Development of a Multi-omics Prediction Model for Immunotherapy Response in Triple-Negative Breast Cancer Subtypes
NCT06833723 · Status: ACTIVE_NOT_RECRUITING · Type: OBSERVATIONAL · Enrollment: 1000
Last updated 2026-01-15
Summary
This study aims to collect clinical samples from breast cancer patients who have undergone or are expected to undergo immunotherapy at our institution. The samples, including fresh tissue from diagnostic punctures, residual tumor tissue post-surgery, blood samples, and imaging data, will be used to build a predictive model for immunotherapy efficacy. The research will employ proteomics, transcriptomics, metabolomics sequencing, imaging mass cytometry (IMC), and spatial transcriptomics to construct a multi-omics, multi-dimensional (temporal and spatial) model to predict the effectiveness of immunotherapy.
Conditions
- Breast Neoplasms
Interventions
- OTHER
-
Retrospective Data Collection and Analysis
This is a retrospective study involving the collection and analysis of existing clinical data from breast cancer patients who received immunotherapy or neoadjuvant immunotherapy between January 1, 2015, and September 30, 2023. No new interventions are administered as part of this study. The data includes diagnostic puncture tissue, residual tumor tissue post-surgery, blood samples, and imaging data. These samples are analyzed using multi-omics approaches (proteomics, transcriptomics, metabolomics) and advanced imaging techniques (imaging mass cytometry and spatial transcriptomics) to build a predictive model for immunotherapy efficacy.
Sponsors & Collaborators
-
Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences
lead OTHER
Principal Investigators
-
Qin Wu · Hangzhou Institute of Medicine (HIM), Chinese Academy
Eligibility
- Min Age
- 18 Years
- Sex
- FEMALE
- Healthy Volunteers
- No
Timeline & Regulatory
- Start
- 2025-04-17
- Primary Completion
- 2025-12-17
- Completion
- 2027-11-17
Countries
- China
Study Locations
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