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造血干细胞龛中的生态决定了慢性髓细胞白血病的临床结果。

The ecology in the hematopoietic stem cell niche determines the clinical outcome in chronic myeloid leukemia.

机构信息

Theoretical Systems Biology, Division of Molecular Biosciences, Imperial College London, London SW7 2AZ, United Kingdom.

出版信息

Proc Natl Acad Sci U S A. 2014 Mar 11;111(10):3883-8. doi: 10.1073/pnas.1317072111. Epub 2014 Feb 24.

Abstract

Chronic myeloid leukemia (CML) is a blood disease that disrupts normal function of the hematopoietic system. Despite the great progress made in terms of molecular therapies for CML, there remain large gaps in our understanding. By comparing mathematical models that describe CML progression and etiology we sought to identify those models that provide the best description of disease dynamics and their underlying mechanisms. Data for two clinical outcomes--disease remission or relapse--are considered, and we investigate these using Bayesian inference techniques throughout. We find that it is not possible to choose between the models based on fits to the data alone; however, by studying model predictions we can discard models that fail to take niche effects into account. More detailed analysis of the remaining models reveals mechanistic differences: for one model, leukemia stem cell dynamics determine the disease outcome; and for the other model disease progression is determined at the stage of progenitor cells, in particular by differences in progenitor death rates. This analysis also reveals distinct transient dynamics that will be experimentally accessible, but are currently at the limits of what is possible to measure. To resolve these differences we need to be able to probe the hematopoietic stem cell niche directly. Our analysis highlights the importance of further mapping of the bone marrow hematopoietic niche microenvironment as the "ecological" interactions between cells in this niche appear to be intricately linked to disease outcome.

摘要

慢性髓性白血病(CML)是一种血液病,会扰乱造血系统的正常功能。尽管在 CML 的分子治疗方面取得了巨大进展,但我们对其的理解仍存在很大差距。通过比较描述 CML 进展和病因的数学模型,我们试图确定那些能够最好地描述疾病动态及其潜在机制的模型。我们考虑了两种临床结果——疾病缓解或复发的数据,并在整个过程中使用贝叶斯推断技术进行了研究。我们发现,仅根据数据拟合来选择模型是不可能的;然而,通过研究模型预测,我们可以排除那些未能考虑生态位效应的模型。对剩余模型的更详细分析揭示了机制上的差异:对于一个模型,白血病干细胞动力学决定疾病的结果;对于另一个模型,疾病的进展是在祖细胞阶段决定的,特别是由于祖细胞死亡率的差异。这项分析还揭示了独特的瞬态动力学,这些动力学将在实验上是可及的,但目前仍处于可测量的极限。为了解决这些差异,我们需要能够直接探测造血干细胞生态位。我们的分析强调了进一步绘制骨髓造血生态位微环境图谱的重要性,因为这个生态位中细胞之间的“生态”相互作用似乎与疾病结果密切相关。

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