Department of Urology, School of Medicine, Xin Hua Hospital Affiliated to Shanghai Jiao Tong University, Shanghai 200092, PR China.
Int Immunopharmacol. 2020 Aug;85:106651. doi: 10.1016/j.intimp.2020.106651. Epub 2020 Jun 5.
To investigate the immune activity scores (IAS) and tumor-infiltrating immune cells (TIIC) in metastatic clear cell renal cell carcinoma (mccRCC) patients and to explore their patterns and potential prognostic values.
The gene expression profiles and clinical information of ccRCC patients from multiple Gene Expression Omnibus (GEO) datasets and TCGA were used as study cohorts. Overall, 3 sets of 69 variables associated with tumor-immune interactions were collected from several tumor immunophenotype analysis websites. Least absolute shrinkage and selection operator (LASSO) and area under receiver operating characteristic (AUC) analyses were performed to establish and evaluate the predictive models.
Several TIIC and IAS variables are significantly different between patients and between different sites within the same patient. The AUC of the multivariable logistic models based on IAS and the two TIIC groups is 0.705 (95%CI 0.643-0.766), 0.719 (95%CI 0.650-0.788), and 0.685 (95%CI 0.623-0.747), respectively. The AUC of the LASSO model is 0.715 (95%CI 0.652-0.777). Certain subtypes identified by the consensus clustering method show a favorable OS (log-rank, p < 0.01) in both nonmetastatic and metastatic ccRCC patients.
IAS and TIIC could vary between patients and different sites within the same patient, and distinct patterns of these variables could correlate with clinical features. Heterogeneity might exist in the biological process of metastasis. LASSO logistic regression reveals that the infiltration of two TIICs would be a predictor of metastatic ccRCC. Last, certain subtypes may have a better prognosis in both ccRCC and mccRCC patients.
研究转移性透明细胞肾细胞癌(mccRCC)患者的免疫活性评分(IAS)和肿瘤浸润免疫细胞(TIIC),并探讨其模式及潜在的预后价值。
本研究使用了来自多个基因表达综合数据库(GEO)数据集和 TCGA 的 ccRCC 患者的基因表达谱和临床信息作为研究队列。总体上,从多个肿瘤免疫表型分析网站共收集了 3 组与肿瘤免疫相互作用相关的 69 个变量。采用最小绝对收缩和选择算子(LASSO)和受试者工作特征曲线下面积(AUC)分析建立和评估预测模型。
患者之间以及同一患者不同部位之间的几个 TIIC 和 IAS 变量存在显著差异。基于 IAS 和两个 TIIC 组的多变量逻辑模型的 AUC 分别为 0.705(95%CI 0.643-0.766)、0.719(95%CI 0.650-0.788)和 0.685(95%CI 0.623-0.747)。LASSO 模型的 AUC 为 0.715(95%CI 0.652-0.777)。共识聚类方法确定的某些亚型在非转移性和转移性 ccRCC 患者中均表现出良好的 OS(对数秩,p<0.01)。
IAS 和 TIIC 可能在患者之间以及同一患者的不同部位之间存在差异,这些变量的不同模式可能与临床特征相关。转移过程中的生物学过程可能存在异质性。LASSO 逻辑回归显示,两种 TIIC 的浸润将是转移性 ccRCC 的预测因子。最后,某些亚型在 ccRCC 和 mccRCC 患者中可能具有更好的预后。