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Bimodal age distribution at diagnosis in breast cancer persists across molecular and genomic classifications
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 distribut...
Autores principales: | , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer US
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6985047/ https://www.ncbi.nlm.nih.gov/pubmed/31535320 http://dx.doi.org/10.1007/s10549-019-05442-2 |
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author | Allott, Emma H. Shan, Yue Chen, Mengjie Sun, Xuezheng Garcia-Recio, Susana Kirk, Erin L. Olshan, Andrew F. Geradts, Joseph Earp, H. Shelton Carey, Lisa A. Perou, Charles M. Pfeiffer, Ruth M. Anderson, William F. Troester, Melissa A. |
author_facet | Allott, Emma H. Shan, Yue Chen, Mengjie Sun, Xuezheng Garcia-Recio, Susana Kirk, Erin L. Olshan, Andrew F. Geradts, Joseph Earp, H. Shelton Carey, Lisa A. Perou, Charles M. Pfeiffer, Ruth M. Anderson, William F. Troester, Melissa A. |
author_sort | Allott, Emma H. |
collection | PubMed |
description | 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. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1007/s10549-019-05442-2) contains supplementary material, which is available to authorized users. |
format | Online Article Text |
id | pubmed-6985047 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | Springer US |
record_format | MEDLINE/PubMed |
spelling | pubmed-69850472020-02-07 Bimodal age distribution at diagnosis in breast cancer persists across molecular and genomic classifications Allott, Emma H. Shan, Yue Chen, Mengjie Sun, Xuezheng Garcia-Recio, Susana Kirk, Erin L. Olshan, Andrew F. Geradts, Joseph Earp, H. Shelton Carey, Lisa A. Perou, Charles M. Pfeiffer, Ruth M. Anderson, William F. Troester, Melissa A. Breast Cancer Res Treat Epidemiology 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. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1007/s10549-019-05442-2) contains supplementary material, which is available to authorized users. Springer US 2019-09-18 2020 /pmc/articles/PMC6985047/ /pubmed/31535320 http://dx.doi.org/10.1007/s10549-019-05442-2 Text en © The Author(s) 2019 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. |
spellingShingle | Epidemiology Allott, Emma H. Shan, Yue Chen, Mengjie Sun, Xuezheng Garcia-Recio, Susana Kirk, Erin L. Olshan, Andrew F. Geradts, Joseph Earp, H. Shelton Carey, Lisa A. Perou, Charles M. Pfeiffer, Ruth M. Anderson, William F. Troester, Melissa A. Bimodal age distribution at diagnosis in breast cancer persists across molecular and genomic classifications |
title | Bimodal age distribution at diagnosis in breast cancer persists across molecular and genomic classifications |
title_full | Bimodal age distribution at diagnosis in breast cancer persists across molecular and genomic classifications |
title_fullStr | Bimodal age distribution at diagnosis in breast cancer persists across molecular and genomic classifications |
title_full_unstemmed | Bimodal age distribution at diagnosis in breast cancer persists across molecular and genomic classifications |
title_short | Bimodal age distribution at diagnosis in breast cancer persists across molecular and genomic classifications |
title_sort | bimodal age distribution at diagnosis in breast cancer persists across molecular and genomic classifications |
topic | Epidemiology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6985047/ https://www.ncbi.nlm.nih.gov/pubmed/31535320 http://dx.doi.org/10.1007/s10549-019-05442-2 |
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