Abstract Introduction: Metastatic castration-resistant prostate cancer (mCRPC) is a heterogeneous disease which can be classified into clinically relevant subtypes based on the expression of genes, such as the androgen receptor (AR) and neuroendocrine markers. Neuroendocrine prostate cancer (NEPC), characterized by gain of stem-like and neuroendocrine features and lack of AR expression is a clinically aggressive variant. Due to the lack of adequate biomarkers, NEPC is usually detected at a very advanced stage. There is mounting evidence that molecular subtype changes seen in NEPC are enforced by widespread epigenetic alterations, in particular DNA methylation changes. In this study, we aim to devise a novel DNA methylation-based assay for molecular subtyping and disease monitoring from cell-free DNA (cfDNA). Methods: We analyzed genome wide methylation patterns in 56 prostate cancer patient-derived xenograft (PDX) and 128 mCRPC tumors using array- and sequencing-based assays. We integrated DNA methylation at promoters, gene bodies and transcription factor binding site (TFBS) to determine the landscape of methylation alterations at key lineage specific genes. Using whole genome methylation derived from tissue with matched expression data we developed a deep learning framework to predict gene expression directly from tissue or cfDNA. Using key marker genes, the model was used to discern tumor molecular phenotypes from tissue and cfDNA in three independent cohorts of mCRPC patients using whole genome bisulfite sequencing and low-pass Enzymatic Methyl-Seq (EM-seq). Results: We observed a tight association between promoter, gene body and TFBS methylation with gene expression. Inferring gene expression from methylation for lineage specific markers such as AR, KLK3, ASCL1, INSM1, SRRM4 and DLL3 we classified molecular subtypes from both tissue and cfDNA. Additionally, for AR and ASCL1, we identified core sets of TFBSs whose differential methylation allowed for accurate assay-independent molecular subtype quantification. Applying the optimized quantitative model to mCRPC patients who underwent comprehensive tissue sampling by rapid autopsy we observed accurate subtype classification from both tissue samples and cfDNA for all cases. A similar analytical performance was observed in additional clinical mCRPC cohorts with cfDNA. Conclusion: Whole-genome methylation analysis of cfDNA allows for the prediction of gene expression patterns in tumor tissues, enabling non-invasive tumor subclassification and assessment of therapeutic targets. Citation Format: Mohamed Adil, Brian Hanratty, Pallabi Mustafi, Chitvan Mittal, Helen Richards, Ilsa Coleman, Radhika Patel, Anna-Lisa Doebley, Robert Patton, Eden Cruikshank, Patricia Galipeau, Ruth Dumpit, Martine Roudier, Jin-Yih Low, Navonil Sarkar, Robert Montgomery, Eva Corey, Colm Morrissey, Peter Nelson, Gavin Ha, Michael Haffner. Advance prostate cancer detection through epigenomic profiling of cell-free DNA [abstract]. In: Proceedings of the AACR Special Conference: Liquid Biopsy: From Discovery to Clinical Implementation; 2024 Nov 13-16; San Diego, CA. Philadelphia (PA): AACR; Clin Cancer Res 2024;30(21_Suppl):Abstract nr PR011.

Abstract PR011: Advance prostate cancer detection through epigenomic profiling of cell-free DNA