臺灣博碩士論文加值系統

近年來研究發現在癌症中，不同病人在特定的基因的小範圍區域內會產生的突變，此特定突變位點為突變熱區(Mutation Hotspots)，且在突變熱區中突變會影響基因的功能導致疾病產生及發展。因此本研究發展出方法在體細胞突變資料中尋找癌症突變熱區，並結合存活資料、表現量、蛋白質功能區及蛋白質結構圖進一步探討所尋找出的突變熱區所影響的基因功能和臨床表現。
本研究假定在基因上單一位點的突變與周圍突變位點數量會有影響，因此設計一個數學模式將突變資料轉呈連續密度資訊，再來利用山岳刪減群聚法(Mountain Subtractive Clustering)依序找出基因上可能為突變熱區的中心位置，之後再利用基因上相鄰突變位點之間距離分布資訊來確定突變熱區的範圍，最後用條件篩選決定最終的突變熱區。
本研究利用TCGA Exome-Seq資料中所提供31種癌症的體細胞突變資訊。為確認本方法找尋癌症突變熱區之效能，我們針對Ion AmpliSeq Cancer Hotspot Panel 中的50個已知和癌症有相關的基因去尋找該基因的突變熱區，本研究針對這50個基因在個別癌症所找出的突變熱區結果和目前研究已知的基因突變熱點比對以進行突變熱區篩選條件參數系統性的調整。在參數調整確定後本研究加入COSMIC資料庫Cancer Gene Census 中列出的目前已被充分研究確定為驅動基因、Foundation Medicine 公司Foundation One CDx中所針對的基因、DNA 修復基因共631個基因。
在30種癌症140個基因共找到368個突變熱區，其中230個突變熱區是落在Pfam 蛋白質的功能區。將找出的突變熱區結合存活資料進行存活分析，以此連結本方法找到的突變熱區和癌症病患存活之相關聯性。總共有33個在突變熱區有突變的病患和在該基因沒有突變的病患存活時間有顯著差異，例如:KRAS在胺基酸位點12-13突變的病患在胰臟腺癌和膀胱尿路上皮癌在存活時間和沒有突變的病患有顯著差異。此外還結合表現量資料，去看基因突變和表現量的關聯，有70個突變熱區在表現量分析中在突變熱區有突變的病人和該基因沒有突變是有顯著差異，其中有6個突變熱區在存活分析和表現量分析結果都是顯著的。例如:在LIHC CTNNB1突變熱區32-37觀察到在存活分析結果和表現量分析的結果都是顯著的，我們將結果顯示在蛋白質結構圖上從蛋白質結構來觀察突變熱區所位在的功能區。最後我們定義了HNSC RAC1 第178胺基酸、LIHC FANCD2第802胺基酸、LUSC CDKN2A第122-124胺基酸三個新的突變熱區。

Recently, the studies have found mutations that occur in different patients in a small region of a specific gene in cancer, and that the specific mutation sites are mutation hotspots. Researches consider that mutations in the hotspots affect the function of genes and leading to the development of disease. We developed a method to find cancer hotspots in somatic mutation data, and combined with survival data, expression, protein domain and protein structure to further explore the gene function and clinical manifestations of the identified hotspot regions.
In this study, we believe that the mutation of a single site in the gene is effected by the neighbor mutation sites. Therefore, a mathematical model is designed to transfer the mutation data to continuous density information, and then to use Mountain Subtractive Clustering which find out the position of the gene that may be the center of the hotspot region and then use the distance distribution information between the adjacent mutations to determine the range of the hotspot region. Finally, selecting the final hotspot region using the filter conditions.
This study used somatic mutation data in 31 cancers from the TCGA. To confirm the effectiveness of this method in the search for cancerous hotspots, we searched for 50 known and cancer-related genes in the Ion AmpliSeq Cancer Hotspot Panel to analysis the hotspot regions of mutations and using the result to adjust the parameter. After the parameter adjustment was confirmed, this study added the COSMIC Cancer Gene Census genes、DNA repair genes and gene list targeted by the Foundation Medicine.
A total of 368 mutation hotspots were found in 140 genes of 30 cancers, among them 230 were in the Pfam domain. This study used survival analysis to link the survival of cancer patients and mutation hotspots. A total of 33 hotspots which patients with mutations had significant differences in survival time between those with no mutation in the gene, such as KRAS 12-13 in both PAAD and BLCA. In addition, we added gene expression data to observe the association between gene mutation and performance. There are 70 mutation hotspots which patients with mutations had significant difference in the expression analysis. Among them, 6 mutation hotspots were significant in both survival analysis and expression analysis. For example, LIHC CTNNB1 32-37. We highlight the hotspot on the protein structure to observe the functional region. Finally, we define three novel mutation hotspots, HNSC RAC1 codon 178, LIHC FANCD2 codon 802 and LUSC CDKN2A 122-124.

目錄
致謝 i
中文摘要 ii
ABSTRACT iv
目錄 vi
圖目錄 vii
表目錄 viii
第一章 背景與介紹 1
1.1基因突變與癌症 1
1.2尋找突變熱區方法 4
1.3所使用資料庫及平台資料簡介 11
1.4動機與主旨 13
第二章 材料與方法 14
2.1 體細胞突變資料 14
2.1.1 基因挑選 15
2.2 利用山岳刪減群聚法搜尋突變熱區 17
2.2.1 突變資訊分數轉換 18
2.2.2 利用山岳刪減群聚法搜尋突變熱區群中心 19
2.2.3 突變熱區範圍 20
2.2.4 突變熱區篩選 22
第三章 結果與討論 23
3.1參數調整 23
3.2 預測突變熱區 24
3.2.1 單一癌症突變熱區預測 24
3.2.1.1 突變熱區與存活分析 24
3.2.1.2 突變熱區與表現量資料關聯 32
3.2.1.3 突變熱區與表現量及存活分析 36
3.3 其他工具比較 40
3.3.1 突變熱區與存活分析和M2C比較 41
3.3.2 突變熱區長度與其他方法比較 44
第四章 結論與未來工作 49
參考 50
附錄 53

圖目錄
圖 1.Cancer Genome Landscapes中ONCOs和TSGs的突變分布圖 3
圖 2.Cosmic資料庫上KRAS的突變分布圖 3
圖 3. e-Driver做法示意圖 5
圖 4. MSEA做法示意圖 6
圖 5. OncodriveCLUST做法之示意圖 8
圖 6. M2C做法示意圖 9
圖 7.本研究的做法與流程 17
圖 8.原始基因突變分布 18
圖 9.進行完分數計算後的連續密度分布 18
圖 10.顯示利用山岳刪減群聚法所找出PTEN在泛癌症中的10個群中心 20
圖 11.為突變熱區延伸範圍的圖例 21
圖 12.AML DNMT3A 突變熱區和存活分析結果 27
圖 13.BLCA KRAS突變熱區和存活分析結果 28
圖 14.HNSC RAC1突變熱區和存活分析結果 29
圖 15.LIHC FANCD2突變熱區和存活分析結果 30
圖 16.LUSC CDKN2A突變熱區和存活分析結果 32
圖 17.UCEC 和 READ 在KRAS中表現量分析 33
圖 18.TP53在突變熱區中表現量顯著差異於非突變熱區 35
圖 19.p53 DNA結合域蛋白質結構 35
圖 20.LIHC CTNNB1突變熱區32-37以及存活分析和表現量分析 38
圖 21.在蛋白質結構中顯示突變熱區的區域 39
圖 22.M2C BLCA FBXW7突變熱區和存活分析結果 42
圖 23.M-hotspot BLCA FBXW7突變熱區和存活分析結果 42
圖 24.M_hotspot、M2C與oncodrive突變熱區長度比較圖 45
圖 25.M_hotspot和M2C所找出突變熱區範圍依cancer census gene分類 46

表目錄
表1.四種尋找突變熱區的方法與特性 5
表2.本研究所用之癌症種類的病患人數和突變數量 7
表3.本研究所選用調整參數之基因 8
表4.存活分析的結果整理 17
表5.單一癌症中找出突變熱區在存活分析上有顯著差異的資訊 18
表6.表現量分析的結果整理 26
表7.在突變熱區中突變表現量與在非突變區表現量有顯著差異 28
表8.存活分析和表現量分析都有統計上顯著差異結果的突變熱區 29
表9.在50個基因中三種方法分別找出的突變熱區比較 33
表10.本方法和M2C存活分析比較 34
表11.M_hotspot找出的突變熱區中長度落在1-5的TSGs 40
表12. M2C所找出突變熱區中長度大於50的OGs 41

Chae, Y.K., Anker, J.F., Carneiro, B.A., Chandra, S., Kaplan, J., Kalyan, A., Santa-Maria, C.A., Platanias, L.C., and Giles, F.J. (2016). Genomic landscape of DNA repair genes in cancer. Oncotarget 7, 23312-23321.
Davis, R.J., Welcker, M., and Clurman, B.E. (2014). Tumor Suppression by the Fbw7 Ubiquitin Ligase: Mechanisms and Opportunities. Cancer cell 26, 455-464.
Eser, S., Schnieke, A., Schneider, G., and Saur, D. (2014). Oncogenic KRAS signalling in pancreatic cancer. British Journal of Cancer 111, 817-822.
Forbes, S.A., Beare, D., Gunasekaran, P., Leung, K., Bindal, N., Boutselakis, H., Ding, M., Bamford, S., Cole, C., Ward, S., et al. (2015). COSMIC: exploring the world's knowledge of somatic mutations in human cancer. Nucleic Acids Research 43, D805-D811.
Garaycoechea, J.I., Crossan, G.P., Langevin, F., Mulderrig, L., Louzada, S., Yang, F., Guilbaud, G., Park, N., Roerink, S., Nik-Zainal, S., et al. (2018). Alcohol and endogenous aldehydes damage chromosomes and mutate stem cells. Nature 553, 171-177.
Ge, Z., Leighton, J.S., Wang, Y., Peng, X., Chen, Z., Chen, H., Sun, Y., Yao, F., Li, J., Zhang, H., et al. (2018). Integrated Genomic Analysis of the Ubiquitin Pathway across Cancer Types. Cell reports 23, 213-226.e213.
Gerstung, M., Pellagatti, A., Malcovati, L., Giagounidis, A., Porta, M.G.D., Jädersten, M., Dolatshad, H., Verma, A., Cross, N.C.P., Vyas, P., et al. (2015). Combining gene mutation with gene expression data improves outcome prediction in myelodysplastic syndromes. Nature Communications 6, 5901.
Goldstein, I., Marcel, V., Olivier, M., Oren, M., Rotter, V., and Hainaut, P. (2010). Understanding wild-type and mutant p53 activities in human cancer: new landmarks on the way to targeted therapies. Cancer Gene Therapy 18, 2.
Jia, P., Wang, Q., Chen, Q., Hutchinson, K.E., Pao, W., and Zhao, Z. (2014). MSEA: detection and quantification of mutation hotspots through mutation set enrichment analysis. Genome Biology 15, 489.
Lin, N., Fu, W., Zhao, C., Li, B., Yan, X., and Li, Y. (2017). Biologico-clinical significance of DNMT3A variants expression in acute myeloid leukemia. Biochemical and Biophysical Research Communications 494, 270-277.
Navarro-Lérida, I., Sánchez-Perales, S., Calvo, M., Rentero, C., Zheng, Y., Enrich, C., and Del Pozo, M.A. (2012). A palmitoylation switch mechanism regulates Rac1 function and membrane organization. The EMBO Journal 31, 534-551.
Pal, N., and Chakraborty, D. (2000). Mountain and subtractive clustering method: Improvements and generalizations, Vol 15.
Ping, Z., Siegal, G.P., Harada, S., Eltoum, I.-E., Youssef, M., Shen, T., He, J., Huang, Y., Chen, D., Li, Y., et al. (2016). ERBB2 mutation is associated with a worse prognosis in patients with CDH1 altered invasive lobular cancer of the breast. Oncotarget 7, 80655-80663.
Poole, W., Leinonen, K., Shmulevich, I., Knijnenburg, T.A., and Bernard, B. (2017). Multiscale mutation clustering algorithm identifies pan-cancer mutational clusters associated with pathway-level changes in gene expression. PLoS Computational Biology 13, e1005347.
Porta-Pardo, E., and Godzik, A. (2014). e-Driver: a novel method to identify protein regions driving cancer. Bioinformatics 30, 3109-3114.
Prior, I.A., Lewis, P.D., and Mattos, C. (2012). A comprehensive survey of Ras mutations in cancer. Cancer Research 72, 2457-2467.
Ramirez-Ardila, D.E., Helmijr, J.C., Look, M.P., Lurkin, I., Ruigrok-Ritstier, K., van Laere, S., Dirix, L., Sweep, F.C., Span, P.N., Linn, S.C., et al. (2013). Hotspot mutations in PIK3CA associate with first-line treatment outcome for aromatase inhibitors but not for tamoxifen. Breast Cancer Research and Treatment 139, 39-49.
Skvortsov, S., Dudás, J., Eichberger, P., Witsch-Baumgartner, M., Loeffler-Ragg, J., Pritz, C., Schartinger, V.H., Maier, H., Hall, J., Debbage, P., et al. (2014). Rac1 as a potential therapeutic target for chemo-radioresistant head and neck squamous cell carcinomas (HNSCC). British Journal of Cancer 110, 2677-2687.
Slamon, D.J., Clark, G.M., Wong, S.G., Levin, W.J., Ullrich, A., and McGuire, W.L. (1987). Human breast cancer: correlation of relapse and survival with amplification of the HER-2/neu oncogene. Science 235, 177.
Tam, K.W., Zhang, W., Soh, J., Stastny, V., Chen, M., Sun, H., Thu, K., Rios, J.J., Yang, C., Marconett, C.N., et al. (2013). CDKN2A/p16 inactivation mechanisms and their relationship to smoke exposure and molecular features in non-small cell lung cancer. Journal of thoracic oncology : official publication of the International Association for the Study of Lung Cancer 8, 1378-1388.
Tamborero, D., Gonzalez-Perez, A., and Lopez-Bigas, N. (2013). OncodriveCLUST: exploiting the positional clustering of somatic mutations to identify cancer genes. Bioinformatics 29, 2238-2244.
Tomczak, K., Czerwińska, P., and Wiznerowicz, M. (2015). The Cancer Genome Atlas (TCGA): an immeasurable source of knowledge. Contemporary Oncology 19, A68-A77.
Vogelstein, B., Papadopoulos, N., Velculescu, V.E., Zhou, S., Diaz, L.A., and Kinzler, K.W. (2013). Cancer Genome Landscapes. Science (New York, NY) 339, 1546-1558.
Wong Chun, M., Fan Sheung, T., and Ng Irene, O.L. (2001). β-catenin mutation and overexpression in hepatocellular carcinoma. Cancer 92, 136-145.
Wu, G., Xu, G., Schulman, B.A., Jeffrey, P.D., Harper, J.W., and Pavletich, N.P. (2003). Structure of a β-TrCP1-Skp1-β-Catenin Complex: Destruction Motif Binding and Lysine Specificity of the SCFβ-TrCP1 Ubiquitin Ligase. Molecular Cell 11, 1445-1456.
Xu, J., Wang, Y.-Y., Dai, Y.-J., Zhang, W., Zhang, W.-N., Xiong, S.-M., Gu, Z.-H., Wang, K.-K., Zeng, R., Chen, Z., et al. (2014). DNMT3A Arg882 mutation drives chronic myelomonocytic leukemia through disturbing gene expression/DNA methylation in hematopoietic cells. Proceedings of the National Academy of Sciences of the United States of America 111, 2620-2625.
Yakobson, E., Shemesh, P., Azizi, E., Winkler, E., Lassam, N., Hogg, D., Brookes, S., Peters, G., Lotem, M., Zlotogorski, A., et al. (2000). Two p16 (CDKN2A) germline mutations in 30 Israeli melanoma families. European Journal Of Human Genetics 8, 590.
Zecchin, D., Boscaro, V., Medico, E., Barault, L., Martini, M., Arena, S., Cancelliere, C., Bartolini, A., Crowley, E.H., Bardelli, A., et al. (2013). BRAF V600E Is a Determinant of Sensitivity to Proteasome Inhibitors. Molecular Cancer Therapeutics 12, 2950.