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.

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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.

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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

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.

Correspondence to Govind V. Kaigala.

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