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Performing Data Mining And Integrative Analysis Of Biomarker in Breast Cancer Using Multiple Publicly Accessible Databases
Published on: May 17, 2019
Concordance among gene-expression-based predictors for breast cancer
Cheng Fan1, Daniel S Oh, Lodewyk Wessels
1Department of Genetics, University of North Carolina at Chapel Hill and Lineberger Comprehensive Cancer Center, Chapel Hill 27599, USA.
The New England Journal of Medicine
|August 11, 2006
Summary
Different gene expression profiles for breast cancer prognosis show high agreement. These prognostic models likely identify similar biological characteristics, improving outcome prediction accuracy for patients.
Area of Science:
- Oncology
- Genomics
- Biomarkers
Background:
- Gene-expression profiling identifies distinct prognostic profiles in breast tumors.
- Limited overlap exists between gene sets identified by different laboratories.
Purpose of the Study:
- To compare prognostic predictions from five gene-expression-based models using a single dataset.
- To assess the concordance of different breast cancer prognostic profiles.
Main Methods:
- Applied five gene-expression models: intrinsic subtypes, 70-gene profile, wound response, recurrence score, and two-gene ratio.
- Utilized a dataset of 295 primary breast tumor samples.
Main Results:
- Most models demonstrated high concordance in outcome predictions for individual samples.
- Basal-like, HER2-positive, estrogen-receptor-negative, and luminal B subtypes showed agreement across models.
- The 70-gene and recurrence score models showed 77-81% agreement.
Conclusions:
- Four of five tested gene-expression-based prognostic models showed significant agreement.
- These models likely reflect common biological phenotypes in breast cancer.
- Prognostic gene sets, despite differences, can reliably predict patient outcomes.
