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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 measuremen...

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Autores principales: 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.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8265007/
https://www.ncbi.nlm.nih.gov/pubmed/34233760
http://dx.doi.org/10.1186/s40364-021-00308-6
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author 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.
author_facet 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.
author_sort Pabla, Sarabjot
collection PubMed
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. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s40364-021-00308-6.
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spelling pubmed-82650072021-07-08 Integration of tumor inflammation, cell proliferation, and traditional biomarkers improves prediction of immunotherapy resistance and response 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. Biomark Res Research 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. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s40364-021-00308-6. BioMed Central 2021-07-07 /pmc/articles/PMC8265007/ /pubmed/34233760 http://dx.doi.org/10.1186/s40364-021-00308-6 Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Research
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.
Integration of tumor inflammation, cell proliferation, and traditional biomarkers improves prediction of immunotherapy resistance and response
title Integration of tumor inflammation, cell proliferation, and traditional biomarkers improves prediction of immunotherapy resistance and response
title_full Integration of tumor inflammation, cell proliferation, and traditional biomarkers improves prediction of immunotherapy resistance and response
title_fullStr Integration of tumor inflammation, cell proliferation, and traditional biomarkers improves prediction of immunotherapy resistance and response
title_full_unstemmed Integration of tumor inflammation, cell proliferation, and traditional biomarkers improves prediction of immunotherapy resistance and response
title_short Integration of tumor inflammation, cell proliferation, and traditional biomarkers improves prediction of immunotherapy resistance and response
title_sort integration of tumor inflammation, cell proliferation, and traditional biomarkers improves prediction of immunotherapy resistance and response
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8265007/
https://www.ncbi.nlm.nih.gov/pubmed/34233760
http://dx.doi.org/10.1186/s40364-021-00308-6
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