RegNetAgents: A Multi-Agent Framework for Cross-Network Regulatory Driver Identification in Cancer Genomics

Researchers introduced RegNetAgents, a multi-agent AI framework for identifying regulatory drivers across cancer gene networks. The system integrates TCGA and single-cell regulatory networks to classify and rank candidate regulators, showing significant enrichment for known cancer genes in breast and colorectal cancer analyses. This framework enables structured evaluation of oncogenic potential and druggability for hypothesis generation.

arXiv:2607.14097v1 Announce Type: new Abstract: We introduce RegNetAgents, an AI-oriented multi-agent framework for structured, query-driven regulatory candidate identification across heterogeneous gene regulatory networks. The system enables unified analysis of bulk tumor and single-cell-derived ARACNe networks by integrating TCGA-derived cancer networks with large-scale single-cell regulatory networks from the GREmLN project. For a given focal gene, the framework performs dual-network classification, cancer gene filtering using OncoKB annotations, and mode-of-action MoA assignment for tumor-derived regulatory relationships. Candidates are ranked by evidence consistency across networks Both, TCGA-only, GREmLN-only . The system is implemented as a multi-agent LangGraph DAG workflow, accessible through a unified Python API and Model Context Protocol MCP client, operating as a downstream analytical layer over precomputed regulatory networks rather than a network inference method. Across eleven breast cancer BRCA and twelve colorectal cancer COAD focal genes, RegNetAgents identifies candidate regulators significantly enriched for OncoKB-annotated cancer genes. TCGA-derived candidates show strong enrichment Stouffer Z = 6.69 for BRCA and 6.95 for COAD , while GREmLN-derived candidates also demonstrate significant enrichment Z = 5.51 for BRCA and 7.06 for COAD; all p < 0.0001 . No enrichment is observed in housekeeping or non-driver control gene sets, supporting signal specificity. An extended module enables structured evaluation of oncogenic potential, druggability, clinical relevance, and network vulnerability, supporting end-to-end interpretation from candidate identification to biological hypothesis generation. RegNetAgents establishes an interpretable AI framework for cross-network regulatory candidate identification in cancer genomics.