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A multi-omics features-based approach integrating immunogenicity and inflammation enhances immunotherapy benefit in
Yanfeng Xue1, Feng Han2, Shuqing Wei3
1Department of Special Needs Medicine, Cancer Hospital Affiliated to Shanxi Medical University/Shanxi Province Cancer Hospital/Shanxi Hospital Affiliated to Cancer Hospital, Chinese Academy of Medical Sciences, Taiyuan, China.
Background:
Programmed cell death 1 (PD-1) or PD-ligand 1 (PD-L1) blocker-based strategies have improved the survival outcomes of clear cell renal cell carcinomas (ccRCCs) in recent years, but only a small number of patients have benefited from them.
Methods:
In this study, we developed a multi-omics machine learning model based on inflammatory and immune signatures (TIs) to predict the response and survival of ccRCC patients to immune checkpoint blockade (ICB) therapy. The research collected RNA-seq and single-cell RNA-seq (scRNA-seq) data from more than 1,900 patients with autoimmune nephropathy and analyzed the genomic and transcriptome profiles of ccRCC patients. The predictive power of the method was validated in more than 1,000 ccRCC patients treated with ICB, and compared to single biomarkers (e.g., PD-L1 expression, TMB).
Results:
Inflammatory signaling was found to be strongly associated with ICB outcome, and 716 inflammation-related genes were identified that are enriched in the "lymphocyte activation regulation" pathway. The findings suggested that ccRCC patients can be categorized into two subtypes with different treatment responses and prognosis by these features. In addition, the TIs-ML model exhibited superior predictive capabilities compared to an individual biomarker (AUC > 0.997) across multiple independent datasets. It demonstrated the capacity to accurately differentiate between responders and non-responders. Furthermore, the model performed more effectively than existing genetic models and functional scores in predicting survival.
Conclusion:
We propose a TIs-ML prediction model based on multi-omics features that can effectively predict ICB treatment response in ccRCC patients. The model integrates inflammatory and immune features, and its high generalization ability was validated in multiple cohorts. Overall, the TIs-ML approach provides a novel method for guiding precise immunotherapy in ccRCC.
Insights
A new multi-omics machine learning model predicts clear cell renal cell carcinoma (ccRCC) patient response to immune checkpoint blockade (ICB) therapy. This model, based on inflammatory and immune signatures (TIs), offers superior prediction accuracy for guiding precise ccRCC immunotherapy.
Area of Science:
- Oncology
- Immunotherapy
- Bioinformatics
Background:
- Immune checkpoint blockade (ICB) therapy, including PD-1/PD-L1 inhibitors, has improved survival for clear cell renal cell carcinoma (ccRCC) patients.
- However, only a subset of ccRCC patients benefit from current ICB strategies, highlighting the need for better predictive biomarkers.
Purpose of the Study:
- To develop and validate a multi-omics machine learning (ML) model to predict ccRCC response and survival to ICB therapy.
- To identify key inflammatory and immune signatures (TIs) associated with ICB treatment outcomes in ccRCC.
Main Methods:
- Collected and analyzed RNA-seq and single-cell RNA-seq (scRNA-seq) data from over 1,900 ccRCC patients.
- Developed a TIs-ML model integrating genomic and transcriptome profiles to predict ICB response.
- Validated the model's predictive power in over 1,000 ccRCC patients treated with ICB, comparing it against single biomarkers and existing models.
Main Results:
- Identified 716 inflammation-related genes significantly associated with ICB outcomes, enriched in lymphocyte activation pathways.
- The TIs-ML model demonstrated superior predictive performance (AUC > 0.997) compared to individual biomarkers (e.g., PD-L1, TMB) and existing models.
- ccRCC patients were categorized into two subtypes with distinct treatment responses and prognoses based on identified features.
Conclusions:
- The developed TIs-ML model effectively predicts ICB treatment response and survival in ccRCC patients using multi-omics inflammatory and immune features.
- The model exhibits high generalization ability across multiple independent cohorts, offering a novel approach for precise immunotherapy guidance in ccRCC.
- This approach has the potential to significantly improve patient selection for ICB therapy, leading to better clinical outcomes.
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