Integration of tumor inflammation, cell proliferation, and traditional biomarkers improves prediction of immunotherapy resistance and response
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 me...
| Main Authors: | , , , , , , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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BMC
2021-07-01
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| Series: | Biomarker Research |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s40364-021-00308-6 |
| _version_ | 1818888627521847296 |
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| author | Sarabjot Pabla R. J. Seager Erik Van Roey Shuang Gao Carrie Hoefer Mary K. Nesline Paul DePietro Blake Burgher Jonathan Andreas Vincent Giamo Yirong Wang Felicia L. Lenzo Margot Schoenborn Shengle Zhang Roger Klein Sean T. Glenn Jeffrey M. Conroy |
| author_facet | Sarabjot Pabla R. J. Seager Erik Van Roey Shuang Gao Carrie Hoefer Mary K. Nesline Paul DePietro Blake Burgher Jonathan Andreas Vincent Giamo Yirong Wang Felicia L. Lenzo Margot Schoenborn Shengle Zhang Roger Klein Sean T. Glenn Jeffrey M. Conroy |
| author_sort | Sarabjot Pabla |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-19T16:56:08Z |
| format | Article |
| id | doaj.art-8e4c928eb92647248416cdd788777bec |
| institution | Directory Open Access Journal |
| issn | 2050-7771 |
| language | English |
| last_indexed | 2024-12-19T16:56:08Z |
| publishDate | 2021-07-01 |
| publisher | BMC |
| record_format | Article |
| series | Biomarker Research |
| spelling | doaj.art-8e4c928eb92647248416cdd788777bec2022-12-21T20:13:25ZengBMCBiomarker Research2050-77712021-07-019111110.1186/s40364-021-00308-6Integration of tumor inflammation, cell proliferation, and traditional biomarkers improves prediction of immunotherapy resistance and responseSarabjot Pabla0R. J. Seager1Erik Van Roey2Shuang Gao3Carrie Hoefer4Mary K. Nesline5Paul DePietro6Blake Burgher7Jonathan Andreas8Vincent Giamo9Yirong Wang10Felicia L. Lenzo11Margot Schoenborn12Shengle Zhang13Roger Klein14Sean T. Glenn15Jeffrey M. Conroy16OmniSeq, IncOmniSeq, IncOmniSeq, IncOmniSeq, IncOmniSeq, IncOmniSeq, IncOmniSeq, IncOmniSeq, IncOmniSeq, IncOmniSeq, IncOmniSeq, IncOmniSeq, IncOmniSeq, IncOmniSeq, IncOmniSeq, IncOmniSeq, IncOmniSeq, IncAbstract 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.https://doi.org/10.1186/s40364-021-00308-6InflammationCell proliferationPembrolizumabNivolumabIpilimumabAlgorithmic analysis |
| spellingShingle | Sarabjot Pabla R. J. Seager Erik Van Roey Shuang Gao Carrie Hoefer Mary K. Nesline Paul DePietro Blake Burgher Jonathan Andreas Vincent Giamo Yirong Wang Felicia L. Lenzo Margot Schoenborn Shengle Zhang Roger Klein Sean T. Glenn Jeffrey M. Conroy Integration of tumor inflammation, cell proliferation, and traditional biomarkers improves prediction of immunotherapy resistance and response Biomarker Research Inflammation Cell proliferation Pembrolizumab Nivolumab Ipilimumab Algorithmic analysis |
| 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 | Inflammation Cell proliferation Pembrolizumab Nivolumab Ipilimumab Algorithmic analysis |
| url | https://doi.org/10.1186/s40364-021-00308-6 |
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