Bivalent chromatin as a therapeutic target in cancer: An in silico predictive approach for combining epigenetic drugs.
Bivalent chromatin as a therapeutic target in cancer: An in silico predictive approach for combining epigenetic drugs.
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二价染色质作为癌症治疗靶点：联合表观遗传药物的计算机预测方法。
DOI：
10.1371/journal.pcbi.1008408
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发表时间：
2021-06
影响因子：
4.3
通讯作者：
Menendez JA
中科院分区：
文献类型：
--
作者：
Alarcón T;Sardanyés J;Guillamon A;Menendez JA
肿瘤细胞异质性是有效设计靶向抗癌疗法的主要障碍。在药物治疗之前，表型不同的肿瘤细胞亚群的多样性分布倾向于不均匀的反应，导致敏感癌细胞的消除，同时使耐药亚群不受伤害。很少有策略被提出来量化与个体癌细胞异质性相关的变异性，并最大限度地减少其对临床结果的不良影响。在这里，我们报告了一种计算方法，允许合理设计的组合疗法，涉及表观遗传药物对染色质修饰剂。我们已经制定了一个随机模型的二价转录因子，使我们能够识别三种不同的定性行为，即：高，高和低基因表达。分析结果和实验数据之间的比较确定了所谓的高表达和高基因表达行为可以分别用未分化和分化的细胞类型来识别。由于具有异常自我更新潜力的未分化细胞可能表现出癌症/转移起始表型，因此我们分析了表观遗传药物结合的效率，其背景是表观遗传子系综内的异质性。虽然单一靶向方法大多未能规避肿瘤异质性所代表的治疗问题，但组合策略表现得更好。具体而言，预测更成功的组合涉及组蛋白H3 K4和H3 K27脱甲基酶KDM 5和KDM 6A/UTX的调节剂。然而，预测涉及H3 K4和H3 K27甲基转移酶MLL 2和EZH 2的那些策略不太有效。我们的理论框架为开发一个计算机平台提供了一致的基础，该平台能够识别最适合治疗管理异质性癌细胞群体的非均匀反应的表观遗传药物组合。癌细胞群中的异源性是对靶向治疗产生耐药性的主要原因之一，因为它在清除敏感亚群的群体内诱导不均匀的反应，同时不影响非反应性细胞。尽管这是一个众所周知的事实，但很少有成功的方法被提出，旨在量化与细胞异质性相关的变异性，并表征规避其耐药性诱导作用的策略。在这里，我们提出了一种计算方法，解决了这些问题的特定背景下，靶向表观遗传调节剂（特别是染色质修饰剂），这已被提出作为治疗目标，在几种类型的癌症，也在衰老相关的疾病。我们的模型预测，更成功的组合涉及脱甲基酶活性的调节剂（特别是KDM 5/6和UTX）。相比之下，涉及EZH 2活性的策略被预测为不太有效。我们的研究结果支持使用我们的框架作为计算机药物试验的平台，因为它解释了细胞群对药物的非同质反应，以及确定哪些亚群更有可能对特定策略做出反应。
Tumour cell heterogeneity is a major barrier for efficient design of targeted anti-cancer therapies. A diverse distribution of phenotypically distinct tumour-cell subpopulations prior to drug treatment predisposes to non-uniform responses, leading to the elimination of sensitive cancer cells whilst leaving resistant subpopulations unharmed. Few strategies have been proposed for quantifying the variability associated to individual cancer-cell heterogeneity and minimizing its undesirable impact on clinical outcomes. Here, we report a computational approach that allows the rational design of combinatorial therapies involving epigenetic drugs against chromatin modifiers. We have formulated a stochastic model of a bivalent transcription factor that allows us to characterise three different qualitative behaviours, namely: bistable, high- and low-gene expression. Comparison between analytical results and experimental data determined that the so-called bistable and high-gene expression behaviours can be identified with undifferentiated and differentiated cell types, respectively. Since undifferentiated cells with an aberrant self-renewing potential might exhibit a cancer/metastasis-initiating phenotype, we analysed the efficiency of combining epigenetic drugs against the background of heterogeneity within the bistable sub-ensemble. Whereas single-targeted approaches mostly failed to circumvent the therapeutic problems represented by tumour heterogeneity, combinatorial strategies fared much better. Specifically, the more successful combinations were predicted to involve modulators of the histone H3K4 and H3K27 demethylases KDM5 and KDM6A/UTX. Those strategies involving the H3K4 and H3K27 methyltransferases MLL2 and EZH2, however, were predicted to be less effective. Our theoretical framework provides a coherent basis for the development of an in silico platform capable of identifying the epigenetic drugs combinations best-suited to therapeutically manage non-uniform responses of heterogenous cancer cell populations. Heterogeneity in cancer cell populations is one of the main engines of resistance to targeted therapies, as it induces nonuniform responses within the population that clears the sensitive subpopulation, whilst leaving unaffected the non-responsive cells. Although this is a well-known fact, few successful approaches have been proposed aimed at both quantifying the variability associated to cell heterogeneity, and characterising strategies that circumvent its drug-resistance inducing effects. Here we present a computational approach that addresses these issues in the particular context of targeting epigenetic regulators (specifically, chromatin modifiers), which have been proposed as therapeutic targets in several types of cancer and also in ageing-related diseases. Our model predicts that the more successful combinations involve modulators of demethylase activity (specifically, KDM5/6 and UTX). By contrast, strategies involving EZH2 activity are predicted to be less effective. Our results support the use of our framework as a platform for in silico drug trials, as it accounts for non-homogeneous response of cell populations to drugs as well as identifying which subpopulations are more likely to respond to specific strategies.
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影响因子：
4.4
作者：
Bruna, Maria;Chapman, S. Jonathan;Smith, Matthew J.
通讯作者：
Smith, Matthew J.
在单细胞水平上表观遗传调节的动力学。
DOI：
10.1126/science.aab2956
发表时间：
2016-02-12
期刊：
Science (New York, N.Y.)
影响因子：
--
作者：
Bintu L;Yong J;Antebi YE;McCue K;Kazuki Y;Uno N;Oshimura M;Elowitz MB
通讯作者：
Elowitz MB
影响因子：
37.3
作者：
Crea F;Sun L;Mai A;Chiang YT;Farrar WL;Danesi R;Helgason CD
通讯作者：
Helgason CD
影响因子：
8.8
作者：
Ezponda T;Dupéré-Richer D;Will CM;Small EC;Varghese N;Patel T;Nabet B;Popovic R;Oyer J;Bulic M;Zheng Y;Huang X;Shah MY;Maji S;Riva A;Occhionorelli M;Tonon G;Kelleher N;Keats J;Licht JD
通讯作者：
Licht JD
影响因子：
44.1
作者：
Cortini, Ruggero;Barbi, Maria;Victor, Jean-Marc
通讯作者：
Victor, Jean-Marc