Deciphering oncogenic drivers: from single genes to integrated pathways

Technological advances in next-generation sequencing have uncovered a wide spectrum of aberrations in cancer genomes. The extreme diversity in cancer mutations necessitates computational approaches to differentiate between the 'drivers' with vital function in cancer progression and those nonfunctional 'passengers'. Although individual driver mutations are routinely identified, mutational profiles of different tumors are highly heterogeneous. There is growing consensus that pathways rather than single genes are the primary target of mutations. Here we review extant bioinformatics approaches to identifying oncogenic drivers at different mutational levels, highlighting the strategies for discovering driver pathways and networks from cancer mutation data. These approaches will help reduce the mutation complexity, thus providing a simplified picture of cancer.

[1]  Steven A. Roberts,et al.  Mutational heterogeneity in cancer and the search for new cancer-associated genes , 2013 .

[2]  Christopher A. Miller,et al.  Discovering functional modules by identifying recurrent and mutually exclusive mutational patterns in tumors , 2011, BMC Medical Genomics.

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

[4]  Alfonso Valencia,et al.  EnrichNet: network-based gene set enrichment analysis , 2012, Bioinform..

[5]  Eli Upfal,et al.  Algorithms for Detecting Significantly Mutated Pathways in Cancer , 2010, RECOMB.

[6]  Trevor J Pugh,et al.  Initial genome sequencing and analysis of multiple myeloma , 2011, Nature.

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

[8]  Michael Krawczak,et al.  Where genotype is not predictive of phenotype: towards an understanding of the molecular basis of reduced penetrance in human inherited disease , 2013, Human Genetics.

[9]  Stefan Fröhling,et al.  Identification of driver and passenger mutations of FLT3 by high-throughput DNA sequence analysis and functional assessment of candidate alleles. , 2007, Cancer cell.

[10]  F. McCormick,et al.  Signalling networks that cause cancer. , 1999, Trends in cell biology.

[11]  A. Valencia,et al.  From cancer genomes to cancer models: bridging the gaps , 2009, EMBO reports.

[12]  A. Gonzalez-Perez,et al.  Improving the prediction of the functional impact of cancer mutations by baseline tolerance transformation , 2012, Genome Medicine.

[13]  David Haussler,et al.  Inference of patient-specific pathway activities from multi-dimensional cancer genomics data using PARADIGM , 2010, Bioinform..

[14]  Eli Upfal,et al.  De Novo Discovery of Mutated Driver Pathways in Cancer , 2011, RECOMB.

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

[16]  Leyla Isik,et al.  Cancer-specific high-throughput annotation of somatic mutations: computational prediction of driver missense mutations. , 2009, Cancer research.

[17]  Brian H. Dunford-Shore,et al.  Somatic mutations affect key pathways in lung adenocarcinoma , 2008, Nature.

[18]  D. Hanahan,et al.  Hallmarks of Cancer: The Next Generation , 2011, Cell.

[19]  Hiroyuki Ogata,et al.  KEGG: Kyoto Encyclopedia of Genes and Genomes , 1999, Nucleic Acids Res..

[20]  P. Ng,et al.  Predicting the effects of frameshifting indels , 2012, Genome Biology.

[21]  Yuedong Yang,et al.  DDIG-in: discriminating between disease-associated and neutral non-frameshifting micro-indels , 2013, Genome Biology.

[22]  K. Kinzler,et al.  Cancer genes and the pathways they control , 2004, Nature Medicine.

[23]  Gary D Bader,et al.  Computational approaches to identify functional genetic variants in cancer genomes , 2013, Nature Methods.

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

[25]  Joshua F. McMichael,et al.  Genome Remodeling in a Basal-like Breast Cancer Metastasis and Xenograft , 2010, Nature.

[26]  Atul J. Butte,et al.  Ten Years of Pathway Analysis: Current Approaches and Outstanding Challenges , 2012, PLoS Comput. Biol..

[27]  C. Sander,et al.  Automated Network Analysis Identifies Core Pathways in Glioblastoma , 2010, PloS one.

[28]  P. Stenson,et al.  The Human Gene Mutation Database: 2008 update , 2009, Genome Medicine.

[29]  David Haussler,et al.  Discovering causal pathways linking genomic events to transcriptional states using Tied Diffusion Through Interacting Events (TieDIE) , 2013, Bioinform..

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

[31]  Joe W. Gray,et al.  Translating insights from the cancer genome into clinical practice , 2008, Nature.

[32]  Gary D Bader,et al.  Systematic analysis of somatic mutations in phosphorylation signaling predicts novel cancer drivers , 2013 .

[33]  Bairong Shen,et al.  Post genome-wide association studies functional characterization of prostate cancer risk loci , 2013, BMC Genomics.

[34]  S. Henikoff,et al.  Predicting deleterious amino acid substitutions. , 2001, Genome research.

[35]  Anaïs Mottaz,et al.  Bioinformatics Applications Note Databases and Ontologies Easy Retrieval of Single Amino-acid Polymorphisms and Phenotype Information Using Swissvar , 2022 .

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

[37]  P. Ng,et al.  SIFT Indel: Predictions for the Functional Effects of Amino Acid Insertions/Deletions in Proteins , 2013, PloS one.

[38]  B. Peters,et al.  Distinguishing cancer-associated missense mutations from common polymorphisms. , 2007, Cancer research.

[39]  G. Parmigiani,et al.  Core Signaling Pathways in Human Pancreatic Cancers Revealed by Global Genomic Analyses , 2008, Science.

[40]  P. Bork,et al.  A method and server for predicting damaging missense mutations , 2010, Nature Methods.

[41]  C. Sander,et al.  Predicting the functional impact of protein mutations: application to cancer genomics , 2011, Nucleic acids research.

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

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

[44]  Kenneth H. Buetow,et al.  PID: the Pathway Interaction Database , 2008, Nucleic Acids Res..

[45]  Tao Xie,et al.  Inferring causal genomic alterations in breast cancer using gene expression data , 2011, BMC Systems Biology.

[46]  Kenneth H. Buetow,et al.  Large-scale analysis of non-synonymous coding region single nucleotide polymorphisms , 2004, Bioinform..

[47]  Yin Li,et al.  Identifying novel glioma associated pathways based on systems biology level meta-analysis , 2013, BMC Systems Biology.

[48]  N. Schork,et al.  Identification of rare cancer driver mutations by network reconstruction. , 2009, Genome research.

[49]  D. Hanahan,et al.  The Hallmarks of Cancer , 2000, Cell.

[50]  Laurent Farinelli,et al.  Impact of replication timing on non-CpG and CpG substitution rates in mammalian genomes. , 2010, Genome research.

[51]  C. Yeang,et al.  Combinatorial patterns of somatic gene mutations in cancer , 2008, FASEB journal : official publication of the Federation of American Societies for Experimental Biology.

[52]  P. A. Futreal,et al.  Intratumor heterogeneity and branched evolution revealed by multiregion sequencing. , 2012, The New England journal of medicine.

[53]  A. McKenna,et al.  The Mutational Landscape of Head and Neck Squamous Cell Carcinoma , 2011, Science.

[54]  Deanna M. Church,et al.  ClinVar: public archive of relationships among sequence variation and human phenotype , 2013, Nucleic Acids Res..

[55]  J. Hicks,et al.  Insight into the heterogeneity of breast cancer through next-generation sequencing. , 2011, The Journal of clinical investigation.

[56]  Jae K. Lee,et al.  Utilizing the molecular gateway: the path to personalized cancer management. , 2009, Clinical chemistry.

[57]  A. Nicholson,et al.  Mutations of the BRAF gene in human cancer , 2002, Nature.

[58]  R. Hruban,et al.  Prioritization of driver mutations in pancreatic cancer using cancer-specific high-throughput annotation of somatic mutations (CHASM) , 2010, Cancer biology & therapy.

[59]  Dana Pe'er,et al.  JISTIC: Identification of Significant Targets in Cancer , 2010, BMC Bioinformatics.

[60]  M. Stratton,et al.  Statistical Analysis of Pathogenicity of Somatic Mutations in Cancer , 2006, Genetics.

[61]  E. Lander,et al.  Assessing the significance of chromosomal aberrations in cancer: Methodology and application to glioma , 2007, Proceedings of the National Academy of Sciences.

[62]  Christopher D. Brown,et al.  Rapid growth of a hepatocellular carcinoma and the driving mutations revealed by cell-population genetic analysis of whole-genome data , 2011, Proceedings of the National Academy of Sciences.

[63]  Elaine R. Mardis,et al.  A decade’s perspective on DNA sequencing technology , 2011, Nature.

[64]  Benjamin J. Raphael,et al.  Identifying driver mutations in sequenced cancer genomes: computational approaches to enable precision medicine , 2014, Genome Medicine.

[65]  Xiaobo Zhou,et al.  A novel missense-mutation-related feature extraction scheme for 'driver' mutation identification , 2012, Bioinform..

[66]  Kei-Hoi Cheung,et al.  A graph theoretic approach to utilizing protein structure to identify non-random somatic mutations , 2013, BMC Bioinformatics.

[67]  Derek Y. Chiang,et al.  The landscape of somatic copy-number alteration across human cancers , 2010, Nature.

[68]  Ling Lin,et al.  PathScan: a tool for discerning mutational significance in groups of putative cancer genes , 2011, Bioinform..

[69]  Alan F. Scott,et al.  McKusick's Online Mendelian Inheritance in Man (OMIM®) , 2008, Nucleic Acids Res..

[70]  Jiajia Chen,et al.  Identification of novel microRNA regulatory pathways associated with heterogeneous prostate cancer , 2013, BMC Systems Biology.

[71]  Lincoln Stein,et al.  Reactome: a knowledgebase of biological pathways , 2004, Nucleic Acids Res..

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

[73]  Yan Zhang,et al.  CanPredict: a computational tool for predicting cancer-associated missense mutations , 2007, Nucleic Acids Res..

[74]  A. Sparks,et al.  The Genomic Landscapes of Human Breast and Colorectal Cancers , 2007, Science.

[75]  Allan Balmain,et al.  Mutually exclusive mutations of the Pten and ras pathways in skin tumor progression. , 2004, Genes & development.

[76]  J. Tchinda,et al.  Recurrent fusion of TMPRSS2 and ETS transcription factor genes in prostate cancer. , 2006, Science.

[77]  G. Parmigiani,et al.  The Consensus Coding Sequences of Human Breast and Colorectal Cancers , 2006, Science.

[78]  G. Getz,et al.  GISTIC2.0 facilitates sensitive and confident localization of the targets of focal somatic copy-number alteration in human cancers , 2011, Genome Biology.

[79]  Roded Sharan,et al.  Simultaneous Identification of Multiple Driver Pathways in Cancer , 2013, PLoS Comput. Biol..

[80]  J. Miller,et al.  Predicting the Functional Effect of Amino Acid Substitutions and Indels , 2012, PloS one.

[81]  G. Parmigiani,et al.  Integrated analysis of homozygous deletions, focal amplifications, and sequence alterations in breast and colorectal cancers , 2008, Proceedings of the National Academy of Sciences.

[82]  N. Schork,et al.  Prediction of cancer driver mutations in protein kinases. , 2008, Cancer research.

[83]  D. Pe’er,et al.  An Integrated Approach to Uncover Drivers of Cancer , 2010, Cell.