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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 -