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Microarray Normalization Revisited for Reproducible Breast Cancer Biomarkers.
Michael Kenn1, Dan Cacsire Castillo-Tong2, Christian F Singer3
1Section of Biosimulation and Bioinformatics, Center for Medical Statistics, Informatics and Intelligent Systems (CeMSIIS), Medical University of Vienna, Spitalgasse 23, 1090 Vienna, Austria.
Biomed Research International
|August 25, 2020
Summary
Choosing the right gene expression data processing is crucial for reliable breast cancer biomarkers. Inconsistent methods can lead to significant discrepancies in patient outcomes, impacting precision medicine.
Area of Science:
- Oncology
- Bioinformatics
- Genomics
Background:
- Precision medicine for breast cancer heavily relies on biomarkers derived from gene expression data.
- The reliability of these biomarkers is often questioned, especially in large datasets, due to variability in data processing.
- Reassessing data normalization procedures is critical for improving biomarker accuracy.
Purpose of the Study:
- To evaluate widely used gene expression data-normalization procedures for breast cancer.
- To identify the most reliable reprocessing methods for biomarker discovery.
- To assess the impact of different normalization pipelines on biomarker consistency and clinical relevance.
Main Methods:
- A comprehensive database of 3753 breast cancer patients from 38 studies was curated from NCBI-GEO.
- Gene expression biomarkers, including receptor status and subtype classification, were assessed.
- Multiple data-normalization and batch correction procedures were applied and compared.
Main Results:
- Significant discrepancies (10% or more) were observed between different normalization pipelines.
- Some methods yielded highly consistent biomarkers (1-2% difference), while others produced unreliable results.
- Batch correction methods were evaluated for their ability to improve receptor prediction accuracy against immunohistochemistry standards.
Conclusions:
- The choice of bioinformatics data preprocessing significantly impacts the reliability of cancer biomarkers.
- Inadequate preprocessing pipelines are a likely source of doubt regarding microarray data.
- Standardizing normalization methods is essential for trustworthy biomarker development in breast cancer research.
