Cancer systems biology in the genome sequencing era: part 2, evolutionary dynamics of tumor clonal networks and drug resistance.

A tumor often consists of multiple cell subpopulations (clones). Current chemo-treatments often target one clone of a tumor. Although the drug kills that clone, other clones overtake it and the tumor recurs. Genome sequencing and computational analysis allows to computational dissection of clones from tumors, while singe-cell genome sequencing including RNA-Seq allows profiling of these clones. This opens a new window for treating a tumor as a system in which clones are evolving. Future cancer systems biology studies should consider a tumor as an evolving system with multiple clones. Therefore, topics discussed in Part 2 of this review include evolutionary dynamics of clonal networks, early-warning signals (e.g., genome duplication events) for formation of fast-growing clones, dissecting tumor heterogeneity, and modeling of clone-clone-stroma interactions for drug resistance. The ultimate goal of the future systems biology analysis is to obtain a 'whole-system' understanding of a tumor and therefore provides a more efficient and personalized management strategies for cancer patients.

[1]  Ruth Nussinov,et al.  Structure and dynamics of molecular networks: A novel paradigm of drug discovery. A comprehensive review , 2012, Pharmacology & therapeutics.

[2]  Q. Cui,et al.  Identification of high-quality cancer prognostic markers and metastasis network modules , 2010, Nature communications.

[3]  Tyler J Moss,et al.  The glucose-deprivation network counteracts lapatinib-induced toxicity in resistant ErbB2-positive breast cancer cells , 2012, Molecular systems biology.

[4]  Antonio Reverter,et al.  A Boolean-based systems biology approach to predict novel genes associated with cancer: Application to colorectal cancer , 2011, BMC Systems Biology.

[5]  H. Ohtsuki,et al.  Accumulation of driver and passenger mutations during tumor progression , 2009, Proceedings of the National Academy of Sciences.

[6]  Torbjörn E. M. Nordling,et al.  Network modeling of the transcriptional effects of copy number aberrations in glioblastoma , 2011, Molecular systems biology.

[7]  A. Arkin,et al.  Sequestration-based bistability enables tuning of the switching boundaries and design of a latch , 2012, Molecular systems biology.

[8]  Orkun S. Soyer,et al.  Bistability in feedback circuits as a byproduct of evolution of evolvability , 2012, Molecular systems biology.

[9]  Otso Ovaskainen,et al.  Transient dynamics in metapopulation response to perturbation. , 2002, Theoretical population biology.

[10]  Simo V. Zhang,et al.  A map of human cancer signaling , 2007, Molecular systems biology.

[11]  E. Wang,et al.  Dynamic modeling and analysis of cancer cellular network motifs. , 2011, Integrative biology : quantitative biosciences from nano to macro.

[12]  Sheila M. Reynolds,et al.  Integrated analyses identify a master microRNA regulatory network for the mesenchymal subtype in serous ovarian cancer. , 2013, Cancer cell.

[13]  Mario Albrecht,et al.  NetworkPrioritizer: a versatile tool for network-based prioritization of candidate disease genes or other molecules , 2013, Bioinform..

[14]  David Warde-Farley,et al.  Dynamic modularity in protein interaction networks predicts breast cancer outcome , 2009, Nature Biotechnology.

[15]  Nir Friedman,et al.  A functional selection model explains evolutionary robustness despite plasticity in regulatory networks , 2012 .

[16]  Edwin Wang,et al.  Cancer systems biology in the genome sequencing era: part 1, dissecting and modeling of tumor clones and their networks. , 2013, Seminars in cancer biology.

[17]  C. Perou,et al.  Allele-specific copy number analysis of tumors , 2010, Proceedings of the National Academy of Sciences.

[18]  Tao Zeng,et al.  Reconstructing dynamic gene regulatory networks from sample-based transcriptional data , 2012, Nucleic acids research.

[19]  E. Wang,et al.  Genetic studies of diseases , 2007, Cellular and Molecular Life Sciences.

[20]  J. Timmer,et al.  Division of labor by dual feedback regulators controls JAK2/STAT5 signaling over broad ligand range , 2011, Molecular systems biology.

[21]  A. McKenna,et al.  Evolution and Impact of Subclonal Mutations in Chronic Lymphocytic Leukemia , 2012, Cell.

[22]  Edwin Wang,et al.  Understanding genomic alterations in cancer genomes using an integrative network approach. , 2013, Cancer letters.

[23]  Teresa M. Przytycka,et al.  Identifying Causal Genes and Dysregulated Pathways in Complex Diseases , 2011, PLoS Comput. Biol..

[24]  Kazuyuki Aihara,et al.  Identifying critical transitions and their leading biomolecular networks in complex diseases , 2012, Scientific Reports.

[25]  Steven J. M. Jones,et al.  Comprehensive molecular portraits of human breast tumours , 2013 .

[26]  Ron Shamir,et al.  Minimally perturbing a gene regulatory network to avoid a disease phenotype: the glioma network as a test case , 2010, BMC Systems Biology.

[27]  Michael Schroeder,et al.  Google Goes Cancer: Improving Outcome Prediction for Cancer Patients by Network-Based Ranking of Marker Genes , 2012, PLoS Comput. Biol..

[28]  Juan Wang,et al.  microRNA evolution in a human transcription factor and microRNA regulatory network , 2010, BMC Systems Biology.

[29]  C. Sander,et al.  Models from experiments: combinatorial drug perturbations of cancer cells , 2008, Molecular systems biology.

[30]  R. Sharan,et al.  INDI: a computational framework for inferring drug interactions and their associated recommendations , 2012, Molecular systems biology.

[31]  T. Ideker,et al.  Network-based classification of breast cancer metastasis , 2007, Molecular systems biology.

[32]  Q. Cui,et al.  Principles of microRNA regulation of a human cellular signaling network , 2006, Molecular systems biology.

[33]  M. Moran,et al.  The human phosphotyrosine signaling network: Evolution and hotspots of hijacking in cancer , 2012, Genome research.

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

[35]  Edwin Wang,et al.  Self-organization of gene regulatory network motifs enriched with short transcript's half-life transcription factors , 2005, q-bio/0504025.

[36]  J. Xiong,et al.  Pre-Clinical Drug Prioritization via Prognosis-Guided Genetic Interaction Networks , 2010, PloS one.

[37]  S. Carpenter,et al.  Early-warning signals for critical transitions , 2009, Nature.

[38]  Steven J. M. Jones,et al.  Comprehensive molecular portraits of human breast tumors , 2012, Nature.

[39]  Peer Bork,et al.  Deciphering a global network of functionally associated post-translational modifications , 2012, Molecular systems biology.

[40]  Q. Cui,et al.  Regulatory network motifs and hotspots of cancer genes in a mammalian cellular signalling network. , 2007, IET systems biology.

[41]  Edwin Wang.  Cancer Systems Biology , 2010 .

[42]  Edwin Wang,et al.  Signaling network analysis of ubiquitin-mediated proteins suggests correlations between the 26S proteasome and tumor progression. , 2009, Molecular bioSystems.

[43]  A. Børresen-Dale,et al.  The Life History of 21 Breast Cancers , 2012, Cell.

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

[45]  Sebastian M. Armasu,et al.  Epigenetic analysis leads to identification of HNF1B as a subtype-specific susceptibility gene for ovarian cancer , 2013, Nature Communications.

[46]  Jeffrey T. Chang,et al.  A pharmacogenomic method for individualized prediction of drug sensitivity , 2011, Molecular systems biology.

[47]  Edwin Wang,et al.  Dynamic rewiring of the androgen receptor protein interaction network correlates with prostate cancer clinical outcomes. , 2011, Integrative biology : quantitative biosciences from nano to macro.

[48]  Edwin Wang,et al.  Protein evolution on a human signaling network , 2009, BMC Systems Biology.

[49]  Manu Setty,et al.  Inferring transcriptional and microRNA-mediated regulatory programs in glioblastoma , 2012, Molecular systems biology.

[50]  Chenguang Wang,et al.  Network analysis of genomic alteration profiles reveals co-altered functional modules and driver genes for glioblastoma. , 2013, Molecular bioSystems.

[51]  Shahab Asgharzadeh,et al.  TARGETgene: A Tool for Identification of Potential Therapeutic Targets in Cancer , 2012, PloS one.

[52]  J. Ferrell,et al.  Bistability in the JNK cascade , 2001, Current Biology.

[53]  Mariano J. Alvarez,et al.  A human B-cell interactome identifies MYB and FOXM1 as master regulators of proliferation in germinal centers , 2010, Molecular systems biology.

[54]  J. Dopazo,et al.  Discovering the hidden sub-network component in a ranked list of genes or proteins derived from genomic experiments , 2012, Nucleic acids research.

[55]  Ido Amit,et al.  Evolvable signaling networks of receptor tyrosine kinases: relevance of robustness to malignancy and to cancer therapy , 2007, Molecular systems biology.

[56]  A. McKenna,et al.  Absolute quantification of somatic DNA alterations in human cancer , 2012, Nature Biotechnology.

[57]  Bertram Klinger,et al.  Reverse engineering a hierarchical regulatory network downstream of oncogenic KRAS , 2012, Molecular systems biology.

[58]  T. Golub,et al.  Tumour micro-environment elicits innate resistance to RAF inhibitors through HGF secretion , 2012, Nature.

[59]  P. Pharoah,et al.  Public health implications from COGS and potential for risk stratification and screening , 2013, Nature Genetics.

[60]  Haiyuan Yu,et al.  Three-dimensional reconstruction of protein networks provides insight into human genetic disease , 2012, Nature Biotechnology.

[61]  Hiroaki Kitano,et al.  Large-Scale Analysis of Network Bistability for Human Cancers , 2010, PLoS Comput. Biol..

[62]  Stefan Wiemann,et al.  Global microRNA level regulation of EGFR-driven cell-cycle protein network in breast cancer , 2012, Molecular systems biology.

[63]  Marten Scheffer,et al.  Slow Recovery from Perturbations as a Generic Indicator of a Nearby Catastrophic Shift , 2007, The American Naturalist.

[64]  Nils Blüthgen,et al.  Strong negative feedback from Erk to Raf confers robustness to MAPK signalling , 2011, Molecular systems biology.

[65]  Chris Wiggins,et al.  ARACNE: An Algorithm for the Reconstruction of Gene Regulatory Networks in a Mammalian Cellular Context , 2004, BMC Bioinformatics.

[66]  Steven J. M. Jones,et al.  Comprehensive genomic characterization of squamous cell lung cancers , 2012, Nature.

[67]  Diogo M. Camacho,et al.  Wisdom of crowds for robust gene network inference , 2012, Nature Methods.

[68]  P. Bork,et al.  Systematic identification of proteins that elicit drug side effects , 2013, Molecular systems biology.

[69]  Matthias Mann,et al.  Proteomic snapshot of the EGF-induced ubiquitin network , 2011, Molecular systems biology.

[70]  Jane Fridlyand,et al.  Widespread potential for growth-factor-driven resistance to anticancer kinase inhibitors , 2012, Nature.