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Development of a Multi-omics Prediction Model for Immunotherapy Response in Triple-Negative Breast Cancer Subtypes
Sponsor: Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences
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.
Official title: Construction and Validation of a Multi-omics Prediction Model to Assess Immunotherapy Efficacy in Patients With Triple-Negative Breast Cancer Subtypes Based on Genomic, Transcriptomic, Proteomic, and Immune Profiling Data
Key Details
Gender
FEMALE
Age Range
18 Years - Any
Study Type
OBSERVATIONAL
Enrollment
1000
Start Date
2025-04-17
Completion Date
2027-11-17
Last Updated
2026-01-15
Healthy Volunteers
No
Conditions
Interventions
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.
Locations (1)
Zhejiang Cancer Hospital
Hangzhou, Zhejiang, China