Michele Grimaldi

Implementation of bone stromal cells (acquisition by MPM and implementation in the A(BM)2)

Impact of tumor positioning on bone resorption, representative pictures

Bone metastasis (BM) is a leading cause of morbidity and mortality in patients with prostate and renal cancer. The complex and dynamic biological processes driving its progression present significant challenges for both understanding and treating this disease. Although current in vivo research provides valuable insights, it is often limited by the inability to fully capture the intricate and multifactorial nature of BM. Thus, complementing existing in vivo models with multiscale computational approaches is crucial for dissecting the complex interactions between tumor cells and the bone microenvironment to advance our understanding of the metastatic process and therapy response. Accordingly, we developed a series of in vivo-inspired, spatially explicit, multicellular agent-based models of BM that effectively recapitulate key aspects of tumor progression, including angiogenesis and bone resorption. The digital twins were rigorously calibrated using in vivo data from prostate and kidney tumors. The models have utility for evaluating therapy response, as verified by the simulation of both the antiangiogenic effects of cabozantinib and the antiresorptive effects of zoledronic acid. These results highlight the predictive character of the agent-based models of BM in anticipating therapeutic outcomes and increasing our understanding of the complex dynamics of BM.Significance:The model offers a transformative tool for evaluating treatment strategies, including combination therapies, with the potential to accelerate the development of highly effective, targeted interventions for bone metastatic cancers.This article is part of a special series: Driving Cancer Discoveries with Computational Research, Data Science, and Machine Learning/AI.

Initialization of tumors in different bones, representative pictures and quantifications

BV parameters, quantification at the edge vs core, in PC3 and RENCA tumors

Prediction of the therapy response in tumors of different sizes in silico and in vivo verification

Initialization of tumors in different positions in the A(BM)2 (representative pictures, quantifications) and representative images, in vivo

Random forest algorithm and sensitivity analysis

Prediction of the therapy response after withdrawal in silico and in vivo verification