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Published on: January 22, 2013
Predicting prognosis and immunotherapeutic response of clear cell renal cell carcinoma
Jun Wang1, Weichao Tu2, Jianxin Qiu1
1Department of Urology, Shanghai General Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Abstract:
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.
Insights
Researchers developed AI-powered models to predict clear cell renal cell carcinoma (ccRCC) patient response to immune checkpoint inhibitors. These models integrate multi-omics data to forecast survival and treatment efficacy, improving personalized immunotherapy strategies.
Area of Science:
- Oncology
- Immunotherapy
- Bioinformatics
Background:
- Immune checkpoint inhibitors (ICIs) are a promising therapy for clear cell renal cell carcinoma (ccRCC).
- However, predicting patient response and managing immune-related adverse reactions remain challenging.
- Existing biomarkers like tumor mutational burden and immune infiltration are insufficient for ccRCC immunotherapy prediction.
Purpose of the Study:
- To develop and validate robust clinical prediction models for immunotherapy efficacy in ccRCC.
- To identify reliable predictors of survival and response to PD-1 inhibitors in advanced ccRCC patients.
Main Methods:
- Integration of multi-omics data (PBRM1 mutation, transcriptome, endogenous retroviruses, gene copy number) from 123 advanced ccRCC patients.
- Application of Artificial Intelligence (AI) for optimized mutation data interpretation.
- Development and internal validation (bootstrap) of predictive models for overall survival, progression-free survival, and objective response rate (ORR).
Main Results:
- The developed models demonstrated high predictive performance: AUC of 0.931 for overall survival, 0.795 for progression-free survival, and 0.763 for ORR.
- Calibration curves confirmed the good fit of the nomogram models.
- The models showed strong internal validity through bootstrap validation.
Conclusions:
- AI-driven clinical prediction models integrating multi-omics data can accurately forecast survival and response to immunotherapy in ccRCC patients.
- These models offer a promising tool for personalized treatment strategies in ccRCC.
- Further research and external validation are warranted to translate these findings into clinical practice.
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