Immune and genomic correlates of response to anti-PD-1 immunotherapy in glioblastoma

Abstract

Immune checkpoint inhibitors have been successful across several tumor types; however, their efficacy has been uncommon and unpredictable in glioblastomas (GBM), where <10% of patients show long-term responses. To understand the molecular determinants of immunotherapeutic response in GBM, we longitudinally profiled 66 patients, including 17 long-term responders, during standard therapy and after treatment with PD-1 inhibitors (nivolumab or pembrolizumab). Genomic and transcriptomic analysis revealed a significant enrichment of PTEN mutations associated with immunosuppressive expression signatures in non-responders, and an enrichment of MAPK pathway alterations (PTPN11, BRAF) in responders. Responsive tumors were also associated with branched patterns of evolution from the elimination of neoepitopes as well as with differences in T cell clonal diversity and tumor microenvironment profiles. Our study shows that clinical response to anti-PD-1 immunotherapy in GBM is associated with specific molecular alterations, immune expression signatures, and immune infiltration that reflect the tumor's clonal evolution during treatment.

Conflict of interest statement

Disclosure of Potential Conflicts of Interest

The authors declare no competing financial interests.

Figures

Extended Data Figure 1.. Additional clinical characteristics of the cohort.

(A) Venn diagram of the data modalities available across the 66-patient cohort. Kaplan-Meier curve comparing post-treatment survival (B) and overall survival from diagnosis (C) of patients who responded to anti-PD-1 therapy (n=13) to those that did not respond (n=29; p-value, two-sided log-rank test), assessed across the entire cohort. (D) Univariate survival analysis reveals that response to anti-PD-1 therapy is still most correlated with post-treatment survival of the patients when assessed across the entire cohort (n=42, 13 responders, 29 non-responders; p-value, two-sided log-rank test).

Extended Data Figure 2.. Additional analysis of genomic correlates of response to anti-PD-1 immunotherapy.

(A) Mutation burden by response group (n=17 patients). (B) Tumor purity, as estimated by ABSOLUTE, by response group. (C) Ratio of subclonal to clonal mutations, as estimated by ABSOLUTE, by response group. (D) Aneuploidy score analysis of non-responders vs. responders. Boxplots show the median, interquartile range, and whiskers (1.5 times interquartile range).

Extended Data Figure 3.. Additional analysis of transcriptomic correlates of response to anti-PD-1 immunotherapy.

(A) GSEA enrichment score of gene set KIM_PTEN_TARGETS_UP for non-responders vs responders (n=12 patients). The boxplot shows the median, interquartile range, and whiskers (1.5 times interquartile range). (B) Boxplot of CD274 (encoding PD-L1) mRNA expression in responders vs. non-responders (n=12 patients). The boxplot shows the median, interquartile range, and whiskers (1.5 times interquartile range).

Extended Data Figure 4.. Clonal diversity of lymphocytes before and after immunotherapy.

Within 7 patients with longitudinal information on TCR and immunoglobulin (Ig) RNA expression, MiXCR was used to group reads into T Cell (A) and B Cell clones (B). Each color on a bar represents the fractional presence of a different clone, with the total clonal read count n listed above.

Extended Data Figure 5.. Non-responders demonstrate a greater increase in clonal diversity of B Cells following immunotherapy.

B-cell clonal diversity before and after immunotherapy was assessed by identifying immunoglobulin RNA sequences within the tumor. Non-responders had a greater increase in Shannon entropy among B cells compared to responders (p = 0.048, two-sided Exact Mann-Whitney U test; n = 16 independent timepoints from 7 patients). The boxplot shows the median, interquartile range, and whiskers (1.5 times interquartile range); the violin plot represents sample distributions via kernel density estimation.

Extended Data Figure 6.. Tumor subtype.

Expression subtyping of tumors from 9 patients (pre- & post-treatment) into proneural, mesenchymal, and classical subtypes.

Extended Data Figure 7.. GSEA analysis.

GSEA enrichment plots (n = 12 patients; 6 responders vs 6 non-responders) of two regulatory T cells (Treg) related gene sets; p-values, two-sided Kolmogorov-Smirnov test.

Extended Data Figure 8.. Enrichment of regulatory T-cells signatures.

(A) Cells associated with the regulatory T-cells signature were enriched in a PTEN-mutated tumor. (B) Tumors associated with the regulatory T-cells signature were enriched in PTEN-mutated samples.

Extended Data Figure 9.. Single cell RNA-seq data analysis.

Topological data analysis of single cell RNA-seq data (n = 4000 cells) from a PTEN-mutated tumor, demonstrating clusters of cells with high expression of CD44 (A, in red) and of microglial signatures (B, in red).

Extended Data Figure 10.. Tumor purity analysis.

PTEN-mutated GBM tumors have significantly lower tumor purity compared to PTEN-wild-type tumors (n = 172, two-sided Wilcoxon rank-sum test). The boxplot shows the median, interquartile range, and whiskers (1.5 times interquartile range); the violin plot represents sample distributions via kernel density estimation.

Figure 1.. Analysis pipeline and clinical characteristics of the cohort.

(A) Sample collection and computational workflow. (B) Brain MRIs of two patients treated with nivolumab, one of whom showed disease progression following 2 months of treatment (Left, NU 7), the other showing stable disease without progression after 17 months of treatment (Right, NU 11). (C) Univariate survival analysis revealed that only response to anti-PD-1 therapy is significantly correlated with overall survival of the patients (n = 25); p-value, two-sided log-rank test. (D) Kaplan-Meier curve comparing overall survival of patients who responded to anti-PD-1 therapy (n = 13) to those that did not respond (n = 12); p-value, two-sided log-rank test.

Figure 2.. Mutational landscape, genomic correlates of response, and tumor evolution under anti-PD-1 therapy.

(A) Clinical and genetic profiles of the cohort. (B) Enrichment of BRAF/PTPN11 and PTEN mutations in tumors from responders and non-responders, respectively, compared to the TCGA-GBM background (left, n = 503 patients) and within the cohort (right, n = 45 patients); two-tailed Fisher’s exact test, see Methods. (C) Locations of identified mutations within the PTEN protein. (D) Evolutionary trees of 5 patients (2 non-responders & 3 responders) evaluated by whole-exome sequencing. Selected driver mutations are labeled in black. The variants that were eliminated after anti-PD-1 therapy and predicted to generate neoantigens are labeled in red. (E) Different tumor evolution models characterize non-responders and responders. The upper panel represents non-responders following a linear pattern of evolution. The lower panel represents responders following a branching pattern of evolution, with the elimination of a clone possessing a neoantigen after anti-PD-1 therapy. (F) Variant allele frequency of protein coding mutations before and after immunotherapy. Predicted expressed neoantigens are depicted in red.

Figure 3.. Transcriptomic signatures related to response to anti-PD-1 therapy.

(A) T cell clonal diversity before and after immunotherapy was assessed by identifying TCR RNA sequences within the tumor. Non-responders had a greater increase in Shannon entropy among T cells compared to responders (p = 0.024, two-sided Exact Mann-Whitney U test; n = 16 independent timepoints from 7 patients). (B) Heatmap showing the top gene sets differentially enriched in responders versus non-responders prior to (upper panel) and after immunotherapy (lower panel). (C) Single-cell RNA-Seq identifies a cluster of CD44 expressing tumor cells that are enriched in displaying an immunosuppressive profile (n = 4000 cells). (D) Heatmap showing the associations between PTEN mutations and immune cell enrichment in TCGA (two-sided Wilcoxon rank-sum test; n=167 samples). All boxplots show the median, interquartile range, and whiskers (1.5 times interquartile range); violin plots represent sample distributions via kernel density estimation.

Figure 4.. Tumor microenvironment profiling through quantitative multiplex immunofluorescence.

(A) Representative Multispectral Images (MSI) from pre-treatment samples showing DAPI (nuclei, blue), SOX2 (tumor, red), CD68 (microglia/macrophages, green), HLA-DR (activation marker, orange), CD3 (T cells, cyan), PD-L1 (immune suppression, yellow), and CD8 (CTLs, magenta), in a non-responder (left) and a responder (right). The white bars represent 10 μm. A total of n = 337 images were acquired. (B) Cellular proportions for identified cell types are shown before and after immunotherapy, as a fraction of the total cell count (n = 17 patients; p-values, two-sided Wilcoxon rank-sum test). Boxplots show the median, interquartile range, and whiskers (1.5 times interquartile range). (C) Pair correlation functions compare the degree of clustering of cells as a function of radius, for macrophages in PTEN-wild-type patients (above, n = 126 images) and for tumor cells prior to immunotherapy (below, n = 204 images). Lines represent the point-wise median across samples; shaded regions represent 95% confidence intervals.