基于 DNA 超分子水凝胶的保护性 NK 细胞储库用于增强三阴性乳腺癌治疗
Protective NK Cell Reservoir Based on DNA Supramolecular Hydrogel for Enhanced Triple-Negative Breast Cancer Therapy.
CELL INTELLIGENCE · 肿瘤细胞治疗研究
肿瘤细胞治疗研究
利用数据挖掘以肿瘤微环境核心活检生物标志物预测乳腺癌新辅助治疗中的淋巴结反应
英文原题：Predicting nodal response to neoadjuvant treatment in breast cancer with core biopsy biomarkers of tumor microenvironment using data mining.
查看英文原题
Predicting nodal response to neoadjuvant treatment in breast cancer with core biopsy biomarkers of tumor microenvironment using data mining.
分数与星级只用于站内排序 —— 不代表疗效、安全性或个人适用性。
研究概要
我们开发了一种用于预测 cN+ 患者接受 NAST 后淋巴结 pCR 的临床工具，该工具纳入 TME 生物标志物，经十折交叉验证后 AUC 达 0.86。
中文摘要
目的
为活检证实淋巴结阳性的乳腺癌患者（临床分期cN+）建立模型，以预测其对新辅助全身治疗（NAST）的淋巴结应答；模型纳入肿瘤微环境（TME）特征，可用于规划腋窝外科分期操作。
方法
回顾性收集437例患者的临床和病理特征。复核粗针活检（CB）样本的间质含量和TIL（肿瘤浸润淋巴细胞）。采用Orange数据挖掘工具箱构建并评估模型。
结果
437例患者中，151例（34.6%）达到淋巴结病理完全缓解（ypN0）。预测模型纳入以下5个变量：ER、HER2、组织学分级、间质含量和TIL。经过分层十折交叉验证后，逻辑回归算法的受试者工作特征曲线下面积（AUC）为0.86，F1分数为0.72。采用列线图进行可视化。
结论
我们开发了一种临床工具，可预测cN+患者接受NAST后的淋巴结病理完全缓解；该工具纳入TME生物标志物，十折交叉验证后的AUC为0.86。
展开英文摘要原文
PURPOSE
To generate a model for predicting nodal response to neoadjuvant systemic treatment (NAST) in biopsy-proven node-positive breast cancer patients (cN+) that incorporates tumor microenvironment (TME) characteristics and could be used for planning the axillary surgical staging procedure.
METHODS
Clinical and pathologic features were retrospectively collected for 437 patients. Core biopsy (CB) samples were reviewed for stromal content and tumor-infiltrating lymphocytes (TIL). Orange Datamining Toolbox was used for model generation and assessment.
RESULTS
151/437 (34.6%) patients achieved nodal pCR (ypN0). The following 5 variables were included in the prediction model: ER, Her-2, grade, stroma content and TILs. After stratified tenfold cross-validation, the logistic regression algorithm achieved and area under the ROC curve (AUC) of 0.86 and F1 score of 0.72. Nomogram was used for visualization.
CONCLUSIONS
We developed a clinical tool to predict nodal pCR for cN+ patients after NAST that includes biomarkers of TME and achieves an AUC of 0.86 after tenfold cross-validation.
论文信息
- 作者
- Pislar N、Gasljevic G、Matos E、Pilko G、Zgajnar J、Perhavec A
- 第一作者单位
- Department of Surgical Oncology, Institute of Oncology Ljubljana, Ljubljana, Slovenia.
- 通讯作者单位
- Department of Surgical Oncology, Institute of Oncology Ljubljana, Ljubljana, Slovenia. aperhavec@onko-i.si.
- 期刊
- Breast cancer research and treatment2025 Feb
- 原文标识
- PubMed 39496911 · DOI 10.1007/s10549-024-07539-9
在 PubMed 查看 → 出版商原文（DOI） 全文 PDF（PMC）· 可下载 治疗专题与资料阅读指南 资料来源与翻译说明 报告译文或资料问题 →