Case Study

PD-1 blockade enriches TIGIT+ memory T cells and confers resistance via PVRL1/PVR axis in Hepatocellular Carcinoma

David Kung-Chun Chiu, Vincent Wai-Hin Yuen, Jacinth Wing-Sum Cheu, Larry Lai Wei, Vox Ting, Michael Fehlings, Hermi Sumatoh, Alessandra Nardin, Evan Newell, Irene Oi-Lin Ng, Thomas Chung-Cheung Yau, Chun-Ming Wong, Carmen Chak-Lui Wong

Relevance

This work showcases the discovery of novel targets for immunomodulation by high dimensional T cell profiling in experimental animals that do not respond to immunotherapy. Similarly, it is a proof-of-concept for the identification of promising biomarker for predicting responsiveness to checkpoint blockade.

 

Introduction

Hepatocellular carcinoma (HCC) is a malignant epithelial tumor arising from hepatocytes and is the third most common cause of death by cancer globally. Programmed cell death protein 1 (PD-1) immune checkpoint inhibitors (nivolumab and pembrolizumab) have provided promising clinical benefit in HCC treatment but only a subset of patients exhibits clear and durable response. Poor anti-tumor responses observed in HCC patients may be mediated by several immune checkpoint pathways such as PD-1 and T cell immunoreceptor with Ig and ITIM domains (TIGIT), which have important regulatory effects and are attractive targets for cancer immunotherapy (1). In this study, we used high-dimensional mass cytometry to screen, identify and phenotypically profile the tumor infiltrating lymphocytes (TILs) in liver tumors of mice in order to gain insight into the mechanisms of resistance during PD-1 blockade. Findings from this study indicate that induction of TIGIT and overexpression of PVRL1, thought to stabilize the TIGIT ligand PVR, contribute to anti-PD-1 resistance in HCC, and imply that targeting the PVRL1/PVR/TIGIT pathway is an attractive therapeutic strategy for combination treatment to address the unmet clinical need in non-responding HCC patients.

 

Figure 1. Induction of TIGIT and overexpression of PVRL1 may contribute to anti-PD-1 resistance in HCC patients.

Effector memory CD8+ T cells expanded upon PD-1 blockade therapy and showed high expression of the inhibitory immunoreceptor, TIGIT. Inhibition of TIGIT increased efficacy of anti-PD-1 therapy, leading to reduction of the tumor burden and improved survival, indicating that anti-TIGIT therapy may be more effective as a combined therapy with anti-PD-1 compared with monotherapy with either anti-PD-1 or anti-TIGIT, alone. It was shown that an increased expression of PVRL1 during HCC plays a supporting role in the TIGIT inhibitory signaling cascade since knockout of PVRL1 not only reduced tumor volume but induced tumor rejection after a single treatment with anti-PD-1. These findings suggest that high expression of PVRL1 in an HCC spontaneous mouse model contribute to PD-1 blockade resistance, and that PVRL1 might serve as a promising biomarker for predicting anti-PD-1 responsiveness in HCC.

Figure 2. Summary figure for the study. Antibody blockade of TIGIT or knockout of PVRL1 enhances the efficacy of anti-PD-1 treatment in HCC.

 

 

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Methods:

To generate a mouse model that mimics the immune tumor microenvironment of human HCC, a CRISPR-Cas9-based platform was utilized that enables the induction of spontaneous HCC (3). A transposon system expressing C-myc, and a CRISPR-Cas9 system expressing a sgRNA targeting Trp53, were delivered to hepatocytes. Spontaneous HCC tumors derived from C-Myc overexpression and Trp53 deletion were developed in 3-5 weeks in immunocompetent C57BL/6 mice. For in vivo knockout of Pvrl1, a CRISPR-Cas9 vector system linking sgRNAs targeting Trp53 and Pvrl1 was co-injected with the transposon system. Mice were treated with anti-PD-1 antibody and treatment-driven alterations of the intratumoral immune response were assessed. We utilized high-dimensional mass cytometry and an antibody panel consisting of 30 markers specific for identifying trafficking, differentiation, activation and exhaustion status of TILs, as well as 10 markers identifying non-T cell lineages.

 

Results:

In this spontaneously induced HCC mouse model, anti-PD-1 treatment failed to suppress tumor growth and improve survival (fig. 3A). Upregulation of TIGIT expression was observed in tumor-infiltrating effector memory CD8+ T after PD-1 treatment compared with the untreated controls (fig. 3B). To gain deeper insight into the effects of PD-1 therapy on the (4). For the analyses, we used Phenograph which identifies cell subpopulations in high-dimensional single-cell data, and factoring in all the markers, were able to discriminate 19 different clusters (fig. 3C). Anti-PD-1 treatment induced expansion of CD8 T cells in clusters 13 and 18 compared to the untreated controls, which were characterized by elevated expression of markers associated with effector memory CD8+ T cells, as well as a high level expression of exhaustion markers, such as PD-1, LAG-3 and TIGIT (fig. 3D). These observations suggest that upregulated expression of inhibitory molecules might be associated with T cell exhaustion and contribute to PD-1 resistance in this HCC model.

A. B.
C. D.

 

Figure 3. Anti-PD-1 treatment elevated TIGIT expression in effector memory CD8+ T cells, but failed to suppress tumor growth. Schematic presentation of the experimental setup (A). Tumors were dissociated and the frequencies of TIGIT-expressing effector memory (CD44+CD62L) T cells were determined (B). High-dimensional analysis of the CD8+ T cell population upon anti-PD-1 treatment and corresponding controls. PhenoGraph analysis of the T cell populations displaying 19 distinct clusters (C). Heatmap representing the frequency of CD8+ T cells positive for all analyzed phenotypic markers (D).

Moreover, analysis of HCC patients who did not respond to PD-1 therapy with nivolumab, showed an elevated TIGIT expression in their peripheral CD4 and CD8 T cells (fig. 4).

 

 

Fig 4. Blockade of PD-1 by nivolumab induces increased TIGIT expression in the peripheral CD4 and CD8 T cells in non-responder HCC patients.

 

Furthermore, liver tissues from HCC patients were analyzed at the mRNA level, and the data show that the PVRL1 transcript was significantly overexpressed in human HCC tissue compared with surrounding non-tumorous liver tissues (fig. 5).

 

 

Figure 5. TIGIT ligand PVRL1 is overexpressed in human HCC. PVRL1 mRNA expression in normal livers (NL) from tumor-free donors, HCC tissues and their corresponding non-tumorous liver (NT) tissues from 66 patients.

 

In the highly aggressive spontaneous HCC mouse model, single treatment of anti-PD-1 or anti-TIGIT therapy failed to reduce tumor burden. Strikingly, dual blockade of PD-1 and TIGIT (combo) greatly suppressed the tumor growth and prolonged the survival of HCC-bearing mice (fig. 6).

 

 

Figure 6. Blockade of TIGIT sensitized HCC to anti-PD-1 therapy. Survival plot of spontaneous HCC mouse model after PD-1/TIGIT dual blockade experiment (n = 8 mice for each group).

 

To evaluate the impact of PVRL1 on HCC tumorigenesis, a spontaneous HCC mouse model was used with the additional knockout of PVRL1. Single treatment of animals with anti-PD-1 induced tumor rejection compared with the untreated controls (fig. 7). Taken together, these data indicate that high expression of PVRL1 in HCC tumors contribute to PD-1 blockade resistance and that PVRL1 might serve as a biomarker for predicting anti-PD-1 responsiveness in HCC disease.

 

Figure 7. Absence of PVRL1 sensitized HCC to anti-PD-1 therapySurvival plot of the HCC mouse model with additional knockout of PVRL1 upon anti-PD-1 treatment (n = 5 mice for each group).

 

 

References:

  1. El-Khoueiry AB, Sangro B, Yau T, et al. Nivolumab in patients with advanced hepatocellular carcinoma (CheckMate 040): an open-label, non-comparative, phase 1/2 dose escalation and expansion trial. Lancet 2017.
  2. Martinet L, Smyth MJ. Balancing natural killer cell activation through paired receptors. Nat Rev Immunol 2015;15:243-54.

3. Tschaharganeh DF, Xue W, Calvisi DF, et al. p53-dependent Nestin regulation links tumor suppression to cellular plasticity in liver cancer. Cell 2014;158:579-92

4. Becht E, McInnes L, Healy J, et al. Dimensionality reduction for visualizing single- cell data using UMAP. Nat Biotechnol 2018.

 

 

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