An Integrative multi-lineage model of variation in leukopoiesis and acute myelogenous leukemia.
An Integrative multi-lineage model of variation in leukopoiesis and acute myelogenous leukemia.
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白细胞和急性粒细胞性白血病的变异的综合多维模型。
DOI：
10.1186/s12918-017-0469-2
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发表时间：
2017-08-25
影响因子：
--
通讯作者：
Rundell AE
中科院分区：
文献类型：
--
作者：
Sarker JM;Pearce SM;Nelson RP Jr;Kinzer-Ursem TL;Umulis DM;Rundell AE
急性髓细胞性白血病（AML）在每个患者中的进展都是独特的。然而，患者通常用相同类型的化疗进行治疗，尽管生物学差异导致对治疗的不同反应。在这里，我们提出了一个多谱系多室模型的造血系统，捕获患者之间的变化，造血细胞群的浓度和变化率。通过将该模型与来自接受诱导化疗的患者的临床造血细胞恢复数据进行约束，我们确定了模型必须满足的参数趋势;例如，有丝分裂率和祖细胞自我更新的概率呈负相关。在数据一致的模型中，我们发现22，796个参数集符合化疗反应标准。这些参数集的模拟显示不同的动态细胞群。为了识别这些模型输出中的大趋势，我们使用k均值聚类对模拟的细胞群体动态进行聚类，并识别出13个“代表性患者”动态。在每个患者群中，我们模拟了AML，发现具有最大有丝分裂能力的患者群更有可能经历临床癌症结果，从而缩短生存时间。相反，其他参数，包括较低的死亡率或动员率，与生存时间无关。使用造血的多谱系模型，我们已经确定了几个决定白细胞稳态的关键特征，包括自我更新概率和有丝分裂率，但不包括动员率。调节AML模型行为的其他有影响力的参数是对外周血中产生的细胞因子/生长因子的响应，其靶向中性粒细胞祖细胞自我更新的概率。最后，我们的模型预测，癌症的有丝分裂率是生存时间最具预测性的参数，其次是影响癌症干细胞自我更新的参数;目前大多数疗法都以有丝分裂率为目标，但根据我们的研究结果，我们提出，针对癌症干细胞自我更新的额外治疗靶向将导致更高的生存率。本文的在线版本（doi：10.1186/s12918-017-0469-2）包含补充材料，可供授权用户使用。
Acute myelogenous leukemia (AML) progresses uniquely in each patient. However, patients are typically treated with the same types of chemotherapy, despite biological differences that lead to differential responses to treatment. Here we present a multi-lineage multi-compartment model of the hematopoietic system that captures patient-to-patient variation in both the concentration and rates of change of hematopoietic cell populations. By constraining the model against clinical hematopoietic cell recovery data derived from patients who have received induction chemotherapy, we identified trends for parameters that must be met by the model; for example, the mitosis rates and the probability of self-renewal of progenitor cells are inversely related. Within the data-consistent models, we found 22,796 parameter sets that meet chemotherapy response criteria. Simulations of these parameter sets display diverse dynamics in the cell populations. To identify large trends in these model outputs, we clustered the simulated cell population dynamics using k-means clustering and identified thirteen ‘representative patient’ dynamics. In each of these patient clusters, we simulated AML and found that clusters with the greatest mitotic capacity experience clinical cancer outcomes more likely to lead to shorter survival times. Conversely, other parameters, including lower death rates or mobilization rates, did not correlate with survival times. Using the multi-lineage model of hematopoiesis, we have identified several key features that determine leukocyte homeostasis, including self-renewal probabilities and mitosis rates, but not mobilization rates. Other influential parameters that regulate AML model behavior are responses to cytokines/growth factors produced in peripheral blood that target the probability of self-renewal of neutrophil progenitors. Finally, our model predicts that the mitosis rate of cancer is the most predictive parameter for survival time, followed closely by parameters that affect the self-renewal of cancer stem cells; most current therapies target mitosis rate, but based on our results, we propose that additional therapeutic targeting of self-renewal of cancer stem cells will lead to even higher survival rates. The online version of this article (doi:10.1186/s12918-017-0469-2) contains supplementary material, which is available to authorized users.
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影响因子：
6.2
作者：
Burnett, Alan K.;Milligan, Donald;Wheatley, Keith
通讯作者：
Wheatley, Keith
影响因子：
2
作者：
Colijn, C;Mackey, MC
通讯作者：
Mackey, MC
影响因子：
2
作者：
Colijn, C;Mackey, MC
通讯作者：
Mackey, MC
影响因子：
4.3
作者：
Getto, Philipp;Marciniak-Czochra, Anna;Vivanco, Maria dM.
通讯作者：
Vivanco, Maria dM.
影响因子：
4.8
作者：
Ashkenazi, Rina;Gentry, Sara N.;Jackson, Trachette L.
通讯作者：
Jackson, Trachette L.