Interrogating differences in expression of targeted gene sets to predict breast cancer outcome

BMC Cancer

Sarah A Andres²  Guy N Brock¹  James L Wittliff² 

[1] Department of Bioinformatics and Biostatistics, University of Louisville, Louisville, KY 40292, USA

[2] Hormone Receptor Laboratory, Department of Biochemistry & Molecular Biology, Brown Cancer Center and the Institute for Molecular Diversity & Drug Design, University of Louisville, Louisville, KY 40292, USA

关键词: Prognostic test;    Risk of recurrence;    Invasive ductal carcinoma;    Breast cancer;   

Others  :  1079664
DOI  :  10.1186/1471-2407-13-326

 received in 2012-12-03, accepted in 2013-06-26,  发布年份 2013

【 摘 要 】

Background

Genomics provides opportunities to develop precise tests for diagnostics, therapy selection and monitoring. From analyses of our studies and those of published results, 32 candidate genes were identified, whose expression appears related to clinical outcome of breast cancer. Expression of these genes was validated by qPCR and correlated with clinical follow-up to identify a gene subset for development of a prognostic test.

Methods

RNA was isolated from 225 frozen invasive ductal carcinomas,and qRT-PCR was performed. Univariate hazard ratios and 95% confidence intervals for breast cancer mortality and recurrence were calculated for each of the 32 candidate genes. A multivariable gene expression model for predicting each outcome was determined using the LASSO, with 1000 splits of the data into training and testing sets to determine predictive accuracy based on the C-index. Models with gene expression data were compared to models with standard clinical covariates and models with both gene expression and clinical covariates.

Results

Univariate analyses revealed over-expression of RABEP1, PGR, NAT1, PTP4A2, SLC39A6, ESR1, EVL, TBC1D9, FUT8, and SCUBE2 were all associated with reduced time to disease-related mortality (HR between 0.8 and 0.91, adjusted p < 0.05), while RABEP1, PGR, SLC39A6, and FUT8 were also associated with reduced recurrence times. Multivariable analyses using the LASSO revealed PGR, ESR1, NAT1, GABRP, TBC1D9, SLC39A6, and LRBA to be the most important predictors for both disease mortality and recurrence. Median C-indexes on test data sets for the gene expression, clinical, and combined models were 0.65, 0.63, and 0.65 for disease mortality and 0.64, 0.63, and 0.66 for disease recurrence, respectively.

Conclusions

Molecular signatures consisting of five genes (PGR, GABRP, TBC1D9, SLC39A6 and LRBA) for disease mortality and of six genes (PGR, ESR1, GABRP, TBC1D9, SLC39A6 and LRBA) for disease recurrence were identified. These signatures were as effective as standard clinical parameters in predicting recurrence/mortality, and when combined, offered some improvement relative to clinical information alone for disease recurrence (median difference in C-values of 0.03, 95% CI of -0.08 to 0.13). Collectively, results suggest that these genes form the basis for a clinical laboratory test to predict clinical outcome of breast cancer.

【 授权许可】

   
2013 Andres et al.; licensee BioMed Central Ltd.

【 图 表 】

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