臺灣博碩士論文加值系統

合成致死是一種存在於兩個基因之間的現象，定義為當兩個基因其中一個無法正常作用的時候，細胞仍能存活，但是當兩個基因同時無作用時，細胞便會死亡；合成存活則是當兩個基因其中之一無法正常運作時造成細胞凋亡，當兩個基因同時無作用，細胞反而存活。近年來，合成交互作用被視為具有潛力的抗癌策略，可應用於專一性殺死癌細胞。目前應用於模式生物例如酵母菌或大腸桿菌的合成交互作用全基因組篩選已完成，然而在人類實行全基因組篩選是相當耗費時間和成本的。目前已發展能在電腦上實行的方法來篩選合成交互作用基因對，主要之策略為找尋模式生物合成作用基因之同源基因，或是在癌症細胞基因組中互斥的體細胞突變現象。本篇研究是將合成交互作用的概念應用於人體，並利用美國癌症基因體圖譜的基因表現量資訊進行存活分析以找尋致死基因及具有合成交互作用關係的基因對。藉由分析合成交互作用網路，我們發現合成交互作用具有癌症獨特性，且癌症必需基因顯著地低表現於合成致死網路與合成存活網絡，更加證明存活分析模型可以找出可能的合成交互作用基因對。合成存活基因與細胞週期及脫氧核醣核酸修復有關聯；合成致死基因則與訊息傳遞及代謝作用相關。利用存活分析模型建立的合成作用網路可以幫助辨識合成致死及合成存活基因對，有助於找尋癌症治療標的與分析基因之間交互作用。

Synthetic interactions (SI) are composed of synthetic lethality (SL) and synthetic viability (SV). SL is defined as the co-occurrence of two genetic perturbations results in cellular death, while single perturbations don’t. SV has been described as the second perturbation buffers the lethal effect of the first one. SIs have been reported as a promising strategy for identifying cancer therapeutic targets. Whole genome screening of SIs has succeeded in model organisms, such as S. cerevisiae and E. coli. However, it is time-consuming and expensive to perform experimental whole genome screening upon human genomes. Current computational approaches predicted SL by exploring homologous genes of experimentally validated SL and mutually exclusive somatic mutations in cancer genomes. By extending the concept of SIs toward organismal levels, we proposed a survival analysis-based model to identify SIs in human cancer genomes. This model applied the mRNA expression profiles of The Cancer Genome Atlas (TCGA) to clarify the relationships between the hazard of patients and gene expression levels. In the SI networks, we observed that SIs are cancer-specific. Moreover, cancer essential genes were under-represented in protective genes and the SI networks, emphasizing the reliability of the survival model. Functional enrichment analysis showed that SV genes are related to cell cycle and DNA repair; SL genes are related to signal transduction and metabolic process. Briefly, our model is useful for identifying novel therapeutic targets in cancer treatment and characterizing under-investigated genetic interactions.

Contents
中文摘要....i
Abstract....ii
Contents....iii
List of Figures....v
List of Tables....vi
Chapter 1 Introduction....1
1.1 Brief introduction of synthetic lethality....1
1.2 Brief Introduction of synthetic viability....2
1.3 Utilizations of identifying synthetic interactions....3
1.4 Current computational approaches for predicting synthetic interactions....3
1.5 Conceptualization of synthetic interactions in our study....4
1.6 Survival analysis....5
1.7 Research objective and specific aims....5
Chapter 2 Materials and Methods....6
2.1 mRNA expression profiles and clinical information....6
2.2 Constructing the survival model....6
2.3 Identifying survival-correlated genes in each cancer type....6
2.4 Categorizing the cancer patients into four groups....6
2.5 Detecting synthetic interactions using the built survival model....7
2.6 Assessing the synergistic effects of the two genes on patient survival....7
2.7 Examining the robustness of the survival model....8
Chapter 3 Results....11
3.1 Significant confounding factors....11
3.2 Lethal genes identified in different cancer types....11
3.3 Double gene survival....15
3.3.1 Synthetic interactions detected in different cancer types....15
3.3.2 Constructed synthetic interaction networks....17
Chapter 4 Discussions....25
Chapter 5 Conclusions....26
References....27
List of Figures
Figure 1 Synthetic Lethality between BRCA and PARP1....1
Figure 2 Synthetic Viability between BRCA1 and 53BP1....2
Figure 3 The concept of Synthetic Interactions in this study....4
Figure 4 Demographic view of workflow....10
Figure 5 Survival-correlated genes in 14 cancer types....13
Figure 6 Synthetic interactions in cancers....16
Figure 7 The constructed synthetic interactions networks....19
Figure 8 Number of cancer types in which synthetic interaction genes got involved....20
Figure 9 Date hub network in the synthetic viable network....24
List of Tables
Table 1 Descriptive statistics of expression profiles and clinical information....9
Table 2 Significant independent confounding factors in 14 cancer types....12
Table 3 Enrichment analysis of survival model detected lethal genes....14
Table 4 Enrichment analysis of synthetic interaction genes....21
Table 5 Edge enrichment in synthetic interaction networks....21
Table 6 Top 10 enriched GO terms in the synthetic interaction networks....23

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