Orchestra: Corroboration-Based Regulatory Candidate Discovery via Composed Bioinformatics MCP Agents

Abstract

Orchestra composes two independently built bioinformatics MCP servers -- RegNetAgents, which infers regulatory network topology, and CASCADE, which provides independent functional evidence (LINCS knockdown, DepMap essentiality, super-enhancer status, DoRothEA transcription-factor confidence) -- into one multi-agent LangGraph workflow. Its central claim is that requiring the two systems to agree on a candidate regulator yields a more trustworthy candidate than either alone. We test this on the TCGA tumor-acquired regulator tier, selecting each query's ten regulators by ARACNe mutual-information edge weight. Across BRCA/COAD focal genes and matched negative controls, requiring at least 2 of 4 evidence sources predicts known-driver status (IntOGen) among focal genes (odds ratio 3.15, Benjamini-Hochberg-adjusted p=0.016; 2.92 after deduplication) but shows no association among negative controls (p=0.70); a single source does not in BRCA/COAD. In a second panel (STAD) the pattern reproduces and is stronger (OR 6.71, adjusted p=0.0047), again with no association in controls. Corroboration adds value beyond MI edge weight in both panels (BRCA/COAD p=0.039; STAD p=0.0001), though modestly in BRCA/COAD. Deduplication, a permutation test and a reproduction of RegNetAgents' published results agree; a second, separately derived driver list (Vogelstein et al. 2013) is directionally consistent but too sparse to reach significance. All experiments invoke Orchestra's real agentic entry point.

arXiv comment20 pages, 3 figures. v4: driver reference set changed from OncoKB to IntOGen; data re-collected on RegNetAgents v1.3.0 / CASCADE v1.5.0 with updated panels; all results, tables and figures updated