模拟肿瘤进展中基因变化的影响

Vanderson Silva, F. Santana, B. Stransky, S. J. Souza
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引用次数: 0

摘要

肿瘤可以被认为是一组积累遗传和表观遗传改变的细胞。根据多阶段Hit理论,正常细胞向肿瘤细胞的转变涉及到许多限制事件,这些事件发生在许多离散的阶段(驱动突变)。然而,并非细胞中发生的所有突变都直接与癌症的发展有关,有些突变可能根本没有作用(乘客突变)。此外,肿瘤进化的过程被有利突变的选择和克隆扩增打断。实际上,目前尚不清楚有多少限制事件,即有多少驱动突变是必要的或足以促进致癌过程的。这一猜想应该得到探索和检验——用数学和统计方法,利用数据库中基因组数据的可用性。在这项工作中,我们探索了Bozic及其合作者(2010)提出的模型,该模型根据高尔顿-沃森过程描述了肿瘤的进化。此外,该模型给出了给定特定驾驶员突变数时乘客突变数之间的关系。我们打算探索一些模型参数,测试一些关于驱动突变数量和选择优势的前提,并将模拟结果与结直肠癌患者的基因组数据进行比较。基因组数据来自DBMutation (http://www.bioinformatics-brazil.org/dbmutation/),这是一个关于癌症基因组突变的综合数据库。我们期望驱动突变和肿瘤过程时间演化之间的相关性将促进基因组信息的解释,使其对临床肿瘤学有用和适用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Modelling the Effects of Genetic Changes in Tumour Progression
Tumours can be considered a set of cells that accumulate genetic and epigenetic alterations. According to the Multi-stage Hit theory, the transformation of a normal into a tumour cell involves a number of limiting events that occur in a number of discrete stages (driver mutations). However, not all mutations that occur in the cell are directly involved in the development of cancer and some probably do not contribute in any way (passenger mutations). Moreover, the process of tumour evolution is punctuated by selection of advantageous mutations and clonal expansions. Actually, it is not known how many limiting-events, i.e., how many driver mutations are necessary or sufficient to promote a carcinogenic process. This conjecture should be explored and tested - mathematically and statistically, with the availability of genomic data on databanks. In this work, we explore the model proposed by Bozic and collaborators (2010) that describes the evolution of the tumour according to a Galton-Watson process. Besides, the model gives the relation between the numbers of passenger mutations giving a specific number of driver mutations. We intend to explore some of the model parameters and test some premises about the number of drive mutations and selective advantage, comparing the simulation results with genomic data from colorectal cancer patients. The genomic data was obtained from the DBMutation (http://www.bioinformatics-brazil.org/dbmutation/), a comprehensive database for genomic mutations in cancer. We expect that correlations between driver mutations and the time evolution of tumour process will facilitate the interpretation of genomic information, to make them useful and applicable to clinical oncology.
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