Optimization of guidelines for Risk Of Recurrence/Prosigna testing using a machine learning model: a Swedish multicenter study - Una Kjällquist; Nikos Tsiknakis; Balazs Acs; Sara Margolin; Luisa Edman Kessler; Scarlett Levy; Maria Ekholm; Christine Lundgren; Erik Olsson; Henrik Lindman; Antonios Valachis; Johan Hartman; et al. (Breast)
Purpose: Gene expression profiles are used for decision making in the adjuvant setting in hormone receptor-positive, HER2-negative (HR+/HER2-) breast cancer. While algorithms to optimize testing exist for RS/Oncotype Dx, no such efforts have focused on ROR/Prosigna. This study aims to enhance pre-selection of patients for testing using machine learning. Methods: We included 348 postmenopausal women with resected HR+/HER2-node-negative breast cancer tested with ROR/Prosigna across four Swedish regions. We developed a machine learning model using simple prognostic factors (size, progesterone receptor expression, grade, and Ki67) to predict ROR/Prosigna output and compared the performance regarding over- and undertreatment with commonly employed risk stratification schemes. Results: Previous classifications resulted in significant undertreatment or large intermediate groups needing gene expression profiling. The machine learning model achieved AUC under ROC of 0.77 in training and 0.83 in validation cohorts for prediction of indication for adjuvant chemotherapy according to ROR/Prosigna. By setting and validating upper and lower cut-offs corresponding to low, intermediate and high-risk disease, we improved risk stratification accuracy and reduced the proportion of patients needing ROR/Prosigna testing compared to current risk stratification. Conclusion: Machine learning algorithms can enhance patient selection for gene expression profiling, though further external validation is needed.