Huang Huafang, Wang Guilin, Zeng Dongyun, Roche Luz Angela Torres-DE LA, Zhuo Rui, Wilde Rudy Leon DE, Wang Wanwan, Kahlert Ulf D, Shi Wenjie
Department of Breast Surgery, EUSOMA Certificate Breast Cancer Center (No.1037/00), Guilin TCM Hospital of China, Guilin, 541002, China.
University Hospital for Gynecology, Pius-Hospital, University Medicine Oldenburg, Oldenburg, 26121, Germany.
Oncol Res. 2025 Feb 28;33(3):631-640. doi: 10.32604/or.2024.054642. eCollection 2025.
Neoadjuvant chemotherapy (NAC) significantly enhances clinical outcomes in patients with triple-negative breast cancer (TNBC); however, chemoresistance frequently results in treatment failure. Consequently, understanding the mechanisms underlying resistance and accurately predicting this phenomenon are crucial for improving treatment efficacy.
Ultrasound images from 62 patients, taken before and after neoadjuvant therapy, were collected. Mitochondrial-related genes were extracted from a public database. Ultrasound features associated with NAC resistance were identified and correlated with significant mitochondrial-related genes. Subsequently, a prognostic model was developed and evaluated using the GSE58812 dataset. We also assessed this model alongside clinical factors and its ability to predict immunotherapy response.
A total of 32 significant differentially expressed genes in TNBC across three groups indicated a strong correlation with ultrasound features. Univariate and multivariate Cox regression analyses identified six genes as independent risk factors for TNBC prognosis. Based on these six mitochondrial-related genes, we constructed a TNBC prognostic model. The model's risk scores indicated that high-risk patients generally have a poorer prognosis compared to low-risk patients, with the model demonstrating high predictive performance ( = 0.002, AUC = 0.745). This conclusion was further supported in the test set ( = 0.026, AUC = 0.718). Additionally, we found that high-risk patients exhibited more advanced tumor characteristics, while low-risk patients were more sensitive to common chemotherapy drugs and immunotherapy. The signature-related genes also predicted immunotherapy response with a high accuracy of 0.765.
We identified resistance-related features from ultrasound images and integrated them with genomic data, enabling effective risk stratification of patients and prediction of the efficacy of neoadjuvant chemotherapy and immunotherapy in patients with TNBC.
新辅助化疗（NAC）显著改善三阴性乳腺癌（TNBC）患者的临床结局；然而，化疗耐药常常导致治疗失败。因此，了解耐药机制并准确预测这一现象对于提高治疗效果至关重要。
收集62例患者新辅助治疗前后的超声图像。从公共数据库中提取线粒体相关基因。识别与NAC耐药相关的超声特征，并将其与重要的线粒体相关基因进行关联。随后，使用GSE58812数据集开发并评估了一个预后模型。我们还将该模型与临床因素一起评估，以及其预测免疫治疗反应的能力。
三组TNBC中共有32个显著差异表达基因与超声特征密切相关。单因素和多因素Cox回归分析确定六个基因是TNBC预后的独立危险因素。基于这六个线粒体相关基因，我们构建了一个TNBC预后模型。该模型的风险评分表明，与低风险患者相比，高风险患者的预后通常较差，该模型具有较高的预测性能（P = 0.002，AUC = 0.745）。这一结论在测试集中得到进一步支持（P = 0.026，AUC = 0.718）。此外，我们发现高风险患者表现出更晚期的肿瘤特征，而低风险患者对常用化疗药物和免疫治疗更敏感。特征相关基因预测免疫治疗反应的准确率也高达0.765。
我们从超声图像中识别出耐药相关特征，并将其与基因组数据整合，能够对患者进行有效的风险分层，并预测TNBC患者新辅助化疗和免疫治疗的疗效。
