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Delineating transcriptional networks of prognostic gene signatures refines treatment recommendations for lymph node-negative breast cancer patients.
- Source :
-
The FEBS journal [FEBS J] 2015 Sep; Vol. 282 (18), pp. 3455-73. Date of Electronic Publication: 2015 Jul 14. - Publication Year :
- 2015
-
Abstract
- The majority of women diagnosed with lymph node-negative breast cancer are unnecessarily treated with damaging chemotherapeutics after surgical resection. This highlights the importance of understanding and more accurately predicting patient prognosis. In the present study, we define the transcriptional networks regulating well-established prognostic gene expression signatures. We find that the same set of transcriptional regulators consistently lie upstream of both 'prognosis' and 'proliferation' gene signatures, suggesting that a central transcriptional network underpins a shared phenotype within these signatures. Strikingly, the master transcriptional regulators within this network predict recurrence risk for lymph node-negative breast cancer better than currently used multigene prognostic assays, particularly in estrogen receptor-positive patients. Simultaneous examination of p16(INK4A) expression, which predicts tumours that have bypassed cellular senescence, revealed that intermediate levels of p16(INK4A) correlate with an intact pRB pathway and improved survival. A combination of these master transcriptional regulators and p16(INK4A), termed the OncoMasTR score, stratifies tumours based on their proliferative and senescence capacity, facilitating a clearer delineation of lymph node-negative breast cancer patients at high risk of recurrence, and thus requiring chemotherapy. Furthermore, OncoMasTR accurately classifies over 60% of patients as 'low risk', an improvement on existing prognostic assays, which has the potential to reduce overtreatment in early-stage patients. Taken together, the present study provides new insights into the transcriptional regulation of cellular proliferation in breast cancer and provides an opportunity to enhance and streamline methods of predicting breast cancer prognosis.<br /> (© 2015 FEBS.)
- Subjects :
- Adult
Aged
Aged, 80 and over
Animals
Breast Neoplasms metabolism
Breast Neoplasms therapy
Cell Proliferation genetics
Cells, Cultured
Cellular Senescence genetics
Cohort Studies
Female
Genes, p16
Humans
Lymphatic Metastasis genetics
Mammary Glands, Human cytology
Mammary Glands, Human metabolism
Mice
Middle Aged
Prognosis
Promoter Regions, Genetic
RNA, Messenger genetics
RNA, Messenger metabolism
RNA, Neoplasm genetics
RNA, Neoplasm metabolism
Receptors, Estrogen metabolism
Risk Factors
Tissue Array Analysis
Breast Neoplasms genetics
Gene Regulatory Networks
Subjects
Details
- Language :
- English
- ISSN :
- 1742-4658
- Volume :
- 282
- Issue :
- 18
- Database :
- MEDLINE
- Journal :
- The FEBS journal
- Publication Type :
- Report
- Accession number :
- 26094870
- Full Text :
- https://doi.org/10.1111/febs.13354