ArticleClinical cancer research : an official journal of the American Association for Cancer Research2008
A gene expression signature that can predict the recurrence of tamoxifen-treated primary breast cancer.
Article in Clinical cancer research : an official journal of the American Association for Cancer Research, 2008. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 107 papers, 1 of them a synthesis that pooled it.
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107 citing papers in PubMed, 1 synthesis or guideline pooled it.
- A data similarity-based strategy for meta-analysis of transcriptional profiles in cancer.PloS one · 2013Pooled it
- AXL is associated with STAT3 activation in breast cancer.Molecular and clinical oncology · 2026Article
- Identification of RPL4 as a biomarker associated with disulfidptosis and arachidonic acid metabolism in breast cancer.Discover oncology · 2026Article
- Residual Breast Cancer Cells Co-opt SOX5-Driven Endochondral Ossification to Maintain Dormancy.Cancer discovery · 2026Article
- KIF5B-driven unfolded protein response reprograms breast cancer immunosuppressive microenvironment for single-cell guided therapeutic targeting.Discover oncology · 2026Article
- FOXA2 as a SETD1A-Regulated Driver of Tamoxifen Resistance in Breast Cancer.Oncology research · 2026Article
- Multi-Scale Transcriptomic Sequencing Data Analysis RevealsOncology research · 2026Article
- JNK pathway suppression mediates insensitivity to combination endocrine therapy and CDK4/6 inhibition in ER+ breast cancer.Journal of experimental & clinical cancer research : CR · 2025Article
- Hi-C profiling in tissues reveals 3D chromatin-regulated breast tumor heterogeneity informing a looping-mediated therapeutic avenue.Cell reports · 2025Article
- Ternary Complex Components Responsible for Rapid LDL Internalization as Biomarkers for Breast Cancer Associated with Proliferation and Early Recurrence.Cancer research communications · 2025Article
- TPD52 as a Therapeutic Target Identified by Machine Learning Shapes the Immune Microenvironment in Breast Cancer.Journal of cellular and molecular medicine · 2025Article
- Integrated single-cell analysis reveals distinct epigenetic-regulated cancer cell states and a heterogeneity-guided core signature in tamoxifen-resistant breast cancer.Genome medicine · 2024Article
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- MIRS: An AI scoring system for predicting the prognosis and therapy of breast cancer.iScience · 2023Article
- Re-evaluation of publicly available gene-expression databases using machine-learning yields a maximum prognostic power in breast cancer.Scientific reports · 2023Article
- The benefits and pitfalls of machine learning for biomarker discovery.Cell and tissue research · 2023Review
- Glutamine synthetase regulates the immune microenvironment and cancer development through the inflammatory pathway.International journal of medical sciences · 2023Article
- Targeting CDK7 reverses tamoxifen resistance through regulating stemness in ER+ breast cancer.Pharmacological reports : PR · 2022Article
- Early myeloid-derived suppressor cells accelerate epithelial-mesenchymal transition by downregulating ARID1A in luminal A breast cancer.Frontiers in bioengineering and biotechnology · 2022Article
47 more citing papers are in PubMed but not listed here.
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11 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeThe identification of a molecular signature predicting the relapse of tamoxifen-treated primary breast cancers should help the therapeutic management of estrogen receptor-positive cancers. EXPERIMENTAL
designA series of 132 primary tumors from patients who received adjuvant tamoxifen were analyzed for expression profiles at the whole-genome level by 70-mer oligonucleotide microarrays. A supervised analysis was done to identify an expression signature.
resultsWe defined a 36-gene signature that correctly classified 78% of patients with relapse and 80% of relapse-free patients (79% accuracy). Using 23 independent tumors, we confirmed the accuracy of the signature (78%) whose relevance was further shown by using published microarray data from 60 tamoxifen-treated patients (63% accuracy). Univariate analysis using the validation set of 83 tumors showed that the 36-gene classifier is more efficient in predicting disease-free survival than the traditional histopathologic prognostic factors and is as effective as the Nottingham Prognostic Index or the "Adjuvant!" software. Multivariate analysis showed that the molecular signature is the only independent prognostic factor. A comparison with several already published signatures demonstrated that the 36-gene signature is among the best to classify tumors from both training and validation sets. Kaplan-Meier analyses emphasized its prognostic power both on the whole cohort of patients and on a subgroup with an intermediate risk of recurrence as defined by the St. Gallen criteria.
conclusionThis study identifies a molecular signature specifying a subgroup of patients who do not gain benefits from tamoxifen treatment. These patients may therefore be eligible for alternative endocrine therapies and/or chemotherapy.
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