Predicting prognosis and immunotherapeutic response of clear cell renal cell carcinoma
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 h...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2022-10-01
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| Series: | Frontiers in Pharmacology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fphar.2022.984080/full |
| _version_ | 1811338895268249600 |
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| author | Jun Wang Weichao Tu Jianxin Qiu Dawei Wang |
| author_facet | Jun Wang Weichao Tu Jianxin Qiu Dawei Wang |
| author_sort | Jun Wang |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-13T18:17:23Z |
| format | Article |
| id | doaj.art-d84ff36f14ea4d639e9b54d36836d679 |
| institution | Directory Open Access Journal |
| issn | 1663-9812 |
| language | English |
| last_indexed | 2024-04-13T18:17:23Z |
| publishDate | 2022-10-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Pharmacology |
| spelling | doaj.art-d84ff36f14ea4d639e9b54d36836d6792022-12-22T02:35:38ZengFrontiers Media S.A.Frontiers in Pharmacology1663-98122022-10-011310.3389/fphar.2022.984080984080Predicting prognosis and immunotherapeutic response of clear cell renal cell carcinomaJun Wang0Weichao Tu1Jianxin Qiu2Dawei Wang3Department of Urology, Shanghai General Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, ChinaDepartment of Urology, Ruijin Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, ChinaDepartment of Urology, Shanghai General Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, ChinaDepartment of Urology, Ruijin Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, ChinaImmune 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.https://www.frontiersin.org/articles/10.3389/fphar.2022.984080/fullclinical predictive modelkidney cancerimmunotherapysurvival analysisimmune checkpoint inhibitors |
| spellingShingle | Jun Wang Weichao Tu Jianxin Qiu Dawei Wang Predicting prognosis and immunotherapeutic response of clear cell renal cell carcinoma Frontiers in Pharmacology clinical predictive model kidney cancer immunotherapy survival analysis immune checkpoint inhibitors |
| title | Predicting prognosis and immunotherapeutic response of clear cell renal cell carcinoma |
| title_full | Predicting prognosis and immunotherapeutic response of clear cell renal cell carcinoma |
| title_fullStr | Predicting prognosis and immunotherapeutic response of clear cell renal cell carcinoma |
| title_full_unstemmed | Predicting prognosis and immunotherapeutic response of clear cell renal cell carcinoma |
| title_short | Predicting prognosis and immunotherapeutic response of clear cell renal cell carcinoma |
| title_sort | predicting prognosis and immunotherapeutic response of clear cell renal cell carcinoma |
| topic | clinical predictive model kidney cancer immunotherapy survival analysis immune checkpoint inhibitors |
| url | https://www.frontiersin.org/articles/10.3389/fphar.2022.984080/full |
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