肾癌免疫治疗疗效评估突变预测模型的建立

doi:10.19723/j.issn.1671-167X.2022.04.013

摘要/Abstract

摘要：

目的: 应用MSKCC(Memorial Sloan Kettering Cancer Center)泛癌免疫治疗队列肾癌患者的基因组测序数据建立疗效评估突变预测模型。方法: 针对MSKCC泛癌免疫治疗队列中121例接受免疫检查点抑制剂(immune checkpoint inhibitors, ICI)治疗的肾透明细胞癌患者基因组测序数据，应用Cox回归分析，鉴定与疗效相关的突变基因，并构建综合ICI药物疗效突变预测模型。结果: PBRM1和ARID1A突变与MSKCC泛癌免疫治疗队列肾癌患者治疗效果相关，基于此建立了年龄、性别、治疗类型、肿瘤突变负荷、PBRM1和ARID1A突变状态的疗效预测模型(1年生存AUC=0.700，2年生存AUC=0.825，3年生存AUC=0.776)。结论: PBRM1和ARID1A突变可作为肾癌免疫治疗疗效评估的潜在生物标志物，基于以上两基因突变状态建立的的疗效预测模型可用以筛选更适宜接受ICI免疫治疗的肾癌患者。

关键词: 肾癌, 免疫检查点抑制剂, 基因突变, 疗效评估

Abstract:

Objective: To establish a mutation prediction model for efficacy assessment, the genomic sequencing data of renal cancer patients from the MSKCC (Memorial Sloan Kettering Cancer Center) pan-cancer immunotherapy cohort was used. Methods: The genomic sequencing data of 121 clear cell renal cell carcinoma patients treated with immune checkpoint inhibitors (ICI) in the MSKCC pan-cancer immunotherapy cohort were obtained from cBioPortal database (http://www.cbioportal.org/) and they were analyzed by univariate and multivariate Cox regression analysis to identify mutated genes associated with ICI treatment efficacy, and we constructed a comprehensive prediction model for drug efficacy of ICI based on mutated genes using nomogram. Survival analysis and time-dependent receiver operator characteristic curves were performed to assess the prognostic value of the model. Transcriptome and genomic sequencing data of 538 renal cell carcinoma patients were obtained from the TCGA database (https://portal.gdc.cancer.gov/). Gene set enrichment analysis was used to identify the potential functions of the mutated genes enrolled in the nomogram. Results: We used multivariate Cox regression analysis and identified mutations in PBRM1 and ARID1A were associated with treatment outcomes in the patients with renal cancer in the MSKCC pan-cancer immunotherapy cohort. Based on this, we established an efficacy prediction model including age, gender, treatment type, tumor mutational burden (TMB), PBRM1 and ARID1A mutation status (HR=4.33, 95%CI: 1.42-13.23, P=0.01, 1-year survival AUC=0.700, 2-year survival AUC=0.825, 3-year survival AUC=0.776). The validation (HR=2.72, 95%CI: 1.12-6.64, P=0.027, 1-year survival AUC=0.694, 2-year survival AUC=0.709, 3-year survival AUC=0.609) and combination (HR=2.20, 95%CI: 1.14-4.26, P=0.019, 1-year survival AUC=0.613, 2-year survival AUC=0.687, 3-year survival AUC=0.526) sets confirmed these results. Gene set enrichment analysis indicated that PBRM1 was involved in positive regulation of epithelial cell differentiation, regulation of the T cell differentiation and regulation of humoral immune response. In addition, ARID1A was involved in regulation of the T cell activation, positive regulation of T cell mediated cyto-toxicity and positive regulation of immune effector process. Conclusion: PBRM1 and ARID1A mutations can be used as potential biomarkers for the evaluation of renal cancer immunotherapy efficacy. The efficacy prediction model established based on the mutation status of the above two genes can be used to screen renal cancer patients who are more suitable for ICI immunotherapy.

Key words: Renal cell carcinoma, Immune checkpoint inhibitors, Gene mutation, Response evaluation

中图分类号:

R737

秦彩朋,宋宇轩,丁梦婷,王飞,林佳兴,杨文博,杜依青,李清,刘士军,徐涛. 肾癌免疫治疗疗效评估突变预测模型的建立[J]. 北京大学学报（医学版）, 2022, 54(4): 663-668.

Cai-peng QIN,Yu-xuan SONG,Meng-ting DING,Fei WANG,Jia-xing LIN,Wen-bo YANG,Yi-qing DU,Qing LI,Shi-jun LIU,Tao XU. Establishment of a mutation prediction model for evaluating the efficacy of immunotherapy in renal carcinoma[J]. Journal of Peking University (Health Sciences), 2022, 54(4): 663-668.

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Characteristic	MSKCC immunotherapy cohort (ccRCC) (n=121)	Training set (n=60)	Model validation set (n=61)	P
Gender				0.56
Male	90 (74.38)	46 (38.02)	44 (36.36)
Female	31 (25.62)	14 (11.57)	17 (14.05)
Age				0.93
>60 years	65 (53.72)	32 (26.45)	33 (27.27)
≤60 years	56 (46.28)	28 (23.14)	28 (23.14)
Drug type				0.45
PD-1/PD-L1	94 (77.69)	45 (37.19)	49 (40.50)
Combo^*	27 (22.31)	15 (12.40)	12 (9.92)