Anti-PD-1 immunotherapy with androgen deprivation therapy induces robust immune infiltration in metastatic castration-sensitive prostate cancer

Abstract

When compared to other malignancies, the tumor microenvironment (TME) of primary and castration-resistant prostate cancer (CRPC) is relatively devoid of immune infiltrates. While androgen deprivation therapy (ADT) induces a complex immune infiltrate in localized prostate cancer, the composition of the TME in metastatic castration-sensitive prostate cancer (mCSPC), and the effects of ADT and other treatments in this context are poorly understood. Here, we perform a comprehensive single-cell RNA sequencing (scRNA-seq) profiling of metastatic sites from patients participating in a phase 2 clinical trial (NCT03951831) that evaluated standard-of-care chemo-hormonal therapy combined with anti-PD-1 immunotherapy. We perform a longitudinal, protein activity-based analysis of TME subpopulations, revealing immune subpopulations conserved across multiple metastatic sites. We also observe dynamic changes in these immune subpopulations in response to treatment and a correlation with clinical outcomes. Our study uncovers a therapy-resistant, transcriptionally distinct tumor subpopulation that expands in cell number in treatment-refractory patients.

Keywords: ADT; anti-PD-1 therapy; clinical translational science; clinical trial; hormone-sensitive; immunotherapy; metastatic prostate cancer; single cell RNA sequencing; tumor microenvironment.

Figure 1.. Baseline Composition of Micro-Environment by Tissue Site

(A) Phase 2 trial design schema. (B) Uniform manifold projection (UMAP) plot constructed from VIPER-inferred protein activity of all cells in aggregate across baseline pre-treatment patient samples. Cells are clustered by resolution-optimized Louvain algorithm with cell type inferred by SingleR. (C) Stacked barplot of the frequency of each major cell lineage within each baseline patient sample, with each column representing a unique patient and patients grouped by metastatic site. Cell clusters from B are aggregated by shared cell type. (D) Stacked barplot of immune vs. non-immune cell frequencies, from C. (E) Boxplot showing distribution of frequencies for each cell cluster in B at baseline, comparing tissue sites. Also see Figure S1.

Figure 2.. Top Protein Activity Cell Cluster Markers

Heatmap of top 5 most differentially activated proteins for each cell type cluster from aggregate scRNA-seq data across all patient samples. Each row represents a protein, grouped by cluster in which they are the most active, with cluster labels on the x and y axes. Each column represents a single cell. Above the x axis cluster label there is also a treatment label indicating time point at which a given cell was profiled. Also see Figure S2.

Figure 3.. Treatment with ADT+anti-PD-1 Induces Dramatic Changes in the Tumor Micro-Environment

(A) UMAP plot of all cells from patients with metastatic bone lesions, split by treatment time point (Baseline, ADT-only, ADT+anti-PD-1, and post-treatment recurrence) and labeled by cell cluster. (B) Stacked barplot showing the relative frequency of each major cell lineage by treatment time point for patients with metastatic Bone lesions, with each column representing aggregate of all samples profiled at a specific treatment time point. (C) UMAP plot, as in A, for patients with metastatic lymph node lesions. (D) Stacked barplot, as in B, for patients with metastatic lymph node lesions. (E) UMAP plot, as in A, for patients with metastatic liver lesions. (F) Stacked barplot, as in B, for patients with metastatic liver lesions. (G) UMAP plot, as in A, for patients with metastatic lung lesions. (H) Stacked barplot, as in B, for patients with metastatic lung lesions. (I) Boxplot showing distribution of frequencies for each cell cluster, comparing frequencies across treatment time points including baseline, ADT-only, and ADT+anti-PD-1. Also see Figure S4.

Figure 4.. Immune Infiltration at Baseline and in Response to Treatment is Recapitulated by Immunofluorescence Analysis

(A) Representative immunofluorescence staining images of samples with low T cell infiltration (top – a baseline lymph node slide section) and high T cell infiltration (bottom – an on-treatment liver slide section). Images on the left show PanCK tumor cell staining and images on the right additionally overlay CD4⁺ and CD8⁺ staining intensity. (B) Comparison of cumulative T cell frequencies in paired specimens as determined by scRNA-seq (y axis) versus quantitative immunofluorescence (x axis), where pre-treatment samples are colored in red at lower T cell frequency and on-treatment samples are colored in blue at higher frequency. Linear regression line is plotted, with correlation value as shown and p value = 0.031. (C) Comparison of cumulative tumor cell frequencies in paired specimens as determined by scRNA-seq (y axis) versus quantitative immunofluorescence (x axis), where pre-treatment samples are colored in red at higher frequency and on-treatment samples are colored in blue. Linear regression line is plotted, with correlation value as shown and p value = 0.026. (D) Comparison of treatment-induced fold-change for a representative patient where baseline and on-treatment tissue was profiled by both scRNA-seq and quantitative immunofluorescence. Fold-change in each major cell population profiled by quantitative immunofluorescence is shown in blue, and fold-change by scRNA-seq is shown in orange. Fold-changes are broadly concordant across all cell populations between the two modalities, with statistically significant correlation across cell types (p value = 0.00442).

Figure 5.. Differences in Baseline Immune Composition Associate with Differences in Treatment Response

(A) Spider-plot of log10(Fold-Change) from baseline in prostate-specific antigen (PSA) over time with treatment, for each patient, such that four patients, labeled in blue, exhibited rapid and dramatic decrease to below 1% of initial PSA and were identified as early responders to treatment, and two patients, labeled in orange, initially responded to treatment with a rapid increase in PSA observed after on-treatment week 28. These were considered late progressors on-treatment. The remaining patients, in gray, generally trended toward a decreasing PSA, though not as rapidly as the early responders. (B) Boxplot showing distribution of frequencies at Baseline for each cell cluster, comparing frequencies in early responders vs. late progressors, such that clusters with significant difference at baseline (p < 0.05 by Student’s t test) included CD4⁺ T cell 1, CD8⁺ T cell 2, Treg 3, and Epithelial 2.

Figure 6.. Sub-Clustering Reveals Heterogeneity of Tumor Cells by Tissue Site

(A) UMAP plot showing sub-clustering by resolution-optimized Louvain algorithm of only tumor cells (Epithelial 1, Epithelial 2, and Epithelial 3 from Figure 1B). Plot shows aggregate of all 2,550 tumor cells across all patients at all time-points. (B) Stacked barplot of tumor cluster frequency by treatment time point in patients with metastatic bone tumors. (C) Stacked barplot of tumor cluster frequency by treatment time point in patients with metastatic liver tumors. (D) Stacked barplot of tumor cluster frequency by treatment time point in patients with metastatic lymph node tumors. (E) Stacked barplot of tumor cluster frequency by treatment time point in patients with metastatic lung tumors. (F) Boxplot showing distribution of frequencies at Baseline for each tumor subcluster, comparing frequencies in early responders vs. late progressors, such that the only cluster with significant difference at baseline (p < 0.05 by Student’s t test) was REF-EPI₂, with higher baseline frequency in late progressors. Also see Figures S3 and S5.

Figure 7.. Tumor Single-Cell Subcluster Signatures Associate with Differential Outcomes in TCGA

(A) Forest plot of Cox regression hazard ratios testing association in TCGA of patient-by-patient normalized enrichment score for each tumor subcluster gene set with recurrence-free survival. REF-EPI₂ gene set enrichment is significantly associated with worse survival outcomes (p = 0.002). (B) Heatmap of leading-edge gene set from REF-EPI₂ comparing all recurrent vs. non-recurrent patients in TCGA. (C) Kaplan-Meier curve testing association of binarized REF-EPI₂ gene set enrichment (greater than 0 = high, less than 0 = low) with recurrence-free survival in TCGA, such that REF-EPI₂ enrichment significantly associates with worse recurrence-free survival. (D) Kaplan-Meier curve testing association of binarized REF-EPI₃ gene set enrichment (greater than 0 = high, less than 0 = low) with recurrence-free survival in TCGA, such that REF-EPI₃ enrichment significantly associates with worse recurrence-free survival. (E) Kaplan-Meier curve testing association of binarized REF-EPI₁ gene set enrichment (greater than 0 = high, less than 0 = low) with recurrence-free survival in TCGA, such that cluster 0 enrichment significantly associates with improved recurrence-free survival. (F) Kaplan-Meier curve testing association of binarized REF-EPI₇ gene set enrichment (greater than 0 = high, less than 0 = low) with recurrence-free survival in TCGA, such that REF-EPI₇ enrichment significantly associates with improved recurrence-free survival, up to 2800 days. Kaplan-Meier curves are not shown for the remaining clusters as log rank p values for these were not statistically significant (p > 0.05). Also see Figures S6 and S7.