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研究生(外文):Feng-Hsiang Chung
論文名稱(外文):A Framework for Searching for Repurposed Drug Compounds for Systems Treatment of Complex Diseases: An Application to Colorectal Adenocarcinoma
指導教授(外文):Hoong-Chien Lee
外文關鍵詞:colorectal cancerTrend of disease Progressionmicroarray analysisFunctional Module Connectivity Mapintracellular side effectConnectivity mapdrug repurposingadenomainflammatory bowel disease
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由於新藥開發上日益增加的成本,與成功上市的藥物因副作用的問題而頻繁地被撤銷,在藥物發展上,找尋舊藥再利用已經成為一項越來越具吸引力的策略。癌症現在已被認為是一種系統性的疾病,是在細胞中整個生物系統的破壞,而非一、兩個基因失常所導致的。因此,人們不能指望癌症被有效地由單一或數個標靶的藥物所治癒。於此,我們設計功能模組連結圖譜 (Functional Module Connectivity Map, FMCM) 來探勘再利用藥物複合物以系統性的方式治療複雜疾病,並應用於大腸直腸腺瘤。FMCM利用多種功能模組來比對連結圖譜 (Connectivity Map, CMap) 。這些功能模組圍繞的樞紐基因,是透過病人的基因表現微陣列晶片中建構特定狀態的基因-基因交互作用網路並利用疾病趨勢進程的程序 (Gene Selection by Trend-of-disease-Progression procedure, GSToP) 所鑑定出來。預測出的藥物必須限制在胞內展現有害副作用最小化的藥物複合物之組成。透過GSToP所篩選出癌症基因的命中率 (~50%) 遠比由eBayes與SAM (~20%) 所獲得的高。我們測試FMCM與以一組獨立基因查詢CMap來選擇藥物的常見做法 (Individual Genes Connectivity Map, IGCM) 做比較,發現FMCM具有較好的穩健性、準確度和再現性來鑑定已知的抗癌藥物。從大腸直腸腺瘤透過FMCM所挑選的46藥物中,65%的藥物有文獻支持具有抗癌活性,且三種藥物被預測對癌症有不良有害反應也曾被報導。在細胞存活測試中,我們驗證出四種候選藥物:GW8510、etacrynic acid、ginkgolide A和6-azathymine,對癌症細胞株有高的抑制能力。透過微陣列晶片實驗,我們驗證了三種候選藥物的新功能聯結:phenoxybenzamin (廣域影響) 、GW-8510 (細胞週期) 、和imipenem (免疫系統) 。我們期望FMCM能廣泛應用於藥物再利用開發來系統性治療其它複雜疾病。
Drug repurposing has become an increasingly attractive approach to drug development owing to the ever-growing cost of new drug discovery and frequent withdrawal of successful drugs caused by side effect issues. Cancer is now recognized as is a systems disease caused by the breakdown of a large part of the cellular system, not just of the failure of one or two genes. Therefore, one cannot expect cancer to be effectively treated by one or a few single-target drugs. Here, we devised Functional Module Connectivity Map (FMCM) for the discovery of repurposed drug compounds for systems treatment of complex diseases, and applied it to colorectal adenocarcinoma. FMCM used multiple functional gene modules to query the Connectivity Map (CMap). The functional modules were built around hub genes identified, through the Gene Selection by Trend-of-disease-Progression (GSToP) procedure, from condition-specific gene-gene interaction networks constructed from sets of cohort gene expression microarrays. The formulated drug compounds were restricted to drugs exhibiting predicted minimal intracellular harmful side effects. Genes selected by GSToP had a much higher cancer gene hit rate (~50%) than that obtained by eBayes and SAM (~20%). We tested FMCM against the common practice of selecting drugs by using a single set of individual genes to query CMap (IGCM), and found that FMCM have higher robustness, accuracy, and reproducibility in identifying known anti-cancer agents. Among the 46 drugs selected by FMCM for colorectal adenocarcinoma, 65% had literature support for association with anti-cancer activities, and three drugs predicted to have adverse harmful effects on cancers had also been reported. In cell viability tests, we validated four candidate drugs: GW-8510, etacrynic acid, ginkgolide A, and 6-azathymine, as having high inhibitory activities against cancer cells. Through microarray experiments we confirmed the novel functional links predicted for three candidate drugs: phenoxybenzamine (broad effects), GW-8510 (cell cycle), and imipenem (immune system). We expect FMCM can be widely applied to repurposed drug discovery for systems treatment of other complex diseases.
Chapter 1 Introduction 1
1-1 Colorectal cancer 1
1-2 Molecular genetics for CRC 2
1-3 Cancer as a systems biology disease 3
1-4 Network-based approaches 5
1-5 Drug repurposing strategies 6
1-6 Cancer biomarker selection 9
1-7 Systems pharmacology on cancer research 10

Chapter 2 Materials and Methods 13
2-1 Samples and microarrays 13
2-2 External databases 14
2-3 Individual gene analysis and individual gene connectivity map 15
2-4 Construction of GGIN and function-function network 16
2-5 GSToP and the functional module connectivity map framework 17
2-6 Hit rate for cancer genes and permutation test 18
2-7 Performance tests on drug prediction 19
2-8 Follow-up experimental validations 20

Chapter 3 Results 23
3-1 Microarray quality and differentially expressed genes 23
3-2 CRC network had highest complexity 24
3-3 Sizes of FFNs generally increased with disease severity 25
3-4 Discovery of cancer genes using the GSToP procedure 26
3-5 Repurposed therapeutic drugs selected by IGCM and FMCM 29
3-6 Comparison between IGCM and FMCM 31
3-7 Follow-up experimental results and test of the perturbagen concept 33

Chapter 4 Summary and discussions 36

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