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研究生：	徐承甫 Hsu, Cheng-Fu
論文名稱：	在癌症病人的Oncomine Panel中定義新的突變特徵 Identify New Mutational Signature in Cancer Patients by Oncomine Panel
指導教授：	蔣榮先 Chiang, Jung-Hsien
共同指導教授:	林鵬展 Lin, Peng-Chan
學位類別：	碩士 Master
系所名稱：	電機資訊學院 - 資訊工程學系 Department of Computer Science and Information Engineering
論文出版年：	2018
畢業學年度：	106
語文別：	英文
論文頁數：	32
中文關鍵詞：	癌症、次世代定序、突變特徵、突變負荷
外文關鍵詞：	Cancer, NGS, mutational signature, mutational load
相關次數：	點閱：110 下載：3
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隨著次世代定序的發明及流行，越來越多用全基因組序列和全外顯子序列的研究。但是，並不是所有人都負擔的起全基因組序列和全外顯子序列的定序費用。然而，從突變負擔的角度來看，Oncomine Panel 可以在很高程度上代表全外顯子序列，並且在Oncomine Panel中的基因都是可以用藥的。所以可以在降低成本情況下，得知病人重要的基因突變訊息。
本研究設計了一套癌症病人定序資料的研究流程去找出癌症背後的生物機制，首先利用次世代定序的方法來取得人類的目標區間序列和全基因組序列，癌症類型包括大腸直腸癌、子宮內膜癌與卵巢癌，以台灣人為目標族群。接著從目標區間定序序列的單核苷酸突變中提取出我們要找的突變特徵，並利用在先前的研究中提出並證實的COSMIC突變特徵 (收錄在COSMIC，the Catalogue Of Somatic Mutations In Cancer)來協助我們分析。發現這群病人中有著突變負擔上的差異，再進一步以基因為單位，利用統計等方法去算出每個基因受到單核苷酸突變或拷貝數變異影響是否為分出這兩群病人的原因。從上述方法獲得的基因集判斷有可能的訊息路徑，就可以得出有可能造成突變負擔上的差異的生物機制。最後在Oncomine Panel的定序資料上，從我們的方法與資料中得出一個與突變負擔過高的相關的突變特徵。

With the invention and popularity of the next generation sequencing, more and more researches that use whole genome sequencing and whole exome sequencing for analyzing are published. However, not all people afford to the cost of whole genome sequencing and whole exome sequences. Besides, In the aspect of mutation load, Oncomine Panel can represent the whole exome sequencing to a high degree, and the genes in Oncomine Panel are druggable. Therefore, it is possible to know the important genetic mutation information of the patient with reducing the cost.
This study designed a pipeline to find out the biological mechanisms underlying in cancer patients by cancer patient sequencing data. First getting target sequencing and Whole-Genome Sequencing(WGS) by Next Generation Sequencing (NGS) technology, and there are three cancer types including Colorectal cancer, Endometrial cancer and ovarian cancer. All patients are Taiwanese. Then, we extract Mutational signatures from all Single Nucleotide Variants(SNV) that in the target sequencing, and use COSMIC signatures that are proposed and confirmed in previous studies (publish on the Catalogue Of Somatic Mutations In Cancer, COSMIC) to help us analyze. After that, we find that there are some differences in the mutational load of our samples. Then, we analyze our samples at gene level[1]. We use statistical methods to find out whether each gene has Single Nucleotide Variation or Copy Number Variation(CNV) or not can be the reason of separating patients. The gene set obtain by the above method can determine the possible pathways, and we can study these pathways for understanding the mechanism under the pathway. Afterwards these pathways may be the reason why there are differences in mutational load. At last, we propose a mutational signature that extract from our dataset relate to higher mutational load on the target sequencing data of Oncomine Panel.

Chapter 1 Introduction ............................................................................................... 1
1 Background ................................................................................................................ 1
2 Motivation ................................................................................................................... 2
3 Research objective and specific aims ....................................................................... 3
4 Thesis Organization ................................................................................................... 3
Chapter 2 Related Work ............................................................................................. 4
1 Mutational Signatures extracting in NGS data ....................................................... 4
2 Mechanisms underlying mutational signatures ...................................................... 5
3 Oncomine Comprehensive Panel (OCP) .................................................................. 6
Chapter 3 Methods and Materials ............................................................................. 7
1 NGS data preprocess ................................................................................................. 8
2 Mutational Signature Extraction .............................................................................. 9
3 DeconstructSigs ........................................................................................................ 10
4 Observations ............................................................................................................. 11
4.1 Principle Components Analysis ................................................................................... 11
4.2 K-means clustering........................................................................................................ 12
4.3 Pearson Correlation ...................................................................................................... 12
4.4 Odds ratio(OR) .............................................................................................................. 12
Chapter 4 Experiments and Results ........................................................................ 14
1 Experimental design ................................................................................................ 14
2 OCP correlation with WES in mutational load .................................................... 15
3 Extracting Signatures .............................................................................................. 15
4 K-mean clustering and Principle component analysis ......................................... 17
5 Annotate by COSMIC signatures ........................................................................... 18
6 Association of COSMIC signatures and mutation load ....................................... 22
7 Gene Integrative analysis ........................................................................................ 24
7.1 Correlation between genes with SNV or CNV and clustering .................................. 24
7.2 Pathway analysis ........................................................................................................... 25
8 Comprehensive Graph ............................................................................................. 27
Chapter 5 Conclusions and Future work ................................................................ 28
1 Conclusions ............................................................................................................... 28
2 Future work .............................................................................................................. 29
References................................................................................................................... 30
                                    

1. Lawrence, M.S., et al., Mutational heterogeneity in cancer and the search for new cancer-associated genes. Nature, 2013. 499(7457): p. 214-218.
2. Helleday, T., S. Eshtad, and S. Nik-Zainal, Mechanisms underlying mutational signatures in human cancers. Nat Rev Genet, 2014. 15(9): p. 585-98.
3. Sinnadurai, M. and K.L. McDonald, Immune checkpoint inhibition and its relationship with hypermutation phenoytype as a potential treatment for Glioblastoma. J Neurooncol, 2017. 132(3): p. 359-372.
4. Pleasance, E.D., et al., A comprehensive catalogue of somatic mutations from a human cancer genome. Nature, 2010. 463(7278): p. 191-6.
5. Stratton, M.R., P.J. Campbell, and P.A. Futreal, The cancer genome. Nature, 2009. 458(7239): p. 719-24.
6. Khurana, E., et al., Integrative annotation of variants from 1092 humans: application to cancer genomics. Science, 2013. 342(6154): p. 1235587.
7. Alexandrov, L.B., et al., Signatures of mutational processes in human cancer. Nature, 2013. 500(7463): p. 415-21.
8. Alexandrov, L.B. and M.R. Stratton, Mutational signatures: the patterns of somatic mutations hidden in cancer genomes. Curr Opin Genet Dev, 2014. 24: p. 52-60.
9. Alexandrov, L.B., et al., Clock-like mutational processes in human somatic cells. Nat Genet, 2015. 47(12): p. 1402-7.
10. Seow, H.F., W.K. Yip, and T. Fifis, Advances in targeted and immunobased therapies for colorectal cancer in the genomic era. Onco Targets Ther, 2016. 9: p. 1899-920.
11. Chuk, M.K., et al., FDA Approval Summary: Accelerated Approval of Pembrolizumab for Second-Line Treatment of Metastatic Melanoma. Clin Cancer Res, 2017. 23(19): p. 5666-5670.
12. O'Neil, N.J., M.L. Bailey, and P. Hieter, Synthetic lethality and cancer. Nat Rev Genet, 2017. 18(10): p. 613-623.
13. Stephens, P.J., et al., The landscape of cancer genes and mutational processes in breast cancer. Nature, 2012. 486(7403): p. 400-4.
14. Morganella, S., et al., The topography of mutational processes in breast cancer genomes. Nat Commun, 2016. 7: p. 11383.
15. Nik-Zainal, S., et al., The life history of 21 breast cancers. Cell, 2012. 149(5): p. 994-1007.
16. Davies, H., et al., Whole-Genome Sequencing Reveals Breast Cancers with Mismatch Repair Deficiency. Cancer Res, 2017. 77(18): p. 4755-4762.
17. Nik-Zainal, S., et al., Mutational processes molding the genomes of 21 breast cancers. Cell, 2012. 149(5): p. 979-93.
18. Nik-Zainal, S., et al., Landscape of somatic mutations in 560 breast cancer whole-genome sequences. Nature, 2016. 534(7605): p. 47-54.
19. Warren, C.J., et al., Roles of APOBEC3A and APOBEC3B in Human Papillomavirus Infection and Disease Progression. Viruses, 2017. 9(8).
20. Middlebrooks, C.D., et al., Association of germline variants in the APOBEC3 region with cancer risk and enrichment with APOBEC-signature mutations in tumors. Nat Genet, 2016. 48(11): p. 1330-1338.
21. Nik-Zainal, S., et al., Association of a germline copy number polymorphism of APOBEC3A and APOBEC3B with burden of putative APOBEC-dependent mutations in breast cancer. Nat Genet, 2014. 46(5): p. 487-91.
22. Yang, B., et al., APOBEC: From mutator to editor. J Genet Genomics, 2017. 44(9): p. 423-437.
23. Hoopes, J.I., et al., APOBEC3A and APOBEC3B Preferentially Deaminate the Lagging Strand Template during DNA Replication. Cell Rep, 2016. 14(6): p. 1273-1282.
24. Alexandrov, L.B., et al., Mutational signatures associated with tobacco smoking in human cancer. Science, 2016. 354(6312): p. 618-622.
25. Pleasance, E.D., et al., A small-cell lung cancer genome with complex signatures of tobacco exposure. Nature, 2010. 463(7278): p. 184-90.
26. Rhodes, D.R., et al., ONCOMINE: a cancer microarray database and integrated data-mining platform. Neoplasia, 2004. 6(1): p. 1-6.
27. Gehring, J.S., et al., SomaticSignatures: inferring mutational signatures from single-nucleotide variants. Bioinformatics, 2015. 31(22): p. 3673-5.
28. Rosenthal, R., et al., DeconstructSigs: delineating mutational processes in single tumors distinguishes DNA repair deficiencies and patterns of carcinoma evolution. Genome Biol, 2016. 17: p. 31.
29. Fabregat, A., et al., The Reactome pathway Knowledgebase. Nucleic Acids Res, 2016. 44(D1): p. D481-7.
30. Forbes, S.A., et al., COSMIC: somatic cancer genetics at high-resolution. Nucleic Acids Res, 2017. 45(D1): p. D777-d783.
31. Wang, K., M. Li, and H. Hakonarson, ANNOVAR: functional annotation of genetic variants from high-throughput sequencing data. Nucleic Acids Res, 2010. 38(16): p. e164.
32. Alexandrov, L.B., et al., Deciphering signatures of mutational processes operative in human cancer. Cell Rep, 2013. 3(1): p. 246-59.
33. Lee, D.D. and H.S. Seung, Learning the parts of objects by non-negative matrix factorization. Nature, 1999. 401(6755): p. 788-91.
34. Campbell, B.B., et al., Comprehensive Analysis of Hypermutation in Human Cancer. Cell, 2017. 171(5): p. 1042-1056.e10.

校內：2019-09-01公開
校外：2019-09-01公開

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