【摘要】：腫瘤細(xì)胞與正常細(xì)胞的差異甲基化分析是癌癥表觀遺傳學(xué)研究的重要內(nèi)容之一。腫瘤組織往往由腫瘤細(xì)胞、正常細(xì)胞、侵入免疫細(xì)胞等多組織混合而成,其中正常細(xì)胞的混入對(duì)隨后的差異甲基化分析有較大影響,但目前尚未有差異甲基化分析的工作考慮到腫瘤細(xì)胞純度的影響�；诙喾N基因組學(xué)數(shù)據(jù)預(yù)測(cè)抗癌藥物的敏感性是實(shí)現(xiàn)腫瘤個(gè)體化醫(yī)療的核心步驟。目前,針對(duì)藥物敏感性預(yù)測(cè)問(wèn)題的研究大多利用各種基因組學(xué)數(shù)據(jù)對(duì)藥物敏感值進(jìn)行稀疏回歸分析,對(duì)于單個(gè)藥物逐一建模。此類方法忽略了藥物自身分子特征對(duì)藥物敏感性的影響,對(duì)大部分藥物的預(yù)測(cè)準(zhǔn)確度難以達(dá)到臨床應(yīng)用的要求。本論文對(duì)上述兩個(gè)計(jì)算生物學(xué)問(wèn)題進(jìn)行了系統(tǒng)的研究。對(duì)于腫瘤細(xì)胞純度估計(jì)問(wèn)題,我們首先利用了腫瘤與正常組織中差異最顯著的若干位點(diǎn)進(jìn)行了密度估計(jì),建立了基于這些位點(diǎn)的純度估計(jì)模型。預(yù)測(cè)的結(jié)果與基于拷貝數(shù)、基因表達(dá)和二代測(cè)序得到的結(jié)果高度一致�；谏鲜黾兌裙烙�(jì)的結(jié)果,我們利用廣義線性模型建立了考慮到腫瘤純度的腫瘤-正常差異甲基化位點(diǎn)估計(jì)模型,針對(duì)肺腺癌、結(jié)直腸癌的差異甲基化分析表明,考慮到腫瘤純度的方法比原有的秩和檢驗(yàn)在差異位點(diǎn)個(gè)數(shù)、腫瘤間統(tǒng)計(jì)量一致性等指標(biāo)上表現(xiàn)更優(yōu)。對(duì)于抗癌藥物敏感性預(yù)測(cè)問(wèn)題,我們首先研究了基因組特征相似的細(xì)胞系對(duì)于同一個(gè)藥物,以及化學(xué)相似的藥物對(duì)于同一個(gè)細(xì)胞系之間藥物敏感性的相似性。隨后構(gòu)建了細(xì)胞系-藥物雙層網(wǎng)絡(luò)模型,并提出局部線性方法預(yù)測(cè)抗癌藥物對(duì)于細(xì)胞系的敏感性。預(yù)測(cè)結(jié)果顯示,相比現(xiàn)有的“彈性網(wǎng)絡(luò)回歸”等稀疏回歸方法,雙層網(wǎng)絡(luò)方法利用更少的模型參數(shù)和特征信息,卻具有更高的預(yù)測(cè)準(zhǔn)確度。我們也對(duì)”癌癥基因組計(jì)劃”(CGP)中缺失的敏感性數(shù)據(jù)進(jìn)行了補(bǔ)充,結(jié)果表明BRAF突變的細(xì)胞系對(duì)三個(gè)MEK抑制劑敏感性更高,這與實(shí)驗(yàn)得到的結(jié)果一致。
[Abstract]:Differential methylation analysis between tumor cells and normal cells is one of the important contents in the study of cancer epigenetics. Tumor tissue is often composed of tumor cells, normal cells, invading immune cells and other tissues, in which the mixing of normal cells has a great influence on the subsequent differential methylation analysis. However, no differential methylation analysis has taken into account the effect of tumor cell purity. Predicting the sensitivity of anticancer drugs based on a variety of genomics data is the core step to realize individualized medical treatment of tumors. At present, most of the studies on the prediction of drug sensitivity use a variety of genomic data to carry out sparse regression analysis of drug sensitivity, and model a single drug one by one. This method ignores the influence of drug molecular characteristics on drug sensitivity, and the prediction accuracy of most drugs is difficult to meet the requirements of clinical application. In this paper, the above two computational biology problems are studied systematically. For the problem of tumor cell purity estimation, we first use some of the most significant differences between tumor and normal tissues to estimate the density of tumor cells, and establish a purity estimation model based on these sites. The predicted results are highly consistent with those based on copy number, gene expression and second generation sequencing. Based on the results of the above purity estimation, we established a tumor-normal differential methylated site estimation model considering tumor purity by using the generalized linear model. The differential methylation analysis for lung cancer and colorectal cancer showed that, The method considering tumor purity is better than the original rank sum test in the number of differentially located points and the consistency of statistics between tumors. For the prediction of anticancer drug sensitivity, we first studied the similarity of drug sensitivity of cell lines with similar genomic characteristics to the same drug and chemical similar drugs to the same cell line. Then the cell line-drug double-layer network model was constructed, and a local linear method was proposed to predict the sensitivity of anticancer drugs to cell lines. The prediction results show that compared with the existing sparse regression methods such as "elastic network regression", the double-layer network method makes use of less model parameters and characteristic information, but has higher prediction accuracy. We also supplemented the missing sensitivity data in Cancer Genome Project (CGP). The results showed that BRAF mutant cell lines were more sensitive to three MEK inhibitors, which was consistent with the experimental results.
【學(xué)位授予單位】：上海師范大學(xué)
【學(xué)位級(jí)別】：博士
【學(xué)位授予年份】：2016
【分類號(hào)】：R96

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