ER阳性乳腺癌中的ET耐药细胞群体：从图谱分析到治疗靶向

Even though most women with estrogen receptor positive (ER+) breast tumors can benefit from endocrine therapy (ET), up to 40% of these patients will eventually experience relapse. Moreover, when these tumors recur, they tend to be more metastatic and therapy-resistant, resulting in disease progression and fatalities. One contributing factor to ET failure and recurrence is intratumoral heterogeneity, where tumors have distinct populations of cells with different sensitivity to ET. Here, we developed an integrated framework that combines clinical and experimental data with cutting edge bioinformatics tools to systematically identify and target ET-resistant cell populations. Leveraging single cell RNA sequencing data from the FELINE clinical trial, we profiled tumors from nine ER+ breast cancer patients treated with letrozole at baseline and after 14 days of therapy. We found that ET-sensitive populations are conserved across patients, whereas ET-resistant populations are more heterogeneous and not defined by ER expression, signaling activity, or molecular subtype. Moreover, gene signatures from ET-resistant clusters are predictive of poor patient survival and adverse pathological features in METABRIC dataset. To functionally validate these findings and target ET-resistant cell populations, we build a panel of patient-derived xenograft organoid (PDxO) models that retain key features of the original tumors and mirror findings from the clinical trial. Using a novel predictive therapeutic pipeline that integrates transcriptional profiling with drug response modeling on PDxOs, we identified and validated candidate drugs targeting both shared and patient-specific ET-resistant populations, including known drugs already used in clinic, such as dasatinib, as well as novel compounds such as pluripotin and AZD8055. These findings demonstrate that ET-resistant cell states emerge early under therapeutic pressure and persist despite treatment, representing a major barrier to ET response. Our integrated framework allowed us to uncover both patient-specific and shared ET-resistant cell populations, and to test personalized therapeutic strategies for targeting ET resistance. Together, our findings reveal a complex and heterogeneous landscape of endocrine therapy response, highlighting the critical importance of single-cell resolution to inform therapeutic strategies aimed at overcoming resistance and improving patient outcomes.