Zhao Jianli, Wang Ying, Lao Zengding, Liang Siting, Hou Jingyi, Yu Yunfang, Yao Herui, You Na, Chen Kai
Breast Tumor Center, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China.
Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, China.
Onco Targets Ther. 2017 Sep 11;10:4423-4433. doi: 10.2147/OTT.S144015. eCollection 2017.
Breast cancer, the most common cancer among women, is a clinically and biologically heterogeneous disease. Numerous prognostic tools have been proposed, including gene signatures. Unlike proliferation-related prognostic gene signatures, many immune-related gene signatures have emerged as principal biology-driven predictors of breast cancer. Diverse statistical methods and data sets were used for building these immune-related prognostic models, making it difficult to compare or use them in clinically meaningful ways. This study evaluated successfully published immune-related prognostic gene signatures through systematic validations of publicly available data sets. Eight prognostic models that were built upon immune-related gene signatures were evaluated. The performances of these models were compared and ranked in ten publicly available data sets, comprising a total of 2,449 breast cancer cases. Predictive accuracies were measured as concordance indices (C-indices). All tests of statistical significance were two-sided. Immune-related gene models performed better in estrogen receptor-negative (ER-) and lymph node-positive (LN+) breast cancer subtypes. The three top-ranked ER- breast cancer models achieved overall C-indices of 0.62-0.63. Two models predicted better than chance for ER+ breast cancer, with C-indices of 0.53 and 0.59, respectively. For LN+ breast cancer, four models showed predictive advantage, with C-indices between 0.56 and 0.61. Predicted prognostic values were positively correlated with ER status when evaluated using univariate analyses in most of the models under investigation. Multivariate analyses indicated that prognostic values of the three models were independent of known clinical prognostic factors. Collectively, these analyses provided a comprehensive evaluation of immune-related prognostic gene signatures. By synthesizing C-indices in multiple independent data sets, immune-related gene signatures were ranked for ER+, ER-, LN+, and LN- breast cancer subtypes. Taken together, these data showed that immune-related gene signatures have good prognostic values in breast cancer, especially for ER- and LN+ tumors.
乳腺癌是女性中最常见的癌症,是一种临床和生物学上具有异质性的疾病。已经提出了许多预后工具,包括基因特征。与增殖相关的预后基因特征不同,许多免疫相关基因特征已成为乳腺癌主要的生物学驱动预测指标。构建这些免疫相关预后模型使用了多种统计方法和数据集,这使得难以以临床有意义的方式对它们进行比较或使用。本研究通过对公开可用数据集进行系统验证,评估了已成功发表的免疫相关预后基因特征。评估了基于免疫相关基因特征构建的八个预后模型。在十个公开可用的数据集中比较了这些模型的性能并进行了排名,这些数据集总共包含2449例乳腺癌病例。预测准确性以一致性指数(C指数)衡量。所有统计学显著性检验均为双侧检验。免疫相关基因模型在雌激素受体阴性(ER-)和淋巴结阳性(LN+)乳腺癌亚型中表现更好。排名前三的ER-乳腺癌模型的总体C指数为0.62-0.63。有两个模型对ER+乳腺癌的预测优于随机水平,C指数分别为0.53和0.59。对于LN+乳腺癌,有四个模型显示出预测优势,C指数在0.56至0.61之间。在大多数研究模型中,使用单变量分析评估时,预测的预后值与ER状态呈正相关。多变量分析表明,三个模型的预后值独立于已知的临床预后因素。总体而言,这些分析对免疫相关预后基因特征进行了全面评估。通过综合多个独立数据集中的C指数,对ER+、ER-、LN+和LN-乳腺癌亚型的免疫相关基因特征进行了排名。综上所述,这些数据表明免疫相关基因特征在乳腺癌中具有良好的预后价值,尤其是对于ER-和LN+肿瘤。