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TY - JOUR JO - Advances in Clinical and Experimental Medicine J2 - Adv Clin Exp Med SN - 1899-5276 PB - Wroclaw Medical University VL - 33 IS - 10 PY - 2024 ID - Zhang2024-10-28 TI - The clinical impact of plasma estrogen receptor-1 mutation in patients with metastatic breast cancer: A meta-analysis AB - Background. The relevance of the discovered plasma ESR1 mutations in positive metastatic breast cancer (BC) patients who had progressing disease after aromatase inhibitor (AI)-based therapy is still being debated.Objectives. We conducted this meta-analysis to explore the prognostic and predictive role of plasma ESR1 mutations in patients with progressive BC who have previously received AI therapy.Material and Methods. We searched for relevant studies in the PubMed, Embase and Cochrane Library databases to be included in the meta-analysis. This study was performed to compute combined hazard ratios (HRs) with 95% confidence intervals (95% CIs) for the progression-free survival (PFS) rate and overall survival (OS) rate. Subgroup and sensitivity analyses were also performed. The heterogeneity between studies was evaluated using the I2 statistic.Results. In this meta-analysis, a total of 1,844 patients with metastatic BC and positive for estrogen receptors (ERs) were enrolled from 8 articles. The analysis revealed that patients with circulating ESR1 mutations had significantly worse PFS (HR: 1.34; 95% CI: 1.17–1.55; p < 0.001) and OS (HR: 1.59; 95% CI: 1.31–1.92; p < 0.001) compared to wild-type ESR1 patients. Subgroup analysis showed that the types of plasma ESR1 mutations were associated with differences in the prognosis of metastatic BC. The D538G mutation showed a statistically significant lower PFS (p = 0.03), while the Y537S mutation was not significantly correlated with PFS (p = 0.354).Conclusion. According to the findings of this meta-analysis, the assessment for plasma ESR1 mutations may provide prognostic and clinical guidance regarding subsequent endocrine therapy decisions for ER-positive, metastatic BC patients who had received prior therapy with AIs. AU - Zhang, Xiaoli AU - Tian, Ye AU - Mo, Dan AU - Chen, Wenli AU - Ding, Yi AU - Yang, Yanjiang AU - Li, Xinning SP - 1069 EP - 1076 DA - 2024-10-28 DO - 10.17219/acem/175816 UR - http://dx.doi.org/10.17219/acem/175816 ER -