Abstract PO4-14-09: A computational model of the mechanisms of action of combined endocrine therapy and CDK4/6 inhibition predicts outcome in patients with Luminal B breast cancer
Authors: Schmiester Leonard , Brasó-Maristany Fara , Kristensen Vessela , Frigessi Arnoldo , Prat Aleix , Köhn-Luque Alvaro 
Published: May 2024

Abstract
Background: Hormone receptor-positive/HER2-negative (HR+/HER2-) breast cancer (BC) is clinically and biologically heterogeneous. CDK4/6 inhibitors (CDK4/6i) are proven to be effective in different molecular subtypes of HR+/HER2- BC, including the PAM50 luminal B. However, not all patients benefit to the same extent and new biomarkers for response are needed. The aim of this study was to develop a computational workflow to predict the response of patients with luminal B BC to treatment with CDK4/6i in combination with endocrine therapy (ET).
Methods: The main part of the workflow is a computational model representing the mechanisms of action of the drugs and their influence on protein signaling and cell proliferation in the tumor. The model was trained on publicly available data from BC cell lines (Western blot time courses and viability dose-response data). The model was then used to predict response in patients by incorporating gene expression profiles obtained at baseline. The output of the model was a score indicating how strongly the tumor responded to the treatment.
Gene expression data determined using the nCounter BC360 panel was available at baseline from two patient cohorts: 1) the CORALLEEN phase II trial which evaluated neoadjuvant ribociclib plus letrozole vs multi-agent chemotherapy in postmenopausal patients with luminal B by PAM50, HR+/HER2- early BC (Prat et al. Lancet Oncology. 2020), 2) a retrospective study of postmenopausal patients with HR+/HER2-/Luminal B advanced BC treated with CDK4/6i plus ET in the first line setting at Hospital Clinic Barcelona (hereafter CDK cohort). PAM50 risk of relapse (ROR) and Ki67 levels were considered as outcome for the CORALLEEN patients. Progression free survival (PFS) was available in the CDK cohort. Area under the ROC Curve (AUC) was used to estimate the discrimination performance of the model. Differences were tested for statistical significance using Wilcoxon rank-sum test. Hazard ratios were estimated using Cox regression to investigate the association with PFS.
Results: The computational model used here describes the dynamics of relevant protein-protein and drug-protein interactions to the treatment of CDK4/6i plus ET. The model showed high agreement with the training data obtained from cell lines (Pearson correlation of 0.88 and 0.95). The ability of the trained model to predict treatment outcome in patients was validated in baseline samples of the CDK4/6i arm (n=51) and the chemotherapy arm (n=52) of the CORALLEEN trial, and baseline Luminal B primary tumors of the CDK cohort (n=19). For each patient, a response score was calculated by the model using the expression of 6 genes at baseline as input. This response score was significantly associated with patient response in both cohorts. The model identified patients with high Ki67 (AUC of 0.80 (95% CI 0.64 - 0.92)) and high ROR (AUC of 0.78 (95% CI 0.64 - 0.89)) after treatment in the CDK4/6i arm of the CORALLEEN cohort and was used to stratify patients into different response groups. The AUC in the chemotherapy arm of CORALLEEN for identifying high Ki67 was 0.44 (95% CI 0.29 - 0.58), indicating that the predictions are specific to CDK4/6i plus ET treatment. Additionally, stratification of patients in the CDK cohort into either two or three groups based on the response score was linked to PFS (HR=3.71 (95% CI 0.97 - 14.16), p=0.055 and HR=2.92 (95% CI 1.08 - 7.86), p=0.034, respectively).
Conclusion: A mechanistic model was developed, trained on cell line data, and then used to predict treatment outcome of patients with HR+/HER2-/PAM50 Luminal B BC treated with CDK4/6i plus ET. This approach showed significant association with observed outcome and could be used to assign patients to different response groups, indicating the usefulness of the model as a potential marker for response.
Citation Format: Leonard Schmiester, Fara Brasó-Maristany, Vessela Kristensen, Arnoldo Frigessi, Aleix Prat, Alvaro Köhn-Luque. A computational model of the mechanisms of action of combined endocrine therapy and CDK4/6 inhibition predicts outcome in patients with Luminal B breast cancer [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO4-14-09.