Discovering potential cancer driver genes by an integrated network-based approach.

Although a lot of methods have been proposed to identify driver genes, how to separate the driver mutations from the passenger mutations is still a challenging problem in cancer genomics. The detection of driver genes with rare mutation and low accuracy is unsolved better. In this study, we present an integrated network-based approach to locate potential driver genes in a cohort of patients. The approach is composed of two steps including a network diffusion step and an aggregated ranking step, which fuses the correlation between the gene mutations and gene expression, the relationship between the mutated genes and the heterogeneous characteristic of the patient mutation. We analyze three cancer datasets including Glioblastoma multiforme, Ovarian cancer and Breast cancer. Our method has not only identified the known driver genes with high-frequency mutations, but also discovered the potential driver genes with a rare mutation. At the same time, validation by literature search and functional enrichment analysis reveal that the predicted genes are obviously related to these three kinds of cancers.

[1]  Susumu Goto,et al.  KEGG: Kyoto Encyclopedia of Genes and Genomes , 2000, Nucleic Acids Res..

[2]  Gary D Bader,et al.  International network of cancer genome projects , 2010, Nature.

[3]  B. Park,et al.  Mutation of the PIK3CA oncogene in human cancers , 2006, British Journal of Cancer.

[4]  Sophie Ahrens,et al.  Recommender Systems , 2012 .

[5]  Paul Resnick,et al.  Recommender systems , 1997, CACM.

[6]  Li Wang,et al.  IndividualizedPath: identifying genetic alterations contributing to the dysfunctional pathways in glioblastoma individuals. , 2014, Molecular bioSystems.

[7]  Benjamin J. Raphael,et al.  Integrated Genomic Analyses of Ovarian Carcinoma , 2011, Nature.

[8]  Holger Moch,et al.  Combined array comparative genomic hybridization and tissue microarray analysis suggest PAK1 at 11q13.5-q14 as a critical oncogene target in ovarian carcinoma. , 2003, The American journal of pathology.

[9]  Teresa M. Przytycka,et al.  MEMCover: integrated analysis of mutual exclusivity and functional network reveals dysregulated pathways across multiple cancer types , 2015, Bioinform..

[10]  Yi-Cheng Zhang,et al.  Collaborative filtering with diffusion-based similarity on tripartite graphs , 2009, ArXiv.

[11]  Mariella G. Filbin,et al.  Coordinate activation of Shh and PI3K signaling in PTEN-deficient glioblastoma: new therapeutic opportunities , 2013, Nature Medicine.

[12]  Gary D Bader,et al.  Systematic analysis of somatic mutations impacting gene expression in 12 tumour types , 2015, Nature Communications.

[13]  Shi-Hua Zhang,et al.  Efficient methods for identifying mutated driver pathways in cancer , 2012, Bioinform..

[14]  W. Park,et al.  Somatic mutations of CASP3 gene in human cancers , 2004, Human Genetics.

[15]  Wei Zeng,et al.  Uncovering the information core in recommender systems , 2014, Scientific Reports.

[16]  D. Haber,et al.  Cancer: Drivers and passengers , 2007, Nature.

[17]  David L. Masica,et al.  Correlation of somatic mutation and expression identifies genes important in human glioblastoma progression and survival. , 2011, Cancer research.

[18]  A. Sivachenko,et al.  Sequence analysis of mutations and translocations across breast cancer subtypes , 2012, Nature.

[19]  C. Sander,et al.  Systematic identification of cancer driving signaling pathways based on mutual exclusivity of genomic alterations , 2014, Genome Biology.

[20]  Kyusam Choi,et al.  Integrative analysis of copy number alteration and gene expression profiling in ovarian clear cell adenocarcinoma. , 2013, Cancer genetics.

[21]  Joshua M. Korn,et al.  Comprehensive genomic characterization defines human glioblastoma genes and core pathways , 2008, Nature.

[22]  Lior Rokach,et al.  Recommender Systems Handbook , 2010 .

[23]  Benjamin J. Raphael,et al.  De novo discovery of mutated driver pathways in cancer , 2011 .

[24]  C. Sander,et al.  Mutual exclusivity analysis identifies oncogenic network modules. , 2012, Genome research.

[25]  R. Reis,et al.  MGMT-independent temozolomide resistance in pediatric glioblastoma cells associated with a PI3-kinase-mediated HOX/stem cell gene signature. , 2010, Cancer research.

[26]  T. Nikaido,et al.  Up-Regulation of Small GTPases, RhoA and RhoC, Is Associated with Tumor Progression in Ovarian Carcinoma , 2003, Laboratory Investigation.

[27]  Steven J. M. Jones,et al.  Comprehensive molecular characterization of human colon and rectal cancer , 2012, Nature.

[28]  Brad T. Sherman,et al.  Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources , 2008, Nature Protocols.

[29]  A. Bashashati,et al.  DriverNet: uncovering the impact of somatic driver mutations on transcriptional networks in cancer , 2012, Genome Biology.

[30]  E. Lengyel.  Ovarian cancer development and metastasis. , 2010, The American journal of pathology.

[31]  M. Stratton,et al.  The cancer genome , 2009, Nature.

[32]  S. Conzen,et al.  Activation of the glucocorticoid receptor is associated with poor prognosis in estrogen receptor-negative breast cancer. , 2011, Cancer research.

[33]  Wei Huang,et al.  Integrative genome analysis reveals an oncomir/oncogene cluster regulating glioblastoma survivorship , 2010, Proceedings of the National Academy of Sciences.

[34]  Hiroyuki Konishi,et al.  The PIK3CA gene is mutated with high frequency in human breast cancers , 2004, Cancer biology & therapy.

[35]  E. Birney,et al.  Patterns of somatic mutation in human cancer genomes , 2007, Nature.

[36]  Francesca D. Ciccarelli,et al.  NCG 4.0: the network of cancer genes in the era of massive mutational screenings of cancer genomes , 2014, Database J. Biol. Databases Curation.

[37]  Wing H Wong,et al.  Expression profiling of serous low malignant potential, low-grade, and high-grade tumors of the ovary. , 2005, Cancer research.

[38]  Sven Laur,et al.  Robust rank aggregation for gene list integration and meta-analysis , 2012, Bioinform..

[39]  R. Kiessling,et al.  IFN-gamma protects short-term ovarian carcinoma cell lines from CTL lysis via a CD94/NKG2A-dependent mechanism. , 2002, The Journal of clinical investigation.

[40]  D. Busam,et al.  An Integrated Genomic Analysis of Human Glioblastoma Multiforme , 2008, Science.

[41]  Irmtraud M. Meyer,et al.  The clonal and mutational evolution spectrum of primary triple-negative breast cancers , 2012, Nature.

[42]  G. Bu,et al.  LRP6 overexpression defines a class of breast cancer subtype and is a target for therapy , 2010, Proceedings of the National Academy of Sciences.

[43]  Anil K Sood,et al.  Biological significance of focal adhesion kinase in ovarian cancer: role in migration and invasion. , 2004, The American journal of pathology.

[44]  Nan Qiao,et al.  CoCiter: An Efficient Tool to Infer Gene Function by Assessing the Significance of Literature Co-Citation , 2013, PloS one.

[45]  J. Ptak,et al.  High Frequency of Mutations of the PIK3CA Gene in Human Cancers , 2004, Science.

[46]  Jian-Guo Liu,et al.  Improved collaborative filtering algorithm via information transformation , 2007, 0712.3807.

[47]  Margaret Wrensch,et al.  Epidemiology and molecular pathology of glioma , 2006, Nature Clinical Practice Neurology.

[48]  Matthew B. Callaway,et al.  MuSiC: Identifying mutational significance in cancer genomes , 2012, Genome research.

[49]  Fan Zhang,et al.  Down-regulation of caspase 3 in breast cancer: a possible mechanism for chemoresistance , 2002, Oncogene.

[50]  Feng Tian,et al.  Evaluation and integration of cancer gene classifiers: identification and ranking of plausible drivers , 2015, Scientific Reports.

[51]  L. Stein,et al.  A human functional protein interaction network and its application to cancer data analysis , 2010, Genome Biology.

[52]  David Haussler,et al.  PARADIGM-SHIFT predicts the function of mutations in multiple cancers using pathway impact analysis , 2012, Bioinform..

[53]  S. Gabriel,et al.  Integrated genomic analysis identifies clinically relevant subtypes of glioblastoma characterized by abnormalities in PDGFRA, IDH1, EGFR, and NF1. , 2010, Cancer cell.

[54]  M. Ringnér,et al.  Impact of DNA amplification on gene expression patterns in breast cancer. , 2002, Cancer research.

[55]  J. P. Hou,et al.  DawnRank: discovering personalized driver genes in cancer , 2014, Genome Medicine.

[56]  Li Wang,et al.  Identifying core gene modules in glioblastoma based on multilayer factor-mediated dysfunctional regulatory networks through integrating multi-dimensional genomic data , 2015, Nucleic acids research.

[57]  A. Corradin,et al.  The side population of ovarian cancer cells is a primary target of IFN-alpha antitumor effects. , 2008, Cancer research.

[58]  T. Hubbard,et al.  A census of human cancer genes , 2004, Nature Reviews Cancer.

[59]  Steven A. Roberts,et al.  Mutational heterogeneity in cancer and the search for new cancer genes , 2014 .

[60]  Yi-Cheng Zhang,et al.  Personalized Recommendation via Integrated Diffusion on User-Item-Tag Tripartite Graphs , 2009, ArXiv.

[61]  Yi-Cheng Zhang,et al.  Bipartite network projection and personal recommendation. , 2007, Physical review. E, Statistical, nonlinear, and soft matter physics.

[62]  Richard Simon,et al.  Identifying cancer driver genes in tumor genome sequencing studies , 2011, Bioinform..