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Thermodynamics and Cancer Dormancy: A Perspective

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Book cover Tumor Dormancy and Recurrence

Part of the book series: Cancer Drug Discovery and Development ((CDD&D))

Abstract

In this review we elaborate on the hypothesis that concepts adapted from statistical thermodynamics, such as entropy and Gibbs free energy, can provide very powerful quantitative measures when applied to cancer research, in particular to cancer dormancy. We discuss how on all size scales of biological organization hierarchy from DNA to tissue and organ representation, cancer progression can be correlated with these thermodynamic measures. Significant diagnostic, prognostic and therapeutic implications of these new organizing principles are presented.

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References

  1. Kuhn TS, Hawkins D (1963) The structure of scientific revolutions. Am J Phys 31:554–555. doi:10.1119/1.1969660

    Article  Google Scholar 

  2. McQuarrie DA (1973) Statistical thermodynamics. Harper & Row, New York

    Google Scholar 

  3. Tseng C-Y, Tuszynski J (2015) A unified approach to computational drug discovery. Drug Discov Today 20:1328–1336. doi:10.1016/j.drudis.2015.07.004

    Article  CAS  PubMed  Google Scholar 

  4. Schrödinger E (1967) What is life?: the physical aspects of living cell with mind and matter and autobiographical sketches. Cambridge University Press, Cambridge

    Google Scholar 

  5. Shannon CE (2001) A mathematical theory of communication. ACM SIGMOBILE Mobile Comput Commun Rev 5:3–55

    Article  Google Scholar 

  6. Hanahan D, Weinberg RA (2000) The hallmarks of cancer. Cell 100:57–70

    Article  CAS  PubMed  Google Scholar 

  7. Hanahan D, Weinberg RA (2011) Hallmarks of cancer: the next generation. Cell 144:646–674. doi:10.1016/j.cell.2011.02.013

    Article  CAS  PubMed  Google Scholar 

  8. Boveri T (1929) The origin of malignant tumors. Lippincott, Williams & Wilkins, Baltimore

    Google Scholar 

  9. Holland AJ, Cleveland DW (2009) Boveri revisited: chromosomal instability, aneuploidy and tumorigenesis. Nat Rev Mol Cell Biol 10:478–487. doi:10.1038/nrm2718

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Stoler DL, Chen N, Basik M, Kahlenberg MS, Rodriguez-Bigas MA, Petrelli NJ, Anderson GR (1999) The onset and extent of genomic instability in sporadic colorectal tumor progression. Proc Natl Acad Sci U S A 96:15121–15126

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Loeb LA, Springgate CF, Battula N (1974) Errors in DNA replication as a basis of malignant changes. Cancer Res 34:2311–2321

    CAS  PubMed  Google Scholar 

  12. Loeb LA, Loeb KR, Anderson JP (2003) Multiple mutations and cancer. Proc Natl Acad Sci U S A 100:776–781. doi:10.1073/pnas.0334858100

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Nowak MA, Komarova NL, Sengupta A, Jallepalli PV, Shih I-M, Vogelstein B, Lengauer C (2002) The role of chromosomal instability in tumor initiation. Proc Natl Acad Sci U S A 99:16226–16231. doi:10.1073/pnas.202617399

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Tomasetti C, Vogelstein B (2015) Cancer etiology. Variation in cancer risk among tissues can be explained by the number of stem cell divisions. Science 347:78–81. doi:10.1126/science.1260825

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Vander Heiden MG, Locasale JW, Swanson KD, Sharfi H, Heffron GJ, Amador-Noguez D, Christofk HR, Wagner G, Rabinowitz JD, Asara JM, Cantley LC (2010) Evidence for an alternative glycolytic pathway in rapidly proliferating cells. Science 329:1492–1499. doi:10.1126/science.1188015

    Article  CAS  PubMed  Google Scholar 

  16. Rietman EA, Friesen DE, Hahnfeldt P, Gatenby R, Hlatky L, Tuszynski JA (2013) An integrated multidisciplinary model describing initiation of cancer and the Warburg hypothesis. Theor Biol Med Model 10:39. doi:10.1186/1742-4682-10-39

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Breitkreutz D, Hlatky L, Rietman E, Tuszynski JA (2012) Molecular signaling network complexity is correlated with cancer patient survivability. Proc Natl Acad Sci U S A 109:9209–9212. doi:10.1073/pnas.1201416109

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Benzekry S, Tuszynski JA, Rietman EA, Lakka Klement G (2015) Design principles for cancer therapy guided by changes in complexity of protein-protein interaction networks. Biol Direct 10:32. doi:10.1186/s13062-015-0058-5

    Article  PubMed  PubMed Central  Google Scholar 

  19. Danø S, Sørensen PG, Hynne F (1999) Sustained oscillations in living cells. Nature 402:320–322. doi:10.1038/46329

    Article  PubMed  Google Scholar 

  20. Voronina S, Sukhomlin T, Johnson PR, Erdemli G, Petersen OH, Tepikin A (2002) Correlation of NADH and Ca2+ signals in mouse pancreatic acinar cells. J Physiol Lond 539:41–52

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Berridge MJ (2008) Smooth muscle cell calcium activation mechanisms. J Physiol Lond 586:5047–5061. doi:10.1113/jphysiol.2008.160440

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Vergun O, Votyakova TV, Reynolds IJ (2003) Spontaneous changes in mitochondrial membrane potential in single isolated brain mitochondria. Biophys J 85:3358–3366. doi:10.1016/S0006-3495(03)74755-9

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Carels N, Tilli T, Tuszynski JA (2015) A computational strategy to select optimized protein targets for drug development toward the control of cancer diseases. PLoS One 10:e0115054. doi:10.1371/journal.pone.0115054

    Article  PubMed  PubMed Central  Google Scholar 

  24. Carels N, Tilli TM, Tuszynski JA (2015) Optimization of combination chemotherapy based on the calculation of network entropy for protein-protein interactions in breast cancer cell lines. EPJ Nonlinear Biomed Phys 3:1–18. doi:10.1140/epjnbp/s40366-015-0023-3

    Article  Google Scholar 

  25. Porat-Shliom N, Chen Y, Tora M, Shitara A, Masedunskas A, Weigert R (2014) In vivo tissue-wide synchronization of mitochondrial metabolic oscillations. Cell Rep 9:514–521. doi:10.1016/j.celrep.2014.09.022

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Rietman E, Bloemendal A, Platig J, Tuszynski J, Klement GL (2015) Gibbs free energy of protein-protein interactions reflects tumor stage. bioRxiv. doi:10.1101/022491

  27. Greenbaum D, Colangelo C, Williams K, Gerstein M (2003) Comparing protein abundance and mRNA expression levels on a genomic scale. Genome Biol 4:117. doi:10.1186/gb-2003-4-9-117

    Article  PubMed  PubMed Central  Google Scholar 

  28. Maier T, Güell M, Serrano L (2009) Correlation of mRNA and protein in complex biological samples. FEBS Lett 583:3966–3973. doi:10.1016/j.febslet.2009.10.036

    Article  CAS  PubMed  Google Scholar 

  29. Kim M-S, Pinto SM, Getnet D, Nirujogi RS, Manda SS, Chaerkady R, Madugundu AK, Kelkar DS, Isserlin R, Jain S, Thomas JK, Muthusamy B, Leal-Rojas P, Kumar P, Sahasrabuddhe NA, Balakrishnan L, Advani J, George B, Renuse S, Selvan LDN, Patil AH, Nanjappa V, Radhakrishnan A, Prasad S, Subbannayya T, Raju R, Kumar M, Sreenivasamurthy SK, Marimuthu A, Sathe GJ, Chavan S, Datta KK, Subbannayya Y, Sahu A, Yelamanchi SD, Jayaram S, Rajagopalan P, Sharma J, Murthy KR, Syed N, Goel R, Khan AA, Ahmad S, Dey G, Mudgal K, Chatterjee A, Huang T-C, Zhong J, Wu X, Shaw PG, Freed D, Zahari MS, Mukherjee KK, Shankar S, Mahadevan A, Lam H, Mitchell CJ, Shankar SK, Satishchandra P, Schroeder JT, Sirdeshmukh R, Maitra A, Leach SD, Drake CG, Halushka MK, Prasad TSK, Hruban RH, Kerr CL, Bader GD, Iacobuzio-Donahue CA, Gowda H, Pandey A (2014) A draft map of the human proteome. Nature 509:575–581. doi:10.1038/nature13302

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Wilhelm M, Schlegl J, Hahne H, Moghaddas Gholami A, Lieberenz M, Savitski MM, Ziegler E, Butzmann L, Gessulat S, Marx H, Mathieson T, Lemeer S, Schnatbaum K, Reimer U, Wenschuh H, Mollenhauer M, Slotta-Huspenina J, Boese J-H, Bantscheff M, Gerstmair A, Faerber F, Kuster B (2014) Mass-spectrometry-based draft of the human proteome. Nature 509:582–587. doi:10.1038/nature13319

    Article  CAS  PubMed  Google Scholar 

  31. Liu R, Li M, Liu Z-P, Wu J, Chen L, Aihara K (2012) Identifying critical transitions and their leading biomolecular networks in complex diseases. Sci Rep 2:813. doi:10.1038/srep00813

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Berretta R, Moscato P (2010) Cancer biomarker discovery: the entropic hallmark. PLoS One 5:e12262. doi:10.1371/journal.pone.0012262

    Article  PubMed  PubMed Central  Google Scholar 

  33. Rietman EA, Platig J, Tuszynski JA, Lakka Klement G (2016) Thermodynamic measures of cancer: Gibbs free energy and entropy of protein-protein interactions. J Biol Phys. doi:10.1007/s10867-016-9410-y

  34. Lapointe J, Li C, Higgins JP, van de Rijn M, Bair E, Montgomery K, Ferrari M, Egevad L, Rayford W, Bergerheim U, Ekman P, DeMarzo AM, Tibshirani R, Botstein D, Brown PO, Brooks JD, Pollack JR (2004) Gene expression profiling identifies clinically relevant subtypes of prostate cancer. Proc Natl Acad Sci U S A 101:811–816. doi:10.1073/pnas.0304146101

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Tomlins SA, Mehra R, Rhodes DR, Cao X, Wang L, Dhanasekaran SM, Kalyana-Sundaram S, Wei JT, Rubin MA, Pienta KJ, Shah RB, Chinnaiyan AM (2007) Integrative molecular concept modeling of prostate cancer progression. Nat Genet 39:41–51. doi:10.1038/ng1935

    Article  CAS  PubMed  Google Scholar 

  36. Wurmbach E, Chen Y, Khitrov G, Zhang W, Roayaie S, Schwartz M, Fiel I, Thung S, Mazzaferro V, Bruix J, Bottinger E, Friedman S, Waxman S, Llovet JM (2007) Genome-wide molecular profiles of HCV-induced dysplasia and hepatocellular carcinoma. Hepatology 45:938–947. doi:10.1002/hep.21622

    Article  CAS  PubMed  Google Scholar 

  37. Gillies RJ, Raghunand N, Karczmar GS, Bhujwalla ZM (2002) MRI of the tumor microenvironment. J Magn Reson Imaging 16:430–450. doi:10.1002/jmri.10181

    Article  PubMed  Google Scholar 

  38. Gatenby RA, Gillies RJ (2004) Why do cancers have high aerobic glycolysis? Nat Rev Cancer 4:891–899. doi:10.1038/nrc1478

    Article  CAS  PubMed  Google Scholar 

  39. Kroemer G, Pouyssegur J (2008) Tumor cell metabolism: cancer’s Achilles’ heel. Cancer Cell 13:472–482. doi:10.1016/j.ccr.2008.05.005

    Article  CAS  PubMed  Google Scholar 

  40. Vander Heiden MG, Cantley LC, Thompson CB (2009) Understanding the Warburg effect: the metabolic requirements of cell proliferation. Science 324:1029–1033. doi:10.1126/science.1160809

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. López-Lázaro M (2010) A new view of carcinogenesis and an alternative approach to cancer therapy. Mol Med 16:144–153. doi:10.2119/molmed.2009.00162

    Article  PubMed  Google Scholar 

  42. Ferrannini E (1988) The theoretical bases of indirect calorimetry: a review. Metab Clin Exp 37:287–301

    Article  CAS  PubMed  Google Scholar 

  43. Friesen DE, Baracos VE, Tuszynski JA (2015) Modeling the energetic cost of cancer as a result of altered energy metabolism: implications for cachexia. Theor Biol Med Model 12:17. doi:10.1186/s12976-015-0015-0

    Article  PubMed  PubMed Central  Google Scholar 

  44. Johns N, Stephens NA, Fearon KCH (2013) Muscle wasting in cancer. Int J Biochem Cell Biol 45:2215–2229. doi:10.1016/j.biocel.2013.05.032

    Article  CAS  PubMed  Google Scholar 

  45. Warburg O (1956) On the origin of cancer cells. Science 123:309–314

    Article  CAS  PubMed  Google Scholar 

  46. Davies PC, Demetrius L, Tuszynski JA (2011) Cancer as a dynamical phase transition. Theor Biol Med Model 8:30. doi:10.1186/1742-4682-8-30

    Article  PubMed  PubMed Central  Google Scholar 

  47. Zmeskal O, Dzik P, Vesely M (2013) Entropy of fractal systems. Comput Math Appl 66:135–146. doi:10.1016/j.camwa.2013.01.017

    Article  Google Scholar 

  48. de Arruda PFF, Gatti M, Facio FN, de Arruda JGF, Moreira RD, Murta LO, de Arruda LF, de Godoy MF (2013) Quantification of fractal dimension and Shannon’s entropy in histological diagnosis of prostate cancer. BMC Clin Pathol 13:6. doi:10.1186/1472-6890-13-6

    Article  PubMed  PubMed Central  Google Scholar 

  49. Weinan E, Lu J, Yao Y (2012) The landscape of complex networks. arXiv:1204.6376 [physics, q-bio, stat]

    Google Scholar 

  50. Van der Toom EE, Verdone JE, Pienta KJ (2016) Disseminated tumor cells and dormancy in prostate cancer metastasis. Curr Opin Biotechnol 40:9–15

    Article  PubMed  PubMed Central  Google Scholar 

  51. Dai Y, Wang L, Tang J, Cao P, Luo Z, Sun J, Kiflu A, Sai B, Zhang M, Wang F, Li G (2016) Activation of anaphase-promoting complex by p53 induces a state of dormancy in cancer cells against chemotherapeutic stress. Oncotarget 7:25478–25492

    Article  PubMed  PubMed Central  Google Scholar 

  52. Kareva I, Berezovskaya F (2015) Cancer immunoediting: a process driven by metabolic competition as a predator–prey–shared resource type model. J Theor Biol 380:463–472

    Article  PubMed  Google Scholar 

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Acknowledgments

E.A.R. acknowledges funding from CSTS Healthcare, Toronto, Canada. J.A.T. has been supported by funding from the Natural Sciences Engineering Research Council of Canada and the Allard Foundation.

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Authors and Affiliations

  1. Computer Science Department, University of Massachusetts, Amherst, MA, USA

    Edward A. Rietman

  2. Department of Oncology, University of Alberta, Edmonton, AB, Canada, T6G 1Z2

    Jack A. Tuszynski

  3. Department of Physics, University of Alberta, Edmonton, AB, Canada, T6G 1Z2

    Jack A. Tuszynski

Authors
  1. Edward A. Rietman
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  2. Jack A. Tuszynski
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Correspondence to Edward A. Rietman .

Editor information

Editors and Affiliations

  1. Department of Experimental Therapeutics and Department of Urologic Sciences, BC Cancer Agency and University of British Columbia, Vancouver, British Columbia, Canada

    Yuzhuo Wang

  2. School of Life, Health & Chemical Sciences, The Open University, Walton Hall, Milton Keynes, Buckinghamshire, United Kingdom

    Francesco Crea

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Rietman, E.A., Tuszynski, J.A. (2017). Thermodynamics and Cancer Dormancy: A Perspective. In: Wang, Y., Crea, F. (eds) Tumor Dormancy and Recurrence. Cancer Drug Discovery and Development. Humana Press, Cham. https://doi.org/10.1007/978-3-319-59242-8_5

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  • DOI: https://doi.org/10.1007/978-3-319-59242-8_5

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  • Publisher Name: Humana Press, Cham

  • Print ISBN: 978-3-319-59240-4

  • Online ISBN: 978-3-319-59242-8

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