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Updated: Jul 30, 2025
Studying Triple Negative Breast Cancer Using Orthotopic Breast Cancer Model
Published on: March 20, 2020
Transcriptome Meta-Analysis of Triple-Negative Breast Cancer Response to Neoadjuvant Chemotherapy
Wei Zhang1, Emma Li2, Lily Wang1,3
1Department of Public Health Sciences, University of Miami Miller School of Medicine, Miami, FL 33136, USA.
Abstract:
Triple-negative breast cancer (TNBC) is a heterogeneous disease with varying responses to neoadjuvant chemotherapy (NAC). The identification of biomarkers to predict NAC response and inform personalized treatment strategies is essential. In this study, we conducted large-scale gene expression meta-analyses to identify genes associated with NAC response and survival outcomes. The results showed that immune, cell cycle/mitotic, and RNA splicing-related pathways were significantly associated with favorable clinical outcomes. Furthermore, we integrated and divided the gene association results from NAC response and survival outcomes into four quadrants, which provided more insights into potential NAC response mechanisms and biomarker discovery.
Insights
Identifying biomarkers for triple-negative breast cancer (TNBC) is crucial. Gene expression meta-analyses revealed immune, cell cycle, and RNA splicing pathways linked to better outcomes with neoadjuvant chemotherapy (NAC).
Area of Science:
- Oncology
- Genomics
- Immunology
Background:
- Triple-negative breast cancer (TNBC) presents significant heterogeneity in treatment response.
- Neoadjuvant chemotherapy (NAC) effectiveness varies widely among TNBC patients.
- Predictive biomarkers for NAC response are critical for personalized treatment strategies.
Purpose of the Study:
- To identify genes and pathways associated with NAC response in TNBC through large-scale meta-analyses.
- To uncover potential biomarkers for predicting treatment outcomes in TNBC.
- To gain deeper insights into the mechanisms underlying NAC response.
Main Methods:
- Conducted large-scale gene expression meta-analyses.
- Analyzed associations between gene expression patterns and NAC response.
- Integrated gene association data for NAC response and survival outcomes.
Main Results:
- Immune-related pathways were significantly associated with favorable clinical outcomes.
- Cell cycle and mitotic pathway genes correlated with better treatment responses.
- RNA splicing-related pathways showed a strong link to improved survival in TNBC patients undergoing NAC.
- A four-quadrant integration of gene association data provided novel insights into NAC response mechanisms.
Conclusions:
- Gene expression meta-analysis is a powerful tool for identifying predictive biomarkers in TNBC.
- Immune, cell cycle, and RNA splicing pathways are key determinants of NAC response and survival in TNBC.
- The study provides a framework for biomarker discovery and personalized treatment strategies for TNBC.
