ArticlePloS one2016
Low Concordance between Gene Expression Signatures in ER Positive HER2 Negative Breast Carcinoma Could Impair Their Clinical Application.
Article in PloS one, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 1 of them a synthesis that pooled it.
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Who cites it
7 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Co-expressed genes enhance precision of receptor status identification in breast cancer patients.Breast cancer research and treatment · 2018Pooled it
- Development and validation of a machine learning clinicogenomic model to improve prognostic stratification in ER-positive/HER2-negative early breast cancer.Breast cancer research : BCR · 2026Article
- Decision Theory versus Conventional Statistics for Personalized Therapy of Breast Cancer.Journal of personalized medicine · 2022Article
- Decision theory for precision therapy of breast cancer.Scientific reports · 2021Article
- Quadruple Negative Breast Cancers (QNBC) Demonstrate Subtype Consistency among Primary and Recurrent or Metastatic Breast Cancer.Translational oncology · 2019Article
- Gene expression information improves reliability of receptor status in breast cancer patients.Oncotarget · 2017Article
- Extracting replicable associations across multiple studies: Empirical Bayes algorithms for controlling the false discovery rate.PLoS computational biology · 2017Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundNumerous prognostic gene expression signatures have been recently described. Among the signatures there is variation in the constituent genes that are utilized. We aim to evaluate prognostic concordance among eight gene expression signatures, on a large dataset of ER positive HER2 negative breast cancers.
methodsWe analysed the performance of eight gene expression signatures on six different datasets of ER+ HER2- breast cancers. Survival analyses were performed using the Kaplan-Meier estimate of survival function. We assessed discrimination and concordance between the 8 signatures on survival and recurrence rates The Nottingham Prognostic Index (NPI) was used to to stratify the risk of recurrence/death.
resultsThe discrimination ability of the whole signatures, showed fair discrimination performances, with AUC ranging from 0.64 (95%CI 0.55-0.73 for the 76-genes signatures, to 0.72 (95%CI 0.64-0.8) for the Molecular Prognosis Index T17. Low concordance was found in predicting events in the intermediate and high-risk group, as defined by the NPI. Low risk group was the only subgroup with a good signatures concordance.
conclusionGenomic signatures may be a good option to predict prognosis as most of them perform well at the population level. They exhibit, however, a high degree of discordance in the intermediate and high-risk groups. The major benefit that we could expect from gene expression signatures is the standardization of proliferation assessment.
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