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Identification of breast cancer prognosis markers via integrative analysis

Author

Listed:
  • Ma, Shuangge
  • Dai, Ying
  • Huang, Jian
  • Xie, Yang

Abstract

In breast cancer research, it is of great interest to identify genomic markers associated with prognosis. Multiple gene profiling studies have been conducted for such a purpose. Genomic markers identified from the analysis of single datasets often do not have satisfactory reproducibility. Among the multiple possible reasons, the most important one is the small sample sizes of individual studies. A cost-effective solution is to pool data from multiple comparable studies and conduct integrative analysis. In this study, we collect four breast cancer prognosis studies with gene expression measurements. We describe the relationship between prognosis and gene expressions using the accelerated failure time (AFT) models. We adopt a 2-norm group bridge penalization approach for marker identification. This integrative analysis approach can effectively identify markers with consistent effects across multiple datasets and naturally accommodate the heterogeneity among studies. Statistical and simulation studies demonstrate satisfactory performance of this approach. Breast cancer prognosis markers identified using this approach have sound biological implications and satisfactory prediction performance.

Suggested Citation

  • Ma, Shuangge & Dai, Ying & Huang, Jian & Xie, Yang, 2012. "Identification of breast cancer prognosis markers via integrative analysis," Computational Statistics & Data Analysis, Elsevier, vol. 56(9), pages 2718-2728.
  • Handle: RePEc:eee:csdana:v:56:y:2012:i:9:p:2718-2728
    DOI: 10.1016/j.csda.2012.02.017
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    References listed on IDEAS

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    1. Jian Huang & Shuangge Ma & Huiliang Xie, 2006. "Regularized Estimation in the Accelerated Failure Time Model with High-Dimensional Covariates," Biometrics, The International Biometric Society, vol. 62(3), pages 813-820, September.
    2. Lukas Meier & Sara van de Geer & Peter B├╝hlmann, 2008. "The group lasso for logistic regression," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 70(1), pages 53-71.
    3. Stute, W., 1993. "Consistent Estimation Under Random Censorship When Covariables Are Present," Journal of Multivariate Analysis, Elsevier, vol. 45(1), pages 89-103, April.
    4. Jian Huang & Shuange Ma & Huiliang Xie & Cun-Hui Zhang, 2009. "A group bridge approach for variable selection," Biometrika, Biometrika Trust, vol. 96(2), pages 339-355.
    5. Susmita Datta & Jennifer Le-Rademacher & Somnath Datta, 2007. "Predicting Patient Survival from Microarray Data by Accelerated Failure Time Modeling Using Partial Least Squares and LASSO," Biometrics, The International Biometric Society, vol. 63(1), pages 259-271, March.
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