Digital twin model shows promise for predicting bone metastasis treatment response in prostate and renal cancers
MD Anderson Research News September 18, 2025
A new study, published in , highlights the development of advanced digital twin models designed to more accurately reflect and predict how bone metastasis in prostate and renal cancers responds to treatment. Led by researchers at Â鶹ӳ» MD Anderson Cancer Center and Houston Methodist, the study may pave the way for more personalized and effective care.
"Integrating experimental data with computational modeling creates biologically driven?digital systems that predict treatment impact and guide preclinical research in a powerful feedback loop," said co-corresponding author, , professor of Genitourinary Medical Oncology at MD Anderson.
Bone metastasis is a primary driver of poor clinical outcomes in patients with prostate and renal cancers. However, current laboratory models are unable to capture the complexity of this process.
These first-of-their-kind and spatially explicit, agent-based computational models ¨C called A(BM)2 ¨C include detailed information about the location and arrangement of cells within tumors, and simulate drivers of tumor progression, including blood vessel formation and bone resorption, based on in vivo data from prostate and kidney tumors. These computations were made possible in collaboration with mathematician Stefano Casarin, Ph.D., assistant professor of Surgery at Houston Methodist, and his team.
The A(BM)2 digital twin models also accurately predicted responses to treatments with cabozantinib and zoledronic acid, enabling exploration of clinically relevant scenarios, such as dosing, drug withdrawal and microenvironmental variability.
These findings highlight A(BM)2 as a promising digital twin for evaluating therapeutic strategies for bone metastatic cancers.
This study was supported by the Cancer Prevention and Research Institute of Texas (CPRIT), National Cancer Institute (NCI), the David H. Koch Center for Applied Research of Genitourinary Cancers at MD Anderson, and the John F. Jr. and Carolyn Bookout Presidential Distinguished Chair Fund. For a full list of collaborating authors, disclosures and research funding support, read the full paper at .
Integrating experimental data with computational modeling creates biologically driven digital systems that predict treatment impact and guide preclinical research in a powerful feedback loop.