Research on AI Models for Predicting Breast Cancer Treatment Effectiveness to Guide Her-2 Targeted ADC Therapy

NCT07689929 | Not Yet Recruiting

• 1 other identifier: IRB-2026-60（ⅡT）
• Study Type: observational
• Target: 900
• Locations: 1 country
• Sites: 1

Brief Summary

This study aims to develop an AI-based predictive tool to help clinicians more accurately determine whether breast cancer patients can benefit from HER-2-targeted antibody-drug conjugate (T-DXd) therapy before treatment. While HER-2-targeted ADC drugs have significantly improved outcomes for patients with HER-2 positive and low-expression advanced breast cancer, there are notable individual differences in efficacy. Currently, there is a lack of precise clinical methods to predict response, which means some patients might receive ineffective treatment and face unnecessary drug side effects and financial burden. This study is a retrospective multicenter observational study, planning to collect pathological images (including HE staining and HER-2, ER, PR, Ki-67 immunohistochemical staining), proteomics data, and clinical efficacy information from HER-2 positive and low-expression advanced breast cancer patients who have received T-DXd treatment. The research will be carried out in five phases:

1. 1.Build a clinical database for ADC drug therapy, integrating basic patient information, treatment plans, efficacy data, and pathology specimen information from multiple centers.
2. 2.Use LC-MS/MS proteomics technology to screen for key protein markers related to T-DXd efficacy and use bioinformatics analysis to identify predictive protein indicators.
3. 3.Extract IHC staining features from pathological images and evaluate their correlation with efficacy alongside clinical data.
4. 4.Integrate proteomics, pathology, and clinical big data, using AI technologies such as foundational pathology models (like TITAN), biomedical large language models (like BioBERT), and protein large language models (like ESM2-15B). Apply a multiple instance learning strategy to build a multimodal efficacy prediction model, and evaluate the model's performance on the training set using 5-fold cross-validation.
5. 5.Establish an internal validation cohort (200 cases) and a multicenter external validation cohort (300 cases). Considering that the external validation group may lack proteomics data, the multimodal model will be fine-tuned and distilled into a simplified predictive model based on standard IHC features (HER-2, ER, PR, Ki-67, plus key protein markers identified from proteomics) and clinical text information, then its performance will be verified in the external cohort.

Conditions

Breast Cancer

Outcome Measures

Primary Outcomes (1)

• Area Under the Receiver Operating Characteristic Curve (AUC) of the Predictive Model

  Baseline (at initial diagnosis)

Study Arms (4)

HER2-positive T-DXd-sensitive cohort

HER2-positive T-DXd-resistant cohort

HER2-low T-DXd-sensitive cohort

HER2-low T-DXd-resistant cohort

Eligibility Criteria

• Sex: female
• Age Groups: Child (0-17), Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

Retrospective cohort (modeling and internal validation): We collected data from breast cancer patients at Zhejiang Cancer Hospital who received Youherde treatment from January 2023 to June 2026, to build a dataset for developing an efficacy prediction model. We integrated the following multimodal information: 1. HE-stained slides, blank slides, and IHC-stained tissue slide images for ER, PR, HER-2, and Ki-67 from the most recent biopsy of recurrent or metastatic lesions before starting Youherde treatment; 2. For patients with post-surgery recurrence/metastasis and available surgical specimens, HE-stained slides, blank slides, and IHC-stained tissue slide images for ER, PR, HER-2, and Ki-67 from the primary tumor; 3. Corresponding clinical efficacy data (PFS, ORR, etc.) and proteomics data. Prospective cohort (multicenter external validation): Working together with multiple centers, we will prospectively include breast cancer patients who are planned to receive second-line or higher treatm

You may qualify if:

• Retrospective Cohort (Modeling and Validation):
• Female, 18 years or older;
• Advanced breast cancer confirmed by pathology (AJCC 8th edition, stage IV);
• HER2 status known;
• Received at least 2 cycles of Pyrotinib monotherapy;
• Complete baseline IHC slides (HER2, ER, PR, Ki-67) and HE-stained slides;
• Efficacy assessed according to RECIST 1.1, with follow-up data (PFS or ORR).
• Prospective Cohort (External Validation):
• Meet criteria 1-3 above;
• Planning to receive Pyrotinib monotherapy as second-line or later treatment; if HER2-positive, previously received neoadjuvant/adjuvant H(P) therapy, and had metastatic recurrence within 12 months after completing treatment, with post-recurrence anti-HER2 therapy considered second-line treatment.
• Signed informed consent, agreeing to provide clinical info like imaging and pathology data before and after treatment.

You may not qualify if:

• All cohorts:
• Baseline IHC or HE slides of poor quality (e.g., faded, folded, or tissue loss \>10%);
• Previous treatment with other HER2-ADC drugs;
• History of other malignancies (except non-melanoma skin cancer or cases with no recurrence for over 5 years);
• Participation in other interventional clinical trials at the same time (past trials already completed are fine);
• Lost to follow-up or missing key clinical data during treatment (e.g., efficacy evaluation, dose adjustment records);
• Special treatment backgrounds that the AI model cannot analyze (e.g., combined local radiotherapy, severe infections, or other confounding factors).
• Pregnant or breastfeeding women;
• Contraindications to Üher (e.g., history of ILD, left ventricular ejection fraction \<50%, etc.);
• Unable to comply with regular follow-up (e.g., living in a remote area, mental disorders).

Sponsors & Collaborators

• Zhejiang Cancer Hospital: lead

Study Sites (1)

Zhejiang Cancer Hospital

Hangzhou, Zhejiang, 310022, China

Location

MeSH Terms

Conditions

Breast Neoplasms

Condition Hierarchy (Ancestors)

Neoplasms by Site; Neoplasms; Breast Diseases; Skin Diseases; Skin and Connective Tissue Diseases

Central Study Contacts

Hai Hu

CONTACT

13556111018; huhai@zjcc.org.cn

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: OTHER
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Chief Physician

Study Record Dates

• First Submitted: July 1, 2026
• First Posted: July 8, 2026
• Study Start: August 1, 2026
• Primary Completion (Estimated): June 1, 2028
• Study Completion (Estimated): December 1, 2028
• Last Updated: July 8, 2026

Record last verified: 2026-07

Data Sharing

• IPD Sharing: Will not share

Locations

CN (1)