Clinical Radiology
Volume 65, Issue 7 , Pages 517-521 , July 2010

The biology underlying molecular imaging in oncology: from genome to anatome and back again

Received 15 December 2009 ,Revised 23 April 2010 ,Accepted 30 April 2010.

References 

  1. Croce CM. Causes and consequences of microRNA dysregulation in cancer. Nature Rev. 2009;10:704–714
  2. Hager GL, McNally JG, Misteli T. Transcription dynamics. Mol Cell. 2009;35:741–753
  3. Munshi A, Shafi G, Aliya N, et al. Histone modifications dictate specific biological readouts. J Genet Genomics. 2009;36:75–88
  4. Cairns BR. The logic of chromatin architecture and remodelling at promoters. Nature. 2009;461:193–198
  5. Zheng YG, Wu J, Chen Z, et al. Chemical regulation of epigenetic modifications: opportunities for new cancer therapy. Med Res Rev. 2008;28:645–687
  6. Carninci P, Yasuda J, Hayashizaki Y. Multifaceted mammalian transcriptome. Curr Opin Cell Biol. 2008;20:274–280
  7. Shi Y. Histone lysine demethylases: emerging roles in development, physiology and disease. Nature Rev. 2007;8:829–833
  8. Ponder BA. Cancer genetics. Nature. 2001;411:336–341
  9. Anderson AR, Rejniak KA, Gerlee P, et al. Microenvironment driven invasion: a multiscale multimodel investigation. J Math Biology. 2009;58:579–624
  10. Gatenby RA, Vincent TL. Application of quantitative models from population biology and evolutionary game theory to tumor therapeutic strategies. Mol Cancer Ther. 2003;2:919–927
  11. Anderson AR, Weaver AM, Cummings PT, et al. Tumor morphology and phenotypic evolution driven by selective pressure from the microenvironment. Cell. 2006;127:905–915
  12. Gerlee P, Anderson AR. Modelling evolutionary cell behaviour using neural networks: application to tumour growth. Biosystems. 2009;95:166–174
  13. Semenza GL, Wang GL. A nuclear factor induced by hypoxia via de novo protein synthesis binds to the human erythropoietin gene enhancer at a site required for transcriptional activation. Mol Cell Biol. 1992;12:5447–5454
  14. Semenza GL. Regulation of cancer cell metabolism by hypoxia-inducible factor 1. Semin Cancer Biol. 2009;19:12–16
  15. Kaelin WG. The von Hippel–Lindau tumour suppressor protein: O2 sensing and cancer. Nat Rev Cancer. 2008;8:865–873
  16. Gillies RJ, Robey I, Gatenby RA. Causes and consequences of increased glucose metabolism of cancers. J Nucl Med. 2008;49(Suppl. 2):24S–42S
  17. Gillies RJ, Schornack PA, Secomb TW, et al. Causes and effects of heterogeneous perfusion in tumors. Neoplasia. 1999;1:197–207
  18. Hanahan D, Weinberg RA. The hallmarks of cancer. Cell. 2000;100:57–70
  19. Kroemer G, Pouyssegur J. Tumor cell metabolism: cancer’s Achilles’ heel. Cancer Cell. 2008;13:472–482
  20. Gatenby RA, Gillies RJ. Why do cancers have high aerobic glycolysis?. Nat Rev Cancer. 2004;4:891–899
  21. Gatenby RA, Gillies RJ. A microenvironmental model of carcinogenesis. Nat Rev Cancer. 2008;8:56–61
  22. Jordan BF, Runquist M, Raghunand N, et al. Dynamic contrast-enhanced and diffusion MRI show rapid and dramatic changes in tumor microenvironment in response to inhibition of HIF-1alpha using PX-478. Neoplasia. 2005;7:475–485
  23. Stephen RM, Gillies RJ. Promise and progress for functional and molecular imaging of response to targeted therapies. Pharm Res. 2007;24:1172–1185
  24. van Baardwijk A, Dooms C, van Suylen RJ, et al. The maximum uptake of (18)F-deoxyglucose on positron emission tomography scan correlates with survival, hypoxia inducible factor-1alpha and GLUT-1 in non-small cell lung cancer. Eur J Cancer. 2007;43:1392–1398
  25. Taylor JS, Tofts PS, Port R, et al. MR imaging of tumor microcirculation: promise for the new millennium. J Magn Reson Imaging. 1999;10:903–907
  26. Leach MO, Brindle KM, Evelhoch JL, et al. The assessment of antiangiogenic and antivascular therapies in early-stage clinical trials using magnetic resonance imaging: issues and recommendations. Br J Cancer. 2005;92:1599–1610
  27. Jackson A, O’Connor JP, Parker GJ, et al. Imaging tumor vascular heterogeneity and angiogenesis using dynamic contrast-enhanced magnetic resonance imaging. Clin Cancer Res. 2007;13:3449–3459
  28. Buonaccorsi GA, O’Connor JP, Caunce A, et al. Tracer kinetic model-driven registration for dynamic contrast-enhanced MRI time-series data. Magn Reson Med. 2007;58:1010–1019
  29. Issa B, Buckley DL, Turnbull LW. Heterogeneity analysis of Gd-DTPA uptake: improvement in breast lesion differentiation. J Comp Assist Tomogr. 1999;23:615–621
  30. Rose CJ, Mills SJ, O’Connor JP, et al. Quantifying spatial heterogeneity in dynamic contrast-enhanced MRI parameter maps. Magn Reson Med. 2009;62:488–499
  31. Prescott JW, Zhang D, Wang JZ, et al. Temporal analysis of tumor heterogeneity and volume for cervical cancer treatment outcome prediction: preliminary evaluation. J Digit Imaging. 2010;23:342–357[Epub 2009 Jan 27]
  32. Som P, Atkins HL, Bandoypadhyay D, et al. A fluorinated glucose analog, 2-fluoro-2-deoxy-d-glucose (F-18): nontoxic tracer for rapid tumor detection. J Nucl Med. 1980;21:670–675
  33. Gambhir SS. Molecular imaging of cancer with positron emission tomography. Nat Rev Cancer. 2002;2:683–693
  34. Zhao S, Kuge Y, Mochizuki T, et al. Biologic correlates of intratumoral heterogeneity in 18F-FDG distribution with regional expression of glucose transporters and hexokinase-II in experimental tumor. J Nucl Med. 2005;46:675–682
  35. Eary JF, O’Sullivan F, O’Sullivan J, et al. Spatial heterogeneity in sarcoma 18F-FDG uptake as a predictor of patient outcome. J Nucl Med. 2008;49:1973–1979
  36. O’Sullivan F, Roy S, O’Sullivan J, et al. Incorporation of tumor shape into an assessment of spatial heterogeneity for human sarcomas imaged with FDG-PET. Biostatistics. 2005;6:293–301
  37. Kidd EA, Grigsby PW. Intratumoral metabolic heterogeneity of cervical cancer. Clin Cancer Res. 2008;14:5236–5241
  38. Petit SF, Aerts HJ, van Loon JG, et al. Metabolic control probability in tumour subvolumes or how to guide tumour dose redistribution in non-small cell lung cancer (NSCLC): an exploratory clinical study. Radiother Oncol. 2009;91:393–398
  39. Hamstra DA, Rehemtulla A, Ross BD. Diffusion magnetic resonance imaging: a biomarker for treatment response in oncology. J Clin Oncol. 2007;25:4104–4109
  40. Chenevert TL, Meyer CR, Moffat BA, et al. Diffusion MRI: a new strategy for assessment of cancer therapeutic efficacy. Mol Imaging. 2002;1:336–343
  41. Evelhoch JL, Gillies RJ, Karczmar GS, et al. Applications of magnetic resonance in model systems: cancer therapeutics. Neoplasia. 2000;2:152–165
  42. Bennett KM, Hyde JS, Schmainda KM. Water diffusion heterogeneity index in the human brain is insensitive to the orientation of applied magnetic field gradients. Magn Reson Med. 2006;56:235–239
  43. Kwee TC, Galban CJ, Tsien C, et al. Intravoxel water diffusion heterogeneity imaging of human high-grade gliomas. NMR Biomed. 2010;23:179–187
  44. Le Bihan D, Poupon C, Amadon A, et al. Artifacts and pitfalls in diffusion MRI. J Magn Reson Imaging. 2006;24:478–488
  45. Hobbs SK, Shi G, Homer R, et al. Magnetic resonance image-guided proteomics of human glioblastoma multiforme. J Magn Reson Imaging. 2003;18:530–536
  46. Van Meter T, Dumur C, Hafez N, et al. Microarray analysis of MRI-defined tissue samples in glioblastoma reveals differences in regional expression of therapeutic targets. Diagn Mol Pathol. 2006;15:195–205
  47. Tebbit CL, Zhai J, Untch BR, et al. Novel tumor sampling strategies to enable microarray gene expression signatures in breast cancer: a study to determine feasibility and reproducibility in the context of clinical care. Breast Cancer Res Treat. 2009;118:635–643
  48. Lee TY, Purdie TG, Stewart E. CT imaging of angiogenesis. Q J Nucl Med. 2003;47:171–187
  49. Mut M, Turba UC, Botella AC, et al. Neuroimaging characteristics in subgroup of GBMs with p53 overexpression. J Neuroimaging. 2007;17:168–174
  50. Sullivan DC. Challenges and opportunities for in vivo imaging in oncology. Technol Cancer Res Treat. 2002;1:419–422
  51. Giger ML, Chan HP, Boone J. Anniversary paper. History and status of CAD and quantitative image analysis: the role of Medical Physics and AAPM. Med Phys. 2008;35:5799–5820
  52. Yuan Y, Giger ML, Li H, et al. Correlative feature analysis on FFDM. Med Phys. 2008;35:5490–5500
  53. Li H, Giger ML, Yuan Y, et al. Evaluation of computer-aided diagnosis on a large clinical full-field digital mammographic dataset. Acad Radiol. 2008;15:1437–1445
  54. Diehn M, Nardini C, Wang DS, et-al. Identification of noninvasive imaging surrogates for brain tumor gene-expression modules. Proc Natl Acad Sci U S A 2008;105:5213–5218.
  55. Segal E, Sirlin CB, Ooi C, et al. Decoding global gene expression programs in liver cancer by noninvasive imaging. Nature Biotechnol. 2007;25:675–680
  56. Henson JW, Gaviani P, Gonzalez RG. MRI in treatment of adult gliomas. Lancet Oncol. 2005;6:167–175
  57. Rees JH, Smirniotopoulos JG, Jones RV, et al. Glioblastoma multiforme: radiologic–pathologic correlation. RadioGraphics. 1996;16:1413–1438quiz 62–63

PII: S0009-9260(10)00182-0

doi: 10.1016/j.crad.2010.04.005

Clinical Radiology
Volume 65, Issue 7 , Pages 517-521 , July 2010