Centre for Cancer Research and Cell Biology, Queen's University Belfast, Belfast, UK.
Centre for Cancer Research and Cell Biology, Queen's University Belfast, Health Sciences Building, Room 2.12, 97 Lisburn Road, Belfast, BT9 7AE, Northern Ireland, UK.
Breast Cancer Res Treat. 2020 Jan;179(1):185-195. doi: 10.1007/s10549-019-05442-2. Epub 2019 Sep 18.
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.
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).
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.
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.
女性乳腺癌在诊断时表现出双峰年龄频率分布模式，可解释为具有共同危险因素的两个主要病因亚型或肿瘤分组。虽然基于 RNA 的方法（包括 PAM50）已经确定了明确的临床亚型，但作为病因亚型替代指标的诊断时年龄分布模式尚未在分子和基因组肿瘤分类中确立。
我们评估了基于免疫组化和基于 RNA 的表达分类的 Carolina Breast Cancer Study 病例的诊断时平滑年龄频率分布。Akaike 信息准则 (AIC) 值比较了单密度与双成分混合模型的拟合度。双成分混合模型通过基于免疫组化的 ER（n=2860）和基于 RNA 的 ESR1 和 PAM50 亚型（n=1965）来估计早发和晚发类别的比例。使用汇总的公开可用数据（n=8103）验证了 PAM50 发现。
无论分子特征如何，乳腺癌在诊断时的特征最好是双峰年龄分布，发病率峰值接近 45 岁和 65 岁。然而，早发和晚发年龄分布的比例组成因分子和基因组特征而异。较高的 ER-蛋白和 ESR1-RNA 类别显示出较大比例的晚发年龄。同样，PAM50 亚型的发病年龄分布也存在转移，基底样和 Luminal A 分别显示出最明显的早发和晚发高峰。
在 Carolina Breast Cancer Study 中检测到诊断时双峰年龄分布，与国家癌症登记数据相似。我们的数据支持两种基本的基于年龄的病因乳腺癌亚型，这些亚型在分子和基因组特征中持续存在。更好的区分病因亚型的标准可以提高对乳腺癌病因的理解，并有助于预防工作。
