Comprehensive Analysis and Prediction of HER2-Targeted Therapy Insensitivity Among HER2-Positive Breast Cancer

Qingyao Shang1,2, Zian Lin3, Jennifer Plichta4

  • 1Department of Breast Surgical Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China.

Cancers
|March 28, 2026
PubMed
Abstract

Insights

Neoadjuvant treatment (NAT) sensitivity in HER2-positive breast cancer varies. Younger age, higher clinical stage, and HR-negative status predict better response, informing risk stratification.

Area of Science:

  • Oncology
  • Breast Cancer Research
  • Clinical Trial Analysis

Background:

  • HER2-targeted therapy is standard for HER2-positive early-stage breast cancer.
  • A significant subset of patients exhibit limited pathological response to neoadjuvant treatment (NAT).

Purpose of the Study:

  • To identify clinicopathological factors associated with NAT sensitivity in HER2-positive breast cancer.
  • To develop a predictive model for NAT response.

Main Methods:

  • Retrospective analysis of 13,004 HER2-positive breast cancer patients from the National Cancer Database (2010-2022).
  • NAT sensitivity defined by pathological complete response (pCR) and clinical-to-pathologic stage migration.
  • Multivariable logistic regression used to develop a predictive model based on baseline variables.

Main Results:

  • 28.1% of patients achieved pCR; 19.6% were NAT-insensitive.
  • NAT-insensitive patients were older, more likely T1c/node-negative; NAT-sensitive patients had higher clinical T/N stages.
  • HR-positive status and lower tumor grade correlated with treatment insensitivity; NAT-insensitive patients had worse overall survival (OS).

Conclusions:

  • NAT sensitivity in HER2-positive breast cancer shows significant heterogeneity.
  • Younger age, higher clinical stage, invasive ductal histology, higher tumor grade, and HR-negative status predict improved response.
  • A validated predictive model using baseline variables can aid in risk stratification.

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