請用此 Handle URI 來引用此文件:
http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/68759
完整後設資料紀錄
DC 欄位 | 值 | 語言 |
---|---|---|
dc.contributor.advisor | 李心予(Hsinyu Lee) | |
dc.contributor.author | Chung-Yueh Wang | en |
dc.contributor.author | 王中岳 | zh_TW |
dc.date.accessioned | 2021-06-17T02:34:00Z | - |
dc.date.available | 2022-08-24 | |
dc.date.copyright | 2017-08-24 | |
dc.date.issued | 2017 | |
dc.date.submitted | 2017-08-17 | |
dc.identifier.citation | 1. Weiss, A., et al., Rapid optimization of drug combinations for the optimal angiostatic treatment of cancer. Angiogenesis, 2015. 18(3): p. 233-44.
2. Devita, V.T., R.C. Young, and G.P. Canellos, Combination versus single agent chemotherapy: A review of the basis for selection of drug treatment of cancer. Cancer, 1975. 35(1): p. 98-110. 3. Lee, Michael J., et al., Sequential Application of Anticancer Drugs Enhances Cell Death by Rewiring Apoptotic Signaling Networks. Cell. 149(4): p. 780-794. 4. Drake, J.M., et al., Metastatic castration-resistant prostate cancer reveals intrapatient similarity and interpatient heterogeneity of therapeutic kinase targets. Proceedings of the National Academy of Sciences, 2013. 110(49): p. E4762-E4769. 5. Jamal-Hanjani, M., et al., Translational Implications of Tumor Heterogeneity. Clinical cancer research : an official journal of the American Association for Cancer Research, 2015. 21(6): p. 1258-1266. 6. Bedard, P.L., et al., Tumour heterogeneity in the clinic. Nature, 2013. 501(7467): p. 355-364. 7. Schena, M., et al., Quantitative Monitoring of Gene Expression Patterns with a Complementary DNA Microarray. Science, 1995. 270(5235): p. 467-470. 8. Gidrol, X., et al., 2D and 3D cell microarrays in pharmacology. Current opinion in pharmacology, 2009. 9(5): p. 664-668. 9. Yarmush, M.L. and K.R. King, Living-cell microarrays. Annual review of biomedical engineering, 2009. 11: p. 235-257. 10. Fernandes, T.G., et al., High-throughput cellular microarray platforms: applications in drug discovery, toxicology and stem cell research. Trends Biotechnol, 2009. 27(6): p. 342-9. 11. Castel, D., et al., Cell microarrays in drug discovery. Drug discovery today, 2006. 11(13): p. 616-622. 12. Hook, A.L., H. Thissen, and N.H. Voelcker, Advanced substrate fabrication for cell microarrays. Biomacromolecules, 2009. 10(3): p. 573-579. 13. Flaim, C.J., S. Chien, and S.N. Bhatia, An extracellular matrix microarray for probing cellular differentiation. Nat Meth, 2005. 2(2): p. 119-125. 14. Fernandes, T.G., et al., On-Chip, Cell-Based Microarray Immunofluorescence Assay for High-Throughput Analysis of Target Proteins. Analytical Chemistry, 2008. 80(17): p. 6633-6639. 15. Lee, J.N., et al., Compatibility of mammalian cells on surfaces of poly (dimethylsiloxane). Langmuir, 2004. 20(26): p. 11684-11691. 16. Hyun, J. and A. Chilkoti, Micropatterning biological molecules on a polymer surface using elastomeric microwells. Journal of the American Chemical Society, 2001. 123(28): p. 6943-6944. 17. Rettig, J.R. and A. Folch, Large-scale single-cell trapping and imaging using microwell arrays. Analytical chemistry, 2005. 77(17): p. 5628-5634. 18. Khademhosseini, A., et al., Co-culture of human embryonic stem cells with murine embryonic fibroblasts on microwell-patterned substrates. Biomaterials, 2006. 27(36): p. 5968-5977. 19. Hung, P.J., et al., Continuous perfusion microfluidic cell culture array for high‐throughput cell‐based assays. Biotechnology and bioengineering, 2005. 89(1): p. 1-8. 20. Bailey, S.N., D.M. Sabatini, and B.R. Stockwell, Microarrays of small molecules embedded in biodegradable polymers for use in mammalian cell-based screens. Proceedings of the National Academy of Sciences of the United States of America, 2004. 101(46): p. 16144-16149. 21. Carstens, M.R., et al., Drug-eluting microarrays to identify effective chemotherapeutic combinations targeting patient-derived cancer stem cells. Proceedings of the National Academy of Sciences, 2015. 112(28): p. 8732-8737. 22. Lee, M.-Y., et al., Three-dimensional cellular microarray for high-throughput toxicology assays. Proceedings of the National Academy of Sciences, 2008. 105(1): p. 59-63. 23. Upadhyaya, S. and P.R. Selvaganapathy, Microfluidic devices for cell based high throughput screening. Lab on a Chip, 2010. 10(3): p. 341-348. 24. Wu, J., et al., A sandwiched microarray platform for benchtop cell-based high throughput screening. Biomaterials, 2011. 32(3): p. 841-848. 25. Chou, T.-C., Drug combination studies and their synergy quantification using the Chou-Talalay method. Cancer research, 2010. 70(2): p. 440-446. 26. Kuddannaya, S., et al., Surface chemical modification of poly (dimethylsiloxane) for the enhanced adhesion and proliferation of mesenchymal stem cells. ACS applied materials & interfaces, 2013. 5(19): p. 9777-9784. 27. Cunningham, J.J., et al., Quantification of fibronectin adsorption to silicone-rubber cell culture substrates. Biotechniques, 2002. 32(4): p. 876-887. 28. Heino, J., The collagen family members as cell adhesion proteins. Bioessays, 2007. 29(10): p. 1001-1010. 29. Zhang, Y., et al., Tissue-specific extracellular matrix coatings for the promotion of cell proliferation and maintenance of cell phenotype. Biomaterials, 2009. 30(23): p. 4021-4028. 30. Ding, X., et al., Cascade search for HSV-1 combinatorial drugs with high antiviral efficacy and low toxicity. International Journal of Nanomedicine, 2012. 7: p. 2281-2292. 31. Foty, R., A Simple Hanging Drop Cell Culture Protocol for Generation of 3D Spheroids. Journal of Visualized Experiments : JoVE, 2011(51): p. 2720. 32. Tung, Y.-C., et al., High-throughput 3D spheroid culture and drug testing using a 384 hanging drop array. Analyst, 2011. 136(3): p. 473-478. 33. Lu, P., V.M. Weaver, and Z. Werb, The extracellular matrix: A dynamic niche in cancer progression. The Journal of Cell Biology, 2012. 196(4): p. 395-406. 34. Godugu, C., et al., AlgiMatrix™ Based 3D Cell Culture System as an In-Vitro Tumor Model for Anticancer Studies. PLOS ONE, 2013. 8(1): p. e53708. 35. Boo, L., et al., MiRNA Transcriptome Profiling of Spheroid-Enriched Cells with Cancer Stem Cell Properties in Human Breast MCF-7 Cell Line. International Journal of Biological Sciences, 2016. 12(4): p. 427-445. | |
dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/68759 | - |
dc.description.abstract | 藥物組合之協同作用已經在臨床被證實具有改善癌症治療、減低藥物使用量,進而達到減少副作用的效果。然而臨床所能取得的病人檢體與分離出之原發腫瘤(primary tumor)數量相當稀少,不宜使用傳統孔盤進行篩選。本研究開發出一種只需使用少量細胞即可進行一百種藥物組合快速篩選的細胞微陣列系統(ECMS)。ECMS包含了一個由聚二甲基矽氧烷 (PDMS) 與蓋玻片製成的膠體晶片,與一台能符合高通量需求的液體列印點膠器 (liquid dispenser) 。目前已經使用攝護腺癌與神經母細胞瘤細胞株完成概念驗證研究,結果顯示在我們的晶片系統中所得到之癌細胞對藥物的敏感性,和於96孔盤上所得並無顯著差異,表示ECMS的藥篩結果是絕對可信的。接著,我們利用ECMS篩選最佳化藥物組合,並將該最佳組合在異種移植小鼠中進行藥效的驗證。結果顯示最佳藥物組合在動物中的腫瘤抑制率和單一藥物相比有顯著的提升。此外,我們還展示了ECMS也可以應用於三維 (3D) 細胞球培養與三維細胞之藥物篩選。總合上述,本研究所研發之細胞微陣列系統在個人化醫療與新藥開發應有寶貴的價值與潛力。 | zh_TW |
dc.description.abstract | Therapeutic synergism has been demonstrated with a potential to improve cancer treatments by screening of optimized drug combinations, reducing drug doses, and therefore attenuating side effects in patients. Here we present an elastomer cellular microarray system (ECMS) that can perform numerous drug screenings as well as requires few cells per assay. The ECMS includes an elastomer device – made with a polydimethylsiloxane (PDMS)-coated cover glass – and a robotic liquid dispenser, allowing to meet the high-throughput requirement in industries. Prostate and neuroblastoma cancer cell lines are utilized for our proof-of-conceptual study. Results show that drug sensitivities derived from our ECMS and conventional 96-well plates have no significant difference. In addition, the optimized drug combination evaluated from our system is further verified in xenograft tumor models. Remarkably, the tumor inhibition rate resulted from this optimized combination is significantly higher than other treatments adopted, i.e. single drug doses, indicating such screening approach by our in vitro platform could recapitulate the in vivo conditions. We demonstrate, furthermore, the ECMS could be employed and applied for applications of three-dimensional (3D) cell culture. Taken together, the ECMS developed herein should have an invaluable insight in the rapid identification of personalized medicine, achieving the new drug development on demand. | en |
dc.description.provenance | Made available in DSpace on 2021-06-17T02:34:00Z (GMT). No. of bitstreams: 1 ntu-106-R04b21014-1.pdf: 3000563 bytes, checksum: 22da8470ad056e7090ea2f5e6e916fc2 (MD5) Previous issue date: 2017 | en |
dc.description.tableofcontents | 中文摘要…………………………………………………Ⅲ
Abstract…………………………………………………Ⅳ Contents…………………………………………………Ⅵ Chapter 1 Introduction…………………………………………………1 1.1 Background and Motivation…………………………………………………1 1.2 Cellular microarray…………………………………………………2 1.2.1 Methods to fabricate cell microarrays…………………………………………………2 1.2.2 Methods of adding drugs into cellular microarray…………………………………………………3 1.3 Combination Index…………………………………………………5 1.4 Rationale…………………………………………………6 Chapter 2 Materials and Methods…………………………………………………7 2.1 Cell culture and maintenance…………………………………………………7 2.2 Fabrication of the ECMS chip…………………………………………………7 2.3 3D cell culture by ECMS platform…………………………………………………8 2.4 Cellular viability assay in 96-well plate…………………………………………………9 2.5 On-chip live/dead cell viability assay and image analysis…………………………………………………9 2.6 Chemosensitivity assay on chip…………………………………………………10 2.7 Procedure of on-chip drug combination screening…………………………………………………11 2.8 Derivation of Combination index (CI) from drug screening…………………………………………………11 2.9 In vivo tumor xenograft model test…………………………………………………11 2.10 Converting of in vitro-derived drug doses into in vivo model…………………………………………………12 2.11 Bioluminescent imaging…………………………………………………12 2.12 Statistical analysis…………………………………………………13 Chapter 3 Results…………………………………………………14 3.1 Chip fabrication, operation and characterization…………………………………………………14 3.1.1 Cell viability assay in ECMS chip…………………………………………………16 3.1.2 Comparison of drug sensitivities before and after cell adhesion…………………………………………………17 3.2 Comparison of toxicity profiles conducted from ECMS and standard 96-well plates…………………………………………………18 3.3 Evaluating efficient drug combinations…………………………………………………19 3.3.1 Identification of the optimal four-drug combination that inhibit PC3 cells …………………………………………………19 3.3.2 Identification of the optimal four-drug combination that inhibit SK-N-DZ cells…………………………………………………21 3.4 Translation of optimal drug combinations into in vivo tumor xenograft model…………………………………………………22 3.5 3D drug screening by ECMS platform…………………………………………………22 Chapter 4 Discussions and conclusions…………………………………………………24 4.1 Discussions…………………………………………………24 4.2 Conclusions…………………………………………………28 References…………………………………………………29 Tables & Figures…………………………………………………34 Table 1. Comparison of IC50 values before and after cell adhesion on 96-well Plates…………………………………………………34 Table 2. Comparison of IC50 value conducted from ECMS and standard 96-well plates…………………………………………………34 Table 3. Selected drug doses for screening of optimized combinations in PC3 cells…………………………………………………35 Table 4. Evaluated combination index from 81 drug conditions in PC3 cells…………………………………………………35 Table 5. Selected drug doses for screening of optimized combinations in SK-N-DZ cells…………………………………………………36 Table 6. Evaluated combination index from 27 drug conditions in SK-N-DZ cells…………………………………………………36 Table 7. Converting of in vitro-derived drug doses into in vivo model…………………………………………………37 Fig. 1. Schematic of experimental design…………………………………………………38 Fig. 2. Elastomer cellular microarray system (ECMS)…………………………………………………39 Fig. 3. Chip characteristics…………………………………………………40 Fig. 4. Comparison of drug sensitivities before and after cell adhesion…………………………………………………41 Fig. 5. Comparison of toxicity profiles conducted from ECMS and standard 96-well plates…………………………………………………42 Fig. 6. Comparison of toxicity profiles conducted from ECMS and standard 96-well plates…………………………………………………43 Fig. 7. Identification of the optimal four-drug combination that inhibit PC3 cells by 96-well plates…………………………………………………44 Fig. 8. Identification of the optimal three-drug combinations that inhibit SK-N-DZ cells by ECMS…………………………………………………46 Fig. 9. Translation of optimal drug combinations into in vivo tumor xenograft model…………………………………………………47 Fig. 10. 3D cell culture on ECMS chip…………………………………………………48 Fig. 11. 3D drug screening on ECMS chip…………………………………………………49 Appendix…………………………………………………50 | |
dc.language.iso | en | |
dc.title | 應用細胞微陣列系統於篩選對腫瘤細胞有效之藥物組合 | zh_TW |
dc.title | Elastomer cellular microarray systems (ECMS) for evaluating efficient drug combinations targeting patient-derived tumors | en |
dc.type | Thesis | |
dc.date.schoolyear | 105-2 | |
dc.description.degree | 碩士 | |
dc.contributor.oralexamcommittee | 董奕鍾(Yi-Chung Tung),許文明(Wen-Ming Hsu),張修豪(Hsiu-Hao Chang) | |
dc.subject.keyword | 細胞微陣列,藥物組合,個人化醫療,高通量,藥物篩選,三維細胞培養, | zh_TW |
dc.subject.keyword | cellular microarray,combination therapy,personalized medicine,high throughput,Drug screening,3D cell culture, | en |
dc.relation.page | 54 | |
dc.identifier.doi | 10.6342/NTU201703418 | |
dc.rights.note | 有償授權 | |
dc.date.accepted | 2017-08-18 | |
dc.contributor.author-college | 生命科學院 | zh_TW |
dc.contributor.author-dept | 生命科學系 | zh_TW |
顯示於系所單位: | 生命科學系 |
文件中的檔案:
檔案 | 大小 | 格式 | |
---|---|---|---|
ntu-106-1.pdf 目前未授權公開取用 | 2.93 MB | Adobe PDF |
系統中的文件,除了特別指名其著作權條款之外,均受到著作權保護,並且保留所有的權利。