计算机科学 ›› 2019, Vol. 46 ›› Issue (9): 310-314.doi: 10.11896/j.issn.1002-137X.2019.09.047
蔡齐荣1, 吴璟莉1,2
CAI Qi-rong1, WU Jing-li1,2
摘要: 通过整合体细胞突变、拷贝数变异和基因表达等3种组学数据,提出识别癌症驱动通路的改进最大权重子矩阵模型。该模型用通路中基因平均权重调控覆盖度和互斥度,对权重大的基因集覆盖度进行加强,同时放松其高互斥度约束。引入基于贪心算法的重组算子,提出求解该模型的单亲遗传算法PGA-MWS。采用胶质母细胞瘤和卵巢癌数据集对算法PGA-MWS和GA进行实验对比分析。实验结果显示,较GA方法,基于改进模型的PGA-MWS算法能识别出覆盖度高但互斥度不太高的基因集,且其识别的基因集中,许多均参与已知信号通路,并被证实与癌细胞密切相关,同时还能识别几种潜在的候选驱动通路,因此PGA-MWS方法可作为检测癌症驱动通路的一种有效补充。
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[1]HANAHAN D,WEINBERG R A.The hallmarks of cancer[J].Cell,2000,100(1):57-70. [2]GREENMAN C,STEPHENS P,SMITH R,et al.Patterns ofsomatic mutation in human cancer genomes [J].European Journal of Cancer Supplements,2008,6(9):153-158. [3]MCLENDON R,FRIEDMAN A,BIGNER D,et al.Comprehensive genomic characterization defines human glioblastoma genes and core pathways [J].Nature,2008,455(7216):1061-1068. [4]THE International Cancer Genome Consortium.Internationalnetwork of cancer genome projects [J].Nature,2010,464(7291):993-998. [5]DING L,GETZ G,WHEELER D A,et al.Somatic mutations affect key pathways in lung adenocarcinoma [J].Nature,2008,455(7216):1069-1075. [6]DEES N D,ZHANG Q,KANDOTH C,et al.MuSiC:Identifying mutational significance in cancer genomes [J].Genome Research,2012,22(8):1589-1598. [7]HAHNAHN W C,WEINBERG R A.Modelling the molecular circuitry of cancer[J].Nature Reviews Cancer,2002,2(5):331-341. [8]BOCA S M,KINZLER K W,VELCULESCU V E,et al.Patientoriented gene set analysis for cancer mutation data [J].Genome Biology,2010,11(11):R112. [9]ZHANG J,ZHANG S.The Discovery of Mutated Driver Pathways in Cancer:Models and Algorithms [J].IEEE/ACM Transactions on Computational Biology & Bioinformatics,2018,15(3):988-998. [10]VANDING F,UPFAL E,RAPHAEL B J.De novo discovery of mutated driver pathways in cancer [J].Genome Research,2012,22(2):375-385. [11] YEANG C H,MCCORMICK F,LEVINE A.Combinatorial patterns of somatic gene mutations in cancer [J].Faseb Journal,2008,22(8):2605-2622. [12]ZHAO J,ZHANG S,WU L Y,et al.Efficient methods for identifying mutated driver pathways in cancer [J].Bioinformatics,2012,28(22):2940-2947. [13]ZHANG J,ZHANG S,WANG Y,et al.Identification of mutated core cancer modules by integrating somatic mutation,copy number variation,and gene expression data [J].Bmc Systems Biology,2013,7(S2):S4. [14]LEISERSON M D,BLOKH D,SHARAN R,et al.Simultaneous identification of multiple driver pathways in cancer [J].PLoS Comput Biol,2013,9(5):e1003054. [15]THE CANCER GENOME ATLAS RESEARCH NETWORK.Integrated genomic analyses of ovarian carcinoma [J].Nature,2011,474(7353):609-615. [16]KEGG(Release86.1)[OL].https://www.kegg.jp/kegg-bin/show_pathway?query=RB&map=map05200&scale=1.0&show_description=hide. [17]KEGG(Release86.1)[OL].http://www.kegg.jp/dbget-bin/www_bget?map04115. [18]WARREN R S,ATREYA C E,NIEDZWIECKI D,et al.Association of TP53 mutational status and gender with survival after adjuvant treatment for stage III colon cancer:results of CALGB 89803 [J].Clinical Cancer Research An Official Journal of the American Association for Cancer Research,2013,19(20):5777-5787. [19]KEGG(Release86.1)[OL].http://www.genome.jp/dbget-bin/www_bget?pathway:map04151. [20]MCLENDON R,FRIEDMAN A,BIGNER D,et al.Comprehensive genomic characterization defines human glioblastoma genes and core pathways [J].Nature,2008,455(7216):1061-1068. [21]KEGG(Release86.1)[OL].http://www.kegg.jp/dbget-bin/www_bget?map04110. [22]NAKAYAMA N,NAKAYAMA K,SHAMIMA Y,et al.Gene amplification CCNE1 is related to poor survival and potential therapeutic target in ovarian cancer [J].Cancer,2010,116(11):2621. [23]ENGLER D A,GUPTA S,GROWDON W B,et al.GenomeWide DNA Copy Number Analysis of Serous Type Ovarian Carcinomas Identifies Genetic Markers Predictive of Clinical Outcome [J].Plos One,2012,7(2):e30996. [24]KEGG(Release86.1)[OL].http://www.kegg.jp/dbget-bin/www_bget?map04261. [25]JIN Y,MERTENS F,KULLENDORFF C M,et al.Fusion of the Tumor-Suppressor Gene CHEK2 and the Gene for the Regulatory Subunit B of Protein Phosphatase 2 PPP2R2A in Childhood Teratoma [J].Neoplasia,2006,8(5):413-418. [26]BARATTA M G,SCHINZEL A C,ZWANG Y,et al.An in-tumor genetic screen reveals that the BET bromodomain protein,BRD4,is a potential therapeutic target in ovarian carcinoma [J].Proceedings of the National Academy of Sciences of the United States of America,2015,112(1):232. [27]KEGG(Release86.1)[OL].http://www.genome.jp/dbget-bin/www_bget?pathway:map04371. [28]RICCIARDELLI C,OEHLER M K.Diverse molecular pathways in ovarian cancer and their clinical significance [J].Maturitas,2009,62(3):270-275. |
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