Abstract 6873: Knowledge graph driven insights and drug repurposing opportunities for neuroendocrine prostate cancer.
Pawan Verma, Abhishek Jha, Nobal Kishor Dhruw, Manimala Sen, Dharani Dadi, Prasanna Kumar Sekar Sekar
Cancer Research
Problems Identified (4)
NEPC aggressive disease: Neuroendocrine prostate cancer is a highly aggressive prostate cancer subtype with poor clinical outcomes.
NEPC therapeutic gap: NEPC has limited effective therapeutic options and loses conventional therapeutic targets after lineage plasticity.
Treatment-induced lineage plasticity: The key drug repurposing challenge in NEPC is described as treatment-induced lineage plasticity driven by specific regulatory alterations.
Limited NEPC datasets: Few publicly available NEPC datasets hamper early-stage research and development.
Proposed Solutions (4)
Biomedical knowledge graph integration: A biomedical knowledge graph integrates more than 20 curated knowledge sources to manage and explore complex biomedical information.
No-code KG exploration GUI: A GUI-based no-code application enables users to derive insights from the knowledge graph.
KG-based dependency-profile query: The knowledge graph is queried to identify genes with dependency profiles similar to MYCN essentiality.
KG-driven drug repurposing: The approach uses upstream or downstream network effects and shared molecular underpinnings to find drugs approved for other diseases that may treat NEPC.
Results (3)
Integrated BKG resource:
No-code insight application:
MYCN-like dependency gene identification:
Research Domain
Bioinformatics and Genomic Networks; neuroendocrine prostate cancer drug repurposing