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Biomarkers for Anti-Angiogenic Therapy in Cancer
Int. J. Mol. Sci. 2013, 14(5), 9686-9702; doi:10.3390/ijms14059686

Identification and Validation of a New Set of Five Genes for Prediction of Risk in Early Breast Cancer

1 Cancer Centre, ASS1 University of Trieste, Trieste 34012, Italy 2 Biostatistics Unit, Department of Health Sciences, University of Genova, Genova 16121, Italy 3 Clinical Epidemiology, National Cancer Research Centre, Genova 16132, Italy 4 Medical Oncology Unit, Galliera Hospital, Genova 16128, Italy 5 Anatomic Pathology Unit, University of Trieste, Trieste 34010, Italy 6 Alphagenics Biotechnologies S.r.l., Area Science Park, Basovizza 34012, Italy 7 Department of Biomedical and Neuromuscular Sciences, Section of Anatomic Pathology "M. Malpighi", Alma Mater Studiorum-University of Bologna, Bologna 40139, Italy
* Author to whom correspondence should be addressed.
Received: 18 February 2013 / Revised: 21 April 2013 / Accepted: 28 April 2013 / Published: 6 May 2013
(This article belongs to the Special Issue Advances in Cancer Diagnosis)
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Molecular tests predicting the outcome of breast cancer patients based on gene expression levels can be used to assist in making treatment decisions after consideration of conventional markers. In this study we identified a subset of 20 mRNA differentially regulated in breast cancer analyzing several publicly available array gene expression data using R/Bioconductor package. Using RTqPCR we evaluate 261 consecutive invasive breast cancer cases not selected for age, adjuvant treatment, nodal and estrogen receptor status from paraffin embedded sections. The biological samples dataset was split into a training (137 cases) and a validation set (124 cases). The gene signature was developed on the training set and a multivariate stepwise Cox analysis selected five genes independently associated with DFS: FGF18 (HR = 1.13, p = 0.05), BCL2 (HR = 0.57, p = 0.001), PRC1 (HR = 1.51, p = 0.001), MMP9 (HR = 1.11, p = 0.08), SERF1a (HR = 0.83, p = 0.007). These five genes were combined into a linear score (signature) weighted according to the coefficients of the Cox model, as: 0.125FGF18 − 0.560BCL2 + 0.409PRC1 + 0.104MMP9 − 0.188SERF1A (HR = 2.7, 95% CI = 1.9–4.0, p < 0.001). The signature was then evaluated on the validation set assessing the discrimination ability by a Kaplan Meier analysis, using the same cut offs classifying patients at low, intermediate or high risk of disease relapse as defined on the training set (p < 0.001). Our signature, after a further clinical validation, could be proposed as prognostic signature for disease free survival in breast cancer patients where the indication for adjuvant chemotherapy added to endocrine treatment is uncertain.
Keywords: breast cancer signature; RTqPCR; algorithm; FFPE; prognostic assay breast cancer signature; RTqPCR; algorithm; FFPE; prognostic assay
This is an open access article distributed under the Creative Commons Attribution License (CC BY) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Mustacchi, G.; Sormani, M.P.; Bruzzi, P.; Gennari, A.; Zanconati, F.; Bonifacio, D.; Monzoni, A.; Morandi, L. Identification and Validation of a New Set of Five Genes for Prediction of Risk in Early Breast Cancer. Int. J. Mol. Sci. 2013, 14, 9686-9702.

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