Observational Study on the Sensitivity of Neoadjuvant Immunotherapy in Early Triple-Negative Breast Cancer Based on High-Resolution Dynamic Immune Signature Analysis
Observational Study on the Sensitivity of Neoadjuvant Immunotherapy in Early Triple-Negative Breast Cancer Based on High-Resolution Dynamic Immune Signature Analysis
Studies have reported that tumors with the same immunogenic mutations may induce T cell receptor (TCR) domains with similar antigen recognition functions. By assembling the complementarity-determining region 3 (CDR3) of TCRs from RNA-seq data and correlating them with 9142 samples from TCGA data, an in-depth analysis of the TCR pool in the tumor microenvironment found a strong correlation between the CDR3 sequences of tumor-infiltrating T cells and tumor mutation burden. Fairfax et al. found that in patients responding to tumor immunotherapy, the TCR immune pool of CD8+ T cells produces many clones with extremely high abundance (exceeding 0.5%) . Cader et al. also found significant changes in the TCR immune pool of patients with Hodgkin's lymphoma responding to PD-1 tumor immunotherapy. Based on these theoretical foundations, evaluating the dynamic changes of the TCR immune pool is expected to be used to analyze the immune characteristics and changes in diseases such as malignant tumors.
I. Background, Approximately 12-17% of breast cancer patients suffer from triple-negative breast cancer (TNBC) , which is prone to visceral and central nervous system metastases, poses significant therapeutic challenges, and has poor prognosis. Clinically starting with early TNBC, strengthening precise and individualized treatment, and reducing the risk of recurrence and metastasis are effective methods to improve the survival rate of TNBC patients.
The optimization of neoadjuvant therapy regimens has always been a research hotspot. Traditional neoadjuvant therapy regimens are primarily based on chemotherapy. The KEYNOTE-522 study showed that traditional chemotherapy combined with PD-1 inhibitors could significantly increase the pCR (pathological complete remission) rate, improving by 13.6% (64.8% vs 51.2%, P=0.00055) and reducing the risk of recurrence. However, this treatment also brings inevitable immune-related adverse events (AEs), with an overall AE incidence rate of 43.6%, a 3-5 grade AE incidence rate of 14.9%, and a fatal AE rate of 0.3%. Many immune-related AEs are lifelong, such as hypothyroidism. Therefore, how to precisely select immunologically sensitive patients for immunotherapy, while administering chemotherapy only to those who are immunologically insensitive, to avoid unnecessary immune-related AEs? How to predict patients who may experience severe immune-related adverse reactions early? These are urgent clinical issues that need to be addressed. Meanwhile, the mechanism of immunotherapy resistance remains unclear.
Studies have reported that tumors with the same immunogenic mutations may induce T cell receptor (TCR) domains with similar antigen recognition functions. By assembling the complementarity-determining region 3 (CDR3) of TCRs from RNA-seq data and correlating them with 9142 samples from TCGA data, an in-depth analysis of the TCR pool in the tumor microenvironment found a strong correlation between the CDR3 sequences of tumor-infiltrating T cells and tumor mutation burden. Fairfax et al. found that in patients responding to tumor immunotherapy, the TCR immune pool of CD8+ T cells produces many clones with extremely high abundance (exceeding 0.5%). Cader et al. also found significant changes in the TCR immune pool of patients with Hodgkin's lymphoma responding to PD-1 tumor immunotherapy. Based on these theoretical foundations, evaluating the dynamic changes of the TCR immune pool is expected to be used to analyze the immune characteristics and changes in diseases such as malignant tumors.
Research Aim and Significance This study aims to utilize deep sequencing technology to capture the molecular characteristics of the TCR immunome pool in the peripheral blood of patients with triple-negative breast cancer at different time points before and after neoadjuvant therapy. By combining artificial intelligence analysis algorithms, the investigators aim to accurately screen the patient population that will benefit from neoadjuvant immunotherapy for triple-negative breast cancer, avoid the occurrence of severe immune-related AEs, and explore the mechanisms of resistance to immunotherapy, ultimately achieving individualized and precise immunotherapy.
Research Content Analyze the changing characteristics of the TCR dynamic molecular group before neoadjuvant therapy in patients with triple-negative breast cancer, and compare the benefited and non-benefited patients receiving neoadjuvant therapy to screen for the population that will benefit from neoadjuvant immunotherapy for triple-negative breast cancer.
Analyze the changing characteristics of the TCR dynamic molecular population during neoadjuvant therapy in patients with triple-negative breast cancer, and compare patients who experience severe immune-related adverse events with those who do not, in order to prevent and detect severe immune-related adverse events early.
Analyze the changing characteristics of the TCR dynamic molecular population in patients with neoadjuvant therapy resistance for triple-negative breast cancer, examine the immune characteristics of resistant patients, and explore the mechanisms of resistance to immunotherapy.
Research Design
Inclusion/Exclusion Criteria and Withdrawal/Termination Criteria of the Subjects
1) Age: 18-60 years old; 2) Histologically confirmed triple-negative breast cancer, with immunohistochemistry showing ER <1%, PR <1%, HER2-negative (IHC 0 or 1+; or IHC 2+ ISH-negative); 3) Imaging-confirmed non-metastatic disease; 4) Clinical efficacy can be evaluated according to RECIST criteria; 5) European Functional Handicap Scale (MRC) of 0-2; 6) The investigator determines that the treatment is tolerable based on the patient's organ function level; 7) Volunteers willing to participate in this study, sign the informed consent, have good compliance, and are willing to cooperate with follow-up.
3. Exclusion Criteria:
4. Have high-risk populations been excluded? Yes; 5. Have interfering factors been excluded? Yes; 6. Withdrawal/Termination Criteria:
Inclusion Criteria:
Exclusion Criteria: