ArticleSignal transduction and targeted therapy2026
Spatially interpretable artificial intelligence framework to tailored neoadjuvant dual HER2 blockade in HER2-positive breast cancer.
Article in Signal transduction and targeted therapy, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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1 citing paper in PubMed.
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22 authors.
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Abstract
Neoadjuvant dual HER2 blockade with trastuzumab and pertuzumab plus chemotherapy represents the current standard-of-care for HER2-positive breast cancer. However, treatment responses remain heterogeneous, underscoring the lack of clinically practical tools for predicting treatment efficacy and informing personalized therapy. Here, we developed HER2-LADDER (Layered AI-based Dual-targeteD anti-HER2 Recommendation), a spatially interpretable and clinically accessible artificial intelligence framework that integrates clinicopathological and spatial topological features from routine hematoxylin and eosin (H&E) and HER2 immunohistochemistry (IHC) slides. Using these spatially derived features, HER2-LADDER accurately predicted response to neoadjuvant TCbHP/PCbHP, achieving AUCs of 0.944 in the model construction cohort (N = 276), 0.917 in the temporal validation cohort (N = 82), and 0.869 in the trial-based validation cohort (N = 85). On the basis of HER2-LADDER scores, patients were stratified into Low (highly responsive), Medium (responsive), and High (resistant) groups, identifying candidates for treatment de-escalation (THP or TCbH/PCbH), standard-of-care (TCbHP/PCbHP), or alternative regimens (e.g., next-generation anti-HER2 antibody-drug conjugates), respectively. Importantly, Xenium in situ profiling further revealed biological correlates underlying model predictions, including HER2-enriched tumor cell aggregation and neutrophil-helper T-cell interactions, thereby highlighting the mechanistic interpretability of the model. Collectively, HER2-LADDER unites digital pathology and high-resolution spatial profiling into a clinically accessible AI framework, offering a robust, transparent, and biologically grounded tool to tailor individualized HER2-targeted therapy optimization.
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