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Journal of Peking University (Health Sciences) ›› 2022, Vol. 54 ›› Issue (4): 663-668. doi: 10.19723/j.issn.1671-167X.2022.04.013

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Establishment of a mutation prediction model for evaluating the efficacy of immunotherapy in renal carcinoma

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*()   

  1. Department of Urology, Peking University People' s Hospital, Beijing 100044, China
  • Received:2022-04-03 Online:2022-08-18 Published:2022-08-11
  • Contact: Tao XU E-mail:xutao@pkuph.edu.cn
  • Supported by:
    the National Natural Science Foundation of China(81872086)

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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

CLC Number: 

  • R737

Table 1

Clinical characteristics of the included cohorts  n(%)"

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)

Figure 1

Cox proportional hazard analysis to model association of mutations in PBRM1 and ARID1A with prognosis in patients who are treated with immune checkpoint inhibitors (ICI)"

Figure 2

Gene set enrichment analysis (GSEA) A, GSEA between the wild-type and PBRM1 mutation groups; B, GSEA between the wild-type and ARID1A mutation groups."

Figure 3

Status of immune cell infiltration induced by mutation in PPBRM1 (A) and ARID1A (B)"

Figure 4

Construction of an integrated prognostic classifier model A nomogram based on age, gender, treatment type, tumor mutation burden (TMB), PBRM1 and ARID1A mutation status in the patients with renal cell carcinoma in MSKCC immune cohort (training set, n=60) (A), receiver operating characteristic (ROC) curves for predicting overall survival (OS) using the nomogram for the cohort(B), The OS curve with the nomogram for the cohort. The risk score is calculated according to the regression coefficient. According to the risk score, the cohort is divided into low-risk and high-risk score groups for Kaplan-Meier survival analysis(C), ROC curve and OS curve (D-E) of model validation set, ROC curve and OS curve entire set (F-G). FPR, false positive rate; TPR, true positive rate."

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