Abstract 137P
Background
Gene expression profiles are used for decision making in the adjuvant setting of hormone receptor-positive, HER2-negative (HR+/HER2-) breast cancer. Previous studies have reported algorithms to optimize the use of RS/Oncotype Dx. However, no such efforts have focused on ROR/Prosigna. In addition, there is no data on adherence to testing guidelines.
Methods
Postmenopausal women with resected HR+/HER2- and node-negative breast cancer that had undergone testing with ROR/Prosigna in four Swedish regions between March 2020 and March 2022 were included (n=348). In addition, we identified all patients that had an indication for gene expression profiling, regardless of whether it was performed. Using the ROR/Prosigna recommendation for chemotherapy as ground truth, we compared the performance of four risk classifications in terms of over- and undertreatment, and patients at intermediate risk. We then developed and validated, by using a 0.7/0.3 splitting rule in the cohort, a machine learning model that comprised simple prognostic factors (size, PgR expression, grade and Ki67) for prediction of ROR/Prosigna outcome.
Results
Adherence to guidelines reached 66.3%, with non-tested patients being older and having more comorbidities (p<0.001). Previous risk classifications led to excessive undertreatments (CTS5: 21.8%, MINDACT/TailorX risk definitions: 28.1%) or large intermediate groups that would need to be tested with gene expression profiling (Ki67 10%/40% cut-offs: 86.5%). The model achieved AUC under ROC for predicting ROR/Prosigna result of 0.77 in the training and 0.83 in the validation cohort. By setting and validating upper and lower cut-offs in the model, we could improve correct risk stratification while decreasing the proportion of patients needing testing with gene expression profiles compared to current management.
Conclusions
We show the feasibility of machine learning algorithms to improve patient selection for gene expression profiling. Further validation in external cohorts is needed.
Legal entity responsible for the study
U. Kjällquist, A. Matikas.
Funding
Has not received any funding.
Disclosure
M. Ekholm: Financial Interests, Personal, Invited Speaker: AstraZeneca; Non-Financial Interests, Personal, Advisory Board: Pfizer, Novartis, Lilly. A. Valachis: Financial Interests, Personal, Funding: Roche, MSD. J. Hartman: Financial Interests, Institutional, Funding: Novartis, Cepheid; Financial Interests, Personal, Ownership Interest: Stratipath AB; Financial Interests, Personal, Invited Speaker: Novartis, Merck, Lilly, Pfizer. T. Foukakis: Financial Interests, Institutional, Invited Speaker: Roche, AstraZeneca, Gilead Sciences; Financial Interests, Personal, Royalties, Authorship of two chapters in UpToDate: Wolters Kluwer; Financial Interests, Institutional, Invited Speaker, Clinical trial support (research grant and study drug): AstraZeneca; Financial Interests, Institutional, Invited Speaker, Clinical trial support (research grant and study drug): Novartis; Financial Interests, Institutional, Invited Speaker, Discount on the Prosigna PAM50 assay in ARIADNE clinical trial: Veracyte. A. Matikas: Financial Interests, Institutional, Invited Speaker: Seagen; Financial Interests, Institutional, Invited Speaker, International co-PI of academic trial ARIADNE (EU CT: 2022-501504-95-00): AstraZeneca, Novartis, Veracyte; Financial Interests, Institutional, Invited Speaker, Registry study: Merck; Non-Financial Interests, Advisory Role: Veracyte, Roche. All other authors have declared no conflicts of interest.