A qualitative transcriptional signature to reclassify estrogen receptor status of breast cancer patients
- 65 Downloads
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.
- 2.American Cancer Society. Available from: https://www.cancer.org/cancer/breast-cancer.html
- 9.Hammond ME, Hayes DF, Dowsett M et al (2010) American Society of Clinical Oncology/College Of American Pathologists guideline recommendations for immunohistochemical testing of estrogen and progesterone receptors in breast cancer. J Clin Oncol 28(16):2784–2795CrossRefPubMedPubMedCentralGoogle Scholar
- 14.Badve SS, Baehner FL, Gray RP et al (2008) Estrogen- and progesterone-receptor status in ECOG 2197: comparison of immunohistochemistry by local and central laboratories and quantitative reverse transcription polymerase chain reaction by central laboratory. J Clin Oncol 26(15):2473–2481CrossRefPubMedGoogle Scholar
- 19.Consortium M, Shi L, Reid LH et al (2006) The MicroArray Quality Control (MAQC) project shows inter- and intraplatform reproducibility of gene expression measurements. Nat Biotechnol 24(9):1151–1161Google Scholar
- 36.Viale G, de Snoo FA, Slaets L et al (2017) Immunohistochemical versus molecular (BluePrint and MammaPrint) subtyping of breast carcinoma. Outcome results from the EORTC 10041/BIG 3-04 MINDACT trial. Breast Cancer Res TreatGoogle Scholar
- 37.Early Breast Cancer Trialists’ Collaborative G, Davies C, Godwin J et al (2011) Relevance of breast cancer hormone receptors and other factors to the efficacy of adjuvant tamoxifen: patient-level meta-analysis of randomised trials. Lancet 378(9793):771–784Google Scholar
- 44.Bahn AK (1969) Application of binomial distribution to medicine: comparison of one sample proportion to an expected proportion (for small samples). Evaluation of a new treatment. Evaluation of a risk factor. J Am Med Women’s Assoc 24(12):957–966Google Scholar
- 45.Schweder T, Spjøtvoll E (1982) A class of rank test procedures for censored survival data. Biometrika 69(3):553–566Google Scholar
- 46.Benjamini Y, Hochberg Y (1995) Controlling the false discovery rate—a practical and powerful approach to multiple testing. J R Stat Soc 57(1):289–300Google Scholar
- 47.National Comprehensive Cancer Network. Available from: https://www.nccn.org/professionals/physician_gls/default.aspx