Plasma-based comprehensive epigenomic profiling enables multiplexed prediction of target gene expression and detection of resistance mechanisms

Background: As novel targeted therapies including drug, radio-, and immune-conjugates, advance toward broad clinical implementation, there is an urgent need for scalable, minimally invasive diagnostics capable of resolving gene expression programs to guide patient selection and therapeutic monitoring. We applied a comprehensive epigenomics liquid biopsy and machine learning platform to infer tumor gene expression, delineate lineage plasticity, and reveal therapeutically relevant molecular programs and resistance mechanisms from only 1 mL of plasma. Methods: 1 mL of plasma from a pan-cancer cohort of patients (95 prostate adenocarcinoma (PRAD), 19 neuroendocrine prostate cancer (NEPC), 45 non-small cell lung cancer, 58 small cell lung cancer, 130 breast cancer, 21 gastroesophageal cancer, and 5 ovarian cancer) was profiled using Precede Biosciences liquid biopsy platform. All samples were assessed for the expression of therapeutically relevant targets. In prostate cancer, samples were further evaluated for the extent of neuroendocrine (NE) transformation, a key mechanism of lineage plasticity and therapeutic resistance. Results: Plasma-derived NE scores distinguished prostate adenocarcinoma (PRAD, n = 97) from neuroendocrine prostate cancer (NEPC, n = 15) along a continuous axis. Intermediate scores suggested partial or heterogeneous NE differentiation, with concurrent expression of PRAD- and NE-associated markers. Within these prostate cancer samples, predicted gene expression profiles clearly differentiated PRAD (high AR, KLK2, KLK3, FOLH1/PSMA) from NEPC (high CHGA, DLL3, SEZ6). DLL3 expression was further assessed across a multi-cancer cohort, and plasma-based predictions demonstrated a dynamic range consistent with published observations in tissue using both IHC and RNA-seq. For a subset of patients with matched FFPE tissue, immunohistochemistry for key drug targets is being performed to assess concordance with plasma-based expression predictions. Conclusion: Plasma-based epigenomic profiling resolved tumor gene expression programs of key therapeutic targets such as DLL3 and delineated a continuum of neuroendocrine differentiation, uncovering molecular states associated with therapeutic response and resistance. Collectively, these findings underscore the potential of a minimally invasive, comprehensive epigenomics platform to deliver real-time, gene expression-level insights into tumor evolution and target expression, thereby guiding therapeutic decision-making.