A mechanistic pan-cancer pathway model informed by multi-omics data interprets stochastic cell fate responses to drugs and mitogens.
A mechanistic pan-cancer pathway model informed by multi-omics data interprets stochastic cell fate responses to drugs and mitogens.
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DOI：
10.1371/journal.pcbi.1005985
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发表时间：
2018-03
影响因子：
4.3
通讯作者：
Birtwistle MR
中科院分区：
文献类型：
--
作者：
Bouhaddou M;Barrette AM;Stern AD;Koch RJ;DiStefano MS;Riesel EA;Santos LC;Tan AL;Mertz AE;Birtwistle MR
大多数癌细胞具有多个驱动因子，其上位性和与表达背景的相互作用使药物和药物组合敏感性预测变得模糊。我们构建了一个机械计算模型，该模型是由组学数据定制的，以捕获泛癌症驱动途径对随机增殖和死亡的调节。模拟和实验探索了RAF/MEK/ERK和PI-3 K/AKT激酶活性的协调动力学如何响应协同促分裂原或药物组合在特定细胞环境中控制细胞命运。在这种MCF 10A细胞背景下，模拟表明协同ERK和AKT介导的死亡可能是由BIM而不是BAD介导的，这得到了先前实验研究的支持。AKT动力学解释了EGF和胰岛素之间的S期进入协同作用，但模拟表明，随机ERK，而不是AKT，动力学似乎驱动细胞间增殖的变化，这在模拟中是可预测的C-Raf/B-Raf水平的刺激前波动。模拟表明MEK改变对转化的影响可忽略不计，与临床数据一致。将模型定制为替代细胞表达和突变背景（神经胶质瘤细胞系），允许预测细胞死亡对AKT抑制的敏感性增加。我们的模型机械地解释了驱动途径和细胞命运之间的特定背景景观，为设计更合理的癌症联合治疗提供了一个框架。癌症是一种复杂而多样的疾病。患有相同癌症类型的两个人对相同治疗的反应往往不同。这些差异主要是由两种类型匹配的肿瘤可能具有不同的突变和基因表达谱，引起对药物的不同敏感性这一事实驱动的。在过去的几十年里，我们已经看到从更广泛的细胞毒性药物转向更有针对性的分子治疗;但如何将患者与特定药物或药物鸡尾酒相匹配仍然是一个难题。在这里，我们建立了一个机制的常微分方程模型，描述了常见的突变泛癌信号通路-受体酪氨酸激酶，Ras/RAF/ERK，PI 3 K/AKT，mTOR，细胞周期，DNA损伤和凋亡之间的相互作用。我们开发了如何将模型定制为来自特定生物背景的多组学数据的方法，设计了一种新的随机算法来诱导mRNA和蛋白质量随时间的非遗传细胞间波动，并针对丰富的生化和细胞命运数据训练模型，以深入了解系统水平，特定于背景的增殖和死亡控制。有一天，我们希望这种模型可以根据患者来源的肿瘤mRNA测序数据进行定制，并用于优先考虑患者特定的药物方案。
Most cancer cells harbor multiple drivers whose epistasis and interactions with expression context clouds drug and drug combination sensitivity prediction. We constructed a mechanistic computational model that is context-tailored by omics data to capture regulation of stochastic proliferation and death by pan-cancer driver pathways. Simulations and experiments explore how the coordinated dynamics of RAF/MEK/ERK and PI-3K/AKT kinase activities in response to synergistic mitogen or drug combinations control cell fate in a specific cellular context. In this MCF10A cell context, simulations suggest that synergistic ERK and AKT inhibitor-induced death is likely mediated by BIM rather than BAD, which is supported by prior experimental studies. AKT dynamics explain S-phase entry synergy between EGF and insulin, but simulations suggest that stochastic ERK, and not AKT, dynamics seem to drive cell-to-cell proliferation variability, which in simulations is predictable from pre-stimulus fluctuations in C-Raf/B-Raf levels. Simulations suggest MEK alteration negligibly influences transformation, consistent with clinical data. Tailoring the model to an alternate cell expression and mutation context, a glioma cell line, allows prediction of increased sensitivity of cell death to AKT inhibition. Our model mechanistically interprets context-specific landscapes between driver pathways and cell fates, providing a framework for designing more rational cancer combination therapy. Cancer is a complex and diverse disease. Two people with the same cancer type often respond differently to the same treatment. These differences are primarily driven by the fact that two type-matched tumors can possess distinct sets of mutations and gene expression profiles, provoking differential sensitivity to drugs. Over the past few decades, we have seen a shift away from more broadly cytotoxic drugs to more targeted molecules therapies; but how to match a patient with a specific drug or drug cocktail remains a difficult problem. Here, we build a mechanistic ordinary differential equation model describing the interactions between commonly mutated pan-cancer signaling pathways—receptor tyrosine kinases, Ras/RAF/ERK, PI3K/AKT, mTOR, cell cycle, DNA damage, and apoptosis. We develop methods for how to tailor the model to multi-omics data from a specific biological context, devise a novel stochastic algorithm to induce non-genetic cell-to-cell fluctuations in mRNA and protein quantities over time, and train the model against a wealth of biochemical and cell fate data to gain insight into the systems-level, context-specific control of proliferation and death. One day, we hope models of this kind could be tailored to patient-derived tumor mRNA sequencing data and used to prioritize patient-specific drug regimens.
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影响因子：
9.8
作者：
Albeck JG;Burke JM;Spencer SL;Lauffenburger DA;Sorger PK
通讯作者：
Sorger PK
影响因子：
12.3
作者：
Carpenter AE;Jones TR;Lamprecht MR;Clarke C;Kang IH;Friman O;Guertin DA;Chang JH;Lindquist RA;Moffat J;Golland P;Sabatini DM
通讯作者：
Sabatini DM
影响因子：
45.3
作者：
Cobleigh, MA;Vogel, CL;Slamon, DJ
通讯作者：
Slamon, DJ
影响因子：
9.9
作者：
Chen, William W.;Schoeberl, Birgit;Jasper, Paul J.;Niepel, Mario;Nielsen, Ulrik B.;Lauffenburger, Douglas A.;Sorger, Peter K.
通讯作者：
Sorger, Peter K.
影响因子：
5.6
作者：
BRANDHORST, BP;MCCONKEY, EH
通讯作者：
MCCONKEY, EH