Li Kunping, Li Yuqing, Lyu Yinfeng, Tan Linyi, Zheng Xinyi, Jiang Haowen, Wen Hui, Feng Chenchen
Department of Urology, Huashan Hospital, Fudan University, Shanghai, China.
Department of Pharmacology, Huashan Hospital, Fudan University, Shanghai, China.
Front Immunol. 2022 May 16;13:853088. doi: 10.3389/fimmu.2022.853088. eCollection 2022.
The action of immune checkpoint inhibition (ICI) largely depends on antibody-dependent cellular phagocytosis (ADCP). We thus aim to develop ADCP-based ccRCC risk stratification as both prognostic and therapeutic markers of ICI.
Genomic data from multiple public datasets (TCGA, etc.) were integrated. A cancer-intrinsic ADCP gene set for ccRCC tailored from a recent report was constructed based on the association with prognosis, immune infiltrates, and response to ICI. Therapeutic potential was profiled using genome-drug sensitivity datasets.
ADCP genes were selected from a recent CRISPR/Cas9 screen report. Following a four-module panel based on clinical traits, we generated a six-gene signature (ARPC3, PHF19, FKBP11, MS4A14, KDELR3, and CD1C), which showed a strong correlation with advanced grade and stage and worsened prognosis, with a nomogram showing predictive efficacies of 0.911, 0.845, and 0.867 (AUC) at 1, 3, and 5 years, respectively. Signatures were further dichotomized, and groups with a higher risk score showed a positive correlation with tumor mutation burden, higher expressions of inhibitory checkpoint molecules, and increased antitumor immune infiltrates and were enriched for antitumor immune pathways. The high risk-score group showed better response to ICI and could benefit from TKIs of axitinib, tivozanib, or sorafenib, preferentially in combination, whereas sunitinib and pazopanib would better fit the low risk-score group.
Here we showed a six-gene ADCP signature that correlated with prognosis and immune modulation in ccRCC. The signature-based risk stratification was associated with response to both ICI and tyrosine kinase inhibition in ccRCC.
免疫检查点抑制(ICI)的作用很大程度上取决于抗体依赖性细胞吞噬作用(ADCP)。因此,我们旨在开发基于ADCP的ccRCC风险分层方法,作为ICI的预后和治疗标志物。
整合来自多个公共数据集(如TCGA等)的基因组数据。根据与预后、免疫浸润和对ICI反应的关联,构建了一个从近期报告中定制的ccRCC癌症内在ADCP基因集。使用基因组药物敏感性数据集分析治疗潜力。
从近期的CRISPR/Cas9筛选报告中选择ADCP基因。基于临床特征构建了一个四模块面板,我们生成了一个六基因特征(ARPC3、PHF19、FKBP11、MS4A14、KDELR3和CD1C),该特征与高级别和晚期以及预后恶化密切相关,列线图显示在1年、3年和5年时的预测效能分别为0.911、0.845和0.867(AUC)。对特征进行进一步二分法分析,风险评分较高的组与肿瘤突变负担呈正相关,抑制性检查点分子表达较高,抗肿瘤免疫浸润增加,且富含抗肿瘤免疫途径。高风险评分组对ICI显示出更好的反应,并且优先联合使用阿昔替尼、替沃扎尼或索拉非尼的酪氨酸激酶抑制剂(TKIs)会受益,而舒尼替尼和帕唑帕尼更适合低风险评分组。
在此我们展示了一个与ccRCC预后和免疫调节相关的六基因ADCP特征。基于该特征的风险分层与ccRCC对ICI和酪氨酸激酶抑制的反应相关。