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 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.

Keywords: Algorithmic analysis; Borderline; Cell proliferation; Inflamed; Inflammation; Ipilimumab; Nivolumab; Non-inflamed; Pembrolizumab.

Fig. 1

Discovery cohort gene expression clusters (A), and association with TIGS clusters (B), CD8 IHC patterns of T-cell infiltration (C–E), and TIGSdistribution within CD8 cohort (F). A. Unsupervised clustering of 1323 clinical RNA-seq profiles yield three immunogenic clusters, namely, inflamed (n = 439/1323; 33.18%), borderline (n = 467/1323; 35.30%) and non-inflamed (n = 417/1323; 31.52%). The tumor immunogenic signature (TIGS) cluster of genes contains 161-genes that are over-represented by T & B cell activation pathways along with IFNg, chemokine, cytokine and interleukin pathways. Mean expression of the 161 genes constituting the TIGS cluster produces the TIGS score. B. Distributions of the TIGS of the samples in each of the three sample clusters. C-E. Representative CD8 immunohistochemistry images of T cell infiltration patterns of Infiltrating (C), Non-infiltrating (D), and excluded (E). F. The distribution of immunogenic scores for tumors in the discovery cohort with strongly infiltrating, non-infiltrating, and excluded CD8 T cell infiltration patterns

Fig. 2

TIGS and ORR to ICI across all tumors in retrospective cohort (A) and within tumors (B). TIGS and survival across all tumors (C), melanoma (D), NSCLC (E), and RCC (F). A. Objective response rates (ORR) observed in the retrospective cohort for each TIGS group. B. ORR observed in each TIGS group for three disease types within the retrospective cohort. C-F. Survival curves for each TIGS group in the retrospective cohort (C), melanoma (D), NSCLC (E), RCC (F)

Fig. 3

ORR to ICI in retrospective cohort combining TIGS with traditional biomarkers; PD-L1 (A) and TMB (B). A. ORR for each subgroup when TIGS is used in conjunction with PD-L1 status, by disease type. B. ORR for each subgroup when TIGS is used in conjunction with TMB status, separated by disease type

Fig. 4

Retrospective cohort combining TIGS and cell proliferation to determine ORR to ICI (A), and survival (B). A. Clinical ORR for each subgroup in the retrospective cohort when TIGS is used in conjunction with cell proliferation score classification. B. Kaplan Meier survival curves of combined TIGS and cell proliferation status for 242 ICI treated retrospective cohort

Fig. 5

Integrative hypothesis for utility of TIGS and cell proliferation for treatment selection. Hypothesized relationship mechanism by which cell proliferation and tumor immunogenicity affect treatment response