臺灣博碩士論文加值系統

為了乳癌是好發於女性的癌症，雖然相較於其他癌症已有較明確之分子分類，但在台灣女性癌症死亡率仍排名第四名，而造成預後不好的主要原因為遠端轉移及抗藥性發生。其中，癌幹細胞（Cancer stem cells，簡稱CSCs）被認為是負責腫瘤轉移與抗藥性的關鍵。近年來相關研究已嘗試開發轉移動物模型來探討轉移機制，主要藉由心臟注射或尾靜脈注射癌細胞（簡稱血液模型）模擬轉移情形，然而相關研究指出，現有血液模型缺乏癌細胞於乳腺（原位）成長並侵入血液循環系統的階段。因此，將腫瘤細胞植入乳腺脂肪墊的自發性轉移模型已經被提出，但是這些研究仍然缺乏多代遠端轉移的全基因組分析，解釋不同代遠端轉移癌細胞轉移能力之差異及機制。
為了解決這個問題，我們提出一個全基因組分析策略，並藉由與台北醫學大學的李嘉華教授合作，發展多代自發性乳癌肺轉移模型（簡稱 (sBLM)2模型），探討轉移、抗藥性與癌幹細胞之間的相關性。(sBLM)2模型是將MDA-MB-231三陰性乳癌細胞株注射進老鼠乳腺脂肪墊上，經過12週後，這些細胞產生自發性肺臟轉移，我們將肺轉移細胞（稱為一代肺轉移細胞）取下，再重新植入另一隻老鼠的乳腺脂肪墊上，待8週後再取下轉移至肺的細胞（稱為二代肺轉移細胞）。透過比較血液模型及(sBLM)2模型，我們發現(sBLM)2模型之二代肺轉移細胞的基因表現圖譜，與血液模型的所有組織細胞具有顯著差異，甚至與(sBLM)2模型的原發性乳癌細胞及一代肺轉移細胞也顯著不同。然而，血液模型中的腫瘤細胞，不論在各代的肺或是其他器官之間，基因表現圖譜皆高度相似。除此之外，生化途徑富集分析 (pathway enrichment analysis)結果顯示，一代肺轉移細胞與二代肺轉移細胞之顯著表達差異基因會調控13條轉移/癌症相關之生化途徑，但是在血液模型中的細胞則不會顯著調控這些途徑。
為了進一步探討一代肺轉移細胞和二代肺轉移細胞之差異，我們整合蛋白質-蛋白質交互作用網路及KEGG生化途徑，建立生化途徑交聯網路。並在二代肺轉移細胞特有調控之交聯網路中，發現CSCs相關途徑會藉由人類疾病相關途徑，與抗藥性相關途徑相互調控。根據腫瘤細胞球體形成試驗 (Tumor-sphere formation assay)與藥物敏感度測試 (MTT assay)，證實二代肺轉移乳癌細胞具有癌幹細胞特性與抗藥性。另外，我們發現基因X在轉移、抗藥性與CSCs中扮演重要的角色。實驗結果顯示當基因X表現被移除 (knock-out)時，細胞不再具有侵襲能力、癌幹細胞特性與抗藥性。綜合上述，我們相信以全基因組分析方法（例如：pathway cross-talk networks）探討(sBLM)2模型，不僅有效用於研究轉移、抗藥性和癌幹細胞之間的關係，更可以用於發現新的生物標誌。

Breast cancer, one of the most common cancer in women, is the fourth leading cause of cancer mortality in Taiwan. Notably, the main reason for poor prognosis is the distant metastasis and drug resistance. Moreover, cancer stem cells (CSCs) is in charge of tumor metastasis and drug resistance. Some studies have developed the metastasis animal models by tail vein/intracardiac injection of tumor cells (called blood models) to simulate metastasis, but blood models were indicated without considering the process of intravasation. Therefore, spontaneous metastasis models have been proposed by introducing the tumor cells into mammary fat pad. However, these studies still lacked a genome-wide analysis for multigenerational distant metastasis, leaving an incomplete picture of breast-to-lung metastasis.
To address these issues, we proposed a genome-wide strategy to explore the relationships among metastasis, drug resistance and CSC using the multigenerational spontaneous breast-to-lung metastasis model (sBLM)2 model, cooperated with Dr. Chia-Hwa Lee (Taipei Medical University). In (sBLM)2 model, MDA-MB-231 cells were injected into the mouse mammary fat pad and these cells exhibited spontaneous pulmonary metastases (called 1st Lung) after 12 weeks. Then, we reimplanted the 1st Lung into the mammary fat pads of another mouse until tumors metastasize to lung (called 2nd Lung) after 8 weeks. By comparing the blood model and (sBLM)2 model, we discovered that gene expression profiles of 2nd Lung in the (sBLM)2 model represent significant difference from those in the ones of blood model and primary tumor cells even 1st Lung. Conversely, the gene expressions of tumor cells between lung for different generation and the other organs in blood model were highly similar. In addition, pathway enrichment analysis shows that the differentially expressed genes of 1st Lung and 2nd Lung were up-regulated in 13 metastasis-/cancer-related pathways but not in blood model.
To further compare differential pathways and genes between 1st Lung and 2nd Lung, we used protein-protein interaction networks and KEGG pathways to establish dynamic pathway cross-talk networks. In the cross-talk network of 2nd Lung, the CSC-related pathways were linked to drug resistance-related pathways through human disease-related pathways. Based on tumor-sphere formation assay and MTT assay, the 2nd Lung represents CSC properties and drug resistance. Furthermore, we discovered gene X plays a key role in metastasis, drug resistance and CSC due to decrease of abilities for invasion, drug resistance, and tumor-sphere formation when knock-out of gene X. In summary, we believe that genome-wide analysis (e.g. pathway cross-talk networks) in (sBLM)2 model is not only useful for studying the relationships between metastasis, drug resistance and CSC but also in discovering the new biomarkers.

中文摘要 I
ABSTRACT II
致謝 III
目錄 IV
圖目錄 VI
表目錄 VIII
第一章、緒論 1
1.1 背景介紹 1
1.2 相關研究 3
第二章、材料與方法 4
2.1 資料集 4
2.1.1 多代自發性乳癌肺轉移模型 4
2.1.2 DNA微陣列資料集 7
2.1.3 轉移與癌症相關之生化途徑 (KEGG pathway) 9
2.1.4 基因本體論資料庫 (Gene Ontology) 13
2.1.5 人類蛋白質與蛋白質交互作用網路 13
2.1.6 癌症基因組圖譜 (The Cancer Genome Atlas)資料庫 14
2.2 基因之表現量差異分析 15
2.2.1 衡量基因變化量（fold change，簡稱FC） 15
2.2.2 衡量均方根偏差（root-mean-square deviation，簡稱RMSD） 16
2.2.3 篩選顯著表達差異之基因 16
2.3 生化途徑交聯網路建立 (Pathway cross-talk networks) 17
2.3.1 基因參與之生化途徑分析 17
2.3.2 生化途徑之交聯網路建立 17
2.4 關鍵基因預測預後的能力 20
2.4.1 關鍵基因預測病患預後的能力 20
第三章、結果與討論 21
3.1 血液模型與多代自發性乳癌肺轉移模型之轉移模式比較結果 21
3.1.1 透過均方根偏差分析各樣本之相似程度 21
3.1.2 顯著變化基因 23
3.1.3 生化途徑分析結果 28
3.1.4 轉移啟動之生化途徑探討 30
3.2 一代與多代自發性乳癌肺轉移模型轉移、抗藥性、癌幹細胞之比較 39
3.2.1 生化途徑交聯網路 39
3.2.2 轉移、抗藥性、癌幹細胞特性之關係 44
3.2.3 篩選潛在之生物標誌 51
3.3 潛在生物標誌之轉移、抗藥性、癌幹細胞特性之驗證 57
3.3.1 細胞癌化程度測試 57
3.3.2 藥物敏感度測試 59
3.3.3 癌幹細胞特性之測試 61
3.3.4 癌幹細胞標誌之測試 62
第四章、結論 63
4.1 總結 63
4.2 未來展望 64
參考文獻 65

1 衛生福利部國民健康署：乳癌防治。取自https://www.hpa.gov.tw/Pages/Detail.aspx?nodeid=205&pid=1124。(2017)。
2 Parker, J. S. et al. Supervised risk predictor of breast cancer based on intrinsic subtypes. Journal of clinical oncology : official journal of the American Society of Clinical Oncology 27, 1160-1167, doi:10.1200/JCO.2008.18.1370 (2009).
3 Gumireddy, K. et al. The mRNA-edited form of GABRA3 suppresses GABRA3-mediated Akt activation and breast cancer metastasis. Nature communications 7, 10715, doi:10.1038/ncomms10715 (2016).
4 Tominaga, K. et al. Addiction to the IGF2-ID1-IGF2 circuit for maintenance of the breast cancer stem-like cells. Oncogene 36, 1276-1286, doi:10.1038/onc.2016.293 (2017).
5 Cassetta, L. & Pollard, J. W. Repolarizing macrophages improves breast cancer therapy. Cell research, doi:10.1038/cr.2017.63 (2017).
6 Miao, Y. et al. MicroRNA-130b targets PTEN to mediate drug resistance and proliferation of breast cancer cells via the PI3K/Akt signaling pathway. Scientific reports 7, 41942, doi:10.1038/srep41942 (2017).
7 Magnani, L. et al. Acquired CYP19A1 amplification is an early specific mechanism of aromatase inhibitor resistance in ERalpha metastatic breast cancer. Nature genetics 49, 444-450, doi:10.1038/ng.3773 (2017).
8 De Cola, A. et al. miR-205-5p-mediated downregulation of ErbB/HER receptors in breast cancer stem cells results in targeted therapy resistance. Cell death & disease 6, e1823, doi:10.1038/cddis.2015.192 (2015).
9 Chen, T. et al. Identification and expansion of cancer stem cells in tumor tissues and peripheral blood derived from gastric adenocarcinoma patients. Cell research 22, 248-258, doi:10.1038/cr.2011.109 (2012).
10 Lin, C. et al. CGI-99 promotes breast cancer metastasis via autocrine interleukin-6 signaling. Oncogene, doi:10.1038/onc.2016.525 (2017).
11 Mitra, A., Mishra, L. & Li, S. EMT, CTCs and CSCs in tumor relapse and drug-resistance. Oncotarget 6, 10697-10711, doi:10.18632/oncotarget.4037 (2015).
12 Minn, A. J. et al. Genes that mediate breast cancer metastasis to lung. Nature 436, 518-524, doi:10.1038/nature03799 (2005).
13 Bos, P. D. et al. Genes that mediate breast cancer metastasis to the brain. Nature 459, 1005-1009, doi:10.1038/nature08021 (2009).
14 Lu, X., Lu, X. & Kang, Y. Organ-specific enhancement of metastasis by spontaneous ploidy duplication and cell size enlargement. Cell research 20, 1012-1022, doi:10.1038/cr.2010.93 (2010).
15 Walsh, L. A. et al. RECK controls breast cancer metastasis by modulating a convergent, STAT3-dependent neoangiogenic switch. Oncogene 34, 2189-2203, doi:10.1038/onc.2014.175 (2015).
16 Liu, H. et al. Cancer stem cells from human breast tumors are involved in spontaneous metastases in orthotopic mouse models. Proceedings of the National Academy of Sciences of the United States of America 107, 18115-18120, doi:10.1073/pnas.1006732107 (2010).
17 Bockhorn, J. et al. Differentiation and loss of malignant character of spontaneous pulmonary metastases in patient-derived breast cancer models. Cancer research 74, 7406-7417, doi:10.1158/0008-5472.CAN-14-1188 (2014).
18 Singh, A. & Settleman, J. EMT, cancer stem cells and drug resistance: an emerging axis of evil in the war on cancer. Oncogene 29, 4741-4751, doi:10.1038/onc.2010.215 (2010).
19 Mani, S. A. et al. The epithelial-mesenchymal transition generates cells with properties of stem cells. Cell 133, 704-715, doi:10.1016/j.cell.2008.03.027 (2008).
20 Wellner, U. et al. The EMT-activator ZEB1 promotes tumorigenicity by repressing stemness-inhibiting microRNAs. Nature cell biology 11, 1487-1495, doi:10.1038/ncb1998 (2009).
21 Witta, S. E. et al. Restoring E-cadherin expression increases sensitivity to epidermal growth factor receptor inhibitors in lung cancer cell lines. Cancer research 66, 944-950, doi:10.1158/0008-5472.CAN-05-1988 (2006).
22 Neve, R. M. et al. A collection of breast cancer cell lines for the study of functionally distinct cancer subtypes. Cancer cell 10, 515-527, doi:10.1016/j.ccr.2006.10.008 (2006).
23 Li, X. et al. Intrinsic resistance of tumorigenic breast cancer cells to chemotherapy. Journal of the National Cancer Institute 100, 672-679, doi:10.1093/jnci/djn123 (2008).
24 Smyth, G. K. Linear models and empirical bayes methods for assessing differential expression in microarray experiments. Statistical applications in genetics and molecular biology 3, Article3, doi:10.2202/1544-6115.1027 (2004).
25 Irizarry, R. A. et al. Summaries of Affymetrix GeneChip probe level data. Nucleic acids research 31, e15 (2003).
26 Kanehisa, M. & Goto, S. KEGG: kyoto encyclopedia of genes and genomes. Nucleic acids research 28, 27-30 (2000).
27 Ashburner, M. et al. Gene ontology: tool for the unification of biology. The Gene Ontology Consortium. Nature genetics 25, 25-29, doi:10.1038/75556 (2000).
28 von Mering, C. et al. Comparative assessment of large-scale data sets of protein-protein interactions. Nature 417, 399-403, doi:10.1038/nature750 (2002).
29 Uetz, P. et al. A comprehensive analysis of protein-protein interactions in Saccharomyces cerevisiae. Nature 403, 623-627, doi:10.1038/35001009 (2000).
30 Rual, J. F. et al. Towards a proteome-scale map of the human protein-protein interaction network. Nature 437, 1173-1178, doi:10.1038/nature04209 (2005).
31 Vidal, M. & Fields, S. The yeast two-hybrid assay: still finding connections after 25 years. Nature methods 11, 1203-1206 (2014).
32 Gingras, A. C., Gstaiger, M., Raught, B. & Aebersold, R. Analysis of protein complexes using mass spectrometry. Nature reviews. Molecular cell biology 8, 645-654, doi:10.1038/nrm2208 (2007).
33 Gstaiger, M. & Aebersold, R. Applying mass spectrometry-based proteomics to genetics, genomics and network biology. Nature reviews. Genetics 10, 617-627, doi:10.1038/nrg2633 (2009).
34 Morris, J. H. et al. Affinity purification-mass spectrometry and network analysis to understand protein-protein interactions. Nature protocols 9, 2539-2554, doi:10.1038/nprot.2014.164 (2014).
35 Gerber, D., Maerkl, S. J. & Quake, S. R. An in vitro microfluidic approach to generating protein-interaction networks. Nature methods 6, 71-74, doi:10.1038/nmeth.1289 (2009).
36 Selbach, M. & Mann, M. Protein interaction screening by quantitative immunoprecipitation combined with knockdown (QUICK). Nature methods 3, 981-983, doi:10.1038/nmeth972 (2006).
37 Aranda, B. et al. The IntAct molecular interaction database in 2010. Nucleic acids research 38, D525-531, doi:10.1093/nar/gkp878 (2010).
38 Xenarios, I. et al. DIP, the Database of Interacting Proteins: a research tool for studying cellular networks of protein interactions. Nucleic acids research 30, 303-305 (2002).
39 Ceol, A. et al. MINT, the molecular interaction database: 2009 update. Nucleic acids research 38, D532-539, doi:10.1093/nar/gkp983 (2010).
40 Stark, C. et al. The BioGRID Interaction Database: 2011 update. Nucleic acids research 39, D698-704, doi:10.1093/nar/gkq1116 (2011).
41 Mewes, H. W. et al. MIPS: analysis and annotation of genome information in 2007. Nucleic acids research 36, D196-201, doi:10.1093/nar/gkm980 (2008).
42 Chen, Y. C., Lo, Y. S., Hsu, W. C. & Yang, J. M. 3D-partner: a web server to infer interacting partners and binding models. Nucleic acids research 35, W561-567, doi:10.1093/nar/gkm346 (2007).
43 Chen, C. C., Lin, C. Y., Lo, Y. S. & Yang, J. M. PPISearch: a web server for searching homologous protein-protein interactions across multiple species. Nucleic acids research 37, W369-375, doi:10.1093/nar/gkp309 (2009).
44 Lin, C. Y., Lin, Y. W., Yu, S. W., Lo, Y. S. & Yang, J. M. MoNetFamily: a web server to infer homologous modules and module-module interaction networks in vertebrates. Nucleic acids research 40, W263-270, doi:10.1093/nar/gks541 (2012).
45 Thiery, J. P. & Sleeman, J. P. Complex networks orchestrate epithelial-mesenchymal transitions. Nature reviews. Molecular cell biology 7, 131-142, doi:10.1038/nrm1835 (2006).
46 Depowski, P. L., Rosenthal, S. I. & Ross, J. S. Loss of expression of the PTEN gene protein product is associated with poor outcome in breast cancer. Modern pathology : an official journal of the United States and Canadian Academy of Pathology, Inc 14, 672-676, doi:10.1038/modpathol.3880371 (2001).
47 Yan, L. X. et al. PIK3R1 targeting by miR-21 suppresses tumor cell migration and invasion by reducing PI3K/AKT signaling and reversing EMT, and predicts clinical outcome of breast cancer. International journal of oncology 48, 471-484, doi:10.3892/ijo.2015.3287 (2016).
48 Lin, Y. et al. PIK3R1 negatively regulates the epithelial-mesenchymal transition and stem-like phenotype of renal cancer cells through the AKT/GSK3beta/CTNNB1 signaling pathway. Scientific reports 5, 8997, doi:10.1038/srep08997 (2015).
49 Vigil, D., Cherfils, J., Rossman, K. L. & Der, C. J. Ras superfamily GEFs and GAPs: validated and tractable targets for cancer therapy? Nature reviews. Cancer 10, 842-857, doi:10.1038/nrc2960 (2010).
50 Bigot, P. et al. Functional characterization of the 12p12.1 renal cancer-susceptibility locus implicates BHLHE41. Nature communications 7, 12098, doi:10.1038/ncomms12098 (2016).
51 Yuan, L. M. et al. Inter-isoform Hetero-dimerization of Human UDP-Glucuronosyltransferases (UGTs) 1A1, 1A9, and 2B7 and Impacts on Glucuronidation Activity. Scientific reports 6, 34450, doi:10.1038/srep34450 (2016).
52 Dellinger, R. W., Matundan, H. H., Ahmed, A. S., Duong, P. H. & Meyskens, F. L., Jr. Anti-cancer drugs elicit re-expression of UDP-glucuronosyltransferases in melanoma cells. PloS one 7, e47696, doi:10.1371/journal.pone.0047696 (2012).
53 Lu, L. et al. Drug-Metabolizing Activity, Protein and Gene Expression of UDP-Glucuronosyltransferases Are Significantly Altered in Hepatocellular Carcinoma Patients. PloS one 10, e0127524, doi:10.1371/journal.pone.0127524 (2015).
54 Rae, J. M. et al. CYP2D6 and UGT2B7 genotype and risk of recurrence in tamoxifen-treated breast cancer patients. Journal of the National Cancer Institute 104, 452-460, doi:10.1093/jnci/djs126 (2012).