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標題: | 運用基因群概念分析癌症中拷貝數變異及藥物組合 Gene Set-based Approaches for Analyzing Copy Number Alterations and Drug Combinations in Cancer |
作者: | Yu-Ching Hsu 徐于晴 |
指導教授: | 莊曜宇(Eric Y. Chuang) |
關鍵字: | 基因群分析,拷貝數變異,藥物組合,乳癌,微陣列生物晶片, gene set analysis,copy number alteration,drug combination,breast cancer,microarray, |
出版年 : | 2015 |
學位: | 碩士 |
摘要: | 伴隨著高通量微陣列技術之成熟,各式各樣統計方法及數學模型因應而生以作為微陣列數據分析之用。近年來,基因群分析方法已被廣為使用在各種分子生物研究之數據分析,由於此方法旨在全面性的分析細胞內生物功能的改變,故其在癌症基因體相關的研究中,能提供相當寶貴而重要的資訊。在此篇論文中,我們運用了基因群分析方法的概念,來提出兩個系統性的方法,分別針對拷貝數變異分析及藥物組合治療這兩個議題來作探討。
拷貝數變異被定義為 1000 個核甘酸以上的基因突變,由於其長度甚長,影響範圍甚廣,常同時影響許多致癌基因或抑癌基因,因而和癌症的發生息息相關。在此論文中,我們系統性的去探討拷貝數變異對於生物功能的影響,並分析了其與癌症病人存活率之關聯性,我們將所設計出來的分析方法運用在從 The Cancer Genome Atlas (TCGA) 所下載之乳癌病人資料,包含有拷貝數變異、基因表現、及臨床相關的資訊。從中,我們利用基因群分析方法找出了 35 個基因群和 10 個基因群分別與拷貝數增加和減少有關,同時,在此 45 個基因群中,有 44 個皆表現出與拷貝數變化相對應之基因表現變化,此外,存活分析也從與拷貝數變異相關的基因群中找出和病人存活率相關的基因群,整體而言,我們的結果顯示拷貝數變異能藉由影響基因表現而干擾生物體內之功能,並進而影響病人之存活率。 由於癌症病變的機制極其複雜性,單一抗癌藥物的治療並不一定能有效的抑制其治療之標靶及其下游的訊息傳導路徑,對該藥物的抗藥性常隨之而生,因而導致病人的癌症復發。欲解決抗藥性所導致的臨床治療失效,不同的藥物組合治療急需被測試是否具有協同作用能避免抗藥性的產生,並有效地抑制癌症細胞之增生。然而,由於已上市的抗癌藥物甚多,基於時間及成本之考量,利用細胞株實驗逐一地檢驗各種可能的藥物組合是相當沒有效率的,因此,在本篇論文中,我們提出了利用電腦預測具有協同作用之藥物組合的分析方法作為可能的解決之道。我們假設藥物協同作用是經由調控相同的生物功能,或是經由調控與某生物功能相關之幾個相似的基因,依此設計出三種預測方法,並藉由 2012 年 DREAM 競賽所提供的資料來測試這些方法的表現。結果顯示這些方法對於預測藥物協同作用有極好的準確率,其中表現最好的預測方法更勝過 DREAM 比賽中所提出的所有預測方法。此外,由於我們的預測方法是建立於基因群分析方法的概念之上,所以我們同時也能探討藥物組合是藉由何種機制來達到協同作用。我們進一步將表現最好的預測方法運用到更大的資料上,也就是來自 connectivity map 的藥物資料,以找出 更多可能具有協同作用之藥物組合。 在本篇論文中,我們證實了我們所提出的分析方法能有系統地去研究拷貝數變異以及藥物協同作用。我們希望從拷貝數變異分析中所得到的結果能夠讓我們對於癌症產生的機制有更多的了解,而我們所提出的藥物組合預測方法能夠促進藥物組合治療之發展。 With the advances in high-throughput microarray and next-generation sequencing technologies, various statistical methods and mathematical models have been developed to comprehensively explore complex cancer genomes. Recently, a knowledge-based gene set analysis was proposed and successfully carried out remarkable findings from different layers of molecular data, such as gene expression and genomic alterations. Due to its power in detecting functional changes resulted from both significantly and modestly changed genes, gene set analysis provides biological insights into cancer genomes. In this study, we proposed two systematic analysis methods based on the concept of gene set analysis, for analyzing copy number alterations (CNAs) and predicting combinatorial drug therapy. CNAs, defined as genomic mutations more than a thousand base pairs, affect a large number of genes simultaneously and play an essential role in tumorigenesis. In the first part of this study, we sought to systematically explore its influences on biological functions and association with patient survival. We devised an algorithm, called Gene Set analysis for Copy number Alterations (GSCA), and analyzed CNA (N = 1,045) and gene expression (N = 529) datasets of breast tumors downloaded from The Cancer Genome Atlas (TCGA). Clinical information of these samples and the identified CNA-affected gene sets were also incorporated. Thirty-five and ten gene sets showed significant enrichment in profiles of copy number gains and losses, respectively. Genes within 44 of the 45 gene sets (98%) exhibited concordant expressional changes with the status of copy numbers. On the other hand, survival analysis revealed the prognostic role of several CNA-affected gene sets. Taken together, the result showed that CNAs can disturb biological functions by altering gene expression, and thus affect the clinical outcomes of patients. Due to the complexity of cancer genome, on the other hand, patients suffered from cancer relapse caused by the occurrence of resistance to individual antitumor drugs. The development of combinatorial drug therapy is of great need since single drugs alone is not able to overcome drug resistance resulting from the continuous activation of drug target or its downstream signaling pathway. However, due to the large amount of FDA- approved drugs, it is impractical to experimentally test every possible drug pairs. We proposed a computational prediction method for drug synergy to address this issue. We hypothesized that drug pairs achieve synergy by targeting similar biological functions and similar genes in a function and validate our devised methods by the datasets provided by the DREAM consortium. The results showed that the devised prediction scores have high performance. The co-gene/GS score even outperformed the methods proposed during the DREAM challenge. In addition, the results also showed that the best performing method devised using the concept of gene set analysis is capable of investigating the underlying mechanism by which drug pairs achieve synergy. We further applied the methods to a larger dataset, the connectivity map dataset, to explore a broader range of synergistic drug combinations. Overall, in the present study we proposed two gene set-based approaches to systematically study the biological roles and clinical significance of CNAs and to predict drug synergy in breast cancer. We demonstrated that these methods can not only identify biologically well-tested results, but also reveal abundant novel candidates for future biological investigations. The findings are expected to enhance our understanding in tumorigenesis and facilitate the development of combinatorial drug therapy for cancers. |
URI: | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/51617 |
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顯示於系所單位: | 生醫電子與資訊學研究所 |
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