title: Prognosis of early breast cancer by immunohistochemistry defined intrinsic sub-types in patients treated with adjuvant chemotherapy in the NEAT/BR9601 trial.
abstract: Breast cancer can be classified into molecular sub-types that have distinct survival patterns. We evaluated the prognostic significance of breast cancer sub-types in a cohort of women taking part in the NEAT and BR9601 clinical trials comparing cyclophosphamide, methotrexate and fluorouracil (CMF) with ECMF (epirubicin and CMF). Furthermore, we evaluated whether the sub-types were predictive of the added benefit of epirubicin in these trials. Tumour tissue microarrays were stained and scored for ER, PR, HER2, EGFR and CK5/6. These were used to classify the tumours into six intrinsic sub-types. We used Cox regression to compare overall survival (OS), breast cancer-specific survival (BCSS) and relapse-free survival (RFS) in the different sub-groups. We also compared the effect of ECMF with CMF by sub-group. Immunohistochemistry data were available for 1,725 cases of whom 805 were luminal 1-basal negative. Median follow-up time was 7 years. The luminal 1-basal negative tumours were associated with the best prognosis in five years after surgery and the HER2-like tumours were associated with the poorest prognosis. There was little evidence for significant heterogeneity of this effect by tumour sub-type (OS p = 0.40, BCSS p = 0.53 RFS p = 0.50) - the largest additional benefit of epirubicin was in women with tumours of the 5-negative phenotype (OS HR = 0.39 95% CI: 0.21-0.73) and the smallest was in Luminal 1-basal negative tumours (OS HR = 0.86 95% CI: 0.64-1.16). We confirmed that breast cancer sub-types show distinct behaviour with differences in short- and long-term survival. The benefit of ECMF over CMF was statistically similar in all disease sub-types.
authors.affiliation: Department of Public Health and Primary Care, University of Cambridge, Cambridge, United Kingdom.
authors.forename: Alaa M
authors.initials: AM
authors.lastname: Ali
authors.forename: Elena
authors.initials: E
authors.lastname: Provenzano
authors.forename: John M S
authors.initials: JM
authors.lastname: Bartlett
authors.forename: Jean
authors.initials: J
authors.lastname: Abraham
authors.forename: Kristy
authors.initials: K
authors.lastname: Driver
authors.forename: Alison F
authors.initials: AF
authors.lastname: Munro
authors.forename: Christopher
authors.initials: C
authors.lastname: Twelves
authors.forename: Christopher J
authors.initials: CJ
authors.lastname: Poole
authors.forename: Louise
authors.initials: L
authors.lastname: Hiller
authors.forename: Janet A
authors.initials: JA
authors.lastname: Dunn
authors.forename: Helena M
authors.initials: HM
authors.lastname: Earl
authors.forename: Carlos
authors.initials: C
authors.lastname: Caldas
authors.forename: Paul D
authors.initials: PD
authors.lastname: Pharoah
mesh_terms: D000328:Adult; D000368:Aged; D000971:Antineoplastic Combined Chemotherapy Protocols / Q000627:therapeutic use*; D001943:Breast Neoplasms / Q000737:chemistry / Q000188:drug therapy / Q000401:mortality* / Q000473:pathology; D017024:Chemotherapy, Adjuvant; D002986:Clinical Trials as Topic; D005260:Female; D006801:Humans; D007150:Immunohistochemistry; D008875:Middle Aged; D009367:Neoplasm Staging; D011379:Prognosis; D016016:Proportional Hazards Models
keywords: breast cancer; chemotherapy; molecular sub-types; prediction; prognosis