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
英文原题:The expression profiles of signature genes from CD103(+)LAG3(+) tumour-infiltrating lymphocyte subsets predict breast cancer survival.
The expression profiles of signature genes from CD103(+)LAG3(+) tumour-infiltrating lymphocyte subsets predict breast cancer survival.
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我们目前的研究表明,CLTRP 评分是一种有前景的生物标志物,可用于区分乳腺癌患者的预后、药物敏感性、分子和免疫特征以及免疫治疗结局。CLTRP 可作为免疫治疗临床决策的有价值工具。
TIL(肿瘤浸润淋巴细胞)(TILs),包括T细胞和B细胞,已被证明与肿瘤进展相关。然而,TILs的不同亚群及其在乳腺癌中的作用仍知之甚少。利用多组学数据进行大规模分析可以揭示潜在机制,并为预测免疫治疗反应提供有前景的生物标志物。
对乳腺癌样本的单细胞转录组数据进行分析,以鉴定独特的 TIL 亚群。基于这些亚群中标记基因的表达谱,通过对包含 1089 名乳腺癌患者的 TCGA 训练队列进行单因素和多因素 Cox 分析及 LASSO 回归,构建了一个 TIL 相关预后模型。采用多重免疫组化确认乳腺癌样本中 TIL 亚群的存在。该模型在包含 3619 名乳腺癌患者的大规模转录组数据集中进行了验证,包括 METABRIC 队列、六个化疗转录组队列和两个免疫治疗转录组队列。
我们鉴定出两个高表达 CD103 和 LAG3 的 TIL 亚群(CD103 + LAG3 +),包括一个 CD8 + T 细胞亚群和一个 B 细胞亚群。基于这两个亚群中标记基因的表达谱,我们进一步开发了一个基于 CXCL13 和 BIRC3 基因的 CD103 + LAG3 + TIL 相关预后模型(CLTRP),用于预测乳腺癌患者的预后。CLTRP-low 患者的预后优于 CLTRP-high 患者。综合结果显示,低 CLTRP 评分与高 TP53 突变率、CD8 T 细胞、辅助 T 细胞和 CD4 T 细胞的高浸润、对化疗药物的高敏感性以及对免疫治疗的良好反应相关。相反,高 CLTRP 评分与低 TP53 突变率、M0 和 M2 巨噬细胞的高浸润、对化疗药物的低敏感性以及对免疫治疗的不良反应相关。
Tumour-infiltrating lymphocytes (TILs), including T and B cells, have been demonstrated to be associated with tumour progression. However, the different subpopulations of TILs and their roles in breast cancer remain poorly understood. Large-scale analysis using multiomics data could uncover potential mechanisms and provide promising biomarkers for predicting immunotherapy response.
Single-cell transcriptome data for breast cancer samples were analysed to identify unique TIL subsets. Based on the expression profiles of marker genes in these subsets, a TIL-related prognostic model was developed by univariate and multivariate Cox analyses and LASSO regression for the TCGA training cohort containing 1089 breast cancer patients. Multiplex immunohistochemistry was used to confirm the presence of TIL subsets in breast cancer samples. The model was validated with a large-scale transcriptomic dataset for 3619 breast cancer patients, including the METABRIC cohort, six chemotherapy transcriptomic cohorts, and two immunotherapy transcriptomic cohorts.
We identified two TIL subsets with high expression of CD103 and LAG3 (CD103 + LAG3 + ), including a CD8 + T-cell subset and a B-cell subset. Based on the expression profiles of marker genes in these two subpopulations, we further developed a CD103 + LAG3 + TIL-related prognostic model (CLTRP) based on CXCL13 and BIRC3 genes for predicting the prognosis of breast cancer patients. CLTRP-low patients had a better prognosis than CLTRP-high patients. The comprehensive results showed that a low CLTRP score was associated with a high TP53 mutation rate, high infiltration of CD8 T cells, helper T cells, and CD4 T cells, high sensitivity to chemotherapeutic drugs, and a good response to immunotherapy. In contrast, a high CLTRP score was correlated with a low TP53 mutation rate, high infiltration of M0 and M2 macrophages, low sensitivity to chemotherapeutic drugs, and a poor response to immunotherapy.
Our present study showed that the CLTRP score is a promising biomarker for distinguishing prognosis, drug sensitivity, molecular and immune characteristics, and immunotherapy outcomes in breast cancer patients. The CLTRP could serve as a valuable tool for clinical decision making regarding immunotherapy.
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