Clinical value of circulating mutations for patients with metastatic breast cancer: a meta-analysis.

Cancer Manag Res

Sun Yat-Sen University Cancer Center, The State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, Guangdong, People's Republic of China,

Published: August 2018

Background: The clinical implication of plasma ESR1 mutations in the estrogen receptor (ER)-positive metastatic breast cancer (MBC) patients who had progressed after prior aromatase inhibitor (AI)-based therapy remains controversial. We conducted the first meta-analysis to investigate the prognostic significance and predictive role of plasma ESR1 mutations in MBC patients with prior exposure to AI therapy.

Materials And Methods: We searched PubMed, Embase, and Cochrane Library databases for eligible studies. Meta-analysis was conducted to calculate combined hazard ratios (HRs) with 95% CIs for progression-free survival (PFS) and overall survival (OS). Subgroup and sensitivity analyses were also performed.

Results: This study enrolled a total of 1,530 patients with ER-positive MBC cases from six articles, including 429 ESR1 mutation carriers (28.04%). Meta-analysis demonstrated that plasma ESR1 mutation carriers had significantly worse PFS (HR: 1.40, 95% CI: 1.17-1.66; <0.0001) and OS (HR: 1.65, 95% CI: 1.36-2.01; <0.0001) compared to wild-type ESR1. Subgroup analysis showed that plasma ESR1 mutations were associated with shorter PFS after AI-based treatment, but were not significantly predictive of outcome on fulvestrant-containing therapy (HR: 1.26, 95% CI: 0.98-1.62; =0.077). As for different ESR1 mutations, D538G mutation implied significantly worse PFS (HR: 1.50, 95% CI: 1.18-1.91; =0.01), while Y537S mutation was not correlated with PFS (HR: 1.65, 95% CI: 0.87-1.73; =0.134).

Conclusion: The meta-analysis indicated that plasma ESR1 mutation assessment may have prognostic significance and clinical value in guiding further endocrine therapy choice in ER+ MBC patients who received prior AI therapy.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6097501PMC
http://dx.doi.org/10.2147/CMAR.S173193DOI Listing

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