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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
Multi-omic Profiling of Recurrence Risk Across Breast Cancer Subtypes
A Eden Cruikshank1,2,3, Pooja Chandra1,2, Christopher I Li1
1Divisions of Public Health Sciences, Fred Hutchinson Cancer Center, 1100 Fairview Ave. N, Seattle, WA 98109.
Medrxiv : the Preprint Server for Health Sciences
|July 30, 2026
Summary
This study reveals subtype-specific molecular and immune features in primary breast cancer tumors that predict recurrence risk. Identifying these distinct biological programs aids in developing personalized recurrence-risk stratification models.
Area of Science:
- Oncology
- Genomics
- Immunology
Background:
- Recurrence risk in breast cancer varies significantly by intrinsic subtype.
- The molecular and immune factors driving recurrence within subtypes are not well understood.
Purpose of the Study:
- To identify tumor-intrinsic and microenvironmental features associated with recurrence across Basal-like, Luminal A, and Luminal B breast cancer subtypes.
- To develop and validate subtype-specific models for breast cancer recurrence risk stratification.
Main Methods:
- Multi-omic analysis (RNA, copy-number, pathway-level mutations) of 340 primary breast cancer tumors.
- Comparison of recurrent versus non-recurrent tumors within each intrinsic subtype.
- Development and external validation (METABRIC cohort) of subtype-specific recurrence risk prediction models.
Main Results:
- Basal-like tumors with recurrence showed reduced lymphocytes/M1 macrophages, enriched TGF-β/EMT activity, specific copy number alterations, and increased pathway tumor mutational burden (pTMB).
- Luminal A recurrent tumors had higher lymphocytes/M1 macrophages, enriched metabolic/stemness pathways, and higher pTMB in multiple signaling pathways.
- Luminal B recurrent tumors were enriched for proliferation/genomic instability pathways, showed 1q amplification, and increased pTMB in Hedgehog signaling.
- Validated recurrence risk scores showed significant association with recurrence-free survival across all subtypes.
Conclusions:
- Primary breast tumors in patients who develop recurrence exhibit distinct, subtype-specific biological programs.
- Multi-omic analysis combined with subtype-informed modeling provides a robust framework for improving breast cancer recurrence risk stratification.
