Integration of tumor inflammation, cell proliferation, and traditional biomarkers improves prediction of immunotherapy resistance and response

Biomarker Research

Vincent Giamo¹  Sarabjot Pabla¹  Shuang Gao¹  Felicia L. Lenzo¹  Paul DePietro¹  Jonathan Andreas¹  Erik Van Roey¹  Shengle Zhang¹  Blake Burgher¹  R. J. Seager¹  Carrie Hoefer¹  Mary K. Nesline¹  Yirong Wang¹  Margot Schoenborn¹  Roger Klein¹  Jeffrey M. Conroy²  Sean T. Glenn² 

[1] OmniSeq, Inc, 700 Ellicott Street, 14203, Buffalo, NY, USA;OmniSeq, Inc, 700 Ellicott Street, 14203, Buffalo, NY, USA;Roswell Park Comprehensive Cancer Center, Elm and Carlton Streets, 14206, Buffalo, NY, USA;

关键词: Inflammation;    Cell proliferation;    Pembrolizumab;    Nivolumab;    Ipilimumab;    Algorithmic analysis;    Inflamed;    Borderline;    Non-inflamed;   

DOI  :  10.1186/s40364-021-00308-6

来源: Springer

【 摘 要 】

BackgroundContemporary 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).MethodsA 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.ResultsUnsupervised 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.ConclusionsTIGS 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.

【 授权许可】

CC BY