Abstract Purpose: Aromatase inhibitors (AI) are used to treat estrogen receptor (ER)–positive low-grade endometrioid endometrial cancer. In breast cancer, ESR1 mutations are rare at diagnosis (<5%) but are frequently acquired in AI-resistant cases and are considered one of the major resistance mechanisms to endocrine therapy. This study aimed to assess the prevalence of ESR1 mutations in hormonotherapy-naïve endometrial cancer samples and correlate them with molecular profiles, ER expression, and clinical outcomes. Experimental Design: A total of 147 patients with advanced endometrial cancer who had responded to first-line chemotherapy were recruited into the UTOLA trial. Archival endometrial cancer tumor tissues underwent sequencing of 127 genes, including ESR1. Only hotspot mutations in the ligand-binding domain were evaluated. ESR1 mutation prevalence was validated in the Genomics England dataset. In UTOLA, tumors were classified as POLE, MMR deficient, TP53abn, or no specific molecular profiles (NSMP) based on the Proactive Molecular Risk Classifier for Endometrial Cancer (PROMISE) classification. Results: Of 147 patients, 137 had sufficient tumor material for sequencing. ESR1 mutations were identified in eight tumors (6%), including Y537S/C/N (n = 4), L536H/P (n = 2), and E380Q (n = 2). A similar prevalence (3.5%) was found among 1,311 tumors in the Genomics England dataset. All ESR1 mutation cases were low-grade endometrioid endometrial cancer, ER-positive, and PR-positive, and classified as NSMP. Among patients with metastatic NSMP low-grade endometrioid endometrial cancer, 22% (8/37) harbored ESR1 mutations. Survival outcomes after platinum chemotherapy were similar between patients with ESR1 mutation endometrial cancer and ESR1 wild type (median, not reached vs. 25.3 months; P = 0.114). Conclusions: ESR1 mutations, while rare overall in treatment-naïve endometrial cancer, are more prevalent in patients with NSMP low-grade endometrioid endometrial cancer, potentially affecting AI efficacy. ESR1 status should be considered in selecting hormonotherapy and as a stratification factor in AI trials.