Angiogenic and immune predictors of neoadjuvant axitinib response in renal cell carcinoma with venous tumour thrombus
Rebecca Wray, Hania Paverd, Inês Machado, Johanna Barbieri, Farhana Easita, A. Edwards, Ferdia A. Gallagher, Iosif Mendichovszky, Thomas J. Mitchell, Maike de la Roche, Jacqueline D. Shields, Stephan Ursprung, Lauren Wallis, Anne Y. Warren, Sarah J. Welsh, Mireia Crispin‐Ortuzar, Grant D. Stewart, James O. Jones, On behalf of the NAXIVA Study Group, Niki Couper, Lisa E. M. Hopcroft, Robert Hill, Athena Matakidou, Cara Caasi, James Watson, Ruby Cross, Sarah W. Burge, Anne George, Tobias Klatte, Tevita F. Aho, James N. Armitage, Sabrina Helena Rossi, Charlie Massie, Shubha Anand, Tiffany Haddow, Marc Dodd, Wenhan Deng, Ezequiel Martin, Philip Howden, Stephanie Wenlock, Evis Sala, Stefan Symeonides, Lynn Ho, Jennifer Baxter, Stuart Leslie, Duncan McLaren, John Brush, Marie O’Donnell, Alisa Griffin, Ruth Orr, Catriona Cowan, Thomas Powles, Anna Pejnovic, Sophia Tincey, Lee Grant, Martin Nuttall, Lucy Willsher, Christian Barnett, David Nicol, James Larkin, Alison Fielding, Christopher G. Smith, Axel Bex, Ekaterini Boleti, Jade Carruthers, Tim Eisen, Kate Fife, Angela Godoy, Abdel Hamid, Alexander Laird, Steve Leung, Jahangeer Malik, Faiz Mumtaz, Grenville Oades, Andrew N. Priest, Antony C. P. Riddick, Balaji Venugopal, Michelle Welsh, Kathleen Riddle, Robert J. Jones
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.