A stochastic Markov chain model to describe lung cancer growth and metastasis.
A stochastic Markov chain model to describe lung cancer growth and metastasis.
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DOI：
10.1371/journal.pone.0034637
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发表时间：
2012
期刊：
影响因子：
3.7
通讯作者：
Kuhn P
中科院分区：
文献类型：
--
作者：
Newton PK;Mason J;Bethel K;Bazhenova LA;Nieva J;Kuhn P
针对原发性肺癌开发了一种用于转移进展的随机马尔可夫链模型，该模型基于转移部位的网络构建，其动力学建模为网络上随机游走者的集合。我们计算一个转移矩阵，其中的条目（转移概率）被解释为随机变量，并使用它构建一个原发和转移位置的圆形双向网络，该网络基于对 3827 例未经治疗的患者尸检的尸检组织分析，记录了该人群的所有原发肿瘤位置和转移部位。由此产生的50个潜在转移位点通过具有分布式权重的有向边连接，其中位点连接和权重是通过计算过渡矩阵集合的条目获得的，使得从马尔可夫链动力系统的长时间极限获得的稳态分布对应于从尸检数据集获得的集合转移分布。我们以从数据集中获得的转移性肿瘤的初始分布为条件来搜索转移矩阵。通过迭代数值搜索过程，我们调整近似序列的条目，直到找到具有正确稳态的转移矩阵（达到数值阈值）。由于此约束线性优化问题是欠定的，因此我们使用奇异值分布的均值和方差作为诊断工具来描述转换矩阵集合的统计方差。我们将集合平均转移概率解释为（近似）正态分布的随机变量。该模型使我们能够模拟和量化疾病进展路径以及从肺部位置到其他部位的进展时间尺度，并且我们强调了基于该模型的几个关键发现。
A stochastic Markov chain model for metastatic progression is developed for primary lung cancer based on a network construction of metastatic sites with dynamics modeled as an ensemble of random walkers on the network. We calculate a transition matrix, with entries (transition probabilities) interpreted as random variables, and use it to construct a circular bi-directional network of primary and metastatic locations based on postmortem tissue analysis of 3827 autopsies on untreated patients documenting all primary tumor locations and metastatic sites from this population. The resulting 50 potential metastatic sites are connected by directed edges with distributed weightings, where the site connections and weightings are obtained by calculating the entries of an ensemble of transition matrices so that the steady-state distribution obtained from the long-time limit of the Markov chain dynamical system corresponds to the ensemble metastatic distribution obtained from the autopsy data set. We condition our search for a transition matrix on an initial distribution of metastatic tumors obtained from the data set. Through an iterative numerical search procedure, we adjust the entries of a sequence of approximations until a transition matrix with the correct steady-state is found (up to a numerical threshold). Since this constrained linear optimization problem is underdetermined, we characterize the statistical variance of the ensemble of transition matrices calculated using the means and variances of their singular value distributions as a diagnostic tool. We interpret the ensemble averaged transition probabilities as (approximately) normally distributed random variables. The model allows us to simulate and quantify disease progression pathways and timescales of progression from the lung position to other sites and we highlight several key findings based on the model.
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影响因子：
45.3
作者：
Cristofanilli, M;Hayes, DF;Terstappen, LWMM
通讯作者：
Terstappen, LWMM
影响因子：
158.5
作者：
Cristofanilli, M;Budd, GT;Hayes, DF
通讯作者：
Hayes, DF
影响因子：
4.6
作者：
AuBuchon, J. P.;de Wildt-Eggen, J.;Dumont, L. J.
通讯作者：
Dumont, L. J.
影响因子：
12.6
作者：
Hsieh, HB;Marrinucci, D;Nieva, J
通讯作者：
Nieva, J
通过循环癌细胞自种肿瘤自种。
影响因子：
64.5
作者：
Kim MY;Oskarsson T;Acharyya S;Nguyen DX;Zhang XH;Norton L;Massagué J
通讯作者：
Massagué J