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.
journal_name
Breast Cancer Res Treatjournal_title
Breast cancer research and treatmentauthors
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 MAdoi
10.1007/s10549-019-05442-2subject
Has Abstractpub_date
2020-01-01 00:00:00pages
185-195issue
1eissn
0167-6806issn
1573-7217pii
10.1007/s10549-019-05442-2journal_volume
179pub_type
杂志文章abstract::Neural networks can be used as pattern recognition systems in complex data sets. We are exploring their utility in performing survival analysis to predict time to relapse or death. This technique has the potential to find easily some types of very complex interactions in data that would not be easily recognized by con...
journal_title:Breast cancer research and treatment
pub_type: 杂志文章
doi:10.1007/BF00666212
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abstract::NF-E2-related factor 2 (Nrf2) is an important transcription factor involved in antioxidant response. Nrf2 binds antioxidant response elements (ARE) within promoters of genes encoding detoxification enzymes (e.g., NAD (P) H-quinone oxidoreductase 1 (NQO1)) leading to their transcriptional activation. Nrf2 function is r...
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journal_title:Breast cancer research and treatment
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journal_title:Breast cancer research and treatment
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journal_title:Breast cancer research and treatment
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journal_title:Breast cancer research and treatment
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journal_title:Breast cancer research and treatment
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