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<p>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&amp;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 (<i>N</i> = 276), 0.917 in the temporal validation cohort (<i>N</i> = 82), and 0.869 in the trial-based validation cohort (<i>N</i> = 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.</p>

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Spatially interpretable artificial intelligence framework to tailored neoadjuvant dual HER2 blockade in HER2-positive breast cancer

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年6月19日

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  • Xiang-Rong Wu,
  • Hong Lv,
  • Shen Zhao,
  • Xiao-Hua Zeng,
  • Lei-Jie Dai,
  • Yu-Zheng Xu,
  • Yu-Wei Li,
  • Zi-Yu Qiu,
  • Ji-Ting Huang,
  • Ning-Ning Zhang,
  • Li Chen,
  • Min He,
  • Yi-Zhi Zhao,
  • Lin Yang,
  • Tong Zhou,
  • Jun-Jie Li,
  • Jiong Wu,
  • Yi-Zhou Jiang,
  • Wen-Tao Yang,
  • Gen-Hong Di,
  • Zhi-Ming Shao,
  • Ding Ma

摘要

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.