC (ORR = 36.6%; 16/44; p = 0.051). Similarly, overall survival for strong TIGS tumors trended upward (median = 25 months; p = 0.19). Integrating the TIGS score categories with neoplastic influence quantified via cell proliferation showed highly proliferative and strong TIGS tumors correlate with significantly higher ICI ORR than poorly proliferative and weak TIGS tumors [14.28%; p = 0.0006]. Importantly, we noted that strong TIGS and highly [median = not achieved; p = 0.025] or moderately [median = 16.2 months; p = 0.025] proliferative tumors had significantly better survival compared to weak TIGS, highly proliferative tumors [median = 7.03 months]. Importantly, TIGS discriminates subpopulations of potential ICI responders that were considered negative for response by TMB and PD-L1. Conclusions TIGS is a comprehensive and informative measurement of immune TME that effectively characterizes host immune response to ICIs in multiple tumors. The results indicate that when combined with PD-L1, TMB and cell proliferation, TIGS provides greater context of both immune and neoplastic influences on the TME for implementation into clinical practice.</description><identifier>ISSN: 2050-7771</identifier><identifier>EISSN: 2050-7771</identifier><identifier>DOI: 10.1186/s40364-021-00308-6</identifier><identifier>PMID: 34233760</identifier><language>eng</language><publisher>LONDON: Springer Nature</publisher><subject>Algorithmic analysis ; Analysis ; Biological markers ; Biomarkers ; Cancer ; Cancer immunotherapy ; Cancer therapies ; Care and treatment ; Cell activation ; Cell growth ; Cell proliferation ; Chemokines ; Gene expression ; Genes ; Genomics ; Immune checkpoint inhibitors ; Immune response ; Immunogenicity ; Immunotherapy ; Inflammation ; Ipilimumab ; Life Sciences & Biomedicine ; Lung cancer, Non-small cell ; Lymphocytes T ; Medicine, Research & Experimental ; Melanoma ; Microenvironments ; Nivolumab ; Non-small cell lung carcinoma ; Oncology ; Patients ; PD-L1 protein ; Pembrolizumab ; Research & Experimental Medicine ; Ribonucleic acid ; RNA ; Science & Technology ; Solid tumors ; T cells ; Tumors</subject><ispartof>Biomarker research, 2021-07, Vol.9 (1), p.56-56, Article 56</ispartof><rights>COPYRIGHT 2021 BioMed Central Ltd.</rights><rights>2021. This work is licensed under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><rights>The Author(s) 2021</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>true</woscitedreferencessubscribed><woscitedreferencescount>17</woscitedreferencescount><woscitedreferencesoriginalsourcerecordid>wos000674361300001</woscitedreferencesoriginalsourcerecordid><citedby>FETCH-LOGICAL-c597t-f67944c843166d0b880d7f30f31b01314abc243cab8cbc9d0b351ce9660d7ca13</citedby><cites>FETCH-LOGICAL-c597t-f67944c843166d0b880d7f30f31b01314abc243cab8cbc9d0b351ce9660d7ca13</cites><orcidid>0000-0003-1764-9706 ; 0000-0002-7746-9144</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/PMC8265007/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC8265007/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,315,728,781,785,865,886,2103,2115,27929,27930,39263,53796,53798</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/34233760$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Pabla, Sarabjot</creatorcontrib><creatorcontrib>Seager, R. J.</creatorcontrib><creatorcontrib>Van Roey, Erik</creatorcontrib><creatorcontrib>Gao, Shuang</creatorcontrib><creatorcontrib>Hoefer, Carrie</creatorcontrib><creatorcontrib>Nesline, Mary K.</creatorcontrib><creatorcontrib>DePietro, Paul</creatorcontrib><creatorcontrib>Burgher, Blake</creatorcontrib><creatorcontrib>Andreas, Jonathan</creatorcontrib><creatorcontrib>Giamo, Vincent</creatorcontrib><creatorcontrib>Wang, Yirong</creatorcontrib><creatorcontrib>Lenzo, Felicia L.</creatorcontrib><creatorcontrib>Schoenborn, Margot</creatorcontrib><creatorcontrib>Zhang, Shengle</creatorcontrib><creatorcontrib>Klein, Roger</creatorcontrib><creatorcontrib>Glenn, Sean T.</creatorcontrib><creatorcontrib>Conroy, Jeffrey M.</creatorcontrib><title>Integration of tumor inflammation, cell proliferation, and traditional biomarkers improves prediction of immunotherapy resistance and response</title><title>Biomarker research</title><addtitle>BIOMARK RES</addtitle><addtitle>Biomark Res</addtitle><description>Background Contemporary to the rapidly evolving landscape of cancer immunotherapy is the equally changing understanding of immune tumor microenvironments (TMEs) which is crucial to the success of these therapies. Their reliance on a robust host immune response necessitates clinical grade measurements of immune TMEs at diagnosis. In this study, we describe a stable tumor immunogenic profile describing immune TMEs in multiple tumor types with ability to predict clinical benefit from immune checkpoint inhibitors (ICIs). Methods A tumor immunogenic signature (TIGS) was derived from targeted RNA-sequencing (RNA-seq) and gene expression analysis of 1323 clinical solid tumor cases spanning 35 histologies using unsupervised analysis. TIGS correlation with ICI response and survival was assessed in a retrospective cohort of NSCLC, melanoma and RCC tumor blocks, alone and combined with TMB, PD-L1 IHC and cell proliferation biomarkers. Results Unsupervised clustering of RNA-seq profiles uncovered a 161 gene signature where T cell and B cell activation, IFNg, chemokine, cytokine and interleukin pathways are over-represented. Mean expression of these genes produced three distinct TIGS score categories: strong (n = 384/1323; 29.02%), moderate (n = 354/1323; 26.76%), and weak (n = 585/1323; 44.22%). Strong TIGS tumors presented an improved ICI response rate of 37% (30/81); with highest response rate advantage occurring in NSCLC (ORR = 36.6%; 16/44; p = 0.051). Similarly, overall survival for strong TIGS tumors trended upward (median = 25 months; p = 0.19). Integrating the TIGS score categories with neoplastic influence quantified via cell proliferation showed highly proliferative and strong TIGS tumors correlate with significantly higher ICI ORR than poorly proliferative and weak TIGS tumors [14.28%; p = 0.0006]. Importantly, we noted that strong TIGS and highly [median = not achieved; p = 0.025] or moderately [median = 16.2 months; p = 0.025] proliferative tumors had significantly better survival compared to weak TIGS, highly proliferative tumors [median = 7.03 months]. Importantly, TIGS discriminates subpopulations of potential ICI responders that were considered negative for response by TMB and PD-L1. Conclusions TIGS is a comprehensive and informative measurement of immune TME that effectively characterizes host immune response to ICIs in multiple tumors. The results indicate that when combined with PD-L1, TMB and cell proliferation, TIGS provides greater context of both immune and neoplastic influences on the TME for implementation into clinical practice.</description><subject>Algorithmic analysis</subject><subject>Analysis</subject><subject>Biological markers</subject><subject>Biomarkers</subject><subject>Cancer</subject><subject>Cancer immunotherapy</subject><subject>Cancer therapies</subject><subject>Care and treatment</subject><subject>Cell activation</subject><subject>Cell growth</subject><subject>Cell proliferation</subject><subject>Chemokines</subject><subject>Gene expression</subject><subject>Genes</subject><subject>Genomics</subject><subject>Immune checkpoint inhibitors</subject><subject>Immune response</subject><subject>Immunogenicity</subject><subject>Immunotherapy</subject><subject>Inflammation</subject><subject>Ipilimumab</subject><subject>Life Sciences & Biomedicine</subject><subject>Lung cancer, Non-small cell</subject><subject>Lymphocytes T</subject><subject>Medicine, Research & Experimental</subject><subject>Melanoma</subject><subject>Microenvironments</subject><subject>Nivolumab</subject><subject>Non-small cell lung carcinoma</subject><subject>Oncology</subject><subject>Patients</subject><subject>PD-L1 protein</subject><subject>Pembrolizumab</subject><subject>Research & Experimental Medicine</subject><subject>Ribonucleic acid</subject><subject>RNA</subject><subject>Science & Technology</subject><subject>Solid tumors</subject><subject>T cells</subject><subject>Tumors</subject><issn>2050-7771</issn><issn>2050-7771</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2021</creationdate><recordtype>article</recordtype><sourceid>HGBXW</sourceid><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><sourceid>DOA</sourceid><recordid>eNqNkstu1DAYhSMEolXpC7BAkdiAIMW32M4GqRpxGakSEpe15TjO1ENiD7ZT6EvwzPyZmY46iAXJIvGf75zEJ6conmJ0gbHkbxJDlLMKEVwhRJGs-IPilKAaVUII_PDe_UlxntIawSElw7h5XJxQRigVHJ0Wv5c-21XU2QVfhr7M0xhi6Xw_6HHcTl-Xxg5DuYlhcL2N-5n2XZmj7ty81EPZujDq-N3GVLoR2BubQGI7Z-6c3ThOPuRrsNjcltEml7L2xm6tYLkJPtknxaNeD8me769nxbf3774uPlZXnz4sF5dXlakbkauei4YxIxnFnHeolRJ1oqeop7hFmGKmW0MYNbqVpjUNELTGxjacA2c0pmfFcufbBb1Wm-jg429V0E5tByGulI7ZmcEqaZlpiLRtQzgThEGG3HSdkBLCba0Br7c7r83UjrYz1kMww5Hp8RPvrtUq3ChJeI2QAIMXe4MYfkw2ZTW6NIeuvQ1TUqRmDZeiljP6_C90HaYIP2CmaiJJ08C-D9RKwwbgZwZ4r5lN1SUXhBLMMAHq4h8UnJ0dnQne9g7mR4KXRwJgsv2VV3pKSS2_fD5myY41MaQUbX_IAyM1F1jtCqygwGpbYMVB9Ox-kgfJXV0BkDvgp21Dn4yzUKADBg3nglGO6Vx2vHB5W9ZFmHwG6av_l9I_AVMNIA</recordid><startdate>20210707</startdate><enddate>20210707</enddate><creator>Pabla, Sarabjot</creator><creator>Seager, R. 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J. ; Van Roey, Erik ; Gao, Shuang ; Hoefer, Carrie ; Nesline, Mary K. ; DePietro, Paul ; Burgher, Blake ; Andreas, Jonathan ; Giamo, Vincent ; Wang, Yirong ; Lenzo, Felicia L. ; Schoenborn, Margot ; Zhang, Shengle ; Klein, Roger ; Glenn, Sean T. ; Conroy, Jeffrey M.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c597t-f67944c843166d0b880d7f30f31b01314abc243cab8cbc9d0b351ce9660d7ca13</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2021</creationdate><topic>Algorithmic analysis</topic><topic>Analysis</topic><topic>Biological markers</topic><topic>Biomarkers</topic><topic>Cancer</topic><topic>Cancer immunotherapy</topic><topic>Cancer therapies</topic><topic>Care and treatment</topic><topic>Cell activation</topic><topic>Cell growth</topic><topic>Cell proliferation</topic><topic>Chemokines</topic><topic>Gene expression</topic><topic>Genes</topic><topic>Genomics</topic><topic>Immune checkpoint inhibitors</topic><topic>Immune response</topic><topic>Immunogenicity</topic><topic>Immunotherapy</topic><topic>Inflammation</topic><topic>Ipilimumab</topic><topic>Life Sciences & Biomedicine</topic><topic>Lung cancer, Non-small cell</topic><topic>Lymphocytes T</topic><topic>Medicine, Research & Experimental</topic><topic>Melanoma</topic><topic>Microenvironments</topic><topic>Nivolumab</topic><topic>Non-small cell lung carcinoma</topic><topic>Oncology</topic><topic>Patients</topic><topic>PD-L1 protein</topic><topic>Pembrolizumab</topic><topic>Research & Experimental Medicine</topic><topic>Ribonucleic acid</topic><topic>RNA</topic><topic>Science & Technology</topic><topic>Solid tumors</topic><topic>T cells</topic><topic>Tumors</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Pabla, Sarabjot</creatorcontrib><creatorcontrib>Seager, R. J.</creatorcontrib><creatorcontrib>Van Roey, Erik</creatorcontrib><creatorcontrib>Gao, Shuang</creatorcontrib><creatorcontrib>Hoefer, Carrie</creatorcontrib><creatorcontrib>Nesline, Mary K.</creatorcontrib><creatorcontrib>DePietro, Paul</creatorcontrib><creatorcontrib>Burgher, Blake</creatorcontrib><creatorcontrib>Andreas, Jonathan</creatorcontrib><creatorcontrib>Giamo, Vincent</creatorcontrib><creatorcontrib>Wang, Yirong</creatorcontrib><creatorcontrib>Lenzo, Felicia L.</creatorcontrib><creatorcontrib>Schoenborn, Margot</creatorcontrib><creatorcontrib>Zhang, Shengle</creatorcontrib><creatorcontrib>Klein, Roger</creatorcontrib><creatorcontrib>Glenn, Sean T.</creatorcontrib><creatorcontrib>Conroy, Jeffrey M.</creatorcontrib><collection>Web of Science Core Collection</collection><collection>Science Citation Index Expanded</collection><collection>Web of Science - Science Citation Index Expanded - 2021</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>Gale In Context: Science</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Natural Science Collection</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>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>ProQuest Central Student</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Biological Science Collection</collection><collection>Biological Science Database</collection><collection>Access via ProQuest (Open Access)</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><collection>DOAJ Directory of Open Access Journals</collection><jtitle>Biomarker research</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Pabla, Sarabjot</au><au>Seager, R. J.</au><au>Van Roey, Erik</au><au>Gao, Shuang</au><au>Hoefer, Carrie</au><au>Nesline, Mary K.</au><au>DePietro, Paul</au><au>Burgher, Blake</au><au>Andreas, Jonathan</au><au>Giamo, Vincent</au><au>Wang, Yirong</au><au>Lenzo, Felicia L.</au><au>Schoenborn, Margot</au><au>Zhang, Shengle</au><au>Klein, Roger</au><au>Glenn, Sean T.</au><au>Conroy, Jeffrey M.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Integration of tumor inflammation, cell proliferation, and traditional biomarkers improves prediction of immunotherapy resistance and response</atitle><jtitle>Biomarker research</jtitle><stitle>BIOMARK RES</stitle><addtitle>Biomark Res</addtitle><date>2021-07-07</date><risdate>2021</risdate><volume>9</volume><issue>1</issue><spage>56</spage><epage>56</epage><pages>56-56</pages><artnum>56</artnum><issn>2050-7771</issn><eissn>2050-7771</eissn><abstract>Background Contemporary to the rapidly evolving landscape of cancer immunotherapy is the equally changing understanding of immune tumor microenvironments (TMEs) which is crucial to the success of these therapies. Their reliance on a robust host immune response necessitates clinical grade measurements of immune TMEs at diagnosis. In this study, we describe a stable tumor immunogenic profile describing immune TMEs in multiple tumor types with ability to predict clinical benefit from immune checkpoint inhibitors (ICIs). Methods A tumor immunogenic signature (TIGS) was derived from targeted RNA-sequencing (RNA-seq) and gene expression analysis of 1323 clinical solid tumor cases spanning 35 histologies using unsupervised analysis. TIGS correlation with ICI response and survival was assessed in a retrospective cohort of NSCLC, melanoma and RCC tumor blocks, alone and combined with TMB, PD-L1 IHC and cell proliferation biomarkers. Results Unsupervised clustering of RNA-seq profiles uncovered a 161 gene signature where T cell and B cell activation, IFNg, chemokine, cytokine and interleukin pathways are over-represented. Mean expression of these genes produced three distinct TIGS score categories: strong (n = 384/1323; 29.02%), moderate (n = 354/1323; 26.76%), and weak (n = 585/1323; 44.22%). Strong TIGS tumors presented an improved ICI response rate of 37% (30/81); with highest response rate advantage occurring in NSCLC (ORR = 36.6%; 16/44; p = 0.051). Similarly, overall survival for strong TIGS tumors trended upward (median = 25 months; p = 0.19). Integrating the TIGS score categories with neoplastic influence quantified via cell proliferation showed highly proliferative and strong TIGS tumors correlate with significantly higher ICI ORR than poorly proliferative and weak TIGS tumors [14.28%; p = 0.0006]. Importantly, we noted that strong TIGS and highly [median = not achieved; p = 0.025] or moderately [median = 16.2 months; p = 0.025] proliferative tumors had significantly better survival compared to weak TIGS, highly proliferative tumors [median = 7.03 months]. Importantly, TIGS discriminates subpopulations of potential ICI responders that were considered negative for response by TMB and PD-L1. Conclusions TIGS is a comprehensive and informative measurement of immune TME that effectively characterizes host immune response to ICIs in multiple tumors. The results indicate that when combined with PD-L1, TMB and cell proliferation, TIGS provides greater context of both immune and neoplastic influences on the TME for implementation into clinical practice.</abstract><cop>LONDON</cop><pub>Springer Nature</pub><pmid>34233760</pmid><doi>10.1186/s40364-021-00308-6</doi><tpages>11</tpages><orcidid>https://orcid.org/0000-0003-1764-9706</orcidid><orcidid>https://orcid.org/0000-0002-7746-9144</orcidid><oa>free_for_read</oa></addata></record> |
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identifier | ISSN: 2050-7771 |
ispartof | Biomarker research, 2021-07, Vol.9 (1), p.56-56, Article 56 |
issn | 2050-7771 2050-7771 |
language | eng |
recordid | cdi_gale_infotracmisc_A672321412 |
source | Springer Online Journals Complete; DOAJ Directory of Open Access Journals; Elektronische Zeitschriftenbibliothek - Frei zugängliche E-Journals; PubMed Central Open Access; Web of Science - Science Citation Index Expanded - 2021<img src="https://exlibris-pub.s3.amazonaws.com/fromwos-v2.jpg" />; PubMed Central; Springer Nature OA/Free Journals |
subjects | Algorithmic analysis Analysis Biological markers Biomarkers Cancer Cancer immunotherapy Cancer therapies Care and treatment Cell activation Cell growth Cell proliferation Chemokines Gene expression Genes Genomics Immune checkpoint inhibitors Immune response Immunogenicity Immunotherapy Inflammation Ipilimumab Life Sciences & Biomedicine Lung cancer, Non-small cell Lymphocytes T Medicine, Research & Experimental Melanoma Microenvironments Nivolumab Non-small cell lung carcinoma Oncology Patients PD-L1 protein Pembrolizumab Research & Experimental Medicine Ribonucleic acid RNA Science & Technology Solid tumors T cells Tumors |
title | Integration of tumor inflammation, cell proliferation, and traditional biomarkers improves prediction of immunotherapy resistance and response |
url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2024-12-14T10%3A44%3A35IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-gale_webof&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Integration%20of%20tumor%20inflammation,%20cell%20proliferation,%20and%20traditional%20biomarkers%20improves%20prediction%20of%20immunotherapy%20resistance%20and%20response&rft.jtitle=Biomarker%20research&rft.au=Pabla,%20Sarabjot&rft.date=2021-07-07&rft.volume=9&rft.issue=1&rft.spage=56&rft.epage=56&rft.pages=56-56&rft.artnum=56&rft.issn=2050-7771&rft.eissn=2050-7771&rft_id=info:doi/10.1186/s40364-021-00308-6&rft_dat=%3Cgale_webof%3EA672321412%3C/gale_webof%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=2552829924&rft_id=info:pmid/34233760&rft_galeid=A672321412&rft_doaj_id=oai_doaj_org_article_8e4c928eb92647248416cdd788777bec&rfr_iscdi=true |