Quantitative microimmunohistochemistry for the grading of immunostains on tumour tissues

Abstract

Immunohistochemistry is the gold-standard method for cancer-biomarker identification and patient stratification. Yet, owing to signal saturation, its use as a quantitative assay is limited as it cannot distinguish tumours with similar biomarker-expression levels. Here, we introduce a quantitative microimmunochemistry assay that enables the acquisition of dynamic information, via a metric of the evolution of the immunohistochemistry signal during tissue staining, for the quantification of relative antigen density on tissue surfaces. We used the assay to stratify 30 patient-derived breast-cancer samples into conventional classes and to determine the proximity of each sample to the other classes. We also show that the assay enables the quantification of multiple biomarkers (human epidermal growth factor receptor, oestrogen receptor and progesterone receptor) in a standard breast-cancer panel. The integration of quantitative microimmunohistochemistry into current pathology workflows may lead to improvements in the precision of biomarker quantification.

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: Quantitative scoring of biomarker expression using a SAM.
Fig. 2: The qµIC assay—create gradients of primary antibody incubation times on FFPE tissue sections using a MFP.
Fig. 3: Analytical pipeline for the qµIC assay.
Fig. 4: Variation of saturation kinetics based on reference cell-block sections with variant HER2 expression.
Fig. 5: SAM for IHC-intensity grading of patient samples using cell blocks as references.
Fig. 6: Multiplexed SAM extraction for different ROIs and different biomarkers.
Fig. 7: Classification of samples using the k-nearest neighbours algorithm for three classes.

Data availability

The data that support the findings of this study are available within the paper and its Supplementary Information. The raw data generated in this study are available from the corresponding author on reasonable request.

Code availability

The custom Python code for the described algorithms is available on request.

References

  1. 1.

    Coons, A. H., Creech, H. J. & Jones, R. N. Immunological properties of an antibody containing a fluorescent group. Exp. Biol. Med. 47, 200–202 (1941).

    CAS  Article  Google Scholar 

  2. 2.

    Coons, A. H. & Kaplan, M. H. Localization of antigen in tissue cells: II. Improvements in a method for the detection of antigen by means of fluorescent antibody. J. Exp. Med. 91, 1–13 (1949).

    Article  Google Scholar 

  3. 3.

    Nakane, P. K. & Pierce, G. B. Enzyme-labeled antibodies: preparation and application for the localization of antigens. J. Histochem. Cytochem. 14, 929–931 (1966).

    CAS  Article  Google Scholar 

  4. 4.

    Nakane, P. Simultaneous localization of multiple tissue antigens using the peroxidase labeled antibody method: a study of pituitary glands of the rat. J. Histochem. Cytochem. 16, 557–560 (1968).

    CAS  Article  Google Scholar 

  5. 5.

    de Matos, L. L., Trufelli, D. C., de Matos, M. G. L. & da Silva Pinhal, M. A. Immunohistochemistry as an important tool in biomarkers detection and clinical practice. Biomark. Insights 5, 9–20 (2010).

    Article  Google Scholar 

  6. 6.

    de Gramont, A. et al. Pragmatic issues in biomarker evaluation for targeted therapies in cancer. Nat. Rev. Clin. Oncol. 12, 197–212 (2014).

    Article  Google Scholar 

  7. 7.

    Smith, I. et al. 2-year follow-up of trastuzumab after adjuvant chemotherapy in HER2-positive breast cancer: a randomised controlled trial. Lancet 369, 29–36 (2007).

    CAS  Article  Google Scholar 

  8. 8.

    Gámez-Pozo, A. et al. The Long-HER study: clinical and molecular analysis of patients with HER2+ advanced breast cancer who become long-term survivors with trastuzumab-based therapy. PLoS ONE 9, e109611 (2014).

    Article  Google Scholar 

  9. 9.

    Zacharakis, N. et al. Immune recognition of somatic mutations leading to complete durable regression in metastatic breast cancer. Nat. Med. 24, 724–730 (2018).

    CAS  Article  Google Scholar 

  10. 10.

    Mason, J. T., Fowler, C. B. & O’leary, T. J. In Antigen Retrieval Immunohistochemistry Based Research and Diagnostics (eds Shi, S.-R. & Taylor, C. R.) 251–285 (John Wiley & Sons, Inc., 2010).

  11. 11.

    Kunz, P. et al. Osteosarcoma microenvironment: whole-slide imaging and optimized antigen detection overcome major limitations in immunohistochemical quantification. PLoS ONE 9, e90727 (2014).

    Article  Google Scholar 

  12. 12.

    Sabattini, E. et al. The EnVision++system: a new immunohistochemical method for diagnostics and research. Critical comparison with the APAAP, ChemMate, CSA, LABC, and SABC techniques. J. Clin. Pathol. 51, 506–511 (1998).

    CAS  Article  Google Scholar 

  13. 13.

    Wu, X. et al. Immunofluorescent labeling of cancer marker Her2 and other cellular targets with semiconductor quantum dots. Nat. Biotechnol. 21, 41–46 (2003).

    CAS  Article  Google Scholar 

  14. 14.

    Barrow, E., Evans, D. G., McMahon, R., Hill, J. & Byers, R. A comparative study of quantitative immunohistochemistry and quantum dot immunohistochemistry for mutation carrier identification in Lynch syndrome. J. Clin. Pathol. 64, 208–214 (2011).

    Article  Google Scholar 

  15. 15.

    Kwon, S., Cho, C. H., Lee, E. S. & Park, J.-K. Automated measurement of multiple cancer biomarkers using quantum-dot-based microfluidic immunohistochemistry. Anal. Chem. 87, 4177–4183 (2015).

    CAS  Article  Google Scholar 

  16. 16.

    Zaha, D. C. Significance of immunohistochemistry in breast cancer. World J. Clin. Oncol. 5, 382–392 (2014).

    Article  Google Scholar 

  17. 17.

    Wolff, A. C. et al. American society of clinical oncology/college of American pathologists guideline recommendations for human epidermal growth factor receptor 2 testing in breast cancer. J. Clin. Oncol. 25, 118–145 (2006).

    Article  Google Scholar 

  18. 18.

    Allred, D. et al. Immunocytochemical analysis of estrogen receptors in human breast carcinomas. Evaluation of 130 cases and review of the literature regarding concordance with biochemical assay and clinical relevance. Arch. Surg. 125, 107–113 (1990).

    CAS  Article  Google Scholar 

  19. 19.

    Carlson, R. W. et al. HER2 testing in breast cancer: NCCN Task Force report and recommendations. J. Natl Compr. Canc. Netw. 4, S1–S22 (2006).

    Article  Google Scholar 

  20. 20.

    Rhodes, A. et al. A formalin-fixed, paraffin-processed cell line standard for quality control of immunohistochemical assay of HER-2/neu expression in breast cancer. Am. J. Clin. Pathol. 117, 81–89 (2002).

    CAS  Article  Google Scholar 

  21. 21.

    Vyberg, M. & Nielsen, S. Proficiency testing in immunohistochemistry—experiences from Nordic Immunohistochemical Quality Control (NordiQC). Virchows Arch. 468, 19–29 (2016).

    CAS  Article  Google Scholar 

  22. 22.

    Grube, D. Constants and variables in immunohistochemistry. Arch. Histol. Cytol. 67, 115–134 (2004).

    CAS  Article  Google Scholar 

  23. 23.

    Taylor, C. R. & Levenson, R. M. Quantification of immunohistochemistry—issues concerning methods, utility and semiquantitative assessment II. Histopathology 49, 411–424 (2006).

    CAS  Article  Google Scholar 

  24. 24.

    Taylor, C. R. Predictive biomarkers and companion diagnostics. The future of immunohistochemistry. Appl. Immunohistochem. Mol. Morphol. 22, 555–561 (2014).

    Article  Google Scholar 

  25. 25.

    Rizzardi, A. E. et al. Quantitative comparison of immunohistochemical staining measured by digital image analysis versus pathologist visual scoring. Diagn. Pathol. 7, 42 (2012).

    Article  Google Scholar 

  26. 26.

    Hall, B. H. et al. Computer-assisted assessment of the human epidermal growth factor receptor 2 immunohistochemical assay in imaged histologic sections using a membrane isolation algorithm and quantitative analysis of positive controls. BMC Med. Imaging 8, 11 (2008).

  27. 27.

    Arar, N. M. et al. Computational immunohistochemistry: recipes for standardization of immunostaining. In Int. Conf. Medical Image Computing and Computer-Assisted Intervention (eds Descoteaux, M. et al.) 48–55 (2017).

    Google Scholar 

  28. 28.

    O’Hurley, G. et al. Garbage in, garbage out: a critical evaluation of strategies used for validation of immunohistochemical biomarkers. Mol. Oncol. 8, 783–798 (2014).

    Article  Google Scholar 

  29. 29.

    Hammond, M. E. H. et al. American society of clinical oncology/college of American pathologists guideline recommendations for immunohistochemical testing of estrogen and progesterone receptors in breast cancer (unabridged version). Arch. Pathol. Lab. Med. 134, e48–e72 (2010).

    CAS  PubMed  Google Scholar 

  30. 30.

    Goldstein, N. S. et al. Recommendations for improved standardization of immunohistochemistry. Appl. Immunohistochem. Mol. Morphol. 15, 124–133 (2007).

    CAS  Article  Google Scholar 

  31. 31.

    Brügmann, A. et al. Digital image analysis of membrane connectivity is a robust measure of HER2 immunostains. Breast Cancer Res. Treat. 132, 41–49 (2012).

    Article  Google Scholar 

  32. 32.

    Masmoudi, H., Hewitt, S. M., Petrick, N., Myers, K. J. & Gavrielides, M. A. Automated quantitative assessment of HER-2/neu immunohistochemical expression in breast cancer. IEEE Trans. Med. Imaging 28, 916–925 (2009).

    Article  Google Scholar 

  33. 33.

    Camp, R. L., Chung, G. G. & Rimm, D. L. Automated subcellular localization and quantification of protein expression in tissue microarrays. Nat. Med. 8, 1323–1328 (2002).

    CAS  Article  Google Scholar 

  34. 34.

    Camp, R. L. X-Tile: a new bio-informatics tool for biomarker assessment and outcome-based cut-point optimization. Clin. Cancer Res. 10, 7252–7259 (2004).

    CAS  Article  Google Scholar 

  35. 35.

    Veta, M., Pluim, J. P. W., van Diest, P. J. & Viergever, M. A. Breast cancer histopathology image analysis: a review. IEEE Trans. Biomed. Eng. 61, 1400–1411 (2014).

    Article  Google Scholar 

  36. 36.

    Zhang, B. et al. Proteogenomic characterization of human colon and rectal cancer. Nature 513, 382–387 (2014).

    CAS  Article  Google Scholar 

  37. 37.

    Dong, F. et al. Computational pathology to discriminate benign from malignant intraductal proliferations of the breast. PLoS ONE 9, e114885 (2014).

    Article  Google Scholar 

  38. 38.

    Djuric, U., Zadeh, G., Aldape, K. & Diamandis, P. Precision histology: how deep learning is poised to revitalize histomorphology for personalized cancer care. NPJ Precis. Oncol. 1, 22 (2017).

    Article  Google Scholar 

  39. 39.

    Harigopal, M. et al. Multiplexed assessment of the southwest oncology group-directed intergroup breast cancer trial S9313 by AQUA shows that both high and low levels of HER2 are associated with poor outcome. Am. J. Pathol. 176, 1639–1647 (2010).

    Article  Google Scholar 

  40. 40.

    Potts, S. J. et al. Evaluating tumour heterogeneity in immunohistochemistry-stained breast cancer tissue. Lab. Invest. 92, 1342–1357 (2012).

    Article  Google Scholar 

  41. 41.

    Zrazhevskiy, P., True, L. D. & Gao, X. Multicolor multicycle molecular profiling with quantum dots for single-cell analysis. Nat. Protoc. 8, 1852–1869 (2013).

    CAS  Article  Google Scholar 

  42. 42.

    Vu, T. Q., Lam, W. Y., Hatch, E. W. & Lidke, D. S. Quantum dots for quantitative imaging: from single molecules to tissue. Cell Tissue Res. 360, 71–86 (2015).

    CAS  Article  Google Scholar 

  43. 43.

    Giesen, C. et al. Highly multiplexed imaging of tumour tissues with subcellular resolution by mass cytometry. Nat. Methods 11, 417–422 (2014).

    CAS  Article  Google Scholar 

  44. 44.

    Ciftlik, A. T., Lehr, H.-A. & Gijs, M. A. M. Microfluidic processor allows rapid HER2 immunohistochemistry of breast carcinomas and significantly reduces ambiguous (2+) read-outs. Proc. Natl Acad. Sci. USA 110, 5363–5368 (2013).

    CAS  Article  Google Scholar 

  45. 45.

    Juncker, D., Schmid, H. & Delamarche, E. Multipurpose microfluidic probe. Nat. Mater. 4, 622–628 (2005).

    CAS  Article  Google Scholar 

  46. 46.

    Kaigala, G. V., Lovchik, R. D., Drechsler, U. & Delamarche, E. A vertical microfluidic probe. Langmuir 27, 5686–5693 (2011).

    CAS  Article  Google Scholar 

  47. 47.

    Lovchik, R. D., Kaigala, G. V., Georgiadis, M. & Delamarche, E. Micro-immunohistochemistry using a microfluidic probe. Lab Chip 12, 1040–1043 (2012).

    CAS  Article  Google Scholar 

  48. 48.

    Kashyap, A., Autebert, J., Delamarche, E. & Kaigala, G. V. Selective local lysis and sampling of live cells for nucleic acid analysis using a microfluidic probe. Sci. Rep. 6, 29579 (2016).

    CAS  Article  Google Scholar 

  49. 49.

    Huber, D., Autebert, J. & Kaigala, G. V. Micro fluorescence in situ hybridization (μFISH) for spatially multiplexed analysis of a cell monolayer. Biomed. Microdevices 18, 40 (2016).

  50. 50.

    Sarkar, A., Kolitz, S., Lauffenburger, D. A. & Han, J. Microfluidic probe for single-cell analysis in adherent tissue culture. Nat. Commun. 5, 3421 (2014).

    Article  Google Scholar 

  51. 51.

    Ainla, A., Jansson, E. T., Stepanyants, N., Orwar, O. & Jesorka, A. A microfluidic pipette for single-cell pharmacology. Anal. Chem. 82, 4529–4536 (2010).

    CAS  Article  Google Scholar 

  52. 52.

    Ainla, A., Xu, S., Sanchez, N., Jeffries, G. D. M. & Jesorka, A. Single-cell electroporation using a multifunctional pipette. Lab Chip 12, 4605–4609 (2012).

    CAS  Article  Google Scholar 

  53. 53.

    Kaigala, G. V., Lovchik, R. D. & Delamarche, E. Microfluidics in the ‘Open Space’ for performing localized chemistry on biological interfaces. Angew. Chem. Int. Ed. 51, 11224–11240 (2012).

    CAS  Article  Google Scholar 

  54. 54.

    Delamarche, E. & Kaigala, G. V. (eds) Open-Space Microfluidics: Concepts, Implementations, Applications (Wiley, 2018).

  55. 55.

    Squires, T. M., Messinger, R. J. & Manalis, S. R. Making it stick: convection, reaction and diffusion in surface-based biosensors. Nat. Biotechnol. 26, 417–426 (2008).

    CAS  Article  Google Scholar 

  56. 56.

    Autebert, J., Cors, J., Taylor, D. & Kaigala, G. V. Convection-enhanced biopatterning with hydrodynamically confined nanoliter volumes of reagents. Anal. Chem. 88, 3235–3242 (2016).

  57. 57.

    De Michele, C., De Los Rios, P., Foffi, G. & Piazza, F. Simulation and theory of antibody binding to crowded antigen-covered surfaces. PLoS Comput. Biol. 12, e1004752 (2016).

  58. 58.

    Thurber, G. M., Schmidt, M. M. & Wittrup, K. D. Antibody tumour penetration: transport opposed by systemic and antigen-mediated clearance. Adv. Drug Deliv. Rev. 60, 1421–1434 (2008).

    CAS  Article  Google Scholar 

  59. 59.

    Worthylake, R., Opresko, L. K. & Wiley, H. S. ErbB-2 amplification inhibits down-regulation and induces constitutive activation of both ErbB-2 and epidermal growth factor receptors. J. Biol. Chem. 274, 8865–8874 (1999).

    CAS  Article  Google Scholar 

  60. 60.

    Van Der Loos, C. M. Chromogens in multiple immunohistochemical staining used for visual assessment and spectral imaging: the colorful future. J. Histochem. 33, 31–40 (2010).

    Google Scholar 

  61. 61.

    Andersson, E., Nie, Y., Roessler, C. & Grimm, O. Color deconvolution method with DAB scatter correction for bright field image analysis. In Medical Imaging 2018: Digital Pathology (eds Gurcan, M. N. & Tomaszewski, J. E.) 19 (SPIE, 2018).

  62. 62.

    Autebert, J., Cors, J. F., Taylor, D. P. & Kaigala, G. V. Convection-enhanced biopatterning with recirculation of hydrodynamically confined nanoliter volumes of reagents. Anal. Chem. 88, 3235–3242 (2016).

    CAS  Article  Google Scholar 

  63. 63.

    Dunnwald, L. K., Rossing, M. A. & Li, C. I. Hormone receptor status, tumour characteristics, and prognosis: a prospective cohort of breast cancer patients. Breast Cancer Res. 9, R6 (2007).

  64. 64.

    Weigelt, B., Peterse, J. L. & van’t Veer, L. J. Breast cancer metastasis: markers and models. Nat. Rev. Cancer 5, 591–602 (2005).

    CAS  Article  Google Scholar 

  65. 65.

    Kim, T. J. et al. Prognostic significance of high expression of ER-beta in surgically treated ER-positive breast cancer following endocrine therapy. J. Breast Cancer 15, 79–86 (2012).

    Article  Google Scholar 

  66. 66.

    Sompuram, S. R., Vani, K., Tracey, B., Kamstock, D. A. & Bogen, S. A. Standardizing immunohistochemistry. J. Histochem. Cytochem. 63, 681–690 (2015).

    CAS  Article  Google Scholar 

  67. 67.

    Taylor, C. R. & Shi, S.-R. Quantifiable internal reference standards for immunohistochemistry and uses thereof. US patent 2007/015417 (2008).

Download references

Acknowledgements

A.K., A.F.K. and G.V.K. are partly supported by a European Research Council Starting Grant, under the 7th Framework Program (project no. 311122, BioProbe). We thank L. Von Voithenberg, N. M. Arar, J. Cors, R. Lovchik, D. Taylor, I. Pereiro, J. Autebert and U. Drechsler for technical assistance and discussions. We thank Z. Varga (Pathology, University Hospital Zurich) for selecting anonymized breast-cancer samples and S. Dettwiler (Tissue Biobank, University Hospital Zurich) for technical assistance. P. Dittrich (ETH Zurich), A. deMello (ETH Zurich), O. Goksel (ETH Zurich), E. Delamarche and W. Riess are acknowledged for their continuous support.

Author information

Affiliations

Authors

Contributions

A.K., A.F.K., P.S. and G.V.K. designed the research project. A.F.K and A.K. performed the experiments. A.K., P.P. and M.G. designed and developed the analytical pipeline. A.K., A.F.K, P.P. and G.V.K. wrote the manuscript. All authors discussed the results and commented on the manuscript.

Corresponding author

Correspondence to Govind V. Kaigala.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

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

Supplementary information

Supplementary Information

Supplementary methods, figures, tables and references.

Reporting Summary

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Kashyap, A., Fomitcheva Khartchenko, A., Pati, P. et al. Quantitative microimmunohistochemistry for the grading of immunostains on tumour tissues. Nat Biomed Eng 3, 478–490 (2019). https://doi.org/10.1038/s41551-019-0386-3

Download citation

Further reading

Search

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