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Bimodal age distribution at diagnosis in breast cancer persists across molecular and genomic classifications.
- Source :
- Breast Cancer Research & Treatment; Jan2020, Vol. 179 Issue 1, p185-195, 11p
- Publication Year :
- 2020
-
Abstract
- Purpose: Female breast cancer demonstrates bimodal age frequency distribution patterns at diagnosis, interpretable as two main etiologic subtypes or groupings of tumors with shared risk factors. While RNA-based methods including PAM50 have identified well-established clinical subtypes, age distribution patterns at diagnosis as a proxy for etiologic subtype are not established for molecular and genomic tumor classifications. Methods: We evaluated smoothed age frequency distributions at diagnosis for Carolina Breast Cancer Study cases within immunohistochemistry-based and RNA-based expression categories. Akaike information criterion (AIC) values compared the fit of single density versus two-component mixture models. Two-component mixture models estimated the proportion of early-onset and late-onset categories by immunohistochemistry-based ER (n = 2860), and by RNA-based ESR1 and PAM50 subtype (n = 1965). PAM50 findings were validated using pooled publicly available data (n = 8103). Results: Breast cancers were best characterized by bimodal age distribution at diagnosis with incidence peaks near 45 and 65 years, regardless of molecular characteristics. However, proportional composition of early-onset and late-onset age distributions varied by molecular and genomic characteristics. Higher ER-protein and ESR1-RNA categories showed a greater proportion of late age-at-onset. Similarly, PAM50 subtypes showed a shifting age-at-onset distribution, with most pronounced early-onset and late-onset peaks found in Basal-like and Luminal A, respectively. Conclusions: Bimodal age distribution at diagnosis was detected in the Carolina Breast Cancer Study, similar to national cancer registry data. Our data support two fundamental age-defined etiologic breast cancer subtypes that persist across molecular and genomic characteristics. Better criteria to distinguish etiologic subtypes could improve understanding of breast cancer etiology and contribute to prevention efforts. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 01676806
- Volume :
- 179
- Issue :
- 1
- Database :
- Complementary Index
- Journal :
- Breast Cancer Research & Treatment
- Publication Type :
- Academic Journal
- Accession number :
- 141414535
- Full Text :
- https://doi.org/10.1007/s10549-019-05442-2