Chen Fahai, Fang Jianmin
CEO Office, RemeGen Co. Ltd., Yantai, China.
School of Life Science and Technology, Tongji University, Shanghai, China.
Front Oncol. 2022 Mar 4;12:824166. doi: 10.3389/fonc.2022.824166. eCollection 2022.
This study aimed to investigate the tumor-related infiltrating lymphocytes (TILs) affecting the response of trastuzumab and identify potential biomarkers based on immune-related genes to improve prognosis and clinical outcomes of targeted therapies in breast cancer.
Estimation of stromal and immune cells in malignant tumors using expression data (ESTIMATE) was adopted to infer the fraction of stromal and immune cells through utilizing gene expression signatures in breast tumor samples. Cell-type identification by estimating relative subsets of RNA transcript (CIBERSORT) algorithm was applied to characterize cell composition of 22 lymphocytes from breast cancer tissues using their gene expression profiles. Immune-related genes were collected from the Immunology Database and Analysis (ImmPort). Univariate and multivariate Cox regression analyses were performed to identify the significant independent risk factors associated with poor overall survival (OS) and breast cancer-specific survival (BCSS) of breast cancer patients. Hub genes were identified based on the protein-protein interaction (PPI) network analysis.
Based on the ESTIMATE algorithm, a significant reduction of stromal scores was observed in tumor tissues and pretreated tumor tissues compared with nontumor and posttreated tumor tissues, respectively, while immune scores failed to present notably statistical differences between both groups. However, from the results of the univariate Cox regression analysis, the immune score was identified to be remarkably associated with the poor OS for breast cancer patients. Subsequently, the infiltrating lymphocytes were evaluated in tumor tissues based on the CIBERSORT algorithm. Furthermore, significance analysis identified 1,244 differentially expressed genes (DEGs) from the GSE114082 dataset, and then 91 overlapping immune-related DEGs were screened between GSE114082 and ImmPort datasets. Subsequently, 10 top hub genes were identified and five (IGF1, ADIPOQ, PPARG, LEP, and NR3C1) significantly correlated with worse OS and BCSS on response to trastuzumab in breast cancer patients.
This study provided an insight into the immune score based on the tumor-related infiltrating lymphocytes in breast cancer tissues and demonstrates the benefits of immune infiltration on the treatment of trastuzumab. Meanwhile, the study established a novel five immune-related gene signature to predict the OS and BCSS of breast cancer treated by trastuzumab.
本研究旨在调查影响曲妥珠单抗反应的肿瘤相关浸润淋巴细胞（TILs），并基于免疫相关基因鉴定潜在生物标志物，以改善乳腺癌靶向治疗的预后和临床结果。
采用基于表达数据的恶性肿瘤基质和免疫细胞估计（ESTIMATE）方法，通过利用乳腺肿瘤样本中的基因表达特征来推断基质和免疫细胞的比例。应用通过估计RNA转录本相对亚群进行细胞类型鉴定（CIBERSORT）算法，利用乳腺癌组织中22种淋巴细胞的基因表达谱来表征其细胞组成。从免疫学数据库和分析（ImmPort）收集免疫相关基因。进行单变量和多变量Cox回归分析，以确定与乳腺癌患者总生存期（OS）和乳腺癌特异性生存期（BCSS）差相关的显著独立危险因素。基于蛋白质-蛋白质相互作用（PPI）网络分析确定枢纽基因。
基于ESTIMATE算法，与非肿瘤组织和治疗后肿瘤组织相比，肿瘤组织和预处理肿瘤组织中的基质评分分别显著降低，而两组之间的免疫评分未呈现明显统计学差异。然而，从单变量Cox回归分析结果来看，免疫评分被确定与乳腺癌患者的不良OS显著相关。随后，基于CIBERSORT算法评估肿瘤组织中的浸润淋巴细胞。此外，显著性分析从GSE114082数据集中鉴定出1244个差异表达基因（DEG），然后在GSE114082和ImmPort数据集之间筛选出91个重叠的免疫相关DEG。随后，确定了10个顶级枢纽基因，其中5个（IGF1、ADIPOQ、PPARG、LEP和NR3C1）与乳腺癌患者曲妥珠单抗反应的较差OS和BCSS显著相关。
本研究深入了解了基于乳腺癌组织中肿瘤相关浸润淋巴细胞的免疫评分，并证明了免疫浸润对曲妥珠单抗治疗的益处。同时，该研究建立了一种新的五个免疫相关基因特征，以预测曲妥珠单抗治疗的乳腺癌的OS和BCSS。
