ArticleFrontiers in oncology2023
A novel immune-related model to predict prognosis and responsiveness to checkpoint and angiogenesis blockade therapy in advanced renal cancer.
Article in Frontiers in oncology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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5 citing papers in PubMed, 4 citations in OpenAlex.
- Transcriptome signatures for the identification of bevacizumab responders in ovarian cancer.Genome medicine · 2026Article
- Rationale of using immune checkpoint inhibitors (ICIs) and anti-angiogenic agents in cancer treatment from a molecular perspective.Clinical and experimental medicine · 2025Review
- Opportunities and Challenges in Soft Tissue Sarcoma Risk Stratification in the Era of Personalised Medicine.Current treatment options in oncology · 2024Review
- The contribution of the CRP/CD64 axis to renal cancer progression by inducing protumor activation of tumor-associated macrophages.Clinical & translational immunology · 2024Article
- Article
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Authors and funding
5 authors at 2 institutions in 1 country.
Funding
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Abstract
Background: Immune checkpoint blockade (ICB) and anti-angiogenic drug combination has prolonged the survival of patients with advanced renal cell carcinoma (RCC). However, not all patients receive clinical benefits from this intervention. In this study, we aimed to establish a promising immune-related prognostic model to stratify the patients responding to ICB and anti-angiogenic drug combination and facilitate the development of personalized therapies for patients with RCC. Materials and methods: Based on clinical annotations and RNA-sequencing (RNA-seq) data of 407 patients with advanced RCC from the IMmotion151 cohort, nine immune-associated differentially expressed genes (DEGs) between responders and non-responders to atezolizumab (anti-programmed death-ligand 1 antibody) plus bevacizumab (anti-vascular endothelial growth factor antibody) treatment were identified Results: The IRS model was constructed using nine immune-associated DEGs: Conclusion: IRS model is a reliable and robust immune signature that can be used for patient selection to optimize the efficacy of ICB plus anti-angiogenic drug therapies in patients with advanced RCC.
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