Abstract LB298: Molecular phenotype classification of metastatic prostate cancer by cell-free DNA methylation analysis

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 transcription factors (TF), 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 absence 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 60 prostate cancer patient-derived xenograft (PDX) and 133 mCRPC tumors using array- and sequencing-based assays. We integrated DNA methylation with TF cistrome data to determine the landscape of methylation alterations at key lineage TF binding sites (TFBS). A linear regression model was trained on low-pass Enzymatic Methyl-Seq (EM-seq) cfDNA data derived from PDXs to identify molecular subtype specific DNA methylation changes at these TFBS. The model performance was optimized with in silico admixture experiments. This model was then used to discern tumor molecular phenotypes from cfDNA in three independent cohorts of mCRPC patients using low-pass whole genome bisulfite sequencing and EM-seq. Results: We observed a strong association between TFBS methylation and TF expression. For lineage specific TFs such as AR and ASCL1, we identified core sets of TFBSs whose differential methylation allowed for accurate assay-independent molecular subtype classification in tumor tissues. Applying an optimized quantitative model to mCRPC patients who underwent comprehensive tissue sampling by rapid autopsy we observed perfect subtype prediction from both tissue samples and cfDNA (AUC=1). A similar analytical performance was observed in additional clinical mCRPC cohorts with cfDNA. Conclusions: We show that methylation patterns at TFBSs can determine TF activity and can be used to classify molecular subtypes from both tumor tissue and cfDNA. For prostate cancer, we demonstrate that this approach can accurately detect NEPC by cost-effective low-pass EM-seq. More broadly, this study provides a novel analysis framework for robustly assessing molecular tumor phenotypes in cfDNA with applications in solid and liquid tumor diagnostics. Citation Format: Mohamed Adil, Brian Hanratty, Pallabi Mustafi, Ilsa Coleman, Radhika Patel, Anna-Lisa Doebley, Robert Patton, Eden Cruikshank, Patricia Galipeau, Ruth Dumpit, Martine Roudier, Jin-Yih Low, Navonil De Sarkar, Robert Montgomery, Eva Corey, Colm Morrissey, Peter Nelson, Gavin Ha, Michael Haffner. Molecular phenotype classification of metastatic prostate cancer by cell-free DNA methylation analysis [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 2 (Clinical Trials and Late-Breaking Research); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(8_Suppl):Abstract nr LB298.