Predictive computational modeling to define effective treatment strategies for bone metastatic prostate cancer.
Predictive computational modeling to define effective treatment strategies for bone metastatic prostate cancer.
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DOI：
10.1038/srep29384
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发表时间：
2016-07-14
影响因子：
4.6
通讯作者：
Lynch CC
中科院分区：
文献类型：
--
作者：
Cook LM;Araujo A;Pow-Sang JM;Budzevich MM;Basanta D;Lynch CC
对于无法治愈的骨转移性前列腺癌，快速评估治疗策略疗效的能力是一个迫切的需求。临床前体内模型在定义治疗对癌-骨微环境中同时多细胞相互作用的时间效应方面受到限制。将生物学和计算建模方法相结合可以克服这一限制。在这里，我们生成了一个生物驱动的离散混合细胞自动机（HCA）模型的骨转移性前列腺癌，以确定最佳的治疗窗口，为推定的靶向治疗。作为原理的证明，我们关注TGFβ，因为其已知的多效性细胞效应。HCA模拟预测了转移前环境中TGFβ抑制的最佳效果，定量输出表明对前列腺癌细胞活力、破骨细胞形成和成骨细胞分化有显著影响。用骨转移性前列腺癌（PAIII和C4-2B）模型在体内验证了计算机模拟预测。人骨转移性前列腺癌标本的分析揭示了TGFβ的异质性癌细胞使用。将患者特异性信息接种到HCA模型中，以预测TGFβ抑制剂治疗对疾病进展的影响。总的来说，我们展示了一个综合的计算/生物学方法可以快速优化骨转移性前列腺癌的潜在靶向治疗的疗效。
The ability to rapidly assess the efficacy of therapeutic strategies for incurable bone metastatic prostate cancer is an urgent need. Pre-clinical in vivo models are limited in their ability to define the temporal effects of therapies on simultaneous multicellular interactions in the cancer-bone microenvironment. Integrating biological and computational modeling approaches can overcome this limitation. Here, we generated a biologically driven discrete hybrid cellular automaton (HCA) model of bone metastatic prostate cancer to identify the optimal therapeutic window for putative targeted therapies. As proof of principle, we focused on TGFβ because of its known pleiotropic cellular effects. HCA simulations predict an optimal effect for TGFβ inhibition in a pre-metastatic setting with quantitative outputs indicating a significant impact on prostate cancer cell viability, osteoclast formation and osteoblast differentiation. In silico predictions were validated in vivo with models of bone metastatic prostate cancer (PAIII and C4-2B). Analysis of human bone metastatic prostate cancer specimens reveals heterogeneous cancer cell use of TGFβ. Patient specific information was seeded into the HCA model to predict the effect of TGFβ inhibitor treatment on disease evolution. Collectively, we demonstrate how an integrated computational/biological approach can rapidly optimize the efficacy of potential targeted therapies on bone metastatic prostate cancer.