Leng Shaolong, Nie Gang, Yi Changhong, Xu Yunsheng, Zhang Lvya, Zhu Linyu
Department of Dermatovenereology, The Seventh Affiliated Hospital of Sun Yat-Sen University, Shenzhen, China.
Department of Interventional Radiology, Cancer Hospital of Shantou University Medical College, Shantou, China.
Cancer Cell Int. 2023 Sep 26;23(1):214. doi: 10.1186/s12935-023-03048-9.
Immunoblockade therapy based on the PD-1 checkpoint has greatly improved the survival rate of patients with skin cutaneous melanoma (SKCM). However, existing anti-PD-1 therapeutic efficacy prediction markers often exhibit a poor situation of poor reliability in identifying potential beneficiary patients in clinical applications, and an ideal biomarker for precision medicine is urgently needed.
10 multicenter cohorts including 4 SKCM cohorts and 6 immunotherapy cohorts were selected. Through the analysis of WGCNA, survival analysis, consensus clustering, we screened 36 prognostic genes. Then, ten machine learning algorithms were used to construct a machine learning-derived immune signature (MLDIS). Finally, the independent data sets (GSE22153, GSE54467, GSE59455, and in-house cohort) were used as the verification set, and the ROC index standard was used to evaluate the model.
Based on computing framework, we found that patients with high MLDIS had poor overall survival and has good prediction performance in all cohorts and in-house cohort. It is worth noting that MLDIS performs better in each data set than almost all models which from 51 prognostic signatures for SKCM. Meanwhile, high MLDIS have a positive prognostic impact on patients treated with anti-PD-1 immunotherapy by driving changes in the level of infiltration of immune cells in the tumor microenvironment. Additionally, patients suffering from SKCM with high MLDIS were more sensitive to immunotherapy.
Our study identified that MLDIS could provide new insights into the prognosis of SKCM and predict the immunotherapy response in patients with SKCM.
基于PD - 1检查点的免疫阻断疗法极大地提高了皮肤黑色素瘤（SKCM）患者的生存率。然而，现有的抗PD - 1治疗疗效预测标志物在临床应用中识别潜在受益患者时，往往存在可靠性差的问题，因此迫切需要一种用于精准医学的理想生物标志物。
选择了10个多中心队列，包括4个SKCM队列和6个免疫治疗队列。通过加权基因共表达网络分析（WGCNA）、生存分析、共识聚类，我们筛选出了36个预后基因。然后，使用十种机器学习算法构建了一种机器学习衍生的免疫特征（MLDIS）。最后，将独立数据集（GSE22153、GSE54467、GSE59455和内部队列）用作验证集，并使用受试者工作特征（ROC）指数标准来评估该模型。
基于计算框架，我们发现MLDIS高的患者总生存期较差，并且在所有队列和内部队列中都具有良好的预测性能。值得注意的是，在每个数据集中，MLDIS的表现都优于几乎所有来自51个SKCM预后特征的模型。同时，高MLDIS通过驱动肿瘤微环境中免疫细胞浸润水平的变化，对接受抗PD - 1免疫治疗的患者具有积极的预后影响。此外，MLDIS高的SKCM患者对免疫治疗更敏感。
我们的研究表明，MLDIS可以为SKCM的预后提供新的见解，并预测SKCM患者的免疫治疗反应。
