Anti-HER2 therapy response assessment for guiding treatment (de-)escalation in early HER2-positive breast cancer
Yiwei Tong1, Zhaoyu Hu2, Haoyu Wang1
1Department of General Surgery, Comprehensive Breast Health Center, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, 197 Ruijin Er Road, Shanghai, 200025, China.
Objectives:
Anti-HER2 targeted therapy significantly reduces risk of relapse in HER2 + breast cancer. New measures are needed for a precise risk stratification to guide (de-)escalation of anti-HER2 strategy.
Methods:
A total of 726 HER2 + cases who received no/single/dual anti-HER2 targeted therapies were split into three respective cohorts. A deep learning model (DeepTEPP) based on preoperative breast magnetic resonance (MR) was developed. Patients were scored and categorized into low-, moderate-, and high-risk groups. Recurrence-free survival (RFS) was compared in patients with different risk groups according to the anti-HER2 treatment they received, to validate the value of DeepTEPP in predicting treatment efficacy and guiding anti-HER2 strategy.
Results:
DeepTEPP was capable of risk stratification and guiding anti-HER2 treatment strategy: DeepTEPP-Low patients (60.5%) did not derive significant RFS benefit from trastuzumab (p = 0.144), proposing an anti-HER2 de-escalation. DeepTEPP-Moderate patients (19.8%) significantly benefited from trastuzumab (p = 0.048), but did not obtain additional improvements from pertuzumab (p = 0.125). DeepTEPP-High patients (19.7%) significantly benefited from dual HER2 blockade (p = 0.045), suggesting an anti-HER2 escalation.
Conclusions:
DeepTEPP represents a pioneering MR-based deep learning model that enables the non-invasive prediction of adjuvant anti-HER2 effectiveness, thereby providing valuable guidance for anti-HER2 (de-)escalation strategies. DeepTEPP provides an important reference for choosing the appropriate individualized treatment in HER2 + breast cancer patients, warranting prospective validation.
Clinical Relevance Statement:
We built an MR-based deep learning model DeepTEPP, which enables the non-invasive prediction of adjuvant anti-HER2 effectiveness, thus guiding anti-HER2 (de-)escalation strategies in early HER2-positive breast cancer patients.
Key Points:
• DeepTEPP is able to predict anti-HER2 effectiveness and to guide treatment (de-)escalation. • DeepTEPP demonstrated an impressive prognostic efficacy for recurrence-free survival and overall survival. • To our knowledge, this is one of the very few, also the largest study to test the efficacy of a deep learning model extracted from breast MR images on HER2-positive breast cancer survival and anti-HER2 therapy effectiveness prediction.
Insights
A novel deep learning model, DeepTEPP, uses breast MRI to predict anti-HER2 therapy effectiveness in HER2-positive breast cancer. This tool aids in personalizing treatment strategies, guiding whether to escalate or de-escalate therapy for better patient outcomes.
Area of Science:
- Oncology
- Radiology
- Artificial Intelligence
Background:
- Anti-HER2 targeted therapy is crucial for HER2-positive breast cancer, but precise risk stratification is needed for optimal treatment.
- Current methods may not fully capture individual patient risk for relapse and treatment response.
Purpose of the Study:
- To develop and validate a deep learning model (DeepTEPP) using preoperative breast MRI for risk stratification in HER2-positive breast cancer.
- To assess DeepTEPP's ability to predict the effectiveness of anti-HER2 therapies and guide treatment de-escalation or escalation.
Main Methods:
- A deep learning model, DeepTEPP, was developed using preoperative breast MRI data from 726 HER2-positive breast cancer patients.
- Patients were categorized into low-, moderate-, and high-risk groups based on DeepTEPP scores.
- Recurrence-free survival (RFS) was analyzed across risk groups according to received anti-HER2 therapies (no/single/dual).
Main Results:
- DeepTEPP effectively stratified patients into low (60.5%), moderate (19.8%), and high (19.7%) risk groups.
- Low-risk patients showed no significant benefit from trastuzumab, suggesting de-escalation.
- High-risk patients significantly benefited from dual HER2 blockade, indicating escalation is appropriate.
Conclusions:
- DeepTEPP is a pioneering MR-based deep learning model for non-invasively predicting adjuvant anti-HER2 effectiveness.
- The model provides valuable guidance for tailoring anti-HER2 (de-)escalation strategies in HER2-positive breast cancer.
- DeepTEPP offers a new reference for individualized treatment selection in early HER2-positive breast cancer, warranting prospective validation.
