Glioma escape signature and clonal development under immune pressure

Abstract

Immunotherapeutic strategies are increasingly important in neuro-oncology, and the elucidation of escape mechanisms that lead to treatment resistance is crucial. We investigated the impact of immune pressure on the clonal dynamics and immune escape signature by comparing glioma growth in immunocompetent versus immunodeficient mice. Glioma-bearing WT and Pd-1-/- mice survived significantly longer than immunodeficient Pfp-/- Rag2-/- mice. While tumors in Pfp-/- Rag2-/- mice were highly polyclonal, immunoedited tumors in WT and Pd-1-/- mice displayed reduced clonality with emergence of immune escape clones. Tumor cells in WT mice were distinguished by an IFN-γ-mediated response signature with upregulation of genes involved in immunosuppression. Tumor-infiltrating stromal cells, which include macrophages/microglia, contributed even more strongly to the immunosuppressive signature than the actual tumor cells. The identified murine immune escape signature was reflected in human patients and correlated with poor survival. In conclusion, immune pressure profoundly shapes the clonal composition and gene regulation in malignant gliomas.

Keywords: Brain cancer; Immunology; Oncology.

Figure 1. Glioma growth in immunocompetent versus immunodeficient mice.

(A) Schematic overview of the experimental setup. (B) Kaplan-Meier survival analysis of WT and Pfp^–/– Rag2^–/– mice injected intracerebrally with GL261 cells (n = 7 per group); log-rank test; *P = 0.0124. (C) Survival analysis of mice injected with CT2A cells; **P = 0.0029; n = 5 WT, 4 Pfp^–/– Rag2^–/–. (D) Macroscopic tumor appearance. (E) H&E staining of tumor sections. (F) Immunohistochemistry for CD3, CD8, IBA1, and CD34. (G) Quantification of macrophages/microglia (IBA1) and tumor microvessels (CD34). Bars represent means ± SEM; unpaired 2-tailed Student’s t test; for GL261, *P = 0.04, n = 4, and for CT2A, *P = 0.01, n = 3. Scale bars: 100 μm; hpf, high-power fields.

Figure 2. Gene regulation during tumor evolution.

(A) Workflow of GL261 tumor analysis. RNA from 2× 2 mice per mouse type (days 7 and 14) or 3× 2 mice per mouse type (symptomatic endpoint [Sympt]) was pooled for expression profiling by Illumina WG-6 v2.0 arrays. i.c., intracerebral. (B) Venn diagrams of genes over- and underexpressed in WT versus Pfp^–/– Rag2^–/– mice. (C) Immunohistochemistry for CD3, IBA1, and PD-L1. Scale bars: 100 μm. (D) Expression values of selected immune-related genes over time. Data are presented as mean ± SEM; day 7, n = 2; day 14, n = 2; symptomatic, n = 3.

Figure 3. Regulation of immune modulators.

Gene expression of stimulatory and inhibitory immune modulators identified by Thorsson et al. (10) in our data set. NA, not applicable.

Figure 4. Expression signatures in tumor and nontumor cell populations.

(A) Schematic workflow of GFP-marked GL261 cell injection, flow cytometric sorting, and RNA sequencing analysis. Stromal cells from Pfp^–/– Rag2^–/– mice could not be analyzed because of very low RNA yield from the sparse nontumor population (see Figure 1E). (B) Principal component analysis of gene expression profiles of GFP⁺ tumor cells from WT and Pfp^–/– Rag2^–/– mice, nontumor stromal cells from WT mice, bulk tumors from both mouse types, and in vitro cultured cells. All samples were analyzed in triplicate. (C) Unsupervised cluster analysis of differentially expressed genes (2-fold cutoff, P < 0.05). (D) GO and KEGG pathway analysis of differentially expressed gene clusters (colors correspond to clusters in C).

Figure 5. Expression intensity of immunosuppressive genes in tumor cells versus nontumor cells.

Expression levels were determined by RNA-Seq as shown in Figure 4A.

Figure 6. Identification of an immunoedited tumor cell expression signature.

(A) Unsupervised hierarchical clustering of differentially expressed genes identifies 6 main clusters, of which 4 represent genes differentially up- or downregulated in tumors in WT mice versus Pfp^–/– Rag2^–/– mice. (B) GO and KEGG pathway analysis of genes upregulated in the 4 clusters.

Figure 7. Network analysis of the genes contained in clusters 1 to 4 (see Figure 6) that are annotated in the STRING database.

Figure 8. Comparison with human gene expression data.

(A) Correlations between different tumor-infiltrating immune and stromal cell subsets in human malignant gliomas. Cytotoxic lymphocytes and CD8⁺ T cells correlate with monocytic lineage in all gliomas (R = 0.52 and 0.31, respectively, P < 0.001), and in particular in IDH-mutated (IDH-mut) gliomas (R = 0.52 and 0.57, respectively, P < 0.001). (B) Overlap between genes overexpressed in the 20% of human gliomas with the highest T cell gene expression signature (fold change and P value, 2-sided Fisher test) and the 4 murine gene clusters identified in Figure 4A. (C) Patient survival according to genes over- or underexpressed in the 4 murine gene clusters (top vs. bottom 20% tumors). (D) Kaplan-Meier analysis for individual genes, comparing the 20% of tumors with the highest versus the lowest expression; log-rank test. See Methods for detailed statistics.

Figure 9. Clonal heterogeneity in RGB-marked tumors.

(A) Schematic representation of lentiviral RGB marking of GL261 cells. Fluorescent proteins of the 3 basic colors are mixed at different but highly stable expression intensities so that all perceivable colors are generated. (B) Confocal fluorescence microscopy of RGB-marked GL261 tumors in WT and Pfp^–/– Rag2^–/– mice. Scale bar: 50 μm. (C) Spherical scatter plot of cells analyzed by flow cytometry. Each data point designates the chromaticity value of a cell. Note the reduced occupancy, i.e., the plot area occupied with data points in WT mice compared with Pfp^–/– Rag2^–/– mice and compared with the preinjection mix. (D) Quantification of occupancy after multistep dimensionality reduction validates significantly higher clonal contraction in tumors in WT mice than in Pfp^–/– Rag2^–/– mice, compared with the multiclonal preinjection mix. Box plots with IQR (box), mean (line), and maximum/minimum (whiskers). One-way ANOVA (P = 0.001) with Tukey’s post hoc test; *P < 0.05, ***P = 0.001; n = 4 per mouse group, n = 2 for the preinjection mix.

Figure 10. Clonal composition in OBC-marked tumors.

(A) Workflow of GL261 optical barcoding (OBC) and analysis of tumors in mice. (B) Survival of mice implanted with OBC-labeled GL261 cells; Kaplan-Meier analysis, log-rank test; ***P < 0.001. (C) Flow cytometry analysis of the clonal tumor composition in mice that became symptomatic at the indicated time points (days). (D) Number of clones that contribute more than 5% to the total number of tumor cells. One-way ANOVA (P = 0.0001) with Tukey’s post hoc test; *P < 0.05, ****P < 0.0001; Pfp^–/– Rag2^–/–, n = 18; WT, n = 20; Pd-1^–/–, n = 10. (E) Mean dominance of clones GL13 and GL19 in repeat experiments. (F) Tumor infiltration with CD3⁺ T cells is maximal on day 10 and parallels the progressive loss of clonal heterogeneity in WT mice. Scale bar: 100 μm.