24 25 26 2 SUMMARY 27Genome-wide DNA methylation profiling has shown that epigenetic abnormalities are 28 biologically important in glioma and can be used to classify these tumors into distinct prognostic 29 groups. Thus far, DNA profiling has required surgically resected glioma tissue; however, 30 gliomas release tumoral material into biofluids, such as blood and cerebrospinal fluid, providing 31 an opportunity for a minimally invasive testing. While prior studies have shown that genetic and 32 epigenetic markers can be detected in blood or cerebrospinal fluid (e.g., liquid biopsy [LB]), 33 there has been low sensitivity for tumor-specific markers. We hypothesize that the low 34 sensitivity is due to the targeted assay methods. Therefore, we profiled the genome-wide CpG 35 methylation levels in DNA of tumor tissue and cell-free DNA in serum of glioma patients, to 36 identify non-invasive epigenetic LB (eLB) markers in the serum that reflect the characteristics of 37 the tumor tissue. From the epigenetic profiles of serum from patients diagnosed with glioma 38 (N=15 IDH mutant and N=7 IDH wildtype) and with epilepsy (N=3), we defined glioma-specific 39 and IDH-specific eLB signatures (Glioma-eLB and IDH-eLB, respectively). The epigenetic 40 profiles of the matched tissue demonstrate that these eLB signatures reflected the signature of the 41 tumor. Through cross-validation we show that Glioma-eLB can accurately predict a patient's 42 glioma from those with other neoplasias (N=6 Colon; N=14 Pituitary; N=3 Breast; N=4 Lung), 43 non-neoplastic immunological conditions (N=22 sepsis; N=9 pancreatic islet transplantation), 44 and from healthy individuals (sensitivity: 98%; specificity: 99%). Finally, IDH-eLB includes 45 promoter methylated markers associated with genes known to be involved in glioma 46 tumorigenesis (PVT1 and CXCR6). The application of the non-invasive eLB signature discovered 47 109 with glioma. We showed that the eLB could differentiate glioma from non-tumoral brain tissue 110 and stratify gliomas based on prognostic class (e.g., IDH mutation status). We further observed 111 that the specificity of the eLB allowed accurate discrimination of patients with glioma from 112 patients with tumors of other origins and from patients with immune-related disease states 113 (pancreatic islet transplantation and sepsis). The IDH-eLB signature includes promoter 114 methylated markers associated with genes known to be involved in glioma tumorigenesis (e.g., 115PVT1 and CXCR6). Finally, we propose a novel clinical approach to apply the eLB panels to 116 complement the standard of care in the diagnosis and follow-up. The ability to monitor patients 117 6 by eLB has the potential to improve the pre-and post-surgical quality of care for patients 118 harboring gliomas. 119 RESULTS 120Glioma cell-free DNA methylome 121 In this study, we selected 22 matching pairs of primary glioma tissue and serum, stored at the 122 Hermelin Brain Tumor Center (HBTC) bank from patients who underwent neurosurgery at the 123 Henr...

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.

No abstract

Background: Although most meningiomas are non-malignant, there is a high recurrence rate among atypical and anaplastic (malignant) meningiomas (grades II/III). In addition, malignant meningioma usually progresses after treatment. Recently, based on DNA methylation, two subgroups of meningioma were described with recurrence-free survival differences (favorable and unfavorable). Epigenetic deregulation at distinct genomic elements such as enhancers can drive changes in gene expression and alter the transcriptional profile of the cancer cells. We seek to understand the mechanisms of meningioma recurrence and progression after initial treatment. Material and Methods: Two favorable and two unfavorable meningioma high grade tumor tissue were selected for this pilot study. Immunoprecipitation (Antibodies) for two different histone modifications (H3K4me3 and H3K27ac) were used to generate genome-wide chromatin-IP sequencing. Genome-wide DNA methylation profile was assessed for 90 meningioma cases including the four samples used in this pilot study. Results: Using a FDR cutoff of 5%, we identified 10,049 H3K27ac and 5,752 H3K4me3 chromatin marks that distinguish favorable from unfavorable meningioma. We dichotomized the results into genomic regions overlapping known gene promoters and non-promoters. Promoter associated chromatin changes (H3K27ac and H3K4me3) coincide with DNA methylation changes in aggressive meningioma. The top 1,000 H3K27ac (sorted by fold difference) without H3K4me3 coincide with highly conserved DNA elements known to be associated with enhancers. These enhancers can drive expression of multiple genes simultaneously. Using DNA methylation differences between favorable and unfavorable meningioma, we will overlap with our unfavorable associated enhancers in order to refine our candidate enhancers for follow up mechanistic studies using aggressive meningioma cell lines. Conclusion: Our preliminary results are the first to unravel the genome-wide chromatin changes associated with unfavorable or clinically aggressive meningioma. Identification of these candidate enhancers will provide knowledge of the role of epigenomics in the development of malignant meningioma and of opportunities for targeted therapy. This project is supported by the Henry Ford Health System, Department of Neurosurgery and the Hermelin Brain Tumor Center Foundation (A30935), United States National Institutes of Health (R01CA222146), and United States Department of Defense (CA170278)