Perturbation biology nominates upstream-downstream drug combinations in RAF inhibitor resistant melanoma cells.
Perturbation biology nominates upstream-downstream drug combinations in RAF inhibitor resistant melanoma cells.
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DOI：
10.7554/elife.04640
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发表时间：
2015-08-18
期刊：
影响因子：
7.7
通讯作者：
Sander C
中科院分区：
文献类型：
--
作者：
Korkut A;Wang W;Demir E;Aksoy BA;Jing X;Molinelli EJ;Babur Ö;Bemis DL;Onur Sumer S;Solit DB;Pratilas CA;Sander C
癌症靶向治疗的耐药性是一个重要的临床问题。抗耐药药物组合的发现是具有挑战性的，因为耐药性可以通过不同的逃逸机制产生。为了应对这一挑战，我们改进并应用了实验-计算微扰生物学方法。使用统计推断，我们建立网络模型，从高通量测量的分子和表型反应的组合有针对性的扰动。这些模型通过计算来预测数千种未经测试的扰动的影响。在RAF抑制剂抗性黑色素瘤细胞中，我们在89个扰动条件下测量了143个蛋白质组/表型实体，并预测c-Myc作为与BRAF或MEK的有效治疗共靶点。使用BET布罗莫结构域抑制剂JQ 1影响c-Myc蛋白水平和靶向ERK途径的蛋白激酶抑制剂的实验证实了该预测。总之，我们提出了一种共同靶向特定上游改变和一般下游脆弱点的抗癌策略，以防止或克服对靶向药物的耐药性。DOI：http://dx.doi.org/10.7554/eLife.04640.001针对特定基因活性的药物可能会形成精确的癌症疗法。一些癌症，包括称为黑色素瘤的侵袭性皮肤癌，最初对这种治疗反应良好。然而，对药物的耐药性迅速发展，导致肿瘤的快速再生长。抗药性可以通过多种方式产生。例如，为了防止药物起作用或补偿药物的作用，癌细胞可以调整其信号传导过程或获得影响基因表达的基因突变或其他修饰。针对多个分子途径的药物组合可以使细胞更难抵抗治疗，因为这限制了绕过药物靶点的可用“逃逸”途径的数量。然而，很难准确预测细胞在接受特定药物治疗时的反应，这使得设计有效的药物组合极具挑战性。2013年，研究人员开发了一种技术，根据从扰动实验收集的数据，然后进行数学建模，建立细胞反应途径的预测模型。Korkut等人包括参与2013年工作的几位研究人员，已经改进了这项技术，并将其应用于预防癌细胞耐药性的问题。使用数学模型的计算机模拟表明，在对临床上成功的药物vemurafenib（RAF蛋白的抑制剂，通常在癌症中突变）不敏感的细胞中，有一种特定的“上游-下游靶向”策略。在信号通路的景观中，上游药物的靶标在突变的RAF蛋白上或附近。c-Myc是下游药物的间接靶点，有助于表达触发信号的基因，这些信号导致细胞生长。同时抑制两种靶点可能具有双重优势，即阻断肿瘤特异性生长途径的激活，同时减少癌细胞绕过激活阻断的尝试。设计的药物组合的初步测试需要从计算机模拟转移到实验室，使用最初来自黑色素瘤肿瘤的细胞培养物。当Korkut等人应用药物组合时，组合治疗成功阻止了细胞生长。结果表明，被称为微扰生物学的数据驱动的计算机建模策略可能是一种有用的工具，用于识别有效的癌症药物组合，以进行进一步的临床前研究，随后可能进行临床试验。DOI：http://dx.doi.org/10.7554/eLife.04640.002网站
Resistance to targeted cancer therapies is an important clinical problem. The discovery of anti-resistance drug combinations is challenging as resistance can arise by diverse escape mechanisms. To address this challenge, we improved and applied the experimental-computational perturbation biology method. Using statistical inference, we build network models from high-throughput measurements of molecular and phenotypic responses to combinatorial targeted perturbations. The models are computationally executed to predict the effects of thousands of untested perturbations. In RAF-inhibitor resistant melanoma cells, we measured 143 proteomic/phenotypic entities under 89 perturbation conditions and predicted c-Myc as an effective therapeutic co-target with BRAF or MEK. Experiments using the BET bromodomain inhibitor JQ1 affecting the level of c-Myc protein and protein kinase inhibitors targeting the ERK pathway confirmed the prediction. In conclusion, we propose an anti-cancer strategy of co-targeting a specific upstream alteration and a general downstream point of vulnerability to prevent or overcome resistance to targeted drugs. DOI: http://dx.doi.org/10.7554/eLife.04640.001 Drugs that target the activity of specific genes could potentially form precise cancer therapies. Some cancers, including the aggressive skin cancer called melanoma, initially respond well to such treatments. However, resistance to drugs develops quickly, leading to the rapid regrowth of the tumors. Resistance can develop in a number of ways. For example, to prevent the drug from working or to compensate for the effects of a drug, cancer cells can adapt their signaling processes or acquire genetic mutations or other modifications that affect how genes are expressed. A well-designed combination of drugs that targets multiple molecular pathways can make it harder for cells to resist treatment, as this limits the number of available ‘escape’ pathways that bypass the drug targets. However, it is difficult to accurately predict how a cell will respond when treated with a particular drug, making it extremely challenging to design effective drug combinations. In 2013, researchers developed a technique to build predictive models of cellular response pathways based on data collected from perturbation experiments followed by mathematical modeling. Now, Korkut et al.—including several of the researchers involved in the 2013 work—have refined this technology and applied it to the problem of preventing drug resistance in cancer cells. Computer simulations that used the mathematical models suggested a particular strategy of ‘upstream–downstream targeting’ in cells that were insensitive to the clinically successful drug vemurafenib (an inhibitor of RAF proteins, which are often mutated in cancers). In the landscape of signaling pathways, the target of the upstream drug is on or near the mutated RAF protein. c-Myc, the indirect target of the downstream drug helps to express genes that trigger signals that cause the cells to grow. Inhibiting both targets in parallel may have the dual advantage of blocking the activation of the tumor-specific growth pathway while reducing the cancer cells' attempts to bypass the activation block. An initial test of the designed drug combination required moving from computer simulations to the laboratory using cell cultures originally derived from melanoma tumors. When Korkut et al. applied the drug combination, the combined treatment successfully blocked cell growth. The results suggest that the data-driven computer modeling strategy termed perturbation biology could be a useful tool for identifying effective cancer drug combinations for further preclinical research, possibly followed by clinical trials. DOI: http://dx.doi.org/10.7554/eLife.04640.002