Functional Contribution and Clinical Implication of Cancer-Associated Fibroblasts in Glioblastoma

Abstract

Purpose: The abundance and biological contribution of cancer-associated fibroblasts (CAF) in glioblastoma (GBM) are poorly understood. Here, we aim to uncover its molecular signature, cellular roles, and potential tumorigenesis implications.

Experimental design: We first applied single-cell RNA sequencing (RNA-seq) and bioinformatics analysis to identify and characterize stromal cells with CAF transcriptomic features in human GBM tumors. Then, we performed functional enrichment analysis and in vitro assays to investigate their interactions with malignant GBM cells.

Results: We found that CAF abundance was low but significantly correlated with tumor grade, poor clinical outcome, and activation of extracellular matrix remodeling using three large cohorts containing bulk RNA-seq data and clinical information. Proteomic analysis of a GBM-derived CAF line and its secretome revealed fibronectin (FN1) as a critical candidate factor mediating CAF functions. This was validated using in vitro cellular models, which demonstrated that CAF-conditioned media and recombinant FN1 could facilitate the migration and invasion of GBM cells. In addition, we showed that CAFs were more abundant in the mesenchymal-like state (or subtype) than in other states of GBMs. Interestingly, cell lines resembling the proneural state responded to the CAF signaling better for the migratory and invasive phenotypes.

Conclusions: Overall, this study characterized the molecular features and functional impacts of CAFs in GBM, alluding to novel cell interactions mediated by CAFs in the GBM microenvironment.

Figure 1.. Identification and molecular characterization of CAFs in glioblastoma.

A, Bubble-plot indicating the average expression of selected marker genes for the cell-types identified in human glioblastomas. The size of the circle corresponds to the percent of cells that express a marker while the color indicates the log₂(count+1). B, Venn diagrams displaying the overlaps of top 100 marker genes between CAFs and endothelial cells, pericyte cells, radial glia cells, and tumor associated macrophages. C, Bar plot indicating the number of cell clusters in the PanglaoDB with markers significantly overlapping with the CAF markers identified here.

Figure 2.. CAF association with tumor grade and clinical outcome in glioma.

A-C, Association between tumor grade and CAF enrichment score in TCGA (Grade II, n=248; Grade III, n=261; Grade IV n=173), CGGA 325 (Grade II, n=109; Grade III, n=72; Grade IV, n=144), and CGGA 693 (Grade II, n=188; Grade III, n=255; Grade IV n=249) datasets (data displayed as mean ± S.D; One-way ANOVA: **** < 0.0001). D-E, Hazard ratios (HRs) associated with cell-type-specific enrichment scores in three datasets describing LGG or glioblastoma samples (data displayed as median with range). Error bars represent 95% confidence intervals (CIs) of calculated HRs. If error bars do not cross the dotted line this indicates the calculated hazard ratio has a p-value of at least less than 0.05 (see Table S2 and S3 for exact HRs, CIs, and p-value calculations. F-H, Kaplan-Meier plots indicating percent survival over time (months) among LGG patients with either a high- or low-CAF enrichment score in TCGA (High CAF; n=127; Low CAF, n=127), CGGA 325 (High CAF, n=45; Low CAF, n=45), and CGGA 693 (High CAF, n=111; Low CAF, n=111) datasets (MSD, median survival difference; HR, hazard ratio; CI, confidence interval). I-K, Kaplan-Meier plots indicating percent survival over time (months) among glioblastoma patients with either a high- or low-CAF enrichment score in TCGA (High CAF, n=40; Low CAF, n=40), CGGA 325 (High CAF, n=36; Low CAF, n=36), and CGGA 693 (High CAF, n=62; Low CAF, n=62) datasets (MSD, median survival difference; HR, hazard ratio; CI, confidence interval).

Figure 3.. CAFs contribute to malignant cell migration and invasion in glioblastoma.

A-C, PCA plots of RNA-seq samples from glioblastomas with high and low CAF enrichment scores for TCGA (High CAF, n=40; Low CAF, n=40), CGGA 325 (High CAF, n=36; Low CAF, n=36), and CGGA 693 (High CAF, n=62; Low CAF, n=62) datasets. Ellipse defines a region that contains 95% of all samples belonging to a particular group. D, Networks of REACTOME terms enriched or depleted in glioblastomas with high CAF enrichment score compared to glioblastomas with low CAF score. Nodes are terms enriched (red circles) or depleted (blue circles) among genes expressed higher in glioblastomas with high CAF enrichment scores, while edges link terms with overlapping genes. Connected nodes with similar functions are further summarized by a more generalized biologically relevant term using Enrichment map. Each node is composed of three parts corresponding to data for TCGA, CGGA 325, and CGGA 693, and thus the color indicates how similar the enrichment was observed. E-G, Transwell migration results for glioblastoma cell lines exposed to conditioned media, T98-G, LN-229, and U87-MG. H-J, Transwell invasion results for glioblastoma cell lines exposed to conditioned media, T98-G, LN-229, and U-118 MG. K-M, Transwell migration results for primary GBM cells exposed to conditioned media, RPCI-1012, RPCI-95, and RPG-429. N-P, Transwell invasion results for primary GBM cells exposed to conditioned media, RPCI-1012, RPCI-95, and RPG-429. Data were generated from n=4 independent experiments and displayed as mean ± S.D. One-way ANOVA: * < 0.05, ** < 0.01, *** < 0.001, and **** < 0.0001. Images used for cell counting were obtained with 20x objective magnification, (scale = 100μm).

Figure 4.. CAF-derived fibronectin facilitates glioblastoma cell migration and invasion.

A, Venn diagram displaying the numbers of top ranked proteins in Fibro^CM and CAF^CM. B-C, Ranks of proteins detected in Fibro^CM and CAF^CM, with the top 10 listed. The x-axis represents the rank of proteins from low (left) to high (right), while the y-axis indicates the average protein expression across two independent replicates. D-F, Pearson’s correlation between CAF enrichment scores and FN1 expression (FPKM) in glioblastoma samples, TCGA (n=161), CGGA_325 (n=144), and CGGA_693 (n=249) datasets. G, Pearson’s correlation between CAF enrichment scores and FN1 protein expression in the CPTAC (n=99) dataset. H, Average FN1 expression across cell-types in the scRNA-seq analysis. Size of the circle indicates individual contribution of a cell type to the total FN1 expression, with colors indicate mean expression. I, Cell-to-cell FN1 interaction network among cell types in the scRNA-seq dataset. J-M, Transwell migration results for glioblastoma cell lines exposed to 10 ng/mL or 20 ng/mL human recombinant FN1, T98-G, LN-229, U87-MG, and U-118 MG. N-O, Transwell invasion results for glioblastoma cell lines exposed to human recombinant FN1, T98-G and LN-229. Data were generated from n=3 independent experiments and displayed as mean ± S.D. One-way ANOVA: * < 0.05, ** < 0.01, *** < 0.001, and **** < 0.0001. Images used for cell counting were obtained with 20x objective magnification, (scale = 100μm).

Figure 5.. CAFs are associated with a proneural-to-mesenchymal transition in glioblastoma.

A, GSEA enrichment plots for the Hallmark epithelial mesenchymal transition gene set, comparing glioblastomas with a high to low CAF enrichment score in the TCGA and CGGA datasets. B, Heatmap depicting individual enrichment scores for signature gene sets describing specific glioblastoma subtypes among glioblastoma samples (columns) with a high and low CAF enrichment score in the TCGA dataset. C, PCA plot for glioblastomas samples in the TCGA cohort, with colors for four groups: proneural CAF low (n=15), proneural CAF high (n=14), mesenchymal CAF low (n=25), and mesenchymal CAF high (n=25). The densities of samples are shown above. D, Heatmap showing changes in the expression of genes associated with inflammation, invasion, mesenchymal-subtype, and proneural-subtype as glioblastomas accumulate more CAFs in the TCGA cohort. E, PCA plot of microarray data for glioma cell-lines (n=2 samples for each cell line). F, Dendrogram showing correlation of microarray samples from glioma cell-lines. G, GSEA enrichment plots for the proneural and mesenchymal glioblastoma subtype gene sets, comparing responder cell lines (T98-G and LN-229) and partial responder cell lines (U118-MG and U87-MG).

Figure 6.

Working model of CAF contribution in glioblastoma plasticity.