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The paucity of clinical treatment data on rare tumors, such as mesenchymal chondrosarcoma (MCS), emphasizes the need in theranostic tools for these diseases. We put forward and validated a new theranostic method, combining tumor xenografts and mathematical models, and used it to suggest an improved treatment schedule for a particular MCS patient. Growth curves and gene expression analysis of xenografts, derived from a patient's lung metastasis, served for creating a mathematical model of MCS progression and adapting it to the xenograft setting. The pharmacokinetics and pharmacodynamics of six drugs were modeled, with model variables being adjusted by patient-specific chemosensitivity tests. The xenografted animals were treated by various monotherapy and combination schedules, and the MCS xenograft model was computer simulated under the same treatment scenario. The mathematical model for xenograft growth was then up-scaled to retrieve the MCS patient's tumor progression under different treatment schedules. An average accuracy of 87.1% was obtained when comparing model predictions with the observed tumor growth inhibition in the xenografted animals. Simulation results suggested that a regimen containing bevacizumab applied i.v. in combination with once-weekly docetaxel would be more efficacious in the MCS patient than all other simulated schedules. Weekly docetaxel in the patient resulted in stable metastatic disease and relief of pancytopenia due to tumor infiltration. We suggest that the advantage of weekly docetaxel on the triweekly regimen is directly related to the angiogenesis rate of the tumor. Further validation of this conclusion, and the theranostic method we provide, may facilitate personalization of solid cancer pharmacotherapy.

cancer research

Efficacy of Weekly Docetaxel and Bevacizumab in Mesenchymal Chondrosarcoma: A New Theranostic Method Combining Xenografted Biopsies with a Mathematical Model