Computational modeling of interactions between multiple myeloma and the bone microenvironment.
Computational modeling of interactions between multiple myeloma and the bone microenvironment.
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多发性骨髓瘤与骨微环境之间相互作用的计算模型。
DOI：
10.1371/journal.pone.0027494
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发表时间：
2011
期刊：
影响因子：
3.7
通讯作者：
Dunstan CR
中科院分区：
文献类型：
--
作者：
Wang Y;Pivonka P;Buenzli PR;Smith DW;Dunstan CR
多发性骨髓瘤（MM）是一种以溶骨性骨病变为特征的B细胞恶性肿瘤。据推测，MM细胞和骨微环境之间相互作用的正反馈回路形成了强化的“恶性循环”，导致骨微环境中更多的骨吸收和MM细胞群生长。尽管确定了许多MM-骨相互作用，但这些相互作用的综合效应及其相对重要性尚不清楚。在本文中，我们开发了一个MM-骨相互作用的计算模型，并阐明了该模型中实施的细胞间信号传导机制是否适当地驱动MM疾病进展。这种新的计算模型是基于Pivonka等人先前的骨重建模型，并明确考虑了IL-6和MM-BMSC（骨髓基质细胞）粘附相关途径，导致在该模型中形成两个正反馈循环。数值模拟MM疾病的进展，从正常的骨生理学到明确的MM疾病状态。我们的模拟与正常骨生理学和MM疾病的已知行为和数据一致。模型结果表明，该模型确定的两个正反馈循环足以共同驱动MM疾病进展。此外，对两个正反馈循环进行定量分析，阐明了两个正反馈循环的相对重要性，并确定了支配两个正反馈循环行为的主导过程。使用我们提出的定量标准，我们确定在这个模型中的正反馈循环可以被认为是“恶性循环”。最后，确定了阻断MM-骨相互作用中正反馈循环的关键点，提示了潜在的药物靶点。
Multiple Myeloma (MM) is a B-cell malignancy that is characterized by osteolytic bone lesions. It has been postulated that positive feedback loops in the interactions between MM cells and the bone microenvironment form reinforcing ‘vicious cycles’, resulting in more bone resorption and MM cell population growth in the bone microenvironment. Despite many identified MM-bone interactions, the combined effect of these interactions and their relative importance are unknown. In this paper, we develop a computational model of MM-bone interactions and clarify whether the intercellular signaling mechanisms implemented in this model appropriately drive MM disease progression. This new computational model is based on the previous bone remodeling model of Pivonka et al., and explicitly considers IL-6 and MM-BMSC (bone marrow stromal cell) adhesion related pathways, leading to formation of two positive feedback cycles in this model. The progression of MM disease is simulated numerically, from normal bone physiology to a well established MM disease state. Our simulations are consistent with known behaviors and data reported for both normal bone physiology and for MM disease. The model results suggest that the two positive feedback cycles identified for this model are sufficient to jointly drive the MM disease progression. Furthermore, quantitative analysis performed on the two positive feedback cycles clarifies the relative importance of the two positive feedback cycles, and identifies the dominant processes that govern the behavior of the two positive feedback cycles. Using our proposed quantitative criteria, we identify which of the positive feedback cycles in this model may be considered to be ‘vicious cycles’. Finally, key points at which to block the positive feedback cycles in MM-bone interactions are identified, suggesting potential drug targets.
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