A qualitative transcriptional signature to reclassify estrogen receptor status of breast cancer patients
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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.
KeywordsBreast cancer Relative expression orderings Estrogen receptor Immunohistochemistry
Relative expression ordering
Pathological complete response
Gene expression omnibus
Robust multichip average algorithm
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.
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