title: Tailoring adjuvant endocrine therapy for postmenopausal breast cancer: a CYP2D6 multiple-genotype-based modeling analysis and validation.
abstract: OBJECTIVE
  Previous studies have suggested that postmenopausal women with breast cancer who present with wild-type CYP2D6 may actually have similar or superior recurrence-free survival outcomes when given tamoxifen in place of aromatase inhibitors (AIs). The present study established a CYP2D6 multiple-genotype-based model to determine the optimal endocrine therapy for patients harboring wild-type CYP2D6.
  
  
  METHODS
  We created a Markov model to determine whether tamoxifen or AIs maximized 5-year disease-free survival (DFS) for extensive metabolizer (EM) patients using annual hazard ratio (HR) data from the BIG 1-98 trial. We then replicated the model by evaluating 9-year event-free survival (EFS) using HR data from the ATAC trial. In addition, we employed two-way sensitivity analyses to explore the impact of HR of decreased-metabolizer (DM) and its frequency on survival by studying a range of estimates.
  
  
  RESULTS
  The 5-year DFS of tamoxifen-treated EM patients was 83.3%, which is similar to that of genotypically unselected patients who received an AI (83.7%). In the validation study, we further demonstrated that the 9-year EFS of tamoxifen-treated EM patients was 81.4%, which is higher than that of genotypically unselected patients receiving tamoxifen (78.4%) and similar to that of patients receiving an AI (83.2%). Two-way sensitivity analyses demonstrated the robustness of the results.
  
  
  CONCLUSIONS
  Our modeling analyses indicate that, among EM patients, the DFS/EFS outcome of patients receiving tamoxifen is similar to that of patients receiving an AI. Further prospective clinical trials are needed to evaluate the value of the CYP2D6 genotype in the selection of endocrine therapy.
authors.affiliation: Department of Oncology, Shanghai Medical College, Cancer Institute and Cancer Center, Fudan University, Shanghai, People's Republic of China.
authors.forename: Ke-Da
authors.initials: KD
authors.lastname: Yu
authors.forename: A-Ji
authors.initials: AJ
authors.lastname: Huang
authors.forename: Zhi-Ming
authors.initials: ZM
authors.lastname: Shao
mesh_terms: D000465:Algorithms; D018931:Antineoplastic Agents, Hormonal / Q000627:therapeutic use; D001943:Breast Neoplasms / Q000188:drug therapy* / Q000235:genetics*; D002986:Clinical Trials as Topic; D019389:Cytochrome P-450 CYP2D6 / Q000235:genetics* / Q000502:physiology*; D004252:DNA Mutational Analysis; D018572:Disease-Free Survival; D004703:Endocrine System; D005260:Female; D005838:Genotype; D006801:Humans; D008390:Markov Chains; D010597:Pharmacogenetics; D017698:Postmenopause; D016016:Proportional Hazards Models
chemical_list: D018931:Antineoplastic Agents, Hormonal; D019389:Cytochrome P-450 CYP2D6