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.

Whole-tissue biopsy phenotyping of three-dimensional tumours reveals patterns of cancer heterogeneity

A Publisher Correction to this article was published on 02 January 2018

This article has been updated

Abstract

Intratumoral heterogeneity is a critical factor when diagnosing and treating patients with cancer. Marked differences in the genetic and epigenetic backgrounds of cancer cells have been revealed by advances in genome sequencing, yet little is known about the phenotypic landscape and the spatial distribution of intratumoral heterogeneity within solid tumours. Here, we show that three-dimensional light-sheet microscopy of cleared solid tumours can identify unique patterns of phenotypic heterogeneity, in the epithelial-to-mesenchymal transition and in angiogenesis, at single-cell resolution in whole formalin-fixed paraffin-embedded (FFPE) biopsy samples. We also show that cleared FFPE samples can be re-embedded in paraffin after examination for future use, and that our tumour-phenotyping pipeline can determine tumour stage and stratify patient prognosis from clinical samples with higher accuracy than current diagnostic methods, thus facilitating the design of more efficient cancer therapies.

Access options

Rent or Buy article

Get time limited or full article access on ReadCube.

from$8.99

All prices are NET prices.

Fig. 1: Assessment of embedding tissues in paraffin before 3D imaging.
Fig. 2: Whole-tissue 3D imaging of mouse bladder tumours.
Fig. 3: Whole-tissue 3D imaging of human FFPE tumours.
Fig. 4: Single-cell analysis of a human FFPE tumour.
Fig. 5: Re-embedding of cleared human FFPE tumours.
Fig. 6: Diagnostic assessment of clinical UC FFPE samples using the DIPCO pipeline.
Fig. 7: Diagnostic assessment of clinical ovarian cancer FFPE samples using the DIPCO pipeline.

Change history

  • 02 January 2018

    In this Article originally published, owing to a technical error, author affiliations were incorrectly assigned in the HTML version; the PDF was correct. These errors have now been corrected.

References

  1. 1.

    Gerlinger, M. et al. Intratumor heterogeneity and branched evolution revealed by multiregion sequencing. N. Engl. J. Med. 366, 883–892 (2012).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  2. 2.

    Marusyk, A., Almendro, V. & Polyak, K. Intra-tumour heterogeneity: a looking glass for cancer? Nat. Rev. Cancer 12, 323–334 (2012).

    CAS  Article  PubMed  Google Scholar 

  3. 3.

    Junttila, M. R. & de Sauvage, F. J. Influence of tumour micro-environment heterogeneity on therapeutic response. Nature 501, 346–354 (2013).

    CAS  Article  PubMed  Google Scholar 

  4. 4.

    Zhang, J. et al. Intratumor heterogeneity in localized lung adenocarcinomas delineated by multiregion sequencing. Science 346, 256–259 (2014).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  5. 5.

    Vogelstein, B. et al. Cancer genome landscapes. Science 339, 1546–1558 (2013).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  6. 6.

    McGranahan, N. & Swanton, C. Biological and therapeutic impact of intratumor heterogeneity in cancer evolution. Cancer Cell 27, 15–26 (2015).

    CAS  Article  PubMed  Google Scholar 

  7. 7.

    Waclaw, B. et al. A spatial model predicts that dispersal and cell turnover limit intratumour heterogeneity. Nature 525, 261–264 (2015).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  8. 8.

    Polyak, K. & Weinberg, R. A. Transitions between epithelial and mesenchymal states: acquisition of malignant and stem cell traits. Nat. Rev. Cancer 9, 265–273 (2009).

    CAS  Article  PubMed  Google Scholar 

  9. 9.

    Plaks, V., Kong, N. & Werb, Z. The cancer stem cell niche: how essential is the niche in regulating stemness of tumor cells? Cell Stem Cell 16, 225–238 (2015).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  10. 10.

    Wang, K. et al. Whole-genome sequencing and comprehensive molecular profiling identify new driver mutations in gastric cancer. Nat. Genet. 46, 573–582 (2014).

    CAS  Article  PubMed  Google Scholar 

  11. 11.

    Andor, N. et al. Pan-cancer analysis of the extent and consequences of intratumor heterogeneity. Nat. Med. 22, 105–113 (2016).

    CAS  Article  PubMed  Google Scholar 

  12. 12.

    Saliba, A. E., Westermann, A. J., Gorski, S. A. & Vogel, J. Single-cell RNA-seq: advances and future challenges. Nucleic Acids Res. 42, 8845–8860 (2014).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  13. 13.

    Patel, A. P. et al. Single-cell RNA-seq highlights intratumoral heterogeneity in primary glioblastoma. Science 344, 1396–1401 (2014).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  14. 14.

    Lawson, D. A. et al. Single-cell analysis reveals a stem-cell program in human metastatic breast cancer cells. Nature 526, 131–135 (2015).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  15. 15.

    Tabassum, D. P. & Polyak, K. Tumorigenesis: it takes a village. Nat. Rev. Cancer 15, 473–483 (2015).

    CAS  Article  PubMed  Google Scholar 

  16. 16.

    Erturk, A. et al. Three-dimensional imaging of solvent-cleared organs using 3DISCO. Nat. Protoc. 7, 1983–1995 (2012).

    CAS  Article  PubMed  Google Scholar 

  17. 17.

    Ke, M. T., Fujimoto, S. & Imai, T. SeeDB: a simple and morphology-preserving optical clearing agent for neuronal circuit reconstruction. Nat. Neurosci. 16, 1154–1161 (2013).

    CAS  Article  PubMed  Google Scholar 

  18. 18.

    Chung, K. & Deisseroth, K. CLARITY for mapping the nervous system. Nat. Methods 10, 508–513 (2013).

    CAS  Article  PubMed  Google Scholar 

  19. 19.

    Susaki, E. A. et al. Whole-brain imaging with single-cell resolution using chemical cocktails and computational analysis. Cell 157, 726–739 (2014).

    CAS  Article  PubMed  Google Scholar 

  20. 20.

    Yang, B. et al. Single-cell phenotyping within transparent intact tissue through whole-body clearing. Cell 158, 945–958 (2014).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  21. 21.

    Hama, H. et al. ScaleS: an optical clearing palette for biological imaging. Nat. Neurosci. 18, 1518–1529 (2015).

    CAS  Article  PubMed  Google Scholar 

  22. 22.

    Tomer, R., Ye, L., Hsueh, B. & Deisseroth, K. Advanced CLARITY for rapid and high-resolution imaging of intact tissues. Nat. Protoc. 9, 1682–1697 (2014).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  23. 23.

    Tainaka, K. et al. Whole-body imaging with single-cell resolution by tissue decolorization. Cell 159, 911–924 (2014).

    CAS  Article  PubMed  Google Scholar 

  24. 24.

    Renier, N. et al. iDISCO: a simple, rapid method to immunolabel large tissue samples for volume imaging. Cell 159, 896–910 (2014).

    CAS  Article  PubMed  Google Scholar 

  25. 25.

    Belle, M. et al. A simple method for 3D analysis of immunolabeled axonal tracts in a transparent nervous system. Cell Rep. 9, 1191–1201 (2014).

    CAS  Article  PubMed  Google Scholar 

  26. 26.

    Susaki, E. A. et al. Advanced CUBIC protocols for whole-brain and whole-body clearing and imaging. Nat. Protoc. 10, 1709–1727 (2015).

    CAS  Article  PubMed  Google Scholar 

  27. 27.

    Eliceiri, K. W. et al. Biological imaging software tools. Nat. Methods 9, 697–710 (2012).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  28. 28.

    Schneider, C. A., Rasband, W. S. & Eliceiri, K. W. NIH Image to ImageJ: 25 years of image analysis. Nat. Methods 9, 671–675 (2012).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  29. 29.

    Peng, H., Bria, A., Zhou, Z., Iannello, G. & Long, F. Extensible visualization and analysis for multidimensional images using Vaa3D. Nat. Protoc. 9, 193–208 (2014).

    CAS  Article  PubMed  Google Scholar 

  30. 30.

    Salois, G. & Smith, J. S. Housing complexity alters GFAP-immunoreactive astrocyte morphology in the rat dentate gyrus. Neural Plast. 2016, 3928726 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  31. 31.

    Peinado, H., Olmeda, D. & Cano, A. Snail, Zeb and bHLH factors in tumour progression: an alliance against the epithelial phenotype? Nat. Rev. Cancer 7, 415–428 (2007).

    CAS  Article  PubMed  Google Scholar 

  32. 32.

    Maier, J., Traenkle, B. & Rothbauer, U. Visualizing epithelial–mesenchymal transition using the chromobody technology. Cancer Res. 76, 5592–5596 (2016).

    CAS  Article  PubMed  Google Scholar 

  33. 33.

    Savagner, P. The epithelial–mesenchymal transition (EMT) phenomenon. Ann. Oncol. 21, vii89–vii92 (2010).

    Article  PubMed  Google Scholar 

  34. 34.

    Connor, J. et al. Regression of bladder tumors in mice treated with interleukin 2 gene-modified tumor cells. J. Exp. Med. 177, 1127–1134 (1993).

    CAS  Article  PubMed  Google Scholar 

  35. 35.

    Matsumoto, K. et al. Intravesical interleukin-15 gene therapy in an orthotopic bladder cancer model. Human Gene Ther. 22, 1423–1432 (2011).

    CAS  Article  Google Scholar 

  36. 36.

    Kobayashi, T., Owczarek, T. B., McKiernan, J. M. & Abate-Shen, C. Modelling bladder cancer in mice: opportunities and challenges. Nat. Rev. Cancer 15, 42–54 (2015).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  37. 37.

    Dobosz, M., Ntziachristos, V., Scheuer, W. & Strobel, S. Multispectral fluorescence ultramicroscopy: three-dimensional visualization and automatic quantification of tumor morphology, drug penetration, and antiangiogenic treatment response. Neoplasia 16, 1–13 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  38. 38.

    Dodt, H. U. et al. Ultramicroscopy: development and outlook. Neurophotonics 2, 041407 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  39. 39.

    Frampton, G. M. et al. Development and validation of a clinical cancer genomic profiling test based on massively parallel DNA sequencing. Nat. Biotechnol. 31, 1023–1031 (2013).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  40. 40.

    Zheng, Z. et al. Anchored multiplex PCR for targeted next-generation sequencing. Nat. Med. 20, 1479–1484 (2014).

    CAS  Article  PubMed  Google Scholar 

  41. 41.

    Brat, D. J. et al. Comprehensive, integrative genomic analysis of diffuse lower-grade gliomas. N. Engl. J. Med. 372, 2481–2498 (2015).

    CAS  Article  PubMed  Google Scholar 

  42. 42.

    Maley, C. C. et al. Genetic clonal diversity predicts progression to esophageal adenocarcinoma. Nat. Genet. 38, 468–473 (2006).

    CAS  Article  PubMed  Google Scholar 

  43. 43.

    Landau, D. A. et al. Evolution and impact of subclonal mutations in chronic lymphocytic leukemia. Cell 152, 714–726 (2013).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  44. 44.

    Bochtler, T. et al. Clonal heterogeneity as detected by metaphase karyotyping is an indicator of poor prognosis in acute myeloid leukemia. J. Clin. Oncol. 31, 3898–3905 (2013).

    Article  PubMed  Google Scholar 

  45. 45.

    Mroz, E. A., Tward, A. D., Hammon, R. J., Ren, Y. & Rocco, J. W. Intra-tumor genetic heterogeneity and mortality in head and neck cancer: analysis of data from the Cancer Genome Atlas. PLoS Med. 12, e1001786 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  46. 46.

    Vergote, I. et al. Re: new guidelines to evaluate the response to treatment in solid tumors [ovarian cancer]. Gynecologic Cancer Intergroup. J. Natl Cancer Inst. 92, 1534–1535 (2000).

    CAS  Article  PubMed  Google Scholar 

  47. 47.

    Tomer, R. et al. SPED light sheet microscopy: fast mapping of biological system structure and function. Cell 163, 1796–1806 (2015).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  48. 48.

    von Dadelszen, P. et al. Prediction of adverse maternal outcomes in pre-eclampsia: development and validation of the fullPIERS model. Lancet 377, 219–227 (2011).

    Article  Google Scholar 

  49. 49.

    Silvestri, G. A. et al. A bronchial genomic classifier for the diagnostic evaluation of lung cancer. N. Engl. J. Med. 373, 243–251 (2015).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  50. 50.

    Stutchfield, P., Whitaker, R. & Russell, I. Antenatal betamethasone and incidence of neonatal respiratory distress after elective caesarean section: pragmatic randomised trial. BMJ 331, 662 (2005).

    Article  PubMed  PubMed Central  Google Scholar 

  51. 51.

    Tierney, W. M., McDonald, C. J., Hui, S. L. & Martin, D. K. Computer predictions of abnormal test results. Effects Outpatient Test. JAMA 259, 1194–1198 (1988).

    CAS  PubMed  Google Scholar 

Download references

Acknowledgements

The authors would like to thank J. Szumiło, Department of Clinical Pathomorphology, Medical University of Lublin, Lublin, Poland for kindly providing human tissue samples. This study was supported by the Swedish Research Council (grants 2009-3364, 2010-4392 and 2013-3189 to P.U.), the Swedish Cancer Society (grant CAN2013/802 and CAN2016/801 to P.U.), the Swedish Brain Foundation (grant FO2017/0107 to P.U.), the Linnaeus Center in Developmental Biology for Regenerative Medicine (DBRM) (P.U.), a Knut and Alice Wallenberg Foundation Grant to the Center for Live Imaging of Cells at the Karolinska (CLICK) Institutet (P.U.), the Royal Swedish Academy of Sciences (P.U.), the David and Astrid Hagelén Foundation (N.T.), the Takeda Science Foundation (N.T.), the Scandinavia-Japan Sasakawa Foundation (N.T. and S.K.), and the Wenner-Gren Foundation (S.K.). The light-sheet microscopy infrastructure used in this work received grants from the Strategic Research Area in Neuroscience – StratNeuro and the Strategic Research Area in Stem Cells and Regenerative Medicine – StratRegen supported by the Swedish government.

Author information

Affiliations

Authors

Contributions

N.T., S.K., A.Mi. and P.U. designed the study. N.T., S.K., D.K., L.L. and K.M. performed the experiments. N.T., S.K. and R.T. performed 3D image processing. R.T. and K.D. developed the custom-built light-sheet microscope system. C.Sa., P.K., L.K., C.L., P.M., A.S., S.C., J.H., P.M., A.Me., C.St., J.W.C., C.F.M., H.D. and A.Mi. provided human tumour samples. P.W., M.O., A.Ö. and K.D. provided conceptual advice. N.T. and P.U. wrote the manuscript.

Corresponding author

Correspondence to Per Uhlén.

Ethics declarations

Competing interests

The authors declare no competing financial interests.

Additional information

Publisher’s note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

A correction to this article is available online at https://doi.org/10.1038/s41551-017-0162-1.

Supplementary information

Supplementary Information

Supplementary figures, tables and video legends.

Life Sciences Reporting Summary

Supplementary Video 1

Three-dimensional volume reconstruction of hTumour 1immunostained for E-cadherin.

Supplementary Video 2

Three-dimensional volume reconstruction of hTumour 3 immunostained for N-cadherin.

Supplementary Video 3

Three-dimensional volume reconstruction of hTumour 6 immunostained for CD34.

Supplementary Video 4

Three-dimensional volume reconstruction of the CD34 signal.

Supplementary Video 5

Single-cell 3D volume reconstruction of hTumour 7 immunostained for Vimentin.

Matlab script 1

Generation of centroids list (point cloud) from Hmaxima images.

Matlab script 2

Calculation of mean intensity value of each dots area.

Matlab script 3

Generation of binary images from XYZ coordinates.

Supplementary Table 1

Clinicopathological characteristics of 50 human urothelial FFPE samples.

Supplementary Table 2

Clinicopathological characteristics of 16 human ovarian cancer FFPE samples.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Tanaka, N., Kanatani, S., Tomer, R. et al. Whole-tissue biopsy phenotyping of three-dimensional tumours reveals patterns of cancer heterogeneity. Nat Biomed Eng 1, 796–806 (2017). https://doi.org/10.1038/s41551-017-0139-0

Download citation

Further reading

Search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing