PATH-44. VARIANT ALLELIC FREQUENCY OF DRIVER MUTATIONS PREDICTS SUCCESS OF GENOMIC METHYLATION CLASSIFICATION IN CNS TUMORS

Abstract

Abstract Whole genome CpG DNA methylation profiling is an extremely valuable tool in the workup of central nervous system (CNS) tumors. Reliability of such profiling depends on sufficient tumor cellularity. Many neoplastic entities have well-known driver mutations that occur in virtually 100% of tumor cells, and next-generation sequencing (NGS) assays can detect those mutations and report their relative amounts in the form of Variant Allelic Frequency (VAF). Since NGS and methylation profiling are often done on the same tumor block, we sought to determine whether driver mutation VAF affects the accuracy of methylation profiling, and whether VAF can help establish more rigorous cutoffs for quality assurance. Using NGS and Infinium Epic850K methylation arrays, we evaluated 153 CNS neoplasms representing a range of entities, including TERT promoter-mutant glioblastoma, IDH-mutant astrocytoma, IDH-mutant oligodendroglioma, SHH-driven medulloblastoma, and CTNNB1-driven adamantinomatous craniopharyngioma. VAFs of each driver mutation ranged between 1-60%. One hundred eleven of 153 cases (73%) had a methylation classification score ≥ 0.9, the most widely accepted cutoff for a successful result. A fit-of-mixture analysis via CutoffFinder (PMID: 23251644) suggested that the optimal VAF cutoff=31%, generating an AUC of 0.87. Ninety-six of 107 (89%) cases with a VAF of 31% or higher had a methylation classification score ≥ 0.9, whereas only 15/46 (33%) below 31% were classifiable with methylation profiling (P< 0.0001 by Fisher’s exact test). An independent validation cohort from NYU (N=50) showed nearly identical results, with 37/50 (74%) of cases having a methylation classification score ≥ 0.9, an optimal cutoff=0.32 with an AUC=0.84, and % classification above and below the 0.32 cutoff being 30/34 (88%) and 7/16 (44%), respectively (P=0.002) These data indicate that VAF of driver mutations can serve as a useful predictor of classification success via methylation profiling, and should be taken into account when interpreting methylation profiling results.