{"data":{"event":{"id":"00db8d2d-b22b-4972-bd48-fc1f72e3bead","slug":"heterogeneous-graph-neural-networks-with-biological-prior-knowledge-for--74e75f51b1","title":"\n\nHeterogeneous graph neural networks with biological prior knowledge for interpretable drug repurposing in triple-negative breast cancer \n\n","short_summary":"Drug repurposing offers a cost-effective path to new therapies for triple-negative breast cancer (TNBC), a subtype with limited targeted treatment options. We present PRECISION, a framework integrating transcription factor (TF) regulatory networks, protein-protein interactions, and drug-target edges into a heterogeneous graph neural network (GNN) to identify TFs mediating drug sensitivity and prioritize repurposing candidates. The knowledge graph has 23,498 nodes and approximately 830,000 edges from CollecTRI, OmniPath and the PRISM screen. In a fair cell-line hold-out evaluation, the GNN matc","full_description":"Drug repurposing offers a cost-effective path to new therapies for triple-negative breast cancer (TNBC), a subtype with limited targeted treatment options. We present PRECISION, a framework integrating transcription factor (TF) regulatory networks, protein-protein interactions, and drug-target edges into a heterogeneous graph neural network (GNN) to identify TFs mediating drug sensitivity and prioritize repurposing candidates. The knowledge graph has 23,498 nodes and approximately 830,000 edges from CollecTRI, OmniPath and the PRISM screen. In a fair cell-line hold-out evaluation, the GNN matches ML baselines in global prediction (Pearson r = 0.76). Per-drug mechanistic attributions via Integrated Gradients on the trained GNN, replicated across three independent training seeds and robust to baseline choice (Spearman's rho = 0.94 between the mean and the random Gaussian baselines), highlight stress-response (CREB3L1), epithelial-mesenchymal transition (EMT; KLF8, ZEB1), stromal/TNBC-specific (AEBP1, MZF1), and epithelial (SPDEF) regulators as stable mediators of drug response. Multi-cohort validation in SCAN-B (n = 7,397), METABRIC (n = 1,979), and TCGA-BRCA (n = 1,072) shows that predicted drug sensitivity is associated with overall survival in 623 drugs in SCAN-B and 74 in METABRIC (FDR < 0.05). Fisher's meta-analysis identifies 551 drugs at FDR < 0.05, validated by positive controls paclitaxel (p_adj = 8.6x10-3), docetaxel (3.1x10-2), epirubicin (3.2x10-6), and talazoparib (1.4x10-4). Paired Wilcoxon tests in 39 AURORA-US patients with matched primary and metastatic samples confirm that 4 of the 10 IG-identified TFs (CREB3L1, KLF8, AEBP1, SPDEF) are significantly altered during metastatic progression after Bonferroni correction. An explicit rule applied to the PAM50-adjusted Cox multivariate results (penalizer = 0.1) selects seven candidates (osimertinib, saracatinib, erlotinib, brigatinib, pelitinib, entinostat, trametinib), revealing pharmacological convergence on the EGFR signaling axis.","ledger_type":"harm","primary_domain_id":"8f1af1b9-7302-4b51-aae9-fdfac04d158a","event_status":"provisional","event_date":"2026-09-14T00:00:00.000Z","discovery_date":"2026-09-14T00:00:00.000Z","first_published_date":"2026-09-14T00:00:00.000Z","last_reviewed_date":"2026-09-14T00:00:00.000Z","geographic_scope":"International","affected_population":null,"base_impact_tier":1,"base_score":"1.00","attribution_multiplier":"0.1000","evidence_multiplier":"0.1000","realization_multiplier":"0.2000","durability_multiplier":"0.5000","current_event_score":"0.001000","confidence_level":"low","score_explanation":"Auto-published from news ingest as a provisional placeholder. 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Attribution stays unspecified until a release is identified.","attribution_confidence":"medium","first_used_date":"2026-09-14T00:00:00.000Z","model_version_if_known":null,"review_status":"approved","created_at":"2026-09-14T21:00:48.792Z","model_slug":"unspecified-ai-system","model_name":"Unspecified AI system","identity_class":"unknown","is_internal":false,"family_name":"Unspecified","family_slug":"unknown-unspecified","organization_name":"Unknown","organization_slug":"unknown"}],"sources":[{"id":"9ba496f8-830d-45c1-acc5-1888efa11a75","event_id":"00db8d2d-b22b-4972-bd48-fc1f72e3bead","url":"\nhttps://www.biorxiv.org/content/10.64898/2026.09.08.750045v1?rss=1\n","canonical_url":"\nhttps://www.biorxiv.org/content/10.64898/2026.09.08.750045v1?rss=1\n","source_type":"preprint","publisher":"bioRxiv","author":null,"publication_date":"2026-09-14T00:00:00.000Z","retrieved_at":"2026-09-14T21:00:48.851Z","title":"\n\nHeterogeneous graph neural networks with biological prior knowledge for interpretable drug repurposing in triple-negative breast cancer \n\n","excerpt":"Drug repurposing offers a cost-effective path to new therapies for triple-negative breast cancer (TNBC), a subtype with limited targeted treatment options. We present PRECISION, a framework integrating transcription factor (TF) regulatory networks, protein-protein interactions, and drug-target edges into a heterogeneous graph neural network (GNN) to identify TFs mediating drug sensitivity and prioritize repurposing candidates. 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