Multi-scale signaling and tumor evolution in high-grade gliomas

Abstract

Although genomic anomalies in glioblastoma (GBM) have been well studied for over a decade, its 5-year survival rate remains lower than 5%. We seek to expand the molecular landscape of high-grade glioma, composed of IDH-wildtype GBM and IDH-mutant grade 4 astrocytoma, by integrating proteomic, metabolomic, lipidomic, and post-translational modifications (PTMs) with genomic and transcriptomic measurements to uncover multi-scale regulatory interactions governing tumor development and evolution. Applying 14 proteogenomic and metabolomic platforms to 228 tumors (212 GBM and 16 grade 4 IDH-mutant astrocytoma), including 28 at recurrence, plus 18 normal brain samples and 14 brain metastases as comparators, reveals heterogeneous upstream alterations converging on common downstream events at the proteomic and metabolomic levels and changes in protein-protein interactions and glycosylation site occupancy at recurrence. Recurrent genetic alterations and phosphorylation events on PTPN11 map to important regulatory domains in three dimensions, suggesting a central role for PTPN11 signaling across high-grade gliomas.

Keywords: CPTAC; glioblastoma; glycoproteomics; lipidome; metabolome; proteomics; single nuclei ATAC-seq; single nuclei RNA-seq; tumor recurrence.

Figure 1.. Overview of study cohort, technology platforms, and assays.

(A) Cohort overview. (B) Features quantified on 14 data platforms (excluding single-nuclei sequencing and multiplex imaging). Specific features quantified on each platform are: WXS-somatic variants, WGS-somatic variants, DNA methylation-CpG probes, RNA-RNA transcripts, miRNA-miRNA transcripts, Proteome-proteins, Phosphoproteome-phosphoproteins, Acetylome-acetylproteins, Glycoproteome-glycoproteins, PRISM SRM-proteins, IMAC SRM-phosphoproteins, Direct SRM-proteins, Metabolome-metabolites, Lipidome-lipids. PRISM: high-pressure, high-resolution separations with intelligent selection and multiplexing. IMAC: immobilized metal affinity chromatography. SRM: selected reaction monitoring. (C) Sub-cohorts used in each analysis. See also Figure S1 and Table S1.

Figure 2.. Proteogenomics of tumor evolution.

(A) Primary and recurrent tumor sample collection for 25 patients with grade 4 astrocytoma. (B) Differentially abundant proteins between recurrent and primary tumors (FDR<0.05, log2FC>0.5). (C) Differentially enriched pathways in the current cohort, and GLASS and GSAM cohorts. (D) Workflow of Kinase Library enrichment analysis. (E) Kinase activity analysis comparing recurrent and primary tumors. (F) Demonstrative variant allele frequency (VAF) correlation plots indicating 4 modes of driver mutation progression observed: stable (all mutations persist, 12 cases), loss (all unique mutations are in primary tumor, 4 cases), gain (all unique mutations are in recurrent tumor, 5 cases) and regain (both gain and loss of different mutations in the same gene, 4 cases). Number of mutations that are shared across timepoints, unique to either primary or recurrence are labeled. Demonstrative cases were randomly chosen. (G) Progression-free survival negatively correlates with tumor clonal similarity statistics (MaxKsi), based on somatic mutations (R² = 0.63, p-value < 0.001). (H) Relationship between fraction of somatic mutations with SBS11 signature and total number of somatic mutations in each tumor sample, categorized by whether patient received TMZ treatment prior to recurrent tumor collection. GLASS and GSAM cohort shown for comparison. Gray line connects tumor samples from same patient. See also Figure S2 and Table S2.

Figure 3.. Single-nuclei sequencing reveals malignant cell-intrinsic and tumor microenvironment (TME)-associated signatures in recurrent grade 4 astrocytomas.

(A) UMAP of snRNA-sequencing data from 10 primary-recurrent pairs. (B) Heatmap showing scaled average snRNA expression of differentially abundant proteins between recurrent and primary tumors. Top two rows: log-fold change of bulk protein and RNA. Middle eight rows: average expression of same genes from snRNA in indicated cell type, scaled by both row and column. Bottom row: cell type having highest snRNA expression of given gene product. (C) Malignant cell-intrinsic dysregulated pathways in recurrent tumors detected from snRNA-based differentially expressed gene (DEG) between each case’s recurrent versus primary tumor. Red in barplot: number of genes differentially expressed in at least 3 cases. (D) Correlation (link score) distribution between accessible chromatin region (ACR) accessibility and gene expression in malignant cells. ACRs overlapping with GeneHancer are annotated. Number of links shown on right. (E) Examples of chromatin accessibility and snRNA expression in primary and recurrent malignant cells for two genes. (F) Motif enrichment in differential ACRs. Motifs significantly enriched (hypergeometric test with Benjamini-Hochberg multiple test correction, FDR<0.05) in red (DACRs in primary tumors), blue (DACRs in recurrent tumors), and gray (both sets of DACRs). (G) Cell-type enrichment score comparison from bulk RNA-seq representing the TME cell-type composition between recurrent and primary tumors (differentially enriched, FDR<0.1). (H) Endothelial cell composition of total tumor microenvironment population is decreased in recurrent tumor, from snRNA cell counts and bulk RNA and bulk protein deconvolution fractions. Boxplots show interquartile range (IQR; boxes span first to third quartiles, middle line indicates median and whiskers show closest values within 1.5x IQR). Paired samples from the same patient are connected by lines. Wilcoxon p values were calculated. (I) Multiplexed immunofluorescence results (CODEX) of three primary-recurrent tumor pairs. (J) Proportion of endothelial cells and Ki67+ malignant cells from 6 primary-recurrent matched tumor sections are quantified in 1mm × 1mm tiles respectively. Boxplots show interquartile range. Wilcoxon p values were calculated (ns: p>0.05, *: p<=0.05, **: p<=0.01, ****: p<=0.0001). See also Figure S3 and Tables S3, S4.

Figure 4.. Cis and trans results highlight similarities between primary tumors at different omics levels.

(A) Somatic alteration mutual exclusivity analysis. Significance of co-occurrence is indicated by both opacity and asterisks. Fisher’s exact test with Benjamini & Hochberg adjusted p-values were calculated (*: p < 0.05, **: p < 0.01, ***: p < 0.001). (B) Cis effects for 13 common drivers demonstrating how gene alterations impact their own RNA and protein (top), and post-translational changes (bottom), relative to tumors WT for the given gene. Heatmaps reflect cis result score (capped at +/−10 for RNA and protein scores, gray where measure was N/A). (C) Trans analysis: effects of somatically altered genes on other proteins, post-translational modifications (PTMs), metabolites, and lipid levels. Trans effects were measured pairwise for each somatically altered gene. Each trans effect was scored for each pair and correlation between pairs was calculated. (D) Driver gene similarity scores were evaluated at the protein/PTM and lipid/metabolite level. Colors indicate the Pearson correlation coefficient (*: p < 0.05, **: p < 0.01, ***: p < 0.001). (E) Pearson correlation in trans scores between TERTp and PTEN effects at protein/PTM levels (p < 1e-100) and lipid/metabolite levels (p < 8.96e-81). Top 5 positively and negatively scored events are labeled and colored according to data type. See also Figure S4 and Table S5.

Figure 5.. Altered glycosylation programs in grade 4 astrocytomas and topologic relation between somatic mutations, glycosites, and phosphosite on EGFR.

(A) Differential analysis of primary tumors vs normal brain in four predominantly tumor-enriched pathways. (B) Glycan type distributions based on upregulated/downregulated intact glycopeptides in primary tumors (vs normal brain). (C) Glycosylation enzymes associated with altered glycosylation activities in primary tumors. (D) Glycan type distribution and biological processes associated with recurrent grade 4 astrocytomas vs primary tumors. (E) The dots (matching ROC curve colors in Fig. S5D) correspond to the AUCs of the glycoproteins and IGPs that may serve as potential biomarkers for recurrent grade 4 astrocytoma (vs primary tumors). (F) Glycan type distribution for intact glycopeptide clusters (IPCs) associated with glycoproteomic subtypes. (G) EGFR dimer, demonstrating spatial relationships between somatic mutations, phosphosite, and glycosites. See also Figure S5 and Table S6.

Figure 6.. Protein-protein interaction (PPI) differences are associated with somatic alterations and recurrence status.

(A) Left, schematic of protein interaction correlations. Right, examples of high correlation in abundance and negative correlation in abundance. (B) Inferred PPIs in EGFR and PDGFRA signaling pathways in indicated contexts. (C) 14 significantly altered PPIs (linear regression with interaction term p-value < 0.01) in primary tumors when one protein partner is somatically altered. (D) Pearson correlations between PPIs according to RB1 and PTPN11 alterations, respectively (Pearson R p-value < 0.05). (E) Subset of protein pairs exhibiting correlation in primary and/or recurrent tumors for all matched samples with protein data available (Pearson R > 0 and Pearson p-value < 0.01 in indicated group). Related to Figure S6B. (F) RB1 and CDK2 protein abundance Pearson correlations in recurrent and matched grade 4 astrocytomas (left). XIAP and AKT1 protein abundance Pearson correlations in recurrent and primary samples (right). (G) Effects of somatic mutations in genes (y-axis), on activity of kinases (x-axis), using Kinase Library applied to differentially abundant phosphoproteins compared to WT tumors. See also Figure S6.

Figure 7.. The PTPN11 signaling hub in grade 4 astrocytoma.

(A) PTPN11, EGFR, IDH1 and PDGFRA alteration status in primary HGGs. The p value was calculated with Fisher’s exact test (***: p<0.001) (B) PTPN11 mutations and observed phosphorylations mapped onto 3D protein structure. (C) cis/trans analyses of phosphorylated residues and driver mutations. Boxplots show interquartile ranges of normalized protein and phosphoprotein abundance. Wilcoxon p values were calculated (ns: p>0.05, **: p<=0.01, ****: p<=0.0001) (D) Kinase activity in samples with PTPN11-Y62 and PTPN11-Y546 phosphorylation events. (E) Kinase substrate analysis for PTPN11-Y62 and PTPN11-Y546 phosphorylation. (F) Analyses of cis and trans effects, PPIs, and kinase/phosphatase-substrate measurements reveal multiple proteins likely regulated by PTPN11. More than 60% of tumors share the PTPN11-centered signaling hub. See also Figure S7.

Figure 8.. Dysregulated pathways in grade 4 IDH-mutant astrocytomas.

(A) Hotspot IDH1 R132H/R132C mutations result in 2-HG production and hypermethylation phenotype. Proteogenomics identified dysregulated pathways in IDH-mutant tumors compared to GBMs. (B) Specific RTK pathway genes are upregulated in IDH-mutant astrocytomas. Boxplots show interquartile ranges of normalized protein abundance. FDR was calculated with Wilcoxon test and Benjamini & Hochberg adjustment. (C) IDH-mutant tumors have less protein expression of hypoxia pathway members compared to GBMs. Boxplots show interquartile ranges of normalized protein abundance. Wilcoxon p values were calculated (ns: p>0.05, *:p<0.05, **: p<=0.01, ***:p<0.001, ****: p<=0.0001) (D) PPIs from STRING among HIF1A pathway genes. (E) CODEX images of IDH-WT (top) and mutant patient samples (middle). Whole-slide percent cellular area with SERPINE1 staining is quantified for each sample (bottom). (F) Kinase activity differences between IDH-mutant astrocytomas and GBMs, based on phosphoproteomic measurements. See also Figure S8 and Table S4.