乳癌在台灣地區女性癌症中發生率排名第一，且死亡率逐年增加。本研究以基質輔助雷射脫附游離質譜法 (Matrix-Assisted Laser Desorption Ionization Mass Spectrometry, MALDI-MS) 分析乳癌細胞株及乳癌腫瘤組織，並將結果結合多變量統計 (multivariate statistic) 進行分析。
一、以基質輔助雷射脫附游離質譜法及影像質譜法結合統計分析研究乳癌腫瘤組織
本研究以MALDI-TOF/MS研究乳癌組織的代謝物變化，分別將乳癌病患之正常乳房組織及腫瘤組織進行萃取後以MALDI-TOF/MS 分析，並結合多變量統計法運算每一個樣品的質譜訊號，成功利用主成分分析 (principle component analysis, PCA) 鑑別正常與癌變組織，並找出因細胞癌變而產生顯著差異的訊號。所開發之萃取方法結合MALDI-TOF/MS 分析，其靈敏度足以偵測細針抽吸之樣品，即使樣品量極小依然可以此方法得到足夠的質譜訊號，而將MALDI-TOF/MS 結合主PCA 統計的分析策略也適用於細針抽吸樣品，成功的將細針抽吸取樣之樣品依癌變組織及正常組織進行區分。結果顯示此分析策略十分有潛力應用於臨床醫學上，在一般細胞學檢查外提供另一項輔助診斷的資訊。
基質輔助雷射脫附游離結合影像質譜法 (Matrix-Assisted Laser Desorption/Ionization Imaging Mass Spectrometry, MALDI-IMS) 是影像與質譜技術的融合，可以影像的方式呈現各成份在組織中之分佈情況與含量多寡，表現出組織特定部位的特異性。以MALDI-TOF/MS 直接分析乳癌腫瘤之組織切片，不僅可完整得到組織上的組成，避免因萃取步驟造成部分組成之遺漏，且保留其在組織上的位置分布。將影像質譜分析結果結合多變量分析統計，成功利用PCA 找出一些顯著差異的訊號能鑑別正常與癌變組織，並將這些訊號以 MALDI imaging 的方式呈現，成功辨識正常與癌變組織，提供乳癌組織之代謝體探討的重要訊息。
二、以基質輔助雷射脫附游離質譜法結合統計分析研究乳癌細胞株
不同賀爾蒙接受體表現型的乳癌對於治療、預後狀況及存活率有很大的影響，本研究以MALDI-TOF/MS 分析不同賀爾蒙表現型之乳癌細胞株，藉此找出其中胜肽及蛋白質組成 (profile) 的差異，並將所得到的質譜圖結合主成分分析及階層式群集分析 (hierarchical clustering analysis) 兩種多變量分析法進行統計。研究中分析六株乳癌細胞株 (BT-474、MCF-7、SKBR3、T-47D、 MDA-MB-453 及MDA-MB-231) 及一株正常細胞株 (M10)，將其結果結合統計方法，可快速的將細胞株依其動情激素受體 (Estrogen Receptor, ER) 及第二型人類表皮生長因子接受體 (Human Epidermal Growth Factor Receptor 2, HER2) 類型進行區分，且結果與基因鑑定 (genotyping) 結果相符。

The incidence of breast cancer became the most common female cancer, and the fourth cause of female cancer death. In this study, matrix-assisted laser desorption ionization time-of-flight mass spectrometry (MALDI-TOF/MS) have been combined with multivariate statistics to investigate breast cancer tissues and cell lines.
Core needle biopsy and fine needle aspiration (FNA) are techniques largely applied in the diagnosis of breast cancer. In this study, we have established an efficient protocol for detecting breast tissue and FNA samples with MALDI-TOF/MS. With the help of statistical analysis software, we can find the lipid-derived ion signals which can be use to distinguish breast cancer tumor tissues from non-tumor parts. This strategy can differentiate normal and tumor tissue, which is potential to apply in clinical diagnoses.
The analysis of breast cancer tissue is challenging as the complexity of the tissue sample. Direct tissue analyses by matrix-assisted laser desorption/ionization imaging mass spectrometry (MALDI-IMS) allows us to investigate the molecular structure and their distribution while maintaining the integrity of the tissue and avoiding the loss of signals from extraction steps. Combined MALDI-IMS with statistic software, tissues can be analyzed and classified based on their molecular content which is helpful to distinguish tumor regions from non-tumor regions of breast cancer tissue. Our result shows the differences in the distribution and content of lipids between tumor and non-tumor tissue which can be supplements of current pathological analysis in tumor margins.
In this study, MALDI-TOF/MS combined with multivariate statistics were used to rapidly differentiate breast cancer cell lines with different estrogen receptor (ER) and human epidermal growth factor receptor 2 (HER2) status. The protocol for efficiently detecting peptides and proteins in breast cancer cells with MALDI-TOF/MS was established, two multivariate statistics including principle component analysis (PCA) and hierarchical clustering analysis were used to process the obtaining MALDI mass spectra of six different breast cancer cell lines and one normal breast cell lines. Based on the difference of the peptide and protein profiles, breast cancer cell lines with same ER and HER-2 status were grouped in nearby region on the PCA score plot. The results of hierarchical cluster analysis also revealed high conformity between breast cancer cell protein profiles and respective hormone receptor types.

中文摘要---------------------------------------------------------------------------------------------I
英文摘要-------------------------------------------------------------------------------------------III
目錄--------------------------------------------------------------------------------------------------V
圖目錄---------------------------------------------------------------------------------------------VII
表目錄----------------------------------------------------------------------------------------------XI
第一章、緒論
1.1 基質輔助雷射脫附游離質譜法------------------------------------------------------------1
1.2 影像質譜法------------------------------------------------------------------------------------5
1.3 主成分分析-----------------------------------------------------------------------------------12
1.4 乳癌--------------------------------------------------------------------------------------------16
第二章、以基質輔助雷射脫附游離質譜法及影像質譜法結合統計分析研究乳癌腫瘤組織
2.1 前言--------------------------------------------------------------------------------------------24
2.2 論文目標--------------------------------------------------------------------------------------25
2.3 實驗--------------------------------------------------------------------------------------------25
2.4 結果與討論-----------------------------------------------------------------------------------31
2.4.1 組織萃取-----------------------------------------------------------------------------31
2.4.2 細針抽吸-----------------------------------------------------------------------------35
2.4.3 訊號探討-----------------------------------------------------------------------------36
2.4.4 影像質譜----------------------------------------------------------------------------42
2.5 結論--------------------------------------------------------------------------------------------50
第三章、以基質輔助雷射脫附游離質譜結合統計分析研究乳癌細胞株
3.1 前言--------------------------------------------------------------------------------------------51
3.2 論文目標--------------------------------------------------------------------------------------51
3.3 實驗--------------------------------------------------------------------------------------------51
3.4 結果與討論-----------------------------------------------------------------------------------55
3.5 結論--------------------------------------------------------------------------------------------61
參考文獻-------------------------------------------------------------------------------------------62
附錄 (Surpporting Information) ------------------------------------------------------------68

1. Posthumus, M. A.; Kistemaker, P. G.; Meuzelaar, H. L. C.; Ten Noever de Brauw, M. C., Laser desorption-mass spectrometry of polar nonvolatile bio-organic molecules. Analytical Chemistry 1978, 50, 985-991.
2. Tanaka, K.; Waki, H.; Ido, Y.; Akita, S.; Yoshida, Y.; Yoshida, T.; Matsuo, T., Protein and polymer analyses up to m/z 100 000 by laser ionization time-of-flight mass spectrometry. Rapid Communications in Mass Spectrometry 1988, 2, 151-153.
3. Karas, M.; Hillenkamp, F., Laser desorption ionization of proteins with molecular masses exceeding 10,000 daltons. Analytical Chemistry 1988, 60, 2299-2301.
4. 陳月枝，現代生化質譜術-基質輔助雷射脫附游離質譜法，化學 2002 60, 229-240.
5. Zenobi, R.; Knochenmuss, R., Ion formation in MALDI mass spectrometry. Mass Spectrometry Reviews 1998, 17, 337-366.
6. Piyadasa, C. K. G.; Håkansson, P.; Ariyaratne, T. R., A high resolving power multiple reflection matrix-assisted laser desorption/ionization time-of-flight mass spectrometer. Rapid Communications in Mass Spectrometry 1999, 13, 620-624.
7. Schiller, J.; Süß, R.; Arnhold, J.; Fuchs, B.; Leßig, J.; Müller, M.; Petković, M.; Spalteholz, H.; Zschörnig, O.; Arnold, K., Matrix-assisted laser desorption and ionization time-of-flight (MALDI-TOF) mass spectrometry in lipid and phospholipid research. Progress in Lipid Research 2004, 43, 449-488.
8. Castaing, R.; Slodzian, G., Microanalyse par ĕ mission ionique secondaire. Journal de Microscopie 1962, 1, 395-410.
9. Caprioli, R. M.; Farmer, T. B.; Gile, J., Molecular Imaging of Biological Samples: Localization of Peptides and Proteins Using MALDI-TOF MS. Analytical Chemistry 1997, 69, 4751-4760.
10. Stoeckli, M.; Chaurand, P.; Hallahan, D. E.; Caprioli, R. M., Imaging mass spectrometry: A new technology for the analysis of protein expression in mammalian tissues. Nature Medicien 2001, 7, 493-496.
11. Todd, P. J.; Schaaff, T. G.; Chaurand, P.; Caprioli, R. M., Organic ion imaging of biological tissue with secondary ion mass spectrometry and matrix-assisted laser desorption/ionization. Journal of Mass Spectrometry 2001, 36, 355-369.
12. Cornett, D. S.; Mobley, J. A.; Dias, E. C.; Andersson, M.; Arteaga, C. L.; Sanders, M. E.; Caprioli, R. M., A Novel Histology-directed Strategy for MALDI-MS Tissue Profiling That Improves Throughput and Cellular Specificity in Human Breast Cancer. Molecular & Cellular Proteomics 2006, 5, 1975-1983.
13. Walch, A.; Rauser, S.; Deininger, S.-O.; Höfler, H., MALDI imaging mass spectrometry for direct tissue analysis: a new frontier for molecular histology. Histochemistry and Cell Biology 2008, 130, 421-434.
14. Seeley, E. H.; Caprioli, R. M., Imaging mass spectrometry: Towards clinical diagnostics. Proteomics - Clinical Applications 2008, 2, 1435-1443.
15. Groseclose, M. R.; Andersson, M.; Hardesty, W. M.; Caprioli, R. M., Identification of proteins directly from tissue: in situ tryptic digestions coupled with imaging mass spectrometry. Journal of Mass Spectrometry 2007, 42, 254-262.
16. Khatib-Shahidi, S.; Andersson, M.; Herman, J. L.; Gillespie, T. A.; Caprioli, R. M., Direct Molecular Analysis of Whole-Body Animal Tissue Sections by Imaging MALDI Mass Spectrometry. Analytical Chemistry 2006, 78, 6448-6456.
17. ClinProTools User Manual, Version 2.2. Bruker Daltonics: 2007.
18. 汪群超，主成份分析的原理 (http://web.ntpu.edu.tw/~ccw/statmath/M_pca.pdf; )
19. 張建邦，多變量分析，三民出版社：1997
20. Davis, J. E.; Shepard, A.; Stanford, N.; Rogers, L. B., Principal-component analysis applied to combined gas chromatographic-mass spectrometric data. Analytical Chemistry 1974, 46 , 821-825.
21. DeLuca, S.; Sarver, E. W.; Harrington, P. d. B.; Voorhees, K. J., Direct analysis of bacterial fatty acids by Curie-point pyrolysis tandem mass spectrometry. Analytical Chemistry 1990, 62, 1465-1472.
22. Aguilera, R.; Becchi, M.; Casabianca, H.; Hatton, C. K.; Catlin, D. H.; Starcevic, B.; Pope, J. H. G., Improved method of detection of testosterone abuse by gas chromatography - combustion - isotope-ratio mass spectrometry analysis of urinary steroids. Journal of Mass Spectrometry 1996, 31, 169-176.
23. Plumb, R. S.; Stumpf, C. L.; Granger, J. H.; Castro-Perez, J.; Haselden, J. N.; Dear, G. J., Use of liquid chromatography/time-of-flight mass spectrometry and multivariate statistical analysis shows promise for the detection of drug metabolites in biological fluids. Rapid Communications in Mass Spectrometry 2003, 17, 2632-2638.
24. Wang, C.; Kong, H.; Guan, Y.; Yang, J.; Gu, J.; Yang, S.; Xu, G., Plasma Phospholipid Metabolic Profiling and Biomarkers of Type 2 Diabetes Mellitus Based on High-Performance Liquid Chromatography/Electrospray Mass Spectrometry and Multivariate Statistical Analysis. Analytical Chemistry 2005, 77, 4108-4116.
25. Pan, Z.; Gu, H.; Talaty, N.; Chen, H.; Shanaiah, N.; Hainline, B.; Cooks, R.; Raftery, D., Principal component analysis of urine metabolites detected by NMR and DESI–MS in patients with inborn errors of metabolism. Analytical and Bioanalytical Chemistry 2007, 387, 539-549.
26. Chen, H.; Wortmann, A.; Zenobi, R., Neutral desorption sampling coupled to extractive electrospray ionization mass spectrometry for rapid differentiation of biosamples by metabolomic fingerprinting. Journal of Mass Spectrometry 2007, 42, 1123-1135.
27. West-Nielsen, M.; Høgdall, E. V.; Marchiori, E.; Høgdall, C. K.; Schou, C.; Heegaard, N. H. H., Sample Handling for Mass Spectrometric Proteomic Investigations of Human Sera. Analytical Chemistry 2005, 77, 5114-5123.
28. Rauser, S.; Marquardt, C.; Balluff, B.; Deininger, S. r.-O.; Al ers, C.; Belau, E.; Hartmer, R.; Suckau, D.; Specht, K.; E ert, M. P.; Schmitt, M.; Au ele, M.; H fler, H.; Walch, A., Classification of HER2 Receptor Status in Breast Cancer Tissues by MALDI Imaging Mass Spectrometry. Journal of Proteome Research 2010, 9, 1854-1863.
29. Yao, I.; Sugiura, Y.; Matsumoto, M.; Setou, M., In situ proteomics with imaging mass spectrometry and principal component analysis in the Scrapper-knockout mouse brain. Proteomics 2008, 8, 3692-3701.
30. Deininger, S. r.-O.; E ert, M. P.; Fütterer, A.; Gerhard, M.; R cken, C., MALDI Imaging Combined with Hierarchical Clustering as a New Tool for the Interpretation of Complex Human Cancers. Journal of Proteome Research 2008, 7, 5230-5236.
31. Zaima, N.; Matsuyama, Y.; Setou, M., Principal Component Analysis of Direct Matrix-Assisted Laser Desorption/Ionization Mass Spectrometric Data Related to Metabolites of Fatty Liver. Journal of Oleo Science 2009, 58, 267-273.
32. 張金堅，郭文宏，王明暘，台灣乳癌之流行病學，中華癌醫會誌 2008, 24, 85-93.
33. 中村清吾 著；孫玉芳 譯，圖解乳癌，世茂出版：2010
34. 彼得帕曼斯，葉歇賀蕭特 著；廖舜茹 譯，乳癌全書，原水文化：2001
35. Breast antomy. (http://www.newsperuvian.com/anatomy/breast-anatomy/)
36. 趙大中，乳癌分期與分類的新觀念，癌症新探 38 期：2009.
37. Perou, C. M.; Sorlie, T.; Eisen, M. B.; van de Rijn, M.; Jeffrey, S. S.; Rees, C. A.; Pollack, J. R.; Ross, D. T.; Johnsen, H.; Akslen, L. A.; Fluge, O.; Pergamenschikov, A.; Williams, C.; Zhu, S. X.; Lonning, P. E.; Borresen-Dale, A.-L.; Brown, P. O.; Botstein, D., Molecular portraits of human breast tumours. Nature 2000, 406, 747-752.
38. Sorlie, T., Gene expression patterns of breast carcinomas distinguish tumor subclasses with clinical implications. Proceedings of the National Academy of Sciences 2001, 98, 10869-10874.
39. Sorlie, T., Repeated observation of breast tumor subtypes in independent gene expression data sets. Proceedings of the National Academy of Sciences 2003, 100, 8418-8423.
40. van 't Veer, L. J.; Dai, H.; van de Vijver, M. J.; He, Y. D.; Hart, A. A. M.; Mao, M.; Peterse, H. L.; van der Kooy, K.; Marton, M. J.; Witteveen, A. T.; Schreiber, G. J.; Kerkhoven, R. M.; Roberts, C.; Linsley, P. S.; Bernards, R.; Friend, S. H., Gene expression profiling predicts clinical outcome of breast cancer. Nature 2002, 415, 530-536.
41. van de Vijver, M. J.; He, Y. D.; van 't Veer, L. J.; Dai, H.; Hart, A. A. M.; Voskuil, D. W.; Schreiber, G. J.; Peterse, J. L.; Roberts, C.; Marton, M. J.; Parrish, M.; Atsma, D.; Witteveen, A.; Glas, A.; Delahaye, L.; van der Velde, T.; Bartelink, H.; Rodenhuis, S.; Rutgers, E. T.; Friend, S. H.; Bernards, R., A Gene-Expression Signature as a Predictor of Survival in Breast Cancer. New England Journal of Medicine 2002, 347, 1999-2009.
42. TCOG乳癌研究委員會，乳癌診斷與治療共識，國家衛生研究院：2004
43. FNA vs CNB. (http://www.cancerquest.org/fna-vs-core-biopsy.)
44. Franzén, B.; Linder, S.; Okuzawa, K.; Kato, H.; Auer, G., Nonenzymatic extraction of cells from clinical tumor material for analysis of gene expression by two-dimensional polyacrylamide gel electrophoresis. Electrophoesis 1993, 14, 1045-1053.
45. Fowler, L. J.; Lovell, M. O.; Izbicka, E., Fine-needle aspiration in PreservCyt®: a novel and reproducible method for possible ancillary proteomic pattern expressionof breast neoplasms by SELDI-TOF. Modern Pathology 2004, 17, 1012-1020.
46. Rapkiewicz, A.; Espina, V.; Zujewski, J. A.; Lebowitz, P. F.; Filie, A.; Wulfkuhle, J.; Camphausen, K.; Petricoin, E. F.; Liotta, L. A.; Abati, A., The needle in the haystack: Application of breast fine-needle aspirate samples to quantitative protein microarray technology. Cancer 2007, 111, 173-184.
47. van Hove, E. R. A.; Blackwell, T. R.; Klinkert, I.; Eijkel, G. B.; Heeren, R. M. A.; Glunde, K., Multimodal Mass Spectrometric Imaging of Small Molecules Reveals Distinct Spatio-Molecular Signatures in Differentially Metastatic Breast Tumor Models. Cancer Research 2010, 70, 9012-9021.
48. Sakai, K.; Okuyama, H.; Yura, J.; Takeyama, H.; Shinagawa, N.; Tsuruga, N.; Kato, K.; Miura, K.; Kawase, K.; Tsujimura, T.; Naruse, T.; Koike, A., Composition and turnover of phospholipids and neutral lipids in human breast cancer and reference tissues. Carcinogenesis 1992, 13, 579-584.
49. Hietanen, E.; Punnonen, K.; Punnonen, R.; Auvinen, O., Fatty acid compsition of phospholipids and neutral lipids and lipid peroxidation in human breast cancer and lipoma tissue. Carcinogenesis 1986, 7, 19659-1969.
50. Glunde, K.; Jacobs, M. A.; Bhujwalla, Z. M., Choline metabolism in cancer: implications for diagnosis and therapy. Expert Review of Molecular Diagnostics 2006, 6, 821-829.
51. Glunde, K.; Bhujwalla, Z. M., Choline kinase alpha in cancer prognosis and treatment. The Lancet Oncology 2007, 8, 855-857.
52. Glunde, K.; Jie, C.; Bhujwalla, Z. M., Mechanisms of Indomethacin-Induced Alterations in the Choline Phospholipid Metabolism of Breast Cancer Cells. Neoplasia 2006, 8, 758–771.
53. Nimmagadda, S.; Glunde, K.; Pomper, M. G.; Bhujwalla, Z. M., Pharmacodynamic Markers for Choline Kinase Down-regulation in Breast Cancer Cells. Neoplasia 2009, 11, 477–484.
54. Glunde, K.; Jacobs, M. A.; Pathak, A. P.; Artemov, D.; Bhujwalla, Z. M., Molecular and functional imaging of breast cancer. NMR in Biomedicine 2009, 22, 92-103.
55. Aboagye, E. O.; Bhujwalla, Z. M., Malignant Transformation Alters Membrane Choline Phospholipid Metabolism of Human Mammary Epithelial Cells. Cancer Research 1999, 59, 80-84.
56. Nielsen, T. O., Immunohistochemical and Clinical Characterization of theBasal-Like Subtype of Invasive Breast Carcinoma. Clinical Cancer Research 2004, 10, 5367-5374.
57. Livasy, C. A.; Karaca, G.; Nanda, R.; Tretiakova, M. S.; Olopade, O. I.; Moore, D. T.; Perou, C. M., Phenotypic evaluation of the basal-like subtype of invasive breast carcinoma. Modern Pathology 2005, 19, 264-271.
58. Harris, R. A.; Yang, A.; Stein, R. C.; Lucy, K.; Brusten, L.; Herath, A.; Parekh, R.; Waterfield, M. D.; O’Hare, M. J.; Neville, M. A.; Page, M. J.; Zvele il, M. J., Cluster analysis of an extensive human breast cancer cell line protein expression map database. Proteomics 2002, 2, 212-223.