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

  • Preclinical study
  • Published:
Breast Cancer Research and Treatment Aims and scope Submit manuscript

Abstract

Purpose

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.

Methods

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.

Results

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.

Conclusions

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.

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Abbreviations

ER:

Estrogen receptor

IHC:

Immunohistochemical

REO:

Relative expression ordering

pCR:

Pathological complete response

RD:

Residual disease

GEO:

Gene expression omnibus

RMA:

Robust multichip average algorithm

RFS:

Relapse-free survival

HR:

Hazard ratio

CI:

Confidence interval

FDR:

False discovery rate

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Acknowledgments

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).

Author information

Author notes

  1. Hao Cai, Wenbing Guo, and Shuobo Zhang contributed equally this work.

Authors and Affiliations

  1. Department of Bioinformatics, Fujian Key Laboratory of Medical Bioinformatics, Key Laboratory of Ministry of Education for Gastrointestinal Cancer, School of Basic Medical Sciences, Fujian Medical University, Fuzhou, 350122, China

    Hao Cai, Na Li, Xianlong Wang, Huaping Liu, Shanshan Wang, Zheng Guo & Jing Li

  2. Fujian Key Laboratory of Tumor Microbiology, Fujian Medical University, Fuzhou, 350122, China

    Hao Cai, Na Li, Xianlong Wang, Huaping Liu, Shanshan Wang, Zheng Guo & Jing Li

  3. Department of Systems Biology, College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150086, China

    Wenbing Guo, Shuobo Zhang & Zheng Guo

  4. Key Laboratory of Ministry of Education for Arrhythmias, Shanghai East Hospital, Tongji University School of Medicine, Shanghai, China

    Rou Chen

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  1. Hao Cai
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  2. Wenbing Guo
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Correspondence to Zheng Guo or Jing Li.

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Cai, H., Guo, W., Zhang, S. et al. A qualitative transcriptional signature to reclassify estrogen receptor status of breast cancer patients. Breast Cancer Res Treat 170, 271–277 (2018). https://doi.org/10.1007/s10549-018-4758-2

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