Bimodal age distribution at diagnosis in breast cancer persists across molecular and genomic classifications.

Published

Journal Article

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.

Full Text

Duke Authors

Cited Authors

  • Allott, EH; Shan, Y; Chen, M; Sun, X; Garcia-Recio, S; Kirk, EL; Olshan, AF; Geradts, J; Earp, HS; Carey, LA; Perou, CM; Pfeiffer, RM; Anderson, WF; Troester, MA

Published Date

  • January 2020

Published In

Volume / Issue

  • 179 / 1

Start / End Page

  • 185 - 195

PubMed ID

  • 31535320

Pubmed Central ID

  • 31535320

Electronic International Standard Serial Number (EISSN)

  • 1573-7217

Digital Object Identifier (DOI)

  • 10.1007/s10549-019-05442-2

Language

  • eng

Conference Location

  • Netherlands