• William F Anderson, Philip S Rosenberg, Aleix Prat, Charles M Perou, and Mark E Sherman.
• Division of Cancer Epidemiology and Genetics Biostatistics Branch (WFA, PSR), and Division of Cancer Prevention (MES), National Cancer Institute, National Institutes of Health, Bethesda, MD; Translational Genomics Group, Vall d'Hebron Institute of Oncology, Barcelona, Spain (AP); Department of Genetics and Pathology & Laboratory Medicine, Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC (CMP). wanderso@mail.nih.gov.
• J. Natl. Cancer Inst. 2014 Aug 1;106(8).

AbstractBreast cancer is a heterogeneous disease, divisible into a variable number of clinical subtypes. A fundamental question is how many etiological classes underlie the clinical spectrum of breast cancer? An etiological subtype reflects a grouping with a common set of causes, whereas a clinical subtype represents a grouping with similar prognosis and/or prediction. Herein, we review the evidence for breast cancer etiological heterogeneity. We then evaluate the etiological evidence with mRNA profiling data. A bimodal age distribution at diagnosis with peak frequencies near ages 50 and 70 years is a fundamental characteristic of breast cancer for important tumor features, clinical characteristics, risk factor profiles, and molecular subtypes. The bimodal peak frequencies at diagnosis divide breast cancer overall into a "mixture" of two main components in varying proportions in different cancer populations. The first breast cancer tends to arise early in life with modal age-at-diagnosis near 50 years and generally behaves aggressively. The second breast cancer occurs later in life with modal age near 70 years and usually portends a more indolent clinical course. These epidemiological and molecular data are consistent with a two-component mixture model and compatible with a hierarchal view of breast cancers arising from two main cell types of origin. Notwithstanding the potential added value of more detailed categorizations for personalized breast cancer treatment, we suggest that the development of better criteria to identify the two proposed etiologic classes would advance breast cancer research and prevention.Published by Oxford University Press 2014.

27 keywords...

hide…