Breast Neoplasms
Conditions
Brief 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.
Detailed description
This research will utilize a comprehensive approach by analyzing various types of clinical samples from breast cancer patients treated with immunotherapy. The integration of proteomic, transcriptomic, and metabolomic data, along with advanced imaging techniques like IMC and spatial transcriptomics, will allow for a detailed understanding of the tumor microenvironment and its response to immunotherapy. This multi-dimensional analysis aims to enhance the accuracy of predicting immunotherapy outcomes, thereby aiding in personalized treatment strategies for breast cancer patients. The study adheres strictly to ethical guidelines, ensuring patient confidentiality and welfare are maintained throughout the research process.
Interventions
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
Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences
Study design
Observational model
COHORT
Time perspective
RETROSPECTIVE
Eligibility
Sex/Gender
FEMALE
Age
18 Years to No maximum
Healthy volunteers
No
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).
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Predictive Accuracy of Immunotherapy Efficacy Model | From the date of sample collection (retrospective cohort: 2015-2023; prospective cohort: 2023-present) until the end of follow-up (up to 5 years post-treatment). | The primary outcome is the predictive accuracy of the multi-omics and multi-dimensional model in determining the efficacy of immunotherapy in breast cancer patients. The model will be evaluated based on its ability to correctly classify patients as responders or non-responders to immunotherapy using clinical outcomes (e.g., progression-free survival, overall survival) as the gold standard. |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| Correlation Between Multi-Omics Profiles and Immunotherapy Response | From the date of sample collection until the end of follow-up (up to 5 years post-treatment). | To assess the relationship between proteomic, transcriptomic, and metabolomic profiles of tumor tissue and the clinical response to immunotherapy. |
Countries
China
Outcome results
None listed