Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

What does physics have to do with cancer?

Key Points

  • Approaches from the physical sciences can contribute to the rate at which powerful new diagnostic tools and therapies can be discovered and brought into the clinic. We provide examples from four areas to describe how teams of physical scientists, cancer biologists, clinicians and cancer advocates are tackling cancer from the perspective of the physical sciences.

  • The principles of evolutionary biology can be used to study the mechanisms and dynamics of tumour initiation, tumour progression, the response to treatment and the emergence of resistance. For example, large-scale cross-sectional genomic data sets can be combined with novel evolutionary approaches to predict the temporal order of somatic events that arise during tumorigenesis. Such knowledge helps to guide the generation of the correct genomic context in animal models of human cancer and helps to prioritize the validation of potential drug targets.

  • DNA in vivo is often sharply distorted away from the canonical Watson–Crick structure; different DNA sequences vary greatly in the ease with which such sharp distortions can be accommodated. Most of the eukaryotic genomic DNA is bent around histones to form nucleosomes. The capacity of the DNA sequence to undergo such distortion can influence the specific preferred locations for many of the nucleosomes.

  • The existence of a cancerous lesion can sometimes be detected through the analysis of the altered behaviour of cells that are located substantial distances away from the primary lesion, a phenomenon that is known as the 'field effect'. Partial wave spectroscopy takes advantage of the field effect to allow for the sensitive and specific detection of cancers in tissues that are difficult to reach.

  • Cancer is an extraordinarily complex disease. Methods that are commonly used in physics can reduce the complexity of cancer to a manageable set of underlying principles and phenomena. In particular, Transport OncoPhysics views cancer as a disease of multiscale mass transport deregulation involving the biological barriers that separate different body compartments. Probes that can be used to investigate the mass transport properties of tissues can be used as directed vectors for the localized, preferential release of therapeutics into tumours.

Abstract

Large-scale cancer genomics, proteomics and RNA-sequencing efforts are currently mapping in fine detail the genetic and biochemical alterations that occur in cancer. However, it is becoming clear that it is difficult to integrate and interpret these data and to translate them into treatments. This difficulty is compounded by the recognition that cancer cells evolve, and that initiation, progression and metastasis are influenced by a wide variety of factors. To help tackle this challenge, the US National Cancer Institute Physical Sciences-Oncology Centers initiative is bringing together physicists, cancer biologists, chemists, mathematicians and engineers. How are we beginning to address cancer from the perspective of the physical sciences?

This is a preview of subscription content, access via your institution

Relevant articles

Open Access articles citing this article.

Access options

Buy article

Get time limited or full article access on ReadCube.

$32.00

All prices are NET prices.

Figure 1: Similarities among soap bubbles, cells in the Drosophila eye and loss of tissue organization in cancer.
Figure 2: Tissues are complex dynamic systems that feature multiscale mechanochemical coupling.
Figure 3: Physical sciences shed light onto nucleosome and transcription factor competition and chromosome packaging.
Figure 4: The application of physical science approaches for understanding deregulated transport in cancer.

References

  1. Paget, S. The distribution of secondary growths in cancer of the breast. Lancet 1, 571–573 (1889).

    Article  Google Scholar 

  2. Boehm, T., Folkman, J., Browder, T. & O'Reilly, M. S. Antiangiogenic therapy of experimental cancer does not induce acquired drug resistance. Nature 390, 404–407 (1997).

    Article  CAS  PubMed  Google Scholar 

  3. Nowell, P. C. The clonal evolution of tumor cell populations. Science 194, 23–28 (1976).

    Article  CAS  PubMed  Google Scholar 

  4. Armitage, P. & Doll, R. A two-stage theory of carcinogenesis in relation to the age distribution of human cancer. Br. J. Cancer 11, 161–169 (1957). One of the first mathematical approaches to explain age-specific cancer incidence curves.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Fisher, J. C. Multiple-mutation theory of carcinogenesis. Nature 181, 651–652 (1958).

    Article  CAS  PubMed  Google Scholar 

  6. [Author unknown.] The Edwin Smith Surgical Papyrus, Vault RB, NY Acad. Med. Rare Book Room, New York (c1,600 BCE).

  7. Butcher, D. T., Alliston, T. & Weaver, V. M. A tense situation: forcing tumour progression. Nature Rev. Cancer 9, 108–122 (2009).

    Article  CAS  PubMed  Google Scholar 

  8. Levental, K. R. et al. Matrix crosslinking forces tumor progression by enhancing integrin signaling. Cell 139, 891–906 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Schedin, P. & Keely, P. J. Mammary gland ECM remodeling, stiffness, and mechanosignaling in normal development and tumor progression. Cold Spring Harb. Perspect. Biol. 3, a003228 (2011).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  10. Montell, D. J. Morphogenetic cell movements: diversity from modular mechanical properties. Science 322, 1502–1505 (2008).

    Article  CAS  PubMed  Google Scholar 

  11. Mariappan, Y. K., Glaser, K. J. & Ehman, R. L. Magnetic resonance elastography: a review. Clin. Anat. 23, 497–511 (2010).

    Article  PubMed  PubMed Central  Google Scholar 

  12. Hansma, P. et al. The tissue diagnostic instrument. Rev. Sci. Instrum. 80, 054303 (2009).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  13. Krieg, M. et al. Tensile forces govern germ-layer organization in zebrafish. Nature Cell Biol. 10, 429–436 (2008).

    Article  CAS  PubMed  Google Scholar 

  14. Salaita, K. et al. Restriction of receptor movement alters cellular response: physical force sensing by EphA2. Science 327, 1380–1385 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Taylor, J. E. Structure of singularities in soap-bubble-like and soap-film-like minimal surfaces. Ann. Math. 103, 489–539 (1976).

    Article  Google Scholar 

  16. Hayashi, T. & Carthew, R. W. Surface mechanics mediate pattern formation in the developing retina. Nature 431, 647–652 (2004).

    Article  CAS  PubMed  Google Scholar 

  17. Kafer, J., Hayashi, T., Maree, A. F., Carthew, R. W. & Graner, F. Cell adhesion and cortex contractility determine cell patterning in the Drosophila retina. Proc. Natl Acad. Sci. USA 104, 18549–18554 (2007).

    Article  PubMed  PubMed Central  Google Scholar 

  18. Hilgenfeldt, S., Erisken, S. & Carthew, R. W. Physical modeling of cell geometric order in an epithelial tissue. Proc. Natl Acad. Sci. USA 105, 907–911 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Acar, M., Pando, B. F., Arnold, F. H., Elowitz, M. B. & van Oudenaarden, A. A general mechanism for network-dosage compensation in gene circuits. Science 329, 1656–1660 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Sprinzak, D. et al. Cis-interactions between Notch and Delta generate mutually exclusive signalling states. Nature 465, 86–90 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Cairns, J. Mutation selection and the natural history of cancer. Nature 255, 197–200 (1975).

    Article  CAS  PubMed  Google Scholar 

  22. Heppner, G. H. & Miller, F. R. The cellular basis of tumor progression. Int. Rev. Cytol. 177, 1–56 (1998).

    CAS  PubMed  Google Scholar 

  23. Crespi, B. & Summers, K. Evolutionary biology of cancer. Trends Ecol. Evol. 20, 545–552 (2005).

    Article  PubMed  Google Scholar 

  24. Michor, F., Iwasa, Y. & Nowak, M. A. Dynamics of cancer progression. Nature Rev. Cancer 4, 197–205 (2004).

    Article  CAS  Google Scholar 

  25. Merlo, L. M., Pepper, J. W., Reid, B. J. & Maley, C. C. Cancer as an evolutionary and ecological process. Nature Rev. Cancer 6, 924–935 (2006). Key reference elucidating evolutionary and ecological approaches to cancer.

    Article  CAS  Google Scholar 

  26. Brash, D. E., Zhang, W., Grossman, D. & Takeuchi, S. Colonization of adjacent stem cell compartments by mutant keratinocytes. Semin. Cancer Biol. 15, 97–102 (2005).

    Article  CAS  PubMed  Google Scholar 

  27. Maley, C. C. et al. Selectively advantageous mutations and hitchhikers in neoplasms: p16 lesions are selected in Barrett's esophagus. Cancer Res. 64, 3414–3427 (2004).

    Article  CAS  PubMed  Google Scholar 

  28. Keller, L. Levels of Selection in Evolution. (Princeton Univ. Press, 1999).

    Google Scholar 

  29. Weinstein, B. S. & Ciszek, D. The reserve-capacity hypothesis: evolutionary origins and modern implications of the trade-off between tumor-suppression and tissue-repair. Exp. Gerontol. 37, 615–627 (2002).

    Article  CAS  PubMed  Google Scholar 

  30. Knudson, A. G. Jr. Mutation and cancer: statistical study of retinoblastoma. Proc. Natl Acad. Sci. USA 68, 820–823 (1971).

    Article  PubMed  PubMed Central  Google Scholar 

  31. Haeno, H., Levine, R. L., Gilliland, D. G. & Michor, F. A progenitor cell origin of myeloid malignancies. Proc. Natl Acad. Sci. USA 106, 16616–16621 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Tomlinson, I. P., Novelli, M. R. & Bodmer, W. F. The mutation rate and cancer. Proc. Natl Acad. Sci. USA 93, 14800–14803 (1996).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Desper, R. et al. Inferring tree models for oncogenesis from comparative genome hybridization data. J. Comput. Biol. 6, 37–51 (1999).

    Article  CAS  PubMed  Google Scholar 

  34. Goldie, J. H. & Coldman, A. J. Quantitative model for multiple levels of drug resistance in clinical tumors. Cancer Treat. Rep. 67, 923–931 (1983).

    CAS  PubMed  Google Scholar 

  35. Coldman, A. J. & Murray, J. M. Optimal control for a stochastic model of cancer chemotherapy. Math. Biosci. 168, 187–200 (2000).

    Article  CAS  PubMed  Google Scholar 

  36. Michor, F. et al. Dynamics of chronic myeloid leukaemia. Nature 435, 1267–1270 (2005).

    Article  CAS  PubMed  Google Scholar 

  37. Coldman, A. J. & Goldie, J. H. A stochastic model for the origin and treatment of tumors containing drug-resistant cells. Bull. Math. Biol. 48, 279–292 (1986).

    Article  CAS  PubMed  Google Scholar 

  38. Skipper, H. E. The forty-year-old mutation theory of Luria and Delbruck and its pertinence to cancer chemotherapy. Adv. Cancer Res. 40, 331–363 (1983).

    Article  CAS  PubMed  Google Scholar 

  39. Iwasa, Y., Michor, F. & Nowak, M. A. Evolutionary dynamics of escape from biomedical intervention. Proc. Biol. Sci. 270, 2573–2578 (2003).

    Article  PubMed  PubMed Central  Google Scholar 

  40. Komarova, N. L. & Wodarz, D. Drug resistance in cancer: principles of emergence and prevention. Proc. Natl Acad. Sci. USA 102, 9714–9719 (2005).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Durrett, R. & Moseley, S. Evolution of resistance and progression to disease during clonal expansion of cancer. Theor. Popul. Biol. 77, 42–48 (2010).

    Article  PubMed  Google Scholar 

  42. Harnevo, L. E. & Agur, Z. The dynamics of gene amplification described as a multitype compartmental model and as a branching process. Math. Biosci. 103, 115–138 (1991).

    Article  CAS  PubMed  Google Scholar 

  43. Goldie, J. H. & Coldman, A. J. The genetic origin of drug resistance in neoplasms: implications for systemic therapy. Cancer Res. 44, 3643–3653 (1984).

    CAS  PubMed  Google Scholar 

  44. Day, R. S. Treatment sequencing, asymmetry, and uncertainty: protocol strategies for combination chemotherapy. Cancer Res. 46, 3876–3885 (1986).

    CAS  PubMed  Google Scholar 

  45. Citron, M. L. et al. Randomized trial of dose-dense versus conventionally scheduled and sequential versus concurrent combination chemotherapy as postoperative adjuvant treatment of node-positive primary breast cancer: first report of Intergroup Trial C9741/Cancer and Leukemia Group B Trial 9741. J. Clin. Oncol. 21, 1431–1439 (2003).

    Article  CAS  PubMed  Google Scholar 

  46. Komarova, N. L., Katouli, A. A. & Wodarz, D. Combination of two but not three current targeted drugs can improve therapy of chronic myeloid leukemia. PLoS ONE 4, e4423 (2009).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  47. Foo, J. & Michor, F. Evolution of resistance to anti-cancer therapy during general dosing schedules. J. Theor. Biol. 263, 179–188 (2010).

    Article  PubMed  Google Scholar 

  48. Knudson, A. G. Two genetic hits (more or less) to cancer. Nature Rev. Cancer 1, 157–162 (2001).

    Article  CAS  Google Scholar 

  49. Nordling, C. O. A new theory on cancer-inducing mechanism. Br. J. Cancer 7, 68–72 (1953).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Varmus, H. The new era in cancer research. Science 312, 1162–1165 (2006).

    Article  CAS  PubMed  Google Scholar 

  51. Weir, B., Zhao, X. & Meyerson, M. Somatic alterations in the human cancer genome. Cancer Cell 6, 433–438 (2004).

    Article  CAS  PubMed  Google Scholar 

  52. Futreal, P. A. et al. A census of human cancer genes. Nature Rev. Cancer 4, 177–183 (2004).

    Article  CAS  Google Scholar 

  53. The Cancer Genome Atlas Research Network. Comprehensive genomic characterization defines human glioblastoma genes and core pathways. Nature 455, 1061–1068 (2008).

  54. Beroukhim, R. et al. The landscape of somatic copy-number alteration across human cancers. Nature 463, 899–905 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  55. Hanahan, D. & Weinberg, R. A. The hallmarks of cancer. Cell 100, 57–70 (2000).

    Article  CAS  PubMed  Google Scholar 

  56. Feinberg, A. P., Ohlsson, R. & Henikoff, S. The epigenetic progenitor origin of human cancer. Nature Rev. Genet. 7, 21–33 (2006).

    Article  CAS  PubMed  Google Scholar 

  57. Hastings, P. J., Lupski, J. R., Rosenberg, S. M. & Ira, G. Mechanisms of change in gene copy number. Nature Rev. Genet. 10, 551–564 (2009).

    Article  CAS  PubMed  Google Scholar 

  58. Stratton, M. R., Campbell, P. J. & Futreal, P. A. The cancer genome. Nature 458, 719–724 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  59. Wang, G. & Vasquez, K. M. Naturally occurring H-DNA-forming sequences are mutagenic in mammalian cells. Proc. Natl Acad. Sci. USA 101, 13448–13453 (2004).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  60. Wang, G., Christensen, L. A. & Vasquez, K. M. Z.-DNA-forming sequences generate large-scale deletions in mammalian cells. Proc. Natl Acad. Sci. USA 103, 2677–2682 (2006).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  61. Zhao, J., Bacolla, A., Wang, G. & Vasquez, K. M. Non-B DNA structure-induced genetic instability and evolution. Cell. Mol. Life Sci. 67, 43–62 (2010).

    Article  CAS  PubMed  Google Scholar 

  62. Huppert, J. L. Structure, location and interactions of G.-quadruplexes. FEBS J. 277, 3452–3458 (2010).

    Article  CAS  PubMed  Google Scholar 

  63. Lipps, H. J. & Rhodes, D. G.-quadruplex structures: in vivo evidence and function. Trends Cell Biol. 19, 414–422 (2009).

    Article  CAS  PubMed  Google Scholar 

  64. Maizels, N. Dynamic roles for G4 DNA in the biology of eukaryotic cells. Nature Struct. Mol. Biol. 13, 1055–1059 (2006).

    Article  CAS  Google Scholar 

  65. Sun, D. & Hurley, L. H. Biochemical techniques for the characterization of G-quadruplex structures: EMSA, DMS footprinting, and DNA polymerase stop assay. Methods Mol. Biol. 608, 65–79 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  66. De, S. & Michor, F. DNA secondary structures and epigenetic determinants of cancer genome evolution. Nature Struct. Mol. Biol. 3 Jul 2011 (doi:10.1038/nsmb.2089).

  67. Kruisselbrink, E. et al. Mutagenic capacity of endogenous G4 DNA underlies genome instability in FANCJ-defective C. elegans. Curr. Biol. 18, 900–905 (2008).

    Article  CAS  PubMed  Google Scholar 

  68. Pontier, D. B., Kruisselbrink, E., Guryev, V. & Tijsterman, M. Isolation of deletion alleles by G4 DNA-induced mutagenesis. Nature Methods 6, 655–657 (2009).

    Article  CAS  PubMed  Google Scholar 

  69. Boan, F. & Gomez-Marquez, J. In vitro recombination mediated by G-quadruplexes. Chembiochem 11, 331–334 (2010).

    Article  CAS  PubMed  Google Scholar 

  70. Attolini, C. S. et al. A mathematical framework to determine the temporal sequence of somatic genetic events in cancer. Proc. Natl Acad. Sci. USA 107, 17604–17609 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  71. Hartl, D. L. & Clark, A. G. Principles of Population Genetics. 4th edn (Sinauer Associates, 2007).

    Google Scholar 

  72. Fearon, E. R. & Vogelstein, B. A genetic model for colorectal tumorigenesis. Cell 61, 759–767 (1990).

    Article  CAS  PubMed  Google Scholar 

  73. Zhu, Y. et al. Early inactivation of p53 tumor suppressor gene cooperating with NF1 loss induces malignant astrocytoma. Cancer Cell 8, 119–130 (2005).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  74. Abdel-Wahab, O. et al. Genetic analysis of transforming events that convert chronic myeloproliferative neoplasms to leukemias. Cancer Res. 70, 447–452 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  75. Beroukhim, R. et al. Assessing the significance of chromosomal aberrations in cancer: methodology and application to glioma. Proc. Natl Acad. Sci. USA 104, 20007–20012 (2007).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  76. Bachoo, R. M. et al. Epidermal growth factor receptor and Ink4a/Arf: convergent mechanisms governing terminal differentiation and transformation along the neural stem cell to astrocyte axis. Cancer Cell 1, 269–277 (2002).

    Article  CAS  PubMed  Google Scholar 

  77. Zhu, H. et al. Oncogenic EGFR signaling cooperates with loss of tumor suppressor gene functions in gliomagenesis. Proc. Natl Acad. Sci. USA 106, 2712–2716 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  78. Segal, E. et al. A genomic code for nucleosome positioning. Nature 442, 772–778 (2006). This paper showed that genomes encode intrinsically preferred locations for many of their nucleosomes, and showed that these positions seemed to facilitate diverse and specific aspects of chromosome function.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  79. Kornberg, R. D. & Lorch, Y. Twenty-five years of the nucleosome, fundamental particle of the eukaryote chromosome. Cell 98, 285–294 (1999).

    Article  CAS  PubMed  Google Scholar 

  80. Richmond, T. J. & Davey, C. A. The structure of DNA in the nucleosome core. Nature 423, 145–150 (2003).

    Article  CAS  PubMed  Google Scholar 

  81. Field, Y. et al. Gene expression divergence in yeast is coupled to evolution of DNA-encoded nucleosome organization. Nature Genet. 41, 438–445 (2009).

    Article  CAS  PubMed  Google Scholar 

  82. Field, Y. et al. Distinct modes of regulation by chromatin encoded through nucleosome positioning signals. PLoS Comput. Biol. 4, e1000216 (2008).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  83. Eaton, M. L., Galani, K., Kang, S., Bell, S. P. & MacAlpine, D. M. Conserved nucleosome positioning defines replication origins. Genes Dev. 24, 748–753 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  84. Getun, I. V., Wu, Z. K., Khalil, A. M. & Bois, P. R. J. Nucleosome occupancy landscape and dynamics at mouse recombination hotspots. EMBO Rep. 11, 555–560 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  85. Sasaki, S. et al. Chromatin-associated periodicity in genetic variation downstream of transcriptional start sites. Science 323, 401–404 (2009).

    Article  CAS  PubMed  Google Scholar 

  86. Lanzer, M., Wertheimer, S. P., de Bruin, D. & Ravetch, J. V. Chromatin structure determines the sites of chromosome breakages in Plasmodium falciparum. Nucleic Acids Res. 22, 3099–3103 (1994).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  87. Wang, G. P., Ciuffi, A., Leipzig, J., Berry, C. C. & Bushman, F. D. HIV integration site selection: analysis by massively parallel pyrosequencing reveals association with epigenetic modifications. Genome Res. 17, 1186–1194 (2007).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  88. Pryciak, P. M. & Varmus, H. E. Nucleosomes, DNA-binding proteins, and DNA sequence modulate retroviral integration target site selection. Cell 69, 769–780 (1992).

    Article  CAS  PubMed  Google Scholar 

  89. Gangadharan, S., Mularoni, L., Fain-Thornton, J., Wheelan, S. J. & Craig, N. L. DNA transposon Hermes inserts into DNA in nucleosome-free regions in vivo. Proc. Natl Acad. Sci. USA 107, 21966–21972 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  90. Palomera-Sanchez, Z. & Zurita, M. Open, repair and close again: chromatin dynamics and the response to UV-induced DNA damage. DNA Repair 10, 119–125 (2010).

    Article  PubMed  CAS  Google Scholar 

  91. Bucceri, A., Kapitza, K. & Thoma, F. Rapid accessibility of nucleosomal DNA in yeast on a second time scale. EMBO J. 25, 3123–3132 (2006).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  92. Prendergast, J. G. D. et al. Chromatin structure and evolution in the human genome. BMC Evol. Biol. 7, 72 (2007).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  93. Widom, J. Role of DNA sequence in nucleosome stability and dynamics. Q. Rev. Biophys. 34, 269–324 (2001).

    Article  CAS  PubMed  Google Scholar 

  94. Cloutier, T. E. & Widom, J. Spontaneous sharp bending of double-stranded DNA. Mol. Cell 14, 355–362 (2004).

    Article  CAS  PubMed  Google Scholar 

  95. Segal, E. & Widom, J. Poly(dA:dT) tracts: major determinants of nucleosome organization. Curr. Opin. Struct. Biol. 19, 65–71 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  96. Thåström, A., Bingham, L. M. & Widom, J. Nucleosomal locations of dominant DNA sequence motifs for histone-DNA interactions and nucleosome positioning. J. Mol. Biol. 338, 695–709 (2004).

    Article  PubMed  CAS  Google Scholar 

  97. Morozov, A. et al. Using DNA mechanics to predict in vitro nucleosome positions and formation energies. Nucleic Acids Res. 37, 4707–4722 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  98. Tolstorukov, M. Y., Colasanti, A. V., McCandlish, D. M., Olson, W. K. & Zhurkin, V. B. A novel roll-and-slide mechanism of DNA folding in chromatin: implications for nucleosome positioning. J. Mol. Biol. 371, 725–738 (2007).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  99. Geggier, S. & Vologodskii, A. Sequence dependence of DNA bending rigidity. Proc. Natl Acad. Sci. USA 107, 15421–15426 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  100. Wiggins, P. A. et al. High flexibility of DNA on short length scales probed by atomic force microscopy. Nature Nanotech. 1, 137–141 (2006).

    Article  CAS  Google Scholar 

  101. Lavery, R. et al. A systematic molecular dynamics study of nearest-neighbor effects on base pair and base pair step conformations and fluctuations in B-DNA. Nucleic Acids Res. 38, 299–313 (2010).

    Article  CAS  PubMed  Google Scholar 

  102. Zakrzewska, K., Bouvier, B., Michon, A., Blanchet, C. & Lavery, R. Protein-DNA binding specificity: a grid-enabled computational approach applied to single and multiple protein assemblies. Phys. Chem. Chem. Phys. 11, 10712–10721 (2009).

    Article  CAS  PubMed  Google Scholar 

  103. Lankas, F. et al. On the parameterization of rigid base and basepair models of DNA from molecular dynamics simulations. Phys. Chem. Chem. Phys. 11, 10565–10588 (2009).

    Article  CAS  PubMed  Google Scholar 

  104. Kaplan, N. et al. The DNA-encoded nucleosome organization of a eukaryotic genome. Nature 458, 362–366 (2009). This study measured intrinsic DNA sequence preferences of nucleosomes in a purely in vitro experiment involving purified yeast genomic DNA and purified histones only. A thermodynamic model of nucleosome–DNA interactions based on these data is highly predictive of the distribution of nucleosomes in vivo , proving that much of the in vivo nucleosome organization is explicitly encoded in the genomic DNA sequence.

    Article  CAS  PubMed  Google Scholar 

  105. Fraser, R. M., Allan, J. & Simmen, M. W. In silico approaches reveal the potential for DNA sequence-dependent histone octamer affinity to influence chromatin structure in vivo. J. Mol. Biol. 364, 582–598 (2006).

    Article  CAS  PubMed  Google Scholar 

  106. Chevereau, G., Palmeira, L., Thermes, C., Arneodo, A. & Vaillant, C. Thermodynamics of intragenic nucleosome ordering. Phys. Rev. Lett. 103, 188103 (2009).

    Article  CAS  PubMed  Google Scholar 

  107. Schwab, D. J., Bruinsma, R. F., Rudnick, J. & Widom, J. Nucleosome switches. Phys. Rev. Lett. 100, 228105 (2008).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  108. Segal, E. & Widom, J. From DNA sequence to transcriptional behaviour: a quantitative approach. Nature Rev. Genet. 10, 443–456 (2009).

    Article  CAS  PubMed  Google Scholar 

  109. Raveh-Sadka, T., Levo, M. & Segal, E. Incorporating nucleosomes into thermodynamic models of transcription regulation. Genome Res. 19, 1480–1496 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  110. Segal, E. & Widom, J. What controls nucleosome positions? Trends Genet. 25, 335–343 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  111. Strukov, Y. G. & Belmont, A. S. Mitotic chromosome structure: reproducibility of folding and symmetry between sister chromatids. Biophys. J. 96, 1617–1628 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  112. Subramanian, H. et al. Optical methodology for detecting histologically unapparent nanoscale consequences of genetic alterations in biological cells. Proc. Natl Acad. Sci. USA 105, 20118–20123 (2008). Key reference showing that partial wave spectroscopy can be a valuable tool for the diagnosis of cancerous lesions by imaging sites far removed from the lesion itself.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  113. Subramanian, H. et al. Nanoscale cellular changes in field carcinogenesis detected by partial wave spectroscopy. Cancer Res. 69, 5357–5363 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  114. Damania, D. et al. Role of cytoskeleton in controlling the disorder strength of cellular nanoscale architecture. Biophys. J. 99, 989–996 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  115. Kim, J. S., Pradhan, P., Backman, V. & Szleifer, I. The influence of chromosome density variations on the increase in nuclear disorder strength in carcinogenesis. Phys. Biol. 8, 015004 (2011).

    Article  PubMed  Google Scholar 

  116. Hudson, T. J. et al. International network of cancer genome projects. Nature 464, 993–998 (2010).

    Article  CAS  PubMed  Google Scholar 

  117. Fidler, I. J. & Kripke, M. L. Metastasis results from preexisting variant cells within a malignant tumor. Science 197, 893–895 (1977).

    Article  CAS  PubMed  Google Scholar 

  118. Navin, N. et al. Inferring tumor progression from genomic heterogeneity. Genome Res. 20, 68–80 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  119. Berger, M. F. et al. The genomic complexity of primary human prostate cancer. Nature 470, 214–220 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  120. Beerenwinkel, N. et al. Genetic progression and the waiting time to cancer. PLoS Comput. Biol. 3, e225 (2007).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  121. Yachida, S. et al. Distant metastasis occurs late during the genetic evolution of pancreatic cancer. Nature 467, 1114–1117 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  122. Campbell, P. J. et al. The patterns and dynamics of genomic instability in metastatic pancreatic cancer. Nature 467, 1109–1113 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  123. Huh, D. & Paulsson, J. Non-genetic heterogeneity from stochastic partitioning at cell division. Nature Genet. 43, 95–100 (2011).

    Article  CAS  PubMed  Google Scholar 

  124. van Engeland, M., Derks, S., Smits, K. M., Meijer, G. A. & Herman, J. G. Colorectal cancer epigenetics: complex simplicity. J. Clin. Oncol. 29, 1382–1391 (2011).

    Article  PubMed  Google Scholar 

  125. Hondermarck, H. Breast cancer: when proteomics challenges biological complexity. Mol. Cell. Proteomics 2, 281–291 (2003).

    Article  CAS  PubMed  Google Scholar 

  126. Fidler, I. J. & Hart, I. R. Biological diversity in metastatic neoplasms: origins and implications. Science 217, 998–1003 (1982).

    Article  CAS  PubMed  Google Scholar 

  127. Copeland, N. G. & Jenkins, N. A. Deciphering the genetic landscape of cancer--from genes to pathways. Trends Genet. 25, 455–462 (2009).

    Article  CAS  PubMed  Google Scholar 

  128. Wooster, R. & Bachman, K. E. Catalogue, cause, complexity and cure; the many uses of cancer genome sequence. Curr. Opin. Genet. Dev. 20, 336–341 (2010).

    Article  CAS  PubMed  Google Scholar 

  129. Auffray, C., Imbeaud, S., Roux-Rouquie, M. & Hood, L. From functional genomics to systems biology: concepts and practices. C. R. Biol. 326, 879–892 (2003).

    Article  CAS  PubMed  Google Scholar 

  130. Liu, E. T., Kuznetsov, V. A. & Miller, L. D. In the pursuit of complexity: systems medicine in cancer biology. Cancer Cell 9, 245–247 (2006).

    Article  CAS  PubMed  Google Scholar 

  131. Check Hayden, E. Cancer complexity slows quest for cure. Nature 455, 148 (2008). Fundamental reference for the understanding of the complexity of cancer.

    Article  CAS  PubMed  Google Scholar 

  132. Sjoblom, T. et al. The consensus coding sequences of human breast and colorectal cancers. Science 314, 268–274 (2006).

    Article  PubMed  Google Scholar 

  133. Ferrari, M. Frontiers in cancer nanomedicine: directing mass transport through biological barriers. Trends Biotechnol. 28, 181–188 (2010). In this paper, cancer is presented as a disease of multiscale mass transport deregulation that requires multiscale physics for its investigation.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  134. Moore, N. M., Kuhn, N. Z., Hanlon, S. E., Lee, J. S. & Nagahara, L. A. De-convoluting cancer's complexity: using a 'physical sciences lens' to provide a different (clearer) perspective of cancer. Phys. Biol. 8, 010302 (2011).

    Article  CAS  PubMed  Google Scholar 

  135. Bearer, E. L. et al. Multiparameter computational modeling of tumor invasion. Cancer Res. 69, 4493–4501 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  136. Cristini, V. & Lowengrub, J. Multiscale Modeling Of Cancer: An Integrated Experimental And Mathematical Modeling Approach (Cambridge Univ. Press, 2010).

    Book  Google Scholar 

  137. Kim, P. et al. In vivo wide-area cellular imaging by side-view endomicroscopy. Nature Methods 7, 303–305 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  138. Ananta, J. S. et al. Geometrical confinement of gadolinium-based contrast agents in nanoporous particles enhances T1 contrast. Nature Nanotechnol. 5, 815–821 (2010).

    Article  CAS  Google Scholar 

  139. Tasciotti, E. et al. Mesoporous silicon particles as a multistage delivery system for imaging and therapeutic applications. Nature Nanotechnol. 3, 151–157 (2008).

    Article  CAS  Google Scholar 

  140. Tanaka, T. et al. Sustained small interfering RNA delivery by mesoporous silicon particles. Cancer Res. 70, 3687–3696 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  141. Ferrari, M. Vectoring siRNA therapeutics into the clinic. Nature Rev. Clin. Oncol. 7, 485–486 (2010).

    Article  CAS  Google Scholar 

  142. Decuzzi, P. & Ferrari, M. Design maps for nanoparticles targeting the diseased microvasculature. Biomaterials 29, 377–384 (2008).

    Article  CAS  PubMed  Google Scholar 

  143. Decuzzi, P. & Ferrari, M. The adhesive strength of non-spherical particles mediated by specific interactions. Biomaterials 27, 5307–5314 (2006).

    Article  CAS  PubMed  Google Scholar 

  144. Gentile, F., Ferrari, M. & Decuzzi, P. The transport of nanoparticles in blood vessels: the effect of vessel permeability and blood rheology. Ann. Biomed. Eng. 36, 254–261 (2008).

    Article  PubMed  Google Scholar 

  145. Serda, R. E. et al. Logic-embedded vectors for intracellular partitioning, endosomal escape, and exocytosis of nanoparticles. Small 6, 2691–2700 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  146. Nickerson, J. A., Krockmalnic, G., Wan, K. M. & Penman, S. The nuclear matrix revealed by eluting chromatin from a cross-linked nucleus. Proc. Natl Acad. Sci. USA 94, 4446–4450 (1997).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

The authors would like to acknowledge support from the US National Cancer Institute Physical Sciences-Oncology Center (PSOC) initiative to fund the Dana-Farber Cancer Institute PSOC (F.M.), Bay Area PSOC (J.L.), The Methodist Hospital Research Institute PSOC (M.F.) and the Northwestern University PSOC (J.W.). M.F.'s research for this article was furthermore supported by grants from DoD/BCRP (W81XWH-09-1-0212), as well as by the Ernest Cockrell Jr. Distinguished Endowed Chair. The authors would like to thank A. Sebeson for her invaluable help. This work is dedicated to Professor Jonathan Widom. With his passing, we have lost both a major intellectual force and a valued member of our community, as well as a trusted friend and colleague.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Franziska Michor.

Ethics declarations

Competing interests

The authors declare no competing financial interests.

Related links

Glossary

Superconductivity

A phenomenon of zero electrical resistance occurring in certain materials below a characteristic temperature.

Fractional quantum hall effect

A property of a collective state in which electrons bind magnetic flux lines to make new quasiparticles, and excitations have a fractional elementary charge.

Monte-Carlo method

A technique in which a large quantity of randomly generated numbers is studied using a probabilistic model to find an approximate solution to a numerical problem that would be difficult to solve by other methods.

Coupled degrees of freedom

The number of values in a study that are free to vary but that are constrained to vary together.

Emergent phenomena

Complex systems and patterns that arise from a multiplicity of relatively simple interactions.

Elastic energy

Energy stored in the configuration of a physical system as work is carried out to distort its volume or shape.

Population genetics

The mathematical study of the dynamics of genetic variation within populations.

Mesoscopic

A subdiscipline of condensed matter physics that deals with materials of an intermediate length scale, between the size of a quantity of atoms (such as, a molecule) and of materials measuring microns.

Probes

Nanoparticles or macromolecules that test the transport properties of tissues and biological barriers.

Delivery vector

A carrier nanoscale or microscale particle, for injection in the systemic circulation, that encapsulates anticancer therapy, and delivers it preferentially to target tissue.

Rights and permissions

Reprints and Permissions

About this article

Cite this article

Michor, F., Liphardt, J., Ferrari, M. et al. What does physics have to do with cancer?. Nat Rev Cancer 11, 657–670 (2011). https://doi.org/10.1038/nrc3092

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/nrc3092

This article is cited by

Search

Quick links

Nature Briefing: Cancer

Sign up for the Nature Briefing: Cancer newsletter — what matters in cancer research, free to your inbox weekly.

Get what matters in cancer research, free to your inbox weekly. Sign up for Nature Briefing: Cancer