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系統識別號 U0026-0108201800333900
論文名稱(中文) 在癌症病人的Oncomine Panel中定義新的突變特徵
論文名稱(英文) Identify New Mutational Signature in Cancer Patients by Oncomine Panel
校院名稱 成功大學
系所名稱(中) 資訊工程學系
系所名稱(英) Institute of Computer Science and Information Engineering
學年度 106
學期 2
出版年 107
研究生(中文) 徐承甫
研究生(英文) Cheng-Fu Hsu
學號 Q56051037
學位類別 碩士
語文別 英文
論文頁數 32頁
口試委員 指導教授-蔣榮先
共同指導教授-林鵬展
口試委員-沈孟儒
口試委員-劉宗霖
口試委員-張文綺
中文關鍵字 癌症  次世代定序  突變特徵  突變負荷 
英文關鍵字 Cancer  NGS  mutational signature  mutational load 
學科別分類
中文摘要 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。
英文摘要 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.
論文目次 Chapter 1 Introduction ............................................................................................... 1
1.1 Background ................................................................................................................ 1
1.2 Motivation ................................................................................................................... 2
1.3 Research objective and specific aims ....................................................................... 3
1.4 Thesis Organization ................................................................................................... 3
Chapter 2 Related Work ............................................................................................. 4
2.1 Mutational Signatures extracting in NGS data ....................................................... 4
2.2 Mechanisms underlying mutational signatures ...................................................... 5
2.3 Oncomine Comprehensive Panel (OCP) .................................................................. 6
Chapter 3 Methods and Materials ............................................................................. 7
3.1 NGS data preprocess ................................................................................................. 8
3.2 Mutational Signature Extraction .............................................................................. 9
3.3 DeconstructSigs ........................................................................................................ 10
3.4 Observations ............................................................................................................. 11
3.4.1 Principle Components Analysis ................................................................................... 11
3.4.2 K-means clustering........................................................................................................ 12
3.4.3 Pearson Correlation ...................................................................................................... 12
3.4.4 Odds ratio(OR) .............................................................................................................. 12
Chapter 4 Experiments and Results ........................................................................ 14
4.1 Experimental design ................................................................................................ 14
4.2 OCP correlation with WES in mutational load .................................................... 15
4.3 Extracting Signatures .............................................................................................. 15
4.4 K-mean clustering and Principle component analysis ......................................... 17
4.5 Annotate by COSMIC signatures ........................................................................... 18
4.6 Association of COSMIC signatures and mutation load ....................................... 22
4.7 Gene Integrative analysis ........................................................................................ 24
4.7.1 Correlation between genes with SNV or CNV and clustering .................................. 24
4.7.2 Pathway analysis ........................................................................................................... 25
4.8 Comprehensive Graph ............................................................................................. 27
Chapter 5 Conclusions and Future work ................................................................ 28
5.1 Conclusions ............................................................................................................... 28
5.2 Future work .............................................................................................................. 29
References................................................................................................................... 30
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