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Background <p>Pathologic complete response (pCR) after neoadjuvant chemotherapy and dual HER2 blockade is associated with improved outcomes in HER2-positive breast cancer, but treatment response remains heterogeneous. We aimed to develop and externally validate an interpretable model based on routine clinicopathological biomarkers to predict pCR and identify clinically meaningful response states.</p> Methods <p>In this multicenter retrospective study, 1,082 patients with HER2-positive breast cancer treated with neoadjuvant chemotherapy and dual HER2 blockade at two Chinese centers were included for model development and internal validation. An independent cohort of 307 patients from a third center was used for external validation. Machine-learning models were developed using routinely available pretreatment clinicopathological variables. Model performance was evaluated using the area under the receiver operating characteristic curve (AUC). Probability-landscape analyses were performed to characterize biomarker-defined response states associated with pCR.</p> Results <p>The final gradient boosting machine model achieved AUCs of 0.78 in internal testing and 0.70 in external validation. Probability-landscape analyses identified distinct response states with substantially different pCR probabilities. Low hormone receptor expression combined with HER2 immunohistochemistry (IHC) 3 + status defined a high-probability pCR state, whereas high hormone receptor expression, low Ki-67 expression, and HER2 IHC 2+/fluorescence in situ hybridization (FISH)-positive disease defined persistently low-probability states. Among baseline node-positive patients who achieved breast pCR, residual nodal disease was observed in only 6.1% of cases.</p> Conclusions <p>Routine clinicopathological biomarkers can be integrated into an interpretable machine-learning model to predict pCR and enable scalable response probability mapping in HER2-positive breast cancer receiving neoadjuvant chemotherapy and dual HER2 blockade. This approach may facilitate individualized treatment selection and support future treatment-adaptation and surgical de-escalation strategies.</p>

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Routine biomarkers map response probability in HER2-positive breast cancer treated with neoadjuvant chemotherapy and dual HER2 blockade

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年7月2日

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  • Zhengjun Yang,
  • Yuqing Dai,
  • Yinuo Li,
  • Li Xia,
  • Tianrui Li,
  • Xiao Chen,
  • Jiangrui Chi,
  • Yue Yu,
  • Xuejiao Lv,
  • Junjie Li,
  • Xi Chen,
  • Xuchen Cao

摘要

Background

Pathologic complete response (pCR) after neoadjuvant chemotherapy and dual HER2 blockade is associated with improved outcomes in HER2-positive breast cancer, but treatment response remains heterogeneous. We aimed to develop and externally validate an interpretable model based on routine clinicopathological biomarkers to predict pCR and identify clinically meaningful response states.

Methods

In this multicenter retrospective study, 1,082 patients with HER2-positive breast cancer treated with neoadjuvant chemotherapy and dual HER2 blockade at two Chinese centers were included for model development and internal validation. An independent cohort of 307 patients from a third center was used for external validation. Machine-learning models were developed using routinely available pretreatment clinicopathological variables. Model performance was evaluated using the area under the receiver operating characteristic curve (AUC). Probability-landscape analyses were performed to characterize biomarker-defined response states associated with pCR.

Results

The final gradient boosting machine model achieved AUCs of 0.78 in internal testing and 0.70 in external validation. Probability-landscape analyses identified distinct response states with substantially different pCR probabilities. Low hormone receptor expression combined with HER2 immunohistochemistry (IHC) 3 + status defined a high-probability pCR state, whereas high hormone receptor expression, low Ki-67 expression, and HER2 IHC 2+/fluorescence in situ hybridization (FISH)-positive disease defined persistently low-probability states. Among baseline node-positive patients who achieved breast pCR, residual nodal disease was observed in only 6.1% of cases.

Conclusions

Routine clinicopathological biomarkers can be integrated into an interpretable machine-learning model to predict pCR and enable scalable response probability mapping in HER2-positive breast cancer receiving neoadjuvant chemotherapy and dual HER2 blockade. This approach may facilitate individualized treatment selection and support future treatment-adaptation and surgical de-escalation strategies.