CD81 通过阻断 CD274/PD-L1 的选择性自噬降解驱动放射抵抗性胶质母细胞瘤的免疫逃逸
CD81 drives immune evasion in radioresistant glioblastoma by blocking selective autophagic degradation of CD274/PD-L1.
CELL INTELLIGENCE · 肿瘤细胞治疗研究
肿瘤细胞治疗研究
英文原题:Cancer-Associated Fibroblasts Together with a Decline in CD8+ T Cells Predict a Worse Prognosis for Breast Cancer Patients.
Cancer-Associated Fibroblasts Together with a Decline in CD8+ T Cells Predict a Worse Prognosis for Breast Cancer Patients.
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本研究强调了 CAFs 在乳腺癌生物学中的重要性,并提供了有力证据表明其对患者预后和治疗反应的影响。这些发现为 CAFs 作为预后和预测生物标志物的潜力提供了宝贵见解,并支持开发靶向 CAFs 的疗法以改善乳腺癌管理。
癌症相关成纤维细胞(CAFs)在肿瘤微环境调控和癌症进展中起着至关重要的作用。本研究评估了CAFs在乳腺癌预后中的重要性和预测潜力。
该研究纳入了1503例乳腺癌患者。通过苏木精-伊红染色切片的形态学特征鉴定癌症相关成纤维细胞。该研究采用基因集富集分析、计算机模拟细胞术、通路分析、体外药物筛选和梯度提升机(GBM)学习,分析了临床病理参数、生存率、免疫细胞、基因集和预后模型。
CAF的存在与年轻年龄、淋巴管侵犯和神经周围侵犯显著相关。计算机模拟细胞术显示,在CAF存在的情况下,白细胞亚群发生改变,CD8+ T细胞减少。基因集富集分析显示其与上皮-间质转化和免疫调节等关键过程相关。在不同成纤维细胞活化蛋白-α表达的乳腺癌细胞系中进行药物敏感性分析表明,靶向CAF的治疗可能增强某些抗癌药物的疗效,包括ARRY-520、ispinesib-mesylate、paclitaxel和docetaxel。将CAF存在与机器学习相结合改善了生存预测。对于乳腺癌患者,CAF是更差疾病特异性生存和 disease-free survival 的独立预后标志物。
Cancer-associated fibroblasts (CAFs) play a crucial role in tumor microenvironment regulation and cancer progression. This study assessed the significance and predictive potential of CAFs in breast cancer prognosis.
The study included 1503 breast cancer patients. Cancer-associated fibroblasts were identified using morphologic features from hematoxylin and eosin slides. The study analyzed clinicopathologic parameters, survival rates, immune cells, gene sets, and prognostic models using gene-set enrichment analysis, in silico cytometry, pathway analysis, in vitro drug-screening, and gradient-boosting machine (GBM)-learning.
The presence of CAFs correlated significantly with young age, lymphatic invasion, and perineural invasion. In silico cytometry showed altered leukocyte subsets in the presence of CAFs, with decreased CD8+ T cells. Gene-set enrichment analysis showed associations with critical processes such as the epithelial-mesenchymal transition and immune modulation. Drug sensitivity analysis in breast cancer cell lines with varying fibroblast activation protein-α expression suggested that CAF-targeted therapies might enhance the efficacy of certain anticancer drugs including ARRY-520, ispinesib-mesylate, paclitaxel, and docetaxel. Integrating CAF presence with machine-learning improved survival prediction. For breast cancer patients, CAFs were independent prognostic markers for worse disease-specific survival and disease-free survival.
This study highlighted the significance of CAFs in breast cancer biology and provided compelling evidence of their impact on patient outcomes and treatment response. The findings offer valuable insights into the potential of CAFs as prognostic and predictive biomarkers and support the development of CAF-targeted therapies to improve breast cancer management.
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