Journal article
Identification of Breast Cancer Prognosis Markers using Integrative Sparse Boosting
Methods of information in medicine, Vol.51(2), pp.152-161
01/01/2012
DOI: 10.3414/ME11-02-0019
PMCID: PMC3598607
PMID: 22344268
Abstract
Objectives: In breast cancer research, it is important to identify genomic markers associated with prognosis. Multiple microarray gene expression profiling studies have been conducted, searching for prognosis markers. Genomic markers identified from the analysis of single datasets often suffer a lack of reproducibility because of small sample sizes. Integrative analysis of data from multiple independent studies has a larger sample size and may provide a cost-effective solution. Methods: We collect four breast cancer prognosis studies with gene expression measurements. An accelerated failure time (AFT) model with an unknown error distribution is adopted to describe survival. An integrative sparse boosting approach is employed for marker selection. The proposed model and boosting approach can effectively accommodate heterogeneity across multiple studies and identify genes with consistent effects. Results: Simulation study shows that the proposed approach outperforms alternatives including meta-analysis and intensity approaches by identifying the majority or all of the true positives, while having a low false positive rate. In the analysis of breast cancer data, 44 genes are identified as associated with prognosis. Many of the identified genes have been previously suggested as associated with tumorigenesis and cancer prognosis. The identified genes and corresponding predicted risk scores differ from those using alternative approaches. Monte Carlo-based prediction evaluation suggests that the proposed approach has the best prediction performance. Conclusions: Integrative analysis may provide an effective way of identifying breast cancer prognosis markers. Markers identified using the integrative sparse boosting analysis have sound biological implications and satisfactory prediction performance. © Schattauer 2012.
Details
- Title: Subtitle
- Identification of Breast Cancer Prognosis Markers using Integrative Sparse Boosting
- Creators
- Shuangge Ma - Yale UniversityJian Huang - University of IowaYang Xie - University of IowaNengjun Yi - University of Alabama
- Resource Type
- Journal article
- Publication Details
- Methods of information in medicine, Vol.51(2), pp.152-161
- DOI
- 10.3414/ME11-02-0019
- PMID
- 22344268
- PMCID
- PMC3598607
- NLM abbreviation
- Methods Inf Med
- ISSN
- 0026-1270
- eISSN
- 2511-705X
- Grant note
- R01 CA142774 || CA / National Cancer Institute : NCI
- Language
- English
- Date published
- 01/01/2012
- Academic Unit
- Statistics and Actuarial Science
- Record Identifier
- 9984257618802771
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