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研究生:盧培春
研究生(外文):Peir-Chuen Lu
論文名稱:以生長行為與基因表現輪廓建立人類胰臟癌細胞之Gemcitabine藥物反應預測模式
論文名稱(外文):A gemcitabine-response prediction model based on growth behavior and gene expression profiling in human pancreatic cancer cells
指導教授:沈家寧
指導教授(外文):Chia-Ning Shen
學位類別:碩士
校院名稱:國立陽明大學
系所名稱:醫學生物技術暨檢驗學系
學門:醫藥衛生學門
學類:醫學技術及檢驗學類
論文種類:學術論文
論文出版年:2016
畢業學年度:104
語文別:中文
論文頁數:79
中文關鍵詞:胰臟癌抗藥性癌幹細胞細胞縮時顯微攝影分裂次數倍增時間
外文關鍵詞:Pancreatic cancerDrug resistanceCancer stem cellsTime-lapse micrography of cellsDivision numberDoubling time
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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
1. Kleeff J, Beckhove P, Esposito I, et al. Pancreatic cancer microen-vironment. Int J Cancer 2007;121(4):699-705.
2. Jemal A, Siegel R, Xu J, et al. Cancer statistics, 2010. CA Cancer J Clin 2010;60(5):277-300.
3. 「103年國人死因統計結果」 中華民國衛生福利部 2015.
4. Shaib YH, Davila JA, El-Serag HB. The epidemiology of pancreatic cancer in the United States: changes below the surface. Aliment Pharmacol Ther 2006;24(1):87-94.
5. Griffin JF, Poruk KE, Wolfgang CL. Pancreatic cancer surgery: past, present, and future. Chin J Cancer Res 2015;27(4):332-48.
6. Neoptolemos JP, Stocken DD, Bassi C, et al. Adjuvant chemotherapy with fluorouracil plus folinic acid vs gemcitabine following pancreatic cancer resection: a randomized controlled trial. JAMA 2010;304(10): 1073-81.
7. Hidalgo M. Pancreatic cancer. N Engl J Med 2010;362(17):1605-17.
8. Ko AH, Tempero MA. Personalized medicine for pancreatic cancer: a step in the right direction. Gastroenterology 2009;136(1):43-5.
9. Michalski CW, Erkan M, Sauliunaite D, et al. Ex vivo chemosensitivity testing and gene expression profiling predict response towards adjuvant gemcitabine treatment in pancreatic cancer. Br J Cancer 2008;99(5): 760-7.
10. Burris HA 3rd, Moore MJ, Andersen J, et al. Improvements in survival and clinical benefit with gemcitabine as first-line therapy for patients with advanced pancreas cancer: a randomized trial. J Clin Oncol 1997; 15(6):2403-13.
11. Reya T, Morrison SJ, Clarke MF, et al. Stem cells, cancer, and cancer stem cells. Nature 2001;414(6859):105-11.
12. Pardal R, Clarke MF, Morrison SJ. Applying the principles of stem-cell biology to cancer. Nat Rev Cancer 2003;3(12):895-902.
13. Li C, Heidt DG, Dalerba P, et al. Identification of pancreatic cancer stem cells. Cancer Res 2007;67(3):1030-7.
14. Hermann PC, Huber SL, Herrler T, et al. Distinct populations of cancer stem cells determine tumor growth and metastatic activity in human pancreatic cancer. Cell Stem Cell 2007;1(3):313-23.
15. Mani SA, Guo W, Liao MJ, et al. The epithelial-mesenchymal transition generates cells with properties of stem cells. Cell 2008;133(4):704-15.
16. Klarmann GJ, Hurt EM, Mathews LA, et al. Invasive prostate cancer cells are tumor initiating cells that have a stem cell-like genomic signature. Clin Exp Metastasis 2009;26(5):433-46.
17. Freshney RI. Culture of animal cells: a manual of basic technique, 5th edition. John Wiley & Sons, Inc 2005.
18. Hanahan D1, Weinberg RA. Hallmarks of cancer: the next generation. Cell 2011;144(5):646-74.
19. Rhim AD, Mirek ET, Aiello NM, et al. EMT and dissemination precede pancreatic tumor formation. Cell 2012;148(1-2):349-61.
20. Sun J, He H, Xiong Y, et al. Fascin protein is critical for transforming growth factor β protein-induced invasion and filopodia formation in spindle-shaped tumor cells. J Biol Chem 2011;286(45):38865-75.
21. Sack U, Walther W, Scudiero D, et al. S100A4-induced cell motility and metastasis is restricted by the Wnt/β-catenin pathway inhibitor calcimycin in colon cancer cells. Mol Biol Cell 2011;22(18):3344-54.
22. Ungefroren H, Sebens S, Giehl K, et al. Rac1b negatively regulates TGF-β1-induced cell motility in pancreatic ductal epithelial cells by suppressing Smad signalling. Oncotarget 2014;5(1):277-90.
23. Preston SP, Waters SL, Jensen OE, et al. T-cell motility in the early stages of the immune response modeled as a random walk amongst targets. Phys Rev E Stat Nonlin Soft Matter Phys 2006;74.
24. Codling EA, Plank MJ, Benhamou S. Random walk models in biology. J R Soc Interface 2008;5(25):813-34.
25. Wu PH, Giri A, Sun SX, et al. Three-dimensional cell migration does not follow a random walk. Proc Natl Acad Sci USA 2014;111(11):3949- 54.
26. Rustom A, Saffrich R, Markovic I, et al. Nanotubular highways for intercellular organelle transport. Science 2004;303(5660):1007-10.
27. Zhang Y. Tunneling-nanotube: A new way of cell-cell communication. Commun Integr Biol 2011;4(3):324-5.
28. Abounit S, Zurzolo C. Wiring through tunneling nanotubes - from electrical signals to organelle transfer. J Cell Sci 2012;125(Pt 5):1089-98.
29. Pasquier J, Guerrouahen BS, Al Thawadi H, et al. Preferential transfer of mitochondria from endothelial to cancer cells through tunneling nanotubes modulates chemoresistance. J Transl Med 2013;11:94.
30. Zahreddine H, Borden KL. Mechanisms and insights into drug resist-ance in cancer. Front Pharmacol 2013;4:28.
31. Drewa T, Styczynski J, Szczepanek J. Is the cancer stem cell population "a player" in multi-drug resistance? Acta Pol Pharm 2008;65(4):493-500.
32. Shachaf CM, Kopelman AM, Arvanitis C, et al. MYC inactivation uncovers pluripotent differentiation and tumour dormancy in hepatocellular cancer. Nature 2004;431(7012):1112-7.
33. Lin WC, Rajbhandari N, Liu C, et al. Dormant cancer cells contribute to residual disease in a model of reversible pancreatic cancer. Cancer Res 2013;73(6):1821-30.
34. Mueller-Klieser W. Multicellular spheroids. A review on cellular aggregates in cancer research. J Cancer Res Clin Oncol 1987;113(2): 101-22.
35. Gutierrez-Barrera AM, Menter DG, Abbruzzese JL, et al. Establish-ment of three-dimensional cultures of human pancreatic duct epithelial cells. Biochem Biophys Res Commun 2007;358(3):698-703.
36. Mishra DK, Sakamoto JH, Thrall MJ, et al. Human lung cancer cells grown in an ex vivo 3D lung model produce matrix metalloproteinases not produced in 2D culture. PLoS One 2012;7(9):e45308.
37. Horning JL, Sahoo SK, Vijayaraghavalu S, et al. 3-D tumor model for in vitro evaluation of anticancer drugs. Mol Pharm 2008;5(5):849-62.
38. Friedrich J, Seidel C, Ebner R, et al. Spheroid-based drug screen: considerations and practical approach. Nat Protoc 2009;4(3):309-24.
39. Longati P, Jia X, Eimer J, et al. 3D pancreatic carcinoma spheroids induce a matrix-rich, chemoresistant phenotype offering a better model for drug testing. BMC Cancer 2013;13:95.
40. Qian J, Dolled-Filhart M, Lin J, et al. Beyond synexpression relation-ships: local clustering of time-shifted and inverted gene expression profiles identifies new, biologically relevant interactions. J Mol Biol 2001;314(5):1053-66.
41. He F, Zeng AP. In search of functional association from time-series microarray data based on the change trend and level of gene expression. BMC Bioinformatics 2006;7:69.
42. Eisen MB, Spellman PT, Brown PO, et al. Cluster analysis and display of genome-wide expression patterns. Proc Natl Acad Sci USA 1998; 95(25):14863-8.
43. Jain AK, Murty MN, Flynn PJ. Data Clustering: A Review. ACM Com-puting Surveys 1999;31(3):264-323.
44. Ben-Dor A, Shamir R, Yakhini Z. Clustering gene expression patterns. J Comput Biol 1999;6(3-4):281-97.
45. Getz G, Levine E, Domany E. Coupled two-way clustering analysis of gene microarray data. Proc Natl Acad Sci USA 2000;97(22):12079-84.
46. Slamon DJ, Leyland-Jones B, Shak S, et al. Use of chemotherapy plus a monoclonal antibody against HER2 for metastatic breast cancer that overexpresses HER2. N Engl J Med 2001;344(11):783-92.
47. Massarelli E, Varella-Garcia M, Tang X, et al. KRAS mutation is an important predictor of resistance to therapy with epidermal growth factor receptor tyrosine kinase inhibitors in non-small-cell lung cancer. Clin Cancer Res 2007;13(10):2890-6.
48. Lièvre A, Bachet JB, Boige V, et al. KRAS mutations as an independent prognostic factor in patients with advanced colorectal cancer treated with cetuximab. J Clin Oncol 2008;26(3):374-9.
49. Zhu CQ, da Cunha Santos G, Ding K, et al. Role of KRAS and EGFR as biomarkers of response to erlotinib in National Cancer Institute of Canada Clinical Trials Group Study BR.21. J Clin Oncol 2008;26(26): 4268-75.
50. Farrell JJ, Elsaleh H, Garcia M, et al. Human equilibrative nucleoside transporter 1 levels predict response to gemcitabine in patients with pancreatic cancer. Gastroenterology 2009;136(1):187-95.
51. Maréchal R, Bachet JB, Mackey JR, et al. Levels of gemcitabine transport and metabolism proteins predict survival times of patients treated with gemcitabine for pancreatic adenocarcinoma. Gastroenter-ology 2012;143(3):664-74.e1-6.
52. Hermann PC, Bhaskar S, Cioffi M, et al. Cancer stem cells in solid tumors. Semin Cancer Biol 2010;20(2):77-84.
53. Rebucci M, Michiels C. Molecular aspects of cancer cell resistance to chemotherapy. Biochem Pharmacol 2013;85(9):1219-26.
54. Baguley BC. Multidrug resistance in cancer. Methods Mol Biol 2010; 596:1-14.
55. Farrell JJ, Elsaleh H, Garcia M, et al. Human equilibrative nucleoside transporter 1 levels predict response to gemcitabine in patients with pancreatic cancer. Gastroenterology 2009;136(1):187-95.
56. Maréchal R, Mackey JR, Lai R, et al. Human equilibrative nucleoside transporter 1 and human concentrative nucleoside transporter 3 predict survival after adjuvant gemcitabine therapy in resected pancreatic adenocarcinoma. Clin Cancer Res 2009;15(8):2913-9.
57. Maréchal R, Mackey JR, Lai R, et al. Deoxycitidine kinase is associated with prolonged survival after adjuvant gemcitabine for resected pancre-atic adenocarcinoma. Cancer 2010;116(22):5200-6.
58. Costantino CL, Witkiewicz AK, Kuwano Y, et al. The role of HuR in gemcitabine efficacy in pancreatic cancer: HuR Up-regulates the ex-pression of the gemcitabine metabolizing enzyme deoxycytidine kinase. Cancer Res 2009;69(11):4567-72.
59. Xu J, Zhu W, Xu W, et al. Silencing of MBD1 reverses pancreatic cancer therapy resistance through inhibition of DNA damage repair. Int J Oncol 2013;42(6):2046-52.
60. Maacke H, Jost K, Opitz S, et al. DNA repair and recombination factor Rad51 is over-expressed in human pancreatic adenocarcinoma. Oncogene 2000;19(23):2791-5.
61. Sims-Mourtada J, Izzo JG, Ajani J, et al. Sonic Hedgehog promotes multiple drug resistance by regulation of drug transport. Oncogene 2007; 26(38):5674-9.
62. Dean M. ABC transporters, drug resistance, and cancer stem cells. J Mammary Gland Biol Neoplasia 2009;14(1):3-9.
63. Hong YB, Kang HJ, Kwon SY, et al. Nuclear factor (erythroid-derived 2)-like 2 regulates drug resistance in pancreatic cancer cells. Pancreas 2010;39(4):463-72.
64. Brunelle JK, Letai A. Control of mitochondrial apoptosis by the Bcl-2 family. J Cell Sci 2009;122(Pt 4):437-41.
65. LaCasse EC, Mahoney DJ, Cheung HH, et al. IAP-targeted therapies for cancer. Oncogene 2008;27(48):6252-75.
66. Yamao M, Naoki H, Ishii S. Multi-cellular logistics of collective cell migration. PLoS One 2011;6(12):e27950.
67. Young HM, Bergner AJ, Simpson MJ, et al. Colonizing while migrating: how do individual enteric neural crest cells behave? BMC Biol 2014;12:23.
68. Treloar KK, Simpson MJ, McElwain DL, et al. Are in vitro estimates of cell diffusivity and cell proliferation rate sensitive to assay geometry? J Theor Biol 2014;356:71-84.
69. Postigo AA. Opposing functions of ZEB proteins in the regulation of the TGFbeta/BMP signaling pathway. EMBO J 2003;22(10):2443-52.
70. Gordon KJ, Blobe GC. Role of transforming growth factor-beta superfamily signaling pathways in human disease. Biochim Biophys Acta 2008;1782(4):197-228.
71. Ritzel MW, Ng AM, Yao SY, et al. Molecular identification and characterization of novel human and mouse concentrative Na+-nucleoside cotransporter proteins (hCNT3 and mCNT3) broadly selective for purine and pyrimidine nucleosides (system cib). J Biol Chem 2001;276(4):2914-27.
72. Hanahan D, Weinberg RA. The hallmarks of cancer. Cell 2000;100(1):57-70.
73. Hanahan D, Weinberg RA. Hallmarks of cancer: the next generation. Cell 2011;144(5):646-74.
74. Stewart MP, Helenius J, Toyoda Y, et al. Hydrostatic pressure and the actomyosin cortex drive mitotic cell rounding. Nature 2011; 469(7329):226-30.

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