跳到主要內容

臺灣博碩士論文加值系統

(18.204.48.64) 您好!臺灣時間:2021/08/04 18:20
字體大小: 字級放大   字級縮小   預設字形  
回查詢結果 :::

詳目顯示

我願授權國圖
: 
twitterline
研究生:謝佳妏
研究生(外文):Chia-Wen Hsieh
論文名稱:腫瘤間質比例在乳癌之預後性價值
論文名稱(外文):The prognostic value of the Tumor-Stroma Ratio (TSR) in breast cancer
指導教授:李耀豐
指導教授(外文):Yao-Feng Li
口試委員:于承平林志恭李耀豐
口試委員(外文):Cheng-Ping YuChih-Kung LinYao-Feng Li
口試日期:2021-05-18
學位類別:碩士
校院名稱:國防醫學院
系所名稱:病理及寄生蟲學研究所
學門:醫藥衛生學門
學類:醫學學類
論文種類:學術論文
論文出版年:2021
畢業學年度:109
語文別:中文
論文頁數:60
中文關鍵詞:乳癌腫瘤間質比例
外文關鍵詞:breast cancerTumor-Stroma Ratio (TSR)
相關次數:
  • 被引用被引用:0
  • 點閱點閱:9
  • 評分評分:
  • 下載下載:0
  • 收藏至我的研究室書目清單書目收藏:0
乳癌是女性最常被診斷出的惡性腫瘤,在病理組織學最常好發的位置為乳管和乳小葉,其中又分為乳管原位癌(Ductal Carcinoma In Situ)、侵襲性乳管癌(Invasive Ductal Carcinoma)、乳小葉原位癌(Lobular Carcinoma In Situ)及侵襲性乳小葉癌(Invasive lobular Carcinoma)。乳癌侵襲範圍及嚴重程度上的分期是以Tumor-Node-Metastasis Staging及Nottingham grading作為評分標準。運用分子生物學基因表現量將乳癌分類為管腔A型(Luminal A)、管腔B型(Luminal B)、HER2高表現量(HER2-enriched)、基底樣(basal-like)、類正常乳腺型(Normal Breast-like)以利於後續的治療。在臨床上都以腫瘤型態作為診斷,較少去探討間質對於腫瘤的影響。
以The Cancer Genome Atlas (TCGA)資料庫收集乳癌H&E病理數位影像,判讀腫瘤間質比例,在先前的參考文獻中,方法學判讀及Cut-off %並無一致性判讀標準。本篇研究中,藉由TCGA資料庫中1061個案例,在研究設計過程中,重新設定方法學及Cut-off %及運用台灣本土數據作為統計,以Visual Eyeballing方法判讀腫瘤間質比例分析技術(Tumor Stroma Ratio,TSR) 以十進位的百分比(%)對病理影像進行評分,透過統計分析軟體 (Statistical Product and Service Solutions, SPSS, v22)、ESTIMATE、生物分子路徑(Ingenuity Pathway Analysis (IPA)、基因集富集Gene Set Enrichment Analysis (GSEA)軟體進一步分析。
在統計結果顯示,(1)描述性統計分析評分TSR Cut-off 50% n=1061。(2) ESTIMATE評估TSR 和TILs判讀結果準確。(3)高TSR預後性差。(4)高TSR存活性低。(5)不同分子亞型,TSR比例不同。(6)經由皮爾森係數與預後性因子分析,呈現低度相關性。(7)IPA分析得出TSR相關基因調控路徑,生物標記物。(8)GSEA分析得出TSR相關基因基因組表現量。
本研究運用TCGA資料庫,以TSR Cut-off 50%來分析乳癌整體生存率具有統計意義。目前判讀乳癌病理數位影像都以人工方式進行判讀,未來將會以深度學習的方式進行腫瘤間質比例分析,以定量方式讓TSR結果更精確,以期作為臨床診斷標準化作業流程。

Breast cancer is the most frequently diagnosed malignant tumor in women. The most common locations in histopathology are the ducts and lobules, Among them, it is divided into ductal carcinoma in situ, invasive ductal carcinoma, lobular carcinoma in situ and invasive lobular carcinoma. Tumor node metastasis staging and Nottingham grade are used as scoring criteria for breast cancer invasion and severity staging. Using molecular biology gene expression to divide breast cancer into Luminal A, Luminal B, HER2-rich, basal-like and normal breast-like, to facilitate subsequent treatment. Clinically, tumor types are used as diagnosis, and the influence of stroma on tumors is rarely discussed.

The Cancer Genome Atlas (TCGA) database collects H&E pathological digital images of breast cancer, Interpret the ratio of tumor stroma,in the previous reference, methodological interpretation and critical value% do not have a consistent interpretation standard. In this study, based on 1061 cases in the TCGA database, In the research and design process, reset the method and critical values, and then use Taiwan’s local data as statistical data, use Visual Eyeballing method to interpret the tumor stroma ratio analysis technology (Tumor Stroma Ratio, TSR) to score pathological images in decimal percentage (%), Statistical analysis software (Statistical Product and Service Solutions, SPSS, v22), ESTIMATE, biomolecular pathway (Ingenuity Pathway Analysis (IPA)), genome enrichment analysis (GSEA) software for further analysis.

In the statistical results, (1) Descriptive statistical analysis score TSR Cut-off 50% n=1061. (2) ESTIMATE assesses the accuracy of the interpretation of TSR and TILs. (3) The prognosis of high TSR is poor. (4) High TSR has low viability. (5) Different molecular subtypes have different TSR ratios. (6) Through the analysis of Pearson coefficient and prognostic factor, there is a low degree of correlation. (7) IPA analysis results in TSR-related gene regulation pathways and biomarkers. (8) GSEA analysis results in the genome expression of TSR-related genes.

This study uses the TCGA database to analyze the overall survival rate of breast cancer with TSR Cut-off 50%, which is statistically significant. Currently, the interpretation of digital images of breast cancer pathology is performed by humans. In the future, deep learning will be used to analyze the tumor-interstitial ratio to make the TSR results more accurate in a quantitative manner, hoping to serve as a standardized workflow for clinical diagnosis.

目錄
目錄 I
表目錄 IV
圖目錄 VI
中文摘要 IX
Abstract XI
第一章 緒論 1
第一節 乳癌(Breast Cancer) 1
第二節 乳癌組織學(Breast Cancer Histologic) 1
第三節 腫瘤、淋巴結及轉移分期系統TNM分期(Tumor-Node-Metastasis Staging) 3
第四節 乳癌組織型態惡性度分級標準Nottingham Criteria 5
第五節 乳癌分子亞型(Molecular Subtype) 6
第六節 腫瘤浸潤性淋巴球(Tumor-Infiltrating Lymphocytes) 7
第七節 間質和纖維化病灶(Stroma and Fibrotic focus,FF) 7
第八節 研究動機與目的 8
第二章 材料與方法 9
第一節 開源資料庫資源下載 9
第二節 相關數據評估(TSR, TIL, Nottingham histologic score, FF) 10
壹. 1. 判讀Tumor Stroma Ratio標準 10
貳. 2. 判讀Tumor Infiltrating Lymphocytes 12
參. 3. 乳癌分化程度 (Nottingham histologic score)評分 13
肆. 4. 判讀Fibrotic Focus 14
第三節 統計分析軟體(Statistical Product and Service Solutions,SPSS) 15
第四節 生物學圖像分析及病理學數據(Bioimsge analysis & digital pathology,QuPath) 15
第五節 生物學路徑分析軟體(Ingenuity Pathway Analysis,IPA) 16
第六節 基因群表達分析軟體(Gene Set Enrichment Analysis,GSEA) 18
第三章 結果 20
第一節 從TCGA收集1061之數位病理玻片及其臨床資料 20
第二節 TSR和TILs與ESTIMATE評分呈正相關 23
第三節 高TSR之乳癌案例之生存率較低 25
第四節 以Kaplan-Meier Method分析TSR 27
第五節 探討TSR與其它預後因子之相關性 29
第六節 腫瘤浸潤淋巴細胞 TIL與TSR及生存率關係 31
第七節 腫瘤間質分析技術Cut off如何定義 36
第八節 TSR在雌激素受體Estrogen receptors與黃體激素受體progesterone receptor 之探討 38
第九節 Breast Cancer分子亞型與TSR之相關性 40
第十節 運用IPA分析TSR相關活化路徑 43
第十一節 運用GSEA分析TSR相關基因中表現量高之基因群 44
第十二節 評估TSGH乳癌數據 46
第四章 討論 50
第一節 TSR在Fibrotic Focus的比例及生存率之探討 50
第二節 運用IPA分析TSR相關基因調控機轉 52
第三節 運用GSEA分析TSR相關基因組 54
第四節 分析TSGH 數據無顯著意義 56
第五章 結論 57
第六章 參考文獻 58


表目錄
表 1、乳癌分子亞型(Moledular Subtype) 6
表 2、 IPA工作流程 16
表 3、各乳癌研究項目之描述性統計資料 21
表 4、TSR_medium 50與Overall Survival之群組統計資料 26
表 5、TSR_medium 50與Overall Survival之N值相關統計 28
表 6、運用皮爾森係數分析TSR與其他預後因子之統計 30
表 7、腫瘤浸潤性淋巴細胞 Tumor Infiltrating Lymphocytes與Tumor Stroma Ratio(TSR%)佔比之分析統計 33
表 8、TIL<20%和≥20%分群與Overall Survival Time(OS Time)統計 34
表 9、以Independent Sample t test分析乳癌分子亞型之統計 35
表 10、TSR分析與乳癌分子之描述性統計 40
表 11、使用ANOVA其中Bonferroni 分析TSR在每個乳癌分子亞型之間的相關性 41
表 12、分析TSGH乳癌相關項目之描述性統計 47
表 13、使用ANOVA中之Bonferroni 評估TSR於每個乳癌分子亞型之間的相關性 48
表 14、TSR_medium 50與Overall Survival之N值相關統計 49

圖目錄
圖 1、乳癌疾病進程 2
圖 2、TNM分期(Tumor-Node-Metastasis Staging) 4
圖 3、乳癌組織型態惡性度分級標準(Nottingham Criteria) 5
圖 4、美國癌症基因體圖譜 (The Cancer GenomeAtlas,TCGA)資料庫。 9
圖 5、腫瘤(Tumor)及間質( Stroma)示意圖。 11
圖 6、TSR評分依據。 11
圖 7、腫瘤浸潤淋巴細胞 (tumor-infiltrating lymphocytes, TIL)判讀的區域周界示意圖。 12
圖 8、乳癌分化程度 (Nottingham histologic score)評分示意圖。 13
圖 9、Fibrotic Focus示意圖。 14
圖 10、基因集富集分析(GSEA)方法的工作流程。 19
圖 11、描述性統計圓餅圖 22
圖 12、運用ESTIMATE評估TSR Cut off 50%及TIL Cut off 20%時在基質評分(Stromal Score)、免疫細胞評分(Immuno Score)、換算腫瘤純度(ESTIMATE Score)之相關性。 24
圖 13、TSR技術在乳癌數位影像Overall Survival的Box-plot 26
圖 14、TSR高表現與低表現的生存曲線 28
圖 15 、在TIL5%與TIL50%中TSR(%)相比p=0.031,具有統計上的意義,代表TIL(%)佔比越高,TSR(%)比例越低。Box plot中間之點線為中位數,藍色圓圈代表平均值。 32
圖 16、Kaplan-Meier Method生存曲線方法分析TIL<20%和≥20%分群與Overall Survival Time(OS Time)統計結果顯示p=0.549。 34
圖 17、Kaplan-Meier Method生存曲線方法分析Overall Survival(Day)和Cumulative Survival在Her2-enriched分子亞型p<0.001,具有顯著意義。 35
圖 18、以ESTIMATE評分TSR groups與Overall Survival之相關性 37
圖 19、Estrogen Receptors(ER)陽性或陰性與TSR之相關性。 38
圖 20、Progesterone Receptor(PR)陽性與陰性與TSR之相關性。 39
圖 21、乳癌分子亞型與TSR(%)之相關性。 42
圖 22、運用IPA生物訊息軟體分析TSR相關基因找尋高度相關常規路徑、生物標誌物、毒性功能預測分析。 43
圖 23、運用GSEA分析以TSR Cut off 50%為區分,在基因組資料庫找尋高度相關之基因組(Gene Set)族群。 45
圖 24、TSR與TSGH臨床檢體overall survival之分析。 47
圖 25、TSGH數據中乳癌分子亞型與TSR(%)之相關性。 48
圖 26、Kaplan-Meier Method生存曲線方法分析TSGH 乳癌數據。 49
圖 27、纖維化病灶(Fibrotic Focus,FF)與TSR(%)之相關性。 50
圖 28、纖維化病灶的有無與總體生存率之分析結果顯示p=0.813,並無顯著上的差異。 51


1.Jemal, A., et al., Global cancer statistics. CA Cancer J Clin, 2011. 61(2): p. 69-90.
2.Ben Aziz, M. and T. Mukhdomi, Regional Anesthesia For Breast Reconstruction, in StatPearls. 2021, StatPearls Publishing
Copyright © 2021, StatPearls Publishing LLC.: Treasure Island (FL).
3.Stanton, S.E. and M.L. Disis, Clinical significance of tumor-infiltrating lymphocytes in breast cancer. J Immunother Cancer, 2016. 4: p. 59.
4.Zolota, V., et al., Epigenetic Alterations in Triple-Negative Breast Cancer-The Critical Role of Extracellular Matrix. Cancers (Basel), 2021. 13(4).
5.Mueller, M.M. and N.E. Fusenig, Friends or foes - bipolar effects of the tumour stroma in cancer. Nat Rev Cancer, 2004. 4(11): p. 839-49.
6.Jeong, Y.J., et al., Association between lysyl oxidase and fibrotic focus in relation with inflammation in breast cancer. Oncol Lett, 2018. 15(2): p. 2431-2440.
7.de Kruijf, E.M., et al., Tumor-stroma ratio in the primary tumor is a prognostic factor in early breast cancer patients, especially in triple-negative carcinoma patients. Breast Cancer Res Treat, 2011. 125(3): p. 687-96.
8.Dekker, T.J., et al., Prognostic significance of the tumor-stroma ratio: validation study in node-negative premenopausal breast cancer patients from the EORTC perioperative chemotherapy (POP) trial (10854). Breast Cancer Res Treat, 2013. 139(2): p. 371-9.
9.Gujam, F.J., et al., The relationship between the tumour stroma percentage, clinicopathological characteristics and outcome in patients with operable ductal breast cancer. Br J Cancer, 2014. 111(1): p. 157-65.
10.Moorman, A.M., et al., The prognostic value of tumour-stroma ratio in triple-negative breast cancer. Eur J Surg Oncol, 2012. 38(4): p. 307-13.
11.Roeke, T., et al., The prognostic value of the tumour-stroma ratio in primary operable invasive cancer of the breast: a validation study. Breast Cancer Res Treat, 2017. 166(2): p. 435-445.
12.He, R., et al., The prognostic value of tumor-stromal ratio combined with TNM staging system in esophagus squamous cell carcinoma. J Cancer, 2021. 12(4): p. 1105-1114.
13.Kang, G., et al., Clinicopathological Significances of Tumor-Stroma Ratio (TSR) in Colorectal Cancers: Prognostic Implication of TSR Compared to Hypoxia-Inducible Factor-1α Expression and Microvessel Density. Curr Oncol, 2021. 28(2): p. 1314-1324.
14.Ogawa, Y., et al., Three Distinct Stroma Types in Human Pancreatic Cancer Identified by Image Analysis of Fibroblast Subpopulations and Collagen. Clin Cancer Res, 2021. 27(1): p. 107-119.
15.Bremnes, R.M., et al., The role of tumor stroma in cancer progression and prognosis: emphasis on carcinoma-associated fibroblasts and non-small cell lung cancer. J Thorac Oncol, 2011. 6(1): p. 209-17.
16.Ohtani, H., et al., Defining lymphocyte-predominant breast cancer by the proportion of lymphocyte-rich stroma and its significance in routine histopathological diagnosis. Pathol Int, 2015. 65(12): p. 644-51.
17.Chen, Q.F., et al., Significance of tumor-infiltrating immunocytes for predicting prognosis of hepatitis B virus-related hepatocellular carcinoma. World J Gastroenterol, 2019. 25(35): p. 5266-5282.
18.Hall, M., et al., Expansion of tumor-infiltrating lymphocytes (TIL) from human pancreatic tumors. J Immunother Cancer, 2016. 4: p. 61.
19.Liu, Z.J., G.L. Semenza, and H.F. Zhang, Hypoxia-inducible factor 1 and breast cancer metastasis. J Zhejiang Univ Sci B, 2015. 16(1): p. 32-43.
20.Zhang, C., et al., Hypoxia induces the breast cancer stem cell phenotype by HIF-dependent and ALKBH5-mediated m⁶A-demethylation of NANOG mRNA. Proc Natl Acad Sci U S A, 2016. 113(14): p. E2047-56.
21.Xiang, L. and G.L. Semenza, Hypoxia-inducible factors promote breast cancer stem cell specification and maintenance in response to hypoxia or cytotoxic chemotherapy. Adv Cancer Res, 2019. 141: p. 175-212.
22.Kowalski, J.P., et al., Design and Characterization of the First Selective and Potent Mechanism-Based Inhibitor of Cytochrome P450 4Z1. J Med Chem, 2020. 63(9): p. 4824-4836.
23.Ennour-Idrissi, K., et al., DNA Methylation and Breast Cancer Risk: An Epigenome-Wide Study of Normal Breast Tissue and Blood. Cancers (Basel), 2020. 12(11).
24.Wang, J., et al., Overexpression of lipid metabolism genes and PBX1 in the contralateral breasts of women with estrogen receptor-negative breast cancer. Int J Cancer, 2017. 140(11): p. 2484-2497.
25.Xiao, B., et al., Identification of methylation sites and signature genes with prognostic value for luminal breast cancer. BMC Cancer, 2018. 18(1): p. 405.
26.Ataca, D., et al., The secreted protease Adamts18 links hormone action to activation of the mammary stem cell niche. Nat Commun, 2020. 11(1): p. 1571.
27.Harbeck, N., et al., Breast cancer. Nat Rev Dis Primers, 2019. 5(1): p. 66.
28.Karamanou, K., et al., Epithelial-to-mesenchymal transition and invadopodia markers in breast cancer: Lumican a key regulator. Semin Cancer Biol, 2020. 62: p. 125-133.
29.Junttila, M.R. and F.J. de Sauvage, Influence of tumour micro-environment heterogeneity on therapeutic response. Nature, 2013. 501(7467): p. 346-54.
30.Hawinkels, L.J., et al., Interaction with colon cancer cells hyperactivates TGF-β signaling in cancer-associated fibroblasts. Oncogene, 2014. 33(1): p. 97-107.
31.Bergers, G., et al., Matrix metalloproteinase-9 triggers the angiogenic switch during carcinogenesis. Nat Cell Biol, 2000. 2(10): p. 737-44.
32.Levental, K.R., et al., Matrix crosslinking forces tumor progression by enhancing integrin signaling. Cell, 2009. 139(5): p. 891-906.


電子全文 電子全文(網際網路公開日期:20260611)
QRCODE
 
 
 
 
 
                                                                                                                                                                                                                                                                                                                                                                                                               
第一頁 上一頁 下一頁 最後一頁 top
無相關論文
 
無相關期刊
 
無相關點閱論文