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. 2013 May 6;14(5):9686-702.
doi: 10.3390/ijms14059686.

Identification and validation of a new set of five genes for prediction of risk in early breast cancer

Giorgio Mustacchi  1 , Maria Pia Sormani, Paolo Bruzzi, Alessandra Gennari, Fabrizio Zanconati, Daniela Bonifacio, Adriana Monzoni, Luca Morandi
Affiliations

Identification and validation of a new set of five genes for prediction of risk in early breast cancer

Giorgio Mustacchi et al. Int J Mol Sci. .

Abstract

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.

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Figures

Figure 1
Figure 1
Construction of the gene-set predictor/gene signature for risk prediction. (A) Gene selection on the published datasets; (B) Gene selection on the merged Gene Expression Omnibus (GEO) datasets; (C) Developing the gene signature.
Figure 2
Figure 2
Training set: Probability of 5 years relapse: Disease free survival (DFS) according to the risk groups defined by the gene signature in the training set: Low risk group (blue curve), intermediate risk group (green curve), high risk group (red curve). The hazard ratio (HR) of DFS for intermediate risk patients as compared to low risk is 6.0 (95% Confidence Intervals (CI) = 1.35–27.0, p = 0.019 and the HR of DFS for high risk patients as compared to low risk is 10.8 (95% CI = 2.51–46.6, p = 0.001).
Figure 3
Figure 3
Validation set: Probability of 5 years relapse. Disease free survival (DFS) according to the risk groups defined by the gene signature in the validation set: low risk group (blue curve), intermediate risk group (green curve), high risk group (red curve). The hazard ratio (HR) of DFS for intermediate risk patients as compared to low risk is 2.1 (95% Confidence Intervals (CI) = 0.72–6.2, p = 0.17) and the HR of DFS for high risk patients as compared to low risk is 5.4 (95% CI = 2.0–14.4, p = 0.001).
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