The standard approach of using one-size-fits-all endocrine therapy for hormone receptor-positive and HER2-negative (HR+/HER2-) breast cancers has faced significant challenges due to variations in treatment response among individuals. To overcome this challenge, we conducted a comprehensive study that integrated data from both "bench" (multi-omics sequencing and functional drug response testing) and "bedside" (multicenter real-world and clinical trial cohorts) studies. Classification of HR+/HER2- breast cancer into four subtypes enabled effective subtyping-directed precision treatment strategies: endocrine therapy alone for the canonical luminal subtype; the addition of CDK4/6 and PARP inhibitors for the proliferative subtype; immunotherapy for the immunogenic subtype; and tyrosine kinase inhibitors for the receptor tyrosine kinase-driven subtype. Development of clinically applicable deep learning-based digital pathology classification demonstrated that matched treatment guided by subtyping-directed precision treatment strategies outperformed unmatched approaches in a real-world cohort and the I-SPY2 trial. Overall, this study emphasizes the superiority of subtyping-directed precision treatment strategies for HR+/HER2- breast cancer.
Zhu et al. (Mon,) studied this question.