Skip to main content

Pathophysiology of Cancer and the Entropy Concept

  • Chapter
Model-Based Reasoning in Science and Technology

Part of the book series: Studies in Computational Intelligence ((SCI,volume 314))

Abstract

Entropy may be seen both from the point of view of thermodynamics and from the information theory, as an expression of system heterogeneity. Entropy, a system-specific entity, measures the distance between the present and the predictable end-stage of a biological system. It is based upon statistics of internal characteristics of the system. A living organism maintains its low entropy and reduces the entropy level of its environment due to communication between the system and its environment. Carcinogenesis is characterized by accumulating genomic mutations and is related to a loss of internal cellular information. The dynamics of this process can be investigated with the help of information theory. It has been suggested that tumor cells might regress to a state of minimum information during carcinogenesis and that information dynamics are integrally related to tumor development and growth. The great variety of chromosomal aberrations in solid tumors has limited its use as a variable to measure tumor aggressiveness or to predict prognosis. The introduction of Shannon’s entropy to express karyotypic diversity and uncertainty associated to sample distribution has overcome this problem. During carcinogenesis, mutations of the genome and epigenetic alterations (e.g. changes in methylation or protein composition) occur, which reduce the information content by increasing the randomness and raising the spatial entropy inside the nucleus. Therefore, we would expect a raise of entropy of nuclear chromatin in cytological or histological preparations with increasing malignancy of a tumor. In this case, entropy is calculated based on the co-occurrence matrix or the histogram of gray values of digitalized images. Studies from different laboratories based on various types of tumors demonstrated that entropy derived variables describing chromatin texture are independent prognostic features. Increasing entropy values are associated with a shorter survival. In summary, the entropy concept helped us to create in a parsimonious way a theoretical model of carcinogenesis, as well as prognostic models regarding survival.

Supported by FAPESP 2007/52015-0 and CNPq 479074/2008-9.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 169.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 219.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 219.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Alfano, F.D.: A stochastic model of oncogene expression and the relevance of this model to cancer therapy. Theoretical Biology and Medical Modelling 3, 5 (2006)

    Article  Google Scholar 

  2. Castro, M.A.A., Onsten, T.T.G., De. Almeida, R.M.C., Moreira, J.C.F.: Profiling cytogenetic diversity with entropy-based karyotypic analysis. Journal of Theoretical Biology 234, 487–495 (2005)

    Article  Google Scholar 

  3. Duesberg, P., Li, R., Fabarius, A., Hehlmann, R.: The chromosomal basis of cancer. Cellular Oncology 27, 293–318 (2005)

    Google Scholar 

  4. Freire, P., Vilela, M., Deus, H., Kim, Y.W., Koul, D., Colman, H., Aldape, K., Bogler, O., Yung, W., Coombes, K., Mills, G.B., Vasconcelos, A.T., Almeida, J.S.: Exploratory analysis of the copy number alterations in glioblastoma multiforme. PLoS ONE 3, e4076 (2008)

    Google Scholar 

  5. Gatenby, R.A., Frieden, B.R.: Information dynamics in carcinogenesis and tumor growth. Mutation Research 568, 259–273 (2004)

    Google Scholar 

  6. Gutierrez, M.I., Siray, A.K., Bhargava, M., Ozbek, U., Banavali, S., Chaudhary, M.A., Soth, H.E.I., Bathia, K.: Concurrent methylation of multiple genes in childhood all: Correlation with phenotype and molecular subgroup. Leukemia 17, 1845–1850 (2003)

    Article  Google Scholar 

  7. Hauptmann, S.: A thermodynamic interpretation of malignancy: Do the genes come later? Medical Hypotheses 58, 144–147 (2002)

    Article  Google Scholar 

  8. Kayser, K., Kayser, G., Metze, K.: The concept of structural entropy in tissue-based diagnosis. Quantitative Cytology and Histology 29, 296–308 (2007)

    Google Scholar 

  9. Metze, K., Adam, R.L., Silva, P.V., Carvalho, R.B.D., Leite, N.J.: Analysis of chromatin texture by pinkus’ approximate entropy. Cytometry Part A 59A, 63 (2004)

    Google Scholar 

  10. Metze, K., Ferreira, R.C., Adam, R.L., Leite, N.J., Ward, L.S., de Matos, P.S.: Chromatin texture is size dependent in follicular adenomas but not in hyperplastic nodules of the thyroid. World Journal of Surgery 32, 2744–2746 (2008)

    Article  Google Scholar 

  11. Metze, K., Mello, M.R.B.D., Adam, R.L., Leite, N.J., Lorand-Metze, I.G.H.: Entropy of the chromatin texture in routinely stained cytology is a prognostic factor in acute lymphoblastic leukemia. Virchows Archiv. 451, 114 (2007)

    Google Scholar 

  12. Nicolis, G.: Self-Organization in Nonequilibrium Systems. Wiley, New York (1977)

    MATH  Google Scholar 

  13. Prigogine, I.: Thermodynamics of Irreversible Processes. Wiley, New York (1967)

    Google Scholar 

  14. Rasnick, D.: Aneuploidy theory explains tumor formation, the absence of immune surveillance, and the failure of chemotherapy. Cancer Genetics and Cytogenetics 135, 66–72 (2002)

    Article  Google Scholar 

  15. Ritchie, W., Granjeaud, S., Puthier, D., Gautheret, D.: Entropy measures quantify global splicing disorders in cancer. PLoS Computational Biology 4, e1000,011 (2008)

    Google Scholar 

  16. Schrödinger, E.: What is Life? Cambridge University Press, Cambridge (1944)

    Google Scholar 

  17. Shannon, C., Weaver, W.: The Mathematical Theory of Communication. University of Illinois Press, Chicago (1949)

    MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2010 Springer-Verlag Berlin Heidelberg

About this chapter

Cite this chapter

Metze, K., Adam, R.L., Kayser, G., Kayser, K. (2010). Pathophysiology of Cancer and the Entropy Concept. In: Magnani, L., Carnielli, W., Pizzi, C. (eds) Model-Based Reasoning in Science and Technology. Studies in Computational Intelligence, vol 314. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15223-8_10

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-15223-8_10

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-15222-1

  • Online ISBN: 978-3-642-15223-8

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics