Angiogenic and immune predictors of neoadjuvant axitinib response in renal cell carcinoma with venous tumour thrombus

Rebecca Wray, Hania Paverd, Ines Machado, Johanna Barbieri, Farhana Easita, Abigail R. Edwards, Ferdia A. Gallagher, Iosif A. Mendichovszky, Thomas J. Mitchell, Maike Roche, Jacqueline D. Shields, Stephan Ursprung, Lauren Wallis, Anne Y. Warren, Sarah J. Welsh, Mireia Crispin-Ortuzar, Grant D. Stewart and James O. Jones ()
Additional contact information
Rebecca Wray: University of Cambridge
Hania Paverd: University of Cambridge
Ines Machado: University of Cambridge
Johanna Barbieri: University of Cambridge
Farhana Easita: University of Cambridge
Abigail R. Edwards: University of Cambridge
Ferdia A. Gallagher: Cambridge University Hospitals NHS Foundation Trust
Iosif A. Mendichovszky: Cambridge University Hospitals NHS Foundation Trust
Thomas J. Mitchell: University of Cambridge
Maike Roche: University of Cambridge
Jacqueline D. Shields: University of Nottingham Biodiscovery Institute
Stephan Ursprung: Cambridge University Hospitals NHS Foundation Trust
Lauren Wallis: University of Warwick
Anne Y. Warren: Cambridge University Hospitals NHS Foundation Trust
Sarah J. Welsh: Royal Devon University Healthcare NHS Foundation Trust
Mireia Crispin-Ortuzar: University of Cambridge
Grant D. Stewart: University of Cambridge
James O. Jones: University of Cambridge

Nature Communications, 2025, vol. 16, issue 1, 1-14

Abstract: 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.

Date: 2025
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.nature.com/articles/s41467-025-58436-8 Abstract (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:nat:natcom:v:16:y:2025:i:1:d:10.1038_s41467-025-58436-8

Ordering information: This journal article can be ordered from
https://www.nature.com/ncomms/

DOI: 10.1038/s41467-025-58436-8

Access Statistics for this article

Nature Communications is currently edited by Nathalie Le Bot, Enda Bergin and Fiona Gillespie

More articles in Nature Communications from Nature
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().