Breast cancer is the most common cancer globally, with the highest mortality rate among malignant tumors in women. Hormone receptor (HR)-positive and human epidermal growth factor receptor-2 (HER2)-negative breast cancer accounts for 60-70% of all breast cancer cases. Although this subtype of breast cancer has a better prognosis compared to HER2-positive and triple-negative breast cancer, the long-term recurrence rate is not low, especially for those tumors with active proliferation. Notably, the treatment options concerning HR-positive/HER2-negative breast cancer are relatively limited. For instance, conventional neoadjuvant chemotherapy often yields poor results in downstaging. In recent years, neoadjuvant endocrine therapy has also become one of the treatment options, but the tumor response rate has not improved. Furthermore, the mechanisms of drug resistance in this subtype remain largely unknown. Herein, we aim to establish multi-omics-based predictive scoring systems for therapeutic response in patients with HR+HER2- breast cancer using data from the UK Biobank database. This will help differentiate patients who can benefit from different treatment strategies.
