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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 | Yu-Sheng Lai | en |
dc.contributor.author | 賴佑昇 | zh_TW |
dc.date.accessioned | 2021-06-17T06:00:54Z | - |
dc.date.available | 2029-02-10 | |
dc.date.copyright | 2019-02-14 | |
dc.date.issued | 2019 | |
dc.date.submitted | 2019-02-12 | |
dc.identifier.citation | 1. Siegel Rebecca L, Miller Kimberly D, Jemal A: Cancer statistics, 2018. CA: A Cancer Journal for Clinicians 2018, 68(1):7-30.
2. Holliday DL, Speirs V: Choosing the right cell line for breast cancer research. Breast Cancer Research 2011, 13(4):215. 3. Toole SA, Beith JM, Millar EKA, West R, McLean A, Cazet A, Swarbrick A, Oakes SR: Therapeutic targets in triple negative breast cancer. Journal of Clinical Pathology 2013, 66(6):530. 4. Sotiriou C, Pusztai L: Gene-Expression Signatures in Breast Cancer. New England Journal of Medicine 2009, 360(8):790-800. 5. Kai M, Kanaya N, Wu SV, Mendez C, Nguyen D, Luu T, Chen S: Targeting breast cancer stem cells in triple-negative breast cancer using a combination of LBH589 and salinomycin. Breast Cancer Research and Treatment 2015, 151(2):281-294. 6. Ovcaricek T, Frkovic SG, Matos E, Mozina B, Borstnar S: Triple negative breast cancer – prognostic factors and survival. Radiology and Oncology 2011, 45(1):46-52. 7. Zhang N, Fu J-N, Chou T-C: Synergistic combination of microtubule targeting anticancer fludelone with cytoprotective panaxytriol derived from panax ginseng against MX-1 cells in vitro: experimental design and data analysis using the combination index method. American Journal of Cancer Research 2016, 6(1):97-104. 8. Devita Vincent T, Young Robert C, Canellos George P: Combination versus single agent chemotherapy: A review of the basis for selection of drug treatment of cancer. Cancer 2006, 35(1):98-110. 9. Weiss A, Ding X, van Beijnum JR, Wong I, Wong TJ, Berndsen RH, Dormond O, Dallinga M, Shen L, Schlingemann RO et al: Rapid optimization of drug combinations for the optimal angiostatic treatment of cancer. Angiogenesis 2015, 18(3):233-244. 10. Chou T-C: Drug Combination Studies and Their Synergy Quantification Using the Chou-Talalay Method. Cancer Research 2010, 70(2):440. 11. Yatsushiro S, Yamamoto T, Yamamura S, Abe K, Obana E, Nogami T, Hayashi T, Sesei T, Oka H, Okello-Onen J et al: Application of a cell microarray chip system for accurate, highly sensitive, and rapid diagnosis for malaria in Uganda. Scientific Reports 2016, 6:30136. 12. Yarmush ML, King KR: Living-Cell Microarrays. Annual Review of Biomedical Engineering 2009, 11(1):235-257. 13. Hong HJ, Koom WS, Koh W-G: Cell Microarray Technologies for High-Throughput Cell-Based Biosensors. Sensors (Basel, Switzerland) 2017, 17(6):1293. 14. Folch A, Toner M: Microengineering of Cellular Interactions. Annual Review of Biomedical Engineering 2000, 2(1):227-256. 15. Castel D, Pitaval A, Debily M-A, Gidrol X: Cell microarrays in drug discovery. Drug Discovery Today 2006, 11(13):616-622. 16. Flaim CJ, Chien S, Bhatia SN: An extracellular matrix microarray for probing cellular differentiation. Nature Methods 2005, 2:119. 17. Fernandes TG, Kwon S-J, Lee M-Y, Clark DS, Cabral JMS, Dordick JS: On-Chip, Cell-Based Microarray Immunofluorescence Assay for High-Throughput Analysis of Target Proteins. Analytical Chemistry 2008, 80(17):6633-6639. 18. Kuo CT, Wang JY, Wo Andrew M, Chen Benjamin PC, Lee H: ParaStamp and Its Applications to Cell Patterning, Drug Synergy Screening, and Rewritable Devices for Droplet Storage. Advanced Biosystems 2017, 1(5):1700048. 19. Zhao C, Wang X, Zhao Y, Li Z, Lin S, Wei Y, Yang H: A Novel Xenograft Model in Zebrafish for High-Resolution Investigating Dynamics of Neovascularization in Tumors. PLOS ONE 2011, 6(7):e21768. 20. Barriuso J, Nagaraju R, Hurlstone A: Zebrafish: A New Companion for Translational Research in Oncology. Clinical Cancer Research 2015, 21(5):969. 21. Fior R, Póvoa V, Mendes RV, Carvalho T, Gomes A, Figueiredo N, Ferreira MG: Single-cell functional and chemosensitive profiling of combinatorial colorectal therapy in zebrafish xenografts. Proceedings of the National Academy of Sciences 2017, 114(39):E8234. 22. Lee HJ, Yang Yeon J, Jeong S, Lee Jong D, Choi SY, Jung DW, Moon In S: Development of a vestibular schwannoma xenograft zebrafish model for in vivo antitumor drug screening. The Laryngoscope 2016, 126(12):E409-E415. 23. Pichler FB, Laurenson S, Williams LC, Dodd A, Copp BR, Love DR: Chemical discovery and global gene expression analysis in zebrafish. Nature Biotechnology 2003, 21:879. 24. Nicoli S, Ribatti D, Cotelli F, Presta M: Mammalian Tumor Xenografts Induce Neovascularization in Zebrafish Embryos. Cancer Research 2007, 67(7):2927. 25. Nicoli S, Presta M: The zebrafish/tumor xenograft angiogenesis assay. Nature Protocols 2007, 2:2918. 26. Stoletov K, Montel V, Lester RD, Gonias SL, Klemke R: High-resolution imaging of the dynamic tumor cell–vascular interface in transparent zebrafish. Proceedings of the National Academy of Sciences 2007, 104(44):17406. 27. Lee SLC, Rouhi P, Jensen LD, Zhang D, Ji H, Hauptmann G, Ingham P, Cao Y: Hypoxia-induced pathological angiogenesis mediates tumor cell dissemination, invasion, and metastasis in a zebrafish tumor model. Proceedings of the National Academy of Sciences 2009, 106(46):19485. 28. Mathias JR, Saxena MT, Mumm JS: Advances in zebrafish chemical screening technologies. Future medicinal chemistry 2012, 4(14):1811-1822. 29. Mokhtari RB, Homayouni TS, Baluch N, Morgatskaya E, Kumar S, Das B, Yeger H: Combination therapy in combating cancer. Oncotarget 2017, 8(23):38022-38043. 30. Hsiung LC, Chiang CL, Wang CH, Huang YH, Kuo CT, Cheng JY, Lin CH, Wu V, Chou HY, Jong DS et al: Dielectrophoresis-based cellular microarray chip for anticancer drug screening in perfusion microenvironments. Lab Chip 2011, 11:2333-2342. 31. Schindelin J, Arganda-Carreras I, Frise E, Kaynig V, Longair M, Pietzsch T, Preibisch S, Rueden C, Saalfeld S, Schmid B et al: Fiji: an open-source platform for biological-image analysis. Nature Methods 2012, 9:676. 32. Kovács R, Bakos K, Urbányi B, Kövesi J, Gazsi G, Csepeli A, Appl ÁJ, Bencsik D, Csenki Z, Horváth Á: Acute and sub-chronic toxicity of four cytostatic drugs in zebrafish. Environmental Science and Pollution Research 2016, 23(15):14718-14729. 33. Khademhosseini A, Ferreira L, Blumling J, Yeh J, Karp JM, Fukuda J, Langer R: Co-culture of human embryonic stem cells with murine embryonic fibroblasts on microwell-patterned substrates. Biomaterials 2006, 27(36):5968-5977. 34. Lee M-Y, Kumar RA, Sukumaran SM, Hogg MG, Clark DS, Dordick JS: Three-dimensional cellular microarray for high-throughput toxicology assays. Proceedings of the National Academy of Sciences 2008, 105(1):59. 35. Carstens MR, Fisher RC, Acharya AP, Butterworth EA, Scott E, Huang EH, Keselowsky BG: Drug-eluting microarrays to identify effective chemotherapeutic combinations targeting patient-derived cancer stem cells. Proceedings of the National Academy of Sciences 2015, 112(28):8732. 36. AshaRani PV, Low Kah Mun G, Hande MP, Valiyaveettil S: Cytotoxicity and Genotoxicity of Silver Nanoparticles in Human Cells. ACS Nano 2009, 3(2):279-290. 37. Kuo CT, Chiang CL, Chang CH, Liu HK, Huang GS, Huang RYJ, Lee H, Huang CS, Wo AM: Modeling of cancer metastasis and drug resistance via biomimetic nano-cilia and microfluidics. Biomaterials 2014, 35:1562-1571. 38. Kuo CT, Wang JY, Lin YF, Wo AM, Chen BPC, ee HL: Three-dimensional spheroid culture targeting versatile tissue bioassays using a PDMS-based hanging drop array. Sci Rep 2017, 7:4363. 39. Lee LMJ, Seftor EA, Bonde G, Cornell RA, Hendrix MJC: The fate of human malignant melanoma cells transplanted into zebrafish embryos: Assessment of migration and cell division in the absence of tumor formation. Developmental Dynamics 2005, 233(4):1560-1570. 40. Au - Ren J, Au - Liu S, Au - Cui C, Au - ten Dijke P: Invasive Behavior of Human Breast Cancer Cells in Embryonic Zebrafish. JoVE 2017(122):e55459. 41. Cabezas-Sainz P, Guerra-Varela J, Carreira MJ, Mariscal J, Roel M, Rubiolo JA, Sciara AA, Abal M, Botana LM, López R et al: Improving zebrafish embryo xenotransplantation conditions by increasing incubation temperature and establishing a proliferation index with ZFtool. BMC Cancer 2018, 18(1):3. 42. Martinez-Ordoñez A, Seoane S, Cabezas P, Eiro N, Sendon-Lago J, Macia M, Garcia-Caballero T, Gonzalez LO, Sanchez L, Vizoso F et al: Breast cancer metastasis to liver and lung is facilitated by Pit-1-CXCL12-CXCR4 axis. Oncogene 2018, 37(11):1430-1444. 43. Jung D-W, Oh E-S, Park S-H, Chang Y-T, Kim C-H, Choi S-Y, Williams DR: A novel zebrafish human tumor xenograft model validated for anti-cancer drug screening. Molecular BioSystems 2012, 8(7):1930-1939. | |
dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/71448 | - |
dc.description.abstract | 乳腺癌為女性最嚴重之惡性腫瘤。儘管乳腺癌已有多種治療方法,化療藥物之協同作用依舊具或多或少之療效。協同作用不僅能增強化療功效,並能減輕患者副作用及抗藥性。然而,來自原發性腫瘤之細胞數量有限,且對於高通量篩選所需大量藥物仍具挑戰性。在此,我們提供了一種新型細胞微陣列晶片系統,它由蠟排列孔洞之晶片及一臺自動液體分配裝置所組成,特別適用於高通量藥物協同作用篩選。我們導入四種常用化療藥物,包含順鉑、5-氟尿嘧啶、環磷酰胺及依托泊苷和兩種乳腺癌細胞,MCF7與MDA-MB-231,進行概念性驗證。並利用協同組合指數方程式運算來篩選出最佳化療藥物組合。此外,晶片系統所評估之最佳藥物組合將再利用斑馬魚腫瘤異種移植模型在動物體內進一步驗證。結果顯示,與單一藥物相比,篩選出之最佳藥物協同作用能減少單一藥物約14.3倍之劑量。與標準96孔盤相比,此晶片可降低500倍之藥物體積。此外,透過注射乳癌細胞至斑馬魚模型,我們成功地證明最佳藥物組合可有效抑制21%之腫瘤生長。值得注意的是,細胞微陣列晶片可以比96孔盤更準確地預測動物實驗結果。總之,本研究整合細胞微陣列晶片及斑馬魚腫瘤異種移植模型,能加速評估對人類乳腺癌之最佳化療藥物組合,以提供個人化藥物之早期篩選及未來新藥開發之新平台。 | zh_TW |
dc.description.abstract | Breast cancer has been recently revealed as the most deadly cancer to females, hitting an astounding 15-year reduction of life. Although numerous planning treatments have been launched, synergistic interactions of current anti-cancer drugs still stun the target. The Synergistic interactions not only improve breast cancer chemotherapy efficacy, but reduce drug resistance and side-effects as well. However, the shortages of less cell amount from primary tumors and massive drugs needed still remain challenging for high throughput evaluation. In this research, we present a cellular microarray ParaStamp (CMP) chip system, which combines of wax-well-arrayed chips and an automatic liquid dispensing machine, particularly for high throughput drug synergy screening. For the proof-of-conceptual demonstration, we conduct four existent chemotherapeutic drugs (e.g. cisplatin, 5-Fluorouracil, cyclophosphamide, and etoposide) and two breast cancer cell lines (e.g. MCF7 and MDA-MB-231 cells) into the system. A screening strategy based on the combination index (CI) equation is then utilized to identify the optimal drug combinations. In addition, the optimizations are further verified by zebrafish (ZF) tumor xenograft models. Results show that the optimal drug combination screened can cause the dose reduction down to approximately 14.3 folds compared with single drugs conducted. In contrast to standard 96-well plate assay, the study conductor demonstrates that the volume of each tested drug can be retained up to 500 folds. Moreover, a significant 21% inhibition of MCF7 breast tumors engrafted in ZF models is successfully presented by the identified drug combination. Remarkably, the CMP chip could predict the in vivo efficacy more accurately than 96-well plate assays. Taken together, our findings demonstrate the integration of the CMP chip platform and zebrafish tumor xenograft model could improve the outcome for breast cancer chemotherapy. It may further offer new opportunities to enhance personalized medicine and drug discovery. | en |
dc.description.provenance | Made available in DSpace on 2021-06-17T06:00:54Z (GMT). No. of bitstreams: 1 ntu-108-R05b21026-1.pdf: 9404714 bytes, checksum: 38d3f80176258c4febba696f60e45f98 (MD5) Previous issue date: 2019 | en |
dc.description.tableofcontents | 誌謝 i
摘要 ii Abstract iii Contents v Chapter 1 Introduction 1 1.1 Breast Cancer and Breast Cancer Subtypes 1 1.2 Drug Combination and Combination Index 1 1.3 Cellular Microarray Chips 2 1.4 Zebrafish Tumor Xenograft Model 3 1.5 Rationale 3 Chapter 2 Materials and Methods 5 2.1 Cell and Cell Culture 5 2.2 High-throughput Drug Synergy Screening by Cellular Microarray ParaStamp (CMP) Chip 5 2.2.1 Fabrication of 9×9 Cellular Microarray ParaStamp (CMP) chip 5 2.2.2 High-throughput drug synergy screening by by Cellular Microarray ParaStamp (CMP) chip 6 2.2.3 Chemosensitivity assay on Cellular Microarray ParaStamp (CMP) chip 7 2.3 Cell Viability Assay in 96-well Plate 7 2.4 Evaluation of the Combination Index from Drug Screening 8 2.5 In Vivo Experiments with Zebra Fish (ZF) Tumor Xenograft Model analysis 8 2.5.1 Cell Labeling to Assess Proliferation 8 2.5.2 Zebrafish (ZF) Tumor Xenograft 9 2.5.3 Quantification of Xenografted Cancer Cells 10 2.5.4 Zebrafish Embryo Toxicity (ZFET) Testing and Treatment 11 2.6 Statistical Analysis 11 Chapter 3 Results 12 3.1 Cellular Microarray ParaStamp (CMP) Chip Fabrication, Operation and Characterization 12 3.2 Comparison of Toxicity Profile from Cellular Microarray ParaStamp (CMP) Chip and Standard 96-well Plate 12 3.3 Evaluating Efficient Drug Combinations 14 3.3.1 Identification of Optimal Four-drug Combination Suppressed Breast Cancer Proliferation 14 3.4 In Vivo Zebrafish Tumor Xenograft Verifying the Drug Efficacy of Drug Combinations 16 3.4.1 Developing Zebrafish Tumor Xenograft Model for In Vivo Drug Efficacy Testing 16 3.4.2 Synergistic Effect of Combination #36 Inhibited Breast Cancer Growth in ZF Tumor Xenograft Model 16 3.4.3 CMP Chip Could Predict the In Vivo Drug Efficacy 17 Chapter 4 Discussions and Conclusions 18 4.1 Discussions 18 4.2 Conclusions 21 Figures 23 Fig. 1. Illustration of experimental design. 23 Fig. 2. Cellular Microarray ParaStamp (CMP) chip system 24 Fig. 3. High-throughput drug screening via a Cellular Microarray ParaStamp (CMP) chip 26 Fig. 4. Toxicity effect of chemotherapeutic medicines on MCF7 cell proliferation conducted from CMP chip and standard 96-well plate. 27 Fig. 5. Toxicity effect of chemotherapeutic medicines on MDA-MB-231 cell proliferation conducted from CMP chip and standard 96-well plate. 28 Fig. 6. Comparison of drug IC50 values derived from CMP chip and 96-well plates. 29 Fig. 7. Identification of the optimal four chemotherapy drug cocktails that inhibit MCF7 cell proliferation by CMP chip. 31 Fig. 8. Identification of the optimal four chemotherapy drug cocktails that inhibit MCF7 cell proliferation by 96-well plate. 32 Fig. 9. Efficacy of optimized combinations versus corresponding single drug used. 33 Fig. 10. Comparison of relative doses used for cellular inhibition by optimized drug combinations and its single-used drugs. 34 Figure S1. Comparison the cell number of the first column and the last column dispensed by robotic liquid handing machine. 41 Figure S2. Comparison the cell number of the first column and the last column dispensed by robotic liquid handing machine. 42 Figure S3. Bioluminescent images showing the viability of MCF7 cells on CMP chip after a 24-h treatment with 81 combinational drugs. 43 Figure S4. Program code of MacroZebrafish for zebrafish tumor analysis. Java programming 44 Tables 45 Table 1. Chemotherapeutic medicines and doses for drug synergy screening 45 Table 2. Comparison of IC50 value derived from CMP chip and standard 96-well plate 45 Table 3. Selected drug doses for screening of optimized combinations in MCF7 45 Table 4. Cell viabilities and the derived combination indexes from 81 drug combinations. 46 Table 5. Selected drug combinations and their dose-reduction index (DRI) 48 Table 6. Zebrafish embryo toxicity (ZFET) testing at 34 °C from 48 hpf to144 hpf 49 Table S1. Characteristics between the CMP chip and 96-well plate. 50 References 51 | |
dc.language.iso | en | |
dc.title | 整合細胞微陣列晶片平台及斑馬魚腫瘤異種移植模型以加速評估對人類乳腺癌之最佳化療藥物組合 | zh_TW |
dc.title | Integration of Cellular Microarray ParaStamp Chip Platform and Zebrafish Tumor Xenograft Model to Boost
The Chemotherapeutic Drug Cocktails Targeting Breast Cancer | en |
dc.type | Thesis | |
dc.date.schoolyear | 107-1 | |
dc.description.degree | 碩士 | |
dc.contributor.oralexamcommittee | 黃元勵(Yuan-Li Huang),蕭崇德(Chung-Der Hsiao),張修豪(Hsiu-Hao Chang) | |
dc.subject.keyword | 乳癌,細胞微陣列晶片,藥物組合,高通量,藥物篩選,斑馬魚異種移植模型, | zh_TW |
dc.subject.keyword | breast cancer,cellular microarray,drug combination,high throughput,drug screening,zebrafish tumor xenograft models, | en |
dc.relation.page | 56 | |
dc.identifier.doi | 10.6342/NTU201900406 | |
dc.rights.note | 有償授權 | |
dc.date.accepted | 2019-02-12 | |
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 |
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