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.
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?
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Paget, S. The distribution of secondary growths in cancer of the breast. Lancet 1, 571–573 (1889).
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).
Nowell, P. C. The clonal evolution of tumor cell populations. Science 194, 23–28 (1976).
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.
Fisher, J. C. Multiple-mutation theory of carcinogenesis. Nature 181, 651–652 (1958).
[Author unknown.] The Edwin Smith Surgical Papyrus, Vault RB, NY Acad. Med. Rare Book Room, New York (c1,600 BCE).
Butcher, D. T., Alliston, T. & Weaver, V. M. A tense situation: forcing tumour progression. Nature Rev. Cancer 9, 108–122 (2009).
Levental, K. R. et al. Matrix crosslinking forces tumor progression by enhancing integrin signaling. Cell 139, 891–906 (2009).
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).
Montell, D. J. Morphogenetic cell movements: diversity from modular mechanical properties. Science 322, 1502–1505 (2008).
Mariappan, Y. K., Glaser, K. J. & Ehman, R. L. Magnetic resonance elastography: a review. Clin. Anat. 23, 497–511 (2010).
Hansma, P. et al. The tissue diagnostic instrument. Rev. Sci. Instrum. 80, 054303 (2009).
Krieg, M. et al. Tensile forces govern germ-layer organization in zebrafish. Nature Cell Biol. 10, 429–436 (2008).
Salaita, K. et al. Restriction of receptor movement alters cellular response: physical force sensing by EphA2. Science 327, 1380–1385 (2010).
Taylor, J. E. Structure of singularities in soap-bubble-like and soap-film-like minimal surfaces. Ann. Math. 103, 489–539 (1976).
Hayashi, T. & Carthew, R. W. Surface mechanics mediate pattern formation in the developing retina. Nature 431, 647–652 (2004).
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).
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).
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).
Sprinzak, D. et al. Cis-interactions between Notch and Delta generate mutually exclusive signalling states. Nature 465, 86–90 (2010).
Cairns, J. Mutation selection and the natural history of cancer. Nature 255, 197–200 (1975).
Heppner, G. H. & Miller, F. R. The cellular basis of tumor progression. Int. Rev. Cytol. 177, 1–56 (1998).
Crespi, B. & Summers, K. Evolutionary biology of cancer. Trends Ecol. Evol. 20, 545–552 (2005).
Michor, F., Iwasa, Y. & Nowak, M. A. Dynamics of cancer progression. Nature Rev. Cancer 4, 197–205 (2004).
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.
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).
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).
Keller, L. Levels of Selection in Evolution. (Princeton Univ. Press, 1999).
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).
Knudson, A. G. Jr. Mutation and cancer: statistical study of retinoblastoma. Proc. Natl Acad. Sci. USA 68, 820–823 (1971).
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).
Tomlinson, I. P., Novelli, M. R. & Bodmer, W. F. The mutation rate and cancer. Proc. Natl Acad. Sci. USA 93, 14800–14803 (1996).
Desper, R. et al. Inferring tree models for oncogenesis from comparative genome hybridization data. J. Comput. Biol. 6, 37–51 (1999).
Goldie, J. H. & Coldman, A. J. Quantitative model for multiple levels of drug resistance in clinical tumors. Cancer Treat. Rep. 67, 923–931 (1983).
Coldman, A. J. & Murray, J. M. Optimal control for a stochastic model of cancer chemotherapy. Math. Biosci. 168, 187–200 (2000).
Michor, F. et al. Dynamics of chronic myeloid leukaemia. Nature 435, 1267–1270 (2005).
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).
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).
Iwasa, Y., Michor, F. & Nowak, M. A. Evolutionary dynamics of escape from biomedical intervention. Proc. Biol. Sci. 270, 2573–2578 (2003).
Komarova, N. L. & Wodarz, D. Drug resistance in cancer: principles of emergence and prevention. Proc. Natl Acad. Sci. USA 102, 9714–9719 (2005).
Durrett, R. & Moseley, S. Evolution of resistance and progression to disease during clonal expansion of cancer. Theor. Popul. Biol. 77, 42–48 (2010).
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).
Goldie, J. H. & Coldman, A. J. The genetic origin of drug resistance in neoplasms: implications for systemic therapy. Cancer Res. 44, 3643–3653 (1984).
Day, R. S. Treatment sequencing, asymmetry, and uncertainty: protocol strategies for combination chemotherapy. Cancer Res. 46, 3876–3885 (1986).
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).
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).
Foo, J. & Michor, F. Evolution of resistance to anti-cancer therapy during general dosing schedules. J. Theor. Biol. 263, 179–188 (2010).
Knudson, A. G. Two genetic hits (more or less) to cancer. Nature Rev. Cancer 1, 157–162 (2001).
Nordling, C. O. A new theory on cancer-inducing mechanism. Br. J. Cancer 7, 68–72 (1953).
Varmus, H. The new era in cancer research. Science 312, 1162–1165 (2006).
Weir, B., Zhao, X. & Meyerson, M. Somatic alterations in the human cancer genome. Cancer Cell 6, 433–438 (2004).
Futreal, P. A. et al. A census of human cancer genes. Nature Rev. Cancer 4, 177–183 (2004).
The Cancer Genome Atlas Research Network. Comprehensive genomic characterization defines human glioblastoma genes and core pathways. Nature 455, 1061–1068 (2008).
Beroukhim, R. et al. The landscape of somatic copy-number alteration across human cancers. Nature 463, 899–905 (2010).
Hanahan, D. & Weinberg, R. A. The hallmarks of cancer. Cell 100, 57–70 (2000).
Feinberg, A. P., Ohlsson, R. & Henikoff, S. The epigenetic progenitor origin of human cancer. Nature Rev. Genet. 7, 21–33 (2006).
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).
Stratton, M. R., Campbell, P. J. & Futreal, P. A. The cancer genome. Nature 458, 719–724 (2009).
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).
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).
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).
Huppert, J. L. Structure, location and interactions of G.-quadruplexes. FEBS J. 277, 3452–3458 (2010).
Lipps, H. J. & Rhodes, D. G.-quadruplex structures: in vivo evidence and function. Trends Cell Biol. 19, 414–422 (2009).
Maizels, N. Dynamic roles for G4 DNA in the biology of eukaryotic cells. Nature Struct. Mol. Biol. 13, 1055–1059 (2006).
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).
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).
Kruisselbrink, E. et al. Mutagenic capacity of endogenous G4 DNA underlies genome instability in FANCJ-defective C. elegans. Curr. Biol. 18, 900–905 (2008).
Pontier, D. B., Kruisselbrink, E., Guryev, V. & Tijsterman, M. Isolation of deletion alleles by G4 DNA-induced mutagenesis. Nature Methods 6, 655–657 (2009).
Boan, F. & Gomez-Marquez, J. In vitro recombination mediated by G-quadruplexes. Chembiochem 11, 331–334 (2010).
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).
Hartl, D. L. & Clark, A. G. Principles of Population Genetics. 4th edn (Sinauer Associates, 2007).
Fearon, E. R. & Vogelstein, B. A genetic model for colorectal tumorigenesis. Cell 61, 759–767 (1990).
Zhu, Y. et al. Early inactivation of p53 tumor suppressor gene cooperating with NF1 loss induces malignant astrocytoma. Cancer Cell 8, 119–130 (2005).
Abdel-Wahab, O. et al. Genetic analysis of transforming events that convert chronic myeloproliferative neoplasms to leukemias. Cancer Res. 70, 447–452 (2010).
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).
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).
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).
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.
Kornberg, R. D. & Lorch, Y. Twenty-five years of the nucleosome, fundamental particle of the eukaryote chromosome. Cell 98, 285–294 (1999).
Richmond, T. J. & Davey, C. A. The structure of DNA in the nucleosome core. Nature 423, 145–150 (2003).
Field, Y. et al. Gene expression divergence in yeast is coupled to evolution of DNA-encoded nucleosome organization. Nature Genet. 41, 438–445 (2009).
Field, Y. et al. Distinct modes of regulation by chromatin encoded through nucleosome positioning signals. PLoS Comput. Biol. 4, e1000216 (2008).
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).
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).
Sasaki, S. et al. Chromatin-associated periodicity in genetic variation downstream of transcriptional start sites. Science 323, 401–404 (2009).
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).
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).
Pryciak, P. M. & Varmus, H. E. Nucleosomes, DNA-binding proteins, and DNA sequence modulate retroviral integration target site selection. Cell 69, 769–780 (1992).
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).
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).
Bucceri, A., Kapitza, K. & Thoma, F. Rapid accessibility of nucleosomal DNA in yeast on a second time scale. EMBO J. 25, 3123–3132 (2006).
Prendergast, J. G. D. et al. Chromatin structure and evolution in the human genome. BMC Evol. Biol. 7, 72 (2007).
Widom, J. Role of DNA sequence in nucleosome stability and dynamics. Q. Rev. Biophys. 34, 269–324 (2001).
Cloutier, T. E. & Widom, J. Spontaneous sharp bending of double-stranded DNA. Mol. Cell 14, 355–362 (2004).
Segal, E. & Widom, J. Poly(dA:dT) tracts: major determinants of nucleosome organization. Curr. Opin. Struct. Biol. 19, 65–71 (2009).
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).
Morozov, A. et al. Using DNA mechanics to predict in vitro nucleosome positions and formation energies. Nucleic Acids Res. 37, 4707–4722 (2009).
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).
Geggier, S. & Vologodskii, A. Sequence dependence of DNA bending rigidity. Proc. Natl Acad. Sci. USA 107, 15421–15426 (2010).
Wiggins, P. A. et al. High flexibility of DNA on short length scales probed by atomic force microscopy. Nature Nanotech. 1, 137–141 (2006).
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).
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).
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).
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.
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).
Chevereau, G., Palmeira, L., Thermes, C., Arneodo, A. & Vaillant, C. Thermodynamics of intragenic nucleosome ordering. Phys. Rev. Lett. 103, 188103 (2009).
Schwab, D. J., Bruinsma, R. F., Rudnick, J. & Widom, J. Nucleosome switches. Phys. Rev. Lett. 100, 228105 (2008).
Segal, E. & Widom, J. From DNA sequence to transcriptional behaviour: a quantitative approach. Nature Rev. Genet. 10, 443–456 (2009).
Raveh-Sadka, T., Levo, M. & Segal, E. Incorporating nucleosomes into thermodynamic models of transcription regulation. Genome Res. 19, 1480–1496 (2009).
Segal, E. & Widom, J. What controls nucleosome positions? Trends Genet. 25, 335–343 (2009).
Strukov, Y. G. & Belmont, A. S. Mitotic chromosome structure: reproducibility of folding and symmetry between sister chromatids. Biophys. J. 96, 1617–1628 (2009).
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.
Subramanian, H. et al. Nanoscale cellular changes in field carcinogenesis detected by partial wave spectroscopy. Cancer Res. 69, 5357–5363 (2009).
Damania, D. et al. Role of cytoskeleton in controlling the disorder strength of cellular nanoscale architecture. Biophys. J. 99, 989–996 (2010).
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).
Hudson, T. J. et al. International network of cancer genome projects. Nature 464, 993–998 (2010).
Fidler, I. J. & Kripke, M. L. Metastasis results from preexisting variant cells within a malignant tumor. Science 197, 893–895 (1977).
Navin, N. et al. Inferring tumor progression from genomic heterogeneity. Genome Res. 20, 68–80 (2010).
Berger, M. F. et al. The genomic complexity of primary human prostate cancer. Nature 470, 214–220 (2011).
Beerenwinkel, N. et al. Genetic progression and the waiting time to cancer. PLoS Comput. Biol. 3, e225 (2007).
Yachida, S. et al. Distant metastasis occurs late during the genetic evolution of pancreatic cancer. Nature 467, 1114–1117 (2010).
Campbell, P. J. et al. The patterns and dynamics of genomic instability in metastatic pancreatic cancer. Nature 467, 1109–1113 (2010).
Huh, D. & Paulsson, J. Non-genetic heterogeneity from stochastic partitioning at cell division. Nature Genet. 43, 95–100 (2011).
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).
Hondermarck, H. Breast cancer: when proteomics challenges biological complexity. Mol. Cell. Proteomics 2, 281–291 (2003).
Fidler, I. J. & Hart, I. R. Biological diversity in metastatic neoplasms: origins and implications. Science 217, 998–1003 (1982).
Copeland, N. G. & Jenkins, N. A. Deciphering the genetic landscape of cancer--from genes to pathways. Trends Genet. 25, 455–462 (2009).
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).
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).
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).
Check Hayden, E. Cancer complexity slows quest for cure. Nature 455, 148 (2008). Fundamental reference for the understanding of the complexity of cancer.
Sjoblom, T. et al. The consensus coding sequences of human breast and colorectal cancers. Science 314, 268–274 (2006).
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.
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).
Bearer, E. L. et al. Multiparameter computational modeling of tumor invasion. Cancer Res. 69, 4493–4501 (2009).
Cristini, V. & Lowengrub, J. Multiscale Modeling Of Cancer: An Integrated Experimental And Mathematical Modeling Approach (Cambridge Univ. Press, 2010).
Kim, P. et al. In vivo wide-area cellular imaging by side-view endomicroscopy. Nature Methods 7, 303–305 (2010).
Ananta, J. S. et al. Geometrical confinement of gadolinium-based contrast agents in nanoporous particles enhances T1 contrast. Nature Nanotechnol. 5, 815–821 (2010).
Tasciotti, E. et al. Mesoporous silicon particles as a multistage delivery system for imaging and therapeutic applications. Nature Nanotechnol. 3, 151–157 (2008).
Tanaka, T. et al. Sustained small interfering RNA delivery by mesoporous silicon particles. Cancer Res. 70, 3687–3696 (2010).
Ferrari, M. Vectoring siRNA therapeutics into the clinic. Nature Rev. Clin. Oncol. 7, 485–486 (2010).
Decuzzi, P. & Ferrari, M. Design maps for nanoparticles targeting the diseased microvasculature. Biomaterials 29, 377–384 (2008).
Decuzzi, P. & Ferrari, M. The adhesive strength of non-spherical particles mediated by specific interactions. Biomaterials 27, 5307–5314 (2006).
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).
Serda, R. E. et al. Logic-embedded vectors for intracellular partitioning, endosomal escape, and exocytosis of nanoparticles. Small 6, 2691–2700 (2010).
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).
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.
The authors declare no competing financial interests.
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.
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.
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.
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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
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