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Multiparametric Analysis of the Tumor Microenvironment: Hypoxia Markers and Beyond

  • Arnulf MayerEmail author
  • Peter Vaupel
Chapter
Part of the Advances in Experimental Medicine and Biology book series (AEMB, volume 977)

Abstract

We have established a novel in situ protein analysis pipeline, which is built upon highly sensitive, multichannel immunofluorescent staining of paraffin sections of human and xenografted tumor tissue. Specimens are digitized using slide scanners equipped with suitable light sources and fluorescence filter combinations. Resulting digital images are subsequently subjected to quantitative image analysis using a primarily object-based approach, which comprises segmentation of single cells or higher-order structures (e.g., blood vessels), cell shape approximation, measurement of signal intensities in individual fluorescent channels and correlation of these data with positional information for each object. Our approach could be particularly useful for the study of the hypoxic tumor microenvironment as it can be utilized to systematically explore the influence of spatial factors on cell phenotypes, e.g., the distance of a given cell type from the nearest blood vessel on the cellular expression of hypoxia-associated biomarkers and other proteins reflecting their specific state of activation or function. In this report, we outline the basic methodology and provide an outlook on possible use cases.

Keywords

Tumor microenvironment Tumor hypoxia Multiparametric image analysis Immunofluorescence Immunohistochemistry 

Notes

Acknowledgment

The authors would like to thank Sysmex GmbH (Norderstedt, Germany) for providing access to slide scanners and scanning services.

References

  1. 1.
    Cancer Genome Atlas Network (2015) Comprehensive genomic characterization of head and neck squamous cell carcinomas. Nature 517(7536):576–582CrossRefGoogle Scholar
  2. 2.
    Nowell PC (1976) The clonal evolution of tumor cell populations. Science 194(4260):23–28CrossRefPubMedGoogle Scholar
  3. 3.
    Gerlinger M, Rowan AJ, Horswell S et al (2012) Intratumor heterogeneity and branched evolution revealed by multiregion sequencing. N Engl J Med 366(10):883–892CrossRefPubMedPubMedCentralGoogle Scholar
  4. 4.
    Chen ZY, Zhong WZ, Zhang XC et al (2012) EGFR mutation heterogeneity and the mixed response to EGFR tyrosine kinase inhibitors of lung adenocarcinomas. Oncologist 17(7):978–985CrossRefPubMedPubMedCentralGoogle Scholar
  5. 5.
    Madar S, Goldstein I, Rotter V (2013) ‘Cancer associated fibroblasts’ – more than meets the eye. Trends Mol Med 19(8):447–453CrossRefPubMedGoogle Scholar
  6. 6.
    Carmeliet P, Jain RK (2011) Molecular mechanisms and clinical applications of angiogenesis. Nature 473(7347):298–307CrossRefPubMedPubMedCentralGoogle Scholar
  7. 7.
    Ruffell B, Coussens LM (2015) Macrophages and therapeutic resistance in cancer. Cancer Cell 27(4):462–472CrossRefPubMedPubMedCentralGoogle Scholar
  8. 8.
    Zou W (2006) Regulatory T cells, tumour immunity and immunotherapy. Nat Rev Immunol 6(4):295–307CrossRefPubMedGoogle Scholar
  9. 9.
    Talmadge JE, Fidler IJ (2010) AACR centennial series: the biology of cancer metastasis: historical perspective. Cancer Res 70(14):5649–5669CrossRefPubMedPubMedCentralGoogle Scholar
  10. 10.
    van der Loos CM (1999) Immunoenzyme multiple staining methods. In: Royal Microscopical Society microscopy handbooks, vol 45. Bios Scientific Publishers, OxfordGoogle Scholar
  11. 11.
    Mayer A, Höckel M, Schlischewsky N et al (2013) Lacking hypoxia-mediated downregulation of E-cadherin in cancers of the uterine cervix. Br J Cancer 108(2):402–408CrossRefPubMedPubMedCentralGoogle Scholar
  12. 12.
    Arganda-Carreras I, Sorzano COS, Marabini R et al. (2006) Consistent and elastic registration of histological sections using vector-spline regularization. In: Beichel RR, Sonka M (eds) Computer vision approaches to medical image analysis: second international ECCV workshop, CVAMIA 2006 Graz, Austria, May 12, 2006 Revised papers. Springer, Berlin\Heidelberg, pp 85–95. doi: 10.1007/11889762_8
  13. 13.
    Mayer A, Schmidt M, Seeger A et al (2014) GLUT-1 expression is largely unrelated to both hypoxia and the Warburg phenotype in squamous cell carcinomas of the vulva. BMC Cancer 14(1):1–9CrossRefGoogle Scholar
  14. 14.
    Toth ZE, Mezey E (2007) Simultaneous visualization of multiple antigens with tyramide signal amplification using antibodies from the same species. J Histochem Cytochem 55(6):545–554CrossRefPubMedGoogle Scholar
  15. 15.
    Mayer A, Brieger J, Vaupel P et al (2015) Multispectral, multiplexed analysis of the tumor microenvironment in FFPE tissue of head and neck cancer. Strahlenther Onkol 191:S45–S45Google Scholar
  16. 16.
    Lamprecht MR, Sabatini DM, Carpenter AE (2007) CellProfiler: free, versatile software for automated biological image analysis. BioTechniques 42(1):71–75CrossRefPubMedGoogle Scholar
  17. 17.
    Henze AT, Garvalov BK, Seidel S et al (2014) Loss of PHD3 allows tumours to overcome hypoxic growth inhibition and sustain proliferation through EGFR. Nat Commun 5:5582CrossRefPubMedPubMedCentralGoogle Scholar
  18. 18.
    Garvalov BK, Foss F, Henze AT et al (2014) PHD3 regulates EGFR internalization and signalling in tumours. Nat Commun 5:5577CrossRefPubMedGoogle Scholar
  19. 19.
    Mayer A, Zahnreich S, Brieger J et al (2016) Downregulation of EGFR in hypoxic, diffusion-limited areas of squamous cell carcinomas of the head and neck. Br J Cancer 115:1351-1358Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Department of Radiation Oncology and Radiotherapy, Tumor Pathophysiology DivisionUniversity Medical CenterMainzGermany

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