The influence of anatomic stage and receptor status on first recurrence for breast cancer within 5 years (AFT-01)

BackgroundRisk-stratified follow-up guidelines that account for the absolute risk and timing of recurrence may improve the quality and efficiency of breast cancer follow-up. The objective of this study was to assess the relationship of anatomic stage and receptor status with timing of the first recurrence for patients with local-regional breast cancer and generate risk-stratified follow-up recommendations. MethodsThe authors conducted a secondary analysis of 8007 patients with stage I-III breast cancer who enrolled in nine Alliance legacy clinical trials from 1997 to 2013 (ClinicalTrials.gov identifier NCT02171078). Patients who received standard-of-care therapy were included. Patients who were missing stage or receptor status were excluded. The primary outcome was days from the earliest treatment start date to the date of first recurrence. The primary explanatory variable was anatomic stage. The analysis was stratified by receptor type. Cox proportional-hazards regression models produced cumulative probabilities of recurrence. A dynamic programming algorithm approach was used to optimize the timing of follow-up intervals based on the timing of recurrence events. ResultsThe time to first recurrence varied significantly between receptor types (p < .0001). Within each receptor type, stage influenced the time to recurrence (p < .0001). The risk of recurrence was highest and occurred earliest for estrogen receptor (ER)-negative/progesterone receptor (PR)-negative/Her2neu-negative tumors (stage III; 5-year probability of recurrence, 45.5%). The risk of recurrence was lower for ER-positive/PR-positive/Her2neu-positive tumors (stage III; 5-year probability of recurrence, 15.3%), with recurrences distributed over time. Model-generated follow-up recommendations by stage and receptor type were created. ConclusionsThis study supports considering both anatomic stage and receptor status in follow-up recommendations. The implementation of risk-stratified guidelines based on these data has the potential to improve the quality and efficiency of follow-up.