Breast Cancer Research and Treatment

, Volume 170, Issue 2, pp 271–277 | Cite as

A qualitative transcriptional signature to reclassify estrogen receptor status of breast cancer patients

  • Hao Cai
  • Wenbing Guo
  • Shuobo Zhang
  • Na Li
  • Xianlong Wang
  • Huaping Liu
  • Rou Chen
  • Shanshan Wang
  • Zheng GuoEmail author
  • Jing LiEmail author
Preclinical study



Immunohistochemistry (IHC) assessment of the estrogen receptor (ER) status has low consensus among pathologists. Quantitative transcriptional signatures are highly sensitive to the measurement variation and sample quality. Here, we developed a robust qualitative signature, based on within-sample relative expression orderings (REOs) of genes, to reclassify ER status.


From the gene pairs with significantly stable REOs in ER+ samples and reversely stable REOs in ER− samples, concordantly identified from four datasets, we extracted a signature to determine a sample’s ER status through evaluating whether the REOs within the sample significantly match with the ER+ REOs or the ER− REOs.


A signature with 112 gene pairs was extracted. It was validated through evaluating whether the reclassified ER+ or ER− patients could benefit from tamoxifen therapy or neoadjuvant chemotherapy. In three datasets for IHC-determined ER+ patients treated with post-operative tamoxifen therapy, 11.6–12.4% patients were reclassified as ER− by the signature and, as expected, they had significantly worse recurrence-free survival than the ER+ patients confirmed by the signature. On another hand, in two datasets for IHC-determined ER− patients treated with neoadjuvant chemotherapy, 18.8 and 7.8% patients were reclassified as ER+ and, as expected, their pathological complete response rate was significantly lower than that of the other ER− patients confirmed by the signature.


The REO-based signature can provide an objective assessment of ER status of breast cancer patients and effectively reduce misjudgments of ER status by IHC.


Breast cancer Relative expression orderings Estrogen receptor Immunohistochemistry 



Estrogen receptor




Relative expression ordering


Pathological complete response


Residual disease


Gene expression omnibus


Robust multichip average algorithm


Relapse-free survival


Hazard ratio


Confidence interval


False discovery rate



The research was supported by Grants from the National Natural Science Foundation of China (Grant No. 81202101, 81172531, 61602119, and 30930038) and the Joint Technology Innovation Fund of Fujian Province (Grant number: 2016Y9044).

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

Supplementary material

10549_2018_4758_MOESM1_ESM.doc (112 kb)
Supplementary material 1 (DOC 112 kb)


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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Bioinformatics, Fujian Key Laboratory of Medical Bioinformatics, Key Laboratory of Ministry of Education for Gastrointestinal Cancer, School of Basic Medical SciencesFujian Medical UniversityFuzhouChina
  2. 2.Fujian Key Laboratory of Tumor MicrobiologyFujian Medical UniversityFuzhouChina
  3. 3.Department of Systems Biology, College of Bioinformatics Science and TechnologyHarbin Medical UniversityHarbinChina
  4. 4.Key Laboratory of Ministry of Education for Arrhythmias, Shanghai East HospitalTongji University School of MedicineShanghaiChina

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