Thermodynamics and Cancer Dormancy: A Perspective

Chapter
Part of the Cancer Drug Discovery and Development book series (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.

Keywords

Cancer Statistical thermodynamics Entropy Information Gibbs free energy Dormancy 

References

  1. 1.
    Kuhn TS, Hawkins D (1963) The structure of scientific revolutions. Am J Phys 31:554–555. doi:10.1119/1.1969660 CrossRefGoogle Scholar
  2. 2.
    McQuarrie DA (1973) Statistical thermodynamics. Harper & Row, New YorkGoogle Scholar
  3. 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 CrossRefPubMedGoogle Scholar
  4. 4.
    Schrödinger E (1967) What is life?: the physical aspects of living cell with mind and matter and autobiographical sketches. Cambridge University Press, CambridgeGoogle Scholar
  5. 5.
    Shannon CE (2001) A mathematical theory of communication. ACM SIGMOBILE Mobile Comput Commun Rev 5:3–55CrossRefGoogle Scholar
  6. 6.
    Hanahan D, Weinberg RA (2000) The hallmarks of cancer. Cell 100:57–70CrossRefPubMedGoogle Scholar
  7. 7.
    Hanahan D, Weinberg RA (2011) Hallmarks of cancer: the next generation. Cell 144:646–674. doi:10.1016/j.cell.2011.02.013 CrossRefPubMedGoogle Scholar
  8. 8.
    Boveri T (1929) The origin of malignant tumors. Lippincott, Williams & Wilkins, BaltimoreGoogle Scholar
  9. 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 CrossRefPubMedPubMedCentralGoogle Scholar
  10. 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 96:15121–15126CrossRefPubMedPubMedCentralGoogle Scholar
  11. 11.
    Loeb LA, Springgate CF, Battula N (1974) Errors in DNA replication as a basis of malignant changes. Cancer Res 34:2311–2321PubMedGoogle Scholar
  12. 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 CrossRefPubMedPubMedCentralGoogle Scholar
  13. 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 CrossRefPubMedPubMedCentralGoogle Scholar
  14. 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 CrossRefPubMedPubMedCentralGoogle Scholar
  15. 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 CrossRefPubMedGoogle Scholar
  16. 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 CrossRefPubMedPubMedCentralGoogle Scholar
  17. 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 CrossRefPubMedPubMedCentralGoogle Scholar
  18. 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 CrossRefPubMedPubMedCentralGoogle Scholar
  19. 19.
    Danø S, Sørensen PG, Hynne F (1999) Sustained oscillations in living cells. Nature 402:320–322. doi:10.1038/46329 CrossRefPubMedGoogle Scholar
  20. 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 539:41–52CrossRefPubMedPubMedCentralGoogle Scholar
  21. 21.
    Berridge MJ (2008) Smooth muscle cell calcium activation mechanisms. J Physiol Lond 586:5047–5061. doi:10.1113/jphysiol.2008.160440 CrossRefPubMedPubMedCentralGoogle Scholar
  22. 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 CrossRefPubMedPubMedCentralGoogle Scholar
  23. 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 CrossRefPubMedPubMedCentralGoogle Scholar
  24. 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 CrossRefGoogle Scholar
  25. 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 CrossRefPubMedPubMedCentralGoogle Scholar
  26. 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. 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 CrossRefPubMedPubMedCentralGoogle Scholar
  28. 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 CrossRefPubMedGoogle Scholar
  29. 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 CrossRefPubMedPubMedCentralGoogle Scholar
  30. 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 CrossRefPubMedGoogle Scholar
  31. 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 CrossRefPubMedPubMedCentralGoogle Scholar
  32. 32.
    Berretta R, Moscato P (2010) Cancer biomarker discovery: the entropic hallmark. PLoS One 5:e12262. doi:10.1371/journal.pone.0012262 CrossRefPubMedPubMedCentralGoogle Scholar
  33. 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. 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 CrossRefPubMedPubMedCentralGoogle Scholar
  35. 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 CrossRefPubMedGoogle Scholar
  36. 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 CrossRefPubMedGoogle Scholar
  37. 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 CrossRefPubMedGoogle Scholar
  38. 38.
    Gatenby RA, Gillies RJ (2004) Why do cancers have high aerobic glycolysis? Nat Rev Cancer 4:891–899. doi:10.1038/nrc1478 CrossRefPubMedGoogle Scholar
  39. 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 CrossRefPubMedGoogle Scholar
  40. 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 CrossRefPubMedPubMedCentralGoogle Scholar
  41. 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 CrossRefPubMedGoogle Scholar
  42. 42.
    Ferrannini E (1988) The theoretical bases of indirect calorimetry: a review. Metab Clin Exp 37:287–301CrossRefPubMedGoogle Scholar
  43. 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 CrossRefPubMedPubMedCentralGoogle Scholar
  44. 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 CrossRefPubMedGoogle Scholar
  45. 45.
    Warburg O (1956) On the origin of cancer cells. Science 123:309–314CrossRefPubMedGoogle Scholar
  46. 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 CrossRefPubMedPubMedCentralGoogle Scholar
  47. 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 CrossRefGoogle Scholar
  48. 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 CrossRefPubMedPubMedCentralGoogle Scholar
  49. 49.
    Weinan E, Lu J, Yao Y (2012) The landscape of complex networks. arXiv:1204.6376 [physics, q-bio, stat]Google Scholar
  50. 50.
    Van der Toom EE, Verdone JE, Pienta KJ (2016) Disseminated tumor cells and dormancy in prostate cancer metastasis. Curr Opin Biotechnol 40:9–15CrossRefPubMedPubMedCentralGoogle Scholar
  51. 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 7:25478–25492CrossRefPubMedPubMedCentralGoogle Scholar
  52. 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 380:463–472CrossRefPubMedGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Computer Science DepartmentUniversity of MassachusettsAmherstUSA
  2. 2.Department of OncologyUniversity of AlbertaEdmontonCanada
  3. 3.Department of PhysicsUniversity of AlbertaEdmontonCanada

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