title: The prognostic impact of age in different molecular subtypes of breast cancer.
abstract: Breast cancer is a heterogeneous entity composed of distinct molecular subgroups with different molecular and clinical features. We analyzed the association between molecular breast cancer subgroups, age at diagnosis, and prognosis in a compilation of publicly available gene expression datasets. Affymetrix gene expression data (U133A or U133Plus2.0 arrays) of 4467 breast cancers from 40 datasets were compiled and homogenized. Breast cancer subgroups were defined based on expression of ESR1, PR, HER2, and Ki67. Event-free survival was calculated as recurrence-free survival or distant metastasis-free survival if recurrence-free survival was not available. Young age at diagnosis is associated with higher frequency of triple negative and HER2 subtypes and lower frequency of luminal A breast cancers. The 5-year event-free survival rates of patients aged less than 40, between 40 and 50, and >50 years were 54.3 ± 3.5, 68.5 ± 1.9, and 70.4 ± 1.3 %, respectively. When controlling for breast cancer subtype, we found that age <40 years remained significantly associated with poor prognosis in triple negative breast cancer. The effect was modest in luminal tumors and not found in HER2 subtype. Both subtypes and age retained their significances in multivariate analysis. Association of age at diagnosis with molecular breast cancer subtype contributes to its important role as prognostic factor among patients with breast cancer. Still, within the group of triple negative breast cancer, young age <40 years has a significant prognostic value which was retained in multivariate analysis.
authors.affiliation: Department of Obstetrics and Gynecology, University Hospital Schleswig-Holstein/Campus Luebeck, Ratzeburger Allee 160, 23538, Lüebeck, Germany, cornelia.liedtke@uksh.de.
authors.forename: Cornelia
authors.initials: C
authors.lastname: Liedtke
authors.forename: Achim
authors.initials: A
authors.lastname: Rody
authors.forename: Oleg
authors.initials: O
authors.lastname: Gluz
authors.forename: Kristin
authors.initials: K
authors.lastname: Baumann
authors.forename: Daniel
authors.initials: D
authors.lastname: Beyer
authors.forename: Eva-Beatrice
authors.initials: EB
authors.lastname: Kohls
authors.forename: Kerstin
authors.initials: K
authors.lastname: Lausen
authors.forename: Lars
authors.initials: L
authors.lastname: Hanker
authors.forename: Uwe
authors.initials: U
authors.lastname: Holtrich
authors.forename: Sven
authors.initials: S
authors.lastname: Becker
authors.forename: Thomas
authors.initials: T
authors.lastname: Karn
mesh_terms: D000328:Adult; D000367:Age Factors; D014408:Biomarkers, Tumor / Q000032:analysis / Q000235:genetics / Q000378:metabolism*; D001943:Breast Neoplasms / Q000188:drug therapy / Q000378:metabolism* / Q000401:mortality* / Q000473:pathology; D018572:Disease-Free Survival; D047628:Estrogen Receptor alpha / Q000235:genetics / Q000378:metabolism; D005260:Female; D020869:Gene Expression Profiling; D006801:Humans; D019394:Ki-67 Antigen / Q000235:genetics / Q000378:metabolism; D008207:Lymphatic Metastasis / Q000473:pathology; D008875:Middle Aged; D015999:Multivariate Analysis; D011379:Prognosis; D018719:Erb-b2 Receptor Tyrosine Kinases / Q000235:genetics / Q000378:metabolism; D011980:Receptors, Progesterone / Q000235:genetics / Q000378:metabolism; D046888:Tissue Array Analysis
chemical_list: D014408:Biomarkers, Tumor; D047628:Estrogen Receptor alpha; D019394:Ki-67 Antigen; D018719:Erb-b2 Receptor Tyrosine Kinases; D011980:Receptors, Progesterone; C506487:ESR1 protein, human; C508053:ERBB2 protein, human