PO.CL01.04 · 临床研究

外周免疫指标预测炎性乳腺癌的病理完全缓解和残余癌负荷

Peripheral immune indices predict pathologic complete response and residual cancer burden in inflammatory breast cancers

海报缩略图:外周免疫指标预测炎性乳腺癌的病理完全缓解和残余癌负荷
编号 3744 展板 16 时间 4/20 02:00–05:00 区域 Section 41 主讲 Bora Lim, MD
分会场 Biomarkers Predictive of Therapeutic Benefit 4
查看 PDF 下载 PDF 🔒 查看 / 下载完整 PDF 需登录并开通下载套餐 · 查看套餐 / 开通 AACR 官方页面

作者与单位 Authors & Affiliations

Hui Gao1, Ranjan Upadhyay1, Angela Alexander2, Angela N. Marx3, Megumi Kai3, Chelain R. Goodman1, Azadeh Nasrazadani4, Savitri Krishnamurthy1, Anthony Lucci5, Rachel M. Layman1, Sadia Saleem3, Vicente Valero6, Michael C. Stauder7, Susie X. Sun8, Gary J. Whitman9, Miral M. Patel9, Huong C. Le-Petross9, Chasity L. Yajima3, Lily Villarreal3, Heather Lopez3, Bora Lim1

1UT MD Anderson Cancer Center, Houston, TX,2TRIUMPH Postdoctoral Fellow, UT MD Anderson Cancer Center, Houston, TX,3Breast Medical Oncology, UT MD Anderson Cancer Center, Houston, TX,4Azadeh Nasrazadani (Individual),5Professor, Dept. of Surgical Oncology, UT MD Anderson Cancer Center, Houston, TX,6Professor of Medicine, UT MD Anderson Cancer Center, Houston, TX,7Assistant Professor, UT MD Anderson Cancer Ctr., Houston, TX,8Breast Surgical Oncology, UT MD Anderson Cancer Center, Houston, TX,9Diagnostic Imaging, UT MD Anderson Cancer Center, Houston, TX

摘要 Abstract

中文摘要
背景:循环免疫谱为监测乳腺癌治疗反应提供了一种非侵入性方法。炎性乳腺癌(IBC)虽在临床上具有独特性,但彼此之间共有耐药和免疫逃逸特征。为识别IBC中的免疫逃逸机制和早期反应预测因子,我们对纵向外周免疫标志物进行了分析,评估其与病理完全缓解(pCR)和残余癌负荷(RCB)在不同治疗方案和亚型间的关联,并开发了复合免疫指标以在类别不平衡的情况下改进预测。 方法:对93例(n=93)接受IBC新辅助或诱导治疗患者的外周血样本进行多参数流式细胞术分析,并按亚型、方案、pCR和RCB进行标注。组间比较采用Mann-Whitney U检验(pCR)和Kruskal-Wallis检验(RCB)。预测模型包括带类别加权的逻辑回归和随机森林分类器。复合指标包括由z标度组分衍生的CD8耗竭、CD8记忆、NK-ADCC评分。采用5折交叉验证并以AUC、PR-AUC和校准评估模型性能。计划的扩展包括留一方案验证、时间分析以及使用预注册流程的外部评估。 结果:单变量分析发现pCR与CD8耗竭指数(p≈0.007)和CD8记忆指数(p≈0.038)存在显著关联,NK-ADCC呈趋势(p≈0.078)。纳入组合指标的逻辑回归和随机森林模型将AUC从单独关联时的约0.70提高至约0.92±0.07(5折CV),尽管由于仅有多达5例pCR事件而存在较宽的不确定性。与pCR相关的单个标志物包括Stem_Memory_CD8、NKG2A_ADCC和CTLA4_CD8,而CD56hi NK和NKG2A⁺CD56hi NK可区分RCB梯度(校正前p<0.05)。鉴于样本量,在FDR校正后无标志物仍保持显著。 结论:外周免疫特征,尤其是组合的CD8耗竭、记忆指数、NK ADCC,与较高的pCR和较低的RCB相关。复合指标优于单一标志物,并捕捉了免疫激活与抑制之间的平衡。正在进行的跨队列验证将评估其在不同方案和亚型间的可推广性。严重的类别不平衡(约5/93 pCR)和较小的亚组规模限制了我们分析的精确度。研究结果具有假设生成意义,需在更大规模的前瞻性队列中进行外部验证。
查看英文原文 English abstract
Background: Circulating immune profiles offer a noninvasive approach to monitor treatment response in breast cancer. Inflammatory breast cancer (IBC), although clinically distinct, shares resistance and immune evasion features among each other. To identify immune escape mechanisms and early predictors of response in IBC, we profiled longitudinal peripheral immune markers and evaluated their association with pathologic complete response (pCR) and residual cancer burden (RCB) across treatment regimens and subtypes and developed composite immune indices to improve prediction under class imbalance. Methods: Peripheral blood samples (n=93) from patients treated with neoadjuvant or induction therapy for IBC were analyzed by multiparameter flow cytometry and annotated for subtype, regimen, pCR, and RCB. Group comparisons used Mann-Whitney U (pCR) and Kruskal-Wallis (RCB). Predictive models included logistic regression and random forest classifiers with class-weighting. Composite indices included CD8 Exhaustion, CD8 Memory, NK-ADCC scores derived from z-scaled components. Model performance was assessed using 5-fold cross-validation with AUC, PR-AUC, and calibration. Planned extensions include leave-one-regimen validation, temporal analyses, and external evaluation using a pre-registered pipeline. Results: Univariate analyses identified significant associations between pCR and the CD8 Exhaustion Index (p≈0.007) and CD8 Memory Index (p≈0.038), with NK-ADCC trending (p≈ 0.078). Logistic regression and random forest models incorporating combined indices improved AUC from ~0.70 when individually associated, to ~0.92 ± 0.07 (5-fold CV), albeit with wide uncertainty due to only up to 5 pCR events. Individual markers linked to pCR included Stem_Memory_CD8, NKG2A_ADCC, and CTLA4_CD8, while CD56hi NK and NKG2A +CD56hi NK distinguished RCB gradients (p<0.05 before correction). No markers remained significant after FDR adjustment given sample size. Conclusions: Peripheral immune signatures, particularly combined CD8 exhaustion, memory indices, NK ADCC were associate with higher pCR and lower RCB. Composite indices outperform single markers and capture the balance between immune activation and suppression. Ongoing cross-cohort validation will assess generalizability across regimens and subtypes. Severe class imbalance (~5/93 pCR) and small subgroup sizes constrain precision of our analysis. Findings are hypothesis-generating and warrant external validation in larger prospective cohorts.
利益披露 Disclosure
H. Gao, None.. A. N. Marx, None.. M. Kai, None.. C. R. Goodman, None.. S. Saleem, None.. S. X. Sun, None.. G. J. Whitman, None.. M. M. Patel, None.. H. C. Le-Petross, None.. C. L. Yajima, None.. L. Villarreal, None.. H. Lopez, None.

← 返回 AACR 2026 检索