Breast cancer is a heterogeneous disease with diverse molecular subtypes, gene expression profiles, and immune infiltration, influencing treatment responses. Currently, numerous breast cancer treatment options have been developed, including chemotherapy, endocrine therapies, CDK4/6 inhibitors, PI3K inhibitors, HER2-targeted agents, ADCs, and immunotherapies. The complexity of disease and treatment underscores the need for personalized strategies to improve recurrence and survival outcomes.
1.Research Questions:
What clinical and molecular factors influence treatment response across drug classes?
How do specific therapies and combinations affect recurrence and survival in breast cancer subtypes?
Can a dynamic model predict recurrence using time-dependent clinical, genetic, and immune factors?
What is the efficacy of combination therapies compared to monotherapy in improving survival?
How can a comprehensive mortality risk model guide personalized treatment?
2.Objectives:
Identify key clinical and molecular markers affecting treatment response.
Analyze drug choices and survival outcomes, including targeted therapies.
Develop a dynamic model for predicting recurrence patterns.
Assess combination therapies’ effectiveness in enhancing survival.
Build a comprehensive risk model for breast cancer-specific mortality.
3.Scientific Rationale:
This study leverages UK Biobank’s clinical and molecular data to address the complexity of breast cancer treatment. Identifying factors influencing outcomes will optimize drug selection, refine personalized strategies, and improve long-term survival. The findings aim to inform therapeutic guidelines, reduce recurrence, and enhance patients’ quality of life.
