PO.EN01.01 · 内分泌肿瘤

肥胖可预测早期ER+乳腺癌新辅助芳香化酶抑制剂治疗后Ki67的更大变化

Obesity predicts greater changes in Ki67 after neoadjuvant aromatase inhibitor therapy in early-stage ER+ breast cancer

海报缩略图:肥胖可预测早期ER+乳腺癌新辅助芳香化酶抑制剂治疗后Ki67的更大变化
编号 2283 展板 5 时间 4/20 09:00–12:00 区域 Section 34 主讲 Lillian Lawrence
分会场 Hormone Receptor Signaling and Therapeutic Targeting
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作者与单位 Authors & Affiliations

Mary Dickinson Chamberlin1, Lillian A. Lawrence2, Roberta M. DiFlorio-Alexander1, Victoria Jones1, Eugene Demidenko1, Jonathan Marotti1

1Dartmouth Cancer Center, Lebanon, NH,2Geisel School of Medicine at Dartmouth, Hanover, NH

摘要 Abstract

中文摘要
背景:近70%的乳腺癌为ER阳性(ER+),抗雌激素治疗(如他莫昔芬和芳香化酶抑制剂(AI))是治疗的支柱。采用芳香化酶抑制剂的新辅助内分泌治疗(NET)可有效减轻肿瘤负荷,并可能预示更好的长期结局。Ki-67的显著降低具有预后意义,但改善NET反应的其他预测性临床因素尚未充分了解。我们在2025年圣安东尼奥乳腺癌研讨会上展示的绝经后接受NET治疗的ER+早期乳腺癌女性队列的初步数据显示,较大的肿瘤尺寸与更大的NET影像学反应相关,68%的肿瘤表现出影像学反应。BMI较高的女性乳腺癌复发和死亡风险更高,假设这是由于芳香化酶抑制不完全所致,但关于NET反应的具体数据尚缺乏。 目的:确定早期乳腺癌患者中BMI与NET后Ki67表达变化之间的关联。 方法:150例I-III期ER+、HER2-乳腺癌的绝经后女性在手术前接受芳香化酶抑制剂治疗7-168天(平均42.83天),并具备BMI、NET前后Ki67免疫组化数据。将NET前诊断活检的数字化Ki-67分析与手术标本的Ki-67进行比较。统计分析通过配对t检验确定p值。对照组分析正在进行中。 结果:NET前的平均Ki-67为18.62%(范围0.69%-61.56%)。NET后的平均Ki-67为4.17%(范围0.04%-46.07%)。影像学基线肿瘤平均尺寸为20.9 mm。我们发现BMI和基线肿瘤尺寸均与Ki-67表达差异显著相关(分别为p=0.038和p=0.026)。例如,根据我们的分析,预计BMI=30的女性,NET后Ki-67表达将有2倍的下降。Ki-67变化与NET天数之间无显著相关。 讨论:在我们的队列中,较高的BMI与Ki-67表达的更大降低相关(相比BMI<30的女性),提示NET对肥胖女性的细胞增殖抑制更强。Ki-67的抑制被认为是良好的预后指标,因此这一初步数据与以下假设相矛盾:即BMI>30的乳腺癌女性预后较差是由于对芳香化酶抑制剂反应不足所致。需要进一步研究以阐明这些发现,并针对长期AI依从性和其他结局预测因素验证这些数据。
查看英文原文 English abstract
Background: Nearly 70% of breast cancers are ER positive (ER+), and anti-estrogen therapy, such as tamoxifen and aromatase inhibitors (AI), are a mainstay of treatment. Neoadjuvant endocrine therapy (NET) with aromatase inhibitors is effective at reducing tumor burden and may be predictive of better long-term outcomes. A significant decrease in Ki-67 is prognostic but additional predictive clinical factors for improved response to NET are not well understood. Preliminary data from our post-menopausal cohort of women with ER+ early-stage breast cancers treated with NET presented at the San Antonio Breast Cancer Symposium 2025, showed correlation between larger tumor sizes and greater radiographic response to NET with 68% of tumors exhibiting a radiographic response. Women with higher BMI have a higher risk of breast cancer recurrence and mortality, hypothesized to be due to incomplete suppression of aromatase but specific data on response to NET is lacking. Objective: Determine the association between BMI and change in Ki67 expression after NET in patients with early-stage breast cancer. Methods: 150 post-menopausal women with Stage I-III ER+, HER2- breast cancer were treated with an aromatase inhibitor for 7-168 days (mean 42.83 days) prior to surgery and had BMI, pre- and post-NET Ki67 immunohistochemistry available. Digital Ki-67 analysis on the diagnostic biopsy before NET was compared to Ki-67 on the surgical specimen. Statistical analysis determined p-values from the pairwise t-test. Control group analysis is in progress. Results: The mean Ki-67 prior to NET was 18.62% (range 0.69%- 61.56%). The mean Ki-67 after NET was 4.17% (range 0.04%- 46.07%). The mean radiographic baseline tumor size 20.9 mm. We found that both BMI and the baseline tumor size significantly correlated with Ki-67 expression difference (p=0.038 and p= 0.026, respectively). For example, based on our analysis, it is expected that women with a BMI = 30, there will be a two-fold decrease in post-NET Ki-67 expression. There was no significant correlation between change in Ki-67 and number of days on NET. Discussion: In our cohort, higher BMI was associated with a greater decrease in Ki-67 expression compared to women with BMI <30, suggesting greater suppression of cell proliferation by NET in obese women. Suppression of Ki-67 is considered a good prognostic indicator, therefore this preliminary data contradicts the hypothesis that poorer outcomes for women with breast cancer and a BMI >30 are due to insufficient response to aromatase inhibitors. Additional research is needed to further elucidate these findings and validate this data against long-term AI adherence and other predictors of outcomes.
利益披露 Disclosure
M. D. Chamberlin, None.. L. A. Lawrence, None.. R. M. DiFlorio-Alexander, None.. V. Jones, None.. E. Demidenko, None.. J. Marotti, None.

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