OS01.7.A Detection of methylation-based prognostic signatures in liquid biopsy specimens from patients with meningiomas
Authors: Herrgott G , Snyder J , She R , Malta T , Sabedot T , Lee I , Pawloski J , Asmaro K , Zhang J , Cannella C , Nelson K , Thomas B , deCarvalho A , Poisson L , Chitale D , Mukherjee A , Mosella M , Robin A , Walbert T , Rosenblum M , Mikkelsen T , Kalkanis S , Podolski-Gondim G , Tirapelli D , Carlotti Jr. C , Rock J , Castro A , Noushmehr H 
Published: September 2022

Abstract

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 &gt;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.