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

Emma H Allott,Mengjie Chen,Joseph Geradts,Melissa A Troester,Lisa A Carey,Susana Garcia-Recio,Andrew F Olshan,William F Anderson,Charles M Perou,Xuezheng Sun,Yue Shan,Erin L Kirk,H Shelton Earp,Ruth M Pfeiffer

doi:10.1007/s10549-019-05442-2

Abstract

PurposeFemale 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.MethodsWe 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).ResultsBreast 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.ConclusionsBimodal 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.

Highlights

Breast cancer heterogeneity may obscure etiologic risk factor associations if tumor subtypes are inadequately or incorrectly classified [1]
While the proportion of cases within the late-onset peak decreased across decreasing categories of estrogen receptor (ER) expression (Fig. 1, green line), the modal ages remained unchanged near 45 and 65 years
One exception was noted for the ER-borderline group, where ΔAIC lay between 4 and 10, still indicating that the two-component mixture model provided better fit, albeit with a slightly lower certainty

Summary

Introduction

Breast cancer heterogeneity may obscure etiologic risk factor associations if tumor subtypes are inadequately or incorrectly classified [1]. Extended author information available on the last page of the article cancer into two or more protein-based subtypes using immunohistochemistry expression of estrogen receptor (ER), progesterone receptor (PR), and HER2 [2]. Efforts to classify breast cancer into four genomic-intrinsic subtypes have focused on determining targeted therapies and cancer-specific clinical outcomes [3]. For cancer prevention efforts, optimizing subtype classification for etiologic subtypes is the key for understanding risk factor associations. There is emerging evidence, based on bimodal age frequency distributions at diagnosis, that breast cancer can be divided into just two etiologically distinct subtypes [4]. Breast cancer bimodality has been observed across categories of ER status, tumor characteristics and histologic

Methods

Results

Discussion

Conclusion