Histopathology based AI model predicts anti-angiogenic therapy response in renal cancer clinical trial

Jay Jasti, Hua Zhong, Vandana Panwar, Vipul Jarmale, Jeffrey Miyata, Deyssy Carrillo, Alana Christie, Dinesh Rakheja, Zora Modrusan, Edward Ernest Kadel, Niha Beig, Mahrukh Huseni, James Brugarolas, Payal Kapur () and Satwik Rajaram ()
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Jay Jasti: University of Texas Southwestern Medical Center
Hua Zhong: University of Texas Southwestern Medical Center
Vandana Panwar: University of Texas Southwestern Medical Center
Vipul Jarmale: University of Texas Southwestern Medical Center
Jeffrey Miyata: University of Texas Southwestern Medical Center
Deyssy Carrillo: University of Texas Southwestern Medical Center
Alana Christie: University of Texas Southwestern Medical Center
Dinesh Rakheja: University of Texas Southwestern Medical Center
Zora Modrusan: Genentech
Edward Ernest Kadel: Genentech
Niha Beig: Genentech
Mahrukh Huseni: Genentech
James Brugarolas: University of Texas Southwestern Medical Center
Payal Kapur: University of Texas Southwestern Medical Center
Satwik Rajaram: University of Texas Southwestern Medical Center

Nature Communications, 2025, vol. 16, issue 1, 1-13

Abstract: Abstract Anti-angiogenic (AA) therapy is a cornerstone of metastatic clear cell renal cell carcinoma (ccRCC) treatment, but not everyone responds, and predictive biomarkers are lacking. CD31, a marker of vasculature, is insufficient, and the Angioscore, an RNA-based angiogenesis quantification method, is costly, associated with delays, difficult to standardize, and does not account for tumor heterogeneity. Here, we developed an interpretable deep learning (DL) model that predicts the Angioscore directly from ubiquitous histopathology slides yielding a visual vascular network (H&E DL Angio). H&E DL Angio achieves a strong correlation with the Angioscore across multiple cohorts (spearman correlations of 0.77 and 0.73). Using this approach, we found that angiogenesis inversely correlates with grade and stage and is associated with driver mutation status. Importantly, DL Angio expediently predicts AA response in both a real-world and IMmotion150 trial cohorts, out-performing CD31, and closely approximating the Angioscore (c-index 0.66 vs 0.67) at a fraction of the cost.

Date: 2025
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DOI: 10.1038/s41467-025-57717-6

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