Merkel cell polyomavirus-specific and CD39+CLA+ CD8 T cells as blood-based predictive biomarkers for PD-1 blockade in Merkel cell carcinoma

Abstract

Merkel cell carcinoma is a skin cancer often driven by Merkel cell polyomavirus (MCPyV) with high rates of response to anti-PD-1 therapy despite low mutational burden. MCPyV-specific CD8 T cells are implicated in anti-PD-1-associated immune responses and provide a means to directly study tumor-specific T cell responses to treatment. Using mass cytometry and combinatorial tetramer staining, we find that baseline frequencies of blood MCPyV-specific cells correlated with response and survival. Frequencies of these cells decrease markedly during response to therapy. Phenotypes of MCPyV-specific CD8 T cells have distinct expression patterns of CD39, cutaneous lymphocyte-associated antigen (CLA), and CD103. Correspondingly, overall bulk CD39⁺CLA⁺ CD8 T cell frequencies in blood correlate with MCPyV-specific cell frequencies and similarly predicted favorable clinical outcomes. Conversely, frequencies of CD39⁺CD103⁺ CD8 T cells are associated with tumor burden and worse outcomes. These cell subsets can be useful as biomarkers and to isolate blood-derived tumor-specific T cells.

Keywords: CD8 T cells; CyTOF; MCPyV; Merkel cell carcinoma; Merkel cell polyomavirus; TCR sequencing; antigen-specific T cells; checkpoint blockade; high-dimensional analysis; immunotherapy; tumor-specific T cells.

Graphical abstract

Figure 1

Identification of MCPyV-specific CD8 T cells and correlation with MCC pembrolizumab treatment clinical outcomes (A) Experimental schematic and representative mass cytometry gating tree and dot plots of CD8 T cells showing the detection of MCPyV-specific and MCC-unrelated epitopes. Representative plots from one patient (P15) and one time point (C01, baseline). (B) Heatmap of individual MCPyV epitopes detected across all VP patients with complete response (CR; n = 8), partial response (PR; n = 5), stable disease (SD; n = 1), and progressive disease (PD; n = 3). (C) Integrated frequencies of MCPyV-specific CD8 T cells in PBMCs prior to the treatment. Data are represented as mean ± SEM; Mann-Whitney test. (D) Kaplan-Meier curve of progression-free survival in patients with detectable (>0.01% of CD8 T cells, violet) or undetectable (<0.01% of CD8 T cells, black) MCPyV-specific CD8 T cells in PBMCs. Detection limit was determined by the highest frequency observed in VN patients. Log-rank test. (E) Frequencies of MCPyV-specific CD8 T cells in PBMCs over the course of the therapy in patients with CR (left) and PR (right). Data are represented as mean ± SEM. Wilcoxon test. SAV, streptavidin.

Figure 2

Phenotypic profiling of MCPyV-specific CD8 T cells in patients with MCC (A) UMAP embedding of CD8 T cells from all patients (left). Normalized expression of selected markers defining CD8 T cell clusters (right). (B) MCPyV-specific (top) and CMV-specific (bottom) cells projected onto a UMAP embedding. Manually gated individual tetramer⁺ cells were concatenated. (C) Expression of markers by indicated tetramer⁺ cells within CD8 T cells from individual patients over the course of pembrolizumab (top: C01 or baseline, middle: C05, bottom: end of treatment [EOT]). Data are represented as mean ± SEM. LTA, large T antigen; STA, small T antigen; CMV, cytomegalovirus; HSV, herpes simplex virus; EBV, Epstein-Barr virus. (D) Expression of CD39, CLA, and CD103 by individual tetramer⁺ cells within CD8 T cells of MCPyV LTA and STA (n = 32), MCPyV viral protein (n = 17), CMV (n = 41), EBV (n = 21), influenza (n = 12), and HSV (n = 9). Mann-Whitney test, ∗p < 0.05, ∗∗p < 0.01, ∗∗∗p < 0.001, and ∗∗∗∗p < 0.0001.

Figure 3

Prognostic potential of CD39, CLA, and CD103 expression among CD8 T cells in the peripheral blood of patients with MCC (A) Contour plots identifying CD39⁺CLA⁺ CD8 T cells by mass cytometry. CD8 T cells were defined and pregated as live/CD45⁺/CD19⁻/CD14⁻/CD3⁺/γδTCR⁻/CD4⁻/CD8⁺. Representative data. (B) Frequencies of CD39⁺CLA⁺ CD8 T cells prior to treatment in all patients. Data are represented as mean ± SEM; Mann-Whitney test. (C) Frequencies of CD39⁺CLA⁺ CD8 T cells prior to treatment in VP patients. Data are represented as mean ± SEM; Mann-Whitney test. (D) Frequencies of CD39⁺CLA⁺ CD8 T cells over the course of pembrolizumab in VP patients. Data are represented as mean ± SEM; Wilcoxon test. (E) Linear regression analysis of frequencies of CD39⁺CLA⁺ CD8 T cells and frequencies of MCPyV-specific CD8 T cells (left) or baseline tumor burden (right) in VP patients. (F) Kaplan-Meier curve of progression-free survival in VP patients with high or low CD39⁺CLA⁺ CD8 T cells. Log-rank test. (G) Contour plots identifying CD39⁺CD103⁺ CD8 T cells by mass cytometry, CD8 T cells were defined and pregated as live/CD45⁺/CD19⁻/CD14⁻/CD3⁺/γδTCR⁻/CD4⁻/CD8⁺. Representative data. (H) Frequencies of CD39⁺CD103⁺ CD8 T cells prior to treatment in all patients. Data are represented as mean ± SEM; Mann-Whitney test. (I) Frequencies of CD39⁺CD103⁺ CD8 T cells prior to treatment in VP patients. Data are represented as mean ± SEM; Mann-Whitney test. (J) Frequencies of CD39⁺CD103⁺ CD8 T cells over the course of pembrolizumab in VP patients. Data are represented as mean ± SEM; Wilcoxon test. (K) Linear regression analysis of frequencies of CD39⁺CD103⁺ CD8 T cells and frequencies of MCPyV-specific CD8 T cells (left) or baseline tumor burden (right) in VP patients. (L) Kaplan-Meier curve of progression-free survival in VP patients with high or low CD39⁺CD103⁺ CD8 T cells. Log-rank test. ∗p < 0.05 and ∗∗p < 0.01. Each symbol represents an individual patient.

Figure 4

TCR repertoire analysis of circulating CD39⁺CLA⁺ and CD39⁺CLA⁻CD103⁺ CD8 T cells from patients with MCC (A) Experimental schematic. Bulk TCRβ sequencing was performed on FFPE tumor biopsy samples (n = 5) obtained from patients with a complete response. Patient-matched PBMC samples collected prior to treatment were subjected to flow-based sorting followed by RNA isolation, bulk TCRβ sequencing, and RNA sequencing. (B) Pie chart illustrating the distribution of unique TCR clonotypes within each population of CD8 T cells. (C) Frequency of bystander-like TCRβ from each CD8 population determined by clustering and quantifying their connection frequency with VDJ database based on sequence similarity (distance ≤ 12). Data are represented as mean ± SEM; Mann-Whitney test. (D) Frequency of tumor-like TCRβ from each CD8 population determined by clustering and quantifying their connection frequency with patient-matched tumor TCRβ based on sequence similarity (distance ≤ 12). (E) Frequency of publicity determined by clustering and quantifying their connection with other patients based on sequence similarity (distance ≤ 12). Data are represented as mean ± SEM, Wilcoxon test. ∗p < 0.05 and ∗∗p < 0.01. Each symbol represents an individual patient.

Figure 5

Distinct transcriptional signatures of circulating CD39⁺CLA⁺ and CD39⁺CLA⁻CD103⁺ CD8 T cells (A) Expression of ENTPD1 in each CD8 T cell population, normalized to those of CD39⁻. (B) PCA projection of sorted CD39⁺CLA⁺, CD39⁺CLA⁻CD103⁺, CD39⁺CLA⁻CD103⁻, and CD39⁻ CD8 T cells from each patient. Each symbol represents an individual patient. (C) DEG^UP overlaps between indicated populations. Bubble size represents the proportion of DEGs^UP from each population (y axis) also found to be upregulated in indicated subsets (x axis). (D) Heatmap illustrating all DEGs (2-fold change, p < 0.05, and false discovery rate [FDR] < 0.1) clustered by using Pearson’s correlation matrix. Color legend indicates Z scores. (E) Pathway enrichment analysis of significantly enriched pathways by log(q value) for each cluster defined in Figure 4D. PCA, principal-component analysis; DEG, differentially expressed gene.