胰臟癌於人類癌症死亡原因的排名，在西方國家列居第四位，在臺灣則排行第八，由於胰臟癌細胞的侵犯性與化學抗藥性都很強，且缺乏早期診斷方法，使得病患的五年存活率低於5%。最近幾個研究團隊認為在胰臟癌之中包含一亞群的腫瘤起始細胞，亦稱為癌幹細胞，其具備幹細胞的諸多特徵性質，例如自我更新、抗藥性以及致使癌症復發的能力。這群癌幹細胞可能是導致化療藥物阻抗性的重要因素，因此，聚焦於癌幹細胞，研究如何改善預測藥物反應的準確度是我們的主要目標。
我們藉由自動定時顯微攝影觀察胰臟癌細胞株的生長行為，並以簡易的自編軟體進行量化分析，發現胰臟癌細胞具備不同於正常細胞的獨特行為，有趣的是這些堆疊現象與移動型態，恰與癌幹細胞的某些特性相符。我們比較PANC-1與已轉移的NTUHPK1兩種不同胰臟癌細胞株在施以化療藥物Gemcitabine前後的行為差異，認為這些存活細胞的獨特行為可能成為預測藥物反應的參考性指標。
針對上述細胞堆疊的現象，我們以Gemcitabine藥物處理PANC-1與NTUHPK1的懸浮球體細胞，分析29個候選基因於不同時間點的表現量，發現藥物處理後的PANC-1細胞調升了CDA、DCK、ABCB1、ZEB2與OCT4基因的表現量，而NTUHPK1細胞則調升CNT3與ABCB1基因的表現量。
綜合以上生長行為與基因表現的量化分析，我們初步完成胰臟癌的Gemcitabine藥物反應預測模式，以3D懸浮細胞的基因表現為主，輔以2D貼附細胞的生長行為差異，盼能預測實際於人體內的胰臟癌細胞對Gemcitabine的抗藥性。未來，本預測模式可納入更多參數，包括其它胰臟癌細胞株與多種化療藥物，將可更精確預測體內的藥物反應，提供醫師對病患的最佳投藥建議，實現個人化醫療的目標。

Pancreatic cancer is the fourth leading cause of cancer-related death in the western world, and the eighth in Taiwan. Because of highly invasive, resistance to current chemotherapy drug, and lack of appropriate early diagnosis approach, the five year survival rate of pancreatic cancer patient is less than 5%. Recently, results of several groups suggested that pancreatic cancer contains a subpopulation of tumor initiating cells, also known as cancer stem cells, which possess self-renewal ability, drug resistance, and capable of causing cancer relapse. The cancer stem cells might lead to drug resistance. Therefore, focused on cancer stem cells, we attempted to improve the accuracy of prediction of drug response.
We analyzed the growth behavior of pancreatic cancer cell lines by time-lapse micrography and self-made quantitative analysis software, and observed that pancreatic cancer cells possessed unique behavior which was distinct to normal cells. Intriguingly, these piled-up phenomenon and motion pattern corresponded with the characteristics of cancer stem cells. In comparing PANC-1 and metastatic NTUHPK1 cell lines with or without gemcitabine treatment, we suggested that the unique behavior of viable cells could be useful indicators to predict drug response.
Comparable to the piled-up cells, we also assessed PANC-1 and NTUHPK1 cells in sphere formation treated with gemcitabine for different duration, then analyzed variations of level of 29 candidate genes respectively. We found that PANC-1 cells treated with gemcitabine upregulated the level of CDA, DCK, ABCB1, ZEB2 and OCT4 genes, and NTUHPK1 cells upregulated the level of CNT3 and ABCB1 genes.
Based on the above analysis to quantify the growth behavior and gene expression of cancer cells, we completed a preliminary model for gemcitabine-response prediction in pancreatic cancer. Relying mainly on gene expression signature of cells in 3D culture and supplementing by the growth behavior variation of cells in 2D culture condition, hopefully, this model could predict the gemcitabine-resistance of human pancreatic cancer cells in vivo. In the future, this prediction model can develop with parameters including other pancreatic cancer cell lines treated with various chemotherapy drugs, it will be a more accurate model for drug-response in vivo and provides physicians with the optimal priority of drug treatment to patient for personalized medicine.

誌謝 ...... i
中文摘要 ...... iii
英文摘要 ...... iv
目錄 ...... vi
圖目錄 ...... ix
表目錄 ...... x
第一章 緒論 ...... 1
1.1 胰臟癌 ...... 1
1.2 幹細胞與癌幹細胞 ...... 2
1.3 細胞生長行為 ...... 4
1.4 癌細胞的化學抗藥性 ...... 5
1.5 基因表現輪廓 ...... 7
1.6 化療藥物反應的預測 ...... 8
1.7 研究目的與範圍 ...... 9
第二章 實驗材料與方法 ...... 11
2.1 細胞培養 ...... 11
2.1.1 細胞株與培養液 ...... 11
2.1.2 繼代培養 ...... 11
2.1.3 細胞冷凍 ...... 12
2.1.4 細胞解凍 ...... 12
2.1.5 細胞計數 ...... 12
2.2 自動定時顯微攝影 ...... 13
2.3 癌幹細胞分離 ...... 13
2.4 藥物處理 ...... 14
2.5 細胞存活率分析 ...... 14
2.6 候選基因 ...... 15
2.7 聚合酶連鎖反應 ...... 16
2.7.1 RNA萃取 ...... 17
2.7.2 反轉錄反應 ...... 17
2.7.3 反轉錄聚合酶連鎖反應 ...... 17
2.7.4 即時聚合酶連鎖反應 ...... 18
2.8 細胞生長行為模組化 ...... 19
2.9 統計分析 ...... 19
第三章 實驗結果 ...... 20
3.1 細胞堆疊行為與移動型態 ...... 20
3.2 細胞生長行為的量化分析 ...... 21
3.3 3D懸浮型較2D貼附型癌細胞有更高的抗藥性 ...... 23
3.4 懸浮型癌細胞經藥物處理之候選基因表現分析 ...... 23
3.5 藥物處理之懸浮型PANC-1細胞調升CDA、DCK、ABCB1、ZEB2與OCT4基因的表現量 ...... 24
3.6 藥物處理之懸浮型NTUHPK1細胞調升CNT3與ABCB1基因的表現量 ...... 26
第四章 胰臟癌細胞之Gemcitabine藥物反應預測模式 ...... 28
4.1 藥物反應實驗 ...... 28
4.2 資料蒐集整理 ...... 29
4.3 藥物反應預測 ...... 30
4.4 未來展望 ...... 35
第五章 討論 ...... 36
5.1 細胞生長行為的蒐集 ...... 36
5.1.1 追蹤時間的界定 ...... 36
5.1.2 細胞圓形化與分裂 ...... 37
5.1.3 細胞移動軌跡 ...... 38
5.1.4 後續改善方向 ...... 39
5.2 懸浮型球體癌細胞 ...... 40
5.3 候選基因的更替 ...... 40
5.4 ABCB1運輸蛋白 ...... 41
5.5 個人化醫療與藥物反應預測模式 ...... 42
參考文獻 ...... 44
圖 ...... 50
表 ...... 70
附錄 中英文名詞對照與索引 ...... 76

圖目錄
圖1-1 研究目的示意圖 ...... 50
圖2-1 PANC-1細胞球體的聚集現象 ...... 51
圖2-2 懸浮球體細胞存活率分析之MTT assay流程 ...... 52
圖2-3 Excel巨集功能Visual Basic程式編寫畫面 ...... 53
圖2-4 細胞生長行為的輸入模組 ...... 54
圖2-5 細胞生長行為的分析模組（單一細胞） ...... 55
圖2-6 細胞生長行為的分析模組（全體細胞） ...... 56
圖3-1 細胞的堆疊行為 ...... 57
圖3-2 細胞的移動型態（紡錘狀的類絲足結構） ...... 58
圖3-3 細胞的移動型態（隧道奈米管結構） ...... 59
圖3-4 細胞的移動型態（走訪行為） ...... 60
圖3-5 PANC-1細胞整體行為之統計摘要 ...... 61
圖3-6 PANC-1細胞之分裂次數分析 ...... 62
圖3-7 NTUHPK1細胞之分裂次數分析 ...... 63
圖3-8 PANC-1與NTUHPK1細胞之倍增時間分析 ...... 64
圖3-9 PANC-1與NTUHPK1細胞之爬行速度分析 ...... 65
圖3-10 懸浮型與貼附型PANC-1細胞之抗藥性比較 ...... 66
圖3-11 懸浮型PANC-1細胞經藥物處理之基因表現分析 ...... 67
圖3-12 PANC-1細胞調升CDA、DCK、ABCB1、ZEB2與OCT4基因 ...... 68
圖3-13 NTUHPK1細胞調升CNT3與ABCB1基因 ...... 69

表目錄
表2-1 懸浮型細胞藥物處理設計 ...... 70
表2-2 候選基因 ...... 71
表2-3 引子序列表 ...... 74

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