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Introduction: Cancer Gene Networks

  • Robert ClarkeEmail author
Protocol
Part of the Methods in Molecular Biology book series (MIMB, volume 1513)

Abstract

Constructing, evaluating, and interpreting gene networks generally sits within the broader field of systems biology, which continues to emerge rapidly, particular with respect to its application to understanding the complexity of signaling in the context of cancer biology. For the purposes of this volume, we take a broad definition of systems biology. Considering an organism or disease within an organism as a system, systems biology is the study of the integrated and coordinated interactions of the network(s) of genes, their variants both natural and mutated (e.g., polymorphisms, rearrangements, alternate splicing, mutations), their proteins and isoforms, and the organic and inorganic molecules with which they interact, to execute the biochemical reactions (e.g., as enzymes, substrates, products) that reflect the function of that system. Central to systems biology, and perhaps the only approach that can effectively manage the complexity of such systems, is the building of quantitative multiscale predictive models. The predictions of the models can vary substantially depending on the nature of the model and its inputoutput relationships. For example, a model may predict the outcome of a specific molecular reaction(s), a cellular phenotype (e.g., alive, dead, growth arrest, proliferation, and motility), a change in the respective prevalence of cell or subpopulations, a patient or patient subgroup outcome(s). Such models necessarily require computers. Computational modeling can be thought of as using machine learning and related tools to integrate the very high dimensional data generated from modern, high throughput omics technologies including genomics (next generation sequencing), transcriptomics (gene expression microarrays; RNAseq), metabolomics and proteomics (ultra high performance liquid chromatography, mass spectrometry), and “subomic” technologies to study the kinome, methylome, and others. Mathematical modeling can be thought of as the use of ordinary differential equations and related tools to create dynamic, semi-mechanistic models of low dimensional data including gene/protein signaling as a function of time/dose. More recently, the integration of imaging technologies into predictive multiscale modeling has begun to extend further the scales across which data can be obtained and used to gain insight into system function.

There are several goals for predictive multiscale modeling including the more academic pursuit of understanding how the system or local feature thereof is regulated or functions, to the more practical or translational goals of identifying predictive (selecting which patient should receive which drug/therapy) or prognostic (disease progress and outcome in an individual patient) biomarkers and/or identifying network vulnerabilities that represent potential targets for therapeutic benefit with existing drugs (including drug repurposing) or for the development of new drugs. These various goals are not necessarily mutually exclusive or inclusive.

Within this volume, readers will find examples of many of the activities noted above. Each chapter contains practical and/or methodological insights to guide readers in the design and interpretation of their own and published work.

Key words

Systems biology Cancer gene networks Quantitative multiscale predictive models Computational modeling Mathematical modeling High throughput omics Subomics 

References

  1. 1.
    Lecca P, Re A (2016) Network-oriented approaches to anticancer drug response. Methods Mol Biol—Cancer Gene Netw 1513:101–115Google Scholar
  2. 2.
    Kim JY, Gatenby R (2016) Quantitative clinical imaging methods for monitoring intratumoral evolution. Methods Mol Biol—Cancer Gene Netw 1513:61–79Google Scholar
  3. 3.
    Verma N, Zhu Z, Hangfu D (2016) CRISPR/Cas-mediated knockin in human pluripotent stem cells. Methods Mol Biol—Cancer Gene Netw 1513:119–139Google Scholar
  4. 4.
    Miller DFB, Yan P, Fang F et al (2016) Complete transcriptome RNA-Seq. Methods Mol Biol—Cancer Gene Netw 1513:141–162Google Scholar
  5. 5.
    Wang J, Ye Z, Huang TH et al (2016) Computational methods and correlation of exon-skipping events with splicing, transcription and epigenetic factors. Methods Mol Biol—Cancer Gene Netw 1513:163–170Google Scholar
  6. 6.
    Zhang W, Lee WY, Zilberberg J (2016) Tissue engineering platforms to replicate the tumor microenvironment of multiple myeloma. Methods Mol Biol—Cancer Gene Netw 1513:171–189Google Scholar
  7. 7.
    Ritchie W (2016) microRNA target prediction. Methods Mol Biol—Cancer Gene Netw 1513:193–199Google Scholar
  8. 8.
    Nguyen C, West GM, Geoghegan KF (2016) Emerging methods in chemoproteomics with relevance to drug discovery. Methods Mol Biol—Cancer Gene Netw 1513:11–21Google Scholar
  9. 9.
    Haznadar M, Mathé E (2016) Experimental and study design considerations in uncovering oncometabolites. Methods Mol Biol—Cancer Gene Netw 1513:37–46Google Scholar
  10. 10.
    Marschall AL, Zhang C, Dübel S (2016) Evaluating the delivery of proteins to the cytosol of mammalian cells. Methods Mol Biol—Cancer Gene Netw (this volume)Google Scholar
  11. 11.
    Jozwik C, Eidelman O, Starr J et al (2016) Validation of biomarker proteins using reverse capture protein microarrays. Methods Mol Biol—Cancer Gene Netw 1513:201–207Google Scholar
  12. 12.
    Clarke R, Cook KL, Hu R et al (2012) Endoplasmic reticulum stress, the unfolded protein response, autophagy, and the integrated regulation of breast cancer cell fate. Cancer Res 72:1321–1331CrossRefPubMedPubMedCentralGoogle Scholar
  13. 13.
    Higgins R, Gendron JM, Rising L et al (2015) The unfolded protein response triggers site-specific regulatory ubiquitylation of 40S ribosomal proteins. Mol Cell 59:35–49CrossRefPubMedPubMedCentralGoogle Scholar
  14. 14.
    Schwartz-Roberts JL, Cook KL, Chen C et al (2015) Interferon regulatory factor-1 signaling regulates the switch between autophagy and apoptosis to determine breast cancer cell fate. Cancer Res 75:1046–1055CrossRefPubMedPubMedCentralGoogle Scholar
  15. 15.
    Cook KL, Wärri A, Soto-Pantoja DR et al (2014) Chloroquine inhibits autophagy to potentiate antiestrogen responsiveness in ER+ breast cancer. Clin Cancer Res 20:3222–3232CrossRefPubMedPubMedCentralGoogle Scholar
  16. 16.
    Clarke R, Tyson JJ, Dixon JM (2015) Endocrine resistance in breast cancer—an overview and update. Mol Cell Endocrinol (in press)Google Scholar
  17. 17.
    Mooneyham A, Bazzaro M (2016) Targeting deubiquitinating enzymes and autophagy in cancer. Methods Mol Biol—Cancer Gene Netw 1513:49–57Google Scholar
  18. 18.
    Li G, Yuan L, Zhuang Z (2016) Chemical synthesis of activity-based diubiquitin probes. Methods Mol Biol—Cancer Gene Netw 1513:223–232Google Scholar
  19. 19.
    Gomez BP, Riggins R, Shajahan AN et al (2007) Human X-Box binding protein-1 confers both estrogen independence and antiestrogen resistance in breast cancer cell lines. FASEB J 21:4013–4027CrossRefPubMedGoogle Scholar
  20. 20.
    Davies MP, Barraclough DL, Stewart C et al (2008) Expression and splicing of the unfolded protein response gene XBP-1 are significantly associated with clinical outcome of endocrine-treated breast cancer. Int J Cancer 123:85–88CrossRefPubMedGoogle Scholar
  21. 21.
    Hu R, Warri A, Jin L et al (2015) NFkappaB signaling is required for XBP1 (U and S) mediated effects on antiestrogen responsiveness and cell fate decisions in breast cancer. Mol Cell Biol 35:379–390CrossRefPubMedGoogle Scholar
  22. 22.
    Feldman HC, Maly DJ (2016) Profiling the dual enzymatic activities of the serine/threonine kinase IRE1a. Methods Mol Biol—Cancer Gene Netw 1513:233–241Google Scholar
  23. 23.
    Leighton X, Eidelman O, Jozwik C et al (2016) ANXA7-GTPase as tumor suppressor: mechanism and therapeutic opportunities. Methods Mol Biol—Cancer Gene Netw 1513:23–33Google Scholar
  24. 24.
    Day TF, Mewani RR, Starr J et al (2016) Transcriptome and proteome analyses of TNFAIP8 knockdown in cancer cells reveal new insights into molecular determinants of cell survival and tumor progression. Methods Mol Biol—Cancer Gene Netw 1513:83–99Google Scholar

Copyright information

© Springer Science+Business Media New York 2017

Authors and Affiliations

  1. 1.Department of OncologyGeorgetown Lombardi Comprehensive Cancer CenterWashingtonUSA

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