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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 施純傑陳璿宇 | zh_TW |
| dc.contributor.advisor | Arthur Chun-Chieh ShihHsuan-Yu Chen | en |
| dc.contributor.author | 張智豪 | zh_TW |
| dc.contributor.author | Chih-Hao Chang | en |
| dc.date.accessioned | 2021-07-11T14:59:49Z | - |
| dc.date.available | 2024-11-11 | - |
| dc.date.copyright | 2019-11-13 | - |
| dc.date.issued | 2019 | - |
| dc.date.submitted | 2002-01-01 | - |
| dc.identifier.citation | REFERENCES
1. Siegel RL, Miller KD, Jemal A. Cancer statistics, 2019. CA Cancer J Clin 2019;69:7-34. 2. Cancer Registry Annual Report, 2016, Taiwan https://www.hpa.gov.tw/Pages/Detail.aspx?nodeid=269&pid=10227. 3. Topalian SL, Drake CG, Pardoll DM. Immune checkpoint blockade: a common denominator approach to cancer therapy. Cancer Cell 2015;27:450-61. 4. Leach DR, Krummel MF, Allison JP. Enhancement of antitumor immunity by CTLA-4 blockade. Science 1996;271:1734-6. 5. Hodi FS, O'Day SJ, McDermott DF, et al. Improved survival with ipilimumab in patients with metastatic melanoma. N Engl J Med 2010;363:711-23. 6. Agata Y, Kawasaki A, Nishimura H, et al. Expression of the PD-1 antigen on the surface of stimulated mouse T and B lymphocytes. Int Immunol 1996;8:765-72. 7. Freeman GJ, Long AJ, Iwai Y, et al. Engagement of the PD-1 immunoinhibitory receptor by a novel B7 family member leads to negative regulation of lymphocyte activation. J Exp Med 2000;192:1027-34. 8. Iwai Y, Okazaki T, Nishimura H, Kawasaki A, Yagita H, Honjo T. Microanatomical localization of PD-1 in human tonsils. Immunol Lett 2002;83:215-20. 9. Keir ME, Butte MJ, Freeman GJ, Sharpe AH. PD-1 and its ligands in tolerance and immunity. Annu Rev Immunol 2008;26:677-704. 10. Ishida M, Iwai Y, Tanaka Y, et al. Differential expression of PD-L1 and PD-L2, ligands for an inhibitory receptor PD-1, in the cells of lymphohematopoietic tissues. Immunol Lett 2002;84:57-62. 11. Pardoll DM. The blockade of immune checkpoints in cancer immunotherapy. Nat Rev Cancer 2012;12:252-64. 12. Sznol M, Chen L. Antagonist antibodies to PD-1 and B7-H1 (PD-L1) in the treatment of advanced human cancer--response. Clin Cancer Res 2013;19:5542. 13. Butte MJ, Keir ME, Phamduy TB, Sharpe AH, Freeman GJ. Programmed death-1 ligand 1 interacts specifically with the B7-1 costimulatory molecule to inhibit T cell responses. Immunity 2007;27:111-22. 14. Mishra AK, Kadoishi T, Wang X, et al. Squamous cell carcinomas escape immune surveillance via inducing chronic activation and exhaustion of CD8+ T Cells co-expressing PD-1 and LAG-3 inhibitory receptors. Oncotarget 2016;7:81341-56. 15. Shen T, Zhou L, Shen H, et al. Prognostic value of programmed cell death protein 1 expression on CD8+ T lymphocytes in pancreatic cancer. Sci Rep 2017;7:7848. 16. Nakano O, Sato M, Naito Y, et al. Proliferative activity of intratumoral CD8(+) T-lymphocytes as a prognostic factor in human renal cell carcinoma: clinicopathologic demonstration of antitumor immunity. Cancer Res 2001;61:5132-6. 17. Asano N, Oshiro A, Matsuo K, et al. Prognostic significance of T-cell or cytotoxic molecules phenotype in classical Hodgkin's lymphoma: a clinicopathologic study. J Clin Oncol 2006;24:4626-33. 18. Badoual C, Hans S, Merillon N, et al. PD-1-expressing tumor-infiltrating T cells are a favorable prognostic biomarker in HPV-associated head and neck cancer. Cancer Res 2013;73:128-38. 19. Carreras J, Lopez-Guillermo A, Roncador G, et al. High numbers of tumor-infiltrating programmed cell death 1-positive regulatory lymphocytes are associated with improved overall survival in follicular lymphoma. J Clin Oncol 2009;27:1470-6. 20. Mlecnik B, Tosolini M, Charoentong P, et al. Biomolecular network reconstruction identifies T-cell homing factors associated with survival in colorectal cancer. Gastroenterology 2010;138:1429-40. 21. Thompson RH, Gillett MD, Cheville JC, et al. Costimulatory B7-H1 in renal cell carcinoma patients: Indicator of tumor aggressiveness and potential therapeutic target. Proc Natl Acad Sci U S A 2004;101:17174-9. 22. Nakanishi J, Wada Y, Matsumoto K, Azuma M, Kikuchi K, Ueda S. Overexpression of B7-H1 (PD-L1) significantly associates with tumor grade and postoperative prognosis in human urothelial cancers. Cancer Immunol Immunother 2007;56:1173-82. 23. Ohigashi Y, Sho M, Yamada Y, et al. Clinical significance of programmed death-1 ligand-1 and programmed death-1 ligand-2 expression in human esophageal cancer. Clin Cancer Res 2005;11:2947-53. 24. Nomi T, Sho M, Akahori T, et al. Clinical significance and therapeutic potential of the programmed death-1 ligand/programmed death-1 pathway in human pancreatic cancer. Clin Cancer Res 2007;13:2151-7. 25. Hamanishi J, Mandai M, Iwasaki M, et al. Programmed cell death 1 ligand 1 and tumor-infiltrating CD8+ T lymphocytes are prognostic factors of human ovarian cancer. Proc Natl Acad Sci U S A 2007;104:3360-5. 26. Ghebeh H, Mohammed S, Al-Omair A, et al. The B7-H1 (PD-L1) T lymphocyte-inhibitory molecule is expressed in breast cancer patients with infiltrating ductal carcinoma: correlation with important high-risk prognostic factors. Neoplasia 2006;8:190-8. 27. Chang YL, Yang CY, Lin MW, Wu CT, Yang PC. High co-expression of PD-L1 and HIF-1alpha correlates with tumour necrosis in pulmonary pleomorphic carcinoma. Eur J Cancer 2016;60:125-35. 28. Yang CY, Lin MW, Chang YL, Wu CT, Yang PC. Programmed cell death-ligand 1 expression is associated with a favourable immune microenvironment and better overall survival in stage I pulmonary squamous cell carcinoma. Eur J Cancer 2016;57:91-103. 29. Yang CY, Lin MW, Chang YL, Wu CT, Yang PC. Programmed cell death-ligand 1 expression in surgically resected stage I pulmonary adenocarcinoma and its correlation with driver mutations and clinical outcomes. Eur J Cancer 2014;50:1361-9. 30. Der SD, Sykes J, Pintilie M, et al. Validation of a histology-independent prognostic gene signature for early-stage, non-small-cell lung cancer including stage IA patients. J Thorac Oncol 2014;9:59-64. 31. Rousseaux S, Debernardi A, Jacquiau B, et al. Ectopic activation of germline and placental genes identifies aggressive metastasis-prone lung cancers. Sci Transl Med 2013;5:186ra66. 32. Okayama H, Kohno T, Ishii Y, et al. Identification of genes upregulated in ALK-positive and EGFR/KRAS/ALK-negative lung adenocarcinomas. Cancer Res 2012;72:100-11. 33. Lee ES, Son DS, Kim SH, et al. Prediction of recurrence-free survival in postoperative non-small cell lung cancer patients by using an integrated model of clinical information and gene expression. Clin Cancer Res 2008;14:7397-404. 34. Botling J, Edlund K, Lohr M, et al. Biomarker discovery in non-small cell lung cancer: integrating gene expression profiling, meta-analysis, and tissue microarray validation. Clin Cancer Res 2013;19:194-204. 35. Bild AH, Yao G, Chang JT, et al. Oncogenic pathway signatures in human cancers as a guide to targeted therapies. Nature 2006;439:353-7. 36. Xie Y, Xiao G, Coombes KR, et al. Robust gene expression signature from formalin-fixed paraffin-embedded samples predicts prognosis of non-small-cell lung cancer patients. Clin Cancer Res 2011;17:5705-14. 37. Gyorffy B, Surowiak P, Budczies J, Lanczky A. Online survival analysis software to assess the prognostic value of biomarkers using transcriptomic data in non-small-cell lung cancer. PLoS One 2013;8:e82241. 38. Johnson WE, Li C, Rabinovic A. Adjusting batch effects in microarray expression data using empirical Bayes methods. Biostatistics 2007;8:118-27. 39. Bolstad BM, Irizarry RA, Astrand M, Speed TP. A comparison of normalization methods for high density oligonucleotide array data based on variance and bias. Bioinformatics 2003;19:185-93. 40. Wang Y, Rodriguez de Gil P, Chen YH, et al. Comparing the Performance of Approaches for Testing the Homogeneity of Variance Assumption in One-Factor ANOVA Models. Educ Psychol Meas 2017;77:305-29. 41. Yan YF, Zheng YF, Ming PP, Deng XX, Ge W, Wu YG. Immune checkpoint inhibitors in non-small-cell lung cancer: current status and future directions. Brief Funct Genomics 2019;18:147-56. 42. Chen DS, Mellman I. Oncology meets immunology: the cancer-immunity cycle. Immunity 2013;39:1-10. 43. Hackl H, Charoentong P, Finotello F, Trajanoski Z. Computational genomics tools for dissecting tumour-immune cell interactions. Nat Rev Genet 2016;17:441-58. 44. Hanahan D, Weinberg RA. Hallmarks of cancer: the next generation. Cell 2011;144:646-74. 45. Vignali DA, Collison LW, Workman CJ. How regulatory T cells work. Nat Rev Immunol 2008;8:523-32. 46. Mahmoud SM, Paish EC, Powe DG, et al. Tumor-infiltrating CD8+ lymphocytes predict clinical outcome in breast cancer. J Clin Oncol 2011;29:1949-55. 47. Barinov A, Galgano A, Krenn G, Tanchot C, Vasseur F, Rocha B. CD4/CD8/Dendritic cell complexes in the spleen: CD8+ T cells can directly bind CD4+ T cells and modulate their response. PLoS One 2017;12:e0180644. 48. Vasaturo A, Di Blasio S, Peeters DG, et al. Clinical Implications of Co-Inhibitory Molecule Expression in the Tumor Microenvironment for DC Vaccination: A Game of Stop and Go. Front Immunol 2013;4:417. 49. Laidlaw BJ, Craft JE, Kaech SM. The multifaceted role of CD4(+) T cells in CD8(+) T cell memory. Nat Rev Immunol 2016;16:102-11. 50. Ott PA, Hodi FS, Robert C. CTLA-4 and PD-1/PD-L1 blockade: new immunotherapeutic modalities with durable clinical benefit in melanoma patients. Clin Cancer Res 2013;19:5300-9. 51. Huang Y, Ma C, Zhang Q, et al. CD4+ and CD8+ T cells have opposing roles in breast cancer progression and outcome. Oncotarget 2015;6:17462-78. 52. Meng F, Zhen S, Song B. HBV-specific CD4+ cytotoxic T cells in hepatocellular carcinoma are less cytolytic toward tumor cells and suppress CD8+ T cell-mediated antitumor immunity. APMIS 2017;125:743-51. 53. Wakabayashi O, Yamazaki K, Oizumi S, et al. CD4+ T cells in cancer stroma, not CD8+ T cells in cancer cell nests, are associated with favorable prognosis in human non-small cell lung cancers. Cancer Sci 2003;94:1003-9. 54. Germain C, Gnjatic S, Tamzalit F, et al. Presence of B cells in tertiary lymphoid structures is associated with a protective immunity in patients with lung cancer. Am J Respir Crit Care Med 2014;189:832-44. 55. Newman AM, Liu CL, Green MR, et al. Robust enumeration of cell subsets from tissue expression profiles. Nat Methods 2015;12:453-7. 56. Shen-Orr SS, Gaujoux R. Computational deconvolution: extracting cell type-specific information from heterogeneous samples. Curr Opin Immunol 2013;25:571-8. 57. Anagnostou VK, Brahmer JR. Cancer immunotherapy: a future paradigm shift in the treatment of non-small cell lung cancer. Clin Cancer Res 2015;21:976-84. 58. Santini FC, Hellmann MD. PD-1/PD-L1 Axis in Lung Cancer. Cancer J 2018;24:15-9. | - |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/78489 | - |
| dc.description.abstract | 中文摘要
台灣和全球癌症死亡率中肺癌排名第一,其中第3B期或第4期肺癌病人3年生存率為17%-4%。目前肺癌治療方法包括化學療法,放射線療法,標靶治療(如表皮生長因子受體酪氨酸激酶抑製劑,EGFRTKI)或免疫療法,以及最終支持療法(如疼痛緩解)。病人單獨使用標靶治療其反應有時效性,而單獨使用免疫療法雖可提供長久反應,但反應僅侷限在一小部分病人中。結合前述兩種療法顯示出協同治療效果,從而為病人帶來長期臨床益處。 免疫系統可利用許多方式殺死癌細胞,包括:針對癌症特異性抗原T細胞的啟動和激活;腫瘤組織中腫瘤浸潤淋巴細胞(TIL)的類型和含量;利用T細胞受體(TCR)與癌細胞上主要組織相容性複合體(MHC)間交互作用(或稱免疫檢查點)的特異性識別並結合癌細胞。 腫瘤微環境是一個複雜的機轉,其結合了腫瘤細胞,免疫細胞和周圍正常細胞間彼此的作用。識別腫瘤微環境基礎分子概況可幫助了解內部作用機轉,並提供抑制癌症進展的解決方案。 為了瞭解腫瘤微環境作用機轉與肺癌臨床表徵的關係,本論文包含兩個研究:1、PD1與PDL1基因表現量顯著影響肺癌病人預後:一整合分析;2、樹突細胞、CD4+T細胞和CD8+T細胞細胞數量與肺癌病人臨床表徵顯著相關:一整合分析。 研究一、PD1與PDL1基因表現量顯著影響肺癌病人預後: 一整合分析 背景 免疫檢查點阻斷療法對於癌症治療有顯著的療效。確認免疫檢查點基因:程序性細胞死亡1(PD1)和程序性細胞死亡配體1(PDL1);基因表現量對於瞭解與建構免疫治療策略至關重要。本研究的目的是探討PD1和PDL1基因表現量與肺癌病人臨床表徵之關聯性。 材料與方法 本研究利用整合分析方法分析8個不同微陣列基因表達數據集共1251名病人的基因表現,評估其臨床預後結果。平均值±標準差(SD)和中位數描述不同臨床病理學特徵中PD1和PDL1基因表現量。採用接收者操作特徵(ROC)曲線最佳切點將PD1 和PDL1區分成高表現與低表現,再用Kaplan-Meier(KM)未調整估計曲線呈現無惡化存活(PFS)和總體存活(OS)曲線。最後,利用Cox回歸模型分析危害比(HR)並呈現相應95%信賴區間(CI)。 結果 肺腺癌和鱗狀細胞癌死亡病人PDL1表現量顯著高於存活病人;肺腺癌和鱗狀細胞癌早期病人攜帶越多PD1與PDL1高表現基因,有累加效應趨勢,統計上顯著降低復發和死亡風險,危害比與95%信賴區間分別為HR = 0.69; 95% CI = 0.53 ~ 0.91; HR = 0.68; 95% CI = 0.54 ~ 0.84於肺腺癌早期病人和HR = 0.53; 95% CI = 0.32 ~ 0.89; HR = 0.78; 95% CI = 0.57 ~ 1.00於鱗狀細胞癌早期病人。相反的,攜帶一個或多個PD1與PDL1高表現基因的鱗狀細胞癌晚期病人復發(HR = 1.51; 95%CI = 1.07~2.11)和死亡(HR = 1.41; 95%CI = 1.08~1.84)風險顯著升高。 結論 本篇研究發現早期肺癌病人中PD1與PDL1高表現有較好的預後,但在晚期肺癌病人中高表現其預後不良。PD1與PDL1基因表現量單獨或合併都可當成預測肺癌復發或死亡的潛在預測因子。 研究二、樹突細胞、CD4+T細胞和CD8+T細胞細胞數量與 肺癌病人臨床表徵顯著相關:一整合分析 背景 腫瘤浸潤淋巴細胞(TIL)在腫瘤微環境中扮演免疫功能的角色。 了解腫瘤浸潤淋巴細胞中樹突狀細胞、CD4 + T和CD8 + T細胞在腫瘤微環境中的密度,對於腫瘤發展是很重要的。 本研究的目的是探討樹突狀細胞、CD4 + T和CD8 + T細胞在腫瘤微環境中的密度和肺癌預後之間的關聯性。 材料與方法 本研究利用整合分析方法分析8個不同微陣列基因表達數據集共1251名病人腫瘤浸潤淋巴細胞中樹突狀細胞、CD4 + T和CD8 + T細胞在腫瘤微環境中的密度,評估其臨床預後結果。平均值±標準差(SD)和中位數描述不同臨床病理學特徵中樹突狀細胞、CD4 + T和CD8 + T細胞的細胞密度。採用接收者操作特徵(ROC)曲線最佳切點將樹突狀細胞、CD4 + T和CD8 + T細胞的細胞密度區分成高密度與低密度,再用Kaplan-Meier(KM)未調整估計曲線呈現無惡化存活(PFS)和總體存活(OS)曲線。最後,利用Cox回歸模型分析危害比(HR)並呈現相應95%信賴區間(CI)。 結果 在早期肺腺癌病人中,復發和死亡病人CD4 + T細胞密度平均值和中位數均低於非複發和存活病人。 在晚期肺腺癌死亡病人和晚期鱗狀細胞癌復發病人中,樹突細胞密度的平均值和中位數達統計上顯著較高。早期肺腺癌與鱗狀細胞癌病人攜帶較多高密度的樹突狀細胞、CD4 + T和CD8 + T細胞,有累加劑量效應顯著降低復發的風險,危害比與95%信賴區間分別為HR = 0.66; 95% CI = 0.53 ~ 0.81和HR = 0.68; 95% CI = 0.47 ~ 0.98。相反的,晚期鱗狀細胞癌病人每多攜帶一個高密度的樹突狀細胞、CD4 + T和CD8 + T細胞將顯著增加死亡風險HR = 1.48; 95% CI = 1.10 ~ 1.98。 結論 本研究發現較高密度的樹突狀細胞、CD4 + T/CD8 + T細胞在早期肺癌病人中有較好預後,但是在晚期肺癌病人預後卻是不良。 單獨或合併樹突狀細胞、CD4 + T和CD8 + T細胞的細胞密度是預測肺癌復發或死亡的潛在預測因子。 | zh_TW |
| dc.description.abstract | ABSTRACT
Lung cancer is ranked first for cancer mortality worldwide, as well as in Taiwan. The 3-year survival of lung patients with stages IIIB or IV were 4 to17%. The current treatments for lung cancer are chemotherapy (CT), radiotherapy (RT), targeting therapy (e.g., epidermal growth factor receptor tyrosine kinase inhibitors, EGFRTKI), or the immunotherapies, in addition to the best supportive care (e.g., pain relief). Responses of targeting therapy alone are often of limited duration while immunotherapies alone provide durable long-lasting responses but only in a fraction of patients. The combination of two treatments show an additive synergistic therapeutic effect resulting in potential long-term clinical benefits for patients. There are several events for an anticancer immune response to kill cancer cells including the priming and activation of T cell responses against the cancer-specific antigens; the type and content of tumor-infiltrating lymphocytes (TIL) contained in the tumor tissue; specifically recognize and bind to cancer cells through the interaction between T cell receptor (TCR) and its major histocompatibility complex (MHC) (immune check point). The tumor micro-environment is a complex issue. It incorporates effects of tumor cells, immune cells, and surrounding normal cells. Understanding the roles of the tumor micro-environments can enable us to identify micro-environment based molecular signatures and provide solutions to inhibit cancer progression. To discover roles of tumor micro-environment and clinical outcomes in lung cancer, two studies of this thesis are as follows: 1. The prognostic significance of PD1 and PDL1 gene expression in lung cancer: a meta-analysis. 2. Dendritic cells and CD4 + T/CD8 + T cell densities are significantly associated with lung cancer patient clinical outcomes: a meta-analysis. Study1 : The prognostic significance of PD1 and PDL1 gene expression in lung cancer: a meta- analysis Background: Immune checkpoint blockade therapy represents an extraordinary advance in lung cancer treatment. It is important to identify the immune checkpoint gene, programmed cell death 1 (PD1) and programmed cell death-ligand 1 (PDL1) expressions to better understand and build up immunotherapeutic strategies. The aim of this study is to explore the association between PD1 and PDL1 gene expressions and the prognostic and outcomes in lung cancer. Materials and methods: This study analyzed 1251 patients from 8 different microarray gene expression datasets were assayed by the meta-analysis method and evaluated for their prognostic implications. The expression levels of PD1/PDL1 genes of different clinicopathological features were described by mean ± standard deviation (SD) and median. Receiver operating characteristic (ROC) curves were calculated an optimal cutoff point to differentiate higher or lower expression of PD1/ PDL1 genes. Two end points: progression free survival (PFS) and over-all survival (OS) were tested by using the Kaplan–Meier (KM) unadjusted estimation curve analysis and Cox regression model. Hazards Ratios (HR) and corresponding 95% confidence intervals (CI) are reported. Results: The mean expression levels of PDL1 in adenocarcinoma (AD) and squamous cell carcinoma (SC) were significantly higher in patients experienced death than in patients alive. There were a trend toward an incremental additive effect on significantly reduced risk of relapse and death among AD (HR = 0.69; 95% CI = 0.53 ~ 0.91; HR = 0.68; 95% CI = 0.54 ~ 0.84) and SC (HR = 0.53; 95% CI = 0.32 ~ 0.89; HR = 0.78; 95% CI = 0.57 ~ 1.00), respectively, as early stage patients in this study carried a greater number of higher expressed PD1 /PDL1 genes (P-trend < 0.05). In contrast, late stage SC patients carrying one or more of higher expressed genes had a significantly elevated risk of relapse (HR = 1.51; 95% CI = 1.07 ~ 2.11) and death (HR = 1.41; 95% CI = 1.08 ~ 1.84). Conclusions: These findings indicate that higher expressions of PD1 and PDL1 was associated with a gainful outcome of early stage lung cancer but an adverse outcome of late stage lung cancer. Expression levels of PD1 and PDL1 individually or jointly are potential prognostic factors for predicting recurrence of or death due to lung cancer. Study 2: Dendritic cells and CD4 + T/CD8 + T cell densities are significantly associated with lung cancer patient clinical outcomes: a meta- analysis Background: Tumor infiltrating lymphocytes (TILs) play an immunological function in the tumor microenvironment. It is important to identify the TILs, dendritic cells (DC), CD4+ T and CD8+ T cell densities to better understand tumor microenvironment development. The aim of this study is to explore the association between DC and CD4+ T/CD8+ T cell densities and the prognostic and outcomes in lung cancer. Materials and methods: This study analyzed 1251 patients’ immune cell types and densities from 8 different microarray gene expression datasets, which were assayed by the meta-analysis method and evaluated for their prognostic implications. The cell densities of DC and CD4+ T/CD8+ T cells of different clinicopathological features are described by mean ± standard deviation (SD) and median. Receiver operating characteristic (ROC) curves was calculated to determine an optimal cutoff point to differentiate higher or lower density of dendritic and CD4+ T/CD8+ T cells. Two end points: progression free survival (PFS) and over-all survival (OS) were tested by using the Kaplan–Meier (KM) unadjusted estimation curve analysis and Cox regression model. Hazards Ratios (HR) and corresponding 95% confidence intervals (CI) are reported. Results: In early stage adenocarcinoma (AD) patients, both cell densities mean and median of CD4+T were lower in relapse and deceased patients than non-relapse and living patients. There was a statistically significant mean and median in deceased patients at late stage AD with higher density DC and in relapse patients at late stage squamous cell carcinoma (SC), respectively. There was a trend toward an incremental additive effect on significantly reduced risk of relapse among AD (HR = 0.66; 95% CI = 0.53 ~ 0.81) and SC (HR = 0.68; 95% CI = 0.47 ~ 0.98), respectively, as early stage patients in this study carried a greater number of higher density DC and CD4+ T/CD8+ T (P-trend < 0.05). In contrast, late stage SC patients carrying one or more of higher density cells had a significantly elevated risk of death (HR = 1.48; 95% CI = 1.10 ~ 1.98). Conclusions: These findings indicate that higher densities of DC and CD4+ T/CD8+ T was associated with a gainful outcome of early stage lung cancer but an adverse outcome of late stage lung cancer. Cell densities of DC and CD4+ T/CD8+ T individually or jointly are potential prognostic factors for predicting recurrence of or death due to lung cancer. | en |
| dc.description.provenance | Made available in DSpace on 2021-07-11T14:59:49Z (GMT). No. of bitstreams: 1 ntu-108-D04B48001-1.pdf: 7248787 bytes, checksum: 22a22485b4f5868f4f5fc747a8ff11fa (MD5) Previous issue date: 2019 | en |
| dc.description.tableofcontents | CONTENTS
中文摘要...........................................................................................I ABSTRACT…………...………….…………………………………….VI CONTENTS………………………….………………………………….1 LIST OF FIGURES…………………..…………...……….…………...4 LIST OF TABLES…………………………….…………………………5 CHAPTER 1…………………………………………...………………..6 Study 1: The prognostic significance of PD1 and PDL1 gene expression in lung cancer: a meta-analysis…….………………6 1.1 Introduction………….....………………………………….6 1.2 Materials and Methods…………….………………..……8 1.2.1 Construction of lung cancer microarray database and covariate variables…………..…..………………….8 1.2.2 Adjusting Batch effects and inverse-variance weighting…………………………………………………..8 1.2.3 Statistical analysis………………………………….9 1.3 Result………………………………….………………….10 1.3.1 The relationship between expression of PD1 and PDL1 genes in lung cancer and clinical parameters...10 1.3.2 Higher expression of PD1 and PDL1 genes act as a protective factor of lung cancer outcome in early stage patients….……………………………...…….…………..11 1.3.3 The correlations between higher expression of PD1 and PDL1 and Poor Clinical Outcome in late stage lung cancer patients………...…………………………..12 1.4 Discussion………………………………………………..13 CHAPTER 2….………………….…………………………………….26 Study 2: Dendritic cells and CD4 + T/CD8 + T cell densities are significantly associated with lung cancer patient clinical outcomes: a meta-analysis…….………………………………..26 2.1 Introduction…..……………………………………………….26 2.2 Materials and Methods….………………………………28 2.2.1 Construction of lung cancer microarray database and covariate variables...……………………………….29 2.2.2 Adjusting batch effects, immune cell types and densities calculating and inverse-variance weighting.29 2.2.3 Statistical analysis...……………….……………..30 2.3 Result…..….………………………………..…………….31 2.3.1 The relationship between cell density of dendritic cell, CD4 + T cell and CD8 + T cell in lung cancer and clinical parameters…………………………………..….31 2.3.2 Higher density of DC, CD4+T and CD8+T cells act as a protective factor of lung cancer outcome in early stage patients..…………..………………………………32 2.3.3 The correlations between higher expression of DC, CD4+T and CD8+T cells and Poor Clinical Outcome in late stage lung cancer patients….......................……..33 2.4 Discussion………………………………………………..34 CHAPTER 3.…………………………………………………………..47 Summarize study1 and study 2…………………………………47 3.1 Introduction.…………………….………………………..47 3.1.1 PD1 gene expression and CD4+T/CD8+T cell densities act as a protective factor of lung cancer outcome in early stage patients.………………………..47 3.1.2 The correlations between higher expression of PD1 and higher density of CD4+T/CD8+T and Poor Clinical Outcome in late stage lung cancer patients...49 3.2 Summary.……………..………………………………….50 CHAPTER 4...…………………………………………………………55 Conclusion and Future Perspective……..………..……………55 REFERENCES.….…………………………………..………………..58 | - |
| dc.language.iso | en | - |
| dc.subject | 肺癌 | zh_TW |
| dc.subject | 免疫檢查點基因 | zh_TW |
| dc.subject | 臨床預後 | zh_TW |
| dc.subject | 腫瘤組織中腫瘤浸潤淋巴細胞 | zh_TW |
| dc.subject | Immune checkpoint genes | en |
| dc.subject | prognosis | en |
| dc.subject | Lung cancer | en |
| dc.subject | Tumor infiltrating lymphocytes | en |
| dc.title | 肺癌微環境中分子概況與臨床表徵 | zh_TW |
| dc.title | Molecular Profiling of Micro-environment and Clinical Outcomes in Lung Cancer | en |
| dc.type | Thesis | - |
| dc.date.schoolyear | 108-1 | - |
| dc.description.degree | 博士 | - |
| dc.contributor.oralexamcommittee | 俞松良;黃耀廷;袁新盛;李美璇;許藝瓊;楊永立 | zh_TW |
| dc.contributor.oralexamcommittee | Sung-Liang Yu;Yao-Ting Huang;Hsin-Sheng Yuan;Mei-Hsuan Li;Yi-Chiung Hsu;Yung-Li Yang | en |
| dc.subject.keyword | 肺癌,臨床預後,免疫檢查點基因,腫瘤組織中腫瘤浸潤淋巴細胞, | zh_TW |
| dc.subject.keyword | Lung cancer,prognosis,Immune checkpoint genes,Tumor infiltrating lymphocytes, | en |
| dc.relation.page | 67 | - |
| dc.identifier.doi | 10.6342/NTU201904261 | - |
| dc.rights.note | 未授權 | - |
| dc.date.accepted | 2019-11-06 | - |
| dc.contributor.author-college | 生命科學院 | - |
| dc.contributor.author-dept | 基因體與系統生物學學位學程 | - |
| dc.date.embargo-lift | 2024-11-13 | - |
| 顯示於系所單位: | 基因體與系統生物學學位學程 | |
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