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研究生： 陳伯軒
Chen, Bo-Hsian
論文名稱： 透過評估突變對基因的功能性影響辨識化療引起的周圍神經病變
CIPN Classification via Gene Mutational Functional Impact
指導教授： 楊士德
Yang, Hsih-Te
學位類別： 碩士
Master
系所名稱： 電機資訊學院 - 醫學資訊研究所
Institute of Medical Informatics
論文出版年： 2017
畢業學年度： 105
語文別： 英文
論文頁數： 36
中文關鍵詞： 化療造成的周遭神經病變 、OncodriveFM 、單核苷酸突變 、功能性突變基因辨識
外文關鍵詞： Chemotherapy-induced peripheral neuropathy (CIPN), mutational functional impact, OncodriveFM, single nucleotide variant
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    • 癌症已經成為全球重大問題之一，單在2012年新罹患癌症的人口已經高達14000000人[1]，癌症的治療也相應成為需要特別重視的領域。化療為癌症治療中一種常見治療方式，而某些化療的藥物可能使周遭神經受損，引起數種不同的症狀，稱為「化療造成的周圍神經病變」(CIPN – Chemotherapy-Induced Peripheral Neuropathy )，可稱其為一種癌症治療用藥的副作用。CIPN症狀包含手腳麻、間歇性疼痛、平衡感喪失、冷熱感喪失問題等等，而有數種症狀可能對病人術中或術後生活造成危害，迫使治療過程需要中斷或更改，降低癌症治療成效，因此本研究著手於分析CIPN造成負面影響的成因，希望能研究其分子生物機轉，達到對CIPN的分析、分類與預測。
    本研究中，我們由成大醫院所提供的癌症病人取得臨床與基因資料，並藉由分析單核苷酸突變(SNV)對基因功能所造成的影響作評分，並整合成單一基因功能突變程度的分數。取代傳統以突變頻率作為對基因突變評分的方式，對CIPN與基因突變之間的關係做深入分析。
    本研究由成大醫院提供癌症病人資料，包含生殖系基因定序資料，與36種針對CIPN所做臨床資料，包括34個神經學測試與2個針對CIPN所做的問卷調查。先由金萬林公司將基因資料做後續分析，得到單核苷酸變異資料，並結合36種臨床資料，進行突變基因與CIPN相關性之分析。我們使用OncodriveFM對「單核苷酸變異對基因功能的影響程度」作評分，並將單核苷酸變異分數整合成「單一基因因突變所造成的功能及結構變化程度」，稱為單一基因的FIS(functional impact score)。再將所有基因的FIS與36個臨床資料作相關性比較，篩選出與各臨床資料有高度相關性的基因，篩選出與CIPN相關的基因名單，再使用CIPN相關基因對病人做分群，區分出CIPN裡的不同子形態，再針對各群病人做特徵選取，找出CIPN不同子形態之間的突變差異，並建立預測模型，達到事前預測CIPN，找出主導化療造成的周圍神經病變成因的路徑(pathway)。希望藉由本研究建立一套系統化流程，以基因資料對CIPN作分析、分類與預測。
    本研究將結果與臨床醫學結合，希望能利用所建預測模型準確預測個別病人在接受化療後造成CIPN的風險，給予相對應的劑量或用藥調整。並使用基因分析得到的成因路徑，尋找降低或避免化療用藥造成的CIPN風險的途徑。

    Cancer is becoming one of the biggest issue all around the world, with approximately 14 million new cases in 2012[1]. According to the massive population of cancer patients, anti-cancer treatment has relatively become an important field which need to pay quite serious attention to. Chemotherapy is common category of cancer treatment, but some chemotherapeutic agents may damage the peripheral nerves witch is called chemotherapy-induced peripheral neuropathy, could be considered as a disabling side effect of cancer treatment. The symptoms of CIPN including Pain, Burning, Tingling, loss of feeling, etc. As a result of this dose reduction, delay, and withdrawal may lead to decreased chemotherapy efficacy and survival. Our research try to profile CIPN by analyzing genomic data and find the pathogenesis. Aim to classify and predict the possibility of getting CIPN for each patient who received anti-cancer treatment.
    We combined genomic data and clinical data to profile and determine CIPN. By identifying mutated gene panel and pathogenesis pathway through analyzing functional impact bias in SNV with our systematic procedure to profile chemotherapy-induced peripheral neuropathy (CIPN) cohort.
    We acquire SNV data in germline sequence and 36 clinical test including 34 NE test and 2 questionnaires directly against CIPN from cancer patients in National Cheng Kung University Hospital (NCKUH). We used OncodriveFM as our mutated genes identifier, which identifies genes with mutational functional impact bias by integrating functional impact score of SNVs in a specific gene. After identifying mutated genes with functional impact score (FIs) for each gene which was found to have SNVs in germline. By analyzing those FIs with 36 clinical test, we aim to distinguish subset in CIPN patients, and go on building predicting model or determining the pathogenesis pathway of each CIPN subset.
    We combine our results with clinical medicine, hoping to build a highly reliable system to predict the consequence caused by chemotherapy, in pursuit of increasing chemotherapy efficacy and survival.

    Contents 摘　要 I Abstract III 致謝 V Contents VII Figure list IX Table list XI Chapter 1. Introduction 1 1.1 Motivation 1 1.2 Objectives 1 Chapter 2. Background 2 2.1 Chemotherapy-induced peripheral neuropathy 2 2.2 Single nucleotide variant 5 Chapter 3. Method and tools 6 3.1 Data 7 3.1.1 Sample source 7 3.1.2 Genomic data 10 3.2 Identifying Mutated genes 11 3.2.1 IntOGen 14 3.2.2 OncodriveCLUST 14 3.2.3 OncodriveFM 15 3.2.4 Transfic 15 3.2.5 Tool adjustment 16 3.3 Feature selection - Clinical Associated Gene 17 3.4 Clustering 17 3.4.1 ConsensusClusterPlus 18 3.5 Feature selection – Group specific genes 18 3.5.1 Gene Panel Analysis 19 3.5.2 Pathogenesis Analysis 19 Chapter 4. Experimental Results 20 4.1 Data 20 4.1.1 Dataset 20 4.1.2 Noise filtering 20 4.2 CIPN Associated Gene 21 4.3 Clustering 22 4.3.1 Group Specific Genes 26 Chapter 5. Conclusion 33 Chapter 6. References 34

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