Immune profiling of uveal melanoma identifies a potential signature associated with response to immunotherapy

Abstract

Background: To date, no systemic therapy, including immunotherapy, exists to improve clinical outcomes in metastatic uveal melanoma (UM) patients. To understand the role of immune infiltrates in the genesis, metastasis, and response to treatment for UM, we systematically characterized immune profiles of UM primary and metastatic tumors, as well as samples from UM patients treated with immunotherapies.

Methods: Relevant immune markers (CD3, CD8, FoxP3, CD68, PD-1, and PD-L1) were analyzed by immunohistochemistry on 27 primary and 31 metastatic tumors from 47 patients with UM. Immune gene expression profiling was conducted by NanoString analysis on pre-treatment and post-treatment tumors from patients (n=6) receiving immune checkpoint blockade or 4-1BB and OX40 dual costimulation. The immune signature of UM tumors responding to immunotherapy was further characterized by Ingenuity Pathways Analysis and validated in The Cancer Genome Atlas data set.

Results: Both primary and metastatic UM tumors showed detectable infiltrating lymphocytes. Compared with primary tumors, treatment-naïve metastatic UM showed significantly higher levels of CD3+, CD8+, FoxP3+ T cells, and CD68+ macrophages. Notably, levels of PD-1+ infiltrates and PD-L1+ tumor cells were low to absent in primary and metastatic UM tumors. No metastatic organ-specific differences were seen in immune infiltrates. Our NanoString analysis revealed significant differences in a set of immune markers between responders and non-responders. A group of genes relevant to the interferon-γ signature was differentially up-expressed in the pre-treatment tumors of responders. Among these genes, suppressor of cytokine signaling 1 was identified as a marker potentially contributing to the response to immunotherapy. A panel of genes that encoded pro-inflammatory cytokines and molecules were expressed significantly higher in pre-treatment tumors of non-responders compared with responders.

Conclusion: Our study provides critical insight into immune profiles of UM primary and metastatic tumors, which suggests a baseline tumor immune signature predictive of response and resistance to immunotherapy in UM.

Keywords: immunotherapy; melanoma; translational medical research; tumor biomarkers.

Figure 1

Immune infiltrates and PD-L1 expression in primary and metastatic uveal melanoma. (A) Representative immunohistochemistry staining for CD3, CD8, CD68, FoxP3, PD-1, and PD-L1 in uveal melanoma (UM) tumor tissues. positive staining denoted by brown/red color. (B) Comparison of immune infiltrate in UM primary and metastatic tumors by quantification of CD3+, CD8+, CD4+, CD68+, FoxP3+, and PD-1+ infiltrates in 27 primary tumors and 29 metastases. (C) Comparison of PD-1+ infiltrate levels between PD-L1-positive and PD-L1 negative primary UM tumors. *=p<0.05; **=p<0.001; ns=not significant.

Figure 2

Quantification of immune infiltrates in metastatic uveal melanoma (UM) tumors receiving treatment. (A) comparison of immune infiltrates (CD3+, CD8+, CD4+, CD68+, FoxP3+, and PD-1+) in UM metastases by organ sites (liver, lung, and others). (B) Immune infiltrates levels in longitudinal metastatic tumor samples of UM patients on systematic therapies. (C) Comparison of immune infiltrates in UM metastases received immunotherapy or targeted therapies. ns=not significant.

Figure 3

The analysis of matching metastatic tumors (pre-treatment and post-treatment) from six patients who received immunotherapy by NanoString. (A) Relative gene expression level change in post-treatment tumors against pre-treatment tumors. the genes above red line show significantly changes of expression levels in post-treatment tumors compared with pre-treatment tumors (p<0.05). IL12A, NUP107, and TNAK expressions were upregulated, and TNFSF4, TIRAP, SBNO2, PIN1, SMAD3, CDKN1A, BID, MS4A1, CTSL, and MAVS were downregulated after treatment in all six patients’ um tumors. (B) Heat map demonstration of NanoString analysis of pre-treatment tumors of uveal melanoma responders (R, n=2) and non-responders (NR, n=4) to immunotherapies. two sets of genes were differentially expressed between the responding versus non-responding pre-treatment tumors (p≤0.05). (C) Functional network analysis by Ingenuity Pathways Analysis (IPA) reveals that interferon-γ is the major upstream regulator for the expression of a group of genes (CASP3, CDH1, HLA-G, IFITM2, SLAMF1, SOCS1, TLR3, and ATF1), which are significantly expressed at a higher level in pre-treatment tumors of responders to immunotherapy. (D) Functional network analysis by IPA reveals that proinflammatory NF-kB, interleukin (IL)-4, and IL-13 are major upstream regulators for the expression of a group of genes (CXCR1, CXCR2, PTGS2, NOS2A, IL4, IL17A, IL19, CCL20, IGLL1, LTK, TAB1, S100A12, and CD3EAP), which are significantly expressed at a higher level in pre-treatment tumors of non-responders to immunotherapy. (E) The expression of CDH1 was significantly higher in the pre-treatment cutaneous melanoma (CM) tumors of responders compared with non-responders who received anti-PD-1 therapy. The cohort of CM tumors was derived from the published data of previous study. IPA analysis: nodes represent genes, with their shape representing the functional class of the gene product (as shown in left panel). Genes with gray nodes are focused genes identified by the NanoString analysis. Genes with purple nodes are generated through the network analysis from IPA as upstream regulators for those genes in gray color. The lines between genes represent known interactions, with solid lines representing direct interactions and dashed lines representing indirect interactions.

Figure 4

NanoString analysis of post-treatment tumors of uveal melanoma (UM) responders (n=4) and non-responders (n=2) to immunotherapies. (A) The heat map demonstrates that two sets of genes were differentially expressed between the responding versus non-responding post-treatment tumors (p≤0.05). (B) Functional network analysis by Ingenuity Pathway Analysis (IPA) reveals that TNF is the major upstream regulator for the expression of HMGB1, PRKCE, and CCL28, which are significantly expressed at a higher level in post-treatment tumors of non-responders to immunotherapy. Genes with gray nodes are focused genes identified by the NanoString analysis. Genes with red and purple nodes are generated through the network analysis from IPA as upstream regulators for those genes in gray color.

Figure 5

SOCS1 related signature in uveal melanoma (UM) tumors. (A) The Kaplan-Meier survival analysis of 80 primary UM tumors in The Cancer Genome Atlas (TCGA) showed that patients with low expression of SOCS1 (medium, black line). p=0.0428. (B) SOCS1 mRNA levels in various tumors (red dots) and normal tissues (green dots) based on based on TCGA and Genotype-Tissue expression (GTEx) data. SOCS1 in UM (red arrows) is significantly lower than cutaneous melanoma (CM; blue arrow), lung adenocarcinoma, and lung squamous cell carcinoma (purple arrows). (C) Comparison of SOCS1, human leukocyte antigen (HLA)-DRB4, and HLA-G expression levels in pretreatment UM tumors of responders versus non-responders who received immunotherapies. The mRNA levels of these three genes were determined by NanoString nCounter platform. (D) Dot plots from TCGA data of 80 UM tumors for the correlations of SOTS1 levels and the expressions of several HLA genes. The significant correlations of gene expressions as measured by RNA-seq of TCGA are shown (p≤0.05).