Quantitative study of transcriptome dynamics during evolution and treatment resistance in cancer

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Title: Quantitative study of transcriptome dynamics during evolution and treatment resistance in cancer
Author: Chen, Ping
Contributor organization: University of Helsinki, Faculty of Medicine, Institute of Biomedicine, Research Programs Unit, Genome-Scale Biology and Medicum
Helsingin yliopisto, lääketieteellinen tiedekunta, biolääketieteen laitos
Helsingfors universitet, medicinska fakulteten, biomedicinska institutionen
Publisher: Helsingin yliopisto
Date: 2016-01-29
Language: eng
URI: http://urn.fi/URN:ISBN:978-951-51-1864-6
Thesis level: Doctoral dissertation (article-based)
Abstract: Transcriptome, defined by the collection of all RNA molecules in a cell, acts as a central bridge that transfers genetic information into molecular functions. Transcriptome regulates the biological characteristics in all living organisms, thus it is one of the most important research subjects in biology. RNAs are transcribed at different levels tightly controlled by cellular conditions. This produces great diversity in cellular transcriptome dynamics, introducing a lot of complexity to the transcriptomic research. Though tremendous challenges exist, the study of transcriptome dynamics is essential to the understanding of the complex systems within the cells and cellular behavior. The dynamics of transcriptome can be investigated by high-throughput technologies, such as microarrays and RNA-sequencing. The large amounts of data introduces challenges to data management, analysis and interpretation. To generate biologically testable and conclusive results, efficient computational methods are urgently needed. This thesis includes theoretical and methodological research. The theoretical part of the research comprehensively studies the characteristics of gene expression, the splicing of ancient and novel exons during the evolution by comparative analysis on transcriptomic data of nine tissues from five species. The methodological research includes new methods developed to solve the research questions related to the study of transcriptomic dynamics in evolution and cancer. The main methods developed in this thesis are 1) exon age classifier, which is able to classify exons according to their evolutionary time, providing the basis for the theoretical study in this thesis; 2) MEAP, a new exon array preprocessing method for expression quantification at multi-levels; 3) PSFinder, a new approach to identify patient prognostic subgroups from treatment naive tumor samples based on their transcriptomic profiles and associated clinical survival times. The theoretical part gives a comprehensive view on the mechanisms of dynamic changes during the evolution of the transcriptome, which provides a solid theoretical basis to the methodological part. The application of MEAP and PSFinder to high-grade serous ovarian cancer revealed a small set of isoform markers with distinct expression profiles for patient prognosis stratification. In combination with experimental validation, the results demonstrate the applicability of these methods in the quantification and stratification of tumor transcriptome dynamics, which provides new insights to the clinical diagnosis and precision medicine for human cancers.转录组是细胞内所有RNA分子的集合,它将细胞内遗传信息编译为具体的功能性 分子。转录组调节了生物体内的各项生物学特性,是生物学中的重要研究对象之 一。在细胞各分子机制的严密调节控制下,RNA被转录为不同的水平。这产生了 细胞转录动态中的多样性,从而给转录组学的研究带来许多复杂性。因此转录组动 态的研究对了解细胞中的复杂系统及细胞行为极为重要,但也意味着转录组研究 的难度较高。 转录组的动态可以通过高通量技术,例如表达芯片及RNA测序来研究。大量的数 据给数据管理,分析及解释带来了挑战。有效的计算分析方法,是生成生物学上 可检测和确定性结果的关键。 本文包含了理论和方法的研究。该研究的理论部分对来自五种物种九种组织的转 录组数据进行比较分析,综合研究了在进化过程中基因表达及新老外显子的剪切 特性。该论文的方法学研究包括了为了解决进化及癌症中转录组动态研究的相关 问题而开发的新算法。本文中开发的主要方法有: 1) 外显子分类器,它能够根据 外显子的进化时间对外显子进行分类。外显子的分类为该论文的理论研究提供了 基础。2) MEAP,一个外显子芯片预处理的新算法。该方法可以对表达进行不同 层面的定量。3) PSFinder,一种基于给药前转录组表达谱及给药后相关的临床存 活时间对病人预后进行亚组分类的新的方法。 理论部分着重分析了转录组进化过程中动态变化的机制,它对方法学的研究提供 了坚实的理论基础。MEAP和PSFinder在高级别浆液性卵巢癌中的应用,发现了一 小组转录物标记带有不同的表达谱,可以用来对病人的预后进行分类。与实验验 证相结合,结果证明了这些方法在定量及癌症转录组动态分类中的适用性。新方 法的开发为人类癌症临床诊断及精准医学提供了新的见解。
Subject: biomedicine
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