BMP4分化疗法的虚拟临床试验

Glioma stem cells (GSCs) are considered a major driver of glioblastoma (GBM) progression and are highly resistant to standard cytotoxic treatments. BMP4 has been shown to promote GSC differentiation, enhance radiosensitivity, slow tumor growth, and extend survival in animal models. Despite this promise, BMP4 has yet to achieve clinical impact, owing largely to heterogeneous and nonlinear responses across preclinical experimental systems. To elucidate how BMP4 could function as an effective targeted differentiation therapy in GBM, we develop a mathematical model that describes the growth of a GBM tumor via a hierarchy of GSCs, progenitor and terminally differentiated cells. We parameterize our model using new radiotherapy and proliferation assay data (with and without BMP4 exposure) from twelve patient-derived GSC lines. This integration of model and data allows us, for the first time, to quantitatively capture patient-specific heterogeneity in BMP4 sensitivity. We perform global sensitivity analysis on the model, identifying proliferation rate and GSC self-renewal sensitivity as key determinants of BMP4 efficacy. These parameters act as model-derived biomarkers that distinguish BMP4-responsive tumors from non-responsive ones. In an in silico analysis across a broad cohort of virtual patients, we find that continuous BMP4 delivery from surgical resection through radiotherapy consistently outperforms a single-dose strategy. Virtual clinical trials further show that, without stratification using these model-derived biomarkers, BMP4 yields little observable therapeutic benefit. In contrast, selecting patients with more proliferative, BMP4-responsive GSCs markedly increases the likelihood of observing a significant treatment effect.