Lin Jianguang, Yang Hainan, Huang Rongfu, Xu Tianwen
Department of Oncology, The Second Affiliated Hospital of Fujian Medical University, Quanzhou, Fujian, China.
Department of Ultrasound, First Affiliated Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, Fujian, China.
Front Oncol. 2025 Mar 25;15:1439516. doi: 10.3389/fonc.2025.1439516. eCollection 2025.
Radiotherapy is a cornerstone of breast cancer (BRCA) treatment. Accurately predicting tumor radiosensitivity is critical for optimizing therapeutic outcomes and personalizing treatment strategies. DNA repair pathways are key determinants of radiotherapy response. Thus, we aimed to develop a novel DNA repair-related radiosensitivity model and to identify potential targets for enhancing radiotherapy efficacy.
A retrospective study was conducted using data from 942 BRCA patients from TCGA database. A radiosensitivity model, comprising a radiosensitivity index, was developed using LASSO regression analysis. Patients were stratified into radiosensitive (RS) and radioresistant (RR) groups based on their radiosensitivity index (RSI). Associations between the RSI, clinicopathological parameters, and PD-L1 status were analyzed. The CIBERSORT and ESTIMATE algorithms were employed to characterize the immune landscape of the tumor microenvironment. The Tumor Immune Dysfunction and Exclusion (TIDE) algorithm and pRRophetic platform were used to predict treatment responses. Key genes identified in the radiosensitivity model were further validated using qRT-PCR experiments.
We successfully constructed a radiosensitivity index incorporating 10 DNA repair-related genes. Patients in the RS group exhibited significantly better prognosis compared to the RR group, but this benefit was limited to those receiving radiotherapy. This survival benefit associated with the radiosensitivity signature was absent in patients who did not receive radiotherapy. The RS group displayed a distinct molecular profile characterized by enrichment of TGF-β signaling and protein secretion pathways, potentially contributing to enhanced radiosensitivity. Furthermore, the RS group exhibited increased infiltration of immune cells. Notably, the RS-PD-L1-high subgroup demonstrated the most favorable survival outcomes and highest immune cell infiltration, highlighting their potential responsiveness to immunotherapy. In addition, the RR group exhibited a distinct profile characterized by enrichment of DNA repair pathways and a heightened sensitivity to CDK and HER2 inhibitors. Conversely, this group displayed resistance to DNA-damaging drugs. These findings were supported by experiments using MCF-7 and radioresistant MCF-7/IR cell lines, confirming differential expression of key radiosensitivity index genes.
In conclusion, we established a radiosensitivity model for predicting radiotherapy benefit in breast cancer. Our study reveals a strong association between radiosensitivity, enhanced antitumor immunity, and potential immunotherapy benefit, particularly within the RS-PD-L1-high subgroup.
放射治疗是乳腺癌（BRCA）治疗的基石。准确预测肿瘤放射敏感性对于优化治疗效果和个性化治疗策略至关重要。DNA修复途径是放射治疗反应的关键决定因素。因此，我们旨在开发一种新型的与DNA修复相关的放射敏感性模型，并确定增强放射治疗疗效的潜在靶点。
使用来自TCGA数据库的942例BRCA患者的数据进行回顾性研究。使用LASSO回归分析开发了一个包含放射敏感性指数的放射敏感性模型。根据患者的放射敏感性指数（RSI）将其分为放射敏感（RS）组和放射抵抗（RR）组。分析RSI、临床病理参数和PD-L1状态之间的关联。采用CIBERSORT和ESTIMATE算法来表征肿瘤微环境的免疫格局。使用肿瘤免疫功能障碍和排除（TIDE）算法和pRRophetic平台预测治疗反应。使用qRT-PCR实验进一步验证放射敏感性模型中鉴定出的关键基因。
我们成功构建了一个包含10个与DNA修复相关基因的放射敏感性指数。与RR组相比，RS组患者的预后明显更好，但这种益处仅限于接受放射治疗的患者。未接受放射治疗的患者中不存在与放射敏感性特征相关的生存益处。RS组表现出独特的分子特征，其特征是TGF-β信号通路和蛋白质分泌途径的富集，这可能有助于提高放射敏感性。此外，RS组免疫细胞浸润增加。值得注意的是，RS-PD-L1高亚组显示出最有利的生存结果和最高的免疫细胞浸润，突出了它们对免疫治疗的潜在反应性。此外，RR组表现出独特的特征，其特征是DNA修复途径的富集以及对CDK和HER2抑制剂的高度敏感性。相反，该组对DNA损伤药物表现出抗性。使用MCF-7和放射抵抗的MCF-7/IR细胞系进行的实验支持了这些发现，证实了关键放射敏感性指数基因的差异表达。
总之，我们建立了一个用于预测乳腺癌放射治疗益处的放射敏感性模型。我们的研究揭示了放射敏感性、增强的抗肿瘤免疫力和潜在的免疫治疗益处之间的紧密关联，特别是在RS-PD-L1高亚组中。
