ArticleNature communications2025
Angiogenic and immune predictors of neoadjuvant axitinib response in renal cell carcinoma with venous tumour thrombus.
Article in Nature communications, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
8 citing papers in PubMed.
- Sequential axitinib and survivin vaccination unlock curative PD-1 immunotherapy in renal carcinoma.Oncoimmunology · 2026Article
- Reviving cancer vaccines in renal cell carcinoma: a new chapter in immunotherapy for kidney cancer.Immuno-oncology technology · 2026Review
- Oligoprogressive renal cell carcinoma: What is the role of surgery?BJU international · 2026Review
- Venous Tumor Thrombus Level in Renal Cell Carcinoma: Impact on Surgical and Oncologic Outcomes.Cancers · 2026Review
- Article
- Review
- Multi-tyrosine kinase inhibitors: exploring immunomodulatory effects on various immune cell types in cancer.Cancer cell international · 2026Review
- Article
Corrections and comments
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Authors and funding
19 authors.
Funding
Abstract
Venous tumour thrombus (VTT), where the primary tumour invades the renal vein and inferior vena cava, affects 10-15% of renal cell carcinoma (RCC) patients. Curative surgery for VTT is high-risk, but neoadjuvant therapy may improve outcomes. The NAXIVA trial demonstrated a 35% VTT response rate after 8 weeks of neoadjuvant axitinib, a VEGFR-directed therapy. However, understanding non-response is critical for better treatment. Here we show that response to axitinib in this setting is characterised by a distinct and predictable set of features. We conduct a multiparametric investigation of samples collected during NAXIVA using digital pathology, flow cytometry, plasma cytokine profiling and RNA sequencing. Responders have higher baseline microvessel density and increased induction of VEGF-A and PlGF during treatment. A multi-modal machine learning model integrating features predict response with an AUC of 0.868, improving to 0.945 when using features from week 3. Key predictive features include plasma CCL17 and IL-12. These findings may guide future treatment strategies for VTT, improving the clinical management of this challenging scenario.
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Registered trials
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