Development of a Multi-omics Prediction Model for Immunotherapy Response in Triple-Negative Breast Cancer Subtypes

Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences

Status: Active, not recruiting

Conditions: Breast Neoplasms

Treatments: Other: Retrospective Data Collection and Analysis

Study type: Observational

Funder types: Other

Identifiers

NCT06833723

HIM-1

Details and patient eligibility

About

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.

Full 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.

Enrollment: 1,000 estimated patients

Sex: Female

Ages: 18+ years old

Volunteers: No Healthy Volunteers

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).

Trial design

1,000 participants in 2 patient groups

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.

Treatment:

Other: Retrospective Data Collection and Analysis

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.

Treatment:

Other: Retrospective Data Collection and Analysis