title: Tailoring adjuvant treatments for the individual breast cancer patient.
abstract: BACKGROUND
  Chemotherapy, tamoxifen and ovarian function suppression have all demonstrated their effectiveness for treating women with early breast cancer. Treatment selection for individual patients, however, requires estimates on the magnitude of treatment effects to be achieved from the application of each modality. Unfortunately, information currently available is insufficient to properly tailor adjuvant treatments.
  
  
  METHODS
  We consider predictive factors to improve our understanding about selection of adjuvant therapies, reassessment of data from previous clinical trials and design of future studies.
  
  
  RESULTS
  Estrogen receptor (ER) and progesterone receptor (PgR) are the primary measures available today to tailor adjuvant therapies. Patient age/menopausal status (ability to obtain treatment effects via ovarian function suppression), measures of the metastatic potential of the tumor (such as number of positive axillary lymph nodes), and concurrent use of chemotherapy and tamoxifen are other factors that modify the magnitude of relative effect associated with chemotherapy and endocrine therapies. The Subpopulation Treatment Effect Pattern Plots (STEPP) method displays the patterns of treatment effects within randomized clinical trials or datasets from meta-analyses to identify features that predict responsiveness to the treatments under study without relying on retrospective subset analysis. Confirmation of hypotheses using independent clinical trial databases is recommended.
  
  
  CONCLUSIONS
  All findings from clinical trials and meta-analyses should be presented primarily according to steroid hormone receptor status and patient age. Future studies should be designed as tailored treatment investigations, with endocrine therapies evaluated within populations of patients with endocrine responsive tumors, and chemotherapy questions focused within populations of patients with endocrine nonresponsive disease.
authors.affiliation: IBCSG Statistical Center, Department of Biostatistical Science, Dana-Farber Cancer Institute, 44 Binney Str., Boston, MA 02115, USA. gelber@jimmy.harvard.edu
authors.forename: R D
authors.initials: RD
authors.lastname: Gelber
authors.forename: M
authors.initials: M
authors.lastname: Bonetti
authors.forename: M
authors.initials: M
authors.lastname: Castiglione-Gertsch
authors.forename: A S
authors.initials: AS
authors.lastname: Coates
authors.forename: A
authors.initials: A
authors.lastname: Goldhirsch
mesh_terms: D018931:Antineoplastic Agents, Hormonal / Q000627:therapeutic use*; D000971:Antineoplastic Combined Chemotherapy Protocols / Q000627:therapeutic use; D001943:Breast Neoplasms / Q000188:drug therapy* / Q000401:mortality / Q000503:physiopathology; D005260:Female; D006801:Humans; D011960:Receptors, Estrogen / Q000502:physiology; D013629:Tamoxifen / Q000627:therapeutic use*; D016896:Treatment Outcome
chemical_list: D018931:Antineoplastic Agents, Hormonal; D011960:Receptors, Estrogen; D013629:Tamoxifen