OBJECTIVES: This study aimed to assess the cost-effectiveness of molecular testing and treatment with alpelisib plus fulvestrant for hormone receptor-positive (HR+)/ human epidermal growth factor receptor 2 negative (HER2-) advanced breast cancer (ABC) in an integrated test-treatment model, focusing on identifying patients with PIK3CA mutations who may benefit from targeted treatment.

METHODS: A probabilistic discrete-time Markov cohort model was developed to compare different strategies: fulvestrant monotherapy for all ABC patients, alpelisib plus fulvestrant for all ABC patients, and treatment based on PIK3CA-positive mutations. The analysis considered healthcare costs, incorporating transition probabilities from randomized controlled trials, local cost data sources, drug tender prices, and utility values from the literature. Sensitivity analyses were performed to address uncertainties.

RESULTS: Treating all patients with fulvestrant monotherapy was the least costly and least effective option. Treating all patients with alpelisib plus fulvestrant was the most effective but more costly alternative. Compared to treating all patients with fulvestrant monotherapy, treating all patients with alpelisib plus fulvestrant resulted in an additional cost for 0.80 gained quality-adjusted life years (QALYs). Treating all patients with PIK3CA-negative tumors with fulvestrant monotherapy and all patients with PIK3CA-positive tumors with alpelisib plus fulvestrant were more effective (0.57 and 0.13 gained QALYs, respectively), but at higher costs than treating all patients with fulvestrant monotherapy. Treating patients with PIK3CA-positive tumors using alpelisib plus fulvestrant was less effective (0.67 fewer QALYs) and less costly then treating alle patients with alpelisib plus fulvestrant. There were some uncertainties associated with the estimation of the transition probabilities and time horizon and drug costs influenced cost-effectiveness outcomes.

CONCLUSIONS: The integrated test-treatment model allowed decision-makers to assess the predictive value of the test and consider the health outcomes and economic consequences of using molecular tests and subsequent treatment in combination.