Advertisement

Computational Simulation of Tumor Hypoxia Based on In Vivo Microvasculature Assessed in a Dorsal Skin Window Chamber

  • Lina Xu
  • Peter Vaupel
  • Siwei Bai
  • Bjoern Menze
  • Kuangyu ShiEmail author
Chapter
Part of the Advances in Experimental Medicine and Biology book series (AEMB, volume 977)

Abstract

Malignant growth usually leads to the depletion of oxygen (O2) supply in most solid tumors. Hypoxia can cause resistance to standard radiotherapy, some chemotherapy and immunotherapy. Furthermore, it can also trigger malignant progression by modulating gene expression and inducing genetic instability. The relationship between microvasculature, perfusion and tumor hypoxia has been intensively studied and many computational simulations have been developed to model tissue O2 transport. Usually simplified 2D phantoms are used to investigate tumor hypoxia and it is assumed that vessels are perpendicular to the region of interest and randomly distributed across the domain. Such idealistic topology overlooks vascular heterogeneity and is not accurate enough to approximate real scenarios. In addition, experimental verification of the spatial gradient of computational simulations is not directly feasible. Realistic vasculature obtained from fluorescence imaging imported as geometry for partial differential equations solving did not receive necessary attention so far. Therefore, we established a computational simulation of in vivo conditions using experimental data obtained from dorsal skin window chamber tumor preparations in nude rats for the verification of computational results. Tumor microvasculature was assessed by fluorescence microscopy. Since the conventional finite difference method can hardly satisfy the real measurements, we established a finite element method (FEM) for the experimental data in this study. Realistic 2D tumor microvasculature was reconstructed by segmenting fluorescence images and then translated into FEM topology. O2 distributions and the O2 gradients were obtained by solving reaction-diffusion equations. The simulation results show that the development of tumor hypoxia is greatly influenced by the irregular architecture and function of microvascular networks.

Keywords

Tumor hypoxia Tumor oxygenation, heterogeneity Tumor oxygenation, computer simulation Tumor microvasculature Oxygen gradients, tumor 

References

  1. 1.
    Vaupel P, Kallinowski F, Okunieff P (1989) Blood flow, oxygen and nutrient supply, and metabolic microenvironment of human tumors: a review. Cancer Res 49:6449–6465PubMedGoogle Scholar
  2. 2.
    Vaupel P (2004) The role of hypoxia-induced factors in tumor progression. Oncologia 9:10–17CrossRefGoogle Scholar
  3. 3.
    Vaupel P, Multhoff G (2016) Adenosine can thwart antitumor immune responses elicited by radiotherapy. Strahlenther Onkol 192:279–287CrossRefPubMedGoogle Scholar
  4. 4.
    Vaupel P (2004) Tumor microenvironmental physiology and its implications for radiation oncology. Semin Radiat Oncol 14:198–206CrossRefPubMedGoogle Scholar
  5. 5.
    Bruley DF (1994) Modeling oxygen transport: development of methods and current state. Adv Exp Med Biol 345:33–42CrossRefPubMedGoogle Scholar
  6. 6.
    Kelly CJ, Brady M (2006) A model to simulate tumour oxygenation and dynamic [18F]-Fmiso PET data. Phys Med Biol 51:5859–5873CrossRefPubMedGoogle Scholar
  7. 7.
    Pogue BW et al (2001) Estimation of oxygen distribution in RIF-1 tumors by diffusion model-based interpretation of pimonidazole hypoxia and Eppendorf measurements. Radiat Res 155:15–25CrossRefPubMedGoogle Scholar
  8. 8.
    Wang Q, Vaupel P, Ziegler SI, Shi K (2015) Exploring the quantitative relationship between metabolism and enzymatic phenotype by physiological modeling of glucose metabolism and lactate oxidation in solid tumors. Phys Med Biol 60:2547–2571CrossRefPubMedGoogle Scholar
  9. 9.
    Schneider M, Hirsch S, Weber B et al (2014) TGIF: Topological gap in-fill for vascular networks/MACCAI. In : Golland P, Hata N, Barillot C, Hornegger J, Howe R (eds) Medical image computing and computer-assisted intervention – MICCAI 2014: 17th International Conference, Boston, MA, USA, September 14-18, 2014, Proceedings, Part II. Springer International Publishing, Cham, ISBN:978-3-319-10470-6, doi: 10.1007/978-3-319-10470-6_12
  10. 10.
    Shi K, Bayer C, Maftei C. et al. (2011) A flow-limited oxygen-dependent diffusion model using heterogeneous perfusion for quantitative analysis of dynamic [18F] FMISO PET. J Nucl Med Meeting Abstracts 52(Suppl1):420Google Scholar
  11. 11.
    Mönnich D et al (2013) Correlation between tumor oxygenation and 18F-fluoromisonidazole PET data simulated based on microvessel images. Acta Oncol 52:1308–1313CrossRefPubMedGoogle Scholar
  12. 12.
    Helmlinger G, Yuan F, Dellian M, Jain RK (1997) Interstitial pH and pO2 gradients in solid tumors in vivo: high-resolution measurements reveal a lack of correlation. Nat Med 3:177–182CrossRefPubMedGoogle Scholar
  13. 13.
    Mönnich D, Troost EGC et al (2011) Modelling and simulation of [18F] fluoromisonidazole dynamics based on histology-derived microvessel maps. Phys Med Biol 56:2045–2057CrossRefPubMedGoogle Scholar
  14. 14.
    Goldman D (2008) Theoretical models of microvascular oxygen transport to tissue. Microcirculation 15:795–811CrossRefPubMedPubMedCentralGoogle Scholar
  15. 15.
    Secomb TW, Hsu R, Ong ET et al (1995) Analysis of the effects of oxygen supply and demand on hypoxic fraction in tumors. Acta Oncol 34:313–316CrossRefPubMedGoogle Scholar
  16. 16.
    Secomb TW, Hsu R, Park EYH et al (2004) Green’s function methods for analysis of oxygen delivery to tissue by microvascular networks [J]. Ann Biomed Eng 32:1519–1529CrossRefPubMedGoogle Scholar
  17. 17.
    Tannock IF (1972) Oxygen diffusion and the distribution of cellular radiosensitivity in tumours. Br J Radiol 45:515–524CrossRefPubMedGoogle Scholar
  18. 18.
    Eggleton CD, Vadapalli A, Roy TK et al (2000) Calculations of intracapillary oxygen tension distributions in muscle. Math Biosci 167:123–143CrossRefPubMedGoogle Scholar
  19. 19.
    Groebe K, Vaupel P (1988) Evaluation of oxygen diffusion distances in human breast cancer xenografts using tumor-specific in vivo data: role of various mechanisms in the development of tumor hypoxia. Int J Radiat Oncol Biol Phys 15:691–697CrossRefPubMedGoogle Scholar
  20. 20.
    Vaupel P (1990) Oxygenation of human tumors. Strahlenther Onkol 166:377–386PubMedGoogle Scholar
  21. 21.
    Vaupel P, Mayer A (2007) Hypoxia in cancer: significance and impact on clinical outcome. Cancer Metastasis Rev 26:225–239CrossRefPubMedGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Lina Xu
    • 1
    • 2
    • 3
  • Peter Vaupel
    • 4
  • Siwei Bai
    • 2
  • Bjoern Menze
    • 1
    • 2
  • Kuangyu Shi
    • 3
    Email author
  1. 1.Department of InformaticsTU MünchenMunichGermany
  2. 2.Department of Medical EngineeringTU MünchenMunichGermany
  3. 3.Department of Nuclear MedicineTU MünchenMunichGermany
  4. 4.Department of Radiation Oncology and RadiotherapyTU MünchenMunichGermany

Personalised recommendations