Development and validation of an immune checkpoint-based signature to predict prognosis in nasopharyngeal carcinoma using computational pathology analysis
Development and validation of an immune checkpoint-based signature to predict prognosis in nasopharyngeal carcinoma using computational pathology analysis
复制标题
开发和验证基于免疫检查点的特征，以使用计算病理学分析预测鼻咽癌的预后。
DOI：
10.1186/s40425-019-0752-4
复制
发表时间：
2019-11-13
影响因子：
10.9
通讯作者：
Ma, Jun
中科院分区：
文献类型：
--
作者：
Wang, Ya-Qin;Zhang, Yu;Ma, Jun
背景免疫疗法，特别是免疫检查点抑制，为抗击癌症提供了强大的工具。我们的目的是检测常见免疫检查点的表达并评估其在鼻咽癌（NPC）中的预后价值。方法通过免疫组织化学检测训练队列（n = 208）中与13个特征一致的9个免疫检查点的表达，并通过计算病理学进行定量。然后，使用 LASSO cox 回归模型构建基于免疫检查点的特征 (ICS)，并在包含 125 名患者的验证队列中进行了验证。结果 PD-L1和B7-H4在肿瘤细胞（TCs）中高表达，PD-L1、B7-H3、B7-H4、IDO-1、VISTA、ICOS和OX40在肿瘤相关免疫细胞（TAICs）中高表达。 13 个免疫特征中的 8 个与患者总生存期相关，并建立了由 5 个特征（B7-H3TAIC、IDO-1TAIC、VISTATAIC、ICOSTAIC 和 LAG3TAIC）组成的 ICS 分类器。训练队列中高危评分的患者总体生存期较短（P < 0.001）、无病生存期（P = 0.002）和无远处转移生存期（P = 0.004），这在验证队列中得到了证实。多变量分析显示ICS分类器是一个独立的预后因素。 ICS 分类器和 TNM 分期的组合比单独的 TNM 分期具有更好的预后价值。此外，ICS 分类器与高 EBV-DNA 载量患者的生存率显着相关。结论 我们确定了与鼻咽癌13个特征一致的9个免疫检查点的表达状态，并进一步构建了ICS预后模型，这可能为TNM分期系统增加预后价值。
Background Immunotherapy, especially immune checkpoint inhibition, has provided powerful tools against cancer. We aimed to detect the expression of common immune checkpoints and evaluate their prognostic values in nasopharyngeal carcinoma (NPC). Methods The expression of 9 immune checkpoints consistent with 13 features was detected in the training cohort (n = 208) by immunohistochemistry and quantified by computational pathology. Then, the LASSO cox regression model was used to construct an immune checkpoint-based signature (ICS), which was validated in a validation cohort containing 125 patients. Results High positive expression of PD-L1 and B7-H4 was observed in tumour cells (TCs), whereas PD-L1, B7-H3, B7-H4, IDO-1, VISTA, ICOS and OX40 were highly expressed in tumour-associated immune cells (TAICs). Eight of the 13 immune features were associated with patient overall survival, and an ICS classifier consisting of 5 features (B7-H3TAIC, IDO-1TAIC, VISTATAIC, ICOSTAIC, and LAG3TAIC) was established. Patients with high-risk scores in the training cohort had shorter overall (P < 0.001), disease-free (P = 0.002), and distant metastasis-free survival (P = 0.004), which were confirmed in the validation cohort. Multivariate analysis revealed that the ICS classifier was an independent prognostic factor. A combination of the ICS classifier and TNM stage had better prognostic value than the TNM stage alone. In addition, the ICS classifier was significantly associated with survivals in patients with high EBV-DNA load. Conclusions We determined the expression status of nine immune checkpoints consistent with 13 features in NPC and further constructed an ICS prognostic model, which might add prognostic value to the TNM staging system.