The central challenge in addressing neuroendocrine prostate neoplasms (NEPC) lies in its aggressive biology and the complete lack of approved, dedicated therapies. Treatment-induced NEPC is an increasingly lethal mechanism of therapeutic resistance, emerging through a dynamic "phenotypic switch" known as lineage plasticity. Prostate adenocarcinoma cells reprogram themselves, shedding their luminal identity to become AR-independent cells that render standard AR-targeted therapies completely ineffective.

At Elucidata, we help biopharma companies tackle these complex biological mechanisms by moving beyond simple, static data associations. Our platform enables the architecture of Causal Knowledge Graphs (CKGs) designed to model the drivers, enablers, and consequences of dynamic biological events. By integrating robust preclinical datasets such as genome-scale CRISPR/RNAi screens from the Cancer Dependency Map (DepMap), we ground biological hypotheses in high-throughput data to predict novel therapeutic responses.

Whether you are seeking to target the MYCN-AURKA-EZH2 driver axis, exploit new phenotypic vulnerabilities like DLL3, or identify acquired dependencies induced by therapy, our framework turns intricate multi-omics data into mechanistically-grounded drug repurposing strategies.

Meet our team at AACR 2026! Pawan Verma (Lead Bioinformatics Engineer) and Kewal Mishra (Associate Solution Architect) will be on-site to discuss how transitioning from associative networks to causal models can help you intercept disease trajectories and discover precise, targetable vulnerabilities.

‍