EPCO-15. INTRA-TUMORAL HETEROGENEITY OF DNA METHYLATION PROFILING AND CELLULAR COMPOSITION IN GLIOBLASTOMA

Abstract BACKGROUND DNA methylation profiling has become an important diagnostic and exploratory tool in neuro-oncology. Intra-tumoral molecular heterogeneity is a crucial factor in the treatment resistance of IDH-wildtype glioblastoma (GBM). However, the intra-tumoral heterogeneity of methylation profiles in GBM remains poorly elucidated. This study aimed to investigate the intra-tumoral heterogeneity of DNA methylation subclasses and deconvolve methylation profiles to infer the cellular composition of the tumor. METHODS We conducted genome-wide DNA methylation analysis and applied the DKFZ/Heidelberg CNS tumor classifier to FFPE tissue samples from a multi-sampling cohort with multiple samples per patient and a single-sampling cohort with one sample per patient. Only samples classified as GBM were included. We conducted two different methylation-based deconvolution analyses. Deconvolution 1 inferred the fractions of tumor subtypes (RTK_I, RTK_II, MES_TYP, and MES_ATYP) and cell types in the microenvironment. Deconvolution 2 estimated the abundance of malignant cell states (stem-like and differentiated cell components) and cell types in the microenvironment. RESULTS The multi-sampling cohort included 32 patients with 113 samples, and the single-sampling cohort included 87 patients with 87 samples. In the multi-sampling cohort, 8 patients (25%) exhibited intra-patient heterogeneity in methylation subclasses across their samples. Deconvolution 1 indicated that each tumor subclass component was heterogeneously distributed within samples, and the abundance of the myeloid cell component correlated with the MES_TYP component across samples. Deconvolution 2 similarly revealed heterogeneity in the immune component abundance across samples within patients. Integrating the results of Deconvolution 1 and Deconvolution 2 using the entire cohort revealed significant correlations between RTK_I and stem-like, RTK_II and differentiated, and MES_TYP and immune components. These findings were also replicated in TCGA dataset. CONCLUSIONS Our findings underscore the importance of considering intra-tumoral heterogeneity in methylation profiles and the biological characteristics of each methylation subclass in developing novel GBM therapies.