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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Veröffentlicht in:Biomarker research 2021-07, Vol.9 (1), p.56-56, Article 56
Hauptverfasser: 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.
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container_end_page 56
container_issue 1
container_start_page 56
container_title Biomarker research
container_volume 9
creator 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.
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 &amp; Biomedicine ; Lung cancer, Non-small cell ; Lymphocytes T ; Medicine, Research &amp; Experimental ; Melanoma ; Microenvironments ; Nivolumab ; Non-small cell lung carcinoma ; Oncology ; Patients ; PD-L1 protein ; Pembrolizumab ; Research &amp; Experimental Medicine ; Ribonucleic acid ; RNA ; Science &amp; 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 &amp; Biomedicine</subject><subject>Lung cancer, Non-small cell</subject><subject>Lymphocytes T</subject><subject>Medicine, Research &amp; 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 &amp; Experimental Medicine</subject><subject>Ribonucleic acid</subject><subject>RNA</subject><subject>Science &amp; 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 &amp; Biomedicine</topic><topic>Lung cancer, Non-small cell</topic><topic>Lymphocytes T</topic><topic>Medicine, Research &amp; 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 &amp; Experimental Medicine</topic><topic>Ribonucleic acid</topic><topic>RNA</topic><topic>Science &amp; 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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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
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