Clinical trial · Observational
Development of a Multi-omics Prediction Model for Immunotherapy Response in Triple-Negative Breast Cancer Subtypes
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
- Source
- ClinicalTrials.gov
- Retrieved
- Sep 8, 2026
- Layer
- normalized (units and labels harmonized; values unchanged)
- Run
- ING-CLINICALTRIALS-20260908-000001
Summary
Brief summary (as posted)
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
Conditions (1)
Free-text conditions as registered, with the CancerIndex entity they were reconciled to and the match type.
| Condition (as posted) | Mapped entity | Match | Confidence |
|---|---|---|---|
| Breast Neoplasms | Breast Neoplasm | ONTOLOGY_EXACT | 0.98 |
Interventions
Interventions (1)
| Intervention | Type | Mapped drug | Match |
|---|---|---|---|
| Retrospective Data Collection and Analysis | Other | — | UNRESOLVED |
Design
Arms and outcomes
Arms (2)
- label
- Cohort (2015-2023)
- description
- This group includes breast cancer patients who were treated at our institution from January 1, 2015, to September 30, 2023, and received immunotherapy or neoadjuvant immunotherapy. Clinical samples (e.g., fresh tissue from diagnostic punctures, residual tumor tissue post-surgery, blood samples, and imaging data) from these patients will be retrospectively collected and analyzed. The data will be used to build and validate the predictive model for immunotherapy efficacy.
- interventionNames
- Other: Retrospective Data Collection and Analysis
- label
- Cohort (2023-Present)
- description
- This group includes breast cancer patients treated at our institution starting from October 1, 2023, who are potential candidates for immunotherapy or neoadjuvant immunotherapy. Clinical samples (e.g., fresh tissue from diagnostic punctures, residual tumor tissue post-surgery, blood samples, and imaging data) will be prospectively collected. These samples will undergo multi-omics analysis (proteomics, transcriptomics, metabolomics) and advanced imaging techniques (imaging mass cytometry and spatial transcriptomics) to further refine and validate the predictive model for immunotherapy efficacy.
- interventionNames
- Other: Retrospective Data Collection and Analysis
Primary outcomes (1)
Eligibility
Eligibility (as posted)
- Sex
- Female
- Minimum age
- 18 Years
Show eligibility criteria text
Inclusion Criteria: \- Female, aged ≥ 18 years. Pathologically confirmed diagnosis of breast cancer. Patients who received immunotherapy/neoadjuvant immunotherapy at our institution between January 1, 2015, and September 30, 2023 (retrospective cohort), or patients who may receive immunotherapy/neoadjuvant immunotherapy starting from October 1, 2023 (prospective cohort). Availability of sufficient tumor tissue samples (e.g., fresh biopsy tissue, residual tumor tissue post-surgery). Availability of blood samples and imaging data. Signed informed consent (for the prospective cohort). Exclusion Criteria: * Male breast cancer patients. Inability to provide sufficient tumor tissue samples or other clinical data. Presence of severe comorbidities (e.g., active infections, severe cardiac, hepatic, or renal dysfunction) that may affect the safety assessment of immunotherapy. Lack of signed informed consent (for the prospective cohort).
References
Publications (1)
- DERIVEDDong H, Wang X, Zheng Y, Li J, Liu Z, Wang A, Shen Y, Wu D, Cui H. Mapping the rapid growth of multi-omics in tumor immunotherapy: Bibliometric evidence of technology convergence and paradigm shifts. Hum Vaccin Immunother. 2025 Dec;21(1):2493539. doi: 10.1080/21645515.2025.2493539. Epub 2025 Apr 24. PMID 40275437