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Comutation and exclusion analysis in human tumors: A tool for cancer biology studies and for rational selection of multitargeted therapeutic approaches

机译:人肿瘤中的计算与排除分析:癌症生物学研究的工具和多目标治疗方法的理性选择

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Abstract Malignant tumors originate from somatic mutations and other genomic and epigenomic alterations, which lead to loss of control of the cellular circuitry. These alterations present patterns of co‐occurrence and mutual exclusivity that can influence prognosis and modify response to drugs, highlighting the need for multitargeted therapies. Studies in this area have generally focused in particular malignancies and considered whole genes instead of specific mutations, ignoring the fact that different alterations in the same gene can have widely different effects. Here, we present a comprehensive analysis of co‐dependencies of individual somatic mutations in the whole spectrum of human tumors. Combining multitesting with conditional and expected mutational probabilities, we have discovered rules governing the codependencies of driver and nondriver mutations. We also uncovered pairs and networks of comutations and exclusions, some of them restricted to certain cancer types and others widespread. These pairs and networks are not only of basic but also of clinical interest, and can be of help in the selection of multitargeted antitumor therapies. In this respect, recurrent driver comutations suggest combinations of drugs that might be effective in the clinical setting, while recurrent exclusions indicate combinations unlikely to be useful.
机译:摘要恶性肿瘤源自躯体突变和其他基因组和表观胶质改变,这导致对细胞电路的控制丧失。这些改变目前的共同发生和相互排斥的模式可以影响预后和修改对药物的反应,突出了对多元疗法的需求。该地区的研究通常集中于特定的恶性肿瘤并考虑了整个基因而不是特定突变,忽略了同一基因中不同改变的事实可以具有广泛不同的效果。在这里,我们对整个人类肿瘤的全体细胞突变的共同依赖性综合分析。结合有条件和预期的突变概率的MultiTiting,我们发现了管理司机和非正载突变的依赖关系的规则。我们还发现了对和排斥的对和网络,其中一些限制为某些癌症类型和其他人普遍存在。这些对和网络不仅是基本的还是临床兴趣,并且可以有助于选择多元抗肿瘤疗法。在这方面,经常性司机的关系表明,在临床环境中可能有效的药物的组合,而复发性排除表明可能是不可用的组合。

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