Corticosteroid-Dependent Association between Prognostic Peripheral Blood Cell-Free DNA Levels and Neutrophil-Mediated NETosis in Patients with Glioblastoma

Abstract

Purpose: Noninvasive prognostic biomarkers to inform clinical decision-making are an urgent unmet need for the management of patients with glioblastoma (GBM). We previously showed that higher circulating cell-free DNA (ccfDNA) concentration is associated with worse survival in GBM. However, the biology underlying this is unknown.

Experimental design: We prospectively enrolled 129 patients with treatment-naïve GBM with blood drawn prior to initial resection (baseline) and at the time of the first postradiotherapy MRI. We performed ccfDNA methylation deconvolution to determine cellular sources of ccfDNA. ELISA was performed to detect citrullinated histone 3 (citH3), a marker of neutrophil extracellular traps (NET). Multiplex proteomic analysis was used to measure soluble inflammatory proteins.

Results: We found that neutrophils contributed the highest proportion of prognostic ccfDNA. The percentage of ccfDNA derived from neutrophils was correlated with total [ccfDNA] but only in patients receiving preoperative corticosteroids. At baseline and on therapy, [citH3] was significantly higher in the plasma of patients with GBM receiving corticosteroids compared with corticosteroid-naïve GBM or no-cancer controls. Unsupervised hierarchical clustering of ccfDNA methylation patterns yielded two clusters, with one enriched for patients with the NETosis phenotype and who received corticosteroids. Unsupervised clustering of circulating inflammatory proteins yielded similar results.

Conclusions: These data suggest neutrophil-mediated NETosis is the dominant source of prognostic ccfDNA in patients with GBM and may be associated with glucocorticoid exposure. If further studies show that pharmacological inhibition of NETosis can mitigate the deleterious effects of corticosteroids, these plasma markers will have important clinical utility as noninvasive correlative biomarkers.

Figure 1.. Sources of DNA in peripheral blood and tissue.

Shown in (A) is deconvolution of plasma ccfDNA methylation results for 110 patients with glioblastoma (GBM) and 31 age- and gender-matched no-cancer controls with percentage of ccfDNA from WBC and tissue types as indicated. Mann-Whitney test with Bonferroni correction was utilized to compare groups with p-value indicated as *P<0.05, **P<0.01 and ****P<0.0001. Black horizontal bars indicate median values. In (B), the correlation of % neutrophil ccfDNA with total ccfDNA concentration is shown for 110 patients with GBM (left) and 31 no-cancer controls (right). In addition, tissue was obtained, and methylation analysis of extracted DNA performed for 79 patients with GBM. Shown in (C) is the deconvolution of cellular sources of tissue DNA. Shown in (D) is the correlation of % neutrophil DNA from tissue DNA vs plasma ccfDNA for the 67 patients with both tissue and ccfDNA methylation analysis. All statistical tests are two-sided with no adjustment for multiple testing (unless indicated otherwise).

Figure 2.. Association of % neutrophil ccfDNA with the NETosis marker citrullinated histone 3.

ELISA was performed on baseline plasma to determine the concentration of citrullinated histone 3 (citH3). Shown in (A) are results for 113 patients with glioblastoma (GBM) and 25 no-cancer controls with associated Mann-Whitney P-value. Shown in (B) are correlations of plasma [citH3] with % neutrophil ccfDNA as determined by methylation deconvolution for the 107 patients with glioblastoma (GBM, left) and 24 no-cancer controls (right). Of the 113 patients with citH3 results, only 107 also had % neutrophil ccfDNA data. In (C), ELISA-determined plasma citH3 values are again shown but the GBM patient cohort is split between those who received (“exposed”) or didn’t receive (“unexposed”) pre-operative steroids. Dunn’s multiple comparison P-values are listed. In (D) the associations of plasma [citH3] and % neutrophil ccfDNA are shown for patients with GBM who were exposed to steroids (left, N=86) and those who weren’t (right, N=21). All statistical tests are two-sided with no adjustment for multiple testing (unless indicated otherwise).

Figure 3.. Unsupervised clustering of plasma ccfDNA methylation data, differential methylation, and pathway analysis.

Shown in (A) is an unsupervised clustering analysis of the 2000 most variably methylated CpGs in the plasma ccfDNA of 110 patients with glioblastoma (GBM). The heatmap depicts the normalized beta values for these CpGs, as well as normalized values for % neutrophil ccfDNA, citH3 concentration (N=107), and pre-operative steroid dose (rows under heat map) for each patient (columns). For citH3, grey indicates sample unavailable for analysis. For pre-operative steroids, white indicates a zero value, i.e., patients unexposed to pre-operative steroids. In (B, C, and D), the associations of % neutrophil ccfDNA, plasma [citH3], and pre-operative steroid dose (respectively) with Cluster 1 and Cluster 2 from (A) are shown as values with Mann-Whitney P-value (top) and non-parametric ROC analysis (bottom). In (D, right), this association is shown for pre-operative steroid exposure (exposed vs. unexposed; Fisher’s exact test). The volcano plot in (E) depicts the association of all CpGs measured with these two clusters; red dots indicate significantly hypomethylated or hypermethylated with an FDR-adjusted P-value > 0.05 and |Δβ| > 0.1, with the number of CpGs meeting these criteria listed below the plot. The top 10 Gene Ontology (GO) gene sets (selected first by ordering by p-value, and when two or more pathways had the same p-value, then ordered by NES) associated with this differential methylation profile are shown in (F) with red arrows identifying pathways associated with the NETosis process. To generate the bubble plot shown, pathways were re-ordered based on gene set size. The Venn diagram shown in (G) demonstrates the overlap of significantly differentially methylated CpGs for patients dichotomized at median [citH3] or median % neutrophil ccfDNA and for patients dichotomized by pre-operative steroid exposure. All statistical tests are two-sided with no adjustment for multiple testing (unless indicated otherwise).

Figure 4.. Analysis of plasma inflammatory biomarker levels.

Shown in (A) is an unsupervised clustering analysis of the plasma levels of 92 inflammatory biomarkers for N=93 patients with glioblastoma (GBM). The heatmap depicts the n5 re-normalized values for these biomarkers (red to blue), normalized values for % neutrophil ccfDNA, citH3 concentration, and pre-operative steroid dose (orange rows under heat map), as well as the unsupervised hierarchical cluster indicated by the ccfDNA methylation profile from Figure 3A (green row under heat map), for each patient (columns). For % neutrophil ccfDNA, grey indicates sample unavailable for analysis. For pre-operative steroids, white indicates a zero value, i.e., patients unexposed to pre-operative steroids. In (B, dot plots from left to right), the associations of % neutrophil ccfDNA (N=87), plasma [citH3], and pre-operative steroid dose with cluster 1 vs cluster 2 from (A) are shown as values and Mann-Whitney P-value (top) and non-parametric ROC analysis (bottom). Additionally, in B (bar charts far right), this association is shown for pre-operative steroid exposure (exposed (black) vs. unexposed (white); Fisher’s exact test), and also for the ccfDNA clusters from Figure 3A (ccfDNA Cluster 1 (light green) vs ccfDNA Cluster 2 (dark green); Fisher’s exact test). The volcano plots in C (left to right) depict the association of the 92 inflammatory biomarkers with % neutrophil ccfDNA dichotomized at the median value, [citH3] dichotomized at the median value, and pre-operative steroid exposure (exposed vs. unexposed); red dots indicate P-adjusted >0.05. The Venn diagram shown in (D) demonstrates the overlap of significantly differentially expressed proteins from C. All statistical tests are two-sided with no adjustment for multiple testing (unless indicated otherwise).