EPCO-30. MACHINE-LEARNING PREDICTIVE MODELS BASED ON DNA METHYLATION SIGNATURES DETECTED IN LIQUID BIOPSY SPECIMENS ACCURATELY PREDICT THE DIAGNOSIS AND PROGNOSIS OF MENINGIOMAS. (12th November 2021)

• Record Type: Journal Article
• Authors: Herrgott, Grayson; Sabedot, Thais; She, Ruicong; Wells, Michael; Asmaro, Karam; Malta, Tathiane; Mosella, Maritza; Nelson, Kevin; deCarvalho, Ana; Poisson, Laila; Robin, Adam; Lee, Ian; Snyder, James; Walbert, Tobias; Kalkanis, Steven; Rock, Jack; Noushmehr, Houtan; Castro, Anavaleria
• Is Part Of: Neuro-oncology. Volume 23: Supplement 6(2021)
• Page Start: vi8
• Page End: vi8
• Publication Date: 2021-11-12
• Subjects: Brain Neoplasms -- Periodicals; Brain -- Tumors -- Periodicals; Brain -- Cancer -- Periodicals; Nervous system -- Cancer -- Periodicals; 616.99481
• DOI: 10.1093/neuonc/noab196.029
• Languages: English
• ISSNs: 1522-8517

Abstract

Abstract: BACKGROUND: Detection of distinct epigenetic features in circulating cell-free DNA (cfDNA) of liquid biopsy specimens (e.g. blood) provides an opportunity to diagnose and prognosticate central nervous system (CNS) tumors. Utilization of distinctive cell-type genome-wide DNA methylation patterns allows for development of machine-learning (ML) models with the ability to predict tumor diagnosis and prognosis, and remains widely unexplored in meningiomas using LB. METHODS: We profiled the cfDNA methylome (EPIC array) in serum specimens from patients with meningiomas, other CNS tumors and nontumor conditions (n= 63, 190 and 18, respectively) and harnessed internal and external meningioma tissue methylome data. For diagnostic model development, we identified "meningioma specific" signatures through comparison of meningioma and non-meningioma serum specimens (Wilcoxon rank-sum test) which exhibited tissue methylation similarities named Meningioma- epigenetic Liquid Biopsy (MeLB), and were then employed to develop and cross-validate a Random-Forest derived "MeLB" score to discriminate meningiomas from the other conditions. To predict recurrence risk (RR), we classified a meningioma tissue cohort as 'favorable' or 'unfavorable' (low and high RR, respectively), using a validated ML outcome model and identified outcome-specific methylation markers with serum subgroup specificities used as input to train a Random-Forest to predict RR in serum-based specimens. RESULTS: Prediction models based on meningioma-specific methylation markers detected in the serum presented a high accuracy in classification of samples as meningioma or not (Accuracy: 89.6%, Sensitivity: 80%, Specificity: 93.8%). The prognostic model using tissue-serum matching methylation markers was validated across an independent tissue-based cohort (Accuracy: 88%, Sensitivity: 86%, Specificity: 88%) and allowed for classification of serum samples according to RR. CONCLUSIONS: Machine-learning models using DNA methylation markers identified in the serum can accurately diagnose and predict prognosis in patients with meningioma. After validation in an external cohort, these approaches may improve presurgical diagnosis and therapeutic management of patients with this type of tumor.