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

International Journal of Molecular Sciences

Giorgio Mustacchi²  Maria Pia Sormani⁴  Paolo Bruzzi⁶  Alessandra Gennari³  Fabrizio Zanconati⁵  Daniela Bonifacio⁵  Adriana Monzoni¹ 

[1]Alphagenics Biotechnologies S.r.l., Area Science Park, Basovizza 34012, Italy

[2] E-Mail:

[3]Cancer Centre, ASS1 University of Trieste, Trieste 34012, Italy

[4] E-Mail:

[5]Medical Oncology Unit, Galliera Hospital, Genova 16128, Italy

[6] E-Mail:

[7]Biostatistics Unit, Department of Health Sciences, University of Genova, Genova 16121, Italy

[8] E-Mail:

[9]Anatomic Pathology Unit, University of Trieste, Trieste 34010, Italy

[10] E-Mails:

[11]Clinical Epidemiology, National Cancer Research Centre, Genova 16132, Italy

[12] E-Mail:

关键词: breast cancer signature;    RTqPCR;    algorithm;    FFPE;    prognostic assay;   

DOI  :  10.3390/ijms14059686

来源: mdpi

【 摘 要 】

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.

【 授权许可】

CC BY   
© 2013 by the authors; licensee MDPI, Basel, Switzerland