Dynamics of breast-cancer relapse reveal late-recurring ER-positive genomic subgroups
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View abstract on PubMed
Summary
This summary is machine-generated.A new statistical model predicts late breast cancer recurrence by analyzing molecular subtypes. This framework identifies high-risk patients, particularly those with ER-positive cancers, enabling better stratification for clinical trials.
Area Of Science
- Oncology
- Genomics
- Biostatistics
Background
- Understanding systemic spread and late recurrence in breast cancer, especially ER-positive types, is limited by data gaps.
- Accurate prediction of late relapse is crucial for patient management and therapeutic strategies.
Purpose Of The Study
- To develop a statistical framework for modeling distinct breast cancer disease stages and competing mortality risks.
- To generate individual risk-of-recurrence predictions using molecular and clinical data.
Main Methods
- Applied a statistical model to 3,240 breast cancer patients, including 1,980 with molecular data.
- Delineated spatiotemporal relapse patterns across immunohistochemical, PAM50, and integrative (IntClust) subtypes.
- Utilized genomic copy-number alterations and gene expression data for subtype classification.
Main Results
- Identified four late-recurring integrative subtypes (26% of ER+/HER2- tumors) with high recurrence risk up to 20 years.
- Defined subgroups of triple-negative breast cancer with distinct late recurrence patterns.
- Integrative subtypes improved late distant relapse prediction beyond clinical covariates.
Conclusions
- The developed statistical framework and integrative subtypes enhance prediction of late breast cancer recurrence.
- Findings support improved patient stratification and the development of biomarker-driven clinical trials.
- Molecularly defined subtypes offer critical insights into distinct breast cancer progression pathways.
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