Wang Jun, Tu Weichao, Qiu Jianxin, Wang Dawei
Department of Urology, Shanghai General Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Department of Urology, Ruijin Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China.
Front Pharmacol. 2022 Oct 14;13:984080. doi: 10.3389/fphar.2022.984080. eCollection 2022.
Immune checkpoint inhibitors have emerged as a novel therapeutic strategy for many different tumors, including clear cell renal cell carcinoma (ccRCC). However, these drugs are only effective in some ccRCC patients, and can produce a wide range of immune-related adverse reactions. Previous studies have found that ccRCC is different from other tumors, and common biomarkers such as tumor mutational burden, HLA type, and degree of immunological infiltration cannot predict the response of ccRCC to immunotherapy. Therefore, it is necessary to further research and construct corresponding clinical prediction models to predict the efficacy of Immune checkpoint inhibitors. We integrated PBRM1 mutation data, transcriptome data, endogenous retrovirus data, and gene copy number data from 123 patients with advanced ccRCC who participated in prospective clinical trials of PD-1 inhibitors (including CheckMate 009, CheckMate 010, and CheckMate 025 trials). We used AI to optimize mutation data interpretation and established clinical prediction models for survival (for overall survival AUC: 0.931; for progression-free survival AUC: 0.795) and response (ORR AUC: 0.763) to immunotherapy of ccRCC. The models were internally validated by bootstrap. Well-fitted calibration curves were also generated for the nomogram models. Our models showed good performance in predicting survival and response to immunotherapy of ccRCC.
免疫检查点抑制剂已成为包括透明细胞肾细胞癌（ccRCC）在内的多种不同肿瘤的一种新型治疗策略。然而，这些药物仅对部分ccRCC患者有效，且会产生广泛的免疫相关不良反应。既往研究发现，ccRCC与其他肿瘤不同，肿瘤突变负荷、HLA类型和免疫浸润程度等常见生物标志物无法预测ccRCC对免疫治疗的反应。因此，有必要进一步研究并构建相应的临床预测模型，以预测免疫检查点抑制剂的疗效。我们整合了123例参与PD-1抑制剂前瞻性临床试验（包括CheckMate 009、CheckMate 010和CheckMate 025试验）的晚期ccRCC患者的PBRM1突变数据、转录组数据、内源性逆转录病毒数据和基因拷贝数数据。我们使用人工智能优化突变数据解读，并建立了ccRCC免疫治疗生存（总生存AUC：0.931；无进展生存AUC：0.795）和反应（客观缓解率AUC：0.763）的临床预测模型。这些模型通过自举法进行内部验证。还为列线图模型生成了拟合良好的校准曲线。我们的模型在预测ccRCC免疫治疗的生存和反应方面表现良好。
