CELL INTELLIGENCE · 肿瘤细胞治疗研究
肿瘤细胞治疗研究
预测 HER2+乳腺癌新辅助治疗疗效的广义非线性模型
英文原题：A generalized non-linear model predicting efficacy of neoadjuvant therapy in HER2+ breast cancer.
查看英文原题
A generalized non-linear model predicting efficacy of neoadjuvant therapy in HER2+ breast cancer.
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中文摘要
新辅助治疗（NAT）目前推荐用于人表皮生长因子受体2阳性乳腺癌（HER2+ BC）患者，这类患者通常预后较差。肿瘤免疫微环境深刻影响NAT的疗效。然而，TIL（肿瘤浸润淋巴细胞）或其特定亚群与HER2+ BC中NAT反应之间的相关性在很大程度上仍不清楚。在我们的研究中，295例患者的免疫浸润状态被分类为“免疫丰富”或“免疫贫乏”表型。“免疫丰富”表型与病理完全缓解（pCR）显著正相关。基于样条和逻辑回归的结果，有十个基因与pCR和免疫表型均相关。我们构建了一个结合线性和非线性基因效应的广义非线性模型，并使用内部和外部验证集（AUC = 0.819，0.797；分别）以及临床集（准确率 = 0.75）成功验证了其预测能力。
展开英文摘要原文
Neoadjuvant therapy (NAT) is currently recommended to patients with human epidermal growth factor receptor 2-positive breast cancer (HER2+ BC) that typically exhibit a poor prognosis. The tumor immune microenvironment profoundly affects the efficacy of NAT.
However, the correlation between tumor-infiltrating lymphocytes or their specific subpopulations and the response to NAT in HER2+ BC remains largely unknown. In our study, the immune infiltration status of 295 patients was classified as "immune-rich" or "immune-poor" phenotypes. The "immune-rich" phenotype was significantly positively related to pathological complete response (pCR). Ten genes were correlated with both pCR and the immune phenotype based on the results of spline and logistic regression.
We constructed a generalized non-linear model combining linear and non-linear gene effects and successfully validated its predictive power using an internal and external validation set (AUC = 0. 819, 0. 797; respectively) and a clinical set (accuracy = 0. 75).
论文信息
- 作者
- Wang Y、Liu X、Yu K、Xu S、Qiu P、Zhang X、Wang M、Xu Y
- 单位
- Department of Breast Surgery, The First Hospital of China Medical University, Shenyang, Liaoning Province 110001, China.China
- 期刊
- iScience2023 Apr 21
- 原文标识
- PubMed 36950120 · DOI 10.1016/j.isci.2023.106330
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