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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...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9614164/ https://www.ncbi.nlm.nih.gov/pubmed/36313281 http://dx.doi.org/10.3389/fphar.2022.984080 |
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author | Wang, Jun Tu, Weichao Qiu, Jianxin Wang, Dawei |
author_facet | Wang, Jun Tu, Weichao Qiu, Jianxin Wang, Dawei |
author_sort | Wang, Jun |
collection | PubMed |
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. |
format | Online Article Text |
id | pubmed-9614164 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-96141642022-10-29 Predicting prognosis and immunotherapeutic response of clear cell renal cell carcinoma Wang, Jun Tu, Weichao Qiu, Jianxin Wang, Dawei Front Pharmacol Pharmacology 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. Frontiers Media S.A. 2022-10-14 /pmc/articles/PMC9614164/ /pubmed/36313281 http://dx.doi.org/10.3389/fphar.2022.984080 Text en Copyright © 2022 Wang, Tu, Qiu and Wang. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Pharmacology Wang, Jun Tu, Weichao Qiu, Jianxin Wang, Dawei Predicting prognosis and immunotherapeutic response of clear cell renal cell carcinoma |
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 | Pharmacology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9614164/ https://www.ncbi.nlm.nih.gov/pubmed/36313281 http://dx.doi.org/10.3389/fphar.2022.984080 |
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