臺灣博碩士論文加值系統

新藥開發是一項長遠、高風險且又高成本的事業。平均10,000個小分子藥物在臨床前開發只會有五個進入臨床試驗，而只會有一個通過FDA核准。在人類基因解碼後，利用生物標記當作篩選病人進入臨床試驗的標準日漸增多。有研究指出當臨床試驗三期有生物標記做引導與沒有生物標記相比，成功率可以由28%上升至62%。而另一方面，末期癌症病人對於現有上市藥物並沒有得到應有的改善。目前末期癌症病人的存活率只有大約2個月。這突顯了病人篩選與個人化醫療在藥物開發中的重要性。利用合成致死的概念，我們可以成功為藥物找到合適的病人群。我們在這裡利用兩種方式來為新藥找到合成致死的對偶基因。首先，並不是所有藥物都能夠有大型的細胞篩選數據，然而每一對合成致死的基因組合都有其特定的機制存在，於是想要找到一個藥物的合成致死基因組合，必然需要先瞭解其作用機轉，我們透過基因微陣列的分析，成功地解開中草藥複方YQ1-EtOH 的作用機轉，並透過LINCS資料庫以及GDSC資料庫找到與其作用機轉與處理細胞後的基因表現圖譜也相似的藥物，並找到其有淺力的合成致死基因，我們推測這合成致死基因有機會與YQ1-EtOH共有。再來，在新藥的開發過程中我們也能透過NCI-60 的藥物篩選平台並結合大型資料庫的細胞的基因資訊找到合成致死的基因對，我們在此找到了幾個臨床前新藥的合成致死對基因。

The drug development process is a lengthy, high risk and costly endeavor. On average, for ~10,000 compounds evaluated in preclinical studies, about five compounds enter clinical trials and about one compound finally receives regulatory approval by FDA. The use of biomarkers as inclusion or exclusion criteria, or selection biomarkers for enrolling patients into clinical studies has increased dramatically since the sequencing of the human genome. The benefit from selection biomarker used raises the likelihood of approval from 62% Phase III compared to 28% when no selection biomarker used. On the other hand, for advanced solid tumor patients, the approval of therapies that many would argue did not achieve clinically meaningful improvement. The average of clinical prognosis in hospitalized patients’ survival were ~2 months with advanced solid tumors. This highlights the importance of precision medicine and biomarker-guided in the process of drug development. In the concept of synthetic lethality (SL), we are able to find the sensitive group of patient for the drugs. Here, we perform two feasible ways to search for the SL genes pair. For the concern of research expense, it is difficult to proceed a large high-throughput for a newly developed drug candidate. First, by using the concept of SL, we successfully uncover the mechanism of a Chinese Herbal medicine YQ1-EtOH through microarray analysis and pathway analysis. We next find the drugs with similar gene profiles and also similar mechanism of action through LINCS database, followed by SL pairs identification through GDSC database. We hypothesized that the drugs we identify would share the same SL pairs with YQ1-EtOH. Second, using the drug screening data from NCI-60, and combined with cell lines gene profile from big-data analysis, we can also find the potential SL pair for the new drugs.

Acknowledge i
摘要 ii
Abstract iii
Content iv
Introduction 1
Synthetic lethality (SL) 1
Lung cancer 2
Genetics of Non-Small Cell Lung Cancer (NSCLC) 2
Chinese herbal medicine 3
Astragalus-based Chinese herbal medicine (YQ1) 3
CCLE (The Cancer Cell Line Encyclopedia project) 4
Precision medicine 4
Library of Integrated Network-based Cellular Signatures (LINCS) 6
Cancer Therapeutics Response Portal (CTRP) 6
Genomics of Drug Sensitivity in Cancer (GDSC) 7
Materials 8
Antibodies for western blotting analysis 8
Methods 9
Cell culture 9
Fractions of Chinese herbal medicine 9
Sulforhodamine B colorimetric (SRB) assay 9
Protein extraction and concentration measurement from cells 10
Western blotting analysis 10
Tube formation assay 11
Objective 12
Specific aims 13
Aim 1: Search for synthetic lethality gene pairs with a new drug without a panel of cell lines screening data. 13
Aim 2: Search for potential synthetic lethality gene pairs based one the pathway results of YQ1-EtOH. 14
Aim 3: Using NCI-60 drug screening data to search for potential synthetic lethality gene pairs. 14
Results 15
Microarray analysis of YQ1-EtOH 15
Pathway analysis of LINCS shRNA query result 15
Validation signal pathway predicted by pathway analysis via western blotting 16
Downstream molecule regulation of STAT3 in YQ1-EtOH treatment 17
YQ1-EtOH regulation on human umbilical vein endothelial cells (HUVECs) 18
Predict the similar compounds with YQ1-EtOH for further synthetic lethality analysis based on the pathway result 19
To predict the genetic dependency of NCI-60 profiled NSC compounds 21
Discussion 23
Reference 27
Tables 30
Table 1. One quarter of the 15 compounds with similar gene expression profiles with YQ1-EtOH are topoisomerase inhibitor 30
Figures 31
Figure 1. Using the LINCS database to predict the candidate genes of YQ1-EtOH. 32
Figure 2. Pathway prediction of YQ1-EtOH by ConsensusPathDB and Ingenuity Pathway Analysis. 34
Figure 3. YQ1-EtOH down-regulates the phosphorylation of EGFR and the downstream activation of JAK and AKT1. 36
Figure 4. YQ1-EtOH down-regulates phosphorylation of STAT3 and the expression of its downstream molecules. 37
Figure 5. YQ1-EtOH decreases the secretion of IL-6 and VEGFA. 38
Figure 6. YQ1-EtOH inhibits the migration ability of Human umbilical vein endothelial cells (HUVECs). 39
Figure 7. YQ1-EtOH inhibits the tube formation ability of HUVECs. 40
Figure 8. Using the LINCS database to predict the candidate compounds with similar gene expression profiles to YQ1-EtOH. 42
Figure 9. Search for candidate compounds for further synthetic lethality analysis. 43
Figure 10. The synthetic lethality analysis of the two topoisomerase inhibitors. 44
Figure 11. The synthetic lethality analysis of BMS-536924 reveals BMS-536924 is resistant to cell lines with PTEN alteration and sensitive to cell lines with U2AF1 mutation 45
Figure 12. New classification of NCI-60 based on genetic profiling. 46
Figure 13. Genes of synthetic lethality gene pairs of NSC777201 and potential cancer types. 48
Figure 14. Genes of synthetic lethality gene pairs of NSC7772865 and potential cancer types. 50
Supplementary 51
The summary of cytotoxicity and clonogenic assays of YQ1-H2O and YQ1-EtOH in various cell lines. 51

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