Predicting neuroblastoma using developmental signals and a logic-based model.
Predicting neuroblastoma using developmental signals and a logic-based model.
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DOI：
10.1016/j.bpc.2018.04.004
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发表时间：
2018-07
影响因子：
3.8
通讯作者：
Kulesa PM
中科院分区：
文献类型：
--
作者：
Kasemeier-Kulesa JC;Schnell S;Woolley T;Spengler JA;Morrison JA;McKinney MC;Pushel I;Wolfe LA;Kulesa PM
来自小儿神经母细胞瘤癌症患者样本的基因组信息以及已知结果已得出特定基因列表，这些基因被认为具有疾病进展的高风险。然而，依赖基因表达相关性而非机制性见解已显示出有限的潜力，并表明迫切需要能更好地预测神经母细胞瘤进展的分子网络模型。在本研究中，我们在一个6基因输入逻辑模型中构建并模拟了发育基因和下游信号的分子网络，该模型根据细胞分化、增殖、凋亡和血管生成这四种细胞状态的结果来预测有利/不利的结果。我们模拟了酪氨酸受体激酶trkA和trkB（神经母细胞瘤的两个预后指标）的错误表达，并发现稳态结果的数量和概率分布存在差异。我们使用人神经母细胞瘤细胞系SHSY5Y的RNA测序来验证机制模型的假设，以确定输入状态，并通过抗体染色确认预测结果。最后，我们应用来自77份已发表的人类患者样本的输入基因特征，结果表明我们的模型对早期疾病的疾病结果预测比目前任何神经母细胞瘤基因列表都更准确。这些发现凸显了基于发育基因的逻辑模型的预测能力，并更好地理解了神经母细胞瘤疾病进展过程中的分子网络相互作用。
Genomic information from human patient samples of pediatric neuroblastoma cancers and known outcomes have led to specific gene lists put forward as high risk for disease progression. However, the reliance on gene expression correlations rather than mechanistic insight has shown limited potential and suggests a critical need for molecular network models that better predict neuroblastoma progression. In this study, we construct and simulate a molecular network of developmental genes and downstream signals in a 6-gene input logic model that predicts a favorable/unfavorable outcome based on the outcome of the four cell states including cell differentiation, proliferation, apoptosis, and angiogenesis. We simulate the mis-expression of the tyrosine receptor kinases, trkA and trkB, two prognostic indicators of neuroblastoma, and find differences in the number and probability distribution of steady state outcomes. We validate the mechanistic model assumptions using RNAseq of the SHSY5Y human neuroblastoma cell line to define the input states and confirm the predicted outcome with antibody staining. Lastly, we apply input gene signatures from 77 published human patient samples and show that our model makes more accurate disease outcome predictions for early stage disease than any current neuroblastoma gene list. These findings highlight the predictive strength of a logic-based model based on developmental genes and offer a better understanding of the molecular network interactions during neuroblastoma disease progression.
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影响因子：
16.6
作者：
Kasemeier-Kulesa, Jennifer C.;Morrison, Jason A.;Kulesa, Paul M.
通讯作者：
Kulesa, Paul M.
影响因子：
5.7
作者：
Kaneko Y;Suenaga Y;Islam SM;Matsumoto D;Nakamura Y;Ohira M;Yokoi S;Nakagawara A
通讯作者：
Nakagawara A
影响因子：
5
作者：
Edsjö, A;Lavenius, E;Påhlman, S
通讯作者：
Påhlman, S
DOI：
10.1158/1078-0432.ccr-08-1815
发表时间：
2009-05-15
期刊：
Clinical cancer research : an official journal of the American Association for Cancer Research
影响因子：
--
作者：
Brodeur GM;Minturn JE;Ho R;Simpson AM;Iyer R;Varela CR;Light JE;Kolla V;Evans AE
通讯作者：
Evans AE
叉头转录因子FOXO1/3/4通过受体酪氨酸激酶PDGFRA调节神经母细胞瘤分化
DOI：
10.1073/pnas.1119535109
发表时间：
2012-03-27
影响因子：
11.1
作者：
Mei, Yang;Wang, Zhanxiang;You, Han
通讯作者：
You, Han