Computational Integrative Analysis of Biological Networks in Cancer

Visa fullständig post

Titel: Computational Integrative Analysis of Biological Networks in Cancer
Author: Liu, Chengyu
Medarbetare: Helsingfors universitet, medicinska fakulteten, forskningsprogramenheten
Nivå: Doktorsavhandling (sammanläggning)
Abstrakt: Cancer is one of the most lethal diseases. By 2030, deaths caused by cancers are estimated to reach 13 million per year worldwide. Cancer is a collection of related diseases distinguished by uncontrolled cell division that is driven by genomic alterations. Cancer is heterogeneous and shows an extraordinary genomic diversity between patients with transcriptionally and histologically similar cancer subtypes, and even between tumors from the same anatomical position. The heterogeneity poses great challenges in understanding cancer mechanisms and drug resistance; this understanding is critical for precise prognosis and improved treatments. Emergence of high-throughput technologies, such as microarrays and next-generation sequencing, has motivated the investigation of cancer cells on a genome-wide scale. Over the last decade, an unprecedented amount of high-throughput data has been generated. The challenge is to turn such a vast amount of raw data into clinically valuable information to benefit cancer patients. Single omics data have failed to fully uncover mechanisms behind cancer phenotypes. Accordingly, integrative approaches have been introduced to systematically analyze and interpret multi-omics data, among which network-based integrative approaches have achieved substantial advances in basic biological studies and cancer treatments. In this thesis, the development and application of network-based integrative methods are included to address challenges in analyzing cancer samples. Two novel methods are introduced to integrate disparate omics data and biological networks at the single-patient level: PerPAS, which takes pathway topology into account and integrates gene expression and clinical data with pathway information; and DERA, which elevates gene expression analysis to the network level and identifies network-based biomarkers that provide functional interpretation. The performance of both methods was demonstrated using biological experiment data, and the results were validated in independent cohorts. The application part of this thesis focuses on understanding cancer mechanisms and identifying clinical biomarkers in breast cancer and diffuse large B-cell lymphoma using PerPAS, DERA, and an existing method SPIA. Our experimental results provided insights into underlying cancer mechanisms and potential prognostic biomarkers for breast cancer, and identified therapeutic targets for diffuse large B-cell lymphoma. The potential of the therapeutic targets was verified in in vitro experiments.癌症是一种复杂的疾病,也是现今最致命的疾病之一。据推算未来二十年后, 在世界范围内, 每年将有一千三百万人死于癌症。癌症是异质性疾病,表现出极大的基因组多样性。取自不同病人但属于相似亚组的基因组样品呈现出显著的差异性, 甚至取自同一个病人同一个位置的基因组样品也是具有差异性。理解癌症致病机理和发展过程才能更好地提供精确诊断及治疗。 高通量技术的出现激发了系统分析学和计算工具的发展。但是单一平台的数据不足以全面揭示癌症机理, 导致理解癌症机理一直是个极大的挑战。基于网络的整合方法的出现促进了基础生物的研究和病人的诊治。这篇论文包括两个部分: 整合方法的开发与应用。在开发新的整合方法方面, 我们研发了新的整合方法来应对整合数据的挑战并回答癌症研究中的问题。两个新开发的整合方法有: 1) PerPAS, 是一个体化治疗分析工具, 支持单个病人样品的分析, 并且能整合信号通路和基因表达数据。2) DERA, 是一个整合细胞网络和基因表达数据的工具。它能把基因表达数据的分析提升到网络层面并能进行单个样品的分析。这两种新型方法的可用性已经在生物数据应用中得以展示, 并且用独立数据验证了发现的结果。 整合方法的应用部分集中在全面整合分析mRNA, miRNA, 信号通路数据, 并在弥漫大B细胞淋巴瘤中识别出新的治疗靶点。在此方法的应用下, 我们发现了几个调控重要的临床存活的细胞通路的靶点。并且这些靶点的可靠性已经被实验验证。
Permanenta länken (URI): URN:ISBN:978-951-51-3586-5
Datum: 2017-09-15
Rights: Publikationen är skyddad av upphovsrätten. Den får läsas och skrivas ut för personligt bruk. Användning i kommersiellt syfte är förbjuden.

Filer under denna titel

Total number of downloads: Loading...

Filer Storlek Format Granska
Computat.pdf 851.3Kb PDF Granska/Öppna

Detta dokument registreras i samling:

Visa fullständig post