OS01.7.A Detection of methylation-based prognostic signatures in liquid biopsy specimens from patients with meningiomas

Background

Detection of distinct epigenetic biomarkers in circulating cell-free DNA (cfDNA) of liquid biopsy (LB) specimens (e.g. blood) fosters opportunity for prognostication of central nervous system (CNS) tumors and has not been thoroughly explored in patients with meningiomas.

Material and Methods

We profiled the cfDNA methylome (EPIC array) in serum specimens from patients with meningiomas (MNG; n= 63) and harnessed internal and external meningioma tissue methylome data with reported follow up (n=48). To predict recurrence risk (RR), we consolidated a tissue cohort with at least 5 years of follow up and divided them into confirmed recurrence (CR; either reported progressive disease in post-surgical imaging, or additional resections following initial surgery) and confirmed no-recurrence (CNR: no confirmed disease progression w/in at least 5-years of follow-up). Then through application of an iterative process consisting of multiple tissue- and serum-based supervised analyses, we identified risk-specific methylation markers with serum specific features which, when inputted into a random forest algorithm allowed for segregation of both tumor tissue and liquid biopsy specimens according to recurrence risk. We estimated immune cell composition using MethylCIBERSORT, where a reference methylome atlas of chosen immune cell types was utilized to deconvolute the MNG samples.

Results

The resulting recurrence risk classifier demonstrated an appreciable predictive power in classifying samples as high or low recurrence risk across the tumor tissue cohort (ACC: 87.5%, CUI+: 85.2%). When compared to another classifier, our model demonstrated statistically significant agreement across primary meningioma samples (κ=0.269, p=0.002), and more accurately predicted samples to recur across an expanded time window (time to recurrence >5yrs). Across resulting liquid biopsy classifications, recurrence risk subgroups were analogous with reported risk factors, including WHO grade, extent of resection, and tumor location. Recurrence risk subgroups (high and low) also demonstrated differential estimated immune cell contributions, with low-risk samples exhibiting a “hot” profile, or enrichment of B-Cells, CD56- and CD4 T-Cells, and natural killer cells. Notably, the estimated neutrophil to lymphocyte ratio, previously purported to be relevant to tumor prognosis, was appreciably higher for those meningioma samples with the highest recurrence risk.

Conclusion

DNA methylation markers identified in the serum are suitable for the development of machine learning-based models which present high predictive power to prognosticate patients with meningioma and estimate a differential immune profile across recurrence risk groups. After validation in an external cohort, this noninvasive approach may improve the presurgical therapeutic management of patients with meningiomas.

OS08.3.A Distinct systemic and tumor microenvironment immune landscapes discriminate across sellar tumor types and controls through a methylation-based deconvolution method

Background

Systemic (Sys) and tumor microenvironment (TME) immune milieus play a pivotal role in tumor development, outcome and immunotherapy response predictions across a variety of central nervous system tumors. Genome-wide methylation profiling can reliably discriminate and estimate immune cell proportions present in the blood and within the tumor and has not been reported across sellar tumor types (STT).

Material and Methods

We estimated cell composition in liquid biopsy (LB, serum/plasma) and tissue specimens from 42 STT collections (i.e., pituitary neuroendocrine tumors [PitNETs; n=37] and craniopharyngiomas [CP; n=5]), and 26 nontumor controls (LB: 11; Tissue: 15) using MethylCIBERSORT, a methylation-based deconvolution algorithm and established immune cell signatures as reference. LB methylation was profiled with EPIC array. Correlations between estimated cell proportions across sample sources were explored (Spearman). Immune cell proportion hierarchical k-means clustering was performed across tissue and LB specimens. Similarly, mean comparisons between and across sample types and subgroups of interest were performed [Non-parametric Kruskal-Wallis, Wilcoxon rank-sum tests; p<0.05].

Results

We identified three immune-clusters across tissue specimens which distinguished controls (k3-cluster) from sellar tumor specimens (k1- and k2- clusters), primarily attributable to differential B-cell and monocyte proportions. Interestingly, a subset of PitNET and CP, belonging to the k2-cluster, presented a distinct immune profile compared to their K1-sellar tumor counterparts. Analysis of plasma-derived immune clusters revealed that PitNETs were distributed across four distinct immune patterns and CP clustered together with controls and a PitNET subset. One of the PitNET clusters was enriched with patients that died during follow-up and presented an enrichment of CD4-(including the regulatory subtype), CD8 and CD56-T and depletion of natural killer cells. Differences across serum- and tissue-derived clusters were present but less prominent than their plasma counterparts. No correlation between immune cell proportions across other clinicopathological features within each tumor type (sex, age, histotypes, invasion etc) was observed.

Conclusion

Our results suggest that PitNETs are characterized by differential TME and systemic immune subtypes which also distinguish these tumors from CP and controls. Additionally, distinct systemic immune composition between tissue and LB sources, more readily observed in plasma, suggest that the systemic response to the presence of the tumor is distinct from the immune response noted in the TME. Tumor immune subtyping may allow the stratification of STT according to immunotherapy response vulnerabilities.