Molecular Diagnostics

Multi-protein spatial signatures in ductal carcinoma in situ (DCIS) of breast



There is limited knowledge about DCIS cellular composition and relationship with breast cancer events (BCE).


Immunofluorescence multiplexing (MxIF) was used to image and quantify 32 cellular biomarkers in FFPE DCIS tissue microarrays. Over 75,000 DCIS cells from 51 patients (median 9 years follow-up for non-BCE cases) were analysed for profiles predictive of BCE. K-means clustering was used to evaluate cellular co-expression of epithelial markers with ER and HER2.


Only ER, PR and HER2 significantly correlated with BCE. Cluster analysis identified 6 distinct cell groups with different levels of ER, Her2, cMET and SLC7A5. Clusters 1 and 3 were not significant. Clusters 2 and 4 (high ER/low HER2 and SLC7A5/mixed cMET) significantly correlated with low BCE risk (P = 0.001 and P = 0.034), while cluster 6 (high HER2/low ER, cMET and SLC7A5) correlated with increased risk (P = 0.018). Cluster 5 (similar to cluster 6, except high SLC7A5) trended towards significance (P = 0.072). A continuous expression score (Escore) based on these 4 clusters predicted likelihood of BCE (AUC = 0.79, log-rank test P = 5E–05; LOOCV AUC = 0.74, log-rank test P = 0.006).


Multiplexed spatial analysis of limited tissue is a novel method for biomarker analysis and predicting BCEs. Further validation of Escore is needed in a larger cohort.

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Fig. 1: A flow chart representing the total number of cells and fields of view (FOV) that were analysed and the filtering process that was used for breast cancer events (BCE) analysis.
Fig. 2: Characteristics of the clinical cohort.
Fig. 3: Contribution of cell clusters to BCE and non-BCE.
Fig. 4: Virtual H&E images for each example cluster, biomaker stains and cluster plot of the cells expressing those markers at different levels of intensity.
Fig. 5: Model performance for prediction of BCE and non-BCE.


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The authors gratefully acknowledge the contributions of Alex Corwin for Cell DIVE imaging workflows and quality control and Sean Dinn and Eric Williams for antibody conjugations.

Author information




Concept, design, interpretation and execution of the entire project: S.S.B., Y.G.-P., M.G., P.H.T., A.H. and F.G. Statistical and bioinformatics analyses and interpretation: S.C., M.T., M.Z. and Y.S. Manuscript writing: S.S.B., S.C., Y.G.-P., P.H.T., M.G., A.H. and F.G. All authors approved the manuscript.

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Correspondence to Sunil S. Badve.

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Ethics approval and consent to participate

The study was performed in accordance with the Declaration of Helsinki. Waiver of IRB was obtained from Indiana, and Oxford Universities.

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Data availability

Additional information can be found in Supplemental data section. The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.

Competing interests

A patent disclosure has been filed at IU for the EScore. The authors (S.S.B., Y.G.P., F.G., S.C. and A.L.H.) have been listed as inventors. S.C., Y.S., C.C., E.M., A.S., M.Z., M.G. and F.G. are employees of GE. The remaining authors do not have any competing interests..

Funding information

This study is supported by the National Cancer Institute of the National Institutes of Health under Award Number R01CA194600 to S. Badve, M. Gerdes and F. Ginty. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.

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Badve, S.S., Cho, S., Gökmen-Polar, Y. et al. Multi-protein spatial signatures in ductal carcinoma in situ (DCIS) of breast. Br J Cancer (2021).

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