Cancer Systems Biology: Current Achievements in – Omics Data Analysis, Network Reconstruction and Mathematical Modelling

Abstract

Systems biology is a multidisciplinary methodology in which quantitative biological experimental data are dissected using mathematical modelling and other computational and network biology tools, aiming at understanding the structure, function and dynamical regulation of biochemical networks. Systems biology will play a major role in the future molecular and clinical oncology because (a) it can be used for the analysis of cancer‐relevant high‐throughput data, (b) it provides tools for the reconstruction of the large multilevel regulatory networks that govern critical cancer phenotypes and (c) it is necessary when investigating cancer‐relevant networks holding multiple overlapping nonlinear regulatory motifs like feedback and feedforward loops. Here, the value of the systems biology approach in handling cancer genomics and transcriptomics data, the reconstruction of cancer networks and the use of mathematical modelling in the elucidation of cancer networks as well as in the design of anticancer therapies are discusses. Some recent case studies as proof of principle are highlighted.

Key Concepts:

  • Systems biology is a multidisciplinary approach that involves the analysis of quantitative experimental data via mathematical modelling and computational and network biology.

  • Modelling of cancer‐related networks is necessary because these networks display multiple nonlinear regulatory motifs like feedback loops.

  • Mathematical models have been used to elucidate the structure, dynamics, dysregulation and crosstalk of cancer pathways like the JAK/STAT, p53, MAPK, NFkB and intrinsinc/extrinsic apoptosis signalling pathways, as well as in microRNA cancer regulation.

  • Multi‐scale mathematical models can accurately describe the spatial configuration of tumours, their crosstalk with the surrounding microenvironment and their role in physiological mechanisms like angiogenesis and immune system response.

  • Mathematical modelling has been used to assess and personalise conventional anticancer therapies, but it is also used in the detection of new anticancer drug targets and the development of combined or immune therapies.

  • Cancer bioinformatics facilitates efficient analyses and integration of diverse types of biomedical high‐throughput data for the elucidation of causes of tumour initiation, progression and metastasis but also for the management, storage and exchange of experimental and clinical data.

  • Cancer systems biology integrates algorithms, methodologies, software tools and web resources into customised analysis workflows to explore and understand the molecular mechanisms underlying this complex disease.

  • Data integration and analysis is used to identify novel robust and precise biomarkers, to identify network biomarkers and to design individual patient‐tailored therapeutic interventions.

Keywords: anticancer therapies; biochemical regulatory networks; feedback and feedforward loops; drug target detection; multi‐scale cancer modelling; miRNA regulation; cancer genomics; cancer transcriptomics; patient stratification

Figure 1.

Sketch of the standard workflow followed for mathematical modelling in cancer systems biology.

Figure 2.

Depicted is an exemplary workflow in cancer bioinformatics. It illustrates that the integration of a wide range of approaches and resources is necessary to better understand the molecular mechanisms underlying the complex disease of cancer as well as for the diagnosis, prognosis and the design of personalised therapies.

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References

Bachmann J, Raue A, Schilling M et al. (2011) Division of labor by dual feedback regulators controls JAK2/STAT5 signaling over broad ligand range. Molecular Systems Biology 7: 516.

Badis G, Berger MF, Philippakis AA et al. (2009) Diversity and complexity in DNA recognition by transcription factors. Science 324: 1720–1723. doi:10.1126/science.1162327.

Bartel DP (2004) MicroRNAs: genomics, biogenesis, mechanism, and function. Cell 116: 281–297.

Becker V, Schilling M, Bachmann J et al. (2010) Covering a broad dynamic range: information processing at the erythropoietin receptor. Science 328: 1404–1408.

Bielas JH, Loeb KR, Rubin BP, True LD, Loeb LA (2006) Human cancers express a mutator phenotype. Proceedings of the National Academy of Sciences of the USA 103: 18238–18242. doi:10.1073/pnas.0607057103.

El‐Metwally S, Hamza T, Zakaria M and Helmy M (2013) Next‐generation sequence assembly: four stages of data processing and computational challenges. PLoS Computational Biology 9: e1003345. doi:10.1371/journal.pcbi.1003345.

Engel C, Scholz M and Loeffler M (2004) A computational model of human granulopoiesis to simulate the hematotoxic effects of multicycle polychemotherapy. Blood 104: 2323–2331.

Erler JT and Linding R (2009) Network‐based drugs and biomarkers. Journal of Pathology. doi:10.1002/path.2646.

Esquela‐Kerscher A and Slack FJ (2006) Oncomirs – microRNAs with a role in cancer. Nature Reviews Cancer 6: 259–269. doi:10.1038/nrc1840.

Fitzgerald JB, Johnson BW, Baum J et al. (2014) MM‐141, an IGF‐1 R and ErbB3 directed bispecific antibody, overcomes network adaptations that limit activity of IGF‐1 R inhibitors. Molecular Cancer Therapeutics 13: 410–425.

Frieboes HB, Lowengrub JS, Wise S et al. (2007) Computer simulation of glioma growth and morphology. NeuroImage 37: S59–S70.

Golub TR, Slonim DK, Tamayo P et al. (1999) Molecular classification of cancer: class discovery and class prediction by gene expression monitoring. Science 286: 531–537.

González‐García I, Solé RV and Costa J (2002) Metapopulation dynamics and spatial heterogeneity in cancer. Proceedings of the National Academy of Sciences of the USA 99: 13085–13089.

Gonzalez‐Perez A, Deu‐Pons J and Lopez‐Bigas N (2012) Improving the prediction of the functional impact of cancer mutations by baseline tolerance transformation. Genome Medicine 4: 89. doi:10.1186/gm390.

Guebel DV, Schmitz U, Wolkenhauer O and Vera J (2012) Analysis of cell adhesion during early stages of colon cancer based on an extended multi‐valued logic approach. Molecular BioSystems 8: 1230–1242.

Gupta SK and Schmitz U (2011) Bioinformatics analysis of high‐throughput experiments. In: Singh MP, Agrawal A and Sharma B (eds) Recent Trends in Biotechnology, vol. 2, pp. 129–156. New York, NY: Nova Science Publishers.

Hector S, Rehm M, Schmid J et al. (2012) Clinical application of a systems model of apoptosis execution for the prediction of colorectal cancer therapy responses and personalisation of therapy. Gut 61: 725–733.

Huang DW, Sherman BT and Lempicki RA (2008) Bioinformatics enrichment tools: paths toward the comprehensive functional analysis of large gene lists. Nucleic Acids Research 37: 1–13. doi:10.1093/nar/gkn923.

Iadevaia S, Lu Y, Morales FC, Mills GB and Ram PT (2010) Identification of optimal drug combinations targeting cellular networks: integrating phospho‐proteomics and computational network analysis. Cancer Research 70: 6704–6714.

Janes KA, Albeck JG, Gaudet S et al. (2005) A systems model of signaling identifies a molecular basis set for cytokine‐induced apoptosis. Science 310: 1646–1653.

Kim D, Rath O, Kolch W and Cho K‐H (2007) A hidden oncogenic positive feedback loop caused by crosstalk between Wnt and ERK Pathways. Oncogene 26: 4571–4579.

Kirouac DC, Du JY, Lahdenranta J et al. (2013) Computational modeling of ERBB2‐amplified breast cancer identifies combined ErbB2/3 blockade as superior to the combination of MEK and AKT inhibitors. Science Signaling 6: ra68.

Klinger B, Sieber A, Fritsche‐Guenther R et al. (2013) Network quantification of EGFR signaling unveils potential for targeted combination therapy. Molecular Systems Biology 9: 673.

Lai X, Schmitz U, Gupta SK et al. (2012) Computational analysis of target hub gene repression regulated by multiple and cooperative miRNAs. Nucleic Acids Research 40: 8818–8834.

Lange F, Rateitschak K, Kossow C, Wolkenhauer O and Jaster R (2012) Insights into erlotinib action in pancreatic cancer cells using a combined experimental and mathematical approach. World Journal of Gastroenterology 18(43): 6226–6234.

Lindner AU, Concannon CG, Boukes GJ et al. (2013) Systems analysis of BCL2 protein family interactions establishes a model to predict responses to chemotherapy. Cancer Research 73: 519–528.

Marin‐Sanguino A, Gupta SK, Voit EO and Vera J (2011) Biochemical pathway modeling tools for drug target detection in cancer and other complex diseases. Methods in Enzymology 487: 319–369.

Nagl S (2005) Cancer Bioinformatics: From Therapy Design to Treatment. Chichester, England; Hoboken, NJ: John Wiley & Sons.

Powathil GG, Adamson DJA and Chaplain MAJ (2013) Towards predicting the response of a solid tumour to chemotherapy and radiotherapy treatments: clinical insights from a computational model. PLoS Computational Biology 9: e1003120.

Raia V, Schilling M, Böhm M et al. (2011) Dynamic mathematical modeling of IL13‐induced signaling in Hodgkin and primary mediastinal B‐cell lymphoma allows prediction of therapeutic targets. Cancer Research 71: 693–704.

Ramis‐Conde I, Drasdo D, Anderson ARA and Chaplain MAJ (2008) Modeling the influence of the E‐cadherin‐beta‐catenin pathway in cancer cell invasion: a multiscale approach. Biophysical Journal 95: 155–165.

Rateitschak K, Winter F, Lange F, Jaster R and Wolkenhauer O (2012) Parameter identifiability and sensitivity analysis predict targets for enhancement of STAT1 activity in pancreatic cancer and stellate cells. PLoS Computational Biology 8: e1002815.

Reiterer V, Fey D, Kolch W, Kholodenko BN and Farhan H (2013) Pseudophosphatase STYX modulates cell‐fate decisions and cell migration by spatiotemporal regulation of ERK1/2. Proceedings of the National Academy of Sciences of the USA 110: E2934–E2943.

Ren X, Wang Y, Wang J and Zhang X‐S (2012) A unified computational model for revealing and predicting subtle subtypes of cancers. BMC Bioinformatics 13: 70. doi:10.1186/1471‐2105‐13‐70.

Ribba B, Colin T and Schnell S (2006) A multiscale mathematical model of cancer, and its use in analyzing irradiation therapies. Theoretical Biology and Medical Modelling 3: 7.

Ritchie W, Rasko JEJ and Flamant S (2013) MicroRNA target prediction and validation. In: Schmitz U, Wolkenhauer O and Vera J (eds) MicroRNA Cancer Regulation, pp. 39–53. Netherlands, Dordrecht: Springer.

Schmid J, Dussmann H, Boukes GJ et al. (2012) Systems analysis of cancer cell heterogeneity in caspase‐dependent apoptosis subsequent to mitochondrial outer membrane permeabilization. Journal of Biological Chemistry 287: 41546–41559.

Schmitz U and Wolkenhauer O (2013) Web resources for microRNA research. In: Schmitz U, Wolkenhauer O and Vera J (eds) MicroRNA Cancer Regulation, pp. 225–250. Netherlands, Dordrecht: Springer.

Schmitz U, Wolkenhauer O and Vera J (2013) MicroRNA Cancer Regulation Advanced Concepts, Bioinformatics and Systems Biology Tools. Dordrecht: Springer.

Schoeberl B, Pace EA, Fitzgerald JB et al. (2009) Therapeutically targeting ErbB3: a key node in ligand‐induced activation of the ErbB receptor‐PI3K axis. Science Signalling 2(77): ra31.

Stolovitzky G, Monroe D and Califano A (2007) Dialogue on reverse‐engineering assessment and methods. Annals of the New York Academy of Sciences 1115: 1–22. doi:10.1196/annals.1407.021.

Swameye I, Muller TG, Timmer J, Sandra O and Klingmuller U (2003) Identification of nucleocytoplasmic cycling as a remote sensor in cellular signaling by databased modeling. Proceedings of the National Academy of Sciences of the USA 100: 1028–1033.

Tian Q, Price ND and Hood L (2012) Systems cancer medicine: towards realization of predictive, preventive, personalized and participatory (P4) medicine: key Symposium: systems cancer medicine. Journal of Internal Medicine 271: 111–121. doi:10.1111/j.1365‐2796.2011.02498.x.

Subramanian A (2005) From the cover: gene set enrichment analysis: a knowledge‐based approach for interpreting genome‐wide expression profiles. Proceedings of the National Academy of Sciences of the USA 102: 15545–15550. doi:10.1073/pnas.0506580102.

Thomas RK, Baker AC, DeBiasi RM et al. (2007) High‐throughput oncogene mutation profiling in human cancer. Nature Genetics 39: 347–351. doi:10.1038/ng1975.

Thusberg J, Olatubosun A and Vihinen M (2011) Performance of mutation pathogenicity prediction methods on missense variants. Human Mutation 32: 358–368. doi:10.1002/humu.21445.

Vera J, Bachmann J, Pfeifer AC et al. (2008) A systems biology approach to analyse amplification in the JAK2‐STAT5 signalling pathway. BMC Systems Biology 2: 38. doi:10.1186/1752-0509-2-38.

Vera J and Wolkenhauer O (2008) A system biology approach to understand functional activity of cell communication systems. Methods in Cell Biology 90: 399–415.

Vera J, Rateitschak K, Lange F et al. (2011) Systems biology of JAK‐STAT signalling in human malignancies. Progress in Biophysics and Molecular Biology 106: 426–434.

Vera J, Schmitz U, Lai X et al. (2013) Kinetic modeling‐based detection of genetic signatures that provide chemoresistance via the E2F1/p73/miR‐205 network. Cancer Research 73: 3511–3524.

Vera J, Schultz J, Ibrahim S et al. (2009) Dynamical effects of epigenetic silencing of 14‐3‐3σ expression. Molecular BioSystems 6: 264–273.

Wu D, Rice CM and Wang X (2012) Cancer bioinformatics: a new approach to systems clinical medicine. BMC Bioinformatics 13: 71. doi:10.1186/1471‐2105‐13‐71.

Zhang X‐P, Liu F and Wang W (2010) Coordination between cell cycle progression and cell fate decision by the p53 and E2F1 pathways in response to DNA damage. Journal of Biological Chemistry 285: 31571–31580.

Zinovyev A, Morozova N, Gorban AN and Harel‐Belan A (2013) Mathematical modeling of microRNA–mediated mechanisms of translation repression. In: Schmitz U, Wolkenhauer O and Vera J (eds) MicroRNA Cancer Regulation, pp. 189–224. Netherlands, Dordrecht: Springer.

Further Reading

Aguda BD, Kim Y, Piper‐Hunter MG, Friedman A and Marsh CB (2008) MicroRNA regulation of a cancer network: consequences of the feedback loops involving miR‐17–92, E2F and Myc. Proceedings of the National Academy of Sciences of the USA 105: 19678–19683.

Ahn AC, Tewari M, Poon C‐S and Phillips RS (2006) The clinical applications of a systems approach. PLOS Medicine 3: e209.

Aldridge BB, Burke JM, Lauffenburger DA and Sorger PK (2006) Physicochemical modelling of cell signalling pathways. Nature Cell Biology 8: 1195–1203.

Bachmann J, Raue A, Schilling M et al. (2012) Predictive mathematical models of cancer signalling pathways. Journal of Internal Medicine 271(2): 155–165.

Byrne HM (2010) Dissecting cancer through mathematics: from the cell to the animal model. Nature Reviews Cancer 10: 221–230.

Schilling M, Maiwald T, Hengl S et al. (2009) Theoretical and experimental analysis links isoform‐specific ERK signalling to cell fate decisions. Molecular Systems Biology 5: 334.

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Vera, Julio, Wolkenhauer, Olaf, and Schmitz, Ulf(Aug 2014) Cancer Systems Biology: Current Achievements in – Omics Data Analysis, Network Reconstruction and Mathematical Modelling. In: eLS. John Wiley & Sons Ltd, Chichester. http://www.els.net [doi: 10.1002/9780470015902.a0025237]